diff --git a/.gitattributes b/.gitattributes index 5f6d3b9084f6adfd57843b624e89733787df50ca..b2cbf75038cd94a0a70c8534e74c70cda929f36c 100644 --- a/.gitattributes +++ b/.gitattributes @@ -37,5 +37,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.txt filter=lfs diff=lfs merge=lfs -text 03-gene-task/data/dna_protein_full.json filter=lfs diff=lfs merge=lfs -text 03-gene-task/img/protein-structure-1-2.png filter=lfs diff=lfs merge=lfs -text +04-gene-sft/data/.ipynb_checkpoints/dna_promoter_300-checkpoint.jsonl filter=lfs diff=lfs merge=lfs -text +04-gene-sft/data/dna_promoter_300.jsonl filter=lfs diff=lfs merge=lfs -text 04-gene-sft/sft_data/train_data.json filter=lfs diff=lfs merge=lfs -text img/gpt2_bridge.png filter=lfs diff=lfs merge=lfs -text diff --git a/02-gpt2_bert/.ipynb_checkpoints/1-dna-bpe-checkpoint.ipynb b/02-gpt2_bert/.ipynb_checkpoints/1-dna-bpe-checkpoint.ipynb index 782dbd71c56458c49719113bbc25a6e0a0291845..45b50acd3b12d99dce399a1a16c4ca7f2c213ae6 100644 --- a/02-gpt2_bert/.ipynb_checkpoints/1-dna-bpe-checkpoint.ipynb +++ b/02-gpt2_bert/.ipynb_checkpoints/1-dna-bpe-checkpoint.ipynb @@ -284,7 +284,7 @@ "id": "c24f10dc-1117-4493-9333-5ed6d898f44a", "metadata": {}, "source": [ - "### 训练DNA BPE分词器\n", + "### **训练DNA BPE分词器**\n", "\n", "以上方法展示了如何对 DNA 和蛋白质序列进行“分词”，以提取有用的特征。选择哪种方法取决于具体的任务需求和数据特性。对于简单的分类或回归任务，K-mer 分解或滑动窗口可能是足够的；而对于更复杂的任务，如序列标注或结构预测，基于词汇表的方法或嵌入表示可能会提供更好的性能。\n", "\n", diff --git a/04-gene-sft/.ipynb_checkpoints/2-gpt2-instruction-ft-checkpoint.ipynb b/04-gene-sft/.ipynb_checkpoints/2-gpt2-instruction-ft-checkpoint.ipynb index d3a93e17ccf6e6574502b5a990af879b9a4144b4..3046ff25db972d80c49d15c8bb0046c847c9a48b 100644 --- a/04-gene-sft/.ipynb_checkpoints/2-gpt2-instruction-ft-checkpoint.ipynb +++ b/04-gene-sft/.ipynb_checkpoints/2-gpt2-instruction-ft-checkpoint.ipynb @@ -127,20 +127,76 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "dc04d5e3-7623-4d59-9f3b-ad03e339db11", + "execution_count": 1, + "id": "b28e3499-bbff-4548-9f85-2baee088cabf", "metadata": {}, "outputs": [], + "source": [ + "import subprocess\n", + "import os\n", + "# 设置环境变量, autodl一般区域\n", + "result = subprocess.run('bash -c \"source /etc/network_turbo && env | grep proxy\"', shell=True, capture_output=True, text=True)\n", + "output = result.stdout\n", + "for line in output.splitlines():\n", + " if '=' in line:\n", + " var, value = line.split('=', 1)\n", + " os.environ[var] = value" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "dc04d5e3-7623-4d59-9f3b-ad03e339db11", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['sequence', 'label'],\n", + " num_rows: 59195\n", + " })\n", + "})" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from datasets import load_dataset\n", "# 1. load ~11k samples from promoters prediction dataset\n", - "dataset = load_dataset(\"dnagpt/dna_promoter_300\")\n", - "dataset" + "dna_dataset = load_dataset(\"dnagpt/dna_promoter_300\")\n", + "dna_dataset" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, + "id": "f7332fa1-3343-4247-b4cc-54733dff6964", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'sequence': 'TAAATACGGAAGTTTATTACTTGAGGAATAGATGGAATCGTCGGGCGTGAGAGATCATAATCGGCTGCTTCTGGGAGCCGCACGTGGGAAAGACTTATCCCCGACGGAGCTGGGACTGGGGCACAAACCGGAAGGAACACATCTGACCGAGAAAGAGACCAAGTGGCTCAGGTAGGACCAAAGCGAGCAAGGCTGCGGGTCCTGTTGCTCTCTGTCCTGTAAATTTAAACGTTACGCCACCTGGTAATGATACCCTCGTCCTCCGAGGCGACAAGTCAGAACTTCCACCAAGGGCATTAC',\n", + " 'label': 0}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dna_dataset[\"train\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "id": "93d09d8d-f521-49f7-b0e0-7ac089dfbf49", "metadata": {}, "outputs": [], @@ -169,10 +225,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "9f9c0e5a-6591-47ac-b358-d746a00dfc0a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'instruction': 'Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.', 'input': 'TAAATACGGAAGTTTATTACTTGAGGAATAGATGGAATCGTCGGGCGTGAGAGATCATAATCGGCTGCTTCTGGGAGCCGCACGTGGGAAAGACTTATCCCCGACGGAGCTGGGACTGGGGCACAAACCGGAAGGAACACATCTGACCGAGAAAGAGACCAAGTGGCTCAGGTAGGACCAAAGCGAGCAAGGCTGCGGGTCCTGTTGCTCTCTGTCCTGTAAATTTAAACGTTACGCCACCTGGTAATGATACCCTCGTCCTCCGAGGCGACAAGTCAGAACTTCCACCAAGGGCATTAC', 'output': 'Non-promoter'}\n" + ] + } + ], "source": [ "example = dna_dataset[\"train\"][0]\n", "print(build_prompt(example))" @@ -180,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "83070a23-1604-4d28-b371-e01060331ed5", "metadata": {}, "outputs": [], @@ -199,10 +263,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "89fb8ed3-aa58-462f-b2a6-ce445c597a33", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7b84d3a64bf645ada13d0cada2d9f524", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Generating train split: 0 examples [00:00, ? examples/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['instruction', 'input', 'output'],\n", + " num_rows: 59195\n", + " })\n", + "})" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dna_ft_dataset = load_dataset(\"json\", data_files='data/dna_promoter_300.jsonl')\n", "dna_ft_dataset" @@ -210,10 +304,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "e4f7b75f-6ccb-4fda-8004-40df7d52678f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['instruction', 'input', 'output'],\n", + " num_rows: 53275\n", + " })\n", + " test: Dataset({\n", + " features: ['instruction', 'input', 'output'],\n", + " num_rows: 5920\n", + " })\n", + "})" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = dna_ft_dataset[\"train\"].train_test_split(train_size=0.9, seed=42)\n", "data" @@ -221,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "36d9ee0e-8423-4529-aa7e-fda2728fab2f", "metadata": {}, "outputs": [], @@ -235,13 +349,14 @@ "from tokenizers import Tokenizer\n", "from transformers import GPT2TokenizerFast\n", "\n", + "#需要使用生物序列+英文的多模态大模型\n", "tokenizer = GPT2Tokenizer.from_pretrained(\"dnagpt/gene_eng_gpt2_v0\")\n", "tokenizer.pad_token = tokenizer.eos_token" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "871baee0-f06f-4422-a741-af533f7d92e1", "metadata": {}, "outputs": [], @@ -270,10 +385,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "bca1c275-cc3d-43df-923e-e6604d584226", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'instruction': 'Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.',\n", + " 'input': 'CCAGGATGCGCTGACGACCCGGCTGGCAGGCGGGTCCTCGTGGGCGAGGCGAGGGAGGCGGCGAGAGAGGAGCAATAGTTTCCCACCGCTCCCTCTCAGGCGCAGGGTCTAGAGAAGCGCGAGGGGATCTAGAGAAGCCGGAGGGGAGGAAGCGCGAGTCCGCGGCCCGCCCCGTTGCGTCCCACCCACCGCGTCCCCTCCCCTCCCCTCCCGCTGCGGGAAAAGCGGCCGCGGGCGGCGGCGCCCACTGTGGGGCGGGCGGAGCGCCGCGGGAGGCGGACGAGATGCGAGCGCGGCCGC',\n", + " 'output': 'promoter'}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "example = data[\"test\"][0]\n", "example" @@ -281,10 +409,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "76f2e027-0a31-4919-bb7e-404c786e1599", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.\n", + "\n", + "### Input:\n", + "CCAGGATGCGCTGACGACCCGGCTGGCAGGCGGGTCCTCGTGGGCGAGGCGAGGGAGGCGGCGAGAGAGGAGCAATAGTTTCCCACCGCTCCCTCTCAGGCGCAGGGTCTAGAGAAGCGCGAGGGGATCTAGAGAAGCCGGAGGGGAGGAAGCGCGAGTCCGCGGCCCGCCCCGTTGCGTCCCACCCACCGCGTCCCCTCCCCTCCCCTCCCGCTGCGGGAAAAGCGGCCGCGGGCGGCGGCGCCCACTGTGGGGCGGGCGGAGCGCCGCGGGAGGCGGACGAGATGCGAGCGCGGCCGC\n", + "\n", + "### Response:\n", + "promoter\n" + ] + } + ], "source": [ "prompt = build_prompt(example)\n", "print(prompt)" @@ -292,24 +437,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "932b54ca-7e27-47cd-b67d-7ef8386b6608", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tokens: Bel ow Ġ is Ġ an Ġ instruc tion Ġ th at Ġ describ es Ġ a Ġ t ask . ĠWrit e Ġ a Ġ respon se Ġ th at Ġ appropri at el y Ġ complet es Ġ the Ġ request . Ċ Ċ # # # ĠIn struc tion : Ċ D eter min e Ġ cor e Ġ promo ter Ġ det ec tion Ġ of Ġ follow ing Ġ d na Ġ sequenc e , ĠTh e Ġ resul t Ġ will Ġ be Ġ on e Ġ of Ġ the Ġ follow ing : ĠN on - promo ter , Ġ promo ter . Ċ Ċ # # # ĠIn put : Ċ CC AGGATGC GC TGACG ACCC GGCTGGC AGGC GGGTCC TCG TGGGCG AGGCG AGGGAGGC GGCG AGAGAGG AGCAATAG TTTCCC ACCGC TCCCTCTC AGGCGC AGGG TCTAG AGAAGC GCG AGGGG ATCTAG AGAAGCC GG AGGGG AGGAAGC GCG AGTCC GCGG CCCGCC CCG TTGCG TCCC ACCCACC GCG TCCCCTCCCC TCCCCTCCC GCTGC GGG AAAAGC GGCCGC GGGCGGC GGCGCCC ACTGTG GGGC GGGC GGAGC GCCGC GGGAGGC GGACG AGATGCG AGCGC GGCCGC Ċ Ċ # # # ĠR esp on se : Ċ promo ter\n" + ] + } + ], "source": [ "print('tokens: ', ' '.join(tokenizer.tokenize(prompt)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "26671faf-68d0-4a44-978e-e1a24e86c9b1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "02d2f083e74a45e6ada46b9872822dfc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/53275 [00:00, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::logic_error@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::locale::~locale()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::basic_string(std::string const&, unsigned long, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_end_catch@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `VTT for std::basic_ofstream >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::logic_error::~logic_error()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for __cxxabiv1::__si_class_type_info@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ios >::_M_cache_locale(std::locale const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `VTT for std::basic_stringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `operator new[](unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::_M_leak_hard()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_ifstream >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_streambuf >::basic_streambuf(std::basic_streambuf > const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::append(char const*, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::basic_string(std::string const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for unsigned short@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::resize(unsigned long, char)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for char const*@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ctype::_M_widen_init() const@GLIBCXX_3.4.11'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__throw_invalid_argument(char const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::locale::operator=(std::locale const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ios >::_M_cache_locale(std::locale const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Rb_tree_decrement(std::_Rb_tree_node_base const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_free_exception@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::condition_variable::notify_one()@GLIBCXX_3.4.11'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ios_base::Init::~Init()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::~basic_string()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_pure_virtual@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream::flush()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for __cxxabiv1::__class_type_info@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_rethrow@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_stringbuf, std::allocator 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/usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Hash_bytes(void const*, unsigned long, unsigned long)@CXXABI_1.3.5'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(long long)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for char*@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__detail::_Prime_rehash_policy::_M_need_rehash(unsigned long, unsigned long, unsigned long) const@GLIBCXX_3.4.18'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::out_of_range@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(unsigned long)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Rb_tree_increment(std::_Rb_tree_node_base const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ios_base::~ios_base()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::range_error::~range_error()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__basic_file::~__basic_file()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_guard_acquire@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(bool)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::overflow_error@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `VTT for std::basic_fstream >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::range_error@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_ios >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_filebuf >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `operator delete[](void*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_stringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::basic_string(unsigned long, char, std::allocator const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__detail::_List_node_base::_M_transfer(std::__detail::_List_node_base*, std::__detail::_List_node_base*)@GLIBCXX_3.4.15'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::replace(unsigned long, unsigned long, char const*, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for std::exception@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::_Rep::_M_destroy(std::allocator const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::istream& std::istream::_M_extract(double&)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_filebuf >::close()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_fstream >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ifstream >::basic_ifstream(char const*, std::_Ios_Openmode)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::append(std::string const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `operator new(unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `VTT for std::basic_istringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for unsigned int@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::append(char const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::domain_error@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::find(char, unsigned long) const@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream::put(char)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for int@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__throw_bad_alloc()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_thread_atexit@CXXABI_1.3.7'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Rb_tree_increment(std::_Rb_tree_node_base*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ifstream >::~basic_ifstream()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ios_base::Init::Init()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::condition_variable::condition_variable()@GLIBCXX_3.4.11'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_filebuf >::basic_filebuf()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `VTT for std::basic_istringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::domain_error::~domain_error()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::cerr@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::find(char const*, unsigned long, unsigned long) const@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_istringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::basic_string(std::allocator const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_stringbuf, std::allocator >::str() const@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::invalid_argument@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for void*@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::assign(std::string const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ostringstream, std::allocator >::~basic_ostringstream()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Rb_tree_rebalance_for_erase(std::_Rb_tree_node_base*, std::_Rb_tree_node_base&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for unsigned long@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__detail::_List_node_base::_M_hook(std::__detail::_List_node_base*)@GLIBCXX_3.4.15'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__detail::_List_node_base::_M_unhook()@GLIBCXX_3.4.15'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_ostringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_stringbuf, std::allocator >::_M_sync(char*, unsigned long, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_iostream >::~basic_iostream()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::locale::locale(std::locale const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_istringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `log2f@GLIBC_2.2.5'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream::operator<<(std::basic_streambuf >*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_streambuf >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::exception::~exception()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::_Rep::_S_create(unsigned long, unsigned long, std::allocator const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__basic_file::is_open() const@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_istringstream, std::allocator >::~basic_istringstream()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::swap(std::string&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for unsigned long*@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_ostringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_streambuf >::basic_streambuf(std::basic_streambuf > const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ios >::init(std::basic_streambuf >*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__throw_bad_cast()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ios >::clear(std::_Ios_Iostate)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_streambuf >::operator=(std::basic_streambuf > const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `operator delete(void*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream::operator<<(int)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::_Rep::_S_empty_rep_storage@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::_Rep::_M_destroy(std::allocator const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_iostream >::~basic_iostream()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::runtime_error@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_ofstream >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Rb_tree_insert_and_rebalance(bool, std::_Rb_tree_node_base*, std::_Rb_tree_node_base*, std::_Rb_tree_node_base&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_stringstream, std::allocator >::~basic_stringstream()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `VTT for std::basic_stringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(long)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::istream::get()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for unsigned long long@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ostream >& std::operator<< >(std::basic_ostream >&, char const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::out_of_range::~out_of_range()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::length_error::~length_error()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ostream >& std::__ostream_insert >(std::basic_ostream >&, char const*, long)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::invalid_argument::~invalid_argument()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::swap(std::basic_string, std::allocator >&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::cout@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(unsigned long long)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for int*@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(void const*)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::underflow_error@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_streambuf >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for std::out_of_range@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_allocate_exception@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_ios >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for void const*@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ios >::init(std::basic_streambuf >*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::reserve(unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_begin_catch@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for long@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::_Rep::_S_empty_rep_storage@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::_M_leak()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_filebuf >::open(char const*, std::_Ios_Openmode)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_stringbuf, std::allocator >::_M_sync(wchar_t*, unsigned long, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::istream::getline(char*, long, char)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_istream >& std::getline, std::allocator >(std::basic_istream >&, std::basic_string, std::allocator >&, char)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_stringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::condition_variable::~condition_variable()@GLIBCXX_3.4.11'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_stringbuf, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::insert(unsigned long, char const*, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::assign(char const*, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for unsigned char@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ios_base::ios_base()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__throw_out_of_range(char const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::overflow_error::~overflow_error()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__throw_length_error(char const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__throw_system_error(int)@GLIBCXX_3.4.11'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ofstream >::close()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(double)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_streambuf >::operator=(std::basic_streambuf > const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for long long@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::basic_string(char const*, unsigned long, std::allocator const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ifstream >::close()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_guard_release@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_throw@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::underflow_error::~underflow_error()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Rb_tree_decrement(std::_Rb_tree_node_base*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::length_error@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_filebuf >::~basic_filebuf()@GLIBCXX_3.4'\n", + "collect2: error: ld returned 1 exit status\n" + ] + } + ], "source": [ "# 初始化Trainer\n", "trainer = Trainer(\n", @@ -454,10 +922,204 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "a9cd936a-5ea6-43e3-9848-27080f818606", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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StepTraining Loss
5002.335700
10002.184100
15002.178500
20002.172400
25002.171400
30002.171900
35002.163100
40002.159300
45002.161000
50002.160200
55002.158400
60002.151600
65002.153700
70002.129500
75002.119100
80002.119800
85002.121600
90002.122500
95002.122300
100002.121500
105002.119500
110002.123500
115002.119600
120002.119300
125002.121800
130002.123500
135002.103200
140002.080700
145002.082100
150002.082900
155002.086400
160002.086600
165002.083800
170002.085000
175002.082800
180002.077600
185002.080300
190002.086600
195002.084200

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "TrainOutput(global_step=19980, training_loss=2.1272921145021977, metrics={'train_runtime': 1315.5944, 'train_samples_per_second': 121.485, 'train_steps_per_second': 15.187, 'total_flos': 2.08804995072e+16, 'train_loss': 2.1272921145021977, 'epoch': 3.0})" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# 开始训练\n", "trainer.train()" @@ -465,10 +1127,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "315aae76-44b4-4513-8139-40ef22934873", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved model to: gpt_ft/final\n" + ] + } + ], "source": [ "save_dir = 'gpt_ft/final'\n", "trainer.save_model(save_dir)\n", @@ -477,7 +1147,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "28d2dbbc-02ff-4120-b230-b19905a786cd", "metadata": {}, "outputs": [], @@ -488,20 +1158,92 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "08987c3c-063a-4e9b-9ebb-e637b0b5bccd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "GPT2LMHeadModel(\n", + " (transformer): GPT2Model(\n", + " (wte): Embedding(90000, 768)\n", + " (wpe): Embedding(1024, 768)\n", + " (drop): Dropout(p=0.1, inplace=False)\n", + " (h): ModuleList(\n", + " (0-11): 12 x GPT2Block(\n", + " (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (attn): GPT2SdpaAttention(\n", + " (c_attn): Conv1D(nf=2304, nx=768)\n", + " (c_proj): Conv1D(nf=768, nx=768)\n", + " (attn_dropout): Dropout(p=0.1, inplace=False)\n", + " (resid_dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (mlp): GPT2MLP(\n", + " (c_fc): Conv1D(nf=3072, nx=768)\n", + " (c_proj): Conv1D(nf=768, nx=3072)\n", + " (act): NewGELUActivation()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (lm_head): Linear(in_features=768, out_features=90000, bias=False)\n", + ")" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "finetuned_model" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "d75010e8-6d6a-40ef-852e-0d705adc3da8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input (test): Below is an instruction that describes a task. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.\n", + "\n", + "### Input:\n", + "CCAGGATGCGCTGACGACCCGGCTGGCAGGCGGGTCCTCGTGGGCGAGGCGAGGGAGGCGGCGAGAGAGGAGCAATAGTTTCCCACCGCTCCCTCTCAGGCGCAGGGTCTAGAGAAGCGCGAGGGGATCTAGAGAAGCCGGAGGGGAGGAAGCGCGAGTCCGCGGCCCGCCCCGTTGCGTCCCACCCACCGCGTCCCCTCCCCTCCCCTCCCGCTGCGGGAAAAGCGGCCGCGGGCGGCGGCGCCCACTGTGGGGCGGGCGGAGCGCCGCGGGAGGCGGACGAGATGCGAGCGCGGCCGC\n", + "\n", + "### Response:\n", + "\n", + "--------------------------\n", + "\n", + "model's answer: \n", + "\n", + "promoterpromoterpromo\n", + "--------------------------\n", + "\n", + "real answer: \n", + "\n", + "promoter\n" + ] + } + ], "source": [ "print(\"input (test):\", input_text)\n", "\n", @@ -517,10 +1259,217 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "64365e15-510e-4abf-92f5-c78b660b37dc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" + ] + } + ], "source": [ "test_data = data[\"test\"].select(range(100))\n", "\n", @@ -543,7 +1492,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "a45fb780-fc3f-401c-b6e0-6f7d0c1682de", "metadata": {}, "outputs": [], @@ -556,19 +1505,128 @@ "# 将 Dataset 对象导出为 JSON 文件\n", "# test_data.to_json(output_file)\n", "with open(output_file, \"w\") as file:\n", - " json.dump(data_list, file, indent=4) # \"indent\" for pretty-printing" + " json.dump(data_list, file, indent=4) # \"indent\" for pretty-printing\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "a83c8881-c763-4bba-8b85-584a6722a38e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "presicion 0.94 same 0.49\n" + ] + } + ], "source": [ "import json\n", "\n", "\n", + "output_file = 'gpt2-small3-1024.json'\n", "\n", "with open(output_file, \"r\") as file:\n", " test_data = json.load(file)\n", @@ -580,15 +1638,30 @@ " output = item[\"output\"]\n", " #output = \" \".join(tokenizer.tokenize(output))\n", " model_response = item[\"model_response\"]\n", + "\n", + " print(output,\"||||||||||||\", model_response)\n", + "\n", " if model_response == output: #same it\n", " same_sum = same_sum + 1\n", " \n", - " if model_response.find(output)!=-1: #find it\n", - " right_sum = right_sum + 1\n", + " if output.find(\"Non\")==-1: # no Non\n", + " if model_response.find(output)!=-1 and model_response.find(\"Non\")==-1: #find it, but no Non\n", + " right_sum = right_sum + 1\n", + " else:\n", + " if model_response.find(output)!=-1: #find it\n", + " right_sum = right_sum + 1\n", "\n", "\n", - "print(\"presicion\", right_sum/all_num, \"same\", same_sum/all_num)" + "print(\"Accuracy\", right_sum/all_num, \"same\", same_sum/all_num)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bf88885-fb33-406e-9644-cd174a8a2f28", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/04-gene-sft/.ipynb_checkpoints/4-deepspeed-intro-checkpoint.ipynb b/04-gene-sft/.ipynb_checkpoints/4-deepspeed-intro-checkpoint.ipynb index cba95bbe310193355908067a03d34d0cd4acb70c..823bc80f97ddb972da9432cb2f6d3eef98776da0 100644 --- a/04-gene-sft/.ipynb_checkpoints/4-deepspeed-intro-checkpoint.ipynb +++ b/04-gene-sft/.ipynb_checkpoints/4-deepspeed-intro-checkpoint.ipynb @@ -10,12 +10,172 @@ }, { "cell_type": "markdown", - "id": "3383c2d7-91a9-4940-b3b2-698fb7d9dbb7", + "id": "75b8219d-8069-4b18-96c8-d5024ee049f1", "metadata": {}, "source": [ - "## 使用gpt2+deepspeed训练" + "## 大模型并行训练简介\n", + "\n", + "大模型的并行训练旨在克服单个 GPU 显存的限制和加速训练过程，通常适用于参数规模较大的模型（如 GPT-3、T5 等）。并行训练主要包括以下几种方法，每种方法适用于不同的场景和模型特性。\n", + "\n", + "---\n", + "\n", + "### **1. 数据并行（Data Parallelism）**\n", + "\n", + "#### **原理**\n", + "- 将数据切分成多个小批次，每个 GPU 处理其中一部分。\n", + "- 模型副本被复制到每个 GPU。\n", + "- 每个 GPU 独立计算梯度，最终通过梯度同步（如 AllReduce 操作）更新参数。\n", + "\n", + "#### **特点**\n", + "- **优点**：\n", + " - 实现简单，是最常用的并行方法。\n", + " - 对模型大小没有限制。\n", + "- **缺点**：\n", + " - 模型副本需要完整加载到每个 GPU，占用显存。\n", + " - 在超大规模模型中，显存压力较大。\n", + "\n", + "#### **适用场景**\n", + "- 参数规模适中，显存可以容纳整个模型的场景。\n", + "\n", + "---\n", + "\n", + "### **2. 模型并行（Model Parallelism）**\n", + "\n", + "#### **原理**\n", + "- 将模型切分成不同的部分，将不同部分分配到不同的 GPU。\n", + "- 前向传播和后向传播时，数据在模型的不同部分之间传递。\n", + "\n", + "#### **特点**\n", + "- **优点**：\n", + " - 不需要复制整个模型，可以支持超大规模模型。\n", + "- **缺点**：\n", + " - GPU 之间通信频繁，可能成为性能瓶颈。\n", + " - 实现复杂，切分模型需要精心设计。\n", + " \n", + "#### **适用场景**\n", + "- 单个 GPU 无法容纳完整模型参数的场景。\n", + "\n", + "#### **具体实现**\n", + "- 将 Transformer 的不同层分配到不同的 GPU。\n", + "- 常用工具：DeepSpeed 的 Pipeline Parallelism、NVIDIA Megatron-LM。\n", + "\n", + "---\n", + "\n", + "### **3. 张量并行（Tensor Parallelism）**\n", + "\n", + "#### **原理**\n", + "- 将模型内部的张量（如权重矩阵）切分为多个子张量，并分配到不同 GPU。\n", + "- GPU 之间协作完成矩阵计算。\n", + "\n", + "#### **特点**\n", + "- **优点**：\n", + " - 减少了每个 GPU 的显存占用，同时保持模型整体完整性。\n", + "- **缺点**：\n", + " - 实现较复杂，需要优化通信操作。\n", + " - 通信开销较高，适合较大批量的训练。\n", + "\n", + "#### **适用场景**\n", + "- 参数非常大的模型（如 GPT-3）。\n", + "- 需要极致优化显存的场景。\n", + "\n", + "#### **具体实现**\n", + "- NVIDIA 的 Megatron-LM 和 Hugging Face Transformers 提供了张量并行的支持。\n", + "\n", + "---\n", + "\n", + "### **4. 管道并行（Pipeline Parallelism）**\n", + "\n", + "#### **原理**\n", + "- 将模型分为不同的部分（通常是按层划分），每部分分配到不同的 GPU。\n", + "- 数据按照流水线的方式流经每个 GPU。\n", + "\n", + "#### **特点**\n", + "- **优点**：\n", + " - 减少每个 GPU 的显存压力。\n", + " - 通过流水线增加计算效率。\n", + "- **缺点**：\n", + " - 引入流水线延迟。\n", + " - 实现复杂，需管理数据依赖和同步。\n", + "\n", + "#### **适用场景**\n", + "- 模型非常深，层数较多的场景。\n", + "\n", + "#### **具体实现**\n", + "- DeepSpeed 的 Pipeline Parallelism。\n", + "\n", + "---\n", + "\n", + "### **5. 混合并行（Hybrid Parallelism）**\n", + "\n", + "#### **原理**\n", + "- 将数据并行、模型并行、张量并行和管道并行组合使用，充分利用多 GPU 资源。\n", + "- 不同的并行方法在不同维度协同工作。\n", + "\n", + "#### **特点**\n", + "- **优点**：\n", + " - 灵活且适应性强，适合超大规模模型。\n", + "- **缺点**：\n", + " - 配置复杂，依赖于框架和训练任务。\n", + "\n", + "#### **适用场景**\n", + "- 超大规模模型（如 GPT-3 或参数量 >1T）。\n", + "- 多机多卡的大型训练环境。\n", + "\n", + "#### **具体实现**\n", + "- NVIDIA Megatron-LM 和 DeepSpeed 的混合并行支持。\n", + "\n", + "---\n", + "\n", + "### **6. ZeRO 优化并行（Zero Redundancy Optimizer）**\n", + "\n", + "#### **原理**\n", + "- 通过分片存储模型参数、优化器状态和梯度，显著减少每个 GPU 的显存占用。\n", + "\n", + "#### **特点**\n", + "- **优点**：\n", + " - 极大降低显存需求。\n", + " - 支持超大规模模型。\n", + "- **缺点**：\n", + " - 对 GPU 间通信要求较高。\n", + " - 比数据并行复杂。\n", + "\n", + "#### **适用场景**\n", + "- 超大模型的高效训练。\n", + "\n", + "#### **具体实现**\n", + "- DeepSpeed 提供的 ZeRO Stage 1/2/3。\n", + "\n", + "---\n", + "\n", + "### **方法对比**\n", + "\n", + "| 并行方法 | 主要优点 | 主要缺点 | 适用场景 |\n", + "|---------------|-------------------------------|-------------------------------|---------------------------|\n", + "| 数据并行 | 简单高效，易实现 | 模型副本占用大量显存 | 模型规模适中，显存足够 |\n", + "| 模型并行 | 支持大模型 | 通信开销大，切分复杂 | 超大模型，显存有限 |\n", + "| 张量并行 | 高效利用显存 | 实现复杂，通信频繁 | 参数规模极大的模型 |\n", + "| 管道并行 | 显存需求降低，适合深模型 | 流水线延迟，数据同步复杂 | 层数多的大型模型 |\n", + "| 混合并行 | 灵活适配超大规模模型 | 配置复杂，依赖框架 | 超大规模模型（如 GPT-3） |\n", + "| ZeRO 并行 | 极大节省显存，占用少 | 通信成本高 | 超大规模模型显存优化 |\n", + "\n", + "---\n", + "\n", + "### **总结**\n", + "- **中等规模模型**：优先使用 **数据并行**。\n", + "- **单卡显存不足**：采用 **模型并行** 或 **张量并行**。\n", + "- **超大规模模型**：使用 **混合并行** 或 DeepSpeed 的 **ZeRO 优化**。\n", + "\n", + "对于现代超大规模模型，通常采用混合并行方法，比如 NVIDIA 的 Megatron-LM 和微软的 DeepSpeed，它们综合了多种并行策略，能够有效利用计算资源并加速训练。如果您有具体的硬件环境或模型需求，可以进一步探讨适合的并行方案！" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "06ddaa4d-e04a-41e0-beb5-f04dfaebcd54", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "c0d29667-1e75-46df-8f65-cae27609ee3f", @@ -169,171 +329,19 @@ ] }, { - "cell_type": "markdown", - "id": "75b8219d-8069-4b18-96c8-d5024ee049f1", + "cell_type": "code", + "execution_count": null, + "id": "a5372798-ced3-420c-b853-badd3ff05dc1", "metadata": {}, - "source": [ - "## 大模型并行方法\n", - "\n", - "大模型的并行训练旨在克服单个 GPU 显存的限制和加速训练过程，通常适用于参数规模较大的模型（如 GPT-3、T5 等）。并行训练主要包括以下几种方法，每种方法适用于不同的场景和模型特性。\n", - "\n", - "---\n", - "\n", - "### **1. 数据并行（Data Parallelism）**\n", - "\n", - "#### **原理**\n", - "- 将数据切分成多个小批次，每个 GPU 处理其中一部分。\n", - "- 模型副本被复制到每个 GPU。\n", - "- 每个 GPU 独立计算梯度，最终通过梯度同步（如 AllReduce 操作）更新参数。\n", - "\n", - "#### **特点**\n", - "- **优点**：\n", - " - 实现简单，是最常用的并行方法。\n", - " - 对模型大小没有限制。\n", - "- **缺点**：\n", - " - 模型副本需要完整加载到每个 GPU，占用显存。\n", - " - 在超大规模模型中，显存压力较大。\n", - "\n", - "#### **适用场景**\n", - "- 参数规模适中，显存可以容纳整个模型的场景。\n", - "\n", - "---\n", - "\n", - "### **2. 模型并行（Model Parallelism）**\n", - "\n", - "#### **原理**\n", - "- 将模型切分成不同的部分，将不同部分分配到不同的 GPU。\n", - "- 前向传播和后向传播时，数据在模型的不同部分之间传递。\n", - "\n", - "#### **特点**\n", - "- **优点**：\n", - " - 不需要复制整个模型，可以支持超大规模模型。\n", - "- **缺点**：\n", - " - GPU 之间通信频繁，可能成为性能瓶颈。\n", - " - 实现复杂，切分模型需要精心设计。\n", - " \n", - "#### **适用场景**\n", - "- 单个 GPU 无法容纳完整模型参数的场景。\n", - "\n", - "#### **具体实现**\n", - "- 将 Transformer 的不同层分配到不同的 GPU。\n", - "- 常用工具：DeepSpeed 的 Pipeline Parallelism、NVIDIA Megatron-LM。\n", - "\n", - "---\n", - "\n", - "### **3. 张量并行（Tensor Parallelism）**\n", - "\n", - "#### **原理**\n", - "- 将模型内部的张量（如权重矩阵）切分为多个子张量，并分配到不同 GPU。\n", - "- GPU 之间协作完成矩阵计算。\n", - "\n", - "#### **特点**\n", - "- **优点**：\n", - " - 减少了每个 GPU 的显存占用，同时保持模型整体完整性。\n", - "- **缺点**：\n", - " - 实现较复杂，需要优化通信操作。\n", - " - 通信开销较高，适合较大批量的训练。\n", - "\n", - "#### **适用场景**\n", - "- 参数非常大的模型（如 GPT-3）。\n", - "- 需要极致优化显存的场景。\n", - "\n", - "#### **具体实现**\n", - "- NVIDIA 的 Megatron-LM 和 Hugging Face Transformers 提供了张量并行的支持。\n", - "\n", - "---\n", - "\n", - "### **4. 管道并行（Pipeline Parallelism）**\n", - "\n", - "#### **原理**\n", - "- 将模型分为不同的部分（通常是按层划分），每部分分配到不同的 GPU。\n", - "- 数据按照流水线的方式流经每个 GPU。\n", - "\n", - "#### **特点**\n", - "- **优点**：\n", - " - 减少每个 GPU 的显存压力。\n", - " - 通过流水线增加计算效率。\n", - "- **缺点**：\n", - " - 引入流水线延迟。\n", - " - 实现复杂，需管理数据依赖和同步。\n", - "\n", - "#### **适用场景**\n", - "- 模型非常深，层数较多的场景。\n", - "\n", - "#### **具体实现**\n", - "- DeepSpeed 的 Pipeline Parallelism。\n", - "\n", - "---\n", - "\n", - "### **5. 混合并行（Hybrid Parallelism）**\n", - "\n", - "#### **原理**\n", - "- 将数据并行、模型并行、张量并行和管道并行组合使用，充分利用多 GPU 资源。\n", - "- 不同的并行方法在不同维度协同工作。\n", - "\n", - "#### **特点**\n", - "- **优点**：\n", - " - 灵活且适应性强，适合超大规模模型。\n", - "- **缺点**：\n", - " - 配置复杂，依赖于框架和训练任务。\n", - "\n", - "#### **适用场景**\n", - "- 超大规模模型（如 GPT-3 或参数量 >1T）。\n", - "- 多机多卡的大型训练环境。\n", - "\n", - "#### **具体实现**\n", - "- NVIDIA Megatron-LM 和 DeepSpeed 的混合并行支持。\n", - "\n", - "---\n", - "\n", - "### **6. ZeRO 优化并行（Zero Redundancy Optimizer）**\n", - "\n", - "#### **原理**\n", - "- 通过分片存储模型参数、优化器状态和梯度，显著减少每个 GPU 的显存占用。\n", - "\n", - "#### **特点**\n", - "- **优点**：\n", - " - 极大降低显存需求。\n", - " - 支持超大规模模型。\n", - "- **缺点**：\n", - " - 对 GPU 间通信要求较高。\n", - " - 比数据并行复杂。\n", - "\n", - "#### **适用场景**\n", - "- 超大模型的高效训练。\n", - "\n", - "#### **具体实现**\n", - "- DeepSpeed 提供的 ZeRO Stage 1/2/3。\n", - "\n", - "---\n", - "\n", - "### **方法对比**\n", - "\n", - "| 并行方法 | 主要优点 | 主要缺点 | 适用场景 |\n", - "|---------------|-------------------------------|-------------------------------|---------------------------|\n", - "| 数据并行 | 简单高效，易实现 | 模型副本占用大量显存 | 模型规模适中，显存足够 |\n", - "| 模型并行 | 支持大模型 | 通信开销大，切分复杂 | 超大模型，显存有限 |\n", - "| 张量并行 | 高效利用显存 | 实现复杂，通信频繁 | 参数规模极大的模型 |\n", - "| 管道并行 | 显存需求降低，适合深模型 | 流水线延迟，数据同步复杂 | 层数多的大型模型 |\n", - "| 混合并行 | 灵活适配超大规模模型 | 配置复杂，依赖框架 | 超大规模模型（如 GPT-3） |\n", - "| ZeRO 并行 | 极大节省显存，占用少 | 通信成本高 | 超大规模模型显存优化 |\n", - "\n", - "---\n", - "\n", - "### **总结**\n", - "- **中等规模模型**：优先使用 **数据并行**。\n", - "- **单卡显存不足**：采用 **模型并行** 或 **张量并行**。\n", - "- **超大规模模型**：使用 **混合并行** 或 DeepSpeed 的 **ZeRO 优化**。\n", - "\n", - "对于现代超大规模模型，通常采用混合并行方法，比如 NVIDIA 的 Megatron-LM 和微软的 DeepSpeed，它们综合了多种并行策略，能够有效利用计算资源并加速训练。如果您有具体的硬件环境或模型需求，可以进一步探讨适合的并行方案！" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", "id": "cd848439-bac8-46b2-9a0f-59ae7c343954", "metadata": {}, "source": [ - "## deepspeed的并行设计\n", + "## deepspeed具体设置\n", "\n", "\n", "是的，DeepSpeed 支持多种并行策略，包括 **数据并行**、**模型并行** 和 **张量并行**，并且可以通过其配置文件灵活地设置这些并行模式。\n", @@ -544,6 +552,22 @@ "DeepSpeed 的配置高度灵活，可以根据模型大小、显存限制和硬件条件选择适合的并行策略。" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8e6de4c-adc1-4a1b-840a-c8542b4ed783", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3383c2d7-91a9-4940-b3b2-698fb7d9dbb7", + "metadata": {}, + "source": [ + "## 使用gpt2+deepspeed训练" + ] + }, { "cell_type": "markdown", "id": "ab2812bc-f743-4f18-b49c-972781484dc6", @@ -569,14 +593,6 @@ "metadata": {}, "outputs": [], "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce701aeb-c8c7-450a-bbf9-b793a19cd0c6", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/04-gene-sft/.ipynb_checkpoints/5-peft-intro-checkpoint.ipynb b/04-gene-sft/.ipynb_checkpoints/5-peft-intro-checkpoint.ipynb index 16d8e0162dab380cd2779ede568d63fd60b2c8a6..152a6ccbd50d08792d4c506ca5a8ad6188fa5f7a 100644 --- a/04-gene-sft/.ipynb_checkpoints/5-peft-intro-checkpoint.ipynb +++ b/04-gene-sft/.ipynb_checkpoints/5-peft-intro-checkpoint.ipynb @@ -5,7 +5,15 @@ "id": "963e9ae0-ac68-44be-8c7d-fb9842784362", "metadata": {}, "source": [ - "# 4.6 基于llama的基因大模型指令微调" + "# 4.5 peft简介" + ] + }, + { + "cell_type": "markdown", + "id": "f4288594-c676-4369-aca1-730446f293d7", + "metadata": {}, + "source": [ + "## peft" ] }, { @@ -139,6 +147,22 @@ "如果您需要实现高效微调，可以结合 Hugging Face 的 PEFT 库快速上手。" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "a70b2631-c9b9-49da-96c6-6760c63040ac", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "7b47ddf3-85c9-4dd8-bbbb-34fc3bd6aa1b", + "metadata": {}, + "source": [ + "## GPT2使用peft样例" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -366,14 +390,6 @@ " print(name)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "37aa6abb-ab1c-4e9c-b968-579dd74044db", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "0add2f79-f35c-4638-80bb-0d8a87a9b6a7", @@ -502,11 +518,19 @@ { "cell_type": "code", "execution_count": null, - "id": "b4a41750-420f-49c4-845d-69db394794f9", + "id": "14f20171-0719-4dfa-b888-147b657ebff4", "metadata": {}, "outputs": [], "source": [] }, + { + "cell_type": "markdown", + "id": "b4e7bff2-2a4f-4a1d-9cb1-dd02aead2f85", + "metadata": {}, + "source": [ + "## LoraConfig具体配置" + ] + }, { "cell_type": "markdown", "id": "10c99eb9-8007-4297-972e-7be71768c9c3", @@ -595,62 +619,6 @@ "\n", "---\n", "\n", - "### **2. 微调哪些参数，冻结哪些参数**\n", - "\n", - "LoRA 的核心思想是通过 **分解矩阵**，只更新少量参数，而冻结模型的大部分参数。以下是常见设置的说明：\n", - "\n", - "#### **微调的参数**\n", - "- LoRA 通过 `target_modules` 指定的模块，例如：\n", - " - GPT-2 的 `c_attn`（自注意力模块）。\n", - " - BERT 的 `query` 和 `key`。\n", - "- 这些模块是模型中对性能贡献最大的部分，通过微调这些模块，任务性能可以显著提升。\n", - "\n", - "#### **冻结的参数**\n", - "- 除了 `target_modules` 中指定的参数外，所有其他模型参数默认冻结，包括：\n", - " - 预训练权重的绝大部分。\n", - " - 偏置参数（如果 `bias=\"none\"`）。\n", - "\n", - "---\n", - "\n", - "### **3. 一般如何设置**\n", - "\n", - "#### **（1）针对不同任务调整**\n", - "- **文本分类任务**：\n", - " - 优先选择自注意力模块（如 `c_attn`）作为 `target_modules`。\n", - " - `r=8` 或 `r=16` 是常见选择，适中计算开销。\n", - " - 设置适当的 dropout（如 `lora_dropout=0.1`）以防止过拟合。\n", - " \n", - "- **语言生成任务**：\n", - " - 对 GPT-2 或 GPT-3，选择 `q_proj` 和 `v_proj`（query 和 value 投影模块）。\n", - " - `r=16` 或更高，适应生成任务的高复杂性。\n", - "\n", - "- **命名实体识别任务**：\n", - " - 优先选择 `q_proj` 和 `k_proj`（query 和 key 模块）。\n", - "\n", - "#### **（2）参数量与显存的权衡**\n", - "- 如果显存有限，减少 `r` 的值。\n", - "- 对小型任务，`r=4` 或 `r=8` 通常已经足够。\n", - "\n", - "#### **（3）偏置设置**\n", - "- 偏置参数的影响较小，在大多数情况下，可以选择 `bias=\"none\"` 保持冻结。\n", - "- 对非常依赖偏置的任务（如生成风格微调），可以尝试 `bias=\"lora_only\"`。\n", - "\n", - "---\n", - "\n", - "### **4. 示例：如何选择目标模块**\n", - "\n", - "#### **GPT-2**\n", - "对 GPT-2 来说，以下模块通常是微调的目标：\n", - "- **`c_attn`**：注意力模块的组合层。\n", - "- **`q_proj` 和 `v_proj`**：Query 和 Value 的线性投影。\n", - "\n", - "#### **BERT**\n", - "对 BERT 来说，以下模块通常是微调的目标：\n", - "- **`query`**：Attention 的 Query 模块。\n", - "- **`key`**：Attention 的 Key 模块。\n", - "\n", - "---\n", - "\n", "### **5. 总结建议**\n", "- **微调的参数**：优先选择模型中注意力相关模块。\n", "- **冻结的参数**：大部分参数默认冻结以节省显存。\n", @@ -664,22 +632,82 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "26d9f362-18cc-471f-b208-f29a6933c06a", + "execution_count": 1, + "id": "bbc080ba-3ee8-4bc6-afd9-2a3241f1bcda", "metadata": {}, "outputs": [], + "source": [ + "import subprocess\n", + "import os\n", + "# 设置环境变量, autodl一般区域\n", + "result = subprocess.run('bash -c \"source /etc/network_turbo && env | grep proxy\"', shell=True, capture_output=True, text=True)\n", + "output = result.stdout\n", + "for line in output.splitlines():\n", + " if '=' in line:\n", + " var, value = line.split('=', 1)\n", + " os.environ[var] = value" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "26d9f362-18cc-471f-b208-f29a6933c06a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of GPT2ForSequenceClassification were not initialized from the model checkpoint at dnagpt/dna_gpt2_v0 and are newly initialized: ['score.weight']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f7e72521368341d38a2b11028715a871", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/5920 [00:00\n", + " \n", + " \n", + " [19980/19980 10:13, Epoch 3/3]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.2997000.3255490.8976350.9081170.8854830.896658
20.3040000.2900040.9048990.8890690.9259010.907111
30.3101000.2896580.9062500.8921380.9248910.908219

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "训练完成，模型已保存至 ./gpt2_lora_text_classification\n" + ] + } + ], + "source": [ "# **6. 计算指标**\n", "def compute_metrics(eval_pred):\n", " predictions, labels = eval_pred\n", @@ -729,7 +845,7 @@ " learning_rate=2e-5, # 学习率\n", " per_device_train_batch_size=8, # 每设备的批量大小\n", " per_device_eval_batch_size=8, # 每设备评估的批量大小\n", - " num_train_epochs=3, # 训练轮数\n", + " num_train_epochs=10, # 训练轮数\n", " weight_decay=0.01, # 权重衰减\n", " logging_dir=\"./logs\", # 日志路径\n", " fp16=True, # 启用混合精度训练\n", @@ -746,6 +862,7 @@ " train_dataset=train_dataset,\n", " eval_dataset=test_dataset,\n", " tokenizer=tokenizer,\n", + " data_collator=data_collator,\n", " compute_metrics=compute_metrics,\n", ")\n", "\n", diff --git a/04-gene-sft/.ipynb_checkpoints/6-llama-continue-train-checkpoint.ipynb b/04-gene-sft/.ipynb_checkpoints/6-llama-continue-train-checkpoint.ipynb index 12125487b0c6b8b3014852e437c7290d6056a63e..2227dfe3c8708f466dc90cbcaccb3a60aa2e4a15 100644 --- a/04-gene-sft/.ipynb_checkpoints/6-llama-continue-train-checkpoint.ipynb +++ b/04-gene-sft/.ipynb_checkpoints/6-llama-continue-train-checkpoint.ipynb @@ -330,6 +330,38 @@ "本节任务是基于llama。训练一个能够处理dna和protein蛋白质数据的基础预训练大模型，数据为第一章中的预训练数据，包括英文数据。" ] }, + { + "cell_type": "markdown", + "id": "aec90d65-ac62-4394-a526-ca62d8bdbad4", + "metadata": {}, + "source": [ + "## 环境设置\n", + "并行环境对transformer、peft等的版本要求比较高，如果版本不匹配可能会出现各种异常问题\n", + "之前的课程，都是单GPU运行，一般不存在版本问题，默认安装的都是最新版本。但运行并行环境时，需要确认下版本再运行，本课程运行并行环境如下：\n", + "\n", + "* Python 3.12.3\n", + "* transformers 4.45.2\n", + "* peft 0.3.0.dev0\n", + "* deepspeed 0.15.2\n", + "* accelerate 1.0.0\n", + "\n", + "如果不是，可以重新安装即可：\n", + "```\n", + "pip install transformers==4.45.2 deepspeed==0.15.2 accelerate==1.0.0\n", + "\n", + "#peft参考使用的是chinese llama的版本，需要git安装\n", + "\n", + "git clone https://github.com/huggingface/peft.git\n", + "\n", + "cd peft\n", + "\n", + "git checkout 13e53fc\n", + "\n", + "pip install . \n", + "```\n", + "如果有环境问题，可以查看本目录下的pip_list.txt" + ] + }, { "cell_type": "markdown", "id": "b1bd33b8-2e05-4b59-9d8f-c48de194cfd6", @@ -339,14 +371,24 @@ "\n", "```\n", "# 复制第一章训练数据,包括dna，protein，还有英文数据，添加英文数据是为了避免遗忘问题\n", + "\n", "mkdir train_data\n", "cp ../01-data_env/data/*.txt train_data/\n", + "使用这些数据，6卡4090大概大致需要训练16个小时，autodl也需要近200块钱了。\n", + "\n", + "建议学习时，可以使用1/10的数据训练：\n", + "awk ‘NR%10==1’ dna_1g.txt > dna.txt\n", + "rm dna_1g.txt\n", + "其他2类数据依次类推\n", + "\n", + "这样大概需要2到3个小时就能训练完成了\n", + "\n", "\n", "#持续预训练\n", "./run_pt.sh\n", "\n", "#合并模型\n", - "./merge_sft_model.sh\n", + "./merge_pt_model.sh\n", "\n", "```" ] @@ -361,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "69b3e97f-a801-4264-a651-a854bcfba9c6", "metadata": {}, "outputs": [], @@ -376,10 +418,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "339435d9-9379-4b30-ae8b-50feee1ba714", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "LlamaTokenizer(name_or_path='dnahlm-merge-hf', vocab_size=91643, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '', 'unk_token': '', 'pad_token': ''}, clean_up_tokenization_spaces=False), added_tokens_decoder={\n", + "\t0: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t1: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t2: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tokenizer = LlamaTokenizer.from_pretrained(\"dnahlm-merge-hf\")\n", "tokenizer.pad_token = tokenizer.eos_token\n", @@ -388,10 +445,61 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "d0f154bb-b1ab-4611-a14c-9b403043fd96", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "342e4ab139b64bb78f0429c2f92c8310", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/3 [00:00', 'KCG', 'FVGP', 'MVHL', 'KV', 'HLE', 'ADV', 'ASSC', 'RSAV', 'I', 'YL', 'TSEE', 'P', 'FEG', 'VLGL', 'RLK', 'EGI', 'AI', 'TGC', 'W', 'PRW', 'P', 'DEM', 'DER', 'SAV', 'W', 'RVE', 'PY', 'TRH', 'FG', 'RVLY', 'SFGV', ',', '<0x0A>', 'The', '▁primary', '▁use', '▁of', '▁L', 'La', 'MA', '▁is', '▁research', '▁on', '▁large', '▁language', '▁models', ',', '▁including']\n" + ] + } + ], "source": [ "text='''GCTGACTCTGCCAGGATGGAATGAAATTAGGTTGTTTTAATTATAATGTAAAGTCAGTTCTAGTCAGACATAGTCACATAGGCAAGTAAGGGAACCTAAAATTGCTTGGAAT,\n", "KCGFVGPMVHLKVHLEADVASSCRSAVIYLTSEEPFEGVLGLRLKEGIAITGCWPRWPDEMDERSAVWRVEPYTRHFGRVLYSFGV,\n", @@ -426,10 +587,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "ebf869c8-866d-4770-8f64-79d671f88663", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e497889a1c3c484cb57c4b6fd93b45ab", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/3 [00:00',\n", + " '<0x0A>',\n", + " '##',\n", + " '#',\n", + " '▁Inst',\n", + " 'ruction',\n", + " ':',\n", + " '<0x0A>',\n", + " 'Det',\n", + " 'erm',\n", + " 'ine',\n", + " '▁core',\n", + " '▁prom',\n", + " 'oter',\n", + " '▁detection',\n", + " '▁of',\n", + " '▁following',\n", + " '▁d',\n", + " 'na',\n", + " '▁sequence',\n", + " ',',\n", + " '▁The',\n", + " '▁result',\n", + " '▁will',\n", + " '▁be',\n", + " '▁one',\n", + " '▁of',\n", + " '▁the',\n", + " '▁following',\n", + " ':',\n", + " '▁Non',\n", + " '-',\n", + " 'prom',\n", + " 'oter',\n", + " ',',\n", + " '▁prom',\n", + " 'oter',\n", + " '.',\n", + " '<0x0A>',\n", + " '<0x0A>',\n", + " '##',\n", + " '#',\n", + " '▁Input',\n", + " ':',\n", + " '<0x0A>',\n", + " 'CCG',\n", + " 'TGCG',\n", + " 'ACCGG',\n", + " 'AAG',\n", + " 'TGGGGC',\n", + " 'GGCG',\n", + " 'ACCCCGG',\n", + " 'AAG',\n", + " 'TCCCC',\n", + " 'GCCGGG',\n", + " 'TGCAGC',\n", + " 'TTGG',\n", + " 'TCGG',\n", + " 'TTCG',\n", + " 'ATCGCC',\n", + " '<0x0A>',\n", + " '<0x0A>',\n", + " '##',\n", + " '#',\n", + " '▁Response',\n", + " ':',\n", + " '<0x0A>',\n", + " 'prom',\n", + " 'oter']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tokenizer.tokenize(prompt)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "11875339-4901-4912-86e5-afe8c74921d9", "metadata": {}, "outputs": [], @@ -498,10 +718,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "1b02644a-8b24-45aa-b22d-0f7ce2270dd9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input (test): Below is an instruction that describes a task. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.\n", + "\n", + "### Input:\n", + "CCGTGCGACCGGAAGTGGGGCGGCGACCCCGGAAGTCCCCGCCGGGTGCAGCTTGGTCGGTTCGATCGCC\n", + "\n", + "### Response:\n", + "\n", + "real answer: promoter\n", + "--------------------------\n", + "\n", + "model's answer: \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/lib/python3.12/site-packages/transformers/generation/configuration_utils.py:601: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.01` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", + " warnings.warn(\n", + "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "promoter\n" + ] + } + ], "source": [ "input_text = format_input(data[\"test\"][0])\n", "\n", @@ -562,10 +820,118 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "68831e19-5a99-46d8-9f40-e8bf6957dbfc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Donor Sites |||||||||||| Non-Splice Sites\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "Donor Sites |||||||||||| Non-Splice Sites\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Donor Sites |||||||||||| Splice Sites\n", + "Background Sequences |||||||||||| Background Sequences\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-Splice Sites |||||||||||| Non-Splice Sites\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Binding Sites |||||||||||| Background Sequences\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-Splice Sites |||||||||||| Non-Splice Sites\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Non-promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Background Sequences |||||||||||| Background Sequences\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Binding Sites |||||||||||| Binding Sites\n", + "promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Donor Sites |||||||||||| Donor Sites\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Binding Sites |||||||||||| Binding Sites\n", + "promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "Background Sequences |||||||||||| Binding Sites\n", + "promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Background Sequences |||||||||||| Background Sequences\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "Donor Sites |||||||||||| Non-Splice Sites\n", + "Binding Sites |||||||||||| Binding Sites\n", + "promoter |||||||||||| promoter\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Non-promoter |||||||||||| promoter\n", + "Binding Sites |||||||||||| Binding Sites\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Non-promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Acceptor Sites |||||||||||| Acceptor Sites\n", + "promoter |||||||||||| promoter\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Donor Sites |||||||||||| Acceptor Sites\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoter\n", + "promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "Background Sequences |||||||||||| Binding Sites\n", + "Acceptor Sites |||||||||||| Splice Sites\n", + "Non-Splice Sites |||||||||||| Non-Splice Sites\n", + "Donor Sites |||||||||||| Non-Splice Sites\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "Background Sequences |||||||||||| Binding Sites\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Acceptor Sites |||||||||||| Splice Sites\n", + "promoter |||||||||||| promoter\n", + "presicion 0.73 same 0.3\n" + ] + } + ], "source": [ "import json\n", "from tqdm import tqdm\n", @@ -598,6 +964,14 @@ "\n", "print(\"presicion\", right_sum/all_num, \"same\", same_sum/all_num)\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bc38f47-4a7d-43eb-abe8-db4310d280e3", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/04-gene-sft/.ipynb_checkpoints/gpt2-small3-1024-checkpoint.json b/04-gene-sft/.ipynb_checkpoints/gpt2-small3-1024-checkpoint.json new file mode 100644 index 0000000000000000000000000000000000000000..851b30b8158403772b2b64b228d84ae0e3625fa5 --- /dev/null +++ b/04-gene-sft/.ipynb_checkpoints/gpt2-small3-1024-checkpoint.json @@ -0,0 +1,602 @@ +[ + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCAGGATGCGCTGACGACCCGGCTGGCAGGCGGGTCCTCGTGGGCGAGGCGAGGGAGGCGGCGAGAGAGGAGCAATAGTTTCCCACCGCTCCCTCTCAGGCGCAGGGTCTAGAGAAGCGCGAGGGGATCTAGAGAAGCCGGAGGGGAGGAAGCGCGAGTCCGCGGCCCGCCCCGTTGCGTCCCACCCACCGCGTCCCCTCCCCTCCCCTCCCGCTGCGGGAAAAGCGGCCGCGGGCGGCGGCGCCCACTGTGGGGCGGGCGGAGCGCCGCGGGAGGCGGACGAGATGCGAGCGCGGCCGC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGTCATGCCTGCAGGGAGAGAGGGCTGGGGCGCTATCTATGGTAGAAGTCAGAAGAGGCTGTCTATATGTCAGTGGGAGGAGAAAAGTGGCTCCAAAGCGGACTGTCTGTCCCTGTGCCAGAAGACATTGCTATTCAACGTTCACTCGCTTCTGCACAGGCCCACGTCACCCACAGACCGAGAACTCATCCACTAAGCGCTGCGGGTGGGCAGTACATACCGACGAAGTGCCTTCGCTACCCCTCTGGGTGTCCGTGCTCCCGCGAACCGGCTGGGGCTGCAGACGGGAAAGGAATGTCC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGGGTTTGCACTAACCGATCCACTCCGCGGGGACCTCTTAGAATTCTGCTGTGGATAAGGATATCGAACTACCGAGTCACTGGACCTAAAGAGAGATTCACTGTATCATCGTCTGCCTTAGACTGTTCCGCACCCAGGAACTGGGGCTGTTGCGCCCTTAAGGTTACTTTGAAGCCAAGGTCGCAAACAGACTTCCGCATTGACGTCAGTAGCCGAACGCTGATTTTCTTAATCTAGTATTTAGGATGGGCCTCTGTCGCCTAGCCGCTATCGCAGAGTGGAGCGGGGCTGGGAGCAAAG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCTCGTAAGGACCTAACTGTTCCAGCACTACACAGCTCATGGTGTCCTCAAGATTAGTACATATCAGAGGCCATGAACTTAGTCTTACGCTCACTGTGGCACTGGACGCGCGGGGGATGCGGGGTTGTTCCCAGCGATTTATGATTCGTTATTTGCCGCGTTGACTCGCCGTCTGTAGCCCCATGACAACGACATTCCTGCATTCTCTGCCTGGAGAGCGAAGTGACAATACTGAATTGAAACGGCTAGAATGTCGCTCGCTGAGGCTCCGGACCTTGGAGCGTCTAGAGTCTGGCTAC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TGCCACATAAGTCGGGTCAGCAAGTCGGTGGCGAAAAGAGGGCCACGTCCCAGACTTTCTGGGAGGAGGCGGGACAAAGGGGCGGGGCGAGCGCAGCGCCCTCCGGGTGGGCGCCGTCAAGAGGCCGGGGGCGGGGCCGAGCGCGGCTGGGCGGGGCCTTGAGAGGCCGGCCGGGGGCGGGGAGGCTGGCGGGTCGGCGCGGGCCCAGCCGTGCGTGCTCACGTGACGGGTCCGCGAGGCCCAGCTCGCGCAGTCGTTCGGGTGAGCGAAGATGGCGGCCGAGAGGGAACCTCCTCCGCT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ATAGGATGCGTCTGTACTCTGATTAACAAACTGCTGAAGGAATAAATATGTACTTGCTGGGCAGCGCCGCCGGCCGAATGGAGATAAGCCTATGCAGCTTCGTGCGCGGCTCCCCAGCCCTTTGCTGCGCCGCGAGCTGCGCCCTGAGACCCCCGCCTCGCTGCCAGCTACTTACCTGCCCCGGCGGAGGGGGCCATGTTGCTACACCTAGGCAGGCGGCAAGAAAGCACGCGTAATGAATTCCTTATATCCCCCGCGCCCCAACGGCGGCGGCGCGCCGGCCGGCATGGAGCCCCGCGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGGGTAAAAAAGGGGACAAATGGAAATTTGTGTTGCAGATAGATGAGGAGCCAACAAAAAAGAGCCTCAGGATCCAGCACACATTATCACAAACTTAGTGTCCATCCATCACTGCTGACCCTCTCCGGACCTGACTCCACCCCTGAGGGACACAGGTCAGCCTTGACCAATGACTTTTAAGTACCATGGAGAACAGGGGGCCAGAACTTCGGCAGTAAAGAATAAAAGGCCAGACAGAGAGGCAGCAGCACATATCTGCTTCCGACACAGCTGCAATCACTAGCAAGCTCTCAGGCCTGG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCTGGGTCCTAGAGGCTACTGAGACAAATGCTGGCTGAAGCCGAGGTACCTAGATTTGTGCAAATCGAGGAGTTCTATTCTGCGTGTTTGTGAAAGGGGGTTAGACGAGAGGAGTGCGGTGGAGGCGAAGATAATAGGATTATTCTTACAGTGTAATAATAATTATTGGTGCTAATGTTTAGGAAGCCGTGAAAGATAACACGAGGGGAGAAAGTGGATAGAAAAACCACCGCTGGCAGTTCTTTTCATTTGAAAGTGAATTGGGAAAAAGCGAATAAAAATTAGTCATGAGTTTTGGTA", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTCGTTGCGTAATCCTCCTTCCGCACCAACGCAAGGCAGGACAGGAGGTCAAATGAGTTCGAGATCAGGCGCCACAGACTCCACTAAAGCACAGACACCAGGTGCGGTAAGACACGCTGGCTCCAAAGTAGGACACCAAACCATTGCTAATGCCTGGGGAGGGCCAAACATGGGCAGGCTGGAGCACTCTCCTCAAAGAGGACCACAAGCCGTGACAGCAGAGCCCCTCGGACTGATGGGACACGGCTTAAACTGAAGTTCGAGGACTCTGTTCTGCAGAGGTCTGAGGCTGGAAGGGGG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGGGAGAGAGCTGGAGTCTCGCGGCCTCGGAGGCCGGGAAGCCAGCCCCGGCGGGGACCCGGGCGGGGCGGAGCCAGCGGAGGCCCCACCCCCGGCGTCACCGGCCCCCAGGGGGGCGTCGCCCCCACCCCGCGCTCCAGGTAGCGGCGGCCTCAGCTGCCGGCGGTGGCTGGCTGAGCTCCCCAGCGGCCCGCGGGCCGGGGCGGGGGCGGGCCGGGGGCGTTCCCGCGGGCCACCGCCCGTGATGTCACAATCGCGGCGGGCCGCGGCGCTCCGGGGTCGGCGGTGGGCGGCGGGCCG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGACTATACCACTTGCCGGCCGCGCAGAGCCCTCCCCAGGTCGCGCAAGGAGCTCGAGCTACTAACGGAGGAGCGGCGCAGGCCCCGCCCTTAGTTCATGAGCTTACTTGTACAGATCTCGGCCCCGGCCCCGGCCCCGCCCCCTGCCTCATCGTGGACCATCACGCTTCCAGCCCAGTCGGCTACGGAGATAATCCATGGAGCTCCGAGGCGCCGAGAGAGCAACCTTAAAGTATCGGGATCAGCGACATTATCAGCGCTGGTCCCTGCAGCTTCTGGTTCGAGATCTGGAAGGGCGGT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AAGTTTAAAAATGAACCCCCAACAGAGCCACCCGCCGGGGACGGTTCTGTTTTCAGCACAACCACGGGGTTCGTTTAGCCTGTCGCCCAAGTTTGTTTCCTCTAGGTCGCCCACCTTCCGTCGAATTCTGTCACTAGACTTTTATACTGGGTTTGTATGTAGCTCGATTCTGCATACAAGCTGGACTGAGAACTGGCAGGAGGCGAAGATGAGGGGGCGGCATCCAATTTGTTGTGATCCTTTTGCGTCAGGCTTCTGCCTGAGCTCGGTGAGGTCAAGCCTCCTCTGCTTCCACCCCTC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGTCCGCTCCGCGGGGCAGGCGTCGTGGGCCCAAGGAGGCCAGGCCAGGAGTCTCGGCCTCGGGGTTACGCGGCGGCGGGCAGCTCTTCCGCTCCGCTCAGGGTGCTGCTAACCCACAGCCAAGCAGCGACCCGCAGGACCGGCGAGAACCCCGCCACTGACTCCGGCCGGAAGAGAACGTGCAGAGATGTACGTCACTTCCGGGGCGTGGCGTGGCCGGGCGTGGCCGTCCCGCCCCTGGCGGCGTGCCTGGTGCCCTGTTCCGCGTCTGTGCGACCGTCCGTCCCGAGCGCGCAGCCG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TACCGGTGCTACTTTACGCTGCGTGTTCCCTAGCAGGGGACCTAGTGTGCTACCATAAAAGCACGACGATAAGACTATACCTCTTCGCCTGCTACTAACATAGGGCACTTCTTCCCCCTCAATCTATCGACGTTCGTTAATATCACCGACTCCCAGGGAGGATCTAATAAAAGGAGGAGGGCTCCTGGAATAATAGAGGAGTGGTGCTGCGAGGGGAAGGAAAAGCCAATCCGTGACCCCTTTCCCGGAAAGCTTCGAACCGACAGCCAGAAGCCCGCACGCACCTCGCACCATGAGATG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGTCGAGAAGACCTGGGCTACTGAATGACGAATGGGACGCACACGTACTGGCTACTTTTGTGGAAGATGGTCCGCCCTGGTTTGGATTGCGCTTTGCCACGAGAACCGCCGGGAAGGTGGGGGACGGGGACGGGAGCAGCCAATCACGCGCCGTCCCGTTCGCTTCGTCAGGGCGCGATCGCGGATTCGGCACTCAGTGTATGGTACTTCGGGCGAGCGAGGAGTCGCGAGCGCTGTGAGTTTCTCCTGGAGTTTTCTGCCGTTTTCGGACCCTAAAGCTTATCTAGGCTCGCGCTCTGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCGTCCTGCTCCAATTGCCAGGAGATACCATTAGCGGACTGACTCCAAGAAAAGAGATCCCTTGAAAAAGGCAGTGCTGAACGCGAGTATGGCGCTGCTTGCGGGCCATTCTGTGCGCTACGCTACCCGCTCGTATTAATCAACAACAGGACCGGACTCTGGTTGCTTGGGAGTTCAGGTGGACAACAGCAACTACTAACAAGGTTTTTCGTTCATAGTGCGGGAATGGCAAAACACGAGAGAAACGCACTCTTCATAGGTCACTATCCGTCTGGAAGAAGGCTTTGGACAGAACAGGG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCCTGCCCCCAGGCACTGACAGGGTAGCAGAGGGAAGACACATGAGCTGCTTTTTAATTTTTTTTAGAATTAATAGAGACGGGGTCTCACTCTGTCACTATGTTGCCCAGGCTAGTTTTGAACTCCTGGCCGCAAGCAGTCCTCCTACCTCAGCCTCCCAAAATGCTGGGATTACAGGTGTGAGCCACCGTGCTTAGCCATGAATTGATTTTAAAACCTTAAATGAGGGGCCAGGTGTTGTCTTTCCGTTTGCCTGAGTGACGCGGGTCTTCTCCAGGACATCCGGCCACAGATCTGCC", + "output": "Non-promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CAGCCCCTATGTTGGTGTGGAGACCCTCACCCGAGCTGGGCCTGCACCCTACACCAATCCGAGGTACCACGATCGGATTACTACAACAGCGGGGTCAGCTCAGGCGCTCGGTCCCCCGGGATCCTGCTCCCAGGCACACGATTCCGTTCGTTATAATTATGTAGACGGGGCCTGCGGTAGACCGACTGCGGGTGACCGAGTAGTGCAAAGTCTGGCTTCTCTTTTGATAGAGCACATCGATCTGTCTTCCTCTTTTCACCGTCGATGAGCCCCGCGTGCCTGGGACTGCTCCAGGCTCCA", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTCGCCGCCCACATTAGTGTGGGGCCCTGCGGCCTAGCGTCCCTCACCAGAGGCCTCCCCTTGCCTAGCTGGACCGCCGAGGGACATCGACGAGTATCCTCCTCCTGCTGTCCCCGGCTTCGCCTGCCGCCCCTAACCGGCCAGTCAAGATGGCCGCCGCTGGGTGAGGCAAGCTGGCGCGCCGCGGGGGCGTCTGGGAGTTGTAGTTCGGGACGGCGGGCTGACGCACTTCGCCGCCGGCCGACGGGCGCCATTGTGCGGCGCGCGCCGGGTGAGTGCCGCGCGAAACCTGCGTCCGTC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCGGGTAGCGATGGGCCGGGCCTCAGTTGTTTAAAGGCCCGATGCGACCCCGCTTAAGACGTCAGATCGTTCACCCCCGAGGGTCGCCGGATTTCGCTCCCGCGCGAGATGGCCTCCACCAGAGACTGCTGCTGCGCCTTGGAGCGGTGCCTCGCCTCCCAATCCTCGTCGTGAATTGCCCCAATTGCTGATCTGGGAACAAGCTCGTGCCAGGCGGGCGGAGAGTACGAACTCCTTATAGACCAGGATGGGCGTAGCAAGAGCGCGGGCGGCCCATGAATGTCTGCAGCGGTGAGGACC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGGATGCGGGGCCGGAGGGGATGGAAGGGGCCGATCTAACACGATATTCCGTTCAAGAACTACAACCGTCTACAGCACACCATAGCCGCTCGCTGAAGGATGTCGAAGGGCGCCCAGGCACCCACCACGTTTGGCCCCTGCCCGGGCCGGTTCCGATATGACTCACTCATTTACTCCCCTAGAGCTCCGAGCCTCGGATCGCCGAGGTGACCCTTACCCGCCTTCCTACCACCTTAGGGGATTTGGCCCGGAGAACGAGATCACCCTCTCATCGTGGCGGGGTATTCCCTTTAAGGTTTG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCCCGTCGGTCCGCGGGGCTCTCGCTGGAGCCTCCCCGACTCCGGTTTCCCCTCGTCCAGAAGCCCGACGTAACCAAAGCCCAGTCTGTCACTTTAAACACGCCCCGCCCCGCCTCCCGCGGCTGTGTTGCCTCCTCGCTGGAGAACACCCTGGTCGACCTCTGTGCGTCCGTGTGCGCGAGCGCGTCCCGCCGAGGCGGTGGGCAGGGCGGACGGTGCGCAGTGCGTTCCCGCTGGTCGGAGCCAGCACACTAACCACGCCACGCGCCCTGCCGTCCCTTCGCCTCCAGCCGCTGCAG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CACCTACTCGGGGTCCGAGGAGGGAGGATTGCCTAGGCCCAGGACTTTGAGAACACGCCTCTACAGAGATTTACATTTTAAGAAAATTAGCTGAGTTCGGGTCGGGCGCAGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGGCTGGCGGATCACCTGAGGTCAGGAATTCGAGACCAGCCTGACCAAAACGGCGAAACCCGTTCTCTACTAAAAATACAAACACTAGCCGGGCGTGGTGGCAGTCGCCTGTAATCCCAGCTACTCCAGAAGCTGAGGCAGGAGAATCGCTT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCGGCCGGTGTGCGGGACACACTATTGTAATCCTAAAAGGCCATGGGCCCCTGCTGGGGTCAAGAATAATGCTCAAAGTTTAAACTACCGAGTGTTGCTGAGGCTTGGGTTGCACATCGAGAGTCGCGATAACACTCACCCAGGGGAACATACCGCGAGGGATTACGGACGATGGAGAGATCACCCAAGGCAGTGGGAGTCCGCGCTTCCTCCCTTAAGTACCGCTGGTTGTCCCCCGACGTAGTGTAGATAGTGAGCCCTCTTGTCCGCTGGCACGTGATGTATGTGTGTCCTTGTCT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGGTTAGGCGCGTGCCGCGAGAACAGAGTGGACGGAGCGTAGGAGAGACCGAAAAGGCTGGGGGTGGGAGTAGCGGATTTGAAGCACTTGTTGGCCTACAGAGGTGTGGCAAGCAGAGCACCTCAGAACTCAGGCGTACTGCCCGCCGCCCGAGCCCTGCGAGGGCCGATAGCGAGGGTGTGGCCCTTATCTGCACCCAGCAGAGCGCCGGCGGGGTACGGTCTTAGGACCTCGATCTCCTTCTCCCTCATTTTCTCTCATCCCTACCTATTGTGGGTGAGTCCTGGCCCCTGGACGGGG", + "output": "promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTTGCATTAATACACCCGGCGCCAGAGGAACGTGGGGATCAATCATATTACACTGCTTCAGACTTGTAATTTTATTTGGTTCAGGCATCGTAGTGGTGAGGCTTGGCAGAGAGCCCGAGATGGAGCTATTGAGACGTTTCAGGCTGGGCGGATAATCGCATCAGTAGGGACTTGGCTAGATCTCCCAAGCCTTCTCCACTCTTCAAACTTTCACCAAACTCTTAGGACCCGTCGGGTCGGTTTCATAGACTCAGCCGCTCAACCTTTTAAGCGAGCGGCGCGGCGGGGCGGGCGAACTCG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGAGCGTCATCTTATCATTCTCTCTTTTTTTTTTTTTTCTGGTAATGATTTTTTTTGTTTTTTTTTTTTTTGATATTGACAAAAGTTTAATCATTTCAATTAAAAATGCCACTAATTTGACTTTTTAAGTAAAAAATGTAGGGGGTTTTAAAACTACTTTCCTACTACCAAAAAATCAGAAAGTATCTAGCTTTCTAAATTGGGAAAGCAAGCAATGTTATAAAAACACTGAAGGAATCTCTTTCTTCGTGACCTTTTGTTAAACTCGGTTTAAGCTGTAGACCTTATTTAAAATAAAAT", + "output": "Non-promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGCTGTGAAACAGGGCCACCTGCAACCGTGGCTTTTCAAAGCTGAAGCCATACCCTGGAGGGAGAGAGGATCCTTGGGCACTGACCCTGGCTGTCCACAGCTAAATGATCATGGGACACCCATGACCATGGGCGTGGCTGGAGGAGGCCCTGCCCTCAGGGGTCTATTGGCATCCAGGGGAAGTGTCTACGGGGTAATGATTAACCCACACCTGCCACCTGGTTTTCAGCCTTCTTGCCCTGCTGACTCATTTTGCAAATCCCACTTGCAAAATTTGGGAGCAGCCTGGGTCCAGCCCCC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTGTGACAATCATGGAGCAGTTGATTGACCCCTTTTCGAGCGCTCGCTATGCTTTAATTTCAAGACCCGCTCTCTAAGGTGCTCTTCCCCTTTAAGACTCACGGCTCTTCCCGTCAGGCTAGCTCGACCTGAGGATTCGTCAACCTCCTTGACAGCTTCCGCTGGCCTGCACCCGAGATTGGGGACGGTTTTCCGCACATCGTGCCTTCATTAGTTAGGTCCTGCGAGTGCATCCAAGGGCCCTCCCCCAAGCCAACAGCAGAACCAGCTCAATTTCTACCCTCACGGCGCTCCGCGGTG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TAGAGAGGCCAGAATCCCTCGGGAGTGCTACCCGGTCAAAAAATCGCGCCGCCCCGTGGGGGACCGAACTTAGCGATGACGGTTCGTCTAATCCCGTAGGAGAGTATCGGGCGAGCTCGGATTGCGAGATTACCTAGTGTTAGTGCGACCGCCCAGGCACATCTGCAGACTGACGTCAGTATGCAAATCAGAGTCGGAAAAGCTGCCAAAATTTTCTCCTGCTGCGCCACCTAACCTGCGTTCGGGGGTAAACCCATCTAGGGCTACTTCAACAAAACTTTGCTGCCCTTCCTGCTCCTC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AAGCTTTGTTTAGGTCCGGAAGGCGGGCTTTCCTGGGAGTGGGTGGGGAGGGGGCGTTGATTCTTGACCAATCCTTTCAGTCCGTTGGGTGGTGACCAGCCAATGGGCCGGATGGATAGGACGCTCCTCCCGGAGAGTAGTGAGACCCCTGGTGCGGGGCGATTGGCGGCGGGAGCGATGAGTGGCAGCCGCACGGCCCAACGGGAGCTGTGCGTGGGCCGCGGGGCGGGGCCAGGGCGGGTGCGCGGCGGCGGCGGGGTGGCTGGGCCGGCGGCGGCGGCGGTACGAGGCGCGCGCTCG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCCATCCAGCGAGAGGGGCAGGTTCCGCATTTTCTCTTCCCCTTTCCCAGCGCTTCCTCCAGCACCCGAAGCCCCAACCCTGCGGGTCAGGAACTCCCTAGTCCCCAAGTCTAGGGATGAGATGGGGGAAGGAGAGCCGTCAGGGTTGACCTGGAGTTTTGTCCGCTCCTCCCCTACAGTGATCCCTCTAGCCTTCTCCAGTCGCCTCCGCCATGTCCGAGGAGCTGGCCCAGGGCCCCAAGGAGAGCCCCCCGGCGCCGCGTGCGGGCCCCAGGGAGGTGTGGAAGAAGGGTGGCCGC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACATCGTATTGCGCTTACTCGGGTCGTGCAATGCACTGACTAGGAAAGGGCTGCGGATGCCTACGTACATAATGCTGTTGCGCACCTATGCACGCGGGTCTGGTCCTGGGCAAGAACCGCCCCCTCTCCGGGCCTGGTTGCCTTTTGATTTGCAGAACAACGGGCCAGGCCCCTTCCCTCTACCGCTCACAAGCGTACATCGCGACCATACCTGGTCCAATGCGCTACGGACGAGGGCAATTCTCGTAATAGGACCCACAGCAAGTCTTGTATCACCTTTGGCTAGCCTGTGCCGGCTGT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACGTGACCGACGTCTTCCGCATACCCGGGGCTTCCCTCACTTCCAGGACAACCTGTTGCGCCTCTATCTCCTCCCCTCCCCGCAGTTTCCCCGCCTTGGCCTCTAATAGTAGTGAGAGCACTTTAGGCGGTATAAAGTCTGACGCTAGAATCAATCTAGCCGCGGCGATTGGCCACCTGGGGAATCGTTAGCAAAATGTCACGTTTACTACTTTACGGCGATCTTCCAGGGTCCGGGGACTGTGCCAAGGATCCTACCGGGGCTGGCAGGAGCCTAAGACAAATACGCTAGCGGCGGAGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GAGATTTCACGCCTAGCGGCTCAGGATCGTGTGTGCTACAAAAATAGGGCCGCAGGGCCGTATAGTCGCACGAACATACAGGTCGTCCGATTCAGGCTCGGGGGCTTGGACGGAGATGACGGCCAACTCTCACCCTCACCCCTCATTTAGGCGGGCGCCGGGGGCATCATCGGGGTCCCTGGGCCCTCCCCCTGCTCAGTGCGTAGCTCCGATAGAAAAGCTAGGGCATACGTGCAGAGACGGGTTGTCAGGCAGACACCCTGTCTGTTGGCAGCTAGCAGCTGGCGGACGCGACCCGGA", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AAACCATGCAATCCGGATGTGGCAAACAAAGGGTGGGAGCCCACTTGTCATGGCATTAATTGCACGTCTGCTTAGGGCGAGTTTTAACCGTGGCACACAAAACAGCACGATCGAGGATACGGGAGGTCGCCGCGTGATATAACGTCTACCCATGGTTCTCATCGAACCTCTACCTAGACACTGACCAAACCAACGCTCCCAGTGCGCACGGCCATTTCTCATAGATATCTTGAGCAGGTTGCACCACCAAGATAGTGAGCAGGGCGGTGGGTGGTGCGGAGTGCCGAGCGGCCTCACCCC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTCGGAGGTCGGCGGCCAGAAGCCAGATTCCCTTGGCCCCAGCTCGTCCACTTCCATCCAGCAGTAGCCAAACAACCGTTGAAAATGGCCTGGCGGCGGCCGGGAAACACCAGAGAACTATCTTCCAACCCCTAGATCCCGCCCATCTCGTTCCGGCGGTGTTTCCGTGGCGACGCTATCCGAAGTGCGGCTGCGCAAGGGTGACGGCGCGCGAGCAAGGGGGAGGGGGTGTTTTGGTTCTAGCCGCTCGCCGTCCTTGCAGGCTCTGCCGTCGGAAAGCCGCTCATTCTCGCTTCCCCT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGTGGTTGTAGGTGGGAAGAATCCTGGGTTCCCATGCTGTCTGAACCAGAGTCATTTGTGGCCCTGCCCTCCCCTGGGACTCAATTTCCCCACCTATAAAATAAGCCCCAGTGTCCGCGAACCCTGGAGGGGCCCGCACCACTGCAGGAGCGGCCGCCGGCGCCAGGGGGCGCCTCCTCGATAACTCGGCGCTCGGCTGGCCAGGCACCGGCGCGTCGGCCGCTCGATTGGTCGGGTCGGGGCCGGCCTGAGCGCCGCGGGCCTGCGCCATTGAGGAGCGGCGGGGAGGAAACGCCGCGC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TAGGAGGCCTGTCTGCACAGCTGTGCTGGGAAGCTGATCCATTCCAACCACCGCCTCATGGCCGCTTACGTGCAGCTCATGCCTGCCCTGGTACAGCGCCGCATCGCAGACTACGAGGCTGCCTCGGCTGTGCCAGGCGTTGCTGCTGAACAGCCTGGGGTCTCTCCATCAGGCAGCTAGCCATACCCAACCCCAGGAAGGAAGGCCTTGGATGGACCCTCAGATTGAAGGACCCGGTGGACCTTGGGGTTGGTGAATCCTAAACAGAGAGAATTCGAGGTTGCCTGAAAGCTGGGTGTC", + "output": "promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TGTTCTAGCTATCTTGAAATACGCAATACTTTGACATTAACACACTGCTATGTTCTCTAATGACATCCCTGGAAACCTCCTAGGGGCAGCCAGATCTTTCATGATAGTGGTTGTCAGTCCTCATATGGAGGGTGGAGGTTTGAAGCAGAGAGCCAAGGGAGGTTTTGTGCACCTATGCTTGTTGTGTTTGTACACAATGACTATGCATACGCTGTGAGTATAAAAGGCTCATTTAATCCTATTGTGTCCCAGGCTTGGTTTGTTTTCAAATCATTACAGCATGAATTTAGAAGGTTTTGT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCCGAAACCCCTCATCTTGGGGGGCCCACGAGACCTCTGAGACAGGAACTGCGAAATGCTCACGAGATTAGGACACGCGCCAAGGCGGGGGCAGGGAGCTGCGAGCGCTGGGGACGCAGCCGGGCGGCCGCAGAAGCGCCCAGGCCCGCGCGCCACCCCTCTGGCGCCACCGTGGTTGAGCCCGTGACGTTTACACTCATTCATAAAACGCTTGTTATAAAAGCAGTGGCTGCGGCGCCTCGTACTCCAACCGCATCTGCAGCGAGCATCTGAGAAGCCAAGACTGAGCCGGCGGCCGC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGCATCTTAGAGTTTCATACAAGGATCAGATTTGTGCTATCGGAGGCCGCCGATCACCCAAGTAAGCTTCATACGGGGAGGGAGGGAGGGCGCGAGCTTGGGAGTGGAGAGGGAAGGAGCAAACTTTGGCATGTATCACAGGGTAGCTCTGCCCCTGCGCGCCCCCTCCAGATCCGGCCCGCGGCTCCCCTCCCGCGGGACATGCTGCGGGGAAGCACGGCGAACAGCCCCTCCTATGATCACAAATGGCTCACCGCTCTCGGAGCCCGGCCGCCACCGCCTGGTGTGCGAGGCTATCCG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCCTCTGACTCGGTTTCCCCTCTCCCCCGGCTCCGCGGTCGCCCTCCTCCTGGACTGCTTATTTCGTCCTCGGCCACTGCCTCTCGGGCTCGAGCTTTCTGCTTCAGAGCAGGAGAGAAACGGAGCGAAAGCAGTTTCTGTCTCCCCGGGGTCTGACTCGGTCCCCCGCGCTCGGTTCTCTGTCCCCTCCCCCTCCCGCCATTGTTCCCGGCAGGAGGAGTGCGCGGCGCAAACTTGCGAGTTCCCCCAGTCTTTGCCCCCGCAGGGCCGCGGACGGAGGTGGTGGCGGAGTTCCCGCT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGCATGATGTTGGCCGACTTGTGCTAAGGTCCTTGGAGAGGTGGAGGCTCTACGGGGGGTGGGCAGTACCCCTTAGAAAACACCCCTCACTCTCAACAACAGAGGCGGTCTGACGTATTGGAACTCATTCTTTAAGGCCAGAGCTGTGGTTGCTCCAGCTGCAATGTGCGCTCTGATGATACTTTATAATAGATTTGAAAAGGGGAGGGACAAGCCTAAGGTCATGGCAGGAGGCTGGGCCAGGGAAGTGTGGGATCCCGTGGTTGTAGTCGGCGAACCGAACGTCTCGGCCTCTCTCCC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TGGGGGTTTTTTGGGGGCTCGGGCGTCGACTGTGGGGCGCTGCGGAAGGGTGGGTCGTCCGCGTGGGGTCCTGGAGCGCAGGAGGCGGAGGGGAAGCCGCGCAGGGGCCACTAGGGAGGGGCTCTGGCGGGGTCGCAGGGGCGCGGTCTGCGGGCGTGGGGGCGGGACCTCGGGGGCGGGGCCTGAGGGCGCGCGCACGCCTCCGGCGCGCCCCCTCCCGGCCGCCATGTTGGCTGGTGTGTGGGTGTCAAACTGAGCCAGACGCGGCGGTGGCGGCGGCTCCGCGGGCTACGGTCGCTC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGAGAGGGGCGGTACGCACCACGGGGGAAGCCAATGAGAAAATCAGGCCCAGCCCTAGGGGGCGGTGCTGTCGGTCACATGCGCACCTGGGGCGGGTGGTGGCGGCGGCGCGGGCACCGCGAGCCGGCGGAAGGGAGAGGGGCCGGCCTGGGGCGGGGTTAGGCAGGTGAGTGACAGGCTCCAGGGGGCCGGCCCCTGCCTGGTGCCCCGAGCGAGCCGGGAGTAGCTGCGGCGGTGCCCGCCCCCTCTCTCCGCCCCTCCAGCGGAGCTGGTCTCCGGCCGGGCACCGTCGCGGGCCCC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGACAACCCGCCCTTGTTCCCGGTAGAGGAACAAAGAGTGCTGATTTACGAATCTAGGCTGCCAGATTCTGTGTTTCCACTGGAGGGCGCGTCCGATGCAGATGTTGGCTCAAATTCCATCTTAACCTATAAACTCAGTTCTAGCGAATACTTCGGGCTAGATGTGAAAATAAACAGTGATGACAATAAACAAATTGGGCTCTTATTAAAGAAATCCTTGGACAGAGAGGAAGCTCCTGCACACAACTTATTCCTGACAGCCACAGATGGGGGCAAACCTGAGCTCACAGGCACTGTTCA", + "output": "promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TTCCAGCACTCCGGGAGCCCGATTGCGAGTTTGTGAGAGTCCTTGGTGGAGGAGGTTGGAAAGCGTTCTCCCTCCCACAAAACCCGGTGCAGAGGGAGGGAGAGCCAGGTCCGTAGCGTGGGTCCAAGAAACTATGGATCCTGGAAGTGAGCACACTTTTGTGTCTTAAAAAGTTTGGTGTCTCGTTTTGAGGTGAAAAATGAATGAGATAAGTGCTTTTTAAAAAGGTAAAGTTTGTCTTACAACTGAAAAAGCTTTTAAACAAAACTATAGTTTTTGCGAGTAACTCCCGTGACACCT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ATCAGCCCTGGCTAGGACCAGCTTCCCACTAAATCGCTTGCGGCAAGGAAGTCTCAGCCCCCGAGGCAGTCCTCACTGTGAGCAAACCAAAAGATGGGATCACTTGGGGCGCGTACGGGCTCCCAGCGCTGGCAGAAACTGTAGCGTTGTAGTAGCCTTTGTGACTGGTTGCCTGTTGGAAGTCTACCCCACCCCCAAACCATAAATCCACCGGAAACTCCTCCAGGACACCCTCTCAAGCAAGCCCCTGGGATGTAACTGCCACAGGGGCTGCGCCACGATGTTCCCTGGCTCTCAGGT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CACCGCGTGAGGATGTGCCGGGTGGTCCTTTCCTCCTCCTCTTCCTCCTCCTCCCGGCTCCCTGCCTAGTCTCCATATAAAAGCGGCGCCGCCTCCCCGCCCTCTCTCACTCCCCGCTCCTCTCCGCCGCGCACTCTCCGCGGCGCTGGGAGAGGGCGGAGGGGGAGGCGGCGCGCGGCGCCAGAGGAGGGGGGACGCAGGGGGCGGAGCGGAGACAGTACCTTCGGAGATAATCCTTTCTCCTGCCGCAGTGGAGAGGAGCGGCCGGAGCGAGACACTTCGCCGAGGCACAGCAGCCGG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCAGGAGTCGGTAGGGGCAAGAACGTTTCCGGCTCCTCAACGGGAGAAGCGCGGGAAATTTGTTGACGTGGAATAGCTCGACGAAATCATCGGCAGGCGCCCGCCAGGGAGCTAGCAACGGAGCATATGCGACCGTCGGTTCGCAAGAGACACACCTCGGGCCTTGCCCCGGAACGCCGCAATGGCTCCTACCTTTCCGCCGAGTGCAGGTTGATGCTGCACACCCAGAACACTTTGCGGCACGGGGGGGGTTATATGACATGCAAAGCCCTGATTCAACTTGGAGATGAGCGGGTCTGT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GAACTCCTGAACTCAGGTGATCCGCTTGCCTCGGCCTCCCAAAATGCTGGGATTACAGGCGTGAGCCACTGCGCCGGCATCCTGGATGCAACACGATCAACGAATGATTGCCCTTTATTCTGCCTGGTCTCCCAGCGGTGCTGTTGTGTCTTCATTTTAATGGATTTTAATTGATGTCACTTAGGGGGGCCCTCAAGCCTTCCTTCCTCTATCCATTTACGTGGTGTACCATCGAACTTTATCGACTCAGAAGTCGTGGGCGGAGGGCGTTCCGATAGAACCAATTGGCTCACGTCTATA", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AAGATGGCCTGAGTCAGGAGCAGGGGCAGAGCTCAGGTTGTCGTGGGGTCCCGCTGCCCACGTCAGACTGGAGGTGAGGGATGGGCGGGGCCTGACAGCAGGCCTGGAAGGAACAGGATGTCTATGCTGGAGATAGAGGGAGAGGACAGTGCCAAAACCCAGCTCCTGGCCAGTCCCCAGCTCCTCCCTGCCTGGCCCTATCCCAGGATCCCCTCCCCGGCCTCCCAGCTATGATCTACCCCGGGGCCCAGACTTCAGGCGCCTTCACGATGCCGGCGGTCAGTGGTCCAGGTCCCTTAT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCTGACACCCTTCGGCCATTTTTTTTAAACGTGTTGGGGTTTGGGTTTCAGCCACAGGCCTAGCACATGGAACCAAAACGCTACCACGGACTATGGAGTTGTGAACCGCGGAACAGCCGGGCGGACTCCGGGGCTCCTGTGTTTGAGATACGCGTGCCATGAACCTCCGCGCCGGAGGCGAGCCACTGGTAATTTACTTTCCAGTTGCGCCAATCCGAGTCTACCTTGATATTGAAAGGTAAGTCAAGAACAAAATTATCGCATAAGACTGTTGAGGTTACCCCGCGGCGGGGTCGGGGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTGAGGCTGGCAGGCACTGGGCAGGCAATGAGTTCTTTCCCTGTAAGTTGGGCACAAAAAGATCGGCATGGGAGCAGCCTCCTTTGAGACAGCTGCTCTGAGAGAATGCAATAAGCAGGGAGCAGCCAGCAATTCCTCCTAGCAGAGGGCGACTCGTGGGAGGAGTTCAGTTTGCCAAGTATTGTCATTTGTTGAGAGAAGGTGTGTGCTCAAGGAGGAGTTTTAACCTGGAGGATCATTAACTCTTTTAGTCAGCTGAGGAGCTGCGGTGGCTCGGCGAGTTGGAGTTCATCCTGGAAG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCGCGCCCCTCCCCCCGGGTCGCGCGCCGCGGGCCCGAGCGCGAGACGCCGCTGCTCCCGCCCCCGCCGCCTCCTGGAGTCGCCGGGGCGGACGCGCAGTCCATGGGGCGCGGTGGGCCGGGGAGTTGCCCCAGGGGCCGCGGGAGTTGCTGAGAGGAGACAGGGTTGGGCTTTCTCCTCGCCCAGACCCCACCCCACCCTGCCGACCCCACCCCCTGCTCCTTCCTCCCCGGGGGCGCGCACTCGGGCACGCGCTCGGAAGTCGGGGGTCGGCGCGGAGTGCAGGCTGCTCCCGGGGTA", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TATCTCCCCTCCTCGGTCCCTCTCCCTCCCTTCCCCCTCCCTTCCCCCGCCCTCCCCCTCTTTTTGCTCCTGCTCCCCCCCCCCACCCCGCCTTTCTCCTTTTGCAAGAAAATAATTTGACAGTCGATTTGCTGACAAGGGAGGAATTTGCATCCTGGATTTAAAAAAAAAAAAAAGGCCGAGAGGAGCTTGGGAACGGTTGCTAGGGGTGGGTAATGGGTGAAAAAAGGGGGGTACCGGGGAGCGGATAAGGAGGGTTAAGGGAGGGGGCGAGGATGGGGAGCAATGCAAAGGTAAGGC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GACCAGTAAGTCGGGGGGGAGGGAGCCGGGGCCTGGCCCCGCCCTATGGGGAGGCACGGGTGGCGAGGTCGGCCGTCCCATGCCCCGCCTGCTCCAGGTGCCGCCGTCCCCACAGGTGCCCGCCCCAGGCCCGCTGGAGCAGCCTGTGGCACGTGGGGTAAGTGGAGGCCGCGGCGGAGCCGTGCGTCCGACGGTTCTGGGGCGGGGGTCACCGGGGCCAGGCCCGCCAGGCCCTTACCTCACCAGGCCGCCTCCGCTGTCGCCCAGTCCCGGCCGCTGGCGGGAACTGACCTGGAGCAA", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCTACCGCTGCCGGAAGAGCGCGGCGCCCGACGGAGCCGTGTGGAGGCCAAAACTCCTCCCGGAAGCCGCTACTGGCCCCGCTTGCCAGGCCCAGCGTCTTTTCTGCATAGGACCCGGGGGAAGCCGGGAAGCCGTTAGGGGGCGGGGCAAGCGGGCGGGCGTGCGTCGGTCAAGTTTCCCGGGTCGCGTCAGGCTTCTTTCTCAGCACCGAGCGAGCGACGTTCGTGAAGCTTTCGTTTTGAGCGGCCAGACTCTGCTGTTCTCAAGCCTGGGAGGCACAGAGAAGCAAGACTTCCGCA", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GAGAATCCCTCTATAATATCCTTGCACAGTTCCTCCGCAAGCACAATATGATTAATTTTTACTTGGCTGATGCCGAGATCTTAACTGCCCTAATCCTCGGCTCGAGTCGAGTTGGGGTTTCCTAAATGAATTACTATTGCAAATCTGGTTATGGCGCTCCACGAGATTATGGCAAAGGCTGAAGAATTTATTGACAGATGTGTTGTTAGACTGTGCTACGTTTTAGTGGCAGGTAGTGGAGCAGCCACCGGCGGACTCGAGAACGCAGTAACATGGCTCAAAAATGGTAGTATTTGTTTT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGGGAACGCACCTGCGTCTAGACGCTGACGCCGTGCGCGGGGCGGGGGCCGGCGAGCATGCGCAGTGTGGTGTGGACAGGGCGTGGTTGGCAAGCATGCGCAATGTGGGCCGGGCGGGGCTGGCGAGCATGCGCAGTATGGGGCGGGGCGGGGCAGGGCTGGTGAGCAAGCCCGCGGGGAGCGAGCGCGGAGGCGGGCGTTGCTACGGCAACGGTGGGCGGCCAGGGAGTTGCCTGAGGGAAAAGGGGCAGACGTCCCTGGGTTCCGGTGTTCGCGGAGGAGTCGAGGCACGGAGAGGCT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTTCTGATTCTCGGTGAAAACGGTAAACATCGGCGTGCTATGTATGGGGATAGCAGGAGCTCTAGCATTTTTGCGCTGGGAAGATTATTCTTCTCTATCCGTCTATGCGAGCCGGCGTTTCGCAGGGGTACCCCCGGGGGCACTGCGTGGGAGGCCAGGAGGTTTTAGAATGGTCACCCGCCGGTTAGCTCCTTGGTAGTATTCTCGATGGACTGCGCGTTTGAGTCTTAAGGGCGTTTTCACGCTTCCGCCTTCTGGAAGCCTGCACTCCGTTTAGAGGAGCCAGAGACCCGTCCCGGT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTATTGGGGATGCAGGGCTCCGCGGTAGTTGACCTAGCAGGGCTAGTCACATCACGCCATAGGGAGCAGCAAGGGGCGCAGCAGTCAGCACGGCCGCTATTCAGATACGGTAACCAGACGCCGTGCGGCGTGCGCACCTGGCATCCAATGGCTTTGGGGATAGGGGCCGCCTGTGGGTTGTGGGTACGAATTAACGGAGTCGGAGCGGCGAATGAGCAGTCGCAAAGCCGAAGCCGGGCGTGGCGCTTTAGAGCGTCCCAGGCTCTCCTTCGGAAAGATGTCGGACACGGCAGTAGCTGA", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGCAGCGGCCGCGGCGACAGCTCCAGCTCCGGCTCCGGCTCCGGCTCCGGCTCCGGCTCCCGCGCCTGCCCCGCTCGGCCCAGCGCGCCCGGGCTCCGCGCCCCGACCCCGCCGCCGCGCCTGCCGGGGGCCTCGGGCGCCCCCGCCGCCCGCCTCACGCTGAAGTTCCTGGCCGTGCTGCTGGCCGCGGGCATGCTGGCGTTCCTCGGTGCCGTCATCTGCATCATCGCCAGCGTGCCCCTGGCGGCCAGCCCGGCGCGGGCGCTGCCCGGCGGCGCCGACAATGCTTCGGTCGCCTCG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTTTTGAACATCTTGTGCCAGGATTTGACATTAAGCTCCAGTTCATCCGCACACCCCGCGGCCGCTTTCGCAAGGGACCGAGAGGGTGGCGGCCTCGCCCGGAGACTCGCGGCGCTGGGAGCTGCCTGCCCTCGGTGCGGGCTGCAGACCGGGCCGCGAGGCGGGCGCGCGGCGCGCACTAGGACCCAGCAGGGCTCCAGGCCGGGGTGGGAGCCGCGCCGCCGACCCCGGGCGGGCGCCGGGCGCAGGAGCCGGGGTTCCGGCCGCGATCTGCTGCAGCTCGGCCGGGAGACGGCGCGA", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TGTGTGGCCAAAGAGAAGAAATGGGTTGAGACAGCAGGCCTGGCACTTACTTTACCTGGCCCAGTCTTGCCTGACAATTAAAAAAAGACGCTTTAGACTGGGCGCGGTGGCTCACGCCTGTAATCCTAGCACTTTGGGAGGCTGAGGCGGGCGGATCACGAGGCCAGGAGATTGAGACGATCCTGGCTAACACAGTGAAATCCCATCTCCACTAAAAATACAAAAACTTAGCCGGGCATGGTGGCGGGCGCCTGTAGTCCCAGCTACTCGGGCGGCTGAGGCAGGAGAATCGCTTGAACC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCATATAAACAGATTAACTGCATTCCCCAACGAGTAGAAATCTGCCCATCACTTTTGGGGTGGTTTGTTGCACCCACTATCGCCCAAGAAACTTCCCCAAGCCGGGTCATATTACAGGTCTCTAGCGCGCTGGCCGCACTCGCAGCCCACCATTCCCACAGAATGGTGTCATCGTAATCTGAAAAGTTGCCACTGGGCACCTGCGCAGGCTTGGCTGCGCCCTCTCGCGCCGCACGCTCCTTCCGGCGACGCGGATAGTCGTAAAGGTTTGAATACCTGATACAGGGAGAGAGGCCGAG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCTTCACAGGGCACAGCCCCTGCAGAGCATCTTGGTCATTTGGAAGAGGACACGGTATCCCCTCTGGCCAGAGTATGTCAGAGAAGGAAGAGTAGGGCTTTTTTGTTTTGTTTTTTTTTAAAGGTGCTTGCTTGTTTAATGTAAATAATAGAAAGCCTTAATATCTTTTCTGTAACACGGAGTAATATTTTAATGTCATGTTTTGGATGTACATAATATATTTATAACAAAGCAGCAAGAGTCTACTTAACCTTGGCTGCCTCGTGGTGTTTCCTGGCTGGGTGGGGTGGGGGGTATCAA", + "output": "Non-promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TGTATTCAGACCAGAACAAACTCGAGTGCCCCAGCTCCTCTGGCAATTAGGTAGCCAATAGTTGAGATTGCGTAGGCAACTACGTAAAATGCGCCGTGCCGTGCCCCTTTCTGCCACTCTGAGTTCGGTAACATAGCTCCGGTTGCTAGGTACTTAAGGTATCCAGTTCCCCCAGCGATTTGCATGAACCGAGAGGGAGTGTCTTCTGCCAACAGATCAGGCGGGGGATCATGAACTCATCCTCCCAGGGAATGCCGGTCGGGGATCCCCCGCGCAGCTCACAGGCCCTGGGAGTGAGCT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACCTCGCTATTCTATGCACAGTTTTGAATCCTGCTTTTAAAATGCGATGTCTGCCTTGTCTTTAAATATTCTGAGGGGGAGGAGAGGAGATGTGTAATGCGACTGTCTATAATTTGAAACCGGACCTCACTATTTAGCGTCTCAGAAAACGCGGGGTTCATTGCCCTGGTCCCCCCCGCCTTCCCCCGCCCCCCGAACCTTCCGGCTGGCTCTCTTGCGGTCCCCGCCTCGGCGCTGATGTGGTCTGGCAGTGGAGATTGGCGCCCGGGCGGAGCACGATGGGCTTCCCCGCGGCAGGCA", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCACGAGCGCCAGAGCCCCGCGCCTCCCCTAGCATATCCGGCGGAGTCCCTGCCCCGCAAGCGCCCACTATACGGCCGGACAACTGTTGTGGGCGTGGATGCAACCACGGGGGAGGGGGCAGGCGTGATCTCAGTCCCGCCCGTGCCCGAGCCCGCGCCCGAGCAGGGACTACATTTCCCTCCCCCCTGGGTCAGGGAGGAGTCTCCTCCTCTATCTATTAAGGATTCTCGCCTCCGCACATGCGTTATCCTTCCAAGGCCGAAAGGATTTGGAGTCCTGTACGCCAGAGGCCGAACTCG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACCCTGCTGGGGGTACAGAGACGTTTCCGAAACTCAGCGGCTCGGTCGCCCCCAAAGGCCAGGCGGAACACACGCCCACACCGCGGCTCCCTTCCCGAAGTAAGACCGCCGGGCCACGGCCGCCCCCAGGAAGCCCCGCGCCCCGCACCCCACACCCGGGGACACAAACAGGCGCCGGGTAGCCGCGAGGGCCGGCGCGGCGCCTTTAAGAGGCGGCGGGCGGCGCTGCCCCCTGGCGGCCGCCCCGCCGCTTCCTCGCCGCCGCGGGCTCAAGCGGGGCGGCCGGGCCAGCGCGGGGCG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGCTTCACAGTCAGAACGCGGTACCGTTGTGCCCCAGCTCGACTGCAGCCTCCCCGCCCCGATACACTTGTCCCATCCTGTCCACGGCGCCCTTGTTTTGATTCGCTGCTGTCCCAGAGAGCTTAGTTATTCGTACGGGCAATCTTGACCCTTTCCCAGGACCTCCCCTAATACTGTTCATTGATCCTGGACCGGCAGCGAGACCCCTTCCGGCATCCAGAGGCCCAGTACTTTCAATCTCCTCCCCCAAAATGCTTTCCCTTTGTGGGGACCCTCGACATAGATGGACACCCAGCGGCC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCGTTCTTGGCATCCTGAGAGGGCCAGTTAGTACTGAAGTCCCTTGGCTGCTCAAGGATTGCAGGGATGAGGCAAGTGGAACAGCCTCGGAACCTCCGAAAATGGGCACGCTCCAGGTCCCAGTTTCTATGGCAACCATACCGGCAAATTGGGCTCCGCAATGGTTTCTCCTGGAAAAACCGTGATTTTGGTTCCCGCGGACGTCTCTATGGTTTCGACAGCCTAGAAGGAACAAAACGGCATTTCCGGGAAGATGGCGGCGCACAAGTCAGGTCCGGCACATGTTTCCGCGGAGCGGAC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCTCTCGCGCCGCTCGCGTGACCGGCCGGTGTGTGCGCGAGGCCCCGGCTCCCGGGGCACGGACGGCCGGGCGCGCGCCTCTGCGAGGGGCGTCCGGGTCCGAGTCGGCGGTCCGGGCCGGCGCGAGGTGCGTGCGGGCGGGCCGCGGGGGTCCCGGACGGACACAAGCGCACACACTCCCGGAGGAGCCTTCGAGGCTGCTCTTCCTCGGCCAGACGGAGAGCGGCACTGTCTCCCCGCCCAGCGCTCACTCGCCCCGCGTCTCCCCCCGCGGCGGCTGCTCCTCCTCGGCACCGCCAG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTCCCTCCTCTGGTAGCCAGAATAAATTTTGCAGTAAGTTAGCAGAGTTGATTCCAAAAAGTCTGCACTGTGGCATCTCAGGAAACCAAACTTATCTGCTTAAGAAATAACTTGCAGAGAGAAAAGGAACTCTGACAAGGCTAATAACAGGGATTTCAAGCTGTGTTTCAGCAAGCCAGTCACAGAGTATTCACGTGTTAATTCACTGGCGTGTTCCGCGGCATAAGCACCCCCTCTCTGCCTTCCCCCAGTTCCAACTGTTGTGCTGCAGCAGATTTGGTCTGAGTCTGGGCAGAGCCC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ATGATGCTGGGGACGGGACACAAACAGGCGTGACAGCAGCGACCAGGCCGGCGCGGCGCCCGTAGACTGAGTCGAGACACCCTGGGCACAGGCGGCCGCCCCGCCTCTGTGATAGTTACTGGGACGTGTGCCCAACGGCCGGGCCAGCGCGGGGCGGCGGCGGGCAGGGGCGGCGAGTGCCTCGACGTTCTAGTGCGCCCTTCTCGGCGGTCTTATGCACTATGGCAGCAAGAGTTTTGAATATGTTGCGGTAAGTTGAAATTATCGAGAATGCCAGGCGAGTTGGCCGCGTGCGCCCCG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGCGACACGCCGTGCGCCTCCGCGGCTGCGCTACGAAAACGAGTCCCGGAGCGGCCCCGCGCCCGCCGCACCCGGCCCTCGCCCGCCCGAAGACAGGCGCCAAGCTGCCCCGCCGTCTCCCCAGCTAGCGCCCGGCCGCCGCCGCCTCGCGGGCCCCGGGCGGAAGGGGGCGGGGTCCCGATTCGCCCCGCCCCCGCGGAGGGATACGCGGCGCCGCGGCCCAAAACCCCCGGGCGAGGCGGCCGGGGCGGGTGAGGCGCTCCGCCTGCTGCGCGTCTACGCGGTCCCCGCGGGCCTTCC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCCTAGCCAGGAGGTAGCATCTGTCTCCAGGTGCCCGTGGGGTGTCCTCCATGAGGACCACCTCTTCTAACCACCTGCCCAGCTCAGAGGACCAGCTGGCAGGAAAGACGCTGCACCGCCCATGAGGCCAGAGCTACAAGTTGCTTCTAGGAGTGGCTGTGGGCGGAGGCTGGTGGTTCTGAAGGTGGCGGTGGTTCTGCAGCGTGGCTCCCCACAGCCCCTTTCATCTGAACAGTAGGGCTCATGCCACTTGTTAATCAGCCTTCCAGTGGCACGAGGAGTCATTTTCTGGGGATAGG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCCTCCTCCCACGCTGCGCGCGCACCTCCCCGCCCCCACCCCTACCCGCTGGCGTGCCCAGTGGAACGGAGCCTTGTGTCTCCGCCTCAAGTCCCCGGATGCTCACCTCCCCGACTCGCCCCCGCTGTGGCCCCGCCCCCGCGCGGCTCTTCGTGCCACGTCACCGCCTGCGTCGCTTCCGGAGGCGCAGCGGGCGATGACGTAGAGGGACGTGCCCTCTATATGAGGTTGGGGAGCGGCTGAGTCGGCCTTTTCCGCCCGCTCCCCCCTCCCCCCGAGCGCCGCTCCGGCTGCACCGCG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCATGCAGCGACGGCCGCCGCGGAGCTCCGAGCAGCGGTAGCGCCCCCCTGTAAAGCGGTTCGCTATGCCGGGGCCACTGTGAACCCTGCCGCCTGCCGGAACACTCTTCGCTCCGGACCAGCTCAGCCTCTGATAAGCTGGACTCGGCACGCCCGCAACAAGCACCGAGGAGTTAAGAGAGCCGCAAGCGCAGGGAAGGCCTCCCCGCACGGGTGGGGGAAAGCGGCCGGTGCAGCGCGGGGACAGGCACTCGGGCTGGCACTGGCTGCTAGGGATGTCGTCCTGGATAAGGTGGCATG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCCTGAGCCAGGCTGAAGCGGGATAGGAAGGGTAAAGGTGCTAGCTGAGCCGTAGTACGGCCCAGCAGTTCTCCAGGGGACAAGCTAGGCCCCCGCTGGAGGGAGTGGTGAGACTCAGCCGGGGCCTGATTCTGGTCAGCACCTTGGACAGCAGCCAGTGCCCTCCCTGGCCCCGCCTGGCCCCGCCTGGCCCCACATCCGCTACTCCTGGGCACCTCCTCAAATAGCACAGCCTCCAGCTGGTGCCCAGGGCCTGAATACACAGAGCGCTGAGAGAGTGGGGCAGTGTGGTCACGGAC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCTGGGAAAAGCTGAGAGCATAATCGAGCGGCAAAGAAAACGCGACCACTGAGGACACTTAGAAGGGGGCGGGGCCTTTTTGTGGTACTAGGAGCTCGAGAAATTGACTAGATTACGAGTGGTTTACGACTCAAGCATACAACTTACAGCTTGGGCCTTCCGTAGCGGGCGTTGCGGGGGAACGTACAGTGCTTGAGGGGCGAGGTTTGCCGGAGCCTCATTCTAAAGGAATCTTCTAGTTCCAAGCGTTGTTCCAATTGGGCCGGAGCGTGCAACGGGACCCACGGAACTACAGGTGTG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AAAGCCACCTCCGCGCCGGCAGCTCCGTTTCTAGGGAAACAGGACCAATTCCAGAAGTTGGGGTGTTACGTGTGCGATATACCTGCCCAAGAGCTGGTATCAATAGAGCGGGCGGCATGGTAGTGTTTCCCCCGGCCATGGTAAAATGCGAACCAGATCCTCGGCGGGTAACCGCGCCCCTAGGCGTTGGCATGTGTCGGGGTAGCATGACGGGGTCGTCCAGTTCTTAGCCAAGATGAATCCGGCTCCGAAGGTCCCCGGCTTCGTGGCCATAAAGGCGAACACCGCTGGAAAGGTGGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGGCTCGGGGACGGGACGGGCGAGCAGTCATTTCTGAATAGCGCGTGCAGAGGGTCATCGCGAAAAGACTAAGACGCGACATAATGGACTGACTCGGGGCATGGCGGGGTGGAGGCGGGAAAGTGTCGAGGCCGGCTAATGCCTGGACAGGGAGGTGGGCATCGCCAGGGCGACGATGAGGGTCGCCACTCCGGAACACGATTAGCATCAGTGCACTGACCTCTGCCCCCACGACTCTCCAACACGCCCGATGATGTTGCGTGCGGCAGCTGTCGGCTAGGAGTCGCGGGGTCTGTGCGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TATGGTATTTCAAGGTCATAAATTACAAATGTGCATATTTATTATTCCCCTATAAAATGTTAATTAAAAAATTTTCATATTTACTTGATGTAACAGAAATGAAACCTGTAATGAGGGATCTAATTTCTTTTTCTTTTTTCATTCCAGAATGTATGACAACATGTCCACAATGGTGTACATAAAGGAAGACAAGTTGGAGAAGCTTACACAGGATGAAATTATTTCTAAGACAAAGCAAGTAATTCAGGGGCTGGAAGCTTTGAAGAATGAGCACAATTCCATTTTACAAAGTTTGCTGGA", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACGACCCTGGCCTCCGACTTCAACGACTTCATAAGGCGGCGTTTCTGGGCGCAGCCGTGTCGCTCCTGGTGAGAGGCCGCCGGCAGGCGGGATCCAGCGCCCTCCGGGGCACCGCGGGCGAGACCGTCGCCTTCGCACCCCCGGCGCGGTCGACCCCGCGGGGCCGTCGGGTCCTGGGTTCCCCGCCGCGTTGCGCTCGTCCCCCTCCTGTCAGAACCTGGGCCCCCGCCCCGCCCACCGGCGCGGGGCCTCTCCTCCTCCCGCTAACGGGCGGTCGGCCGCCTCCTTCCTCTCTTGGCT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGTTTCTGAGGCTGAGGAGGTACCCAGGTAAGCACACTCTGGGTTCTCTGGTGAGCACACACCGAGCTGCCTGCATCTCCCCTACCCATGAAGGCAGCTAGCCGCCGGGCCAGTGGTGGGAAGGGGAGGCGGCCATTAGCGCCGTTCCCGCCGGGACTTGAAGCGCCCGGCCGCGGCAAGCCCCGCCCTCGGCGCGCCCCCGCGTCCGCGCGCGCTCCTCGGGTCTGCGCGGAGCCGGCGTCGGCGCGCGCTTGGGCGCCTGGCGAAGACCGAGAGAGGCTGGCGGGATCTCAGCGGCGC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GACCGCCCGTTGGACATACCTCTAAATTGGGAGCGCGGTGCCGATTTACGTGACCGCCGTGGTTCGCACGGTGGGTAAACAGTGGATCGTGGGGTGGTGAGAAGGTTCCGATGGGTGTTCTTACTACGGCCCGATCCTAGCAGTCGCCGACTTTGTTATTCTTATTGGTGGATGGGGGGCCGCTGATATAAATCTGGGAGATGAGGAGATAGGCGGGAGCGAGGGCTGACTCTGCGTGTCGAAGGAGGTAGAGAAAAAGCGTTGCTTAAAGGGAGGGAGCGTAGCTTGGTTGCTCCGTAG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GACCTCTCTTCGGGCGGCAAGTGCCATACGTATAGATAAGCGGAATTTACATGGTGTCACAGTTAGCGCAAGGTCGAATAAATGCGGCTTAACACGACAGGTATCTCCAAATAAGTGTGTCTTACGGCGTAGATCGCGAGTTGAGCTGTCATTTCTTTCCAAGCGGCGCAGGACAGGACCCAGAAGCCAGAGCGCAGGAACTGCTTCCGGTTTGAGTTTTTATTAGAATAACGGGCTGTGGGAGAGGCAAGTGGCGGATCTTCAGGTCGGCGAGGGTGTGACATTCGACCCACAGGCCGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCATATTCTGTCTGAAATCGTGTGCACCGAAATCCCCGCCTTGCGGTGGAGGCTGGCGCTAGGCGGCCTCAGCCTCGGCCTGCTGCGCTCAGGAACCCGCGCCCCGGCTCCTCGGCGATCCATTGCTCTTTCCTCTGGCGCCGGCCGCAGGCCTCGGTCACGCCCCCAGCGGCCCGTTGGTTTCCGGGTCCCGCGGGGTGCCCCCGCCCACACGCTATGCCTTAAATTGGGCCAGGCTGAGGCGCTGCTGCTGGAGCGGCCGATCCGAGACGTGGCTCCCTGGGCGGCAGAACCATGTTG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TGGTCTTGTGTCTCGGCGTACCATATCTTTTGGGTTCCCCGGCTGCCCCGGAGGTTCGCGGCGAGAGGACGGGCCGGGTGCGGCTAGTTTACCTGAGCAGTGTGAACTACCTCACTCCCATCGGGACCAGCCGGCATAAACACTCTTATTGCCTGTCAACGTTTTCGAGACATATTTTGTTCGCGAGCCCGCAGACAACGCGCATTCTGGAGTTCGAACGCTCAGTTCGCGTCCCCGAGAAGATATTATTCAGATGCCTGCGAGCTGGGGCCGGGTGGCCGCGCCGGGTCGGAGTGTGGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCCGGAGACAGGTGGGAGGGTGGGGGATGCCGGCAGGAGAGGGAAGCAGGACCGTGCGCGCCGCCACCCACCCGCAGTCCGTCCGCCCCCGGGCGCCCAGCGCGTGGCCCGAGCGGCGCTGCCCGCCGAGCCGGGAGCCCCCTCGGCCCCTCCCTTCAGGCGCGGGCCGGGGGCGGCTTCTCCGCGACCTTATGTAACCGGGCGGGAGGGGCCGGGCGGGCATGGGCCTTCCCGGCCCGGAGCTGGGAGTCGAAGGGGCGGGAGGCGTGATGGTGAACTCGCAAGAAGTTTGAGGGACG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCAGGACTGAGTCACCTCCTGAAGGCCCCGTTCCAAAGCACTCCCACGGGGGATTAGGTTTCAACACAGGAATTTTTGGCGACACAAACATTGAGACCACAGCACCCAGCTCTCTGGGTTCCCAGAACCCTGTGGGAGGGCTGGGTCGCGCCTGCCCCCTTTTAAACACGAGGCTTCCATGGAAGCTTCTTGCCGGAGGCTGGACACTGAGGAATGGACAGGATCAACACTCTTCCACCGGCTCTTCCACCTCCCGATCACTTCCACCATCGCTGGGGGAAGAACGAATGCACCGCGTGG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CACAGCCCCAGATGGTGACTACTGGAGACTGCTGCCCCCAGGTATCCACATTGTCATTGCCCAAGCCCCTGGCTACGCCAAAGTCATCAAGAAAGTCATCATCCCCGCCCGGATGAAGAGGGCTGGCCGTGTGGACTTCATTCTGCAACCTCTGGGGATGGGACCCAAGAACTTTATTCATGGGCTGCGGAGGACTGGGCCCCACGACCCACTGGGAGGTGCCAGCTCTTTGGGGGAGGCCACGGAGCCCGACCCGCTCCGGGCGCGCAGGCAGCCCTCGGCCGACGGGAGTAAGCCCTG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AATCGTGTAATTCCCGCTTCCTATGGCAAGCGGCGCCCAGTTCTGGGTGACTTGTGAAATGAGCGTCCTGCTAGGCCATAGTCTCTACGAGGAGGATATAATGTAAAGTCTGGAGCGAACGTAAATGCCGTGACGTGTATTTGTGAGACTTCGCCCGGTCGGTGAGCTGGGTGCATAGGTTCCGAGCGCGCGACCACTTAGCTTCCCTTCTACAACAACCACCTGTGTGCGCATGCCCTCTCATCTTACAGTTGTCAAAGTGCTTTTGCCAGAGCGGTCACATAATCAACCTGAAAGAAG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCTTAGAATATTGGGCATACAATTTACTTTTATAAATGTCTATGAAATAAAATTCTTATATAATTATATAAAATTACATAATATATAATTATATTGAAATAATTATAACTGTTGAATAAATGTATTATTTTTCCTTTGAACTATTTAACAGGGTTATATAACAAACTCATCACGAGTTGTGTCTAATAAGTCATCAGAGTTACTGTTTGACTTGACCCAGGATACAGGATTATCACATTACCAAGGGGGACCAACACTTTCTATGGCAGGTTGGTTTTAGTATTTTTTTCATAAAGGTT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGCGCGCGCGCCGGGCCGAGGGGCCGAGAGGCGGGGTCTTACAGCGACCGCGGGAAGAGGGCGCCCCAGAGCTGAGCCGGAGGCCGGCTGCCAGCGGGGCGAGGGTGGGCGCGAGCGCAGGGGCGGGCCCCGAGGGAGGGCGGGGACGGTGAGGGGGCGGGGTCGGGCTAGGCGGGGACGCGCTCGCGGGGTGGGGAAGGCGGGGGCGCGGCGGTGGCGGGAGCGTGCCCGGTCCCCGCCCCTGTTCCCACTCTCCTTCCACCTCGGACCGGCCGGGGCTCCGCAGAGCCAAAGCTCGCT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TATTTCCGATTCCTTGACTAACGTACGGGGGCCCAGCACTTTCATTCCATCCCATCACCCCATGATCCTTCCCCAGACTCTAATACTGGCCGTAATATTTACCCCCCCCTGCTCCATGTGCGTAGATCAATGACACGAGCAAGCCATTGGTCTAAAACCCAGGGTTCTCTCGCAGAGGCCCTCCATTGTCTCTGCGGTGTCACTTCGCGCACACACATCAGAGCGGGCCTCTCTGTGAGGCGCGGGGTTGATGTACTCACACGCTGCGTTGATCGGAGGTCGCTGCCAAGCATGGCGCCC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGTCAGCAAAGGCTTCCCTGAGGAATTGGGAATAGCCAGCCATACGGGTGGAGCCGGTCAACTCCTTGTGGTTGGGAGCTAACCGATAGCCCCTACAAGGAGCATTCCAACTATGTTATATAACATTGTGATTTTCGCGAAGGCAACGACTTATTCTTAAGACAGTATCTATCTGAATCTGAAGATCCTTGGTCTGGAGATTGGTAAAAATCTCCAGTCATCTTATGTTTCTATTATACTCTACTGGCGTTTTTACGTGACGCGGGGAGTCTACCTCCCCCAACCATAATTTAACAGTAA", + "output": "Non-promoter", + "model_response": "Non-promoter" + } +] \ No newline at end of file diff --git a/04-gene-sft/.ipynb_checkpoints/merge_llama_with_dna_lora-checkpoint.py b/04-gene-sft/.ipynb_checkpoints/merge_llama_with_dna_lora-checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..2994b9f8d0bca0d91050b86f534b140f27e03ae7 --- /dev/null +++ b/04-gene-sft/.ipynb_checkpoints/merge_llama_with_dna_lora-checkpoint.py @@ -0,0 +1,368 @@ +""" +Usage: +python merge_llama_with_chinese_lora.py \ + --base_model path/to/llama/model \ + --lora_model path/to/first/lora/model [path/to/second/lora/model] \ + --output_type [pth|huggingface] \ + --output_dir path/to/output/dir +""" +import subprocess +import os +# 设置环境变量, autodl一般区域 +result = subprocess.run('bash -c "source /etc/network_turbo && env | grep proxy"', shell=True, capture_output=True, text=True) +output = result.stdout +for line in output.splitlines(): + if '=' in line: + var, value = line.split('=', 1) + os.environ[var] = value + +import argparse +import json +import os +import gc +import torch +import peft +from peft import PeftModel +from transformers import LlamaForCausalLM, LlamaTokenizer +from huggingface_hub import hf_hub_download + +parser = argparse.ArgumentParser() +parser.add_argument('--base_model', default=None, required=True, + type=str, help="Please specify a base_model") +parser.add_argument('--lora_model', default=None, required=True, + type=str, help="Please specify LoRA models to be merged (ordered); use commas to separate multiple LoRA models.") +parser.add_argument('--offload_dir', default=None, type=str, + help="(Optional) Please specify a temp folder for offloading (useful for low-RAM machines). Default None (disable offload).") +parser.add_argument('--output_type', default='pth',choices=['pth','huggingface'], type=str, + help="save the merged model in pth or huggingface format.") +parser.add_argument('--output_dir', default='./', type=str) + + +emb_to_model_size = { + 4096 : '7B', + 5120 : '13B', + 6656 : '33B', + 8192 : '65B', +} +num_shards_of_models = {'7B': 1, '13B': 2, '33B': 4, '65B': 8} +params_of_models = { + '7B': + { + "dim": 4096, + "multiple_of": 256, + "n_heads": 32, + "n_layers": 32, + "norm_eps": 1e-06, + "vocab_size": -1, + }, + '13B': + { + "dim": 5120, + "multiple_of": 256, + "n_heads": 40, + "n_layers": 40, + "norm_eps": 1e-06, + "vocab_size": -1, + }, + '33B': + { + "dim": 6656, + "multiple_of": 256, + "n_heads": 52, + "n_layers": 60, + "norm_eps": 1e-06, + "vocab_size": -1, + }, + '65B': + { + "dim": 8192, + "multiple_of": 256, + "n_heads": 64, + "n_layers": 80, + "norm_eps": 1e-05, + "vocab_size": -1, + }, +} + +def transpose(weight, fan_in_fan_out): + return weight.T if fan_in_fan_out else weight + +# Borrowed and modified from https://github.com/tloen/alpaca-lora +def translate_state_dict_key(k): + k = k.replace("base_model.model.", "") + if k == "model.embed_tokens.weight": + return "tok_embeddings.weight" + elif k == "model.norm.weight": + return "norm.weight" + elif k == "lm_head.weight": + return "output.weight" + elif k.startswith("model.layers."): + layer = k.split(".")[2] + if k.endswith(".self_attn.q_proj.weight"): + return f"layers.{layer}.attention.wq.weight" + elif k.endswith(".self_attn.k_proj.weight"): + return f"layers.{layer}.attention.wk.weight" + elif k.endswith(".self_attn.v_proj.weight"): + return f"layers.{layer}.attention.wv.weight" + elif k.endswith(".self_attn.o_proj.weight"): + return f"layers.{layer}.attention.wo.weight" + elif k.endswith(".mlp.gate_proj.weight"): + return f"layers.{layer}.feed_forward.w1.weight" + elif k.endswith(".mlp.down_proj.weight"): + return f"layers.{layer}.feed_forward.w2.weight" + elif k.endswith(".mlp.up_proj.weight"): + return f"layers.{layer}.feed_forward.w3.weight" + elif k.endswith(".input_layernorm.weight"): + return f"layers.{layer}.attention_norm.weight" + elif k.endswith(".post_attention_layernorm.weight"): + return f"layers.{layer}.ffn_norm.weight" + elif k.endswith("rotary_emb.inv_freq") or "lora" in k: + return None + else: + print(layer, k) + raise NotImplementedError + else: + print(k) + raise NotImplementedError + + +def unpermute(w): + return ( + w.view(n_heads, 2, dim // n_heads // 2, dim).transpose(1, 2).reshape(dim, dim) + ) + + +def save_shards(model_sd, num_shards: int): + # Add the no_grad context manager + with torch.no_grad(): + if num_shards == 1: + new_state_dict = {} + for k, v in model_sd.items(): + new_k = translate_state_dict_key(k) + if new_k is not None: + if "wq" in new_k or "wk" in new_k: + new_state_dict[new_k] = unpermute(v) + else: + new_state_dict[new_k] = v + + os.makedirs(output_dir, exist_ok=True) + print(f"Saving shard 1 of {num_shards} into {output_dir}/consolidated.00.pth") + torch.save(new_state_dict, output_dir + "/consolidated.00.pth") + with open(output_dir + "/params.json", "w") as f: + json.dump(params, f) + else: + new_state_dicts = [dict() for _ in range(num_shards)] + for k in list(model_sd.keys()): + v = model_sd[k] + new_k = translate_state_dict_key(k) + if new_k is not None: + if new_k=='tok_embeddings.weight': + print(f"Processing {new_k}") + assert v.size(1)%num_shards==0 + splits = v.split(v.size(1)//num_shards,dim=1) + elif new_k=='output.weight': + print(f"Processing {new_k}") + if v.size(0)%num_shards==0: + splits = v.split(v.size(0)//num_shards,dim=0) + else: + size_list = [v.size(0)//num_shards] * num_shards + size_list[-1] += v.size(0)%num_shards + splits = v.split(size_list, dim=0) # 13B: size_list == [24976,24977] + elif new_k=='norm.weight': + print(f"Processing {new_k}") + splits = [v] * num_shards + elif 'ffn_norm.weight' in new_k: + print(f"Processing {new_k}") + splits = [v] * num_shards + elif 'attention_norm.weight' in new_k: + print(f"Processing {new_k}") + splits = [v] * num_shards + + + elif 'w1.weight' in new_k: + print(f"Processing {new_k}") + splits = v.split(v.size(0)//num_shards,dim=0) + elif 'w2.weight' in new_k: + print(f"Processing {new_k}") + splits = v.split(v.size(1)//num_shards,dim=1) + elif 'w3.weight' in new_k: + print(f"Processing {new_k}") + splits = v.split(v.size(0)//num_shards,dim=0) + + + elif 'wo.weight' in new_k: + print(f"Processing {new_k}") + splits = v.split(v.size(1)//num_shards,dim=1) + + elif 'wv.weight' in new_k: + print(f"Processing {new_k}") + splits = v.split(v.size(0)//num_shards,dim=0) + + elif "wq.weight" in new_k or "wk.weight" in new_k: + print(f"Processing {new_k}") + v = unpermute(v) + splits = v.split(v.size(0)//num_shards,dim=0) + else: + print(f"Unexpected key {new_k}") + raise ValueError + for sd,split in zip(new_state_dicts,splits): + sd[new_k] = split.clone() + del split + del splits + del model_sd[k],v + gc.collect() # Effectively enforce garbage collection + + os.makedirs(output_dir, exist_ok=True) + for i,new_state_dict in enumerate(new_state_dicts): + print(f"Saving shard {i+1} of {num_shards} into {output_dir}/consolidated.0{i}.pth") + torch.save(new_state_dict, output_dir + f"/consolidated.0{i}.pth") + with open(output_dir + "/params.json", "w") as f: + print(f"Saving params.json into {output_dir}/params.json") + json.dump(params, f) + + +if __name__=='__main__': + + args = parser.parse_args() + base_model_path = args.base_model + lora_model_paths = [s.strip() for s in args.lora_model.split(',') if len(s.strip())!=0] + output_dir = args.output_dir + output_type = args.output_type + offload_dir = args.offload_dir + + print(f"Base model: {base_model_path}") + print(f"LoRA model(s) {lora_model_paths}:") + + if offload_dir is not None: + # Load with offloading, which is useful for low-RAM machines. + # Note that if you have enough RAM, please use original method instead, as it is faster. + base_model = LlamaForCausalLM.from_pretrained( + base_model_path, + load_in_8bit=False, + torch_dtype=torch.float16, + offload_folder=offload_dir, + offload_state_dict=True, + low_cpu_mem_usage=True, + device_map={"": "cpu"}, + ) + else: + # Original method without offloading + base_model = LlamaForCausalLM.from_pretrained( + base_model_path, + load_in_8bit=False, + torch_dtype=torch.float16, + device_map={"": "cpu"}, + cache_dir=None, # 不使用缓存目录 + force_download=False, # 禁止从远程下载 + local_files_only=True # 强制仅从本地文件加载 + ) + + ## infer the model size from the checkpoint + embedding_size = base_model.get_input_embeddings().weight.size(1) + model_size = emb_to_model_size[embedding_size] + print(f"Peft version: {peft.__version__}") + print(f"Loading LoRA for {model_size} model") + + lora_model = None + lora_model_sd = None + for lora_index, lora_model_path in enumerate(lora_model_paths): + print(f"Loading LoRA {lora_model_path}...") + tokenizer = LlamaTokenizer.from_pretrained(lora_model_path, + cache_dir=None, # 不使用缓存目录 + force_download=False, # 禁止从远程下载 + local_files_only=True # 强制仅从本地文件加载 + ) + + print(f"base_model vocab size: {base_model.get_input_embeddings().weight.size(0)}") + print(f"tokenizer vocab size: {len(tokenizer)}") + + model_vocab_size = base_model.get_input_embeddings().weight.size(0) + assert len(tokenizer) >= model_vocab_size, \ + (f"The vocab size of the tokenizer {len(tokenizer)} is smaller than the vocab size of the base model {model_vocab_size}\n" + "This is not the intended use. Please check your model and tokenizer.") + if model_vocab_size != len(tokenizer): + base_model.resize_token_embeddings(len(tokenizer)) + print(f"Extended vocabulary size to {len(tokenizer)}") + + first_weight = base_model.model.layers[0].self_attn.q_proj.weight + first_weight_old = first_weight.clone() + + print(f"Loading LoRA weights") + if hasattr(peft.LoraModel,'merge_and_unload'): + try: + lora_model = PeftModel.from_pretrained( + base_model, + lora_model_path, + device_map={"": "cpu"}, + torch_dtype=torch.float16, + local_files_only=True + ) + except RuntimeError as e: + if '[49953, 4096]' in str(e): + print("The vocab size of the tokenizer does not match the vocab size of the LoRA weight. \n" + "Did you misuse the LLaMA tokenizer with the Alpaca-LoRA weight?\n" + "Make sure that you use LLaMA tokenizer with the LLaMA-LoRA weight and Alpaca tokenizer with the Alpaca-LoRA weight!") + raise e + assert torch.allclose(first_weight_old, first_weight) + print(f"Merging with merge_and_unload...") + base_model = lora_model.merge_and_unload() + else: + base_model_sd = base_model.state_dict() + try: + lora_model_sd = torch.load(os.path.join(lora_model_path,'adapter_model.bin'),map_location='cpu') + except FileNotFoundError: + print("Cannot find lora model on the disk. Downloading lora model from hub...") + filename = hf_hub_download(repo_id=lora_model_path,filename='adapter_model.bin') + lora_model_sd = torch.load(filename,map_location='cpu') + if 'base_model.model.model.embed_tokens.weight' in lora_model_sd: + assert lora_model_sd['base_model.model.model.embed_tokens.weight'].shape[0]==len(tokenizer), \ + ("The vocab size of the tokenizer does not match the vocab size of the LoRA weight. \n" + "Did you misuse the LLaMA tokenizer with the Alpaca-LoRA weight?\n" + "Make sure that you use LLaMA tokenizer with the LLaMA-LoRA weight and Alpaca tokenizer with the Alpaca-LoRA weight!") + + lora_config = peft.LoraConfig.from_pretrained(lora_model_path) + lora_scaling = lora_config.lora_alpha / lora_config.r + fan_in_fan_out = lora_config.fan_in_fan_out + lora_keys = [k for k in lora_model_sd if 'lora_A' in k] + non_lora_keys = [k for k in lora_model_sd if not 'lora_' in k] + + for k in non_lora_keys: + print(f"merging {k}") + original_k = k.replace('base_model.model.','') + base_model_sd[original_k].copy_(lora_model_sd[k]) + + for k in lora_keys: + print(f"merging {k}") + original_key = k.replace('.lora_A','').replace('base_model.model.','') + assert original_key in base_model_sd + lora_a_key = k + lora_b_key = k.replace('lora_A','lora_B') + base_model_sd[original_key] += ( + transpose(lora_model_sd[lora_b_key].float() @ lora_model_sd[lora_a_key].float(),fan_in_fan_out) * lora_scaling + ) + assert base_model_sd[original_key].dtype == torch.float16 + + # did we do anything? + assert not torch.allclose(first_weight_old, first_weight) + + tokenizer.save_pretrained(output_dir) + + if output_type=='huggingface': + print("Saving to Hugging Face format...") + LlamaForCausalLM.save_pretrained(base_model, output_dir) #, state_dict=deloreanized_sd) + else: # output_type=='pth + print("Saving to pth format...") + + base_model_sd = base_model.state_dict() + del lora_model, base_model, lora_model_sd + + params = params_of_models[model_size] + num_shards = num_shards_of_models[model_size] + n_layers = params["n_layers"] + n_heads = params["n_heads"] + dim = params["dim"] + dims_per_head = dim // n_heads + base = 10000.0 + inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head)) + + save_shards(model_sd=base_model_sd, num_shards=num_shards) diff --git a/04-gene-sft/.ipynb_checkpoints/pip_list-checkpoint.txt b/04-gene-sft/.ipynb_checkpoints/pip_list-checkpoint.txt new file mode 100644 index 0000000000000000000000000000000000000000..0f64a502f7e4eb45f58e5b38a9224ef29f88575e --- /dev/null +++ b/04-gene-sft/.ipynb_checkpoints/pip_list-checkpoint.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:828ede2bc2aeec77ad37dd1e5ba9ff2fbbb481dbe3c2729b6cdafc4feb7ac669 +size 7419 diff --git a/04-gene-sft/.ipynb_checkpoints/run_clm_pt_with_peft-checkpoint.py b/04-gene-sft/.ipynb_checkpoints/run_clm_pt_with_peft-checkpoint.py index 18d496816de64ce38d1bc4ed66960cc53980cf94..d1124de99ad47931b0d74016036e28699b352693 100644 --- a/04-gene-sft/.ipynb_checkpoints/run_clm_pt_with_peft-checkpoint.py +++ b/04-gene-sft/.ipynb_checkpoints/run_clm_pt_with_peft-checkpoint.py @@ -21,6 +21,17 @@ https://huggingface.co/models?filter=text-generation """ # You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments. +import subprocess +import os +# 设置环境变量, autodl一般区域 +result = subprocess.run('bash -c "source /etc/network_turbo && env | grep proxy"', shell=True, capture_output=True, text=True) +output = result.stdout +for line in output.splitlines(): + if '=' in line: + var, value = line.split('=', 1) + os.environ[var] = value + + import logging import numpy as np import math diff --git a/04-gene-sft/.ipynb_checkpoints/run_pt-checkpoint.sh b/04-gene-sft/.ipynb_checkpoints/run_pt-checkpoint.sh index 49910149663b2183acdcb38eea0703d567a0f7ba..b12e274a3f3416ed2b0f332bbf2d1ca0d00515fd 100644 --- a/04-gene-sft/.ipynb_checkpoints/run_pt-checkpoint.sh +++ b/04-gene-sft/.ipynb_checkpoints/run_pt-checkpoint.sh @@ -6,17 +6,17 @@ modules_to_save="embed_tokens,lm_head" lora_dropout=0.05 pretrained_model=./llama-7b-hf -dna_eng_tokenizer_path=./merged_dna_eng_tokenizer_hf +dna_eng_tokenizer_path=./merged_gene_eng_tokenizer_hf dataset_dir=./train_data data_cache=temp_data_cache_dir -per_device_train_batch_size=32 -per_device_eval_batch_size=32 +per_device_train_batch_size=4 +per_device_eval_batch_size=4 gradient_accumulation_steps=8 output_dir=dnahlm_llama_7b deepspeed_config_file=ds_zero2_no_offload.json -torchrun --nnodes 1 --nproc_per_node 6 run_clm_pt_with_peft.py \ +torchrun --nnodes 1 --nproc_per_node 10 run_clm_pt_with_peft.py \ --deepspeed ${deepspeed_config_file} \ --model_name_or_path ${pretrained_model} \ --tokenizer_name_or_path ${dna_eng_tokenizer_path} \ @@ -39,8 +39,8 @@ torchrun --nnodes 1 --nproc_per_node 6 run_clm_pt_with_peft.py \ --save_total_limit 3 \ --save_steps 200 \ --gradient_accumulation_steps ${gradient_accumulation_steps} \ - --preprocessing_num_workers 128 \ - --block_size 512 \ + --preprocessing_num_workers 64 \ + --block_size 256 \ --output_dir ${output_dir} \ --overwrite_output_dir \ --ddp_timeout 30000 \ diff --git a/04-gene-sft/.ipynb_checkpoints/run_sft-checkpoint.sh b/04-gene-sft/.ipynb_checkpoints/run_sft-checkpoint.sh index e8b9bbebf109f97ed621611baee90fa0465b6bd1..26b6fd178fc2dfda8cc14cbdbf6d183ab0b0dd84 100644 --- a/04-gene-sft/.ipynb_checkpoints/run_sft-checkpoint.sh +++ b/04-gene-sft/.ipynb_checkpoints/run_sft-checkpoint.sh @@ -8,8 +8,8 @@ lora_dropout=0.05 pretrained_model=dnahlm-merge-hf chinese_tokenizer_path=dnahlm-merge-hf dataset_dir=sft_data -per_device_train_batch_size=32 -per_device_eval_batch_size=32 +per_device_train_batch_size=8 +per_device_eval_batch_size=8 gradient_accumulation_steps=8 output_dir=dnahlm-llama7b-sft #peft_model=peft_model/dir @@ -29,7 +29,7 @@ torchrun --nnodes 1 --nproc_per_node 6 run_clm_sft_with_peft.py \ --do_eval \ --seed $RANDOM \ --fp16 \ - --num_train_epochs 8 \ + --num_train_epochs 3 \ --lr_scheduler_type cosine \ --learning_rate ${lr} \ --warmup_ratio 0.03 \ @@ -43,7 +43,7 @@ torchrun --nnodes 1 --nproc_per_node 6 run_clm_sft_with_peft.py \ --save_steps 200 \ --gradient_accumulation_steps ${gradient_accumulation_steps} \ --preprocessing_num_workers 4 \ - --max_seq_length 512 \ + --max_seq_length 256 \ --output_dir ${output_dir} \ --overwrite_output_dir \ --ddp_timeout 30000 \ diff --git a/04-gene-sft/2-gpt2-instruction-ft.ipynb b/04-gene-sft/2-gpt2-instruction-ft.ipynb index d3a93e17ccf6e6574502b5a990af879b9a4144b4..3046ff25db972d80c49d15c8bb0046c847c9a48b 100644 --- a/04-gene-sft/2-gpt2-instruction-ft.ipynb +++ b/04-gene-sft/2-gpt2-instruction-ft.ipynb @@ -127,20 +127,76 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "dc04d5e3-7623-4d59-9f3b-ad03e339db11", + "execution_count": 1, + "id": "b28e3499-bbff-4548-9f85-2baee088cabf", "metadata": {}, "outputs": [], + "source": [ + "import subprocess\n", + "import os\n", + "# 设置环境变量, autodl一般区域\n", + "result = subprocess.run('bash -c \"source /etc/network_turbo && env | grep proxy\"', shell=True, capture_output=True, text=True)\n", + "output = result.stdout\n", + "for line in output.splitlines():\n", + " if '=' in line:\n", + " var, value = line.split('=', 1)\n", + " os.environ[var] = value" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "dc04d5e3-7623-4d59-9f3b-ad03e339db11", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['sequence', 'label'],\n", + " num_rows: 59195\n", + " })\n", + "})" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from datasets import load_dataset\n", "# 1. load ~11k samples from promoters prediction dataset\n", - "dataset = load_dataset(\"dnagpt/dna_promoter_300\")\n", - "dataset" + "dna_dataset = load_dataset(\"dnagpt/dna_promoter_300\")\n", + "dna_dataset" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, + "id": "f7332fa1-3343-4247-b4cc-54733dff6964", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'sequence': 'TAAATACGGAAGTTTATTACTTGAGGAATAGATGGAATCGTCGGGCGTGAGAGATCATAATCGGCTGCTTCTGGGAGCCGCACGTGGGAAAGACTTATCCCCGACGGAGCTGGGACTGGGGCACAAACCGGAAGGAACACATCTGACCGAGAAAGAGACCAAGTGGCTCAGGTAGGACCAAAGCGAGCAAGGCTGCGGGTCCTGTTGCTCTCTGTCCTGTAAATTTAAACGTTACGCCACCTGGTAATGATACCCTCGTCCTCCGAGGCGACAAGTCAGAACTTCCACCAAGGGCATTAC',\n", + " 'label': 0}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dna_dataset[\"train\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "id": "93d09d8d-f521-49f7-b0e0-7ac089dfbf49", "metadata": {}, "outputs": [], @@ -169,10 +225,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "9f9c0e5a-6591-47ac-b358-d746a00dfc0a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'instruction': 'Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.', 'input': 'TAAATACGGAAGTTTATTACTTGAGGAATAGATGGAATCGTCGGGCGTGAGAGATCATAATCGGCTGCTTCTGGGAGCCGCACGTGGGAAAGACTTATCCCCGACGGAGCTGGGACTGGGGCACAAACCGGAAGGAACACATCTGACCGAGAAAGAGACCAAGTGGCTCAGGTAGGACCAAAGCGAGCAAGGCTGCGGGTCCTGTTGCTCTCTGTCCTGTAAATTTAAACGTTACGCCACCTGGTAATGATACCCTCGTCCTCCGAGGCGACAAGTCAGAACTTCCACCAAGGGCATTAC', 'output': 'Non-promoter'}\n" + ] + } + ], "source": [ "example = dna_dataset[\"train\"][0]\n", "print(build_prompt(example))" @@ -180,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "83070a23-1604-4d28-b371-e01060331ed5", "metadata": {}, "outputs": [], @@ -199,10 +263,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "89fb8ed3-aa58-462f-b2a6-ce445c597a33", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7b84d3a64bf645ada13d0cada2d9f524", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Generating train split: 0 examples [00:00, ? examples/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['instruction', 'input', 'output'],\n", + " num_rows: 59195\n", + " })\n", + "})" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dna_ft_dataset = load_dataset(\"json\", data_files='data/dna_promoter_300.jsonl')\n", "dna_ft_dataset" @@ -210,10 +304,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "e4f7b75f-6ccb-4fda-8004-40df7d52678f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['instruction', 'input', 'output'],\n", + " num_rows: 53275\n", + " })\n", + " test: Dataset({\n", + " features: ['instruction', 'input', 'output'],\n", + " num_rows: 5920\n", + " })\n", + "})" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = dna_ft_dataset[\"train\"].train_test_split(train_size=0.9, seed=42)\n", "data" @@ -221,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "36d9ee0e-8423-4529-aa7e-fda2728fab2f", "metadata": {}, "outputs": [], @@ -235,13 +349,14 @@ "from tokenizers import Tokenizer\n", "from transformers import GPT2TokenizerFast\n", "\n", + "#需要使用生物序列+英文的多模态大模型\n", "tokenizer = GPT2Tokenizer.from_pretrained(\"dnagpt/gene_eng_gpt2_v0\")\n", "tokenizer.pad_token = tokenizer.eos_token" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "871baee0-f06f-4422-a741-af533f7d92e1", "metadata": {}, "outputs": [], @@ -270,10 +385,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "bca1c275-cc3d-43df-923e-e6604d584226", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'instruction': 'Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.',\n", + " 'input': 'CCAGGATGCGCTGACGACCCGGCTGGCAGGCGGGTCCTCGTGGGCGAGGCGAGGGAGGCGGCGAGAGAGGAGCAATAGTTTCCCACCGCTCCCTCTCAGGCGCAGGGTCTAGAGAAGCGCGAGGGGATCTAGAGAAGCCGGAGGGGAGGAAGCGCGAGTCCGCGGCCCGCCCCGTTGCGTCCCACCCACCGCGTCCCCTCCCCTCCCCTCCCGCTGCGGGAAAAGCGGCCGCGGGCGGCGGCGCCCACTGTGGGGCGGGCGGAGCGCCGCGGGAGGCGGACGAGATGCGAGCGCGGCCGC',\n", + " 'output': 'promoter'}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "example = data[\"test\"][0]\n", "example" @@ -281,10 +409,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "76f2e027-0a31-4919-bb7e-404c786e1599", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.\n", + "\n", + "### Input:\n", + "CCAGGATGCGCTGACGACCCGGCTGGCAGGCGGGTCCTCGTGGGCGAGGCGAGGGAGGCGGCGAGAGAGGAGCAATAGTTTCCCACCGCTCCCTCTCAGGCGCAGGGTCTAGAGAAGCGCGAGGGGATCTAGAGAAGCCGGAGGGGAGGAAGCGCGAGTCCGCGGCCCGCCCCGTTGCGTCCCACCCACCGCGTCCCCTCCCCTCCCCTCCCGCTGCGGGAAAAGCGGCCGCGGGCGGCGGCGCCCACTGTGGGGCGGGCGGAGCGCCGCGGGAGGCGGACGAGATGCGAGCGCGGCCGC\n", + "\n", + "### Response:\n", + "promoter\n" + ] + } + ], "source": [ "prompt = build_prompt(example)\n", "print(prompt)" @@ -292,24 +437,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "932b54ca-7e27-47cd-b67d-7ef8386b6608", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tokens: Bel ow Ġ is Ġ an Ġ instruc tion Ġ th at Ġ describ es Ġ a Ġ t ask . ĠWrit e Ġ a Ġ respon se Ġ th at Ġ appropri at el y Ġ complet es Ġ the Ġ request . Ċ Ċ # # # ĠIn struc tion : Ċ D eter min e Ġ cor e Ġ promo ter Ġ det ec tion Ġ of Ġ follow ing Ġ d na Ġ sequenc e , ĠTh e Ġ resul t Ġ will Ġ be Ġ on e Ġ of Ġ the Ġ follow ing : ĠN on - promo ter , Ġ promo ter . Ċ Ċ # # # ĠIn put : Ċ CC AGGATGC GC TGACG ACCC GGCTGGC AGGC GGGTCC TCG TGGGCG AGGCG AGGGAGGC GGCG AGAGAGG AGCAATAG TTTCCC ACCGC TCCCTCTC AGGCGC AGGG TCTAG AGAAGC GCG AGGGG ATCTAG AGAAGCC GG AGGGG AGGAAGC GCG AGTCC GCGG CCCGCC CCG TTGCG TCCC ACCCACC GCG TCCCCTCCCC TCCCCTCCC GCTGC GGG AAAAGC GGCCGC GGGCGGC GGCGCCC ACTGTG GGGC GGGC GGAGC GCCGC GGGAGGC GGACG AGATGCG AGCGC GGCCGC Ċ Ċ # # # ĠR esp on se : Ċ promo ter\n" + ] + } + ], "source": [ "print('tokens: ', ' '.join(tokenizer.tokenize(prompt)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "26671faf-68d0-4a44-978e-e1a24e86c9b1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "02d2f083e74a45e6ada46b9872822dfc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/53275 [00:00, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::logic_error@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::locale::~locale()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::basic_string(std::string const&, unsigned long, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_end_catch@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `VTT for std::basic_ofstream >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::logic_error::~logic_error()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for __cxxabiv1::__si_class_type_info@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ios >::_M_cache_locale(std::locale const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `VTT for std::basic_stringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `operator new[](unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::_M_leak_hard()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_ifstream >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_streambuf >::basic_streambuf(std::basic_streambuf > const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::append(char const*, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::basic_string(std::string const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for unsigned short@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::resize(unsigned long, char)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for char const*@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ctype::_M_widen_init() const@GLIBCXX_3.4.11'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__throw_invalid_argument(char const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::locale::operator=(std::locale const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ios >::_M_cache_locale(std::locale const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Rb_tree_decrement(std::_Rb_tree_node_base const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_free_exception@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::condition_variable::notify_one()@GLIBCXX_3.4.11'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ios_base::Init::~Init()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::~basic_string()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_pure_virtual@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream::flush()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for __cxxabiv1::__class_type_info@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_rethrow@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_stringbuf, std::allocator 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/usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Hash_bytes(void const*, unsigned long, unsigned long)@CXXABI_1.3.5'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(long long)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for char*@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__detail::_Prime_rehash_policy::_M_need_rehash(unsigned long, unsigned long, unsigned long) const@GLIBCXX_3.4.18'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::out_of_range@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(unsigned long)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Rb_tree_increment(std::_Rb_tree_node_base const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ios_base::~ios_base()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::range_error::~range_error()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__basic_file::~__basic_file()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_guard_acquire@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(bool)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: 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"/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::basic_string(unsigned long, char, std::allocator const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__detail::_List_node_base::_M_transfer(std::__detail::_List_node_base*, std::__detail::_List_node_base*)@GLIBCXX_3.4.15'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::replace(unsigned long, unsigned long, char const*, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for std::exception@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::_Rep::_M_destroy(std::allocator const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::istream& std::istream::_M_extract(double&)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_filebuf >::close()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_fstream >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ifstream >::basic_ifstream(char const*, std::_Ios_Openmode)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::append(std::string const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `operator new(unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `VTT for std::basic_istringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for unsigned int@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::append(char const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::domain_error@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::find(char, unsigned long) const@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream::put(char)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for int@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__throw_bad_alloc()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_thread_atexit@CXXABI_1.3.7'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Rb_tree_increment(std::_Rb_tree_node_base*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ifstream >::~basic_ifstream()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ios_base::Init::Init()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::condition_variable::condition_variable()@GLIBCXX_3.4.11'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_filebuf >::basic_filebuf()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `VTT for std::basic_istringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::domain_error::~domain_error()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::cerr@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::find(char const*, unsigned long, unsigned long) const@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_istringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::basic_string(std::allocator const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_stringbuf, std::allocator >::str() const@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::invalid_argument@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for void*@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::assign(std::string const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ostringstream, std::allocator >::~basic_ostringstream()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Rb_tree_rebalance_for_erase(std::_Rb_tree_node_base*, std::_Rb_tree_node_base&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for unsigned long@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__detail::_List_node_base::_M_hook(std::__detail::_List_node_base*)@GLIBCXX_3.4.15'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__detail::_List_node_base::_M_unhook()@GLIBCXX_3.4.15'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_ostringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_stringbuf, std::allocator >::_M_sync(char*, unsigned long, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_iostream >::~basic_iostream()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::locale::locale(std::locale const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_istringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `log2f@GLIBC_2.2.5'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream::operator<<(std::basic_streambuf >*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_streambuf >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::exception::~exception()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::_Rep::_S_create(unsigned long, unsigned long, std::allocator const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__basic_file::is_open() const@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_istringstream, std::allocator >::~basic_istringstream()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::swap(std::string&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for unsigned long*@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_ostringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_streambuf >::basic_streambuf(std::basic_streambuf > const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ios >::init(std::basic_streambuf >*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__throw_bad_cast()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ios >::clear(std::_Ios_Iostate)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_streambuf >::operator=(std::basic_streambuf > const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `operator delete(void*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream::operator<<(int)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::_Rep::_S_empty_rep_storage@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::_Rep::_M_destroy(std::allocator const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_iostream >::~basic_iostream()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::runtime_error@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_ofstream >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Rb_tree_insert_and_rebalance(bool, std::_Rb_tree_node_base*, std::_Rb_tree_node_base*, std::_Rb_tree_node_base&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_stringstream, std::allocator >::~basic_stringstream()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `VTT for std::basic_stringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(long)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::istream::get()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for unsigned long long@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ostream >& std::operator<< >(std::basic_ostream >&, char const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::out_of_range::~out_of_range()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::length_error::~length_error()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ostream >& std::__ostream_insert >(std::basic_ostream >&, char const*, long)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::invalid_argument::~invalid_argument()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::swap(std::basic_string, std::allocator >&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::cout@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(unsigned long long)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for int*@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(void const*)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::underflow_error@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_streambuf >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for std::out_of_range@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_allocate_exception@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_ios >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for void const*@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ios >::init(std::basic_streambuf >*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::reserve(unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_begin_catch@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for long@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::_Rep::_S_empty_rep_storage@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::_M_leak()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_filebuf >::open(char const*, std::_Ios_Openmode)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_stringbuf, std::allocator >::_M_sync(wchar_t*, unsigned long, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::istream::getline(char*, long, char)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_istream >& std::getline, std::allocator >(std::basic_istream >&, std::basic_string, std::allocator >&, char)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_stringstream, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::condition_variable::~condition_variable()@GLIBCXX_3.4.11'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::basic_stringbuf, std::allocator >@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::insert(unsigned long, char const*, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::string::assign(char const*, unsigned long)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for unsigned char@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ios_base::ios_base()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__throw_out_of_range(char const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::overflow_error::~overflow_error()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__throw_length_error(char const*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::__throw_system_error(int)@GLIBCXX_3.4.11'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ofstream >::close()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::ostream& std::ostream::_M_insert(double)@GLIBCXX_3.4.9'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_streambuf >::operator=(std::basic_streambuf > const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `typeinfo for long long@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_string, std::allocator >::basic_string(char const*, unsigned long, std::allocator const&)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_ifstream >::close()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_guard_release@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `__cxa_throw@CXXABI_1.3'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::underflow_error::~underflow_error()@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::_Rb_tree_decrement(std::_Rb_tree_node_base*)@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `vtable for std::length_error@GLIBCXX_3.4'\n", + "/root/miniconda3/compiler_compat/ld: /usr/local/cuda/lib64/libcufile.so: undefined reference to `std::basic_filebuf >::~basic_filebuf()@GLIBCXX_3.4'\n", + "collect2: error: ld returned 1 exit status\n" + ] + } + ], "source": [ "# 初始化Trainer\n", "trainer = Trainer(\n", @@ -454,10 +922,204 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "a9cd936a-5ea6-43e3-9848-27080f818606", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "

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StepTraining Loss
5002.335700
10002.184100
15002.178500
20002.172400
25002.171400
30002.171900
35002.163100
40002.159300
45002.161000
50002.160200
55002.158400
60002.151600
65002.153700
70002.129500
75002.119100
80002.119800
85002.121600
90002.122500
95002.122300
100002.121500
105002.119500
110002.123500
115002.119600
120002.119300
125002.121800
130002.123500
135002.103200
140002.080700
145002.082100
150002.082900
155002.086400
160002.086600
165002.083800
170002.085000
175002.082800
180002.077600
185002.080300
190002.086600
195002.084200

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "TrainOutput(global_step=19980, training_loss=2.1272921145021977, metrics={'train_runtime': 1315.5944, 'train_samples_per_second': 121.485, 'train_steps_per_second': 15.187, 'total_flos': 2.08804995072e+16, 'train_loss': 2.1272921145021977, 'epoch': 3.0})" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# 开始训练\n", "trainer.train()" @@ -465,10 +1127,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "315aae76-44b4-4513-8139-40ef22934873", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved model to: gpt_ft/final\n" + ] + } + ], "source": [ "save_dir = 'gpt_ft/final'\n", "trainer.save_model(save_dir)\n", @@ -477,7 +1147,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "28d2dbbc-02ff-4120-b230-b19905a786cd", "metadata": {}, "outputs": [], @@ -488,20 +1158,92 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "08987c3c-063a-4e9b-9ebb-e637b0b5bccd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "GPT2LMHeadModel(\n", + " (transformer): GPT2Model(\n", + " (wte): Embedding(90000, 768)\n", + " (wpe): Embedding(1024, 768)\n", + " (drop): Dropout(p=0.1, inplace=False)\n", + " (h): ModuleList(\n", + " (0-11): 12 x GPT2Block(\n", + " (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (attn): GPT2SdpaAttention(\n", + " (c_attn): Conv1D(nf=2304, nx=768)\n", + " (c_proj): Conv1D(nf=768, nx=768)\n", + " (attn_dropout): Dropout(p=0.1, inplace=False)\n", + " (resid_dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (mlp): GPT2MLP(\n", + " (c_fc): Conv1D(nf=3072, nx=768)\n", + " (c_proj): Conv1D(nf=768, nx=3072)\n", + " (act): NewGELUActivation()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (lm_head): Linear(in_features=768, out_features=90000, bias=False)\n", + ")" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "finetuned_model" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "d75010e8-6d6a-40ef-852e-0d705adc3da8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input (test): Below is an instruction that describes a task. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.\n", + "\n", + "### Input:\n", + "CCAGGATGCGCTGACGACCCGGCTGGCAGGCGGGTCCTCGTGGGCGAGGCGAGGGAGGCGGCGAGAGAGGAGCAATAGTTTCCCACCGCTCCCTCTCAGGCGCAGGGTCTAGAGAAGCGCGAGGGGATCTAGAGAAGCCGGAGGGGAGGAAGCGCGAGTCCGCGGCCCGCCCCGTTGCGTCCCACCCACCGCGTCCCCTCCCCTCCCCTCCCGCTGCGGGAAAAGCGGCCGCGGGCGGCGGCGCCCACTGTGGGGCGGGCGGAGCGCCGCGGGAGGCGGACGAGATGCGAGCGCGGCCGC\n", + "\n", + "### Response:\n", + "\n", + "--------------------------\n", + "\n", + "model's answer: \n", + "\n", + "promoterpromoterpromo\n", + "--------------------------\n", + "\n", + "real answer: \n", + "\n", + "promoter\n" + ] + } + ], "source": [ "print(\"input (test):\", input_text)\n", "\n", @@ -517,10 +1259,217 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "64365e15-510e-4abf-92f5-c78b660b37dc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n", + "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", + "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" + ] + } + ], "source": [ "test_data = data[\"test\"].select(range(100))\n", "\n", @@ -543,7 +1492,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "a45fb780-fc3f-401c-b6e0-6f7d0c1682de", "metadata": {}, "outputs": [], @@ -556,19 +1505,128 @@ "# 将 Dataset 对象导出为 JSON 文件\n", "# test_data.to_json(output_file)\n", "with open(output_file, \"w\") as file:\n", - " json.dump(data_list, file, indent=4) # \"indent\" for pretty-printing" + " json.dump(data_list, file, indent=4) # \"indent\" for pretty-printing\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "a83c8881-c763-4bba-8b85-584a6722a38e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "promoter |||||||||||| promoterpromoterpromo\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "presicion 0.94 same 0.49\n" + ] + } + ], "source": [ "import json\n", "\n", "\n", + "output_file = 'gpt2-small3-1024.json'\n", "\n", "with open(output_file, \"r\") as file:\n", " test_data = json.load(file)\n", @@ -580,15 +1638,30 @@ " output = item[\"output\"]\n", " #output = \" \".join(tokenizer.tokenize(output))\n", " model_response = item[\"model_response\"]\n", + "\n", + " print(output,\"||||||||||||\", model_response)\n", + "\n", " if model_response == output: #same it\n", " same_sum = same_sum + 1\n", " \n", - " if model_response.find(output)!=-1: #find it\n", - " right_sum = right_sum + 1\n", + " if output.find(\"Non\")==-1: # no Non\n", + " if model_response.find(output)!=-1 and model_response.find(\"Non\")==-1: #find it, but no Non\n", + " right_sum = right_sum + 1\n", + " else:\n", + " if model_response.find(output)!=-1: #find it\n", + " right_sum = right_sum + 1\n", "\n", "\n", - "print(\"presicion\", right_sum/all_num, \"same\", same_sum/all_num)" + "print(\"Accuracy\", right_sum/all_num, \"same\", same_sum/all_num)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bf88885-fb33-406e-9644-cd174a8a2f28", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/04-gene-sft/4-deepspeed-intro.ipynb b/04-gene-sft/4-deepspeed-intro.ipynb index cba95bbe310193355908067a03d34d0cd4acb70c..823bc80f97ddb972da9432cb2f6d3eef98776da0 100644 --- a/04-gene-sft/4-deepspeed-intro.ipynb +++ b/04-gene-sft/4-deepspeed-intro.ipynb @@ -10,12 +10,172 @@ }, { "cell_type": "markdown", - "id": "3383c2d7-91a9-4940-b3b2-698fb7d9dbb7", + "id": "75b8219d-8069-4b18-96c8-d5024ee049f1", "metadata": {}, "source": [ - "## 使用gpt2+deepspeed训练" + "## 大模型并行训练简介\n", + "\n", + "大模型的并行训练旨在克服单个 GPU 显存的限制和加速训练过程，通常适用于参数规模较大的模型（如 GPT-3、T5 等）。并行训练主要包括以下几种方法，每种方法适用于不同的场景和模型特性。\n", + "\n", + "---\n", + "\n", + "### **1. 数据并行（Data Parallelism）**\n", + "\n", + "#### **原理**\n", + "- 将数据切分成多个小批次，每个 GPU 处理其中一部分。\n", + "- 模型副本被复制到每个 GPU。\n", + "- 每个 GPU 独立计算梯度，最终通过梯度同步（如 AllReduce 操作）更新参数。\n", + "\n", + "#### **特点**\n", + "- **优点**：\n", + " - 实现简单，是最常用的并行方法。\n", + " - 对模型大小没有限制。\n", + "- **缺点**：\n", + " - 模型副本需要完整加载到每个 GPU，占用显存。\n", + " - 在超大规模模型中，显存压力较大。\n", + "\n", + "#### **适用场景**\n", + "- 参数规模适中，显存可以容纳整个模型的场景。\n", + "\n", + "---\n", + "\n", + "### **2. 模型并行（Model Parallelism）**\n", + "\n", + "#### **原理**\n", + "- 将模型切分成不同的部分，将不同部分分配到不同的 GPU。\n", + "- 前向传播和后向传播时，数据在模型的不同部分之间传递。\n", + "\n", + "#### **特点**\n", + "- **优点**：\n", + " - 不需要复制整个模型，可以支持超大规模模型。\n", + "- **缺点**：\n", + " - GPU 之间通信频繁，可能成为性能瓶颈。\n", + " - 实现复杂，切分模型需要精心设计。\n", + " \n", + "#### **适用场景**\n", + "- 单个 GPU 无法容纳完整模型参数的场景。\n", + "\n", + "#### **具体实现**\n", + "- 将 Transformer 的不同层分配到不同的 GPU。\n", + "- 常用工具：DeepSpeed 的 Pipeline Parallelism、NVIDIA Megatron-LM。\n", + "\n", + "---\n", + "\n", + "### **3. 张量并行（Tensor Parallelism）**\n", + "\n", + "#### **原理**\n", + "- 将模型内部的张量（如权重矩阵）切分为多个子张量，并分配到不同 GPU。\n", + "- GPU 之间协作完成矩阵计算。\n", + "\n", + "#### **特点**\n", + "- **优点**：\n", + " - 减少了每个 GPU 的显存占用，同时保持模型整体完整性。\n", + "- **缺点**：\n", + " - 实现较复杂，需要优化通信操作。\n", + " - 通信开销较高，适合较大批量的训练。\n", + "\n", + "#### **适用场景**\n", + "- 参数非常大的模型（如 GPT-3）。\n", + "- 需要极致优化显存的场景。\n", + "\n", + "#### **具体实现**\n", + "- NVIDIA 的 Megatron-LM 和 Hugging Face Transformers 提供了张量并行的支持。\n", + "\n", + "---\n", + "\n", + "### **4. 管道并行（Pipeline Parallelism）**\n", + "\n", + "#### **原理**\n", + "- 将模型分为不同的部分（通常是按层划分），每部分分配到不同的 GPU。\n", + "- 数据按照流水线的方式流经每个 GPU。\n", + "\n", + "#### **特点**\n", + "- **优点**：\n", + " - 减少每个 GPU 的显存压力。\n", + " - 通过流水线增加计算效率。\n", + "- **缺点**：\n", + " - 引入流水线延迟。\n", + " - 实现复杂，需管理数据依赖和同步。\n", + "\n", + "#### **适用场景**\n", + "- 模型非常深，层数较多的场景。\n", + "\n", + "#### **具体实现**\n", + "- DeepSpeed 的 Pipeline Parallelism。\n", + "\n", + "---\n", + "\n", + "### **5. 混合并行（Hybrid Parallelism）**\n", + "\n", + "#### **原理**\n", + "- 将数据并行、模型并行、张量并行和管道并行组合使用，充分利用多 GPU 资源。\n", + "- 不同的并行方法在不同维度协同工作。\n", + "\n", + "#### **特点**\n", + "- **优点**：\n", + " - 灵活且适应性强，适合超大规模模型。\n", + "- **缺点**：\n", + " - 配置复杂，依赖于框架和训练任务。\n", + "\n", + "#### **适用场景**\n", + "- 超大规模模型（如 GPT-3 或参数量 >1T）。\n", + "- 多机多卡的大型训练环境。\n", + "\n", + "#### **具体实现**\n", + "- NVIDIA Megatron-LM 和 DeepSpeed 的混合并行支持。\n", + "\n", + "---\n", + "\n", + "### **6. ZeRO 优化并行（Zero Redundancy Optimizer）**\n", + "\n", + "#### **原理**\n", + "- 通过分片存储模型参数、优化器状态和梯度，显著减少每个 GPU 的显存占用。\n", + "\n", + "#### **特点**\n", + "- **优点**：\n", + " - 极大降低显存需求。\n", + " - 支持超大规模模型。\n", + "- **缺点**：\n", + " - 对 GPU 间通信要求较高。\n", + " - 比数据并行复杂。\n", + "\n", + "#### **适用场景**\n", + "- 超大模型的高效训练。\n", + "\n", + "#### **具体实现**\n", + "- DeepSpeed 提供的 ZeRO Stage 1/2/3。\n", + "\n", + "---\n", + "\n", + "### **方法对比**\n", + "\n", + "| 并行方法 | 主要优点 | 主要缺点 | 适用场景 |\n", + "|---------------|-------------------------------|-------------------------------|---------------------------|\n", + "| 数据并行 | 简单高效，易实现 | 模型副本占用大量显存 | 模型规模适中，显存足够 |\n", + "| 模型并行 | 支持大模型 | 通信开销大，切分复杂 | 超大模型，显存有限 |\n", + "| 张量并行 | 高效利用显存 | 实现复杂，通信频繁 | 参数规模极大的模型 |\n", + "| 管道并行 | 显存需求降低，适合深模型 | 流水线延迟，数据同步复杂 | 层数多的大型模型 |\n", + "| 混合并行 | 灵活适配超大规模模型 | 配置复杂，依赖框架 | 超大规模模型（如 GPT-3） |\n", + "| ZeRO 并行 | 极大节省显存，占用少 | 通信成本高 | 超大规模模型显存优化 |\n", + "\n", + "---\n", + "\n", + "### **总结**\n", + "- **中等规模模型**：优先使用 **数据并行**。\n", + "- **单卡显存不足**：采用 **模型并行** 或 **张量并行**。\n", + "- **超大规模模型**：使用 **混合并行** 或 DeepSpeed 的 **ZeRO 优化**。\n", + "\n", + "对于现代超大规模模型，通常采用混合并行方法，比如 NVIDIA 的 Megatron-LM 和微软的 DeepSpeed，它们综合了多种并行策略，能够有效利用计算资源并加速训练。如果您有具体的硬件环境或模型需求，可以进一步探讨适合的并行方案！" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "06ddaa4d-e04a-41e0-beb5-f04dfaebcd54", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "c0d29667-1e75-46df-8f65-cae27609ee3f", @@ -169,171 +329,19 @@ ] }, { - "cell_type": "markdown", - "id": "75b8219d-8069-4b18-96c8-d5024ee049f1", + "cell_type": "code", + "execution_count": null, + "id": "a5372798-ced3-420c-b853-badd3ff05dc1", "metadata": {}, - "source": [ - "## 大模型并行方法\n", - "\n", - "大模型的并行训练旨在克服单个 GPU 显存的限制和加速训练过程，通常适用于参数规模较大的模型（如 GPT-3、T5 等）。并行训练主要包括以下几种方法，每种方法适用于不同的场景和模型特性。\n", - "\n", - "---\n", - "\n", - "### **1. 数据并行（Data Parallelism）**\n", - "\n", - "#### **原理**\n", - "- 将数据切分成多个小批次，每个 GPU 处理其中一部分。\n", - "- 模型副本被复制到每个 GPU。\n", - "- 每个 GPU 独立计算梯度，最终通过梯度同步（如 AllReduce 操作）更新参数。\n", - "\n", - "#### **特点**\n", - "- **优点**：\n", - " - 实现简单，是最常用的并行方法。\n", - " - 对模型大小没有限制。\n", - "- **缺点**：\n", - " - 模型副本需要完整加载到每个 GPU，占用显存。\n", - " - 在超大规模模型中，显存压力较大。\n", - "\n", - "#### **适用场景**\n", - "- 参数规模适中，显存可以容纳整个模型的场景。\n", - "\n", - "---\n", - "\n", - "### **2. 模型并行（Model Parallelism）**\n", - "\n", - "#### **原理**\n", - "- 将模型切分成不同的部分，将不同部分分配到不同的 GPU。\n", - "- 前向传播和后向传播时，数据在模型的不同部分之间传递。\n", - "\n", - "#### **特点**\n", - "- **优点**：\n", - " - 不需要复制整个模型，可以支持超大规模模型。\n", - "- **缺点**：\n", - " - GPU 之间通信频繁，可能成为性能瓶颈。\n", - " - 实现复杂，切分模型需要精心设计。\n", - " \n", - "#### **适用场景**\n", - "- 单个 GPU 无法容纳完整模型参数的场景。\n", - "\n", - "#### **具体实现**\n", - "- 将 Transformer 的不同层分配到不同的 GPU。\n", - "- 常用工具：DeepSpeed 的 Pipeline Parallelism、NVIDIA Megatron-LM。\n", - "\n", - "---\n", - "\n", - "### **3. 张量并行（Tensor Parallelism）**\n", - "\n", - "#### **原理**\n", - "- 将模型内部的张量（如权重矩阵）切分为多个子张量，并分配到不同 GPU。\n", - "- GPU 之间协作完成矩阵计算。\n", - "\n", - "#### **特点**\n", - "- **优点**：\n", - " - 减少了每个 GPU 的显存占用，同时保持模型整体完整性。\n", - "- **缺点**：\n", - " - 实现较复杂，需要优化通信操作。\n", - " - 通信开销较高，适合较大批量的训练。\n", - "\n", - "#### **适用场景**\n", - "- 参数非常大的模型（如 GPT-3）。\n", - "- 需要极致优化显存的场景。\n", - "\n", - "#### **具体实现**\n", - "- NVIDIA 的 Megatron-LM 和 Hugging Face Transformers 提供了张量并行的支持。\n", - "\n", - "---\n", - "\n", - "### **4. 管道并行（Pipeline Parallelism）**\n", - "\n", - "#### **原理**\n", - "- 将模型分为不同的部分（通常是按层划分），每部分分配到不同的 GPU。\n", - "- 数据按照流水线的方式流经每个 GPU。\n", - "\n", - "#### **特点**\n", - "- **优点**：\n", - " - 减少每个 GPU 的显存压力。\n", - " - 通过流水线增加计算效率。\n", - "- **缺点**：\n", - " - 引入流水线延迟。\n", - " - 实现复杂，需管理数据依赖和同步。\n", - "\n", - "#### **适用场景**\n", - "- 模型非常深，层数较多的场景。\n", - "\n", - "#### **具体实现**\n", - "- DeepSpeed 的 Pipeline Parallelism。\n", - "\n", - "---\n", - "\n", - "### **5. 混合并行（Hybrid Parallelism）**\n", - "\n", - "#### **原理**\n", - "- 将数据并行、模型并行、张量并行和管道并行组合使用，充分利用多 GPU 资源。\n", - "- 不同的并行方法在不同维度协同工作。\n", - "\n", - "#### **特点**\n", - "- **优点**：\n", - " - 灵活且适应性强，适合超大规模模型。\n", - "- **缺点**：\n", - " - 配置复杂，依赖于框架和训练任务。\n", - "\n", - "#### **适用场景**\n", - "- 超大规模模型（如 GPT-3 或参数量 >1T）。\n", - "- 多机多卡的大型训练环境。\n", - "\n", - "#### **具体实现**\n", - "- NVIDIA Megatron-LM 和 DeepSpeed 的混合并行支持。\n", - "\n", - "---\n", - "\n", - "### **6. ZeRO 优化并行（Zero Redundancy Optimizer）**\n", - "\n", - "#### **原理**\n", - "- 通过分片存储模型参数、优化器状态和梯度，显著减少每个 GPU 的显存占用。\n", - "\n", - "#### **特点**\n", - "- **优点**：\n", - " - 极大降低显存需求。\n", - " - 支持超大规模模型。\n", - "- **缺点**：\n", - " - 对 GPU 间通信要求较高。\n", - " - 比数据并行复杂。\n", - "\n", - "#### **适用场景**\n", - "- 超大模型的高效训练。\n", - "\n", - "#### **具体实现**\n", - "- DeepSpeed 提供的 ZeRO Stage 1/2/3。\n", - "\n", - "---\n", - "\n", - "### **方法对比**\n", - "\n", - "| 并行方法 | 主要优点 | 主要缺点 | 适用场景 |\n", - "|---------------|-------------------------------|-------------------------------|---------------------------|\n", - "| 数据并行 | 简单高效，易实现 | 模型副本占用大量显存 | 模型规模适中，显存足够 |\n", - "| 模型并行 | 支持大模型 | 通信开销大，切分复杂 | 超大模型，显存有限 |\n", - "| 张量并行 | 高效利用显存 | 实现复杂，通信频繁 | 参数规模极大的模型 |\n", - "| 管道并行 | 显存需求降低，适合深模型 | 流水线延迟，数据同步复杂 | 层数多的大型模型 |\n", - "| 混合并行 | 灵活适配超大规模模型 | 配置复杂，依赖框架 | 超大规模模型（如 GPT-3） |\n", - "| ZeRO 并行 | 极大节省显存，占用少 | 通信成本高 | 超大规模模型显存优化 |\n", - "\n", - "---\n", - "\n", - "### **总结**\n", - "- **中等规模模型**：优先使用 **数据并行**。\n", - "- **单卡显存不足**：采用 **模型并行** 或 **张量并行**。\n", - "- **超大规模模型**：使用 **混合并行** 或 DeepSpeed 的 **ZeRO 优化**。\n", - "\n", - "对于现代超大规模模型，通常采用混合并行方法，比如 NVIDIA 的 Megatron-LM 和微软的 DeepSpeed，它们综合了多种并行策略，能够有效利用计算资源并加速训练。如果您有具体的硬件环境或模型需求，可以进一步探讨适合的并行方案！" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", "id": "cd848439-bac8-46b2-9a0f-59ae7c343954", "metadata": {}, "source": [ - "## deepspeed的并行设计\n", + "## deepspeed具体设置\n", "\n", "\n", "是的，DeepSpeed 支持多种并行策略，包括 **数据并行**、**模型并行** 和 **张量并行**，并且可以通过其配置文件灵活地设置这些并行模式。\n", @@ -544,6 +552,22 @@ "DeepSpeed 的配置高度灵活，可以根据模型大小、显存限制和硬件条件选择适合的并行策略。" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8e6de4c-adc1-4a1b-840a-c8542b4ed783", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3383c2d7-91a9-4940-b3b2-698fb7d9dbb7", + "metadata": {}, + "source": [ + "## 使用gpt2+deepspeed训练" + ] + }, { "cell_type": "markdown", "id": "ab2812bc-f743-4f18-b49c-972781484dc6", @@ -569,14 +593,6 @@ "metadata": {}, "outputs": [], "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce701aeb-c8c7-450a-bbf9-b793a19cd0c6", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/04-gene-sft/5-peft-intro.ipynb b/04-gene-sft/5-peft-intro.ipynb index 03c47ea420b052218d35094090abedd321a0e521..45bc08c45065ec4c977fd5ec228be8bb46670336 100644 --- a/04-gene-sft/5-peft-intro.ipynb +++ b/04-gene-sft/5-peft-intro.ipynb @@ -8,6 +8,14 @@ "# 4.5 peft简介" ] }, + { + "cell_type": "markdown", + "id": "f4288594-c676-4369-aca1-730446f293d7", + "metadata": {}, + "source": [ + "## peft" + ] + }, { "cell_type": "markdown", "id": "182b82c4-d484-4c15-a600-03c3b51367ec", @@ -139,6 +147,22 @@ "如果您需要实现高效微调，可以结合 Hugging Face 的 PEFT 库快速上手。" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "a70b2631-c9b9-49da-96c6-6760c63040ac", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "7b47ddf3-85c9-4dd8-bbbb-34fc3bd6aa1b", + "metadata": {}, + "source": [ + "## GPT2使用peft样例" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -366,14 +390,6 @@ " print(name)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "37aa6abb-ab1c-4e9c-b968-579dd74044db", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "0add2f79-f35c-4638-80bb-0d8a87a9b6a7", @@ -502,11 +518,19 @@ { "cell_type": "code", "execution_count": null, - "id": "b4a41750-420f-49c4-845d-69db394794f9", + "id": "14f20171-0719-4dfa-b888-147b657ebff4", "metadata": {}, "outputs": [], "source": [] }, + { + "cell_type": "markdown", + "id": "b4e7bff2-2a4f-4a1d-9cb1-dd02aead2f85", + "metadata": {}, + "source": [ + "## LoraConfig具体配置" + ] + }, { "cell_type": "markdown", "id": "10c99eb9-8007-4297-972e-7be71768c9c3", @@ -595,62 +619,6 @@ "\n", "---\n", "\n", - "### **2. 微调哪些参数，冻结哪些参数**\n", - "\n", - "LoRA 的核心思想是通过 **分解矩阵**，只更新少量参数，而冻结模型的大部分参数。以下是常见设置的说明：\n", - "\n", - "#### **微调的参数**\n", - "- LoRA 通过 `target_modules` 指定的模块，例如：\n", - " - GPT-2 的 `c_attn`（自注意力模块）。\n", - " - BERT 的 `query` 和 `key`。\n", - "- 这些模块是模型中对性能贡献最大的部分，通过微调这些模块，任务性能可以显著提升。\n", - "\n", - "#### **冻结的参数**\n", - "- 除了 `target_modules` 中指定的参数外，所有其他模型参数默认冻结，包括：\n", - " - 预训练权重的绝大部分。\n", - " - 偏置参数（如果 `bias=\"none\"`）。\n", - "\n", - "---\n", - "\n", - "### **3. 一般如何设置**\n", - "\n", - "#### **（1）针对不同任务调整**\n", - "- **文本分类任务**：\n", - " - 优先选择自注意力模块（如 `c_attn`）作为 `target_modules`。\n", - " - `r=8` 或 `r=16` 是常见选择，适中计算开销。\n", - " - 设置适当的 dropout（如 `lora_dropout=0.1`）以防止过拟合。\n", - " \n", - "- **语言生成任务**：\n", - " - 对 GPT-2 或 GPT-3，选择 `q_proj` 和 `v_proj`（query 和 value 投影模块）。\n", - " - `r=16` 或更高，适应生成任务的高复杂性。\n", - "\n", - "- **命名实体识别任务**：\n", - " - 优先选择 `q_proj` 和 `k_proj`（query 和 key 模块）。\n", - "\n", - "#### **（2）参数量与显存的权衡**\n", - "- 如果显存有限，减少 `r` 的值。\n", - "- 对小型任务，`r=4` 或 `r=8` 通常已经足够。\n", - "\n", - "#### **（3）偏置设置**\n", - "- 偏置参数的影响较小，在大多数情况下，可以选择 `bias=\"none\"` 保持冻结。\n", - "- 对非常依赖偏置的任务（如生成风格微调），可以尝试 `bias=\"lora_only\"`。\n", - "\n", - "---\n", - "\n", - "### **4. 示例：如何选择目标模块**\n", - "\n", - "#### **GPT-2**\n", - "对 GPT-2 来说，以下模块通常是微调的目标：\n", - "- **`c_attn`**：注意力模块的组合层。\n", - "- **`q_proj` 和 `v_proj`**：Query 和 Value 的线性投影。\n", - "\n", - "#### **BERT**\n", - "对 BERT 来说，以下模块通常是微调的目标：\n", - "- **`query`**：Attention 的 Query 模块。\n", - "- **`key`**：Attention 的 Key 模块。\n", - "\n", - "---\n", - "\n", "### **5. 总结建议**\n", "- **微调的参数**：优先选择模型中注意力相关模块。\n", "- **冻结的参数**：大部分参数默认冻结以节省显存。\n", @@ -664,22 +632,82 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "26d9f362-18cc-471f-b208-f29a6933c06a", + "execution_count": 1, + "id": "bbc080ba-3ee8-4bc6-afd9-2a3241f1bcda", "metadata": {}, "outputs": [], + "source": [ + "import subprocess\n", + "import os\n", + "# 设置环境变量, autodl一般区域\n", + "result = subprocess.run('bash -c \"source /etc/network_turbo && env | grep proxy\"', shell=True, capture_output=True, text=True)\n", + "output = result.stdout\n", + "for line in output.splitlines():\n", + " if '=' in line:\n", + " var, value = line.split('=', 1)\n", + " os.environ[var] = value" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "26d9f362-18cc-471f-b208-f29a6933c06a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of GPT2ForSequenceClassification were not initialized from the model checkpoint at dnagpt/dna_gpt2_v0 and are newly initialized: ['score.weight']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f7e72521368341d38a2b11028715a871", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/5920 [00:00\n", + " \n", + " \n", + " [66600/66600 34:07, Epoch 10/10]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.2683000.3078430.9097970.9168090.9019870.909338
20.2874000.2788040.9135140.9013390.9292690.915091
30.2828000.2912220.9145270.9131160.9168070.914958
40.2552000.2815720.9163850.8964740.9420680.918706
50.2520000.2719500.9145270.9131160.9168070.914958
60.2423000.2881990.9163850.9164980.9168070.916653
70.2535000.2686730.9187500.9094800.9306160.919927
80.2359000.2778930.9175680.9068550.9312900.918910
90.2386000.2806470.9175680.9133620.9232060.918258
100.2379000.2841490.9177360.9133910.9235430.918439

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "训练完成，模型已保存至 ./gpt2_lora_text_classification\n" + ] + } + ], + "source": [ "# **6. 计算指标**\n", "def compute_metrics(eval_pred):\n", " predictions, labels = eval_pred\n", @@ -729,7 +908,7 @@ " learning_rate=2e-5, # 学习率\n", " per_device_train_batch_size=8, # 每设备的批量大小\n", " per_device_eval_batch_size=8, # 每设备评估的批量大小\n", - " num_train_epochs=3, # 训练轮数\n", + " num_train_epochs=10, # 训练轮数\n", " weight_decay=0.01, # 权重衰减\n", " logging_dir=\"./logs\", # 日志路径\n", " fp16=True, # 启用混合精度训练\n", @@ -746,6 +925,7 @@ " train_dataset=train_dataset,\n", " eval_dataset=test_dataset,\n", " tokenizer=tokenizer,\n", + " data_collator=data_collator,\n", " compute_metrics=compute_metrics,\n", ")\n", "\n", diff --git a/04-gene-sft/6-llama-continue-train.ipynb b/04-gene-sft/6-llama-continue-train.ipynb index e975dc3c3d3fec6f7854d8a5fd245ce9997776ca..2227dfe3c8708f466dc90cbcaccb3a60aa2e4a15 100644 --- a/04-gene-sft/6-llama-continue-train.ipynb +++ b/04-gene-sft/6-llama-continue-train.ipynb @@ -330,6 +330,38 @@ "本节任务是基于llama。训练一个能够处理dna和protein蛋白质数据的基础预训练大模型，数据为第一章中的预训练数据，包括英文数据。" ] }, + { + "cell_type": "markdown", + "id": "aec90d65-ac62-4394-a526-ca62d8bdbad4", + "metadata": {}, + "source": [ + "## 环境设置\n", + "并行环境对transformer、peft等的版本要求比较高，如果版本不匹配可能会出现各种异常问题\n", + "之前的课程，都是单GPU运行，一般不存在版本问题，默认安装的都是最新版本。但运行并行环境时，需要确认下版本再运行，本课程运行并行环境如下：\n", + "\n", + "* Python 3.12.3\n", + "* transformers 4.45.2\n", + "* peft 0.3.0.dev0\n", + "* deepspeed 0.15.2\n", + "* accelerate 1.0.0\n", + "\n", + "如果不是，可以重新安装即可：\n", + "```\n", + "pip install transformers==4.45.2 deepspeed==0.15.2 accelerate==1.0.0\n", + "\n", + "#peft参考使用的是chinese llama的版本，需要git安装\n", + "\n", + "git clone https://github.com/huggingface/peft.git\n", + "\n", + "cd peft\n", + "\n", + "git checkout 13e53fc\n", + "\n", + "pip install . \n", + "```\n", + "如果有环境问题，可以查看本目录下的pip_list.txt" + ] + }, { "cell_type": "markdown", "id": "b1bd33b8-2e05-4b59-9d8f-c48de194cfd6", @@ -339,8 +371,18 @@ "\n", "```\n", "# 复制第一章训练数据,包括dna，protein，还有英文数据，添加英文数据是为了避免遗忘问题\n", + "\n", "mkdir train_data\n", "cp ../01-data_env/data/*.txt train_data/\n", + "使用这些数据，6卡4090大概大致需要训练16个小时，autodl也需要近200块钱了。\n", + "\n", + "建议学习时，可以使用1/10的数据训练：\n", + "awk ‘NR%10==1’ dna_1g.txt > dna.txt\n", + "rm dna_1g.txt\n", + "其他2类数据依次类推\n", + "\n", + "这样大概需要2到3个小时就能训练完成了\n", + "\n", "\n", "#持续预训练\n", "./run_pt.sh\n", @@ -361,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "69b3e97f-a801-4264-a651-a854bcfba9c6", "metadata": {}, "outputs": [], @@ -376,10 +418,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "339435d9-9379-4b30-ae8b-50feee1ba714", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "LlamaTokenizer(name_or_path='dnahlm-merge-hf', vocab_size=91643, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '', 'unk_token': '', 'pad_token': ''}, clean_up_tokenization_spaces=False), added_tokens_decoder={\n", + "\t0: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t1: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t2: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tokenizer = LlamaTokenizer.from_pretrained(\"dnahlm-merge-hf\")\n", "tokenizer.pad_token = tokenizer.eos_token\n", @@ -388,10 +445,61 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "d0f154bb-b1ab-4611-a14c-9b403043fd96", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "342e4ab139b64bb78f0429c2f92c8310", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/3 [00:00', 'KCG', 'FVGP', 'MVHL', 'KV', 'HLE', 'ADV', 'ASSC', 'RSAV', 'I', 'YL', 'TSEE', 'P', 'FEG', 'VLGL', 'RLK', 'EGI', 'AI', 'TGC', 'W', 'PRW', 'P', 'DEM', 'DER', 'SAV', 'W', 'RVE', 'PY', 'TRH', 'FG', 'RVLY', 'SFGV', ',', '<0x0A>', 'The', '▁primary', '▁use', '▁of', '▁L', 'La', 'MA', '▁is', '▁research', '▁on', '▁large', '▁language', '▁models', ',', '▁including']\n" + ] + } + ], "source": [ "text='''GCTGACTCTGCCAGGATGGAATGAAATTAGGTTGTTTTAATTATAATGTAAAGTCAGTTCTAGTCAGACATAGTCACATAGGCAAGTAAGGGAACCTAAAATTGCTTGGAAT,\n", "KCGFVGPMVHLKVHLEADVASSCRSAVIYLTSEEPFEGVLGLRLKEGIAITGCWPRWPDEMDERSAVWRVEPYTRHFGRVLYSFGV,\n", @@ -426,10 +587,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "ebf869c8-866d-4770-8f64-79d671f88663", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e497889a1c3c484cb57c4b6fd93b45ab", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/3 [00:00',\n", + " '<0x0A>',\n", + " '##',\n", + " '#',\n", + " '▁Inst',\n", + " 'ruction',\n", + " ':',\n", + " '<0x0A>',\n", + " 'Det',\n", + " 'erm',\n", + " 'ine',\n", + " '▁core',\n", + " '▁prom',\n", + " 'oter',\n", + " '▁detection',\n", + " '▁of',\n", + " '▁following',\n", + " '▁d',\n", + " 'na',\n", + " '▁sequence',\n", + " ',',\n", + " '▁The',\n", + " '▁result',\n", + " '▁will',\n", + " '▁be',\n", + " '▁one',\n", + " '▁of',\n", + " '▁the',\n", + " '▁following',\n", + " ':',\n", + " '▁Non',\n", + " '-',\n", + " 'prom',\n", + " 'oter',\n", + " ',',\n", + " '▁prom',\n", + " 'oter',\n", + " '.',\n", + " '<0x0A>',\n", + " '<0x0A>',\n", + " '##',\n", + " '#',\n", + " '▁Input',\n", + " ':',\n", + " '<0x0A>',\n", + " 'CCG',\n", + " 'TGCG',\n", + " 'ACCGG',\n", + " 'AAG',\n", + " 'TGGGGC',\n", + " 'GGCG',\n", + " 'ACCCCGG',\n", + " 'AAG',\n", + " 'TCCCC',\n", + " 'GCCGGG',\n", + " 'TGCAGC',\n", + " 'TTGG',\n", + " 'TCGG',\n", + " 'TTCG',\n", + " 'ATCGCC',\n", + " '<0x0A>',\n", + " '<0x0A>',\n", + " '##',\n", + " '#',\n", + " '▁Response',\n", + " ':',\n", + " '<0x0A>',\n", + " 'prom',\n", + " 'oter']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tokenizer.tokenize(prompt)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "11875339-4901-4912-86e5-afe8c74921d9", "metadata": {}, "outputs": [], @@ -498,10 +718,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "1b02644a-8b24-45aa-b22d-0f7ce2270dd9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input (test): Below is an instruction that describes a task. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.\n", + "\n", + "### Input:\n", + "CCGTGCGACCGGAAGTGGGGCGGCGACCCCGGAAGTCCCCGCCGGGTGCAGCTTGGTCGGTTCGATCGCC\n", + "\n", + "### Response:\n", + "\n", + "real answer: promoter\n", + "--------------------------\n", + "\n", + "model's answer: \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/root/miniconda3/lib/python3.12/site-packages/transformers/generation/configuration_utils.py:601: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.01` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", + " warnings.warn(\n", + "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "promoter\n" + ] + } + ], "source": [ "input_text = format_input(data[\"test\"][0])\n", "\n", @@ -562,10 +820,118 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "68831e19-5a99-46d8-9f40-e8bf6957dbfc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Donor Sites |||||||||||| Non-Splice Sites\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "Donor Sites |||||||||||| Non-Splice Sites\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Donor Sites |||||||||||| Splice Sites\n", + "Background Sequences |||||||||||| Background Sequences\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-Splice Sites |||||||||||| Non-Splice Sites\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Binding Sites |||||||||||| Background Sequences\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-Splice Sites |||||||||||| Non-Splice Sites\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Non-promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Background Sequences |||||||||||| Background Sequences\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Binding Sites |||||||||||| Binding Sites\n", + "promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Donor Sites |||||||||||| Donor Sites\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Binding Sites |||||||||||| Binding Sites\n", + "promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "Background Sequences |||||||||||| Binding Sites\n", + "promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Background Sequences |||||||||||| Background Sequences\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "Donor Sites |||||||||||| Non-Splice Sites\n", + "Binding Sites |||||||||||| Binding Sites\n", + "promoter |||||||||||| promoter\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Non-promoter |||||||||||| promoter\n", + "Binding Sites |||||||||||| Binding Sites\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Non-promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Acceptor Sites |||||||||||| Acceptor Sites\n", + "promoter |||||||||||| promoter\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Donor Sites |||||||||||| Acceptor Sites\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| promoter\n", + "promoter |||||||||||| Non-promoter\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "Background Sequences |||||||||||| Binding Sites\n", + "Acceptor Sites |||||||||||| Splice Sites\n", + "Non-Splice Sites |||||||||||| Non-Splice Sites\n", + "Donor Sites |||||||||||| Non-Splice Sites\n", + "Donor Sites |||||||||||| Donor Sites\n", + "Non-promoter |||||||||||| Non-promoter\n", + "promoter |||||||||||| promoter\n", + "Background Sequences |||||||||||| Binding Sites\n", + "promoter |||||||||||| promoter\n", + "promoter |||||||||||| promoter\n", + "Acceptor Sites |||||||||||| Splice Sites\n", + "promoter |||||||||||| promoter\n", + "presicion 0.73 same 0.3\n" + ] + } + ], "source": [ "import json\n", "from tqdm import tqdm\n", @@ -598,6 +964,14 @@ "\n", "print(\"presicion\", right_sum/all_num, \"same\", same_sum/all_num)\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bc38f47-4a7d-43eb-abe8-db4310d280e3", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/04-gene-sft/__pycache__/build_dataset.cpython-312.pyc b/04-gene-sft/__pycache__/build_dataset.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3f80dc7c3c63b66af2bdd814eac8623cf371b0bb Binary files /dev/null and b/04-gene-sft/__pycache__/build_dataset.cpython-312.pyc differ diff --git a/04-gene-sft/data/.ipynb_checkpoints/dna_promoter_300-checkpoint.jsonl b/04-gene-sft/data/.ipynb_checkpoints/dna_promoter_300-checkpoint.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9ecda059cf82f6962bb6eaafa39e6bc5d2222e40 --- /dev/null +++ b/04-gene-sft/data/.ipynb_checkpoints/dna_promoter_300-checkpoint.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:490769d2870d4336ccd377292d22b3f78cfac78a4f0776043103562168c347b2 +size 28531988 diff --git a/04-gene-sft/data/dna_promoter_300.jsonl b/04-gene-sft/data/dna_promoter_300.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9ecda059cf82f6962bb6eaafa39e6bc5d2222e40 --- /dev/null +++ b/04-gene-sft/data/dna_promoter_300.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:490769d2870d4336ccd377292d22b3f78cfac78a4f0776043103562168c347b2 +size 28531988 diff --git a/04-gene-sft/gpt2-small3-1024.json b/04-gene-sft/gpt2-small3-1024.json new file mode 100644 index 0000000000000000000000000000000000000000..851b30b8158403772b2b64b228d84ae0e3625fa5 --- /dev/null +++ b/04-gene-sft/gpt2-small3-1024.json @@ -0,0 +1,602 @@ +[ + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCAGGATGCGCTGACGACCCGGCTGGCAGGCGGGTCCTCGTGGGCGAGGCGAGGGAGGCGGCGAGAGAGGAGCAATAGTTTCCCACCGCTCCCTCTCAGGCGCAGGGTCTAGAGAAGCGCGAGGGGATCTAGAGAAGCCGGAGGGGAGGAAGCGCGAGTCCGCGGCCCGCCCCGTTGCGTCCCACCCACCGCGTCCCCTCCCCTCCCCTCCCGCTGCGGGAAAAGCGGCCGCGGGCGGCGGCGCCCACTGTGGGGCGGGCGGAGCGCCGCGGGAGGCGGACGAGATGCGAGCGCGGCCGC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGTCATGCCTGCAGGGAGAGAGGGCTGGGGCGCTATCTATGGTAGAAGTCAGAAGAGGCTGTCTATATGTCAGTGGGAGGAGAAAAGTGGCTCCAAAGCGGACTGTCTGTCCCTGTGCCAGAAGACATTGCTATTCAACGTTCACTCGCTTCTGCACAGGCCCACGTCACCCACAGACCGAGAACTCATCCACTAAGCGCTGCGGGTGGGCAGTACATACCGACGAAGTGCCTTCGCTACCCCTCTGGGTGTCCGTGCTCCCGCGAACCGGCTGGGGCTGCAGACGGGAAAGGAATGTCC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGGGTTTGCACTAACCGATCCACTCCGCGGGGACCTCTTAGAATTCTGCTGTGGATAAGGATATCGAACTACCGAGTCACTGGACCTAAAGAGAGATTCACTGTATCATCGTCTGCCTTAGACTGTTCCGCACCCAGGAACTGGGGCTGTTGCGCCCTTAAGGTTACTTTGAAGCCAAGGTCGCAAACAGACTTCCGCATTGACGTCAGTAGCCGAACGCTGATTTTCTTAATCTAGTATTTAGGATGGGCCTCTGTCGCCTAGCCGCTATCGCAGAGTGGAGCGGGGCTGGGAGCAAAG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCTCGTAAGGACCTAACTGTTCCAGCACTACACAGCTCATGGTGTCCTCAAGATTAGTACATATCAGAGGCCATGAACTTAGTCTTACGCTCACTGTGGCACTGGACGCGCGGGGGATGCGGGGTTGTTCCCAGCGATTTATGATTCGTTATTTGCCGCGTTGACTCGCCGTCTGTAGCCCCATGACAACGACATTCCTGCATTCTCTGCCTGGAGAGCGAAGTGACAATACTGAATTGAAACGGCTAGAATGTCGCTCGCTGAGGCTCCGGACCTTGGAGCGTCTAGAGTCTGGCTAC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TGCCACATAAGTCGGGTCAGCAAGTCGGTGGCGAAAAGAGGGCCACGTCCCAGACTTTCTGGGAGGAGGCGGGACAAAGGGGCGGGGCGAGCGCAGCGCCCTCCGGGTGGGCGCCGTCAAGAGGCCGGGGGCGGGGCCGAGCGCGGCTGGGCGGGGCCTTGAGAGGCCGGCCGGGGGCGGGGAGGCTGGCGGGTCGGCGCGGGCCCAGCCGTGCGTGCTCACGTGACGGGTCCGCGAGGCCCAGCTCGCGCAGTCGTTCGGGTGAGCGAAGATGGCGGCCGAGAGGGAACCTCCTCCGCT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ATAGGATGCGTCTGTACTCTGATTAACAAACTGCTGAAGGAATAAATATGTACTTGCTGGGCAGCGCCGCCGGCCGAATGGAGATAAGCCTATGCAGCTTCGTGCGCGGCTCCCCAGCCCTTTGCTGCGCCGCGAGCTGCGCCCTGAGACCCCCGCCTCGCTGCCAGCTACTTACCTGCCCCGGCGGAGGGGGCCATGTTGCTACACCTAGGCAGGCGGCAAGAAAGCACGCGTAATGAATTCCTTATATCCCCCGCGCCCCAACGGCGGCGGCGCGCCGGCCGGCATGGAGCCCCGCGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGGGTAAAAAAGGGGACAAATGGAAATTTGTGTTGCAGATAGATGAGGAGCCAACAAAAAAGAGCCTCAGGATCCAGCACACATTATCACAAACTTAGTGTCCATCCATCACTGCTGACCCTCTCCGGACCTGACTCCACCCCTGAGGGACACAGGTCAGCCTTGACCAATGACTTTTAAGTACCATGGAGAACAGGGGGCCAGAACTTCGGCAGTAAAGAATAAAAGGCCAGACAGAGAGGCAGCAGCACATATCTGCTTCCGACACAGCTGCAATCACTAGCAAGCTCTCAGGCCTGG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCTGGGTCCTAGAGGCTACTGAGACAAATGCTGGCTGAAGCCGAGGTACCTAGATTTGTGCAAATCGAGGAGTTCTATTCTGCGTGTTTGTGAAAGGGGGTTAGACGAGAGGAGTGCGGTGGAGGCGAAGATAATAGGATTATTCTTACAGTGTAATAATAATTATTGGTGCTAATGTTTAGGAAGCCGTGAAAGATAACACGAGGGGAGAAAGTGGATAGAAAAACCACCGCTGGCAGTTCTTTTCATTTGAAAGTGAATTGGGAAAAAGCGAATAAAAATTAGTCATGAGTTTTGGTA", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTCGTTGCGTAATCCTCCTTCCGCACCAACGCAAGGCAGGACAGGAGGTCAAATGAGTTCGAGATCAGGCGCCACAGACTCCACTAAAGCACAGACACCAGGTGCGGTAAGACACGCTGGCTCCAAAGTAGGACACCAAACCATTGCTAATGCCTGGGGAGGGCCAAACATGGGCAGGCTGGAGCACTCTCCTCAAAGAGGACCACAAGCCGTGACAGCAGAGCCCCTCGGACTGATGGGACACGGCTTAAACTGAAGTTCGAGGACTCTGTTCTGCAGAGGTCTGAGGCTGGAAGGGGG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGGGAGAGAGCTGGAGTCTCGCGGCCTCGGAGGCCGGGAAGCCAGCCCCGGCGGGGACCCGGGCGGGGCGGAGCCAGCGGAGGCCCCACCCCCGGCGTCACCGGCCCCCAGGGGGGCGTCGCCCCCACCCCGCGCTCCAGGTAGCGGCGGCCTCAGCTGCCGGCGGTGGCTGGCTGAGCTCCCCAGCGGCCCGCGGGCCGGGGCGGGGGCGGGCCGGGGGCGTTCCCGCGGGCCACCGCCCGTGATGTCACAATCGCGGCGGGCCGCGGCGCTCCGGGGTCGGCGGTGGGCGGCGGGCCG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGACTATACCACTTGCCGGCCGCGCAGAGCCCTCCCCAGGTCGCGCAAGGAGCTCGAGCTACTAACGGAGGAGCGGCGCAGGCCCCGCCCTTAGTTCATGAGCTTACTTGTACAGATCTCGGCCCCGGCCCCGGCCCCGCCCCCTGCCTCATCGTGGACCATCACGCTTCCAGCCCAGTCGGCTACGGAGATAATCCATGGAGCTCCGAGGCGCCGAGAGAGCAACCTTAAAGTATCGGGATCAGCGACATTATCAGCGCTGGTCCCTGCAGCTTCTGGTTCGAGATCTGGAAGGGCGGT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AAGTTTAAAAATGAACCCCCAACAGAGCCACCCGCCGGGGACGGTTCTGTTTTCAGCACAACCACGGGGTTCGTTTAGCCTGTCGCCCAAGTTTGTTTCCTCTAGGTCGCCCACCTTCCGTCGAATTCTGTCACTAGACTTTTATACTGGGTTTGTATGTAGCTCGATTCTGCATACAAGCTGGACTGAGAACTGGCAGGAGGCGAAGATGAGGGGGCGGCATCCAATTTGTTGTGATCCTTTTGCGTCAGGCTTCTGCCTGAGCTCGGTGAGGTCAAGCCTCCTCTGCTTCCACCCCTC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGTCCGCTCCGCGGGGCAGGCGTCGTGGGCCCAAGGAGGCCAGGCCAGGAGTCTCGGCCTCGGGGTTACGCGGCGGCGGGCAGCTCTTCCGCTCCGCTCAGGGTGCTGCTAACCCACAGCCAAGCAGCGACCCGCAGGACCGGCGAGAACCCCGCCACTGACTCCGGCCGGAAGAGAACGTGCAGAGATGTACGTCACTTCCGGGGCGTGGCGTGGCCGGGCGTGGCCGTCCCGCCCCTGGCGGCGTGCCTGGTGCCCTGTTCCGCGTCTGTGCGACCGTCCGTCCCGAGCGCGCAGCCG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TACCGGTGCTACTTTACGCTGCGTGTTCCCTAGCAGGGGACCTAGTGTGCTACCATAAAAGCACGACGATAAGACTATACCTCTTCGCCTGCTACTAACATAGGGCACTTCTTCCCCCTCAATCTATCGACGTTCGTTAATATCACCGACTCCCAGGGAGGATCTAATAAAAGGAGGAGGGCTCCTGGAATAATAGAGGAGTGGTGCTGCGAGGGGAAGGAAAAGCCAATCCGTGACCCCTTTCCCGGAAAGCTTCGAACCGACAGCCAGAAGCCCGCACGCACCTCGCACCATGAGATG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGTCGAGAAGACCTGGGCTACTGAATGACGAATGGGACGCACACGTACTGGCTACTTTTGTGGAAGATGGTCCGCCCTGGTTTGGATTGCGCTTTGCCACGAGAACCGCCGGGAAGGTGGGGGACGGGGACGGGAGCAGCCAATCACGCGCCGTCCCGTTCGCTTCGTCAGGGCGCGATCGCGGATTCGGCACTCAGTGTATGGTACTTCGGGCGAGCGAGGAGTCGCGAGCGCTGTGAGTTTCTCCTGGAGTTTTCTGCCGTTTTCGGACCCTAAAGCTTATCTAGGCTCGCGCTCTGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCGTCCTGCTCCAATTGCCAGGAGATACCATTAGCGGACTGACTCCAAGAAAAGAGATCCCTTGAAAAAGGCAGTGCTGAACGCGAGTATGGCGCTGCTTGCGGGCCATTCTGTGCGCTACGCTACCCGCTCGTATTAATCAACAACAGGACCGGACTCTGGTTGCTTGGGAGTTCAGGTGGACAACAGCAACTACTAACAAGGTTTTTCGTTCATAGTGCGGGAATGGCAAAACACGAGAGAAACGCACTCTTCATAGGTCACTATCCGTCTGGAAGAAGGCTTTGGACAGAACAGGG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCCTGCCCCCAGGCACTGACAGGGTAGCAGAGGGAAGACACATGAGCTGCTTTTTAATTTTTTTTAGAATTAATAGAGACGGGGTCTCACTCTGTCACTATGTTGCCCAGGCTAGTTTTGAACTCCTGGCCGCAAGCAGTCCTCCTACCTCAGCCTCCCAAAATGCTGGGATTACAGGTGTGAGCCACCGTGCTTAGCCATGAATTGATTTTAAAACCTTAAATGAGGGGCCAGGTGTTGTCTTTCCGTTTGCCTGAGTGACGCGGGTCTTCTCCAGGACATCCGGCCACAGATCTGCC", + "output": "Non-promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CAGCCCCTATGTTGGTGTGGAGACCCTCACCCGAGCTGGGCCTGCACCCTACACCAATCCGAGGTACCACGATCGGATTACTACAACAGCGGGGTCAGCTCAGGCGCTCGGTCCCCCGGGATCCTGCTCCCAGGCACACGATTCCGTTCGTTATAATTATGTAGACGGGGCCTGCGGTAGACCGACTGCGGGTGACCGAGTAGTGCAAAGTCTGGCTTCTCTTTTGATAGAGCACATCGATCTGTCTTCCTCTTTTCACCGTCGATGAGCCCCGCGTGCCTGGGACTGCTCCAGGCTCCA", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTCGCCGCCCACATTAGTGTGGGGCCCTGCGGCCTAGCGTCCCTCACCAGAGGCCTCCCCTTGCCTAGCTGGACCGCCGAGGGACATCGACGAGTATCCTCCTCCTGCTGTCCCCGGCTTCGCCTGCCGCCCCTAACCGGCCAGTCAAGATGGCCGCCGCTGGGTGAGGCAAGCTGGCGCGCCGCGGGGGCGTCTGGGAGTTGTAGTTCGGGACGGCGGGCTGACGCACTTCGCCGCCGGCCGACGGGCGCCATTGTGCGGCGCGCGCCGGGTGAGTGCCGCGCGAAACCTGCGTCCGTC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCGGGTAGCGATGGGCCGGGCCTCAGTTGTTTAAAGGCCCGATGCGACCCCGCTTAAGACGTCAGATCGTTCACCCCCGAGGGTCGCCGGATTTCGCTCCCGCGCGAGATGGCCTCCACCAGAGACTGCTGCTGCGCCTTGGAGCGGTGCCTCGCCTCCCAATCCTCGTCGTGAATTGCCCCAATTGCTGATCTGGGAACAAGCTCGTGCCAGGCGGGCGGAGAGTACGAACTCCTTATAGACCAGGATGGGCGTAGCAAGAGCGCGGGCGGCCCATGAATGTCTGCAGCGGTGAGGACC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGGATGCGGGGCCGGAGGGGATGGAAGGGGCCGATCTAACACGATATTCCGTTCAAGAACTACAACCGTCTACAGCACACCATAGCCGCTCGCTGAAGGATGTCGAAGGGCGCCCAGGCACCCACCACGTTTGGCCCCTGCCCGGGCCGGTTCCGATATGACTCACTCATTTACTCCCCTAGAGCTCCGAGCCTCGGATCGCCGAGGTGACCCTTACCCGCCTTCCTACCACCTTAGGGGATTTGGCCCGGAGAACGAGATCACCCTCTCATCGTGGCGGGGTATTCCCTTTAAGGTTTG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCCCGTCGGTCCGCGGGGCTCTCGCTGGAGCCTCCCCGACTCCGGTTTCCCCTCGTCCAGAAGCCCGACGTAACCAAAGCCCAGTCTGTCACTTTAAACACGCCCCGCCCCGCCTCCCGCGGCTGTGTTGCCTCCTCGCTGGAGAACACCCTGGTCGACCTCTGTGCGTCCGTGTGCGCGAGCGCGTCCCGCCGAGGCGGTGGGCAGGGCGGACGGTGCGCAGTGCGTTCCCGCTGGTCGGAGCCAGCACACTAACCACGCCACGCGCCCTGCCGTCCCTTCGCCTCCAGCCGCTGCAG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CACCTACTCGGGGTCCGAGGAGGGAGGATTGCCTAGGCCCAGGACTTTGAGAACACGCCTCTACAGAGATTTACATTTTAAGAAAATTAGCTGAGTTCGGGTCGGGCGCAGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGGCTGGCGGATCACCTGAGGTCAGGAATTCGAGACCAGCCTGACCAAAACGGCGAAACCCGTTCTCTACTAAAAATACAAACACTAGCCGGGCGTGGTGGCAGTCGCCTGTAATCCCAGCTACTCCAGAAGCTGAGGCAGGAGAATCGCTT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCGGCCGGTGTGCGGGACACACTATTGTAATCCTAAAAGGCCATGGGCCCCTGCTGGGGTCAAGAATAATGCTCAAAGTTTAAACTACCGAGTGTTGCTGAGGCTTGGGTTGCACATCGAGAGTCGCGATAACACTCACCCAGGGGAACATACCGCGAGGGATTACGGACGATGGAGAGATCACCCAAGGCAGTGGGAGTCCGCGCTTCCTCCCTTAAGTACCGCTGGTTGTCCCCCGACGTAGTGTAGATAGTGAGCCCTCTTGTCCGCTGGCACGTGATGTATGTGTGTCCTTGTCT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGGTTAGGCGCGTGCCGCGAGAACAGAGTGGACGGAGCGTAGGAGAGACCGAAAAGGCTGGGGGTGGGAGTAGCGGATTTGAAGCACTTGTTGGCCTACAGAGGTGTGGCAAGCAGAGCACCTCAGAACTCAGGCGTACTGCCCGCCGCCCGAGCCCTGCGAGGGCCGATAGCGAGGGTGTGGCCCTTATCTGCACCCAGCAGAGCGCCGGCGGGGTACGGTCTTAGGACCTCGATCTCCTTCTCCCTCATTTTCTCTCATCCCTACCTATTGTGGGTGAGTCCTGGCCCCTGGACGGGG", + "output": "promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTTGCATTAATACACCCGGCGCCAGAGGAACGTGGGGATCAATCATATTACACTGCTTCAGACTTGTAATTTTATTTGGTTCAGGCATCGTAGTGGTGAGGCTTGGCAGAGAGCCCGAGATGGAGCTATTGAGACGTTTCAGGCTGGGCGGATAATCGCATCAGTAGGGACTTGGCTAGATCTCCCAAGCCTTCTCCACTCTTCAAACTTTCACCAAACTCTTAGGACCCGTCGGGTCGGTTTCATAGACTCAGCCGCTCAACCTTTTAAGCGAGCGGCGCGGCGGGGCGGGCGAACTCG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGAGCGTCATCTTATCATTCTCTCTTTTTTTTTTTTTTCTGGTAATGATTTTTTTTGTTTTTTTTTTTTTTGATATTGACAAAAGTTTAATCATTTCAATTAAAAATGCCACTAATTTGACTTTTTAAGTAAAAAATGTAGGGGGTTTTAAAACTACTTTCCTACTACCAAAAAATCAGAAAGTATCTAGCTTTCTAAATTGGGAAAGCAAGCAATGTTATAAAAACACTGAAGGAATCTCTTTCTTCGTGACCTTTTGTTAAACTCGGTTTAAGCTGTAGACCTTATTTAAAATAAAAT", + "output": "Non-promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGCTGTGAAACAGGGCCACCTGCAACCGTGGCTTTTCAAAGCTGAAGCCATACCCTGGAGGGAGAGAGGATCCTTGGGCACTGACCCTGGCTGTCCACAGCTAAATGATCATGGGACACCCATGACCATGGGCGTGGCTGGAGGAGGCCCTGCCCTCAGGGGTCTATTGGCATCCAGGGGAAGTGTCTACGGGGTAATGATTAACCCACACCTGCCACCTGGTTTTCAGCCTTCTTGCCCTGCTGACTCATTTTGCAAATCCCACTTGCAAAATTTGGGAGCAGCCTGGGTCCAGCCCCC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTGTGACAATCATGGAGCAGTTGATTGACCCCTTTTCGAGCGCTCGCTATGCTTTAATTTCAAGACCCGCTCTCTAAGGTGCTCTTCCCCTTTAAGACTCACGGCTCTTCCCGTCAGGCTAGCTCGACCTGAGGATTCGTCAACCTCCTTGACAGCTTCCGCTGGCCTGCACCCGAGATTGGGGACGGTTTTCCGCACATCGTGCCTTCATTAGTTAGGTCCTGCGAGTGCATCCAAGGGCCCTCCCCCAAGCCAACAGCAGAACCAGCTCAATTTCTACCCTCACGGCGCTCCGCGGTG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TAGAGAGGCCAGAATCCCTCGGGAGTGCTACCCGGTCAAAAAATCGCGCCGCCCCGTGGGGGACCGAACTTAGCGATGACGGTTCGTCTAATCCCGTAGGAGAGTATCGGGCGAGCTCGGATTGCGAGATTACCTAGTGTTAGTGCGACCGCCCAGGCACATCTGCAGACTGACGTCAGTATGCAAATCAGAGTCGGAAAAGCTGCCAAAATTTTCTCCTGCTGCGCCACCTAACCTGCGTTCGGGGGTAAACCCATCTAGGGCTACTTCAACAAAACTTTGCTGCCCTTCCTGCTCCTC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AAGCTTTGTTTAGGTCCGGAAGGCGGGCTTTCCTGGGAGTGGGTGGGGAGGGGGCGTTGATTCTTGACCAATCCTTTCAGTCCGTTGGGTGGTGACCAGCCAATGGGCCGGATGGATAGGACGCTCCTCCCGGAGAGTAGTGAGACCCCTGGTGCGGGGCGATTGGCGGCGGGAGCGATGAGTGGCAGCCGCACGGCCCAACGGGAGCTGTGCGTGGGCCGCGGGGCGGGGCCAGGGCGGGTGCGCGGCGGCGGCGGGGTGGCTGGGCCGGCGGCGGCGGCGGTACGAGGCGCGCGCTCG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCCATCCAGCGAGAGGGGCAGGTTCCGCATTTTCTCTTCCCCTTTCCCAGCGCTTCCTCCAGCACCCGAAGCCCCAACCCTGCGGGTCAGGAACTCCCTAGTCCCCAAGTCTAGGGATGAGATGGGGGAAGGAGAGCCGTCAGGGTTGACCTGGAGTTTTGTCCGCTCCTCCCCTACAGTGATCCCTCTAGCCTTCTCCAGTCGCCTCCGCCATGTCCGAGGAGCTGGCCCAGGGCCCCAAGGAGAGCCCCCCGGCGCCGCGTGCGGGCCCCAGGGAGGTGTGGAAGAAGGGTGGCCGC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACATCGTATTGCGCTTACTCGGGTCGTGCAATGCACTGACTAGGAAAGGGCTGCGGATGCCTACGTACATAATGCTGTTGCGCACCTATGCACGCGGGTCTGGTCCTGGGCAAGAACCGCCCCCTCTCCGGGCCTGGTTGCCTTTTGATTTGCAGAACAACGGGCCAGGCCCCTTCCCTCTACCGCTCACAAGCGTACATCGCGACCATACCTGGTCCAATGCGCTACGGACGAGGGCAATTCTCGTAATAGGACCCACAGCAAGTCTTGTATCACCTTTGGCTAGCCTGTGCCGGCTGT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACGTGACCGACGTCTTCCGCATACCCGGGGCTTCCCTCACTTCCAGGACAACCTGTTGCGCCTCTATCTCCTCCCCTCCCCGCAGTTTCCCCGCCTTGGCCTCTAATAGTAGTGAGAGCACTTTAGGCGGTATAAAGTCTGACGCTAGAATCAATCTAGCCGCGGCGATTGGCCACCTGGGGAATCGTTAGCAAAATGTCACGTTTACTACTTTACGGCGATCTTCCAGGGTCCGGGGACTGTGCCAAGGATCCTACCGGGGCTGGCAGGAGCCTAAGACAAATACGCTAGCGGCGGAGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GAGATTTCACGCCTAGCGGCTCAGGATCGTGTGTGCTACAAAAATAGGGCCGCAGGGCCGTATAGTCGCACGAACATACAGGTCGTCCGATTCAGGCTCGGGGGCTTGGACGGAGATGACGGCCAACTCTCACCCTCACCCCTCATTTAGGCGGGCGCCGGGGGCATCATCGGGGTCCCTGGGCCCTCCCCCTGCTCAGTGCGTAGCTCCGATAGAAAAGCTAGGGCATACGTGCAGAGACGGGTTGTCAGGCAGACACCCTGTCTGTTGGCAGCTAGCAGCTGGCGGACGCGACCCGGA", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AAACCATGCAATCCGGATGTGGCAAACAAAGGGTGGGAGCCCACTTGTCATGGCATTAATTGCACGTCTGCTTAGGGCGAGTTTTAACCGTGGCACACAAAACAGCACGATCGAGGATACGGGAGGTCGCCGCGTGATATAACGTCTACCCATGGTTCTCATCGAACCTCTACCTAGACACTGACCAAACCAACGCTCCCAGTGCGCACGGCCATTTCTCATAGATATCTTGAGCAGGTTGCACCACCAAGATAGTGAGCAGGGCGGTGGGTGGTGCGGAGTGCCGAGCGGCCTCACCCC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTCGGAGGTCGGCGGCCAGAAGCCAGATTCCCTTGGCCCCAGCTCGTCCACTTCCATCCAGCAGTAGCCAAACAACCGTTGAAAATGGCCTGGCGGCGGCCGGGAAACACCAGAGAACTATCTTCCAACCCCTAGATCCCGCCCATCTCGTTCCGGCGGTGTTTCCGTGGCGACGCTATCCGAAGTGCGGCTGCGCAAGGGTGACGGCGCGCGAGCAAGGGGGAGGGGGTGTTTTGGTTCTAGCCGCTCGCCGTCCTTGCAGGCTCTGCCGTCGGAAAGCCGCTCATTCTCGCTTCCCCT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGTGGTTGTAGGTGGGAAGAATCCTGGGTTCCCATGCTGTCTGAACCAGAGTCATTTGTGGCCCTGCCCTCCCCTGGGACTCAATTTCCCCACCTATAAAATAAGCCCCAGTGTCCGCGAACCCTGGAGGGGCCCGCACCACTGCAGGAGCGGCCGCCGGCGCCAGGGGGCGCCTCCTCGATAACTCGGCGCTCGGCTGGCCAGGCACCGGCGCGTCGGCCGCTCGATTGGTCGGGTCGGGGCCGGCCTGAGCGCCGCGGGCCTGCGCCATTGAGGAGCGGCGGGGAGGAAACGCCGCGC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TAGGAGGCCTGTCTGCACAGCTGTGCTGGGAAGCTGATCCATTCCAACCACCGCCTCATGGCCGCTTACGTGCAGCTCATGCCTGCCCTGGTACAGCGCCGCATCGCAGACTACGAGGCTGCCTCGGCTGTGCCAGGCGTTGCTGCTGAACAGCCTGGGGTCTCTCCATCAGGCAGCTAGCCATACCCAACCCCAGGAAGGAAGGCCTTGGATGGACCCTCAGATTGAAGGACCCGGTGGACCTTGGGGTTGGTGAATCCTAAACAGAGAGAATTCGAGGTTGCCTGAAAGCTGGGTGTC", + "output": "promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TGTTCTAGCTATCTTGAAATACGCAATACTTTGACATTAACACACTGCTATGTTCTCTAATGACATCCCTGGAAACCTCCTAGGGGCAGCCAGATCTTTCATGATAGTGGTTGTCAGTCCTCATATGGAGGGTGGAGGTTTGAAGCAGAGAGCCAAGGGAGGTTTTGTGCACCTATGCTTGTTGTGTTTGTACACAATGACTATGCATACGCTGTGAGTATAAAAGGCTCATTTAATCCTATTGTGTCCCAGGCTTGGTTTGTTTTCAAATCATTACAGCATGAATTTAGAAGGTTTTGT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCCGAAACCCCTCATCTTGGGGGGCCCACGAGACCTCTGAGACAGGAACTGCGAAATGCTCACGAGATTAGGACACGCGCCAAGGCGGGGGCAGGGAGCTGCGAGCGCTGGGGACGCAGCCGGGCGGCCGCAGAAGCGCCCAGGCCCGCGCGCCACCCCTCTGGCGCCACCGTGGTTGAGCCCGTGACGTTTACACTCATTCATAAAACGCTTGTTATAAAAGCAGTGGCTGCGGCGCCTCGTACTCCAACCGCATCTGCAGCGAGCATCTGAGAAGCCAAGACTGAGCCGGCGGCCGC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGCATCTTAGAGTTTCATACAAGGATCAGATTTGTGCTATCGGAGGCCGCCGATCACCCAAGTAAGCTTCATACGGGGAGGGAGGGAGGGCGCGAGCTTGGGAGTGGAGAGGGAAGGAGCAAACTTTGGCATGTATCACAGGGTAGCTCTGCCCCTGCGCGCCCCCTCCAGATCCGGCCCGCGGCTCCCCTCCCGCGGGACATGCTGCGGGGAAGCACGGCGAACAGCCCCTCCTATGATCACAAATGGCTCACCGCTCTCGGAGCCCGGCCGCCACCGCCTGGTGTGCGAGGCTATCCG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCCTCTGACTCGGTTTCCCCTCTCCCCCGGCTCCGCGGTCGCCCTCCTCCTGGACTGCTTATTTCGTCCTCGGCCACTGCCTCTCGGGCTCGAGCTTTCTGCTTCAGAGCAGGAGAGAAACGGAGCGAAAGCAGTTTCTGTCTCCCCGGGGTCTGACTCGGTCCCCCGCGCTCGGTTCTCTGTCCCCTCCCCCTCCCGCCATTGTTCCCGGCAGGAGGAGTGCGCGGCGCAAACTTGCGAGTTCCCCCAGTCTTTGCCCCCGCAGGGCCGCGGACGGAGGTGGTGGCGGAGTTCCCGCT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGCATGATGTTGGCCGACTTGTGCTAAGGTCCTTGGAGAGGTGGAGGCTCTACGGGGGGTGGGCAGTACCCCTTAGAAAACACCCCTCACTCTCAACAACAGAGGCGGTCTGACGTATTGGAACTCATTCTTTAAGGCCAGAGCTGTGGTTGCTCCAGCTGCAATGTGCGCTCTGATGATACTTTATAATAGATTTGAAAAGGGGAGGGACAAGCCTAAGGTCATGGCAGGAGGCTGGGCCAGGGAAGTGTGGGATCCCGTGGTTGTAGTCGGCGAACCGAACGTCTCGGCCTCTCTCCC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TGGGGGTTTTTTGGGGGCTCGGGCGTCGACTGTGGGGCGCTGCGGAAGGGTGGGTCGTCCGCGTGGGGTCCTGGAGCGCAGGAGGCGGAGGGGAAGCCGCGCAGGGGCCACTAGGGAGGGGCTCTGGCGGGGTCGCAGGGGCGCGGTCTGCGGGCGTGGGGGCGGGACCTCGGGGGCGGGGCCTGAGGGCGCGCGCACGCCTCCGGCGCGCCCCCTCCCGGCCGCCATGTTGGCTGGTGTGTGGGTGTCAAACTGAGCCAGACGCGGCGGTGGCGGCGGCTCCGCGGGCTACGGTCGCTC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGAGAGGGGCGGTACGCACCACGGGGGAAGCCAATGAGAAAATCAGGCCCAGCCCTAGGGGGCGGTGCTGTCGGTCACATGCGCACCTGGGGCGGGTGGTGGCGGCGGCGCGGGCACCGCGAGCCGGCGGAAGGGAGAGGGGCCGGCCTGGGGCGGGGTTAGGCAGGTGAGTGACAGGCTCCAGGGGGCCGGCCCCTGCCTGGTGCCCCGAGCGAGCCGGGAGTAGCTGCGGCGGTGCCCGCCCCCTCTCTCCGCCCCTCCAGCGGAGCTGGTCTCCGGCCGGGCACCGTCGCGGGCCCC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGACAACCCGCCCTTGTTCCCGGTAGAGGAACAAAGAGTGCTGATTTACGAATCTAGGCTGCCAGATTCTGTGTTTCCACTGGAGGGCGCGTCCGATGCAGATGTTGGCTCAAATTCCATCTTAACCTATAAACTCAGTTCTAGCGAATACTTCGGGCTAGATGTGAAAATAAACAGTGATGACAATAAACAAATTGGGCTCTTATTAAAGAAATCCTTGGACAGAGAGGAAGCTCCTGCACACAACTTATTCCTGACAGCCACAGATGGGGGCAAACCTGAGCTCACAGGCACTGTTCA", + "output": "promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TTCCAGCACTCCGGGAGCCCGATTGCGAGTTTGTGAGAGTCCTTGGTGGAGGAGGTTGGAAAGCGTTCTCCCTCCCACAAAACCCGGTGCAGAGGGAGGGAGAGCCAGGTCCGTAGCGTGGGTCCAAGAAACTATGGATCCTGGAAGTGAGCACACTTTTGTGTCTTAAAAAGTTTGGTGTCTCGTTTTGAGGTGAAAAATGAATGAGATAAGTGCTTTTTAAAAAGGTAAAGTTTGTCTTACAACTGAAAAAGCTTTTAAACAAAACTATAGTTTTTGCGAGTAACTCCCGTGACACCT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ATCAGCCCTGGCTAGGACCAGCTTCCCACTAAATCGCTTGCGGCAAGGAAGTCTCAGCCCCCGAGGCAGTCCTCACTGTGAGCAAACCAAAAGATGGGATCACTTGGGGCGCGTACGGGCTCCCAGCGCTGGCAGAAACTGTAGCGTTGTAGTAGCCTTTGTGACTGGTTGCCTGTTGGAAGTCTACCCCACCCCCAAACCATAAATCCACCGGAAACTCCTCCAGGACACCCTCTCAAGCAAGCCCCTGGGATGTAACTGCCACAGGGGCTGCGCCACGATGTTCCCTGGCTCTCAGGT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CACCGCGTGAGGATGTGCCGGGTGGTCCTTTCCTCCTCCTCTTCCTCCTCCTCCCGGCTCCCTGCCTAGTCTCCATATAAAAGCGGCGCCGCCTCCCCGCCCTCTCTCACTCCCCGCTCCTCTCCGCCGCGCACTCTCCGCGGCGCTGGGAGAGGGCGGAGGGGGAGGCGGCGCGCGGCGCCAGAGGAGGGGGGACGCAGGGGGCGGAGCGGAGACAGTACCTTCGGAGATAATCCTTTCTCCTGCCGCAGTGGAGAGGAGCGGCCGGAGCGAGACACTTCGCCGAGGCACAGCAGCCGG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCAGGAGTCGGTAGGGGCAAGAACGTTTCCGGCTCCTCAACGGGAGAAGCGCGGGAAATTTGTTGACGTGGAATAGCTCGACGAAATCATCGGCAGGCGCCCGCCAGGGAGCTAGCAACGGAGCATATGCGACCGTCGGTTCGCAAGAGACACACCTCGGGCCTTGCCCCGGAACGCCGCAATGGCTCCTACCTTTCCGCCGAGTGCAGGTTGATGCTGCACACCCAGAACACTTTGCGGCACGGGGGGGGTTATATGACATGCAAAGCCCTGATTCAACTTGGAGATGAGCGGGTCTGT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GAACTCCTGAACTCAGGTGATCCGCTTGCCTCGGCCTCCCAAAATGCTGGGATTACAGGCGTGAGCCACTGCGCCGGCATCCTGGATGCAACACGATCAACGAATGATTGCCCTTTATTCTGCCTGGTCTCCCAGCGGTGCTGTTGTGTCTTCATTTTAATGGATTTTAATTGATGTCACTTAGGGGGGCCCTCAAGCCTTCCTTCCTCTATCCATTTACGTGGTGTACCATCGAACTTTATCGACTCAGAAGTCGTGGGCGGAGGGCGTTCCGATAGAACCAATTGGCTCACGTCTATA", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AAGATGGCCTGAGTCAGGAGCAGGGGCAGAGCTCAGGTTGTCGTGGGGTCCCGCTGCCCACGTCAGACTGGAGGTGAGGGATGGGCGGGGCCTGACAGCAGGCCTGGAAGGAACAGGATGTCTATGCTGGAGATAGAGGGAGAGGACAGTGCCAAAACCCAGCTCCTGGCCAGTCCCCAGCTCCTCCCTGCCTGGCCCTATCCCAGGATCCCCTCCCCGGCCTCCCAGCTATGATCTACCCCGGGGCCCAGACTTCAGGCGCCTTCACGATGCCGGCGGTCAGTGGTCCAGGTCCCTTAT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCTGACACCCTTCGGCCATTTTTTTTAAACGTGTTGGGGTTTGGGTTTCAGCCACAGGCCTAGCACATGGAACCAAAACGCTACCACGGACTATGGAGTTGTGAACCGCGGAACAGCCGGGCGGACTCCGGGGCTCCTGTGTTTGAGATACGCGTGCCATGAACCTCCGCGCCGGAGGCGAGCCACTGGTAATTTACTTTCCAGTTGCGCCAATCCGAGTCTACCTTGATATTGAAAGGTAAGTCAAGAACAAAATTATCGCATAAGACTGTTGAGGTTACCCCGCGGCGGGGTCGGGGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTGAGGCTGGCAGGCACTGGGCAGGCAATGAGTTCTTTCCCTGTAAGTTGGGCACAAAAAGATCGGCATGGGAGCAGCCTCCTTTGAGACAGCTGCTCTGAGAGAATGCAATAAGCAGGGAGCAGCCAGCAATTCCTCCTAGCAGAGGGCGACTCGTGGGAGGAGTTCAGTTTGCCAAGTATTGTCATTTGTTGAGAGAAGGTGTGTGCTCAAGGAGGAGTTTTAACCTGGAGGATCATTAACTCTTTTAGTCAGCTGAGGAGCTGCGGTGGCTCGGCGAGTTGGAGTTCATCCTGGAAG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCGCGCCCCTCCCCCCGGGTCGCGCGCCGCGGGCCCGAGCGCGAGACGCCGCTGCTCCCGCCCCCGCCGCCTCCTGGAGTCGCCGGGGCGGACGCGCAGTCCATGGGGCGCGGTGGGCCGGGGAGTTGCCCCAGGGGCCGCGGGAGTTGCTGAGAGGAGACAGGGTTGGGCTTTCTCCTCGCCCAGACCCCACCCCACCCTGCCGACCCCACCCCCTGCTCCTTCCTCCCCGGGGGCGCGCACTCGGGCACGCGCTCGGAAGTCGGGGGTCGGCGCGGAGTGCAGGCTGCTCCCGGGGTA", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TATCTCCCCTCCTCGGTCCCTCTCCCTCCCTTCCCCCTCCCTTCCCCCGCCCTCCCCCTCTTTTTGCTCCTGCTCCCCCCCCCCACCCCGCCTTTCTCCTTTTGCAAGAAAATAATTTGACAGTCGATTTGCTGACAAGGGAGGAATTTGCATCCTGGATTTAAAAAAAAAAAAAAGGCCGAGAGGAGCTTGGGAACGGTTGCTAGGGGTGGGTAATGGGTGAAAAAAGGGGGGTACCGGGGAGCGGATAAGGAGGGTTAAGGGAGGGGGCGAGGATGGGGAGCAATGCAAAGGTAAGGC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GACCAGTAAGTCGGGGGGGAGGGAGCCGGGGCCTGGCCCCGCCCTATGGGGAGGCACGGGTGGCGAGGTCGGCCGTCCCATGCCCCGCCTGCTCCAGGTGCCGCCGTCCCCACAGGTGCCCGCCCCAGGCCCGCTGGAGCAGCCTGTGGCACGTGGGGTAAGTGGAGGCCGCGGCGGAGCCGTGCGTCCGACGGTTCTGGGGCGGGGGTCACCGGGGCCAGGCCCGCCAGGCCCTTACCTCACCAGGCCGCCTCCGCTGTCGCCCAGTCCCGGCCGCTGGCGGGAACTGACCTGGAGCAA", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCTACCGCTGCCGGAAGAGCGCGGCGCCCGACGGAGCCGTGTGGAGGCCAAAACTCCTCCCGGAAGCCGCTACTGGCCCCGCTTGCCAGGCCCAGCGTCTTTTCTGCATAGGACCCGGGGGAAGCCGGGAAGCCGTTAGGGGGCGGGGCAAGCGGGCGGGCGTGCGTCGGTCAAGTTTCCCGGGTCGCGTCAGGCTTCTTTCTCAGCACCGAGCGAGCGACGTTCGTGAAGCTTTCGTTTTGAGCGGCCAGACTCTGCTGTTCTCAAGCCTGGGAGGCACAGAGAAGCAAGACTTCCGCA", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GAGAATCCCTCTATAATATCCTTGCACAGTTCCTCCGCAAGCACAATATGATTAATTTTTACTTGGCTGATGCCGAGATCTTAACTGCCCTAATCCTCGGCTCGAGTCGAGTTGGGGTTTCCTAAATGAATTACTATTGCAAATCTGGTTATGGCGCTCCACGAGATTATGGCAAAGGCTGAAGAATTTATTGACAGATGTGTTGTTAGACTGTGCTACGTTTTAGTGGCAGGTAGTGGAGCAGCCACCGGCGGACTCGAGAACGCAGTAACATGGCTCAAAAATGGTAGTATTTGTTTT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGGGAACGCACCTGCGTCTAGACGCTGACGCCGTGCGCGGGGCGGGGGCCGGCGAGCATGCGCAGTGTGGTGTGGACAGGGCGTGGTTGGCAAGCATGCGCAATGTGGGCCGGGCGGGGCTGGCGAGCATGCGCAGTATGGGGCGGGGCGGGGCAGGGCTGGTGAGCAAGCCCGCGGGGAGCGAGCGCGGAGGCGGGCGTTGCTACGGCAACGGTGGGCGGCCAGGGAGTTGCCTGAGGGAAAAGGGGCAGACGTCCCTGGGTTCCGGTGTTCGCGGAGGAGTCGAGGCACGGAGAGGCT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTTCTGATTCTCGGTGAAAACGGTAAACATCGGCGTGCTATGTATGGGGATAGCAGGAGCTCTAGCATTTTTGCGCTGGGAAGATTATTCTTCTCTATCCGTCTATGCGAGCCGGCGTTTCGCAGGGGTACCCCCGGGGGCACTGCGTGGGAGGCCAGGAGGTTTTAGAATGGTCACCCGCCGGTTAGCTCCTTGGTAGTATTCTCGATGGACTGCGCGTTTGAGTCTTAAGGGCGTTTTCACGCTTCCGCCTTCTGGAAGCCTGCACTCCGTTTAGAGGAGCCAGAGACCCGTCCCGGT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTATTGGGGATGCAGGGCTCCGCGGTAGTTGACCTAGCAGGGCTAGTCACATCACGCCATAGGGAGCAGCAAGGGGCGCAGCAGTCAGCACGGCCGCTATTCAGATACGGTAACCAGACGCCGTGCGGCGTGCGCACCTGGCATCCAATGGCTTTGGGGATAGGGGCCGCCTGTGGGTTGTGGGTACGAATTAACGGAGTCGGAGCGGCGAATGAGCAGTCGCAAAGCCGAAGCCGGGCGTGGCGCTTTAGAGCGTCCCAGGCTCTCCTTCGGAAAGATGTCGGACACGGCAGTAGCTGA", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGCAGCGGCCGCGGCGACAGCTCCAGCTCCGGCTCCGGCTCCGGCTCCGGCTCCGGCTCCCGCGCCTGCCCCGCTCGGCCCAGCGCGCCCGGGCTCCGCGCCCCGACCCCGCCGCCGCGCCTGCCGGGGGCCTCGGGCGCCCCCGCCGCCCGCCTCACGCTGAAGTTCCTGGCCGTGCTGCTGGCCGCGGGCATGCTGGCGTTCCTCGGTGCCGTCATCTGCATCATCGCCAGCGTGCCCCTGGCGGCCAGCCCGGCGCGGGCGCTGCCCGGCGGCGCCGACAATGCTTCGGTCGCCTCG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTTTTGAACATCTTGTGCCAGGATTTGACATTAAGCTCCAGTTCATCCGCACACCCCGCGGCCGCTTTCGCAAGGGACCGAGAGGGTGGCGGCCTCGCCCGGAGACTCGCGGCGCTGGGAGCTGCCTGCCCTCGGTGCGGGCTGCAGACCGGGCCGCGAGGCGGGCGCGCGGCGCGCACTAGGACCCAGCAGGGCTCCAGGCCGGGGTGGGAGCCGCGCCGCCGACCCCGGGCGGGCGCCGGGCGCAGGAGCCGGGGTTCCGGCCGCGATCTGCTGCAGCTCGGCCGGGAGACGGCGCGA", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TGTGTGGCCAAAGAGAAGAAATGGGTTGAGACAGCAGGCCTGGCACTTACTTTACCTGGCCCAGTCTTGCCTGACAATTAAAAAAAGACGCTTTAGACTGGGCGCGGTGGCTCACGCCTGTAATCCTAGCACTTTGGGAGGCTGAGGCGGGCGGATCACGAGGCCAGGAGATTGAGACGATCCTGGCTAACACAGTGAAATCCCATCTCCACTAAAAATACAAAAACTTAGCCGGGCATGGTGGCGGGCGCCTGTAGTCCCAGCTACTCGGGCGGCTGAGGCAGGAGAATCGCTTGAACC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCATATAAACAGATTAACTGCATTCCCCAACGAGTAGAAATCTGCCCATCACTTTTGGGGTGGTTTGTTGCACCCACTATCGCCCAAGAAACTTCCCCAAGCCGGGTCATATTACAGGTCTCTAGCGCGCTGGCCGCACTCGCAGCCCACCATTCCCACAGAATGGTGTCATCGTAATCTGAAAAGTTGCCACTGGGCACCTGCGCAGGCTTGGCTGCGCCCTCTCGCGCCGCACGCTCCTTCCGGCGACGCGGATAGTCGTAAAGGTTTGAATACCTGATACAGGGAGAGAGGCCGAG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCTTCACAGGGCACAGCCCCTGCAGAGCATCTTGGTCATTTGGAAGAGGACACGGTATCCCCTCTGGCCAGAGTATGTCAGAGAAGGAAGAGTAGGGCTTTTTTGTTTTGTTTTTTTTTAAAGGTGCTTGCTTGTTTAATGTAAATAATAGAAAGCCTTAATATCTTTTCTGTAACACGGAGTAATATTTTAATGTCATGTTTTGGATGTACATAATATATTTATAACAAAGCAGCAAGAGTCTACTTAACCTTGGCTGCCTCGTGGTGTTTCCTGGCTGGGTGGGGTGGGGGGTATCAA", + "output": "Non-promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TGTATTCAGACCAGAACAAACTCGAGTGCCCCAGCTCCTCTGGCAATTAGGTAGCCAATAGTTGAGATTGCGTAGGCAACTACGTAAAATGCGCCGTGCCGTGCCCCTTTCTGCCACTCTGAGTTCGGTAACATAGCTCCGGTTGCTAGGTACTTAAGGTATCCAGTTCCCCCAGCGATTTGCATGAACCGAGAGGGAGTGTCTTCTGCCAACAGATCAGGCGGGGGATCATGAACTCATCCTCCCAGGGAATGCCGGTCGGGGATCCCCCGCGCAGCTCACAGGCCCTGGGAGTGAGCT", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACCTCGCTATTCTATGCACAGTTTTGAATCCTGCTTTTAAAATGCGATGTCTGCCTTGTCTTTAAATATTCTGAGGGGGAGGAGAGGAGATGTGTAATGCGACTGTCTATAATTTGAAACCGGACCTCACTATTTAGCGTCTCAGAAAACGCGGGGTTCATTGCCCTGGTCCCCCCCGCCTTCCCCCGCCCCCCGAACCTTCCGGCTGGCTCTCTTGCGGTCCCCGCCTCGGCGCTGATGTGGTCTGGCAGTGGAGATTGGCGCCCGGGCGGAGCACGATGGGCTTCCCCGCGGCAGGCA", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCACGAGCGCCAGAGCCCCGCGCCTCCCCTAGCATATCCGGCGGAGTCCCTGCCCCGCAAGCGCCCACTATACGGCCGGACAACTGTTGTGGGCGTGGATGCAACCACGGGGGAGGGGGCAGGCGTGATCTCAGTCCCGCCCGTGCCCGAGCCCGCGCCCGAGCAGGGACTACATTTCCCTCCCCCCTGGGTCAGGGAGGAGTCTCCTCCTCTATCTATTAAGGATTCTCGCCTCCGCACATGCGTTATCCTTCCAAGGCCGAAAGGATTTGGAGTCCTGTACGCCAGAGGCCGAACTCG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACCCTGCTGGGGGTACAGAGACGTTTCCGAAACTCAGCGGCTCGGTCGCCCCCAAAGGCCAGGCGGAACACACGCCCACACCGCGGCTCCCTTCCCGAAGTAAGACCGCCGGGCCACGGCCGCCCCCAGGAAGCCCCGCGCCCCGCACCCCACACCCGGGGACACAAACAGGCGCCGGGTAGCCGCGAGGGCCGGCGCGGCGCCTTTAAGAGGCGGCGGGCGGCGCTGCCCCCTGGCGGCCGCCCCGCCGCTTCCTCGCCGCCGCGGGCTCAAGCGGGGCGGCCGGGCCAGCGCGGGGCG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGCTTCACAGTCAGAACGCGGTACCGTTGTGCCCCAGCTCGACTGCAGCCTCCCCGCCCCGATACACTTGTCCCATCCTGTCCACGGCGCCCTTGTTTTGATTCGCTGCTGTCCCAGAGAGCTTAGTTATTCGTACGGGCAATCTTGACCCTTTCCCAGGACCTCCCCTAATACTGTTCATTGATCCTGGACCGGCAGCGAGACCCCTTCCGGCATCCAGAGGCCCAGTACTTTCAATCTCCTCCCCCAAAATGCTTTCCCTTTGTGGGGACCCTCGACATAGATGGACACCCAGCGGCC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCGTTCTTGGCATCCTGAGAGGGCCAGTTAGTACTGAAGTCCCTTGGCTGCTCAAGGATTGCAGGGATGAGGCAAGTGGAACAGCCTCGGAACCTCCGAAAATGGGCACGCTCCAGGTCCCAGTTTCTATGGCAACCATACCGGCAAATTGGGCTCCGCAATGGTTTCTCCTGGAAAAACCGTGATTTTGGTTCCCGCGGACGTCTCTATGGTTTCGACAGCCTAGAAGGAACAAAACGGCATTTCCGGGAAGATGGCGGCGCACAAGTCAGGTCCGGCACATGTTTCCGCGGAGCGGAC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCTCTCGCGCCGCTCGCGTGACCGGCCGGTGTGTGCGCGAGGCCCCGGCTCCCGGGGCACGGACGGCCGGGCGCGCGCCTCTGCGAGGGGCGTCCGGGTCCGAGTCGGCGGTCCGGGCCGGCGCGAGGTGCGTGCGGGCGGGCCGCGGGGGTCCCGGACGGACACAAGCGCACACACTCCCGGAGGAGCCTTCGAGGCTGCTCTTCCTCGGCCAGACGGAGAGCGGCACTGTCTCCCCGCCCAGCGCTCACTCGCCCCGCGTCTCCCCCCGCGGCGGCTGCTCCTCCTCGGCACCGCCAG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTCCCTCCTCTGGTAGCCAGAATAAATTTTGCAGTAAGTTAGCAGAGTTGATTCCAAAAAGTCTGCACTGTGGCATCTCAGGAAACCAAACTTATCTGCTTAAGAAATAACTTGCAGAGAGAAAAGGAACTCTGACAAGGCTAATAACAGGGATTTCAAGCTGTGTTTCAGCAAGCCAGTCACAGAGTATTCACGTGTTAATTCACTGGCGTGTTCCGCGGCATAAGCACCCCCTCTCTGCCTTCCCCCAGTTCCAACTGTTGTGCTGCAGCAGATTTGGTCTGAGTCTGGGCAGAGCCC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ATGATGCTGGGGACGGGACACAAACAGGCGTGACAGCAGCGACCAGGCCGGCGCGGCGCCCGTAGACTGAGTCGAGACACCCTGGGCACAGGCGGCCGCCCCGCCTCTGTGATAGTTACTGGGACGTGTGCCCAACGGCCGGGCCAGCGCGGGGCGGCGGCGGGCAGGGGCGGCGAGTGCCTCGACGTTCTAGTGCGCCCTTCTCGGCGGTCTTATGCACTATGGCAGCAAGAGTTTTGAATATGTTGCGGTAAGTTGAAATTATCGAGAATGCCAGGCGAGTTGGCCGCGTGCGCCCCG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGCGACACGCCGTGCGCCTCCGCGGCTGCGCTACGAAAACGAGTCCCGGAGCGGCCCCGCGCCCGCCGCACCCGGCCCTCGCCCGCCCGAAGACAGGCGCCAAGCTGCCCCGCCGTCTCCCCAGCTAGCGCCCGGCCGCCGCCGCCTCGCGGGCCCCGGGCGGAAGGGGGCGGGGTCCCGATTCGCCCCGCCCCCGCGGAGGGATACGCGGCGCCGCGGCCCAAAACCCCCGGGCGAGGCGGCCGGGGCGGGTGAGGCGCTCCGCCTGCTGCGCGTCTACGCGGTCCCCGCGGGCCTTCC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCCTAGCCAGGAGGTAGCATCTGTCTCCAGGTGCCCGTGGGGTGTCCTCCATGAGGACCACCTCTTCTAACCACCTGCCCAGCTCAGAGGACCAGCTGGCAGGAAAGACGCTGCACCGCCCATGAGGCCAGAGCTACAAGTTGCTTCTAGGAGTGGCTGTGGGCGGAGGCTGGTGGTTCTGAAGGTGGCGGTGGTTCTGCAGCGTGGCTCCCCACAGCCCCTTTCATCTGAACAGTAGGGCTCATGCCACTTGTTAATCAGCCTTCCAGTGGCACGAGGAGTCATTTTCTGGGGATAGG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCCTCCTCCCACGCTGCGCGCGCACCTCCCCGCCCCCACCCCTACCCGCTGGCGTGCCCAGTGGAACGGAGCCTTGTGTCTCCGCCTCAAGTCCCCGGATGCTCACCTCCCCGACTCGCCCCCGCTGTGGCCCCGCCCCCGCGCGGCTCTTCGTGCCACGTCACCGCCTGCGTCGCTTCCGGAGGCGCAGCGGGCGATGACGTAGAGGGACGTGCCCTCTATATGAGGTTGGGGAGCGGCTGAGTCGGCCTTTTCCGCCCGCTCCCCCCTCCCCCCGAGCGCCGCTCCGGCTGCACCGCG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCATGCAGCGACGGCCGCCGCGGAGCTCCGAGCAGCGGTAGCGCCCCCCTGTAAAGCGGTTCGCTATGCCGGGGCCACTGTGAACCCTGCCGCCTGCCGGAACACTCTTCGCTCCGGACCAGCTCAGCCTCTGATAAGCTGGACTCGGCACGCCCGCAACAAGCACCGAGGAGTTAAGAGAGCCGCAAGCGCAGGGAAGGCCTCCCCGCACGGGTGGGGGAAAGCGGCCGGTGCAGCGCGGGGACAGGCACTCGGGCTGGCACTGGCTGCTAGGGATGTCGTCCTGGATAAGGTGGCATG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCCTGAGCCAGGCTGAAGCGGGATAGGAAGGGTAAAGGTGCTAGCTGAGCCGTAGTACGGCCCAGCAGTTCTCCAGGGGACAAGCTAGGCCCCCGCTGGAGGGAGTGGTGAGACTCAGCCGGGGCCTGATTCTGGTCAGCACCTTGGACAGCAGCCAGTGCCCTCCCTGGCCCCGCCTGGCCCCGCCTGGCCCCACATCCGCTACTCCTGGGCACCTCCTCAAATAGCACAGCCTCCAGCTGGTGCCCAGGGCCTGAATACACAGAGCGCTGAGAGAGTGGGGCAGTGTGGTCACGGAC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCTGGGAAAAGCTGAGAGCATAATCGAGCGGCAAAGAAAACGCGACCACTGAGGACACTTAGAAGGGGGCGGGGCCTTTTTGTGGTACTAGGAGCTCGAGAAATTGACTAGATTACGAGTGGTTTACGACTCAAGCATACAACTTACAGCTTGGGCCTTCCGTAGCGGGCGTTGCGGGGGAACGTACAGTGCTTGAGGGGCGAGGTTTGCCGGAGCCTCATTCTAAAGGAATCTTCTAGTTCCAAGCGTTGTTCCAATTGGGCCGGAGCGTGCAACGGGACCCACGGAACTACAGGTGTG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AAAGCCACCTCCGCGCCGGCAGCTCCGTTTCTAGGGAAACAGGACCAATTCCAGAAGTTGGGGTGTTACGTGTGCGATATACCTGCCCAAGAGCTGGTATCAATAGAGCGGGCGGCATGGTAGTGTTTCCCCCGGCCATGGTAAAATGCGAACCAGATCCTCGGCGGGTAACCGCGCCCCTAGGCGTTGGCATGTGTCGGGGTAGCATGACGGGGTCGTCCAGTTCTTAGCCAAGATGAATCCGGCTCCGAAGGTCCCCGGCTTCGTGGCCATAAAGGCGAACACCGCTGGAAAGGTGGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGGCTCGGGGACGGGACGGGCGAGCAGTCATTTCTGAATAGCGCGTGCAGAGGGTCATCGCGAAAAGACTAAGACGCGACATAATGGACTGACTCGGGGCATGGCGGGGTGGAGGCGGGAAAGTGTCGAGGCCGGCTAATGCCTGGACAGGGAGGTGGGCATCGCCAGGGCGACGATGAGGGTCGCCACTCCGGAACACGATTAGCATCAGTGCACTGACCTCTGCCCCCACGACTCTCCAACACGCCCGATGATGTTGCGTGCGGCAGCTGTCGGCTAGGAGTCGCGGGGTCTGTGCGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TATGGTATTTCAAGGTCATAAATTACAAATGTGCATATTTATTATTCCCCTATAAAATGTTAATTAAAAAATTTTCATATTTACTTGATGTAACAGAAATGAAACCTGTAATGAGGGATCTAATTTCTTTTTCTTTTTTCATTCCAGAATGTATGACAACATGTCCACAATGGTGTACATAAAGGAAGACAAGTTGGAGAAGCTTACACAGGATGAAATTATTTCTAAGACAAAGCAAGTAATTCAGGGGCTGGAAGCTTTGAAGAATGAGCACAATTCCATTTTACAAAGTTTGCTGGA", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACGACCCTGGCCTCCGACTTCAACGACTTCATAAGGCGGCGTTTCTGGGCGCAGCCGTGTCGCTCCTGGTGAGAGGCCGCCGGCAGGCGGGATCCAGCGCCCTCCGGGGCACCGCGGGCGAGACCGTCGCCTTCGCACCCCCGGCGCGGTCGACCCCGCGGGGCCGTCGGGTCCTGGGTTCCCCGCCGCGTTGCGCTCGTCCCCCTCCTGTCAGAACCTGGGCCCCCGCCCCGCCCACCGGCGCGGGGCCTCTCCTCCTCCCGCTAACGGGCGGTCGGCCGCCTCCTTCCTCTCTTGGCT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGTTTCTGAGGCTGAGGAGGTACCCAGGTAAGCACACTCTGGGTTCTCTGGTGAGCACACACCGAGCTGCCTGCATCTCCCCTACCCATGAAGGCAGCTAGCCGCCGGGCCAGTGGTGGGAAGGGGAGGCGGCCATTAGCGCCGTTCCCGCCGGGACTTGAAGCGCCCGGCCGCGGCAAGCCCCGCCCTCGGCGCGCCCCCGCGTCCGCGCGCGCTCCTCGGGTCTGCGCGGAGCCGGCGTCGGCGCGCGCTTGGGCGCCTGGCGAAGACCGAGAGAGGCTGGCGGGATCTCAGCGGCGC", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GACCGCCCGTTGGACATACCTCTAAATTGGGAGCGCGGTGCCGATTTACGTGACCGCCGTGGTTCGCACGGTGGGTAAACAGTGGATCGTGGGGTGGTGAGAAGGTTCCGATGGGTGTTCTTACTACGGCCCGATCCTAGCAGTCGCCGACTTTGTTATTCTTATTGGTGGATGGGGGGCCGCTGATATAAATCTGGGAGATGAGGAGATAGGCGGGAGCGAGGGCTGACTCTGCGTGTCGAAGGAGGTAGAGAAAAAGCGTTGCTTAAAGGGAGGGAGCGTAGCTTGGTTGCTCCGTAG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GACCTCTCTTCGGGCGGCAAGTGCCATACGTATAGATAAGCGGAATTTACATGGTGTCACAGTTAGCGCAAGGTCGAATAAATGCGGCTTAACACGACAGGTATCTCCAAATAAGTGTGTCTTACGGCGTAGATCGCGAGTTGAGCTGTCATTTCTTTCCAAGCGGCGCAGGACAGGACCCAGAAGCCAGAGCGCAGGAACTGCTTCCGGTTTGAGTTTTTATTAGAATAACGGGCTGTGGGAGAGGCAAGTGGCGGATCTTCAGGTCGGCGAGGGTGTGACATTCGACCCACAGGCCGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCATATTCTGTCTGAAATCGTGTGCACCGAAATCCCCGCCTTGCGGTGGAGGCTGGCGCTAGGCGGCCTCAGCCTCGGCCTGCTGCGCTCAGGAACCCGCGCCCCGGCTCCTCGGCGATCCATTGCTCTTTCCTCTGGCGCCGGCCGCAGGCCTCGGTCACGCCCCCAGCGGCCCGTTGGTTTCCGGGTCCCGCGGGGTGCCCCCGCCCACACGCTATGCCTTAAATTGGGCCAGGCTGAGGCGCTGCTGCTGGAGCGGCCGATCCGAGACGTGGCTCCCTGGGCGGCAGAACCATGTTG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TGGTCTTGTGTCTCGGCGTACCATATCTTTTGGGTTCCCCGGCTGCCCCGGAGGTTCGCGGCGAGAGGACGGGCCGGGTGCGGCTAGTTTACCTGAGCAGTGTGAACTACCTCACTCCCATCGGGACCAGCCGGCATAAACACTCTTATTGCCTGTCAACGTTTTCGAGACATATTTTGTTCGCGAGCCCGCAGACAACGCGCATTCTGGAGTTCGAACGCTCAGTTCGCGTCCCCGAGAAGATATTATTCAGATGCCTGCGAGCTGGGGCCGGGTGGCCGCGCCGGGTCGGAGTGTGGC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCCGGAGACAGGTGGGAGGGTGGGGGATGCCGGCAGGAGAGGGAAGCAGGACCGTGCGCGCCGCCACCCACCCGCAGTCCGTCCGCCCCCGGGCGCCCAGCGCGTGGCCCGAGCGGCGCTGCCCGCCGAGCCGGGAGCCCCCTCGGCCCCTCCCTTCAGGCGCGGGCCGGGGGCGGCTTCTCCGCGACCTTATGTAACCGGGCGGGAGGGGCCGGGCGGGCATGGGCCTTCCCGGCCCGGAGCTGGGAGTCGAAGGGGCGGGAGGCGTGATGGTGAACTCGCAAGAAGTTTGAGGGACG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCAGGACTGAGTCACCTCCTGAAGGCCCCGTTCCAAAGCACTCCCACGGGGGATTAGGTTTCAACACAGGAATTTTTGGCGACACAAACATTGAGACCACAGCACCCAGCTCTCTGGGTTCCCAGAACCCTGTGGGAGGGCTGGGTCGCGCCTGCCCCCTTTTAAACACGAGGCTTCCATGGAAGCTTCTTGCCGGAGGCTGGACACTGAGGAATGGACAGGATCAACACTCTTCCACCGGCTCTTCCACCTCCCGATCACTTCCACCATCGCTGGGGGAAGAACGAATGCACCGCGTGG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CACAGCCCCAGATGGTGACTACTGGAGACTGCTGCCCCCAGGTATCCACATTGTCATTGCCCAAGCCCCTGGCTACGCCAAAGTCATCAAGAAAGTCATCATCCCCGCCCGGATGAAGAGGGCTGGCCGTGTGGACTTCATTCTGCAACCTCTGGGGATGGGACCCAAGAACTTTATTCATGGGCTGCGGAGGACTGGGCCCCACGACCCACTGGGAGGTGCCAGCTCTTTGGGGGAGGCCACGGAGCCCGACCCGCTCCGGGCGCGCAGGCAGCCCTCGGCCGACGGGAGTAAGCCCTG", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AATCGTGTAATTCCCGCTTCCTATGGCAAGCGGCGCCCAGTTCTGGGTGACTTGTGAAATGAGCGTCCTGCTAGGCCATAGTCTCTACGAGGAGGATATAATGTAAAGTCTGGAGCGAACGTAAATGCCGTGACGTGTATTTGTGAGACTTCGCCCGGTCGGTGAGCTGGGTGCATAGGTTCCGAGCGCGCGACCACTTAGCTTCCCTTCTACAACAACCACCTGTGTGCGCATGCCCTCTCATCTTACAGTTGTCAAAGTGCTTTTGCCAGAGCGGTCACATAATCAACCTGAAAGAAG", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCCTTAGAATATTGGGCATACAATTTACTTTTATAAATGTCTATGAAATAAAATTCTTATATAATTATATAAAATTACATAATATATAATTATATTGAAATAATTATAACTGTTGAATAAATGTATTATTTTTCCTTTGAACTATTTAACAGGGTTATATAACAAACTCATCACGAGTTGTGTCTAATAAGTCATCAGAGTTACTGTTTGACTTGACCCAGGATACAGGATTATCACATTACCAAGGGGGACCAACACTTTCTATGGCAGGTTGGTTTTAGTATTTTTTTCATAAAGGTT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGCGCGCGCGCCGGGCCGAGGGGCCGAGAGGCGGGGTCTTACAGCGACCGCGGGAAGAGGGCGCCCCAGAGCTGAGCCGGAGGCCGGCTGCCAGCGGGGCGAGGGTGGGCGCGAGCGCAGGGGCGGGCCCCGAGGGAGGGCGGGGACGGTGAGGGGGCGGGGTCGGGCTAGGCGGGGACGCGCTCGCGGGGTGGGGAAGGCGGGGGCGCGGCGGTGGCGGGAGCGTGCCCGGTCCCCGCCCCTGTTCCCACTCTCCTTCCACCTCGGACCGGCCGGGGCTCCGCAGAGCCAAAGCTCGCT", + "output": "promoter", + "model_response": "promoterpromoterpromo" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TATTTCCGATTCCTTGACTAACGTACGGGGGCCCAGCACTTTCATTCCATCCCATCACCCCATGATCCTTCCCCAGACTCTAATACTGGCCGTAATATTTACCCCCCCCTGCTCCATGTGCGTAGATCAATGACACGAGCAAGCCATTGGTCTAAAACCCAGGGTTCTCTCGCAGAGGCCCTCCATTGTCTCTGCGGTGTCACTTCGCGCACACACATCAGAGCGGGCCTCTCTGTGAGGCGCGGGGTTGATGTACTCACACGCTGCGTTGATCGGAGGTCGCTGCCAAGCATGGCGCCC", + "output": "Non-promoter", + "model_response": "Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGTCAGCAAAGGCTTCCCTGAGGAATTGGGAATAGCCAGCCATACGGGTGGAGCCGGTCAACTCCTTGTGGTTGGGAGCTAACCGATAGCCCCTACAAGGAGCATTCCAACTATGTTATATAACATTGTGATTTTCGCGAAGGCAACGACTTATTCTTAAGACAGTATCTATCTGAATCTGAAGATCCTTGGTCTGGAGATTGGTAAAAATCTCCAGTCATCTTATGTTTCTATTATACTCTACTGGCGTTTTTACGTGACGCGGGGAGTCTACCTCCCCCAACCATAATTTAACAGTAA", + "output": "Non-promoter", + "model_response": "Non-promoter" + } +] \ No newline at end of file diff --git a/04-gene-sft/gpt2_lora_text_classification/README.md b/04-gene-sft/gpt2_lora_text_classification/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1d1ea3b65e48fcbd558e61ed55bb0102b413cd74 --- /dev/null +++ b/04-gene-sft/gpt2_lora_text_classification/README.md @@ -0,0 +1,202 @@ +--- +base_model: dnagpt/dna_gpt2_v0 +library_name: peft +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Funded by [optional]:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] +### Framework versions + +- PEFT 0.14.0 \ No newline at end of file diff --git a/04-gene-sft/gpt2_lora_text_classification/adapter_config.json b/04-gene-sft/gpt2_lora_text_classification/adapter_config.json new file mode 100644 index 0000000000000000000000000000000000000000..561731bcd64ca13b2bcd7c050f0752953fbf70c3 --- /dev/null +++ b/04-gene-sft/gpt2_lora_text_classification/adapter_config.json @@ -0,0 +1,34 @@ +{ + "alpha_pattern": {}, + "auto_mapping": null, + "base_model_name_or_path": "dnagpt/dna_gpt2_v0", + "bias": "none", + "eva_config": null, + "exclude_modules": null, + "fan_in_fan_out": true, + "inference_mode": true, + "init_lora_weights": true, + "layer_replication": null, + "layers_pattern": null, + "layers_to_transform": null, + "loftq_config": {}, + "lora_alpha": 32, + "lora_bias": false, + "lora_dropout": 0.1, + "megatron_config": null, + "megatron_core": "megatron.core", + "modules_to_save": [ + "classifier", + "score" + ], + "peft_type": "LORA", + "r": 8, + "rank_pattern": {}, + "revision": null, + "target_modules": [ + "c_attn" + ], + "task_type": "SEQ_CLS", + "use_dora": false, + "use_rslora": false +} \ No newline at end of file diff --git a/04-gene-sft/gpt2_lora_text_classification/adapter_model.safetensors b/04-gene-sft/gpt2_lora_text_classification/adapter_model.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..85b813f68df1a50e58f1157e110d7fa0007895ad --- /dev/null +++ b/04-gene-sft/gpt2_lora_text_classification/adapter_model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:420bd2ac394927379af63039e7cf7cc8ce58744863a2ed076cc1710f3a5b22f1 +size 1188920 diff --git a/04-gene-sft/gpt2_lora_text_classification/checkpoint-46620/README.md b/04-gene-sft/gpt2_lora_text_classification/checkpoint-46620/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1d1ea3b65e48fcbd558e61ed55bb0102b413cd74 --- /dev/null +++ b/04-gene-sft/gpt2_lora_text_classification/checkpoint-46620/README.md @@ -0,0 +1,202 @@ +--- +base_model: dnagpt/dna_gpt2_v0 +library_name: peft +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Funded by [optional]:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] +### Framework versions + +- PEFT 0.14.0 \ No newline at end of file diff --git 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3611, + "CAAAGCC": 3612, + "TAGATAGATAGATAGA": 3613, + "CCTTAAAA": 3614, + "GTACGG": 3615, + "CAAAGGAA": 3616, + "CAAATTTG": 3617, + "CCCCCTCC": 3618, + "GATTATG": 3619, + "GCTGTCA": 3620, + "GCTCTCTG": 3621, + "GGAAGGAAGGAAGGAA": 3622, + "GGTCATT": 3623, + "CCTGGCA": 3624, + "GGCCCTG": 3625, + "CCAGAAAA": 3626, + "CTAAGAA": 3627, + "GTAGAAAA": 3628, + "TGTTAAAAA": 3629, + "GTATAAAA": 3630, + "CTACGG": 3631, + "GCCGGGC": 3632, + "GTTTTAAAA": 3633, + "CAAACATT": 3634, + "GTCATTTA": 3635, + "GGAGACC": 3636, + "GAAAGCAA": 3637, + "TACTGTT": 3638, + "CCTGGGTT": 3639, + "TCTTTAAA": 3640, + "CTCTAAAA": 3641, + "GTGCAGTG": 3642, + "TGGGAAAA": 3643, + "CATCCTG": 3644, + "TAAGGAAA": 3645, + "GGAAGAGG": 3646, + "CGAGTT": 3647, + "TCTTCTTTT": 3648, + "CCTGTAATCCCAGCTA": 3649, + "TAGCATT": 3650, + "TGATAGG": 3651, + "GTAAGAA": 3652, + "TGAGACC": 3653, + "TCCTAGA": 3654, + "TCTCAAAAA": 3655, + "GCCCACC": 3656, + "CTCAGTT": 3657, + "CTTTGCC": 3658, + "TAAGGCA": 3659, + "GAGAGCA": 3660, + "TGAAGTCA": 3661, + "CGCTCA": 3662, + "GTGTTTTA": 3663, + "GGGTGAA": 3664, + "GGGCATT": 3665, + "CAAGGCA": 3666, + "CAGATCA": 3667, + "CCTTCCTTCCTTCCTT": 3668, + "CAGAGCTT": 3669, + "TATACTA": 3670, + "TGAACTA": 3671, + "CTGGAAAA": 3672, + "TACAAAAA": 3673, + "CTCACACA": 3674, + "CCTCTCTT": 3675, + "GGGCAGG": 3676, + "TCTATTAA": 3677, + "CAGTCTG": 3678, + "GTTTGAAA": 3679, + "GTGGGCTG": 3680, + "GTTATTTA": 3681, + "TCCCATA": 3682, + "GACTCCA": 3683, + "GTGTCCA": 3684, + "TGTGGTA": 3685, + "CCAGCTA": 3686, + "GAAATCAA": 3687, + "GCTCATGCCTG": 3688, + "CTTTCCTT": 3689, + "TATTGCA": 3690, + "CCCAATT": 3691, + "CCCGTC": 3692, + "TTATATG": 3693, + "TACAGGA": 3694, + "GCCCTAA": 3695, + "GCAATTTT": 3696, + "TATAAATG": 3697, + "GATTGATT": 3698, + "CTTGAAAA": 3699, + "CCATTAAA": 3700, + "CTTATTTG": 3701, + "CTCCAGC": 3702, + "CCTGCCA": 3703, + "TAAACATG": 3704, + "TGAATTAA": 3705, + "GAGTGGGTT": 3706, + "GGAAGTG": 3707, + "GTAGATA": 3708, + "CTCGTG": 3709, + "CTGAGCAA": 3710, + "TATCCAA": 3711, + 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"CATTGCA": 3763, + "CCGGGA": 3764, + "TCTCAGCTCACTGCAA": 3765, + "GAGGTTA": 3766, + "CTCCAGGA": 3767, + "CCTCAAAA": 3768, + "GTGCTCC": 3769, + "GTAATTAA": 3770, + "TCTTTGAA": 3771, + "TCGGCCTCCCAAA": 3772, + "GGATTCA": 3773, + "CCTGTAGTCC": 3774, + "TCCATTTG": 3775, + "TTTTTATA": 3776, + "TAGTGAA": 3777, + "CACATCA": 3778, + "GTCCTCA": 3779, + "TATGGTA": 3780, + "TAATCAAA": 3781, + "GGATCAA": 3782, + "TAAATAAATAAATAAA": 3783, + "GACTGAAA": 3784, + "TGTTTCTG": 3785, + "CAGCTCTG": 3786, + "CACAGCAA": 3787, + "GCCTATA": 3788, + "CGGAGCTT": 3789, + "TACTTAAA": 3790, + "TATTGAAA": 3791, + "TGTTCCA": 3792, + "GTCACTCA": 3793, + "GGAGCCA": 3794, + "TAAATTCA": 3795, + "CCTTTCTT": 3796, + "CTTACTG": 3797, + "CTTACTT": 3798, + "GAGACGG": 3799, + "TAATCTCA": 3800, + "TCTCGGCTCACTGCAA": 3801, + "TGTGGTT": 3802, + "CATTTTAAA": 3803, + "TAAGCAAA": 3804, + "TGTTTGAA": 3805, + "GCCTC": 3806, + "TAGTATG": 3807, + "TAAAGATG": 3808, + "GTACTC": 3809, + "CAGAAATG": 3810, + "TATGCAA": 3811, + "TCAAGTG": 3812, + "GATTTGA": 3813, + "CGGAGGTT": 3814, + "GTAGTC": 3815, + "TGTGTCA": 3816, + "TGGATTA": 3817, + "GTGCTGGGATTA": 3818, + "TCTTGTG": 3819, + "GCTTGTG": 3820, + "TCTATTTG": 3821, + "TCAATATT": 3822, + "GAGGGCA": 3823, + "GGAGAGAA": 3824, + "CCAAGCA": 3825, + "TAGGTTA": 3826, + "CGCAAAA": 3827, + "GCTACTG": 3828, + "TCTTTTTTTT": 3829, + "TCTGGTT": 3830, + "GTCCAAAA": 3831, + "GGAACAA": 3832, + "GAATACA": 3833, + "CGTCATT": 3834, + "GCTGGCC": 3835, + "CGTTTTTT": 3836, + "GAACGA": 3837, + "TAATGTTA": 3838, + "GGAGGAGG": 3839, + "GTCTTAAA": 3840, + "CACACCA": 3841, + "GTGATGA": 3842, + "CAGGCAA": 3843, + "GTCACCA": 3844, + "GAATGTA": 3845, + "CATGCAA": 3846, + "CGGCAA": 3847, + "GGTGCTG": 3848, + "GGACTAA": 3849, + "GTATAGA": 3850, + "GCCAGAAA": 3851, + "CTATGGG": 3852, + "TACTGAAA": 3853, + "CTGATGA": 3854, + "CAAACAAAA": 3855, + "GGAAGTT": 3856, + "CTCAGAGA": 3857, + "TCTCCATT": 3858, + "GTCCCCA": 3859, + "TCCATCTT": 3860, + "CTGACAA": 3861, + "TCTCTCTCC": 3862, + "CTCAGCC": 3863, + "CCCGTA": 3864, + "GGAACTG": 3865, + "GCTGTGTG": 3866, + "CACAGTT": 3867, + "TCAAATTA": 3868, + "CCCATTTT": 3869, + "CAAATCAA": 3870, + "GTAATTTA": 3871, + "TACATGA": 3872, + "GATGGCA": 3873, + "GAAATATG": 3874, + "TCTGGTG": 3875, + "GTTATTTG": 3876, + "TAAATCTT": 3877, + "CATGGCA": 3878, + "CAGATGAA": 3879, + "CTTTTATG": 3880, + "CGGAAAA": 3881, + "CATATAA": 3882, + "TCTCTCTA": 3883, + "TGTGGCA": 3884, + "GTCCTGGG": 3885, + "TATATGA": 3886, + "TGCCTTA": 3887, + "GGACTTA": 3888, + "CCCCATGGA": 3889, + "TGAACATT": 3890, + "TTTTATTAA": 3891, + "GAAATTTA": 3892, + "CCGACA": 3893, + "CTAGTCA": 3894, + "GTAGGGG": 3895, + "GCCCAGC": 3896, + "GCCATGTT": 3897, + "GTGAGTG": 3898, + "CATCTTTT": 3899, + "CGACTG": 3900, + "TTTTCATT": 3901, + "GCTGGCTG": 3902, + "CCCTGTCTC": 3903, + "CTTTCATT": 3904, + "CCAGCCTGGG": 3905, + "GACGTC": 3906, + "GCTTTCC": 3907, + "GGCATTA": 3908, + "CTGAGGCAGGAGAATG": 3909, + "TAGGGAA": 3910, + "TATTCTAA": 3911, + "CTGACTGA": 3912, + "TCGAGC": 3913, + "GATCTAA": 3914, + "CCTGCCTT": 3915, + "TATGTGA": 3916, + "TAACTCC": 3917, + "TGTTTAAA": 3918, + "GGGAGAA": 3919, + "TTTTGTG": 3920, + "CTTTATTTT": 3921, + "GTTCGAGA": 3922, + "TCAATTAA": 3923, + "CTCCAGTG": 3924, + "GCGGCGG": 3925, + "CCATGTA": 3926, + "GAAAAGAA": 3927, + "TCTCAATT": 3928, + "CAATTAAAA": 3929, + "CATGTCA": 3930, + "TATGTATT": 3931, + "TATACAAA": 3932, + "GGCTAAA": 3933, + "CTTTACCA": 3934, + "CACAGTGA": 3935, + "CTGAGGTG": 3936, + "TAAAAGAA": 3937, + "TACCTTA": 3938, + "CACCCTG": 3939, + "CTTCCCTG": 3940, + "GCTGCTA": 3941, + "GCCTAAA": 3942, + "CCTGCAA": 3943, + "CGATTAA": 3944, + "GCGACTT": 3945, + "TACATAAA": 3946, + "CCCCTTA": 3947, + "GCACCCA": 3948, + "CAGAGTG": 3949, + "TTTTGGA": 3950, + "GGCCTCC": 3951, + "TCATTATT": 3952, + "TCAATGA": 3953, + "GTTACAA": 3954, + "CCAAGGA": 3955, + "TATAGTT": 3956, + "AAAAAAAAAAAAAAA": 3957, + "GGGAAAAA": 3958, + "TAGACAA": 3959, + "CCCCCTG": 3960, + "CTTGGTA": 3961, + "TGTGCAA": 3962, + "TACTCTT": 3963, + "CTATGCA": 3964, + "GGGTGTT": 3965, + 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+ "GTCAAATT": 4119, + "TGTGTTTA": 4120, + "CACCTCC": 4121, + "TATTCTTA": 4122, + "CAGGCTA": 4123, + "GAAAGATG": 4124, + "TCTACTG": 4125, + "CTCCAGTATT": 4126, + "GGGCCTT": 4127, + "TCGGAAA": 4128, + "GCTĊ": 4129, + "TATACATA": 4130, + "CTGACCA": 4131, + "GTCGCC": 4132, + "GTATGGG": 4133, + "GTCTCTCA": 4134, + "CCCAGTT": 4135, + "CGGGCC": 4136, + "GGCTATT": 4137, + "TATCCATT": 4138, + "TGAAGATG": 4139, + "CTAACTG": 4140, + "GTTAGAA": 4141, + "CCTCAGCCTCC": 4142, + "TCAGGAAA": 4143, + "CTAAAAAAA": 4144, + "TAAGGGA": 4145, + "CATCAAAA": 4146, + "TGAAGAGA": 4147, + "CAAAGATG": 4148, + "GCTGCAGG": 4149, + "CATGCCA": 4150, + "TATATAAAA": 4151, + "CATGCTT": 4152, + "CGGGAA": 4153, + "TCTTGCTT": 4154, + "GCTAGAA": 4155, + "TCCTAAAA": 4156, + "GGCAGAGA": 4157, + "GCAATGA": 4158, + "TCGCGG": 4159, + "CAAGTAGCTG": 4160, + "GGGGCAGG": 4161, + "GATTTCTG": 4162, + "TGCCTTTT": 4163, + "GCATTTTA": 4164, + "TGTGCTA": 4165, + "GGGCTCC": 4166, + "GACCATG": 4167, + "GTGTAGA": 4168, + "TTTTTGTTTT": 4169, + 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"CTAATAA": 4474, + "CCTGTCA": 4475, + "TAGGTTTT": 4476, + "CCAACTCC": 4477, + "GGTGAGG": 4478, + "GTGCTGGGATTACAGGCGTGA": 4479, + "TATACCA": 4480, + "CCATTGCA": 4481, + "GCAAGTG": 4482, + "TATCCAAA": 4483, + "CTCCACCA": 4484, + "TATTTAGA": 4485, + "CCAATCA": 4486, + "CTCCAGTT": 4487, + "CATATAAA": 4488, + "TAAAATGA": 4489, + "CCTTTGA": 4490, + "TCTGGGTT": 4491, + "GTATACA": 4492, + "GTCAGTCA": 4493, + "CTTTGTTTT": 4494, + "TATGCTA": 4495, + "GGGTATT": 4496, + "TGAACACA": 4497, + "TTATGTG": 4498, + "GCAGTGAGCCAA": 4499, + "TAAAACTA": 4500, + "TCAATTTA": 4501, + "GGTCCTG": 4502, + "CAAAGACA": 4503, + "GAAAAGAAA": 4504, + "TCCCAAAA": 4505, + "CCTGCC": 4506, + "GTGATTTG": 4507, + "TATGGATG": 4508, + "CTGAGATG": 4509, + "CACTTAAA": 4510, + "TACTCTG": 4511, + "TAACATTA": 4512, + "GGATGTA": 4513, + "TCTTGCA": 4514, + "GGGTGTG": 4515, + "CCTGTGA": 4516, + "CAGGAATG": 4517, + "CTTTCTTTT": 4518, + "CATTCACA": 4519, + "GTAATTTG": 4520, + "TATTTTTTTT": 4521, + "CTCCATCC": 4522, + "CGAGCC": 4523, + 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4574, + "TCAACATT": 4575, + "CCTCCATG": 4576, + "CCAGGTA": 4577, + "CAGTTAAA": 4578, + "TATTGAGA": 4579, + "CAGCCAAA": 4580, + "TGACAGAA": 4581, + "CAGGCAGA": 4582, + "CACGTC": 4583, + "GATCTTTT": 4584, + "TAGACTG": 4585, + "TGCCCGG": 4586, + "GAGGCAA": 4587, + "TAATAATG": 4588, + "TGTGTCC": 4589, + "GAGGCTGA": 4590, + "TCATCTC": 4591, + "TCTTTTATT": 4592, + "TTTTGAAA": 4593, + "CCAACTG": 4594, + "GGATCTA": 4595, + "GCTGGGAA": 4596, + "CATATATT": 4597, + "TGTTCTCA": 4598, + "TGTGTCTG": 4599, + "CCTTGTG": 4600, + "CGGAATT": 4601, + "GGTGAGA": 4602, + "GCTCTGAA": 4603, + "GTTTAATT": 4604, + "GCCACTT": 4605, + "GACCCTGA": 4606, + "TACATGAA": 4607, + "TAGGATT": 4608, + "TCACGAGG": 4609, + "GCCATCTT": 4610, + "TCGATTA": 4611, + "GAGTAAAA": 4612, + "CCTTCC": 4613, + "TAAATATTA": 4614, + "TAAATAATT": 4615, + "TATAGAAA": 4616, + "TAGCCTT": 4617, + "TCCTCCTT": 4618, + "GGAATTTG": 4619, + "CCAATTTG": 4620, + "GCGAGCA": 4621, + "CATGGATG": 4622, + "GTCTCCTT": 4623, + "CGGGTG": 4624, + "GCAAGTT": 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+ "TCGACTT": 4777, + "GAAAGTCA": 4778, + "GAATAGG": 4779, + "CTTTGAAAA": 4780, + "CTCĊ": 4781, + "CCAATAA": 4782, + "TGTGAAAAA": 4783, + "CCAACATG": 4784, + "GATTGTT": 4785, + "TCTGTATG": 4786, + "CCAACTT": 4787, + "GAGCTCC": 4788, + "TAAAATTTA": 4789, + "TATATATAA": 4790, + "CATAGTG": 4791, + "TGTTTGTA": 4792, + "CGAAAAAA": 4793, + "TCTTTATG": 4794, + "TCAAGAGA": 4795, + "GATATTAA": 4796, + "TGAGACAGA": 4797, + "TGATTATT": 4798, + "CCCACCTT": 4799, + "TGAGAGAA": 4800, + "CTCGTA": 4801, + "CATGGGA": 4802, + "TGGAGGA": 4803, + "TCCAGATT": 4804, + "GTCCTCC": 4805, + "CTCCAATG": 4806, + "CACAGAGG": 4807, + "CACAGACA": 4808, + "TACAGCAA": 4809, + "GTCGCTT": 4810, + "GTCACTA": 4811, + "GGCATTTT": 4812, + "CCTTATG": 4813, + "CAAGGGA": 4814, + "GAGCTGAA": 4815, + "CTCATTTA": 4816, + "CCTGCTCC": 4817, + "AATT": 4818, + "TAGAGTT": 4819, + "GATTTAAA": 4820, + "CATGGTT": 4821, + "CCTATAA": 4822, + "GCCAGTGA": 4823, + "CCAAGAAA": 4824, + "GCGTCTG": 4825, + "TTAATAA": 4826, + "TATATTAA": 4827, + 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"CCTTTCTG": 5480, + "GTCACATG": 5481, + "TCCACATG": 5482, + "GTTĊ": 5483, + "TGATGCC": 5484, + "TTTTCTTA": 5485, + "TCCCCATT": 5486, + "GACACCA": 5487, + "CTCAGTGA": 5488, + "CTCCCAGG": 5489, + "GGCCAAAA": 5490, + "CGAGGTG": 5491, + "GTTTCTTA": 5492, + "GCTGCAGA": 5493, + "CTGTGCC": 5494, + "GTTACCA": 5495, + "TTTTTCTG": 5496, + "TCCGAA": 5497, + "TCAAGATT": 5498, + "TTTTATATA": 5499, + "GTCCCTGA": 5500, + "GTTTTTTTG": 5501, + "CACAGTAA": 5502, + "GTTTTTCA": 5503, + "GCCACCGCGCC": 5504, + "GGATAAAA": 5505, + "GCCATTTG": 5506, + "GTCTCTCC": 5507, + "CAATGAAAA": 5508, + "GTTGGAAA": 5509, + "CCTGGTA": 5510, + "TGTGTGTGTT": 5511, + "GAGAGTG": 5512, + "GCGCTCC": 5513, + "TCCCAGGA": 5514, + "GCAGTGAA": 5515, + "CAGGGGAA": 5516, + "CTCAGTCA": 5517, + "TAAACCAA": 5518, + "CACGGTGAAA": 5519, + "CATAGTA": 5520, + "CTAGCTA": 5521, + "CACATTCA": 5522, + "TCTCTATA": 5523, + "TGATGAAAA": 5524, + "TAGTTTTA": 5525, + "TAACATCA": 5526, + "TGATATTA": 5527, + "GTTAGTTA": 5528, + "TGTTCAAA": 5529, + "GTTTACC": 5530, + "GCGAGTG": 5531, + "TCAGCTA": 5532, + "CTTCTGTT": 5533, + "CAGATGGG": 5534, + "TTTGGAA": 5535, + "CTTAATTA": 5536, + "GGAGGCAGAGGTT": 5537, + "TATCTGAA": 5538, + "TAAGAATT": 5539, + "TAAAGCTT": 5540, + "TCCAGTCA": 5541, + "CTTTCTGA": 5542, + "GAGĊ": 5543, + "TGTGGATG": 5544, + "CAATTATT": 5545, + "TCTCCA": 5546, + "GCAGACC": 5547, + "CTGATTTA": 5548, + "TATCTCTG": 5549, + "GCCACCTG": 5550, + "CAAAATTTT": 5551, + "CCCAAAAA": 5552, + "GGTAGAA": 5553, + "GCTGCTCA": 5554, + "TCTCTGTGAGTT": 5555, + "CCCACAAA": 5556, + "GCAAGAAA": 5557, + "CTCCCAAA": 5558, + "GGGATTTT": 5559, + "CATTATTTT": 5560, + "TCCTACC": 5561, + "CCTTCATT": 5562, + "TGAATTAAA": 5563, + "TGCATGTG": 5564, + "CCTGTAGTCCCAGCTA": 5565, + "GCGCATG": 5566, + "GGAACCA": 5567, + "TATGCATT": 5568, + "TGAACTAA": 5569, + "GTGACTTA": 5570, + "CGGCGC": 5571, + "GTGGAGAA": 5572, + "CTCTCTTA": 5573, + "TCTTGTTTT": 5574, + "GCTGGAAA": 5575, + "TCCACCAA": 5576, + "GATAGAA": 5577, + "TGAAGAAAA": 5578, + "CTGACAAA": 5579, + "TAACATTTT": 5580, + "TCAATATA": 5581, + "CTCATTAA": 5582, + "GTCĊ": 5583, + "TATTGG": 5584, + "TAATGCC": 5585, + "GCACAGTG": 5586, + "GAGAGTT": 5587, + "CGAGATT": 5588, + "GGAGCTGA": 5589, + "TAAGACC": 5590, + "CTGGTTTT": 5591, + "TTAGCC": 5592, + "GCTGGGGG": 5593, + "CCCAGCTT": 5594, + "CTGAGGTT": 5595, + "CTTCCTCA": 5596, + "CAGGCC": 5597, + "CGCTCTG": 5598, + "GTTAATTTT": 5599, + "CCACTTTT": 5600, + "CTCGATG": 5601, + "GTTATGA": 5602, + "TCTAGAAA": 5603, + "CCCCTCTG": 5604, + "GTGGGAAA": 5605, + "CTCTACTT": 5606, + "TCCAGATG": 5607, + "TGTGTGGG": 5608, + "TCCAGAAAA": 5609, + "GCATTCTCA": 5610, + "TCTTTCTTTT": 5611, + "TCTCAAAAAA": 5612, + "CCTATCA": 5613, + "TAAATAGG": 5614, + "TGTTCTTA": 5615, + "TTTGTGA": 5616, + "GTCCCAA": 5617, + "TAGATATA": 5618, + "TCTTGTAA": 5619, + "CATTCCAA": 5620, + "GTCAGAGA": 5621, + "CTAAGTT": 5622, + "TAAAGTC": 5623, + "TTATCTG": 5624, + "GTGTCTGA": 5625, + "GTCCTCTG": 5626, + "TCTTGGCTCACTGCAA": 5627, + "CATTCTTTT": 5628, + "GAAAAAAAAAA": 5629, + "CTTGCTA": 5630, + "GGAGGAAAA": 5631, + "TCAGGAGATCGAGA": 5632, + "GTAGCCA": 5633, + "GGCCTCA": 5634, + "GCGCCAA": 5635, + "GGAAATGGCAACCCA": 5636, + "GCTCCATG": 5637, + "GCCGGGG": 5638, + "GAAGACC": 5639, + "CCTGCAGG": 5640, + "TCGTCTG": 5641, + "CTCAGTAA": 5642, + "CAAGCAAA": 5643, + "CAAATACA": 5644, + "TAAGAGAA": 5645, + "GGAĊ": 5646, + "TGAGGAAAA": 5647, + "GCCCCAAA": 5648, + "TTACAGG": 5649, + "GGAATGAA": 5650, + "CATAAATT": 5651, + "CTTTATAA": 5652, + "CCCCTTTT": 5653, + "TACTTTTA": 5654, + "CATTCTTA": 5655, + "GAGACTA": 5656, + "GCCAGGTG": 5657, + "TATGAGA": 5658, + "GTACAAAA": 5659, + "TCTTTGGG": 5660, + "TGTTCATG": 5661, + "CTCAGAGG": 5662, + "CATAGCA": 5663, + "TACCATG": 5664, + "CACACTGA": 5665, + "CTTTGGAA": 5666, + "CATTGTCA": 5667, + "CCTGGAAA": 5668, + "GGACAAAA": 5669, + "TGTTGCC": 5670, + "CAAAGA": 5671, + "CTCCTGACC": 5672, + "TAGGTGA": 5673, + "GCGATCA": 5674, + "CATACATG": 5675, + "TCACACC": 5676, + "GACAGTGA": 5677, + "CAAGGTA": 5678, + "CATAATTA": 5679, + "GCAGCAGCAGCA": 5680, + "TGTTTGGG": 5681, + "CAAACCAA": 5682, + "TAATATAA": 5683, + "TCTTATTA": 5684, + "GCGGGCGCC": 5685, + "CTTCAGTT": 5686, + "TATTCAGA": 5687, + "CATACTA": 5688, + "GTCCCAGG": 5689, + "TATTATTTA": 5690, + "TGTTCACA": 5691, + "TCTCTAAA": 5692, + "CGCGTG": 5693, + "TTTTTTTTAAA": 5694, + "TTTTTGCA": 5695, + "GTTAGGA": 5696, + "CTCATGA": 5697, + "GAACTGAA": 5698, + "CAATCAAA": 5699, + "GCCCAAAA": 5700, + "GCTAATAA": 5701, + "CATATGA": 5702, + "GTAGGGA": 5703, + "CCACGTG": 5704, + "GCGTCTT": 5705, + "GCCCAGAA": 5706, + "CAGĊ": 5707, + "GTTTCTTTT": 5708, + "GTCAGCTT": 5709, + "CTCCTGTG": 5710, + "CTTATATA": 5711, + "GGGCTTA": 5712, + "CCATACA": 5713, + "CTTĊ": 5714, + "GCACCCCA": 5715, + "GCACAGAA": 5716, + "GAAATGCA": 5717, + "CATATTAA": 5718, + "GGACCCA": 5719, + "TATGCATG": 5720, + "TGATTAAAA": 5721, + "GAACAGAA": 5722, + "GATGGAGG": 5723, + "TCTGCCCA": 5724, + "GGAAGATT": 5725, + "GCCACATG": 5726, + "CTTCTGAA": 5727, + "CTTCAGAA": 5728, + "CAGATTTA": 5729, + "GAAACTAA": 5730, + "TGAAGATT": 5731, + "TCTCCTCTCC": 5732, + "CTCCCCTT": 5733, + "TCATCATCATCA": 5734, + "CTTCCAAA": 5735, + "GTCCAGAA": 5736, + "GCCAGCAA": 5737, + "CATTGTAA": 5738, + "TGAATTCA": 5739, + "CCAACC": 5740, + "CCACGCC": 5741, + "TGAATGGA": 5742, + "GATGTGA": 5743, + "TGTGCATG": 5744, + "GCAGCAAA": 5745, + "GGAATGGG": 5746, + "CCAGACC": 5747, + "GTCCAGCA": 5748, + "CCTTCAAA": 5749, + "CTCCTTCA": 5750, + "TAACACC": 5751, + "TCAĊ": 5752, + "TCGGCAA": 5753, + "TAAATATTTT": 5754, + "TTTTACAA": 5755, + "CTTGGAAA": 5756, + "CACAGATT": 5757, + "CTGGAGAA": 5758, + "GCCTGAAA": 5759, + "GTCCCTA": 5760, + "CACAAAAAA": 5761, + "GCCAAATT": 5762, + "GGACTTTT": 5763, + "CACATACA": 5764, + "GACACTA": 5765, + "CCCCCGG": 5766, + "GGCCCGG": 5767, + "GTTTGATT": 5768, + "CTCCTCCA": 5769, + "GGCCCATT": 5770, + "TCCCAGTG": 5771, + "GTCTCTAA": 5772, + "TTTTTATG": 5773, + "TCACAGCA": 5774, + "GCCACAAA": 5775, + "TCGAGGA": 5776, + "TCTGCAGG": 5777, + "TCTTCAGG": 5778, + "CACAGA": 5779, + "GCCTGGAAAA": 5780, + "CAGACTGA": 5781, + "CAGAGTTA": 5782, + "GCAAGCAA": 5783, + "CATĊ": 5784, + "CTGGGAAA": 5785, + "TATATGCA": 5786, + "TCAGAATT": 5787, + "GTCTACC": 5788, + "TAAAAGAAA": 5789, + "CCTTCTC": 5790, + "CCTTGTGA": 5791, + "GATTTTAAA": 5792, + "TTTTAATTA": 5793, + "GCACAAAA": 5794, + "CTAAGTG": 5795, + "TAGAAAAAA": 5796, + "TGAACATG": 5797, + "CAGATGTT": 5798, + "TGAGTAAA": 5799, + "CATTGCTT": 5800, + "GTTTAGTT": 5801, + "GGATGAAA": 5802, + "AAAAAATTA": 5803, + "GATGCAA": 5804, + "GTACCTA": 5805, + "CCCTAAAA": 5806, + "GTAGGCA": 5807, + "TTAATTTG": 5808, + "GAGATAA": 5809, + "CCCCCAGG": 5810, + "TCAATGTG": 5811, + "GCCACTGA": 5812, + "TCGACTG": 5813, + "CTTCTCTG": 5814, + "GATGGAAA": 5815, + "TAGATAAA": 5816, + "GGAGTTTT": 5817, + "GAGGAGAGGA": 5818, + "TTTTGCA": 5819, + "GTGATGTT": 5820, + "GCTGTCTT": 5821, + "TCTAGTG": 5822, + "GGTTATG": 5823, + "GATGCTCA": 5824, + "GTTGTAAA": 5825, + "GTTCAATT": 5826, + "GGATACA": 5827, + "CGCCCGCCA": 5828, + "TCCTACAA": 5829, + "GGAAGGTG": 5830, + "GCGGAGG": 5831, + "TAAAATATG": 5832, + "CCTTGCA": 5833, + "GCTCTTAA": 5834, + "TCACCTA": 5835, + "GTGAGAGG": 5836, + "GAGAGTCC": 5837, + "TCAGTGAA": 5838, + "GCTGGCTT": 5839, + "GCCAGGATGGTCTCGA": 5840, + "TCTGGGTG": 5841, + "TGGAGAAA": 5842, + "TTTTGATT": 5843, + "GAAĊ": 5844, + "TAAATGGG": 5845, + "CGCTTAA": 5846, + "CCACCTA": 5847, + "CATTTGGG": 5848, + "TCCCTCTT": 5849, + "CTGGTCC": 5850, + "TCTGCTGA": 5851, + "TTTTTTTTTTTTTTTTTTTT": 5852, + "TCTATATG": 5853, + "TTTTTTTTA": 5854, + "GTTCCCC": 5855, + "GGAACACA": 5856, + "TGAATATG": 5857, + "GCAACTA": 5858, + "GCCAGCCA": 5859, + "GCTCCACA": 5860, + "CTGCTGAA": 5861, + "TAGACTT": 5862, + "CAGATTAA": 5863, + "GTCACACA": 5864, + "CATCTCTT": 5865, + "TATAGTTA": 5866, + "GACTTTTA": 5867, + "TCCCCCTT": 5868, + "CCCACCTG": 5869, + "TATATATATATATATATATATATA": 5870, + "TCCTCCAA": 5871, + "GAAACAGG": 5872, + "TCCCCCTG": 5873, + "TGAAGCTG": 5874, + "CGGGAGA": 5875, + "GCGACTG": 5876, + "GTTCCAAA": 5877, + "CAAAGATA": 5878, + "TTACTTTT": 5879, + "TTAATTAAA": 5880, + "GCTTCTC": 5881, + "TCGAATG": 5882, + "TACTTTTTT": 5883, + "TCGGTCA": 5884, + "AAAAAAAAAAAAAAAAA": 5885, + "GAAAGACA": 5886, + "CAGAGGCA": 5887, + "GGGACAGG": 5888, + "TATATTTG": 5889, + "TCGCATT": 5890, + "TCAAGG": 5891, + "TAAAACTG": 5892, + "CTTGGCC": 5893, + "CATCCATT": 5894, + "GTCAGTGA": 5895, + "CCACGTT": 5896, + "CCAACTGA": 5897, + "CTTACACA": 5898, + "CGATTTG": 5899, + "CTTCCTGA": 5900, + "CTAAATGA": 5901, + "GAAGCAAA": 5902, + "CCTTAAAAA": 5903, + "TCGAGTG": 5904, + "TGAAGCC": 5905, + "TCAAGCAA": 5906, + "CGTGTGTG": 5907, + "CTCAAATG": 5908, + "CATACATT": 5909, + "GACTCAAA": 5910, + "CTAATAAA": 5911, + "GCCAGCTG": 5912, + "TAGACCA": 5913, + "GATTTATA": 5914, + "CTTCAGCA": 5915, + "CTAACCA": 5916, + "TAAGTATT": 5917, + "TAACCAAA": 5918, + "TCAGGCC": 5919, + "TTAATTTA": 5920, + "CTCACCTT": 5921, + "GAAAGCTT": 5922, + "GAAAATGA": 5923, + "TCCGTT": 5924, + "GAGACTGA": 5925, + "GTCACC": 5926, + "GATGCTA": 5927, + "GTCCTGTG": 5928, + "CAAACC": 5929, + "TAAAAGTAA": 5930, + "CTCCCTC": 5931, + "GGCTTAAA": 5932, + "TCCTCATT": 5933, + "TGAAGTAA": 5934, + "TCAACC": 5935, + "GAAACC": 5936, + "CAGAGTAA": 5937, + "CTTTATCA": 5938, + "GGAGAGCA": 5939, + "TCCATACA": 5940, + "GCCCGCC": 5941, + "GATTTGCA": 5942, + "CAAGGCC": 5943, + "GAAGGTA": 5944, + "GCGATCC": 5945, + "TCTTCATA": 5946, + "TCAGTAAA": 5947, + "GTATATTA": 5948, + "CTGAATTA": 5949, + "TCTTGTTA": 5950, + "TAGACCAGGCTG": 5951, + "CCTTTAAA": 5952, + "CTGATGTT": 5953, + "CCCAGTCA": 5954, + "TCGACCA": 5955, + "CTCATTCA": 5956, + "AAAATATT": 5957, + "GCACTTTT": 5958, + "TAATTTATA": 5959, + "TCCCAGTT": 5960, + "GCCATGAA": 5961, + "TTTTAATTTT": 5962, + "CATGTATT": 5963, + "GCTTCATT": 5964, + "GAAGAAAAA": 5965, + "GTCTGATT": 5966, + "GCGTCAA": 5967, + "CTCGGGG": 5968, + "TCTTTCTC": 5969, + "GCTCCAGA": 5970, + "CGGTTTG": 5971, + "TGAGTGAA": 5972, + "CCTGCCAA": 5973, + "TCCĊ": 5974, + "CGCGTC": 5975, + "CTAAGGA": 5976, + "GTGATTAA": 5977, + "TGAGACGGA": 5978, + "GTGATGGG": 5979, + "CTTATATT": 5980, + "TAAAAGTT": 5981, + "GTTATATT": 5982, + "TATCTATT": 5983, + "GCGCCTT": 5984, + "GCTATGTT": 5985, + "GGTTGTT": 5986, + "CACCTGTG": 5987, + "GAGCTGTT": 5988, + "GCCCTTTT": 5989, + "GAAATATTA": 5990, + "CGCTCAA": 5991, + "TCTGTCTA": 5992, + "GTTTTCTG": 5993, + "GATACTT": 5994, + "GGTCGG": 5995, + "TGTATATT": 5996, + "TAAGTGTGTA": 5997, + "TTTTCCAA": 5998, + "GCTCTCAA": 5999, + "CAAGGTCA": 6000, + "GAAAGGAAA": 6001, + "GATATAA": 6002, + "GTCTCACTCTG": 6003, + "TGAAGCAA": 6004, + "TAGATATT": 6005, + "TGCACTG": 6006, + "GGTTTGA": 6007, + "TCTCCATA": 6008, + "TAATTGAA": 6009, + "TAATTTAAA": 6010, + "CCTAGCA": 6011, + "GACTAAAA": 6012, + "GCCTCTGA": 6013, + "CTCTAGAA": 6014, + "CAAAGCTT": 6015, + "GTCCCC": 6016, + "CCCATGA": 6017, + "CCTCCAGA": 6018, + "CGTCATG": 6019, + "GGATCAAA": 6020, + "CGACTA": 6021, + "TTTTCTAA": 6022, + "GTCGCTCA": 6023, + "TCTTGTGA": 6024, + "CAGCTGTT": 6025, + "GGATTGCTT": 6026, + "GATCGTG": 6027, + "TAACAGCA": 6028, + "CATTCC": 6029, + "CGAGGAA": 6030, + "GAGAGACA": 6031, + "TATCTCAA": 6032, + "GAGAGAGAA": 6033, + "TTTATCA": 6034, + "GACAAATT": 6035, + "TTTAGCA": 6036, + "CCAACCTG": 6037, + "GTTATATA": 6038, + "GCAGGCTG": 6039, + "TAAATCTG": 6040, + "GCTCACACCTG": 6041, + "TGATGATGATGA": 6042, + "GAAAGCC": 6043, + "GAAAATTTT": 6044, + "GAGCTCTG": 6045, + "TACAAATT": 6046, + "GGATCCA": 6047, + "CTTACATT": 6048, + "TCTTTCCTT": 6049, + "GATAAAAAA": 6050, + "TAAGTGAA": 6051, + "GAAATGAAA": 6052, + "GCTGCATA": 6053, + "TCTACACA": 6054, + "CTTCAGTG": 6055, + "CCTTTATT": 6056, + "CAAGAGAA": 6057, + "CTGGGTA": 6058, + "CCAGAGAA": 6059, + "CTCTGATG": 6060, + "GAGGTTTT": 6061, + "TATGCACA": 6062, + "GTCATTCA": 6063, + "GTCACAAA": 6064, + "CCTTTGAA": 6065, + "TAATTCTA": 6066, + "GTCTTGCTCTG": 6067, + "GTTTAAAAA": 6068, + "TCTCTCCTT": 6069, + "GAAGAGCA": 6070, + "TAAGAAAAA": 6071, + "CCATAAAA": 6072, + "CTAATCA": 6073, + "CGCCACA": 6074, + "TAACAATT": 6075, + "TCTCCACC": 6076, + "CCCCCTA": 6077, + "GTCCAATT": 6078, + "GCAACACA": 6079, + "TCCACGG": 6080, + "CGCGGTG": 6081, + "GGATTTTA": 6082, + "CCTCCCGG": 6083, + "CAGACGG": 6084, + "GTTAGTA": 6085, + "GCTGGGCA": 6086, + "CATGGCC": 6087, + "TCTAGCA": 6088, + "TTATGAAAA": 6089, + "CGTAAAAA": 6090, + "GTTACCTG": 6091, + "CCACAGAA": 6092, + "GCCACCAA": 6093, + "CATTGTTTT": 6094, + "GTGACTGA": 6095, + "TATTATTAA": 6096, + "TAGGCAA": 6097, + "GATATTTG": 6098, + "GCAAGCC": 6099, + "GCCATCTCA": 6100, + "CATAAATG": 6101, + "CTTACAAA": 6102, + "GTTACAAA": 6103, + "GGGAGTG": 6104, + "CAATGCAA": 6105, + "CATGGAGA": 6106, + "GTTTTAAAAA": 6107, + "TCACTTGAGG": 6108, + "TCTGCACA": 6109, + "TACACTGA": 6110, + "GAAACTCA": 6111, + "TAATCATT": 6112, + "TCACTGAA": 6113, + "GGACAGAA": 6114, + "TAAAATGTA": 6115, + "TAAAAAAATT": 6116, + "TCTGTCTGTCTGTCTG": 6117, + "GTGAGATG": 6118, + "GCTGCACA": 6119, + "TAATGATT": 6120, + "TCCCACTT": 6121, + "CTGCCTA": 6122, + "CAGAGGGG": 6123, + "GCTTTCAA": 6124, + "CTAATATT": 6125, + "TCCAGGTG": 6126, + "TAGTGTG": 6127, + "TACGCC": 6128, + "GACGTGA": 6129, + "CTTAAATT": 6130, + "GAAAATATT": 6131, + "TGTTCCTG": 6132, + "CTCAAGTGA": 6133, + "CCGGGC": 6134, + "TTTGCAA": 6135, + "TAAACCTT": 6136, + "GCGTTAA": 6137, + "GCCATTAA": 6138, + "GTTGCAAA": 6139, + "GGTTGAA": 6140, + "CATTCCTG": 6141, + "TCTCTACTAAAAATACAAAAATTA": 6142, + "TGTGGCC": 6143, + "GCTTGGTG": 6144, + "TCTGGAGG": 6145, + "CTGCAAAA": 6146, + "TCTTGGAA": 6147, + "GTAATATA": 6148, + "TGTGCCTG": 6149, + "CTGGGAAAA": 6150, + "GATATCA": 6151, + "TTTTATATT": 6152, + "TACATCAA": 6153, + "CAATACAA": 6154, + "CTCCCGG": 6155, + "GATAATTA": 6156, + "GTGACTCA": 6157, + "CTAAAAATACAAAAATTA": 6158, + "GTGTGCC": 6159, + "CTCCCCTG": 6160, + "TCTAATTA": 6161, + "TAGATGAA": 6162, + "GTATCCA": 6163, + "GCAACAAA": 6164, + "CTTAGAAA": 6165, + "TCTACATT": 6166, + "TCCCTGTG": 6167, + "GCAAGGAA": 6168, + "TATGGGA": 6169, + "TCTTGAGA": 6170, + "TTTGCTT": 6171, + "CTAGGGG": 6172, + "CAGGTGGG": 6173, + "TCTCTAGG": 6174, + "GTAATGAA": 6175, + "TAGCCTG": 6176, + "TGTGTTCA": 6177, + "CTTTTCTG": 6178, + "GAAAATGAA": 6179, + "TAAATTAAAA": 6180, + "TAGACAAA": 6181, + "CACGCTG": 6182, + "GCAĊ": 6183, + "GCCCAGTG": 6184, + "CTCTACTG": 6185, + "GCCGAGG": 6186, + "TGTGTATT": 6187, + "TGATAATT": 6188, + "GTGTCCTT": 6189, + "CTCGG": 6190, + "CTAACAAA": 6191, + "TGGACAA": 6192, + "CACAAATT": 6193, + "TTAACAA": 6194, + "GCCCCTCC": 6195, + "TAACTTAA": 6196, + "TCCCACCA": 6197, + "TGAGGCTG": 6198, + "GTATTTTTT": 6199, + "TCCCAGC": 6200, + "CATAGAAA": 6201, + "TCAAGATG": 6202, + "GATACTG": 6203, + "GCCAGCTT": 6204, + "CCTGCGG": 6205, + "CAGCATCA": 6206, + "CTTTTTCTT": 6207, + "TGTGGAGG": 6208, + "GTCAGATT": 6209, + "TAAATAATA": 6210, + "CTCCACTA": 6211, + "CGAAAATT": 6212, + "TGTGGGTG": 6213, + "GGAATTTA": 6214, + "CCTTCCAA": 6215, + "CGACTGAA": 6216, + "GAATTTCA": 6217, + "GCTAGGA": 6218, + "TGACCTA": 6219, + "TGTACAA": 6220, + "TGTGAGCCA": 6221, + "TGATCAAA": 6222, + "GGAAGCTG": 6223, + "CCACATTTT": 6224, + "TAAAGTTTT": 6225, + "TCAAGTAA": 6226, + "GCAGGGAA": 6227, + 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"CTCTAATT": 6278, + "CTGAGCTT": 6279, + "TCACCCAGGCTGGAGTGCAGTG": 6280, + "TCACGTG": 6281, + "CTAGGCA": 6282, + "CCAAGATG": 6283, + "TCCACTCA": 6284, + "CCCACCCCA": 6285, + "CTGTCTC": 6286, + "TATTCAAAA": 6287, + "GCAAGATG": 6288, + "TCTATGAA": 6289, + "GCCTCTCC": 6290, + "TCTCTGTGTA": 6291, + "TCACTTAA": 6292, + "TCATATCA": 6293, + "TAGACATT": 6294, + "TATTTTTTTA": 6295, + "GTGATGAA": 6296, + "TATGTCTT": 6297, + "GCCTCTCA": 6298, + "TCTAGGTT": 6299, + "TCATGCC": 6300, + "GTTTACTT": 6301, + "GTGACACA": 6302, + "GTTATTAAA": 6303, + "TAGTAAAA": 6304, + "GTTTTTATT": 6305, + "GTCTCTTA": 6306, + "CAGATAAA": 6307, + "GTCAGAGG": 6308, + "TCCTCCTCCTCC": 6309, + "TCCCATCA": 6310, + "TGTGCAAA": 6311, + "CTTATTCA": 6312, + "TTTTCTCA": 6313, + "CTTCTAAA": 6314, + "CTTTTCCTT": 6315, + "GATĊ": 6316, + "TGATGATG": 6317, + "CTAACACA": 6318, + "GCTATAA": 6319, + "TTACCAA": 6320, + "CATTTCAAA": 6321, + "TCTAGGA": 6322, + "GTCCTGGA": 6323, + "GTCAGGAA": 6324, + "GATTCAAA": 6325, + "TAGGATG": 6326, + "CTTGGGAA": 6327, + "CATTTTATT": 6328, + "TTACATA": 6329, + "GCCCATA": 6330, + "GAAGGGAA": 6331, + "TATTCATA": 6332, + "TGAGAATT": 6333, + "TCCTTATT": 6334, + "TCTTTTAATT": 6335, + "TCGTGTG": 6336, + "CTTCACTT": 6337, + "TATTGATG": 6338, + "GCTGGGTT": 6339, + "CATGCCTT": 6340, + "GGTGGCA": 6341, + "AAAAAAAAAAAAAAAAAA": 6342, + "GTCCAGC": 6343, + "GAGGTGGA": 6344, + "CGGCAGG": 6345, + "CCCTTAAA": 6346, + "TGACAGC": 6347, + "GCTGCAAA": 6348, + "GCTTTGGA": 6349, + "CCAACACA": 6350, + "CCCCATCTCTA": 6351, + "GTTTCCAA": 6352, + "GTTATAAA": 6353, + "GTAATTAAA": 6354, + "CTCTCTTTT": 6355, + "GGATGCC": 6356, + "CAGAGAAAA": 6357, + "GTTGCTTA": 6358, + "CTTTTCTA": 6359, + "GCTTGCA": 6360, + "CAACAGCA": 6361, + "GTGATGGA": 6362, + "GCAATAAA": 6363, + "TAGACACA": 6364, + "TCAGACC": 6365, + "CATGTATA": 6366, + "TTTTCTCC": 6367, + "TCCCTCAA": 6368, + "CATGCTCA": 6369, + "TCGCTTA": 6370, + "GCAATTTA": 6371, + "TATĊ": 6372, + "TCACACCA": 6373, + "GTGAGGAA": 6374, + "TCCACTTA": 6375, + "GCCTCCTT": 6376, + "CGGTGTG": 6377, + "TCCTGGAACTCA": 6378, + "GGTGGAGG": 6379, + "TCCTGATT": 6380, + "CCTGTCTG": 6381, + "GAATTATT": 6382, + "TGTAGTA": 6383, + "CCTAGGA": 6384, + "TAGGAGG": 6385, + "TCACGTT": 6386, + "TGTTGTAA": 6387, + "CTCCAGTGTT": 6388, + "TCGCCCAGGCTGGAGTGCAGTG": 6389, + "GTCATGA": 6390, + "CGGGTT": 6391, + "CAAAGGTT": 6392, + "CAAATGGA": 6393, + "TCGCTCC": 6394, + "GAAAGGGG": 6395, + "GTGAGGGG": 6396, + "GTCCAGTT": 6397, + "CTTCACTG": 6398, + "TAATATATT": 6399, + "CATGGAGG": 6400, + "GGGTATG": 6401, + "CTCTATCA": 6402, + "CTTAGGA": 6403, + "GGCCCAGG": 6404, + "GAAAGTTA": 6405, + "CTCACAGAGA": 6406, + "CTGAGACA": 6407, + "CCCAGCCA": 6408, + "CAATATAA": 6409, + "TCTGCATA": 6410, + "CAAGACCA": 6411, + "GGGAGCC": 6412, + "GCGTATT": 6413, + "GCAGCTCA": 6414, + "TAGCTGTG": 6415, + "CGTTATG": 6416, + "GGGACTG": 6417, + "GCTGAAAAA": 6418, + "TTATTTTTT": 6419, + "TGTTTGCA": 6420, + "TCAATAAAA": 6421, + "CTTTGGGAGGCC": 6422, + "CAACCCC": 6423, + "CCAATTTA": 6424, + "CCACACCTG": 6425, + "CGAGATG": 6426, + "CACATGAA": 6427, + "TACCCAAA": 6428, + "CTTTACTT": 6429, + "CTGACTCA": 6430, + "TCTCATTTT": 6431, + "CAACAAAAA": 6432, + "CTACTCC": 6433, + "CTGACATT": 6434, + "TCTCCTTCA": 6435, + "TAATTATG": 6436, + "TTATACA": 6437, + "TGACTGAA": 6438, + "CTGTGATT": 6439, + "GTGATTCA": 6440, + "TCTACCTT": 6441, + "GATAAATG": 6442, + "CACATGCA": 6443, + "TGACAGCA": 6444, + "GCTTGG": 6445, + "GTAAGGA": 6446, + "GTGĊ": 6447, + "GGATTCTCCAGGCAAGAA": 6448, + "GCTTAAAAA": 6449, + "TGTTTACA": 6450, + "TTATAGA": 6451, + "TCAACATG": 6452, + "GAGCCAGA": 6453, + "TCCCTCTG": 6454, + "CAGGGTA": 6455, + "TCCCGTG": 6456, + "GCGACAA": 6457, + "TGACCTCA": 6458, + "TCGGATT": 6459, + "GTGCGTG": 6460, + "GTCTATTA": 6461, + "GCTGCTC": 6462, + "TTTTCC": 6463, + "GACAGCAA": 6464, + "GGGAGCA": 6465, + "TGTTGTCA": 6466, + "CCCACTCC": 6467, + "TCAACACA": 6468, + "GGTGATGGA": 6469, + "TCCCTGTT": 6470, + "GTTTCTAA": 6471, + "TTTATAAA": 6472, + "GTTTTCAA": 6473, + "GATCTCTT": 6474, + "GAGAGATT": 6475, + "TAAGAGTT": 6476, + "CATTTTTTTT": 6477, + "GACGAAA": 6478, + "TGTGGGAA": 6479, + "TCAATTAAA": 6480, + "GTTCTCTT": 6481, + "GGACACCA": 6482, + "TGTTTGGA": 6483, + "GTTAGCA": 6484, + "GTGTGAAA": 6485, + "CCACTGAA": 6486, + "GTCTATAA": 6487, + "CTAATGAA": 6488, + "GAGAGTGA": 6489, + "CAAAAAAAAA": 6490, + "CCATCTC": 6491, + "TCTCAGAA": 6492, + "TTTTTTATT": 6493, + "GCCATCTG": 6494, + "GAAAATATA": 6495, + "TAGACAGA": 6496, + "GCTACAGA": 6497, + "GATGTGTT": 6498, + "CCCCCATG": 6499, + "GCGACCA": 6500, + "AAAACTA": 6501, + "TCAATATG": 6502, + "CAAGGCTG": 6503, + "CATTGGA": 6504, + "TCGTTTTA": 6505, + "TCATAATT": 6506, + "CTCCAAAAA": 6507, + "CTGAGAGG": 6508, + "TGACGTT": 6509, + "TCAAGTCA": 6510, + "GGAAGTCA": 6511, + "CCCTCCTT": 6512, + "TCAGACTT": 6513, + "CAGCCAGA": 6514, + "CCCTATG": 6515, + "TCTGGATG": 6516, + "CCTTCATG": 6517, + "TAGTCTG": 6518, + "CGAGAAA": 6519, + "CAGCTCTT": 6520, + "CACAGGTG": 6521, + "GACTCATT": 6522, + "TAAAGCTG": 6523, + "GCATTATT": 6524, + "GTTACAGA": 6525, + "TCACAGTG": 6526, + "CCTACAAA": 6527, + "GGTCAGGCTG": 6528, + "GCCTCCTG": 6529, + "TCTGCAGA": 6530, + "GCTTCAGG": 6531, + "GAGAGGGA": 6532, + "CTAATATA": 6533, + "TAGAGTG": 6534, + "TAGGTACA": 6535, + "GTTGAGAA": 6536, + "CATTGAAA": 6537, + "TAGCATG": 6538, + "CTTAGTG": 6539, + "GTCCCATG": 6540, + "GGTTTTAA": 6541, + "GCTTGGTT": 6542, + "CACATATG": 6543, + "TAATCCCAGCACTTTGGGAGGCTGA": 6544, + "TCCCAGCC": 6545, + "CAGGTGCA": 6546, + "CAGGCCTG": 6547, + "CTCCCTGA": 6548, + "GCAAATTA": 6549, + "GTGATAA": 6550, + "TCTGAGTT": 6551, + "GCTACACAGAGAAA": 6552, + "CTTTACAA": 6553, + "GGGGCCA": 6554, + "TCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTT": 6555, + "GAACCAAA": 6556, + "GTATAATT": 6557, + "GCCAGATT": 6558, + "TAAAAGCA": 6559, + "GCAACC": 6560, + "TCTATGTG": 6561, + "GGGGTCA": 6562, + "GAGATTAA": 6563, + "TATGAGG": 6564, + "GCTTTTAAA": 6565, + "CAAAACTA": 6566, + "GTAACAAA": 6567, + "GAAAACTA": 6568, + "GTTTTCTA": 6569, + "TTTACTA": 6570, + "CTTGACTT": 6571, + "GCCGTT": 6572, + "TGTACTG": 6573, + "GTAGGAAA": 6574, + "AAAATGA": 6575, + "GAGGGGAA": 6576, + "GACTCTA": 6577, + "CTTTTGTT": 6578, + "TCTTGTCA": 6579, + "GTGACATT": 6580, + "CGGTCC": 6581, + "CGGTGAA": 6582, + "AAAATATA": 6583, + "CTCCATTA": 6584, + "TAGATCA": 6585, + "GCCCAGGCTGGAGTGCAGTG": 6586, + "CTTATTAAA": 6587, + "GGAGAGC": 6588, + "CCTTTGTG": 6589, + "TTAATATT": 6590, + "GGCATCA": 6591, + "CCCCTGTG": 6592, + "CTTATAAA": 6593, + "TATGTACA": 6594, + "GATCCAAA": 6595, + "GCAGAGTG": 6596, + "TCTGTGTGTG": 6597, + "CTCACAAA": 6598, + "CCGCTA": 6599, + "GAGACAGAA": 6600, + "TCAACCAA": 6601, + "GAGTCAGACA": 6602, + "TGAAGACA": 6603, + "CTCCCACC": 6604, + "GCCGTA": 6605, + "CCTTGG": 6606, + "TAAAAATAAAA": 6607, + "CTTTCTAA": 6608, + "GAGGCTA": 6609, + "TTAGATG": 6610, + "GCTGTCTG": 6611, + "TACTTTAA": 6612, + "CCATGCCCA": 6613, + "TAGGCATG": 6614, + "GTGTTATT": 6615, + "GGAGATCA": 6616, + "TAGGTTTG": 6617, + "CAGATATT": 6618, + "CATAGTT": 6619, + "GCTGGATT": 6620, + "CTCCCACA": 6621, + "CCTATGA": 6622, + "GCACGCA": 6623, + "TCAATTCA": 6624, + "GCCCGTG": 6625, + "CTCTCACA": 6626, + "CCTGCACA": 6627, + "TAATAATAATAATAATAATAATAA": 6628, + "CTCTGAGG": 6629, + "CGGTAA": 6630, + "CGGTGGG": 6631, + "GAAAAATTA": 6632, + "CCTGCTCA": 6633, + "CACAAATG": 6634, + "TATTGCAA": 6635, + "TATAGATA": 6636, + "CAGGCAAA": 6637, + "GTTACATT": 6638, + "TTTAGTT": 6639, + "CTCCGTCTCAAAA": 6640, + "TACAGCTT": 6641, + "GCCACTCA": 6642, + "GCTAGAAA": 6643, + "GCAGAGC": 6644, + "GAGAGCTT": 6645, + "CATGTAAA": 6646, + "TCCAGTTTT": 6647, + "GGTCTTTT": 6648, + "CATATGAA": 6649, + "CAACCCCA": 6650, + "GCTACAAA": 6651, + "CCATGAAA": 6652, + "CAAACAAACAAACAAA": 6653, + "GGAACAAA": 6654, + "GTGATATT": 6655, + "GCGATTA": 6656, + "CAATGCC": 6657, + "GCTCCATT": 6658, + "TAAGACAA": 6659, + "TTAGTCA": 6660, + "CCAGAGCA": 6661, + "GTGCCCC": 6662, + "GTGGTAAA": 6663, + "TGCAGGA": 6664, + "TGATATAA": 6665, + "TCACCTGAGGTCAGGA": 6666, + "GTGATGTG": 6667, + "GCTGAATG": 6668, + "GCACGC": 6669, + "GAAAAGTA": 6670, + "CAAAATATT": 6671, + "CTCCAGCCTG": 6672, + "GATCGAA": 6673, + "TAATTTTAAA": 6674, + "CCTCCATT": 6675, + "TGTTCCAA": 6676, + "CTGCAGTG": 6677, + "CTAGGGA": 6678, + "CTCGGTG": 6679, + "GATGAATT": 6680, + "CAGGCTCA": 6681, + "CCTTCCCC": 6682, + "TCAGAGCA": 6683, + "CTCCCCAA": 6684, + "TCTGATTTT": 6685, + "TCATTCTCA": 6686, + "TACTAAAA": 6687, + "TAAGAGCA": 6688, + "GGAATAAA": 6689, + "CGGGCTG": 6690, + "CTTTTTATT": 6691, + "GAACACC": 6692, + "CACATGGA": 6693, + "CAGAGGCAGG": 6694, + "CCTCCGCCTCC": 6695, + "TCTCAATG": 6696, + "GATGTTAA": 6697, + "TGCAGTA": 6698, + "TAATACAA": 6699, + "TTATTTATA": 6700, + "CCAGGCTT": 6701, + "TAAAAATTAA": 6702, + "CTTAATTTT": 6703, + "GTGAAATG": 6704, + "GCACACC": 6705, + "CTCCCA": 6706, + "GTCCACCA": 6707, + "GAGAGCC": 6708, + "GACACC": 6709, + "TGCAGAAA": 6710, + "GGGCTAA": 6711, + "GATGTGAA": 6712, + "CTGĊ": 6713, + "CTTCAGCC": 6714, + "TGTTCTAA": 6715, + "GGAGATGA": 6716, + "CTCCTTTA": 6717, + "TACATCTT": 6718, + "TCATTTTTT": 6719, + "CTCGTCA": 6720, + "TCTATTTTA": 6721, + "CAGACAGACAGACAGA": 6722, + "TAGAGGAA": 6723, + "GAGAGCAA": 6724, + "CGCACACA": 6725, + "GTTTATCA": 6726, + "TCTACC": 6727, + "GCACTGAA": 6728, + "TCAAGTTA": 6729, + "GATATAAA": 6730, + "CTGAGTTA": 6731, + "CATAGCAA": 6732, + "GAGATGTG": 6733, + "CCTGTCTT": 6734, + "CATATTTTA": 6735, + "GGCAGTA": 6736, + "CCTTTCTTTT": 6737, + "CTCCCAGA": 6738, + "TAAAACAAAA": 6739, + "CATATGTT": 6740, + "CTCCTAA": 6741, + "CTGCTGCTGCTG": 6742, + "GATCTGTT": 6743, + "CAAGACC": 6744, + "CTAATTTTTT": 6745, + "GAATGTTA": 6746, + "TGACAGTG": 6747, + "CTGCTGTT": 6748, + "TGGGGAAA": 6749, + "TATGTCTG": 6750, + "GAGGTAA": 6751, + "CTCCATGA": 6752, + "CCTTGCTT": 6753, + "GCATGCC": 6754, + "CAGCTCCA": 6755, + "CTCTATTA": 6756, + "GCGATAA": 6757, + "GAAGTAAA": 6758, + "GCCĊ": 6759, + "GTTTTTTTTTT": 6760, + "GGACAGCA": 6761, + "TGTGCATT": 6762, + "GAATGCC": 6763, + "CCAĊ": 6764, + "GAATCAAA": 6765, + "GCATAAAA": 6766, + "CCACACTG": 6767, + "TAATAGTA": 6768, + "TTCAGTT": 6769, + "TATATATATT": 6770, + "GAGGGTA": 6771, + "GAATAATT": 6772, + "CCACCCTG": 6773, + "TCTTGCTG": 6774, + "TGTTCTTTT": 6775, + "TATGGTAA": 6776, + "GGCAGCTG": 6777, + "GCCGTC": 6778, + "GCTTTAAA": 6779, + "CAGAAAAAA": 6780, + "TCCATATA": 6781, + "TAATCCCAGCACTTTGGGAGGCCAA": 6782, + "TACACCAA": 6783, + "GTGTAAAA": 6784, + "CAAACTAA": 6785, + "GAGGAATT": 6786, + "CATGGATT": 6787, + "GATCGA": 6788, + "TCATATTA": 6789, + "CTCTGAGA": 6790, + "CGATGAA": 6791, + "GAATACTG": 6792, + "GTCCCGG": 6793, + "CCCACTCA": 6794, + "GATACTA": 6795, + "CATATTTG": 6796, + "TATTGAAAA": 6797, + "GAGAGAAAA": 6798, + "GCGCAGG": 6799, + "GTTGTGTT": 6800, + "TCACATCA": 6801, + "CTTCATTA": 6802, + "GTCTTTTTT": 6803, + "CCACAGTG": 6804, + "CACACCTT": 6805, + "CTATTAAAA": 6806, + "CCACCCCA": 6807, + "TCCAGTAA": 6808, + "GGTTTCC": 6809, + "TCGCTAA": 6810, + "GTCACTGA": 6811, + "TGTTGTGA": 6812, + "TAGCAGG": 6813, + "GACATTAA": 6814, + "GAACTTTT": 6815, + "TATTCCTG": 6816, + "TGGGTTTG": 6817, + "GGCCTCCCAAAGTGCTGGGATTACAGG": 6818, + "GACGTCA": 6819, + "TAACAGTA": 6820, + "CAGATGGA": 6821, + "TAATTTCTT": 6822, + "CCTAGGAA": 6823, + "TGACCCCA": 6824, + "GTGGGCC": 6825, + "GCGCATT": 6826, + "CCACATGA": 6827, + "TACCCTG": 6828, + "TACACACACA": 6829, + "CTTGACC": 6830, + "CTTTAGAA": 6831, + "CTTTGCTG": 6832, + "TGATTTTTT": 6833, + "CAAAATGTG": 6834, + "GATCTGTG": 6835, + "GTTGAATT": 6836, + "GCAGCTGA": 6837, + "CTCAGATT": 6838, + "TTCAGTA": 6839, + "CTCAGCTG": 6840, + "GTCAGACA": 6841, + "TAAAACATT": 6842, + "GCAGACGC": 6843, + "TAGACGG": 6844, + "CATCTTTG": 6845, + "CTAGGTA": 6846, + "CCGAAAA": 6847, + "TCACTGTT": 6848, + "TTAGAAAA": 6849, + "TTATCTA": 6850, + "CAACATCA": 6851, + "CCTTTGGG": 6852, + "TCTTTAGG": 6853, + "CGAGTC": 6854, + "CCTĊ": 6855, + "CATGTGTG": 6856, + "CAATCCA": 6857, + "TCTGCCTCC": 6858, + "CTCTCTAA": 6859, + "GATGGGAA": 6860, + "GAGTCAAA": 6861, + "TCTGTCAA": 6862, + "CCAAGCAA": 6863, + "GAACTAAA": 6864, + "CAAGCTA": 6865, + "GCTCAAGCCTG": 6866, + "TTTTGTAA": 6867, + "TAATTGTA": 6868, + "CACATTAAA": 6869, + "GAAGCTA": 6870, + "CAAGTAAA": 6871, + "GTGACAAA": 6872, + "CTTTAGTT": 6873, + "GTTGCTCA": 6874, + "CTTCTCAA": 6875, + "TGCAGCCA": 6876, + "GAGGTTAA": 6877, + "TAATATAAA": 6878, + "CCTAGAAA": 6879, + "GCTAAATG": 6880, + "TAAAAATAA": 6881, + "TCAGATGA": 6882, + "CATATGTG": 6883, + "GATATGA": 6884, + "TCCAAATG": 6885, + "CCAGGCCA": 6886, + "CTTATTTTA": 6887, + "GTCCTTAA": 6888, + "GATTAATG": 6889, + "GGTGGTA": 6890, + "CTTTACTG": 6891, + "CATGTGTT": 6892, + "GCATGAAA": 6893, + "CTTTGATT": 6894, + "TGTTCTC": 6895, + "TTATTATTTT": 6896, + "CTTCATCA": 6897, + "GTTCAGTG": 6898, + "CAGGGTGA": 6899, + "CCCCCTCA": 6900, + "CCTACAGA": 6901, + "GACACTGA": 6902, + "TCTCCCTG": 6903, + "TCATGAAAA": 6904, + "GTTTAGAA": 6905, + "CCATCAAA": 6906, + "GATTACAGG": 6907, + "CATGTCTG": 6908, + "TCGCCTG": 6909, + "CATCCAGA": 6910, + "GTTCAGAA": 6911, + "TACAGCC": 6912, + "CGACAAA": 6913, + "TCCCCTCA": 6914, + "CTTGCATT": 6915, + "GCTCCGCCTCCCGG": 6916, + "CACATGGG": 6917, + "TATTTTTTAA": 6918, + "TAGCTCC": 6919, + "GTGGCTA": 6920, + "TGACATTTT": 6921, + "GAAAGGTT": 6922, + "GCTTGATG": 6923, + "CATCCAGG": 6924, + "CCAGAGGA": 6925, + "TCTGCCAA": 6926, + "CCAAATTA": 6927, + "TCAAATTAA": 6928, + "CTTCACAA": 6929, + "GGAGACTG": 6930, + "CCCAGTC": 6931, + "CATGCTAA": 6932, + "GGTTGGTT": 6933, + "TATATTATT": 6934, + "CCTTCTGA": 6935, + "GTTTTTAAA": 6936, + "TAATAATTA": 6937, + "TAATCTC": 6938, + "GTCCCAGA": 6939, + "CGAGCTG": 6940, + "TGACACAA": 6941, + "CCCAGGTG": 6942, + "TGAGATTA": 6943, + "GCGAGCC": 6944, + "GCAGATGA": 6945, + "TCTATCAA": 6946, + "TTTTGGAA": 6947, + "TCACACTG": 6948, + "GATTTATG": 6949, + "TCGAGTA": 6950, + "GTAGAGAA": 6951, + "GTGAGCC": 6952, + "GATATATT": 6953, + "GGAAATATCTT": 6954, + "CTCAGG": 6955, + "TAGGTGTG": 6956, + "TGAAGTTA": 6957, + "TTTTGG": 6958, + "GAAGGCC": 6959, + "GTTCTGAA": 6960, + "TAGAAATT": 6961, + "TTTTATAAA": 6962, + "TCTCTGAAA": 6963, + "TAACACTT": 6964, + "TAAGATAA": 6965, + "CTATATTA": 6966, + "GCCACCTT": 6967, + "TCTACCTG": 6968, + "TCGGAGG": 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7018, + "CATTCATG": 7019, + "TCTGGTGA": 7020, + "TCAATTTTA": 7021, + "CCACAGTT": 7022, + "GAAATCTT": 7023, + "CTCGCTG": 7024, + "CTTACCTT": 7025, + "TCTGTGAAA": 7026, + "TACGTC": 7027, + "TCAACTAA": 7028, + "CTTTTTAAA": 7029, + "GGTTCTA": 7030, + "TCATGTTA": 7031, + "CTTGCCTT": 7032, + "CTAAAAATACAAAAAATTA": 7033, + "TCACCAAA": 7034, + "GTCGGGG": 7035, + "TGAAAAATT": 7036, + "TCATATTTT": 7037, + "GATGGCTG": 7038, + "GACGAGG": 7039, + "TCCGTA": 7040, + "GGTACTG": 7041, + "TCTAATTTT": 7042, + "CGCAATG": 7043, + "CAAACTGA": 7044, + "CAGGTGGA": 7045, + "TACACTTA": 7046, + "TAATAATAA": 7047, + "TCATATAA": 7048, + "TAAAATTCA": 7049, + "GTTTTTGTT": 7050, + "GGAGGTGGG": 7051, + "TTTTATTAAA": 7052, + "TCTCCTTTG": 7053, + "TGTGCTTA": 7054, + "TATGGCAA": 7055, + "TGAAGAGG": 7056, + "CATGGCTG": 7057, + "GTGTGGTG": 7058, + "GATGTCTT": 7059, + "GGACCCC": 7060, + "GCGTGGG": 7061, + "CTTGTAAA": 7062, + "GTATGAAA": 7063, + "TCTATCTT": 7064, + "TATGAAATT": 7065, + "CAACATTA": 7066, + "CATGCC": 7067, + 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"TATGGTCA": 7117, + "CCAACATT": 7118, + "CGATGTT": 7119, + "CTCGGGAGGCTGAGGCAGGAGAATG": 7120, + "GCATCAAA": 7121, + "TCATAGTT": 7122, + "GCCCTGAA": 7123, + "CACAGAAAA": 7124, + "TCTGCTTA": 7125, + "GTAACTAA": 7126, + "TATATTTTTT": 7127, + "CTCAGCCA": 7128, + "TATAATTAA": 7129, + "GTCTAATT": 7130, + "GATTCTGA": 7131, + "TCCTTCTG": 7132, + "CTCCAGCTT": 7133, + "GTTGTGAA": 7134, + "GAAGACAA": 7135, + "TATCATTA": 7136, + "GTTTTTAA": 7137, + "TCTACTTA": 7138, + "GTCCTTCA": 7139, + "GGCCTGTG": 7140, + "GTCCTTTA": 7141, + "CCACTGCACTCCAGCCTGGG": 7142, + "CAGATATA": 7143, + "CCAAGG": 7144, + "CCTGGATG": 7145, + "TCACATGA": 7146, + "GCAGCATT": 7147, + "CAGGGTCA": 7148, + "CAGAGGGA": 7149, + "CCCGGGAGGCGGAGCTT": 7150, + "TGGGTTA": 7151, + "GTAGGCTG": 7152, + "TGAATGAAA": 7153, + "GAATGGAA": 7154, + "CTGTCAAA": 7155, + "CTCTGTTA": 7156, + "TTTTTATTTT": 7157, + "CTCAGGGG": 7158, + "CTCGTGA": 7159, + "CTGATGTG": 7160, + "CCAATAAA": 7161, + "TCACTGTG": 7162, + "TCTCTCTGA": 7163, + "TCTATCC": 7164, + 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"GAATATAA": 7215, + "CAAAGGCA": 7216, + "TACACAAAA": 7217, + "TCCCATTA": 7218, + "GAGAGGTG": 7219, + "TCTGTAAAA": 7220, + "CCTGGGGG": 7221, + "GTCACCTT": 7222, + "TAAAGGTG": 7223, + "CCCTAACCCTAA": 7224, + "GTGCTGAA": 7225, + "TATTCTC": 7226, + "TTTATGTG": 7227, + "TATAAATTA": 7228, + "TCAGCTCA": 7229, + "TGAATGCA": 7230, + "GATTTGTG": 7231, + "TATGTTAA": 7232, + "CACGTGA": 7233, + "CACTGATT": 7234, + "GACACGG": 7235, + "GATCTCTG": 7236, + "GTCCATTA": 7237, + "CAGATCTT": 7238, + "GTTACTTA": 7239, + "GAAATCTG": 7240, + "CAGGCGG": 7241, + "TCTAGGAA": 7242, + "GAAAGAGAA": 7243, + "GATACAAA": 7244, + "TAACGAA": 7245, + "TCAACTGA": 7246, + "CCTCAGCCTCCCAAA": 7247, + "CGCAAAAA": 7248, + "CTCGAGG": 7249, + "CCCATCCTAATT": 7250, + "TATCTCTT": 7251, + "GTTCACTG": 7252, + "GCAGGAAAA": 7253, + "CATGTTAA": 7254, + "CTAGGAAA": 7255, + "GTGACCTT": 7256, + "CCAAGTCA": 7257, + "TACAGCTG": 7258, + "CAGCCATT": 7259, + "CCAGCCTT": 7260, + "GTTTCTCC": 7261, + "TCAAGCC": 7262, + "CAATTCAA": 7263, + "GATTATTTT": 7264, + "CTCGCTT": 7265, + "TATCTAAA": 7266, + "CCTCCACCTCC": 7267, + "TACATAAAA": 7268, + "CCCATCTT": 7269, + "TATTGGTT": 7270, + "CAATGTTA": 7271, + "TTATTATTATTATTATTATTATTA": 7272, + "GCTAATTTTTGTATTTTTAGTAGAGA": 7273, + "GCCAGATG": 7274, + "GTAACATT": 7275, + "TAAGGCC": 7276, + "CTGACACA": 7277, + "CACCCACA": 7278, + "GTGACC": 7279, + "TGCCTCC": 7280, + "GACCTTTT": 7281, + "AAAAA": 7282, + "TCAACTCA": 7283, + "TCACCTGA": 7284, + "GCAGCCAA": 7285, + "CGATGTG": 7286, + "AAATGTTTT": 7287, + "TAGCAAAA": 7288, + "TAAGCCAA": 7289, + "TCATCTGA": 7290, + "TGATCTCA": 7291, + "TGAAGGTT": 7292, + "CAGATATG": 7293, + "GTGAGCTG": 7294, + "CTCTGAAAA": 7295, + "GAAGGGGA": 7296, + "TCATAGAA": 7297, + "GTAĊ": 7298, + "GTGAGG": 7299, + "GAGGCCAA": 7300, + "CACCACCA": 7301, + "CGGAATG": 7302, + "TCGACTA": 7303, + "CTCAAAATT": 7304, + "TATTTCTTA": 7305, + "CCTGCCCC": 7306, + "CAAACATA": 7307, + "CAAATGAAA": 7308, + "TCTATATA": 7309, + "CAACCAAA": 7310, + "CTACCAAA": 7311, + "CCTGGATT": 7312, + "GTAAGGAA": 7313, + "TCCTTCCTT": 7314, + "CTTCGTT": 7315, + "CATTTGAAA": 7316, + "CTCCTGCCTCA": 7317, + "GTTTTCCTT": 7318, + "CGTGCGTG": 7319, + "GTGGCTGA": 7320, + "GAGAGGTT": 7321, + "GTCAGCC": 7322, + "CTCTGCTG": 7323, + "TCCAGTTA": 7324, + "CTGATGGA": 7325, + "CGCTCTT": 7326, + "CACCAGGCTCC": 7327, + "TAATTTGA": 7328, + "GTCTAGAA": 7329, + "TCTCCAAAA": 7330, + "TGCCCAA": 7331, + "CAGTTATT": 7332, + "TAACTATT": 7333, + "CTTGGCCA": 7334, + "TGAATAAAA": 7335, + "TCTGGCAA": 7336, + "GATTGGA": 7337, + "GGGCTGTG": 7338, + "GGACACGACTGA": 7339, + "GCGTGCTG": 7340, + "TAATCTTA": 7341, + "GTTCTAAA": 7342, + "GAAAGATA": 7343, + "GAATACC": 7344, + "CTTAGCAA": 7345, + "TCCACGA": 7346, + "CAGATGCA": 7347, + "TAAAGGTT": 7348, + "TTTACTG": 7349, + "TGGGTGGG": 7350, + "GCCTTTTA": 7351, + "TAGGTAGG": 7352, + "TCCCTAAA": 7353, + "TATTGTAAA": 7354, + "CGGGGGG": 7355, + "TATGTGAA": 7356, + "GACGATG": 7357, + "GTTCCTCA": 7358, + "TTTAGTA": 7359, + "TCTGCTTTT": 7360, + "GAATTCCA": 7361, + "TCATGCATT": 7362, + "TATAGATG": 7363, + "TCTCTCTCTT": 7364, + "TAACAAAAA": 7365, + "GCCCACTG": 7366, + "TGTGTGTGTGTGTGTGTGTGTGTG": 7367, + "GTCGGTG": 7368, + "CTAAAAATT": 7369, + "GCGGATG": 7370, + "CTCATCTT": 7371, + "GTTATGTT": 7372, + "TACATAGA": 7373, + "CTCTATAA": 7374, + "GTGACAGG": 7375, + "CCCAGTAA": 7376, + "TCACCTCC": 7377, + "GGAAATTA": 7378, + "CCACACAA": 7379, + "GCCTCCAA": 7380, + "GCCACGA": 7381, + "TATTAATTA": 7382, + "CATGTGAA": 7383, + "TCTACATG": 7384, + "TATATATATG": 7385, + "GTGTGTGTG": 7386, + "CCTCTCAA": 7387, + "GAGACC": 7388, + "CAGACCTG": 7389, + "CAAAAGAAA": 7390, + "CAAATAGG": 7391, + "GTCCCCTT": 7392, + "TGTGGG": 7393, + "TGAATCC": 7394, + "GACACTTA": 7395, + "GTTTGGAAA": 7396, + "CTGCAGTT": 7397, + "TAAGATTA": 7398, + "GCCTAAAA": 7399, + "TCATTCTT": 7400, + "CAAGAGCA": 7401, + "TATTGATA": 7402, + "TATGGCTT": 7403, + "CTGTACC": 7404, + "GTGGGAGG": 7405, + "TCTACCAA": 7406, + "GGTAGGA": 7407, + "TGATATCA": 7408, + "GCCGGCC": 7409, + "CAAAATATA": 7410, + "CAGTCATT": 7411, + "CTTGCAAA": 7412, + "CCATTATT": 7413, + "CGGAAAAA": 7414, + "CATTGCTG": 7415, + "GTCACCAA": 7416, + "GACAGTAA": 7417, + "GCAGTGAGCC": 7418, + "CAGCTTTG": 7419, + "CTGCCCTG": 7420, + "GTTCGAA": 7421, + "CATTGG": 7422, + "TCTAGAGA": 7423, + "CAATATCA": 7424, + "CCATCTCC": 7425, + "TGTTGGTG": 7426, + "CTGTGTTTT": 7427, + "TCAAAAATT": 7428, + "TAAACGA": 7429, + "TGAATGGG": 7430, + "GCTTTGCA": 7431, + "CCCCCCAA": 7432, + "GATCTGAA": 7433, + "GTCCAGCC": 7434, + "TTAGCTG": 7435, + "GCCGGTG": 7436, + "GTTTTCAAA": 7437, + "TAATTGTT": 7438, + "GGAGACCA": 7439, + "CTATATAA": 7440, + "TCTGCTAA": 7441, + "CTTTGGGAGGCCAA": 7442, + "CATATATG": 7443, + "CTTCAGC": 7444, + "CTATACTA": 7445, + "TTTGGTA": 7446, + "GAACACCA": 7447, + "TAATTATTA": 7448, + "GGACCTCA": 7449, + "CATGTCC": 7450, + "TCTACTCA": 7451, + "GAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGA": 7452, + "TATGTGTG": 7453, + "CATTTAGA": 7454, + "CTCCTTAA": 7455, + "CTTGGCTT": 7456, + "TCCATATG": 7457, + "CTGTGAAAA": 7458, + "CTTTCCTG": 7459, + "CAAACAAAAA": 7460, + "CATTGCAA": 7461, + "TCAGGCTG": 7462, + "TCAGAGGA": 7463, + "GGAGACTT": 7464, + "CTTAGGAA": 7465, + "TCAGGAAAA": 7466, + "TCAGAGTT": 7467, + "CTTTCTCTT": 7468, + "CAAAATGA": 7469, + "GGGACAA": 7470, + "TCTAAAATT": 7471, + "GGAGAGTG": 7472, + "GTCTACTT": 7473, + "GAAATAGG": 7474, + "TCTTGATG": 7475, + "CATGCAGG": 7476, + "GTCGCA": 7477, + "CACATCTG": 7478, + "CACAGGTT": 7479, + "GACAGTCA": 7480, + "CTGAAGAA": 7481, + "GGGCCAGG": 7482, + "CATTTAGG": 7483, + "CCTTCAGG": 7484, + "GGCAGTGA": 7485, + "TGTAGTT": 7486, + "GATCGCA": 7487, + "TATTAAATT": 7488, + "CACACCAA": 7489, + "TCTTCCTA": 7490, + "GGAAGGTT": 7491, + "TCTACAGA": 7492, + "TTCAGTG": 7493, + "CTAATTAAA": 7494, + "GGCAGTT": 7495, + "GGACAGTG": 7496, + "TGAGGATT": 7497, + "GAAAGCTG": 7498, + "TATAGCAA": 7499, + "CTCACCTG": 7500, + "CCTTTCAA": 7501, + "GGGTTAAA": 7502, + "CTTCTTTG": 7503, + "GGAGAAAAA": 7504, + "GTAATGTT": 7505, + "GAATGTTTT": 7506, + "CCACCTCA": 7507, + "CTCATCC": 7508, + "TATGCTAA": 7509, + "TCAAAGAA": 7510, + "CTGAACAA": 7511, + "TCACATTA": 7512, + "TCTTTCTCA": 7513, + "TAATTCTG": 7514, + "CTCTCTCTG": 7515, + "GAAAATA": 7516, + "GACATTTG": 7517, + "CCAGTGAA": 7518, + "CAGACCAA": 7519, + "TTTTACTA": 7520, + "TATGTCAA": 7521, + "CTTATGAA": 7522, + "CCTCCCGA": 7523, + "GAAATGAAAA": 7524, + "GCAGGAGG": 7525, + "CTCAGGTT": 7526, + "TCAGTCTT": 7527, + "GCTAGTA": 7528, + "TCATTCCTT": 7529, + "TCACTCTT": 7530, + "GACGAGA": 7531, + "TAATCCCAGCTA": 7532, + "CTGGGGAGG": 7533, + "GCAAGAGG": 7534, + "GATCGC": 7535, + "GTCACTAA": 7536, + "GCCAGTAA": 7537, + "TAGACTGA": 7538, + "TCGCGA": 7539, + "TATTATTTG": 7540, + "CATTTAATT": 7541, + "GGAACTGA": 7542, + "TCTATCTG": 7543, + "GGGGAGGGG": 7544, + "CCTCCTCC": 7545, + "CACĊ": 7546, + "GCCCATCA": 7547, + "TAAAATCAA": 7548, + "GTCACAGG": 7549, + "TCTCTCTCTG": 7550, + "CATAGTGA": 7551, + "TTTTTTTTTTAA": 7552, + "GTTTTATTTT": 7553, + "GTTACC": 7554, + "TAAAGAGG": 7555, + "GACAAAAAA": 7556, + "TGACTAAA": 7557, + "GTGCGCA": 7558, + "TAACCCC": 7559, + "TAATCCAA": 7560, + "CATTCAGA": 7561, + "TCTGCGG": 7562, + "TCATTTATT": 7563, + "TCAGATCA": 7564, + "CTGAGGCAGGAGAATCGCTT": 7565, + "GGGAAATT": 7566, + "CTTGTCTT": 7567, + "TATGTTTTA": 7568, + "GTGTCCGACTC": 7569, + "CTGGATCA": 7570, + "TGGGGAGG": 7571, + "CTTGTGTT": 7572, + "CCATATGA": 7573, + "GATTCTCA": 7574, + "GGATGGCA": 7575, + "TCGGCCTCCCAAAGTGCTGGGATTACAGG": 7576, + "TCACTCTG": 7577, + "CTGCAGC": 7578, + "CACATAAA": 7579, + "CATTGATG": 7580, + "TACAATTA": 7581, + "GGAATGTG": 7582, + "GTTTTTTTA": 7583, + "GAAACCTG": 7584, + "CCTTGGGCA": 7585, + "CACCTTTT": 7586, + "TCTTGGTG": 7587, + "GCAGTAAA": 7588, + "TGTTCTGA": 7589, + "TATGCATA": 7590, + "TGAGGGAA": 7591, + "TAGGGTG": 7592, + "CAAAAATTAA": 7593, + "CGACAGG": 7594, + "GGCCTTTT": 7595, + "CAACACCA": 7596, + "GGGGCC": 7597, + "TCTCCTTTT": 7598, + "GTTTTTTAAA": 7599, + "CAATAGAA": 7600, + "TGAGCTGA": 7601, + "CTTAAAAAA": 7602, + "GGACGC": 7603, + "GCTTCGG": 7604, + 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"CTGAATTTT": 8241, + "GAGATAAA": 8242, + "CTAAGATT": 8243, + "CTCGATT": 8244, + "TAACGC": 8245, + "TCAGATAA": 8246, + "TCCTAATT": 8247, + "CCCATGTG": 8248, + "CGTATTA": 8249, + "GTGCTGTG": 8250, + "GTAGTATT": 8251, + "GTGACATG": 8252, + "TAGCGG": 8253, + "GCCCACTT": 8254, + "GTCTACCA": 8255, + "CAAACCTG": 8256, + "GCTGCTCC": 8257, + "CATATCAA": 8258, + "CTGTTAAAA": 8259, + "GGAGGGAA": 8260, + "GAAGAAAAAA": 8261, + "GACCCAA": 8262, + "GTTCACTT": 8263, + "TATCCAGG": 8264, + "CAAATGGG": 8265, + "CCAAGATT": 8266, + "CCCACCTA": 8267, + "TAAATGAAAA": 8268, + "GGACTACAGGCGCCCGCCA": 8269, + "TGACAATT": 8270, + "GGCACGG": 8271, + "TCTGAGAA": 8272, + "TCAGCCTG": 8273, + "GTTTCCTG": 8274, + "GTAGTCC": 8275, + "GGATTTGAA": 8276, + "TGACACTT": 8277, + "GCACAGGA": 8278, + "CTTACC": 8279, + "CAGAAAATT": 8280, + "TAACTCTT": 8281, + "GCACAGTT": 8282, + "GCAATATT": 8283, + "TACTTATT": 8284, + "CTTGACTG": 8285, + "GCTTTGTA": 8286, + "GCCACTGCA": 8287, + "CGACATT": 8288, + "GATAGAGA": 8289, + "CTGTTATT": 8290, + "GCTGGGTGTG": 8291, + "TGAGCTCA": 8292, + "GTAGCTCA": 8293, + "TATATGGA": 8294, + "GGCAGGTGGA": 8295, + "CTATCCA": 8296, + "CAAAATGAA": 8297, + "GCTACTGA": 8298, + "TGACAGAGTGA": 8299, + "GATGTTTG": 8300, + "TCACCATT": 8301, + "CTTAGTAA": 8302, + "TCTTGGGG": 8303, + "TGTTTGTTTG": 8304, + "CAACAGTG": 8305, + "GTGTCAAA": 8306, + "TCTTTCTCTT": 8307, + "CTCACTAA": 8308, + "GGTTTTTG": 8309, + "TTTTGCTT": 8310, + "TAAACAGAA": 8311, + "TGAGAAAAA": 8312, + "GAACCCGGGAGG": 8313, + "GACAGATT": 8314, + "GCTGGTCA": 8315, + "CTGAGGGG": 8316, + "GAAGAGGAA": 8317, + "GGGGGGGGGGGG": 8318, + "CCCGTCC": 8319, + "TGTTGAGG": 8320, + "CTAAGTCA": 8321, + "CTTGAGTT": 8322, + "GCCCCCTG": 8323, + "CCAGTGTT": 8324, + "TATATATAAA": 8325, + "TATGCCAA": 8326, + "CCTGTTTG": 8327, + "TAATCTAA": 8328, + "TGACCCTG": 8329, + "TATAGTCA": 8330, + "GGTTTCTG": 8331, + "CTCTAGGA": 8332, + "GAGACATG": 8333, + "GCAACAGA": 8334, + "TAAGATGA": 8335, + "TCAGTCAA": 8336, + "GTGTCCAA": 8337, + "GTTCAGCA": 8338, + 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"GTCCCAAA": 8438, + "GCAAAGAA": 8439, + "GCAAACTA": 8440, + "TGAAGGCA": 8441, + "TTCACTA": 8442, + "TGAGTCTT": 8443, + "TGAGTGTG": 8444, + "TTTGGAAA": 8445, + "TTTACAAA": 8446, + "GGAGGGAGA": 8447, + "GTTGTCTT": 8448, + "GGACCCTG": 8449, + "GTAAGTTA": 8450, + "CGGGGAA": 8451, + "TCCCGCC": 8452, + "GTGTCATT": 8453, + "GTCCCTCA": 8454, + "GCGAATG": 8455, + "TCCTCTAA": 8456, + "CTAAAAATG": 8457, + "GCTCTATA": 8458, + "CCTAACCTAA": 8459, + "CAAGTGAA": 8460, + "GTACAGTG": 8461, + "GAGATATT": 8462, + "GTTGGCTG": 8463, + "CCTTGCTG": 8464, + "CCTTTTTG": 8465, + "GAGTAATT": 8466, + "TCAGCCTT": 8467, + "TTAAAAATT": 8468, + "CGAATTA": 8469, + "CATTGGTT": 8470, + "TAGACATG": 8471, + "GTAAATTTT": 8472, + "TATGGTTA": 8473, + "CCTACCTA": 8474, + "CACCCCAA": 8475, + "TCTATTAAA": 8476, + "TCAGTTTG": 8477, + "TAAGGGAA": 8478, + "GGATGAAAA": 8479, + "GATGGGTG": 8480, + "TCAGACAA": 8481, + "TGTTGGGG": 8482, + "CACCCTA": 8483, + "TCTCTTAAA": 8484, + "TAGCATTA": 8485, + "GGACTAAA": 8486, + "GCTAATTA": 8487, + "TCAACTTA": 8488, + "TATACTTTT": 8489, + "CTGGACC": 8490, + "GTTGGCC": 8491, + "GTTCTATT": 8492, + "TGAATTTTA": 8493, + "TGCAGCC": 8494, + "TAATACTA": 8495, + "GTATTCAGA": 8496, + "CTGTGGTGTT": 8497, + "GTCCATTTT": 8498, + "GTAAGTGA": 8499, + "GCACAGCC": 8500, + "GGACGCA": 8501, + "CACAGCCA": 8502, + "GCAACTAA": 8503, + "CCAGCTCC": 8504, + "GGGATTACAGG": 8505, + "TGATATGA": 8506, + "CTGCCTCA": 8507, + "CTCTATGA": 8508, + "TTATTGAA": 8509, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 8510, + "TCTGGGCA": 8511, + "TCAGCCTCC": 8512, + "TCTTCCAAA": 8513, + "CAAAGCCA": 8514, + "GCTGCCAA": 8515, + "TCTCTTTTA": 8516, + "GTTTACTG": 8517, + "CCAGTCAA": 8518, + "CTGACCTG": 8519, + "GCTACATG": 8520, + "GGACCTA": 8521, + "TGTGTAAA": 8522, + "TATTTCCTT": 8523, + "CTCATCAA": 8524, + "TTAAGTA": 8525, + "TGACGTA": 8526, + "CACACACACACACACACACACACACACACACACACACACA": 8527, + "GTGCAGTT": 8528, + "GTTATCTT": 8529, + "CTTTACTA": 8530, + "CAAAACTG": 8531, + "CATAGATG": 8532, + 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"GTCTACTG": 8582, + "GTCATCC": 8583, + "CCTTAATG": 8584, + "CACTCTA": 8585, + "GAATATCA": 8586, + "CTGAGTTTT": 8587, + "TCTGACTCTTTG": 8588, + "TATTTAATA": 8589, + "GGAATATG": 8590, + "GCTTCATG": 8591, + "GTCCCCTG": 8592, + "GATAGAAA": 8593, + "CTTTTTTTTTTTTTT": 8594, + "GAAGTGAA": 8595, + "CTCGGGA": 8596, + "TATAGCTG": 8597, + "GTAACTTA": 8598, + "TTATTTCAA": 8599, + "GCCCGCA": 8600, + "GAGGGTGA": 8601, + "GAAAACCA": 8602, + "TGATACTG": 8603, + "TTTTACATT": 8604, + "GAAGAGTG": 8605, + "GAGGCTT": 8606, + "CTGGAGTT": 8607, + "GTCATCTG": 8608, + "CAAGGGAA": 8609, + "GTTCTCTG": 8610, + "GTCCTCTT": 8611, + "TCTTTTTTTTTT": 8612, + "TTAGTTTT": 8613, + "TATTGACTT": 8614, + "GTCCTGCC": 8615, + "GTTTAATA": 8616, + "GCTGCTAA": 8617, + "GGAGGGATT": 8618, + "TCACATAA": 8619, + "GCCGGGA": 8620, + "CTATCAAA": 8621, + "GATGTCC": 8622, + "TTTGGG": 8623, + "CCCCTCAA": 8624, + "TAACCTCA": 8625, + "CAAGAAAAA": 8626, + "GTCGAGA": 8627, + "GAAACCTT": 8628, + "TTTTATGTT": 8629, + "TCCAGCCA": 8630, + "CGACCTT": 8631, + "TCAGAGC": 8632, + "GTCTCGG": 8633, + "CCCCTGAA": 8634, + "GCAATTCA": 8635, + "TTATTTGAA": 8636, + "CACGTAA": 8637, + "TAAATACAA": 8638, + "TAATCCTAGCA": 8639, + "GTAAGAGA": 8640, + "TATGTAGG": 8641, + "GTCCCAGC": 8642, + "TATAATATA": 8643, + "CAAAATTAAA": 8644, + "CTAATACA": 8645, + "GGTAGTA": 8646, + "TGACAGGA": 8647, + "CTCTAGCA": 8648, + "GATTGAGA": 8649, + "CACGATT": 8650, + "TGATAATA": 8651, + "GTGGCAAA": 8652, + "CTTACCTG": 8653, + "TGTGTGGA": 8654, + "TCTTTATTA": 8655, + "GGAATGTT": 8656, + "GTCAGG": 8657, + "GAAAATTAAA": 8658, + "GAATAAAAA": 8659, + "GTGTAATT": 8660, + "GGAGTGGG": 8661, + "TAAAGCAAA": 8662, + "GGAATTCA": 8663, + "CTTTTTCC": 8664, + "GAAGAGTT": 8665, + "CTTAAATG": 8666, + "GCACACAA": 8667, + "GATGGATT": 8668, + "GGGACTCTCAA": 8669, + "TCTCCCTCC": 8670, + "CTCTTAAAA": 8671, + "CAGCAATG": 8672, + "GGAGTGTT": 8673, + "CCATACC": 8674, + "CACGCTT": 8675, + "GAAGACTG": 8676, + "CTACAGCA": 8677, + "GACCTCC": 8678, + "GCGCTTA": 8679, + "GTGGTTAA": 8680, + "GATGTAA": 8681, + "GATGGCAA": 8682, + "TCTCCTTTA": 8683, + "GAAGCCAA": 8684, + "GAAAGGTG": 8685, + "GATTCCAA": 8686, + "AAAATTAAAA": 8687, + "TATAGAAAA": 8688, + "GTGACGG": 8689, + "TATGTAAAA": 8690, + "TCAGGAGATCGAGACCATCCTGGCTAA": 8691, + "CTCCTCAA": 8692, + "CCACAGCC": 8693, + "TTTTAGTA": 8694, + "GGAGGCGG": 8695, + "TCAGGCTT": 8696, + "TTACGG": 8697, + "CTACAGTT": 8698, + "CATAGAGA": 8699, + "CTCTAAAAA": 8700, + "GTCAGGTG": 8701, + "TAGGTTTA": 8702, + "CCTACCTG": 8703, + "GATAATTTT": 8704, + "GCCCAGTT": 8705, + "TGTGTCAA": 8706, + "TATTCAGG": 8707, + "CATTTCATT": 8708, + "CTGTTCTT": 8709, + "TCACCATG": 8710, + "CAACATGA": 8711, + "CTGCACTG": 8712, + "GTTAAATG": 8713, + "TATGCAGA": 8714, + "TACACCTT": 8715, + "GTTTATGA": 8716, + "GTTGGCTT": 8717, + "CTATTTTTT": 8718, + "CACACACTG": 8719, + "TCAGAATG": 8720, + "TCTTTGAAA": 8721, + "GTGAGCAA": 8722, + "GTAATCAA": 8723, + "GAAGCCTG": 8724, + "TCCTGAGA": 8725, + "TCACCCAA": 8726, + "GAAGGCTG": 8727, + "GAATACTA": 8728, + "TGAACTGA": 8729, + "GGCCAGGCTGGTCTCGAA": 8730, + "TTATATTA": 8731, + "CTCTCATG": 8732, + "TCTGGGGG": 8733, + "TCCACTAA": 8734, + "TCATCCTT": 8735, + "CCACAGGA": 8736, + "GCCCGGA": 8737, + "GGAAGAGGA": 8738, + "GTCAGGGA": 8739, + "CTGTGCCTG": 8740, + "CTATTGTGAA": 8741, + "TCTAGTTA": 8742, + "TGTATGTT": 8743, + "TGCAGCAA": 8744, + "GGCACAAA": 8745, + "CACGGTG": 8746, + "GTGGTGAA": 8747, + "GTGTGTGTGTG": 8748, + "GTTTCTCTT": 8749, + "TATTTATGA": 8750, + "TAAAACGA": 8751, + "CAGTGATG": 8752, + "TAATAAATT": 8753, + "GCAGACTT": 8754, + "GCCCCCCA": 8755, + "GAAATCAAA": 8756, + "CTAGCCA": 8757, + "CCTCTTTG": 8758, + "GCCCCAGG": 8759, + "CTTGGTCA": 8760, + "TATTGTATT": 8761, + "TCTACTTTT": 8762, + "CTCCAGGGGATCTT": 8763, + "CTGCACTCCAGCCTGGGCGACAGA": 8764, + "CACTCATT": 8765, + "GTGGCTTA": 8766, + "GCCTAGAA": 8767, + "TATACCAA": 8768, + "TGAACTTA": 8769, + "CAACCTA": 8770, + "CCAGGTCA": 8771, + "TAAAATATTA": 8772, + "GCTTGCTG": 8773, + "TCTACAAAA": 8774, + "TCTCCTGGG": 8775, + "TGATCTGA": 8776, + "GTCGTCA": 8777, + "CTGACATG": 8778, + "TAATCCTT": 8779, + "GTTCGC": 8780, + "CTAGAGAA": 8781, + "TATATAAAAA": 8782, + "CCTTGAGA": 8783, + "GCCCCCAA": 8784, + "CGGCATT": 8785, + "CTCCAGTACTCTT": 8786, + "CCGTGTT": 8787, + "CGGCGCA": 8788, + "CGTTAAAA": 8789, + "TCTAGATG": 8790, + "CATCTATG": 8791, + "TCGGATG": 8792, + "TCGTCAAA": 8793, + "CTTGTGAA": 8794, + "TAAATACC": 8795, + "CAGAGTC": 8796, + "TCAAAAGACTACACACTTATTTCAAAAAAA": 8797, + "CTTCATAA": 8798, + "TTTGGTTTT": 8799, + "TCTAGTTTT": 8800, + "GACACCAA": 8801, + "GTTCATTTT": 8802, + "TCTTTACA": 8803, + "GTCTGAAAA": 8804, + "CTTCGTG": 8805, + "GGATCTC": 8806, + "GTAGCTGA": 8807, + "CTCCTCCTT": 8808, + "GATGGAGGA": 8809, + "CTGTTGCTT": 8810, + "TCGTAAAA": 8811, + "GATATATG": 8812, + "GGATTAAAA": 8813, + "GTCTGTTA": 8814, + "GACGATT": 8815, + "GTCAGGTT": 8816, + "TACACGA": 8817, + "GCCTCGG": 8818, + "CTTTTGAAA": 8819, + "TAAGTCTT": 8820, + "TATAGGAA": 8821, + "TATACAAAA": 8822, + "TGAGTCAA": 8823, + "CTCGTAA": 8824, + "GTCATATT": 8825, + "TTCAGCA": 8826, + "CTGAAGCA": 8827, + "GCAAACACA": 8828, + "CTCCCTCCCTCCCTCC": 8829, + "GGCACCTG": 8830, + "GTTCCAGGA": 8831, + "TCCCTTTG": 8832, + "CTTCAGTA": 8833, + "CCAGGATG": 8834, + "TCACGC": 8835, + "TCACAGTA": 8836, + "GCCTGAGG": 8837, + "TTTTTTGTTTT": 8838, + "AAAAAAAAATT": 8839, + "GCCTCATT": 8840, + "TTCACAA": 8841, + "CTTGCTCA": 8842, + "GGGGTGGGGG": 8843, + "GAGGGAGGGAGGGAGG": 8844, + "TCCTATTA": 8845, + "TCAGACTG": 8846, + "CTTTATGA": 8847, + "TAAATCCA": 8848, + "CACACTTA": 8849, + "GCTTCTAA": 8850, + "GAAAATCAA": 8851, + "TTTTTGAAAA": 8852, + "GACTCCAA": 8853, + "TAAATCATT": 8854, + "TTTTTTTTTTTTTTTTTTTTTT": 8855, + "TTTTCTGA": 8856, + "TCTCAGAAA": 8857, + "GTGAAAGTGAA": 8858, + "TCAGAGTG": 8859, + "CTGTAATT": 8860, + "GGATCATT": 8861, + "TCTTTCAAA": 8862, + "TAGGGAGA": 8863, + "CAACGAA": 8864, + "CCTTGGGG": 8865, + "TTAACTA": 8866, + "TACGTAA": 8867, + "TCATGATT": 8868, + "CACAGAGCA": 8869, + "TCTTGTC": 8870, + "GATACATG": 8871, + "GCAACAGG": 8872, + "CGAGTGA": 8873, + "TCCATCACCAACTCC": 8874, + "TCCGCCTG": 8875, + "CTGTCCTT": 8876, + "TCTTCCTCC": 8877, + "GCTTGTTTT": 8878, + "CGGCTGA": 8879, + "CGACGAA": 8880, + "TCATTTTAA": 8881, + "GATTGGAA": 8882, + "TCCCATAA": 8883, + "TATACCTT": 8884, + "GCGCCCC": 8885, + "CCATATAA": 8886, + "CCTGCAAA": 8887, + "TGTGCTGA": 8888, + "GTACTCC": 8889, + "TGTTTAGA": 8890, + "TCTGTCTC": 8891, + "GTAACTCA": 8892, + "CACGGAA": 8893, + "TGAGAATG": 8894, + "GTTCATTA": 8895, + "GAAATTAAAA": 8896, + "GCGCTAA": 8897, + "TGTGGTCA": 8898, + "CAGATGTA": 8899, + "GTATTTATT": 8900, + "TCACCCAGG": 8901, + "GTAAACAA": 8902, + "TATTTTAATT": 8903, + "GATCTCAA": 8904, + "GCAGTTTG": 8905, + "GTATCTCA": 8906, + "CACATCAA": 8907, + "GATTTGGA": 8908, + "TACTTAAAA": 8909, + "CCATGAAAA": 8910, + "GAACGAA": 8911, + "GAAGATCA": 8912, + "TAAACAATT": 8913, + "TGAAGCCA": 8914, + "GGAAGTTA": 8915, + "GAAAAGCAA": 8916, + "TTATAATT": 8917, + "GTGTATTA": 8918, + "GGATTTTCCAGGCAA": 8919, + "TCGTGGG": 8920, + "GATGAATG": 8921, + "GCATCATT": 8922, + "GCACACCA": 8923, + "CTCTCAGG": 8924, + "TGCACCTG": 8925, + "GTTTGAGG": 8926, + "TGAATACA": 8927, + "TGTTTATTA": 8928, + "GGAGATCCAA": 8929, + "GGGCGCGGTG": 8930, + "CCGTGAA": 8931, + "GTTCACAA": 8932, + "GTTACCTT": 8933, + "GGTCTCTT": 8934, + "CAGGGTTTCA": 8935, + "TCTGTTTTA": 8936, + "GCTTGAGG": 8937, + "TATGTGTT": 8938, + "CTCGGTT": 8939, + "GTAAGCAA": 8940, + "GAAAATGTA": 8941, + "TCTTTTAAAA": 8942, + "TCATCAAACCCATCCTAATT": 8943, + "GGTGCCTG": 8944, + "CAAAGGGG": 8945, + "CTGAATGTT": 8946, + "TGAGCATT": 8947, + "CTGGAGTGCAGTG": 8948, + "CTTATATG": 8949, + "GGAAAGAAA": 8950, + "TCCTTTCCTT": 8951, + "GTGTTTTTT": 8952, + "GTGAGTAAA": 8953, + "CCTTCTTTT": 8954, + "CATGGGTT": 8955, + "GGGCTCTG": 8956, + "TACGAAA": 8957, + "CAATCTC": 8958, + "GAGACATT": 8959, + "TCTTATTTA": 8960, + "CAGGGAGGCCTG": 8961, + "TGACGGA": 8962, + "CACAGTTA": 8963, + "CGGGATG": 8964, + "CGACCAA": 8965, + "CCCAGGAGG": 8966, + "GGAAACTGA": 8967, + "GAACATCA": 8968, + "CTTGTTAA": 8969, + "CTTTAATG": 8970, + "GAAAAGCA": 8971, + "TCTCACTG": 8972, + "TAATGAATT": 8973, + "CTTCGC": 8974, + "TCCAATTA": 8975, + "CTCCTGCCTCAGCCTCCCAAGTAGCTG": 8976, + "TCTTTAAAAA": 8977, + "CTTACTAA": 8978, + "CGATATA": 8979, + "CCTCCCAAA": 8980, + "CCTCGGCCTCCCAAA": 8981, + "GAGCAATT": 8982, + "TTTTGGGG": 8983, + "CTTTTTAAAA": 8984, + "CCAGAGTT": 8985, + "GTCTCATG": 8986, + "CAGATCAA": 8987, + "GTAATAAAA": 8988, + "CAAAACACA": 8989, + "TTATTCTG": 8990, + "GGCCAGGCTGGTCTTGAA": 8991, + "GACGTTA": 8992, + "CTCGGCC": 8993, + "GAAACATA": 8994, + "GTTACTGA": 8995, + "CTTAGCTT": 8996, + "GTGTACC": 8997, + "CTCATGTT": 8998, + "CTCCCCTCC": 8999, + "CTTTGCCTT": 9000, + "CAAGATGA": 9001, + "TTTTGGTT": 9002, + "TATTĊ": 9003, + "CAAATCAAA": 9004, + "CTCATGGG": 9005, + "GTCATCAA": 9006, + "CTCAGGAGGCTGA": 9007, + "CATCTATA": 9008, + "GTTCTGTG": 9009, + "GTTGAGTT": 9010, + "GTGATAAA": 9011, + "CGTGCTG": 9012, + "CAAATGAAAA": 9013, + "GGGTCGCAAA": 9014, + "TAAGGCTG": 9015, + "CTCTCTGTT": 9016, + "GCTACCTG": 9017, + "CATCAATT": 9018, + "CCAGACTG": 9019, + "TGTGGGGG": 9020, + "GAACTCAA": 9021, + "GAACACTT": 9022, + "TCCCCAGG": 9023, + "GTTGAGGA": 9024, + "CAGGGGTG": 9025, + "GGGACGG": 9026, + "GACTTATT": 9027, + "GATGAAAAA": 9028, + "TCTTGGCTCA": 9029, + "CTGTTGTG": 9030, + "CAGCAATT": 9031, + "CAAGAGGA": 9032, + "CCACAGC": 9033, + "GAAATAAAAA": 9034, + "GCCTGGAAAATCCCATG": 9035, + "TCTTTGCC": 9036, + "GCTTTATA": 9037, + "TTCACTG": 9038, + "TAAAAAAAAAAAA": 9039, + "TAAGAATG": 9040, + "TTTTTCTA": 9041, + "GGACATCA": 9042, + "TAACAAAAAA": 9043, + "CAAAGTATT": 9044, + "CTCAGTCC": 9045, + "GCAAGG": 9046, + "TAGAGAAAA": 9047, + "CCTGTGAA": 9048, + "CAGGCCAA": 9049, + "GTATGAAAA": 9050, + "TAATGAGA": 9051, + "GAAGAATG": 9052, + "GTATCTTA": 9053, + "GGTAGAAA": 9054, + "TAGAATTA": 9055, + "TGACTCTT": 9056, + "TCATTATG": 9057, + "CCACGCA": 9058, + "TATTCCCA": 9059, + "TATTGACA": 9060, + "GCTTGATT": 9061, + "CGACACA": 9062, + "GATACATT": 9063, + "TCCTGTTA": 9064, + "TCATATGA": 9065, + "GCATAATT": 9066, + "GCCCTCTG": 9067, + "TGTGGACA": 9068, + "CATGGTAA": 9069, + "CAAAATGTT": 9070, + "CTGGAAAAA": 9071, + "TATGTCAAA": 9072, + "TCTCTAAAA": 9073, + "GTAAATTAA": 9074, + "CCTTCTAA": 9075, + "CTTAGTGA": 9076, + "GTCTCTGTG": 9077, + "TCAAGGCTG": 9078, + "TCTGCAAGTGGA": 9079, + "CAGATAGA": 9080, + "GTTTTCATT": 9081, + "GTGCACTG": 9082, + "CCAGACTT": 9083, + "GTTACATG": 9084, + "GCTACATT": 9085, + "TGACTGTG": 9086, + "GAAGAGC": 9087, + "CAGGTGTT": 9088, + "GATTTCTA": 9089, + "CGACTC": 9090, + "GGATACC": 9091, + "CCTCCTGGG": 9092, + "TCGGCGG": 9093, + "GCCAGGTGTG": 9094, + "GTGCAGGA": 9095, + "TCTGTACA": 9096, + "TCTGGTCC": 9097, + "GGAACTCA": 9098, + "CCTTGGCCTCCCAAA": 9099, + "CCCACTTTT": 9100, + "GCTCACTGCAA": 9101, + "CAAGGCAA": 9102, + "GGAGGATG": 9103, + "TAAACTC": 9104, + "TCTGGTCA": 9105, + "TACTATTA": 9106, + "GTTCACGCCATT": 9107, + "CCTGGCAA": 9108, + "GCTTCAGA": 9109, + "CTTACTGA": 9110, + "GGAATATA": 9111, + "CCAGAGCC": 9112, + "TAAATATCA": 9113, + "TGTGGCAA": 9114, + "TAAAACACA": 9115, + "TACTCAAA": 9116, + "AAAAAAAAAAAAAAAAAAAA": 9117, + "GATCCAGG": 9118, + "TAATTTTTTTT": 9119, + "CTACTGTT": 9120, + "CAAAGAGAA": 9121, + "CAGGGCAA": 9122, + "CCAAGGTT": 9123, + "CTTATCAA": 9124, + "TATCTACA": 9125, + "CTGGAGCA": 9126, + "TAGGGTA": 9127, + "CTGAAGTA": 9128, + "CCCTTTTA": 9129, + "GTGCCCTG": 9130, + "CGAGTCA": 9131, + "CCGTAAA": 9132, + "GTGAGGGA": 9133, + "GTTAGTAA": 9134, + "CGTGAAAA": 9135, + "TACTGAAAA": 9136, + "GTGTGAAAA": 9137, + "CGTĊ": 9138, + "GAAAACACA": 9139, + "CCCCCATT": 9140, + "GGACACTT": 9141, + "CTAAGTAA": 9142, + "TCGCATG": 9143, + "TGATAATG": 9144, + "TGACAGTA": 9145, + "TATTCGG": 9146, + "GTGATGTA": 9147, + "GCGATTTT": 9148, + "CAACTCAA": 9149, + "CCACACTT": 9150, + "TATGGCC": 9151, + "GACGTAA": 9152, + "CAGAGCCAGG": 9153, + "TCATCTTA": 9154, + "CTTGCTGA": 9155, + "GCTTCACA": 9156, + "TCCCAGTA": 9157, + "GTCTATGA": 9158, + "TCGAAATT": 9159, + "TTTTCATG": 9160, + "GAACAGTG": 9161, + "TCTGTAGA": 9162, + "GCTTCCCTG": 9163, + "GTGAGGTT": 9164, + "CCTTCAGA": 9165, + "TATATACC": 9166, + "CAGGTGAA": 9167, + "GATATGAA": 9168, + "GAGTACTGGAGTG": 9169, + "CCCAAATT": 9170, + "CAATAGCA": 9171, + "GTTCCTGA": 9172, + "GCCTATCA": 9173, + "GTTAGGAA": 9174, + "CTTAGAGA": 9175, + "GTGTAGTG": 9176, + "CCCCTCCA": 9177, + "CTCTGTCTC": 9178, + "GTGACTAA": 9179, + "CCTTCCTCA": 9180, + "CTCCCCCA": 9181, + "CAATACTT": 9182, + "GTTGACTT": 9183, + "GTAGAGCA": 9184, + "GCCTACTT": 9185, + "CTCCTCTA": 9186, + "TTTTTTTATT": 9187, + "CACGTTA": 9188, + "TATAGACA": 9189, + "CCAAGAGG": 9190, + "CCCCCGCC": 9191, + "TCACTCAA": 9192, + "GGAGTGAA": 9193, + "TAAAGGGA": 9194, + "TCTGGTTTT": 9195, + "GATGTGTG": 9196, + "TTATGGG": 9197, + "CAAATTTTA": 9198, + "CGAGTTA": 9199, + "CAGCTCAA": 9200, + "TCCATCCATCCATCCA": 9201, + "GGGTAAAA": 9202, + "TGATACTT": 9203, + "TTATGCA": 9204, + "GCAGAAAAA": 9205, + "TAAAGGTA": 9206, + "CTGATCTT": 9207, + "GCAAGGCA": 9208, + "GCTATGTG": 9209, + "TCTTTTTCTT": 9210, + "CTAAATTAA": 9211, + "TTTTGGTA": 9212, + "GTTGGCAA": 9213, + "GATACACA": 9214, + "CTCCTGACCTCA": 9215, + "CTGAGGCAGGAGAATTGCTT": 9216, + "GTTAATTAA": 9217, + "TAGCTGTT": 9218, + "TCCTATCA": 9219, + "CCAGAATT": 9220, + "CTCTATCC": 9221, + "CAGCTATA": 9222, + "GTCGGTT": 9223, + "GCTTCTCTT": 9224, + "GAAAATTTA": 9225, + "CACCTGTT": 9226, + "GCTGTAGG": 9227, + "CCAAGCCA": 9228, + "GCTATATT": 9229, + "GAAGACTT": 9230, + "TAAAATACA": 9231, + "CCTTTATG": 9232, + "GAGGAGAGG": 9233, + "GTGGGAAAA": 9234, + "TCGCTTTT": 9235, + "CCAAGGTG": 9236, + "CTCCCATG": 9237, + "CCAGCCTGGCCAACATGGTGAAA": 9238, + "CAATGATT": 9239, + "CAATACCA": 9240, + "GTCATGGG": 9241, + "GATTCC": 9242, + "GAGGAATG": 9243, + "TGACAATG": 9244, + "CTATAATT": 9245, + "CAGGTAA": 9246, + "CTCCTGTA": 9247, + "GGAGGATCACTT": 9248, + "CATTATTTA": 9249, + "CTCATGAA": 9250, + "GATTCTTA": 9251, + "GCCAGTTTT": 9252, + "CTACAGTA": 9253, + "TAATATAATA": 9254, + "GGACGGA": 9255, + "GTATCTC": 9256, + "TCAAGTCAA": 9257, + "GAGGTGAA": 9258, + "GAAAATATG": 9259, + "GAGGAGAGA": 9260, + "GATCTATT": 9261, + "CTGTCTCA": 9262, + "CTGGAATT": 9263, + "GCACCTCA": 9264, + "GCTTGCC": 9265, + "GCTTTCTA": 9266, + "TCTCCTCC": 9267, + "CTTCTTAAA": 9268, + "TATTTATTTTA": 9269, + "GTGGAAGG": 9270, + "GGAAGGGAA": 9271, + "GGGGCCTG": 9272, + "CTCTGTTTT": 9273, + "CGTGAGCCA": 9274, + "TATTGGTG": 9275, + "TCTGCAAAA": 9276, + "CCTATGTT": 9277, + "CACAGGCA": 9278, + "TCGCAGG": 9279, + "GATCAATT": 9280, + "GACTCTGA": 9281, + "TCATACTT": 9282, + "GTTCCCAA": 9283, + "CCTGGAAAA": 9284, + "CTTTTTTCA": 9285, + "GGTTTCTT": 9286, + "GCTCCAAAA": 9287, + "TAAAATATAA": 9288, + "GCTGGCAA": 9289, + "CGGGAAA": 9290, + "CCCGCTG": 9291, + "TATAGCC": 9292, + "CCAATTGTG": 9293, + "GAGGTAGG": 9294, + "TTATGAAA": 9295, + "CCATCCTT": 9296, + "CGATCAA": 9297, + "TCAAATCAAA": 9298, + "CGGCTCC": 9299, + "GTAGTAAA": 9300, + "CTGAGGCC": 9301, + "TCCAATTTG": 9302, + "AAAAAAAAAAAAAAAAAAA": 9303, + "TAACCATT": 9304, + "CAAATAAAAA": 9305, + "GATAATAAA": 9306, + "CATATATATA": 9307, + "TGTTGAGA": 9308, + "CTTATCTT": 9309, + "CTTCATGA": 9310, + "GTGGCCTT": 9311, + "GTATTGGA": 9312, + "GCTTATTTT": 9313, + "GAACGTT": 9314, + "GCCCCCTT": 9315, + "CGGTGGA": 9316, + "GATTCTAA": 9317, + "CCTACATG": 9318, + "TCCCGCA": 9319, + "CTCCATCTC": 9320, + "CTTACAGA": 9321, + "CGCCAGA": 9322, + "CTGCACTT": 9323, + "GTTAGAGG": 9324, + "CGGCTCA": 9325, + "TTATTCAA": 9326, + "TCCTGTTTT": 9327, + "CCAGCCTGGCCAA": 9328, + "TCTGTCCTT": 9329, + "CCTGGGCA": 9330, + "GAACAGTT": 9331, + "GCCATAA": 9332, + "CTACAGTG": 9333, + "GTTTTTTTTTTTT": 9334, + "TCTAGAAAA": 9335, + "CTGTGGGA": 9336, + "TAAATGTTA": 9337, + "GACTCCTT": 9338, + "GCCTCAGG": 9339, + "TCCATA": 9340, + "GGTCAGGAA": 9341, + "CATACTCA": 9342, + "TCACGTA": 9343, + "GTGTAGTT": 9344, + "TTTTTTTCC": 9345, + "TCTCCTGCATT": 9346, + "CCTGTGTG": 9347, + "TATGGGTT": 9348, + "GATGCCTT": 9349, + "GGGAGGTG": 9350, + "CTACTAAA": 9351, + "GTTTTCTTTT": 9352, + "GCCAGTTA": 9353, + "TAGAGTCA": 9354, + "GACACACACA": 9355, + "TTAGAGG": 9356, + "CAACAGTT": 9357, + "GCATCTGA": 9358, + "CCTAGAGA": 9359, + "GTTGGGGA": 9360, + "GAAAGCAAA": 9361, + "CATTCTC": 9362, + "TGAACCAA": 9363, + "GGAGAGGAA": 9364, + "GTGCACCA": 9365, + "GTTGCCTT": 9366, + "TAAATAGAA": 9367, + "GCTTCCCA": 9368, + "CATACTTA": 9369, + "TCACGCA": 9370, + "TAAACCTG": 9371, + "TGAAGGTA": 9372, + "GATTGTAA": 9373, + "GTTTTCCA": 9374, + "CTGGGGAA": 9375, + "TAAAAGTTA": 9376, + "TATCCAGA": 9377, + "TCAGAAAAA": 9378, + "CAAGAGTT": 9379, + "CGGCACA": 9380, + "CTAATGTT": 9381, + "GTCCTGTA": 9382, + "TGAGTTTG": 9383, + "CAACCTCA": 9384, + "GTGGACTG": 9385, + "TCTTTATTTT": 9386, + "CTGCCCTT": 9387, + "GATATGTT": 9388, + "GTGAATTTT": 9389, + "GATCCCCTGGAGAA": 9390, + "CCACATCC": 9391, + "GGAGTAAA": 9392, + "GAGGGG": 9393, + "CGAAAAAAA": 9394, + "TCTGCTC": 9395, + "GTAATA": 9396, + "CAAAATCAA": 9397, + "GACATAA": 9398, + "GTGTTAAAA": 9399, + "CCAAGGGG": 9400, + "CTTAGG": 9401, + "GTTTTGATT": 9402, + "CCAATTAAA": 9403, + "TCGCAGCA": 9404, + "TCATAAAAA": 9405, + "CCACGGA": 9406, + "GAGATAGG": 9407, + "CATTTATAA": 9408, + "GATGCAGA": 9409, + "CAAAAAAATT": 9410, + "TGGAGGAA": 9411, + "GCGTGGTG": 9412, + "TCATCTCC": 9413, + "GAATAATA": 9414, + "GCACATCA": 9415, + "GTAAGTCA": 9416, + "GAGGGCTG": 9417, + "GTCCTGCA": 9418, + "TGATTGTA": 9419, + "CCAGCCTGGTCTA": 9420, + "CTTGGATT": 9421, + "GTCGTGA": 9422, + "TATATACAA": 9423, + "CAAATAATT": 9424, + "TAGACTAA": 9425, + "GATTTTTTTGAAA": 9426, + "GTGTATTTT": 9427, + "GTCATTAAA": 9428, + "CAAAGCAAA": 9429, + "GTTACTTTT": 9430, + "CTTATGTT": 9431, + "CGACATG": 9432, + "GGAGATAA": 9433, + "GCCCATCC": 9434, + "GGTGGTGA": 9435, + "TCATTCTA": 9436, + "TCATCCAA": 9437, + "TGATAGCA": 9438, + "GCAGACCA": 9439, + "GCAGCATG": 9440, + "CACATCTT": 9441, + "CCTTTGCA": 9442, + "TGATTTCTT": 9443, + "TTTTTGTTA": 9444, + "GCACCATT": 9445, + "CCTTTCCA": 9446, + "GGAGACAGA": 9447, + "GTTTTAATG": 9448, + "GTGCCATT": 9449, + "TACTGGAGTG": 9450, + "CTTGCTTA": 9451, + "GTCCCATT": 9452, + "TTTTAATTAA": 9453, + "GAAGATAA": 9454, + "GTGAAAAAA": 9455, + "GCAGATAA": 9456, + "CCAGAGC": 9457, + "CACAGGGG": 9458, + "TAAAATAATT": 9459, + "CATCTACA": 9460, + "CAGGTTTA": 9461, + "TCATTTAA": 9462, + "CTGCTGTG": 9463, + "CACATTTTA": 9464, + "CAAAAGCAA": 9465, + "TAGAGTAA": 9466, + "CCTCTATT": 9467, + "GTGGGCAAA": 9468, + "CTCCTGACCTCAAGTGA": 9469, + "TGACTCAA": 9470, + "CTGCAATT": 9471, + "GCATTTTTT": 9472, + "TATGGTTTT": 9473, + "CAGAATTA": 9474, + "TAAGTGTGTAGTCTTTT": 9475, + "GCACACTT": 9476, + "GAAAATATTA": 9477, + "TTATTTCC": 9478, + "GTCTTAAAA": 9479, + "GTACAGCA": 9480, + "GTCGTTA": 9481, + "GGAGGTTA": 9482, + "CTGGGATT": 9483, + "GAAAGGCA": 9484, + "CTGCACTCCAGCC": 9485, + "GCTCTGGATAA": 9486, + "GACAGATG": 9487, + "GTTCATGA": 9488, + "TGACTATT": 9489, + "TGACAGAGCAA": 9490, + "TACAGGAA": 9491, + "GAACCCC": 9492, + "GGTCTGTT": 9493, + "TAAAGTAAA": 9494, + "GGAACCTG": 9495, + "TATTTGTTA": 9496, + "GCTGGGCGTG": 9497, + "CAGTCCTT": 9498, + "GACGCCA": 9499, + "TCACTATT": 9500, + "TCATCATG": 9501, + "CAAGACTG": 9502, + "GTTTAGTG": 9503, + "GAAAAGGAAA": 9504, + "AAAAAAAAAAAAAAAAAAAAA": 9505, + "CCGGGCA": 9506, + "CTTCCTTTT": 9507, + "GGACAGTT": 9508, + "CCAGACAA": 9509, + "TCAAGCTG": 9510, + "GCTTTAGG": 9511, + "TATGGCTG": 9512, + "GGTGGCTGA": 9513, + "CGCGCGC": 9514, + "TATTTCTAA": 9515, + "TCTGTCCATG": 9516, + "CAGACTAA": 9517, + "GAGGCAAA": 9518, + "CTACCCC": 9519, + "GAGAATTTT": 9520, + "CCTCTATA": 9521, + "GTTCAGC": 9522, + "TTAATATA": 9523, + "TCTAGTGA": 9524, + "CGAGTAA": 9525, + "GCAATGTG": 9526, + "TAATCTTTT": 9527, + "TAAGGCAA": 9528, + "GTTCCCCA": 9529, + "GTTGACAA": 9530, + "TCTTGCTA": 9531, + "TCATTGTA": 9532, + "TCGTTTA": 9533, + "TCCCGGA": 9534, + "CATTCAGG": 9535, + "TCTGCCTA": 9536, + "TAAAATGAAA": 9537, + "TAGAGTTA": 9538, + "TGTACATG": 9539, + "GAGGTGTG": 9540, + "TCAATACA": 9541, + "TCCAAAAAA": 9542, + "GTGGGCTT": 9543, + "GATTGGTT": 9544, + "TCTCCAGCA": 9545, + "GTGCAGC": 9546, + "CCACATTA": 9547, + "TCTGGTTA": 9548, + "CCTGGGAGG": 9549, + "CTCCCCAAA": 9550, + "GAAAACTCA": 9551, + "TCAATGAAA": 9552, + "CATAGATT": 9553, + "CTTGCCTGGAGAATCCCAGG": 9554, + "TCCCTGCC": 9555, + "CTGTATAA": 9556, + "TATGCTCA": 9557, + "GGTGGAGA": 9558, + "GATACAGA": 9559, + "TAACAATG": 9560, + "GCCCGTT": 9561, + "GGAACAAAA": 9562, + "CTCCTAGG": 9563, + "TTTGCAAA": 9564, + "TCCCTTAA": 9565, + "CCACCATT": 9566, + "TCAATGCA": 9567, + "TCTCTACAA": 9568, + "TATCTTAA": 9569, + "TCGCGCC": 9570, + "GAGTCTA": 9571, + "GTCAGTTA": 9572, + "CCAAGTGA": 9573, + "CACCCAGG": 9574, + "GAACTGTG": 9575, + "GGTAGAGA": 9576, + "GCTCTATT": 9577, + "GGATTACAGGTG": 9578, + "CCATATTA": 9579, + "TATACTCA": 9580, + "GTAACTGA": 9581, + "TCCTGCCTT": 9582, + "CGGGGCC": 9583, + "TGGAGTA": 9584, + "TCAAAATTA": 9585, + "GGAGACAGG": 9586, + "GACAGAGGAGCCTG": 9587, + "GGGCAAAA": 9588, + "GTTGCATT": 9589, + "GGAGAGAAA": 9590, + "GTCAAATA": 9591, + "TATACAGTA": 9592, + "TGTTGCAA": 9593, + "CGGTTAA": 9594, + "TAGCCATG": 9595, + "GGCTTTTA": 9596, + "CAACAGC": 9597, + "GGACGTT": 9598, + "GTCTTTTAA": 9599, + "CGGCAGA": 9600, + "GTTTTATTA": 9601, + "CTCATTAAA": 9602, + "TAACACTA": 9603, + "TCAGCTCC": 9604, + "CCCACCTCC": 9605, + "CTCTAATG": 9606, + "GATTGAAA": 9607, + "CCAACAGA": 9608, + "CCCATGAA": 9609, + "CTGCTCTG": 9610, + "GTCCATAA": 9611, + "TATGGGAA": 9612, + "TATTTCTGA": 9613, + "GCCTTATT": 9614, + "CTCTCCTCC": 9615, + "TGTTGTTGTT": 9616, + "TAAAAGAAAA": 9617, + "TCCTGAGTT": 9618, + "GTGCGTT": 9619, + "GTGTGTTTT": 9620, + "GATGGATGGATGGATG": 9621, + "CCCĊ": 9622, + "TAAGGTAA": 9623, + "GTAGAGTT": 9624, + "CCTTGAGG": 9625, + "TGGATGA": 9626, + "TCATTTCTT": 9627, + "GCTCAATG": 9628, + "GTTTAATG": 9629, + "GAGATAGA": 9630, + "GGGGTAGG": 9631, + "TAATTCTT": 9632, + "GCATTGAA": 9633, + "CTGCGTG": 9634, + "GTTTTTCAA": 9635, + "CCTACATT": 9636, + "TGATTCTA": 9637, + "TAGCCACA": 9638, + "GGTTAGGTTA": 9639, + "GAACCCGG": 9640, + "CCGAGC": 9641, + "GCCCCTGA": 9642, + "TATATAATA": 9643, + "GCAAGTAA": 9644, + "CCCAGCCTG": 9645, + "TCATGGCTG": 9646, + "CAAAAAAAAAA": 9647, + "GCTATATG": 9648, + "TCCTACTG": 9649, + "CTGTATCA": 9650, + 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+ "GGTCTGTG": 9700, + "GATCTTAA": 9701, + "GGAAGGCA": 9702, + "GTAAGATG": 9703, + "GAGGCTCA": 9704, + "CTAATTTTA": 9705, + "TAGGGAAA": 9706, + "GCTATCTT": 9707, + "TCAAAAAAAA": 9708, + "GCATCACTGA": 9709, + "GCCGTCA": 9710, + "GCTCTATG": 9711, + "GTATACTT": 9712, + "GCAAGGTG": 9713, + "GACAGAAAA": 9714, + "TCCGGA": 9715, + "TCAAGATA": 9716, + "TAGAGCTG": 9717, + "GGTTTATT": 9718, + "GACACATG": 9719, + "GAAGTCAA": 9720, + "TGTTCAGA": 9721, + "CCCCCGA": 9722, + "GTACAGTT": 9723, + "CTAACATG": 9724, + "GAAGTTAA": 9725, + "TGAAGGGG": 9726, + "GAGGGGTG": 9727, + "GTCCCCCA": 9728, + "GCTAAAAAA": 9729, + "TCATGAGG": 9730, + "CAAAAAATT": 9731, + "GCCGCTT": 9732, + "CTGATGTA": 9733, + "TCTCCCCA": 9734, + "GTCCAAAAA": 9735, + "TCACAAGGTCA": 9736, + "TAACGCA": 9737, + "GTAAAAATG": 9738, + "TTTTAAATT": 9739, + "GAACGTG": 9740, + "CAAATATAA": 9741, + "CACCCATG": 9742, + "GAGTCCTT": 9743, + "TCCGGCC": 9744, + "GCACAGTA": 9745, + "TTAATGAA": 9746, + "TGGGGTT": 9747, + "CTCTTTCTT": 9748, + 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+ "CCAGCATG": 10033, + "GTCAGAATG": 10034, + "TGGAGAAAA": 10035, + "CATCTAAA": 10036, + "GGGCTGTT": 10037, + "GTTTAGCA": 10038, + "GCACGAA": 10039, + "CCTCTGAAA": 10040, + "GGACCAAA": 10041, + "CACACACCA": 10042, + "TAATTTATG": 10043, + "CTTTCATG": 10044, + "GTACTGAA": 10045, + "GCCCATGA": 10046, + "CCTCCCCTCC": 10047, + "GGTCTGAA": 10048, + "TGACAAAAA": 10049, + "GGACCCCA": 10050, + "TCAATGGG": 10051, + "GTCATATG": 10052, + "GGGATATT": 10053, + "TCATCTAA": 10054, + "CAATCTCA": 10055, + "CTGTAGTT": 10056, + "CTTTTTCTG": 10057, + "CGATCTT": 10058, + "TCAAATTTG": 10059, + "CGCCCAGC": 10060, + "TCCTCATG": 10061, + "GTAGGCC": 10062, + "TAGGCAAA": 10063, + "GGTGGCC": 10064, + "GGGAGGTT": 10065, + "GTAGGCTA": 10066, + "CTGAGGCTG": 10067, + "GATTTCCA": 10068, + "GCGAGACTCCGTCTCAAAA": 10069, + "GTGCTCTT": 10070, + "CCAGTCTG": 10071, + "CCACTGGA": 10072, + "CATTTAAAAA": 10073, + "CCCAACCCAGG": 10074, + "GTATAGTT": 10075, + "GAAATTTTA": 10076, + "TGTTTATAA": 10077, + "GAGCGTCTG": 10078, + 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+ "GCATATTTT": 10399, + "TTTTACAAA": 10400, + "TGGACTA": 10401, + "CTTTATAAA": 10402, + "CTTTAGGA": 10403, + "GAGAGTAA": 10404, + "CATTTCTCA": 10405, + "CCAACTCA": 10406, + "GTAGTTTG": 10407, + "CATTTCTAA": 10408, + "CATAGTTA": 10409, + "TCAAATATTA": 10410, + "CTGCGC": 10411, + "CTTGATTA": 10412, + "GTTTATAAA": 10413, + "CACTAATT": 10414, + "CCTGCCCA": 10415, + "GCCACTAA": 10416, + "CCCCCCTCC": 10417, + "GAATTCTCA": 10418, + "GCATGTTA": 10419, + "TATTTGATT": 10420, + "TAAAAGATT": 10421, + "GCTGTAAA": 10422, + "CTTATGA": 10423, + "CTCTCGG": 10424, + "GGTTTGTT": 10425, + "CTACAGTAA": 10426, + "TCGCTCTT": 10427, + "TAACCCTT": 10428, + "CACACAGCA": 10429, + "GTGAGACC": 10430, + "TTTACTAA": 10431, + "CCAGCCTGACCAA": 10432, + "GATAAATA": 10433, + "GAAGACCA": 10434, + "CTGCACCA": 10435, + "TGTGGTTA": 10436, + "CAAATAATA": 10437, + "CCCAAGGA": 10438, + "CTACACTG": 10439, + "GCTTGCAA": 10440, + "GCTAGTAA": 10441, + "TCTTTTTCTTTT": 10442, + "GAAGAAGAAGAAGAAGAAGAAGAA": 10443, + "CTGGTTGGA": 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"CTGTCTTA": 10584, + "TAAACACC": 10585, + "GTCGTCC": 10586, + "TTAATCA": 10587, + "GAAAAGAAAAA": 10588, + "GGGTGAAA": 10589, + "GTTGGTGA": 10590, + "TAAATATAAA": 10591, + "GGAACTAA": 10592, + "GAGGGGGA": 10593, + "CGTCTTA": 10594, + "CGTAAATT": 10595, + "CCATCTTA": 10596, + "TAAAGCCA": 10597, + "GAGAGATA": 10598, + "CCTGGTCA": 10599, + "CTTCCTGTT": 10600, + "TGACCATT": 10601, + "TAAATAATG": 10602, + "TAAAATGCA": 10603, + "GATGCAAA": 10604, + "CTTTTTATG": 10605, + "TCGGAAAA": 10606, + "GAGAAAATT": 10607, + "CTGCTTTG": 10608, + "CTGGACTT": 10609, + "GCTGTCAA": 10610, + "GCTAGCTT": 10611, + "GCCCTCAA": 10612, + "GCCTCTAA": 10613, + "TGTACACA": 10614, + "CTGTTCTA": 10615, + "CATAAAATT": 10616, + "CCGTCTT": 10617, + "GAAAAAATT": 10618, + "CAATACTG": 10619, + "GGATACAA": 10620, + "GAAAATGAAA": 10621, + "TAGAGCTT": 10622, + "TCCTCCAAA": 10623, + "TAAGAAAAAA": 10624, + "GGTTATTA": 10625, + "GCGACTA": 10626, + "CTCTGTGTT": 10627, + "TTTTATGGA": 10628, + "GCACAATT": 10629, + "GATTCACA": 10630, + 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10859, + "GTTACCAA": 10860, + "TTTATTATT": 10861, + "CCCCCCTG": 10862, + "GCTAGCAA": 10863, + "CCTATGTG": 10864, + "TATCTTAAA": 10865, + "GTTGTGTG": 10866, + "GCCTACAA": 10867, + "GAAAGTAAA": 10868, + "GAGATCC": 10869, + "CGGGTGA": 10870, + "CGATCTG": 10871, + "GTTTGCAA": 10872, + "GACCCTGATGCTG": 10873, + "CCGCAAA": 10874, + "GACTAATT": 10875, + "TAACAACAA": 10876, + "CCACAATG": 10877, + "CTTCTGGG": 10878, + "TCTTCAGAA": 10879, + "GATTGTGA": 10880, + "TAGGCAGA": 10881, + "CAGCTCTA": 10882, + "CCCAGGTT": 10883, + "CTTGACAA": 10884, + "GCATATCA": 10885, + "GAGGGCAA": 10886, + "GTCCCATCA": 10887, + "TGATTTCA": 10888, + "GCCCCGG": 10889, + "TTTTACAGA": 10890, + "TACAGTAAA": 10891, + "TCCCAGCAA": 10892, + "GTGCTGAAA": 10893, + "CCGAGAA": 10894, + "TCCCCCTC": 10895, + "GCCAGGCGTG": 10896, + "GGCTGATT": 10897, + "GATAAAATT": 10898, + "GACCTTTG": 10899, + "CTGGACAA": 10900, + "CTGGGCAA": 10901, + "GAGGCACA": 10902, + "TACATACAA": 10903, + "GAAAACATG": 10904, + "TTTTTATTTTTT": 10905, + "TGTGCTAA": 10906, + "GAGGGGCA": 10907, + "GTTATTATT": 10908, + "GTGAAGGA": 10909, + "TAATAATTTT": 10910, + "GTTGTTAAA": 10911, + "GATCTATA": 10912, + "GAATGAGA": 10913, + "GTGGGCCA": 10914, + "GCTTTCCA": 10915, + "GATTGCTG": 10916, + "GTAGATTA": 10917, + "TATTTATATT": 10918, + "CTTACCAA": 10919, + "GCTAGGAA": 10920, + "TAATTAATG": 10921, + "CATGAATG": 10922, + "GTGGACTT": 10923, + "CTATAGTT": 10924, + "GAATTGAAAA": 10925, + "GGTACCA": 10926, + "CGCCTGTAGTCCCAGCTA": 10927, + "TATATACACA": 10928, + "CTGGGTCA": 10929, + "CTGACTAA": 10930, + "CACCTCAGCCTCC": 10931, + "GAGGCGA": 10932, + "TGGACAGA": 10933, + "TCCTTCAA": 10934, + "TCCTGGAA": 10935, + "TACAGATG": 10936, + "TAAAAACAA": 10937, + "CTTGGACTGCAA": 10938, + "GACGGTT": 10939, + "GTATGCC": 10940, + "TCAAACTA": 10941, + "CGAAAATG": 10942, + "TCACGCC": 10943, + "GTGCTCTG": 10944, + "GTGAGCCA": 10945, + "GTAATGTA": 10946, + "GTTAGCAA": 10947, + "TACATCTA": 10948, + "CTTAATTAA": 10949, + "GGGAAGGGAA": 10950, + "TAGTCCCAGCTA": 10951, + 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10998, + "GAATAGTT": 10999, + "TAAATAGTT": 11000, + "TATTGTTAA": 11001, + "GAATTTGAA": 11002, + "GTACACTG": 11003, + "GCAATATG": 11004, + "GGTTGAGG": 11005, + "GTACACC": 11006, + "GATACAGG": 11007, + "TCGTTAAA": 11008, + "GAAGGAGG": 11009, + "GTCCAGTC": 11010, + "CCAATATA": 11011, + "TGCATATA": 11012, + "CCCAGATT": 11013, + "GAAGTCTT": 11014, + "CTGGTCTT": 11015, + "CTGGCATT": 11016, + "TCAAGTTTT": 11017, + "CCCATCCA": 11018, + "TCTCCTTAA": 11019, + "GATTTACA": 11020, + "TCTCTCAAA": 11021, + "CTGTTTCCA": 11022, + "TGACCCTT": 11023, + "CTGGGATG": 11024, + "TGTGTTTTA": 11025, + "TCAACGG": 11026, + "GTTAGATG": 11027, + "CACACACATG": 11028, + "CTAAGATG": 11029, + "GATATCTT": 11030, + "GGACCTGA": 11031, + "GAGACCTT": 11032, + "GAATTTCTT": 11033, + "TGATTTATT": 11034, + "GCTCCGCCTCC": 11035, + "GCCGGGCA": 11036, + "GAGCAATG": 11037, + "CTTGATTTT": 11038, + "TCAATCC": 11039, + "GCTGGTAA": 11040, + "CACCCCTG": 11041, + "TAACCTAA": 11042, + "GCAGATTA": 11043, + "CATTTCTTTT": 11044, + "TCAACAGG": 11045, + "TACAGTCC": 11046, + "TTTTTTTTTTA": 11047, + "ATTA": 11048, + "GAAGTATT": 11049, + "TAGGCTCA": 11050, + "CTGCAGCCTT": 11051, + "TCACAGCAA": 11052, + "GACTTTTTT": 11053, + "GAAGGATG": 11054, + "GTGGCAGG": 11055, + "GGAATGTA": 11056, + "CATGCACCA": 11057, + "CCTATGAA": 11058, + "CTGTTCTCA": 11059, + "CCAGGGAGG": 11060, + "CAATAGTT": 11061, + "TATTTACTA": 11062, + "GCTAGATG": 11063, + "CCACATCCA": 11064, + "CCCTAATT": 11065, + "GTTGGTAA": 11066, + "CCATAGAA": 11067, + "GTAGTGAA": 11068, + "TTTTATCA": 11069, + "TAATTCTAA": 11070, + "TCTGATTTA": 11071, + "TGCCCTCA": 11072, + "CTGGTAAA": 11073, + "TAAACTAAA": 11074, + "TAGGCTTA": 11075, + "GGGAGTGA": 11076, + "CAATTCTA": 11077, + "GGTATTAA": 11078, + "CAACTATT": 11079, + "CTGGACTG": 11080, + "GTATAATA": 11081, + "GCTATTTA": 11082, + "GGAGTCAA": 11083, + "GGAGATTTT": 11084, + "GCTTTAAAA": 11085, + "TTTGCCTT": 11086, + "GCAGAGGAA": 11087, + "GGCGAGA": 11088, + "TATATGTATA": 11089, + "TATAAAATT": 11090, + "TAAGCTGA": 11091, + "GGTGGAAA": 11092, + "CATATCTG": 11093, + "CATAGAAAA": 11094, + "TCATTTCC": 11095, + "TACCTGTT": 11096, + "CAGACAAAA": 11097, + "GCAAGGTT": 11098, + "GTTAGATT": 11099, + "GGGATGTG": 11100, + "TCGTTTCA": 11101, + "CCCCCCTT": 11102, + "TAATTGTTTT": 11103, + "CTCTGCCTT": 11104, + "GAGTCCTG": 11105, + "TCCGTCC": 11106, + "CTAAGAGA": 11107, + "TAATTATTAA": 11108, + "GGAGATCGAGA": 11109, + "CCTTATTA": 11110, + "GAGTCCAA": 11111, + "GCACATAA": 11112, + "GTATACTG": 11113, + "GTAATGGG": 11114, + "CTCCCGCC": 11115, + "TTCATGTG": 11116, + "CTGCACCTG": 11117, + "GGCĊ": 11118, + "TAAATAAAAAA": 11119, + "GTCGGAA": 11120, + "CAGAGCTGA": 11121, + "CATGTACA": 11122, + "CATGGACA": 11123, + "TAGTATTA": 11124, + "GTATGTTTT": 11125, + "GTGGTGGTGGTG": 11126, + "CCACCCCCA": 11127, + "CTCCAACACCACAGTT": 11128, + "GCGGAGGA": 11129, + "TAAGGATT": 11130, + "TCATACCA": 11131, + "CAAGATTA": 11132, + "GTTGAGC": 11133, + "CTAGTAAA": 11134, + "CGACTCA": 11135, + "GCGCTCTG": 11136, + "TCTCCAGTT": 11137, + "CGTGTTTT": 11138, + "TACCTGA": 11139, + "GGGCTCCA": 11140, + "GGGACTCTCAAGAGTCTT": 11141, + "CTGGTGTT": 11142, + "GCAGAAGA": 11143, + "TCTTTCTCC": 11144, + "GTGACTC": 11145, + "GTCATGGA": 11146, + "CGGCTAA": 11147, + "GGCCCCTG": 11148, + "GAGGAGGGG": 11149, + "GAAACTTTT": 11150, + "GCTAGTT": 11151, + "GCAAATTAA": 11152, + "TGCCTGA": 11153, + "GACCTGA": 11154, + "CTCTGCCTG": 11155, + "CAGCTTAA": 11156, + "TAACTAAAA": 11157, + "CAAATCTA": 11158, + "TCTGTCTCC": 11159, + "CGACTTA": 11160, + "GCTAGATT": 11161, + "TAGCTATT": 11162, + "GAACATTTT": 11163, + "CCACTTAA": 11164, + "TTTGTAAA": 11165, + "CTCCTCTGTCCATG": 11166, + "GCCTCTTA": 11167, + "CATTGACA": 11168, + "TGTAGAGA": 11169, + "GGAAATTAA": 11170, + "CACTCCAA": 11171, + "CAGTCCAA": 11172, + "TACGGAA": 11173, + "GAGAAAATG": 11174, + "GAAGAAGG": 11175, + "CTTAAAATT": 11176, + "CACACCCA": 11177, + "TCTTCTGAA": 11178, + "CACTCTTTTTGTA": 11179, + "GAACCCTG": 11180, + "GGAGCCTT": 11181, + "TCAAAGAAA": 11182, + "GGGACTA": 11183, + "CCTCGCCCGG": 11184, + "CAACAGCAA": 11185, + "CCCCAAAAA": 11186, + "TATTCCTA": 11187, + "TGTATGA": 11188, + "TACCTAAA": 11189, + "GCATGTTTT": 11190, + "TCGACGG": 11191, + "TCTTTCTTA": 11192, + "TCAATCTA": 11193, + "CCTTTTTTTT": 11194, + "TCAAGAGTT": 11195, + "GCTTGTGA": 11196, + "CCAAGGCA": 11197, + "GGCCCAA": 11198, + "GCCACCAGGGAA": 11199, + "GCCTCGAACTCACAGAGA": 11200, + "TAGCCTA": 11201, + "GTGCATTA": 11202, + "GTGTACTT": 11203, + "GGATAGAA": 11204, + "GATGCTTA": 11205, + "GCTATTTTA": 11206, + "GAAGAGTA": 11207, + "GTGCTCAA": 11208, + "CAAACAAAAAA": 11209, + "CTCCCAGCA": 11210, + "CACGGTT": 11211, + "TATTTACTG": 11212, + "GCTGATTTT": 11213, + "CAATCATT": 11214, + "GTGGTGTG": 11215, + "GTTTGATG": 11216, + "GTCCATC": 11217, + "GGAATCTG": 11218, + "CTGACCCA": 11219, + "GCTTTCTGA": 11220, + "TCAGCAAAA": 11221, + "GATGTATG": 11222, + "TCTCAGCCTCC": 11223, + "CGCGGGG": 11224, + "CTGTGGTA": 11225, + "CGCTGAAA": 11226, + "TAACAAACCTG": 11227, + "GGCTCTGA": 11228, + "GTTGGATT": 11229, + "GCAGTGGG": 11230, + "GGACTGTT": 11231, + "GCCGTCC": 11232, + "TATTTATTAA": 11233, + "TCAAAAATG": 11234, + "TACAGCCA": 11235, + "CCTACTAA": 11236, + "CTAGGCCA": 11237, + "TAGATCTT": 11238, + "CTACATAA": 11239, + "CATTCAAAA": 11240, + "GTTTTCTCA": 11241, + "CAAGAATG": 11242, + "CCATGATT": 11243, + "CTACATCA": 11244, + "GTAAAAAAAA": 11245, + "CGCATTA": 11246, + "CTCCTGGCCTCAA": 11247, + "CCTCAATT": 11248, + "GGATGATT": 11249, + "GGAGTCTG": 11250, + "GCCTACTG": 11251, + "CTACTCTT": 11252, + "GAAATACAA": 11253, + "GCGGCAGG": 11254, + "CCCTCCTG": 11255, + "GACGTCC": 11256, + "GCTAAAATT": 11257, + "CTGGATTA": 11258, + "TTTTACTTA": 11259, + "CACCCTCC": 11260, + "CAATTTATT": 11261, + "TCACTTTTCACTT": 11262, + "TCAGAGTA": 11263, + "TCCTCGG": 11264, + "GCATACAA": 11265, + "TGGGGTTTCACCATGTT": 11266, + "GCAAATTTT": 11267, + "GGTAATTA": 11268, + "CAACTTTG": 11269, + "CAAGTCTT": 11270, + "GGAAGCCA": 11271, + "TTTACTCA": 11272, + "CATACCTT": 11273, + "CTGCGTT": 11274, + "CTTTATC": 11275, + "GTAGATGA": 11276, + "GCCAAAATT": 11277, + "TCTTGGCA": 11278, + "TTATTCTAA": 11279, + "GGATGAGATG": 11280, + "GAACCTGA": 11281, + "GCATACTT": 11282, + "GGATACTT": 11283, + "TAAACAAAAA": 11284, + "TAGGCAGG": 11285, + "CCTAATAA": 11286, + "GAGTAAAAA": 11287, + "GTACACAA": 11288, + "AAAATGTT": 11289, + "TGTGTACA": 11290, + "GGAATGCA": 11291, + "CGGTGCC": 11292, + "TCTAGGTG": 11293, + "CTTTTTGGA": 11294, + "TCCGTC": 11295, + "TCTTCCCTT": 11296, + "GAGATGCA": 11297, + "TCTACCCA": 11298, + "CGACTAA": 11299, + "CAATTTAA": 11300, + "GGACACGACTGAA": 11301, + "TAGĊ": 11302, + "GTGTCATG": 11303, + "CCATCATG": 11304, + "CTATCTTA": 11305, + "TTTGTAA": 11306, + "GGGGGGTG": 11307, + "GTAGGGAA": 11308, + "CATGTAGA": 11309, + "CCAAGACA": 11310, + "CGGGGTTTCACCATGTT": 11311, + "CCATAATT": 11312, + "CTCTCAAAA": 11313, + "GATTCAGG": 11314, + "CCAATGTT": 11315, + "GATGTAAA": 11316, + "TTTATAAAA": 11317, + "CTGCAGTA": 11318, + "CCAGATCA": 11319, + "CACACCTA": 11320, + "TCTCCACTG": 11321, + "GTTTTTAAAA": 11322, + "CAGAGGAGCCTG": 11323, + "CCGACTT": 11324, + "GTTGACTG": 11325, + "GGATATGA": 11326, + "GAAACAGTG": 11327, + "GTGAGGTAA": 11328, + "CCAATCAA": 11329, + "TCCCTTCC": 11330, + "GCAGGATG": 11331, + "CACCCAAA": 11332, + "GCACCTCC": 11333, + "GTTATAGA": 11334, + "GAAAATTCA": 11335, + "CAGAATTTT": 11336, + "CCGACTG": 11337, + "TGATCATG": 11338, + "CAATTTCA": 11339, + "TGAGGTTTG": 11340, + "TCCGGTG": 11341, + "TGTGTTATT": 11342, + "CCTTCGA": 11343, + "TGGAGCTG": 11344, + "CAAGCCAA": 11345, + "GAGGGACA": 11346, + "TCTAATTAA": 11347, + "CATTTACATT": 11348, + "TCAACTTTT": 11349, + "CTTACAGG": 11350, + "TTTTTTCTT": 11351, + "GACTTGGAA": 11352, + "GTCATAGA": 11353, + "TTTTAGTG": 11354, + "CCTTATTTT": 11355, + "CACGTTTT": 11356, + "GCACTCAA": 11357, + "GTCTCTCTT": 11358, + "GTCTCTGTT": 11359, + "TATACCTG": 11360, + "GTTTTCTGTT": 11361, + "CATAGGAA": 11362, + "TCATTGATG": 11363, + "GAATAATG": 11364, + "GTAAACTA": 11365, + "TGGGTTTGATGAAA": 11366, + "TGAGAAGG": 11367, + "GTTTCTGTT": 11368, + "CTGATCAA": 11369, + "GCTGTATT": 11370, + "CCATTCCTT": 11371, + "GCAGGCAGA": 11372, + "GCATGGTGGTG": 11373, + "TCGCAAAA": 11374, + "GGAGACTA": 11375, + "GTACGTA": 11376, + "TCCACAAAA": 11377, + "TCTAGTTCTA": 11378, + "TAATTCTTA": 11379, + "GAACCCAA": 11380, + "GCCGAAA": 11381, + "GTGAGGCC": 11382, + "CCTCTATG": 11383, + "TCTCCAAAAA": 11384, + "CAATTTTAA": 11385, + "TCTGTTATT": 11386, + "TGTATTTTTT": 11387, + "TCCTTCTT": 11388, + "AAAAAAAATG": 11389, + "GATCCGGCCA": 11390, + "TAGATTAA": 11391, + "GTGCCCTT": 11392, + "TAATAGAAA": 11393, + "TAAGGCTT": 11394, + "GTACATTTT": 11395, + "TATTTAATG": 11396, + "TGTTCCCA": 11397, + "TAGGTCTT": 11398, + "TCGAATA": 11399, + "CAAAGGTA": 11400, + "CATTGAGA": 11401, + "GTGAGCTT": 11402, + "GATTTATTTT": 11403, + "GCTCATTA": 11404, + "GTCAATTA": 11405, + "AAAAGAAA": 11406, + "GCTGCAGCA": 11407, + "CCACACGG": 11408, + "CAACCCTG": 11409, + "CATTAAATT": 11410, + "GCTATCTG": 11411, + "TAATTTCC": 11412, + "GCAAAGTA": 11413, + "GCCTGCTG": 11414, + "CTTGTGTG": 11415, + "GTGGTCTG": 11416, + "TCAGGTTA": 11417, + "GCAGATCC": 11418, + "GGAAGATA": 11419, + "CTTTTCTAA": 11420, + "TAGACTTA": 11421, + "CAATTTAAA": 11422, + "TGGAATACTA": 11423, + "CAACAGCC": 11424, + "GGATCTTA": 11425, + "GTTAGATA": 11426, + "TAGATACA": 11427, + "CATAGAGG": 11428, + "CAGCTATG": 11429, + "TAATCCCAACA": 11430, + "CTTGAGGA": 11431, + "GTGGACAA": 11432, + "GTAATGGA": 11433, + "TGTATCA": 11434, + "CCATGTTTT": 11435, + "TACTAATT": 11436, + "GTGGATTA": 11437, + "GTTGGAGG": 11438, + "GTAGACTT": 11439, + "TGTTTCAAA": 11440, + "GAAAATCTA": 11441, + "CGAATTTT": 11442, + "TCCCACCAA": 11443, + "GAACACTGA": 11444, + "CCGTATT": 11445, + "TAACCTGA": 11446, + "GAACTTAA": 11447, + "TTTGTATA": 11448, + "TGCCCCTG": 11449, + "CGATTAAA": 11450, + "TACTTATTTT": 11451, + "TCGGAGA": 11452, + "GCCTGAGA": 11453, + "TAAAATAGG": 11454, + "GTGAGGAAA": 11455, + "CCTTGTTTT": 11456, + "GATTGATG": 11457, + "TGCATATG": 11458, + "TACCTCAA": 11459, + "CCCAAGGAA": 11460, + "CCGCTCC": 11461, + "GGTAATAA": 11462, + "CTTGAAAAA": 11463, + "TGTGGTTTT": 11464, + "CAGACTTA": 11465, + "CTTTTGCTT": 11466, + "GAATTCTG": 11467, + "GTGGTGGG": 11468, + "GCCTAGTT": 11469, + "TAAACCCA": 11470, + "TCTTTATCA": 11471, + "GCAACTC": 11472, + "CACCCCCA": 11473, + "GCTAAATA": 11474, + "CAACTGTT": 11475, + "CAATAATAA": 11476, + "CAAATCCA": 11477, + "GAATTTCAA": 11478, + "TGAGGGAGG": 11479, + "GCATTTTAA": 11480, + "GTACGCA": 11481, + "CTATCTC": 11482, + "GAAAGTTTT": 11483, + "TTTTTTCTTTT": 11484, + "CCGCTC": 11485, + "GTGCAGCC": 11486, + "GTGTGTGTT": 11487, + "GTTGCTTTT": 11488, + "TACATATTTT": 11489, + "TGTATGAA": 11490, + "GATTTGCATT": 11491, + "CACTCTCA": 11492, + "TGACGTCA": 11493, + "TAAAATTATT": 11494, + "CTGACTCC": 11495, + "TTAATTCA": 11496, + "TTTGGGAA": 11497, + "TCTATTATT": 11498, + "TGTGCCCA": 11499, + "TATGTTATT": 11500, + "TGGAGTCA": 11501, + "TAACTGTG": 11502, + "CAACTCTT": 11503, + "AAAAAAATT": 11504, + "TATTTGCC": 11505, + "TGTTAAATT": 11506, + "CATTTACTT": 11507, + "CTGAGGAAA": 11508, + "TTTTATCTT": 11509, + "CTGCCTGA": 11510, + "GTGTAGTA": 11511, + "GCCTCATG": 11512, + "CAAAGTAAA": 11513, + "CGGGCTT": 11514, + "CACCTGAA": 11515, + "TCGCTGTG": 11516, + "CTTCAGTC": 11517, + "GTGCTATT": 11518, + "TAAATATATT": 11519, + "CATTGGTG": 11520, + "CCTTCCCTT": 11521, + "CATAGTCA": 11522, + "GGTACTA": 11523, + "GGTAGATG": 11524, + "TAAATGTTTT": 11525, + "TCACAAAAA": 11526, + "TGACCTAA": 11527, + "GAAAAACC": 11528, + "GAATGCTGA": 11529, + "TCGCCATT": 11530, + "AAAATGAA": 11531, + "CAGAGGCTG": 11532, + "TAAAACTTA": 11533, + "CTTTTTTATT": 11534, + "TTTAGATT": 11535, + "GACATATG": 11536, + "CTGAATTTG": 11537, + "TCAAAAGACTACACACTTATT": 11538, + "TCAATATTTT": 11539, + "GTCCCTTA": 11540, + "GCTAGTTTT": 11541, + "GAATACACA": 11542, + "CTTCAGCTT": 11543, + "CCCGAGA": 11544, + "CTTCACTA": 11545, + "GTTGAGCA": 11546, + "GGAGTTAA": 11547, + "CAAAGTCAA": 11548, + "TGGGGAGA": 11549, + "TCAGCAGG": 11550, + "CAAAGGACA": 11551, + "GTGAAGTAA": 11552, + "TACCTGAA": 11553, + "GCTAATTTTA": 11554, + "CAAAAGTAA": 11555, + "GGCGCAAA": 11556, + "TTAGATA": 11557, + "AAAATATAA": 11558, + "TCAACTC": 11559, + "TACATAATT": 11560, + "CCATTGCCTT": 11561, + "GCCTTGAACTCA": 11562, + "GCTCAAAAA": 11563, + "CAACGCA": 11564, + "CTTTAGTA": 11565, + "CCTGCCCTG": 11566, + "GATGCTAA": 11567, + "CATAGCTT": 11568, + "TCATTTCTG": 11569, + "TCCGCCCGCC": 11570, + "GCCGGGCGTGGTG": 11571, + "GTGCATCA": 11572, + "TAAATTATG": 11573, + "GTGATGCA": 11574, + "CCAATATG": 11575, + "GGTTCCA": 11576, + "TGTATCTT": 11577, + "CGCTCTC": 11578, + "GCGCACC": 11579, + "TAAAAGGA": 11580, + "CAACAATG": 11581, + "CTATGATT": 11582, + "CATGTAGG": 11583, + "CGACCCC": 11584, + "CACTCTTTT": 11585, + "CCCGAAA": 11586, + "TAAATAATAA": 11587, + "CACATAGA": 11588, + "GTGCTGGGATTATAGG": 11589, + "TAAGCCTT": 11590, + "TCCGTAA": 11591, + "CCTTCCCTG": 11592, + "GTCACCCTG": 11593, + "TTTGTATG": 11594, + "CTGTAATA": 11595, + "GTTTAGGA": 11596, + "CCACTCAA": 11597, + "GGAATGAAA": 11598, + "GTTTACATT": 11599, + "TAAGCTCA": 11600, + "GTTTCCTGA": 11601, + "GCCGGCA": 11602, + "GCAACCTG": 11603, + "TTTTATGTG": 11604, + "GTTATGAAA": 11605, + "GGGGCTCA": 11606, + "GTGGAAAAA": 11607, + "CTGATATG": 11608, + "GCCATCCA": 11609, + "TTTATGA": 11610, + "CAGTATTTT": 11611, + "GTGGCACATG": 11612, + "TCCTCTCTT": 11613, + "GTGCGGA": 11614, + "TATAĊ": 11615, + "GTGAGGCA": 11616, + "GATGTATT": 11617, + "TCGAAGA": 11618, + "TCCAAATA": 11619, + "CTGGCCAA": 11620, + "CCATTTTTT": 11621, + "CTTAGAAAA": 11622, + "GGTAGCC": 11623, + "GGGCGGGG": 11624, + "GCGTTGG": 11625, + "TCATTTAAA": 11626, + "CTACCATT": 11627, + "CGAGAAAA": 11628, + "GGGATGTT": 11629, + "GGGACAAA": 11630, + "TTTTCTC": 11631, + "GTTTATATT": 11632, + "CCTTGTGAA": 11633, + "CTCCTCTC": 11634, + "CTTATGTA": 11635, + "CACCCTGA": 11636, + "CTCTGGAGG": 11637, + "GACCAGGCTG": 11638, + "GGGCCATG": 11639, + "TCTTTATAA": 11640, + "TGTTTACTT": 11641, + "CAGATGAAA": 11642, + "TCAAATATT": 11643, + "GAGCTAAA": 11644, + "CTGATGCTGAA": 11645, + "TCGTTGA": 11646, + "CCACCTGA": 11647, + "GTATATGA": 11648, + "CTACTTAA": 11649, + "CTACAATT": 11650, + "TAGACTCA": 11651, + "CTATTTATT": 11652, + "CCAGGATT": 11653, + "CCTGGGTA": 11654, + "CTAATGCA": 11655, + "TGCCTCTT": 11656, + "GATCGTT": 11657, + "GGATGAGG": 11658, + "CACATAAAA": 11659, + "TCAAATTAAA": 11660, + "TCTTCAAACCCATG": 11661, + "CAAGCTCA": 11662, + "CCTATCAA": 11663, + "CGGGGTTTCA": 11664, + "CCGCCTG": 11665, + "TAGTCAAA": 11666, + "CTTGAGTG": 11667, + "GCATAGCA": 11668, + "CGGCGTG": 11669, + "CCAAGTTA": 11670, + "GCTATCAA": 11671, + "GCTACTTTT": 11672, + "CAGCTACA": 11673, + "CTGCGCA": 11674, + "GTGCCCCA": 11675, + "GTCTAATG": 11676, + "GCAATGGA": 11677, + "GGTGCAGG": 11678, + "CCGCGCC": 11679, + "TGAGTCTG": 11680, + "CAAATATCA": 11681, + "CAAGAGTG": 11682, + "GTGCGAA": 11683, + "TAAAGTTAA": 11684, + "CAAATAAATT": 11685, + "GTCAGTTTT": 11686, + "GATGCTTTT": 11687, + "GACATTTTA": 11688, + "GCAGGCAGG": 11689, + "CCGATCA": 11690, + "CTGGTGAA": 11691, + "CTTGCTTTT": 11692, + "GTAGTGTT": 11693, + "GCTGCTCTT": 11694, + "GATGTCAA": 11695, + "CCCAGCTCC": 11696, + "TACCCAGG": 11697, + "CTCTGTCA": 11698, + "CCAGGCCTG": 11699, + "CAGAGACAGAGA": 11700, + "TTTTAAAAAA": 11701, + "TCCGTGA": 11702, + "GGATGATG": 11703, + "CTCGTTTT": 11704, + "GGAATAAAA": 11705, + "TGCCCACA": 11706, + "GCGGAGA": 11707, + "CTGCATCC": 11708, + "CTATTTTAA": 11709, + "GGAAGCAAA": 11710, + "TAGATCAA": 11711, + "TCTCCTCTA": 11712, + "CTAAATATA": 11713, + "GCAGGGCTG": 11714, + "TCCGCTT": 11715, + "GTAGCATT": 11716, + "TCTGCCCC": 11717, + "CCGTGTG": 11718, + "CCCGCCA": 11719, + "TAAGGTGA": 11720, + "GCCTATTA": 11721, + "GAAACAGGA": 11722, + "GAGATATA": 11723, + "TAAAACATA": 11724, + "GATTGCAA": 11725, + "CTTAGGTT": 11726, + "GACCTCAA": 11727, + "GTTTATTTTA": 11728, + "TCTAAAAAAA": 11729, + "GTAGAAAAA": 11730, + "TGAAGTC": 11731, + "CTGACGA": 11732, + "CCTTGTC": 11733, + "CTAACTCA": 11734, + "GACACGA": 11735, + "GGGACATG": 11736, + "CCTACTTTT": 11737, + "GTTCACTA": 11738, + "CCAGGAGG": 11739, + "GTACACTT": 11740, + "CACCCATT": 11741, + "TCCAAGAA": 11742, + "TCTAATTTG": 11743, + "CAGTAATG": 11744, + "CCGACAA": 11745, + "GCATAGTT": 11746, + "TATTCTCC": 11747, + "TATTCATAA": 11748, + "CAAACCAAA": 11749, + "CCAACAGG": 11750, + "CTATTTCTT": 11751, + "CCACTCTG": 11752, + "GGAAAAATG": 11753, + "TCTCCCGG": 11754, + "CATGATTA": 11755, + "TCCGG": 11756, + "CTGGTTAA": 11757, + "GTCACCTA": 11758, + "CAAAATGTA": 11759, + "GACCCTCA": 11760, + "CGGAGCC": 11761, + "GAGCAGGGG": 11762, + "GAAGATTA": 11763, + "GAAAGAGAAA": 11764, + "CCGGGTT": 11765, + "TCTTĊ": 11766, + "TACTATTTT": 11767, + "TATCCTTA": 11768, + "CTGCAGCTG": 11769, + "GTTTATC": 11770, + "CTCTAGC": 11771, + "CTGATCTG": 11772, + "GTCATTTTA": 11773, + "CAAAAGAAAA": 11774, + "GGGACCTG": 11775, + "GCAAAGAGG": 11776, + "CCCATTAA": 11777, + "GTGCGTA": 11778, + "GTTTTGCTT": 11779, + "TAGGTGGG": 11780, + "GATATCTG": 11781, + "GCGGCGA": 11782, + "TAACAGAAA": 11783, + "CAAGTTTG": 11784, + "CTGGAGC": 11785, + "GTGGAATG": 11786, + "CTAATGTG": 11787, + "CCCAGAAAA": 11788, + "GATATATA": 11789, + "TGAGCCTT": 11790, + "GAACTGTT": 11791, + "CTGCACAA": 11792, + "CCACGTA": 11793, + "CTACAGCC": 11794, + "CACACGTA": 11795, + "CCTTCGTT": 11796, + "GGAGGTTTT": 11797, + "TCTTTTCTTTT": 11798, + "GCGAAGA": 11799, + "TGAGCCTG": 11800, + "GCCCAGTA": 11801, + "CCAAGAAAA": 11802, + "GCCACTGTG": 11803, + "TATTTTTAGTAGAGA": 11804, + "GCAGGTTA": 11805, + "TCTTTCCCAGCA": 11806, + "CAAATACTT": 11807, + "GAGAGTTTT": 11808, + "CGGTCAA": 11809, + "AAAATTTA": 11810, + "TCTTGAATT": 11811, + "CTCAGGAAA": 11812, + "GAGGTGTT": 11813, + "TTTGGGA": 11814, + "TCGTTGG": 11815, + "GAAACTC": 11816, + "GAAAGGGAA": 11817, + "CATGAAATT": 11818, + "GCAAGCTT": 11819, + "GATTGTTA": 11820, + "GACAATTA": 11821, + "TAAACATTTT": 11822, + "TGTAGCTG": 11823, + "GGGTGGGGG": 11824, + "CACGCCA": 11825, + "CAAGGTGA": 11826, + "TGATTTCAA": 11827, + "GGGCTGAA": 11828, + "GCATAATG": 11829, + "GTACATAA": 11830, + "TGTGTCATT": 11831, + "CAGACATA": 11832, + "CTAATATG": 11833, + "GATGTCTG": 11834, + "TCTGAGGTT": 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"GTTTTTGTTTT": 11881, + "GCAGAGAAA": 11882, + "TCTTGTCC": 11883, + "GCTGGTC": 11884, + "TCGTGAAA": 11885, + "TAAAAAAAAAAA": 11886, + "GGATTTTTT": 11887, + "CCAGAATG": 11888, + "TAAAACCAA": 11889, + "CACAGTCC": 11890, + "TCTCTCATT": 11891, + "GCCATATA": 11892, + "TCAGGAGTTTGAGA": 11893, + "TACĊ": 11894, + "GCAGCTC": 11895, + "CTAAGTGA": 11896, + "CATACCAA": 11897, + "GCCTAATT": 11898, + "CATACGG": 11899, + "GGCATGA": 11900, + "TATTTTTTTG": 11901, + "GGAGGGGA": 11902, + "CACACACACACACACACACA": 11903, + "TATTTATTTATT": 11904, + "TAAGACTA": 11905, + "CTGTATGA": 11906, + "GTTCCAGG": 11907, + "GAAAGCTGA": 11908, + "TCTCCAGCC": 11909, + "CAGTCAAAA": 11910, + "GACGTTTT": 11911, + "CTGTTTTTT": 11912, + "GTGCACAA": 11913, + "GTAAGGAAA": 11914, + "GAGCTGAAA": 11915, + "CTTCTTCTTCTTCTTCTTCTTCTT": 11916, + "TAAGAAGG": 11917, + "CAATCCAA": 11918, + "GTACAGGA": 11919, + "CACTTTCTG": 11920, + "TAAATTGGTT": 11921, + "CCGAGCC": 11922, + "GTCTCCAAA": 11923, + "GAGGGCTT": 11924, + "TCTCCATGTT": 11925, + "GATGGTCA": 11926, + "CAAAATTCA": 11927, + "CGTGTTTG": 11928, + "GGCCCCTT": 11929, + "GGCGGTG": 11930, + "TCGTTCC": 11931, + "GTAGATCA": 11932, + "TATAGGTT": 11933, + "CAAACTC": 11934, + "CAATTTCTT": 11935, + "TCATGATG": 11936, + "GTTCCTCTG": 11937, + "CTTATGTG": 11938, + "TCCTTCCTG": 11939, + "TTTTTATTTTA": 11940, + "GGTGTGAA": 11941, + "TCGĊ": 11942, + "CTGGAGCC": 11943, + "GTGCATGA": 11944, + "GTTTAAGA": 11945, + "GCATAAAAA": 11946, + "CAAACTCC": 11947, + "GCTGCCCC": 11948, + "GCTGGGTCA": 11949, + "CAGTAAAAA": 11950, + "CCGTTAA": 11951, + "TTTTGCAA": 11952, + "GCCTATAA": 11953, + "CTAGACC": 11954, + "TACAĊ": 11955, + "TACCTCTG": 11956, + "CATTACTTTG": 11957, + "GCCATACTG": 11958, + "TAAGTGTG": 11959, + "CTACACTA": 11960, + "TATATCCA": 11961, + "GAGATCAA": 11962, + "CTCAGTTTT": 11963, + "TCTTTTTTAA": 11964, + "GAACATGTG": 11965, + "TACAGATT": 11966, + "GGCTCCTT": 11967, + "TGATCTAA": 11968, + "TCCTAGTG": 11969, + "CAATAGTG": 11970, + "GTACTGTT": 11971, + "GAAATATTTT": 11972, + "TAATTGCTT": 11973, + "CTGGGTTA": 11974, + "TCACATCC": 11975, + "GCAGTCTG": 11976, + "GGACATTTT": 11977, + "CCACCCAA": 11978, + "TAAAAAAATG": 11979, + "GAGAAATGCAAATCAAAA": 11980, + "TTTTCAGA": 11981, + "GAACAGC": 11982, + "GTGGGCAA": 11983, + "CCAGTTAA": 11984, + "CCTTGTTA": 11985, + "CCTTCTCTT": 11986, + "CTTTTGATT": 11987, + "TAGGCATT": 11988, + "GGTTGGTG": 11989, + "TGCCCAGA": 11990, + "GGCTGGAGTGCAGTG": 11991, + "TCTAATTAAA": 11992, + "TATCTTTG": 11993, + "CTTCGCC": 11994, + "TGTTGGCCA": 11995, + "GAGAGGGAA": 11996, + "GCAGACGCTCTTATCCAGA": 11997, + "TTTTCAGG": 11998, + "CTGGTTTG": 11999, + "GTGTGTGTGTGTGTGTGTG": 12000, + "GTCTAGCA": 12001, + "CCACCCCTG": 12002, + "CACACAGAA": 12003, + "GCTACCCA": 12004, + "TTCAGTTA": 12005, + "TGACACTA": 12006, + "GTGTACTG": 12007, + "TCTTCGA": 12008, + "TCTTCCTGA": 12009, + "GAGATGGAA": 12010, + "CAGAGGTA": 12011, + "GAAAAAAATG": 12012, + "GACATATT": 12013, + "GCAGGCAGATT": 12014, + "GCGCAGTG": 12015, + "TGACTCTG": 12016, + "GTGCTTAA": 12017, + "GTGCATTTT": 12018, + "CTTTTCTTA": 12019, + "TTATTATTATT": 12020, + "GATCGTA": 12021, + "GCGCTGTG": 12022, + "GAATCTC": 12023, + "GTTCCTAA": 12024, + "GGAGCATT": 12025, + "GAAATCCA": 12026, + "CATTTCTGA": 12027, + "GCCATCTA": 12028, + "GGTTGGTCA": 12029, + "GTATTTATA": 12030, + "TCGCCCC": 12031, + "GTGTATCA": 12032, + "GCATCCTG": 12033, + "CAAACATTA": 12034, + "TATGCAAAA": 12035, + "CTAAGTTA": 12036, + "CTAAAAAAAA": 12037, + "CCTCACTCA": 12038, + "GAGGAGGAA": 12039, + "TCTCCAATT": 12040, + "TAATTCCAGCA": 12041, + "TCAGGTTTG": 12042, + "CTGGAGTA": 12043, + "CCATAGCA": 12044, + "GTACGC": 12045, + "GTACATCA": 12046, + "GTAGACAA": 12047, + "AAAATTTG": 12048, + "CATTCCCTT": 12049, + "GTGACGA": 12050, + "CTCCTCTCC": 12051, + "CTCCCACCA": 12052, + "GGCCCTCA": 12053, + "GGCGAAA": 12054, + "GTAGAATG": 12055, + "GAGAGTTA": 12056, + "TTAATTTTA": 12057, + "GACAGTTA": 12058, + "CCCCTCTCC": 12059, + "CCCGCTT": 12060, + "TAACCTTA": 12061, + "TAAATACTG": 12062, + "CTAATGTA": 12063, + "GGCATCTG": 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12111, + "GCAGCTAA": 12112, + "GCAAGAAAA": 12113, + "GCATATGA": 12114, + "TATTCGA": 12115, + "TCTCCACCA": 12116, + "TACACTTTT": 12117, + "GAACTTAAA": 12118, + "GGAGAGTA": 12119, + "CTCTCCCA": 12120, + "GTGAAGACA": 12121, + "CCACCATGCCCA": 12122, + "TCGAGAAA": 12123, + "CTCTCCATT": 12124, + "GTCTAATA": 12125, + "GAAAGCCA": 12126, + "GGGGGATG": 12127, + "GGACTACAGGTG": 12128, + "TCAGAGAAA": 12129, + "CTAATAAAA": 12130, + "GGCCTGAA": 12131, + "CAACAGAGTGA": 12132, + "CCCTCATT": 12133, + "TCGTTGTT": 12134, + "TCCCCTTTT": 12135, + "CTGATGAAA": 12136, + "CTCATAGA": 12137, + "CCAATGTG": 12138, + "GCACCATG": 12139, + "CTACATGA": 12140, + "TGATCCTG": 12141, + "TGATAGTG": 12142, + "CTCCCCTA": 12143, + "TAGCTATA": 12144, + "GGAGTTCGAGA": 12145, + "CCGGGGA": 12146, + "TAATTAAAAA": 12147, + "GAATACCA": 12148, + "CTGAGTCC": 12149, + "TATGGGTG": 12150, + "TCCACTTTT": 12151, + "TTCACAAA": 12152, + "TGTACTT": 12153, + "GCGCTTTT": 12154, + "TAAGCTTA": 12155, + "TAAGTATG": 12156, + "TCATTATTA": 12157, + "CAAGTGATT": 12158, + "GGATGAGTT": 12159, + "TAAAGAATT": 12160, + "TGTAGATT": 12161, + "GGGTCACAAA": 12162, + "GCACTGGG": 12163, + "TATACATTA": 12164, + "GCTGGATCA": 12165, + "CTAACTTA": 12166, + "AAAAAATTAA": 12167, + "GAAAAATTAA": 12168, + "GTTCAAGCGATT": 12169, + "TCCCGTA": 12170, + "GCCCTTAA": 12171, + "GAAACAGCA": 12172, + "CTCATATT": 12173, + "CAAAACCAA": 12174, + "CTCTTTGAA": 12175, + "GCTGATTA": 12176, + "GCTGAGATT": 12177, + "GACCCCTG": 12178, + "CCTGTTAA": 12179, + "GTTGGGTG": 12180, + "GGATAGGA": 12181, + "GTCCACTA": 12182, + "CAGGTCAA": 12183, + "CAGATACA": 12184, + "CGACGTG": 12185, + "TGGATAAA": 12186, + "TCACCCAGGCTG": 12187, + "CAGAAAAAAA": 12188, + "GAGTGATG": 12189, + "CCCACTTA": 12190, + "CATATCTA": 12191, + "TGCCCTGA": 12192, + "GAATGCCAA": 12193, + "TACGTCA": 12194, + "TTTTCACA": 12195, + "TCCTTATG": 12196, + "TCCTAGGA": 12197, + "CAATCTGA": 12198, + "GCACTCTG": 12199, + "GGACATAA": 12200, + "TATGCTTTT": 12201, + "CCATTGTA": 12202, + "CAGTCCTG": 12203, + "TCATAAATT": 12204, + "CTACCTCA": 12205, + "TTTAGTAA": 12206, + "CTCTTCTT": 12207, + "GAAGGGAGG": 12208, + "GCGCACGCCTTTAATCCCAGCA": 12209, + "GGAGTCTT": 12210, + "CCATAGTT": 12211, + "GTCTCAGGA": 12212, + "GAAATAATA": 12213, + "TATATTTTAA": 12214, + "GCCATTTTA": 12215, + "TAATCCCAGCACTTTGGGAGG": 12216, + "GTCTCTCTG": 12217, + "TATGTATAA": 12218, + "TGCAGTAA": 12219, + "TACACATG": 12220, + "GCTATTAAA": 12221, + "GAAACCAAA": 12222, + "GCAATCAA": 12223, + "CTAAGAAAA": 12224, + "CATACAAAA": 12225, + "TGCACAAA": 12226, + "CGGATTCA": 12227, + "GCCTGGCCAA": 12228, + "GGAGACTGA": 12229, + "TATTTATTTATTTT": 12230, + "GAATCTAA": 12231, + "GTTGATCA": 12232, + "GAAGATGAA": 12233, + "CTTGACCA": 12234, + "GTTCTGGG": 12235, + "CTACACTT": 12236, + "TAAACGTT": 12237, + "GCTGGAGAA": 12238, + "CCCATTTTA": 12239, + "TTATTTAAAA": 12240, + "GGATGTTA": 12241, + "TATGTATTTT": 12242, + "GATTTATTA": 12243, + "CATCAATG": 12244, + "CCGGCTT": 12245, + "TTTTTAGA": 12246, + "CTGGATGA": 12247, + "AAAACAAA": 12248, + "CATTGAGG": 12249, + "CCTGGTCC": 12250, + "TCTCTCCTG": 12251, + "TCTAGATA": 12252, + "CTGTGATG": 12253, + "TATACATAA": 12254, + "CCAACCTT": 12255, + "CTAATCTT": 12256, + "CTGAATTAA": 12257, + "GGTAGGAA": 12258, + "GAAGTAAAA": 12259, + "TGGGTGTG": 12260, + "GGTTCTCA": 12261, + "CACACACTCA": 12262, + "GTGGTCAA": 12263, + "GTATGATT": 12264, + "TATTCTGTT": 12265, + "TGTAGAGG": 12266, + "GGCACTA": 12267, + "GGTCGTG": 12268, + "GTTGAATG": 12269, + "GTTTATTAA": 12270, + "GTCAAAATT": 12271, + "GGTGTTTTA": 12272, + "GGTGCAGTG": 12273, + "TGCATGAA": 12274, + "TGATACTA": 12275, + "CTGTAGTG": 12276, + "GGACGCC": 12277, + "GTACCCC": 12278, + "TCTCAGCTT": 12279, + "GGTATGTG": 12280, + "GCTGGACA": 12281, + "GTAAGAAAA": 12282, + "GGGGGTGA": 12283, + "GAAAAAGAAAA": 12284, + "CCTCGGCC": 12285, + "TCTTGGGA": 12286, + "CCTTTAGG": 12287, + "TCCACCTA": 12288, + "CTTTTTCAA": 12289, + "TATATGAAAA": 12290, + "GACTCCGTCTCAAAA": 12291, + "CTAATACAA": 12292, + "GTAAATATA": 12293, + "CCATTTATT": 12294, + "GGCTGGGAA": 12295, + "GCGGGTG": 12296, + "CTTGGTTA": 12297, + "CCCTCTTTT": 12298, + "ATTTT": 12299, + "GCACTCCAGCCTGGG": 12300, + "CCATACTT": 12301, + "TAAAAGAGA": 12302, + "GGGGCTA": 12303, + "CTGCAGCTT": 12304, + "TGACTATG": 12305, + "TCCGGTT": 12306, + "TCTTCCCTG": 12307, + "CAGAGTTTT": 12308, + "GTCACGA": 12309, + "GTAATAGA": 12310, + "TCTCCCTCA": 12311, + "GCAAAAGAAA": 12312, + "TAAAGCTA": 12313, + "TAAATGCC": 12314, + "TCTTCAGTT": 12315, + "CAAACAGCA": 12316, + "CTTTTTTTTTTTTTTTT": 12317, + "CAATTTCAA": 12318, + "GCATTTATT": 12319, + "CCGAATT": 12320, + "GAAGGATT": 12321, + "CAATCTAA": 12322, + "GGACTCTG": 12323, + "CCACACTA": 12324, + "GCTCCTGGG": 12325, + "GAACTCTG": 12326, + "GCACCTGA": 12327, + "CCATAGTG": 12328, + "GCTGCGA": 12329, + "TATGTCTA": 12330, + "TCTCAAAAAAA": 12331, + "GATGCATG": 12332, + "TCCGAGG": 12333, + "GGAGAGCC": 12334, + "TACACGTG": 12335, + "GGGTCGCACAGA": 12336, + "TACGTGA": 12337, + "TCGTTCA": 12338, + "GACCTTAA": 12339, + "CAAGGTAA": 12340, + 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+ "TCTATCCA": 12388, + "TAAAAAGAAA": 12389, + "CTGTTCTTA": 12390, + "TGAGATCTT": 12391, + "TGAGATGAA": 12392, + "TAATAATTAA": 12393, + "TATATATATATATA": 12394, + "GTCCTTAAA": 12395, + "TCTTTTATG": 12396, + "TCGCTGTT": 12397, + "TAAGGGTT": 12398, + "GTGCATAA": 12399, + "GCCTAGCA": 12400, + "CATGTTTTA": 12401, + "CCCCTACC": 12402, + "TGCACTA": 12403, + "GTGTTTGTG": 12404, + "GAATCCAA": 12405, + "GTAAATGAA": 12406, + "CAGGTTAAA": 12407, + "TCGCGTG": 12408, + "CTTCTTGG": 12409, + "CCTGCCTCA": 12410, + "CAAAACAGA": 12411, + "CTGAAGAAA": 12412, + "CACTGTTA": 12413, + "CTTTATTTTA": 12414, + "GTTGGTTTT": 12415, + "CCCCATGGACTGTA": 12416, + "CTGATAGA": 12417, + "CCTGTATT": 12418, + "CGGATTA": 12419, + "TCAGGAATT": 12420, + "TAAACCAAA": 12421, + "TCTTTCACTT": 12422, + "CTAATGGG": 12423, + "GCTATATA": 12424, + "TCCTAATG": 12425, + "GTACGAA": 12426, + "GCCGGAA": 12427, + "GCCCCATG": 12428, + "TAAAGACAA": 12429, + "TGTTTCATT": 12430, + "GGGAGTTA": 12431, + "GACCCACA": 12432, + "TAGTAATT": 12433, + "TCGAACA": 12434, + "GAACCTCA": 12435, + "GAAAAAAAAAAA": 12436, + "CCATGATG": 12437, + "CTAGAGCA": 12438, + "CCTGCCTCC": 12439, + "GAAAAGTTA": 12440, + "GTTTTGTTA": 12441, + "GTCCTGCTG": 12442, + "TGCATATT": 12443, + "TGAGCATG": 12444, + "CAAGCCTT": 12445, + "CTGTCCTG": 12446, + "TTATTTCTG": 12447, + "TTCACTCA": 12448, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 12449, + "TAATATTTG": 12450, + "TTTTTTTTAA": 12451, + "GCTTTCAAA": 12452, + "TATGGCTA": 12453, + "CAAAATGAAA": 12454, + "TCTCCTGACCTT": 12455, + "TCTGATTTG": 12456, + "GCGCAGCA": 12457, + "CTACAAAAA": 12458, + "TGTTGGCAA": 12459, + "GGCCTGTT": 12460, + "GCCTGGTCTA": 12461, + "CCGGCGG": 12462, + "GTTGGATG": 12463, + "CCAGTTTG": 12464, + "TGTGGGGA": 12465, + "TATATATGA": 12466, + "CAAACACC": 12467, + "CATTGATA": 12468, + "CTCCCACTT": 12469, + "TAGATTTTA": 12470, + "CCTATTTTA": 12471, + "CAGTTTTAA": 12472, + "CTGGCTTA": 12473, + "TACATCCA": 12474, + "CGACGCA": 12475, + "TGCAGCTT": 12476, + "CTTTGGGAA": 12477, + "GCCTGATG": 12478, + "CGTGCGCA": 12479, + "TGCCTGAA": 12480, + "GAGAAGAAA": 12481, + "TATGAAAAA": 12482, + "GGCTCATT": 12483, + "TGAGCTAA": 12484, + "CAAGGATG": 12485, + "GTGTGAGG": 12486, + "CAGGCAGAA": 12487, + "TCTAGACA": 12488, + "GGAGACACA": 12489, + "CAACAGAGCAA": 12490, + "TGATAGTA": 12491, + "TAAAGGGAA": 12492, + "TCAAGGTA": 12493, + "TCATTCTTA": 12494, + "CTCCTGGGCTCAA": 12495, + "GATATTAAA": 12496, + "TCAGCTTA": 12497, + "CTGCATAA": 12498, + "TATTTATAAA": 12499, + "TGTACAAA": 12500, + "GTGAATTTG": 12501, + "GTTAATTTA": 12502, + "GTATTTCTT": 12503, + "GGGAGGAAA": 12504, + "TACTCCA": 12505, + "TAACCATG": 12506, + "TCAGCGG": 12507, + "GCCGCGG": 12508, + "CATTTCTTA": 12509, + "CCTTGGCA": 12510, + "GCCACGCC": 12511, + "TGTATCAA": 12512, + "CCAAAGTAA": 12513, + "CGTTTCC": 12514, + "CTCCAGTGTTCTTGCCTGGAGAATCCCAGG": 12515, + "TGAGCTCC": 12516, + "GAATGATG": 12517, + "GCCGATT": 12518, + "GCTCACTGCAGCC": 12519, + "TACCTCTT": 12520, + "GAGCGAGA": 12521, + "TAAGCAAAA": 12522, + "CACACACACACACACACACACACACACACACACACACACACACA": 12523, + "GGTGATGGACAGGGAGGCCTG": 12524, + "CCAGCAGG": 12525, + "GTACATGA": 12526, + "GCCCATTA": 12527, + "CCACAGTGA": 12528, + "GGACTCTT": 12529, + "GTATAATAA": 12530, + "CATTTATCA": 12531, + "GCTGTATG": 12532, + "CGGGTC": 12533, + "CTTACTTTT": 12534, + "TGGATGTG": 12535, + "TAATTGATT": 12536, + "GTTCAGCC": 12537, + "GGAGAAGAA": 12538, + "CTATAATA": 12539, + "TAACTTTA": 12540, + "TCACAGAAA": 12541, + "GCCCGTC": 12542, + "CTCTCATCA": 12543, + "CAGACAGCA": 12544, + "TACAGTGAA": 12545, + "TTCATGTT": 12546, + "CTGTTTCTG": 12547, + "CCTGTAGG": 12548, + "TCCAĊ": 12549, + "TGGACATG": 12550, + "TCTCCTCTG": 12551, + "GTAGAGC": 12552, + "TATGGAAAA": 12553, + "CCTGCACCA": 12554, + "GTTATAGG": 12555, + "GCCAATTTT": 12556, + "GAAGGTTA": 12557, + "CTTTAAATT": 12558, + "TCTCTACTT": 12559, + "TCCACGTG": 12560, + "TGAGGTTTT": 12561, + "TAGCTAAA": 12562, + "TCCCCTC": 12563, + "AAAATAAAA": 12564, + "TTTTACCTT": 12565, + "TCTCTCTTA": 12566, + "TTATTTCTA": 12567, + "CCATTTCTT": 12568, + "TCAAAAGACTA": 12569, + "GTAACAAAA": 12570, + "GAGGCAGAA": 12571, + "CTAACTGA": 12572, + "GGTTCAAA": 12573, + "TCATAGTA": 12574, + "GTGGAGTA": 12575, + "TGGGGCTG": 12576, + "GCTAGTCA": 12577, + "GGTAGATT": 12578, + "CTTCCCAAA": 12579, + "GTGGCTCC": 12580, + "GTTGGGCA": 12581, + "GTTAGTTTT": 12582, + "CTCCTCCTG": 12583, + "GGCATGTT": 12584, + "GGGAGCTT": 12585, + "CCGATGA": 12586, + "GTAGCCTT": 12587, + "TCTGGAGAA": 12588, + "CAGACCCA": 12589, + "TGGGCAGG": 12590, + "CTATGTTTT": 12591, + "GTGAAGAA": 12592, + "GGAGGAGAAGG": 12593, + "GTCATGTATGGATG": 12594, + "CTAGGATT": 12595, + "CTCTĊ": 12596, + "CATTCACC": 12597, + "TTTTACTTTT": 12598, + "GCCATACA": 12599, + "TTTACCTT": 12600, + "CTTTGGTA": 12601, + "CTTTGGATG": 12602, + "TCTTTTTTTG": 12603, + "TTATTTTTTA": 12604, + "TAAACATCA": 12605, + "GGAACTTA": 12606, + "GTGAGAAAA": 12607, + "GCAACGA": 12608, + "GTTACGG": 12609, + "GGTTGATT": 12610, + "TAATTTCTA": 12611, + "CTTTGGAAA": 12612, + "TAGCTATG": 12613, + "GAGCAAAAA": 12614, + "CTGCTGGG": 12615, + "CACAGGAAA": 12616, + "GCAATAGG": 12617, + "GATGTAGG": 12618, + "TTTACTGA": 12619, + "TAATTGAAA": 12620, + "GCGTTTTA": 12621, + "GCTCTGATT": 12622, + "CCACAAAAA": 12623, + "TAAAACTGA": 12624, + "CTGTGCTA": 12625, + "GAATCCCATG": 12626, + "GGTCTATA": 12627, + "GGGAGATG": 12628, + "GTGGGATG": 12629, + "GTTCGCA": 12630, + "GCACTCTT": 12631, + "TATACAGTG": 12632, + "GGTGGCTT": 12633, + "CGCGGA": 12634, + "CTGAAAAAA": 12635, + "CTTCGGA": 12636, + "CTTTCAGG": 12637, + "CTTTATATT": 12638, + "GTTCCATG": 12639, + "GTTTCTCTG": 12640, + "GTGTTTGTT": 12641, + "AAAAAAATTA": 12642, + "CAACTCTG": 12643, + "CTGTCCAA": 12644, + "GTGGACTA": 12645, + "GCATAGTG": 12646, + "CTCGCCA": 12647, + "CAGAĊ": 12648, + "TCAACCTCC": 12649, + "GAGGCATG": 12650, + "CCAACGG": 12651, + "GCCTAGTG": 12652, + "TCAACATTA": 12653, + "TTTTATGTA": 12654, + "GTTACATA": 12655, + "CCAAACCAAA": 12656, + "TAATATAAAA": 12657, + "GCCGGATCTCAGCTCACTGCAA": 12658, + "TCATCAAAA": 12659, + "TCAGCTAA": 12660, + "CTAGAGTT": 12661, + "TAAATCATG": 12662, + "TATACATTTT": 12663, + "CTAACCTT": 12664, + "TTATTGATT": 12665, + "CAATGCCA": 12666, + "CTAGCCGGGC": 12667, + "CAGGTTTGTTACA": 12668, + "TCGAAGAA": 12669, + "CTTTATTAA": 12670, + "TGTGGCCTT": 12671, + "TAATGGAA": 12672, + "TGACAGTGA": 12673, + "TTTTTGAAA": 12674, + "CTTCGCA": 12675, + "TGTTCATCA": 12676, + "GGTTTCAAA": 12677, + "GATACTGA": 12678, + "GACCCCTT": 12679, + "TCGTGGA": 12680, + "GTATTATG": 12681, + "GTCGCAA": 12682, + "GATGGAAAA": 12683, + "GATGGACA": 12684, + "GGCACTCA": 12685, + "CAAAAACAAA": 12686, + "GCTCTGGATAAGAGCGTCTG": 12687, + "CACACGTG": 12688, + "GAAAGAGGA": 12689, + "CTTATACA": 12690, + "TCCTGGCTAA": 12691, + "CAAGAAAAAA": 12692, + "TCAGGATG": 12693, + "CAAGAGTA": 12694, + "CTCGCAA": 12695, + "TCTTGACA": 12696, + "CCAATGGG": 12697, + "GCCAACAAA": 12698, + "TAAAAGACA": 12699, + "GCTTGAGA": 12700, + "GTTAATTTG": 12701, + "CTCCTGCCTCAGCC": 12702, + "GTGCCTCC": 12703, + "CTAGGCAA": 12704, + "TCTTCACC": 12705, + "TCTACCTA": 12706, + "CGACGCC": 12707, + "GAAGACATT": 12708, + "GGTCCCC": 12709, + "TCATACTA": 12710, + "GAAGCAGG": 12711, + "TAAATGATT": 12712, + "CAGGTAGG": 12713, + "GTAAATTTG": 12714, + "GCTCCCACA": 12715, + "TAGTCTA": 12716, + "GAGCTGGG": 12717, + "CAACAGAAA": 12718, + "CTTTAAAAAA": 12719, + "CCTGGGCC": 12720, + "TCTCCAGTG": 12721, + "GAGCTAA": 12722, + "GTCCAACTCTCACA": 12723, + "TCATAGTG": 12724, + "CAACCCTT": 12725, + "TATTCTTAA": 12726, + "TATTCCATT": 12727, + "GCTGGTTA": 12728, + "GATTAAATT": 12729, + "TTTAGTTA": 12730, + "TCAAACAAA": 12731, + "TCGAGTC": 12732, + "GCGAATA": 12733, + "GCTGCATATG": 12734, + "GTTTTTCTG": 12735, + "GGTGTCTG": 12736, + "TAATTCAAA": 12737, + "CTTGGTTTT": 12738, + "CACCTTTG": 12739, + "GAATTATG": 12740, + "CAGGTAGA": 12741, + "CGGCAGC": 12742, + "TGTGATTTT": 12743, + "CCCGTCA": 12744, + "TGTTĊ": 12745, + "GCCATAGG": 12746, + "GGCATGAA": 12747, + "CGCTAAA": 12748, + "CGGACGGA": 12749, + "CCGCATT": 12750, + "TAACCCTA": 12751, + "CAACCATT": 12752, + "GGACAATG": 12753, + "GTAGTCTT": 12754, + "GACTTAAAA": 12755, + "TGAATAGG": 12756, + "CCTTGACA": 12757, + "CTTTTGTTA": 12758, + "GGGCCAGA": 12759, + "GTCTCACTCTGTT": 12760, + "GGAGTGTG": 12761, + "GAAATATCA": 12762, + "CTGATAGG": 12763, + "GTAATGAAA": 12764, + "CCTGCTAA": 12765, + "TCCCATGAA": 12766, + "GATTTGCATTTCTC": 12767, + "TAACGTAA": 12768, + "GAACAATG": 12769, + "CTATAAATT": 12770, + "CATTTCCAA": 12771, + "GTGATCTT": 12772, + "GCAACAGAA": 12773, + "GTTAGAATG": 12774, + "TATATTAAAA": 12775, + "CCATCCAGCCATCTCA": 12776, + "CTGCTTAA": 12777, + "GCATCTAA": 12778, + "CTACACCA": 12779, + "CATTTAGAA": 12780, + "GCTGTATA": 12781, + "GCAAAAATG": 12782, + "TACATACACA": 12783, + "GGGGAAAAAA": 12784, + "TTATTTGA": 12785, + "TATATGTTA": 12786, + "CCGTGTTA": 12787, + "TCGATTAA": 12788, + "CTGTAGTA": 12789, + "GAATTTGTT": 12790, + "GGTATGTT": 12791, + "AAATATAAAA": 12792, + "GGAATGAAAA": 12793, + "GCGACTTCACTT": 12794, + "CTCATATA": 12795, + "GTAACAGG": 12796, + "GAGGGAGAA": 12797, + "CCAATAAAA": 12798, + "CATAGCC": 12799, + "TCAAAGTA": 12800, + "GTAAATGTT": 12801, + "GGGTACATGTG": 12802, + "CTAGGCC": 12803, + "CATTĊ": 12804, + "GTAATATTA": 12805, + "TTTTATAGA": 12806, + "CCTAGAAAA": 12807, + "TCTCCTCTT": 12808, + "TACTCTA": 12809, + "GTTGAGTA": 12810, + "TCTTGATA": 12811, + "CGGCACC": 12812, + "CCTAGTGA": 12813, + "GTCTGGAA": 12814, + "TCGCCACA": 12815, + "TAACAATA": 12816, + "TTTTCAAAA": 12817, + "CAATATTAA": 12818, + "TCAAGGGA": 12819, + "GGAAGTTTT": 12820, + "CTCACTCC": 12821, + "CCTTGGCTT": 12822, + "TACAGTATT": 12823, + "TGATTTGTT": 12824, + "GGCTGCAGTGA": 12825, + "TAGTCATT": 12826, + "CAACCCAA": 12827, + "GCATTATG": 12828, + "TATTTCAAAA": 12829, + "CAGGGTTTT": 12830, + "GGAAGTC": 12831, + "GCTGTAGA": 12832, + "CCAACTAA": 12833, + "TTAATGTT": 12834, + "GTAAACAAA": 12835, + "CATTAATTA": 12836, + "CTGCAGAGA": 12837, + "TAACCTCC": 12838, + "CTAGTTAA": 12839, + "TAAACACTT": 12840, + "TATGGACA": 12841, + "CCTGCTC": 12842, + "CGATAGA": 12843, + "CTTCCCCTT": 12844, + "GAGTTTTTT": 12845, + "GGACTCAA": 12846, + "GTACTTTG": 12847, + "TCAAGGCA": 12848, + "TAAAAGTCA": 12849, + "CCTAGCTT": 12850, + "GCCTGTTA": 12851, + "GCCTCCAAA": 12852, + "GGCAGATT": 12853, + "GATCAAAAA": 12854, + "CTAGAAAAA": 12855, + "GAGATTTTAA": 12856, + "GATGTATA": 12857, + "CGTATTTG": 12858, + "TAACATCC": 12859, + "TCAGGAGG": 12860, + "GGAGGAGA": 12861, + "CGGTGCA": 12862, + "CCCATCTCA": 12863, + "GGTTGCTT": 12864, + "TGATTTATA": 12865, + "GGTATTTG": 12866, + "CTGTACTA": 12867, + "GTTTCCAAA": 12868, + "CTATATGA": 12869, + "TAATTAAATT": 12870, + "CTTTTGAACTGTGGTGTT": 12871, + "CATATTAAA": 12872, + "GACTGATG": 12873, + "TAAGGGTG": 12874, + "CTTGAATG": 12875, + "GGACTGTG": 12876, + "TATTTCCAA": 12877, + "CCTTCAGAA": 12878, + "GATCGCC": 12879, + "TAAGGGCA": 12880, + "GCACAATG": 12881, + "GATTCTTTT": 12882, + "CATATCC": 12883, + "GAAGACAAA": 12884, + "TCAGCATG": 12885, + "CTGCTATT": 12886, + "CTGTACCA": 12887, + "GTGGAGCC": 12888, + "GTCTCTTTG": 12889, + "CACAGGTA": 12890, + "CCTGCAAAA": 12891, + "TCATTTGA": 12892, + "GTTCATATT": 12893, + "TCCGCCCGTC": 12894, + "CCTATTAA": 12895, + "GCTGCGC": 12896, + "GCTGGGATTACAGG": 12897, + "TAGGTGGAA": 12898, + "CTGCAGTCCATG": 12899, + "CTCCTTAAA": 12900, + "TAACAGCC": 12901, + "GTGAAAATG": 12902, + "CACAGCTA": 12903, + "CCTTGTCC": 12904, + "CCTGTAATCC": 12905, + "TCTCCAGGA": 12906, + "GTGAACAGG": 12907, + "GTGGGTTA": 12908, + "GTTAAAATT": 12909, + "CTCTATC": 12910, + "TTTACTTTT": 12911, + "CAAGACTA": 12912, + "CTCTCTGAA": 12913, + "GTCTAGTA": 12914, + "TCAATAGG": 12915, + "GCTTTAGA": 12916, + "GCTTCCTGA": 12917, + "GGGATGA": 12918, + "CCTCATTA": 12919, + "GCCATGAAA": 12920, + "GCAGTGAGCTGAGATCCGGCCA": 12921, + "GGCGGAA": 12922, + "GGATCCTG": 12923, + "GCCCATTTT": 12924, + "CAAATACC": 12925, + "CAGGCAAAA": 12926, + "CCTGGACA": 12927, + "CCCATATT": 12928, + "TCAAATGAA": 12929, + "TCATTCTTTT": 12930, + "CACTATTA": 12931, + "GAAGCCTT": 12932, + "CACAGAGAAA": 12933, + "GTTAGAAAA": 12934, + "GCTATCC": 12935, + "GAGGTTAAA": 12936, + "GGGTTATT": 12937, + "GGCGCTT": 12938, + "GTGGCATG": 12939, + "CAGGTGTA": 12940, + "GTTCGTA": 12941, + "GATGTCTA": 12942, + "TGATGCCA": 12943, + "AAATATTTTA": 12944, + "TGTTTGTTTGTTTGTT": 12945, + "GGTGTTTG": 12946, + "GCGĊ": 12947, + "GTAGAGTG": 12948, + "CTCCAGGAA": 12949, + "GTCTCACAA": 12950, + "TATTAAATG": 12951, + "CGGGTCA": 12952, + "GTTTTCTAA": 12953, + "GGCAAAAAA": 12954, + "TAAGAGAAA": 12955, + "CTGCATTTT": 12956, + "CAGGCTCC": 12957, + "GGCATCC": 12958, + "TTCAGAGA": 12959, + "TCTCATTTA": 12960, + "TGGGTTTGAA": 12961, + "GTGCGCC": 12962, + "GCCCTGCC": 12963, + "TCTTCTAAA": 12964, + "CAATGGAAA": 12965, + "GCACAAAAA": 12966, + "GCACTATT": 12967, + "TAAACTTAA": 12968, + "GTCCCAGCA": 12969, + "TCTCCTGCA": 12970, + "GAATTCAA": 12971, + "CCATTCCATT": 12972, + "TGAGATGGAGTC": 12973, + "GCTCTCCTT": 12974, + "TTTTGAAAA": 12975, + "TTCAGCTT": 12976, + "CTGTTGTCA": 12977, + "CGTCTTTG": 12978, + "TAGTGTTA": 12979, + "GAGGTCC": 12980, + "GATGCTCAA": 12981, + "TCTACTCC": 12982, + "GTATTTGTT": 12983, + "TAATTTTAAAA": 12984, + "CCCAGATG": 12985, + "GACTCCTG": 12986, + "GTTCCTTTT": 12987, + "TATTGCTA": 12988, + "CAAAGACAA": 12989, + "TCTCTATAA": 12990, + "GTTAGGTT": 12991, + "TCCACGCA": 12992, + "GGTGTGA": 12993, + "GGAGGGGAA": 12994, + "CATCCTTA": 12995, + "TAGGAATT": 12996, + "GACACACA": 12997, + "CAACCTCC": 12998, + "CTGCATGA": 12999, + "CTCTTTTTG": 13000, + "CAGGTCTT": 13001, + "GCTGCGTG": 13002, + "GTTTTCTGA": 13003, + "TCATTTATA": 13004, + "GGTATTTTA": 13005, + "GCCTGAAAA": 13006, + "TGGAACAGAA": 13007, + "GCACCAGG": 13008, + "GGACAAAAA": 13009, + "CACACTCTT": 13010, + "GATTGAGG": 13011, + "TTATTCTTA": 13012, + "TAATATGTT": 13013, + "CACTCAAAA": 13014, + "CCCGTGA": 13015, + "TCACCTTA": 13016, + "TCAAGGGG": 13017, + "GCTGAAATT": 13018, + "GATACTTA": 13019, + "GTTAAGAA": 13020, + "TCGGTTTT": 13021, + "CTTTATCTT": 13022, + "TCCTCCCACC": 13023, + "CCCTGATG": 13024, + "TAGAGATT": 13025, + "TAAGAACA": 13026, + "TTAATAAAA": 13027, + "TAATTCCAA": 13028, + "GGCCCAGA": 13029, + "GCTCCTTA": 13030, + "GATTATATTTT": 13031, + "GGGCCACA": 13032, + "GCGCCATT": 13033, + "CTACTATT": 13034, + "TATTTAGGA": 13035, + "CCTTGCAA": 13036, + "TCTACGA": 13037, + "TTATTCATT": 13038, + "GGTACAAA": 13039, + "GCCTGGCTT": 13040, + "GCTCGCC": 13041, + "CTGGCTAA": 13042, + "TGTGAAATT": 13043, + "TATATACTT": 13044, + "CTCAGAAAA": 13045, + "GTTATATTTT": 13046, + "CCCACCTCA": 13047, + "GGGAGCAA": 13048, + "GAATTTAAA": 13049, + "TCCCTGGGTTGGGAA": 13050, + "CAAGGAGAA": 13051, + "CTGGACCA": 13052, + "CTCATAAA": 13053, + "CGGTGAAA": 13054, + "GATGAAATT": 13055, + "CCACCAGG": 13056, + "GTGTATGTG": 13057, + "CCCTGATT": 13058, + "GCCCAGTC": 13059, + "CTCTCCCCA": 13060, + "TATTCTATT": 13061, + "CAGATAAAA": 13062, + "GAGAAGAGA": 13063, + "TGATGGTGA": 13064, + "GAAATGTTA": 13065, + "GTTCTTCTT": 13066, + "CATTGTC": 13067, + "TGAAĊ": 13068, + "CTAACCAA": 13069, + "CCAAAGAA": 13070, + "CTCAATTTT": 13071, + "GTTGACTA": 13072, + "TATTTTTATTTT": 13073, + "GCGTTTA": 13074, + "TGATAGGA": 13075, + "GCTAGGTG": 13076, + "TCTTCCCCA": 13077, + "GAAAACCAA": 13078, + "TGTTAATTA": 13079, + "GGAATAGA": 13080, + "CTTAGAGG": 13081, + "GATATAAAA": 13082, + "GACCCAAA": 13083, + "CAAAAACAAAA": 13084, + "CCCTGTCTT": 13085, + "GGCGTCA": 13086, + "GACCAATT": 13087, + "GACAAAATT": 13088, + "GTAGACTG": 13089, + "GAGACGA": 13090, + "GTGACCCC": 13091, + "GAAAATCTG": 13092, + "CTTTTTGAA": 13093, + "TGTAGTTA": 13094, + "TCTGAGAAA": 13095, + "TAGTGAAAA": 13096, + "CCCAGGAAA": 13097, + "GTAGCTGGGACTACAGG": 13098, + "GGACAGCC": 13099, + "GTCACAAAA": 13100, + "GCCAATTA": 13101, + "TGAGAGAAA": 13102, + "TCCCTCATT": 13103, + "CTCAGGCA": 13104, + "CATGGGGG": 13105, + "GTCCCACC": 13106, + "CCTAGTAA": 13107, + "TCCTTTGAA": 13108, + "TCATGCAA": 13109, + "GTGTAGCA": 13110, + "CCAGATAA": 13111, + "GCTGTCTA": 13112, + "GCAATGAAA": 13113, + "CCCATATA": 13114, + "GAATTTCTG": 13115, + "TAATAGCCA": 13116, + "GCCTGGAGG": 13117, + "GCTATGAAA": 13118, + "GCGTGAACCCGGGAGGCGGAGCTT": 13119, + "GTTGACCA": 13120, + "GCATACTG": 13121, + "CAGCTAAA": 13122, + "GGCAGGCAGA": 13123, + "CACTCCTG": 13124, + "CCCGATT": 13125, + "CAAGGTTA": 13126, + "GTTAAATA": 13127, + "CCCAGCTA": 13128, + "GCGACGC": 13129, + "CCACCACACCTG": 13130, + "TAGTTTTTT": 13131, + "TCAAAAAATT": 13132, + "GTTGTTTTA": 13133, + "TGAATATTA": 13134, + "CATGGGCA": 13135, + "TACACTCC": 13136, + "TTTTTTTCA": 13137, + "TCCTTCTTA": 13138, + "GCATAATA": 13139, + "TAAATATTAA": 13140, + "GCAATGTA": 13141, + "CGTAAATG": 13142, + "GTTGGAAAA": 13143, + "GTAACGG": 13144, + "GTTTTTATG": 13145, + "CGCTCTA": 13146, + "TTTTTTAAATT": 13147, + "CTAAAGAA": 13148, + "CGCCTCC": 13149, + "CACCAATT": 13150, + "TGACATC": 13151, + "GTTTAAATT": 13152, + "GAGATGTA": 13153, + "GTGATGAAA": 13154, + "CTAAGAGG": 13155, + "TCCATAAAA": 13156, + "CCCACTC": 13157, + "GGGAAGGGG": 13158, + "GCAGGTGGG": 13159, + "GTCAGGCA": 13160, + "CCCCTAAA": 13161, + "TCAAACACA": 13162, + "TTTTTTCTA": 13163, + "GTGAATGGG": 13164, + "GCTGAGATTA": 13165, + "TTTTAATTTA": 13166, + "GCTCTTTTA": 13167, + "GCGACGG": 13168, + "GGACTTTG": 13169, + "CTATACCA": 13170, + "GTCTTTTTG": 13171, + "CCCCCTGA": 13172, + "CTGGGTATA": 13173, + "TCCCAGTCC": 13174, + "CTTAAATA": 13175, + "GGATTTTTG": 13176, + "TTCAGTGA": 13177, + "TGACCTCC": 13178, + "CAAGTGGA": 13179, + "TAAAGATAA": 13180, + "CTCCCTCTT": 13181, + "TTAACAAA": 13182, + "TAATACTAA": 13183, + "TAATCCCAGCACTTTGGGAGGCCGAGGCGG": 13184, + "GGCTAATT": 13185, + "CAAAAAATG": 13186, + "GCCTAGGA": 13187, + "CATGGAAAA": 13188, + "TTTGCTGA": 13189, + "CCCACATG": 13190, + "TAAGCATG": 13191, + "GTTGTCTG": 13192, + "GATAGTAA": 13193, + "TAAAAATGTT": 13194, + "GCTCĊ": 13195, + "CCGTTTG": 13196, + "CCGTCTG": 13197, + "CTGCCATG": 13198, + "CACAGAGTT": 13199, + "TGAACCTT": 13200, + "CGGTAGG": 13201, + "CTAAATTTA": 13202, + "TATATATGTG": 13203, + "TACGTTTT": 13204, + "TAACTATG": 13205, + "TCACTATG": 13206, + "CCCATTTG": 13207, + "GAAGTTTG": 13208, + "GCAAACAAA": 13209, + "CTCTGTGA": 13210, + "CTTCCTCTG": 13211, + "GTTCAAAAA": 13212, + "GAAATACC": 13213, + "TTCAGTAA": 13214, + "CGCGTA": 13215, + "TACTGATT": 13216, + "TAATGTTAA": 13217, + "GTGCCCAA": 13218, + "TATATCAAA": 13219, + "TCTTTACTT": 13220, + "CTTAGTCA": 13221, + "GATGGTAAA": 13222, + "TAGGTGTT": 13223, + "GTGTTCTT": 13224, + "CTTCCCAGG": 13225, + "TATTCCAAA": 13226, + "GAAACAAAAA": 13227, + "CAGACACC": 13228, + "TTTTTTTCTA": 13229, + "CTCTGCTTA": 13230, + "CTATCATT": 13231, + "CTCGACA": 13232, + "TCTTCTGTT": 13233, + "GCAAGGGA": 13234, + "GATGCACA": 13235, + "CGAGCCA": 13236, + "TCTCATTAA": 13237, + "TCGTTCTT": 13238, + "GAATCTTA": 13239, + "TCAATCTG": 13240, + "GAGGTAGA": 13241, + "CCTGTAAA": 13242, + "TCTCTAGTT": 13243, + "CAAAAGACA": 13244, + "TCGTATA": 13245, + "TAACATC": 13246, + "TCTCTTATT": 13247, + "CCCACGTG": 13248, + "CATAGCTG": 13249, + "GTCCTTCCAA": 13250, + "TAAGGATG": 13251, + "CAAGGGGA": 13252, + "CCATTTTAA": 13253, + "GCCCTTGG": 13254, + "GAGACTAA": 13255, + "CGGGGCTG": 13256, + "CCTGGTTA": 13257, + "CTCCGTC": 13258, + "GTTTTGGGG": 13259, + "CAAAACATG": 13260, + "TGGAGTGA": 13261, + "CTTCATCCA": 13262, + "GCTGTTAAA": 13263, + "GTGGACCA": 13264, + "GGACTGGG": 13265, + "TGATTTCTG": 13266, + "TACTGTTTT": 13267, + "TCAGGTGGCCAAA": 13268, + "GGTGTTAA": 13269, + "GTGTGAGA": 13270, + "GACATGAAA": 13271, + "TATACATATG": 13272, + "GTCATGCA": 13273, + "GAGAAGGAA": 13274, + "CCCCATCTC": 13275, + "TACCATTA": 13276, + "TATATATTTTA": 13277, + "TATATACCCA": 13278, + "TACGATT": 13279, + "GAAATACTT": 13280, + "TGGGTTCA": 13281, + "TCCTTTCC": 13282, + "TCTTGGTA": 13283, + "CAGGTAAA": 13284, + "CTAATATTA": 13285, + "GACATTTA": 13286, + "TCTTGTCTT": 13287, + "TCAAGGAAA": 13288, + "TAATATGAA": 13289, + "TGATTTGAA": 13290, + "CCTCĊ": 13291, + "GTGTCTAA": 13292, + "TGTGAAATG": 13293, + "GAAACTAAA": 13294, + "CCTTTAGA": 13295, + "CGCTATA": 13296, + "CTGTTGTA": 13297, + "GAAAAAGAAAAA": 13298, + "GGAGAGCTG": 13299, + "CTTCCCCAA": 13300, + "TCCTGAATG": 13301, + "CTTTAATAA": 13302, + "CACACACAAA": 13303, + "GTGAATGAA": 13304, + "CCCCACCCCA": 13305, + "GGGCTAATA": 13306, + "CTTCCTGTG": 13307, + "TCCTACTA": 13308, + "CAACATCC": 13309, + "CTTCAGAAA": 13310, + "AAAAAAAAAAAAA": 13311, + "TCTTTACC": 13312, + "TCTCGAA": 13313, + "GCCAĊ": 13314, + "TAGGTGAA": 13315, + "CGTTTATT": 13316, + "CTTAATTTG": 13317, + "GACCTGTT": 13318, + "CTCTGCCA": 13319, + "CCACATAAATTTT": 13320, + "TCACCACCA": 13321, + "TAAAACTCA": 13322, + "TGGGCATG": 13323, + "GTCGTTTT": 13324, + "GTCTAAAAA": 13325, + "CAGAGGGAA": 13326, + "CTTAATTTA": 13327, + "TCTTAGTTTT": 13328, + "TAATĊ": 13329, + "TTATATTTT": 13330, + "TCCGCA": 13331, + "CGGCGAA": 13332, + "GGGGTCTG": 13333, + "CCTCTACA": 13334, + "TGACGAAA": 13335, + "TCCGTCTG": 13336, + "GAAAGGTA": 13337, + "GGTTGCA": 13338, + "GGCACTTA": 13339, + "CAAAAATAAA": 13340, + "TCGACGA": 13341, + "GGGCCCTG": 13342, + "TGACAGAAA": 13343, + "CTTGCTCC": 13344, + "TCAAĊ": 13345, + "TAAAATACTG": 13346, + "GTGATTTTA": 13347, + "TGATTTAAA": 13348, + "GCCTGGCC": 13349, + "TATTATTAAA": 13350, + "GACTCATG": 13351, + "CTACGAA": 13352, + "CAGGGTAA": 13353, + "GCTTTGCC": 13354, + "CCTGCTTTT": 13355, + "TGGGGGAA": 13356, + "GGTGCTCA": 13357, + "GATTTTAAAA": 13358, + "CAGGTGTCTTTTA": 13359, + "TAGCAATT": 13360, + "CAGAGGCCA": 13361, + "GTGATAGA": 13362, + "GTCAGAAAA": 13363, + "TAGGGGAA": 13364, + "GGGACATT": 13365, + "CTCAAGGA": 13366, + "GGCGTTA": 13367, + "CAAGGATT": 13368, + "GTACCTCA": 13369, + "CCTTTGAAA": 13370, + "GGTTTAAA": 13371, + "TTCATATT": 13372, + "TCATTGTTTT": 13373, + "CCAAATTAA": 13374, + "CTATTCTATT": 13375, + "CTCCACCAA": 13376, + "CAAGAGGAA": 13377, + "CCGTCAA": 13378, + "GCGAACA": 13379, + "GAATTTTAA": 13380, + "CAATTATG": 13381, + "TCTCCAGC": 13382, + "TTTGCTTA": 13383, + "CAAAAGGAA": 13384, + "GGTTCTGA": 13385, + "CCCCAAATT": 13386, + "TTCAGGA": 13387, + "CCACGTC": 13388, + "GTAGGTAA": 13389, + "GATGGCTCA": 13390, + "TTATTTGTG": 13391, + "TATAATATT": 13392, + "AAAAAAAAAAAAAAAAAAAAAA": 13393, + "TCGTCATT": 13394, + "GATCAATG": 13395, + "TCCTAATA": 13396, + "CGGTAAA": 13397, + "CTTTTTTTTA": 13398, + "TGGGTGCA": 13399, + "TAGGGCTG": 13400, + "GGATGTTTT": 13401, + "TCACATTGTA": 13402, + "TATGTGTGTG": 13403, + "GCGCAGA": 13404, + "TAACATTAA": 13405, + "CTGCCTTA": 13406, + "GTATAATG": 13407, + "GTAGGCAA": 13408, + "TAGACATA": 13409, + "GGGACTGA": 13410, + "CTAAATATT": 13411, + "TATTATTTTA": 13412, + "CTCTTTCTG": 13413, + "TAAGTTTTA": 13414, + "GTCGGCA": 13415, + "CTTATGAAA": 13416, + "GGTTTATA": 13417, + "CTTTGGGA": 13418, + "GAAGAGAAAA": 13419, + "GGTGGCTGACTAAA": 13420, + "GGTCCAGG": 13421, + "GGCAGATG": 13422, + "CCATCTAA": 13423, + "GTACACCA": 13424, + "CATGTACTG": 13425, + "GAGGTCAA": 13426, + "CGGACGG": 13427, + "CTTTCTTTCTT": 13428, + "GTGTCTTTT": 13429, + "CTATGACAGA": 13430, + "TCTTGCCTT": 13431, + "TAAAAGTGA": 13432, + "CTAATAATT": 13433, + "CTAAATTTG": 13434, + "GAGAAGCAA": 13435, + "CTTCAGCAA": 13436, + "TGGGTTTGATGAAACCGAA": 13437, + "CAATGATG": 13438, + "GTACTATT": 13439, + "TATTGGTA": 13440, + "GTCACAGAA": 13441, + "TACATACC": 13442, + "CACCCCTT": 13443, + "TAATCTCC": 13444, + "GTTGTATG": 13445, + "GAGACAAAA": 13446, + "GGTTCTT": 13447, + "TAAAAATTTA": 13448, + "GAATTTATA": 13449, + "TCCTGCCTG": 13450, + "GAGCACTGA": 13451, + "CTACCTGA": 13452, + "TGTTCCCTT": 13453, + "CAGACACTG": 13454, + "CTCATGCA": 13455, + "GTCATGCTG": 13456, + "GCTATAAAA": 13457, + "TAAATACTA": 13458, + "CACAGGGAA": 13459, + "TCTCAGCA": 13460, + "GCCAAGAA": 13461, + "TTTTAAAAATT": 13462, + "GAATATGAA": 13463, + "GTTCAAGTGATT": 13464, + "CTACAGC": 13465, + "GCTTCCCTT": 13466, + "TCTCTATCA": 13467, + "TACATCAAA": 13468, + "GGGGTCC": 13469, + "TCGAAAAAA": 13470, + "CACACACGCA": 13471, + "GCGCCAGG": 13472, + "TTTGTCA": 13473, + "GTAAATTTA": 13474, + "TGCCCATG": 13475, + "GACCCATG": 13476, + "CTCAACATT": 13477, + "GAAAAATAAA": 13478, + "GTGGTGCATG": 13479, + "TGAGAAATG": 13480, + "GTTCCTC": 13481, + "GCCTCCACA": 13482, + "CAGGTATT": 13483, + "GAGGGAAAA": 13484, + "CTTAGTTTT": 13485, + "TTATTCTT": 13486, + "GTAGGTGTG": 13487, + "TCTTTTAAAAA": 13488, + "TGACGTC": 13489, + "CACATCCA": 13490, + "CCTTCCAAA": 13491, + "GCAACAGCA": 13492, + "GCCACAAAA": 13493, + "CGACTCC": 13494, + "GGTGGGAGGA": 13495, + "CTGAATTTA": 13496, + "TGTTTGTTA": 13497, + "GCCGACA": 13498, + "CTCTCCCTT": 13499, + "GTCTGTGTT": 13500, + "CTTTTTCTTTT": 13501, + "GCTATCTA": 13502, + "GAGGACAAA": 13503, + "CTTGCACAATT": 13504, + "TCCTATCC": 13505, + "TCTTCACTT": 13506, + "TGTTTCTTTT": 13507, + "GAAAATGCA": 13508, + "TTTTATTTATT": 13509, + "CTAGGGAA": 13510, + "CTAGTTTTTT": 13511, + "TGTGTCTA": 13512, + "TGATGAAAATTA": 13513, + "TACTCCAA": 13514, + "CTGCCCAA": 13515, + "GTTCAATG": 13516, + "GTTTATCC": 13517, + "TGAACGG": 13518, + "CTCATCCA": 13519, + "CTAAGCC": 13520, + "CTTATTTTTT": 13521, + "CAGAAGAA": 13522, + "GCCTGCCTG": 13523, + "CTCTAGCCTGGG": 13524, + "GGCTTATT": 13525, + "GGCGCCA": 13526, + "GCGTAAAA": 13527, + "TAATGAGG": 13528, + "TAACTGAAA": 13529, + "TTTTCATA": 13530, + "GTGGCTAA": 13531, + "TGTTGGCA": 13532, + "GCATTCTCAGAAA": 13533, + "CCGGTGA": 13534, + "TACTCCTT": 13535, + "TCAAATATA": 13536, + "TTAATTAAAA": 13537, + "TATTTTATG": 13538, + "GAATAGTA": 13539, + "CTCTGAGAA": 13540, + "CCAAAAATA": 13541, + "CTGTGGCTT": 13542, + "GATCATTA": 13543, + "CACTCTTA": 13544, + "GGGGAATT": 13545, + "GGCTCCTG": 13546, + "GTGTTATG": 13547, + "GTTGTTTA": 13548, + "GCATAGTA": 13549, + "GTAGCCAA": 13550, + "CTATAGTA": 13551, + "CGGGGCA": 13552, + "GAAGAGCAA": 13553, + "TCTCATTGTGGTTTT": 13554, + "GGTAAATG": 13555, + "TCTTCATTTT": 13556, + "TCTCTTTTTT": 13557, + "TACATTTTAA": 13558, + "GGTGCCA": 13559, + "GAAGGCAATG": 13560, + "TCCTCTGTG": 13561, + "TCTTCTTTG": 13562, + "CAGGCAGCA": 13563, + "TCTGGCCA": 13564, + "CTCAGAGAA": 13565, + "CACAAAATT": 13566, + "TAAGAAATT": 13567, + "TTTTTGGA": 13568, + "GATAGGA": 13569, + "GCAAAGAAA": 13570, + "GAGAGGCC": 13571, + "GCTGCTGCTG": 13572, + "GTAAGAGG": 13573, + "CTTTTCACA": 13574, + "CCATGCTG": 13575, + "CAAGGAAAA": 13576, + "CTTCATC": 13577, + "GTTTAATTA": 13578, + "CAAAGAATT": 13579, + "TGAACGA": 13580, + "CTCACGA": 13581, + "CCAACCCC": 13582, + "CTCAAGAAA": 13583, + "TAATTTTCATCA": 13584, + "TAAGTGGGAGAA": 13585, + "CCGGCAA": 13586, + "TAAAGTGAA": 13587, + "CCAAGGGA": 13588, + "CTCCTATG": 13589, + "CTAATGAAA": 13590, + "CGAAGCCA": 13591, + "TCGCGCCA": 13592, + "TAGATTTG": 13593, + "TCGCTGAA": 13594, + "TTTTTCAAA": 13595, + "TGAGCTTA": 13596, + "GTTAGCTT": 13597, + "GTTTTTCCA": 13598, + "GTCCTCTA": 13599, + "GGGGTGAA": 13600, + "TCCTTTACA": 13601, + "GAAGGCAGA": 13602, + "CTGCCTGTG": 13603, + "GCACCCTT": 13604, + "CTATCCAA": 13605, + "CTAGGCTG": 13606, + "CATTTACTG": 13607, + "GGAACCTT": 13608, + "GAGGGGTT": 13609, + "CCCATATG": 13610, + "CTTGGGAGGCTGA": 13611, + "GTTTCGG": 13612, + "GTTCAGTC": 13613, + "CAAAGACC": 13614, + "CATTGAAAA": 13615, + "GGAATCC": 13616, + "TAGGGGA": 13617, + "TAATATCAA": 13618, + "TAATAGCAA": 13619, + "GAAGGCAGG": 13620, + "CAGGTTCAAGCGATT": 13621, + "TAAGAATA": 13622, + "TAACTCCTT": 13623, + "CAAGGGTG": 13624, + "CTTTCTCTG": 13625, + "GAGATCTT": 13626, + "CTGAGTAAA": 13627, + "TCTCAAATG": 13628, + "GCCATAGA": 13629, + "TAATTTAAAAA": 13630, + "CGCTACA": 13631, + "CGTTTCTT": 13632, + "TACCCTCC": 13633, + "GAGAGAGAGAGAGAGAGAGAGAGA": 13634, + "GGGCTTTG": 13635, + "GCGCTGTT": 13636, + "CTGGATCC": 13637, + "GTGTCTGTG": 13638, + "TGTGTGAAA": 13639, + "CAAACACTT": 13640, + "TCTGGGCC": 13641, + "TAAAAGCTT": 13642, + "CCAACAAAA": 13643, + "CAATTCCTG": 13644, + "GTGCAGCTG": 13645, + "CTGTAATG": 13646, + "CTATCTCC": 13647, + "TATTCTGAA": 13648, + "GAAAGTGAA": 13649, + "CATTCTGTT": 13650, + "CAAAATGCTG": 13651, + "TAATTCTGA": 13652, + "GTTCCAGGACAGCCAGG": 13653, + "GAACCCCA": 13654, + "CTCTCCAAA": 13655, + "TCTGGGAAA": 13656, + "GCTTCAGTT": 13657, + "CATGCGG": 13658, + "TCTAGTC": 13659, + "CATACGA": 13660, + "TAATATCA": 13661, + "GCCTTGACTA": 13662, + "CTGCGAA": 13663, + "GGAATACCA": 13664, + "CTAACAGAA": 13665, + "TGCAGTTA": 13666, + "GTATTCCCA": 13667, + "TGATGTCAA": 13668, + "GTGCCCGG": 13669, + "TAATAAATA": 13670, + "TGAGCAGG": 13671, + "CTTCAATG": 13672, + "CTCTCAGA": 13673, + "TATACAGAA": 13674, + "TCCTGGAGG": 13675, + "TCCTGGATG": 13676, + "TACTTTCC": 13677, + "CCTCAAAAA": 13678, + "CCCGGTG": 13679, + "TAACCAAAA": 13680, + "GAATATAAA": 13681, + "CTGGATAA": 13682, + "GTGTAGGA": 13683, + "TAAAATACAA": 13684, + "CTGATCC": 13685, + "GTAATACA": 13686, + "TATGTCCA": 13687, + "TCAAATTCA": 13688, + "GGGAGTA": 13689, + "TCCTTCACA": 13690, + "GAATGGAAA": 13691, + "TATCTTATT": 13692, + "TTACAGTGA": 13693, + "GGGGAAAAA": 13694, + "GTTCGGA": 13695, + "GCATCTTTT": 13696, + "GCAGACTA": 13697, + "GACATACAA": 13698, + "GGTGGTCA": 13699, + "TGTAGATG": 13700, + "CTAAAGAAA": 13701, + "TAGCTAGG": 13702, + "GGATGTGGAGAAA": 13703, + "CAGGGTC": 13704, + "CCTTTAATCCCAGCA": 13705, + "TACCAGGCTCC": 13706, + "CCGCCCC": 13707, + "GGGCCCA": 13708, + "GTTTCTTAA": 13709, + "CTCACCCA": 13710, + "GTAACTTTT": 13711, + "CCAACGA": 13712, + "TGCCCTCC": 13713, + "GCTCAGGCTG": 13714, + "CGTCTCC": 13715, + "CCACAATGAGATA": 13716, + "CTTGACTA": 13717, + "GCAACATA": 13718, + "GTATTCTA": 13719, + "TAGTTCACC": 13720, + "TTTTATGA": 13721, + "GAGGTAAA": 13722, + "GAAAATCA": 13723, + "CCTAGCC": 13724, + "CCGCCAA": 13725, + "TTTTGTCC": 13726, + "CAATATAAA": 13727, + "GTCCAGAAA": 13728, + "GTTACAAAA": 13729, + "CATTATTTG": 13730, + "GCTCTACA": 13731, + "GCTCTTAAA": 13732, + "CAAGGGTT": 13733, + "GTGGATTTT": 13734, + "CAAATGATT": 13735, + "TATGGTGTT": 13736, + "CCTGCTTA": 13737, + "CCCAGCCTT": 13738, + "GGCAGCCA": 13739, + "TCGTAATT": 13740, + "GTGTCTCTT": 13741, + "CTCAGC": 13742, + "CGGGAGGA": 13743, + "CCCCCCCCCCCCCCCC": 13744, + "CCTGTATG": 13745, + "TCAAATGA": 13746, + "CGCAGCC": 13747, + "TGATGTCA": 13748, + "GTGGCCTG": 13749, + "GGACACTA": 13750, + "GTATCTAA": 13751, + "TGTGCTC": 13752, + "TATTTAGTA": 13753, + "TGTTTACTG": 13754, + "CATTCAGAA": 13755, + "CAGGCCTCTC": 13756, + "GTAAGTTTT": 13757, + "GTAATATTTT": 13758, + "GACATAGA": 13759, + "GTTCTTGG": 13760, + "GCAGGAGGA": 13761, + "GGAGCTCC": 13762, + "TCTGAAATG": 13763, + "GTAATGCA": 13764, + "CCTGCCCCA": 13765, + "GGGACCA": 13766, + "GCGGAAAA": 13767, + "CAGTTATG": 13768, + "TGTTTCTTA": 13769, + "TCTAGCC": 13770, + "GCGGTTA": 13771, + "GGATGAGATGGTT": 13772, + "CACCTTAA": 13773, + "CAACCTGA": 13774, + "GTATTTTTG": 13775, + "TCAATTATT": 13776, + "GTCAGGGAA": 13777, + "CTTAGATG": 13778, + "TCCAGCTA": 13779, + "GGGACTCA": 13780, + "TCTCCCAGG": 13781, + "CTGTTTCA": 13782, + "GGCACATT": 13783, + "CTTGAGTA": 13784, + "CTACTCAA": 13785, + "TCTTCCCAA": 13786, + "TCTGTCTCA": 13787, + "GGAACAGAA": 13788, + "TTTTATAAAA": 13789, + "CCAGGGATG": 13790, + "GTTTTGTTTTGTTTT": 13791, + "GCTAAAATG": 13792, + "GTTTCCATT": 13793, + "CAGATAGG": 13794, + "GCTGGGCTT": 13795, + "TCCATCTC": 13796, + "TTAAAAATG": 13797, + "GACAGTC": 13798, + "GGAGCTAA": 13799, + "GCCGAGC": 13800, + "TGTGGATCA": 13801, + "CATTCTCTT": 13802, + "CCTGTCAA": 13803, + "TCCAGAAAAA": 13804, + "CGCGCTT": 13805, + "CATCATTTT": 13806, + "CTCCAGAGA": 13807, + "TACTCATT": 13808, + "GTATCCTT": 13809, + "TGTGCATA": 13810, + "CATTGTCC": 13811, + "TGAATTATT": 13812, + "GCAAGAGAA": 13813, + "GTCAGAGAA": 13814, + "CGGCGTT": 13815, + "CCAAGTCC": 13816, + "CAGTGTTTT": 13817, + "CTTCAGCAAA": 13818, + "GAAGACTA": 13819, + "GGATTATG": 13820, + "CAGGTATG": 13821, + "GAAAATGGA": 13822, + "CAAATGTTA": 13823, + "TAACAGTAA": 13824, + "GAGTCGGACACGACTGA": 13825, + "TCGAGACAGGGTTTCTCTGTGTA": 13826, + "GATCTATG": 13827, + "GAACTATT": 13828, + "TCCTCTCTG": 13829, + "GCCCGTA": 13830, + "TCTGĊ": 13831, + "GCCATCTCTCCA": 13832, + "CGATTATT": 13833, + "TCAAACTCA": 13834, + "GGTACAGA": 13835, + "CATGTGTGTG": 13836, + "CTGGCCTG": 13837, + "TAAAGTATT": 13838, + "CACAGACC": 13839, + "TTAATGA": 13840, + "GACATCC": 13841, + "TGCCTCAA": 13842, + "CAGTCTTA": 13843, + "CAGGTTCAAGTGATT": 13844, + "CTTCTCCTT": 13845, + "TGTTTATCA": 13846, + "CAAATACCA": 13847, + "TAAAATAATA": 13848, + "GTTAGCTG": 13849, + "GAAAAGATG": 13850, + "CATGATTTT": 13851, + "CACATGAAAA": 13852, + "TACGAGA": 13853, + "TCTATATATA": 13854, + "GCGCCCA": 13855, + "CAAGCATG": 13856, + "CAGGGTTTCACCATGTT": 13857, + "GGAAĊ": 13858, + "GTGAGTTTT": 13859, + "GTCAGATA": 13860, + "GAGGCTAA": 13861, + "CCCACAAAA": 13862, + "TAGATAGG": 13863, + "TGTAGCA": 13864, + "TAATTCTCA": 13865, + "TATTATTATTA": 13866, + "TGAATGAAAA": 13867, + "TAATAATAATAATAATAA": 13868, + "CAATGAGA": 13869, + "CACACACTT": 13870, + "CATTGAATT": 13871, + "GCCACCTGA": 13872, + "CTCTTCTCA": 13873, + "TGAGGCAGG": 13874, + "GTAGGTCA": 13875, + "TATTCAGAA": 13876, + "GAAAGAGCA": 13877, + "GTAACATA": 13878, + "CCAAGGAAA": 13879, + "TGTGTGTATG": 13880, + "TAAGGTTTT": 13881, + "TCCCCTAA": 13882, + "CAATCCTT": 13883, + "CTGGCTCC": 13884, + "CTGTAGGA": 13885, + "GTATATAAA": 13886, + "GCCTCTGTG": 13887, + "CTATAGGA": 13888, + "CTAACATA": 13889, + "CTCCTGCCTCAGCCTCCCGAGTAGCTGGGACTACAGG": 13890, + "GAGAGTCCCTTGGACTGCAA": 13891, + "GGTCCCTG": 13892, + "CTGTCTTTT": 13893, + "GCCGGGTG": 13894, + "GAAAGACAA": 13895, + "TGTTTCTGA": 13896, + "GTAAGGTT": 13897, + "TTATATGAA": 13898, + "GAACACAAA": 13899, + "CAGAAAATG": 13900, + "GTTCAAGG": 13901, + "GCCGCAA": 13902, + "TAGGGTTA": 13903, + "CAGTGTTTG": 13904, + "CTGCAGTCA": 13905, + "GAATAGCA": 13906, + "GAAAAAATG": 13907, + "GCCCCTC": 13908, + "CTACGTA": 13909, + "TAAACAGCA": 13910, + "TCTGTCTGA": 13911, + "TAAAAGATG": 13912, + "GCTTCCTCA": 13913, + "CTTTTCTGA": 13914, + "TAAAAATATG": 13915, + "TTTTAATTTG": 13916, + "GAGCTAGA": 13917, + "CCAGGCATG": 13918, + "CTCCCACTTAAAAA": 13919, + "GGCGCAA": 13920, + "CAATATATT": 13921, + "GTTCCCTA": 13922, + "CATTCTAAA": 13923, + "CTTATTATT": 13924, + "TTAATGTG": 13925, + "CCTAGTTA": 13926, + "GAATTTCTA": 13927, + "CTTTGTCTT": 13928, + "CTCTGTAGACCAGGCTG": 13929, + "GTGGAAAATAAGTATTTG": 13930, + "CTGTCATG": 13931, + "GCACCCAA": 13932, + "CTCAAAATG": 13933, + "GAGAGGGCA": 13934, + "GACATACA": 13935, + "GGCCAGGATG": 13936, + "TCAGAGCAA": 13937, + "CCAGCCTGGCCAACATG": 13938, + "GCGCTGAA": 13939, + "CTCTGAATT": 13940, + "TATATACTA": 13941, + "GCTGCCCTG": 13942, + "GATTGTCA": 13943, + "TGTATCTG": 13944, + "CAAGAAGG": 13945, + "GGATACTG": 13946, + "CTACCTCC": 13947, + "GTCCAGGCTG": 13948, + "TATAGGTA": 13949, + "GTGATATTTT": 13950, + "GCTGCAGGA": 13951, + "GATTTACAA": 13952, + "CTAACCTG": 13953, + "TGCAGAAAA": 13954, + "TTCATCA": 13955, + "CTGTTTGTT": 13956, + "GCAGGCGG": 13957, + "TAAAGACTT": 13958, + "TCAAAGATCA": 13959, + "TAATTTGTG": 13960, + "GGCAGTAA": 13961, + "GTGAATTAA": 13962, + "GTCTTGCTCTGTT": 13963, + "GCATTTGA": 13964, + "TTTTTTTTTTTG": 13965, + "TGGGTTCAAGCGATT": 13966, + "GGCAGGTT": 13967, + "CTTTCTAAA": 13968, + "GGATCCAA": 13969, + "CTCTCCAGG": 13970, + "CACACAGTG": 13971, + "CTTATCTA": 13972, + "TCAAAGAAAA": 13973, + "TTCATCTT": 13974, + "CGCATTTT": 13975, + "GCGGTGGG": 13976, + "CACTATTATT": 13977, + "GAGACTGGCTAGG": 13978, + "GGCTCTA": 13979, + "CTGGAATG": 13980, + "CACATACACA": 13981, + "CATGCCCA": 13982, + "GCAAGCCA": 13983, + "TGAAATTTG": 13984, + "CCAATTGTGCAAGTT": 13985, + "GCGGAGAA": 13986, + "GTGCCATG": 13987, + "GTTTĊ": 13988, + "TCTTCACAA": 13989, + "TCCATCTCA": 13990, + "CGATTTTA": 13991, + "TCATTTGTG": 13992, + "CCTTCTCCTCCTG": 13993, + "GCGAGAAA": 13994, + "TAATTTTATT": 13995, + "GAACAGCC": 13996, + "CAAGCCTG": 13997, + "GGATGAGA": 13998, + "CTAGTGTT": 13999, + "TATTCATTTT": 14000, + "TATGGTGAAA": 14001, + "GGGGGAAAA": 14002, + "GGCGGGTGGATCA": 14003, + "CAACCCTA": 14004, + "CTTTCACTT": 14005, + "CTGAGCTA": 14006, + "GCAAGGAAA": 14007, + "TCATTTCTA": 14008, + "TCATAGC": 14009, + "GAACGGA": 14010, + "GGACAGAAA": 14011, + "TATACCCA": 14012, + "TATATACTG": 14013, + "CAGATTTTA": 14014, + "GTTTTTGAA": 14015, + "GTATTCACA": 14016, + "TCCCTGCTG": 14017, + "CTTTĊ": 14018, + "GTAGGTTA": 14019, + "CTAGAGTG": 14020, + "CTAGATAA": 14021, + "TAAAAAAATA": 14022, + "CTTACGG": 14023, + "TACAAAAAATTA": 14024, + "CAAAAAATTA": 14025, + "GTGCTGGGATTACAGGCATGA": 14026, + "GTTCGTC": 14027, + "GCCTTTTTT": 14028, + "GCTGCAGTG": 14029, + "CAAAAGTGA": 14030, + "CACCTCTT": 14031, + "TACGCTT": 14032, + "GACTTTTAA": 14033, + "GTGTCTCTG": 14034, + "TGTGGAAAA": 14035, + "CAGAGCAAA": 14036, + "CTCAGGCC": 14037, + "CTCCTGAAA": 14038, + "GTATTTGAA": 14039, + "GATGATTA": 14040, + "CCAGGTTA": 14041, + "GCCAGTCTCCAGA": 14042, + "GGGAAAAAAA": 14043, + "GTGTTTTAA": 14044, + "GCAGAAATT": 14045, + "CTATATAAA": 14046, + "GATGGATA": 14047, + "TAGGCTGA": 14048, + "GCCAAGATT": 14049, + "CTCTGTCTT": 14050, + "TAATGTATG": 14051, + "CAGGACCTT": 14052, + "GAGCATGTG": 14053, + "TAAACTGAA": 14054, + "GTAATATAA": 14055, + "TCCATTATT": 14056, + "TTCAGTCA": 14057, + "TCTCCTGTT": 14058, + "TAAAAAGTA": 14059, + "CAGCTCCTT": 14060, + "CATAATTAA": 14061, + "GCTCCGCCTCCCGGGTTCACGCCATT": 14062, + "TAGTGATT": 14063, + "GGCGGTT": 14064, + "GTGCACTA": 14065, + "GGAACTGAA": 14066, + "GTGACATCA": 14067, + "GTAATTATT": 14068, + "GTTTTGGTT": 14069, + "CACTCCAAA": 14070, + "GGTGAAAAA": 14071, + "GGGAGATT": 14072, + "CAACGCC": 14073, + "TAAAATCC": 14074, + "GCTGGTCC": 14075, + "CCCAĊ": 14076, + "CCTATCTT": 14077, + "TCTCCTGTG": 14078, + "TACCCATG": 14079, + "GAAGGGAGA": 14080, + "TAAAAAAAAATT": 14081, + "TCTGGGTA": 14082, + "GCTTTCCTT": 14083, + "GGCAGAAAA": 14084, + "GCAAATATT": 14085, + "CTGTGCTCA": 14086, + "GCCTTAAAA": 14087, + "GGTCTTTG": 14088, + "TCAAAAATA": 14089, + "TACCTCC": 14090, + "GAGCATTA": 14091, + "TTTTCAAACCACATAAATTTT": 14092, + "TTTTGGTG": 14093, + "GAATCCTT": 14094, + "TCTCAGTT": 14095, + "CTCTGCTCA": 14096, + "CCCCATCTCTACTAAAAATA": 14097, + "GAGCTCCA": 14098, + "GGTCGCA": 14099, + "TAATGATA": 14100, + "CAAGATTTT": 14101, + "GGACCCAA": 14102, + "CTAGACTT": 14103, + "TGAACTCC": 14104, + "GCTGCAGAA": 14105, + "GTCCCTAA": 14106, + "GGGGTGA": 14107, + "CTCTTCACA": 14108, + "TCGCCAGG": 14109, + "GCTGGGAAA": 14110, + "TTCATATG": 14111, + "TACCCTGA": 14112, + "GCATTTCTT": 14113, + "CGTTTTAAA": 14114, + "GACAGGAAA": 14115, + "TATAGGGTT": 14116, + "CAGGCTTA": 14117, + "TCTGGATA": 14118, + "TCTGTACTT": 14119, + "TTTGCATA": 14120, + "GACATCTA": 14121, + "GACCTCTT": 14122, + "CAATTTATA": 14123, + "TAATGTAAA": 14124, + "TAATGGAAA": 14125, + "TAGCTCTTA": 14126, + "CCACCACACCCA": 14127, + "GCGAAGAA": 14128, + "TAACTTCTT": 14129, + "TTTTTGGG": 14130, + "CTTGGATG": 14131, + "GTGAGTCC": 14132, + "GCTGGAGTG": 14133, + "TTAAGATT": 14134, + "TTTGGCAA": 14135, + "CAGCTCTGA": 14136, + "GAAGGGAAA": 14137, + "GTATTATTA": 14138, + "CAAACTGAA": 14139, + "GCCACCATG": 14140, + "TATTTTTATT": 14141, + "TCCTGCATT": 14142, + "GCTCCTCC": 14143, + "TCAGAGTCTTTT": 14144, + "GCGTTCA": 14145, + "CTTGGAGG": 14146, + "GGATCCTT": 14147, + "CCTGGGAAA": 14148, + "GTTATACA": 14149, + "GTTATTTTTT": 14150, + "CCAATCTT": 14151, + "GCCACGC": 14152, + "TAGAGCATG": 14153, + "GGTTCGA": 14154, + "CACTGTTTT": 14155, + "GTAGTCAA": 14156, + "CATGTAAAA": 14157, + "GTCATGTA": 14158, + "CTCCTGCTG": 14159, + "GGTATATT": 14160, + "GTTTGGCTG": 14161, + "CAACACAAA": 14162, + "GCTGCATAGTATTCCATG": 14163, + "TAAAGTCC": 14164, + "GAAACACC": 14165, + "GCTTCAGAA": 14166, + "TGGGTGGA": 14167, + "TAGACAGCA": 14168, + "CTTAATCCA": 14169, + "TTACAGTAA": 14170, + "GTTCAAGCAATT": 14171, + "TGATCTCTG": 14172, + "CCACTATT": 14173, + "CCACTGTA": 14174, + "TATATAGAA": 14175, + "TCTGTGATT": 14176, + "CGGGGGA": 14177, + "CCAACTC": 14178, + "TTAATATG": 14179, + "TCATAGTTTT": 14180, + "TACTTATTTTCCACCA": 14181, + "TAACAAATT": 14182, + "GTGGGGGTG": 14183, + "TCTGCAGCA": 14184, + "GGGGGTA": 14185, + "CATAGTTTT": 14186, + "TGATGCTA": 14187, + "TGACAGCAA": 14188, + "TACGATG": 14189, + "GTCGGCC": 14190, + "CAAACTAAA": 14191, + "GTGATCAA": 14192, + "GGGGGCC": 14193, + "CACTAAAAA": 14194, + "TCAGTTTTA": 14195, + "GTGCTGCA": 14196, + "GCACATCC": 14197, + "TAAACAGTT": 14198, + "GTAAGACA": 14199, + "TCTATTTTTT": 14200, + "TAATGTATA": 14201, + "GTTTGGGAA": 14202, + "CCAGGCAGG": 14203, + "TAGCTAGA": 14204, + "GAGTACTGGAGTGGGGTG": 14205, + "GAGTCATG": 14206, + "GCGTTTCA": 14207, + "TAACGTC": 14208, + "TCATGAGA": 14209, + "GAACGTA": 14210, + "GTTATGCA": 14211, + "CTTTTTAAAAA": 14212, + "CAAAACTTA": 14213, + "CCTCAGCC": 14214, + "CAGTCCATGGGGTC": 14215, + "TCGTGCTG": 14216, + "TTTTTCCA": 14217, + "TGATATATT": 14218, + "TGTGTCCA": 14219, + "TGTTGTCC": 14220, + "CATTTCAAAA": 14221, + "CAGGTGCC": 14222, + "TTTGGGTT": 14223, + "TTTACACA": 14224, + "GCTCAGAGA": 14225, + "GTCTTCTA": 14226, + "CAATGCTA": 14227, + "GCAAAATTA": 14228, + "CTCCTAGA": 14229, + "CTAAGGTT": 14230, + "TAGGCACA": 14231, + "CTGAATGAA": 14232, + "GTAAAGAAA": 14233, + "TCGATTTG": 14234, + "CTGCGCC": 14235, + "TGTTCGG": 14236, + "CTGATTTTA": 14237, + "GTCACCCA": 14238, + "GTTTTTGTG": 14239, + "GTGTTGAA": 14240, + "TTTTTGTTTTTG": 14241, + "TCCCTTCTT": 14242, + "CTTCTGAAA": 14243, + "GTTTCTATT": 14244, + "GGAATCTT": 14245, + "TAAAATCTG": 14246, + "CCAAAAATG": 14247, + "TGGGGATT": 14248, + "CCCCCCCA": 14249, + "TATTAATTAA": 14250, + "CCCGTAA": 14251, + "TCGCCCA": 14252, + "TCCTCAAAA": 14253, + "CTTGAGCC": 14254, + "GTTCATCC": 14255, + "GTATCCAA": 14256, + "GTACTCTT": 14257, + "TATAGAGG": 14258, + "GTAATCTT": 14259, + "GCACCCGG": 14260, + "TCAGTATG": 14261, + "GTGACAAAA": 14262, + "GATGGAGCA": 14263, + "TAATTTACA": 14264, + "TGCAGTTTT": 14265, + "GGTATAAA": 14266, + "CGATTTATTA": 14267, + "CCTTTTATT": 14268, + "CTCAGTAAA": 14269, + "GATAGCTT": 14270, + "TCGGCGA": 14271, + "TCTTTGAAAA": 14272, + "TCCTCTGTCGTCC": 14273, + "CAGATGTGA": 14274, + "CTAACACAA": 14275, + "GTCCCTC": 14276, + "TTTAGAGA": 14277, + "GATAGCAA": 14278, + "CTTTCTGAA": 14279, + "GTGCCTTA": 14280, + "CATGGAGAA": 14281, + "GAAAATAGA": 14282, + "CTTTTTGTG": 14283, + "TAATTCCTT": 14284, + "TAATTTCAAA": 14285, + "CAGGTTCAAGCAATT": 14286, + "TCATAGGA": 14287, + "TCCTTTTAA": 14288, + "CCAGAAGG": 14289, + "CATTCCAAA": 14290, + "TTTGGAGA": 14291, + "TGGACAAA": 14292, + "TAAGCTTTT": 14293, + "TAAGTGAAA": 14294, + "GTGTAATG": 14295, + "GCTGGCCA": 14296, + "CTCACAGTT": 14297, + "CATAGTGAAA": 14298, + "GGGAGCCA": 14299, + "CCCTGAAAA": 14300, + "GGTCCAAA": 14301, + "TGATGAGG": 14302, + "CCAGCTAA": 14303, + "TAAACGTA": 14304, + "GATTGGTG": 14305, + "TTTAGTTTT": 14306, + "CTGAACTGA": 14307, + "CGCCTATAA": 14308, + "CATGTTTA": 14309, + "GAACCATT": 14310, + "CAATAAAAAA": 14311, + "CTAGATTA": 14312, + "CTACCCTC": 14313, + "TCTGCAGAA": 14314, + "CTAACAGG": 14315, + "GGCCTCTT": 14316, + "TGATGCTGA": 14317, + "CCTGGAGGA": 14318, + "TTTACCAA": 14319, + "GTGAAGAAA": 14320, + "GACTCTCC": 14321, + "CCATCATCA": 14322, + "GTACCATT": 14323, + "TAAATCTTA": 14324, + "CTCGACC": 14325, + "GAGAĊ": 14326, + "GCAATCCA": 14327, + "CTGTTGTTTT": 14328, + "CCTCCAAAA": 14329, + "TAGAATAGAA": 14330, + "TAGTGTTTT": 14331, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTG": 14332, + "TATTCAGGA": 14333, + "CAAAGAAAAA": 14334, + "CATTTGCC": 14335, + "CTGAAGTCA": 14336, + "GTCTTCCTT": 14337, + "GTGGGTTTT": 14338, + "CAACTCACAGA": 14339, + "TGATGTGTG": 14340, + "GTTGCTCC": 14341, + "GTAGCTAA": 14342, + "TAAACTCC": 14343, + "TGTGCGTG": 14344, + "CAAATGCC": 14345, + "CAAACATCA": 14346, + "CATGCCTA": 14347, + "TTTTATCAA": 14348, + "TCAAATACTT": 14349, + "TTCAGCAA": 14350, + "GGCTTCCCTG": 14351, + "CCTGAAAAA": 14352, + "TGATCTTTT": 14353, + "GAACACTA": 14354, + "CTGTGAGA": 14355, + "GGACGTA": 14356, + "GTCCAGGAA": 14357, + "TATACGG": 14358, + "GCTGĊ": 14359, + "CATGTGATT": 14360, + "GATGTAGA": 14361, + "GGTTCCTT": 14362, + "CATATTATT": 14363, + "GCCATTAAA": 14364, + "TGGGTTCAAGTGATT": 14365, + "TGACATCC": 14366, + "GAAGCTAA": 14367, + "CATGTCCA": 14368, + "GATGGTTA": 14369, + "CGACACC": 14370, + "TGGAGCC": 14371, + "GAGTTAAAA": 14372, + "GAAGACAGA": 14373, + "CCGGTCA": 14374, + "CTGTGAGG": 14375, + "GAAAGAATT": 14376, + "CATTTGATT": 14377, + "TCTGCCTCA": 14378, + "CCTTCCTA": 14379, + "CTAACAAAA": 14380, + "TTTAGAAAA": 14381, + "TAGGGATT": 14382, + "TGTAGAAAA": 14383, + "TACATTAAA": 14384, + "GGGCTGGG": 14385, + "TCCCAACC": 14386, + "CAAATAGAA": 14387, + "TGAAGTCC": 14388, + "CCCCTCTA": 14389, + "GATAGTGA": 14390, + "CCCTCTTA": 14391, + "CCGTCTATG": 14392, + "CCATCCAACCATCTCA": 14393, + "GGTCTAAA": 14394, + "GTACAGC": 14395, + "GCTTTCTCA": 14396, + "CTTTTTCTA": 14397, + "TTTACAGA": 14398, + "CCCACTCCAGTATT": 14399, + "TCCTTCTCA": 14400, + "GACCCTT": 14401, + "GCCTGTTTT": 14402, + "TCACCTCCA": 14403, + "GTTCCTCTT": 14404, + "CCGATAA": 14405, + "GGTGGGTT": 14406, + "CTGAAATA": 14407, + "GGACCATT": 14408, + "GCAATCTT": 14409, + "CTTACAGTT": 14410, + "GCCAGCAAA": 14411, + "TAGGTATT": 14412, + "GCTATTATT": 14413, + "GCATTTGTT": 14414, + "TAACCCTAA": 14415, + "TGATATAAA": 14416, + "CAAGAGAAA": 14417, + "GGATAATG": 14418, + "CTCTGACA": 14419, + "CATGCCATT": 14420, + "TCTCCATGA": 14421, + "GTCTGTCTG": 14422, + "GACTGTTA": 14423, + "GTATTTTTAGTA": 14424, + "CAGGGTCC": 14425, + "GAGGGTAA": 14426, + "TCATTCTAA": 14427, + "GGTAGCTG": 14428, + "CTGTGTCTG": 14429, + "GAGTTTGGA": 14430, + "GCAGTTTTGAAA": 14431, + "CACCAATG": 14432, + "GGTCCCCA": 14433, + "TCCGCTG": 14434, + "TAAACGAA": 14435, + "TTAAGTAA": 14436, + "TGTAGCTT": 14437, + "TAATATTATT": 14438, + "GCTCATTTG": 14439, + "TCCGGAA": 14440, + "GTTTCAGA": 14441, + "CATTCGA": 14442, + "GAGGGGAAA": 14443, + "GAAAACTT": 14444, + "GCTACGA": 14445, + "CTGAAGTG": 14446, + "TAATATCTT": 14447, + "CGTCTTTT": 14448, + "CTAAATTGCTCAAA": 14449, + "TAATCCCA": 14450, + "TCAGTGGG": 14451, + "GTCGAATT": 14452, + "GTCTCACTG": 14453, + "CACAGAGGA": 14454, + "TCAATCAAA": 14455, + "TCTCTAATT": 14456, + "GGCAGGAGGA": 14457, + "GTGTTGGGATTACAGG": 14458, + "GTTCAAGGCCA": 14459, + "TCGTTTTTT": 14460, + "TCAGCTTTT": 14461, + "GCCTACTA": 14462, + "GAGAGGGGA": 14463, + "CATTCCTA": 14464, + "GCAAGTTTT": 14465, + "CTTTGCAA": 14466, + "TACCCCAA": 14467, + "CATTTGTTTT": 14468, + "TCACAGTCA": 14469, + "GTGATGTTA": 14470, + "TAGAGATG": 14471, + "TATTCTCTT": 14472, + "CACAĊ": 14473, + "TGAAGAGAA": 14474, + "TAAAATATCA": 14475, + "CCTGCAGCA": 14476, + "TAGGCCA": 14477, + "GTTAATTTTA": 14478, + "CTCACAGAGTTGAA": 14479, + "GTTAAAATG": 14480, + "TAAATCTCA": 14481, + "CAGGGAGAA": 14482, + "GCTTCAGCA": 14483, + "GCAATAGA": 14484, + "CCGACAGA": 14485, + "GTTCGCC": 14486, + "CATTCACTT": 14487, + "GATGCGA": 14488, + "TCTACGG": 14489, + "TCATTCAGA": 14490, + "TCATTCTGA": 14491, + "CTGTGGGAA": 14492, + "CCTAATTTT": 14493, + "GTTCAGAAA": 14494, + "TATACGA": 14495, + "GTTAGTCA": 14496, + "TCCACCAAA": 14497, + "GGGGCAA": 14498, + "CCCCATCTCTACTAAAAATACAAAAATTA": 14499, + "TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC": 14500, + "GTAGGTGA": 14501, + "TAAACACTG": 14502, + "GTCTATCC": 14503, + "GAGAGATGA": 14504, + "TTTGCTTTT": 14505, + "GCTCAAGCCTGTAATCCCAGCACTTTGGGAGGCCGAGACGG": 14506, + "GTTGAAGG": 14507, + "CTGAGAGAA": 14508, + "CATGCTTTT": 14509, + "CGGTCTG": 14510, + "TCTCCACAA": 14511, + "TACACCTA": 14512, + "TTAAGAAAA": 14513, + "TCTAGGGA": 14514, + "TTCACTT": 14515, + "GGAAAGGAA": 14516, + "TAATGTATT": 14517, + "TCACTGGG": 14518, + "TCCCAATG": 14519, + "CCACCCTC": 14520, + "CTCTCCACA": 14521, + "TGTGGGCA": 14522, + "TCAATATCA": 14523, + "GAGGTCAGG": 14524, + "GTAAAGAATCTGCCTG": 14525, + "GGGTCAAA": 14526, + "GCTCTCATT": 14527, + "TGACTTCTT": 14528, + "GTTCAAGA": 14529, + "GAGACAGCA": 14530, + "GATTTGATT": 14531, + "CTCCCAGCC": 14532, + "CTTAGCC": 14533, + "GCGGGAGG": 14534, + "CCCTTATT": 14535, + "GACCAAAAA": 14536, + "GTATACTA": 14537, + "CTCAGATA": 14538, + "CCTTGAAAA": 14539, + "CCTGGAGCA": 14540, + "GTTACGTT": 14541, + "TTTAGGA": 14542, + "TAGACAGAA": 14543, + "CATACTTTT": 14544, + "TGGAGCA": 14545, + "TAAGTCTG": 14546, + "TTAĊ": 14547, + "CAATAAATT": 14548, + "CTGGGAGG": 14549, + "GTGGCTC": 14550, + "GAATTGTAA": 14551, + "CACCCTT": 14552, + "TAACATTTTA": 14553, + "TCCCTCCCAGCA": 14554, + "TAACTTAAA": 14555, + "CAACGGA": 14556, + "GTCTCCTGA": 14557, + "TAAAAATGTA": 14558, + "TATAATAAA": 14559, + "GGATGGATG": 14560, + "TATATGTATG": 14561, + "CACTCATG": 14562, + "CACCAAAAA": 14563, + "CCACCTC": 14564, + "GTATAGTG": 14565, + "TATTCATCA": 14566, + "TCTCTGAGTT": 14567, + "CCCACCCTG": 14568, + "TTATTGTTTT": 14569, + "CTGAAGTGA": 14570, + "CTGTTCCTT": 14571, + "TCAGGGAAATG": 14572, + "CTGCCCGG": 14573, + "GAAATTAGGA": 14574, + "TGTGCCACATTTT": 14575, + "TACAAAAATTA": 14576, + "GGAGTATT": 14577, + "GTAGGGGA": 14578, + "GCCCAGGTT": 14579, + "TCTTGAGTT": 14580, + "CTGAGGGAA": 14581, + "TTATTATTTA": 14582, + "GGGCTCAA": 14583, + "GTATTTAGTCAGCCA": 14584, + "GTTGCCATT": 14585, + "CTAGACCA": 14586, + "CATTGGGG": 14587, + "CATGGATA": 14588, + "TACACTC": 14589, + "TCCATCTCC": 14590, + "TTTTTTTCTTTT": 14591, + "GTGTTGGAA": 14592, + "TACCCTCA": 14593, + "CTACCCTG": 14594, + "GAAACTCC": 14595, + "GTGAGTTCCA": 14596, + "CAAAACTAA": 14597, + "CACACACATT": 14598, + "CTTCCTGAA": 14599, + "TGCTGGAGA": 14600, + "TCCATGAGCATG": 14601, + "TCATATAAA": 14602, + "GAACTTTG": 14603, + "CTGCGGA": 14604, + "GCACTATG": 14605, + "CACAGTTTT": 14606, + "GAAACAGAAA": 14607, + "GTCAGGGTT": 14608, + "GTCCCGCC": 14609, + "TGGATGTT": 14610, + "CTCTGCTTTT": 14611, + "GATGTTAAA": 14612, + "CACGCTA": 14613, + "CCCGTTA": 14614, + "CACATAGG": 14615, + "GTTATCTA": 14616, + "TTTAGGAA": 14617, + "CGCTAGG": 14618, + "TGGAAAAAA": 14619, + "GTACAGTAA": 14620, + "CTATAGCA": 14621, + "TATAGGGAA": 14622, + "TCTTCATTA": 14623, + "CTTTGCTGAA": 14624, + "CACCATTA": 14625, + "GGCGATT": 14626, + "TCGCAGA": 14627, + "GTGTACCA": 14628, + "CTCGGTA": 14629, + "CAGACTTTT": 14630, + "GATGCAAAAA": 14631, + "GAAAACGG": 14632, + "TTTGGCTT": 14633, + "TCTAĊ": 14634, + "TAGATAAAA": 14635, + "GGCATATA": 14636, + "TAATACTTA": 14637, + "CTGTTCATT": 14638, + "TACCCATT": 14639, + "GTCATTAAAA": 14640, + "TAAATATTTTA": 14641, + "TGATCTCC": 14642, + "GTTTAATAA": 14643, + "GGACATC": 14644, + "TAAATCAAAA": 14645, + "CCTGGGTGA": 14646, + "TGTACTCA": 14647, + "TATTATATT": 14648, + "CTCTGGCTG": 14649, + "CTATTCCTT": 14650, + "GAAGGTGAA": 14651, + "CACTTTTAA": 14652, + "GAACCCTT": 14653, + "CCAGCGG": 14654, + "GTGAGATA": 14655, + "TCTCTGAGA": 14656, + "GGGGGGGGGGGGGGGG": 14657, + "TCAGTAAAA": 14658, + "GTGGCTTTT": 14659, + "TGTTCGA": 14660, + "CAAACAATT": 14661, + "GTGACAGAA": 14662, + "GTAAGGGG": 14663, + "TCAGGTTTT": 14664, + "TCAGCAGCA": 14665, + "GTGATAGG": 14666, + "TACAGCTA": 14667, + "CGCGAA": 14668, + "TTTTTTTAATT": 14669, + "GGATTTCTT": 14670, + "CTCCAGCTG": 14671, + "CTCCCTTTT": 14672, + "TTCATGAA": 14673, + "CGCTCGG": 14674, + "CAGCATTTT": 14675, + "CAATATCC": 14676, + "GCCCAGAGA": 14677, + "CACAGTC": 14678, + "GAAATATGA": 14679, + "CATTTAGTT": 14680, + "CCCATAAA": 14681, + "GGGAGAATG": 14682, + "CTCTTCCA": 14683, + "TGAGAAAAAA": 14684, + "GTTCCCTC": 14685, + "TATTTACCA": 14686, + "TCTTTCTAA": 14687, + "CAGACTC": 14688, + "GTAAGCTT": 14689, + "GAAAAGAGG": 14690, + "GACACAAAA": 14691, + "TTTTTTTATA": 14692, + "GAACCCAGGAGGCAGAGGTT": 14693, + "GAGTGAAAAA": 14694, + "CTAGTATT": 14695, + "CTATCTAA": 14696, + "GAAACGAA": 14697, + "TGTTGGTA": 14698, + "GATTTCTTA": 14699, + "CTAAGTTTT": 14700, + "TACACTGAA": 14701, + "TACATAAAAA": 14702, + "CCCAGCCAA": 14703, + "GCTACAAAA": 14704, + "GGCCTATG": 14705, + "TGATTGGTG": 14706, + "GCATTCTTTG": 14707, + "TGTTTGAAAA": 14708, + "GTGATGTTTT": 14709, + "TCATATCC": 14710, + "TCAGAAGA": 14711, + "CTGGTCAA": 14712, + "CATTGTCTT": 14713, + "TCTCTATTA": 14714, + "CTTTGCTT": 14715, + "GTGTTGTG": 14716, + "TATCAAAAA": 14717, + "GCCGAGCA": 14718, + "CAGGTCTA": 14719, + "CCAACATCA": 14720, + "CTTAGCTG": 14721, + "GACAGCCA": 14722, + "GATAGAGG": 14723, + "TTTTTTTAAAA": 14724, + "CGTGCCTG": 14725, + "CAATTTGTT": 14726, + "CGCCCGCC": 14727, + "TAGCTCTA": 14728, + "GCCACCATGTG": 14729, + "CCATATCC": 14730, + "GCCTCTCTG": 14731, + "TGTGTGCC": 14732, + "CAAAGTGAA": 14733, + "CTCACAGCA": 14734, + "GAAAAGTCA": 14735, + "TTTGGCA": 14736, + "TGCATCTT": 14737, + "TGCATCTG": 14738, + "GGCCCTAA": 14739, + "CACCCTAA": 14740, + "GCTCAGGAA": 14741, + "CTGCTTCTG": 14742, + "TGTGCGCA": 14743, + "CAAACACTG": 14744, + "CATGGGGA": 14745, + "CCAACACCA": 14746, + "TGGAGTGGCCTA": 14747, + "GGTTTTTTTT": 14748, + "TCACGCCA": 14749, + "TGATTTTTG": 14750, + "TTATTTTAA": 14751, + "CATTAAAAAA": 14752, + "GTCATCCA": 14753, + "CCCCTCCCC": 14754, + "TCCTTTGTT": 14755, + "CCAGGCACA": 14756, + "CTGTAGCCTT": 14757, + "TCCCCAAAA": 14758, + "GGATAGTT": 14759, + "GGATAGCA": 14760, + "TATTGATTA": 14761, + "TAAAATCAAA": 14762, + "CCTTGGGA": 14763, + "GAAAAGTG": 14764, + "GGAAAGAGA": 14765, + "CATCTTTTA": 14766, + "TCGTGTA": 14767, + "GAATGAATT": 14768, + "TCCTAGTA": 14769, + "GGATCATG": 14770, + "CCACTGGG": 14771, + "CTCTCCCTG": 14772, + "GGAAGAGCA": 14773, + "CTCACACTG": 14774, + "TCTCTCTAA": 14775, + "CTCCTCTGA": 14776, + "GTTTTTTCA": 14777, + "GTCCCCAAA": 14778, + "GGCATCAA": 14779, + "GGCACGA": 14780, + "GCTCAGCTG": 14781, + "TAAAAACTAGACAGAA": 14782, + "GGTCCACA": 14783, + "TCGCCATG": 14784, + "TGATAATAA": 14785, + "GGACCCTT": 14786, + "CTACTTTG": 14787, + "GTCTĊ": 14788, + "CAGACAGTG": 14789, + "TATGAAATG": 14790, + "GATTCGG": 14791, + "CATACACACACA": 14792, + "TAATTGTG": 14793, + "TCCTTTCTG": 14794, + "CAAGGAGG": 14795, + "GTTGGCTA": 14796, + "CCATAATG": 14797, + "GCCTATGA": 14798, + "CTAGGCTT": 14799, + "CTCGGGAA": 14800, + "TCTCTACTG": 14801, + "TCTCTACCA": 14802, + "TTTGTTTTA": 14803, + "CCTACGA": 14804, + "GCTGACTG": 14805, + "GGATGGGGG": 14806, + "GGAGGAGAGG": 14807, + "TCGCACC": 14808, + "CCAGATTA": 14809, + "GATTTCTGA": 14810, + "TCCAGCAAA": 14811, + "TGGGGTATT": 14812, + "GGAAATCAA": 14813, + "GTGGGGATG": 14814, + "TCCTCCTGCC": 14815, + "TATTTGCAAATTATG": 14816, + "TACATTTA": 14817, + "TCATTTTTG": 14818, + "CAAAGGGAA": 14819, + "CATGTCTA": 14820, + "TATGTAGAA": 14821, + "CTGTGGAAA": 14822, + "CTCCATCTT": 14823, + "CTGGAGCTG": 14824, + "TAAGGTTAA": 14825, + "GTAATTAAAA": 14826, + "GACCAATG": 14827, + "GAGGCTTTT": 14828, + "CCTGTATA": 14829, + "GTCCTATT": 14830, + "CCATGTCCA": 14831, + "GTTTCTAAA": 14832, + "CATTTCATG": 14833, + "CAAAATAGA": 14834, + "GGTGCAA": 14835, + "CATATAAAAA": 14836, + "GTGTTTCTG": 14837, + "GTGTTGCTT": 14838, + "TGATGGAGG": 14839, + "GCGGTTTT": 14840, + "TCCCAGCTT": 14841, + "CTGGAGGAA": 14842, + "GGGTTTTTT": 14843, + "GAATAGGA": 14844, + "GCCCATAA": 14845, + "CTATTATTA": 14846, + "TGTGĊ": 14847, + "TGTTCTCTT": 14848, + "CTGATACA": 14849, + "TATGGGGG": 14850, + "GACATAGG": 14851, + "CCCCCGTG": 14852, + "GCCAAGATG": 14853, + "GATCACACCA": 14854, + "TCAGACTA": 14855, + "GTGGCTCATGCCTG": 14856, + "GCATACCA": 14857, + "GCACAGAAA": 14858, + "CTCGCTCA": 14859, + "TTTGCTAA": 14860, + "TTATTGTAA": 14861, + "TCATTGTCA": 14862, + "TGATTGGTT": 14863, + "CAAAACAAAACAAAA": 14864, + "GGCTCCAA": 14865, + "GTTGTAAAA": 14866, + "GTACTCAA": 14867, + "CTCGATA": 14868, + "CTCTCACC": 14869, + "TGTTTAGTT": 14870, + "GGCAGTTA": 14871, + "TGAAATAAA": 14872, + "TAATACCAA": 14873, + "GTTTGCTGA": 14874, + "GTGGGGAGA": 14875, + "AAAATGTTA": 14876, + "GCTCAAATT": 14877, + "TCACTTGG": 14878, + "TCCGATG": 14879, + "GTTGCCTG": 14880, + "CTCTATAAA": 14881, + "CTCATGTA": 14882, + "CGGTCTT": 14883, + "CCTGCATA": 14884, + "CCTGGGCTG": 14885, + "CTAAGATA": 14886, + "CAAAAGATT": 14887, + "CTGTGCCTT": 14888, + "CATCCAAAA": 14889, + "TAACAACA": 14890, + "CTATCCTT": 14891, + "TGTGGCCA": 14892, + "CCAAGATA": 14893, + "GGAAATTTT": 14894, + "GAATTCTT": 14895, + "CTCTGGAGA": 14896, + "CAGCTCTCA": 14897, + "GATAATTAA": 14898, + "TAAGTTTA": 14899, + "TCACTTTTA": 14900, + "TCCTAAAAA": 14901, + "GGATACTA": 14902, + "CTATCTGA": 14903, + "TCAACATCA": 14904, + "GCTGGGTA": 14905, + "CGGGACA": 14906, + "TCCATCCTT": 14907, + "CGATAAAA": 14908, + "CAGCTCAAA": 14909, + "TTTTTTTTTTTA": 14910, + "GCTTTTAAAA": 14911, + "CATTTGTTA": 14912, + "TCACCCAGGCTGGAGTG": 14913, + "TCTTCAAACCCATGCTAAATTGCTCAAA": 14914, + "GATCTACA": 14915, + "GCTCTCAAA": 14916, + "TGACCCTA": 14917, + "TCCGCCA": 14918, + "CTTTCCAAA": 14919, + "TAAATAGTA": 14920, + "CAAAGACTT": 14921, + "CAGATGCC": 14922, + "CTCCTCCAA": 14923, + "CTAATAGA": 14924, + "TACAGTTAA": 14925, + "TTATTTAATT": 14926, + "TTTTTTAAAAAA": 14927, + "TGATTGATT": 14928, + "GTTTGCTAA": 14929, + "GCGGCGCC": 14930, + "AAAATTTATG": 14931, + "TCACTGCAGATG": 14932, + "CGGTGGCTCACGCCTG": 14933, + "CCCCTTTG": 14934, + "GTCGATA": 14935, + "GAAACGTT": 14936, + "GAAACACTG": 14937, + "GTGACATA": 14938, + "TCTCCAGTA": 14939, + "TACATACTT": 14940, + "CTGAAGGAA": 14941, + "CAATTCTGA": 14942, + "CCTCCTTA": 14943, + "CTTCAGACGG": 14944, + "GGTCCAGA": 14945, + "GTGTCAGG": 14946, + "CTAGCTAA": 14947, + "CTCTCAATT": 14948, + "CTTATGCA": 14949, + "GAAAACAGAA": 14950, + "CTTTTTATTTT": 14951, + "TCATTTCCA": 14952, + "GCTGTGCAGAA": 14953, + "CAAGTCTG": 14954, + "CAGACGGA": 14955, + "GTCACATA": 14956, + "GATTTCTCA": 14957, + "CCCAGCAAA": 14958, + "TACCTATA": 14959, + "CTGTGGAA": 14960, + "GAGCAGAGA": 14961, + "GCTCTGCCTCCCGA": 14962, + "GCGCGCC": 14963, + "CCATATGAA": 14964, + "CCAATGAGTCAA": 14965, + "GGACCAGG": 14966, + "CTAGATGA": 14967, + "TAAAGGAAAA": 14968, + "GGAAGGTA": 14969, + "GTTATATAA": 14970, + "CCCAGCTCA": 14971, + "TAATTTTTTA": 14972, + "GCGACGA": 14973, + "CCCGATG": 14974, + "CCTGAATG": 14975, + "GGCCAATG": 14976, + "GGACTTGG": 14977, + "GTCGACA": 14978, + "TCCACTTCTG": 14979, + "TTTGTAGA": 14980, + "GTGCTAGGATTACAGG": 14981, + "CCCGCAA": 14982, + "CAAGATCC": 14983, + "TGTGGATA": 14984, + "TCTGCCCTG": 14985, + "GCTTTAGAA": 14986, + "TTTGGAGG": 14987, + "GTCCTAA": 14988, + "GGGGGCTT": 14989, + "TCCGTTA": 14990, + "GTTTAGC": 14991, + "CTAGCCTG": 14992, + "TAAATTTTAA": 14993, + "TGTGCAGTT": 14994, + "TATTTCTGTG": 14995, + "TATATAGTT": 14996, + "GAGACATA": 14997, + "GCTGGTTTT": 14998, + "GATTTAATT": 14999, + "CCCAGTTTT": 15000, + "CAAAATGCA": 15001, + "TGTACCA": 15002, + "GCTGAGGAA": 15003, + "TCAAAATTAA": 15004, + "GCATGCTG": 15005, + "CTGGGGCTG": 15006, + "CTCCACCTT": 15007, + "CTTTCCATT": 15008, + "GTAGCTTA": 15009, + "CTCACAGAA": 15010, + "TTTTAGTTTT": 15011, + "GAGGCAGCA": 15012, + "TTTGCAGA": 15013, + "GCCACCCCA": 15014, + "TGTATAAAA": 15015, + "CTGTTCCTG": 15016, + "CGCCAAAA": 15017, + "GAGCTATG": 15018, + "GCTCCCAGG": 15019, + "CCTGTAATCTCA": 15020, + "TACGCTG": 15021, + "TAGCTTTG": 15022, + "GCGTGAAA": 15023, + "TTAGGTG": 15024, + "GTATAGCA": 15025, + "CACAGACTT": 15026, + "TGTTGGAAA": 15027, + "TGTTGGCTT": 15028, + "CTGATCTGA": 15029, + "CTATTTCTA": 15030, + "GAGTATTTT": 15031, + "CGTCAACTATGACAGA": 15032, + "TCATCCAGG": 15033, + "CTTCTAAAA": 15034, + "GGATAAAAA": 15035, + "TATTGAGTG": 15036, + "GAAATAATG": 15037, + "CCTGGGCAA": 15038, + "CCCCCGCA": 15039, + "GTAATGTCTCTGCTTTT": 15040, + "CTTGTTTTA": 15041, + "TGTTCTTAA": 15042, + "CTGAĊ": 15043, + "GTCATCTA": 15044, + "GGTTTAA": 15045, + "CTATTTATA": 15046, + "GCCTATTTT": 15047, + "GCCTCAGTT": 15048, + "CTACCCCA": 15049, + "TGTTCAGTG": 15050, + "CAGAGTCAA": 15051, + "TCTCCATTTT": 15052, + "CAATTCTAA": 15053, + "GCATTTCTG": 15054, + "GATGAGAGAGGCCTG": 15055, + "TCGCCTCC": 15056, + "GGAGCTTA": 15057, + "GTAGTTTTA": 15058, + "CATGGAGAAA": 15059, + "TCTCTAGAA": 15060, + "CAAAAGAGA": 15061, + "TAGGTAGA": 15062, + "GCTAGCCA": 15063, + "CACCTCAA": 15064, + "CCTCCCAGG": 15065, + "GCAGGGAGA": 15066, + "TACAGGTAACGA": 15067, + "GGTATTTA": 15068, + "TCGCATA": 15069, + "CTTTGTGTT": 15070, + "GCACCTC": 15071, + "GGAGCATG": 15072, + "CCCAGCACC": 15073, + "TCTTTGGTCTT": 15074, + "GCCTCAGCCTCC": 15075, + "TCACAAGG": 15076, + "CAACCTAA": 15077, + "GTTTCCCCA": 15078, + "GCCGTAA": 15079, + "TATTCACAA": 15080, + "CAGACACCA": 15081, + "GATTGGGG": 15082, + "CTCCTACA": 15083, + "TGGGTTAA": 15084, + "CCTACCATT": 15085, + "TCATTGCAA": 15086, + "TCCCATCTG": 15087, + "TGATTATTA": 15088, + "GTAGGGTT": 15089, + "CTCTCTCAA": 15090, + "CTAATGGA": 15091, + "TGTAGTGA": 15092, + "GAAGGGGAA": 15093, + "GGAGAGTCA": 15094, + "CTACATATG": 15095, + "CCATCTCTT": 15096, + "TCTTCAGCA": 15097, + "GTTATGGA": 15098, + "TGCACTCA": 15099, + "GCTCTTTAGTT": 15100, + "TACCAATT": 15101, + "GGGATTAA": 15102, + "GCCCTGCTG": 15103, + "GTCGCGG": 15104, + "GTCTCCTCC": 15105, + "TATTGGCA": 15106, + "GAAACACTT": 15107, + "CAAACATAA": 15108, + "TCAATACAA": 15109, + "TACAGAAAAA": 15110, + "CCCAGAATT": 15111, + "TCTCATTCA": 15112, + "GCGCCATG": 15113, + "TAAAGACACATG": 15114, + "TGACTTGG": 15115, + "CTGCACTA": 15116, + "CTTCCAAAA": 15117, + "GTAGACTA": 15118, + "CACACTTTT": 15119, + "TGAAGTAAA": 15120, + "CAGACACTT": 15121, + "TACAGTGTT": 15122, + "GGTTGAGA": 15123, + "CCTAGTGAA": 15124, + "GTCTTCATT": 15125, + "GTGTGTATG": 15126, + "CTGGGCATG": 15127, + "TGATATCC": 15128, + "TTACAGAA": 15129, + "CTGGTATT": 15130, + "GGAGAAATG": 15131, + "CAAAGGCC": 15132, + "CAGGGAAAA": 15133, + "CTGACTTTT": 15134, + "GCTGGGGAA": 15135, + "CCTTCATA": 15136, + "CCTTTCTCA": 15137, + "CGGCAGCA": 15138, + "GTTTTTATTTT": 15139, + "TGTATCTA": 15140, + "GCCAATTTG": 15141, + "TCGCCAAA": 15142, + "GAAAGATAA": 15143, + "TGAAGATGA": 15144, + "TCTGCTAAA": 15145, + "GCTTCCCC": 15146, + "CTCATCTA": 15147, + "GCCAGAAAA": 15148, + "TAGGTAA": 15149, + "CAACAGCAAA": 15150, + "CCAGAGGAA": 15151, + "CGGTGGCTCA": 15152, + "CTTGCACAATTGGTGGCTGACTAAA": 15153, + "GCAGATTTT": 15154, + "GCTTGGCC": 15155, + "CCTTTCAAA": 15156, + "TAATTTCCA": 15157, + "CGCGTCC": 15158, + "TCATTCAA": 15159, + "GGTAGTGA": 15160, + "TACCTATG": 15161, + "CTATTTCAA": 15162, + "GGAGAAAAAA": 15163, + "CCGCAGG": 15164, + "GAGTAATG": 15165, + "GTTCATC": 15166, + "GCCGTTA": 15167, + "CTATGAGA": 15168, + "CTAGACTA": 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"TTTTCAAACCACATAAATTTTCGTTCGGTTTCATCAAACCCATCCTAATT": 15254, + "TGATATTAA": 15255, + "GAATAGTG": 15256, + "TGTGCTCC": 15257, + "TCTTCAGTG": 15258, + "TGAAGTTTG": 15259, + "TCTGTAATT": 15260, + "GCAAGTCC": 15261, + "CCAAGGCC": 15262, + "TGTAGTTTT": 15263, + "CCGGGCTG": 15264, + "TAAAAAAATTA": 15265, + "TGAGTAAAA": 15266, + "TTAGACA": 15267, + "GCTGCACCA": 15268, + "TATGCACC": 15269, + "CCTGTCCA": 15270, + "TTAATCAA": 15271, + "GTCTTTCTG": 15272, + "TCTTTGGAGG": 15273, + "GTGGCTCACAACCA": 15274, + "CTCCAGTACTCTTGCCTGGAAAATCCCATG": 15275, + "TCATATC": 15276, + "TCATATATT": 15277, + "GAATATATT": 15278, + "CTACCCTT": 15279, + "TAAAGATGA": 15280, + "TAAATAATTA": 15281, + "GCTTTCTTA": 15282, + "GTAAGTAAA": 15283, + "CAAAATACA": 15284, + "GGTTGGAA": 15285, + "GTTTGGGGG": 15286, + "TAGGAAAAA": 15287, + "GCATGAGG": 15288, + "TATACGTA": 15289, + "CCAATGGA": 15290, + "GTCCTCTGA": 15291, + "GGTGGATT": 15292, + "GCGTCCAA": 15293, + "TGACGCC": 15294, + "CTACGC": 15295, + "TAAATCTAA": 15296, + "TGTTGCTA": 15297, + "CATGCGTG": 15298, + "GATGGGCA": 15299, + "GGTATCTG": 15300, + "TAATGCTG": 15301, + "GAACAGCAA": 15302, + "TCTGTCTGTCTG": 15303, + "GGGATTTG": 15304, + "TCGTTCTG": 15305, + "CTTGCATG": 15306, + "GTGCTTGG": 15307, + "CTAGGTCA": 15308, + "GTCGCCA": 15309, + "GAGGTTCA": 15310, + "CCCATAGG": 15311, + "CTGAAGAGA": 15312, + "TACCTCCA": 15313, + "TAGCTACA": 15314, + "CCCTCCAAA": 15315, + "TCGTAGG": 15316, + "GGACTATT": 15317, + "CTCTCATA": 15318, + "TGTTGTGTT": 15319, + "GCTGGAAAA": 15320, + "TCTGTAGATT": 15321, + "TCTCCTGACCTCA": 15322, + "CCAATTGTGCAAGTTCTCCCACTTAAAAA": 15323, + "TTATCCA": 15324, + "CAAGTGTG": 15325, + "GTTGTTATT": 15326, + "GGATACCA": 15327, + "CATTCCCC": 15328, + "TAAAAĊ": 15329, + "GTCCCAGGA": 15330, + "CTTTTCCTG": 15331, + "GGGGCGTG": 15332, + "CGACAGAA": 15333, + "GGTTCAGG": 15334, + "GATCATTTT": 15335, + "TATCAATG": 15336, + "CTTCTATG": 15337, + "GCAGTATG": 15338, + "GTATCTTTT": 15339, + "CACACTCC": 15340, + "GGTTGCTG": 15341, + "CCTATCTG": 15342, + "TCTCCCAAA": 15343, + "CTTTATCCA": 15344, + "TCGTCTA": 15345, + "GTACGCC": 15346, + "GTACTGTG": 15347, + "CTCAGGTGA": 15348, + "CCTTCCCAA": 15349, + "TCAAATAA": 15350, + "TAAAAATTAAA": 15351, + "GACATTAAA": 15352, + "CAATAACAA": 15353, + "GTGTAATA": 15354, + "CCAGCTC": 15355, + "GTAGGAGG": 15356, + "CTAGCTCA": 15357, + "CTAGAATG": 15358, + "CTCTGTGTG": 15359, + "CCTTTATCA": 15360, + "GTTATATTA": 15361, + "TACAATTAA": 15362, + "GGACACGG": 15363, + "GGTCCTGGAGTGGCCTA": 15364, + "TCCTCTCCAGCA": 15365, + "GAATGAGG": 15366, + "GCACGTC": 15367, + "CATTCCATT": 15368, + "GCTTCAAAA": 15369, + "GTCAGAGCA": 15370, + "GACAGTTTT": 15371, + "TACCCAGA": 15372, + "TAACACAAA": 15373, + "TGTGTGTGTGTGTGTGTG": 15374, + "CATCTTAAA": 15375, + "CGCCTGTAATCCCAGCTA": 15376, + "GGGCCAAA": 15377, + "GCGTGTA": 15378, + "TCCCTTGG": 15379, + "TATTGCCA": 15380, + "TGAATCTA": 15381, + "CTCATAA": 15382, + "CCTTGGAAA": 15383, + "CGACAGC": 15384, + "GGTTGTTA": 15385, + "GCAAAGAGA": 15386, + "GCCAAGACCAAA": 15387, + "GGTTATTTT": 15388, + "TCACAGTGA": 15389, + "TAAAATGAAAA": 15390, + "TCCCCAGA": 15391, + "GTACAATG": 15392, + "TCTGTAGAA": 15393, + "GCTGTGATT": 15394, + "TTTTAGAGA": 15395, + "GCCAAGGA": 15396, + "TGATGGCCA": 15397, + "TATCGTGA": 15398, + "TCCCCCTA": 15399, + "CAAGCGATT": 15400, + "GTTGCTGTT": 15401, + "GCCCAATT": 15402, + "TATTGGAAA": 15403, + "CACAGGCC": 15404, + "CCTTCAAAA": 15405, + "GCAAĊ": 15406, + "CCCCCTTA": 15407, + "CCTATCTA": 15408, + "GAATTGTGA": 15409, + "CGCCCACCA": 15410, + "TAATTATTTA": 15411, + "GTTTACAAA": 15412, + "GGAGAATCACTT": 15413, + "GCCCACCAGGCTCC": 15414, + "GAATCATG": 15415, + "CTAGTCTT": 15416, + "TGTTTGCC": 15417, + "TTTGAAATG": 15418, + "TAATGTCA": 15419, + "GAGGTGGAGGTT": 15420, + "TCTCCTGACCTTGTGA": 15421, + "GAGACTGGCTAGGCCACTC": 15422, + "TAGTCCA": 15423, + "GAAGCGG": 15424, + "CCATCAGA": 15425, + "CCCCCTAA": 15426, + "GACCCTAA": 15427, + "TGAATTAAAA": 15428, + "TCGATATA": 15429, + "TGACCTC": 15430, + "GTGAAAATT": 15431, + "TCTTGCCA": 15432, + "CAGGTGTGA": 15433, + "TCAATATAA": 15434, + "CTGACATA": 15435, + "GTAACCTG": 15436, + "CCCAGCTAA": 15437, + "TATGTGAAA": 15438, + "CGTTTTATT": 15439, + "GCTTCTGAA": 15440, + "TACAGAATT": 15441, + "GTCCTGGAA": 15442, + "GGTTTATG": 15443, + "GCTCAGAGG": 15444, + "CTCTGCATT": 15445, + "GAATATTATT": 15446, + "CACACTACGCC": 15447, + "TACGAGG": 15448, + "GACTAAAAA": 15449, + "GAACAGAAA": 15450, + "GCACACTA": 15451, + "CTATTATG": 15452, + "GTCTCAGAA": 15453, + "GCTGCTGTT": 15454, + "GATTTCATT": 15455, + "TACACGCA": 15456, + "TTTGTCAA": 15457, + "TTTGCGA": 15458, + "CTGTGGAGG": 15459, + "GTTTAGAAA": 15460, + "GCCCCCTCC": 15461, + "TCATTATA": 15462, + "TCCAAAATT": 15463, + "CTTGAAATT": 15464, + "CCAGCTTA": 15465, + "TAGGCTAA": 15466, + "TCAAAGCTG": 15467, + "CGTGGATG": 15468, + "TGCCCAAA": 15469, + "CGCCCAGCC": 15470, + "CTTCACCTT": 15471, + "CTCCAATGCATGAAAGTGAAAA": 15472, + "GTTTCCTCA": 15473, + "GTAGGGCA": 15474, + "GCCTCAAAA": 15475, + "GTTTATTTG": 15476, + "TATTTATTTA": 15477, + "TGAAGACAA": 15478, + "CAGATAATT": 15479, + "GAGGAGAAA": 15480, + "GAAGACAGG": 15481, + "CCACAGCAA": 15482, + "GACGTGTACCTA": 15483, + "TGAGCAAAA": 15484, + "GTTGTGGG": 15485, + "TCTTTAGAA": 15486, + "TGTTGTTAA": 15487, + "CTCACTTTT": 15488, + "GACAGTAAA": 15489, + "GGTGGCAA": 15490, + "GATAGTTTT": 15491, + "GCAAACAA": 15492, + "GCCTTGAA": 15493, + "GAAGGCCAA": 15494, + "GTGTAAAAA": 15495, + "TAAATAAATG": 15496, + "TATGĊ": 15497, + "GGGGGGA": 15498, + "GCGGGCTG": 15499, + "TGATACAAA": 15500, + "CCGATTA": 15501, + "TACGCAA": 15502, + "CTTTCTATT": 15503, + "GTTTCTGAA": 15504, + "CCAGATCC": 15505, + "GCCTCAGA": 15506, + "TATACTTAA": 15507, + "GTCACTTTT": 15508, + "GGGGCCAA": 15509, + "TGGGTTTA": 15510, + "GCAAACTG": 15511, + "GGCCCCCA": 15512, + "GCTCAGTC": 15513, + "CGAAGAA": 15514, + "CCCTCAAAA": 15515, + "TCTAAATTA": 15516, + "GCTGGCTGA": 15517, + "CTCACATA": 15518, + "GTAATAGG": 15519, + "CTCCĊ": 15520, + "TGGACAGG": 15521, + "TAATACACA": 15522, + "CTATTGTTTT": 15523, + "TACCATTTT": 15524, + "TAGACGGCAGCC": 15525, + "GTTTCTGTG": 15526, + "TAAACCCTT": 15527, + "TGTTTACAA": 15528, + "CATTCATTA": 15529, + "TCAACTGTG": 15530, + "TACATAGAA": 15531, + "TGGGGCA": 15532, + "CGGAGCTG": 15533, + "CTGCACTCCAGCCTG": 15534, + "GCTGGGTCTT": 15535, + "TGACCAACCTAGA": 15536, + "TAGCTTAA": 15537, + "TGTGCGA": 15538, + "TGAAAAATA": 15539, + "TCAATAGAA": 15540, + "TATGTACAA": 15541, + "TTAATATTA": 15542, + "TGGGGACA": 15543, + "GGTACTTA": 15544, + "CAAAAATATT": 15545, + "CGTATTTA": 15546, + "CCTCCTGA": 15547, + "GATGATTTT": 15548, + "GTACATATA": 15549, + "GTGCTGGGATTACAGGTGTGA": 15550, + "CTGTAAAAA": 15551, + "CCTTGACC": 15552, + "TAGATCTG": 15553, + "CCAAAGCAA": 15554, + "GAGAAGAA": 15555, + "TCGGAGGA": 15556, + "TCACGTGA": 15557, + "TAAAGATTA": 15558, + "GTCGGGA": 15559, + "TGAACTTTT": 15560, + "GGAAGGGGA": 15561, + "TTTTAGTAA": 15562, + "TTTGAAATT": 15563, + "CAATTGATT": 15564, + "GGAGGCCTG": 15565, + "GTTTGCATT": 15566, + "CTTCCCTGA": 15567, + "GCTGAAACTCCAA": 15568, + "GGGTCTCA": 15569, + "TCGCCCTG": 15570, + "GCGTTGA": 15571, + "CAAGCTCC": 15572, + "TGTGGGAAA": 15573, + "TATTGTGTT": 15574, + "TAAAAGTTTT": 15575, + "CCAATACAA": 15576, + "CATTTTTTAA": 15577, + "CTCCAGTGA": 15578, + "TCTTAATTA": 15579, + "TCAGACAGG": 15580, + "GTGCTGGGATT": 15581, + "GCTTTGCGCCTT": 15582, + "CCAAAAAAAA": 15583, + "GGTTTTATT": 15584, + "CATTCAGTT": 15585, + "CCAACAGGA": 15586, + "TGGGTGTT": 15587, + "GTCTTTCTT": 15588, + "CAGTGGAA": 15589, + "TAACATATT": 15590, + "GAACATCTT": 15591, + "GGCACATG": 15592, + "TCACATC": 15593, + "GGAGAAATT": 15594, + "GTCGGTA": 15595, + "GCTGCAGCC": 15596, + "CTCAGCAAA": 15597, + "TCTCCATTA": 15598, + "GTTAGGGG": 15599, + "TGCCCCTT": 15600, + "GTTCATATG": 15601, + "CCATAGCTT": 15602, + "GCGCCTCC": 15603, + "CCAGACTA": 15604, + "GTACCCTT": 15605, + "GTACCTGA": 15606, + "TGAATATTTT": 15607, + "CTAAGGCA": 15608, + "CAAAACAGG": 15609, + "TGTGTGTTTT": 15610, + "GTTATTAAAA": 15611, + "TATTCAGTT": 15612, + "TATTTCATG": 15613, + "CTGAGATTA": 15614, + "GATTGCC": 15615, + "TTAATTATT": 15616, + "CCCATCTA": 15617, + "GACAGTGTG": 15618, + "CCAAACAAA": 15619, + "TAATAGAGA": 15620, + "TGATTTAA": 15621, + "GCCAAGACAA": 15622, + "CGCACGG": 15623, + "GGAGGGAAA": 15624, + "TACCCGG": 15625, + "GAGTTTCAGCTT": 15626, + "GACTTTGTT": 15627, + "CTGGGCAGG": 15628, + "TGACAGAGA": 15629, + "TAAACATGA": 15630, + "TCAAGCCA": 15631, + "TCAACAGCA": 15632, + "GTGAGGCTG": 15633, + "GAGGCTTA": 15634, + "CCCCGGGG": 15635, + "CCAAATGAA": 15636, + "GGTAGTG": 15637, + "CGCCTTA": 15638, + "CCCTCTCC": 15639, + "CTGCCCAGG": 15640, + "GGGTCCTG": 15641, + "GTTCTATG": 15642, + "GTTGAAGA": 15643, + "GTAGATTTT": 15644, + "GCTGTCCTG": 15645, + "GGTGCTT": 15646, + "GCTAGAAAA": 15647, + "TCAAAGGAA": 15648, + "GCAAATGTT": 15649, + "CTGGGTTTG": 15650, + "TAGCTCTGA": 15651, + "CTTTACAAA": 15652, + "GGCGTCC": 15653, + "GCGCCACA": 15654, + "CAATCAAAA": 15655, + "CCACTATG": 15656, + "GTAGGATT": 15657, + "TAAACTATT": 15658, + "GGAAGAAAAA": 15659, + "TAGGCCTT": 15660, + "GGTGTATG": 15661, + "TCAAAGTCA": 15662, + "CTGAAGCAA": 15663, + "TATAAATATA": 15664, + "TCACGTC": 15665, + "CTGTCTAA": 15666, + "GTACCTTA": 15667, + "TCTGCGA": 15668, + "CAGACATGA": 15669, + "CTCATATG": 15670, + "CTAATATAA": 15671, + "CCCCTAA": 15672, + "TTCACTTA": 15673, + "CGCTCTCC": 15674, + "GGAAAGAAAA": 15675, + "GGTACACA": 15676, + "GCTGAGAA": 15677, + "GTTTGGTTTT": 15678, + "CCGGCTA": 15679, + "TCGTCCA": 15680, + "TAAACGTG": 15681, + "TATTCCCTT": 15682, + "CAGAGCCAA": 15683, + "GCTTGGGA": 15684, + "GATGGCCA": 15685, + "GAGTGTGTG": 15686, + "CATGATGAAA": 15687, + "GGAGACGG": 15688, + "TAGTCTCA": 15689, + "TCGGAATT": 15690, + "GTTCTGTA": 15691, + "GAAAGGAAAA": 15692, + "TCTGTATTTT": 15693, + "CCTTTATTTT": 15694, + "TCTAGTCC": 15695, + "TAGAGCC": 15696, + "GGTACAGG": 15697, + "GGTACATG": 15698, + "GTTAATAAA": 15699, + "GGGCAGAGG": 15700, + "TCCCAGAGA": 15701, + "GACGAAAA": 15702, + "TTTTTATAA": 15703, + "CTGTTTTAA": 15704, + "GTTCTCAAA": 15705, + "GTCTCCAGG": 15706, + "TCCATCCATG": 15707, + "TGGGGGA": 15708, + "GGTGTCA": 15709, + "TCAAAGAGA": 15710, + "TGATGGAGA": 15711, + "GCCTGGGA": 15712, + "GAATACAAA": 15713, + "TCCGTTTT": 15714, + "GTTTGAGAA": 15715, + "GCTGCAGC": 15716, + "CTCAGGCTG": 15717, + "CTTTTGGTT": 15718, + "GGGGTGTT": 15719, + "TTCATTTTA": 15720, + "CATAATTTA": 15721, + "TATAAAATG": 15722, + "GGCTCATG": 15723, + "GCTCTCTA": 15724, + "CTTTCAGA": 15725, + "TAAACCTA": 15726, + "TATTCACTT": 15727, + "TCAAGTCC": 15728, + "GCTTTATTA": 15729, + "TCTAGTCAA": 15730, + "TGATTCCATT": 15731, + "TGAGAGGAA": 15732, + "GGTCATTA": 15733, + "CCGGTAA": 15734, + "GTATCAAAA": 15735, + "GAAACCTA": 15736, + "CAGAGTGAA": 15737, + "TATGTATGTG": 15738, + "CCAACTTA": 15739, + "GATAGCA": 15740, + "GATACTCA": 15741, + "GGAAATGGAA": 15742, + "CATCAAATT": 15743, + "TTTTCATTTT": 15744, + "GTAGGCCA": 15745, + "TTTTATATAA": 15746, + "GAAAACAGG": 15747, + "CTTTTTTTTTTTTTTTTTTTT": 15748, + "CAGCCAAAA": 15749, + "TCAATTAAAA": 15750, + "TGGGTTCAAGCAATT": 15751, + 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"GCTTTCCAA": 15798, + "GTCAGTCC": 15799, + "GATTCTCC": 15800, + "TTTTTTGGTT": 15801, + "CGTGGAA": 15802, + "GGACCTCTG": 15803, + "GAGAGGCTG": 15804, + "GCTTCCAAA": 15805, + "GCTGCAAAA": 15806, + "CGAĊ": 15807, + "CGGATCTCTGTGAGTTCGAGG": 15808, + "CCTCCTCTG": 15809, + "GGATGGAGG": 15810, + "TCTCTTGTGA": 15811, + "CCAGGTGTG": 15812, + "CTTCTCAAA": 15813, + "CTATATATT": 15814, + "TGTTTAAAAA": 15815, + "CCAATTTATTTT": 15816, + "GTTTTGTAA": 15817, + "CTTTTTGTA": 15818, + "GATACGG": 15819, + "AAAAAATAAA": 15820, + "CAATTTCTG": 15821, + "CAAGAGCAA": 15822, + "GGTTTGAAA": 15823, + "CCTCTGCCTCCCGG": 15824, + "TAGAAAATT": 15825, + "GGGTCCTT": 15826, + "TGATAAATT": 15827, + "CTTTCTCTA": 15828, + "TGTTGCCA": 15829, + "CTTACAGAA": 15830, + "GAAAATACA": 15831, + "TTTAGCAA": 15832, + "GGTGGTAA": 15833, + "GTGTTTGAA": 15834, + "TGTGTGTGAA": 15835, + "CCAGACATG": 15836, + "TAGGTTAAA": 15837, + "TACTTATTTTCCACCATAATTTG": 15838, + "CAAGCAAAA": 15839, + "CTGGATTTT": 15840, + "CTCCAGGTT": 15841, + "CAGAGCTCA": 15842, + "GGAAGTAAA": 15843, + "CTTACAAAA": 15844, + "TCTATCAAA": 15845, + "TGGATATG": 15846, + "TCATTGATT": 15847, + "CGTAGTGTGTTA": 15848, + "TGAGACTGA": 15849, + "CCAGGTTTG": 15850, + "GGCCAATT": 15851, + "TAATGATGA": 15852, + "TCCCTCTA": 15853, + "CAAGCTAA": 15854, + "GTTCTTCC": 15855, + "CTAGCTGA": 15856, + "CAAAGAGCA": 15857, + "GGAACATA": 15858, + "GATGCCAAA": 15859, + "TGGATATA": 15860, + "TCCTGTCTG": 15861, + "GAGCTAGG": 15862, + "CCACAGCTG": 15863, + "TCCCCGG": 15864, + "GTACAGAAA": 15865, + "TCTTTACAA": 15866, + "GGAACTTTT": 15867, + "CTCATCTCA": 15868, + "CATGGTTTT": 15869, + "CGATACC": 15870, + "TTTATCTG": 15871, + "GGTTGGGA": 15872, + "CGCĊ": 15873, + "TCATTTATG": 15874, + "GAATTCCTGA": 15875, + "GGCCGAGG": 15876, + "GTTTCATTA": 15877, + "GAATAGAAA": 15878, + "GCTAATTTTTGTA": 15879, + "GTCTCGCTCTGTCGCCCAGGCTGGAGTGCAGTG": 15880, + "GGTTAATG": 15881, + "GAACCCAGG": 15882, + "CAATATC": 15883, + "CTGCAATG": 15884, + "GTGGAGAAA": 15885, + "GTTTCCATG": 15886, + "CTACGCA": 15887, + "GAAAAGCTG": 15888, + "TCTATAATT": 15889, + "GTCCAAATT": 15890, + "TAGACCTT": 15891, + "GGTGGCTCA": 15892, + "GCCTTCCTT": 15893, + "GGGCTCTT": 15894, + "CCTGTAATCCCAGCTACTTGGGAGG": 15895, + "TGGGTTTGAAGAAATTAGGA": 15896, + "GGTGAATG": 15897, + "TGAGAAATT": 15898, + "CAACTGTG": 15899, + "CAGACACAA": 15900, + "GTAATAAAAA": 15901, + "TCCACTGAA": 15902, + "TTTACAAAA": 15903, + "GGTTGTCA": 15904, + "CCTATAAAA": 15905, + "GTAAATAA": 15906, + "CACCATGGAATACTA": 15907, + "GACTATTTT": 15908, + "CTCCACCCC": 15909, + "GAAGAGCTG": 15910, + "CTCATTGGAAAA": 15911, + "GAGATTTCAA": 15912, + "CTCGGGAGGCAGAGCCAGG": 15913, + "GAAGTCTG": 15914, + "CTTCTTTTA": 15915, + "GCAAATAAA": 15916, + "TCCTTGTA": 15917, + "GTATTTCA": 15918, + "GAGCTATA": 15919, + "GGAGAATCGCTT": 15920, + "TAACATATA": 15921, + "GGACACAAA": 15922, + "TGATATC": 15923, + "CTGCCTC": 15924, + "GCCGACTA": 15925, + "CTAGACTG": 15926, + "CAGGTCC": 15927, + "GAGGAAATG": 15928, + "TAATTGCA": 15929, + "GTAAATAAAA": 15930, + "CAACCCTGA": 15931, + "CAGCTGGG": 15932, + "TGAGTGGG": 15933, + "GAGAGCCA": 15934, + "CAGGGCCA": 15935, + "GCAAGACC": 15936, + "GAGGTATG": 15937, + "TCAAATATG": 15938, + "CGCAAATG": 15939, + "TCACCTGAA": 15940, + "TGATATTTTA": 15941, + "CGTCAATT": 15942, + "GAACCTGGGAGGCAGAGGTT": 15943, + "GAGTTATG": 15944, + "TTTTTACA": 15945, + "CTGCATC": 15946, + "GTTCTCCTT": 15947, + "GCTTGGCA": 15948, + "CATGCAAAA": 15949, + "GTTTTCCTG": 15950, + "CGTGCGG": 15951, + "TGATGGATG": 15952, + "CAGTTTCTT": 15953, + "CTCCAGTCA": 15954, + "CTTTATATGCC": 15955, + "GCCCAGGCTGGAGTG": 15956, + "CTCTCAGAA": 15957, + "CATTCATCA": 15958, + "TTTGTCTG": 15959, + "TCTAGAGAA": 15960, + "CTTTTCTCC": 15961, + "CGCTTTTA": 15962, + "CGCGATG": 15963, + "GCTGAGGA": 15964, + "CTGTGGCTG": 15965, + "TATCATTAA": 15966, + "TGATGAAAATTACAGGCCTCTC": 15967, + "CTGGTCTG": 15968, + "CTTCTCTGA": 15969, + "GCAGAAGAA": 15970, + "AAAATATG": 15971, + "GTGAĊ": 15972, + "GTTACAGAA": 15973, + "TCTTTTTATT": 15974, + "GCGAAATT": 15975, + "TTACCAAA": 15976, + "CTACAATG": 15977, + "GTCTGTGAA": 15978, + "CATTTAATA": 15979, + "TCAAGCTA": 15980, + "TCTCTAGTG": 15981, + "TAGGTTCA": 15982, + "GGGACTT": 15983, + "CGTGTGCA": 15984, + "TGATTGTAA": 15985, + "GAGCCTCTT": 15986, + "CATTGGCA": 15987, + "GTGAGAGAA": 15988, + "TCTCCTGGA": 15989, + "TTTTTTTCTG": 15990, + "CCTGAGGAA": 15991, + "GCAGACACA": 15992, + "TCGGCCTCCCAAAGTGCTGGGATTACAGGCGTGA": 15993, + "TCACTGAAA": 15994, + "TGTTTCTAA": 15995, + "CAAACTATT": 15996, + "GCTTCGA": 15997, + "CTCAGAGCA": 15998, + "CCCATAA": 15999, + "TAGGCCTG": 16000, + "GACCCTCC": 16001, + "TGAGATCTG": 16002, + "GAATAGCAA": 16003, + "TCATCATTA": 16004, + "TCCCATC": 16005, + "TAAAGGCC": 16006, + "TGTGCAGAA": 16007, + "TGTGTATGA": 16008, + "GTGAGCTA": 16009, + "GTTACGA": 16010, + "GAATTCTGA": 16011, + "TGATTCAGA": 16012, + "GAAAAAGAA": 16013, + "GAAGATATG": 16014, + "CAACAGTAA": 16015, + "CAACAAATG": 16016, + "CCAGCAGCA": 16017, + "GGGGCACA": 16018, + "CTCTTCTGA": 16019, + "CTCTGGGA": 16020, + "TGATGGTA": 16021, + "GGGAATTA": 16022, + "TACAAAATT": 16023, + "TCGAAATG": 16024, + "CAACTTAAA": 16025, + "CCACAGAAA": 16026, + "CCTTAAATG": 16027, + "GCCACCCTG": 16028, + "CTTTTCATG": 16029, + "CAGCCTTA": 16030, + "GAACATGAA": 16031, + "CTGGCATG": 16032, + "GCATCGG": 16033, + "GCACTTGG": 16034, + "GAAACATCA": 16035, + "GGAACTC": 16036, + "GCTTTACA": 16037, + "GAGGGCC": 16038, + "GAGGTCTT": 16039, + "TTTGCCTG": 16040, + "TAGAĊ": 16041, + "TGTACCTG": 16042, + "CCTACTCC": 16043, + "CGCACCAA": 16044, + "CAATTCAAA": 16045, + "TAGCTCCTT": 16046, + "GGGGAGGGA": 16047, + "CCACACTGA": 16048, + "GCGTTGTT": 16049, + "CTGTATCC": 16050, + "TATATTATTA": 16051, + "CGGGTCC": 16052, + "CATATCCA": 16053, + "TCAAATCAA": 16054, + "CTGTTTATT": 16055, + "TGATGGTTTCCA": 16056, + "GATATGAAA": 16057, + "GTTCCAAAA": 16058, + "TCTGCTCTT": 16059, + "TAAAATAAAAAA": 16060, + "GCTGTCTC": 16061, + "GAAAAGAGAA": 16062, + "GCCAGCTA": 16063, + "GGTACCTG": 16064, + "CAGTTGAA": 16065, + "TCATATTTTA": 16066, + "CCACACTCA": 16067, + "CTTAAAAAAA": 16068, + "TAAATACCA": 16069, + "GAAATCATT": 16070, + "TGTTGCCTT": 16071, + "CCTTTCTCC": 16072, + "TTATTTACA": 16073, + "GAATTCATT": 16074, + "GCCTGGAA": 16075, + "GATAAAATG": 16076, + "CTTTCTTAA": 16077, + "CCAATCC": 16078, + "CTAATATTTT": 16079, + "TTTGGTAA": 16080, + "CTAAACATG": 16081, + "CTAAAGCAA": 16082, + "TCCTGGGA": 16083, + "CAATATTTTA": 16084, + "CCTCTCCTT": 16085, + "GGTCGGA": 16086, + "TCGTAGA": 16087, + "TCACCTC": 16088, + "CAATAGGA": 16089, + "TCTTGGAAA": 16090, + "TCTTTAGTT": 16091, + "TAAAACTTTT": 16092, + "CTGACAGAA": 16093, + "TATGGGGA": 16094, + "GATTTACC": 16095, + "CTCCTATT": 16096, + "TCCAGCTCA": 16097, + "CCCCCCCCA": 16098, + "GATACCTT": 16099, + "GTAAAGATG": 16100, + "CTGTTCTAA": 16101, + "GTGTTCCTT": 16102, + "TGATGGTG": 16103, + "GTTTATGTT": 16104, + "AAAAAAAAAAAAAAAAAAAAAAAA": 16105, + "GACTCATTGGAAAA": 16106, + "CTGGCGG": 16107, + "GCACAGTC": 16108, + "CCACCTTA": 16109, + "CGGCTTA": 16110, + "TCCATGTGTT": 16111, + "GAAAATCAAA": 16112, + "GAAAACAATT": 16113, + "TAGATATTA": 16114, + "CTAAATGAA": 16115, + "TCTTTTTTTA": 16116, + "CGGAGGGG": 16117, + "CTCTGCAGG": 16118, + "TGATGGAAA": 16119, + "CCCTGTTA": 16120, + "GAGCTCTGA": 16121, + "TTATTTTAAAA": 16122, + "TCGCACA": 16123, + "TCACCCTC": 16124, + "CTGCAAAAA": 16125, + "CTTTAATTA": 16126, + "TAAAGTCAA": 16127, + "CAAACGTT": 16128, + "GCTTCCCAA": 16129, + "GTGACTTTT": 16130, + "CATGGCCA": 16131, + "GAGGGTTA": 16132, + "TCTCTATTTT": 16133, + "TCTCTAGCA": 16134, + "TTAAGGAA": 16135, + "TTTGGGGA": 16136, + "GTAAATATG": 16137, + "TAGAAGAA": 16138, + "CTGGGTGTG": 16139, + "CTTTAGAAA": 16140, + "GTAAĊ": 16141, + "GGTGCAAA": 16142, + "TGTAGCAA": 16143, + "GTTAACAAA": 16144, + "CACAATTTT": 16145, + "GATGTGAAA": 16146, + "GTCGCTA": 16147, + "CTTATATTA": 16148, + "CAAAAGGA": 16149, + "GAATTTAA": 16150, + "CTGGGTTTT": 16151, + "CAAGGCAGG": 16152, + "TTACAGTTA": 16153, + "CCTGTAATCCCAGCTACTCGGGAGG": 16154, + "GATAGGTT": 16155, + "CTGAAAATT": 16156, + "GTTCTGAAA": 16157, + "CCATCCCTG": 16158, + "GCCTAGTA": 16159, + "GCCCCAGA": 16160, + "GCAAGGCC": 16161, + "CCAAĊ": 16162, + "CTCCTACC": 16163, + "CCCCCTTTT": 16164, + "CGCGATT": 16165, + "GACCCATT": 16166, + "GAGGAGGA": 16167, + "CCTCCTTTT": 16168, + "GTATATTTTA": 16169, + "GCCCATCTG": 16170, + "GGCCTTTG": 16171, + "GCTCTCCAA": 16172, + "GTTTCTTTG": 16173, + "GCAGCTTTT": 16174, + "CTCTAAATT": 16175, + "CGGCGTC": 16176, + "GGGGCAGGA": 16177, + "TGTACAGA": 16178, + "TCTCCCCTT": 16179, + "GAAAACATA": 16180, + "TCTCAGGTG": 16181, + "GTGTGGACA": 16182, + "TAGCTCCA": 16183, + "CTTCAGTCA": 16184, + "CACACACCTG": 16185, + "GTTTGTGTT": 16186, + "GTTTCCACA": 16187, + "CTAGATCA": 16188, + "CTCTCACCA": 16189, + "TATTGTCC": 16190, + "CACAGTGAA": 16191, + "GCTGCACC": 16192, + "CCAACATA": 16193, + "TGGATAAAA": 16194, + "CTCTGGGAA": 16195, + "TACTGGGGCA": 16196, + "CTTTATGTT": 16197, + "CTTCTGTTA": 16198, + "CCGCGA": 16199, + "TAACTTTTTT": 16200, + "TCACCACC": 16201, + "GCTGCCCCA": 16202, + "CTAACTTTT": 16203, + "GAAAAGGGG": 16204, + "TCTAGGAAA": 16205, + "CTGTTGATT": 16206, + "GATAATTTA": 16207, + "GGAGAGTGA": 16208, + "TAACAGTC": 16209, + "GCCCAGGAA": 16210, + "GCTTGCTCA": 16211, + "GCAAAGGGG": 16212, + "CTCTGGAA": 16213, + "CTCTGCTA": 16214, + "GGGAAGAGA": 16215, + "TAATGACAAA": 16216, + "CTAATTTTTGTATTTTTAGTAGAGA": 16217, + "TCCTCACC": 16218, + "CAATAAATG": 16219, + "CTGCAGAAA": 16220, + "GCAGTAAAA": 16221, + "CAGGGGGG": 16222, + "CTAATAGG": 16223, + "GGCATCTT": 16224, + "TCATTGTG": 16225, + "TGAGGGAGA": 16226, + "TATCĊ": 16227, + "GAGTATTA": 16228, + "CAAGATCTCA": 16229, + "CAACATGAA": 16230, + "TGAGTATG": 16231, + "CTGGTGTG": 16232, + "GTTAAAAAAA": 16233, + "TCTGTACC": 16234, + "CAGACGTG": 16235, + "CTCAGCTA": 16236, + "CATGĊ": 16237, + "TCTCGACGTT": 16238, + "TAATTGTGA": 16239, + "GTCTTTGTT": 16240, + "CTATTTCA": 16241, + "GATTTTTTA": 16242, + "CAGTGATTTT": 16243, + "CAGAGAATT": 16244, + "GGAAGGCTT": 16245, + "CTGACCTCA": 16246, + "GTAACCTT": 16247, + "GAGGAAATT": 16248, + "TTAATGAAA": 16249, + "TAGAGCCA": 16250, + "GGTTGATG": 16251, + "TCTCAGGA": 16252, + "GGAGATCAA": 16253, + "CTTCATCTT": 16254, + "CTGTAGATT": 16255, + "CGGTTAAA": 16256, + "CGTTAAAAA": 16257, + "GGCTCTCA": 16258, + "CTTGTAAAA": 16259, + "TCTGGGCTG": 16260, + "CATGCTTTG": 16261, + "TGGGCTA": 16262, + "GGTTGTTTT": 16263, + "GGCACCAA": 16264, + "CCTACAAAA": 16265, + "TGATTCATT": 16266, + "CTGTTTGAA": 16267, + "TGAGGCCA": 16268, + "GTGGGCGG": 16269, + "TGTGGTCC": 16270, + "CAAACGCA": 16271, + "CAAACGAA": 16272, + "GTGACTGAA": 16273, + "GTCACTCC": 16274, + "GTCATTATT": 16275, + "CCTGGCCAA": 16276, + "TCTCTATGA": 16277, + "TTTGGATG": 16278, + "TGCACCAA": 16279, + "TAATAGATT": 16280, + "GCTCCTTTT": 16281, + "TAAAGAGTT": 16282, + "CATTCACAA": 16283, + "GCTTĊ": 16284, + "TCTAGGGG": 16285, + "TGCCCCAA": 16286, + "CTGTTTCC": 16287, + "CTGTTTATG": 16288, + "CTCAAGATG": 16289, + "GTCAAGGGG": 16290, + "GTTTTATTTA": 16291, + "CTTGATCTT": 16292, + "TAAGTTAAA": 16293, + "TCACAGTC": 16294, + "GAAGCATG": 16295, + "TAAAAGATA": 16296, + "CTCAGGTA": 16297, + "CTAACGA": 16298, + "TACATACTG": 16299, + "GCAAATGAA": 16300, + "TGATTTATG": 16301, + "TCATGCATTGGAGAAGGAAATGGCAACCCA": 16302, + "TCGCTTTG": 16303, + "GTTGCGG": 16304, + "TGTTTAGAA": 16305, + "GTGACAGCA": 16306, + "GCTGGAATCAA": 16307, + "CCCATAGA": 16308, + "GCAAAGGAA": 16309, + "CTCTGCTGA": 16310, + "CTAGCCGGGCGAGGTG": 16311, + "CCGATCC": 16312, + "TGAGCTC": 16313, + "TCCCAGAAA": 16314, + "GCACAGAGA": 16315, + "GGAGCTC": 16316, + "GTAGGAAAA": 16317, + "CACAGAGTG": 16318, + "TCTTGGCC": 16319, + "TGTTGGGA": 16320, + "CAGGGGCTG": 16321, + "GCAAGTC": 16322, + "CTAATTATT": 16323, + "TCCACGTT": 16324, + "GACAĊ": 16325, + "GCTAGGGG": 16326, + "TGCATTTTA": 16327, + "GGCATATG": 16328, + "GATACCTG": 16329, + "CAGCTAGG": 16330, + "CAGCTCCAA": 16331, + "TAGCATTTT": 16332, + "CAAGCAGG": 16333, + "GCATACTA": 16334, + "TAAATATC": 16335, + "GTGAGGTA": 16336, + "GATTTCCTT": 16337, + "CTCCCAGAA": 16338, + "GAAAATATTTT": 16339, + "TTTGCCAA": 16340, + "GCCCCCGG": 16341, + "CAACGTC": 16342, + "GGATCTAA": 16343, + "TAAAGTGTT": 16344, + "TGTTTCCAA": 16345, + "CATTCTTAA": 16346, + "TCAATAAAAA": 16347, + "TCCAGAATT": 16348, + "TGGGCAGA": 16349, + "GTATTTATG": 16350, + "TCAAAATTTT": 16351, + "TCACAGCTT": 16352, + "TGTATTTTTAGTA": 16353, + "CATAAAAAAA": 16354, + "GGCGTAA": 16355, + "GCGTCCA": 16356, + "GCAGCGA": 16357, + "CCTGGTC": 16358, + "TGGAGTTA": 16359, + "TCATTTAAAA": 16360, + "TGATTTGA": 16361, + "TGATTCATG": 16362, + "TCATCTTTTTAAGTGGGAGAA": 16363, + "TAGGAATG": 16364, + "GGTTTTAAA": 16365, + "TCCCTGGA": 16366, + "GAAACAGTT": 16367, + "TCCACCCCA": 16368, + "TGCACTT": 16369, + "TTTTTGTTTTTT": 16370, + "CTTTGTGATGTGTG": 16371, + "GGTATATG": 16372, + "TCGTATTA": 16373, + "GATCGGA": 16374, + "TGTTTCTC": 16375, + "CAGGCATGA": 16376, + "TCTGGCTTA": 16377, + "CTGAGACC": 16378, + "TCTCACTCA": 16379, + "GGAGGGATG": 16380, + "GACTATTA": 16381, + "GGAGATGTG": 16382, + "GGAGATATA": 16383, + "GTGCAGTGA": 16384, + "CTCATGTCCATT": 16385, + "CCGCGC": 16386, + "TAGTCTTA": 16387, + "TAAAAGGAAA": 16388, + "CTCCTATA": 16389, + "TCTAGCCA": 16390, + "TGGACTAA": 16391, + "TCTCAGTAA": 16392, + "CTGAAATTA": 16393, + "GCAGAGCTT": 16394, + "GAACATCTG": 16395, + "TCAGAATTA": 16396, + "GAAGTAGTGATT": 16397, + "CCTATATG": 16398, + "GAAGAAATT": 16399, + "CTGTAGC": 16400, + "GTGTACTA": 16401, + "GTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 16402, + "GTCTATAAA": 16403, + "TGTGCACCA": 16404, + "GCAATTTTA": 16405, + "CCAAGGCAA": 16406, + "TACACTAAA": 16407, + "TTCATATA": 16408, + "GCAAATGGG": 16409, + "GGCCTCAA": 16410, + "GAAGGCAAA": 16411, + "AAATACAAA": 16412, + "CCCTCATG": 16413, + "TAATGACA": 16414, + "TTTTATCAAA": 16415, + "TTAACTAA": 16416, + "TGTAGGAA": 16417, + "CCTACCCTG": 16418, + "GCAAACTAA": 16419, + "TCATTCAAA": 16420, + "TAAATTTATT": 16421, + "CAGTCGTGTCCGACTC": 16422, + "TCAGAAAAAA": 16423, + "TCACCAGG": 16424, + "GCCCCCTC": 16425, + "CACATCTTAAA": 16426, + "TCTTCTTCTT": 16427, + "GCCTGGGAA": 16428, + "TATATATATATATATATATATATATATA": 16429, + "TGACAGAGCGAGA": 16430, + "CATCTGAAA": 16431, + "GTCACGTG": 16432, + "CCCACCACCA": 16433, + "GGTGTATA": 16434, + "CGCGGTT": 16435, + "CTGAAGATG": 16436, + "CGTTCAA": 16437, + "CAATTTGAA": 16438, + "CTATTTGTT": 16439, + "CTTTACATT": 16440, + "TAACATAAA": 16441, + "CCGAAGG": 16442, + "TAAGGGGA": 16443, + "GTACCCTG": 16444, + "TCTGCAGTG": 16445, + "GATTCGA": 16446, + "CTTTTTATTA": 16447, + "GGTGCCTT": 16448, + "GGTACGG": 16449, + "CATGTGAAA": 16450, + "GCAGAGACATTACTTTG": 16451, + "GGATTTTAA": 16452, + "CCACCCTA": 16453, + "CACACCCC": 16454, + "TCAACGTT": 16455, + "TGTGTTTGTG": 16456, + "GAAAATGAAAA": 16457, + "CGAGAGAGA": 16458, + "GGTCGTT": 16459, + "TGACTGGG": 16460, + "CAACTAAAA": 16461, + "GTGTATATT": 16462, + "CTCGAAAA": 16463, + "GAGATAAAA": 16464, + "GAGAGAGTG": 16465, + "CCTGGCAAA": 16466, + "GTTACAGCA": 16467, + "TGGGGTCA": 16468, + "GAATTCCTT": 16469, + "CTAAAGAGCTT": 16470, + "GCTGAGATG": 16471, + "GCATTTCA": 16472, + "GAGCTATT": 16473, + "CTCCAGCCA": 16474, + "TCCCACCTT": 16475, + "CTTCAGTGA": 16476, + "GCACAGCTG": 16477, + "GGCGTGTG": 16478, + "GCGTAGG": 16479, + "GTGCTTCTT": 16480, + "GTTGCATG": 16481, + "GCAGCCATG": 16482, + "CTCTGATA": 16483, + "GGAATACA": 16484, + "CTCACACC": 16485, + "CCAATGAAA": 16486, + "GGGGCATG": 16487, + "TTTAGATG": 16488, + "GGTGTCC": 16489, + "TGCACGG": 16490, + "GATACGA": 16491, + "TCCTTGCTT": 16492, + "CTGCAGCAA": 16493, + "GCTCCAAAAA": 16494, + "GGGTCCA": 16495, + "GGACATCC": 16496, + "CAAACACCA": 16497, + "CATTGCTA": 16498, + "TGAATACTT": 16499, + "TGCATCA": 16500, + "TAATATGTG": 16501, + "GTGTTCTA": 16502, + "CAATTCCA": 16503, + "TCCTGCAGG": 16504, + "TCAGGCCTT": 16505, + "TATTTACTTA": 16506, + "TCGTATTTT": 16507, + "GTTCTAAAA": 16508, + "GTCAAAATG": 16509, + "CAGAGCGAGA": 16510, + "CCTGTCTA": 16511, + "TGTACTGA": 16512, + "CCTACCTCA": 16513, + "GAATATTTTA": 16514, + "CAATAGAAA": 16515, + "CGTCCAAA": 16516, + "TCAATGAAAA": 16517, + "CCCTAACCCTAACCCTAACCCTAA": 16518, + "GGTCCTA": 16519, + "GAATGTGAA": 16520, + "GTTGTTTTTT": 16521, + "TAAATCTGA": 16522, + "TAAAACAAAAA": 16523, + "CAAAATATAA": 16524, + "TCAAAGCAA": 16525, + "CCATTTCTG": 16526, + "CAGCCTTATT": 16527, + "CCAGGCTGA": 16528, + "CTTCCCTCC": 16529, + "GGATTCTCCAGGCAAGAACACTGGAGTGGGTTGCCATTTCCTT": 16530, + "GTAGACCA": 16531, + "GTCACAGCA": 16532, + "TACACGTA": 16533, + "TTATTTCATT": 16534, + "GTAAACACA": 16535, + "CTGTTCTGA": 16536, + "GCTAATTAA": 16537, + "GATAATTTG": 16538, + "TAACAGCAA": 16539, + "TAATTTTCATCATAGGTACA": 16540, + "TCCAAAATG": 16541, + "CTAGCCAA": 16542, + "TGTGGTC": 16543, + "TATACAAAAA": 16544, + "CAAATAGTT": 16545, + "TCAATATGA": 16546, + "CCAACAGTG": 16547, + "TTATTTCCA": 16548, + "TTCACTGA": 16549, + "GGCCTCCA": 16550, + "CTCAATTTG": 16551, + "GCCCAGCTG": 16552, + "TAACGCC": 16553, + "GCCCCACC": 16554, + "GTCAGGTA": 16555, + "CCTGCAGAA": 16556, + "GTTACTTAA": 16557, + "GGTTCATT": 16558, + "TGCATAAA": 16559, + "TAATACTT": 16560, + "TGATTCAA": 16561, + "TGATTCTAA": 16562, + 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16655, + "CTCACCCC": 16656, + "GATTCGAA": 16657, + "CCAACCAAA": 16658, + "CTTATGGA": 16659, + "CGCGACC": 16660, + "TGAAATGAA": 16661, + "GGCCGCC": 16662, + "GCTCAGGA": 16663, + "CAAAAAAAAAAAA": 16664, + "TACTTTTAA": 16665, + "TGATTTTTTTT": 16666, + "CAAGTAAAA": 16667, + "GCTTGTCC": 16668, + "CTCAĊ": 16669, + "GTCAGGCTG": 16670, + "TCTCCATAA": 16671, + "GGAGGGTGA": 16672, + "CAGTGTGTG": 16673, + "GCCTGGCTG": 16674, + "GTGAAATTA": 16675, + "TATTTGCAAATTATGGTGGAAAATAAGTATTTG": 16676, + "TAACGTTA": 16677, + "TCACTAAAA": 16678, + "TGACAGTC": 16679, + "TCCCTATG": 16680, + "CAATCCTG": 16681, + "CTAGTCAA": 16682, + "TATTAAATA": 16683, + "CAGGCACC": 16684, + "GTAAAAATA": 16685, + "GGGGGCAA": 16686, + "TAGACAAAA": 16687, + "TCTCAGGAA": 16688, + "GGCCCTCC": 16689, + "CTGTTCATG": 16690, + "CTTTGCTGA": 16691, + "TAACATGAA": 16692, + "TCTGCAAGTGGATATTTGGA": 16693, + "GTGGGAGGA": 16694, + "GGACAGAGA": 16695, + "CCATCTTTT": 16696, + "CACACAGGA": 16697, + "CAGGCAGGA": 16698, + "CCTGCAGGA": 16699, + 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16745, + "GCGCCCTG": 16746, + "TAACAGGAA": 16747, + "TCAGAAATT": 16748, + "TAAAGACC": 16749, + "CTCTATATT": 16750, + "GTTTTTATTA": 16751, + "CCCAGCTGA": 16752, + "TGAAAGAAA": 16753, + "TAATATCTA": 16754, + "CTGTTGAA": 16755, + "CTTTGGTG": 16756, + "TGAGGCTGA": 16757, + "CCATGCTGTTTT": 16758, + "GCAGCATCATG": 16759, + "CCACATCTG": 16760, + "GGTCCTGGAGTGGCCTAGCCAGTCTCCAGA": 16761, + "CCCCTTAA": 16762, + "GAACTTTAA": 16763, + "CCAGCTCTG": 16764, + "CATTCAGCA": 16765, + "TATGCGG": 16766, + "TCCATACAA": 16767, + "GGTGCAGA": 16768, + "TGTATTATT": 16769, + "GTGAATGTT": 16770, + "CTCTGGCTT": 16771, + "GAAAAATGAA": 16772, + "CTGTGCTGA": 16773, + "CTGTATGTT": 16774, + "CTGCGTA": 16775, + "CTAGGCTA": 16776, + "CTCATCAAA": 16777, + "CTCCCACAA": 16778, + "TGGGGTGA": 16779, + "TTCATAAA": 16780, + "GAGTGTTA": 16781, + "GTTATTCTG": 16782, + "GCAGAGCAA": 16783, + "CCAGAGTAAA": 16784, + "GGATATGAA": 16785, + "GGCTCTCC": 16786, + "TCAGTTTCA": 16787, + "TGACTTTTA": 16788, + "GAATATC": 16789, + 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16835, + "GCAAAGCAA": 16836, + "GTATTTCAA": 16837, + "GTGTTTATA": 16838, + "GTTTGCTG": 16839, + "CATTTTATG": 16840, + "GCTTTTATTTT": 16841, + "CTCGGAAA": 16842, + "TCTTCACTG": 16843, + "CAGGCTAA": 16844, + "CAGACAGGA": 16845, + "GGAATCAAA": 16846, + "GCTTGTC": 16847, + "GTGATGCTG": 16848, + "CGCGCGG": 16849, + "CTCTTCAA": 16850, + "CTTTGCAAA": 16851, + "TACCTAA": 16852, + "CGGAGGTG": 16853, + "GGAGACAAA": 16854, + "TCCTCCCACCTCAGCCTCC": 16855, + "CCGAGTA": 16856, + "GTGTTTTTG": 16857, + "GGAAGAATT": 16858, + "GTAACTCC": 16859, + "TAAAAAGAA": 16860, + "CACCAGGCC": 16861, + "CAACCTTA": 16862, + "GTTCTTAAA": 16863, + "GCATGAGA": 16864, + "GGAACGA": 16865, + "TACATATCA": 16866, + "GTTTTTCTA": 16867, + "CCCATCTC": 16868, + "TAGGCCTA": 16869, + "TCTCACGA": 16870, + "GATACAAAA": 16871, + "TCCTTGAGA": 16872, + "CAGAAGAAA": 16873, + "GATTTTTTTTTT": 16874, + "TTATAGAAA": 16875, + "TCTCCAATG": 16876, + "CACTAATG": 16877, + "CCGGTTA": 16878, + "TAGTGATG": 16879, + "GGGTGATT": 16880, + "TTAGGAAA": 16881, + "GTTTGAATT": 16882, + "CTAGGGCA": 16883, + "TATTGTCAA": 16884, + "TCAAATGAAA": 16885, + "CACCCGA": 16886, + "CTGTTTCTA": 16887, + "CTCTTTGTT": 16888, + "CCATTTCC": 16889, + "GGGAAGAGG": 16890, + "TACTCTTA": 16891, + "GAAGTGTG": 16892, + "CTACCCAA": 16893, + "CTACCTTA": 16894, + "CATTCGTT": 16895, + "TCTGCACTG": 16896, + "CCTGCCTA": 16897, + "TTTGCCA": 16898, + "TGGGCGG": 16899, + "GTGAAGAGG": 16900, + "CGGATTTT": 16901, + "CTGGGCTGA": 16902, + "GTTGTGGGTT": 16903, + "TAGTAATG": 16904, + "GGAGCTGAA": 16905, + "GAGAGAAAAA": 16906, + "CATGCTCC": 16907, + "GATTTATAA": 16908, + "GTCCTGAAA": 16909, + "CTTTTGGAA": 16910, + "TGGGCTGA": 16911, + "GATAGTG": 16912, + "GGTAGTTA": 16913, + "GTGTTCCA": 16914, + "CAGCĊ": 16915, + "GGCTCTTA": 16916, + "CAATTAATT": 16917, + "GTACCCAA": 16918, + "GCCCAAGG": 16919, + "CTACCATG": 16920, + "TATTGAATT": 16921, + "TATTCAGCA": 16922, + "CACACTGAA": 16923, + "TATAGGTG": 16924, + "GAGACACCA": 16925, + "CAGGCCAAA": 16926, + "CTTAGGTG": 16927, + "GTTTTGTCA": 16928, + "CATATTTTTT": 16929, + "TGTAGGA": 16930, + "TCAAACAGA": 16931, + "TGATTCAAA": 16932, + "CAACAACAA": 16933, + "TCTTGGCTT": 16934, + "CTAAĊ": 16935, + "GGGGGTGGG": 16936, + "TGGGGAAAA": 16937, + "TCCAAGATG": 16938, + "CTCTGCTT": 16939, + "CTGCAGCCA": 16940, + "TCCTGATA": 16941, + "GCCTCGA": 16942, + "TATTGGGA": 16943, + "CTGAGGCAA": 16944, + "TTCACGG": 16945, + "CAATTCTG": 16946, + "CCATTCCA": 16947, + "CGGAGGA": 16948, + "TCATGTCA": 16949, + "GAATACATT": 16950, + "GTAGGTTTT": 16951, + "TTAGTAGAGA": 16952, + "CTCTTAATT": 16953, + "GAAAGCATT": 16954, + "CAGAGTAAA": 16955, + "GCTGCAGTT": 16956, + "CCAACAGAA": 16957, + "TACACGTT": 16958, + "TACATATATATA": 16959, + "GTTTTTATCA": 16960, + "CGACGTC": 16961, + "CCAAATTTA": 16962, + "TGATTTCTA": 16963, + "CTGTGGTCA": 16964, + "GCAGGGATG": 16965, + "GAGTCTTA": 16966, + "GGAGTGCAGTG": 16967, + "CATAGGTT": 16968, + "CCTTĊ": 16969, + "CCTTGTCTT": 16970, + "TTTGCTCA": 16971, + "GACAGCAAA": 16972, + "TGGAGCAA": 16973, + "TCTGAGTTA": 16974, + "CTTAAGCCA": 16975, + "GTTAAGAGCA": 16976, + "TCCTGGAGA": 16977, + "CTTTATGAA": 16978, + "GAAATACCATT": 16979, + "CAAGAAATT": 16980, + "GGACCTCC": 16981, + "CTAGGGTG": 16982, + "CCTGGGGAA": 16983, + "CCAAGAGAA": 16984, + "CGTGGCC": 16985, + "TCATTGCTT": 16986, + "TCTTTTTTTTTTTTTT": 16987, + "TAAGACAAA": 16988, + "TAACAATTA": 16989, + "GTTCAACA": 16990, + "TGTGGGTA": 16991, + "GAAATACTG": 16992, + "CCTTGAATT": 16993, + "CTAATCTA": 16994, + "CTTAGGGA": 16995, + "CTTTGGCTT": 16996, + "TCTTTTTTTTTTTTTTTT": 16997, + "TAATGTCTG": 16998, + "CTATTTCTG": 16999, + "GCTCTAAAA": 17000, + "GCGCTCTT": 17001, + "TCCTĊ": 17002, + "CTTGAAGG": 17003, + "GCACCAAAA": 17004, + "GTATGATG": 17005, + "CATTTACCA": 17006, + "TTAACACA": 17007, + "GAAAAGTGA": 17008, + "TCAAACTG": 17009, + "TATTACTA": 17010, + "CCATTTCCTT": 17011, + "CTGTGGTG": 17012, + "GAATGGAATG": 17013, + "GGATGCTG": 17014, + "CACTATTTT": 17015, + "TCACAGTAA": 17016, + "GCGCGATCTCGGCTCACTGCAA": 17017, + "GCATTCTCAGAAACTT": 17018, + "TGATTAATT": 17019, + "CCACGCTG": 17020, + "GCCTGAATG": 17021, + "CTAGACAGAA": 17022, + "CATGGCAAA": 17023, + "GTCAGTAAA": 17024, + "GCCACCTA": 17025, + "CTTTTTCCA": 17026, + "CTTTTGGTG": 17027, + "TGCATCAA": 17028, + "TCTCCCTGA": 17029, + "TATAATTTTA": 17030, + "TACCTATT": 17031, + "GGTCGC": 17032, + "TCCTGACTG": 17033, + "TGTTGTAAA": 17034, + "GGAAGACAA": 17035, + "GCTTTGCTG": 17036, + "GCTGCATCA": 17037, + "GCTAGCC": 17038, + "TCAAAGTAA": 17039, + "TCTCAGTGA": 17040, + "AAAAAATAAAA": 17041, + "GCAACCCACTCCAGTATT": 17042, + "GGATGAGATGGCTGGATG": 17043, + "GCTCTGGATAAGAGCGTCTGCTAAATGA": 17044, + "CTCGGCCA": 17045, + "CTCTCAGCA": 17046, + "GGGGCAGCA": 17047, + "CCCATCTCC": 17048, + "TGGATATT": 17049, + "CGATTCATG": 17050, + "GTTATGTTA": 17051, + "CTAATTTTTTGTATTTTTAGTAGAGA": 17052, + "CCTCAATG": 17053, + "GCAGTGGTG": 17054, + "GTACAGCC": 17055, + "TGTGTATAA": 17056, + "TATATACCA": 17057, + "TCTGGTC": 17058, + "GGTTTCCA": 17059, + "TGGAGAATT": 17060, + "TAATAGTTA": 17061, + "TACCCTT": 17062, + "GCCTTCCTG": 17063, + "CTATTTAAA": 17064, + "TTATGCAAA": 17065, + "CTTTTATTAA": 17066, + "GAATATGTT": 17067, + "GGAGAATTTT": 17068, + "CAATGAGAA": 17069, + "CACAGGGCA": 17070, + "TTCAGTTTT": 17071, + "TATTATTATT": 17072, + "GCTGAGCTG": 17073, + "GCATTCTA": 17074, + "CTCCAGCAA": 17075, + "TCACAGCTG": 17076, + "CACAGACAA": 17077, + "TCAAGAGAA": 17078, + "TTTGGTT": 17079, + "TCTAGGTA": 17080, + "TGGACTCA": 17081, + "GGGATCCA": 17082, + "GTCTTCTG": 17083, + "GCCTTGCA": 17084, + "GTTCATCTT": 17085, + "TGTTCAGAA": 17086, + "TCTGCTGTT": 17087, + "TCAACACAA": 17088, + "TAAAATAGAA": 17089, + "CCCACTGAA": 17090, + "CAAAACTCA": 17091, + "CTCTGCAGA": 17092, + "AAATTAAATT": 17093, + "CTTGGGAGA": 17094, + "GGGGAGAGG": 17095, + "GAACAGCTT": 17096, + "CCGAATG": 17097, + "GGTTTTTA": 17098, + "CCATAGTA": 17099, + "GTAGTGTG": 17100, + "TGAAGAATT": 17101, + "TTAAGTTA": 17102, + "GGGGCTCC": 17103, + "CCTACTGAA": 17104, + "TCTCAGAGG": 17105, + "GATTATTTG": 17106, + 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"GATATATTA": 17152, + "CTGTATATT": 17153, + "CTATAGTG": 17154, + "TGTTCTTTG": 17155, + "CATTGCAAA": 17156, + "CTGATGCC": 17157, + "CTCATGCC": 17158, + "GATTTAGAA": 17159, + "GATGCGCA": 17160, + "CGCGAGG": 17161, + "CCTGATTA": 17162, + "TATCCTTTT": 17163, + "GTTTACTTA": 17164, + "GGTCCTCC": 17165, + "CCATAATA": 17166, + "CTCAAAAAAA": 17167, + "CAAAGATAA": 17168, + "GGAAGACC": 17169, + "TAAAACAGG": 17170, + "GCAAGAGTT": 17171, + "TATGCAGAA": 17172, + "TCTCTCCTCC": 17173, + "GTTAĊ": 17174, + "GTTTTGTGA": 17175, + "TTTAGTG": 17176, + "TAGGGCTT": 17177, + "TAATGCATT": 17178, + "GAGCTACA": 17179, + "GCTCGGTG": 17180, + "TTATACAGA": 17181, + "CTGCCCACA": 17182, + "GATCTAAAA": 17183, + "GCGCAGGA": 17184, + "CAAGGGC": 17185, + "TATTGAGTT": 17186, + "CACACTTAA": 17187, + "TCTGTATTA": 17188, + "CTCAGAGGA": 17189, + "GTCACACTG": 17190, + "CCAAGGCTG": 17191, + "CCCACGCA": 17192, + "TTCATCAA": 17193, + "CTTATTAAAA": 17194, + "CATGTTAAA": 17195, + "GGTGGGCAGA": 17196, + "TCGTCCTT": 17197, + "TCGCTATT": 17198, + "TCACCTTTT": 17199, + "TCCCAAAAA": 17200, + "GCATATAAA": 17201, + "GGAGCTCTT": 17202, + "GAAATATAAA": 17203, + "TTTTATATTTT": 17204, + "CGGCGGA": 17205, + "CTAAGGAAA": 17206, + "TAGATATTTT": 17207, + "CAATTTAAAA": 17208, + "CAGTTCTG": 17209, + "CCCTGTCTCTACTAAAAATA": 17210, + "GCCGGGCATG": 17211, + "GGTAAAAAA": 17212, + "TATTCTTTG": 17213, + "CTGAGCAAA": 17214, + "CATGGACTG": 17215, + "TAGGGACATG": 17216, + "CACCTATG": 17217, + "GTGTGTCTG": 17218, + "GGATGGAGA": 17219, + "GGAGGAGGGG": 17220, + "CCCGGTT": 17221, + "TAATATATG": 17222, + "GGACGTC": 17223, + "GGAGTATG": 17224, + "CTCTTACA": 17225, + "CACATACAA": 17226, + "TTAAGATG": 17227, + "TGCACTTA": 17228, + "CTCAACAAA": 17229, + "GAAGGTTTT": 17230, + "CTTCCTCCA": 17231, + "TTTTGGGAGG": 17232, + "TGATAGC": 17233, + "GTAAGGTG": 17234, + "CCAAGCAAA": 17235, + "CTCCCACTG": 17236, + "GAAAATAAAAA": 17237, + "CCCACCCCCA": 17238, + "AAAAAAAAATG": 17239, + "GAGCCAAAA": 17240, + "TCAGAGTCA": 17241, + "TCATATGAA": 17242, + 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17288, + "GGCCTTAA": 17289, + "TCATCTCTT": 17290, + "GTACCCCA": 17291, + "GCCTCCAGG": 17292, + "GCTGGACC": 17293, + "GCTGGGACTACAGG": 17294, + "GATGGGAAA": 17295, + "GGTAGCAA": 17296, + "GTCTTCAA": 17297, + "GAGGAGAA": 17298, + "CGTATGTA": 17299, + "GCGGCGCA": 17300, + "GAAGACACA": 17301, + "GCAGACAGG": 17302, + "GTGTATATGTGCCACATTTT": 17303, + "GGTCCCTT": 17304, + "CTTTATTTG": 17305, + "GCAGAGGTT": 17306, + "GGACCTTA": 17307, + "GAAAGAGTT": 17308, + "GAAAGATCA": 17309, + "GAAAATAATT": 17310, + "TTTGTATTTT": 17311, + "TTTATCC": 17312, + "TGGGCAA": 17313, + "GGTTCACA": 17314, + "TGGAGTT": 17315, + "TAAAAATATTTT": 17316, + "CTAAAGTA": 17317, + "TCCTGGCTT": 17318, + "GCCTTCCAA": 17319, + "TCGATGTG": 17320, + "TCGCTAAA": 17321, + "CATTCTGAA": 17322, + "GCCACCTCA": 17323, + "GACACTTTT": 17324, + "TTATTAAATT": 17325, + "CTAAATGTT": 17326, + "GGTACTGA": 17327, + "TGATTCCA": 17328, + "TGATTCTGA": 17329, + "CTCTTTCC": 17330, + "CGTATTAA": 17331, + "GTGGGTGTG": 17332, + "GGAGACCTG": 17333, + 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"TTTTATACA": 17380, + "TCATTCACA": 17381, + "TATCATTTG": 17382, + "CCCTTAAAA": 17383, + "GGACACATT": 17384, + "GTCTCCCA": 17385, + "GAAAGCCAA": 17386, + "GAAATACCA": 17387, + "CAGGCGA": 17388, + "TCTGGATGA": 17389, + "GATTTAAAAA": 17390, + "GTAAATGTA": 17391, + "TCTCCCTT": 17392, + "GTTTGTCTT": 17393, + "GCTAATTTG": 17394, + "CTTGAGCCA": 17395, + "GCCCAGGCTGGTCTCAAA": 17396, + "GCAGTGCTG": 17397, + "GAAACAATT": 17398, + "TCTGGAATT": 17399, + "CTGACAGCA": 17400, + "CCTTGACTT": 17401, + "GTTACAGAGA": 17402, + "GGTTGTAA": 17403, + "TCTGATTAA": 17404, + "CTGTTGTT": 17405, + "GTGTTTCA": 17406, + "CAATTTATG": 17407, + "TGAGGTGGG": 17408, + "GTTTTATTTG": 17409, + "TACTCTGA": 17410, + "TAAAAAAAATT": 17411, + "GTGCATCC": 17412, + "GCCCACTA": 17413, + "GTCTGAGAA": 17414, + "CAGATCTA": 17415, + "CGGGTAA": 17416, + "CGGGACC": 17417, + "CTATTCATT": 17418, + "CCATGTCA": 17419, + "GCATGTTTTA": 17420, + "TCAGAGTGA": 17421, + "CAATATCTT": 17422, + "TGACAGCTT": 17423, + "CGCTTAAA": 17424, + "TAACTTAAAA": 17425, + "GAAATACAAA": 17426, + "GCTGCACTG": 17427, + "GCTGGGGTG": 17428, + "GCCACCCCCA": 17429, + "GACATAAAA": 17430, + "TCAAAGTTA": 17431, + "GAGCGAAA": 17432, + "GAAGATATA": 17433, + "CTGTTTTTTCA": 17434, + "CCAGTCCATCC": 17435, + "TAAGAAGAA": 17436, + "TGACCTCTG": 17437, + "GCCGTTTT": 17438, + "CAAAGGCAA": 17439, + "CCTGTAGA": 17440, + "CTCCTCTCA": 17441, + "CTTATATAA": 17442, + "TTTATAATT": 17443, + "TCCTTGGGA": 17444, + "GGCCCATG": 17445, + "CTTTGTAAA": 17446, + "CGAATTAA": 17447, + "GACAACACA": 17448, + "GCTCCTTTG": 17449, + "GAAATGTTTT": 17450, + "CTGTATGTG": 17451, + "CAATACATT": 17452, + "GCCCCCAGG": 17453, + "TAGTTTTAA": 17454, + "GGAGCAGCA": 17455, + "CTAGGGAGG": 17456, + "TCTGCAGTT": 17457, + "GTGATACA": 17458, + "GTCACGTT": 17459, + "CCTGCGA": 17460, + "GTTATCC": 17461, + "CTTTTGGA": 17462, + "TTATTCAAA": 17463, + "TTTTTTCCTT": 17464, + "TCATTGTAA": 17465, + "GGCCCACA": 17466, + "GGCCATTA": 17467, + "TAAATTTAAA": 17468, + "CCATGGCAA": 17469, + "GCCCTGGCTGTCCTGGAACTCA": 17470, + "TAGCCCTG": 17471, + "GAGTTTTTG": 17472, + "TAACTTGG": 17473, + "TAAGTATA": 17474, + "TCATTATTTT": 17475, + "TGAGTTGG": 17476, + "GAACGCC": 17477, + "GCACCCTA": 17478, + "CTCTGAGTT": 17479, + "CTCTCACTG": 17480, + "TATTTAAAAAA": 17481, + "GAGAGGGTG": 17482, + "CTGACCCC": 17483, + "CCAACACC": 17484, + "TGTACCCA": 17485, + "GCAAATGCA": 17486, + "GCTGATGGAA": 17487, + "GTGTTCTG": 17488, + "TTATACATT": 17489, + "TGTGTGTGTGTGTGTGTGTGTG": 17490, + "GACGGGGGA": 17491, + "GTGGTGATG": 17492, + "GAAAGACTT": 17493, + "TATAGCCA": 17494, + "TCTGGCTA": 17495, + "CTTTTAAATT": 17496, + "GGTGGTCC": 17497, + "TCTCCTGAA": 17498, + "TCTGAGAGA": 17499, + "TGAAACCAA": 17500, + "TGATTGTTTT": 17501, + "CTCTTTCCA": 17502, + "TCAGAGCTG": 17503, + "TCGATGTT": 17504, + "CAATACACA": 17505, + "CCGCTTA": 17506, + "GCTCTCTCTC": 17507, + "TCACCCTA": 17508, + "TCAATACC": 17509, + "TTAACTG": 17510, + "TGTAGTCA": 17511, + "TCAAACATT": 17512, + "CGTGTTCA": 17513, + "CTGTGGTGA": 17514, + "GAGCCCAGG": 17515, + "GTTTAGATT": 17516, + "GGACACGA": 17517, + "CTAAAAATACAAAAAA": 17518, + "GAAGTGGATGCTCA": 17519, + "GAGGCAGGTGGA": 17520, + "CTGAGGCCA": 17521, + "TCTCAGAGA": 17522, + "TAATATAATATT": 17523, + "GTGTTCATT": 17524, + "TCCTGGGAA": 17525, + "GAGGATTA": 17526, + "GATTATTTA": 17527, + "CTCCAGTCC": 17528, + "GTTTCATTTT": 17529, + "TCATAGCTCA": 17530, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 17531, + "GTGTCGG": 17532, + "CCACCTAA": 17533, + "GCAAGCAAA": 17534, + "CCTGGTTTT": 17535, + "CTAAGGTG": 17536, + "TACACAAAAA": 17537, + "CGACAAAA": 17538, + "TAGGGTAA": 17539, + "CCAAACAA": 17540, + "GAATTTCC": 17541, + "TAATAGCTGA": 17542, + "CAGGATTA": 17543, + "CACATTAAAA": 17544, + "CAAGATGAA": 17545, + "CCGGAGG": 17546, + "GAAAGCTA": 17547, + "GAAAGTATT": 17548, + "CAGGTATA": 17549, + "GCTTCCTA": 17550, + "TATGCCCA": 17551, + "CGGTATA": 17552, + "TCTAGAACTA": 17553, + "TAATTCACA": 17554, + "TGCACCTT": 17555, + "GGGATCC": 17556, + "GTTTGGAGA": 17557, + "TGAGAGCTG": 17558, + "GAGCTCTA": 17559, + "GCTCCCGG": 17560, + "TCGGATA": 17561, + "GCATAGGA": 17562, + "CTACATCC": 17563, + "TATTTATTTG": 17564, + "CAAAGCCAA": 17565, + "CTGACACC": 17566, + "CTGATGCTG": 17567, + "CATGGTC": 17568, + "GTCAGGATG": 17569, + "GAGGCTGAA": 17570, + "CCTGGTCAA": 17571, + "CCTGGGCTT": 17572, + "GAAAACCTT": 17573, + "GACACGTG": 17574, + "GCAAATTTA": 17575, + "GCTGAGGTG": 17576, + "CCTCCTCCTCC": 17577, + "TCATCACTG": 17578, + "TCCAGGGGATCTT": 17579, + "CCACCCACA": 17580, + "TTTTAAATTA": 17581, + "CAGCTTAAA": 17582, + "GTTGCAAAA": 17583, + "GAAACATAA": 17584, + "CAGAGGCAA": 17585, + "TCAACACC": 17586, + "TTAAGAGA": 17587, + "CCTACCCA": 17588, + "TAAAAAGGAA": 17589, + "TAAAAATTTG": 17590, + "CGGATCACGAGG": 17591, + "TTTTTTTTTTTTA": 17592, + "GGTAATTTA": 17593, + "CGTGAAAAA": 17594, + "GAAGCAAAA": 17595, + "GTACGGA": 17596, + "TCTTTGATT": 17597, + "TCTTCAATT": 17598, + "TCTGGGTCA": 17599, + "CCAAGTTTT": 17600, + "GGGGGAGTG": 17601, + "CGAGTTTT": 17602, + "TGTAGCC": 17603, + "TATTTTTAAA": 17604, + "CTGTGCCCA": 17605, + "GCCTGCCAGG": 17606, + "GCCTGGCCAACATGGTGAAA": 17607, + "GAATGCTA": 17608, + "GAAGACATG": 17609, + "TAACACTAA": 17610, + "TAAGATGTGAA": 17611, + "GTCCACGG": 17612, + "GACTAATG": 17613, + "GCACATC": 17614, + "GGATAGTG": 17615, + "TGTGTAAAA": 17616, + "TATTGTGAA": 17617, + "CACAGAGCC": 17618, + "CAGGAAATG": 17619, + "CTGAGAGCA": 17620, + "GCTTTACAA": 17621, + "CCAATTTTA": 17622, + "GCCATCTC": 17623, + "GGGGCTT": 17624, + "GTGTTTCC": 17625, + "TAATGGATT": 17626, + "TGAGATGTT": 17627, + "GGATTCTCCAGG": 17628, + "GAAGGTGGG": 17629, + "TTATTATTTG": 17630, + "TCTCTGCAGG": 17631, + "CCACAGTCA": 17632, + "CGTGAATG": 17633, + "TGAGGTGGCCAAA": 17634, + "CACTTATG": 17635, + "GGTCCTCA": 17636, + "GCGTTCCA": 17637, + "CTGCCAGG": 17638, + "GTGCCGG": 17639, + "CCAGATTTT": 17640, + "CTCTCTATT": 17641, + "CTCTCTAAA": 17642, + "GAAATGATT": 17643, + "CCTTTCTC": 17644, + "TCCAGCAGCA": 17645, + "GGAAATGAA": 17646, + "TATTATATG": 17647, + "CACAACACA": 17648, + "TAATAATTTA": 17649, + "GTTTCACTCTT": 17650, + "GCAGAGTCA": 17651, + "GGATTACAGGCGTGA": 17652, + "TATCTCAAA": 17653, + "GCGAAAAAA": 17654, + "CTCTAAGA": 17655, + "GTCTCAGTT": 17656, + "GCTTTCTTTT": 17657, + "CCTGCCCTT": 17658, + "CTCCCCCTT": 17659, + "CTTTTTGA": 17660, + "GGCACTTAA": 17661, + "TAAAAAGAAAA": 17662, + "GGAAACAA": 17663, + "CCATTTCAA": 17664, + "GCTTTTTTA": 17665, + "CTGCATGTG": 17666, + "CAAGACAAA": 17667, + "TCCTGAGTGAGGTAA": 17668, + "GTAATGAAAA": 17669, + "CAAAACAAAACAAAACAAAA": 17670, + "TCGTGATT": 17671, + "CCAGTCCATT": 17672, + "GCCCCCTA": 17673, + "TCTTAAAAAA": 17674, + "TCTGTCATT": 17675, + "GCAAGGCTG": 17676, + "CTTAGAGAA": 17677, + "CAGCACAA": 17678, + "GTGTTGATG": 17679, + "CCCTCTTCA": 17680, + "TATATATATATATATATATA": 17681, + "CAAAATGAAAA": 17682, + "CGCCAGGCCTCCCTG": 17683, + "GGTCCATG": 17684, + "TATTCAATT": 17685, + "GAAAGAGTA": 17686, + "CCAACCCA": 17687, + "CTCCTGGAA": 17688, + "CTTAĊ": 17689, + "GGTTGAGAA": 17690, + "TCAAATGCA": 17691, + "GCGTTATT": 17692, + "TGATCCCTG": 17693, + "CTTGATCC": 17694, + "TCTTCTTAA": 17695, + "CATTTACTA": 17696, + "TGAATAGAA": 17697, + "GGAAGGAGG": 17698, + "CCTTTAATT": 17699, + "CCTTTACC": 17700, + "CTTATGGG": 17701, + "GATGTACCA": 17702, + "GTTTTTTTTA": 17703, + "GTCCCCTA": 17704, + "GGGGCCCC": 17705, + "GTTAATATA": 17706, + "GTATTTCTA": 17707, + "GAGGAGATG": 17708, + "GCCTGCCTT": 17709, + "TACTGTATA": 17710, + "TTATATAAAA": 17711, + "TAGAGGTT": 17712, + "GTACACTA": 17713, + "TAAACAGTG": 17714, + "TACATACTA": 17715, + "GAAAAGCAAA": 17716, + "GAATGCAAA": 17717, + "TCACACCTG": 17718, + "GTTCTTTTA": 17719, + "CCATGAGG": 17720, + "GTACCTAA": 17721, + "TCTGGACC": 17722, + "TCTGGGGAA": 17723, + "GTAATCC": 17724, + "TTTAGGTT": 17725, + "GGGACCAA": 17726, + "CGCGGGC": 17727, + "GGAGATCAAA": 17728, + "TAACATGTT": 17729, + "TCCCCCAAA": 17730, + "TATTTCTTCACTA": 17731, + "GACTCTGTCTC": 17732, + "GTTGAGAAA": 17733, + "CTCAGTGAA": 17734, + "GAGGCCAGGA": 17735, + "GATTTCTTTT": 17736, + "CTCCAAATT": 17737, + "TACATATGA": 17738, + "CAAAAGTCA": 17739, + "TAGACCTG": 17740, + "GGCTGAAAA": 17741, + "GTTAAATAAA": 17742, + "TGTGTGTGTGTGTGTGTGTG": 17743, + "TACTTATG": 17744, + "GGCTGATG": 17745, + "GAAGAGCC": 17746, + "CTTTAAATG": 17747, + "TATATGCC": 17748, + "TGAAGAGTT": 17749, + "GCTATGATT": 17750, + "GGAAAGGA": 17751, + "CTAAACTA": 17752, + "GTTAATATT": 17753, + "TGAGAGAAAA": 17754, + "TCAGATGTG": 17755, + "TCCCACGG": 17756, + "CTTCACTGA": 17757, + "TCCACATTTT": 17758, + "GTGGAGGAA": 17759, + "GAGTCAAAA": 17760, + "CCGCTAA": 17761, + "GGGCAATT": 17762, + "GAATAAATT": 17763, + "CTTGCAGG": 17764, + "CACAGATGA": 17765, + "TTATTTCAAA": 17766, + "CGCGGCA": 17767, + "TAGCTTTTA": 17768, + "CTATTCACAATAGCAAA": 17769, + "TAAGATCC": 17770, + "GAAGTGGG": 17771, + "CTGCCTAA": 17772, + "GGAGCACA": 17773, + "CAAATACTG": 17774, + "CCTTCGCC": 17775, + "CCTGTCTC": 17776, + "GGGGGATT": 17777, + "CAAAACTGA": 17778, + "GAAAAATAAAA": 17779, + "TATCATTTA": 17780, + "TCTTAATTTT": 17781, + "GACCTGAAA": 17782, + "GCCGGGCGCGGTG": 17783, + "GGCGCAAAGCTACACAGAGAAACCCTGTCTC": 17784, + "TATGTTTA": 17785, + "GATCGTC": 17786, + "CAAGCTTA": 17787, + "GTGGTATG": 17788, + "TGTTCAGTT": 17789, + "CATTGGGA": 17790, + "CAGGGATA": 17791, + "GTGAGTGAA": 17792, + "GCAAGATA": 17793, + "GTTACATTA": 17794, + "TTAATAATT": 17795, + "GCCACGGA": 17796, + "CGACGGA": 17797, + "CCTAGCTG": 17798, + "CTCTTTCA": 17799, + "TATTAATTTA": 17800, + "CAGCCCCAAA": 17801, + "GAACACTTA": 17802, + "GGACACTGA": 17803, + "CCATAGCCTTGACTA": 17804, + "GCTAGAGAAAGTA": 17805, + "CAATAATTA": 17806, + "GTTGCAGG": 17807, + "CACAGAATT": 17808, + "TCTGTCTTA": 17809, + "CTCAGGGAA": 17810, + "CTCACACAA": 17811, + "GTCAĊ": 17812, + "TATGTATGA": 17813, + "GTTTGGTTTG": 17814, + "GAGCTTTTA": 17815, + "GGAGATCTT": 17816, + "GAGGAATGGG": 17817, + "TCTGTAGTT": 17818, + "CTCAGACC": 17819, + "TGAAATAAAA": 17820, + "GTGTTTATT": 17821, + "GCATTTGTG": 17822, + "TGACAATGA": 17823, + "GGAGGAGGAGGAGGAGGAGGAGGA": 17824, + "GGTCGCC": 17825, + "TAAGCCTG": 17826, + "TGAGTTTA": 17827, + "TTAGGGA": 17828, + "CCATCAGG": 17829, + "CTAGAGTA": 17830, + "TCAAGTTAA": 17831, + "TGGGCCTG": 17832, + "GGTACATT": 17833, + "TGATGGAA": 17834, + "GGTCAGGA": 17835, + "TAAAAATTGATTTTTT": 17836, + "GCGTTCTT": 17837, + "GCGGGGTG": 17838, + "TGACTAAAA": 17839, + "GGATCTTTT": 17840, + "GTAGGGTG": 17841, + "TGTGTACTT": 17842, + "CAGGCCCA": 17843, + "TCTGTAATG": 17844, + "CTCATGAAA": 17845, + "GATTCATA": 17846, + "CTTTTTCAAA": 17847, + "TGGGCAAAA": 17848, + "TAGGTCA": 17849, + "TTATTGAAA": 17850, + "GCCTTCTGA": 17851, + "TGAGACACA": 17852, + "TGACAGTAA": 17853, + "CACGAAAA": 17854, + "GATCTGAAA": 17855, + "CAACTTTTA": 17856, + "GTACTTTTA": 17857, + "TAAACAATG": 17858, + "TATAGTAAA": 17859, + "CATTCCATG": 17860, + "TCTAGGCA": 17861, + "GCCAGCTCA": 17862, + "GTCCTAGA": 17863, + "CTGTTGATG": 17864, + "GGAAAAAAAAAA": 17865, + "CTTCATGAA": 17866, + "CAACAGTGA": 17867, + "TCGTGAAAAAA": 17868, + "CTGGAGAAA": 17869, + "GTGGTAAAA": 17870, + "GTCTGTTTG": 17871, + "GTCTCCAGA": 17872, + "TATGCGA": 17873, + "GGGGTTTA": 17874, + "TCTCCCACA": 17875, + "GGGCAGGGG": 17876, + "GTGCTGAGATTACAGG": 17877, + "CCGTGGG": 17878, + "TCGCAGAA": 17879, + "TGAGTTTTA": 17880, + "CAACTTCTT": 17881, + "CTGCTTGG": 17882, + "TATTTAATAA": 17883, + "GAAACGTG": 17884, + "CAGGGAGCA": 17885, + "GCTTTATTTT": 17886, + "TCCACCCTG": 17887, + "TGTACCTT": 17888, + "TAATTCATA": 17889, + "GTGAAAGAGGA": 17890, + "TTATAGTAA": 17891, + "TGACCAGG": 17892, + "CTGTTATG": 17893, + "GTGGATCC": 17894, + "CTACTGTG": 17895, + "CTATAATG": 17896, + "GCTTGGCTT": 17897, + "CCTGGCTCA": 17898, + "TCTCGTGA": 17899, + "TCCAGCTCC": 17900, + "GTTTTGCAA": 17901, + "GGGGTCTT": 17902, + "CAAAACCAAA": 17903, + "GGTTGCC": 17904, + "GGTTCAGA": 17905, + "CGCAAATT": 17906, + "CTTTGCTTA": 17907, + "CAAAAATTTT": 17908, + "CAATAGTGA": 17909, + "GTTGGTTTG": 17910, + "GCACAGTGA": 17911, + "GTTGAAATT": 17912, + "GTAGAAGG": 17913, + "GTAGGGTA": 17914, + "TATGTACTT": 17915, + "GAGGĊ": 17916, + "GATTCAGAA": 17917, + "TCCAGCCAA": 17918, + "CCCAGTCTT": 17919, + "TTATTCCTT": 17920, + "GTGAAGATG": 17921, + "CTGTTCCA": 17922, + "CTTTGCATT": 17923, + "TATATTTATA": 17924, + "TTTTATTAAAA": 17925, + "GTTGGTTAA": 17926, + "GAAGTTTACATACA": 17927, + "TCTGAGTTCGAGG": 17928, + "TACGGTT": 17929, + "TATAAAAAAA": 17930, + "TAAGAAATG": 17931, + "GTTCTCTA": 17932, + "GTTCCAGA": 17933, + "GCATATC": 17934, + "TGAATGGAA": 17935, + "TCTGCACC": 17936, + "CAGACAGTT": 17937, + "CTGACGAA": 17938, + "GTCATGAAA": 17939, + "TTTTACTGA": 17940, + "GATTTCTAA": 17941, + "CCTGTCTCC": 17942, + "TCTCTAGGA": 17943, + "CTAACGG": 17944, + "GAATTGAAA": 17945, + "CAATTCATT": 17946, + "GTTTGTTTTTT": 17947, + "GATTATTAA": 17948, + "TAAATAAATAAATAAATAAATAAATAAATAAA": 17949, + "TAATGTGTT": 17950, + "CTTTCGG": 17951, + "CTTTCACA": 17952, + "GCAGGAGAA": 17953, + "TGTTGACTT": 17954, + "GCTGGATA": 17955, + "CTTTTCAAAA": 17956, + "GGGGCCTT": 17957, + "GTCTTTGAA": 17958, + "CCCTGTCTCTACTAAAAATACAAAAATTA": 17959, + "CTCCACGG": 17960, + "CCCCGTCTCTACTAAAAATA": 17961, + "GGTAAATA": 17962, + "TAATGAGAA": 17963, + "GTGCCTAA": 17964, + "GTGGTGCTG": 17965, + "TAAATTATTA": 17966, + "TCCATCCAA": 17967, + "GAAAACTTA": 17968, + "TCTACCCC": 17969, + "TCAAAGATG": 17970, + "CTTTGGAGG": 17971, + "CCTCAGCTT": 17972, + "TTACATATA": 17973, + "CACGTGTT": 17974, + "GCCCTATT": 17975, + "CTATATCC": 17976, + "GTCTGTGTG": 17977, + "TCTTCTATT": 17978, + "TGTTCATTA": 17979, + "GAGAGAGTT": 17980, + "TGAAGCAAA": 17981, + "GTCCTATG": 17982, + "TCTCCCATT": 17983, + "GTGTTCTCA": 17984, + "CTCTGTCC": 17985, + "GCGGGCC": 17986, + "TAGTATATA": 17987, + "TATTCCCC": 17988, + "GAGAGCAAA": 17989, + "TAAAATATAAA": 17990, + "CCCATACA": 17991, + "GTAAAGTAA": 17992, + "TCTGAGGTTATT": 17993, + "CTCTTCGCA": 17994, + "CCATTTCA": 17995, + "TCTAAGAA": 17996, + "CCTGATTTT": 17997, + "GAACAGTGA": 17998, + "TCATGGCTGCAGTCA": 17999, + "TAAGAGCC": 18000, + "TAATATTTA": 18001, + "GTATCATG": 18002, + "CTCTGATGA": 18003, + "CAGGGGGA": 18004, + "GCTTTCATT": 18005, + 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"CATCTCAAA": 18049, + "GCGTTCAA": 18050, + "TGATGTTTG": 18051, + "GTATTTTTA": 18052, + "TATTCGTT": 18053, + "TCTTCCTTA": 18054, + "GTGACAGTG": 18055, + "GATGGGGGA": 18056, + "GGGGTGCA": 18057, + "GGTTCATG": 18058, + "TAAAAATGAAA": 18059, + "GTGAATTTA": 18060, + "CTGTTTGTG": 18061, + "CATTATTATT": 18062, + "GCATGGAA": 18063, + "TATTTAGAAAA": 18064, + "GGGTGATG": 18065, + "CTTGTTTA": 18066, + "GTCTCTAAA": 18067, + "TTTTATACC": 18068, + "CTTAGGCA": 18069, + "GCCATGATT": 18070, + "GGGAGTAA": 18071, + "GAATTTGTG": 18072, + "TAATGGTAA": 18073, + "TGATGCAA": 18074, + "CGTCTGTG": 18075, + "TGAGTGAAA": 18076, + "GTGTAGC": 18077, + "GTCATAGG": 18078, + "CTTATCC": 18079, + "GCCACCAAA": 18080, + "CGACATA": 18081, + "CAAAATAATT": 18082, + "GCTCCTCCATT": 18083, + "CCTCAAAAAA": 18084, + "CTTCACAAA": 18085, + "GCTTTTATG": 18086, + "GCAGTGAAA": 18087, + "GTCTTAATT": 18088, + "TATTCACTG": 18089, + "TATTTAATTA": 18090, + "TCTGCGTG": 18091, + "GCTTCACTT": 18092, + "CCTGCACTG": 18093, + "CCTGCACAA": 18094, + "CTAACATTA": 18095, + "TCTAGCTA": 18096, + "GCCAGCTCC": 18097, + "CAAAACATA": 18098, + "TTCACCAA": 18099, + "TGAGACAA": 18100, + "GGTAATTTT": 18101, + "CAACCTCAAA": 18102, + "TATTTATC": 18103, + "TGTTCACTG": 18104, + "GGAAGCCAA": 18105, + "CTCACTGAA": 18106, + "CTTATAGG": 18107, + "GAAAAGGGAA": 18108, + "CAAAAAAATG": 18109, + "CGCTCTGA": 18110, + "GGCCGGGC": 18111, + "CTTAAAAATT": 18112, + "CCATTCATT": 18113, + "CAGCTTTTA": 18114, + "CCCTTCCTT": 18115, + "GTATTTAGTCAGCCACCAATTGTGCAAGTTCTCCCACTTAAAAA": 18116, + "GGGTCTA": 18117, + "TCCTCTGAA": 18118, + "CTTTCAGAA": 18119, + "TATAGTCC": 18120, + "TGTTTATGA": 18121, + "GAGGCGGA": 18122, + "CTTACCCA": 18123, + "GATGCCATGA": 18124, + "GGTAGTAA": 18125, + "CTCAAGAA": 18126, + "CTGGGGAGA": 18127, + "CCACCACGCCTG": 18128, + "GTTGCTC": 18129, + "CACACTC": 18130, + "TATATCTTA": 18131, + "TGAAGAAAAA": 18132, + "CAGAGGGGA": 18133, + "CCTTCTGAA": 18134, + "TGGGCTCA": 18135, + "TAGATAGAA": 18136, + "TGTACTAA": 18137, + "TCTGACTG": 18138, + "GAATTCTAA": 18139, + "TAATATAATT": 18140, + "GCTCAGTGA": 18141, + "TCCAAGGA": 18142, + "GGAGGAGGTG": 18143, + "GAATATCTT": 18144, + "TAACACTGA": 18145, + "CTCTAGCTT": 18146, + "CTACAGTGA": 18147, + "TACAGGTT": 18148, + "TCAGTGATT": 18149, + "CTATCATG": 18150, + "TAAACAGTA": 18151, + "TATTCCTCA": 18152, + "GAAAGACC": 18153, + "GAAAGCCTT": 18154, + "TGTTTGATT": 18155, + "TGAACCCA": 18156, + "TCTGCTCTG": 18157, + "CAGACAGAAA": 18158, + "GGAAGGGCA": 18159, + "CCTTTATTA": 18160, + "GCCACTTTT": 18161, + "GACAGTCC": 18162, + "TGTACTTTT": 18163, + "GATATCC": 18164, + "CTAAATTCA": 18165, + "TAATATGA": 18166, + "CAAAAACAA": 18167, + "GGGCTAGG": 18168, + "TATTTACATT": 18169, + "CTGCCCCTG": 18170, + "GTTCAGCTT": 18171, + "TAGTCCTT": 18172, + "TTTTĊ": 18173, + "GTAGCCTG": 18174, + "GCCTAATG": 18175, + "TCTTGGCTG": 18176, + "TAAAACACAA": 18177, + "GTCACTGAA": 18178, + "GTAATCCA": 18179, + "CCAATACA": 18180, + "CGAAGAGTT": 18181, + "TATTAAAAATA": 18182, + "CCAGAGCTG": 18183, + "GCACACGG": 18184, + "CTATAGTTTT": 18185, + "TGAGTGAGAACATG": 18186, + "CTTGCACAATTGGTGGCTGACTAAATACTTTTTT": 18187, + "TAATCGA": 18188, + "CAACTGTA": 18189, + "GTGCCTC": 18190, + "GGAATATAA": 18191, + "GGGGGGTT": 18192, + "CGCTCTCA": 18193, + "CAGCAGAGG": 18194, + "GTCTTTGTG": 18195, + "TACCTACA": 18196, + "CCATTCTG": 18197, + "TTTTTACTT": 18198, + "GCAGTTTTA": 18199, + "CAGGĊ": 18200, + "CAGATGACA": 18201, + "GGAAGGCC": 18202, + "CATGCAGAA": 18203, + "CTCCTTTTA": 18204, + "GTTTTTCC": 18205, + "GGTGCCAA": 18206, + "TGATTTGTG": 18207, + "TGTGTGTGTGTA": 18208, + "GAGATGAAAA": 18209, + "GCCCCTAA": 18210, + "GAAAGTTAA": 18211, + "TAAAAGCAAA": 18212, + "CTCACCCAA": 18213, + "CCTTCTCTG": 18214, + "CAAAATCTT": 18215, + "TCTTTGTCA": 18216, + "CAACACTGA": 18217, + "TGGGTTAAA": 18218, + "TAACAAATG": 18219, + "TAATCATTA": 18220, + "GAATATTAA": 18221, + "GTGCAGTC": 18222, + "GCACCTAA": 18223, + "CTACGGA": 18224, + "CTTAGATA": 18225, + "GGGGCAAA": 18226, + "TGGGTATG": 18227, + "GTAAACATT": 18228, + "TAAAAATATAA": 18229, + "GATCACGCCA": 18230, + "TCCCAGTTA": 18231, + "GCCCACGG": 18232, + "GAAGATGAAA": 18233, + "CCACTGAAA": 18234, + "CCATCACCA": 18235, + "CTAGCCTT": 18236, + "CTAGATTTT": 18237, + "CTAGGTAA": 18238, + "GTCTATC": 18239, + "GAAACGC": 18240, + "CAGAGGTCA": 18241, + "GTAAGAGAA": 18242, + "CCTGGCCTT": 18243, + "GTTAGGGA": 18244, + "GGTTGCAA": 18245, + "TATAATCAA": 18246, + "GTCTGGGAA": 18247, + "GTTCAAGACCAGCCTGGG": 18248, + "GCGTCAAA": 18249, + "GCGTTGTG": 18250, + "TAAAGATCA": 18251, + "TATATTATG": 18252, + "CAGAGGAGCC": 18253, + "CATGCGA": 18254, + "GTAATAATT": 18255, + "CTCCCATA": 18256, + "GATGGTTTT": 18257, + "GATGGATCA": 18258, + "TTATTTCCTT": 18259, + "TAATAGTTTT": 18260, + "CTTTGTTTTA": 18261, + "CAATTTCTA": 18262, + "TCCTGTCTC": 18263, + "CAGGAGCAA": 18264, + "GAGTGTTTT": 18265, + "GAAGACTGA": 18266, + "TAATTTTTTAA": 18267, + "TAACTCGTTAAAAAA": 18268, + "TCACTTGAGGTCAGGA": 18269, + "CCTAAATA": 18270, + "TAAGGGC": 18271, + "TGATAATTA": 18272, + "GCAGTTTCA": 18273, + "TATATCTCA": 18274, + "TATACAGTT": 18275, + "GAGAGACC": 18276, + "TGAATCAAA": 18277, + "CAGGTACA": 18278, + "GATGCAGAA": 18279, + "TTTAGCTT": 18280, + "TTTATCCA": 18281, + "TAGGAAATT": 18282, + "GACAGTGAA": 18283, + "CATATAATT": 18284, + "TGAAATGAAA": 18285, + "TGATTCCTG": 18286, + "CCTTTTAAAA": 18287, + "TGACAGCTG": 18288, + "CAAGAGTGA": 18289, + "GAGAGATGGCTCA": 18290, + "GTGAAAGTGAAGTCGCTCA": 18291, + "CCGAAAAA": 18292, + "TAGTCCAA": 18293, + "GCAGCCAGG": 18294, + "CACAGGCTT": 18295, + "GAAACTCAA": 18296, + "TCTGCCCTT": 18297, + "TCAATCCA": 18298, + "TATGTAGCA": 18299, + "GTTAGCTA": 18300, + "GGTGGTT": 18301, + "GTAAAGTA": 18302, + "TCTCCTCA": 18303, + "TCCTTCCTCA": 18304, + "CTAAACAAA": 18305, + "GCTGAGGCA": 18306, + "TCACCCTCA": 18307, + "CTGCCCTCA": 18308, + "GGGGTTAAA": 18309, + "CAGCCATGAAATTAAAA": 18310, + "TGACTTAAA": 18311, + "CTCTCCAGA": 18312, + "CATGCACAA": 18313, + "GGTGCACA": 18314, + "TCCTTCAGG": 18315, + "GCATTTAAA": 18316, + "GTGTGGGA": 18317, + "CAAGGGTTTT": 18318, + "TTATACAAA": 18319, + "CTTTTGAACTGTGGTGTTGGAGAAGACTCTT": 18320, + "TACGTCC": 18321, + "GATAAATAAA": 18322, + "GCGTTAAA": 18323, + "TAAGTAATT": 18324, + "CTTCTGCTG": 18325, + "CTCTCACTT": 18326, + "TGTTCTGTT": 18327, + "CTGAGGCTT": 18328, + "GCTTCCCCA": 18329, + "GGGGGAGGA": 18330, + "TTATTCTGA": 18331, + "TTATTGTCA": 18332, + "GGGATCTG": 18333, + "GGTATCAA": 18334, + "TCTTAGAA": 18335, + "CTTTGGAGA": 18336, + "GAGCATTTT": 18337, + "TCTCTTTCTT": 18338, + "TCCAAATGCA": 18339, + "CTGGAGTTA": 18340, + "GTCATGTATGGATGTGAGAGTTGGA": 18341, + "TCGTTCAA": 18342, + "TCGCTTAA": 18343, + "TAATCAGG": 18344, + "CAATTATTA": 18345, + "CTGTCTCTG": 18346, + "GCACCTTA": 18347, + "CCATAGGA": 18348, + "CTCGGCTG": 18349, + "GAGATACTG": 18350, + "CAGAGGGCA": 18351, + "GTGATAAAA": 18352, + "CCTGGCTA": 18353, + "GAAAAGGTT": 18354, + "TAGGTCCA": 18355, + "GAATTTAAAA": 18356, + "GTGGGGCTG": 18357, + "GTTTATGAA": 18358, + "GTGGCAGGTG": 18359, + "CCATGAGA": 18360, + "GTCCAGAGA": 18361, + "TATTGATGA": 18362, + "CATTGTTAA": 18363, + "GCTGCTGTG": 18364, + "CTCCCCTCA": 18365, + "TCAAATAGA": 18366, + "CCATGTCTG": 18367, + "GACCATTA": 18368, + "CTTGGTTTG": 18369, + "CAATATGAA": 18370, + "CTTTCATA": 18371, + "GTGCAAAAA": 18372, + "GCATATATT": 18373, + "CCACAGTC": 18374, + "TATTGTCTT": 18375, + "TATACTAAA": 18376, + "CAGAGAAGG": 18377, + "GTCACAGTG": 18378, + "GTAAGCTG": 18379, + "TGGGCATT": 18380, + "GTAAAGGAA": 18381, + "GCTGAGAGG": 18382, + "CTTTGCTCA": 18383, + "GAATGGCTT": 18384, + "CTAATTAAAA": 18385, + "TCGCTGGGCAA": 18386, + "TCTCCTTGCAGTCCAAGGGACTCTCAAGAGTCTT": 18387, + "CCGCATG": 18388, + "TACCTTTG": 18389, + "GGCGTTTT": 18390, + "TCGGAGAA": 18391, + "CAAGAAGAA": 18392, + "CCACAAATG": 18393, + "TCTTCTCAA": 18394, + "GAGACAGTG": 18395, + "CAGACCAAA": 18396, + "TATGGGAAA": 18397, + "TCTCTGATG": 18398, + "GATGĊ": 18399, + "CCCACGCC": 18400, + "GGCACGTG": 18401, + "TCTGACTTA": 18402, + "GAATTCACA": 18403, + "CTATTCCAA": 18404, + "GATTTTTTTG": 18405, + "GCAGTGAGCTGAGATT": 18406, + "CTGAACTGAACTGAA": 18407, + "TAAGGAGG": 18408, + "GTGCTATG": 18409, + "GTCTGAATT": 18410, + "GTCTCCATG": 18411, + "GAGACACAA": 18412, + "GAGAGAGC": 18413, + "TCAACACCA": 18414, + "CCTTCACTT": 18415, + "TTTTATGCA": 18416, + "TACAGTATG": 18417, + "CTTTTGAAAA": 18418, + "CCTAACCTAACCTAACCTAA": 18419, + "GCAGĊ": 18420, + "GAAAGTCAA": 18421, + "CATTAAATG": 18422, + "CGGTTTTA": 18423, + "TACACTTAA": 18424, + "GTGTTGTA": 18425, + "CCTCTAAAA": 18426, + "GGAGGAGGAA": 18427, + "TGACATGAA": 18428, + "GCGTGATT": 18429, + "TAAACACTA": 18430, + "TATACAGCA": 18431, + "CAAAGATGA": 18432, + "TATGAAAAAA": 18433, + "CCTGGATA": 18434, + "CCAACAGCA": 18435, + "GTTTTTGAAA": 18436, + "GACATCCA": 18437, + "GGTGCGA": 18438, + "GAGAATTTG": 18439, + "TATTTTCTA": 18440, + "CCTCAGATATG": 18441, + "GTTCAACCATT": 18442, + "GAGTCGGACATGA": 18443, + "GGTCCTTTG": 18444, + "TAAGCTCC": 18445, + "CTCTAATTA": 18446, + "TGTGCAAAA": 18447, + "TGTGGCAAA": 18448, + "GTCATGTGA": 18449, + "GTAAGGGA": 18450, + "TTAATGTA": 18451, + "TTCATCTG": 18452, + "TCTCACATG": 18453, + "CCCATTAAA": 18454, + "TAATCTAAA": 18455, + "TGATGAGAA": 18456, + "TCCCAGGAA": 18457, + "GTCGAGC": 18458, + "CACACGTT": 18459, + "CTGAGGTCA": 18460, + "GCTGCCCTT": 18461, + "GAGGTATT": 18462, + "GAAAAGTT": 18463, + "CGTGTTTA": 18464, + "GTGAACTCC": 18465, + "CTTTGGATT": 18466, + "GTTTGGCTT": 18467, + "TCCCACCCC": 18468, + "TGTGATTTTCTG": 18469, + "TAACGAAA": 18470, + "TAAATATTTG": 18471, + "TCTGCCCGCC": 18472, + "CAGATACTT": 18473, + "CTGACGTT": 18474, + "CCAACCCCA": 18475, + "TTTGCAGG": 18476, + "CATATTTTAA": 18477, + "TCAAATGTG": 18478, + "GTCTTCAAA": 18479, + "CATTAATTTT": 18480, + "GAAGACGG": 18481, + "GCAGACAAA": 18482, + "AAAATGTTTT": 18483, + "TAAATTAATT": 18484, + "CAGACTGAA": 18485, + "CTGACTC": 18486, + "CCTTGCCA": 18487, + "TATGGAGAA": 18488, + "CGTGCATG": 18489, + "GGCCCAAA": 18490, + "CTCTTTCAA": 18491, + "CAATTCATG": 18492, + "TGAGAGTGA": 18493, + "GTGGAGCTG": 18494, + "CAGATGACACCA": 18495, + "TGATGTGTT": 18496, + "GTGCATC": 18497, + "TAAACGCA": 18498, + "TATTAAAATT": 18499, + "CGGCCCTG": 18500, + "TCTCTGAGG": 18501, + "CTCCCAGC": 18502, + "TCCACGAA": 18503, + "TCCAGCCTG": 18504, + "GAAAAGACA": 18505, + "GAAAATATCA": 18506, + "TTTACAGG": 18507, + "TCAAATCA": 18508, + "TAATTGCAA": 18509, + "TTCACCTG": 18510, + "CTGTTTCAA": 18511, + "GTCAATTAA": 18512, + "CTCTGCAA": 18513, + "CCAAAATTA": 18514, + "GCGACCCCATGGA": 18515, + "CTCACTCATAGGTG": 18516, + "GGGTCTTA": 18517, + "TGAGCGG": 18518, + "TGAGCTGTT": 18519, + "CTGGATATTA": 18520, + "TAAAGACCA": 18521, + "GAAAGGCAA": 18522, + "TATACTC": 18523, + "CTCATACA": 18524, + "GAGGGGCTG": 18525, + "GATTGACA": 18526, + "GTCCCTTTT": 18527, + "GGTGCATG": 18528, + "TCAAATAAAA": 18529, + "GGCCCTT": 18530, + "CTTTGGGGG": 18531, + "GCATTTCAA": 18532, + "TGATGTCTG": 18533, + "CCCCAGGAA": 18534, + "CAAATGGATG": 18535, + "CAACATCAA": 18536, + "GATAAAAAAA": 18537, + "TGACTTTA": 18538, + "GAATATCC": 18539, + "TAAACTCAA": 18540, + "GTCTCCCTG": 18541, + "TGTGGAGAAA": 18542, + 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"TCCACTCTT": 18588, + "GTATTCTT": 18589, + "CCCTCCACC": 18590, + "CAAATGTTTT": 18591, + "TGAGCAATTTAGCA": 18592, + "TAATGTTTG": 18593, + "CACACCAAA": 18594, + "GAAAGTGTT": 18595, + "GAAATAGCA": 18596, + "TATGTTTTTT": 18597, + "GAGGAAAAAA": 18598, + "CTAATCTG": 18599, + "TTAACATG": 18600, + "CGATAGG": 18601, + "TTATTCTTTT": 18602, + "CGCGGCGG": 18603, + "GAGAAAAAAAA": 18604, + "CAGCGCC": 18605, + "TACAATATA": 18606, + "GCACAGCAA": 18607, + "GTACATGTG": 18608, + "GATCTCAAA": 18609, + "TCACTTTA": 18610, + "CTATCTTTT": 18611, + "CTCTCTTAA": 18612, + "TCTTCCTTTT": 18613, + "CAAATATGA": 18614, + "GGCATATT": 18615, + "TGATGGCTT": 18616, + "GCAGGTTTGAAA": 18617, + "GCATCTCTT": 18618, + "GGACAGGAA": 18619, + "GCCTCAATT": 18620, + "AAAACATT": 18621, + "CTCTCCATG": 18622, + "TGTTTCCTG": 18623, + "CGTGGAGA": 18624, + "TCGGTCTG": 18625, + "GGGGACAGGA": 18626, + "GAATAGGAA": 18627, + "TGAGAACA": 18628, + "GAATCCTG": 18629, + "CAGGGACC": 18630, + "GCAACAGTT": 18631, + "GTCACATGA": 18632, + "CGGĊ": 18633, + "CGCTAGA": 18634, + "TGAGGCTA": 18635, + "CTGGGCCTG": 18636, + "CTGCACTGA": 18637, + "TGACCTTTT": 18638, + "TCCTCACCA": 18639, + "CTGGCAGG": 18640, + "GGACAAGG": 18641, + "CCATCTCTG": 18642, + "GTCGAAGG": 18643, + "CACATAAAAA": 18644, + "CACAGCTCA": 18645, + "TCTTTACCA": 18646, + "CAGAGAAAAA": 18647, + "CCTTTGCTG": 18648, + "CCAAGGCCA": 18649, + "CTGAAAAATT": 18650, + "GAATTGTTTT": 18651, + "GAATGGCC": 18652, + "GCTCCTCTG": 18653, + "GTTTATGTG": 18654, + "TAACAGTTA": 18655, + "TCACAGCTCA": 18656, + "TGACACTGA": 18657, + "GGACAGTCA": 18658, + "CACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACA": 18659, + "TGAGTAGCTGGGATTACAGG": 18660, + "TGATCAGG": 18661, + "AAAATGTA": 18662, + "GAAATAGAAA": 18663, + "GGAAGATGA": 18664, + "CCTTCCTTA": 18665, + "GTCATACA": 18666, + "TATGTACC": 18667, + "CTCCTCAAA": 18668, + "CGCGTT": 18669, + "CAGCATTTG": 18670, + "CAAGACCAA": 18671, + "CCTATTTCTCCACA": 18672, + "GCTCATGCCTA": 18673, + "GGATATC": 18674, + "TATTCATGA": 18675, + 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"GATTAAAAAA": 18720, + "TCCACAATG": 18721, + "TAATTCATG": 18722, + "CGTTTGTG": 18723, + "CTGTGCATG": 18724, + "CCCACAACCCTG": 18725, + "TCGTCTCA": 18726, + "TTAGGTA": 18727, + "CTGCGTC": 18728, + "TATTCAGTG": 18729, + "TATTGCATT": 18730, + "TCTCTCTGTT": 18731, + "GTTAGGTG": 18732, + "GTCCTAAA": 18733, + "TGGAGTTTT": 18734, + "TTCAGCTG": 18735, + "CAGGAGGGA": 18736, + "TAATAATAAA": 18737, + "GTGTGGCTG": 18738, + "CCAAAACAGCATG": 18739, + "TTATAGTTA": 18740, + "AAAATTTATGTGAGGTT": 18741, + "CATAAATAAA": 18742, + "TCGCAATT": 18743, + "TCAGTCCTT": 18744, + "CAATCTCC": 18745, + "GTTTGATA": 18746, + "GTAGGATG": 18747, + "CATTCACTG": 18748, + "CAGGGCTGA": 18749, + "CCTTTATAA": 18750, + "GTCATAAAA": 18751, + "GATGCTC": 18752, + "CTAAAGTAA": 18753, + "CGCACACC": 18754, + "GCTCTTATT": 18755, + "GCGGGCA": 18756, + "TAGGATTTT": 18757, + "GAACTAAAA": 18758, + "GAAACATGA": 18759, + "TCTTTATGA": 18760, + "CATTCATTTT": 18761, + "CATACC": 18762, + "CTAAATTAAA": 18763, + "CTGTTTATA": 18764, + "GGAGGCTAA": 18765, + "CAACATGGTGAAA": 18766, + "TCCCCACA": 18767, + "GTTTAATTTT": 18768, + "CCACATC": 18769, + "CTAGAGAAA": 18770, + "TGAAGACC": 18771, + "GCTTTGGAA": 18772, + "TCTCGCC": 18773, + "CAAAACTT": 18774, + "CGTTGTGA": 18775, + "CTAAATAAAA": 18776, + "CAATTAAATT": 18777, + "CTCAAATTA": 18778, + "CTTCAGCTG": 18779, + "GAATCAAAA": 18780, + "TCCTTTTTG": 18781, + "CAACAGAGA": 18782, + "TATATAATG": 18783, + "CAGGCCCCA": 18784, + "CAGGCCCTG": 18785, + "CGGTGTA": 18786, + "TCAAACATG": 18787, + "TGGACTGA": 18788, + "GTCTTGA": 18789, + "CGCCTGTA": 18790, + "TCCTGCTCA": 18791, + "CTGTGGCC": 18792, + "GGCCAGGTG": 18793, + "TATTTATTATT": 18794, + "GGACATGAA": 18795, + "CTCTTACCTG": 18796, + "CCAAATTTCAAGTCAA": 18797, + "CAAGAGCC": 18798, + "GCAGGAGA": 18799, + "GGAGCAGTG": 18800, + "CTACTCTG": 18801, + "GGAACAGCA": 18802, + "TCTCGGTT": 18803, + "CCAACACAA": 18804, + "CCAAGACCA": 18805, + "GATGTAAAA": 18806, + "GATGTAGAA": 18807, + "TGGGCACA": 18808, + "CCTAĊ": 18809, + "TGATTCTT": 18810, + "GAAAAACAAAA": 18811, + "TATCTAATT": 18812, + "CTCCATGTT": 18813, + "CAGTCTTTT": 18814, + "GGACAGTGA": 18815, + "GCACCCTATT": 18816, + "CACACATACA": 18817, + "TGAGTGTGTG": 18818, + "TATAGAGAA": 18819, + "TCTGTCTCTG": 18820, + "GTAATAGAA": 18821, + "TCTCTACTA": 18822, + "CTTTTCCCCA": 18823, + "TAAAAATAGA": 18824, + "GGTAGCA": 18825, + "TGATTGAA": 18826, + "GAAGATAAA": 18827, + "TCCCAGTGA": 18828, + "CAAGGCTA": 18829, + "GATTTGAAAA": 18830, + "CGCCTTGGCCTCCCAAA": 18831, + "CAATAGC": 18832, + "TGTGTATTA": 18833, + "TATATAGCA": 18834, + "CATTCAGAAAA": 18835, + "TCTGCTGAA": 18836, + "GCTTCTGTT": 18837, + "GTAACACAA": 18838, + "CCTGCTCTT": 18839, + "GGGGAAATG": 18840, + "CCCCĊ": 18841, + "CGCGTCA": 18842, + "GTAAATTCA": 18843, + "CGTTGTA": 18844, + "TAATAGTCA": 18845, + "GAGAAAGAGA": 18846, + "TGATGATGATGATGATGATGATGA": 18847, + "TCACAAGGTCAGGAGATCGAGA": 18848, + "TATATACCCAGTAATG": 18849, + "TAAGTGATT": 18850, + "TATTCTCAA": 18851, + "GAAAGGCTT": 18852, + "GCTTGCCA": 18853, + "CCTTAAAAAA": 18854, + "CCAACACTT": 18855, + "TCCAGTGTT": 18856, + "TCCATACC": 18857, + "CCTAGTC": 18858, + "TCTTATTTTA": 18859, + "GTCAAGTG": 18860, + "GTAAAATTAA": 18861, + "CTTCCCGG": 18862, + "GAACAGAAAA": 18863, + "TCTCTCACTGTT": 18864, + "TCACTGTA": 18865, + "TATTGAGAA": 18866, + "TGAACACTG": 18867, + "TCAACATTTT": 18868, + "CCTTGCCTT": 18869, + "CCTGTTTTA": 18870, + "GTTACAGTT": 18871, + "CTAAGTCGCTT": 18872, + "TACATAGTT": 18873, + "TAATTGTATT": 18874, + "TGATTTCC": 18875, + "GGGAATTTT": 18876, + "CTTCCCTCA": 18877, + "TATATGTTTT": 18878, + "TAATGATTA": 18879, + "GTTGCCCAGGCTGGAGTGCAATG": 18880, + "TAATTTTCATCATAGGTACACGTCAACTATGACAGA": 18881, + "CCCGTTTT": 18882, + "GAACATCC": 18883, + "CACATGGAA": 18884, + "TGTTCAGCA": 18885, + "GAGACTTTT": 18886, + "CATTCTCAA": 18887, + "CAGAGCCTT": 18888, + "GCCAGCATG": 18889, + "CAAAACATTA": 18890, + "TCCTTTGTG": 18891, + "TCCTTCTGA": 18892, + "TGATTCTCA": 18893, + "TCCAATATG": 18894, + "TCTTTTCTT": 18895, + "GTCAAGAAA": 18896, + "TCCTGCTCC": 18897, + "GAAAAACAA": 18898, + "TAGAAGAAAA": 18899, + "CATCTAAAA": 18900, + "TATATGCCCA": 18901, + "GCTTTGTTTT": 18902, + "CTCGAGGA": 18903, + "CTCTAAGAA": 18904, + "TGTGGAATT": 18905, + "TATGGAGTT": 18906, + "GTTAGACA": 18907, + "TAAAAATAAAAAA": 18908, + "TAATACCA": 18909, + "GTCTTCTGA": 18910, + "CGCCCTG": 18911, + "CAATGTCA": 18912, + "TCAGGGAGG": 18913, + "GTGAAATAA": 18914, + "TAACAGTGA": 18915, + "GTTATTTTCAATT": 18916, + "CTCCTGCCTCAGCCTCCCGAGTAGCTGGGACTACAGGCGCCCGCCA": 18917, + "TTACATTA": 18918, + "GAAGATTTT": 18919, + "TGTGGAGTT": 18920, + "CAAATAATG": 18921, + "CAAAGACCA": 18922, + "CCTGCAGTG": 18923, + "TTTGGAAAA": 18924, + "CCCCTAGG": 18925, + "GGAGGCTGAA": 18926, + "TTTTTTTTTTTTAA": 18927, + "GGATGCTGA": 18928, + "GAAAGAAAGAAA": 18929, + "TGACAAATT": 18930, + "CTGGACTA": 18931, + "GGACCCAGA": 18932, + "CAAATACTA": 18933, + "CAGATACC": 18934, + "CAGATGGAA": 18935, + "TCAAGACC": 18936, + "CCTTCAGCA": 18937, + "CATGTACC": 18938, + "TATGTAGTA": 18939, + "CGGCCCA": 18940, + "GATGGTGAA": 18941, + "GTCCTATA": 18942, + "GGTGTCTT": 18943, + "GAATTCAGG": 18944, + "CGTTTCTG": 18945, + "CAGAAGCAA": 18946, + "GTCTTCCA": 18947, + "GGAGGCGGA": 18948, + "TACTTTGTT": 18949, + "GTGTGGATG": 18950, + "CGTCGAGATTTTAA": 18951, + "TAACAGAAAA": 18952, + "TCATAATTA": 18953, + "GGACCATG": 18954, + "GGACCTC": 18955, + "TATTGTTTTA": 18956, + "CACAGACTG": 18957, + "CATTGGTA": 18958, + "CATTCCCCA": 18959, + "CAGATACAA": 18960, + "CAGACATCA": 18961, + "TCAACATAA": 18962, + "GGAAGGCTG": 18963, + "GTGACTCC": 18964, + "CCTGCAGCC": 18965, + "CTTTTGACCA": 18966, + "GGGGGTCA": 18967, + "CGTTCGG": 18968, + "GCTGAGCAA": 18969, + "TCTTACTA": 18970, + "GAAGATGTT": 18971, + "TCACATGAA": 18972, + "CTTTTTTAAAA": 18973, + "CCATCTCACACCA": 18974, + "TCATAATAA": 18975, + "GTCTCCCC": 18976, + "GTCCACAAA": 18977, + "TATTGACAA": 18978, + "CACACACTA": 18979, + "CAGACGCA": 18980, + "TCAACAGTT": 18981, + "GATTTGGAA": 18982, + "CCAAGACC": 18983, + "GTTTTGAGG": 18984, + "CGTGGAGG": 18985, + "TAATAGCA": 18986, + "CAATTTCC": 18987, + "TATAAACAAA": 18988, + "CTGCCTCTT": 18989, + "CAGGTCTGTGAGA": 18990, + "TAATAAAATT": 18991, + "GTTCGATT": 18992, + "GTAGCATG": 18993, + "TCAACAGGA": 18994, + "GCTTCTTTG": 18995, + "GCTTCACTG": 18996, + "GGTTCTAA": 18997, + "GCATTCTTA": 18998, + "TCATGGAA": 18999, + "GCAATTAAAA": 19000, + "GCACAGTCA": 19001, + "GGGAGGGAGG": 19002, + "TAAAATGTTA": 19003, + "GGGATTAAA": 19004, + "TCCAGAAAATCACATTGTA": 19005, + "TAAGTTATT": 19006, + "TCCCTCTCC": 19007, + "GTACCATG": 19008, + "TAAACACCA": 19009, + "TATATCTCC": 19010, + "GAGATCCTG": 19011, + "GCTTTAATT": 19012, + "GCTGGGCCA": 19013, + "CTCCCATCC": 19014, + "CTTTTGTGA": 19015, + "CAGCAGCA": 19016, + "TCTTAGTTA": 19017, + "CGTCCATC": 19018, + "GCAGATGAA": 19019, + "CACTCCAGCCTGGG": 19020, + "CTCCTGACCTCGTGA": 19021, + "GTGCTGGGATTACAGGCATGAGCCA": 19022, + "CAAGTGAAA": 19023, + "TAAATGATG": 19024, + "GCTGGAGTT": 19025, + "CCTTCACCA": 19026, + "CCTTCCTTTT": 19027, + "GTCAGACC": 19028, + "GATTTACTT": 19029, + "CCAAGAGTT": 19030, + "GGTTGTGA": 19031, + "CATATCAAA": 19032, + "GGGAGTCC": 19033, + "CCTAGTT": 19034, + "GTAAACAAAA": 19035, + "TCCAATTAA": 19036, + "GGGTTGTTTT": 19037, + "GAATATGTG": 19038, + "TAAGAGTAA": 19039, + "GCGTCCTT": 19040, + "TCATATTAA": 19041, + "GTATATTAA": 19042, + "TACATATAAA": 19043, + "CTTATATTTT": 19044, + "CGTTCCTG": 19045, + "GGCCTAGG": 19046, + "TAATACTGA": 19047, + "TGCCCGCCA": 19048, + "GAGAAGGGG": 19049, + "GTCAACATG": 19050, + "CCATTCAA": 19051, + "TGAGGTAGG": 19052, + "CTGCAGTAA": 19053, + "CAACAACAAAA": 19054, + "TAGGGGTT": 19055, + "GCGTTCTG": 19056, + "TAATCTCTT": 19057, + "TGATTATA": 19058, + "GCCCTGGA": 19059, + "GTCGTCTT": 19060, + "CACAGACACA": 19061, + "GAAAGGCTG": 19062, + "GAGACCCTG": 19063, + "CAGGCAGTG": 19064, + "GCTTTACC": 19065, + "GCTTTACTT": 19066, + "TTTGGTCA": 19067, + "GCAAACATT": 19068, + "CCAAAGCCTT": 19069, + "CTTAAGAA": 19070, + "CTTTGGAAAA": 19071, + "CAAAAATAAAA": 19072, + "CTGTGGCAA": 19073, + "GAGGAGGGA": 19074, + "TTATGCTAA": 19075, + "TATCTTTTTT": 19076, + "CTGGGGATG": 19077, + "GAGCCTGGA": 19078, + "CTCCATGAA": 19079, + "CTTCAGTTTT": 19080, + "CAGATGGTTGTGA": 19081, + "TAGCCCC": 19082, + "GCGTATA": 19083, + "TAACTCAAA": 19084, + "TCACAGAGA": 19085, + "TCCGCAA": 19086, + "CCACAGAGA": 19087, + "GTATTTTATT": 19088, + "GCCTCAGTG": 19089, + "CTGATGATG": 19090, + "TTTGGGTG": 19091, + "TGCACGCA": 19092, + "TCTCACTCTG": 19093, + "CTGAACTCA": 19094, + "TGCCCTCAA": 19095, + "CACCCTCTT": 19096, + "GGAGGTGAA": 19097, + "CCCTGCCCC": 19098, + "CCCTTCCCC": 19099, + "CCAAAACATCA": 19100, + "GTTTATCAA": 19101, + "CTGCCTGTT": 19102, + "CTTGCCTGGAGAATCCCATG": 19103, + "CAACAGTC": 19104, + "CTGCCCTC": 19105, + "CACATATAA": 19106, + "GCTTGGAAA": 19107, + "CGGCAGGA": 19108, + "CCAACTTTT": 19109, + "TTAATCTT": 19110, + "GATACTTTT": 19111, + "TCCTTGTTTT": 19112, + "GAGAAGGA": 19113, + "CTTTGTAGA": 19114, + "GGGGATTA": 19115, + "GTTCATGTT": 19116, + "CACACATATA": 19117, + "CTTAAAATG": 19118, + "GTTGTCCTT": 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19163, + "TAAAATAATG": 19164, + "TAAAACATTA": 19165, + "GCTTCAGGA": 19166, + "CCTGTACC": 19167, + "GTTAGGAAA": 19168, + "CTCCCAGTG": 19169, + "CTTAGGAAA": 19170, + "TGGGCAAAAA": 19171, + "TAGGCAGAA": 19172, + "GTGAAGGAA": 19173, + "TAATAGATG": 19174, + "GCCTTCATT": 19175, + "TGAGACTG": 19176, + "CAGCCTCAA": 19177, + "TGGGTTTGATGAAACCGAACGAAAATTTATGTG": 19178, + "GCGCATA": 19179, + "TCCTGAATT": 19180, + "GGAGCACC": 19181, + "TAAAGGCAA": 19182, + "TCTGCATCA": 19183, + "TCTGTATCA": 19184, + "CAGAGGTGA": 19185, + "TAATATTTTTT": 19186, + "CCATTTATA": 19187, + "CTCTGCTCC": 19188, + "CAGTATTTG": 19189, + "GTGCCACTGCACTCCAGCCTGGG": 19190, + "GTAGTGCACTA": 19191, + "GATATATATA": 19192, + "GCGATTAA": 19193, + "CTTCAAGG": 19194, + "CCACTTGG": 19195, + "CACAGCTGA": 19196, + "GAAAGAAATT": 19197, + "CAAACAGTT": 19198, + "CAAACAGGA": 19199, + "GGAATCTA": 19200, + "GCTGTTTTA": 19201, + "TAAAAATCAA": 19202, + "TGAAAGAA": 19203, + "TCATGTTTTA": 19204, + "GCAGGTGTG": 19205, + "TTATATCTA": 19206, + 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"TGACAGTCA": 19382, + "TAAATACAAA": 19383, + "TGAAAAATTA": 19384, + "CAACAGGAA": 19385, + "CAACTATG": 19386, + "CTTGAGAAA": 19387, + "CTTGTTAAA": 19388, + "GGAGTTTTA": 19389, + "TAAACTCTT": 19390, + "TCAACCCA": 19391, + "TCAAGATGA": 19392, + "TGCATGA": 19393, + "GCAAATGTG": 19394, + "GTCTTCCTG": 19395, + "CAATTGTA": 19396, + "CCCCATGAA": 19397, + "GTCTGTCC": 19398, + "GCGAGCTG": 19399, + "GCTAATTTTTTGTATTTTTAGTAGAGA": 19400, + "GGTAGGTT": 19401, + "GTGTATC": 19402, + "GCCTCTCTT": 19403, + "CTCTTATTA": 19404, + "GAGACATGA": 19405, + "CTCCCAGTT": 19406, + "TCCATATCA": 19407, + "GCCACCTCC": 19408, + "TGTACAAAA": 19409, + "CGTGTTAA": 19410, + "GGCCGGGCGCGGTG": 19411, + "GCTCAGCTT": 19412, + "TAATGTTTTA": 19413, + "CATCATTAA": 19414, + "GCAGGGTGA": 19415, + "TAATTATTTG": 19416, + "TGTTTGCATTCAA": 19417, + "GCAAAAAAAAA": 19418, + "GCGTCTCC": 19419, + "TTTTCTTCTA": 19420, + "TATTCCATG": 19421, + "TGAACACTT": 19422, + "TCTGGAGGA": 19423, + "CAGAGGGTG": 19424, + "GGAAGAGTG": 19425, + 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"GAATTCTCCAGG": 19515, + "TCCAAGGAA": 19516, + "CGGAGAA": 19517, + "TAATGCATG": 19518, + "TAATGGTAAA": 19519, + "CGTATATA": 19520, + "TAAGTGGATATTA": 19521, + "GCGTTTAA": 19522, + "TTAGCTA": 19523, + "CTTCTCCA": 19524, + "CAAAGTTAA": 19525, + "TCAACACTG": 19526, + "CGGTGTTTG": 19527, + "GAAAAGATA": 19528, + "GTCCCAGTG": 19529, + "TCTCAGCTG": 19530, + "CGCGTGA": 19531, + "TAATACAAAA": 19532, + "GCCAAGGAA": 19533, + "GACCCCCA": 19534, + "CCCTGTTTG": 19535, + "GCTCGGGG": 19536, + "CCCAATTTT": 19537, + "CTTTACTTA": 19538, + "GCGCAGAA": 19539, + "GCGAGATT": 19540, + "TGAGAATA": 19541, + "TCCCTTCTG": 19542, + "GTGCTTCA": 19543, + "GCACACATT": 19544, + "GCAGCGCC": 19545, + "GGAGCAGGA": 19546, + "CAAACAGTG": 19547, + "CAGGTAGAA": 19548, + "TCTGGATGTG": 19549, + "TAAAATAATAA": 19550, + "CCTTTCATT": 19551, + "CATGCAGCA": 19552, + "GTTACCAAA": 19553, + "GTTTTGAGA": 19554, + "GCCAGAACTT": 19555, + "CTTTTCTCTT": 19556, + "TGGACTT": 19557, + "TAAGACCAA": 19558, + "TCATAGCTT": 19559, + "GTCATGCTGAAA": 19560, + "TGACATTAA": 19561, + "GTGTATCC": 19562, + "GTTTAAGG": 19563, + "GCCTCTGAA": 19564, + "TAAATCTC": 19565, + "TAAAGAATG": 19566, + "CTCTTTTTTTT": 19567, + "TGTGGCTA": 19568, + "TATTCGAA": 19569, + "CAGGGGCC": 19570, + "CAGATTATT": 19571, + "GATTTCATG": 19572, + "TTAAGCAA": 19573, + "CGTGGGTG": 19574, + "CGTTCTG": 19575, + "TGCCTCTA": 19576, + "GCAGGCCTG": 19577, + "CCTGATTTG": 19578, + "GGAATTGAACTCA": 19579, + "CTTTTCTTTTCTTTTCTTTT": 19580, + "CACAAAATG": 19581, + "CCCACACA": 19582, + "TGACTTTCA": 19583, + "GTTGATCC": 19584, + "GTTTGAGTT": 19585, + "TCTTGAGAA": 19586, + "CCAACACTG": 19587, + "TAGGTGTA": 19588, + "CGTGGTA": 19589, + "TAAAAATTCA": 19590, + "TAAAAATATTA": 19591, + "CTTTGCCA": 19592, + "CAAAAATGAA": 19593, + "GCATTTATA": 19594, + "TATCAAAAAA": 19595, + "TCAGATATA": 19596, + "GTGGCCAGG": 19597, + "GTTCAAATT": 19598, + "GAAACAGTA": 19599, + "TCTTCATGA": 19600, + "GCTGCCTA": 19601, + "CTCACAAAA": 19602, + "GCAAGGCCA": 19603, + "CGGCCTCA": 19604, + 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"CTTGCAAAA": 19650, + "CTTTCTCAA": 19651, + "GCCTCTGTT": 19652, + "TAAACAGGA": 19653, + "CTCTCGA": 19654, + "CACAGCCTT": 19655, + "TCAAGAAAAA": 19656, + "CTTACCTA": 19657, + "TCCAGTCTT": 19658, + "GACACGTT": 19659, + "CCCCTCTC": 19660, + "TCAAACTGA": 19661, + "GGGATCTT": 19662, + "GGTAGTCA": 19663, + "GAGAAGCTAA": 19664, + "TCATATCCA": 19665, + "CTTGCTCTG": 19666, + "GCCGATA": 19667, + "GAAATCCAA": 19668, + "GAGATTTTA": 19669, + "CAGGCACTG": 19670, + "CGTGTGTGTGTG": 19671, + "GTGAATGTG": 19672, + "CTTTTATTTG": 19673, + "TCTATATTA": 19674, + "TCACTACTTCCA": 19675, + "GTCTCGCTCTGTT": 19676, + "GAGGTGGGTGGA": 19677, + "GAGAGTCCCTTGGACTGCAAGGAGATCCAA": 19678, + "TAATCGG": 19679, + "GCAGAAATG": 19680, + "GGATCAGG": 19681, + "GGAGTTGAA": 19682, + "GCCCATC": 19683, + "GCAATGCC": 19684, + "CCTGTACA": 19685, + "TAGGGAAAA": 19686, + "CTGAATATT": 19687, + "TCCTTTATT": 19688, + "GAATTCCAA": 19689, + "CAGTGGCTCA": 19690, + "TAATGCCA": 19691, + "TGTTTGTTTGTT": 19692, + "TACTGATG": 19693, + "TAACACATT": 19694, + "TCAGTTTA": 19695, + "GAAGTGGA": 19696, + "CAACCATG": 19697, + "CAAGAATA": 19698, + "CTCTAAGG": 19699, + "GTCCAGGTT": 19700, + "GTCTGTTGTT": 19701, + "TATTGGGTG": 19702, + "CACATATTA": 19703, + "CATTGAGAA": 19704, + "GCTGTACA": 19705, + "GCTGCGTT": 19706, + "CCTGGACTG": 19707, + "GATGGAGTT": 19708, + "GGGGTTCA": 19709, + "CATACCCA": 19710, + "CAGCAGCTG": 19711, + "GTATTGTA": 19712, + "CAATTGTAA": 19713, + "TACCCTTA": 19714, + "CAGCTCTC": 19715, + "GCTCCTGCC": 19716, + "CCTTATATA": 19717, + "TCTTTTTTAAA": 19718, + "GAAATATTACAACTAAA": 19719, + "CATCTCCTT": 19720, + "TACTCTCA": 19721, + "TCATTAAAAA": 19722, + "CAATTTTTTTT": 19723, + "CTTCAAATT": 19724, + "GAAAGACTG": 19725, + "TGTTCAGTA": 19726, + "CATTCCTCA": 19727, + "GCTTTAGCA": 19728, + "CCTTAAATT": 19729, + "GGGGTATG": 19730, + "TAGGTCTG": 19731, + "TAGGGCAA": 19732, + "TCAAAGGA": 19733, + "TGCACTAA": 19734, + "GAATTTCCA": 19735, + "TACCCTAA": 19736, + "GAATGGATT": 19737, + "GTGTGGAGG": 19738, + "CCTTTTTTA": 19739, + "CTTCCCTTTT": 19740, + "CTGTAGTTA": 19741, + "CAATAGTAA": 19742, + "TGACATGTT": 19743, + "TGAATGTTA": 19744, + "TAATTTATTTT": 19745, + "TCCTCTGTCATCC": 19746, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 19747, + "CCTAAAATT": 19748, + "TAATCCATT": 19749, + "GAACTGAAA": 19750, + "TCCGGGA": 19751, + "GCATTTTTG": 19752, + "TCTTCATAA": 19753, + "TATGGCTGA": 19754, + "GAAAAGCTT": 19755, + "GTTTTAAATT": 19756, + "TCTACCTCC": 19757, + "TGTACAGG": 19758, + "CTGAACTA": 19759, + "TTTTTTTAAAAA": 19760, + "CTTAAAAATG": 19761, + "TCCTGCCA": 19762, + "CTCTGCAAA": 19763, + "CTGTGCCA": 19764, + "CTGTGGAGA": 19765, + "CCTCCGCCTCCCGG": 19766, + "GTGTGCATG": 19767, + "GCACATGTG": 19768, + "CAGATGAAAA": 19769, + "TGAGCAATTTAGCATGGGTTTGAAGAAATTAGGA": 19770, + "TCAGCTGTT": 19771, + "CAATCTTTT": 19772, + "TAAAACTAAA": 19773, + "TACATAATA": 19774, + "TTAATATAA": 19775, + "TTTGGTGA": 19776, + "CCCACGGA": 19777, + "TCTCACTGA": 19778, + "TCTCACTTA": 19779, + "GCAGGCTGA": 19780, + "CTTCCCATT": 19781, + "TGACATCTG": 19782, + "TAATGAAAAA": 19783, + "CTACGCC": 19784, + "GAAACACCA": 19785, + "TATAGGCA": 19786, + "CCTTGCAAA": 19787, + "CCAAGGTCA": 19788, + "GAAAACGA": 19789, + "CGAGCTA": 19790, + "CAATTCTTA": 19791, + "GTGGGGAAA": 19792, + "TATAAATTTT": 19793, + "TCAGATGAA": 19794, + "CAATAGCAA": 19795, + "TAATAAATTA": 19796, + "CAAAATTAAAA": 19797, + "GACTCCATCTC": 19798, + "CCGCCAGA": 19799, + "CCGAGTC": 19800, + "TAGCAAAAA": 19801, + "GAACATC": 19802, + "CTTGCTC": 19803, + "TAAAGTAAAA": 19804, + "CAAACTCAA": 19805, + "GCAACTTTT": 19806, + "TTTTAGGA": 19807, + "TATGCATTA": 19808, + "GATGTTATT": 19809, + "CAAAATAAAAA": 19810, + "CGTGTGGG": 19811, + "GAGGAGGTG": 19812, + "TCAGATATT": 19813, + "TCAGACATG": 19814, + "GCCAGGCATGGTG": 19815, + "TAAGACATT": 19816, + "GCCCACCTG": 19817, + "GTGTGAGATGGTA": 19818, + "TCATĊ": 19819, + "TCAGAATA": 19820, + "GCATCTGTT": 19821, + "GTATATCC": 19822, + "TAAACGC": 19823, + "CTCTGTTTG": 19824, + "GAAATAAATT": 19825, + "GCTTCTCTG": 19826, + "CTCAGAGTT": 19827, + "GTTACAGTA": 19828, + "TCTATCATT": 19829, + "GCCATTTTAA": 19830, + "CGACCCTG": 19831, + "CGTTTGAA": 19832, + "CACCCTTA": 19833, + "CTCTTCTTTT": 19834, + "CGCAGAGG": 19835, + "GTGTTTGA": 19836, + "TAATGTCTT": 19837, + "CCTCACTG": 19838, + "GGATTTCAA": 19839, + "AAATGTGA": 19840, + "CTTCAGTAA": 19841, + "GCTGGGCATGGTG": 19842, + "CTCCAAAGATTTT": 19843, + "CAAGTATG": 19844, + "CAATGAATT": 19845, + "TAAAGCTGA": 19846, + "CTCTCAGGA": 19847, + "TGTTCATAA": 19848, + "TGTTTCCATT": 19849, + "GCTTGACA": 19850, + "GTTACATAA": 19851, + "CGACATCA": 19852, + "TCATTGGTT": 19853, + "GTGTTGGGG": 19854, + "GTCTTCTCA": 19855, + "CAATTCACA": 19856, + "GCCTTCTG": 19857, + "GAAGAGTAA": 19858, + "CTGCATATG": 19859, + "CAAGGGATT": 19860, + "TCATCAGACCA": 19861, + "GCCCCTGTG": 19862, + "TAAATATAAAA": 19863, + "GTTCCTGTT": 19864, + "CAGTTTTTTAACGA": 19865, + "CTTGGGAGGCTGAGGCA": 19866, + "TCTTTCCCAGCATCAGGGTCTTTT": 19867, + "TACTAATG": 19868, + "TCGTCCAA": 19869, + "GAGTĊ": 19870, + 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"GGAACCCA": 19916, + "CCTTCACTG": 19917, + "TACAGTCAA": 19918, + "GTTTTCTCC": 19919, + "CGACTTTT": 19920, + "CAAAAGCAAA": 19921, + "TGATTGTG": 19922, + "TGATTCTTA": 19923, + "CCTCCTCCA": 19924, + "TCCCAGTCA": 19925, + "CAACAGCTG": 19926, + "GTATAGTAA": 19927, + "GTAGGGATG": 19928, + "GGTATTTAGTA": 19929, + "TAAAATAAAATAAAATAAAA": 19930, + "GACAAAAAAA": 19931, + "TCCTCACA": 19932, + "GTTCCAGAA": 19933, + "GTAGTATG": 19934, + "GTCGACC": 19935, + "GAAAGGCC": 19936, + "TGTTGTATT": 19937, + "GCTTTCTC": 19938, + "CTCATTATT": 19939, + "CCTTCTGTT": 19940, + "CCTTTACTT": 19941, + "GCAACGTT": 19942, + "GATTGGAAA": 19943, + "TTAATAGA": 19944, + "TTCAGCCA": 19945, + "TCCTTGAAA": 19946, + "CTGTGCAGG": 19947, + "GCAGACGG": 19948, + "GGTGGTGGCGCACGCCTTTAATCCCAGCA": 19949, + "CAAAAAAAAAAA": 19950, + "GTTTCCAGG": 19951, + "CCAGCCTCC": 19952, + "CTCTTATA": 19953, + "TCAAGGCC": 19954, + "CATGTATAA": 19955, + "GATGCTGAA": 19956, + "GTCTTCCAA": 19957, + "CCATTCCAA": 19958, + "GAAAAATATA": 19959, + "CAGCCCTG": 19960, + "TCTCTGTTA": 19961, + "GCGTACA": 19962, + "GCGTTTGA": 19963, + "CAATTATTTT": 19964, + "GTAGTCTG": 19965, + "TATAGATAA": 19966, + "TGTTGAATT": 19967, + "CAAATCCAA": 19968, + "GGAAGTGAA": 19969, + "GGAACAGGA": 19970, + "GCTTGGTA": 19971, + "GTGAGCTGA": 19972, + "TTTTATCTG": 19973, + "TCCAGTAAA": 19974, + "CAAAAĊ": 19975, + "GACAGCTA": 19976, + "CTGAATAAA": 19977, + "TCTGATTCA": 19978, + "TGATTTAAAA": 19979, + "GCATTCATT": 19980, + "GGCTGGAGTG": 19981, + "CATCATTTG": 19982, + "CCCTGGAGG": 19983, + "GTTTACTGA": 19984, + "CTTCCCAGA": 19985, + "AAAATACAAAAATTA": 19986, + "GCCCAATG": 19987, + "GTCTCACA": 19988, + "TCTTCAGTA": 19989, + "CTCCAAATG": 19990, + "CATACGTA": 19991, + "TCAAACGA": 19992, + "CGTGGCTG": 19993, + "GCTCAGCA": 19994, + "GCATTCCTT": 19995, + "GCTTATTTA": 19996, + "CGAAATTA": 19997, + "GGACAGCTT": 19998, + "GGTTAGGTTAGGTTAGGTTA": 19999, + "TCCACACACA": 20000, + "TCGTCCTG": 20001, + "TTTTCCCA": 20002, + "CTTGGAGAA": 20003, + "AAAATTCA": 20004, + "GTCGGGTG": 20005, + "TCTGCCAAA": 20006, + "CAGATATTTT": 20007, + "TCAAGATCA": 20008, + "TTTTAGGTT": 20009, + "GATTGAAAA": 20010, + "CCAAGATAA": 20011, + "GTTTTTGA": 20012, + "GTTTTCATG": 20013, + "GCCAGTGTT": 20014, + "GGGGCTGGG": 20015, + "GACACGGA": 20016, + "CTGAATTCA": 20017, + "CAGAAATTA": 20018, + "CTCCATGTG": 20019, + "TAAGAGCAA": 20020, + "GCGCGCGC": 20021, + "GAGGTGGAAA": 20022, + "TCAAATTGCCAA": 20023, + "CCTAGGTT": 20024, + "TACGAAAA": 20025, + "GGTCAATG": 20026, + "TCCGGCA": 20027, + "GCCTATCC": 20028, + "CTATTATTTT": 20029, + "TCTGTATAA": 20030, + "TTTTACCA": 20031, + "CCTGGAGTT": 20032, + "TCCACTGTT": 20033, + "CAAAAGCTT": 20034, + "TGGACGG": 20035, + "TGCATACA": 20036, + "TTTTTTGAGA": 20037, + "GTCTTGAAA": 20038, + "CAATTCCCA": 20039, + "CTATTGTAAA": 20040, + "CCAGACACA": 20041, + "TATTTATTTATTTATTTATTTATTTATTTATT": 20042, + "GCGAAATG": 20043, + "GAAGGAGGA": 20044, + "TCCTCATCA": 20045, + "TGTTCTAAA": 20046, + "CAAACCCC": 20047, + "CTGAGCTGA": 20048, + "CTCATAAAA": 20049, + "CATGCATTA": 20050, + "TCTCCACTA": 20051, + "CCAAGGTA": 20052, + "CTAAGTGAA": 20053, + "TAAAAATACA": 20054, + "TGATTGCTT": 20055, + "GTCTTTATT": 20056, + "CTGTGGATG": 20057, + "CTGTGGCTCA": 20058, + "CACTGCTG": 20059, + "CAATGCCAAA": 20060, + "TAAGGCAGG": 20061, + "TCAGAAGAA": 20062, + "CTTCAGAGA": 20063, + "GTGCGTC": 20064, + "GTTCTGCTG": 20065, + "GCACAAGG": 20066, + "GTACCTC": 20067, + "TGTGGAGAA": 20068, + "TCTTTACTG": 20069, + "GAGAGGTGA": 20070, + "TCAATGGAA": 20071, + "GCAAGCTGA": 20072, + "GTTATTATTA": 20073, + "TTTAGTGA": 20074, + "TCTGATGCA": 20075, + "GGAAAGAGG": 20076, + "CTTAATGAA": 20077, + "TACCTCTA": 20078, + "TCCTGGAAA": 20079, + "TAAAAAATAAA": 20080, + "GGAGACATG": 20081, + "GTTGTTGTTTT": 20082, + "CTGGAGTCA": 20083, + "TCACTTCTT": 20084, + "CAATGAGG": 20085, + "GTGGCACA": 20086, + "GGAGTTGG": 20087, + "CACACAGTT": 20088, + "CAGGCATA": 20089, + "GAGGCTCC": 20090, + "CCTGTCTCA": 20091, + "CTAAGGGA": 20092, + "CGTGTGTT": 20093, + "CGTGCGC": 20094, + "TAAAAACTG": 20095, + "TGATTCCTT": 20096, + "CTCTTGCTT": 20097, + "CTTTGTATT": 20098, + "CTGTGTGA": 20099, + "TGATGGGAA": 20100, + "GTTTAGTTA": 20101, + "CTGTACATT": 20102, + "CCTTTGTTTT": 20103, + "CACCTGAAA": 20104, + "GTAGTATAGTT": 20105, + "CAGACAGACAGA": 20106, + "TAAACTTCTGA": 20107, + "TCGCTCAA": 20108, + "TAACCAGG": 20109, + "TAATCAGCA": 20110, + "GAACTGGG": 20111, + "CTAGTTTG": 20112, + "GTCGGGCA": 20113, + "CTGATAAAA": 20114, + "TTAACCA": 20115, + "TTTGGTTA": 20116, + "TCATTGCTG": 20117, + "GTATTCTG": 20118, + "GGGTGAAAA": 20119, + "CTTTATGTG": 20120, + "CAATATCAA": 20121, + "GTCAAATTA": 20122, + "GGAGTGGCTT": 20123, + "GTGGTTTTA": 20124, + "GCAGCCCA": 20125, + "TCTTGATGA": 20126, + "GAGAGGAGG": 20127, + "GAGAGGGAGA": 20128, + "CCTTTGGAA": 20129, + "CCCCGTCC": 20130, + "TGATTGTTA": 20131, + "GTCAATGAA": 20132, + "CCTGAGGTT": 20133, + "TCGGCGC": 20134, + "GTGAAATTTT": 20135, + "CCCTCTCTG": 20136, + "TATTTACAAA": 20137, + "CTATAGTAA": 20138, + "TCTATATTTT": 20139, + "CCATCAGCAA": 20140, + "GACTTATG": 20141, + "TAATCATCA": 20142, + "TAATCCAGG": 20143, + "TCCCTTTTA": 20144, + "CTTCAAGA": 20145, + "CAGATATTA": 20146, + "CGGCAAAA": 20147, + "CCAAACCAA": 20148, + "CTCAATAAA": 20149, + "CACTGCAA": 20150, + "GGATGGCTT": 20151, + "TGATAGAAA": 20152, + "TTACACAAA": 20153, + "GTATATGTG": 20154, + "TCATGTTAA": 20155, + "GCCGGCCA": 20156, + "CACATTATT": 20157, + "TATAGGGG": 20158, + "GAGAGACAA": 20159, + "CAGATACTG": 20160, + "GTCATCTGA": 20161, + "GTCAGAGTT": 20162, + "GTAACTGAA": 20163, + "CTTTTGGGG": 20164, + "CTTTTAAAAAA": 20165, + "GTAAAGCAA": 20166, + "CAATTTCAAA": 20167, + "CTCTGGAAA": 20168, + "CCAGGCGG": 20169, + "TCTCTGTATA": 20170, + "GCCAGGCTG": 20171, + "CGTAAAAAA": 20172, + "TAAGGATA": 20173, + "TCCTGTGTT": 20174, + "GTGGCAGCA": 20175, + "GTATAGGA": 20176, + "CTCTAATAA": 20177, + "CTCTGAATG": 20178, + "CATTTAGCA": 20179, + "CATGCGCA": 20180, + "GTCCTGTGA": 20181, + "TAGGGTTTT": 20182, + "TGAAATATT": 20183, + "CACCCGCCA": 20184, + "CTTAACTA": 20185, + "TATAACTA": 20186, + "TACAATGTT": 20187, + "TAACATATG": 20188, + "CTCATTAAAA": 20189, + "GTTCTTGTGA": 20190, + "GTCATGTCTGA": 20191, + "GTAAATTTGTT": 20192, + "TCCGCCTGCCTCTG": 20193, + "CATCTCTA": 20194, + "TAGAAAAAAA": 20195, + "TAAATTATA": 20196, + "GTCTCACC": 20197, + "TATATCTAA": 20198, + "TCTTGCTCTG": 20199, + "CATTTCTCC": 20200, + "TAAAAGAAAAA": 20201, + "GAGGTGCA": 20202, + "CTTTTCACTT": 20203, + "CGATACA": 20204, + "TAGGAAATG": 20205, + "CGTGCAGG": 20206, + "TCCTGGCC": 20207, + "GAGTGCTG": 20208, + "CATGACTTA": 20209, + "GCGGCTC": 20210, + "GAAGAGCTT": 20211, + "CTTCCTCTA": 20212, + "CCCTCTGGG": 20213, + "GTTCACTGA": 20214, + "GTGCACATGTA": 20215, + "TCGTAGAA": 20216, + "TAACTTGA": 20217, + "TAATATATTA": 20218, + "GCAGAGATT": 20219, + "TATTCCTTA": 20220, + "CATTTAATG": 20221, + "GTCATACACA": 20222, + "TTTTAGGAA": 20223, + "GATTCCCA": 20224, + "TTAATATTTT": 20225, + "GAAAATCTT": 20226, + "TAATTGGAA": 20227, + "CTTTGTCTCTG": 20228, + "CAATGCAAA": 20229, + "GAAGACCCA": 20230, + "TAAATATGAA": 20231, + "TTAGGCA": 20232, + "CTTTAATTTT": 20233, + "CTTTGAGAA": 20234, + "GTCTCGC": 20235, + "CACAGGCAA": 20236, + "GAGATGCC": 20237, + "GAGAGGCCA": 20238, + "TAAAACATTTT": 20239, + "TTTTAGCTT": 20240, + "GATGGGGAA": 20241, + "CATACCTA": 20242, + "TCATTGTGA": 20243, + "TCCTTGGTT": 20244, + "CTAAAGGAA": 20245, + "GCTCACTG": 20246, + "CTTTATTTTTT": 20247, + "TAATCCCAGC": 20248, + "GGACCACAGG": 20249, + "GAAAATGTGGCACATATA": 20250, + "TAGCCTCC": 20251, + "GCGGGGTT": 20252, + "TTTTCTGTT": 20253, + "TGATATTTG": 20254, + "GTACTTGG": 20255, + "CTCTTATTTT": 20256, + "TGTTCGTT": 20257, + "GAGACACC": 20258, + "TCTGGCCTT": 20259, + "TCAACACTT": 20260, + "CGGCCGG": 20261, + "GAAAACAAAAA": 20262, + "GGGGGTTA": 20263, + "TCTCAGTCA": 20264, + "CTGAAGAGG": 20265, + "TCCTTGTCTT": 20266, + "GTTTGCCA": 20267, + "CTATAGTCA": 20268, + "CAGGTTTGTTACATATGTATA": 20269, + "GGGAAAATT": 20270, + "TCGAGGTT": 20271, + "TCCCTTTCA": 20272, + "GTGCTTTTA": 20273, + "GCCTCCAGA": 20274, + "GTCTCAGCA": 20275, + "GTCTCCTA": 20276, + "GAAATAGTA": 20277, + "TATATTTTTG": 20278, + "TCTTGGTCA": 20279, + "CAAAGCAAAA": 20280, + "GAGACGC": 20281, + "TCAATAGTT": 20282, + "CTCACTAAA": 20283, + "CTCAGGCTT": 20284, + "GTAAGGCAA": 20285, + "CTAAGCCA": 20286, + "TAGGTCAA": 20287, + "TCAAACCA": 20288, + "TGGACGA": 20289, + "TAATATTTTAA": 20290, + "TACAATATT": 20291, + "TGAGATAGA": 20292, + "GAAGGTTTG": 20293, + "GTGGGGTGGG": 20294, + "TGTGTGAAAA": 20295, + "GTTCAGTCA": 20296, + "CCGTGCC": 20297, + "TAACAAGA": 20298, + "TTAGGGG": 20299, + "GAACCTTA": 20300, + "GTGCCTTTT": 20301, + "GCAGCTGAA": 20302, + "TGTTCCCC": 20303, + "TGTTCACTT": 20304, + "TTTTAGTT": 20305, + "GATTGCATT": 20306, + "CTCCCCCCA": 20307, + "CTTTTGTCA": 20308, + "GCTACCTA": 20309, + "TATAACACA": 20310, + "GAGCACAGG": 20311, + "CTGGGCACA": 20312, + "TCGGTTTG": 20313, + "CTTCATATT": 20314, + "TCGCGGCC": 20315, + "GCCGGGTGTG": 20316, + "ATTAA": 20317, + "TCGCACTG": 20318, + "GGACCGG": 20319, + "GTAGCTCC": 20320, + "GAAATCTCA": 20321, + "CAAACTTAA": 20322, + "GGAAGGGGAA": 20323, + "TAAAACATAA": 20324, + "CTCAGACTG": 20325, + "CCTTCCTGA": 20326, + "CGGTATG": 20327, + "GGGAĊ": 20328, + "GCAAAGATG": 20329, + "GGATTGACTG": 20330, + "CAATGGTTGAA": 20331, + "GAACAGTAA": 20332, + "GCCATTCCCTT": 20333, + "TCGTCAAACCCATG": 20334, + "TTTTCTCTT": 20335, + "CTGCTTTTA": 20336, + "GGAGCAAAA": 20337, + "GCCCCAAAA": 20338, + "CTCTAACC": 20339, + "CTCGTTACCTG": 20340, + "TCTTCTGTG": 20341, + "GAGAGCTGA": 20342, + "CATTCATGA": 20343, + "CAGACCCC": 20344, + "CCTTGATA": 20345, + "GATTCTTAA": 20346, + "GTCCCATA": 20347, + "CGAGTTGA": 20348, + "GGTTCATA": 20349, + "TGTAĊ": 20350, + "TTATTGCAA": 20351, + "CCAAAGGAA": 20352, + "CGCCTGTG": 20353, + "GAGGAGGAGGA": 20354, + "GCTCGTG": 20355, + "TACTATTTA": 20356, + "CTGGGGATT": 20357, + "CTCCACCTG": 20358, + "GAAGAGTGA": 20359, + "CTTCCCCTG": 20360, + "CTTCATATG": 20361, + "GCGTAGA": 20362, + "CTTCTGCA": 20363, + "GTACCTCC": 20364, + "GTCGACTT": 20365, + "GAGACAGC": 20366, + "CTGACATCA": 20367, + "GAAAATAGAA": 20368, + "GAAAAAAATA": 20369, + "CAAAAGGAAA": 20370, + "TGTACC": 20371, + "TTTTTTGTT": 20372, + "CGCAGCTG": 20373, + "CCATGGAA": 20374, + "TAAAAAATTAA": 20375, + "CCCTCTGCC": 20376, + "TGATACATT": 20377, + "CTGGATGAA": 20378, + "CTTCTGCCTCAGCCTCC": 20379, + "GGATGACAGA": 20380, + "CACCAACAGTG": 20381, + "TCCATCACCAACTCCCGGA": 20382, + "TGAGCTTTT": 20383, + "TTAGCCA": 20384, + "TCCTCATA": 20385, + "CTCTCAGTG": 20386, + "GATTGATGA": 20387, + "CTAAGGGG": 20388, + "GCCAGCCCGG": 20389, + "CATAGTAAA": 20390, + "CGCAGGTG": 20391, + "CGAATAA": 20392, + "GCGGTTAA": 20393, + "TCACACTGA": 20394, + "GGAAATGGCAA": 20395, + "TAATAAATAAA": 20396, + "GTATGAGA": 20397, + "CTACGTC": 20398, + "GAAACAAAATT": 20399, + "TCTTCTCTG": 20400, + "TGAACGTA": 20401, + "TCTGCATTTT": 20402, + "TCAAGCATT": 20403, + "GAGGTGAAA": 20404, + "CCTGCAGC": 20405, + "TAGAGAAAAA": 20406, + "GATATAATT": 20407, + "GTAAATTAAA": 20408, + "TCTGAGATT": 20409, + "TCATTTCAAA": 20410, + "CCAAACACA": 20411, + "CAGAAGGAA": 20412, + "TGATGCGG": 20413, + "TACAAGAA": 20414, + "CAGCCCGG": 20415, + "CCAGGTGGG": 20416, + "CTACAGTCA": 20417, + "GGGGTGGAAA": 20418, + "CCCATGAAA": 20419, + "CCATAAGGGTG": 20420, + "CAGAGTACATCA": 20421, + "TCAGTTCTT": 20422, + "CAAGCAGA": 20423, + "CAAAGCATT": 20424, + "GTAACATTA": 20425, + "CTAATAATA": 20426, + "GAAAAGGGA": 20427, + "CTTTTTGCA": 20428, + "TGAAATATG": 20429, + "GAGAATGAA": 20430, + "CTCTTCTTA": 20431, + "CGAAGTCA": 20432, + "TGATGGATT": 20433, + "GGCTGTGGG": 20434, + "GCGGCTCC": 20435, + "GATCAGATCA": 20436, + "CCTTTGAAAA": 20437, + "GTCATGTTTT": 20438, + "GCGTCATT": 20439, + "GAAAACAGCA": 20440, + "TTTAGAGG": 20441, + "GTTTGCTCA": 20442, + "CCATGGCC": 20443, + "GCAGGTTTG": 20444, + "GGAGACTCA": 20445, + "TGTGTGTATA": 20446, + "TCGATAAA": 20447, + "TTATACTTA": 20448, + "CCACATCTT": 20449, + "GGATGAGATGGTTGGATG": 20450, + "ATTTG": 20451, + "CAGCAAATT": 20452, + "GGTCTACA": 20453, + "TTACGTT": 20454, + "TCCCTGAAA": 20455, + "CATTCTATT": 20456, + "CAGATATCA": 20457, + "GCTGTCTCA": 20458, + "GCTGCTCTG": 20459, + "CGACACCA": 20460, + "TGGACCA": 20461, + "TCTGACTCA": 20462, + "GGTACTT": 20463, + "CTTCACTCA": 20464, + "GAACACGG": 20465, + "TATTTGAACAGTGA": 20466, + "TCCCAAGG": 20467, + "GCACAGGAA": 20468, + "CATTGACTT": 20469, + "TCAACTCAA": 20470, + "GCAAGAGCA": 20471, + "GATGTACA": 20472, + "GCCAGCCAA": 20473, + "TTATTGGTT": 20474, + "TCCAACAAA": 20475, + "GCTGAGGGAA": 20476, + "TAATGCTGA": 20477, + "TCTCTGAAAA": 20478, + "CGGGGTTTCACCATGTTA": 20479, + "GATCTTTTA": 20480, + "GTGTĊ": 20481, + "GGATATCC": 20482, + "TGTTGATGA": 20483, + "TCTGGAGCA": 20484, + "GGAAGCGG": 20485, + "GGAAGGTGA": 20486, + "GCTGGAGC": 20487, + "CATGAAATG": 20488, + "TCTCTAGTA": 20489, + "CTTAGAATG": 20490, + "TCCATTTTTT": 20491, + "CTTTTTTTTAA": 20492, + "CAAAACAGAAA": 20493, + "CATAGCTA": 20494, + "CGGAGAGA": 20495, + "TTATGTATA": 20496, + "GGATGCAGG": 20497, + "GCATGCATG": 20498, + "GACTAGAGA": 20499, + "CTGCAGTGAA": 20500, + "CGTAATTA": 20501, + "TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC": 20502, + "GCTCAAATG": 20503, + "TAAGGAATT": 20504, + "TTTTCCCC": 20505, + "GTGAAAAAAA": 20506, + "CCAGCCCC": 20507, + "GTACTCTG": 20508, + "GGAAGATCA": 20509, + "GCTTGGCTG": 20510, + "GCAATATTA": 20511, + "CCAATCCA": 20512, + "CTAACACTT": 20513, + "CCCACCAAA": 20514, + "TAGGCGA": 20515, + "TAATTGGTT": 20516, + "CCTAGGGG": 20517, + "TGCCCTTA": 20518, + "TCTTTTGCTGTGCAGAA": 20519, + "TACAATTTA": 20520, + "GAGTTTAGTTGTAA": 20521, + "GGGCTATG": 20522, + "CCACCACCATG": 20523, + "GCAGTGAGCCAAGATT": 20524, + "TACGGTG": 20525, + "GGGTCTCC": 20526, + "TAACATTTG": 20527, + "GCATCATCA": 20528, + "CCAGCCCTG": 20529, + "TCTTGTATT": 20530, + "CATTCAGTG": 20531, + "TGAACAGCA": 20532, + "GGAAGACTT": 20533, + "GATTCTGAA": 20534, + "CTTTTGATG": 20535, + "CTTTTGACTG": 20536, + "TAGGCAAAA": 20537, + "TCATTCCAA": 20538, + "AAATTTAAA": 20539, + "GCAGGCATG": 20540, + "GCTTATTTG": 20541, + "GAATAGCTT": 20542, + "CAAGTAGCTGGGATTACAGG": 20543, + "GGGCTAATATCCAGAA": 20544, + "TAACTGTA": 20545, + "TTTTGCTG": 20546, + "GTGTGTGAA": 20547, + "GTAGCGG": 20548, + "TATATAATTA": 20549, + "TGTTCATTTT": 20550, + "CCTTCATCA": 20551, + "TATGTATATT": 20552, + "CCTGCTGAA": 20553, + "GGCAGCGG": 20554, + "GTCTTGGTT": 20555, + "CGAACGAA": 20556, + "TCTAAGAAA": 20557, + "TAATGCTAA": 20558, + "GGCTGGAGG": 20559, + "GTCTGCTG": 20560, + "TATCTAAAAA": 20561, + "TCCCACTGA": 20562, + "TAAGATATT": 20563, + "GTCCAGTGA": 20564, + "CCAATTAAAA": 20565, + "TCACTTCC": 20566, + "CCATCTATG": 20567, + "CTACAGTC": 20568, + "TAAACCTCA": 20569, + "CTCCACATT": 20570, + "TGAAGGCC": 20571, + "TGAAGTGAA": 20572, + "CGAGTTAA": 20573, + "CCTAGTTTT": 20574, + "TCTCACACA": 20575, + "GCAAAGCTG": 20576, + "TCTGAGTCA": 20577, + "TCTGAGAGG": 20578, + "CCAAATATA": 20579, + "TGCCCTAA": 20580, + "CTCTTCCAA": 20581, + "GTTTGTATT": 20582, + "TGAGAGCAA": 20583, + "TAAATTTAAAA": 20584, + "GAACATGTT": 20585, + "CAACAGCTT": 20586, + "GAGGTGGAGG": 20587, + "GGCTCTGGG": 20588, + "TCTTTATAGCAGCA": 20589, + "GTCCTTCCAATGAATATT": 20590, + "GGGTCCAA": 20591, + "GACGTAAA": 20592, + "GAACTATG": 20593, + "GTTTAAGAA": 20594, + "TAAATAACAA": 20595, + "CTCCATTTG": 20596, + "CACAGATTTT": 20597, + "GAAACCCC": 20598, + "TACACTGTT": 20599, + "TCCAGCTGA": 20600, + "GCTACTGAA": 20601, + "GTAAATGCA": 20602, + "CTTTGCCAA": 20603, + "TCATGCTG": 20604, + "GTGTGTATA": 20605, + "GAAGAAGTTA": 20606, + "GGAGACCAA": 20607, + "CTTTATCAA": 20608, + "TGATACCTT": 20609, + "CCAGAAGAGG": 20610, + "TCCCTAAAA": 20611, + "CTTCTCATT": 20612, + "GTTCTATA": 20613, + "CACACCCCA": 20614, + "GAAAGAATG": 20615, + "CTCACTGTT": 20616, + "TATGTATATATA": 20617, + "CTCCCATCA": 20618, + "TACAGCATT": 20619, + "TCTACTGAA": 20620, + "GTCCCAGCC": 20621, + "CAAAATGGG": 20622, + "GACATAGAA": 20623, + "TCCTTTCAA": 20624, + "CTAAACTAA": 20625, + "TAATAĊ": 20626, + "CGCATTAA": 20627, + "GAAGAGTCA": 20628, + "GGACTATAGG": 20629, + "TCTGGAAGTGGA": 20630, + "GACCCTGTCTC": 20631, + "GTGCTGGGATTAAAGGCGTGCGCCACCACCGCCCGG": 20632, + "CATCTCATT": 20633, + "CCGTTTTA": 20634, + "CCCCAATG": 20635, + "GATTTTATG": 20636, + "GACTTTTTG": 20637, + "GTACATCC": 20638, + "GCCTĊ": 20639, + "TATTTATCC": 20640, + "CTCAGCCAA": 20641, + "CTCACCCTT": 20642, + "CATGGATGAA": 20643, + "CGGCATCA": 20644, + "GATGCAGTG": 20645, + "TTCATCC": 20646, + "TCTTAGAAA": 20647, + "CTCAAGAGA": 20648, + "CTCTGGTG": 20649, + "GGGAAGAA": 20650, + "GGATTGCTG": 20651, + "TAGTATTTG": 20652, + "GGGTTAAAA": 20653, + "GTGTACTTA": 20654, + "CCATAGCAA": 20655, + "CAAAATCAGCAGG": 20656, + "TCGGATTA": 20657, + "TCTAAAAATT": 20658, + "TAACCTC": 20659, + "TCAGĊ": 20660, + "GTAACTC": 20661, + "CGACCTCA": 20662, + "GGCAGGAGG": 20663, + "GGGACCAGGA": 20664, + "TCTCATATT": 20665, + "TAAAAAGTTA": 20666, + "GAATTTGA": 20667, + "GAAAAATTTT": 20668, + "GGCTGCTG": 20669, + "CAGGAGGA": 20670, + "GAATGGGAA": 20671, + "GAATAGATT": 20672, + "TTAAAAATTA": 20673, + "CCTGTTAAA": 20674, + "CTTGGGAGGCAGAGGCAGG": 20675, + "CAATTTTTG": 20676, + "CCAGCCCCA": 20677, + "CAAATAAATG": 20678, + "CCAAGAGCA": 20679, + "TTATTGTGA": 20680, + "CTTTGCAGA": 20681, + "TCCTGCTA": 20682, + "GTTTGGGA": 20683, + "TGATGCCAA": 20684, + "TCATGGAAA": 20685, + "GACCAGAGA": 20686, + "GATTTTTTAA": 20687, + "GGGTTGGGG": 20688, + "CAACAACAAAAA": 20689, + "TTATAGATT": 20690, + "GTGGTGCACA": 20691, + "CAACACGG": 20692, + "TCATAGCAA": 20693, + "GATTTGTTTT": 20694, + "CTTGGGAGGCTGAGGCAGGAGAATG": 20695, + "CCTCTCTCTC": 20696, + "TCCCAAGA": 20697, + "GCACTGCTG": 20698, + "GCCCCTCTG": 20699, + "TAAAGTCTT": 20700, + "TCAACCCC": 20701, + "GATTCCCTG": 20702, + "GGTGGACA": 20703, + "TTATTCCA": 20704, + "GGGACTCC": 20705, + "GATAĊ": 20706, + "GTAAATGTG": 20707, + "GTAAAGGAAA": 20708, + "CTCTTTCTA": 20709, + "CTCAAGAGG": 20710, + "CCTGACATT": 20711, + "TAAAAAAAAAAAAAAA": 20712, + "TCAGATATG": 20713, + "TCCAGGCCCGTC": 20714, + "TAACTGAAAA": 20715, + "CTGGGTATATACCCAAA": 20716, + "CAAGTTTTA": 20717, + "GTTTCCCTG": 20718, + "TCTTAAATG": 20719, + 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"TATATATATATATATATATATATATATATATATATATATATATATATATATATATA": 20762, + "TCCGCCTGGCTCTGCCTCCCGA": 20763, + "TCGGAGCA": 20764, + "TCAAAAAAAAA": 20765, + "TAATCTGAA": 20766, + "CTGTGAGCA": 20767, + "CTTTCAATT": 20768, + "GCCTCAGCA": 20769, + "CTATCAAAA": 20770, + "CTACTTTTA": 20771, + "TAAACAAATT": 20772, + "CTCGGGTT": 20773, + "TCAACTAAA": 20774, + "TCAAGGTCA": 20775, + "CTGAGACTT": 20776, + "GCTTCAGTG": 20777, + "CCTTTCCAAA": 20778, + "CATGTAGAA": 20779, + "GATGCAAAA": 20780, + "GGTTGGAAA": 20781, + "GAATTAAATT": 20782, + "CAGAATTAA": 20783, + "CAATTGAA": 20784, + "CGGAGGAA": 20785, + "GCATTTCTA": 20786, + "TGAGGCAGA": 20787, + "GCGGCCTT": 20788, + "TATAAATTAA": 20789, + "CTATAGTTA": 20790, + "CATGTGCCACCA": 20791, + "TGACTCCAA": 20792, + "CACAGCCAA": 20793, + "CACAGGCCA": 20794, + "GAGACCTA": 20795, + "GTGAGTGCA": 20796, + "CATGTCCTTTG": 20797, + "GATGAAATG": 20798, + "GATAGCTG": 20799, + "CGTTTACTCTG": 20800, + "CTCTTTATT": 20801, + "GTATTGGAA": 20802, + "CTGTGGTTTT": 20803, + "CGTATGTG": 20804, + "CTTTACCTT": 20805, + "CCCTCTCTCA": 20806, + "GTTTTTTAAAA": 20807, + "CCCAGGTCTCC": 20808, + "CGTCAAAAA": 20809, + "GGATCGG": 20810, + "GTATAAATT": 20811, + "CATTCCTTTT": 20812, + "TATGTAGTT": 20813, + "GTTATAATT": 20814, + "CTTTTGGGA": 20815, + "TAGATTATT": 20816, + "GCTCAGGTG": 20817, + "GTGTTCTTA": 20818, + "CTCAAGGAA": 20819, + "TTTTAATGAGG": 20820, + "CATGAGCCACCA": 20821, + "GAAGGCAA": 20822, + "CCTAATTTA": 20823, + "CTCCACCCA": 20824, + "CGGTGGCTCACACCTG": 20825, + "CCACATGGTGGCTCACAACCA": 20826, + "CCTGTAATCCCAGCTACTCAGGAGG": 20827, + "GATCAAATT": 20828, + "TTTTTACAA": 20829, + "GTGTCAAAA": 20830, + "GGAGCTGTG": 20831, + "CACACTAAA": 20832, + "CACAGAGC": 20833, + "CACAGATAA": 20834, + "GAAATACTA": 20835, + "TCAACTTAA": 20836, + "GCTGCCTCA": 20837, + "TTTTAGAAAA": 20838, + "TATGCACTCA": 20839, + "TCCAGTGATG": 20840, + "GCTACTC": 20841, + "TGTACCAA": 20842, + "TCTCCCCTG": 20843, + "AAAAAATAAAAA": 20844, + "GGATGGGAA": 20845, + 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20889, + "GACCTCTA": 20890, + "CTATTTATG": 20891, + "TATATATATATATT": 20892, + "CCAGGCTCA": 20893, + "GAGCCCGG": 20894, + "TAAGAGGAA": 20895, + "GTTCCCAGG": 20896, + "TTAAAAAGTCAGGAAA": 20897, + "CAACCTACAGAATG": 20898, + "GAACCTGGGAGGTGGAGGTT": 20899, + "ATTTA": 20900, + "GCGGACA": 20901, + "TTATCAAA": 20902, + "TGTGTCTGA": 20903, + "TGTTGTCTT": 20904, + "CAAAGATTA": 20905, + "TCTGCAGCC": 20906, + "CTGAGACTG": 20907, + "TTTTATCCAAA": 20908, + "CAAAACCTT": 20909, + "TTATTCCAA": 20910, + "CGTTTATG": 20911, + "GTGTTGTT": 20912, + "CAGTGCCTG": 20913, + "GTCTGTCTT": 20914, + "TCACACTCA": 20915, + "CGGTGGCTCATGCCTG": 20916, + "GCCTGACCAA": 20917, + "GGTGGGCGGA": 20918, + "TCTGCAACCCTA": 20919, + "TAAATATCC": 20920, + "GTCTGACA": 20921, + "GAGACTCC": 20922, + "GCTTCCATG": 20923, + "GTGATGCC": 20924, + "GCTGGGATT": 20925, + "GCTGGACTG": 20926, + "GCAACACAA": 20927, + "CTAATATCA": 20928, + "GATGGCCTA": 20929, + "TCCTTGTGA": 20930, + "GGTAGCCTT": 20931, + "TCCTGCAGA": 20932, + "GTTTGCCAA": 20933, + "TGAGGTCA": 20934, + "TCGGTCTGTCTG": 20935, + "TAACACTTA": 20936, + "TTAATGAAAA": 20937, + "CTCAATGGACATGAGTT": 20938, + "GGAGGGATTGGGGGCA": 20939, + "GCGTCTA": 20940, + "TGATĊ": 20941, + "GCAGCCCTG": 20942, + "CTCCAGCCAGG": 20943, + "CACATCAAA": 20944, + "GAAACGTC": 20945, + "TATAGATTA": 20946, + "CAAAGGCTG": 20947, + "TGAACTGAA": 20948, + "TCAATGATT": 20949, + "GGAACCAAA": 20950, + "CTGAGATGA": 20951, + "GTCACCAAA": 20952, + "GAGGGGAGG": 20953, + "TCTCTCATG": 20954, + "CTTACCTATT": 20955, + "CAAAAAAATA": 20956, + "CATATTATG": 20957, + "TGAAATTTA": 20958, + "GGTATAAAA": 20959, + "TCTTACATG": 20960, + "CGAAGAGTA": 20961, + "GAGGACATT": 20962, + "CCTCCCCA": 20963, + "GGTGATTTT": 20964, + "TCGATATT": 20965, + "TAAGATAAA": 20966, + "CTGATTTCCTTTA": 20967, + "TGACCCAGG": 20968, + "GCTTTGAAAA": 20969, + "GCCCAGGCTGGTCTTGAA": 20970, + "GAACCCGGGAGGCAGAGGTT": 20971, + "CCGTATG": 20972, + "TCCGACA": 20973, + "CTTGAACC": 20974, + "GTGTCTATT": 20975, + "GTGTTTTAAA": 20976, + "CTCTCATTA": 20977, + "GTCTAAATT": 20978, + "TATACTGAA": 20979, + "CAAACCTA": 20980, + "TGAATATCA": 20981, + "TAAAAGGTT": 20982, + "TATGCAGCA": 20983, + "TCATTGTT": 20984, + "CGTTGCC": 20985, + "CTTTGTAGACCAGGCTG": 20986, + "GTATTGTTA": 20987, + "GAGCAGAA": 20988, + "CCACCTGTG": 20989, + "TAAGTTTCA": 20990, + "TCCTCAGAA": 20991, + "CAACTTTA": 20992, + "CTTTCCCCA": 20993, + "GTAGTAAAA": 20994, + "GAAACTGTT": 20995, + "CATTGTGAA": 20996, + "CTGACACCA": 20997, + "CATGTCCTT": 20998, + "CTTTTGAGA": 20999, + "TAATTĊ": 21000, + "TTTTTTCAA": 21001, + "GCTCAGTGGTA": 21002, + "GAGAAGATG": 21003, + "CTCTGCCCA": 21004, + "CTGTGGAAAATT": 21005, + "CCTCCCCTT": 21006, + "TATTAATTTG": 21007, + "CCTCTGCCTCTG": 21008, + "GTGTATGTT": 21009, + "CAAGAAACACA": 21010, + "TTTTTTTTTTTTTTTTTTTTTTTTTTTT": 21011, + "CCGCCTCC": 21012, + "TTATATCA": 21013, + "GTCGGACACGACTGAA": 21014, + "CAAACGTA": 21015, + "GGAATAGTT": 21016, + "GTCACCCAGG": 21017, + "CGGTAGA": 21018, + "TAGGGCA": 21019, + "TCTCAGATT": 21020, + "GGAGGGACA": 21021, + "TCCTGGATT": 21022, + "CCTCACACCA": 21023, + "GCCTGGGGG": 21024, + "TACAATTTG": 21025, + "GCGGTTTG": 21026, + "GTTGGGAGA": 21027, + "TGACCCAGA": 21028, + "TAGTTATG": 21029, + "GACGTGAA": 21030, + "TAATCGAA": 21031, + "TTTTCGA": 21032, + "CTTGCTGTG": 21033, + "CTTCTCTCA": 21034, + "GTATTAATT": 21035, + "CTATATC": 21036, + "GTCGGCTG": 21037, + "GTCTTATTTT": 21038, + "CACATGCTG": 21039, + "GAAATCAAAA": 21040, + "GATTGGGA": 21041, + "CTTAGACA": 21042, + "TCCATAGAA": 21043, + "TCTATCTCA": 21044, + "GCCAGCCTT": 21045, + "CTTTTTAGA": 21046, + "GCTACTCC": 21047, + "CGCGCAA": 21048, + "TCTGACAGA": 21049, + "GGTACTCA": 21050, + "GTGTTGGTG": 21051, + "CTGTGGGGG": 21052, + "TAATGTAGA": 21053, + "GCAGGGCTT": 21054, + "GTGGGGAAAA": 21055, + "TATCCCCTT": 21056, + "TAAGACAGA": 21057, + "TCTTATTAAA": 21058, + "CAAACAAGCAA": 21059, + "TGATGACA": 21060, + "TCCCTTTA": 21061, + "TCCCAACA": 21062, + "CTCTCTCCTT": 21063, + "GAAATTTTAA": 21064, + "TATAGTTAA": 21065, + "TGTTGAGAA": 21066, + "GTGACATTA": 21067, + "TATGCATTTT": 21068, + "GTTATTTTAA": 21069, + "CTTAGAATT": 21070, + "CCCAGTGTG": 21071, + "GTAAAGTTA": 21072, + "TCTGAGCTG": 21073, + "TGCCCGA": 21074, + "TCCAATGGG": 21075, + "CGCAGTG": 21076, + "GTATTGTAA": 21077, + "CGGATTTG": 21078, + "TAGAATTTG": 21079, + "TAGCATGAA": 21080, + "CCTTATTTG": 21081, + "GTTTACCTT": 21082, + "GAATATCAA": 21083, + "CCAGAGCAA": 21084, + "GCCCATGTT": 21085, + "GCCCAGTGA": 21086, + "GAGGTTTCTG": 21087, + "TCGAAAATT": 21088, + "TCCGGGTG": 21089, + "CTTTAAGAA": 21090, + "GTTTCCAGA": 21091, + "CACAGACCA": 21092, + "CTGATACC": 21093, + "GAGGCTGGG": 21094, + "CTTAGCAAA": 21095, + "GGGACTAA": 21096, + "GCAAACAGG": 21097, + "TAATATAAAAA": 21098, + "GTATTTGATCC": 21099, + "GAAGAATTA": 21100, + "CTGTACATG": 21101, + "TAAGACAAAAA": 21102, + "TGATAGTGA": 21103, + "CTGGAGCTT": 21104, + "GTAGAATAATA": 21105, + "GTGCATGTG": 21106, + "TAAGTTAAAA": 21107, + "GAACCTGGGAGGCGGAGGTT": 21108, + "GCGTAATT": 21109, + "GCGGATA": 21110, + "TGAGCTGAA": 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"GCAGGGGAA": 21156, + "TAGTATTTA": 21157, + "CCCTATTA": 21158, + "AAATATGTT": 21159, + "CAGTCGCTCA": 21160, + "CTTCATCTG": 21161, + "TCATACAAA": 21162, + "TTACATGTT": 21163, + "CTACATGATT": 21164, + "GAACCCAGGAGGCGGAGGTT": 21165, + "CTCCTGGGCTCAAGTGA": 21166, + "GCCATCCCATTA": 21167, + "GCGGATCACGAGGTCAGGAGATCGAGACCATCCTGGCTAA": 21168, + "GCTGAAACTCCAATACTTTG": 21169, + "CCAGTGGG": 21170, + "TAAAAGAGG": 21171, + "CTCAGCCTG": 21172, + "CTCACACTT": 21173, + "CATGGTCC": 21174, + "CCTGGCTAA": 21175, + "TTAACTGA": 21176, + "CTTTTGTTATT": 21177, + "TAGGTAAGCA": 21178, + "TCAAATTTTA": 21179, + "TCAAATGTA": 21180, + "CCTACTTAA": 21181, + "TCTCACAA": 21182, + "GCAAATATG": 21183, + "TCATTTAATT": 21184, + "TGAAATCAA": 21185, + "GGCCTCTA": 21186, + "GGCCGGGG": 21187, + "GTCTTTAATT": 21188, + "CCATTTCTA": 21189, + "GTCTGGAGA": 21190, + "CAGCCTCTG": 21191, + "CCACACAAA": 21192, + "CTTAAAATCTCGACGTT": 21193, + "TAAAAATTAAAA": 21194, + "GCCACCGCGCCTG": 21195, + "TCTTCTTTTGAGAA": 21196, + "TCCCCACCA": 21197, + "GTGGTGAAA": 21198, + "GTTTGGGTT": 21199, + "CTAGAGC": 21200, + "TAAATGAGA": 21201, + "TGTGGGTAA": 21202, + "CAGGCTGAA": 21203, + "CAGGTGCTG": 21204, + "GTGACTAAA": 21205, + "GATTCCAAA": 21206, + "GCCAGGAGG": 21207, + "TAGGAAAAAA": 21208, + "GGCAĊ": 21209, + "CCTAGTCC": 21210, + "GCAAATGTA": 21211, + "CTTAACCAA": 21212, + "GTGTTGGTT": 21213, + "TAATGTCAA": 21214, + "GTTTGCTTTT": 21215, + "GAAGGCGG": 21216, + "CAAGACACA": 21217, + "GGATTACAGA": 21218, + "CTGCCCCCA": 21219, + "GGACACCAA": 21220, + "TAATCATGTG": 21221, + "TAACTGGG": 21222, + "TCCCTCCA": 21223, + "TCCTCCAGA": 21224, + "CTTTCTATA": 21225, + "GGAGCTAAA": 21226, + "CCAGAAGAA": 21227, + "GAAAGAAAAAA": 21228, + "CAAACCTCA": 21229, + "TCTCTTATG": 21230, + "CTAATAAAAA": 21231, + "GACACCCA": 21232, + "TAAAAATGTG": 21233, + "CGTTCCTT": 21234, + "GGCCTATA": 21235, + "GCTGAGGTT": 21236, + "TATTTTTATA": 21237, + "GCCAAGAAA": 21238, + "GACCCGA": 21239, + "CAACATATA": 21240, + "CTCTCCCTCC": 21241, + "CTCAAAATAAATTGGTT": 21242, + "CCTGAGGTCAGGA": 21243, + "GCGACTTAGCAGCAGCAGCA": 21244, + "GAAGCTCC": 21245, + "CTGCCAAAA": 21246, + "GTTTCCTTA": 21247, + "GCAGCTGTT": 21248, + "CCAGCTGTG": 21249, + "GTCTCATCA": 21250, + "GAAACAATG": 21251, + "CAAAGAGAAA": 21252, + "CAGGCTTTT": 21253, + "TAAAACCAAA": 21254, + "CCTGGGTCA": 21255, + "GATGCAGCA": 21256, + "TTAAGTG": 21257, + "TTAATTTTTT": 21258, + "GGTGGTTA": 21259, + "GGTGCGTG": 21260, + "TGATGGAATT": 21261, + "TCACATCTG": 21262, + "CCCCCAATCCCTCCCAGCA": 21263, + "TGAGĊ": 21264, + "CTTGTGAAA": 21265, + "GTTGCTGTG": 21266, + "GCCTTTTAA": 21267, + "TATTTCTAAA": 21268, + "GCTGGGTGA": 21269, + "GCAACACTG": 21270, + "GATTTCCAA": 21271, + "TCCATATGA": 21272, + "GGCATAGA": 21273, + "TCTCAGATG": 21274, + "CTGAACAAA": 21275, + "TAATACCTT": 21276, + "GAAAAACTA": 21277, + "GTTTGCCTT": 21278, + "GCATGGAGG": 21279, + "CACTCTGATG": 21280, + "TGATCATTA": 21281, + "CTTGTTCTT": 21282, + "GTTTCAATT": 21283, + "GCATCCCTG": 21284, + "TGTGCAGTG": 21285, + "CAGGCTC": 21286, + "GTCACAGGA": 21287, + "TTTTATCTA": 21288, + "TCTCCCAGA": 21289, + "TTTTTTGAA": 21290, + "GCAAACTGA": 21291, + "GGCCTCTGA": 21292, + "CTTTGGTGA": 21293, + "CTTTGTATG": 21294, + "TATAAAAAAAA": 21295, + "CCTCAGTGA": 21296, + "TACTATTAA": 21297, + "TAAAAAATAAAA": 21298, + "CTTTAAGAGG": 21299, + "CACTCTGTTTG": 21300, + "GTTTCCTTTG": 21301, + "CATATTTATT": 21302, + "TACCAAAAA": 21303, + "GCGAACAA": 21304, + "TCCTCGAA": 21305, + "CAAGCCACA": 21306, + "CAATGACC": 21307, + "CTGTCAAAA": 21308, + "TGTTTAGCA": 21309, + "CAGGGAGTA": 21310, + "CCTTCTAAA": 21311, + "GATTCAGTT": 21312, + "TCTCTATCC": 21313, + "CTCCTGTGA": 21314, + "GCCATCTGA": 21315, + "CAAAATCTG": 21316, + "CTTTGCATG": 21317, + "CCATTGTG": 21318, + "GGCTGGGTG": 21319, + "CAGTATTTA": 21320, + "TAAGATATA": 21321, + "TCATCTCTG": 21322, + "TCCTCACTG": 21323, + "CTGTTTTTG": 21324, + "CTTCAGGAA": 21325, + "CATTGATCA": 21326, + "CAGGCTGGG": 21327, + "TCAACCTA": 21328, + "GGAATACAA": 21329, + "GCTGCACTT": 21330, + "GTAAGTGAA": 21331, + 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"GCCGTGTT": 21421, + "CTAGCATG": 21422, + "GAAAGGGCA": 21423, + "CAGGCTCAA": 21424, + "CTCCCTGAA": 21425, + "CTAAGACAA": 21426, + "TCTGAGTGA": 21427, + "GGTAGAAAA": 21428, + "GCCAATATCA": 21429, + "CTGTGCTT": 21430, + "GTTTGTTTTA": 21431, + "CTATTGATT": 21432, + "GATGAGAA": 21433, + "CTCAAACTCA": 21434, + "CAGATTAAAA": 21435, + "TAAGGGATT": 21436, + "CAAGAATACTG": 21437, + "TATATATAAAA": 21438, + "CCCCCACCCC": 21439, + "CAGTTTTTG": 21440, + "TAAGCAGCA": 21441, + "TAATCCAAA": 21442, + "TCAGCTCTG": 21443, + "TCACGTCA": 21444, + "GTCGGTTA": 21445, + "CAAAGAGGA": 21446, + "GAGATCTA": 21447, + "CAGACACACACA": 21448, + "GCTTCTAAA": 21449, + "CATGGCTA": 21450, + "TAATTCAGA": 21451, + "GTGAAGTCA": 21452, + "TGATTTCCA": 21453, + "TATTACAAA": 21454, + "TCTAATAAA": 21455, + "GTTTGGTA": 21456, + "CGTACGG": 21457, + "TAATAATTTG": 21458, + "TCTCTTAAAA": 21459, + "CTATGGAA": 21460, + "CTTGGCCTT": 21461, + "GAGAGAGAGAGAAA": 21462, + "GTTTACTAA": 21463, + "TGATATGTG": 21464, + "GTTTAACCTTTCTTTT": 21465, + "GCACCACTGCACTCCAGCCTGGG": 21466, + "CTTCAAACTATACTA": 21467, + "CCTGTTTA": 21468, + "GTTGAGCC": 21469, + "GCCCGAGG": 21470, + "CTCGCTA": 21471, + "GTCGAGAA": 21472, + "TATTGACC": 21473, + "CCAATCTA": 21474, + "TCTACAATTTT": 21475, + "GTCCĊ": 21476, + "TCTCACAAA": 21477, + "TATTTTTTTTA": 21478, + "CGCCTTTT": 21479, + "TAATGGGAA": 21480, + "CTAAAATTAA": 21481, + "TCTGTGTTA": 21482, + "GATCCTTA": 21483, + "GTGCCTGTG": 21484, + "CACTCTTTTTGTG": 21485, + "CTGGTTCCAAA": 21486, + "TAAGCATA": 21487, + "TCATCAGA": 21488, + "TCAGCCAGG": 21489, + "TGAACGTG": 21490, + "GTAATCAAA": 21491, + "CTCCCAAAA": 21492, + "TCTACCATT": 21493, + "CCCACCCAA": 21494, + "GTAAACATG": 21495, + "CCAAACATT": 21496, + "GCTGAGTGA": 21497, + "GTTAATGAA": 21498, + "GTCAATTTG": 21499, + "CAATATAAAA": 21500, + "TCACATCAA": 21501, + "TCTCAACCCCA": 21502, + "CACTGAATT": 21503, + "TGATAAATG": 21504, + "CCAGCGTG": 21505, + "GAAAGCATG": 21506, + "GAAAGGGGA": 21507, + "CAAACTCTT": 21508, + "GCTTCATCA": 21509, + "CTCAGTCTT": 21510, + "CCTGCCACCA": 21511, + "TACACAATT": 21512, + "TACAGCCTT": 21513, + "TCCAGGATG": 21514, + "GCCACCCAA": 21515, + "GGGGGAGAA": 21516, + "TTTAGATA": 21517, + "GGTGCTAA": 21518, + "GGTGCTTA": 21519, + "TGCAGCAAA": 21520, + "TTCACTAA": 21521, + "TCTCCCCTCC": 21522, + "GAATTCATA": 21523, + "GGCTGTTA": 21524, + "TTATGGAA": 21525, + "GTATGTATA": 21526, + "GCGACCTT": 21527, + "CAATATTATT": 21528, + "TCACATATA": 21529, + "TGACATCAA": 21530, + "GGACATAGGCATG": 21531, + "GCCCAGCAA": 21532, + "GGGCAATG": 21533, + "TAAGTGGA": 21534, + "TGACATTTG": 21535, + "CTTTCCTA": 21536, + "GTGCCCTA": 21537, + "GTTTGTTAA": 21538, + "GTATTTTAAA": 21539, + "CTAGGAAAA": 21540, + "TAAACGGA": 21541, + "TAAATTATTTT": 21542, + "TATTGAAAAA": 21543, + "TGAATACAA": 21544, + "CAGGCACCA": 21545, + "CAGGCGTGA": 21546, + "CTGACAGTG": 21547, + "CCTGGGGCA": 21548, + "GAAAATATGA": 21549, + "GTTTTCACA": 21550, + "CCCAGTGTT": 21551, + "CAAAAGAATTGAAAA": 21552, + "GCAAAGATT": 21553, + "TGAAATATA": 21554, + "CAGTACAGTA": 21555, + "TAGTATTAA": 21556, + "TAAATAAATAAAA": 21557, + "TCGCCCAGGCTG": 21558, + "GCTAGGGAGG": 21559, + "CAGTAAATT": 21560, + "TCCCTGCA": 21561, + "CTTTCATCA": 21562, + "GTAGCTTTT": 21563, + "TAAAGCCAA": 21564, + "TCTGCAGGA": 21565, + "TCAAGAATT": 21566, + "GCTGCTCAA": 21567, + "GCAACGAA": 21568, + "GGTTTAAAA": 21569, + "GGTGTATT": 21570, + "TGCACGTG": 21571, + "CCAAACCA": 21572, + "CGCCTGCC": 21573, + "CCATTCCTG": 21574, + "TAATGCTTA": 21575, + "CCCCATGTGA": 21576, + "CTCCACTCC": 21577, + "GTTTACTTTT": 21578, + "CTTCCTCCAA": 21579, + "GAACAGTCA": 21580, + "CTGCCTCCA": 21581, + "TCACTGTTA": 21582, + "TGATGAATT": 21583, + "GCACCACTG": 21584, + "GGAGCAGAA": 21585, + "CTCCACTATG": 21586, + "TGTTTAATA": 21587, + "TCTGTAGCA": 21588, + "GTGACACC": 21589, + "TTTTACCAA": 21590, + "TATGCATAA": 21591, + "TTTACCTG": 21592, + "TGGACTGTA": 21593, + "GGGAGGATT": 21594, + "GTAAATCAA": 21595, + "GCAAAGTCA": 21596, + "TCCTTCATG": 21597, + "CCAAACTA": 21598, + "CCAAATTCA": 21599, + "GCTCATTAA": 21600, + "CTGTGCTTA": 21601, + "TGATGCTAA": 21602, + "TGAGGTGTG": 21603, + "TCATGCTA": 21604, + "CCCTTTCTT": 21605, + "CAATGGTG": 21606, + "TCGGCCTG": 21607, + "CTGCACCCATCAA": 21608, + "CTTTAGTTA": 21609, + "TAATTTATTA": 21610, + "TGTTGAATAGGA": 21611, + "GCTAATTTTTGTATTTTTAGTA": 21612, + "TCCTATAAA": 21613, + "TCCTCCACA": 21614, + "GGACAAGA": 21615, + "CCAGCCTCA": 21616, + "GTAGCAGG": 21617, + "TAAATAAATA": 21618, + "CTCTAATGA": 21619, + "GTCTCAGTG": 21620, + "CAAACATGA": 21621, + "CAGACAGTA": 21622, + "GCTGGCTA": 21623, + "TCAAACTAA": 21624, + "CTTAATATT": 21625, + "CTTTGCAGG": 21626, + "GACCTCCA": 21627, + "GTATAGTAACGA": 21628, + "GTGGATCACGAGG": 21629, + "CTTCAGACGGGCCTGGA": 21630, + "CCTATTTA": 21631, + "GGCTAATG": 21632, + "GTGGGAGA": 21633, + "GTTTCCCC": 21634, + "CAAAGCCTT": 21635, + "CAAACAGTA": 21636, + "GAGAGATCA": 21637, + "CTCAGGGCA": 21638, + "TTTTATTTTATTTT": 21639, + "GAGGTATA": 21640, + "TAGAGCTA": 21641, + "GTATTTAAAA": 21642, + "GTATTGCTT": 21643, + "CTCTGCGG": 21644, + "TCAAAACAA": 21645, + "GTGTGGGGG": 21646, + "TCGGTTAA": 21647, + "TAAGACATG": 21648, + "TCTATCTATCTA": 21649, + "CTTTATATGCCCCAGTA": 21650, + "TCTCTCACTGTTCAAATAAA": 21651, + "TAGCCCCA": 21652, + "TAAAGGTAA": 21653, + "CACACAGCC": 21654, + "TCTTCCTAA": 21655, + "CAAATCTAA": 21656, + "TGAATGCC": 21657, + "CAGATCCTT": 21658, + "CTCATGCTG": 21659, + "TATGGGTA": 21660, + "TCTCTTTTAA": 21661, + "TACACCCC": 21662, + "GATGTCCA": 21663, + "TTTGAAAAAA": 21664, + "GCCATTATT": 21665, + "CGCTGACA": 21666, + "CTGTTTCTCA": 21667, + "GTGTTTCTA": 21668, + "GTCAATTAAA": 21669, + "CAAAAACTA": 21670, + "GTTTGGTTA": 21671, + "CTATTCCA": 21672, + "TGATGCCTT": 21673, + "TATCACATT": 21674, + "TATCACAATT": 21675, + "TCATGTCCAA": 21676, + "GCTTATCCA": 21677, + "TCGGTGTG": 21678, + "GTGGTGTGTG": 21679, + "CCGGCGA": 21680, + "GCCCAGCTT": 21681, + "TCCTAATAATT": 21682, + "GCTCTGATG": 21683, + "CGCCAATT": 21684, + "GACAAATGGGA": 21685, + "TGATGTGAA": 21686, + "CTTTAAGG": 21687, + "GCCTAATA": 21688, + "CTCGCCTT": 21689, + "GTCTGTTAA": 21690, + "TGTTTATATT": 21691, + "CTGAGACAA": 21692, + "GTGAGACTG": 21693, + "GTTTTTTATT": 21694, + "TGGGGCCATG": 21695, + "TGCAGCTA": 21696, + "GGCATCCA": 21697, + "TTTTTTCCCC": 21698, + "TAAAAATAATT": 21699, + "CTCTTCTAA": 21700, + "GTCAAGGA": 21701, + "CCAGGCTA": 21702, + "GGTGAGAAGG": 21703, + "CTTTACTCA": 21704, + "GTTTACATG": 21705, + "GTTGGTGAA": 21706, + "GTTCCCCTG": 21707, + "CTTTCCAAAA": 21708, + "CCGACTC": 21709, + "TGAGGAGGA": 21710, + "GTATCTGTT": 21711, + "GTCTCCCCA": 21712, + "TCTTTAATA": 21713, + "TCTTCAGGA": 21714, + "CAAAGATCA": 21715, + "TCTGGACTT": 21716, + "GTGACGTT": 21717, + "CCTTGGTA": 21718, + "TATGGTAAA": 21719, + "CTTATCCA": 21720, + "CCCACGTT": 21721, + "TAATTGCAAA": 21722, + "CCAAAGTCA": 21723, + "CTAAACATT": 21724, + "GTTAAGTAA": 21725, + "CAATTTGTG": 21726, + "CCATTCTGA": 21727, + "GCCTGCTCA": 21728, + "TGAGGTTAA": 21729, + "CATTTTTTTG": 21730, + "GTTTATTATT": 21731, + "TCTATTAAAA": 21732, + "GTCAAAGCTATG": 21733, + "TATTAAATTA": 21734, + "GAAATAAGTATT": 21735, + "TCTCATTGTGGTTTTGATTTGCATTTCTC": 21736, + "TGCTGGAGAGGATGTGGAGAAA": 21737, + "GCGTTTTTT": 21738, + "TCAGAACA": 21739, + "TGACTGTA": 21740, + "GAAGCCAGA": 21741, + "CTACATC": 21742, + "GAGACAGTT": 21743, + "GGAAGACTG": 21744, + "GTGATATTA": 21745, + "CCTGCGTG": 21746, + "GTTTTTAAAAA": 21747, + "CTTTTGCAA": 21748, + "CCCACCCTT": 21749, + "CATATCCTTTG": 21750, + "TATTTTTAAAA": 21751, + "CTTTGCTAA": 21752, + "TCTTTTTTTTTTTTTTTTTTTT": 21753, + "TACCCAAAA": 21754, + "TACCTAGG": 21755, + "TCCTGCCAA": 21756, + "CTGTGTATG": 21757, + "GCATGGCAA": 21758, + "GTGCTGGGA": 21759, + "GTGCTGGAATTACAGG": 21760, + "GTTCCATAAACAA": 21761, + "GTCCTGGTCC": 21762, + "CAGAGGAATGGA": 21763, + "CCTCGCCCGGCTAGTTTTTT": 21764, + "GCGAGGTG": 21765, + "TAACGTCA": 21766, + "CTTGTGGG": 21767, + "GCATTATTA": 21768, + "GTATAATTA": 21769, + "GCCTCAGAA": 21770, + "CTAGCTGTG": 21771, + "CAAAGGCTT": 21772, + "CATTCTTAAA": 21773, + "TCTGCACTT": 21774, + "CTCAGAAGG": 21775, + "GATTCAGCA": 21776, + "GTTATCAAA": 21777, + "CACAATTAA": 21778, + "CTGCAGTTA": 21779, + "GTTTATCTA": 21780, + "CAGTCTTTG": 21781, + "CTCTACCAACTGA": 21782, + "GTGTCATCTGCATA": 21783, + "CTTCAAGAA": 21784, + "CTCCATTAA": 21785, + "GAAAGAGTG": 21786, + "TGAACACC": 21787, + "CAGATATAA": 21788, + "GGAATAGAA": 21789, + "GCTGCCAAA": 21790, + "CTCAGCTGA": 21791, + "GCCAGAATT": 21792, + "CCCCAAAAAA": 21793, + "GGGACTTA": 21794, + "GTAAACAGA": 21795, + "GCTCAGGGG": 21796, + "GACCTCAAA": 21797, + "CCATTTAAA": 21798, + "CTATTGTAA": 21799, + "TCATGTCATCTG": 21800, + "GCATCACCAA": 21801, + "GAACAAAGCTGGAGG": 21802, + "CACTĊ": 21803, + "TACCTGAAA": 21804, + "GCTAAATAAA": 21805, + "TTTTGCC": 21806, + "TCACAGGAA": 21807, + "TGAGCAGCA": 21808, + "GAACCAGG": 21809, + "TCCTGTGAA": 21810, + "CAACAAATT": 21811, + "GTTTGTGAA": 21812, + "GCTGTAGTT": 21813, + "CGGCCTCC": 21814, + "GATTCTGTT": 21815, + "CCCCTCTGA": 21816, + "CCCCCCCAA": 21817, + "TTATTTCTGA": 21818, + "GGCAGTTTT": 21819, + "TCTCATCTG": 21820, + "CGTTTAAA": 21821, + "CTTTGCTCC": 21822, + "GACCCTTA": 21823, + "CGGACGA": 21824, + "CTATTTAAAA": 21825, + "AAATTTATT": 21826, + "TGAGGCCAA": 21827, + "GCAGGGCC": 21828, + "CTTTATTATT": 21829, + "GAATAGAGA": 21830, + "GTGGTGGGTG": 21831, + "TCAATGTTA": 21832, + "CCGACGA": 21833, + "CTTGCTGTT": 21834, + "GTGGCAAAA": 21835, + "GCAGCTCTT": 21836, + "CTCTCTACA": 21837, + "GTCGAGGA": 21838, + "GTCTCAATT": 21839, + "TATACACTT": 21840, + "TCTTGGGTT": 21841, + "CAAATATATT": 21842, + "GGAACACTG": 21843, + "GCTGGGGCA": 21844, + "GAGGCAAAA": 21845, + "TCTAACAA": 21846, + "TTATTATTATTTT": 21847, + "TCAGATAGG": 21848, + "CTTTACTAA": 21849, + "TTATAGATG": 21850, + "GTTCAGTTA": 21851, + "CTCCTGTCTCAGCCTCC": 21852, + "GAACCCAGGAGGTGGAGGTT": 21853, + "GAAGTAGTGATTCCTTGGGCA": 21854, + "GGTCTCAAA": 21855, + "GTGGCGTG": 21856, + "CTACTGCTG": 21857, + "CTACCATCA": 21858, + "GTCTCGTT": 21859, + "TGAAGCTA": 21860, + "GGAATATTA": 21861, + "GGAAAAATA": 21862, + "GCAACTCC": 21863, + "GTCACATCA": 21864, + "CTTTTTAATT": 21865, + "CCCCTATG": 21866, + "TCCTTTCCA": 21867, + "CTCTTCATT": 21868, + "GTGTTGGGA": 21869, + "CAGTGCTG": 21870, + "CAGTGTTAA": 21871, + "CCTCAGGGA": 21872, + "TAAGATGTG": 21873, + "TTTTGTAAA": 21874, + "TCCTGACC": 21875, + "TCCCTTCCA": 21876, + "GGATGAATT": 21877, + "CACAGAAAAA": 21878, + "CATTCCAGG": 21879, + "CAGGGCTA": 21880, + "TCTGGGCCA": 21881, + "GTGACAATATA": 21882, + "CATGCTGAA": 21883, + "TATGGTTTTA": 21884, + "CCCCAAATG": 21885, + "TGCATAAAA": 21886, + "CTGAAGCTG": 21887, + "GCAAAGAAAA": 21888, + "CTTAAGTGA": 21889, + "GCCAAGCTT": 21890, + "GTCTGGAGG": 21891, + "GACTTGAAA": 21892, + "CCCTTCCCCA": 21893, + "CATGAGCCACTG": 21894, + "CGTCTTTA": 21895, + "GAGAAATTA": 21896, + "TCTGTGTTTT": 21897, + "CAACAATATGAA": 21898, + "GTTTACAGA": 21899, + "GTCCAGCTT": 21900, + "GTGGCAGGCA": 21901, + "GGTCGTA": 21902, + "TCGTCTGA": 21903, + "TCAGTTATT": 21904, + "CTCGAGC": 21905, + "CTCGGGGA": 21906, + "CTGACTGAA": 21907, + "GCAAGGGAA": 21908, + "CCCATAAAA": 21909, + "CAATTGCTT": 21910, + "GCCTTTGTT": 21911, + "GAGCAGGAA": 21912, + "TGAGGCGG": 21913, + "GTGTGCGG": 21914, + "CCTCTTATT": 21915, + "CCAAAATACCA": 21916, + "CCTCAAGGATCTA": 21917, + "GATTCTTCCTA": 21918, + "TTTTTCTTA": 21919, + "TCATTTTAAA": 21920, + "GAAGGAGAA": 21921, + "CTCAGAATT": 21922, + "TTTTATGAAA": 21923, + "GTTACACAA": 21924, + "GCCATACCA": 21925, + "TGGATAGA": 21926, + "TCTCAGGTT": 21927, + "GTAAAGAAAA": 21928, + "GGAAATTTG": 21929, + "TCCTTCTAA": 21930, + "CTAAACAAAA": 21931, + "GGCCCGA": 21932, + "CACCTACA": 21933, + "CAGAATTTG": 21934, + "GTATTTGATACA": 21935, + "CAATTGTTTT": 21936, + "CGAACGA": 21937, + "CTCTGTCTCAAAAAAAAAAAAAAAA": 21938, + "GCATTTATG": 21939, + "TGAGGCCTTCGTT": 21940, + "GAATGCAA": 21941, + "CAATGCTG": 21942, + "GGGCCTGGG": 21943, + "TGATGTTAA": 21944, + "TCCCTTAAA": 21945, + "GGAGCAGA": 21946, + "AAAAGGAA": 21947, + "GAGACGCA": 21948, + "TCAAGCCAA": 21949, + "CTGAGTGAA": 21950, + "GATTTATCA": 21951, + "CCAATATCA": 21952, + "CCTACGTT": 21953, + "TCTGAGACA": 21954, + "TCCTTCCCTT": 21955, + "CCAAATGTT": 21956, + "GTATTGTTTT": 21957, + "GTGTTGTTTT": 21958, + "TGATGTATT": 21959, + "TATGATTTA": 21960, + "CCCAACACA": 21961, + "TATCGTCA": 21962, + "TATCGATT": 21963, + "GGAAAATTA": 21964, + "TCAGAGCTT": 21965, + "TCCCACTCC": 21966, + "CTTTACATG": 21967, + "GTTTACACA": 21968, + "CCACAGTAA": 21969, + "GAGACTGGCTAGGCCACTCCAGGACCTT": 21970, + "GCGAAGCA": 21971, + "GCGTCTGA": 21972, + "TTTTTATCA": 21973, + "GCACGCTG": 21974, + "GGACCCTGA": 21975, + "TAAAGCTCA": 21976, + "CTCTATTAA": 21977, + "GTCTCATA": 21978, + "TGTTGAGTT": 21979, + "CATTCCCTG": 21980, + "GTGAGAGCA": 21981, + "GCAATTATT": 21982, + "TCTCTCAGG": 21983, + "CCAACCTA": 21984, + "CTTAACAAA": 21985, + "CTCTTCCCA": 21986, + "GTCAACAAA": 21987, + "CTGTGCTAA": 21988, + "TGATGGTAA": 21989, + "GTCTGCCTG": 21990, + "GAATGCATT": 21991, + "GCTCGCTG": 21992, + "CCTCGGGG": 21993, + "CTGCACTCA": 21994, + "TTACAGTTTT": 21995, + "GAACCCATG": 21996, + "GAAAATTAAAA": 21997, + "TCCCAAACACA": 21998, + "TCATCGA": 21999, + "GAAGAAATG": 22000, + "CTTCTACA": 22001, + "CCAGCCACA": 22002, + "TAAACAGC": 22003, + "CTCTCGTT": 22004, + "CACATACCA": 22005, + "CAGGGCTCC": 22006, + "TCAATGCC": 22007, + "TTTTAGCA": 22008, + "CGGGAAAA": 22009, + "TCTCTCCCC": 22010, + "TCCACCTCCA": 22011, + "TAATTGAGA": 22012, + "TCTCACAGA": 22013, + "CTTAATGTT": 22014, + "TCTTACAGA": 22015, + "CTATTGTTA": 22016, + "CAGGAGGAA": 22017, + "TCAGACCTT": 22018, + "TCACATGTG": 22019, + "CAACACCAA": 22020, + "GCACAGCTT": 22021, + "TCAGTATAAAA": 22022, + "TAGCCAAAA": 22023, + "CTAAAGAGCTTCTGCACA": 22024, + "GCAAAAAATT": 22025, + "TGACAGGAA": 22026, + "TGACAAGG": 22027, + "GAACCTAA": 22028, + "GTGTCACTG": 22029, + "GTTGTGAAA": 22030, + "CCATCGG": 22031, + "CCATCACC": 22032, + "GCCAAAATG": 22033, + "TATTTĊ": 22034, + "TATATATTAA": 22035, + "TCTTGACC": 22036, + "TGAACAGTT": 22037, + "TGAAGATTTT": 22038, + "CAAAATTATT": 22039, + "CGAACAA": 22040, + "GGGAAGGGA": 22041, + "CCTCCTTTG": 22042, + "CAGTAGAGA": 22043, + "TCTGAAGACA": 22044, + "GAGGTTTAATT": 22045, + "GCTGGGCGTGGTG": 22046, + "TCACATTAA": 22047, + "TTACAGCA": 22048, + "GAATCTTTT": 22049, + "CCACGTGA": 22050, + "CTATAATAA": 22051, + "GAAATAGTG": 22052, + "CAAACGGA": 22053, + "CAAAGACTG": 22054, + "CATTTCCTG": 22055, + "TGAATAAAAA": 22056, + "TCTGTACAA": 22057, + "TCAAGATAA": 22058, + "GGAAGGCCA": 22059, + "TAAAATGGAA": 22060, + "GTGATAAAAA": 22061, + "GCAACTAAA": 22062, + "GTCAGAGTG": 22063, + "TCTCTGTGTG": 22064, + "TACAGCTCA": 22065, + "GTTTTCAGA": 22066, + "TGCATAGG": 22067, + "CGTTGATT": 22068, + "CGCAGCTT": 22069, + "CCAGGGCC": 22070, + "TAAGAGAAAA": 22071, + "GTTCAAGACCAGCCTGGCCAACATGGTGAAA": 22072, + "TTTTAAATG": 22073, + "TGAGCCAGG": 22074, + "CTTCATTAA": 22075, + "GTCTGACTG": 22076, + "CACAGCATT": 22077, + "CACACATGA": 22078, + "CTGATATTA": 22079, + "GTTATAGTT": 22080, + "GATGCTCC": 22081, + "GCCATCAAA": 22082, + "GACACCCC": 22083, + "CGTGCTCA": 22084, + "GAATTCATG": 22085, + "TCTTACTG": 22086, + "TACCTTTTA": 22087, + "GTTTGCATG": 22088, + "GAATGGTA": 22089, + "CTATGGAAAA": 22090, + "CAGAGATATA": 22091, + "CTTCACCTG": 22092, + "TGATATGTT": 22093, + "CTAAAAAATACAAAAAA": 22094, + "GAATGGACTGGTTGGA": 22095, + "TCTGATCTTTGACAAA": 22096, + "GGGTCATG": 22097, + "GACTĊ": 22098, + "GCGTTGAA": 22099, + "TAAGĊ": 22100, + "TGAGTGCTG": 22101, + "TGAGTTTCA": 22102, + "TGAGCAGA": 22103, + "TCCTCAGCA": 22104, + "GTTGAAGAA": 22105, + "CATTCACCA": 22106, + "CTGATCCA": 22107, + "GTGACACTG": 22108, + "GCTGGACTT": 22109, + "GCTGGAGTA": 22110, + "CCTTGGCCA": 22111, + "GTCAGTGAA": 22112, + "CGACAGTG": 22113, + "GACATTATT": 22114, + "TCTGACATT": 22115, + "TCATTCCTG": 22116, + "GTATTCTAA": 22117, + "GTATTTCCA": 22118, + "CGCCTTTG": 22119, + "CTGTGGATT": 22120, + "TACTTTATT": 22121, + "GCTCCTTCA": 22122, + "GTTCATGAA": 22123, + "GCGCGGTG": 22124, + "GAGAGAGAGAGAGAGAGAGAGAGAGAGA": 22125, + "CCATGCTGTTTTGGTTA": 22126, + "TAGAAAATG": 22127, + "GGGTAAAAA": 22128, + "GCGAGATG": 22129, + "TCAGCTCTT": 22130, + "TTACATAA": 22131, + "CCACTGCC": 22132, + "TAAAGGATT": 22133, + "CCTTCCATT": 22134, + "GTTATACAA": 22135, + "CTTACTCC": 22136, + "TAGGCC": 22137, + "GACACCAAA": 22138, + "TTATTTCCACA": 22139, + "CGTGTCTG": 22140, + "GCAAAGCA": 22141, + "TATTTTTCTT": 22142, + "GTCTTGATT": 22143, + "TATAATAAAA": 22144, + "TCTAATTTTA": 22145, + "TGATGTATG": 22146, + "CAGTTGGTG": 22147, + "CCCTGCCTG": 22148, + "TCATGGCTCA": 22149, + "TCCCAGCCA": 22150, + "CTTTACAGA": 22151, + "TAACATAAAA": 22152, + "GTATAGAGG": 22153, + "TGATGTTCCCCTT": 22154, + "TACGCTA": 22155, + "GGTAAAATT": 22156, + "TAAGTGTA": 22157, + "GCAGCCTCC": 22158, + "GCAGTTCC": 22159, + "GGACTATG": 22160, + "GTCGAGCA": 22161, + "GAAATATATT": 22162, + "CAGGGCAGG": 22163, + "CAGGTATGA": 22164, + "GCAACCCCA": 22165, + "GTAATGCC": 22166, + "CTAACACC": 22167, + "GCCACTGTT": 22168, + "TGGGGCTCA": 22169, + "CGCATGTG": 22170, + "GTTTGGATG": 22171, + "CTATTCTG": 22172, + "TAGAAGAAA": 22173, + "GGATTCAAA": 22174, + "TAGCĊ": 22175, + "CTGGATGTG": 22176, + "GCCCAGGCTA": 22177, + "TAAGCCAAA": 22178, + "TCCTGAGAA": 22179, + "GTTTCCTA": 22180, + "CCATCCCCA": 22181, + "TAAAGGCTT": 22182, + "CTCGTGAA": 22183, + "CAAATACAAA": 22184, + "GTGACAGTT": 22185, + "GTAAGCC": 22186, + "GTAAGGCA": 22187, + "GTAATAATA": 22188, + "TATGCCTA": 22189, + "CGGGGGTG": 22190, + "GATTTAGTT": 22191, + "GTTATACTT": 22192, + "TAGATACTG": 22193, + "GAATTGTTA": 22194, + "CGCACTG": 22195, + "TCCTGCATG": 22196, + "CCTCATTTG": 22197, + "GCCTGCATG": 22198, + "TGAGGCTCA": 22199, + "GCTCCTGTG": 22200, + "CTTCATCAA": 22201, + "TCACATTTTA": 22202, + "TAATCCTTTGGG": 22203, + "GTATTTAGTCAGCCACCAATTGTGCAAGTTCTCCCACTTAAAAAGATGAGAGAGGCCTG": 22204, + "TAGTCATG": 22205, + "GCGTATTA": 22206, + "TCATCTGAA": 22207, + "GAACAGAGA": 22208, + "GAATTAATT": 22209, + "CTATAATTA": 22210, + "CTACCCTA": 22211, + "CTCGGGCA": 22212, + "GTCTGACC": 22213, + "GCTGTTATT": 22214, + "GCAAGACTT": 22215, + "CTAACTCC": 22216, + "GTTTTGCTCTT": 22217, + "CAAAAGGCA": 22218, + "CCTACCCCA": 22219, + "CTGAATGTG": 22220, + "GCAAACAAAA": 22221, + "TCATTGACAA": 22222, + "GAGAAGAAAA": 22223, + "TATTATGTT": 22224, + "CTCAATTAA": 22225, + "CAAAAATCAA": 22226, + "CTCTGGATT": 22227, + "GTTTGCATA": 22228, + "CTATTGTA": 22229, + "GCCTGCGG": 22230, + "CCTCCTCTT": 22231, + "TCATGGTG": 22232, + "CAGCCTGTG": 22233, + "TATCCTTTG": 22234, + "CTTCAGTATT": 22235, + "CTTTCCTTTT": 22236, + "TCCGCCTGCC": 22237, + "CATTTTAATT": 22238, + "TAATAAAAAAA": 22239, + "CAAGCGG": 22240, + "CTTTATTTA": 22241, + "GTTTCTATA": 22242, + "TATACAATT": 22243, + "GTGACATTTT": 22244, + "CTTATAATT": 22245, + "TCTAGAGTT": 22246, + "TGCACAAAA": 22247, + "CTGAACATT": 22248, + "CGTGTATG": 22249, + "CTAAATGAAA": 22250, + "CAGGAGAGG": 22251, + "GAAGGGATG": 22252, + "CACTATTAA": 22253, + "GATCCTGGA": 22254, + "TCACCTGTG": 22255, + "TAACATGTG": 22256, + "GAACCCACA": 22257, + "GACTCTTTG": 22258, + "GTGGCATGTG": 22259, + "TCAGGGAAATGCAAATCAAAA": 22260, + "GACGTCTT": 22261, + "TAAGCAATT": 22262, + "CACAGATCA": 22263, + "GAGAGTAAA": 22264, + "GAGACAAGG": 22265, + "TGAATATGA": 22266, + "TCAATAATA": 22267, + "CCTTTCCTG": 22268, + "CCTTGGCTG": 22269, + "GTCACTTAA": 22270, + "GTAATACAA": 22271, + "CGGGGAAA": 22272, + "TACAGCTGA": 22273, + "CTTAGCCA": 22274, + "TCAAACCAA": 22275, + "GCTCAGGTT": 22276, + "GTCTTGGCTG": 22277, + "GTATGGCAA": 22278, + "GAATAGAATT": 22279, + "GATACATATG": 22280, + "GCAGTGAGCCGAGATCGCGCCA": 22281, + "TTTTTAGTT": 22282, + "TCAGTCATT": 22283, + "CTGCTTCTT": 22284, + "GTCTGATA": 22285, + "GAGAGTGAA": 22286, + "GAGACGAA": 22287, + "GGAAGATAA": 22288, + "GGAAGGGAGG": 22289, + "TAAAATACTA": 22290, + "TTTTAGATG": 22291, + "GAGGTGGAA": 22292, + "CCTGGATGA": 22293, + "CCTGCCTTA": 22294, + "GTTACATTTT": 22295, + "CTCCTGACTT": 22296, + "TTTTTTCATT": 22297, + "TCCTTCAAA": 22298, + "GCCTTCTCA": 22299, + "TGATGGTTA": 22300, + "GCAGGCAA": 22301, + "GCCCCCAAA": 22302, + "GCCCCCGCC": 22303, + "TGACTACTGGAAAAA": 22304, + "GTCTCGATCTCCTGACCTCGTGA": 22305, + "TCAGAGTCTTTTCCAATGAGTCAA": 22306, + "GAAGTGGATGCTCACAGTCA": 22307, + "TAGCCTCA": 22308, + "TTTTGAGTT": 22309, + "GTGCCCTC": 22310, + "GTGGCTCTT": 22311, + "TAAAGACTG": 22312, + "GTCTATTAA": 22313, + "TGTTGGCTG": 22314, + "GAGACAGAAA": 22315, + "CTCATAGG": 22316, + "CCTGCATCA": 22317, + "GATGTACAA": 22318, + "CTTTGGCA": 22319, + "TCTCTTCAA": 22320, + "GAAGATATT": 22321, + "CTTATTTAACTTA": 22322, + "TGACATATT": 22323, + "CCACCTATAGGGTT": 22324, + "TCGTCATG": 22325, + "TCATTTTATT": 22326, + "GTAGAAGAA": 22327, + "CTCGGTGA": 22328, + "TATATATC": 22329, + "TCTTGTGTT": 22330, + "TGTTGGTGA": 22331, + "GTGACCCA": 22332, + "GCAACCCC": 22333, + "CGGGTTTT": 22334, + "CCAAGGGAA": 22335, + "CTTACACAA": 22336, + "GATGCCCA": 22337, + "GGGGGGGA": 22338, + "CCTATTATT": 22339, + "CTGAAGACA": 22340, + "GCAAACCA": 22341, + "TAATGCCAA": 22342, + "GTTAAATAAGCAGG": 22343, + "GCAGAAAAAA": 22344, + "TCAGTGTTA": 22345, + "TCTCCTCTCCTCTCCTCTCC": 22346, + "TAAGTCAAA": 22347, + "TCCCGAGG": 22348, + "CTATATTAA": 22349, + "TATATAGTG": 22350, + "TGAATGCTG": 22351, + "CAGGTTTTA": 22352, + "GCTTTATCA": 22353, + "GTAACACTT": 22354, + "TACAGCTAA": 22355, + "TACATTTTTG": 22356, + "GGTTGCCATT": 22357, + "CCAAACTG": 22358, + "GCTGAGTCA": 22359, + "TATAATGAA": 22360, + "CTATTCTTA": 22361, + "TGATGTCTT": 22362, + "GTAAAATTTT": 22363, + "GACCACAGTG": 22364, + "TCAGGTGTG": 22365, + "GATAAATTA": 22366, + "GGCAGGAAAA": 22367, + "CGCCTGTAATCC": 22368, + "GGCACACA": 22369, + "GCGCAATT": 22370, + "TAATCTTAA": 22371, + "TGACAAAATT": 22372, + "GAAGCTGTT": 22373, + "GTGGCAGTG": 22374, + "GTTGTTTCA": 22375, + "CTCTGAAATT": 22376, + "GTCTGATGA": 22377, + "TATATCATT": 22378, + "CAGAGCCTG": 22379, + "GCTTTAAAAA": 22380, + "CTCAGGCCA": 22381, + "CATGCAGTT": 22382, + "CATGGGCCA": 22383, + "TTTTATAGG": 22384, + "TATGTAATA": 22385, + "GAAAATACC": 22386, + "GTAAATAGA": 22387, + "GTCTTGCTG": 22388, + "CTCAAGCCA": 22389, + "CAAAAATCACAA": 22390, + "CTGTGCATT": 22391, + "CATTATTTTA": 22392, + "CTTTACACA": 22393, + "TAACACAAAA": 22394, + "CTGTAGCTG": 22395, + "TGAATGTTTT": 22396, + "GTGATGAAAA": 22397, + "CAAACAAACAAAAAA": 22398, + "TGGGGAAAGGATT": 22399, + "TCGGGGTT": 22400, + "GATACATA": 22401, + "CTTTCCAGA": 22402, + "GTGGAAATG": 22403, + "TAAATCTTTT": 22404, + "CATTGATTTT": 22405, + "TCTGTAGTA": 22406, + "CATGTACTT": 22407, + "GTTATGACCAACCTAGA": 22408, + "GACATCAAA": 22409, + "GGTTCCCA": 22410, + "TTTTTTCTTA": 22411, + "CTCTTCAAA": 22412, + "TATTTTTTATT": 22413, + "GACCGAGG": 22414, + "CAATTCCAA": 22415, + "CAAAAAGAAA": 22416, + "CCTCCCCCA": 22417, + "GTCTGCCTT": 22418, + "GTTTAGTTTT": 22419, + "TCACCCTCC": 22420, + "GTTGGCCAA": 22421, + "GGCAGGAGAATCGCTT": 22422, + "TAAGTGGG": 22423, + "CCATTTTTG": 22424, + "CCAGTATG": 22425, + "CACAGAGGAA": 22426, + "CAAATTATG": 22427, + "CAGGCGCA": 22428, + "CAGACAGAGA": 22429, + "CTGAGGGCA": 22430, + "GCTTGATA": 22431, + "GTCACACC": 22432, + "GTTACAGTG": 22433, + "CCAATGAGTCA": 22434, + "TCCACGTA": 22435, + "TCTACCAAA": 22436, + "CGATAAAAA": 22437, + "CCCCTCCCCA": 22438, + "CTGAAGCC": 22439, + "TTTTTTTGAA": 22440, + "GCAAAGTGA": 22441, + "GGTACTAA": 22442, + "TCCAACAA": 22443, + "CTGTTCAA": 22444, + "CTCAACATCA": 22445, + "GAATACAGA": 22446, + "TTTTAAATTTT": 22447, + "CCAGCCTGGTCTACAGAGTGA": 22448, + "CAGTGATTTTGGAGCC": 22449, + "TAAGTATTA": 22450, + "TCATAATTTT": 22451, + "CTGTCATCA": 22452, + "CTTGAAGAA": 22453, + "CTTGTGCCA": 22454, + "GTTGGAAAAA": 22455, + "CCACCTCTG": 22456, + "GTCGATGA": 22457, + "TATTTCTGTT": 22458, + "CAGACAGC": 22459, + "GGAAGTCAA": 22460, + "GGAAGTGTG": 22461, + "CTGATTATT": 22462, + "GCTTAAATT": 22463, + "GTAACAGCA": 22464, + "GTTAGAGAA": 22465, + "GTTATAGAA": 22466, + "CTTACAGCA": 22467, + "TCCAGCATT": 22468, + "GATGGATGA": 22469, + "GGGACGA": 22470, + "AAAAAAGAAA": 22471, + "GAGGAGAGAA": 22472, + "GGATTGTCTT": 22473, + "TAACATCTG": 22474, + "GGCAAAGTAATGTCTCTGCTTTT": 22475, + "GCTAAAAAAA": 22476, + "TAATCAATT": 22477, + "CTACTGGG": 22478, + "GTCGGGAA": 22479, + "GTCGAGTT": 22480, + "TGTGGAGTG": 22481, + "CTGACACTG": 22482, + "CTGAGAGTT": 22483, + "GTAATATCA": 22484, + "GATTGTAAA": 22485, + "GCTACTGTT": 22486, + "TGCACCTGA": 22487, + "TCATTAAATT": 22488, + "TGATTGCATT": 22489, + "TCCAACTG": 22490, + "TCTTATATT": 22491, + "CTTTGGCAA": 22492, + "CGAAGAAA": 22493, + "TACCCCTG": 22494, + "CTGTGTCCA": 22495, + "TAACAGAGA": 22496, + "CTGTCTCTT": 22497, + "CTTCTAAAAA": 22498, + "GTTCTCCAA": 22499, + "GGAGCTTTT": 22500, + "CCATATAAA": 22501, + "CAGGGACTT": 22502, + "TATGGTTTG": 22503, + "TCTCCATC": 22504, + "GACATATTA": 22505, + "GCAAATATA": 22506, + "GGTATCTA": 22507, + "TCTTACCTT": 22508, + "GAGCGCC": 22509, + "GATCATTAA": 22510, + "GAGAGAGAGAGG": 22511, + "TCAGATGTT": 22512, + "TTATATCTT": 22513, + "TTTTTAGAAA": 22514, + "TCACTACATC": 22515, + "GTGTGACTGTT": 22516, + "CAGAAAACCAAA": 22517, + "TGAAGTCAGGTA": 22518, + "CATAGGAAA": 22519, + "CAACATTAA": 22520, + "GGACCCTA": 22521, + "CCAGGAGAA": 22522, + "TAAATAAGA": 22523, + "GCTTCTTAA": 22524, + "GTGAGATGA": 22525, + "CTCAGACTT": 22526, + "GTCAGATGAAA": 22527, + "TCTCAAATA": 22528, + "CTCCAAAATT": 22529, + "TACATAGCA": 22530, + "GAAAACCTG": 22531, + "TTATTCACAA": 22532, + "TTTTTTGTAGAGA": 22533, + "CGTGTGTA": 22534, + "CGTGCACCA": 22535, + "CGTTGCTT": 22536, + "CTAAACACA": 22537, + "CTCTTGCTG": 22538, + "TATTTTTATTA": 22539, + "GGCCATAGTG": 22540, + "CTAAAAGTA": 22541, + "CTAAAACTA": 22542, + "TGTGTGCATG": 22543, + "TTATAGCAA": 22544, + "TGATATCTG": 22545, + "TCATATGTT": 22546, + "GGACATCTA": 22547, + "TATATACATG": 22548, + "TAAATACATT": 22549, + "CTACATCTT": 22550, + "TAACTATTA": 22551, + "CGACAGAGCAA": 22552, + "GAATAATTA": 22553, + "GTTTCGTT": 22554, + "CCAGAAATT": 22555, + "GCCTGTGTT": 22556, + "GCCGGGGA": 22557, + "CAAATAGTA": 22558, + "CTTTTTTTTTTTTTTTTTT": 22559, + "TAGATATCA": 22560, + "CATACAAAAA": 22561, + "CCAAAGAGA": 22562, + "GTATTCCTT": 22563, + "GTGTTTCCA": 22564, + "TTATACTAA": 22565, + "CTTCAGAAAA": 22566, + "CTGCCTGAA": 22567, + "GGAATTAAAA": 22568, + "GGCCTGAAA": 22569, + "CCTATGTCCTGAATG": 22570, + "TCTGAGGTTATTGATATT": 22571, + "CCGCGTG": 22572, + "TGAGGAGA": 22573, + "GAATAGC": 22574, + "CTTCCACA": 22575, + "GTGGTTTA": 22576, + "CTCGGGAGA": 22577, + "TATTGCTTA": 22578, + "CAAAGCTGA": 22579, + "CAGACATTTT": 22580, + "CTCACGGA": 22581, + "CTTACTGAA": 22582, + "GAAAATGGAA": 22583, + "CAAAAGAGG": 22584, + "TAGATACAA": 22585, + "TCAAACTTA": 22586, + "TGGACCAA": 22587, + "CGTTTCAA": 22588, + "CTAAATACCAA": 22589, + "CTTAATTCA": 22590, + "CTGTGCACA": 22591, + "TAATAATAAAA": 22592, + "TATGATTTG": 22593, + "CTGCATCTG": 22594, + "GCCAGGCGTGGTG": 22595, + "TTATAGTTTT": 22596, + "AAAAAAATTAA": 22597, + "TGATATCAA": 22598, + "GGCTCCAAAA": 22599, + "TGAGTAGCTGGGACTACAGG": 22600, + "GCTGCATAGTATTCCATGGTGTATATGTGCCACATTTT": 22601, + "GCGTCATG": 22602, + "CTTGTCAAA": 22603, + "CTTGCTCTT": 22604, + "GTAGAGCC": 22605, + "CTCCAGATT": 22606, + "GTCTCAAAAAAA": 22607, + "CACACGGA": 22608, + "TCTTGTTAA": 22609, + "CAAAGGCCA": 22610, + "TGAACTAAA": 22611, + "GCTTTCCTG": 22612, + "CTCATAGAA": 22613, + "CCCCCCTCA": 22614, + "TGTACCACATTTT": 22615, + "GCAAATGAAA": 22616, + "TCTGACCTT": 22617, + "TCCTTCCCA": 22618, + "CGTTCTCA": 22619, + "TAATAGTT": 22620, + "TAATAGTGA": 22621, + "GGTACCAAA": 22622, + "GAGAATTTTA": 22623, + "GCCAACAA": 22624, + "GACCTAAA": 22625, + "CCCTGCCCTG": 22626, + "GATCCCATTTG": 22627, + "CCGGGAGG": 22628, + "GCCCACCCC": 22629, + "CTGTAAATT": 22630, + "GCATCACC": 22631, + "GCAGTTGG": 22632, + "GCAGCTCCA": 22633, + "CACAGAGTC": 22634, + "TGTTGATTA": 22635, + "GAGAGAGGAA": 22636, + "CAGAGGAGG": 22637, + "GGAAGGATT": 22638, + "CTGACACTT": 22639, + "CGGGCCA": 22640, + "TACAGTATA": 22641, + "CAAAATAGG": 22642, + "GGTGGATCA": 22643, + "TCCTTGCTG": 22644, + "TAATAGTAAA": 22645, + "GCTCAGAAA": 22646, + "TCCAAGTGA": 22647, + "CGCACGC": 22648, + "GCAGGTGAA": 22649, + "GATCCAAAA": 22650, + "TAACAAGTTCAAA": 22651, + "TTACATATG": 22652, + "CGAGTAGCTGGGATTACAGG": 22653, + "CCTCACTCAGGATGA": 22654, + "GCGACTTCACTTTCACTTTTCACTT": 22655, + "GATAGGAAA": 22656, + "TTTTCCAAA": 22657, + "CTGGCTTTT": 22658, + "CTTCAACAA": 22659, + "GCCCCAGCA": 22660, + "TATTTAGC": 22661, + "CACAGGTGA": 22662, + "GAGAGAAGG": 22663, + "TAAAAGCATT": 22664, + "TAATATAATAA": 22665, + "TGATTGATG": 22666, + "AAAAAATATA": 22667, + "GCCAACAGA": 22668, + "TAATGGCAA": 22669, + "CAGCCCAGG": 22670, + "CCCTTCTCC": 22671, + "GCTCCCTCC": 22672, + "GAAGACTCA": 22673, + "CTTTAGATT": 22674, + "GTGGACGG": 22675, + "GTTGATGTT": 22676, + "CAGATGTTA": 22677, + "TAAGTGAAAA": 22678, + "TACACAATGGAGTACTA": 22679, + "GCCACCGCGCCCGG": 22680, + "TATCAAATT": 22681, + "GTAGCAGCA": 22682, + "CACACAGAGA": 22683, + "TCTTGTTTG": 22684, + "TGTTCTCATT": 22685, + "CATTCAGTA": 22686, + "CTGAGAGGA": 22687, + "CATGTACAA": 22688, + "GAAAAGGTG": 22689, + "GTCCCACCA": 22690, + "CGAGCTGA": 22691, + "TGGGGCAA": 22692, + "TAATTCACTG": 22693, + "GGCAGCTA": 22694, + "CGCGGAA": 22695, + "TCTGAGCCA": 22696, + "TCCTTGAGAA": 22697, + "CTAAAGTTA": 22698, + "CTGTTGGAA": 22699, + "CATGATTTG": 22700, + "GCGGGGA": 22701, + "GAAATTTCTT": 22702, + "GAGAAAGAAA": 22703, + "CCACCCAGG": 22704, + "TTACATGAA": 22705, + "CTCACCCTCATG": 22706, + "GAAGTTAAA": 22707, + "TCTTGCTTA": 22708, + "TGTTCACAA": 22709, + "CAGACCCTG": 22710, + "GTGATAGAA": 22711, + "TCTCGGTG": 22712, + "CTTTTCATA": 22713, + "CGATCAAA": 22714, + "CCCAGTGAA": 22715, + "TGGGTAGG": 22716, + "GACACTCC": 22717, + "TGTACGG": 22718, + "TCAAACAAAA": 22719, + "TCCTTCCTCC": 22720, + "GGCCTATT": 22721, + "CTGTGTATT": 22722, + "GTGTGTGA": 22723, + "CTGGGGAAA": 22724, + "CTCCACTCA": 22725, + "CAAGGCTACAGTAA": 22726, + "CAACATAAA": 22727, + "CCAATGAAAA": 22728, + "TCTTTTAATG": 22729, + "CTCGGGAGGCAGAGGCAGG": 22730, + "TACTCCTG": 22731, + "GACGTGTT": 22732, + "TCAGTGCTG": 22733, + "CAAGCTCTG": 22734, + "CTTGCTGAA": 22735, + "GCCCTTCC": 22736, + "CTCTGAAAAA": 22737, + "TGTTCTGAA": 22738, + "CCTTCAGTG": 22739, + "GCAACATCA": 22740, + "TTTTAGCAA": 22741, + "TCCATACTA": 22742, + "GATGGCAAA": 22743, + "TTTAGTC": 22744, + "TAGACATTA": 22745, + "TCAAATGGA": 22746, + "TCTCCCCAA": 22747, + "TCTGAGGCTG": 22748, + "CACCTATT": 22749, + "GTTAAAAATG": 22750, + "CAGTGAAATG": 22751, + "GTTTGTATA": 22752, + "TGATGGGA": 22753, + "CAATGCCTG": 22754, + "CATCCTTTT": 22755, + "TGACATGTG": 22756, + "CCAGAGTCA": 22757, + "TCAATGTTTT": 22758, + "CTAGGCAAA": 22759, + "GAGCTGTCAAA": 22760, + "TCAGCCCAGAA": 22761, + "CCTGCTGATTTTGTA": 22762, + "GGTATATA": 22763, + "TAAAAAAAAAAAAA": 22764, + "TTACACAA": 22765, + "GTGGGAGAA": 22766, + "GTTCTCATT": 22767, + "GTATCTATA": 22768, + "GCCGGGCC": 22769, + "GAGAGATAA": 22770, + "GAGAGACAGA": 22771, + "TGAAGGGAA": 22772, + "CTGAGACCA": 22773, + "GTGACAGGA": 22774, + "TTTTACAAAA": 22775, + "GATTGGTA": 22776, + "CTTATAGAA": 22777, + "GTTTTTCTTTT": 22778, + "TCTAGAATT": 22779, + "TAATTGAGG": 22780, + "TCTCAGAAAA": 22781, + "TCTCCTGTA": 22782, + "TCTGATGTT": 22783, + "TGATTCACA": 22784, + "GTTAAGAAA": 22785, + "CTCTGTATG": 22786, + "GTTTGTATG": 22787, + "GCCTGCACA": 22788, + "TGAGATAGG": 22789, + "TTTTTTTTTTTTTG": 22790, + "CATAATATA": 22791, + "GCATGGTGA": 22792, + "CGTCGCC": 22793, + "TCCCATGTT": 22794, + "GTACAGTCA": 22795, + "TCAAAAAATG": 22796, + "CTGTATC": 22797, + "CTTTAAGA": 22798, + "CTTCTTTTTT": 22799, + "GTTTCGAA": 22800, + "GTATĊ": 22801, + "CTACCTC": 22802, + "TGAAGAGCA": 22803, + "CAGACATAA": 22804, + "GTGACCTCA": 22805, + "CATGGACC": 22806, + "GTAACC": 22807, + "GTAACATCA": 22808, + "TCTCTGTGTT": 22809, + "TCTAGCATT": 22810, + "GACACGCA": 22811, + "GATATCCA": 22812, + "CGCTAAAA": 22813, + "GCAAATTCA": 22814, + "GAATTGCAA": 22815, + "CGAACGG": 22816, + "CTCTGTATT": 22817, + "CCTCCTGCC": 22818, + "TCATGCCA": 22819, + "CAGCCTTTT": 22820, + "TCAGGCAGG": 22821, + "TCAGACATT": 22822, + "GAATACTTA": 22823, + "GAACACCAA": 22824, + "GTGGTGGGCA": 22825, + "CACATGTATA": 22826, + "CAAACAAACAAAAA": 22827, + "CTTCTCCAA": 22828, + "CTAGCTTA": 22829, + "GAAAGATGAA": 22830, + "CAAATCATT": 22831, + "CAAACAGC": 22832, + "TGAAGACTT": 22833, + "CAGGGACAA": 22834, + "TTTGTAGG": 22835, + "GTTTTTATAA": 22836, + "GGGGCCCTG": 22837, + "CCCCGGCC": 22838, + "TAATTTCTTTT": 22839, + "TCCTTCTTTT": 22840, + "GAGCGAGG": 22841, + "TCATGTCTG": 22842, + "GAATACCCA": 22843, + "CAAGACATG": 22844, + "TATTTATTTATTTATTTT": 22845, + "TAGAAAATCTTTGGAGG": 22846, + "CACAAAAATTA": 22847, + "GAGTTAATT": 22848, + "TAATCTCTG": 22849, + "TCATTTTTA": 22850, + "TTACATTTT": 22851, + "TCCGTCCATG": 22852, + "CTGGAAGAA": 22853, + "CTGCCCTA": 22854, + "GTTCTTCA": 22855, + "GCCTAAAAA": 22856, + "GCCCAAGAA": 22857, + "CTAGAAGG": 22858, + "TGTGCATGTG": 22859, + "TATTGAATG": 22860, + "TCTTGCATT": 22861, + "CAGGAAATA": 22862, + "GTTATCCA": 22863, + "TCTACTC": 22864, + "GTCCGTCC": 22865, + "TTATTGCTT": 22866, + "TCTCATATG": 22867, + "CTGAAGCTT": 22868, + "GTAAATGAAA": 22869, + "GCAAAGGA": 22870, + "TAATATCTG": 22871, + "GCTCACAGAA": 22872, + "GTGTTGCTG": 22873, + "CGCCCAGG": 22874, + "CAGTGTCTG": 22875, + "GGCTGTTTT": 22876, + "GAGAGAGATG": 22877, + "CAATACAAAA": 22878, + "TCACACGG": 22879, + "TTTTTTTTTTTTTTTG": 22880, + "CTCGGGAGGCTGA": 22881, + "GACCCCAAA": 22882, + "TGACAACAA": 22883, + "TCCGACC": 22884, + "CTTCTGTA": 22885, + "TAAAGCTAA": 22886, + "TATTCCTAA": 22887, + "TATTTCTATT": 22888, + "GAGAGACTT": 22889, + "TGAACATCA": 22890, + "TCAACGAA": 22891, + "GGAATCGAA": 22892, + "CTGACCCTG": 22893, + "CCTGGACTT": 22894, + "GTTTTCGA": 22895, + "GTTTTGGTA": 22896, + "TGGGTCTG": 22897, + "CGCTCCTG": 22898, + "CGTGCACA": 22899, + "GGAGGTTAA": 22900, + "TGAGATCTGA": 22901, + "GGCCAGGCA": 22902, + "GCAGGGAAA": 22903, + "CTGGGTTAA": 22904, + "CTGCACCCA": 22905, + "CTTTACCAA": 22906, + "CCACAGCCA": 22907, + "GCGCACTG": 22908, + "TCAGTTAAA": 22909, + "CTTCACATT": 22910, + "GCATCAATT": 22911, + "CTCTCTCTCTCTCTCTCTC": 22912, + "GTCTCACCA": 22913, + "TGTGGAAAAA": 22914, + "CACATACAAA": 22915, + "TAAAACACTT": 22916, + "GCTGGGGGA": 22917, + "CTCACTCTT": 22918, + "CTCCTAAAA": 22919, + "GAAAAGGCA": 22920, + "GAAAATACTT": 22921, + "TTATTTAAAAA": 22922, + "CCTACTC": 22923, + "GTAAAGAGA": 22924, + "TTTTTTGGGG": 22925, + "GCAAACAGA": 22926, + "TCTTATCTT": 22927, + "CTGTGGCA": 22928, + "GAGGACAGG": 22929, + "TGAGAGCCA": 22930, + "GTTCTTAAAA": 22931, + "TTACATAAA": 22932, + "CAGACCGACAGA": 22933, + "GGTCTTTTA": 22934, + "TAACTTCA": 22935, + "TAATCAGAA": 22936, + "TCATGAATT": 22937, + "TTACATCA": 22938, + "CTTGCGG": 22939, + "GTGGĊ": 22940, + "GTAGTGGG": 22941, + "TAAACCTAA": 22942, + "GTCTCTCAA": 22943, + "CATTGTCAA": 22944, + "TGAACATGA": 22945, + "TCAAGGGAA": 22946, + "GGAATCCA": 22947, + "GTGATGTGA": 22948, + "CCTGGCCTG": 22949, + "TTTGGCCA": 22950, + "TCTATTTTAA": 22951, + "GCAAACTCA": 22952, + "TCATTCCCA": 22953, + "CTGTTCCCA": 22954, + "GTTAATTCA": 22955, + "TCTAATGAA": 22956, + "TGAGGGAAA": 22957, + "GTTCCCTTTT": 22958, + "GTCCATCCTT": 22959, + "TACAAAATG": 22960, + "GGGAAAATG": 22961, + "GAGTAAATT": 22962, + "TAACAAGG": 22963, + "TCACAAATT": 22964, + "TCCTCAGTG": 22965, + "CTTGTTTCA": 22966, + "CTTTGATA": 22967, + "TAAAGGTCA": 22968, + "GTGAGCCAA": 22969, + "CCTTTAAAAA": 22970, + "GAGGCCCTG": 22971, + "GTTTTCAAAA": 22972, + "TCTGACATG": 22973, + "CGTTCAAA": 22974, + "CTAAATGTA": 22975, + "CGCCTGCA": 22976, + "TGAGATATT": 22977, + "GAGCTAAAA": 22978, + "CATTATATA": 22979, + "GAGTGGAGG": 22980, + "GGATTTGTT": 22981, + "TACTGTTAA": 22982, + "TTATATTTTTT": 22983, + "GTGATGGGACCA": 22984, + "GTCATGTCCGACTC": 22985, + "GTTGCATCCAAA": 22986, + "TCAAATATTATATTATTAAA": 22987, + "TGACTTCA": 22988, + "GGATATTAA": 22989, + "CTCGTCATT": 22990, + "GTCGGGGA": 22991, + "CACATACTT": 22992, + "GCAACGTG": 22993, + "CCAAGACAA": 22994, + "CTTACTTAA": 22995, + "GATGCAGTT": 22996, + "GACAGAAAAA": 22997, + "CCCCTCTCA": 22998, + "TCAAATCTA": 22999, + "GTAAATATTA": 23000, + "TCTCCCATG": 23001, + "CCAAAGGA": 23002, + "CGTTCTA": 23003, + "GCTGAGTAA": 23004, + "CAATTAAAAAA": 23005, + "CCATTGACTT": 23006, + "TACTACAGA": 23007, + "TCATACATT": 23008, + "GCACCCACA": 23009, + "CGGGGTTTCACCA": 23010, + "GATCCTTGAGGAA": 23011, + "TGCCTCCAGCTTTGTT": 23012, + "GGGTCTTTT": 23013, + "TTTTTGCC": 23014, + "GTGCTAAAA": 23015, + "GTTTCCCTT": 23016, + "CCACAAGG": 23017, + "GCCTAGC": 23018, + "CTCGGCGG": 23019, + "GTCGCTGA": 23020, + "TGTTGGCC": 23021, + "CTGACCCCA": 23022, + "CATGCATCA": 23023, + "TATGGAATCA": 23024, + "CTTACACTT": 23025, + "GAAAATACAA": 23026, + "CTTTTTGAAA": 23027, + "GGGGGGCA": 23028, + "TCTGAGGTG": 23029, + "GGAAACTG": 23030, + "TCCTTCCCTCA": 23031, + "GGTAGCCA": 23032, + "TGATTCCAA": 23033, + "TCCAAGCA": 23034, + "GGAGGCAA": 23035, + "TAATGGACA": 23036, + "TAAAAAATAAAAA": 23037, + "GGGTTTGGG": 23038, + "GGGCGTGGTG": 23039, + "TCATAGAAA": 23040, + "CTGCCCTCC": 23041, + "CAACAGAGCGAGA": 23042, + "CGAGTAGCTGGGACTACAGG": 23043, + "TTACCCA": 23044, + "GAACGTC": 23045, + "TCCTCGA": 23046, + "GTGTTAATT": 23047, + "GTCTGAGGA": 23048, + "TGTTCTATT": 23049, + "CAAACAGAGA": 23050, + "TCAACAGTG": 23051, + "GTCATATTA": 23052, + "TATGCTTAA": 23053, + "TCTCTAAAAA": 23054, + "TTAAGAGG": 23055, + "TTAACATA": 23056, + "TCAAATACA": 23057, + "TAATTTCTAA": 23058, + "GAATTGGAA": 23059, + "CTTTGTCTG": 23060, + "GTTTGGTGA": 23061, + "GAGCAGAGCA": 23062, + "TAAATTTATA": 23063, + "TCAGGGATG": 23064, + "GATCTTATT": 23065, + "TCCTTAATT": 23066, + "GTGCCACTG": 23067, + "GCATCTATT": 23068, + "GGACAGTC": 23069, + "GTACAGAGA": 23070, + "CTCGACTG": 23071, + "CTCTAACA": 23072, + "TGAACGTT": 23073, + "CGGCAGAA": 23074, + "CCTGCAGTT": 23075, + "CCTGGACCA": 23076, + "TCTCTCCCTC": 23077, + "TACAGTGTG": 23078, + "CTTACATTTT": 23079, + "TTTGGGAAA": 23080, + "TCTGACAA": 23081, + "TAAATTCTA": 23082, + "TTTTTGTATT": 23083, + "AAATAGAAA": 23084, + "TCACATCTT": 23085, + "CATTTGGAGCGCTT": 23086, + "TACTCTTTT": 23087, + "TGAGCTCTG": 23088, + "TTATACTT": 23089, + "GAAGCAGCA": 23090, + "CTGTGTGTG": 23091, + "CTGAAAATG": 23092, + "GTATCGG": 23093, + "GCCTATTTGAA": 23094, + "TATTGGCCA": 23095, + "CATTGCCTT": 23096, + "CAGGGCTCA": 23097, + "TCAAGGCCA": 23098, + "GCTTTGAGG": 23099, + "GAAAATTTTA": 23100, + "CTTTTCCCA": 23101, + "GGGGCGA": 23102, + "TTTATAATA": 23103, + "GACACTGTT": 23104, + "GCTACTAAA": 23105, + "GGAAATGGG": 23106, + "GTATTCAAA": 23107, + "CGTACATA": 23108, + "CAGGAGTGA": 23109, + "GAGCTCAAA": 23110, + "TAGTTCTA": 23111, + "GCGGTGTG": 23112, + "GCGGCTCA": 23113, + "CTCCATCTCAAAAAAAAAAAAAAAA": 23114, + "GTTTATTTTTT": 23115, + "TAACATCTT": 23116, + "TGACACGA": 23117, + "GTGCAGTCA": 23118, + "CAGCTTCTA": 23119, + "GGTGTGTGTG": 23120, + "TAATCACAA": 23121, + "TTATCTTA": 23122, + "CTTGTAATT": 23123, + "GGACTGAAA": 23124, + "GAGACGTT": 23125, + "TGAATAATG": 23126, + "GCAACAGC": 23127, + "CTCCCATGA": 23128, + "TACAGTCTT": 23129, + "TCCATTTTAA": 23130, + "TCCATACTG": 23131, + "TCTAGCTAA": 23132, + "GTCCCCCTG": 23133, + "CAAAATATCA": 23134, + "CCCCGTGGG": 23135, + "CCCCTCCTCC": 23136, + "TCATTTCTTTT": 23137, + "GCATTCACA": 23138, + "CCCCATTTT": 23139, + "GAACATTTTTT": 23140, + "GCGATATG": 23141, + "TGACATAAA": 23142, + "CAACATCTG": 23143, + "GTTGAGGAA": 23144, + "CTTGGTAGCCCAGGCTG": 23145, + "GGAAACGGGATTTCTT": 23146, + "GGAAATGGCAACCCACTCCAGTATT": 23147, + "TGACAAATG": 23148, + "TCCTCTTTG": 23149, + "TATTGCCAA": 23150, + "TAAAAGAGAA": 23151, + "GATTAAATG": 23152, + "CTAACCCA": 23153, + "TCTAGATTA": 23154, + "CTTTTCAGA": 23155, + "GGGGGTT": 23156, + "GCTACGTT": 23157, + "TGTATTTTAA": 23158, + "CGCGGCTG": 23159, + "TAAAAATGCA": 23160, + "TCCTTTAAA": 23161, + "CATCCTGGG": 23162, + "GCGTGGAGCC": 23163, + "GATGTGGGGTA": 23164, + "TTTTAATGGTAGGTT": 23165, + "GTCGTGTCTGA": 23166, + "TGAGTTCTT": 23167, + "GCAGCCCCA": 23168, + "GAAACTATT": 23169, + "TATACCAAA": 23170, + "TGTTCCTCA": 23171, + "GCTTTCTAA": 23172, + "CGGGGCCA": 23173, + "GATTTATGA": 23174, + "TCCACGCC": 23175, + "TCTACTTAA": 23176, + "TGGAGCTGA": 23177, + "CTTTGTATA": 23178, + "TCCTGGGGG": 23179, + "CAGCTCTAA": 23180, + "GAGGAGCTG": 23181, + "GCTCCTGCA": 23182, + "GCGGCACA": 23183, + "CCCTCTCTT": 23184, + "CCCTCTTTG": 23185, + "TCACCTAGG": 23186, + "TCTCAATAGATG": 23187, + "GCACATAAA": 23188, + "CAAAATCAGCAGGGGATCAAA": 23189, + "CATAGGTG": 23190, + "GGCTTATG": 23191, + "TAATATTAAA": 23192, + "TCATGATTA": 23193, + "TCCCTTGA": 23194, + "CAACTGGG": 23195, + "GTGGCGCA": 23196, + "GTCTGGGTT": 23197, + "TATTGATCA": 23198, + "TCTTGGTGA": 23199, + "GAGACCAAA": 23200, + "CATGGAGCA": 23201, + "TTTTACTCA": 23202, + "GTCCTCTCC": 23203, + "GTCCCGGA": 23204, + "TTTATTATTA": 23205, + "GGTTCTTTT": 23206, + "GTAAAGCA": 23207, + "GGAAACAGA": 23208, + "GACCTAGA": 23209, + "CAATTCTCA": 23210, + "CGAATGTT": 23211, + "CTCTGTCAA": 23212, + "CCTCGGTG": 23213, + "TCAGGGATT": 23214, + "CTTCCTCCTT": 23215, + "GGAGGAGGGA": 23216, + "TAAAGAAAATGTG": 23217, + "GTCATTTAGCAGACGCTCTTATCCAGAGCGACTTA": 23218, + "GAAGCAGAA": 23219, + "CTTGTTATT": 23220, + "GTACATC": 23221, + "CTCGGTCA": 23222, + "GAAAGAACC": 23223, + "GTGAGGGTG": 23224, + "GCAATATCA": 23225, + "CCAACATGA": 23226, + "CTTACAGTA": 23227, + "TGGACAAAA": 23228, + "TAATTGCTG": 23229, + "CCTAGCTA": 23230, + "TCCTTGCC": 23231, + "GTGAACTA": 23232, + "TATTTTTTCA": 23233, + "GTCTTGTTTT": 23234, + "CCATTTATTAAA": 23235, + "CCTCCCTA": 23236, + "CGTCTGTA": 23237, + "GAGAGAGATT": 23238, + "TCTCTCTCTCTCC": 23239, + "GTTTATCTG": 23240, + "CCCTCCATCA": 23241, + "GTTGGTAGAGCATG": 23242, + "TATTTATGAA": 23243, + "GATTAATTA": 23244, + "TAATCCCAGAA": 23245, + "GAAAGAGAGGCCCATT": 23246, + "GAAAAGAAAAGAAAAGAAAA": 23247, + "TGAACTAACCAGTA": 23248, + "GATTTGCATTTCCCTGA": 23249, + "CAGTTAATT": 23250, + "GAGCAAATT": 23251, + "TGACACATT": 23252, + "GAATTATTA": 23253, + "GAAGCAGA": 23254, + "GTGGCCACA": 23255, + "GTTTAACAA": 23256, + "TGTGCACTT": 23257, + "GTGATGATG": 23258, + "GTCATCAAA": 23259, + "CTAAGAGAA": 23260, + "TGAAATGTT": 23261, + "CTTAAGTA": 23262, + "CTTAATTAAA": 23263, + "TCTTATAAA": 23264, + "GTGTTGGA": 23265, + "GGGAAGGTG": 23266, + "CATTACATT": 23267, + "GTCTGGCTT": 23268, + "GAATGGGA": 23269, + "CAGCCTTTG": 23270, + "GAATAGTAA": 23271, + "TGACAGTTA": 23272, + "TAAATAAGGCTG": 23273, + "TACACTAGCAA": 23274, + "CCTCACATAAATTTTCAAACCACATAAATTTTCGTTCGGTTTCATCAAACCCATCCTAATT": 23275, + "TTAGATAA": 23276, + "TCCTAAATT": 23277, + "CAACTGAAA": 23278, + "CAAGAACA": 23279, + "GAGAGGGAGG": 23280, + "CCTTGCTCA": 23281, + "CATGCACC": 23282, + "GTCAGGGCA": 23283, + "GAGGTACTG": 23284, + "GTTAGGTA": 23285, + "TAGACACAA": 23286, + "GCAAAGACA": 23287, + "TGATTTCAAA": 23288, + "TGATTTCATT": 23289, + "AAAAAATTAAA": 23290, + "GCCAATTAA": 23291, + "CAATTTCCA": 23292, + "CAATTCAATT": 23293, + "GGAGGGCTG": 23294, + "TCTAACTA": 23295, + "GAAAAAGCAA": 23296, + "TGAGAGTAA": 23297, + "CCAGGGAAA": 23298, + "TCATATCAA": 23299, + "TATTCAACATA": 23300, + "GCTGGAATCAAGATT": 23301, + "GGCAAAATT": 23302, + "TCATCTAAA": 23303, + "TCAGTGGA": 23304, + "TGACTGGA": 23305, + "CAATĊ": 23306, + "CTTCAGATT": 23307, + "CTTTCCCTG": 23308, + "CCATATC": 23309, + "CCACTTCTT": 23310, + "CTCGAGAA": 23311, + "CAAACCCACA": 23312, + "CATTTATTAA": 23313, + "TGAAGCTGA": 23314, + "TGAAGACTG": 23315, + "TCTGCACCA": 23316, + "CAGACGTT": 23317, + "TTTTATTTTATT": 23318, + "TATGGGCA": 23319, + "TTTGGCTG": 23320, + "TCTATAAAAA": 23321, + "GCCACGAA": 23322, + "GTCCCTGAA": 23323, + "CAAAATACAA": 23324, + "TGGGGTAA": 23325, + "GCTACCAAA": 23326, + "TCCAAGAAA": 23327, + "TCCAACTCA": 23328, + "TGATGCAAA": 23329, + "CCCTCCAGG": 23330, + "CAATATGTT": 23331, + "GGACAGCTG": 23332, + "TAAATATGTT": 23333, + "GACCTTTGTT": 23334, + "GGTAGGTG": 23335, + "TTACAGTT": 23336, + "GTTTAACTT": 23337, + "CCATĊ": 23338, + "CCACTAAAA": 23339, + "CCAGCTCTT": 23340, + "CTCGCTGA": 23341, + "CTCGCGG": 23342, + "CACATACTG": 23343, + "TGAAGGCAA": 23344, + "GGAAGAAGG": 23345, + "GATGGGTA": 23346, + "TCTACCTCA": 23347, + "GCCATACC": 23348, + "CATACTAAA": 23349, + "GAATTGAATT": 23350, + "GTGAAAAATT": 23351, + "TCCAAGATT": 23352, + "GCCTGGAAA": 23353, + "GTGCCACTGCA": 23354, + "TCCTACAATGTGATTTT": 23355, + "TCACCTGAGGTCAGGAGTTTGAGA": 23356, + "TATAAAATA": 23357, + "CTTTCCCA": 23358, + "GTTCTGCC": 23359, + "GCACTGGA": 23360, + "GTCTTATTA": 23361, + "CTGATACAA": 23362, + "CTGACAGTT": 23363, + "CATGCAGTG": 23364, + "TATGCAGTG": 23365, + "GAGGGGGAA": 23366, + "CCTGCATTA": 23367, + "CCTGGCTCC": 23368, + "GAAAATCC": 23369, + "CTTTTGATA": 23370, + "TGCAĊ": 23371, + "GCAAAGTG": 23372, + "TGAAATTAAA": 23373, + "CCAAATAGG": 23374, + "GTGAATAAA": 23375, + "CGCCCTCC": 23376, + "GCCTTTCTT": 23377, + "GTTTGTAAA": 23378, + "TAGAATTTA": 23379, + "GCAGGCACA": 23380, + "GACCATTTT": 23381, + "TGACACCAA": 23382, + "GTCCAGTCA": 23383, + "TAGGTGACCAAA": 23384, + "TCTGTCTGTCTGTCTGTCTGTCTGTCTGTCTG": 23385, + "TCATCTGTG": 23386, + "GAAGCTGAA": 23387, + "TCCTCACTT": 23388, + "GTTTCCTCC": 23389, + "GTACTGAAA": 23390, + "CTATGAGG": 23391, + "TGTTGTGTG": 23392, + "TGTTCTTGG": 23393, + "CATGCACTT": 23394, + "GCAACAGGA": 23395, + "GAGGGGCC": 23396, + "TACATCTTA": 23397, + "GATGGTCTT": 23398, + "GGGATCAA": 23399, + "TAAAAAGACA": 23400, + "TGAAATTCA": 23401, + "CCATTCCCTT": 23402, + "TGTTAATTAA": 23403, + "GAGCCTTA": 23404, + "GTTCACCAA": 23405, + "TTAGGGTTA": 23406, + "CACTTTTTG": 23407, + "TATCTCATT": 23408, + "TCGAAATA": 23409, + "TAAGTAGA": 23410, + "TCACTTCA": 23411, + "GAACAAATT": 23412, + "GTTCTTATT": 23413, + "GTATGTGAA": 23414, + "TAAAGATTTT": 23415, + "CTCGAGCA": 23416, + "TGTGGGTGA": 23417, + "CATTTATAAA": 23418, + "TGAATACC": 23419, + "CAGGAAAAAA": 23420, + "CAGAGCTCC": 23421, + "TAAAAGGCA": 23422, + "TCTCCATCTT": 23423, + "CCAATCAAAA": 23424, + "CTAACACTG": 23425, + "CCCAGCATT": 23426, + "TAGACC": 23427, + "GAGAAGGGA": 23428, + "GAAAAACATT": 23429, + "GAGCGGGG": 23430, + "GGATGGAAA": 23431, + "CAGGAATTTT": 23432, + "CAACACCTG": 23433, + "CCACCCCAA": 23434, + "GTTATGAAAA": 23435, + "TCTGTCATAGTT": 23436, + "TGAATGAATGAATGAA": 23437, + "CCGCTGCCCAAGGAA": 23438, + "GAGAAATGCAAATCAAAACCACAATGAGATA": 23439, + "TAATCCATG": 23440, + "TAAGTACA": 23441, + "TGACCCCATGGA": 23442, + "GTAGCAAAA": 23443, + "CTCTCTCTCC": 23444, + "GTCTCAAAAA": 23445, + "TATTCCTGA": 23446, + "TCTTGGCCA": 23447, + "TCTTGACTG": 23448, + "TGTTGATTTT": 23449, + "TGAACGAA": 23450, + "TTTTATGGG": 23451, + "CTAACAGCA": 23452, + "TCCATCCATT": 23453, + "CGATATTTT": 23454, + "TTTACGG": 23455, + "GGTGAAATT": 23456, + "GGAAACAGG": 23457, + "TCCTTGGGG": 23458, + "GTGAAGGGG": 23459, + "GTTAATTAAA": 23460, + "GAAAAAGGAA": 23461, + "TAATGGCTG": 23462, + "CCCTGTTTT": 23463, + "CTCCAGTAA": 23464, + "CCATAGATT": 23465, + "TAAAATGCAAA": 23466, + "CTGTGAAGAAA": 23467, + "CGTAAAATT": 23468, + "GATCTCATT": 23469, + "GCCACACACA": 23470, + "TAAGCAGAA": 23471, + "CAACTCAAA": 23472, + "GCAGTTCTG": 23473, + "GGATATATT": 23474, + "GCCCGCTG": 23475, + "TGTGCCTCA": 23476, + "TGTTTCTCC": 23477, + "GAGAGCAGG": 23478, + "CTGAGATAA": 23479, + "TTTTATAATT": 23480, + "CTAAGACC": 23481, + "CTTACATAA": 23482, + "CGATCTA": 23483, + "CCCACGC": 23484, + "GACAGTGTT": 23485, + "CATAGCAAA": 23486, + "CATACTTAA": 23487, + "TTATTCATG": 23488, + "TTATTAAAAAA": 23489, + "TCCTTCTCTT": 23490, + "CAAAAAGAA": 23491, + "TCAAAAGAAA": 23492, + "CCCTGCTG": 23493, + "TAGTGTTTA": 23494, + "TAAGACACA": 23495, + "GGATTTTTAATGAATT": 23496, + "TCAATTTTGGCTTTT": 23497, + "CCTATTTCTCCACATCCTCTCCAGCA": 23498, + "CCTCCAATT": 23499, + "TACCAATG": 23500, + "GCATTAATT": 23501, + "TCTTTCTGTG": 23502, + "TGTTCTCTG": 23503, + "CAGGTCATT": 23504, + "TCTGCAGC": 23505, + "TAAAATTTTAA": 23506, + "TTAACCAA": 23507, + "GTTTTGCTG": 23508, + "GTCCCCCC": 23509, + "TAATTAAATG": 23510, + "TCTCATGGA": 23511, + "CGTGTGCATGTG": 23512, + "GCAAATGGA": 23513, + "TCCTTTCTA": 23514, + "GGCCGGGCA": 23515, + "CAGAACTG": 23516, + "TCTTACAAA": 23517, + "CTTTGCACA": 23518, + "GACCCGTG": 23519, + "GTATTCTGTT": 23520, + "TGATGTGA": 23521, + "GAACAGCTG": 23522, + "CTTCTGTTTT": 23523, + "CTCCTGCCTCAGCCTCCTGAGTA": 23524, + "TAGACGGCAGCCCACCAGGCTCC": 23525, + "TACTCATG": 23526, + "GACCAAATT": 23527, + "CTTCTGGA": 23528, + "GTGGAGATT": 23529, + "GTTGCCATG": 23530, + "GCACAAGAA": 23531, + "CTAGATCC": 23532, + "GTCTAAGG": 23533, + "GTCTCCCTT": 23534, + "TCTTGAAAAA": 23535, + "TGTTGGGAA": 23536, + "GAGACCTCA": 23537, + "GTAATAGTT": 23538, + "GAGGCTGCAGTGA": 23539, + "CTTACTAAA": 23540, + "CGACATC": 23541, + "GCTCAGATG": 23542, + "GTCTTCATG": 23543, + "CGCCCACC": 23544, + "TATAACAAA": 23545, + "CTGTGCAAA": 23546, + "GGGAAGGA": 23547, + "GAATGCAAAA": 23548, + "TCATGGTCA": 23549, + "GTGTGGAA": 23550, + "GGCTTCATCC": 23551, + "TAAAAAATTTT": 23552, + "TTATATAAAAA": 23553, + "CTTTAACAA": 23554, + "CTTTATTTTAA": 23555, + "GCCTTTTTG": 23556, + "TCTGGGTTTG": 23557, + "CAGATAGAA": 23558, + "GTGAGATCA": 23559, + "TATGCAGTT": 23560, + "CCTGTAGTT": 23561, + "GCCATCTCC": 23562, + "TTCACCAAA": 23563, + "TCCTTGTCA": 23564, + "GTGAAGCTG": 23565, + "CGCAGCAA": 23566, + "CGCACACACACA": 23567, + "GTGTTGTGTT": 23568, + "CTCTGGCAA": 23569, + "CCATGGAGG": 23570, + "CCCTCTGCA": 23571, + "TGAGAATTA": 23572, + "GTTCCTGTG": 23573, + "CTATACAAA": 23574, + "TATTTGAACAGTGAGAGACAGAA": 23575, + "TAATCTATA": 23576, + "TCACAATA": 23577, + "CTTGĊ": 23578, + "CTTTAACA": 23579, + "GCCGAGGA": 23580, + "CTCTCACAA": 23581, + "CTCACCTA": 23582, + "CATGTACCA": 23583, + "TTTTATTATTA": 23584, + "CTTACCAAA": 23585, + "CTTAGAGCA": 23586, + "TCCATAATT": 23587, + "GATGCCCTT": 23588, + "GGAAATGTT": 23589, + "TCCTTGGTG": 23590, + "GAATTGTT": 23591, + "CGTTGAA": 23592, + "CGTTGTCA": 23593, + "TCTTACATT": 23594, + "CAATTGTTA": 23595, + "CAGTGCAGTG": 23596, + "GCATTTCC": 23597, + "CCCTCTGTG": 23598, + "GCACACAAA": 23599, + "GAATAAGGCTG": 23600, + "TTAGTAGAAAATATTA": 23601, + "TAAGCAGA": 23602, + "CAATAATGA": 23603, + "GTGTCTTTG": 23604, + "GTCGGTCA": 23605, + "GTGAGAGGA": 23606, + "CATGAAAAAA": 23607, + "CATGGGCTT": 23608, + "CCTGAAATG": 23609, + "CTTTTGGCA": 23610, + "CGTTTATA": 23611, + "GCTCATCTG": 23612, + "GTTAAGTTA": 23613, + "CTTTGGTAA": 23614, + "GCCTTTCTG": 23615, + "GTCTGGAAA": 23616, + "AAAAAAAATTA": 23617, + "TCAGGGCTG": 23618, + "GGCTATTA": 23619, + "TATATGTATATATA": 23620, + "GCACAGTTA": 23621, + "GGACAGCCA": 23622, + "TGACTGTTA": 23623, + "CACGGAAA": 23624, + "GGGCTGAAA": 23625, + "CTACCATCAGA": 23626, + "AGAA": 23627, + "TAACGATT": 23628, + "CTTTCCAGG": 23629, + "GCAGTGGA": 23630, + "GTACTGTA": 23631, + "GCCGAGAA": 23632, + "TAAAGACTA": 23633, + "TGTGTACAA": 23634, + "CATTGCTGA": 23635, + "CAGGCCTCA": 23636, + "CAGGGAGCC": 23637, + "TCAATTTTAA": 23638, + "TCAAGGCAA": 23639, + "GCTTTGATT": 23640, + "CCTTGCTTA": 23641, + "CTAAGATCA": 23642, + "GGGGCAGAA": 23643, + "TTTAGGGG": 23644, + "TAGACATTTT": 23645, + "GACATTTTAA": 23646, + "CTGTTGTGA": 23647, + "TCCTGCAA": 23648, + "TCCTGCTGA": 23649, + "CAGTTCTGA": 23650, + "TCAGGGAAA": 23651, + "TAAATGTATA": 23652, + "TTACAGCTT": 23653, + "GTGGCATGCA": 23654, + "TTATTATTATTATTATTA": 23655, + "GGAGAAAATTTTTG": 23656, + "TGATGTACTCTG": 23657, + "TCCGCCCGTCTCGGCCTCCCAAAGTGCTGGGATTACAGGCTTGAGCCACCGCGCC": 23658, + "GCGTCTCA": 23659, + "TTTTTCTC": 23660, + "CTGTĊ": 23661, + "CTGGCTCTG": 23662, + "GTATTTTTTTT": 23663, + "GAGACCCC": 23664, + "CAGATCTCA": 23665, + "TCAACGTG": 23666, + "TAAAAGCTA": 23667, + "GTGACGTCA": 23668, + "GCTGCCTGA": 23669, + "CATGTCTAA": 23670, + "GAGGGGGGA": 23671, + "GATTCCTA": 23672, + "CTTAGTAAA": 23673, + "TCCATATTTT": 23674, + "TAGGGTT": 23675, + "TCCTTTGA": 23676, + "CGTTGTTTT": 23677, + "CGTTTGGG": 23678, + "TGCCTATT": 23679, + "GTGTTCAA": 23680, + "CAAAAATTTA": 23681, + "CTGTGTATA": 23682, + "GATGAGGAA": 23683, + "CCATGGTAA": 23684, + "TCAGACTCC": 23685, + "TCATAGTAA": 23686, + "GAAGTTTTA": 23687, + "GGAGCCCC": 23688, + "CCAGGAGA": 23689, + "CACATATTTT": 23690, + "CACATATCA": 23691, + "GTGACGAA": 23692, + "GTGACAGTA": 23693, + "TATGGAGGA": 23694, + "CCTGCCAAA": 23695, + "CCTGGGGGA": 23696, + "GTTTTTCAAA": 23697, + "TCATTCGA": 23698, + "TGATTGAAA": 23699, + "TCCAATGAA": 23700, + "CAGAAAAATG": 23701, + "CTCAACCAA": 23702, + "GGAGGGTAA": 23703, + "CTGTGTCC": 23704, + "TAATAAAAAAAA": 23705, + "GCAGGTAAGA": 23706, + "TACTATATA": 23707, + "CAACAATTA": 23708, + "GGGTCTGAATA": 23709, + "CGCCAATG": 23710, + "GTGCTGGGATTACAGGTGTGAGCCA": 23711, + "CACGCAAA": 23712, + "GTGGCAGAA": 23713, + "GTCGGTAA": 23714, + "CACATCTCA": 23715, + "CACACCTCA": 23716, + "CACAGGTCA": 23717, + "TCTTCAGC": 23718, + "TCAACTGAA": 23719, + "CATGTAATT": 23720, + "TATGTACCA": 23721, + "CGGGAGAA": 23722, + "GCCACCCTA": 23723, + "TAGAGCAAA": 23724, + "TTATTTCTTA": 23725, + "GATAGGGG": 23726, + "CGTGGTGA": 23727, + "CTCAACAA": 23728, + "GGAGGCTCA": 23729, + "GCCTGGATG": 23730, + "CGTATTCA": 23731, + "GAGTGGAGA": 23732, + "CAAGGCAAAA": 23733, + "TTATATGTG": 23734, + "TAAGAGTTA": 23735, + "GTTCAGCTG": 23736, + "CAGGTGCCATTAA": 23737, + "GTCTGAGAGA": 23738, + "GCTCTCACAGA": 23739, + "GAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGA": 23740, + "GTCCCATCACTTCATG": 23741, + "CCAAAACAGCATGGTACTG": 23742, + "CACCAAATT": 23743, + "TAAGCGG": 23744, + "TTTTCTAAA": 23745, + "CCAGTAAAA": 23746, + "GCCTGAGAA": 23747, + "CTAGTAAAA": 23748, + "AAAATGAAA": 23749, + "GTCGATCA": 23750, + "CATTTAGTA": 23751, + "CAGGCAGTT": 23752, + "TCTGGGATG": 23753, + "GTCAGCTA": 23754, + "GAGGCGC": 23755, + "GAGGCAGTT": 23756, + "TCTCGATT": 23757, + "CTAACATAA": 23758, + "CTTATATCA": 23759, + "TCTACAATT": 23760, + "CGACACAA": 23761, + "TGGAĊ": 23762, + "TCTCCTCAA": 23763, + "TCCTTTCTCA": 23764, + "GTATTGTG": 23765, + "GTATTTACA": 23766, + "GCCTGCAA": 23767, + "TCATGCAAA": 23768, + "CAAGGGATG": 23769, + "GTTCTTGTA": 23770, + "CTACATATA": 23771, + "CCTTTGTTA": 23772, + "TAGGAACACTTTTA": 23773, + "GGATGGACTGGTTGGA": 23774, + "TCACTTGAGGCCAGGA": 23775, + "CCCATTTA": 23776, + "CTACTGTTA": 23777, + "TAAAGCAAAA": 23778, + "CTCGTAAA": 23779, + "CACAGTTAA": 23780, + "TCTTCATCC": 23781, + "CTCACACCA": 23782, + "CTTATCAAA": 23783, + "TTAACTCA": 23784, + "CTTTTGAGG": 23785, + "CGATATAA": 23786, + "TAGGCATA": 23787, + "CGCTGATT": 23788, + "TCATTGAAA": 23789, + "TCATTTAAAAA": 23790, + "TCCTTGTCC": 23791, + "CGGATCC": 23792, + "GAAGGTATA": 23793, + "TCTCTGCCTCC": 23794, + "GGAGATGTT": 23795, + "TCAGATCTCA": 23796, + "TCATTATTTA": 23797, + "GTATTTTTTAGTAGAGA": 23798, + "GTCTTTTGATTTTTTTGAAA": 23799, + "TACCTACTGTGAA": 23800, + "GCGTCTTA": 23801, + "TGATCTCTT": 23802, + "GCCCTATG": 23803, + "TGTTGCAAA": 23804, + "TCTGAAAAAA": 23805, + "TCAAGTGAA": 23806, + "CTAACC": 23807, + "TTAATTAATT": 23808, + "TCTAGACC": 23809, + "TCATTCTCC": 23810, + "CTAAAGATA": 23811, + "CTCTTGGTG": 23812, + "GGAGGTTTG": 23813, + "CTCTGTAGG": 23814, + "CTCTGCCCC": 23815, + "TGAGGGTG": 23816, + "CATTTTTGGG": 23817, + "GGCTTTGGG": 23818, + "CTCCACAAA": 23819, + "CCCTAGAACTTAAA": 23820, + "CAAGGCAAA": 23821, + "GAACATATG": 23822, + "GAGGGAGGGAGG": 23823, + "CTCAGCTATTAAAAA": 23824, + "TACAAAAAAA": 23825, + "TACAGGAAA": 23826, + "CAATATTTG": 23827, + "GTTTCTATG": 23828, + "TAAATTAAAAA": 23829, + "CACATAGAA": 23830, + "TGTTGCATT": 23831, + "TCAATACCA": 23832, + "CTGACATTTT": 23833, + "CTCAGCATT": 23834, + "CCTGGAGC": 23835, + "CTCCCTCCTT": 23836, + "TTTGTAAAA": 23837, + "CAAAACGG": 23838, + "TAGAGTAAA": 23839, + "TGGACTGAA": 23840, + "TCCTTTATA": 23841, + "CCAAATATT": 23842, + "CTGTGCCAA": 23843, + "GCCTTCCAAA": 23844, + "TGATGGCAA": 23845, + "TGAGAGTCA": 23846, + "CATGATTTTA": 23847, + "CTTCTTAAAA": 23848, + "TGATAGTAA": 23849, + "CAACATATG": 23850, + "TTACAGAAAA": 23851, + "CTTGAGGAA": 23852, + "CCTTAATTA": 23853, + "CTCCTGCCTCAGCCTCCTGAGTAGCTGGGATTACAGG": 23854, + "GTGGAACAA": 23855, + "GCAGCCAAA": 23856, + "GCAGGGGTT": 23857, + "CTAGAAATT": 23858, + "CAAACGC": 23859, + "TGAATAGTT": 23860, + "CAGGGAGTG": 23861, + "CCTTGATGA": 23862, + "GATTCTAAA": 23863, + "GTTATAGCA": 23864, + "TACATACATG": 23865, + "TCCAGTATT": 23866, + "CCCAGCAGG": 23867, + "TAGACCTA": 23868, + "CAGAATGAA": 23869, + "GTGTTGTGA": 23870, + "CAATTGCAA": 23871, + "GAGGAGGCA": 23872, + "GGATTTCCCAGG": 23873, + "TCATGGTA": 23874, + "GATAAGAA": 23875, + "CGTCTGTT": 23876, + "TCAGACTGA": 23877, + "GTCCAGCTG": 23878, + "AAAAAAAAAAAAAAAAAAAAAAAAAA": 23879, + "TCACTGATT": 23880, + "GAACAGGAA": 23881, + "GTTTGTGTG": 23882, + "TCTGCCTGA": 23883, + "CCTTGAGAA": 23884, + "CCTTGCTGA": 23885, + "GTCAGAATT": 23886, + "TCTACAAAAA": 23887, + "GGTGCC": 23888, + "CTGAAGTAA": 23889, + "TCATTTCCAA": 23890, + "CGTTTGACC": 23891, + "CTCAATGGA": 23892, + "TACCTGATG": 23893, + "TCCCCGGGA": 23894, + "GCGCCCGG": 23895, + "GCAGCCTTATTTA": 23896, + "GTATTCCCATCTCTT": 23897, + "CGTCGAGATTTTAAGTTTGA": 23898, + "GAACCTCC": 23899, + "GGAGCTCTG": 23900, + "TAAACATAAA": 23901, + "TATTTAATTTT": 23902, + "TCTTCAGCC": 23903, + "TAAAACC": 23904, + "GTGATATGA": 23905, + "CCTTTACAA": 23906, + "CATGTCTCA": 23907, + "CATGGTAAA": 23908, + "CATGGCAAAA": 23909, + "TTTTATGTTA": 23910, + "TCTCGGCC": 23911, + "CTCCCTAAA": 23912, + "TACAGCCAA": 23913, + "TTTGGTG": 23914, + "CTTTTCCCC": 23915, + "GGTGTCAA": 23916, + "TTATTĊ": 23917, + "GTAAACTGA": 23918, + "TCATTTCTGA": 23919, + "TATTACATT": 23920, + "TCTTACTCA": 23921, + "TCTTAGCAA": 23922, + "TTTTAAAAATG": 23923, + "TAATGGAGA": 23924, + "TTTTTTTTTTCA": 23925, + "GTGTGGTGA": 23926, + "CCTTATTAA": 23927, + "GAAGACGA": 23928, + "CTGCACGG": 23929, + "TTTTATTGAGGA": 23930, + "GCACACTGA": 23931, + "GGACACCTG": 23932, + "TTTTTATGTT": 23933, + "TCTGACAAAGGGCTAATATCCAGAA": 23934, + "GGTGTTTA": 23935, + "GAATCGG": 23936, + "GTTCATTAA": 23937, + "AAAAAAATG": 23938, + "CAGAGTTAA": 23939, + "CTCAGCCTCC": 23940, + "CCTTGAGTT": 23941, + "TATGTACTG": 23942, + "CTAATACTT": 23943, + "GACACCCTG": 23944, + "GCTATCCA": 23945, + "TCAAACCTG": 23946, + "TGCATAGA": 23947, + "GTAAACTG": 23948, + "GGTAGTTTT": 23949, + "CCATTGCTT": 23950, + "TGATGCATT": 23951, + "GGCTGCAGG": 23952, + "TCCCATGTG": 23953, + "CTTTAGAGA": 23954, + "GGACATCAA": 23955, + "GCGCGGGG": 23956, + "CACACACTAAA": 23957, + "CCTGTGTTA": 23958, + "TACTTTAAGTTCTA": 23959, + "GATAGGTG": 23960, + "GACTCCAAA": 23961, + "GCGGAATT": 23962, + "TAAGCTGAA": 23963, + "TGATCCAGAA": 23964, + "TCCCTTCA": 23965, + "CTTTAATGA": 23966, + "CTAGCAAAA": 23967, + "GAAACCTCA": 23968, + "GTCACTAAA": 23969, + "GATTCAGTG": 23970, + "GTTTTGCA": 23971, + "GTCCCACTT": 23972, + "TAATTCCCA": 23973, + "GGCATTTTA": 23974, + "GCAAATATTTT": 23975, + "CTCTTGGTT": 23976, + "TCTTATATG": 23977, + "GTCAAAAATT": 23978, + "GCCTGTCTG": 23979, + "CAATGCCAA": 23980, + "AAAAAAAAAAATT": 23981, + "CGTCCAGG": 23982, + "GCACACCTG": 23983, + "TCCCTGTCTT": 23984, + "TGAGGTTGTGGA": 23985, + "CCAGCCTGGGCTACCAA": 23986, + "GACGTGTACCTATGATGAAAATTACAGGCCTCTCTCATCTTTTTAAGTGGGAGAA": 23987, + "GGCATTTA": 23988, + "TGACGTGA": 23989, + "GCAGAACA": 23990, + "GTACTGGG": 23991, + "GAAAGGATT": 23992, + "GAGATCCA": 23993, + "GTGACCCCA": 23994, + "CCTTGGGAA": 23995, + "GTCAGCTGA": 23996, + "GTCAGACAA": 23997, + "GAGGTGCTG": 23998, + "GTTACAGGA": 23999, + "TACATTATG": 24000, + "TTTAGCC": 24001, + "GACAGCTGA": 24002, + "GGTGTAGG": 24003, + "TAATTGATA": 24004, + "TGAAAGGAA": 24005, + "GGTATCA": 24006, + "CTGTGCAA": 24007, + "GCCTTTGTG": 24008, + "GGCTGGGCA": 24009, + "CCTCCCCTG": 24010, + "GCGGCGTG": 24011, + "CCATGCTCA": 24012, + "TATTTGTATT": 24013, + "CTGCTGCTG": 24014, + "TCACATATT": 24015, + "TGACCTCAA": 24016, + "GAACCTGTG": 24017, + "GACGGAAA": 24018, + "CAAAATGTGGAAAAA": 24019, + "GCGCTGGG": 24020, + "TAACTCTA": 24021, + "TAAGTATTTT": 24022, + "CTTGAAGA": 24023, + "CTCGAGTT": 24024, + "CACACTTATTTCAAAAAAA": 24025, + "GAAAGGCCA": 24026, + "TATATCTGA": 24027, + "CAAACTGTT": 24028, + "CAGACCTCA": 24029, + "GGAACATCA": 24030, + "GCTGCGCC": 24031, + "GTCAGGCC": 24032, + "GATTTACTG": 24033, + "CTAATAGAA": 24034, + "GAAAACACAA": 24035, + "GGGATAGA": 24036, + "GATATTTTAA": 24037, + "CTAAACAGG": 24038, + "TACCTAGA": 24039, + "CCATTTCCA": 24040, + "TAATGGGA": 24041, + "CATAATATT": 24042, + "CAAGGTTAA": 24043, + "CTATATTTTA": 24044, + "CCACTGGAAAAA": 24045, + "TGGGTGTTTT": 24046, + "TAAAATACCTAGGAA": 24047, + "CTTGCCTGGAGAATT": 24048, + "GACTTAATT": 24049, + "TAAGTTGTT": 24050, + "TAATTTGAAA": 24051, + "GAAGCTC": 24052, + "CTTCĊ": 24053, + "CTTGTTCA": 24054, + "CTTGAAAAAA": 24055, + "GTGGAGGTG": 24056, + "TAAAGGCCA": 24057, + "TAAACCCAA": 24058, + "TAAAGAGTA": 24059, + "AAAAGTA": 24060, + "TCTGGATCA": 24061, + "TCTGGAGTA": 24062, + "GGAATTATT": 24063, + "GTGATCTA": 24064, + "CCTTTCTAA": 24065, + "GAGGCAGC": 24066, + "GTTACTGAA": 24067, + "CCAAGCTA": 24068, + "CTTATACAA": 24069, + "TTTGTTTTTT": 24070, + "GTCCCAGTT": 24071, + "GGTATGA": 24072, + "CTGTTGGTT": 24073, + "CAGAAGAAAA": 24074, + "TATTTTTGTT": 24075, + "CTTTGTCA": 24076, + "TCTAACATT": 24077, + "CCTAATTAA": 24078, + "GGGCTACA": 24079, + "CAAGGGAGA": 24080, + "CAATATGTG": 24081, + "TCATAGATG": 24082, + "GCACACTCA": 24083, + "CCGGAGGA": 24084, + "TAAGTTTAA": 24085, + "TAAGTCTCA": 24086, + "TTTTCCTA": 24087, + "GTGGAAATT": 24088, + "GTGGCTGTT": 24089, + "GTTCGCTG": 24090, + "GCATĊ": 24091, + "GTACGTC": 24092, + "CAAAGATTTT": 24093, + "GAGAGAATT": 24094, + "TCTGGCTCA": 24095, + "TAAAACTGAA": 24096, + "GCAAGAGC": 24097, + "GAGGTACAA": 24098, + "CGGGGCGG": 24099, + "CTCCTACTT": 24100, + "GCAAATTAAA": 24101, + "CGTTTACTT": 24102, + "TAATATCC": 24103, + "GTATTCAA": 24104, + "TCATGGCTT": 24105, + "GCGGCCCC": 24106, + "TATATTTATT": 24107, + "CAGTCTGTG": 24108, + "CCACCCCCACCCCA": 24109, + "CTCCTCTGTCCTCCA": 24110, + "GGTTTCATCTGA": 24111, + "GCCTCGAACTCAGAAA": 24112, + "GAAGGCAATGGCACCCCA": 24113, + "TACTCAAAA": 24114, + "GATTTTAATT": 24115, + "GACGTCAA": 24116, + "TTTTCAGTT": 24117, + "CTTTAACTT": 24118, + "GTATTATA": 24119, + "TAAAGTTTTA": 24120, + "AAAAGTAA": 24121, + "GTCTGAGTG": 24122, + "TATAGAATT": 24123, + "TCAAGACTG": 24124, + "CTCACCCCA": 24125, + "CCTTCTTAA": 24126, + "TTTTACAGG": 24127, + "GTTACATCA": 24128, + "CTTACGTT": 24129, + "CAAAAGATA": 24130, + "TTATTGATG": 24131, + "GGGATATA": 24132, + "GGGACCCA": 24133, + "TCATTGACA": 24134, + "GGAAATGTG": 24135, + "TGAAACTA": 24136, + "GCTGAGCC": 24137, + "TCTTATGTT": 24138, + "GCCAAGACC": 24139, + "GTGTTCCTG": 24140, + "CAATTGAAA": 24141, + "CAATTTAAAAA": 24142, + "CTCTGGGGG": 24143, + "CTATTTCC": 24144, + "CTATTGCTG": 24145, + "CAATGGCTT": 24146, + "CAATGGCAA": 24147, + "CATAACAA": 24148, + "GGATGGGA": 24149, + "AAATATCTT": 24150, + "CCGGCGC": 24151, + "TATATACTTA": 24152, + "GTAGACCTGCACCA": 24153, + "GGCAGGCAGATT": 24154, + "TGACCTGCAGAGA": 24155, + "CAACCTCAAACAGTCA": 24156, + "TTATACAA": 24157, + "GAATCTCC": 24158, + "GTTGĊ": 24159, + "CCATCCAAA": 24160, + "GCCCGAGA": 24161, + "GTCGCTC": 24162, + "TGTTCCCCA": 24163, + "GAGAGAATG": 24164, + "CATTGAGCA": 24165, + "TCTGTACTG": 24166, + "CAGACTAAA": 24167, + "GGAAGAGAAA": 24168, + "CGGCGTA": 24169, + "CCAATAGAA": 24170, + "TCCACCCTT": 24171, + "GAAAAGAAAAAA": 24172, + "GCCATACAA": 24173, + "GTCCCAAAA": 24174, + "GGTTGCCATTTCCTT": 24175, + "TCTCAGTTA": 24176, + "GTAAATGA": 24177, + "TCTGATGGG": 24178, + "GGAAAGGGG": 24179, + "GCATTTCTAA": 24180, + "CATCACTGA": 24181, + "CACAATGTG": 24182, + "TAGTTTCTT": 24183, + "GCTTTTGGTGTTTTA": 24184, + "CTTTACTTTT": 24185, + "CAAGACATT": 24186, + "CAACATATT": 24187, + "CGTTATTTT": 24188, + "CACATGCACA": 24189, + "GACGCTTA": 24190, + "CGTTTACTCTGGAACC": 24191, + "TAGTCCTG": 24192, + "TAGAGGAAA": 24193, + "TGAGCCAAA": 24194, + "CCAGCCTA": 24195, + "TAAACCATT": 24196, + "TGAACATTTT": 24197, + "TGAATGTGA": 24198, + "GCTGGTCTT": 24199, + "CATGCTGTT": 24200, + "GTAACAGTT": 24201, + "GATTGAGAA": 24202, + "CTCCCTGTT": 24203, + "GATGGTTTTA": 24204, + "TTTGCACA": 24205, + "GCAAAGCAAA": 24206, + "TAAAAAGCAA": 24207, + "GTGTTGAAA": 24208, + "GGCTTGGTG": 24209, + "GGAGAACTG": 24210, + "TCACATATG": 24211, + "TTTTTACTTTT": 24212, + "CAACCTCCA": 24213, + "GAGGATTGGGAGAATG": 24214, + "CCACATCCAGTGGAA": 24215, + "TTAGAGAA": 24216, + "GGAGTAAAA": 24217, + "CCAGGAGGA": 24218, + "GTATAGC": 24219, + "GTCCACATT": 24220, + "GCTTCTCAA": 24221, + "CGGGAGC": 24222, + "GATTGAGTT": 24223, + "TTAATCCATT": 24224, + "CCCATAAAACGA": 24225, + "CCCCTACAA": 24226, + "CATAGAAAAA": 24227, + "TTTTTTTCCA": 24228, + "CTTAAGAAA": 24229, + "GACCTAGG": 24230, + "CGGAGAAA": 24231, + "TATCAGAA": 24232, + "TAAATTCAA": 24233, + "GATAATGAA": 24234, + "GGATGGTGA": 24235, + "GAGCCTGTG": 24236, + "GAATATAAAA": 24237, + "CAAGAACAGTA": 24238, + "CAACCACTCTG": 24239, + "CAAAGCCTACCA": 24240, + "TACTGGGGCATATAAA": 24241, + "TGATTTTATT": 24242, + "GTGCAGAGA": 24243, + "GTTGCCAGG": 24244, + "TATACGTT": 24245, + "TGTTGTGAA": 24246, + "CAAACGTG": 24247, + "CAGGTAAAA": 24248, + "CAGACGAA": 24249, + "TCAAGACTT": 24250, + "GCAACGC": 24251, + "GTCACACTT": 24252, + "GAGGCACTG": 24253, + "CTCCCCATT": 24254, + "CTCCCTCTG": 24255, + "TAGATACTA": 24256, + "GCTATAATT": 24257, + "TCAAAGCTT": 24258, + "TTCATAGA": 24259, + "CAAAAATTTG": 24260, + "TAATGCACA": 24261, + "GGCTGGGGG": 24262, + "GAGCAGCTG": 24263, + "GTCTGGCTG": 24264, + "GTGGGCATG": 24265, + "TATATTGTCACCCTG": 24266, + "CTAATGAAAA": 24267, + "CTCGGGAGGCAGAGCCAGGCGGATCTCTGTGAGTTCGAGG": 24268, + "CAGTGAATT": 24269, + "TGAGTCCCTG": 24270, + "TTACACTG": 24271, + "GGAGCCAAA": 24272, + "GCCTCGTG": 24273, + "TAAAGGTTA": 24274, + "GTCGAAAA": 24275, + "TCTTGCTCA": 24276, + "GTCACAGTT": 24277, + "CCCAGCTCTG": 24278, + "GGCAGTCC": 24279, + "TTCATAAAA": 24280, + "GGAAATTAAA": 24281, + "CTGTGCAAAA": 24282, + "GCATTTCCA": 24283, + "CATAATGTT": 24284, + "TAACAGCTG": 24285, + "CTTTGTTACTTTG": 24286, + "CAGTAAATG": 24287, + "TCCGATA": 24288, + "CTGTCTGTT": 24289, + "CTTGTTGG": 24290, + "CTTTGACTT": 24291, + "GGATGTGAA": 24292, + "GCCGGTA": 24293, + "CTCTCTATA": 24294, + "CACAGGGTG": 24295, + "TCTGCCCCA": 24296, + "GCTGTAATT": 24297, + "GCTGTCCAA": 24298, + "GTAATATGA": 24299, + "CGGTCCA": 24300, + "GATTCGTT": 24301, + "CCTGTCTGA": 24302, + "CTCCCGC": 24303, + "TAAAAATCA": 24304, + "GAATTGAGG": 24305, + "CTAAATGTG": 24306, + "CTCTTGAGA": 24307, + "CTTTGGTGAA": 24308, + "CAGCTAAAA": 24309, + "CTATTGAA": 24310, + "CTATTTGTG": 24311, + "TTTTTTTTTTTTTTTTTTTTTTTTTT": 24312, + "GCTAATTTA": 24313, + "CAGTATTAA": 24314, + "TGACATCTT": 24315, + "GTTCACAAA": 24316, + "TTACAGTCA": 24317, + "CTACAGCAA": 24318, + "CGGTTCCAGAGTAAA": 24319, + "GGTCCCCCTG": 24320, + "GAAGTGAAAA": 24321, + "CGACAGAGTGA": 24322, + "CACCCCAAA": 24323, + "TCGTTTTAA": 24324, + "GCGTCCTG": 24325, + "TTTTCTTAA": 24326, + "TCAGTGTA": 24327, + "CAAGCTTTT": 24328, + "GTGGCAGA": 24329, + "GTGTCCAGA": 24330, + "GTTCTCTGA": 24331, + "CCAGCAAAA": 24332, + "GTATGTGTT": 24333, + "TGTTTGTGTT": 24334, + "TGAAGAGTG": 24335, + "CAGATATGA": 24336, + "CTTTTCAGG": 24337, + "GGGGAAATT": 24338, + "CAAAACAAAAAA": 24339, + "CCCCGTGTG": 24340, + "TCAAATCTT": 24341, + "TCTCACAGG": 24342, + "TTTTTTGAAA": 24343, + "TGAAACATG": 24344, + "TCCTTGAGG": 24345, + "GGCCTCGG": 24346, + "TGATTTACA": 24347, + "CTCAATTTA": 24348, + "TCAAAATATT": 24349, + "CATTAATTAA": 24350, + "CTTCCCATG": 24351, + "CAATACATG": 24352, + "CAACATCTT": 24353, + "GAGTGAATT": 24354, + "TAAGCTC": 24355, + "CAAGCTC": 24356, + "CTGTCAGG": 24357, + "TATAGTATT": 24358, + "CAAACACACACA": 24359, + "TGAAGAGGA": 24360, + "TCTGTATGA": 24361, + "CAGACTCAA": 24362, + "TCAATGTGA": 24363, + "CGGGGGC": 24364, + "CGATCCCTG": 24365, + "TGTAGCTA": 24366, + "GGCATCTA": 24367, + "GGGACCCC": 24368, + "GGGAGAAAAA": 24369, + "TTTTTTCAAA": 24370, + "CTCTTGACC": 24371, + "CAGAATTTA": 24372, + "GCCAACATT": 24373, + "CGCAGAGA": 24374, + "TATAAGAAA": 24375, + "TAATGCCTT": 24376, + "GCCTGCTGA": 24377, + "GAATGGATG": 24378, + "TATTAAAAATT": 24379, + "GGATGGCAA": 24380, + "CCCAAGAGA": 24381, + "TCCCATCTT": 24382, + "CTTCACGGTT": 24383, + "TAACACCAA": 24384, + "CTGTACTCCAGCCTGGG": 24385, + "TCACAGTTA": 24386, + "CCACATGTG": 24387, + "GTTCCCTCA": 24388, + "GCACCTGTG": 24389, + "GTCATGTCTGACTCTTTG": 24390, + "GTAGTACTCCATT": 24391, + "AAAATTATAGACTGA": 24392, + "TTTTCTGTAAAATTA": 24393, + "TCCCTCATTAAAATG": 24394, + "TAAGTCATT": 24395, + "GTTCTCCA": 24396, + "GTATGAGG": 24397, + "GTAGAAATT": 24398, + "GCCGCTA": 24399, + "CTATCCTG": 24400, + "CTAGCTCC": 24401, + "CTAGGGGA": 24402, + "TATTGGCTG": 24403, + "TGTTCATGA": 24404, + "CAGACCTA": 24405, + "CAGAGTTTG": 24406, + "CTAAAAAAAAA": 24407, + "CATAGCCA": 24408, + "CTGAAGTTA": 24409, + "GGAAATGAAA": 24410, + "CTGTTCTGTT": 24411, + "TATTAGGAA": 24412, + "GTCTTTCAA": 24413, + "CCATTTGTG": 24414, + "CTGTGGTTA": 24415, + "TGATGTATA": 24416, + "TGATGGTCA": 24417, + "CAATGCAGG": 24418, + "CGTCTGCC": 24419, + "GAAGAAGGAA": 24420, + "CTGCATGAA": 24421, + "TAATTTTTTTA": 24422, + "CCACATGAA": 24423, + "GTTCCCTGTG": 24424, + "GTATTTTAAAA": 24425, + "TAAAATGTTTT": 24426, + "TCATCTTCAA": 24427, + "CTATGGGGTTGAGA": 24428, + "TAATATAATATTTGAAA": 24429, + "CACGTCTT": 24430, + "CATTTTTTAAA": 24431, + "TCAGTTGTT": 24432, + "TCAGCGA": 24433, + "GAACCATG": 24434, + "GAAGGAAAAA": 24435, + "GTGTATAAA": 24436, + "GTGGCTCTG": 24437, + "GGAGGAATG": 24438, + "GGACAACCA": 24439, + "TAAATGTTAA": 24440, + "CTCGAGTG": 24441, + "TGTTTCAAAA": 24442, + "TGTTTAGTA": 24443, + "GCTGTAAAA": 24444, + "GCTGTATTA": 24445, + "GCAATGGAA": 24446, + "GATTGATTA": 24447, + "CGACCAAA": 24448, + "TAGACACTG": 24449, + "CATACGTT": 24450, + "GTAAATACA": 24451, + "CAGCAGATG": 24452, + "GTGTTGTCA": 24453, + "GCATTGCTT": 24454, + "TGAGACTCA": 24455, + "CACTTCTCAAAA": 24456, + "GTCTGCCAA": 24457, + "GGTGATTA": 24458, + "TCTGTTGGCTG": 24459, + "CTGTAGAGA": 24460, + "GAACAGTTTT": 24461, + "CTCCTTCGTT": 24462, + "CAACAGTTA": 24463, + "TAAGGTTTG": 24464, + "CCACCCGG": 24465, + "GCACAGTAA": 24466, + "GCACACCAA": 24467, + "GCCCATGTG": 24468, + "GTAGAATTCA": 24469, + "CCTCCATGCAGA": 24470, + "GGATGAGATGGCTGGATGGCATCACTGA": 24471, + "GATATTTATT": 24472, + "TCATCCATT": 24473, + "TCCTAGC": 24474, + "CTAGTTTTA": 24475, + "CTCTTTTCTT": 24476, + "TATTGACTG": 24477, + "TGTTCCATT": 24478, + "GGAACAGTT": 24479, + "GCAACATTA": 24480, + "TTTTAGATA": 24481, + "CTCCGGGG": 24482, + "TCTATAATA": 24483, + "TCTATTTTTG": 24484, + "GGGGAAAAAAA": 24485, + "CCCATCTGA": 24486, + "TAGAGTCC": 24487, + "CGCTCTTA": 24488, + "TCCAACTTA": 24489, + "GCTGAGGCTG": 24490, + "GCCAAGAGG": 24491, + "GCCAAGGCTG": 24492, + "CAATTCCCAGCAA": 24493, + "GGGAATTTG": 24494, + "TACTTTAAAA": 24495, + "GTCTGGCCA": 24496, + "GCGGCCTG": 24497, + "CCATGGAAA": 24498, + "GAATACTGAA": 24499, + "CAAGACCTT": 24500, + "CCACACCAA": 24501, + "CTGCCCTGA": 24502, + "GTTCAGTTTT": 24503, + "GCCCAGTCC": 24504, + "GAGATGTTA": 24505, + "GAACTGAAAA": 24506, + "CTGCTATAAAGACACATG": 24507, + "CCCACAACCCTGGCGTT": 24508, + "CACGTAAA": 24509, + "CACGTTTG": 24510, + "TAACĊ": 24511, + "TAATCATTTT": 24512, + "TCATTACA": 24513, + "GCACTTTTA": 24514, + "GCATGTTTG": 24515, + "CTCGATCA": 24516, + "CTCTTAAAAA": 24517, + "TCAAGTATT": 24518, + "GTAATAGTA": 24519, + "GATTGTCC": 24520, + "TCTAGTAAA": 24521, + "TGGGAAATG": 24522, + "CATACCAAA": 24523, + "TGGATACA": 24524, + "CAGCAGCTT": 24525, + "TAATGTGA": 24526, + "TAATGTAGG": 24527, + "GGATTGGGG": 24528, + "CCTGAGAGG": 24529, + "CTGGGGCTT": 24530, + "TAGTGTTTG": 24531, + "TCATACACA": 24532, + "CTCTAGAGA": 24533, + "TTAGGTTTA": 24534, + "TCCTTTTTTATG": 24535, + "TAGATAGATAGA": 24536, + "CTCTCAACCAGCTT": 24537, + "CTCCACTATGTTCATA": 24538, + "GGCAGGAGAATCACTT": 24539, + "GCATATTAA": 24540, + "GCATCGA": 24541, + "GCCTCAGGA": 24542, + "TAAATCCTT": 24543, + "CTCTTTTAAA": 24544, + "TCTTCACTA": 24545, + "TGTTTATAAA": 24546, + "CAAACAGAAA": 24547, + "CATTCCTGA": 24548, + "CCTGGAATT": 24549, + "GATGCACAA": 24550, + "GCCATCATT": 24551, + "GGAAAGGTG": 24552, + "CCAAACTCA": 24553, + "TCCAAGTA": 24554, + "CTCTTCCCC": 24555, + "CTCTTCCTCC": 24556, + "GTTAATATG": 24557, + "GTCTTGTA": 24558, + "GGATTGGAA": 24559, + "CCAGGGTGA": 24560, + "TCTCTGTTTT": 24561, + "TCAGAGAAAA": 24562, + "TCCCACTTA": 24563, + "GAATATCTG": 24564, + "TTATACATG": 24565, + "TCCAGGAAAA": 24566, + "TGATACATG": 24567, + "GTAGGGAGG": 24568, + "GGCAGGAGAATTGCTT": 24569, + "CTGAATGTTGAGCTT": 24570, + "TCGTCTTTT": 24571, + "TCCCĊ": 24572, + "CAAGAAATG": 24573, + "TAAAATCCA": 24574, + "CTGACATTA": 24575, + "CGGGACAA": 24576, + "CTAAGAGTT": 24577, + "CAAAATAATA": 24578, + "TAGGGATA": 24579, + "TAAAAACATG": 24580, + "TCTGAGCTT": 24581, + "AAAAAAGAAAA": 24582, + "CTGTTCTTTT": 24583, + "CTTAACATT": 24584, + "CTCAATGAA": 24585, + "GAGCATTTG": 24586, + "GACCACTGATAA": 24587, + "TCTCTTCCTT": 24588, + "CTGGGTCTG": 24589, + "CCAGGCTCC": 24590, + "CAAGGGAGG": 24591, + "TCCAAATTA": 24592, + "CAAGAGAAAA": 24593, + "TAAATACTTA": 24594, + "CTATAGATT": 24595, + "CCTGTTGTTTCCTGA": 24596, + "TCTTGCTTGTTTG": 24597, + "CCAACCCAAATG": 24598, + "GGCTGGGAAGACTGAA": 24599, + "GCTTTTAATT": 24600, + "TCACTCAAA": 24601, + "GAACACATT": 24602, + "CAACCTC": 24603, + "GGAGAGAGAGA": 24604, + "GAAATAATAA": 24605, + "GAGACGGAA": 24606, + "CTGATAGAA": 24607, + "GTAACCAAA": 24608, + "GTAAGGTA": 24609, + "CGGGCTC": 24610, + "CTAAGGTA": 24611, + "GTTTTTAATT": 24612, + "GGTGGAGAA": 24613, + "TGGACCTT": 24614, + "TCTCACCTG": 24615, + "TTTTTTCCA": 24616, + "TCTGATGA": 24617, + "CTAAATCAA": 24618, + "CTCTTCCAAA": 24619, + "TCTAATATG": 24620, + "TGATGGCTG": 24621, + "CGTAGAGG": 24622, + "GAGCAGGGA": 24623, + "CTGGGCTCA": 24624, + "GAAGAAGAAA": 24625, + "TCACCTCTT": 24626, + "CCCCTGAAA": 24627, + "GTCCAACTCTCACATCCATACA": 24628, + "GACGAATT": 24629, + "TAAGTTGA": 24630, + "TCATCCACA": 24631, + "GTGTTATTA": 24632, + "CTCGATGA": 24633, + "CCTTCATTTT": 24634, + "GCAAGTGAA": 24635, + "TCTCTCCCTT": 24636, + "CGATATCA": 24637, + "TGGACCTG": 24638, + "TAATTGGA": 24639, + "CGCTCCTT": 24640, + "CTGAAGGAAA": 24641, + "TGATTAAATT": 24642, + "CTGTTCCAA": 24643, + "GTATTTCC": 24644, + "GTCAAGAGA": 24645, + "GAAAAATATG": 24646, + "TCATGGGGG": 24647, + "CCTTATTTA": 24648, + "TCAGGTTTA": 24649, + "TGTGTTAAAA": 24650, + "GTATGTATG": 24651, + "TTATAGAAAA": 24652, + "GAACACTAA": 24653, + "GCACCCCCA": 24654, + "GGATACAAA": 24655, + "TCTACATTTT": 24656, + "TGTTTGAACTTGTTA": 24657, + "GGGCAAAAA": 24658, + "TCACTGCC": 24659, + "GCCTGACC": 24660, + "TAAAGCTTA": 24661, + "CACACTTATT": 24662, + "TGAAGTTAA": 24663, + "TCTGCAGTA": 24664, + "GGAACATTA": 24665, + "CATGGACTT": 24666, + "GCAAGACTG": 24667, + "GATTCCCTT": 24668, + "GGGGCAGTG": 24669, + "CAAAACGA": 24670, + "TGTACGA": 24671, + "TGCATTATT": 24672, + "GGAAAGAATGAA": 24673, + "GTGTTTCAA": 24674, + "GTGTTCTGA": 24675, + "CGAATCC": 24676, + "CTCTGTCCA": 24677, + "GAGGATGGG": 24678, + "CAGGAGAGA": 24679, + "CAGGAGATG": 24680, + "CCTCCCTCA": 24681, + "CACCATGTT": 24682, + "GTGTGGAGA": 24683, + "GACCACCAA": 24684, + "CGTCATTTA": 24685, + "GAACATCAA": 24686, + "TGACACCTG": 24687, + "TGTGATGAAA": 24688, + "GTTCCCCAA": 24689, + "GGGAAAGGA": 24690, + "CCTAGGTATTTTATT": 24691, + "GCAGATTCAGTCTT": 24692, + "GGAGCAATTATTA": 24693, + "CCCCGTCTCTACTAAAAATACAAAAAATTA": 24694, + "TAATCACTT": 24695, + "TAACTTCTG": 24696, + "TCAGTTGG": 24697, + "GTGTCTGAA": 24698, + "GTTCAGGAA": 24699, + "GTCTGAGTT": 24700, + "TATTCCCAA": 24701, + "TATAGAGTT": 24702, + "CATTCAATT": 24703, + "TGAAGGCTT": 24704, + "GGAACTCC": 24705, + "GCTTCGTG": 24706, + "GTGAGAGTG": 24707, + "TACATCATT": 24708, + "TCTAGCAAA": 24709, + "TGTATAATT": 24710, + "TAATATCAAA": 24711, + "CTGTTCTCC": 24712, + "TCTTAGAGA": 24713, + "CTTTGGTCA": 24714, + "CAAAAATTAAA": 24715, + "CAATGCTGA": 24716, + "CCAGGGAGA": 24717, + "CTTGGGCTT": 24718, + "GAATAGATG": 24719, + "TCCACACAGTCAAA": 24720, + "CTGATTAAAA": 24721, + "GCGTGTGTG": 24722, + "CTCCCAATCCTCTT": 24723, + "TGAGGAATT": 24724, + "GTGCCACCA": 24725, + "CCAGAGATT": 24726, + "CTACACAAA": 24727, + "TAAAGAAAAAA": 24728, + "TATTCCCTG": 24729, + "TATTGCTAA": 24730, + "CACATCCAA": 24731, + "TCTTGGAGG": 24732, + "GCTTTAGTT": 24733, + "CTCAGGTCA": 24734, + "GATTGTATT": 24735, + "GGTGGCCAAA": 24736, + "GATATCAAA": 24737, + "CGCGAGTG": 24738, + "CCAAACTGA": 24739, + "CTAAAGAGA": 24740, + "CAGAATGTG": 24741, + "CCATTGTTA": 24742, + "GTTTGTAGA": 24743, + "CCTCAGCTG": 24744, + "CATCAGTCA": 24745, + "CTCCAGCCTT": 24746, + "CTTTATAAAA": 24747, + "GTTTACTCA": 24748, + "GCAGATGTT": 24749, + "GCTAAAGGGA": 24750, + "CACACAATACCCCA": 24751, + "GCTCTTTAGTTTAATTA": 24752, + "TCGTAAAAA": 24753, + "TAATCACTG": 24754, + "TTTTCATTA": 24755, + "CCAGTTTTA": 24756, + "GCCGCTCC": 24757, + "TAAATAAGG": 24758, + "GTCTCTATA": 24759, + "TATTCACTA": 24760, + "CAGGGGTA": 24761, + "GCTGTATCA": 24762, + "CCTTGCTA": 24763, + "GTAACACTG": 24764, + "GAAAAGTAAA": 24765, + "GCTACTTGGGAGG": 24766, + "TGTATCCA": 24767, + "TCTCACCA": 24768, + "CAGCACTG": 24769, + "CTCTTCAGA": 24770, + "CTCAAGTCA": 24771, + "TTTTAATTTTAA": 24772, + "GAGGAGGAAA": 24773, + "TGAGGGATG": 24774, + "TTATAGCTG": 24775, + "CTGGAGTGA": 24776, + "TCAGCATGACAATGA": 24777, + "TCATTTTTTA": 24778, + "GCACCACTGCA": 24779, + "TATTAATGGCACC": 24780, + "GAGAAGAGAAGAGAAGAGAA": 24781, + "TCAGCCAGA": 24782, + "CAACTTGG": 24783, + "CTTTGTTTG": 24784, + "CTTGAAGGA": 24785, + "GTTGTAGA": 24786, + "GCAGTGATT": 24787, + "GGAGAGATG": 24788, + "GTACTTAAA": 24789, + "TAAATAATTTT": 24790, + "TATTTCATTA": 24791, + "TCTTTCATCA": 24792, + "CATTGACAA": 24793, + "TCTGCCATT": 24794, + "GGAAGGCAA": 24795, + "GCTTCCTAA": 24796, + "CATGGGGTT": 24797, + "GTTAGGGTT": 24798, + "CCAACCCAAA": 24799, + "CTAAGCAAA": 24800, + "CTAATTATG": 24801, + "CTTAGCTA": 24802, + "TTAAĊ": 24803, + "CCCAGTAAA": 24804, + "TAGGTGAAA": 24805, + "CGCTCTAA": 24806, + "TCTCCCACC": 24807, + "CTAAACAGAA": 24808, + "TAATGGTGA": 24809, + "GTTTGTGA": 24810, + "GCCTGCAGA": 24811, + "CACCACACA": 24812, + "CATTTTCTG": 24813, + "GCAGGGTCA": 24814, + "GTAAAATAAA": 24815, + "TCGGGAGG": 24816, + "TCCATTAAAA": 24817, + "GAATACTGA": 24818, + "CCCTCTGAA": 24819, + "CTTCATGTG": 24820, + "CAACACAAAA": 24821, + "GCGCGGCC": 24822, + "TAGGAAGAATCAA": 24823, + "TAATAGCCAGAAGCTG": 24824, + "TACACACACACACA": 24825, + "TCGTGATG": 24826, + "TCGCAAAAA": 24827, + "TAACTAATT": 24828, + "TTTTGCTA": 24829, + "GCACCACC": 24830, + "GCCCCTTTT": 24831, + "GCCCAAGA": 24832, + "TAAACAAATG": 24833, + "CTCGGGC": 24834, + "TATTGAAATT": 24835, + "TCTTGGGAA": 24836, + "TCTTTAGGA": 24837, + "TGAACCAAA": 24838, + "GGAACACTT": 24839, + "CATGGGCTG": 24840, + "CATGGACAA": 24841, + "GATGCAGCTG": 24842, + "TTAATGGA": 24843, + "TAGATCC": 24844, + "TTATTGTAAA": 24845, + "TCTGACACA": 24846, + "CTAAACATA": 24847, + "CTGTTCAAA": 24848, + "CTTTGTCAA": 24849, + "CTGTGCTGTG": 24850, + "GGATTCTGA": 24851, + "GGCCATCTTTT": 24852, + "GCGGGCACCTG": 24853, + "GAATATAATT": 24854, + "CAACAGTCA": 24855, + "GCAGTGGCCACA": 24856, + "CCTGTAACTTCTT": 24857, + "TTTTTTTGTATTTTTAGTAGAGA": 24858, + "GTTCAAGACCAGCCTGGCCAA": 24859, + "TAATGAATG": 24860, + "TTTTCACTT": 24861, + "TCACĊ": 24862, + "GGATGAGAA": 24863, + "TAAATAGGA": 24864, + "GTCGTGTCCGACTC": 24865, + "TATACATCA": 24866, + "TCTCGCTT": 24867, + "GTCCTCCAA": 24868, + "GACATATAA": 24869, + "TCTCAGGGG": 24870, + "TTTTTTGTAA": 24871, + "CGTGTCC": 24872, + "CGTGGGTT": 24873, + "GAATTCAAAA": 24874, + "CTAAATATAA": 24875, + "CAGCAGGAA": 24876, + "CGAAAAATT": 24877, + "GTCAAGAA": 24878, + "GTCAACTTA": 24879, + "GAAAAATGTT": 24880, + "GGATTTCTTTG": 24881, + "GAATGGCTG": 24882, + "GAATGCCTT": 24883, + "GGGTGTTTG": 24884, + "TGACATATG": 24885, + "CCAGAGTGA": 24886, + "CTTCTGAAAA": 24887, + "GAACCCAGA": 24888, + "CTGATGGTAGG": 24889, + "TCGCCCAGGCTGGAGTG": 24890, + "TGTGTATGTGTGTG": 24891, + "TCTTGATTCCAGCTT": 24892, + "GTGAGTTCCAGGAAA": 24893, + "CTGCTGAAA": 24894, + "CCAGCAGTG": 24895, + "CTACTGAAA": 24896, + "GTCTAAGA": 24897, + "TATAGCAAA": 24898, + "TCTTTCATG": 24899, + "CATTTAAAAAA": 24900, + "CTCAGCTCC": 24901, + "CCTTCAGC": 24902, + "CCAAGGCTT": 24903, + "GCCACCCTT": 24904, + "CGAGCGA": 24905, + "CAAAATGTTA": 24906, + "TGGGAAATT": 24907, + "TAGACATCA": 24908, + "TTATTGTG": 24909, + "TCTGAGTA": 24910, + "CTAAAGAAAA": 24911, + "GCTCAGAA": 24912, + "GAGAACAAA": 24913, + "CAATTCTT": 24914, + "GCATTCCTG": 24915, + "TACAATGAA": 24916, + "GGGAAGATG": 24917, + "GAAGAĊ": 24918, + "TAACACTCA": 24919, + "CTGTACCTG": 24920, + "TAATTAATTA": 24921, + "GATCĊ": 24922, + "GCCACCACGCC": 24923, + "CACTCCTCCATT": 24924, + "CTGGAGTGCAATG": 24925, + "GCTCCCACAGTTGATT": 24926, + "GAAATACCATTTGACCCA": 24927, + "CCATCTCACACCAGTTAGAATG": 24928, + "GTTGCATCCAAAGAACACCA": 24929, + "CAGCAAATG": 24930, + "TCGTTATG": 24931, + "TAATGAGTT": 24932, + "TCATCCATG": 24933, + "TGACGAGA": 24934, + "GTTCTGATT": 24935, + "CTAGCTTTT": 24936, + "CTAGGGC": 24937, + "TAAATAGC": 24938, + "TGTGTCAAA": 24939, + "GAAAGACCA": 24940, + "CGGAAATT": 24941, + "CCAATATAA": 24942, + "CTTACGTA": 24943, + "TAGGTTTTA": 24944, + "GGCAGCTGA": 24945, + "TCTGAGTAA": 24946, + "GGAAATAAAA": 24947, + "TCCTTGTG": 24948, + "CGTTGGTG": 24949, + "CGTTTGAAA": 24950, + "TAATAGCTA": 24951, + "CTCAAGTA": 24952, + "TCCTGCTT": 24953, + "TAATGCAGA": 24954, + "TAATGTAAAA": 24955, + "CCTCCTTCA": 24956, + "GGCAAGAGAA": 24957, + "TCTCTCTCTCTG": 24958, + "CCCCAATTTA": 24959, + "AAAAAATGCTCA": 24960, + "TCTTCAAACCAAGCTG": 24961, + "TCGTAATG": 24962, + "GCGTATTTT": 24963, + "GCGATTTG": 24964, + "TCATCTATT": 24965, + "TGACGTAA": 24966, + "GCACTAAAA": 24967, + "CCAGĊ": 24968, + "CTCTCGTG": 24969, + "TGAACATAA": 24970, + "TCTGGATTTT": 24971, + "CAGATACCA": 24972, + "CTGAGCTCC": 24973, + "GTCAGTGTG": 24974, + "GAGGTAGAA": 24975, + "GTTATATCA": 24976, + "TCCAGTCAA": 24977, + "CCCCCGGA": 24978, + "TGTATGTGTGTG": 24979, + "GGCACTCC": 24980, + "CCTACCAAA": 24981, + "CGCGTTA": 24982, + "GTGAATTCA": 24983, + "CACCTAGA": 24984, + "CAAAAATAGA": 24985, + "CCTCAGATAAA": 24986, + "CCTGAGCCA": 24987, + "CGAGGAAAA": 24988, + "TGATAGATT": 24989, + "GCCACAGTCA": 24990, + "CATTTATTTTA": 24991, + "GTCTGATGAAA": 24992, + "GTTTAATGA": 24993, + "CTCGCTC": 24994, + "GTCTCATTA": 24995, + "TCAAGTCTT": 24996, + "GGAATGCC": 24997, + "CCTGCTGTT": 24998, + "CCAATGAGAA": 24999, + "CTAAGATCATG": 25000, + "TTTGTCAAA": 25001, + "GTTTTĊ": 25002, + "TCCTTTATG": 25003, + "CGTTCCA": 25004, + "CCATTGTAA": 25005, + "CGGATTTA": 25006, + "CTGTGGTAA": 25007, + "GCCTTGGGG": 25008, + "TGAGAGTG": 25009, + "GTCTGTTTTA": 25010, + "GAATGTAAA": 25011, + "CATTTTGAAA": 25012, + "TTATGTATG": 25013, + "CTTTATCTG": 25014, + "CTTTAGAAAA": 25015, + "CAAGGCAGA": 25016, + "TGACACCTT": 25017, + "GTGGACTGA": 25018, + "TCAGGAGATCAAGACCA": 25019, + "CCGCTCTG": 25020, + "GCGAGGTT": 25021, + "TAAGCACA": 25022, + "TGACTTGA": 25023, + "GAAGGAGA": 25024, + "TCCTGACTT": 25025, + "CTTGTTGTT": 25026, + "CTTGTATA": 25027, + "GTCGTAAA": 25028, + "GTCATGCC": 25029, + "TGGGGATGA": 25030, + "CGCGGCCA": 25031, + "CTGAATATG": 25032, + "TAAAAACATA": 25033, + "GGAAATATG": 25034, + "TCCTTCCCCA": 25035, + "CAATTGCA": 25036, + "GGAGGTAGG": 25037, + "CTGTGGAAAA": 25038, + "GCATTGTA": 25039, + "GTTTGCAGA": 25040, + "TGATGCATG": 25041, + "TCAAAACTATGAAA": 25042, + "TCATGCCTT": 25043, + "TATTAATATT": 25044, + "GCTCGTTA": 25045, + "CATCGCC": 25046, + "CTGCATGTT": 25047, + "GTTTAGTAA": 25048, + "TCACCCTGA": 25049, + "TGACAGAAAA": 25050, + "GTTCTAACTGTTGCTT": 25051, + "CTAACTGGTGTGAGATGGTA": 25052, + "TGAGCTGTG": 25053, + "CTGTAACC": 25054, + "GGAGTTGGA": 25055, + "GTATCTCTT": 25056, + "CAAACCCTG": 25057, + "CAAAGGGCA": 25058, + "GAGATACAA": 25059, + "GTGATTATT": 25060, + "CATGCCAAA": 25061, + "CATGCTAAA": 25062, + "GAGGGGGTG": 25063, + "CTCCTAGAA": 25064, + "GCCACTGCACTCCAGCCTGGG": 25065, + "CTTTTTATAA": 25066, + "CAAAAGCTG": 25067, + "GGGAGAAGG": 25068, + "CGTGGGGG": 25069, + "TAAAAACACA": 25070, + "GGCCTCAAA": 25071, + "GTGAATATA": 25072, + "CAATTTACA": 25073, + "CCTCCCACC": 25074, + "GTCTGCCA": 25075, + "GTGTGCAGG": 25076, + "GATCATTTG": 25077, + "AAATACACA": 25078, + "TCACACCAA": 25079, + "GCCCACCTT": 25080, + "GAGTCCAGGA": 25081, + "TCTTTTATTTT": 25082, + "GGCAGAGGCAGA": 25083, + "GGCAGGAGAATG": 25084, + "GAAACCATCATTCTCA": 25085, + "TGACAAGA": 25086, + "GTGGGAATG": 25087, + "GTTGGAGAA": 25088, + "GTTGTCAAA": 25089, + "GTTCCGG": 25090, + "GCATCAGA": 25091, + "CTATTTTAAA": 25092, + "TAAAATAAAATT": 25093, + "GCTGTCAAA": 25094, + "CTCACGCA": 25095, + "TCCACGGA": 25096, + "TTAATGCA": 25097, + "GGTGĊ": 25098, + "GGTGGTTTT": 25099, + "CGCTCGA": 25100, + "TTTTTTAAATG": 25101, + "TCCTTGAGTG": 25102, + "GGTATCTC": 25103, + "GCTCACACA": 25104, + "GGATTCTG": 25105, + "GCAGGTGTT": 25106, + "GGATGGAA": 25107, + "CCTAACAGAA": 25108, + "TCTGTGAAAA": 25109, + "CTTCCCTTA": 25110, + "GCAGACATT": 25111, + "CTGTATGAA": 25112, + "TGATGATTA": 25113, + "CCAGATGTG": 25114, + "CTACATATT": 25115, + "CCTGTTCCTG": 25116, + "TGTTTGTTTGTTTG": 25117, + "TTTTCCACAGTT": 25118, + "TCGATGTAGTGACACTA": 25119, + "CTTCACAGTAGG": 25120, + "CAGGGAAGCCTG": 25121, + "TACCTCAGCCTCC": 25122, + "GATCTCCTT": 25123, + "TCAGTTCTG": 25124, + "GTTGTCATT": 25125, + "GTTGTATA": 25126, + "GCACCAGCA": 25127, + "GAAACGTA": 25128, + "GAAAGCAAAA": 25129, + "TAAAACCTG": 25130, + "GCTGGGGTT": 25131, + "CTCAGACAA": 25132, + "GTCACATTA": 25133, + "GAGGGTCC": 25134, + "GATGCTGTT": 25135, + "CTTTTGACC": 25136, + "TTTATAAAAA": 25137, + "GACATCATT": 25138, + "CATATCTGA": 25139, + "TTATTCCAAA": 25140, + "GGCACCCA": 25141, + "TCTCACAGTT": 25142, + "CTGAACATG": 25143, + "CAGCAGAA": 25144, + "TCTTTTTTCA": 25145, + "TGATGCAGG": 25146, + "TATCATATG": 25147, + "GAGTGTTTG": 25148, + "TGAGGGTGAGTAAA": 25149, + "GAGTTCTA": 25150, + "CTGGGGACA": 25151, + "TCCCATCTA": 25152, + "GGAGGAGAAGGGGACGACAGA": 25153, + "GAATATCTCA": 25154, + "CAAGGCCAA": 25155, + "TAAGATGTT": 25156, + "GCTGTGTTA": 25157, + "GCTCATGCCTGTAATCCCAGCA": 25158, + "TCCTCCTCCTCCTCCTCCTCCTCC": 25159, + "CAAATAAATTCATTAAAAA": 25160, + "GAAATAAGTATTTGACCCC": 25161, + "CCGAAATT": 25162, + "GATCAAATG": 25163, + "GCGCTTAA": 25164, + "TAACTCATT": 25165, + "CAACGTCA": 25166, + "GTGCCCAGG": 25167, + "TAAAGGGCA": 25168, + "GAAAGAAGG": 25169, + "TCTGCCTTA": 25170, + "CAGAGGGCC": 25171, + "CATGCCCC": 25172, + "GTCAGGGTG": 25173, + "TCTCTCAAAA": 25174, + "CTTACATCA": 25175, + "CGACACTG": 25176, + "TGGATCTG": 25177, + "TAATTGAAAA": 25178, + "TCTCATGTG": 25179, + "TCTCCCTTTT": 25180, + "GGAAAGCAA": 25181, + "GAATTTCAAA": 25182, + "GTCTTCTCTT": 25183, + "GGAGGCCA": 25184, + "TCTAATCTA": 25185, + "TGAGGGGAA": 25186, + "GAAGACCTG": 25187, + "TAACATTTTTT": 25188, + "TATTTATGTT": 25189, + "GAAAAAAAAAAAAAAA": 25190, + "GTACAGCCA": 25191, + "CACGTGTG": 25192, + "CCGTAAAA": 25193, + "CCGCACC": 25194, + "CCCGCTA": 25195, + "TCACAAGA": 25196, + "TGACTGCTG": 25197, + "CAACGAAA": 25198, + "GTTGAATTA": 25199, + "GGACTTGA": 25200, + "CTCGCTAA": 25201, + "CTCTCGAA": 25202, + "GCAAGTAAA": 25203, + "GTCAGACTT": 25204, + "TATGCACAA": 25205, + "CTCCCCCTG": 25206, + "GTCCTACA": 25207, + "GTCCCACAA": 25208, + "TCCTTCTTCTT": 25209, + "TATTATATAA": 25210, + "GTTAACAA": 25211, + "TCTTAGATT": 25212, + "CGTACTG": 25213, + "TGAGACCA": 25214, + "GAATGGCTA": 25215, + "GTGGGGGAA": 25216, + "TCAGGCCTG": 25217, + "CTCAAACAA": 25218, + "TTTTTAGTTTT": 25219, + "CTCTCCAAAA": 25220, + "GCTGGGTGTGGTG": 25221, + "TAATCTCAGCACTTTG": 25222, + "TCATATTTG": 25223, + "TGAGCCACA": 25224, + "TCCTCTCAA": 25225, + "CTTTGAGTT": 25226, + "GTAGAGGAA": 25227, + "CTAGGTTTT": 25228, + "GTCTCCTCA": 25229, + "GAAACCCAA": 25230, + "TATACATGA": 25231, + "TCTTGTGAA": 25232, + "CATTGATGA": 25233, + "TGAACTGTT": 25234, + "TCTCAAAATT": 25235, + "GTTACATGA": 25236, + "CTTATACTT": 25237, + "CTTTTCTGTT": 25238, + "TAATTCCTG": 25239, + "TCTCATGAA": 25240, + "CTCTTGGGA": 25241, + "GCCAAGGTG": 25242, + "GTTTGGTAA": 25243, + "GCCTGGATT": 25244, + "GCTCCCCTG": 25245, + "TAGCATTTG": 25246, + "CCTTAGTTA": 25247, + "GAAGACCAA": 25248, + "CTTTAGGAA": 25249, + "CTTTATCTA": 25250, + "GCCACCACGCCTG": 25251, + "TGACAGCTCTTTGGTCTT": 25252, + "TCAGCCACA": 25253, + "GAACAGTC": 25254, + "GCAGAGATG": 25255, + "CTACTATG": 25256, + "CTCGACTT": 25257, + "CTCTAAATG": 25258, + "GTCGTGAA": 25259, + "TATTTCTCTT": 25260, + "TATAGACAA": 25261, + "TCTTTAGTG": 25262, + "TGTTTAGTG": 25263, + "CAAAGAGTA": 25264, + "TCTGTCCAA": 25265, + "CAGATGAGA": 25266, + "GTGAGCAAA": 25267, + "GTGACGTA": 25268, + "CGGTACC": 25269, + "GAAAAGAAATT": 25270, + "TCTGACCA": 25271, + "CACCĊ": 25272, + "GCTGAGGGA": 25273, + "GTCAATAACAAAA": 25274, + "CGGATGGG": 25275, + "CCCTGCCTT": 25276, + "CACTTCCTG": 25277, + "TTATGTCAA": 25278, + "CCATGGGA": 25279, + "GGGCGGTG": 25280, + "CCCTCTCTCC": 25281, + "GTTCATGTG": 25282, + "CTACAGCTT": 25283, + "GACTTACAGAAAA": 25284, + "GTGACTGCAGCCATGAAATTAAAA": 25285, + "TACTTTGTTATATA": 25286, + "TCTTCCATTTCCTAATAATT": 25287, + "GAATTATTTT": 25288, + "GTTGTGTA": 25289, + "TAAAGAAATT": 25290, + "GAAACCATT": 25291, + "TCTTGCTTTT": 25292, + "CAGGCAGC": 25293, + "CAGACCCTT": 25294, + "GCTGGTGAA": 25295, + "CGGCAGTT": 25296, + "CTAACTAAA": 25297, + "TCTGACTGA": 25298, + "GGCCCGTT": 25299, + "GCTGATTCA": 25300, + "GTCTTGCTT": 25301, + "CTCAAGTAA": 25302, + "TATAACAA": 25303, + "CCTCCTTTA": 25304, + "GAGCTCCTG": 25305, + "CGTCTGCA": 25306, + "TTTTTGGTTTT": 25307, + "CAATACTTA": 25308, + "TATTTATGTG": 25309, + "CCAGAGCTT": 25310, + "TATATATGAA": 25311, + "GAGATGGGAATACCA": 25312, + "TCATTTTGACC": 25313, + "CTTGCCTGGGAAA": 25314, + "CATTCATTCATTCATT": 25315, + "GACTGAATT": 25316, + "TAAGCCATT": 25317, + "TAACCATA": 25318, + "TTTTCGTT": 25319, + "TTTTGTTAA": 25320, + "TGATCTAAA": 25321, + "GAATĊ": 25322, + "GAACAACAA": 25323, + "GGACAAATT": 25324, + "GCCTGTGAA": 25325, + "GCCCTTCTT": 25326, + "GTCGGCTT": 25327, + "GTCGGCGG": 25328, + "CACATGTGA": 25329, + "GAGATAATT": 25330, + "CATTTCTCTT": 25331, + "GTGACGTG": 25332, + "CCTTCAGGA": 25333, + "GCAATATTTT": 25334, + "GCAACACCA": 25335, + "TTTTAGTCA": 25336, + "TCTCTCCCTG": 25337, + "TCCATACTT": 25338, + "TTAACTTTT": 25339, + "TTAAGGTT": 25340, + "GTCCCTGGG": 25341, + "TTTACTTAA": 25342, + "TCTCATATA": 25343, + "CTGAATTAAA": 25344, + "CTTTGGTTA": 25345, + "CCTCAGGAAA": 25346, + "CCTCCTGTG": 25347, + "CAGCCTCC": 25348, + "CTTCACCAA": 25349, + "CTGTATCTG": 25350, + "TCACATGTT": 25351, + "GTGGATGTG": 25352, + "GTTCCCAAA": 25353, + "GTAGGTGTT": 25354, + "TCACAATTA": 25355, + "TGACAATTA": 25356, + "GACCTTAAA": 25357, + "CTTAAGCCAGTACATG": 25358, + "CCTCAAATT": 25359, + "GATTTTATTTT": 25360, + "GACGGAGG": 25361, + "TAATATATATA": 25362, + "TAAGGAGAA": 25363, + "TTTTCTATT": 25364, + "TCAGTTGA": 25365, + "CTGTGTGAA": 25366, + "CTTGCTATT": 25367, + "CCAGTGAAA": 25368, + "TGTGCTTCTT": 25369, + "TATTCACCA": 25370, + "GAGAGGTCA": 25371, + "CAGGGACCA": 25372, + "TCAAGAGCA": 25373, + "GCTGTACC": 25374, + "CTCAGATGA": 25375, + "GCAACAAAAA": 25376, + "CGGAAATG": 25377, + "TCTCTĊ": 25378, + "TCCACAACCTCAAACAGTCA": 25379, + "TCTAGGGTT": 25380, + "TAGACGTT": 25381, + "GGGATCAAA": 25382, + "CCTAGCCA": 25383, + "CTCTTCTCAA": 25384, + "TAAATTTGTT": 25385, + "GCAGAATTA": 25386, + "GCTTAAGCAGGGG": 25387, + "CTGAGGCAGGAGGA": 25388, + "TCTGCAATAGGTAAGCA": 25389, + "TACGTAAA": 25390, + "TGAGGACA": 25391, + "GTGGAAGA": 25392, + "GTGGTTGTT": 25393, + "GTGCCTCTG": 25394, + "GCCCACAAA": 25395, + "GAAAGTCTT": 25396, + "TCTTTATCC": 25397, + "GGAAGACCA": 25398, + "CTCACGTG": 25399, + "GAGGGTTTT": 25400, + "CGGTACA": 25401, + "CGGCAGTG": 25402, + "GTTACTAAA": 25403, + "CTAATCAAA": 25404, + "GGGGGAAAAA": 25405, + "CTGAAGGTG": 25406, + "TTTTTTTCAAA": 25407, + "TCTGATGTG": 25408, + "GTGAATATG": 25409, + "TGCCTAGG": 25410, + "CACTGTTAA": 25411, + "TCATGGTAA": 25412, + "GCGGTCC": 25413, + "GCAGGGACA": 25414, + "GAAGGCCTG": 25415, + "CCAGGCAGA": 25416, + "GAAATGCTTCTTA": 25417, + "GTTTACCAA": 25418, + "TAATTTTTTTG": 25419, + "GAACACCTG": 25420, + "CTCTACAAA": 25421, + "GTTCCTGAA": 25422, + "CCCGGAAA": 25423, + "CGTTTGCCCACTGA": 25424, + "CTATCCCAAAA": 25425, + "CCAAGAACACCA": 25426, + "CATCATGCTTTGGG": 25427, + "TGCCCTTGCTGATGGAA": 25428, + "CTTTGGGAGGCCGAGGCGG": 25429, + "TCACACATT": 25430, + "TGACTAGG": 25431, + "GAATAAAAAA": 25432, + "CAATCCCTG": 25433, + "GTGTCCACA": 25434, + "GCCGGCGG": 25435, + "GAAAGGTAA": 25436, + "TCAACCTCA": 25437, + "GCTTCATTA": 25438, + "TACATAATG": 25439, + "GATGGTCAA": 25440, + "GAAAACTAAA": 25441, + "CGAGCAAA": 25442, + "CAAAATACTA": 25443, + "TTCAGTAAA": 25444, + "GCAAAGTTA": 25445, + "TCCTTGATT": 25446, + "TCCTTTCTTTT": 25447, + "TAATACTCA": 25448, + "TATTACTTA": 25449, + "CTTTGCCCA": 25450, + "CGCCTCTG": 25451, + "CCATTTAAAA": 25452, + "AAATTTCAA": 25453, + "GCGGCATG": 25454, + "TCCCATATT": 25455, + "TAAGATTTTA": 25456, + "TGTGTGTGTGTGTGTGTGTGTGTGTG": 25457, + "GTATTTTGGCCCATT": 25458, + "GTGGGCAAACGTA": 25459, + "GCATTCTTTGTCCTCCAAA": 25460, + "GTGTGAGTG": 25461, + "GGATTTTTTTT": 25462, + "CCACCGG": 25463, + "CTACAGAGA": 25464, + "CTAGGGTA": 25465, + "CACACGAA": 25466, + "TCTTTCAAAA": 25467, + "CAGATCAAA": 25468, + "CTGAGTCAA": 25469, + "CTGACACAA": 25470, + "CCTTTAGAA": 25471, + "TATGTCTTA": 25472, + "CTAAAAAATG": 25473, + "GGGGCCCA": 25474, + "TAGATACTT": 25475, + "CGTGCAA": 25476, + "TCTGAGCAA": 25477, + "TCATTTCTCA": 25478, + "TCCAACAATT": 25479, + "GCTGAGACA": 25480, + "GTGTTGTTA": 25481, + "CGCCCCCC": 25482, + "CTCTGTCTCC": 25483, + "TTTTAATAAA": 25484, + "TAATGGATG": 25485, + "GGCTGTGTG": 25486, + "TGAGGGCTG": 25487, + "GATAATATT": 25488, + "CTTTAGTTTT": 25489, + "TCGAGGAA": 25490, + "GGAGCAGGTT": 25491, + "CTGTAAGTCTTCA": 25492, + "GACTACACACTTATTTCAAAAAAA": 25493, + "CTTTGGATGCAACTCA": 25494, + "TCGAGACAGGGTTTCTCTGTGTAGCTTTGCGCCTT": 25495, + "GCGAGAGAGA": 25496, + "TGATTTTAAA": 25497, + "CTTCATTTG": 25498, + "CTTCTCTCC": 25499, + "GTGGTTTCA": 25500, + "CTACAAGA": 25501, + "CTCTAACTT": 25502, + "GAAAGGAGG": 25503, + "GAAACAGC": 25504, + "GAGATCAAA": 25505, + "CATTCTTAAAA": 25506, + "TCTGGGGCA": 25507, + "TAAAATATTAA": 25508, + "TAAAATGTGA": 25509, + "GCAATAATT": 25510, + "CGGGGCTT": 25511, + "TACACTCAA": 25512, + "TCCAGGATT": 25513, + "TCCAGGAGAA": 25514, + "TTTAGACA": 25515, + "TGGATCTT": 25516, + "TGAAATGCA": 25517, + "TGATTGCAA": 25518, + "GCTGATGGG": 25519, + "TACCTGAGA": 25520, + "TAATGGCA": 25521, + "GTCTGTCATAGTT": 25522, + "TCATGCCAA": 25523, + "GCTCCCTCA": 25524, + "CTGAAATTTT": 25525, + "CTGTAGTAA": 25526, + "GTGGTGCGTG": 25527, + "GAAAAAAAAATT": 25528, + "CACACACACACACACACACACA": 25529, + "GGCGCGGTG": 25530, + "CCTATAATCCCAGCTA": 25531, + "CAGAGGAGCCTCTTAAA": 25532, + "TAGTTTTTG": 25533, + "GGGTTATG": 25534, + "TCGGGGTG": 25535, + "TGAGTTATT": 25536, + "TTACCTA": 25537, + "GTTGGAGGA": 25538, + "GCCTAGAAA": 25539, + "TATTTCAATT": 25540, + "TATTAAAAAAA": 25541, + "CAAAGGTCA": 25542, + "TGAAGACCA": 25543, + "TCTGGACTG": 25544, + "CTGAGAATT": 25545, + "CTGAGGGTG": 25546, + "GCTTGAGAA": 25547, + "GTGACTTAA": 25548, + "CATGTCAAA": 25549, + "GTCATCTCC": 25550, + "GTCAGATAA": 25551, + "TATGCTCC": 25552, + "TATGGCCA": 25553, + "GTTTTCTTGG": 25554, + "GTCCCTCTT": 25555, + "CGACCCCATGGA": 25556, + "TGGAGGAGG": 25557, + "TTCACAAAA": 25558, + "GTAAACATA": 25559, + "GCAAAGAAAAA": 25560, + "GCCAACAGG": 25561, + "GTGTTGAGG": 25562, + "CTCTGGATG": 25563, + "GCCTTGGTG": 25564, + "CAGGAGACA": 25565, + "GAGCTCTCA": 25566, + "GATGAGATT": 25567, + "GTGGGGTGA": 25568, + "AAATATCAA": 25569, + "AAATACTTA": 25570, + "TCAGAGCCTG": 25571, + "GGCTATTTT": 25572, + "GTTCAGTAA": 25573, + "CCACCCCTT": 25574, + "TGACCCCAA": 25575, + "CGCCAGGGTTGTGGGTT": 25576, + "CCCAGCCTGGTGCAGG": 25577, + "TGACCTGCAGAGAGCTGGGA": 25578, + "TAACTCCAA": 25579, + "TGATCTGTT": 25580, + "CTTTCCATG": 25581, + "GCATCTGTG": 25582, + "GCACCACCA": 25583, + "TAAACTATG": 25584, + "TATATAATATA": 25585, + "TCTTCCCTA": 25586, + "TAAAAGGTA": 25587, + "CTCAGGAGA": 25588, + "CATGCTCTT": 25589, + "GTCAGACTG": 25590, + "TCTACCCTG": 25591, + "GCCACCTCTT": 25592, + "CTTTTTACA": 25593, + "GATATAAAAA": 25594, + "TCCTTGGA": 25595, + "GTGAACACA": 25596, + "CTGTTTCCCCA": 25597, + "GCCAAGGTT": 25598, + "GTCAAGCTG": 25599, + "GCATTCAAA": 25600, + "GCCTTCAA": 25601, + "TGAGACCTT": 25602, + "GAGCAGGTG": 25603, + "CTTCATAAA": 25604, + "TATATACAAA": 25605, + "TCTATGAAAA": 25606, + "GTAATGTTA": 25607, + "GTGCCATTTG": 25608, + "GGTGTGAAA": 25609, + "GCCACCCTTTGCCTT": 25610, + "CCATCAGCAAGGGCATT": 25611, + "CCGTTTCA": 25612, + "TATTTTTTAAA": 25613, + "CTGTATAAA": 25614, + "GTTTAACA": 25615, + "GCACCTTTT": 25616, + "CTACCAAAA": 25617, + "TGTGTACC": 25618, + "TCTTGGATT": 25619, + "GAGAGGCAA": 25620, + "TCTGGCAAA": 25621, + "CTCACATCA": 25622, + "CTCAGACCA": 25623, + "CTCAGGGTT": 25624, + "GCAACCAAA": 25625, + "CCAATCAAA": 25626, + "CTAATAGTT": 25627, + "TACATACAAA": 25628, + "GAAAATACTG": 25629, + "GCCAGTAAA": 25630, + "GGGGĊ": 25631, + "GGGGTCAA": 25632, + "GCTATCAAA": 25633, + "CGTGTGAA": 25634, + "GCAAAGAGTT": 25635, + "TAAAAATACAA": 25636, + "TGAAACTG": 25637, + "TAATACAGG": 25638, + "CGCAGAA": 25639, + "TCTTTTTGTA": 25640, + "TACTATTTG": 25641, + "TATTTGCATA": 25642, + "CTTTAGCTT": 25643, + "GCGACGAA": 25644, + "TGATAGGAA": 25645, + "TCACTGAAAA": 25646, + "GTAGATGAA": 25647, + "GCAAAAATCCCAGAA": 25648, + "TCCCTCTGGG": 25649, + "CAACATAGTGA": 25650, + "GGAAATGGAAGACATACAA": 25651, + "GTCAGATGAAACCAAAA": 25652, + "TAACCTCTT": 25653, + "TAAGAAATA": 25654, + "TGACAATA": 25655, + "CAAGTTAAA": 25656, + "GTGTAAATT": 25657, + "GTTGGCTTAAA": 25658, + "CCATCTGTG": 25659, + "GTACAAGGA": 25660, + "TGTGCACAA": 25661, + "GAGACGTG": 25662, + "GAGATCTGA": 25663, + "TGAACATTA": 25664, + "CTCACCTCC": 25665, + "TATGTAAAAA": 25666, + "TATGTCATT": 25667, + "GAGGTCAGGA": 25668, + "CGGCTGAA": 25669, + "CCAACCTCA": 25670, + "TTTGĊ": 25671, + "CAAAATCC": 25672, + "GGGATAGTT": 25673, + "CCAAATGCA": 25674, + "TAATATCCA": 25675, + "TGCCTTTTA": 25676, + "TCCAACCTGA": 25677, + "TCTAACAAA": 25678, + "CGTACACA": 25679, + "TCATGTCTT": 25680, + "CAACACTAA": 25681, + "CAACAGAAAA": 25682, + "CTGCCCCTT": 25683, + "GTTGATATT": 25684, + "GGCAAAGGGTG": 25685, + "CTTTCTTAAA": 25686, + "CTCCTGACCTTGTGA": 25687, + "GCCCAGGCTGGAGTGCAATG": 25688, + "GATTTGCATTTTAATGAGG": 25689, + "GTTTGCTAGAGTG": 25690, + "GATGGAGGAGCCTG": 25691, + "TCAGTAATT": 25692, + "TGAGTGGA": 25693, + "GAATCACTT": 25694, + "CTTCAATA": 25695, + "GTGCTTCTG": 25696, + "GAAATAAAAAA": 25697, + "CAGAGTCTT": 25698, + "GCTGCCATT": 25699, + "CATGCTCAA": 25700, + "CATGGAGTT": 25701, + "GCAACGCA": 25702, + "GTAAGAGTT": 25703, + "CGGCATA": 25704, + "GATGCACC": 25705, + "TGGGTATA": 25706, + "TGGACTTA": 25707, + "TAATTGGTG": 25708, + "CCTATAATT": 25709, + "GCAAAGAGAA": 25710, + "GGCCCGC": 25711, + "GCTCACAGA": 25712, + "TCCAAAAATT": 25713, + "GAGAAGTGA": 25714, + "GTATTGTATT": 25715, + "TCTTTTCTA": 25716, + "TACCTTATT": 25717, + "CTCTGCACA": 25718, + "GCTAACAAA": 25719, + "GTGTGTATT": 25720, + "GCATGCAGG": 25721, + "TAAGACTGA": 25722, + "TATTTATAAAA": 25723, + "GCCATGCCCTCC": 25724, + "TGACTGTGTGGATCA": 25725, + "GTAATGTTTT": 25726, + "CTCCTGCCTCAGCCTCCCGA": 25727, + "CAAAAATGAAAATT": 25728, + "CAGACAGACAGACAGACAGACAGACAGACAGA": 25729, + "GAAGCATTTCAA": 25730, + "CACTAAATT": 25731, + "CTTCTGATT": 25732, + "GTGGTTGG": 25733, + "GTTGCAGCA": 25734, + "GGAGCTGTT": 25735, + "GTACCCTA": 25736, + "TGTGTTTTTT": 25737, + "TCTTGATTTT": 25738, + "GTGATCTGA": 25739, + "GCTGCACAA": 25740, + "TCTACTCTT": 25741, + "GGTTTACA": 25742, + "GGAAAGATG": 25743, + "GCTCAGTTTG": 25744, + "TATTATGTA": 25745, + "CTTAACAA": 25746, + "CAGAAGAGA": 25747, + "CGCCCCTG": 25748, + "GATTATCTT": 25749, + "TCGGGTTA": 25750, + "TCAGAGTTA": 25751, + "GGTAATTTG": 25752, + "GGAATTGAACAA": 25753, + "TAACAATTTT": 25754, + "CAAAAAACAAAAAA": 25755, + "GTCTGATGGG": 25756, + "TGAAATTAAAA": 25757, + "CAGATCACGAGG": 25758, + "GCCCAGCGACAGCC": 25759, + "GTTCATTTG": 25760, + "GTTCĊ": 25761, + "GTTGCTCTT": 25762, + "CCAGCCAGA": 25763, + "GTATCTATT": 25764, + "CTACTAAAA": 25765, + "TCTTCGTG": 25766, + "TGTTGTCAA": 25767, + "CATTGTAAAA": 25768, + "TCAATAGCA": 25769, + "GGAAGGTCA": 25770, + "GAGGCACC": 25771, + "TCTCGCTCTG": 25772, + "CTCCGCC": 25773, + "CTTTTGTAAA": 25774, + "GGGGTGCTG": 25775, + "CCCATCCCTG": 25776, + "TAGGĊ": 25777, + "TGAAAGAAAA": 25778, + "TACCCGA": 25779, + "CCATTCTCC": 25780, + "GAAAAAGAGA": 25781, + "GTGTGGGAA": 25782, + "TTATGGAAA": 25783, + "TTATGGAAAA": 25784, + "CCAGGTGAA": 25785, + "GAACAGAAAACCAAA": 25786, + "GCGAGTGA": 25787, + "TCACATAAA": 25788, + "TTACACTGA": 25789, + "TTTTTACATT": 25790, + "TGATCACAAGAA": 25791, + "GCCTCGAACTCACAGAGATCCGCCTGGCTCTGCCTCCCGA": 25792, + "GAGCAGGGGGACCTT": 25793, + "GAAAATGTGGCACATATACACCATGGAATACTA": 25794, + "CAAACAAGCAAGATTTCTG": 25795, + "CAAAGCCTACCATCAGTAA": 25796, + "GATCTCTA": 25797, + "GCGAATTA": 25798, + "TGATAAGAA": 25799, + "CCAGGAATT": 25800, + "GCTGGTTTTA": 25801, + "CCTTCCTCTT": 25802, + "GTAATACTT": 25803, + "CCAATACCA": 25804, + "GTTTTTGGG": 25805, + "GTTTTCATA": 25806, + "CGATACAA": 25807, + "TCTGATATG": 25808, + "TCATTTCCTT": 25809, + "CGAATGAA": 25810, + "CAGTGGTA": 25811, + "CTGTGCAGA": 25812, + "CTATTCTCA": 25813, + "TCATGGCTA": 25814, + "GTGTGGCTT": 25815, + "CAATGTAAA": 25816, + "GGATGCAGA": 25817, + "TCAGGGTCA": 25818, + "TCGATAAAA": 25819, + "GTTTAGTCA": 25820, + "CTGTATTTTA": 25821, + "GCACAGCCA": 25822, + "GACTCTTTA": 25823, + "TCTCTACTAAAAA": 25824, + "GGCTAATTTT": 25825, + "CCCTATTTAATAAATG": 25826, + "GGTCAGGCTGGTCTCAAA": 25827, + "TCGGAATG": 25828, + "TAACTGGAA": 25829, + "TTTTGTGTT": 25830, + "TTATAATA": 25831, + "CAAGTGTGA": 25832, + "GCATCAGCA": 25833, + "CTCGTCTT": 25834, + "TCTGTTTTTT": 25835, + "CATGGAATT": 25836, + "CCTGGGGCC": 25837, + "GATGGGGATT": 25838, + "TTATTCACA": 25839, + "CCTATCAAA": 25840, + "CTAAAGGA": 25841, + "TGATTGCTG": 25842, + "TCTTTTCCAA": 25843, + "CACAATATT": 25844, + "CATTTTTTTA": 25845, + "TACTGTTTG": 25846, + "CAATGGCTG": 25847, + "TCTGTGTTTG": 25848, + "CTTCTTCCTG": 25849, + "CAACAAGAGTGAAA": 25850, + "GGTAATTAA": 25851, + "TCACCCCAA": 25852, + "TAAGACTAA": 25853, + "CTCCTTGGAAGGAAA": 25854, + "CTGGATGTT": 25855, + "GCCCAGTCA": 25856, + "GCTTGGTTTGAAGAAA": 25857, + "TCTCCTCTAGAGCA": 25858, + "GACGGGGGAGCCTGGTGGGCTG": 25859, + "TAGAGGTG": 25860, + "TAATCTTTG": 25861, + "TCAGCAATT": 25862, + "TCCTTATTA": 25863, + "CTGGCTC": 25864, + "GCATCCACA": 25865, + "CTAGTGTG": 25866, + "TATGGACAA": 25867, + "GATTGAGCA": 25868, + "CCTGCTCTG": 25869, + "CTAAAAAATA": 25870, + "GATGGTGTT": 25871, + "CGTGGGA": 25872, + 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"GTATTCAGACCCCTT": 25960, + "GTCCCAGCTCTCTGCAGG": 25961, + "CCACAGGAA": 25962, + "TAAACTAAAA": 25963, + "TAAATAATAAA": 25964, + "TATATTATTTT": 25965, + "TGTTTATTAA": 25966, + "CAAACCATT": 25967, + "CAGGTCCA": 25968, + "TCTGGCTCTA": 25969, + "CATATAATA": 25970, + "GGCATAGG": 25971, + "TGCCTAAA": 25972, + "CACCCAAAA": 25973, + "GCTGAGCTT": 25974, + "GTTAAGAAAA": 25975, + "GTCTTTCTA": 25976, + "CTCAAGGTCA": 25977, + "CGGAGATG": 25978, + "TAATGTACA": 25979, + "CCCTGTGGG": 25980, + "GAATGTATT": 25981, + "GGCCATTTT": 25982, + "GCGGGGAA": 25983, + "TAAGACAGG": 25984, + "GAACAGTTA": 25985, + "CCACCACGCC": 25986, + "CAAGAGCTT": 25987, + "CAACATGTG": 25988, + "TGACCTGTG": 25989, + "TGTTTATTTTA": 25990, + "GAAGTTAAAA": 25991, + "CTTTGCAGAAAAA": 25992, + "TCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTT": 25993, + "CCGATC": 25994, + "GGTTTATTTT": 25995, + "GACGTTAA": 25996, + "TGATTTTTA": 25997, + "TGATTAAAAA": 25998, + "GTTGCTGAA": 25999, + "CCAGTTTCA": 26000, + "GTACACAAA": 26001, + "CTAGGATG": 26002, + "CTAGAGCC": 26003, + "GTCTGACTT": 26004, + "TATTTCCAAA": 26005, + "GAAACCCCA": 26006, + "TATACGAA": 26007, + "CAAATTTTAA": 26008, + "CAGGGAGTT": 26009, + "CAGATGATT": 26010, + "GGAAGAGGAA": 26011, + "TAAAAGTATT": 26012, + "CTGATATAA": 26013, + "GCAATATAA": 26014, + "GCAATAGAA": 26015, + "CCTGGGGTG": 26016, + "GTTTTCCCA": 26017, + "TTATTCCTG": 26018, + "CCTAGAATT": 26019, + "CCAAATATG": 26020, + "GGCCTAGA": 26021, + "TCTAAAAAAAA": 26022, + "GCCTTGGTT": 26023, + "GTGTGGAAA": 26024, + "CCTCGTCC": 26025, + "CATCGTCA": 26026, + "TCTCTCTCTCTCTT": 26027, + "TGTGTGCGTG": 26028, + "TCAGATAAA": 26029, + "TTATAGTCA": 26030, + "TAAGGTTTA": 26031, + "TGATGAGCATTTT": 26032, + "GGGTATTTT": 26033, + "TCATTTTAAAA": 26034, + "GTAAAAACTCAA": 26035, + "GAGAGAGAGAGAGAGAGAGA": 26036, + "CTCTTCTCTTCTCTTCTCTT": 26037, + "GTCTCAGGATACAAAA": 26038, + "CACTCCAAACTCCACTATG": 26039, + "TCCACTTCTGTGTTTG": 26040, + "TAATCTGTG": 26041, + "TAACCTTAA": 26042, + "TCATGTGAA": 26043, + "TGATCGA": 26044, + "GAAGTCAAA": 26045, + "CAATGTGAA": 26046, + "GTGTGAATT": 26047, + "GTTCTGCA": 26048, + "TATATAATAA": 26049, + "CATTCTTTG": 26050, + "CAGAGGAAAA": 26051, + "GGAATATCA": 26052, + "GCTGGAGTGCAGTG": 26053, + "CTCACGTA": 26054, + "CCTTTACCA": 26055, + "CATGGAGGA": 26056, + "GCAAGCTA": 26057, + "GATTCCATT": 26058, + "GATTTCCTG": 26059, + "CTAATCCA": 26060, + "GACAGGATT": 26061, + "TCAAAĊ": 26062, + "CCTATCCA": 26063, + "TCATTTCTAA": 26064, + "CTAAAGCCA": 26065, + "CGCAGATG": 26066, + "GTATTCTTA": 26067, + "GTGTTGTAA": 26068, + "CATCATTTA": 26069, + "GTGTGTAGG": 26070, + "GCTCCCTGA": 26071, + "CGTCCATT": 26072, + "CTGTACTTA": 26073, + "TATTTATCAA": 26074, + "TAAGAATGTT": 26075, + "GTTCTGGGATTTTTG": 26076, + "GCCTGGGAAA": 26077, + "GGGGCTGTCGCTGGGCAA": 26078, + "GTCATGCTGAAAGACCCA": 26079, + "CCCTĊ": 26080, + "TACCAAATT": 26081, + "TCATGTGTT": 26082, + "TCATCCCC": 26083, + "TGAGGAGAA": 26084, + "TGACTCAAA": 26085, + "GTTGCTCTG": 26086, + "CCAGCTGAA": 26087, + "CTCCAGGTG": 26088, + "GGAATAATT": 26089, + "GCAAGAGGA": 26090, + "GTTTTCCATT": 26091, + "TCTATCTTA": 26092, + "TAGACAGTA": 26093, + "TGCATCCA": 26094, + "GATAGTAAA": 26095, + "CGTTTGCA": 26096, + "TATTATGAA": 26097, + "CTTAACTG": 26098, + "GACAATTTG": 26099, + "CCATGCTCC": 26100, + "GCAGGGCA": 26101, + "CCCTAGAAGAAAA": 26102, + "CTTCCGACTT": 26103, + "CAAGGCTGA": 26104, + "TCACCCACA": 26105, + "TCATCATTTT": 26106, + "TCGTTAAAA": 26107, + "GACTCTGTA": 26108, + "TAATATGCTGTCTA": 26109, + "GCAACAGTGTGTGAAAA": 26110, + "GCTCTGGATAAGAGCGTCTGCTAAATGACTAAAATG": 26111, + "GCTGAAACTCCAGTACTTTGGCCA": 26112, + "CCCAAAATT": 26113, + "TAATCCACA": 26114, + "TTACTGAA": 26115, + "CTGTCTATG": 26116, + "GTGTATATG": 26117, + "GCAGCCATT": 26118, + "GGAGATTAA": 26119, + "CCAGGACA": 26120, + "TATAGAAAAA": 26121, + "GCTTGAGTT": 26122, + "CTCAGAGCC": 26123, + "GTCAGGCTT": 26124, + "TTTTAGTGA": 26125, + "TTAACCTT": 26126, + "CTTTTTAGG": 26127, + "TGGGGACC": 26128, + "CCTACCCAA": 26129, + "CGCGTAA": 26130, + "CTGAAGGGG": 26131, + "TCTGATCTGA": 26132, + "CCAAATGTA": 26133, + "CAGCAGAGA": 26134, + "GTATTGGAGCTT": 26135, + "CAAAAACATG": 26136, + "TAATGTTATT": 26137, + "TGAGACCAA": 26138, + "TTATGTTTTA": 26139, + "TAGCTCAAA": 26140, + "GGAGACATT": 26141, + "TTATAGAGA": 26142, + "CAACACCTT": 26143, + "CTGCCCATG": 26144, + "CCAGACAGA": 26145, + "TTACATGTG": 26146, + "GTCCATGTG": 26147, + "GAGATTATCAGTG": 26148, + "CATCTCCAA": 26149, + "TGACTTCTG": 26150, + "GTTCAAGAA": 26151, + "GTACAGTC": 26152, + "CTCTATTTG": 26153, + "TCTTCAAAAA": 26154, + "TCTGCACAA": 26155, + "TCTGCCCAA": 26156, + "GGAATATTTT": 26157, + "CGGCACCA": 26158, + "TAGATCCA": 26159, + "GGTTTCTC": 26160, + "GGGAGTTTT": 26161, + "GTAAATATAA": 26162, + "GAGAATACTA": 26163, + "TATTACAA": 26164, + "CAGCAGAAA": 26165, + "CAGTGCTGA": 26166, + "GGGAAAAAAAA": 26167, + "GAGCAGAAA": 26168, + "TTATGTAATG": 26169, + "CTGTACAAA": 26170, + "TCATAGTTA": 26171, + "TAAGGCAAA": 26172, + "CCACTGAAAA": 26173, + "GAACTTAAAA": 26174, + "GCAGAGACATTACTTTGCCAACAAA": 26175, + "TCATCTTTG": 26176, + "TTACGTA": 26177, + "TCCGTGTAAA": 26178, + "GTTGAATA": 26179, + "GTTGCATTA": 26180, + "GGATCACTT": 26181, + "GCCGAGTG": 26182, + "GTCTGAGCA": 26183, + "TATTGGCTT": 26184, + "TATAGTCAA": 26185, + "TATATTTTAAA": 26186, + "GGAACATGA": 26187, + "TATGCCAAA": 26188, + "GATTTCTC": 26189, + "CCTGTAAAA": 26190, + "CCTGCACTT": 26191, + "GGTGGAAAA": 26192, + "CGTGGTCA": 26193, + "GCAAAGCTT": 26194, + "GTGAACTG": 26195, + "CACCTCTGA": 26196, + "CAGCAGTGA": 26197, + "CAAAAATGTG": 26198, + "CAGCTCTTA": 26199, + "CATCACTA": 26200, + "CAGGAGGGG": 26201, + "TACTTCTA": 26202, + "GTCTGCTA": 26203, + "TCATGGAGG": 26204, + "GCGGCAGA": 26205, + "TCGGTGGG": 26206, + "TCCCACAAA": 26207, + "GAACACTCA": 26208, + "TCTAAATTTT": 26209, + "CCACCATGCCCGG": 26210, + "TCTCAATTTG": 26211, + "TTAATTCAAGATG": 26212, + "GTTCAGCAA": 26213, + "CTATATGAA": 26214, + "GTACAGTGA": 26215, + "CATCTTGTTCC": 26216, + "GGGGTTGGGG": 26217, + "CTGCTCTAGAGGAGA": 26218, + "CAATCCCAGCA": 26219, + "CTATGAACATAGTG": 26220, + "TCTCAGCTCACTGCAACCTCTGCCTCC": 26221, + "CAAGTAGCTGGGACTACAGG": 26222, + "AGAGA": 26223, + "TCAGTCAAA": 26224, + "TCACTCTGA": 26225, + "GTGCCAAAA": 26226, + "GCACTGCA": 26227, + "CCATATTAA": 26228, + "CCATGTGTT": 26229, + "TAAAGGCTG": 26230, + "GTCGAGTG": 26231, + "TATTGCTGA": 26232, + "GAAAGTAAAA": 26233, + "TCTTGCTGA": 26234, + "CAGGGACATG": 26235, + "TCTGGTCTT": 26236, + "GCTGGACCA": 26237, + "CATGTCTAAAA": 26238, + "TATGTGATT": 26239, + "TATGGATGA": 26240, + "GACAGCTCA": 26241, + "GGTTTCCTT": 26242, + "TTTTTTCTAA": 26243, + "GAATTGGA": 26244, + "GAGAACACA": 26245, + "CGCAGGGG": 26246, + "CAAAAAGTA": 26247, + "CAGCTCTCC": 26248, + "TTTTAATTAAA": 26249, + "GCCTTGCC": 26250, + "TGAGGCCTT": 26251, + "TTATGCATT": 26252, + "GGTCATTTG": 26253, + "TTATAGCTT": 26254, + "TAAGAATTTT": 26255, + "CTTGACCTT": 26256, + "CTAGGCCCC": 26257, + "CTCCTGGCCTCAAGTGA": 26258, + "GTATTTGATCCCCTGCTGATTTTGTA": 26259, + "GGTTTCATCTGACCATATGA": 26260, + "TAAGTTGAA": 26261, + "TAAGCTATT": 26262, + "TCCCTCAAA": 26263, + "GTGTAATAA": 26264, + "GCAGTTGTT": 26265, + "CCAGTGCTG": 26266, + "GTAGAAATG": 26267, + "TATTTCCATT": 26268, + "GAAAGTGAAA": 26269, + "GAGAGGGTT": 26270, + "CATTCCTCC": 26271, + "CAGGCACTT": 26272, + "GTAACTAAA": 26273, + "TATGTAGTG": 26274, + "GAGGGACTG": 26275, + "CCTGGGGTT": 26276, + "CCAAGTCTT": 26277, + "CCAATACC": 26278, + "TACATCTGA": 26279, + "TACATCCAA": 26280, + "CAAAATACCA": 26281, + "TCTCCTAAGCAA": 26282, + "CCAAATGTG": 26283, + "CCAAAGAAAA": 26284, + "GGCCCGGTT": 26285, + "GTGAACAAA": 26286, + "GCATTGTGA": 26287, + "GAGCGTGA": 26288, + "GGATTTAAA": 26289, + "TCATGTCAA": 26290, + "GATGATGAA": 26291, + "CTGGGCGG": 26292, + "GAGTATTTG": 26293, + "TAGCTCTAA": 26294, + "GTTGGCCTT": 26295, + "CTTTTTTCTTTT": 26296, + "TAAATAAATAAAAA": 26297, + "TACTGATAACAAGTTCAAA": 26298, + "GAGTCAGACATGACTGA": 26299, + "CTGGATCATCCAAATGCA": 26300, + "GAGCTGTCAAAGGACACCA": 26301, + "TACTCTCC": 26302, + "TAACTACA": 26303, + "CTGAAAAAAA": 26304, + "GCACCCTC": 26305, + "CCATCACA": 26306, + "TGTGTCTC": 26307, + "GAAATCTAA": 26308, + "CATGCCCCA": 26309, + "CATGTATGA": 26310, + "GTCACCCC": 26311, + "TATGGTC": 26312, + "CTCCCTCAA": 26313, + "TAGACAGTG": 26314, + "CCCCCTCTT": 26315, + "CATAGTTAA": 26316, + "TTCAGTCC": 26317, + "CGCTCATT": 26318, + "GAGAAGGTG": 26319, + "CGCACCTG": 26320, + "GGAGGCTA": 26321, + "AAATTCTA": 26322, + "TCATGCTGA": 26323, + "CAATGCATT": 26324, + "GACCACGA": 26325, + "GGCAATTTT": 26326, + "TAAGATCAA": 26327, + "TTATTTTTTTA": 26328, + "CAAGACAAAA": 26329, + "TCATTATTAA": 26330, + "GTAGGGGAGG": 26331, + "TGACCCTGA": 26332, + "GTCCATAGG": 26333, + "TAAGTAGGAAAA": 26334, + "GTTGTGTTA": 26335, + "GTCATGAAAA": 26336, + "TCGCACCAA": 26337, + "TGAGATGGAGTCTT": 26338, + "TCTCCCTCGATCTG": 26339, + "GGGTTAGGGTTAGGGTTAGGGTTA": 26340, + "TAGAGAGAGA": 26341, + "TCTGTGTGTGTGTG": 26342, + "TAACCCTC": 26343, + "TCATCACC": 26344, + "TGACAGATT": 26345, + "GAATTTTTG": 26346, + "CTTGCTTTG": 26347, + "GTTGTATTTT": 26348, + "CCACGCCCGG": 26349, + "CTCGCTCC": 26350, + "CACAGCAAAA": 26351, + "TCTGTAGGA": 26352, + "TATGCGTT": 26353, + "CGGCTTTT": 26354, + "GATGGGGTT": 26355, + "GGTTĊ": 26356, + "GCTACCTCA": 26357, + "TAATTTCTCA": 26358, + "TTATTTCTAA": 26359, + "TCTCATGTA": 26360, + "CGCTCCAA": 26361, + "CTCAACTG": 26362, + "CAGCTGATT": 26363, + "TTTTAATTTTA": 26364, + "GGATTCTCA": 26365, + "CCCTTCTCA": 26366, + "GGATGGATT": 26367, + "GCATGGCTG": 26368, + "CCAGGCATT": 26369, + "CCAGGGCTT": 26370, + "TCGGTGTT": 26371, + "AAATAGGAA": 26372, + "TCGATCAA": 26373, + "CAATAGTTA": 26374, + "TCCAGAGTGGTT": 26375, + "GTAAAGAATCCACCTG": 26376, + "CCTCTCTA": 26377, + "TCATAAATG": 26378, + "TTACACC": 26379, + "GAACCCTA": 26380, + "CTGGCAAAA": 26381, + "GTTCTGGA": 26382, + "GCATCAGTT": 26383, + "GTCCAGATT": 26384, + "TCTGGATTA": 26385, + "CTGATACTT": 26386, + "CATGGCTGA": 26387, + "CATGTATCA": 26388, + "GTCACTGTT": 26389, + "GTAATGGAA": 26390, + "GAGGGAAGG": 26391, + "TCTCGTTA": 26392, + "TCCATCCTG": 26393, + "TGCATCTA": 26394, + "GGGACTGAA": 26395, + "GGAAATATT": 26396, + "TGCCTCTGA": 26397, + "CAGCAGCC": 26398, + "TCTTAGGTCA": 26399, + "GTTTGTCAA": 26400, + "TGAGAGTA": 26401, + "GTCTGCAGG": 26402, + "CAATGGGAA": 26403, + "GCATGGCTT": 26404, + "CTAAAATTTT": 26405, + "CCAGGGGA": 26406, + "TATTTGCATT": 26407, + "CAACAACAAAAAA": 26408, + "CTTTATCCAA": 26409, + "TAACACTTTT": 26410, + "CAAGATATT": 26411, + "GTTGGTTTA": 26412, + "GATTAATTTT": 26413, + "TCCCCTCTT": 26414, + "CCAGCATGACAA": 26415, + "GAAGGAGAGA": 26416, + "GTTTGATTTT": 26417, + "TTAGGGTTAA": 26418, + "CAAACAACCCCA": 26419, + "GAACCCGGGAGGCGGAGGTT": 26420, + "CGGGGTTTCACCGTGTTAGCCAGGATGGTCTCGATCTCCTGACCTCGTGA": 26421, + "CAATGACAA": 26422, + "GCATGTGTG": 26423, + "CACAGAATG": 26424, + "TCTTTAGCA": 26425, + "TCTGGGGTG": 26426, + "GTGAGGCAA": 26427, + "CTCAGATAA": 26428, + "GTCACCATT": 26429, + "GTCATACCA": 26430, + "GTAAGAGCA": 26431, + "GTAACATTTT": 26432, + "TCTCGCTG": 26433, + "CTCCCCCAA": 26434, + "GGGGGCCA": 26435, + "CCCACTCTT": 26436, + "GGTGGCCA": 26437, + "CGCGGGA": 26438, + "GACCTATG": 26439, + "CTCTGTCTA": 26440, + "GCCTTCTA": 26441, + "CCTCAGGA": 26442, + "GCTCCTGAA": 26443, + "GACTATGCC": 26444, + "TCTCTCTCTCTCTG": 26445, + "GGGGAGAGA": 26446, + "CAAGGTTTG": 26447, + "TAATTTTTTTTTT": 26448, + "CAAGACAGG": 26449, + "CTTGAGCTT": 26450, + "GTTCCAGAAAAA": 26451, + "TCTCTTAATT": 26452, + "CTGTAGCCTTGTAGTATAGTT": 26453, + "CAGCCCCAAAGCATGA": 26454, + "GTACTGGAGTTTCAGCTTTAGCA": 26455, + "GTGTGACTGTTTGAGGTTGTGGA": 26456, + "CCGAGGAA": 26457, + "TCTAAAAATG": 26458, + "GACGCAAA": 26459, + "TAACTGATT": 26460, + "TTACAATT": 26461, + "TCCTCAGTT": 26462, + "CTTGTCATT": 26463, + "CTTGTTTAA": 26464, + "TAAAGAGTG": 26465, + "TAAACAGCTT": 26466, + "CACATAGTA": 26467, + "GAAACTCTT": 26468, + "GAAACACTA": 26469, + "CATTCAGGA": 26470, + "TCTGCATAA": 26471, + "CCTTCATTA": 26472, + "TCTCTCTGAA": 26473, + "TCCATACATGA": 26474, + "TAGACTGAA": 26475, + "GGTTTCTGA": 26476, + "CGCGGTA": 26477, + "GTAAATTTTA": 26478, + "TAAAAACTAA": 26479, + "GGAAACCAA": 26480, + "TAATAGTG": 26481, + "CTCTTGCC": 26482, + "CAGTTTGTG": 26483, + "GGATTTGTG": 26484, + "TGAGGCAA": 26485, + "CAATGTCAA": 26486, + "GCAGGCCAA": 26487, + "GATTTTTTTA": 26488, + "CATCCTTTG": 26489, + "TCAGGTGGG": 26490, + "TAGGAGAGG": 26491, + "TAATTATTTTA": 26492, + "CTGCAGTCC": 26493, + "TCGATGAA": 26494, + "CAAATGGAAGAA": 26495, + "CTCAAACTA": 26496, + "CAATACCAA": 26497, + "TGACATATA": 26498, + "TGACCTCTT": 26499, + "CTCATGGATT": 26500, + "CCGCTTTT": 26501, + "TAGGGGTG": 26502, + "TCGCAGGA": 26503, + "CCAGCAGA": 26504, + "TGTGTATCA": 26505, + "TATTGTATG": 26506, + "CAAATATTAA": 26507, + "CAAACCCAA": 26508, + "CATTGGCC": 26509, + "TCTGTCTCTT": 26510, + "TCTGGGGTT": 26511, + "CAGACGCC": 26512, + "TCAACAGCC": 26513, + "GCAACACTT": 26514, + "CCTGGAGCC": 26515, + "CCCACGAA": 26516, + "CATATCATT": 26517, + "CGCGCTA": 26518, + "TCTCCCTTA": 26519, + "CGTGCTGA": 26520, + "GCAAACATG": 26521, + "GTGAATATT": 26522, + "GCCTGCCAA": 26523, + "CTGGGGCC": 26524, + "TCGACGTT": 26525, + "GTTTAGGAA": 26526, + "CAAGATGTT": 26527, + "GCCCCGCC": 26528, + "GTGTATTTTA": 26529, + "TCACAACAA": 26530, + "GAACGAAA": 26531, + "GCAGTTGTG": 26532, + "CCACTTTCA": 26533, + "GAAAGGTGA": 26534, + "TATACATGTG": 26535, + "CAAAGTCTT": 26536, + "GAGACACTT": 26537, + "GTGACACTT": 26538, + "GCTGGCTCA": 26539, + "TATGTCCTT": 26540, + "TCTACCCTT": 26541, + "CTTTTTAATGA": 26542, + "CGAGTAAA": 26543, + "GACACCTA": 26544, + "CATACGTG": 26545, + "TAATTTAAATT": 26546, + "GATAGCC": 26547, + "CCAAACCTG": 26548, + "GCCAAGAGAA": 26549, + "GGAGGGCTT": 26550, + "GCTCGGCA": 26551, + "GATGATTTG": 26552, + "GCAGGGGA": 26553, + "GGCAAGAGAAA": 26554, + "CTTGGCTA": 26555, + "CTTGGTTAA": 26556, + "GTTTAGAGA": 26557, + "CTCTACCTT": 26558, + "CTCTACCTG": 26559, + "CCGGTTTG": 26560, + "TAAATACATG": 26561, + "GTAGGCAGA": 26562, + "CGTATTAAA": 26563, + "GAGTCCAAA": 26564, + "GCTCTCATG": 26565, + "CACGTTAA": 26566, + "TAACTTCC": 26567, + "TAACGTTTT": 26568, + "TCATCCAAA": 26569, + "TGACTATA": 26570, + 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26614, + "GCTCTAGGAA": 26615, + "CCCCAAATTTA": 26616, + "TGACACAGGTCAAA": 26617, + "TGAGATCTTGCATGGA": 26618, + "GAGGAATGGGCCAAAATACCA": 26619, + "TCTTGCTTGTTTGTAGGTGACCAAA": 26620, + "GATTTTAAAAA": 26621, + "TCACGCTG": 26622, + "TCAGCAGAA": 26623, + "TCCTCCATG": 26624, + "TCCTGAGTG": 26625, + "GTTTATTTA": 26626, + "CCAGTTCTT": 26627, + "GCCTCACAA": 26628, + "CTCGACAA": 26629, + "TATTGGCC": 26630, + "CATTAAAATT": 26631, + "CATTCTAATT": 26632, + "TCTGGACAA": 26633, + "TCTGGACCA": 26634, + "CAGAGGTAA": 26635, + "TCAAGTGTT": 26636, + "TAAAACTTAA": 26637, + "CTGAGTCTT": 26638, + "CTAACCCC": 26639, + "GTTTTAAAAAA": 26640, + "GGTTGTCC": 26641, + "GGTGTCCA": 26642, + "CGTGGTGAAA": 26643, + "GGAAACACA": 26644, + "TGAAACAA": 26645, + "CGAAGGAA": 26646, + "TCTTTTCTTCTT": 26647, + "CCCTGGAGA": 26648, + "GTCTGTAGA": 26649, + "GCTAAGGTG": 26650, + "TATGAGTGA": 26651, + "GATCAGGAA": 26652, + "GTTTTATTTTA": 26653, + "CTTGGCAGG": 26654, + "TCAGACTCA": 26655, + "CAATACGAA": 26656, + "TGATATTATT": 26657, + "CCGGGATT": 26658, + "TCCCCCTCA": 26659, + "GTTCTGTTTT": 26660, + "GCTATTAAAA": 26661, + "GAGGTGGGGG": 26662, + "TAATGCTATCCCTCC": 26663, + "CACTCTGATGGTAGTT": 26664, + "CTATGGGGTTGAGATCTG": 26665, + "CATCTGATT": 26666, + "GAATCAGG": 26667, + "TCCGGTA": 26668, + "CAAGAAGA": 26669, + "CTGCTTTCA": 26670, + "GTCTTACA": 26671, + "TATTCCTTTT": 26672, + "CACAGTGTT": 26673, + "TGAATCTCA": 26674, + "TGAATTTTTT": 26675, + "TAAAATAGTA": 26676, + "GTAAGTGTT": 26677, + "GTTACAATT": 26678, + "TACATCTC": 26679, + "CTTACAGTG": 26680, + "GGTTGCCA": 26681, + "TCTCCTCCTCC": 26682, + "CGTGGATT": 26683, + "TCCTTGGAA": 26684, + "GAATTGTG": 26685, + "GAATTGAGA": 26686, + "CTGTTTAAA": 26687, + "GTTAACTA": 26688, + "GACCCTGAA": 26689, + "CGCACATG": 26690, + "GTATTCGA": 26691, + "CGGAGATT": 26692, + "CAAAAATATG": 26693, + "CTCTGGTGA": 26694, + "CAGGAGGTG": 26695, + "GAGTGTATG": 26696, + "TAGGATTTG": 26697, + "TCTGTTAAAA": 26698, + "GAATACTAA": 26699, + "CTTCATTTTA": 26700, + "TGATAGATG": 26701, + "GTGATTAAAA": 26702, + "CCGGCGCC": 26703, + "CTTGAAGAGA": 26704, + "TGATCAGTCTATAA": 26705, + "GGCTAAAAA": 26706, + "TCAGATTAA": 26707, + "CTGTCTGTG": 26708, + "GTTTGATTA": 26709, + "GCATAAATT": 26710, + "CCACCTTTT": 26711, + "GTACTAAAA": 26712, + "GTAGTTTTTT": 26713, + "TGAAGACTA": 26714, + "TGAAGGAAAA": 26715, + "TAAAAGCC": 26716, + "TAAAAGACAA": 26717, + "GCTGTCATT": 26718, + "GAAAACCTA": 26719, + "GTCCGCC": 26720, + "CTTTTĊ": 26721, + "GACAGTCAA": 26722, + "GGCAGTC": 26723, + "CTCTTGATT": 26724, + "TAATAATAATT": 26725, + "TATGATATA": 26726, + "GGGCTCTA": 26727, + "GAATAGAAAA": 26728, + "GTCTCCCAAA": 26729, + "CCAGTACCATGCTGTTTTGGTTA": 26730, + "TCCTTTGTAGGGACATG": 26731, + "AGAAA": 26732, + "CCGCAGCC": 26733, + "GGATAAATT": 26734, + "GGACTGCAA": 26735, + "GGACTTCCA": 26736, + "CCAGAAGA": 26737, + "CCACTTCC": 26738, + "TCTTGGGCA": 26739, + "TGTTCTCAA": 26740, + "CATTTAAATT": 26741, + "TCTGTCTGTG": 26742, + "CAGAGCTC": 26743, + "GCTTTACTG": 26744, + "CATGCGCCA": 26745, + "GCAAGATGA": 26746, + "GTTATCTGA": 26747, + "GCCAGAATG": 26748, + "GCAAAGGCA": 26749, + "TAAAAATTATT": 26750, + "TAATAGCTG": 26751, + "GGTACAAAA": 26752, + "TGATTGTCA": 26753, + "GCTCATTTA": 26754, + "GCTGATAAGCAA": 26755, + "GTTAATGGG": 26756, + "TCTTTTTAAA": 26757, + "TATAATTATT": 26758, + "CTCTGGGAGTT": 26759, + "TGATGTAGG": 26760, + "TGATGGTGTT": 26761, + "CAGGACAGG": 26762, + "TATCCTTAA": 26763, + "GGGCTAGA": 26764, + "TCGACCTG": 26765, + "GAATACAAAA": 26766, + "GCGAGGAA": 26767, + "CTATATCTA": 26768, + "TAACTATTTT": 26769, + "GATATTAAAA": 26770, + "CCTTCCTTCCTT": 26771, + "CGAATTAAA": 26772, + "GAAATCAGTCTG": 26773, + "CTCCTGCCTCAGCCTCCCAAGTA": 26774, + "TCAGCATGACAATGATCCCAAACACA": 26775, + "CCCTTTTAA": 26776, + "GGGTTTTAA": 26777, + "TAACGGAA": 26778, + "TAATCTATG": 26779, + "TCAGCAGTG": 26780, + "CTGCCAGA": 26781, + "CTTTGACA": 26782, + "CTTTGATTTT": 26783, + "GTTTCCCA": 26784, + "GGACTTTTA": 26785, + "GTCGCTAA": 26786, + "TGTGTAGAA": 26787, + "TGAAGATCA": 26788, + "TCTGGTAAA": 26789, + "CTGAGAGTG": 26790, + "CCTTGGTCA": 26791, + "TCTCGAAAA": 26792, + "GTTAGGGTTA": 26793, + "GCCAGTCTT": 26794, + "GCCAGTGTG": 26795, + "TTTTTTGTTA": 26796, + "GTGAAGTGA": 26797, + "CACCTAAA": 26798, + "TCCAAGTCA": 26799, + "CAGAAGATG": 26800, + "GTTAAGAGA": 26801, + "TCTTTTCAAA": 26802, + "CAGCGCTG": 26803, + "GGCTGGAAAA": 26804, + "CGTATGTT": 26805, + "TCAAAATAAA": 26806, + "CCTCCCACCA": 26807, + "TTATGTAAA": 26808, + "GCGGGTGCCTG": 26809, + "TAGCATTTA": 26810, + "TATCCTTTA": 26811, + "GTTGGGGAA": 26812, + "CTCTAGTGAA": 26813, + "CCGGTTTT": 26814, + "GTTCCAGACCA": 26815, + "CATATTAAAA": 26816, + "CTCATGCCTG": 26817, + "GATGGATGGATG": 26818, + "GTATTGGAGTTTCAGCTT": 26819, + "GTTATTCTGTCTCTCACTGTTCAAATAAA": 26820, + "TAGTCAAAA": 26821, + "GACGCCAA": 26822, + "GCGAACTG": 26823, + "GCTCCAATT": 26824, + "TGAGATTAA": 26825, + "TCCTGTTTG": 26826, + "GTGCAAGG": 26827, + "GGAGTTAAA": 26828, + "GTACAAATT": 26829, + "GTCTTTTTA": 26830, + "CATTCTCCCAATCCTCTT": 26831, + "CTCACCAAA": 26832, + "CTCAGAGC": 26833, + "GTAACATAA": 26834, + "GTTACCCA": 26835, + "CTAACGCA": 26836, + "CTAATACTG": 26837, + "TACATCTCA": 26838, + "TACACGC": 26839, + "CTTACCCC": 26840, + "GTCCTCCTT": 26841, + "GGGGCGGA": 26842, + "CCCACCTGA": 26843, + "TAGATATGA": 26844, + "TGGATTTTA": 26845, + "CGTGCGA": 26846, + "TAAAAAGATA": 26847, + "GGAAACATT": 26848, + "TATTAGAAA": 26849, + "CAATTTAATT": 26850, + "CAAAAATGTT": 26851, + "TTTTAATATT": 26852, + "CATTTTTTTTTT": 26853, + "GCTCGGCC": 26854, + "GCATGCCTG": 26855, + "TGTGAATTTT": 26856, + "TAAGAGCTG": 26857, + "TCCCAGGGCTGA": 26858, + "CTTTTTCACTCTCC": 26859, + "GCTAATTTTACAGAAAATT": 26860, + "GGTGATGGACAGGGAGGCCTGGCGTGCTG": 26861, + "CAGGTCTGTGAGAGCCAGAAA": 26862, + "CCTAAAATG": 26863, + "TGACĊ": 26864, + "TGAGCTCTT": 26865, + "TATTGGTCA": 26866, + "TGTTGGCTA": 26867, + "TGAAGCTTA": 26868, + "CAGGCGCC": 26869, + "CCCCGAGG": 26870, + "GTAAACCAA": 26871, + "GGAAACTA": 26872, + "GGAAAGTGA": 26873, + "GCCTTGCTT": 26874, + "GGCTGGATG": 26875, + "TGAGATGAGTG": 26876, + "TTTTTTTTTAAA": 26877, + "GAGCAGTGA": 26878, + "GGATTTCA": 26879, + "AAATGTATG": 26880, + "CTGGGCTA": 26881, + "AAATAGATG": 26882, + "CTTCATATA": 26883, + "CTGTAGAAA": 26884, + "CTGGACAGG": 26885, + "TCAGAATTTT": 26886, + "TCGCTCTC": 26887, + "GGCGGCGG": 26888, + "TCCCATGGACAGA": 26889, + "GAGATGGGGTTTCA": 26890, + "GCTGCATATGTATCAAAA": 26891, + "TACCTACTGTGAAGCATG": 26892, + "CCTATTAAA": 26893, + "TCGAGGTG": 26894, + "CAACACATT": 26895, + "CTTCCTTAA": 26896, + "CTTTCCTCA": 26897, + "GTGTCCCCA": 26898, + "GCACTGCC": 26899, + "GGACCCTCA": 26900, + "CCATATATT": 26901, + "CTCGTGTT": 26902, + "TGTGTGATT": 26903, + "CAAAGAATG": 26904, + "TAAAACGTT": 26905, + "GCAATGCTG": 26906, + "TTTTATAGTT": 26907, + "TGGGTAAA": 26908, + "TGTATGTGTG": 26909, + "TCAAATATCA": 26910, + "GGGAGCTGA": 26911, + "TAAAAACTTA": 26912, + "CTCTTGTAA": 26913, + "TACAATTCA": 26914, + "TCATGTATT": 26915, + "CTTGGGAAA": 26916, + "CTGCATCTT": 26917, + "CCCTATTTT": 26918, + "AAATACAAAA": 26919, + "TCATAGCAAA": 26920, + "TCCCCTCTCC": 26921, + "CTTGACATT": 26922, + "TCTATACCTT": 26923, + "CCACTGGACCA": 26924, + "TCTATTTGCCATGAA": 26925, + "CCATCCAGCCATCTCATCCTCTGTCGTCC": 26926, + "TCTTCAAACCAAGCTGCTTACCTATT": 26927, + "TAACGTGA": 26928, + "TAAGTAGG": 26929, + "CAACCCCATGGA": 26930, + "CTTCAAATG": 26931, + "GTGGCAGGA": 26932, + "GCATCTGAA": 26933, + "GCTGCATCC": 26934, + "CCAAGCTCA": 26935, + "GTCCTAGAA": 26936, + "CCCCGGTG": 26937, + "CACCGCATGTT": 26938, + "TCTTAGCTT": 26939, + "TCTTACTTA": 26940, + "CTTTGTAGG": 26941, + "CAGTGTTTA": 26942, + "CCCTTCCTG": 26943, + "GCTAACTA": 26944, + "CAATGTATG": 26945, + "TATATATATATG": 26946, + "GATGACCTG": 26947, + "TAGCATTAA": 26948, + "CTTTTATTTTA": 26949, + "GAAGACCTT": 26950, + "CCTTTTTTTG": 26951, + "TAACATAGA": 26952, + "CAATACTAA": 26953, + "CTCTAGATT": 26954, + "GTGCAGCCA": 26955, + "TGTTTACATT": 26956, + "TCCGCCCACC": 26957, + "GCCAGGATGGTCTCAA": 26958, + "TAGTCTGA": 26959, + "TACTGAATT": 26960, + "GAATGTGTT": 26961, + "GAAGĊ": 26962, + "CTTGGAATT": 26963, + "CTTTCCCAA": 26964, + "CTCTCATTTT": 26965, + "TGTGCCTA": 26966, + "TCTTAAAATT": 26967, + "GCTGGGCAA": 26968, + "GTCATATTTT": 26969, + "TACATAGTG": 26970, + "CTTACACTG": 26971, + "CTTACAGGA": 26972, + "CTTAGACTT": 26973, + "CTTTTTCATT": 26974, + "TGGGCGA": 26975, + "TGCAGCTCA": 26976, + "TCTCATTTTA": 26977, + "CGCACGA": 26978, + "GTGTTCTAA": 26979, + "TCTTTTTGTT": 26980, + "CCATTCACA": 26981, + "GTTTGGCC": 26982, + "GGGCTATA": 26983, + "CTTTAGTGA": 26984, + "TCACCTCAA": 26985, + "TTATATAATT": 26986, + "CCAGACGG": 26987, + "GTTCCCAGA": 26988, + "GCTTTGTTA": 26989, + "GCCCACTGA": 26990, + "CGTTTTTTA": 26991, + "GAAATAGAAAA": 26992, + "GCGTCTTTTAATT": 26993, + "TCATATGGTCAGATGAAACCAAAA": 26994, + "GTCACAGAGTTGAA": 26995, + "ACAAA": 26996, + "CTTCTAATT": 26997, + "CCAGTTGTG": 26998, + "GTAGTGGTT": 26999, + "TATACTATT": 27000, + "TGAAGGCTG": 27001, + "TCTGCCTCTG": 27002, + "CTGAGCCTT": 27003, + "GCTGTCTGA": 27004, + "GCTGTACTT": 27005, + "CCTTGGTGA": 27006, + "CATGGTGAA": 27007, + "TCTCTCCCCA": 27008, + "CTAAGTCACTT": 27009, + "TCTACTCAA": 27010, + "GGGGTAAA": 27011, + "TGGGGAGAA": 27012, + "GACAGAATT": 27013, + "GGTGCATCC": 27014, + "CATAGTCC": 27015, + "CGTTCAACTCACAGA": 27016, + "GCTGATTAA": 27017, + "GTTAATGCA": 27018, + "GTCTTCAGG": 27019, + "GGAGGGTCA": 27020, + "TATAATGTA": 27021, + "CAAAAATCA": 27022, + "TTTTAAGAAA": 27023, + "TCATGCAGA": 27024, + "GCATGCACA": 27025, + "TTATACTTTT": 27026, + "GTTAAATTTT": 27027, + "CTGGACACA": 27028, + "GCATATTTTA": 27029, + "CTGATGTTTT": 27030, + "TAACTGTTA": 27031, + "CAGTCACTCA": 27032, + "CAACAACAACAACAACAACAACAA": 27033, + "CCGTGGTG": 27034, + "GGGTAATG": 27035, + "GAACTCAAA": 27036, + "GTTGCTATT": 27037, + "GCACGAGG": 27038, + "CCATCCACA": 27039, + "GTCTCTATG": 27040, + "TCTTGATCA": 27041, + "TCAACAGTA": 27042, + "GCTTGAGCC": 27043, + "GCTGGAGCC": 27044, + "GCAATACC": 27045, + "GAGGCCCA": 27046, + "CGGGGTA": 27047, + "TCCACGC": 27048, + "TGGAGTCC": 27049, + "TAATTGTTTATG": 27050, + 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"GTAAGACTT": 27093, + "TATGTTATG": 27094, + "GAGGCCAAA": 27095, + "CGGGAGCA": 27096, + "TCTACGTT": 27097, + "GACACGAA": 27098, + "CATACGCA": 27099, + "CTCTTCCCTT": 27100, + "CTCAACCTT": 27101, + "CAGCGGTG": 27102, + "CCCTGGTGGCTCA": 27103, + "GGCCATTTG": 27104, + "CATCAAAAAA": 27105, + "TCAGGGTAA": 27106, + "CTTTAGCAA": 27107, + "GCCAGGGACTCAAA": 27108, + "GAACATAAA": 27109, + "GTCCATGTT": 27110, + "GAGATGGAGA": 27111, + "CTCATGTCCA": 27112, + "GAGTGGGTTGCCATT": 27113, + "GGTCGTC": 27114, + "TCGCAATG": 27115, + "TAACTTTTAA": 27116, + "CTGTGTGTT": 27117, + "CTTTATATG": 27118, + "GTGGAGGTT": 27119, + "CTCTCTATG": 27120, + "TATTGGTTA": 27121, + "TATACACC": 27122, + "CAAATAATAA": 27123, + "TCTGGAGTG": 27124, + "TCAAGCTCA": 27125, + "GGAATACC": 27126, + "TTTTACATTTT": 27127, + "GTTATAGTA": 27128, + "CCAATAATT": 27129, + "TACATGATT": 27130, + "TCTAGTTAA": 27131, + "TAGGTGTGA": 27132, + "GGTTGTAAA": 27133, + "TGTACCTA": 27134, + "TTCACTTTT": 27135, + "CGTGGAAA": 27136, + "GGCCĊ": 27137, + "TGCCTAGA": 27138, + "TATTAGTAA": 27139, + "GTGTTTAAA": 27140, + "GCCTGGTCC": 27141, + "TGAGAGCTT": 27142, + "TATCAGTAA": 27143, + "GAAGATCAA": 27144, + "AAATACATG": 27145, + "TCCCACCCA": 27146, + "CAAGGGCAA": 27147, + "GAACACGA": 27148, + "CAACATGTT": 27149, + "CCACATGTT": 27150, + "CCAGATCGAGG": 27151, + "GTAGATGTT": 27152, + "CTTCTATTTT": 27153, + "CCGTGCCTG": 27154, + "GTTTGTTTGTT": 27155, + "GCACATGTATACATATG": 27156, + "GTGGTCCAGTG": 27157, + "TCAGTATAAAAGACACCTG": 27158, + "CTGTAAGTCTTCACAAGGTTTT": 27159, + "CCCACACACA": 27160, + "GCGTAAAAA": 27161, + "TCACTGCAA": 27162, + "TGAGCAGAA": 27163, + "CAAGCGCC": 27164, + "GCATCAGAA": 27165, + "GTACAGGAA": 27166, + "GTCTCATTTT": 27167, + "TGTGCATCA": 27168, + "TATTCGC": 27169, + "CACACTGTT": 27170, + "TCAATCATT": 27171, + "TCAAGCTGA": 27172, + "TCAACAATT": 27173, + "GTGACCAAA": 27174, + "GCAATATGA": 27175, + "GATGCCCTG": 27176, + "GTTTTGCC": 27177, + "TTTACCCA": 27178, + "TAGATTTTTT": 27179, + "CCCCTAGTG": 27180, + "TGGAGCCA": 27181, + "GTAAACTAA": 27182, + "TCCTTGTTAA": 27183, + "TCCAACATT": 27184, + "GCCTGGTGA": 27185, + "CAGTTTGAA": 27186, + "TGTGTGTGTGTT": 27187, + "CGTCTTAA": 27188, + "GCAGCAGCAA": 27189, + "GTTTAGCTT": 27190, + "CTTCACTAA": 27191, + "TCACTTAAAA": 27192, + "CTCTACTGA": 27193, + "CCACTGCATT": 27194, + "GTTTAATTAA": 27195, + "CAACTGAAAA": 27196, + "GGAGGTGAAA": 27197, + "GTCGGCCATCACTG": 27198, + "GTAGACCTGCACCAGGCTGGGAAGACTGAA": 27199, + "CACGTCAA": 27200, + "GGCTTTTAA": 27201, + "GACAGGCTG": 27202, + "TAACTCAAAA": 27203, + "TCACGCTT": 27204, + "CTTTCGA": 27205, + "GTGGCCAAA": 27206, + "GTGGAAGGA": 27207, + "GTTTCCCAA": 27208, + "GCACTTAAA": 27209, + "CTCTAACAA": 27210, + "GTCGTTAA": 27211, + "GTCGACAA": 27212, + "GTCGACTA": 27213, + "GTCAAAAAAA": 27214, + "GTCTCCTTA": 27215, + "GAAAGGGTA": 27216, + "TATATGATT": 27217, + "CAAAGGTAA": 27218, + "GGAATGCTG": 27219, + "TAAAATCAAAA": 27220, + "CTGAGCCAA": 27221, + "CTGAGATCTT": 27222, + "GCTGTCCTT": 27223, + "CATGCATGA": 27224, + "CGGTCGG": 27225, + "GATTTGCTG": 27226, + "CCAAGACTT": 27227, + "CCAAGATCA": 27228, + "GTTTTGAAAA": 27229, + "GACAGCATT": 27230, + "TCTCACATT": 27231, + "CTGAAGAAAA": 27232, + "GGAAATGCA": 27233, + "CAGCACAGG": 27234, + "GTATTCCATT": 27235, + "TATAATGTT": 27236, + "GCATTGATT": 27237, + "GCATTGTTTT": 27238, + "GTCTGGGGG": 27239, + "TGAGGGTAA": 27240, + "GTGTGGTCA": 27241, + "GCGGGAGA": 27242, + "TCTCTGCAGCA": 27243, + "GGAGATAGA": 27244, + "TGTGTGCAAA": 27245, + "CTTCCCGA": 27246, + "TAACACCTT": 27247, + "CAAGAATTA": 27248, + "GTACACATATG": 27249, + "GCGTCTGCTAAATG": 27250, + "GAATGCTGAGTTGCATCCAAAGAACACCA": 27251, + "CCAAAGTAACAAAGCCTACCATCAGTAA": 27252, + "CATTCTGTAGGTTGCCTGTT": 27253, + "CACGTCTG": 27254, + "GGCGCTA": 27255, + "CAACCAGG": 27256, + "GCATTTTAAA": 27257, + "CTGAGAATG": 27258, + "GCTTGACC": 27259, + "TATGGTCC": 27260, + "GTTATAAAAA": 27261, + "GATGGGCTG": 27262, + "TTTGTAATT": 27263, + "TTATTTCTTTT": 27264, + "TTTTTTCGTT": 27265, + "TCCTTTCAAA": 27266, + "CAGAAGATA": 27267, + "CAGCTCATT": 27268, + "TACAATGCA": 27269, + "CAGTTCATG": 27270, + "CACAACGTG": 27271, + "GGATTCGAA": 27272, + "TCATGGTGA": 27273, + "TAGTTTATT": 27274, + "TCGGGGA": 27275, + "GGAGACCTT": 27276, + "GTTTACGCCATT": 27277, + "CAAGGGAAA": 27278, + "CTTGAGTCA": 27279, + "TAACCCAAA": 27280, + "CTTCACAGTAGGTATGGTGTT": 27281, + "CCGGAAAA": 27282, + "CCGAACC": 27283, + "TCACGAAA": 27284, + "TCAGCCATT": 27285, + "GTGTGTTTG": 27286, + "GGAGTAGG": 27287, + "GCCCAAAAA": 27288, + "TGTGCTTAA": 27289, + "TCTTTATAAA": 27290, + "CATTGCTCA": 27291, + "TCTGCATGA": 27292, + "TAAAACAATT": 27293, + "GCTGGCCTT": 27294, + "CTCACCTCA": 27295, + "GCAAGATCA": 27296, + "TATGTATAAA": 27297, + "GCCATCCAA": 27298, + "GGTTCCCC": 27299, + "CTAAATATGTCAAA": 27300, + "TGCCĊ": 27301, + "TGATTGTT": 27302, + "TCTTACCTG": 27303, + "CGCAGTCA": 27304, + "CTCTGCTCTG": 27305, + "CTATTCCTG": 27306, + "GCCTGGCCAACATG": 27307, + "GTCTGGATG": 27308, + "CTCCATCTA": 27309, + "GCGACAGA": 27310, + "TGACACGG": 27311, + "CTGGACAAA": 27312, + "TTACATACA": 27313, + "TTACATAGA": 27314, + "GTTCCCCTT": 27315, + "CCTCCTTCCATCAGCAAGGGCATT": 27316, + "CAAAGCTGTCATCAA": 27317, + "TACGTGAA": 27318, + "TAAGTAAAAA": 27319, + "TCAGCTGAA": 27320, + "TGATAACAA": 27321, + "TCCCACATT": 27322, + "CTTTCCTGA": 27323, + "TGAAGAATG": 27324, + "CTGAGGAGTG": 27325, + "CTCACAGGA": 27326, + "CCTTTAATCC": 27327, + "GCAAGGCTT": 27328, + "CCTGTCCCTG": 27329, + "TCTCCAGCTT": 27330, + "CTTAGAGTT": 27331, + "TTAATATCA": 27332, + "TCTAGACTT": 27333, + "GGGGGGAAA": 27334, + "CGAGCAAAA": 27335, + "CAAAAGAATT": 27336, + "TTCATCCA": 27337, + "CAGCAGGTG": 27338, + "GCTGATTTA": 27339, + "CTTTGGTCTT": 27340, + "TCTAAGTA": 27341, + "CTATTGTG": 27342, + "TGAGATAAA": 27343, + "GATGACGG": 27344, + "GAAGGCATT": 27345, + "GCATGGAGA": 27346, + "GGCAAGATA": 27347, + "GAGAGAGAGAGAA": 27348, + "GCAGAGTAA": 27349, + "CTGTAGTCA": 27350, + "CAAGATGTG": 27351, + "CAACATTTTA": 27352, + "TCATAGTCA": 27353, + "TATTTAGAAA": 27354, + "TATTTACTGA": 27355, + "CTTGACACA": 27356, + "GTTCAAAAAA": 27357, + "TACCTTAAA": 27358, + "CCCCATCTCTACTAAAAA": 27359, + "TAAAAATTGATTTTTTGATAATAAA": 27360, + "TATTAATGGCACCTGTTTGAACTTGTTA": 27361, + "CCTAGGTG": 27362, + "TTATGATT": 27363, + "CAATTATA": 27364, + "CTGTAATTTT": 27365, + "GTGGTTAAA": 27366, + "GCCCCACCA": 27367, + "CTATTAATT": 27368, + "TATTGACTA": 27369, + "CAGGCAGCC": 27370, + "CAGGTGGAA": 27371, + "TCAAGAGTA": 27372, + "TCAAAAATGTTA": 27373, + "GTGAGTGTG": 27374, + "GCTGGAATT": 27375, + "CTCACAGCC": 27376, + "TATGGAATT": 27377, + "TACAGCTTA": 27378, + "TGGAGAAATG": 27379, + "TGCACTAAA": 27380, + "CTCTTGTA": 27381, + "GTATTCCAA": 27382, + "TACCTAATG": 27383, + "CGGATCAGTC": 27384, + "AAATTTAAAA": 27385, + "CAGGAGAAA": 27386, + "TGAGGTTTA": 27387, + "CAATGCCTT": 27388, + "TATCGTAA": 27389, + "CAATACTTAA": 27390, + "TCACAGCCA": 27391, + "GTATACAAA": 27392, + "CGACAGAGCGAGA": 27393, + "CCAGGCCTCCCTG": 27394, + "CCCGTCCCTG": 27395, + "GTTCAACCATTGTGGAA": 27396, + "TCATTTTTTTT": 27397, + "TCATGATGA": 27398, + "TCACTCTCC": 27399, + "TCATAATCA": 27400, + "TCCTCACAA": 27401, + "GTTGTAATT": 27402, + "GCACAAATT": 27403, + "CTATAGC": 27404, + "CTAGAGCTG": 27405, + "CTGATATGA": 27406, + "CTCATCCTT": 27407, + "TTTTACCTG": 27408, + "GTTAGGCA": 27409, + "TCTAGAGCA": 27410, + "CAAAATACTT": 27411, + "CCCCTCAAA": 27412, + "CGCGAGA": 27413, + "CTGAATGCA": 27414, + "TCCAAGTAA": 27415, + "TCTTACTAA": 27416, + "CGAAGGTT": 27417, + "TATAACTTTT": 27418, + "CTGTGTCTA": 27419, + "CCCCAGAGG": 27420, + "CATAAGAA": 27421, + "GAAGAAGAA": 27422, + "GTTTAGAAAA": 27423, + "CAAATGCTG": 27424, + "TGATATCTT": 27425, + "TCATTATTTG": 27426, + "GCTATACAGAGAAA": 27427, + "GTGGTTAAAA": 27428, + "TCTCTGTGAGTTCAAGGCCA": 27429, + "TCAACTGTGGGAGCAATTATTA": 27430, + "CTCTTACCTGCTTGATT": 27431, + "CACTTAATT": 27432, + "CCGTGCTG": 27433, + "TATCTCTA": 27434, + "GGCAAAATG": 27435, + "GATAAAATA": 27436, + "GCGAAGTT": 27437, + "TCCCGCTG": 27438, + "CAACGCTG": 27439, + "CTTGGAGGA": 27440, + "CTTTGAAAAA": 27441, + "GTTGAACA": 27442, + "GCATGTGAA": 27443, + "GCAGTTCA": 27444, + "CTATĊ": 27445, + "TAAAGAGC": 27446, + "CTCTTTTATT": 27447, + "TGTTGACAA": 27448, + "GAGACAGTA": 27449, + "GAGATAGAAA": 27450, + "GCTTCGTT": 27451, + "GCTGCTAAA": 27452, + "CTAACCAGTA": 27453, + "GATGGGGCA": 27454, + "GGTGGAGTG": 27455, + "GGTGGTGGTG": 27456, + "CCTACCCTT": 27457, + "TCTCAGGGA": 27458, + "CCAAACAAAA": 27459, + "AAAAAACAAAA": 27460, + "GAGAATTTA": 27461, + "GTCTTGGAA": 27462, + "CCATTCCCA": 27463, + "TCTAAGGAA": 27464, + "TTTTAATATA": 27465, + "TATTAATATTA": 27466, + "CATAATAAAA": 27467, + "TAGCACTC": 27468, + "CTGGGCAGA": 27469, + "TCAGGCTCC": 27470, + "TAAAAAATATA": 27471, + "GGGTGTTA": 27472, + "CTTTAGTAAA": 27473, + "TCATACATG": 27474, + "GCACATGAA": 27475, + "CTGAGAATGATGGTTTCCA": 27476, + "CAAAGTATTGAGAAA": 27477, + "GTGTGTGTGTGTGTG": 27478, + "GTTAAAAAAAA": 27479, + "CCAGTCCA": 27480, + "GCCGACTT": 27481, + "CAAACCCTT": 27482, + "CTGACGTG": 27483, + "GCTGTAGAA": 27484, + "GCAAGAGTG": 27485, + "GTAAGCCA": 27486, + "TATGCACTT": 27487, + "GAGGTCAAA": 27488, + "CCTGCTGTG": 27489, + "TACACCATG": 27490, + "TACATATGTG": 27491, + "GGTGTAGA": 27492, + "TGTAGCCA": 27493, + "TTATTGTTAA": 27494, + "TTCATCTA": 27495, + "GCAAAGGTG": 27496, + "TCATTĊ": 27497, + "GGAAATTTA": 27498, + "GCTCAGTCC": 27499, + "CTCTTCAGG": 27500, + "CAGAATATA": 27501, + "GTTAATAAAA": 27502, + "GCCAAGAGA": 27503, + "CAATTGAGA": 27504, + "TGATGCCTG": 27505, + "GCTCTAATT": 27506, + "GATGATTAA": 27507, + "GTGGGGACA": 27508, + "TCGGGGAA": 27509, + "GGGGATGGG": 27510, + "GGGCTCTGA": 27511, + "TCAGATCTT": 27512, + "GAAATGGAA": 27513, + "TCTTTGGGATT": 27514, + "TCGACCAA": 27515, + "TAACAGTTTT": 27516, + "GTTGGCAAA": 27517, + "CCACAGTTA": 27518, + "TATATATGTT": 27519, + "TCCCCATCAA": 27520, + "TAATGATTTT": 27521, + "GCGCCACTGCACTCCAGCCTGGG": 27522, + "CCTCTCCTG": 27523, + "TGATCTGTG": 27524, + "TCCTCTAAA": 27525, + "CTTCTCTTA": 27526, + "GTTGCAGA": 27527, + "GTTCAGATT": 27528, + "GCATAATAA": 27529, + "GCAGCGCA": 27530, + "CCATCTGTT": 27531, + "TATTCTAAAA": 27532, + "CTGACTGTT": 27533, + "CCTTGGCAA": 27534, + "TATGCGTG": 27535, + "TCTCTAATG": 27536, + "CAAAAGGTT": 27537, + "TAGGCTTTT": 27538, + "GCTACGTG": 27539, + "TTATTGTGAA": 27540, + "GCAAACCAA": 27541, + "TCCTTGTTA": 27542, + "TCTTATGAA": 27543, + "CGAAGGA": 27544, + "TATAAGTAA": 27545, + "CCATTAAAAAA": 27546, + "TCTAACTTA": 27547, + "CAAAAAGAAAA": 27548, + "GTTTGCGG": 27549, + "GTCTGGGA": 27550, + "TGAGGGTCA": 27551, + "TTATGCAA": 27552, + "GAAGGTGTG": 27553, + "GACCAGGTT": 27554, + "TTATTATTTTA": 27555, + "TCTGTTTCTCA": 27556, + "GTTCTGTTA": 27557, + "GTTGAATCACATG": 27558, + "GTGGCACGTG": 27559, + "TGTGTTTGGGATCATT": 27560, + "TACTTTTTTCCCTCA": 27561, + "CCTTGTGAAGACTTACAGAAAA": 27562, + "TGTGTCATTGCCAACAAA": 27563, + "GTTTCTCAATACTTTGTTATATA": 27564, + "CATAAAATA": 27565, + "CCGGAGA": 27566, + "TCGAAATTA": 27567, + "TCTATTAATT": 27568, + "TAACTGGA": 27569, + "TGACTCTGA": 27570, + "TCCCGGAA": 27571, + "TCCCAGGTT": 27572, + "GTTTGAAAAA": 27573, + "GCCCTAAAA": 27574, + "TGTGAAAAAA": 27575, + "CATTGACC": 27576, + "CATTGTTTTA": 27577, + "TCAACTGTT": 27578, + "TCAAGGCTT": 27579, + "CATGCCTCA": 27580, + "GCAAGGCAA": 27581, + "TATGGATTA": 27582, + "GATTCACC": 27583, + "TCTACGAA": 27584, + "GGGGCACCA": 27585, + "CAAAATCTC": 27586, + "TAGGTTTTTT": 27587, + "TAAAAACTCA": 27588, + "CTAAATACTT": 27589, + "TGATTCTTTT": 27590, + "TATAAGAA": 27591, + "CACTGGAA": 27592, + "CAGGAGCTG": 27593, + "TTTTTTTTAAAA": 27594, + "GGCCAGACTG": 27595, + "CCATGGACA": 27596, + "CAAGGTCAA": 27597, + "TGATAGAGA": 27598, + "GTACATGAA": 27599, + "GCGGATCACAAGGTCAGGAGATCGAGA": 27600, + "TTTTATGTTTT": 27601, + "CCGTTCC": 27602, + "TAAGCCACA": 27603, + "GTTGGAGA": 27604, + "GTTCTCTCA": 27605, + "GCAGTAGG": 27606, + "CCATAAATT": 27607, + "GCCTCCATT": 27608, + "CTCTGGGTG": 27609, + "TCTTGATAA": 27610, + "GCTTGCTA": 27611, + "CATGGTCTT": 27612, + "CATGGGGAA": 27613, + "CTAAGTTAA": 27614, + "GATGTCCTT": 27615, + "GTTTTCAGG": 27616, + "GTCCCGTG": 27617, + "CGATTTTTT": 27618, + "TCATTCAGG": 27619, + "GAGAAAAATT": 27620, + "GCATTCCAA": 27621, + "GTCTGGTCTG": 27622, + "GCGGGTGA": 27623, + "CATCCTTCA": 27624, + "TAATTATATA": 27625, + "CTCCAĊ": 27626, + "TCCATTTCTTCTA": 27627, + "CAATTACTTTT": 27628, + "TTTTTTATTTA": 27629, + "TAAGCAGAGTT": 27630, + "TATTTTATTTT": 27631, + "CTGTTTTTTCACGATTTATTA": 27632, + "CCTCTCAAA": 27633, + "TAGATGAAA": 27634, + "TCGAGATG": 27635, + "TCAGCGTG": 27636, + "TTACACCA": 27637, + "CTGCAGGAA": 27638, + "CTTGTTGAA": 27639, + "GTGCTTCC": 27640, + "GTGTCCCTG": 27641, + "GTTCCATA": 27642, + "GCAGAATA": 27643, + "GCACTTTCA": 27644, + "GCACCCAGG": 27645, + "GTAGTTAAA": 27646, + "CTATCTCTT": 27647, + "TAAATATATATA": 27648, + "TAAACCTGA": 27649, + "GTCTAATTA": 27650, + "CACATTTTTT": 27651, + "CACACATTTT": 27652, + "CATTGATAA": 27653, + "CAGGGGCCA": 27654, + "GCTTCCATT": 27655, + "GTCATGGAA": 27656, + "GTAACCCA": 27657, + "TCTCGGGG": 27658, + "CTCCCTGTG": 27659, + "CTAATCCAA": 27660, + "CTAAGAAAAA": 27661, + "TCCACCATT": 27662, + "GATGGACATT": 27663, + "TTAATACA": 27664, + "GTTTTTGCA": 27665, + "CTTTTGACA": 27666, + "CGAGTGAA": 27667, + "TAATTTAATA": 27668, + "CGCTGATG": 27669, + "GGTATTATT": 27670, + "AAAAAATTTA": 27671, + "GAGAAGCTG": 27672, + "CTGTTGAAA": 27673, + "CAGAAGTGA": 27674, + "TATTTTTATG": 27675, + "GTGTTCATG": 27676, + "CTCAAGATT": 27677, + "GGAGGCGA": 27678, + "TACCTAAAA": 27679, + "TACCCCCA": 27680, + "TAGTTTGTT": 27681, + "CAATGTATT": 27682, + "CTGCACAAA": 27683, + "AAATATTATT": 27684, + "TAACATTTTAA": 27685, + "CTCCTGCCTCAGCCTCCCAAGTAGCTGGGATTACAGG": 27686, + "GCCTTGAACTCACAGAGA": 27687, + "GCGCTGCTG": 27688, + "GCTTTTAAAAA": 27689, + "TAACCACA": 27690, + "TCACTCATT": 27691, + "TGAGTTCA": 27692, + "TCCTTATTTT": 27693, + "GCAGCGTT": 27694, + "GTAGAGATG": 27695, + "CTACTGTA": 27696, + "GTCGGAAA": 27697, + "GTCGTCAA": 27698, + "TGTGTGAATT": 27699, + "CACAGAGTA": 27700, + "TCTTCATC": 27701, + "TCTTTCCATT": 27702, + "TCTGCGCA": 27703, + "TCTGCAATT": 27704, + "GCTTGGGAA": 27705, + "CTAAGACTT": 27706, + "CTAATGGAA": 27707, + "TCTAGATAA": 27708, + "GTCCTTTTA": 27709, + "CAAAATAGAA": 27710, + "TGGGTGAAA": 27711, + "TAGATAGTA": 27712, + "GCTCACAGG": 27713, + "CAGCACCA": 27714, + "CTCTTTAAA": 27715, + "GACCTATA": 27716, + "GTATTGTCA": 27717, + "GTCAAGATT": 27718, + "GCATTGTAA": 27719, + "CCTCAGAAAATT": 27720, + "GAGTTTGAA": 27721, + "GCTAAGAA": 27722, + "TACTGTATG": 27723, + "GAAGGTTAA": 27724, + "GGTCACACA": 27725, + "GTGGGTGTT": 27726, + "GACTATTAA": 27727, + "TCACAGAAAA": 27728, + "TCCTCCTGCCTCA": 27729, + "GTGGAGTGA": 27730, + "GTATATTATT": 27731, + "CTGATGTTA": 27732, + "GATTTGTTA": 27733, + "CATAAATTA": 27734, + "GTGCTGAGATTA": 27735, + "GCTCTCCAAA": 27736, + "GCTGTTTTTCTGCAAA": 27737, + "CTCCTGCCTCAGCCTCCCGAGTAGCTGGGATTACAGG": 27738, + "TCGTTTTATGGGTTTT": 27739, + "CTGCACTCCAGCCTGGGCGACAGAGCGAGA": 27740, + "CCGCAGCA": 27741, + "GCGAACTT": 27742, + "TAACGAGA": 27743, + "TAAGTTGG": 27744, + "TCACGAGA": 27745, + "CAACCAGA": 27746, + "GCCGAGCC": 27747, + "GCCGCGC": 27748, + "CTACTTGG": 27749, + "CTCGATTTT": 27750, + "CTCTCGCC": 27751, + "GTCGGGC": 27752, + "GTCTTAAAAA": 27753, + "GAAATAGGA": 27754, + "GAAAGGGAAA": 27755, + "TGTTTCGG": 27756, + "GGAACACCA": 27757, + "GCTTTCTCC": 27758, + "CTCAGGCAA": 27759, + "CCTTGAGCA": 27760, + "CCTGGAATG": 27761, + "GTTAGTAAA": 27762, + "GTTATATGA": 27763, + "GATGTCAAA": 27764, + "GTTTTTGGA": 27765, + "GTCCTAAAA": 27766, + "CGAGCCAA": 27767, + "CCCAGCATG": 27768, + "TGGGTAGA": 27769, + "GCTATAAAAA": 27770, + "TCTCAGGTGA": 27771, + "GGTAĊ": 27772, + "CTCTTGTGA": 27773, + "GTGTTGAGA": 27774, + "TCTAAGATT": 27775, + "CAAAAATGTA": 27776, + "CTATTGCAA": 27777, + "TCAAAATATG": 27778, + "CCATGGCTG": 27779, + "GAAGGGATT": 27780, + "TGATACTGA": 27781, + "TAACCATTA": 27782, + "TCACTATGTT": 27783, + "GAATTTGCAAGTG": 27784, + "TTTATAACATTTT": 27785, + "CATTTGGATGATCCAGAA": 27786, + "GAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAA": 27787, + "GGCAGAGGAACCAGAGA": 27788, + "TTTTCTCAA": 27789, + "TCACATTTG": 27790, + 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"TCTCACGATACATG": 27835, + "CCGTGGA": 27836, + "TAACAGATT": 27837, + "TGACCCTC": 27838, + "TGAGTCAAA": 27839, + "CTTGCAGA": 27840, + "CTTCTATA": 27841, + "GTGTAATTA": 27842, + "GTGTCCCC": 27843, + "GCACTTTA": 27844, + "GCAGTCCTT": 27845, + "TAAACAGAAA": 27846, + "GTCTCGTG": 27847, + "TATACGTG": 27848, + "TATATCAAAA": 27849, + "TCTTCTTGG": 27850, + "CAAACCAAAA": 27851, + "GAGATGAGG": 27852, + "CAGGCATAA": 27853, + "CATGCATAA": 27854, + "CGGCATC": 27855, + "TCTCTGTGAA": 27856, + "CCAAGTAAA": 27857, + "CTAATGCC": 27858, + "GGGGTAAATTTG": 27859, + "CATAGTATT": 27860, + "GCTACTCTT": 27861, + "TGAAATGTA": 27862, + "TGATTGGA": 27863, + "GCCTGCTCC": 27864, + "TATCATATA": 27865, + "GTCTGCAA": 27866, + "TCATGGTTA": 27867, + "GTGTGTAGA": 27868, + "CCATGGTA": 27869, + "CCTGAGATG": 27870, + "CTGGGCTCC": 27871, + "CTCCACGCA": 27872, + "AAATAGTAA": 27873, + "TCAGACCTG": 27874, + "TAAGATATG": 27875, + "GCGACGCA": 27876, + "CAAGACTGA": 27877, + "TATTTACTTTT": 27878, + "CCACCCTGA": 27879, + "GAAGGAGTTACAGAGA": 27880, + "CATTTACTTA": 27881, + "CTTATGAAAA": 27882, + "GACCTGAAAA": 27883, + "GACTCTGTCTCAAAAAAAAAAAAAAAA": 27884, + "TATCGTGAGATTTT": 27885, + "CTGCTCTAGAGGAGATCTGCATG": 27886, + "CCTCTCATT": 27887, + "TGAGTGTA": 27888, + "TCCTAATTA": 27889, + "TCCTCTATT": 27890, + "GTTCAACAA": 27891, + "GCATAATTA": 27892, + "CCACĊ": 27893, + "CACAGTCTT": 27894, + "TATACTGTT": 27895, + "GGAACGCA": 27896, + "GCAACCCTG": 27897, + "GTCATCTCA": 27898, + "CGGTGGAA": 27899, + "TCCATAGTA": 27900, + "TCAAAGATAA": 27901, + "TAATTTCCAA": 27902, + "TTCATTATT": 27903, + "TTCACAATT": 27904, + "CAGAACAA": 27905, + "CTTTGGCTG": 27906, + "GTCAACAA": 27907, + "GCATTGTG": 27908, + "GAGCTCCTT": 27909, + "GACTTTGGG": 27910, + "GTGTGCTGA": 27911, + "CATGACTGA": 27912, + "GGCTTCCTG": 27913, + "TCTCTTGACC": 27914, + "GTGGGGGA": 27915, + "TATTTGTTTTA": 27916, + "AAATAGAAAA": 27917, + "CAGTCTTCA": 27918, + "GCAGACTGA": 27919, + "TAACATCAAA": 27920, + "TCTCAATTA": 27921, + "CTACAGCTG": 27922, + "GCCATGAAAA": 27923, + "GGCTCTTTG": 27924, + "GGAAATATCTTCGTA": 27925, + "GTCACCTACAAACAAGCAAGATTTCTG": 27926, + "TATATACCCAGTAATGGGATG": 27927, + "CACGTATT": 27928, + "TAGATTAAA": 27929, + "TAAGGACA": 27930, + "TTTTGACA": 27931, + "TGATAATTTT": 27932, + "GAACCAAAA": 27933, + "CTGCAATA": 27934, + "CTTGTTGA": 27935, + "GTGCTTTA": 27936, + "GTTTCAAAAA": 27937, + "GCCCTGCA": 27938, + "CTCGATTA": 27939, + "TGTTGCTTA": 27940, + "CAAATATCC": 27941, + "CATTTAAATG": 27942, + "TCTGTGAGG": 27943, + "TCTGTACCA": 27944, + "CAGATAGTT": 27945, + "GGAATAGGA": 27946, + "TAAAAGGGG": 27947, + "CATGGATGA": 27948, + "TTTTACATTA": 27949, + "TACATACATT": 27950, + "TTTAGGTG": 27951, + "TCTCATCAA": 27952, + "CGTGGCTGGGTCTT": 27953, + "GTGAAGGAAA": 27954, + "TGATTCAATTA": 27955, + "GCATTGCAA": 27956, + "CAGTTCTA": 27957, + "TGAGAGTTA": 27958, + "GTCTGCTGA": 27959, + "GAATGGAGG": 27960, + "TATTAATAAA": 27961, + "TTATGGTAA": 27962, + "TAAATTAAATT": 27963, + "GTGGGGTGGGGG": 27964, + "GTATGTATT": 27965, + "CTCCAGTTA": 27966, + "TCACCCGG": 27967, + "CCACCTCTT": 27968, + "CAGGTGGATT": 27969, + "GTATTATTAA": 27970, + "TTACCAGAAAACAA": 27971, + "GTGTTTGGAGGACAAA": 27972, + "TGAGGATTTCGTTGGAAACGG": 27973, + "CCGCTGTG": 27974, + "TCATGTTTG": 27975, + "CTTCTCCTG": 27976, + "CTTCCTTTG": 27977, + "GCCGCTGA": 27978, + "GCCCTTCTG": 27979, + "CTCTAATCA": 27980, + "TATTTGTTAA": 27981, + "CAGACCTGA": 27982, + "CTCACGTT": 27983, + "TCTCGTA": 27984, + "CTCCCGCA": 27985, + "CTCCCCCTCC": 27986, + "CTTATAATA": 27987, + "TCTAGACAA": 27988, + "TAGACACCA": 27989, + "CCCCTCCTG": 27990, + "GGTGTACA": 27991, + "CATAGGGG": 27992, + "TAATTTCCTT": 27993, + "GGGACCCTG": 27994, + "TCTCCCTCTT": 27995, + "GCAAACGG": 27996, + "CCAAACGA": 27997, + "TATTAGTTA": 27998, + "CAGCATGGG": 27999, + "GTTAACTTA": 28000, + "GGAGGCACA": 28001, + "CTGTGTAGG": 28002, + "GCCTTCTTTG": 28003, + "TGATGTCATG": 28004, + "GGATTAAATG": 28005, + "CTGGGGTCA": 28006, + "TCGCTCAAA": 28007, + "GTTTGAGACCAGCCTGGG": 28008, + "CCTGGCTGGAGTCA": 28009, + "ACACA": 28010, + "CCTCTCCAA": 28011, + "CAAGGAGGA": 28012, + "CTTGAAATG": 28013, + "CCACAAGAA": 28014, + "GCCCTCCTT": 28015, + "GTCTTATA": 28016, + "TGTGTAGTG": 28017, + "TCTTCGAA": 28018, + "TCTTGGTTA": 28019, + "CAGGCGTG": 28020, + "TCAATAAATG": 28021, + "GGAACTAAA": 28022, + "GCTTGTCTT": 28023, + "GCTTCAGTA": 28024, + "GTGACACCA": 28025, + "CTCATCATT": 28026, + "GCAATGTGA": 28027, + "GTCATACAA": 28028, + "CTCCTCATT": 28029, + "CTAAGCTA": 28030, + "GTCCCGTT": 28031, + "CTTTTTCCTT": 28032, + "CCAAATTAAA": 28033, + "GAGAACTG": 28034, + "CTGTGCGG": 28035, + "GCCTTGGCTG": 28036, + "TGATGCTCA": 28037, + "GGGAACAAAA": 28038, + "CCATGCCTT": 28039, + "TAGCTCATT": 28040, + "CTCTAGAAA": 28041, + "TGACCCAAAA": 28042, + "TGACTGAAAA": 28043, + "GATCAAAAATT": 28044, + "GACCTGGAGG": 28045, + "CTTTTTGGTAAAAACTCAA": 28046, + "CCCACTTTTTGATGGG": 28047, + "GCTCTCACAGACCTGTAACTTCTT": 28048, + "TCATCTTCAATGCCCTTGCTGATGGAA": 28049, + "TAAGCCAGG": 28050, + "CACACTGCTT": 28051, + "TCTTCTTTTA": 28052, + "GAGAGACCA": 28053, + "GAGAGAGTA": 28054, + "TCTGCTTAA": 28055, + "TCTGCATCC": 28056, + "TCTGGGTGA": 28057, + "CAGATCTGA": 28058, + "GGAACGAA": 28059, + "CATGGCTCA": 28060, + "GAAAACACTG": 28061, + "GTTTTTAGG": 28062, + "CGATATC": 28063, + "GCTATTTTTT": 28064, + "GTAAATGGG": 28065, + "TATTTTCCTT": 28066, + "CGCATTTA": 28067, + "GTGTTCCAA": 28068, + "CGGACGC": 28069, + "GCTAATTTTTG": 28070, + "GAAGACAAAA": 28071, + "GAATACAGG": 28072, + "GAACATATT": 28073, + "CCACACCCC": 28074, + "GTACAGTTA": 28075, + "GCTGATTAAA": 28076, + "GGAAGCACAGTT": 28077, + "GTACCTTCAGG": 28078, + "CGACCCCATGAA": 28079, + "TCAAAAAATCAATTTTTACACAA": 28080, + "TAAGCTAAA": 28081, + "TCATATATG": 28082, + "TCCTGTGTG": 28083, + "GTTCAATA": 28084, + "GCACTTCTT": 28085, + "GTCTCTACA": 28086, + "TGTGTCCTT": 28087, + "TGTGTAGTT": 28088, + "TATATCCAA": 28089, + "TCTTTAATG": 28090, + "TCTTGACAA": 28091, + "CATTTAGGA": 28092, + "CAGAGGCAGA": 28093, + "GAAAATGCC": 28094, + "TTTATCAAA": 28095, + "TTCAĊ": 28096, + "CTGAACCTG": 28097, + "GCAAACAGAA": 28098, + "TCTTAGGAA": 28099, + "GTATTGAAA": 28100, + "CTATTCAAA": 28101, + "CTATTTCAAA": 28102, + "GGATTTATG": 28103, + "TGAGGTGAA": 28104, + "GAATGCAGA": 28105, + "TATATATATTA": 28106, + "CCATGGATG": 28107, + "GGGTGCAGTG": 28108, + "TCGACATG": 28109, + "CAAGAGCTG": 28110, + "CCACCTCAA": 28111, + "GCATATGAA": 28112, + "TACAAATTA": 28113, + "GTATTTTGGCCCATTCCTCCATGCAGA": 28114, + "TAATGTGTG": 28115, + "TCATAAAAAA": 28116, + "GAAGTATA": 28117, + "CTTGCCTA": 28118, + "GTTGCCAAA": 28119, + "GGAGTGAAA": 28120, + "CCATGTGAA": 28121, + "GTAGCTC": 28122, + "CTAGTCTG": 28123, + "CACAGATTA": 28124, + "TGTTGATAA": 28125, + "TAAAATGCC": 28126, + "GCAATAAAAA": 28127, + "TACAGCATG": 28128, + "CTTACTGTT": 28129, + "CTTACCTCA": 28130, + "CTTATAGTT": 28131, + "TCTACTTTG": 28132, + "CAAAAGTG": 28133, + "TCAAAGAGG": 28134, + "TAATTCCAAA": 28135, + "GGCACGTT": 28136, + "GAATTCTTTT": 28137, + "TGATTTCTTTT": 28138, + "GAGAAGACA": 28139, + "CAATTCAAAA": 28140, + "CTCAATATT": 28141, + "CAAAAATAGAA": 28142, + "GCATTCTAA": 28143, + "GTTTGCAGG": 28144, + "CCCCAGAGA": 28145, + "GGATGCCAA": 28146, + "TTTTTGGGGG": 28147, + "GCAGAAGGAA": 28148, + "TTTTTATGAA": 28149, + "TGGGTTTGACGAAA": 28150, + "TCCTGAGGCC": 28151, + "GACTAAATT": 28152, + "GCGTTCTA": 28153, + "TTTTTAGAA": 28154, + "TCATGAGAA": 28155, + "TCACGTTA": 28156, + "CTGTAATTA": 28157, + "CTTCGAAA": 28158, + "CTTGTTTTTT": 28159, + "GCAGTCATT": 28160, + "GGACGCAA": 28161, + "GGACCTGTG": 28162, + "CCACAAATT": 28163, + "CCAGCTCCA": 28164, + "GTATCAGG": 28165, + "GCCGGGAA": 28166, + "GCCTGAGTT": 28167, + "GCCCCAGAA": 28168, + "GCCTCGCC": 28169, + "TAAAGGGTT": 28170, + "CTCGTCAA": 28171, + "GTCTAAGAA": 28172, + "TCTTTAAAAAA": 28173, + "TGTTCCTTTT": 28174, + "TCAATGCTG": 28175, + "GTCACGTA": 28176, + "GTAACTTAA": 28177, + "GGTTGATA": 28178, + "TAATTTAAAAAA": 28179, + "TCTGACAGG": 28180, + "CCATTGAA": 28181, + "CTGTGGACA": 28182, + "TTTTAAGAA": 28183, + "GAAGGGCTG": 28184, + "CATCTTTTTT": 28185, + "CAACAAGAGCAAAA": 28186, + "TCGACGC": 28187, + "CTTCCCCCA": 28188, + "GAATAGTTA": 28189, + "TGACTTAAAA": 28190, + "GGCTCCAAA": 28191, + "CCATGCCCGG": 28192, + "CCTGCTCCCCGCA": 28193, + "CTTTCTCTAGCTCCTCCATT": 28194, + "TTTTAAAATT": 28195, + "TGACTTTTTT": 28196, + "TTAGCAAA": 28197, + "CTTTGATTA": 28198, + "GTTCTCCTG": 28199, + "CCATAGC": 28200, + "GCCTCTAAA": 28201, + "GTCGATTA": 28202, + "GAAACAGCC": 28203, + "GAAATAATTA": 28204, + "TCTGCGTT": 28205, + "TAAAACAGCA": 28206, + "TAAAAGTTAA": 28207, + "GTAACACC": 28208, + "GTAAGACAA": 28209, + "TATGGTATT": 28210, + "CCTGGAGTG": 28211, + "GATGGAGTC": 28212, + "GGTTCTGTT": 28213, + "TGCACGA": 28214, + "GATACCAAA": 28215, + "CGTGGGAA": 28216, + "TGAAAGCAA": 28217, + "GCTGAGCCA": 28218, + "GACCCTGTT": 28219, + "GGCTGCCTG": 28220, + "CGTACAA": 28221, + "CCTCCTGGA": 28222, + "GAGTTGGTG": 28223, + "CAATGCACA": 28224, + "GCGGCTGA": 28225, + "CCATGCCAA": 28226, + "CTATGCCTG": 28227, + "GAGCCTTTG": 28228, + "CAAGGTGGG": 28229, + "TTATACTGA": 28230, + "GCTGTGAAAA": 28231, + "CGCTGCTG": 28232, + "CTCTACCAA": 28233, + "CCAGATGAA": 28234, + "GTTTTAATTTG": 28235, + "TATATTTTAAAA": 28236, + "CTCTGTGCGACCCCA": 28237, + "TGTTCTTCCATT": 28238, + "TACAGGTAACGAGTGGAGGA": 28239, + "TTTTGGAAA": 28240, + "TCAGGGGTT": 28241, + "TTATCATT": 28242, + "TCCGACTG": 28243, + "GTTGTGGTG": 28244, + "GTTTGAATG": 28245, + "GTAGATCC": 28246, + "GCCTGAATT": 28247, + "GCCTCACTT": 28248, + "TATATATCC": 28249, + "CAGGGCAAA": 28250, + "CTGACATGA": 28251, + "CTCACTCAA": 28252, + "CTCACATAA": 28253, + "CCTTGTGTT": 28254, + "GAGGTGCC": 28255, + "GAGGCATTTT": 28256, + "CTAACTGAA": 28257, + "CTTACTCTT": 28258, + "TCCATCTTA": 28259, + "TCCATCCTCC": 28260, + "TCTAGTCTT": 28261, + "GTCCCCTCA": 28262, + "GTCCAAATG": 28263, + "TCTCCCCCA": 28264, + "CTAAACTGA": 28265, + "CAAAAACACA": 28266, + "GAAAAATCAA": 28267, + "TAATGCTTTT": 28268, + "TCAAAATTTG": 28269, + "CACAACACAA": 28270, + "TACTGGAA": 28271, + "CTGGGTAATTTA": 28272, + "CTGCACCTT": 28273, + "GTTTAGCAA": 28274, + "CCACCACGCCCGG": 28275, + "CGTAAGAAGCATTTCAA": 28276, + "CAACACGA": 28277, + "GTGCCTGAA": 28278, + "GGCTCTGTG": 28279, + "CAGTTTTAAA": 28280, + "CCTACCATTAAAATTATAGACTGA": 28281, + "TCTTCCATTTCCTAATAATTGCTCCCACAGTTGATT": 28282, + "GGAAATGGAAGACATACAAGACCACTGATAA": 28283, + "CACGGAGG": 28284, + "GCGTAGAA": 28285, + "TGACGATG": 28286, + "TTACACTT": 28287, + "CTTCGATT": 28288, + "TAAACTTTG": 28289, + "CTCGGGTG": 28290, + "TATTTGAGA": 28291, + "CAAATCTC": 28292, + "GAGACTGCCA": 28293, + "TGAACAATT": 28294, + "TCTGGCTGA": 28295, + "GGAAGCTCA": 28296, + "GTGACCTA": 28297, + "GTGATATCA": 28298, + "CCTTCCAGAA": 28299, + "GTAAGTCC": 28300, + "TATGCCATT": 28301, + "CGGCGATG": 28302, + "CCAAGATGA": 28303, + "GAAAATGTGA": 28304, + "CAAAACTGAA": 28305, + "GGTTAAATT": 28306, + "CCCCTTTTA": 28307, + "TAATTCATAA": 28308, + "TCTGACCAA": 28309, + "TCCTTCCCTG": 28310, + "CTAAAGTCA": 28311, + "CTAAAGATT": 28312, + "GGTACCTA": 28313, + "TGATTTAAAAA": 28314, + "TCTTTTCTG": 28315, + "GCATTGAATCTA": 28316, + "TCAAAATATA": 28317, + "CAGTTTCAA": 28318, + "GAGCAGCAA": 28319, + "GAGTGGATT": 28320, + "GCTCCTCTT": 28321, + "CATGATAAAA": 28322, + "TACCACTA": 28323, + "CGTCTCTA": 28324, + "TATCGTA": 28325, + "TCTCTGTTTTGGTA": 28326, + "TAACAĊ": 28327, + "TGATAGTCC": 28328, + "GTCAAATTTT": 28329, + "TAAGGGAAA": 28330, + "CCGGCTCC": 28331, + "GTGGAGTCA": 28332, + "TCCCCCGA": 28333, + "TTTTTAGTAAA": 28334, + "TCAAAAAGTGGGCAAA": 28335, + "TCGTCAAACCCATGCTAAATATGTCAAA": 28336, + "GACGCCTT": 28337, + "GCGCAATG": 28338, + "TCACTTATT": 28339, + "TTACGAA": 28340, + "TCCGGCCA": 28341, + "CTGTATTAA": 28342, + "GTTGAAATG": 28343, + "GTTTATTAAA": 28344, + "GCACAACAA": 28345, + "GCATGACAA": 28346, + "CCACTTTTA": 28347, + "TGTGGGGAA": 28348, + "CACAGTCAA": 28349, + "CACACAGAAA": 28350, + "GAAAGGATG": 28351, + "TGTTGCTAA": 28352, + "CAAAGGAGTG": 28353, + "GAGAGGCTT": 28354, + "CAGGCTGTT": 28355, + "TAAAATAATTA": 28356, + "CTCATCTC": 28357, + "TTAATAAAAA": 28358, + "CAAAACCCA": 28359, + "TTATTCATA": 28360, + "CTGAATCAA": 28361, + "TTTTTTĊ": 28362, + "CCAAATGGA": 28363, + "TAATATTATTA": 28364, + "GAGAACAA": 28365, + "GCTGAGGCTT": 28366, + "TATTTTTGTA": 28367, + "GTGTTCGA": 28368, + "TCCTGGCAA": 28369, + "GAGCAGATG": 28370, + "TGAGGCAAA": 28371, + "TGAGGTCAA": 28372, + "CAACAGTAAA": 28373, + "TCATAGATT": 28374, + "TATTTAGTTA": 28375, + "GCCATTCTGACTG": 28376, + "GTGGACATG": 28377, + "GTAGGGAAAA": 28378, + "TTAGGTCCA": 28379, + "CTCGGGAGGCTGAGGCA": 28380, + "TCTCGGCTCACTGCAACCTCTGCCTCC": 28381, + "GCCAAAATTGACAAATGGGA": 28382, + "GAAATATCAATAACCTCAGATATG": 28383, + "CTTTTGTTATTGACCAAA": 28384, + "CAGAGGAATGGATACAGAAAA": 28385, + "CATCTCCTG": 28386, + "GGCGAAAA": 28387, + "GGGATGAAA": 28388, + "TTTTTCCAA": 28389, + "TGATAAAAAA": 28390, + "TGAGTTGTT": 28391, + "TGATCATCA": 28392, + "TCCCGTGA": 28393, + "GTGGCTTAA": 28394, + "GTTGCAGAA": 28395, + "GGACGTGA": 28396, + "GGACAAGGA": 28397, + "GCCGGTGA": 28398, + "TAAAGACACA": 28399, + "CTCGACCA": 28400, + "GTCTATTTG": 28401, + "TGTGGTAAA": 28402, + "TATACAATG": 28403, + "TCTTTAGTA": 28404, + "CATTCGAA": 28405, + "CAGAGCAGG": 28406, + "CCAAGGGCA": 28407, + "GACAGCATG": 28408, + "TCAAAGGAAA": 28409, + "GGGACC": 28410, + "GCAAACCAAA": 28411, + "GAATTCTCAGTAA": 28412, + "GAGAACTA": 28413, + "CTGTTGTTCTG": 28414, + "CTGTGTAAA": 28415, + "CATCACTAA": 28416, + "GAGCAGCTT": 28417, + "GGATTCCTG": 28418, + "GAATGGTGA": 28419, + "TAATAATATT": 28420, + "CAGCCTGCA": 28421, + "TTATGGATT": 28422, + "TAGCAGAGA": 28423, + "GCGATGAA": 28424, + "CCGGCCCC": 28425, + "GCAATTCGA": 28426, + "GCACTGCAGGATT": 28427, + "CTCACCGTTCTTGTGA": 28428, + "GTTCCTCTGCCTTTT": 28429, + "GGTAAATTA": 28430, + "GATAAAAATT": 28431, + "TAACCTGAAA": 28432, + "TTTTTATGA": 28433, + "TTAGTAAA": 28434, + "GAATGACTT": 28435, + "GTGCTGATT": 28436, + "GTTCCAGCA": 28437, + "CCACTTCTG": 28438, + "TCTTTATATT": 28439, + "TGTTGACTG": 28440, + "CATTTCTGTT": 28441, + "GTCATTTTAA": 28442, + "TCTCTAATA": 28443, + "TACATCTAA": 28444, + "GCCACTAAA": 28445, + "CGAGTTGATG": 28446, + "CAAAATCTA": 28447, + "GCTACAATT": 28448, + "GGAAATGA": 28449, + "GGAAATGTA": 28450, + "TCCTTGATG": 28451, + "GGCCCTTA": 28452, + "TATTATAAAA": 28453, + "GTGTTCACA": 28454, + "TCCTGGTCA": 28455, + "CAGCGAGG": 28456, + "GTTTGGAAAA": 28457, + "CATTATATT": 28458, + "TACTTCAA": 28459, + "TAGTTTCAGAA": 28460, + "CAACAGTTTT": 28461, + "CCACACCTT": 28462, + "TCATGACATTCTAA": 28463, + "GCCCGGGCGG": 28464, + "TAAAAAAAATG": 28465, + "TGAGACGGAGTCTCGCTCTG": 28466, + "GTTTTTACCAAAAA": 28467, + "TCCAGGCCCGTCTGAA": 28468, + "CACTCCAAACTCCACTATGGCCAAGACCAAA": 28469, + "TAGTGAATT": 28470, + "GCTTTTTTTTTTTT": 28471, + "TCACAAATG": 28472, + "CTTGTTCAA": 28473, + "GTGCAGGAA": 28474, + "GGACAAGAA": 28475, + "GTAGCCAGG": 28476, + "TAAACATATA": 28477, + "TATATCTC": 28478, + "GAGATGTGA": 28479, + "TGAAGATTA": 28480, + "CAGGCCTGA": 28481, + "CTCAGATCA": 28482, + "CTCCCGTT": 28483, + "TACAGTAAAA": 28484, + "CGACACTT": 28485, + "GGCAGTGAA": 28486, + "TAATAGCAAA": 28487, + "CTCTTGTTTT": 28488, + "GTATTTCAAA": 28489, + "GGAGGTAGA": 28490, + "CAAAAAGTTA": 28491, + "GAAAAATTTG": 28492, + "CATTTTTATT": 28493, + "TCAGGGCTT": 28494, + "GTATGGTA": 28495, + "GGAGATCTG": 28496, + "CCCTCCACA": 28497, + "TCACCCCCA": 28498, + "TAAGAGTCA": 28499, + "TCACAGTTTT": 28500, + "CACACACGG": 28501, + "GTTTCCTTTT": 28502, + "GGATTACAGGCATGAGCCA": 28503, + "CAGCATCAGTCCTT": 28504, + "GCAGGCAGATTCTTTACCA": 28505, + "TCCCAACCGTGAA": 28506, + "GGGACCAGGACGACTGA": 28507, + "CTTTAAGAGGCTCCTCTGTCCTCCA": 28508, + "TCTGCAATAGGTAAGCAGCTTGGTTTGAAGAAA": 28509, + "TGATCACAAGAACGGTGA": 28510, + "GTCATGCTGAAAGACCCAGCCACGTT": 28511, + "TGGGTTTGACGAAATTAGGA": 28512, + "GCGAAATA": 28513, + "TAATGTTTA": 28514, + "TCCTTATA": 28515, + "TCCCTCCTG": 28516, + "CTTCTGCAA": 28517, + "GTTCTAATT": 28518, + "GTTGCTAAA": 28519, + "GGATĊ": 28520, + "TAAAGAAGG": 28521, + "TGTGGATTA": 28522, + "GAAAGAGC": 28523, + "CTGAGGTTA": 28524, + "GCTGTACCA": 28525, + "TTTTAGGTG": 28526, + "GAGGGGGCA": 28527, + "GTTATACC": 28528, + "CTCCTGATT": 28529, + "TCCAGTCTG": 28530, + "GATGTATTA": 28531, + "CCCCCCAAAA": 28532, + "CATAGAATT": 28533, + "GCTACGAA": 28534, + "CGCTCGCC": 28535, + "TTTTTTGCTT": 28536, + "TTTTTTGAAAA": 28537, + "TAAAAATGGA": 28538, + "TGAAAGAGA": 28539, + "CCATTCAAA": 28540, + "CCATTGTCA": 28541, + "GCATTCAA": 28542, + "GCATTTAAAA": 28543, + "GGCTGCGG": 28544, + "GGCTGTTTG": 28545, + "TTTTTTTTTTTTTTA": 28546, + "TGAGGGCAA": 28547, + "TATATATATATAA": 28548, + "GACTGTAGCC": 28549, + "CCTCGGTT": 28550, + "CTCCATCTCC": 28551, + "CTTCACGG": 28552, + "TAACATCTA": 28553, + "CTGTACACA": 28554, + "GCGATGTT": 28555, + "CAATAGATT": 28556, + "CCCAGGCTGGAGTGCAGTG": 28557, + "GAGTCGGACACAA": 28558, + "GTGCAGCTT": 28559, + "TGATTTTAAAA": 28560, + "CCTCCACCTCCCGG": 28561, + "TCTTCACAGAATT": 28562, + "CTATCTAGGTTGGTCA": 28563, + "GAGACGGAGTCTCGCTCTG": 28564, + "GCAGACGCTTTTATCCAAA": 28565, + "TCTTTCACTTTCATCAA": 28566, + "TAGGAACACTTTTACACTGTTGGTG": 28567, + "CATCTCTGA": 28568, + "TAATTTTATG": 28569, + "TGATCTGAA": 28570, + "TTAGGGAAA": 28571, + "GAAGGAATT": 28572, + "TCCAAAAAAA": 28573, + "TCCTCGTG": 28574, + "GGACCCAGG": 28575, + "CACATCTGA": 28576, + "CAGGTGATT": 28577, + "GTGAGGTGA": 28578, + "CTCATATAA": 28579, + "CCTTCAGCC": 28580, + "TATGCTGAA": 28581, + "TATGAAATA": 28582, + "GAGGTGTA": 28583, + "CCAACATTA": 28584, + "TACATATATA": 28585, + "CTTACGAA": 28586, + "TCCACCTCTG": 28587, + "GGGGTGCC": 28588, + "TGGGCTCC": 28589, + "CCCCCGAA": 28590, + "TAATTCAGAA": 28591, + "TTCATACA": 28592, + "GATACAATT": 28593, + "TTTTTTTAAATT": 28594, + "GCAAACTGAA": 28595, + "GCTCACTGA": 28596, + "GAGAATGTG": 28597, + "CTTAAGCAA": 28598, + "GTTTGGCAA": 28599, + "TGATGCTGCCA": 28600, + "CAATGGAGA": 28601, + "CAGTATGATATT": 28602, + "CCATGGTG": 28603, + "GGGCĊ": 28604, + "CTTTACAAAA": 28605, + "CTTCCTAGA": 28606, + "CTTCACTTTT": 28607, + "TCCTCCAAAA": 28608, + "CAAGATCAA": 28609, + "TAAGGCAGA": 28610, + "GTTGACATT": 28611, + "GCCCAGCCTG": 28612, + "CACACATGTG": 28613, + "CTCTTAACCACTGA": 28614, + "GCTATTATAAATAAGGCTG": 28615, + "TACTTTAACGAGA": 28616, + "GCAGATTCAGTCTTCCCAGCCTGGTGCAGG": 28617, + "CCGAGAAA": 28618, + "CCGCTCAA": 28619, + "TTTTTCATT": 28620, + "TGACGCTG": 28621, + "TGAGGATA": 28622, + "GAATAAATG": 28623, + "CAAGTTATT": 28624, + "GTTCTTTTTT": 28625, + "CTCGTATT": 28626, + "GTCCATTAA": 28627, + "TCTTGGCAA": 28628, + "TGTTCGTA": 28629, + "CAAAGACAAA": 28630, + "CATTCCTTA": 28631, + "GCTGTACTG": 28632, + "GTAACAGTA": 28633, + "CTAAGATAA": 28634, + "GCCATATCA": 28635, + "CGATAGAA": 28636, + "TGAAATGTG": 28637, + "GTGTTTCTCA": 28638, + "GTCTTTAAA": 28639, + "TATAAGAATG": 28640, + "TCTAAGTTA": 28641, + "CTCTGTAAA": 28642, + "GTTTGGTCA": 28643, + "TGATGCACA": 28644, + "TGAGAĊ": 28645, + "GAGCGGTT": 28646, + "GACTTCTCA": 28647, + "GATGAGAAA": 28648, + "CCAGGCCCA": 28649, + "TATAAATTTA": 28650, + "TCGACATT": 28651, + "CAAGGCCTG": 28652, + "CAAGGCCCA": 28653, + "TCACCCCTG": 28654, + "TGACACTCA": 28655, + "TGACACTAA": 28656, + "CTCCTTCCTT": 28657, + "CCCCCACCCCA": 28658, + "CAACTCCGTTTATGAAA": 28659, + "CCAAAAAATG": 28660, + "TACTCTAA": 28661, + "TAAGAGATT": 28662, + "TAACCACAA": 28663, + "TTTTTTAAAAA": 28664, + "TCAGTATA": 28665, + "GAATGAGAA": 28666, + "GTGTCAGA": 28667, + "GCACTTGA": 28668, + "GGATAGC": 28669, + "CTATAAATG": 28670, + "CTCTTAATG": 28671, + "GAAAGGTCA": 28672, + "TGAACAGTG": 28673, + "CCTTGAGGA": 28674, + "CATGCAGTA": 28675, + "GCAACCTA": 28676, + "GTAAGAATT": 28677, + "TATGCAAATT": 28678, + "CCAAGGCTA": 28679, + "TCTACTGTT": 28680, + "GCCACGTA": 28681, + "GGGGGTGGGGG": 28682, + "CATACTGTT": 28683, + "GGCACTGAA": 28684, + "GATATTTTCTT": 28685, + "TCTGATATT": 28686, + "TCTGATGGA": 28687, + "TCATTGGTG": 28688, + "GGAAAGGGAA": 28689, + "CGTTCGA": 28690, + "GTGAATGGA": 28691, + "CACCCGCC": 28692, + "TGATTGCCATT": 28693, + "GCTCACCTG": 28694, + "GACCCAAAA": 28695, + "CAGTTTGTT": 28696, + "CCCCACCTG": 28697, + "CCCTGGGAA": 28698, + "CCTCCCTGA": 28699, + "TGAGGGCTT": 28700, + "CAATGGTA": 28701, + "CCTCTTTTTT": 28702, + "TCGGCTCC": 28703, + "TCAGGTTAA": 28704, + "TCTGTGGAGG": 28705, + "GTGCTGGAGAA": 28706, + "TCATACAGG": 28707, + "CCGGCCTG": 28708, + "CTACAGTTA": 28709, + "CTCTCCTTA": 28710, + "GTTTCTCTGTGTA": 28711, + "CACATCTTAAACCAATTTATTTT": 28712, + "TAAAAAAAAAAAAAA": 28713, + "TCCGAAAA": 28714, + "CAATAAGAA": 28715, + "CTTGAATA": 28716, + "CTTTCTATG": 28717, + "GCAGTTATT": 28718, + "GCATTATA": 28719, + "GCACĊ": 28720, + "CCAGTCTGTG": 28721, + "CTATTTTTA": 28722, + "GTCGACTG": 28723, + "TCTTTGAGTT": 28724, + "GTGAGGCCA": 28725, + "TCTCTCCCTCC": 28726, + "GTTTTTTTTTTTTTT": 28727, + "TCTAGGCC": 28728, + "TCTATCTAA": 28729, + "GCCACCATT": 28730, + "CTTTTGAAAAA": 28731, + "GACAGAATG": 28732, + "GGTGCCCA": 28733, + "TAATTGTAAA": 28734, + "GGCAGCCTT": 28735, + "CCAAAGCTT": 28736, + "GCTCACGG": 28737, + "CAGCATATTAAAAA": 28738, + "GTGTTCAAA": 28739, + "CAGTGCCTT": 28740, + "TCCTGCGG": 28741, + "CTCTGTATA": 28742, + "GCATTGGTT": 28743, + "TAATGGAGG": 28744, + "GTTTGAAAATTTATGTGAGGTT": 28745, + "AAATTTATA": 28746, + "TCAAAATGAA": 28747, + "CACTGGGA": 28748, + "CACCATTTG": 28749, + "TAGTTTATA": 28750, + "TTATGGTCA": 28751, + "CTTGGTTTA": 28752, + "TCATATCTT": 28753, + "GCACAGCTA": 28754, + "GTTCTGAAAA": 28755, + "GACTCTGCA": 28756, + "GGCGGAAA": 28757, + "CACCTCAGCCTCCCAAA": 28758, + "CTCAAAATTCTCCAA": 28759, + "GTTCATATTGTTGTT": 28760, + "CCCGAAAA": 28761, + "TAACTGCTG": 28762, + "TTTTGAATT": 28763, + "TCAGTCTA": 28764, + "TGACAACA": 28765, + "GAACAAAAAA": 28766, + "TCCTATATT": 28767, + "GGAGTGGTG": 28768, + "GTAGTTGTT": 28769, + "CACAGGGTT": 28770, + "CAGGTACTT": 28771, + "CTCACAGTA": 28772, + "GCAATCAAA": 28773, + "GTTTTTAGA": 28774, + "CGAGCGG": 28775, + "CAAAATACC": 28776, + "GGTTGAAAA": 28777, + "GGGACAAAA": 28778, + "CGCTCGC": 28779, + "GCAAAGCC": 28780, + "TCATTCATA": 28781, + "CGTTCAGCA": 28782, + "GGCCGGCC": 28783, + "CAGAACCAAA": 28784, + "GAAAAATTAAA": 28785, + "GTTTGCCTG": 28786, + "TGATGGAAAA": 28787, + "CACTGTAGCTG": 28788, + "GAGTTTAGA": 28789, + "CAGCCCATG": 28790, + "GCTTAGTGA": 28791, + "TCTCTGCCTT": 28792, + "TAGGAGGAA": 28793, + "GGAGAĊ": 28794, + "GGAGAGTAA": 28795, + "CTTTACCTG": 28796, + "TCACCCATG": 28797, + "CCACATCAA": 28798, + "GTTCATCAA": 28799, + "TCCATGTAGTT": 28800, + "GCACACGCA": 28801, + "GAACCAGAAA": 28802, + "CACTTAAAAA": 28803, + "TGACTTATT": 28804, + "TTATAAAAA": 28805, + "CTGTCCCCA": 28806, + "TGAATATC": 28807, + "TGAAGGTGA": 28808, + "GGAAGGGAAA": 28809, + "GTGAGACAA": 28810, + "GCTGGACAA": 28811, + "TTTTACAGAA": 28812, + "TATGCATCA": 28813, + "GATTTCAAAA": 28814, + "CTAACAGTG": 28815, + "CTAAGGGAA": 28816, + "GCCATATTA": 28817, + "GGGGTCCA": 28818, + "TGTACTTAA": 28819, + "TCTCATGGG": 28820, + "CGTGCTA": 28821, + "TCCTTCCTGA": 28822, + "CTAAACTAAA": 28823, + "TGCCTACA": 28824, + "TGATTGCCAA": 28825, + "CAGCAGATGCTG": 28826, + "TCTTATCTG": 28827, + "GACCTCTGA": 28828, + "CTCTGCTGTT": 28829, + "TGATGTAGA": 28830, + "TACAAAAAAAA": 28831, + "TACTTTAAA": 28832, + "GATAATATA": 28833, + "GAAGGCGA": 28834, + "GAAGGTAGA": 28835, + "TTTTTGTTTTA": 28836, + "CTGCATCAA": 28837, + "TCCATTTGTT": 28838, + "CAGTCTGGG": 28839, + "CGTCATTA": 28840, + "CAATACTTTT": 28841, + "TGACAGTTTT": 28842, + "TGATACAGA": 28843, + "CTATACATT": 28844, + "CTACATCTG": 28845, + "GGTTTTAAAA": 28846, + "GGCAGGGATG": 28847, + "CTTTGAGAGG": 28848, + "TATCCACTTA": 28849, + "TATCCATTCCTCTGTT": 28850, + "CGCCTATAATCCCAGCA": 28851, + "GTGATGTTTTGGGGCTGTCGCTGGGCAA": 28852, + "TTTTGGCA": 28853, + "GAACAAGAA": 28854, + "CAACCTTTT": 28855, + "CTTTCGTT": 28856, + "GCAGAGGTG": 28857, + "GCCTGGGTT": 28858, + "GTCTAATAA": 28859, + "GTCTTTTAAA": 28860, + "TGTGGCCAA": 28861, + "GAAATATC": 28862, + "TCTTGAGTA": 28863, + "GAGATATAA": 28864, + "CAGAGGGTT": 28865, + "TACATTAGG": 28866, + "TTAACACAA": 28867, + "CGATAATT": 28868, + "CCCACAATT": 28869, + "GACATAATT": 28870, + "TCTCAGTCC": 28871, + "TCTGAGCCACCAGGGAA": 28872, + "GGAAATAGG": 28873, + "CGTTGATG": 28874, + "GTGAAAAATG": 28875, + "CTCTTGGA": 28876, + "GTATTCATA": 28877, + "CTCTGTCTCAAAA": 28878, + "TGAGGGATT": 28879, + "TTATGCTG": 28880, + "GAAGGCTA": 28881, + "CGTCCATG": 28882, + "GTGGGGTG": 28883, + "CCTAACTCA": 28884, + "TATAAATAAA": 28885, + "CAAGGTGAA": 28886, + "GGAGAAGGAGAA": 28887, + "CTCATTTCAA": 28888, + "CCAGACAAA": 28889, + "GTAGATATT": 28890, + "GAGTCGGTGATG": 28891, + "GTGCCCGA": 28892, + "CAACAGGTGCTGGAGAGGATGTGGAGAAA": 28893, + "CTTTTTTTTTTTTTTTTTTTTTTTT": 28894, + "CCCACCAACAGTG": 28895, + "TCATAACAA": 28896, + "TGAGTAGG": 28897, + "GTTGCATA": 28898, + "GCATCACTG": 28899, + "GGAGTTTGA": 28900, + "CCATCACTT": 28901, + "CCATCCCC": 28902, + "GCCCĊ": 28903, + "TATTTAAATG": 28904, + "GAGAGGGTA": 28905, + "TGAATACTG": 28906, + "TAAAACATCA": 28907, + "CATGTCTGA": 28908, + "GTAATACC": 28909, + "TTTTATTTTTTA": 28910, + "CGGCACTT": 28911, + "GTTACTGTT": 28912, + "CTCCTACCA": 28913, + "CTCCCCAGG": 28914, + "TTAAGCTT": 28915, + "GCTAGTAAA": 28916, + "GGCACGCA": 28917, + "TCTCACTAA": 28918, + "CGCAAAAAA": 28919, + "TCTCCTGGAA": 28920, + "GGAAATGGA": 28921, + "TCCTTTCTTA": 28922, + "CTCTTTATG": 28923, + "GCTGACATT": 28924, + "TCTTACTTTT": 28925, + "CGCCTGAA": 28926, + "CACCAGAGA": 28927, + "GTGTGGTTTT": 28928, + "CATCTAAAAA": 28929, + "CATCCCTG": 28930, + "CTTGGGATG": 28931, + "CAAATTTCTG": 28932, + "CTCCATATG": 28933, + "CAAGGCACA": 28934, + "GAACACCTT": 28935, + "GCGACGCC": 28936, + "TATTTATCTT": 28937, + "GTCCAGTCC": 28938, + "GGGCCTTTG": 28939, + "GGGTCTGGG": 28940, + "TCCACCTGCCTCTGCCTCC": 28941, + "TCAGGTGGTCTG": 28942, + "GTCGTGTCTGACTCTTTG": 28943, + "GTAAAAAAATATTTT": 28944, + "CCGGGCGCGGTG": 28945, + "GGTCAGGCTGGTCTTGAA": 28946, + "GAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAA": 28947, + "GGAGGTTTTCACTCAAAA": 28948, + "GAACAGGTT": 28949, + "CTTGTACA": 28950, + "CTTGTGTTA": 28951, + "CCACTTCCA": 28952, + "GCCGCTC": 28953, + "CTCGAATT": 28954, + "TGTGCCCC": 28955, + "CACAGCCTG": 28956, + "CAAACACTA": 28957, + "CAGGGGTCA": 28958, + "GTGAGATAA": 28959, + "CCTTGACAA": 28960, + "GCAATCTCA": 28961, + "GAGGCTGTT": 28962, + "GAGGCTCTTTA": 28963, + "GATTCCATG": 28964, + "CTCCCGTG": 28965, + "GAAAACGTT": 28966, + "GCCATCTTA": 28967, + "TAGGCAGCA": 28968, + "GACATATTTT": 28969, + "CTGAATGTA": 28970, + "TGAAATGGA": 28971, + "CACCCGTT": 28972, + "TATTATCAA": 28973, + "CAGAACAAA": 28974, + "TGAGATCAA": 28975, + "GAGCACTG": 28976, + "CACACACACATA": 28977, + "TACTTTCTT": 28978, + "TTATGGTTA": 28979, + "TAAATTGTA": 28980, + "GAAGGGCTT": 28981, + "GCATGGAAA": 28982, + "CTTGGTGGG": 28983, + "GGAGACGA": 28984, + "CTTCTTCCTT": 28985, + "GGGCGAGG": 28986, + "TCCCATAGA": 28987, + "CTCAAACAAATTTA": 28988, + "TTACATCAA": 28989, + "CTTTCATTTA": 28990, + "GCAAAAGAAACTACCATCAGA": 28991, + "TCATGGCTGCAGTCACCATCTG": 28992, + "TATAAAAATT": 28993, + "TCACAAGAA": 28994, + "TTACTAAA": 28995, + "GTTGAGATT": 28996, + "GTTTGACTT": 28997, + "GCAGGAATT": 28998, + "GGATGAGGA": 28999, + "GCCTGTGTG": 29000, + "TAAATATATG": 29001, + "CACATTTTAA": 29002, + "CATTCACTA": 29003, + "TGAAGTATT": 29004, + "CAGGCGTT": 29005, + "GGAATGTGA": 29006, + "TAAAACCTA": 29007, + "CCTTCTCAA": 29008, + "GAAAACATTA": 29009, + "CCCACCCTA": 29010, + "CTCTTTATA": 29011, + "GTTAAGATG": 29012, + "TCTTAGTA": 29013, + "CGCAGGTT": 29014, + "CAATTGTCA": 29015, + "TCCTGCAAA": 29016, + "GGATTGTTTT": 29017, + "GAGTTGGGG": 29018, + "TTATGGCAA": 29019, + "GCTCCTTTA": 29020, + "TAAATTAAAAAA": 29021, + "GGATGCGG": 29022, + "TAGCTCTCA": 29023, + "GAAGATCTCA": 29024, + "TCAGATCCA": 29025, + "CTTTAGTCA": 29026, + "GGTTATTTG": 29027, + "TCACCCAGA": 29028, + "GCGATGTG": 29029, + "TCATAGCTG": 29030, + "CCAGACCTG": 29031, + "TCCATGAAAA": 29032, + "CACATGCTCCACTATGTTCATA": 29033, + "CAAGATCTCACCTC": 29034, + "CGGTTCCAGAGTAAACGTGCGCA": 29035, + "TTACCTCA": 29036, + "CTGTAATAA": 29037, + "CTTGGATA": 29038, + "CTTTAAAAAAA": 29039, + "GGACGAGG": 29040, + "TAAACAGCC": 29041, + "CTCGATAA": 29042, + "GTCTTTTTTTT": 29043, + "GAGATAGTT": 29044, + "GCTTTATGA": 29045, + "GAGGTAGGA": 29046, + "GATTCATCA": 29047, + "CTAAGCATT": 29048, + "GCCATACTT": 29049, + "TAGAGTTAA": 29050, + "GGTTGGGGA": 29051, + "GGCACCCC": 29052, + "GGAAAGGGA": 29053, + "GGAAATTCA": 29054, + "GCTGATGAA": 29055, + "TCTTATCTA": 29056, + "CTTTGTTTTTT": 29057, + "GTATTCTGA": 29058, + "CGAACAAA": 29059, + "TCCTGCTTA": 29060, + "CTATTGAAA": 29061, + "GCCTGCTA": 29062, + "GCCTGGCAA": 29063, + "TGAGGCCTG": 29064, + "GAATGTCTG": 29065, + "TCATGGATG": 29066, + "GTGTGCACA": 29067, + "CAATGGTAA": 29068, + "GAGTATTTA": 29069, + "CCAGGCCTT": 29070, + "GAGAAAGAAAAA": 29071, + "TATATTTATG": 29072, + "TCGACAAA": 29073, + "GCCAGGAAAA": 29074, + "TCCAAATTTT": 29075, + "GAACATATA": 29076, + "TGATACCAA": 29077, + "TATTTACATG": 29078, + "CAAGAATTTG": 29079, + "GCATGACATAA": 29080, + "CTTGTGATTTTTG": 29081, + "CATCCATGTCCCTACAAAGGACA": 29082, + "CTTTGGATGCAACTCAGCATTCTTTGTCCTCCAAA": 29083, + "TAACCAGAA": 29084, + "CAATGTTAA": 29085, + "CTGCTTCC": 29086, + "GTTCAAATG": 29087, + "GCATGAGTT": 29088, + "GGACCAAAA": 29089, + "CCACGTCA": 29090, + "CCACGATG": 29091, + "CTATTATA": 29092, + "TATTCTGTG": 29093, + "TCTGTCCTG": 29094, + "CTGACATAA": 29095, + "GTGATACAA": 29096, + "GTGAGACTT": 29097, + "CTCACATGA": 29098, + "CCTTTAGTT": 29099, + "GCAAGGGCA": 29100, + "GTCAGGATT": 29101, + "CGGTCAAA": 29102, + "CGGGATA": 29103, + "CGGCCTC": 29104, + "GATTGGCA": 29105, + "GTTATACTG": 29106, + "TACACCCTG": 29107, + "CAAAACCTG": 29108, + "TGCCCTGAA": 29109, + "CACCCGTG": 29110, + "GAGAAGGGAA": 29111, + "TCTTAGTAA": 29112, + "GTATTTCTAGTTCTA": 29113, + "TGATGCTTA": 29114, + "GAGCTCTC": 29115, + "GTCTGCATT": 29116, + "GAATGTCTT": 29117, + "GAATGTCAA": 29118, + "TATTTGGAA": 29119, + "CTTGGCATT": 29120, + "GTGGAGCTT": 29121, + "GTTCCTCAA": 29122, + "CTATAGAAA": 29123, + "TCAGCATTTT": 29124, + "GTCTCACTATGTT": 29125, + "CTGAGGTGGGAGGA": 29126, + "GTGAACAGGCAACCTACAGAATG": 29127, + "TAACTGCA": 29128, + "TCATAATGA": 29129, + "TCAGTAGA": 29130, + "TGATAAAATT": 29131, + "TCCTAACC": 29132, + "CTGTCGG": 29133, + "GCATTATTTT": 29134, + "GCATCCTCA": 29135, + "CCATCTGAA": 29136, + "CCATCACTG": 29137, + "GTAGTTTCA": 29138, + "GTAGAATA": 29139, + "TAAACCTTA": 29140, + "GTCGATTTT": 29141, + "GTCTGTTTA": 29142, + "TGTGCCCTG": 29143, + "TATTTCTTAA": 29144, + "TGTTTCATG": 29145, + "TGAAGTCTT": 29146, + "CAGGCGC": 29147, + "CTGAGGTGA": 29148, + "GCAACATTTT": 29149, + "GTCACCTCA": 29150, + "TATGCTGTT": 29151, + "TCCATCTAA": 29152, + "GAAAACTGAA": 29153, + "GTTTTGATA": 29154, + "CGATACTG": 29155, + "GACATACTT": 29156, + "CATACCATG": 29157, + "GCTACTTAA": 29158, + "TTATTCATTA": 29159, + "CTAAACCAA": 29160, + "GTGAATGGAA": 29161, + "GTTAAGGAA": 29162, + "CAATTCTTTT": 29163, + "TCCTGGTG": 29164, + "CTCTGCGA": 29165, + "CTATTCAA": 29166, + "CAGGATTTG": 29167, + "GAATGTATA": 29168, + "GAATGCTTA": 29169, + "CAATGCTAA": 29170, + "GCTCGAGG": 29171, + "GTGGGCGCCTGTAGTCCCAGCTA": 29172, + "GCAAAATTAA": 29173, + "CTCCATCAA": 29174, + "CGTGAGCCACTG": 29175, + "CTGCTGGATT": 29176, + "CAAGGGGAA": 29177, + "CCAGACATT": 29178, + "CAGTCCAAA": 29179, + "GTTGAGAGAATGCCAA": 29180, + "GGAAAGAATGAATGGGGCCATG": 29181, + "GACGTATT": 29182, + "TGATCCATT": 29183, + "GTATATATG": 29184, + "CAAATTTTTT": 29185, + "TCTGGGCAA": 29186, + "GGAAGCATT": 29187, + "GTGACACAA": 29188, + "GATTCATTA": 29189, + "CCAAGACTG": 29190, + "TCCATAGTG": 29191, + "GAAAAGCC": 29192, + "GTTTTCTAAA": 29193, + "CATACTGAA": 29194, + "TGGATAGG": 29195, + "GTATTTCATT": 29196, + "CCATTTATG": 29197, + "CAGCGTCA": 29198, + "GGATTTATA": 29199, + "TGAGGTAGA": 29200, + "TAATAATAAAAA": 29201, + "GGCCAGAGG": 29202, + "GCAGGCTCA": 29203, + "TAAGACTTA": 29204, + "CAATATCTA": 29205, + "TAAATATGTG": 29206, + "CTATAGATG": 29207, + "CCATATGTG": 29208, + "GGATAGAAA": 29209, + "GGCTCCAGG": 29210, + "GTCACATGGGGTAA": 29211, + "TACTGATAACAAGTTCAAACAGGTGCCATTAA": 29212, + "TAAGTTCTA": 29213, + "TCAGGAGGA": 29214, + "GCACCTCTG": 29215, + "CCAGCCCA": 29216, + "CTCTAGGTT": 29217, + "GTCCAGGTG": 29218, + "CACACTCAA": 29219, + "TGTTTAGGA": 29220, + "GAGAGCTCA": 29221, + "CATGGTAAAA": 29222, + "GTCATAGAA": 29223, + "TCTCTCGG": 29224, + "TCCACCTGA": 29225, + "TAGACATGA": 29226, + "TGGAGAAAAA": 29227, + "GAATTGTCA": 29228, + "CTTAAGTAA": 29229, + "GTATTGATG": 29230, + "GGAGGTGA": 29231, + "CCATTCTTA": 29232, + "CTCTGCTAA": 29233, + "CTATTCTGA": 29234, + "GCCTGGTG": 29235, + "TACAACAAA": 29236, + "GTCTGCATG": 29237, + "CAATGCAGA": 29238, + "CCATGCTA": 29239, + "GATCAGAAA": 29240, + "GACCATCTG": 29241, + "GGGTGGAGG": 29242, + "GCAGCACTCTT": 29243, + "CTTCCTATG": 29244, + "GCAGAGCCA": 29245, + "CAAGATATG": 29246, + "CTCTACATT": 29247, + "CATACATTTT": 29248, + "GGTCTGAAA": 29249, + "TTACAGTA": 29250, + "GAATTAAAAA": 29251, + "TCCCGTCC": 29252, + "GTTTCTTTTA": 29253, + "GCAGTTGA": 29254, + "TATTCCCCA": 29255, + "TATATATTTG": 29256, + "TCTTTCTGTT": 29257, + "GAGAGATTA": 29258, + "TCTGGTTTG": 29259, + "GCTGCGCA": 29260, + "CTCATCTGA": 29261, + "TTTTATATCA": 29262, + "CGGGGTCA": 29263, + "GTTATCATT": 29264, + "CCAATATCC": 29265, + "TACAGCAAAA": 29266, + "TACATTTTTA": 29267, + "GGGGCACC": 29268, + "CCCAGCCCC": 29269, + "CAAAAGGTG": 29270, + "GACATCTCA": 29271, + "TCAAACCCATCCTAATT": 29272, + "GGAAATATCC": 29273, + "TCCTTCAGA": 29274, + "CGTTTCCA": 29275, + "GTGAACATT": 29276, + "CTTTGTCTA": 29277, + "GTATTCTCA": 29278, + "TCTTTTCATT": 29279, + "TATAATGAAA": 29280, + "CGGATGAA": 29281, + "CTGTGCTTTT": 29282, + "GAGGAGGAGGAA": 29283, + "AAATTAAAAAA": 29284, + "CAGTTCCTT": 29285, + "CCTCCCCCC": 29286, + "CCCAAGAA": 29287, + "CCCAACAA": 29288, + "GGCTTTGTG": 29289, + "GTATGGCCATTTT": 29290, + "TCAGACTTA": 29291, + "TCGACGTG": 29292, + "TCGACGAA": 29293, + "CTTATTTATT": 29294, + "TTATATCTG": 29295, + "TGATACACA": 29296, + "TCATATGTG": 29297, + "GTATACTTA": 29298, + "GGTCCTGCC": 29299, + "GAGTTTTAAA": 29300, + "TCTTTGTCAGTGGGCAAACGTA": 29301, + "GCCCAGCGACAGCCCCAAAACATCA": 29302, + "GGCTĊ": 29303, + "GTGGAAAAAA": 29304, + "GTTCAGGTT": 29305, + "GCAGTCAAA": 29306, + "GTACCTTTT": 29307, + "GTACCTCTG": 29308, + "GCCTATC": 29309, + "CTCGAGCC": 29310, + "GAAACACAAA": 29311, + "GCTTAAAAAA": 29312, + "GCTTCCTTA": 29313, + "GTGATAGTT": 29314, + "GTTAGTTAA": 29315, + "CTCCCGAA": 29316, + "GAAAAGACAA": 29317, + "TTTGTATTA": 29318, + "TCTAGTGTT": 29319, + "GCCAGCTTTT": 29320, + "GTCCCGC": 29321, + "CTTTTCATTTT": 29322, + "CGATACTT": 29323, + "GACATACC": 29324, + "GGTTGGCA": 29325, + "CATACAATT": 29326, + "GGCAGCTCC": 29327, + "TTTTTTTCTTA": 29328, + "CTAAATGCA": 29329, + "CACCTCCAA": 29330, + "TGATTTAATT": 29331, + "GTCAAGATG": 29332, + "CAAAAACAAAAAA": 29333, + "GCATTCCCA": 29334, + "GCATTTCACTG": 29335, + "GCCTGTAGG": 29336, + "AAATTTGAA": 29337, + "TGAGATATA": 29338, + "CATTACAA": 29339, + "GGATTGATT": 29340, + "CTTGGGGAA": 29341, + "AAATACAAAAATTA": 29342, + "TCCCATCAA": 29343, + "GTTGATCTTTT": 29344, + "GATCGATT": 29345, + "GCTCTTATG": 29346, + "GGAAAGGAAAGGAAAGGAAA": 29347, + "TGGGGTAGAGGATT": 29348, + "TAATCAATG": 29349, + "TAAGTTTGA": 29350, + "TCACAGATT": 29351, + "TCACAACA": 29352, + "TCATAAGG": 29353, + "TCAGCCCTG": 29354, + "GAACCTC": 29355, + "GAATCTGAA": 29356, + "CAATTTTATT": 29357, + "CAAGTTCTT": 29358, + "CTTTCACC": 29359, + "GTGGCCAGA": 29360, + "GTTGGAATT": 29361, + "GCATTACA": 29362, + "GCCCTCAAA": 29363, + "GCCTCACTG": 29364, + "TAAAGCCTT": 29365, + "TAAAGATAAA": 29366, + "GTCTCGGA": 29367, + "CAGGCATCA": 29368, + "TCAATCAAAA": 29369, + "GCTTTATGAA": 29370, + "GTGACGCA": 29371, + "GTGAGTTTG": 29372, + "CTCAGTGAAA": 29373, + "TTTTAGAGG": 29374, + "CTCCCCTGA": 29375, + "CTAACAGTT": 29376, + "GACAGCCAA": 29377, + "GGTGCCCC": 29378, + "GCTAGCCAA": 29379, + "TCTCAGACA": 29380, + "TCCTTGCCTT": 29381, + "CACCTAGG": 29382, + "CAGCACACA": 29383, + "GTTAAGCAA": 29384, + "TAATGCCTG": 29385, + "CATAATAAA": 29386, + "TCAGGCTGA": 29387, + "GGAGATAAA": 29388, + "GGAGACGGA": 29389, + "GTTTAĊ": 29390, + "TGACAGAATT": 29391, + "CTGGATATA": 29392, + "CCAGATGTT": 29393, + "CTGCAAAAAA": 29394, + "GTCTACAAA": 29395, + "CCTCTTGAGAAA": 29396, + "GTCCACCTG": 29397, + "GATGAAGCTG": 29398, + "GACAGGAAAA": 29399, + "TTTTATGAAAA": 29400, + "TAATCCCAGCACTTTGGGAGGCCGAGGTGGG": 29401, + "TCCTCTTTTATT": 29402, + "GCAGTGAGCCGAGATCGTG": 29403, + "TGACACAGGTCAAACGTTTT": 29404, + "CAGTTTTATT": 29405, + "GGTATTAAA": 29406, + "TCCTGAGGA": 29407, + "CTTTAAAATT": 29408, + "GTTTATATG": 29409, + "GCCGATGA": 29410, + "GCCCCACTG": 29411, + "TGTGGACC": 29412, + "TATTGTAAAA": 29413, + "TATTGGCAA": 29414, + "GAGAGGAAAA": 29415, + "GGAACAGTG": 29416, + "CCTTTATGA": 29417, + "CCTTTCTCTT": 29418, + "GCAAGACTAA": 29419, + "GTAATTTTAA": 29420, + "GATTTACTA": 29421, + "CCAACGTG": 29422, + "CCAAGTGTT": 29423, + "TTTGTTATT": 29424, + "CTTTTCTAAA": 29425, + "CGTTGCTG": 29426, + "GGTACGTT": 29427, + "CTCTTGTCA": 29428, + "CTCTTCCCCA": 29429, + "GTCTTCAGA": 29430, + "CTCAACTA": 29431, + "GTCAAGGAA": 29432, + "CCATTGATT": 29433, + "TAATGTCTA": 29434, + "GAGCATTTA": 29435, + "TCATGTATG": 29436, + "TAAATTCTAA": 29437, + "AAATGTATA": 29438, + "TCAGGCATT": 29439, + "CTTCTTGAGTT": 29440, + "GCCAGGTGTGGTG": 29441, + "TGATACTAA": 29442, + "GTGGACACA": 29443, + "TCTATATCTATA": 29444, + "GTACATATG": 29445, + "GAACCTGAA": 29446, + "GATGAAAGTGAAAGAGGA": 29447, + "CAAGATGAAAATATAA": 29448, + "GTTCACTCAGACTCA": 29449, + "TGAGTCCCTGGCGG": 29450, + "TCATCTTAA": 29451, + "TCACTACA": 29452, + "TGATCCAAA": 29453, + "TGAGCGTG": 29454, + "GAACCGG": 29455, + "CTTCAACA": 29456, + "CCAGCTGTT": 29457, + "CTGATATTTT": 29458, + "GTGAGGGCA": 29459, + "GCTGGGAGG": 29460, + "GTCAGGTCA": 29461, + "TTTTACAGTT": 29462, + "CCTGCGCA": 29463, + "TCCACTAAA": 29464, + "TTAAGATA": 29465, + "CGACATAA": 29466, + "CAAAATACTG": 29467, + "TGTAGGGG": 29468, + "TGCACCCA": 29469, + "GTAAAGATA": 29470, + "GCAAATACA": 29471, + "TCCTTGCAA": 29472, + "CAGAACAGAA": 29473, + "GTTAAGTA": 29474, + "CAGCGGCA": 29475, + "TAATGCTCA": 29476, + "CTATTCACA": 29477, + "GAGCATCTTTT": 29478, + "TATCAGAAA": 29479, + "TACTTCCAAA": 29480, + "GAATGGGGG": 29481, + "GCAGGCAAA": 29482, + "TGTGTTTGTT": 29483, + "GAGTCTTTG": 29484, + "GGGCTCCACGCAAGATCTCA": 29485, + "TCAGCATGACAATGATCCCAAACACACCGCC": 29486, + "CATAAAAATT": 29487, + "TTTTCCATT": 29488, + "TGACTCCTT": 29489, + "CAATTTTAAA": 29490, + "CAAGCATA": 29491, + "CTTTCAAAAA": 29492, + "GCACTTCC": 29493, + "CCACTTTAA": 29494, + "GAGATGATT": 29495, + "GAGATATTA": 29496, + "TAAAACAGTT": 29497, + "CATGTAGTT": 29498, + "GTAAGATAA": 29499, + "TATGTATATG": 29500, + "GATTCAATT": 29501, + "GATTTGTGA": 29502, + "TCTCTCTTTG": 29503, + "TCTCTAGAAA": 29504, + "CTCCTACTG": 29505, + "TCCATCACC": 29506, + "GAAAACACTT": 29507, + "GAAAATATATT": 29508, + "TCTACGTA": 29509, + "GCCATATC": 29510, + "CTTTTCCAAA": 29511, + "CGATACCA": 29512, + "CGATCGG": 29513, + "GGTTTAATT": 29514, + "TTTTTTCTG": 29515, + "CGTTCTTA": 29516, + "CTCTTGGAA": 29517, + "TATTTTCTTA": 29518, + "GCATTCAGA": 29519, + "GCCTTGAAA": 29520, + "GCCTTGGCC": 29521, + "GACTTTGAA": 29522, + "TTATGTTATG": 29523, + "TAAATTTCAA": 29524, + "CCATGGTGA": 29525, + "CCTGAGTTA": 29526, + "GATTATTTTA": 29527, + "TGACACTTA": 29528, + "GTTGGTAGG": 29529, + "GTTTTAATTA": 29530, + "GCTCAAGGGCA": 29531, + "TCTACATTA": 29532, + "GTAATGGGGGTG": 29533, + "GGTTTGTTTT": 29534, + "TCTTTACCTA": 29535, + "CATTCTTTCCTTTACA": 29536, + "TAAAAGTGTTCCTATTTCTCCACATCCTCTCCAGCA": 29537, + "CCGTCTATGGGGTCGCACAGA": 29538, + "TGATAAGG": 29539, + "TGAGCACA": 29540, + "GAAGTGATT": 29541, + "TCCCTCTCA": 29542, + "GTGCAATA": 29543, + "GTGCACAAA": 29544, + "GTATGAATT": 29545, + "TATTCAATA": 29546, + "TCTTGGCTA": 29547, + "CTCATGGAA": 29548, + "CCTTGTAAA": 29549, + "CATGCCCTT": 29550, + "GTAAGACC": 29551, + "CCAATTATT": 29552, + "CTAAGTCC": 29553, + "TCCATCCATTTG": 29554, + "GAAAAGCTA": 29555, + "GTTTTTACTT": 29556, + "CCCAGGATT": 29557, + "TAGACAGTT": 29558, + "GACACTAAA": 29559, + "TTCAGCAAA": 29560, + "GGAAATATA": 29561, + "GGCCTACA": 29562, + "GCTGACGG": 29563, + "GTTAACTAA": 29564, + "GCATTGTTA": 29565, + "TGATGCAGA": 29566, + "TACAATAAA": 29567, + "GAGCAGGTT": 29568, + "GCTCGGA": 29569, + "GCAGGGCAA": 29570, + "CTTGGCAAA": 29571, + "CTTTAGATG": 29572, + "TCGAGCAA": 29573, + "GTTGGCAGA": 29574, + "GCCCAGGAGG": 29575, + "GTTGAATTTT": 29576, + "TATAAAAGACACCTGTCCACA": 29577, + "TCAGTCCTGAA": 29578, + "CAGGCAGGCAGGCAGG": 29579, + "TCAAATACTTATG": 29580, + "GGTAGGAAA": 29581, + "TAAGCGA": 29582, + "TGACTGCA": 29583, + "GAAAAAAAAAAAAA": 29584, + "TCCTATTAA": 29585, + "CCAGTTTA": 29586, + "GTATATTTG": 29587, + "CTATCCCTT": 29588, + "CTCTAATTTT": 29589, + "GTCTGAATG": 29590, + "TGTGCTGTT": 29591, + "TGTGCACTG": 29592, + "TATTGGATT": 29593, + "TCAATACTG": 29594, + "TCTCGGA": 29595, + "CCAACCCTT": 29596, + "TCCACTCAA": 29597, + "GCCACGCTG": 29598, + "CCCATCCTT": 29599, + "TGGGGTCC": 29600, + "TAGATACC": 29601, + "TAATTCTTAA": 29602, + "CGTTCGTT": 29603, + "GGCCCGTG": 29604, + "GTGAATGTA": 29605, + "GTATTCTTTT": 29606, + "GTCTTCTTA": 29607, + "TCTTTTGATTTT": 29608, + "TAATGTCAAA": 29609, + "GGGAAGGGTA": 29610, + "GAGCTCCAA": 29611, + "CTATGCTA": 29612, + "GAGAAATTAA": 29613, + "CTTGGCACA": 29614, + "TTTTTGGTAGAGA": 29615, + "GAAGAATGAA": 29616, + "GTTGTTAAAA": 29617, + "TTTTTAGATT": 29618, + "GAAAAAAATTA": 29619, + "CATATACATATA": 29620, + "GCCTGACCAACATG": 29621, + "GGTGAGATCTT": 29622, + "CTCCTGACCTCAGGTGATCCACCCA": 29623, + "GTAGAATAATAGTGAAGACA": 29624, + "TAACCCAGG": 29625, + "TCAGCCCC": 29626, + "GAAGTTCTT": 29627, + "TCCGTAAA": 29628, + "TCCCTGTA": 29629, + "TCCCGAAA": 29630, + "CTTGCCAAA": 29631, + "CTTCGGTG": 29632, + "GCATCAGTG": 29633, + "CCATTTTAAA": 29634, + "GCCTCTCAA": 29635, + "GCCCGCTT": 29636, + "GCCTACAAA": 29637, + "CTCGCCTG": 29638, + "TGTGCATTTT": 29639, + "TATATAAATT": 29640, + "CAAAGAAATA": 29641, + "CTGAGTCTG": 29642, + "GCTTGACAA": 29643, + "GTGATACTG": 29644, + "GCTGCAGTA": 29645, + "GCAAGGTCA": 29646, + "CGGGCTA": 29647, + "GATTCTCTT": 29648, + "GTTAGGGAA": 29649, + "TACAGTGGG": 29650, + "TCCAGTTTG": 29651, + "GCCATCACAAA": 29652, + "CCCAGCAGTG": 29653, + "TAGACGTA": 29654, + "GATACCTA": 29655, + "TCTCCCCCC": 29656, + "CGTGTCCCCCTG": 29657, + "TGATTTCTTA": 29658, + "GCTCAGTA": 29659, + "TCTTTTTTTTTTTTTTTTTT": 29660, + "CGGAGACA": 29661, + "GAAAAAGAGG": 29662, + "TTTTAATGTT": 29663, + "GCCTTGGAA": 29664, + "GGCTGTGGGTTTG": 29665, + 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\ No newline at end of file diff --git a/04-gene-sft/gpt2_lora_text_classification/checkpoint-66600/README.md b/04-gene-sft/gpt2_lora_text_classification/checkpoint-66600/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1d1ea3b65e48fcbd558e61ed55bb0102b413cd74 --- /dev/null +++ b/04-gene-sft/gpt2_lora_text_classification/checkpoint-66600/README.md @@ -0,0 +1,202 @@ +--- +base_model: dnagpt/dna_gpt2_v0 +library_name: peft +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Funded by [optional]:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] +### Framework versions + +- PEFT 0.14.0 \ No newline at end of file diff --git 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3611, + "CAAAGCC": 3612, + "TAGATAGATAGATAGA": 3613, + "CCTTAAAA": 3614, + "GTACGG": 3615, + "CAAAGGAA": 3616, + "CAAATTTG": 3617, + "CCCCCTCC": 3618, + "GATTATG": 3619, + "GCTGTCA": 3620, + "GCTCTCTG": 3621, + "GGAAGGAAGGAAGGAA": 3622, + "GGTCATT": 3623, + "CCTGGCA": 3624, + "GGCCCTG": 3625, + "CCAGAAAA": 3626, + "CTAAGAA": 3627, + "GTAGAAAA": 3628, + "TGTTAAAAA": 3629, + "GTATAAAA": 3630, + "CTACGG": 3631, + "GCCGGGC": 3632, + "GTTTTAAAA": 3633, + "CAAACATT": 3634, + "GTCATTTA": 3635, + "GGAGACC": 3636, + "GAAAGCAA": 3637, + "TACTGTT": 3638, + "CCTGGGTT": 3639, + "TCTTTAAA": 3640, + "CTCTAAAA": 3641, + "GTGCAGTG": 3642, + "TGGGAAAA": 3643, + "CATCCTG": 3644, + "TAAGGAAA": 3645, + "GGAAGAGG": 3646, + "CGAGTT": 3647, + "TCTTCTTTT": 3648, + "CCTGTAATCCCAGCTA": 3649, + "TAGCATT": 3650, + "TGATAGG": 3651, + "GTAAGAA": 3652, + "TGAGACC": 3653, + "TCCTAGA": 3654, + "TCTCAAAAA": 3655, + "GCCCACC": 3656, + "CTCAGTT": 3657, + "CTTTGCC": 3658, + "TAAGGCA": 3659, + "GAGAGCA": 3660, + "TGAAGTCA": 3661, + "CGCTCA": 3662, + "GTGTTTTA": 3663, + "GGGTGAA": 3664, + "GGGCATT": 3665, + "CAAGGCA": 3666, + "CAGATCA": 3667, + "CCTTCCTTCCTTCCTT": 3668, + "CAGAGCTT": 3669, + "TATACTA": 3670, + "TGAACTA": 3671, + "CTGGAAAA": 3672, + "TACAAAAA": 3673, + "CTCACACA": 3674, + "CCTCTCTT": 3675, + "GGGCAGG": 3676, + "TCTATTAA": 3677, + "CAGTCTG": 3678, + "GTTTGAAA": 3679, + "GTGGGCTG": 3680, + "GTTATTTA": 3681, + "TCCCATA": 3682, + "GACTCCA": 3683, + "GTGTCCA": 3684, + "TGTGGTA": 3685, + "CCAGCTA": 3686, + "GAAATCAA": 3687, + "GCTCATGCCTG": 3688, + "CTTTCCTT": 3689, + "TATTGCA": 3690, + "CCCAATT": 3691, + "CCCGTC": 3692, + "TTATATG": 3693, + "TACAGGA": 3694, + "GCCCTAA": 3695, + "GCAATTTT": 3696, + "TATAAATG": 3697, + "GATTGATT": 3698, + "CTTGAAAA": 3699, + "CCATTAAA": 3700, + "CTTATTTG": 3701, + "CTCCAGC": 3702, + "CCTGCCA": 3703, + "TAAACATG": 3704, + "TGAATTAA": 3705, + "GAGTGGGTT": 3706, + "GGAAGTG": 3707, + "GTAGATA": 3708, + "CTCGTG": 3709, + "CTGAGCAA": 3710, + "TATCCAA": 3711, + 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3812, + "GATTTGA": 3813, + "CGGAGGTT": 3814, + "GTAGTC": 3815, + "TGTGTCA": 3816, + "TGGATTA": 3817, + "GTGCTGGGATTA": 3818, + "TCTTGTG": 3819, + "GCTTGTG": 3820, + "TCTATTTG": 3821, + "TCAATATT": 3822, + "GAGGGCA": 3823, + "GGAGAGAA": 3824, + "CCAAGCA": 3825, + "TAGGTTA": 3826, + "CGCAAAA": 3827, + "GCTACTG": 3828, + "TCTTTTTTTT": 3829, + "TCTGGTT": 3830, + "GTCCAAAA": 3831, + "GGAACAA": 3832, + "GAATACA": 3833, + "CGTCATT": 3834, + "GCTGGCC": 3835, + "CGTTTTTT": 3836, + "GAACGA": 3837, + "TAATGTTA": 3838, + "GGAGGAGG": 3839, + "GTCTTAAA": 3840, + "CACACCA": 3841, + "GTGATGA": 3842, + "CAGGCAA": 3843, + "GTCACCA": 3844, + "GAATGTA": 3845, + "CATGCAA": 3846, + "CGGCAA": 3847, + "GGTGCTG": 3848, + "GGACTAA": 3849, + "GTATAGA": 3850, + "GCCAGAAA": 3851, + "CTATGGG": 3852, + "TACTGAAA": 3853, + "CTGATGA": 3854, + "CAAACAAAA": 3855, + "GGAAGTT": 3856, + "CTCAGAGA": 3857, + "TCTCCATT": 3858, + "GTCCCCA": 3859, + "TCCATCTT": 3860, + "CTGACAA": 3861, + "TCTCTCTCC": 3862, + "CTCAGCC": 3863, + "CCCGTA": 3864, + "GGAACTG": 3865, + "GCTGTGTG": 3866, + "CACAGTT": 3867, + "TCAAATTA": 3868, + "CCCATTTT": 3869, + "CAAATCAA": 3870, + "GTAATTTA": 3871, + "TACATGA": 3872, + "GATGGCA": 3873, + "GAAATATG": 3874, + "TCTGGTG": 3875, + "GTTATTTG": 3876, + "TAAATCTT": 3877, + "CATGGCA": 3878, + "CAGATGAA": 3879, + "CTTTTATG": 3880, + "CGGAAAA": 3881, + "CATATAA": 3882, + "TCTCTCTA": 3883, + "TGTGGCA": 3884, + "GTCCTGGG": 3885, + "TATATGA": 3886, + "TGCCTTA": 3887, + "GGACTTA": 3888, + "CCCCATGGA": 3889, + "TGAACATT": 3890, + "TTTTATTAA": 3891, + "GAAATTTA": 3892, + "CCGACA": 3893, + "CTAGTCA": 3894, + "GTAGGGG": 3895, + "GCCCAGC": 3896, + "GCCATGTT": 3897, + "GTGAGTG": 3898, + "CATCTTTT": 3899, + "CGACTG": 3900, + "TTTTCATT": 3901, + "GCTGGCTG": 3902, + "CCCTGTCTC": 3903, + "CTTTCATT": 3904, + "CCAGCCTGGG": 3905, + "GACGTC": 3906, + "GCTTTCC": 3907, + "GGCATTA": 3908, + "CTGAGGCAGGAGAATG": 3909, + "TAGGGAA": 3910, + "TATTCTAA": 3911, + "CTGACTGA": 3912, + "TCGAGC": 3913, + "GATCTAA": 3914, + "CCTGCCTT": 3915, + "TATGTGA": 3916, + "TAACTCC": 3917, + "TGTTTAAA": 3918, + "GGGAGAA": 3919, + "TTTTGTG": 3920, + "CTTTATTTT": 3921, + "GTTCGAGA": 3922, + "TCAATTAA": 3923, + "CTCCAGTG": 3924, + "GCGGCGG": 3925, + "CCATGTA": 3926, + "GAAAAGAA": 3927, + "TCTCAATT": 3928, + "CAATTAAAA": 3929, + "CATGTCA": 3930, + "TATGTATT": 3931, + "TATACAAA": 3932, + "GGCTAAA": 3933, + "CTTTACCA": 3934, + "CACAGTGA": 3935, + "CTGAGGTG": 3936, + "TAAAAGAA": 3937, + "TACCTTA": 3938, + "CACCCTG": 3939, + "CTTCCCTG": 3940, + "GCTGCTA": 3941, + "GCCTAAA": 3942, + "CCTGCAA": 3943, + "CGATTAA": 3944, + "GCGACTT": 3945, + "TACATAAA": 3946, + "CCCCTTA": 3947, + "GCACCCA": 3948, + "CAGAGTG": 3949, + "TTTTGGA": 3950, + "GGCCTCC": 3951, + "TCATTATT": 3952, + "TCAATGA": 3953, + "GTTACAA": 3954, + "CCAAGGA": 3955, + "TATAGTT": 3956, + "AAAAAAAAAAAAAAA": 3957, + "GGGAAAAA": 3958, + "TAGACAA": 3959, + "CCCCCTG": 3960, + "CTTGGTA": 3961, + "TGTGCAA": 3962, + "TACTCTT": 3963, + "CTATGCA": 3964, + "GGGTGTT": 3965, + 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4016, + "TATTTTTTA": 4017, + "TAAGGTA": 4018, + "CTGAGTG": 4019, + "TACTCAA": 4020, + "TCATAGG": 4021, + "GTAACTG": 4022, + "TACCATT": 4023, + "CTACAAAA": 4024, + "TGTGGGA": 4025, + "GTTGATA": 4026, + "TCCTGAAA": 4027, + "CCAGGCAA": 4028, + "CTCTC": 4029, + "TCACTCC": 4030, + "TGACATCA": 4031, + "CATAGAA": 4032, + "CCTAATG": 4033, + "CAACAACAACAA": 4034, + "GCTGGAGA": 4035, + "TATGCTG": 4036, + "CGCGCC": 4037, + "TACGCA": 4038, + "TCCCCAAA": 4039, + "GGGGATG": 4040, + "GTTTATTA": 4041, + "TAAAGATT": 4042, + "TAGAGAAA": 4043, + "TCACAGAA": 4044, + "GTAAGTG": 4045, + "CTGAGTA": 4046, + "CTGAGAGA": 4047, + "CAATGGG": 4048, + "CTTCACC": 4049, + "CTTATTAA": 4050, + "GAAGAAGAAGAA": 4051, + "CACACATG": 4052, + "CAGGTGA": 4053, + "AAAATAA": 4054, + "GCGAAAA": 4055, + "GGAAAAAAA": 4056, + "CGAGTA": 4057, + "TGAACAAA": 4058, + "GCTCAAAA": 4059, + "CTAACAA": 4060, + "GGGTGGG": 4061, + "GTTACTG": 4062, + "TAGTCAA": 4063, + "TGAATTTA": 4064, + "CCCAGTA": 4065, + "AAAATTTT": 4066, + "TCTTGATT": 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+ "GTCAAATT": 4119, + "TGTGTTTA": 4120, + "CACCTCC": 4121, + "TATTCTTA": 4122, + "CAGGCTA": 4123, + "GAAAGATG": 4124, + "TCTACTG": 4125, + "CTCCAGTATT": 4126, + "GGGCCTT": 4127, + "TCGGAAA": 4128, + "GCTĊ": 4129, + "TATACATA": 4130, + "CTGACCA": 4131, + "GTCGCC": 4132, + "GTATGGG": 4133, + "GTCTCTCA": 4134, + "CCCAGTT": 4135, + "CGGGCC": 4136, + "GGCTATT": 4137, + "TATCCATT": 4138, + "TGAAGATG": 4139, + "CTAACTG": 4140, + "GTTAGAA": 4141, + "CCTCAGCCTCC": 4142, + "TCAGGAAA": 4143, + "CTAAAAAAA": 4144, + "TAAGGGA": 4145, + "CATCAAAA": 4146, + "TGAAGAGA": 4147, + "CAAAGATG": 4148, + "GCTGCAGG": 4149, + "CATGCCA": 4150, + "TATATAAAA": 4151, + "CATGCTT": 4152, + "CGGGAA": 4153, + "TCTTGCTT": 4154, + "GCTAGAA": 4155, + "TCCTAAAA": 4156, + "GGCAGAGA": 4157, + "GCAATGA": 4158, + "TCGCGG": 4159, + "CAAGTAGCTG": 4160, + "GGGGCAGG": 4161, + "GATTTCTG": 4162, + "TGCCTTTT": 4163, + "GCATTTTA": 4164, + "TGTGCTA": 4165, + "GGGCTCC": 4166, + "GACCATG": 4167, + "GTGTAGA": 4168, + "TTTTTGTTTT": 4169, + 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"CTAATAA": 4474, + "CCTGTCA": 4475, + "TAGGTTTT": 4476, + "CCAACTCC": 4477, + "GGTGAGG": 4478, + "GTGCTGGGATTACAGGCGTGA": 4479, + "TATACCA": 4480, + "CCATTGCA": 4481, + "GCAAGTG": 4482, + "TATCCAAA": 4483, + "CTCCACCA": 4484, + "TATTTAGA": 4485, + "CCAATCA": 4486, + "CTCCAGTT": 4487, + "CATATAAA": 4488, + "TAAAATGA": 4489, + "CCTTTGA": 4490, + "TCTGGGTT": 4491, + "GTATACA": 4492, + "GTCAGTCA": 4493, + "CTTTGTTTT": 4494, + "TATGCTA": 4495, + "GGGTATT": 4496, + "TGAACACA": 4497, + "TTATGTG": 4498, + "GCAGTGAGCCAA": 4499, + "TAAAACTA": 4500, + "TCAATTTA": 4501, + "GGTCCTG": 4502, + "CAAAGACA": 4503, + "GAAAAGAAA": 4504, + "TCCCAAAA": 4505, + "CCTGCC": 4506, + "GTGATTTG": 4507, + "TATGGATG": 4508, + "CTGAGATG": 4509, + "CACTTAAA": 4510, + "TACTCTG": 4511, + "TAACATTA": 4512, + "GGATGTA": 4513, + "TCTTGCA": 4514, + "GGGTGTG": 4515, + "CCTGTGA": 4516, + "CAGGAATG": 4517, + "CTTTCTTTT": 4518, + "CATTCACA": 4519, + "GTAATTTG": 4520, + "TATTTTTTTT": 4521, + "CTCCATCC": 4522, + "CGAGCC": 4523, + 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4574, + "TCAACATT": 4575, + "CCTCCATG": 4576, + "CCAGGTA": 4577, + "CAGTTAAA": 4578, + "TATTGAGA": 4579, + "CAGCCAAA": 4580, + "TGACAGAA": 4581, + "CAGGCAGA": 4582, + "CACGTC": 4583, + "GATCTTTT": 4584, + "TAGACTG": 4585, + "TGCCCGG": 4586, + "GAGGCAA": 4587, + "TAATAATG": 4588, + "TGTGTCC": 4589, + "GAGGCTGA": 4590, + "TCATCTC": 4591, + "TCTTTTATT": 4592, + "TTTTGAAA": 4593, + "CCAACTG": 4594, + "GGATCTA": 4595, + "GCTGGGAA": 4596, + "CATATATT": 4597, + "TGTTCTCA": 4598, + "TGTGTCTG": 4599, + "CCTTGTG": 4600, + "CGGAATT": 4601, + "GGTGAGA": 4602, + "GCTCTGAA": 4603, + "GTTTAATT": 4604, + "GCCACTT": 4605, + "GACCCTGA": 4606, + "TACATGAA": 4607, + "TAGGATT": 4608, + "TCACGAGG": 4609, + "GCCATCTT": 4610, + "TCGATTA": 4611, + "GAGTAAAA": 4612, + "CCTTCC": 4613, + "TAAATATTA": 4614, + "TAAATAATT": 4615, + "TATAGAAA": 4616, + "TAGCCTT": 4617, + "TCCTCCTT": 4618, + "GGAATTTG": 4619, + "CCAATTTG": 4620, + "GCGAGCA": 4621, + "CATGGATG": 4622, + "GTCTCCTT": 4623, + "CGGGTG": 4624, + "GCAAGTT": 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5029, + "CTTTTTTTG": 5030, + "TCTCATTA": 5031, + "GTCCTGA": 5032, + "TCTTGAAA": 5033, + "CCAAGCTG": 5034, + "GGTAATG": 5035, + "CACCCCA": 5036, + "CTGATCA": 5037, + "TCTCTCAA": 5038, + "GTTAAAAAA": 5039, + "GATCCCCTG": 5040, + "CTCTGATT": 5041, + "TAAACC": 5042, + "CCTTGTA": 5043, + "TTTTTTTAA": 5044, + "TAAAACCA": 5045, + "GCCCAGCA": 5046, + "CTAGTC": 5047, + "CAAAGTTA": 5048, + "CCTGGCTT": 5049, + "GATGGGA": 5050, + "TGTGTGAA": 5051, + "TTTTGTTA": 5052, + "CACACAGG": 5053, + "CTATTATT": 5054, + "TAAAATGTT": 5055, + "CAAAATTAA": 5056, + "TAATTCAA": 5057, + "TATTCTCA": 5058, + "GACACTT": 5059, + "GCCACATT": 5060, + "GGTTCTG": 5061, + "CATTTGTA": 5062, + "GCACTTTG": 5063, + "CCAAGGAA": 5064, + "GCGTGTG": 5065, + "TCATTAAAA": 5066, + "GGTGAAAA": 5067, + "TCCTCTGA": 5068, + "TACAGTGA": 5069, + "TCGATGA": 5070, + "TAAAGCC": 5071, + "GATTTCAA": 5072, + "GGTCTAA": 5073, + "TAAGCTA": 5074, + "CAGGTCTG": 5075, + "CACACTCA": 5076, + "CACATGTA": 5077, + "GTCACAGA": 5078, + "TACCCAA": 5079, + 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5180, + "TATATCAA": 5181, + "TCCTCAAA": 5182, + "CTAAGTA": 5183, + "GAGAAAAAA": 5184, + "CAATAAAAA": 5185, + "GCGAGTT": 5186, + "CCAAGCC": 5187, + "TCTGCTCA": 5188, + "TTTAGAA": 5189, + "GACAAATG": 5190, + "CCTTGCC": 5191, + "CCCCTGA": 5192, + "GTCAGAAA": 5193, + "GAGCCCAGGA": 5194, + "CCTGCTGA": 5195, + "TAAATGAAA": 5196, + "CTTATCA": 5197, + "TATCCTG": 5198, + "CAGAGGTG": 5199, + "GGAGGGAGG": 5200, + "CAGACTCA": 5201, + "TCTGGATT": 5202, + "TCTCTATT": 5203, + "GTTTGAAAA": 5204, + "TCGAGCA": 5205, + "GGAACTT": 5206, + "CAAGGCCA": 5207, + "TGGGGAA": 5208, + "GTCTCATT": 5209, + "CTGTAAAA": 5210, + "CTCTTATT": 5211, + "CCCAGTGA": 5212, + "GTTTGTTA": 5213, + "CTGCGA": 5214, + "GCAGATCA": 5215, + "GCTACTA": 5216, + "GCTGTTAA": 5217, + "GTGTACA": 5218, + "CTTTACC": 5219, + "CGAGGTT": 5220, + "TTAGAGA": 5221, + "CACCCGG": 5222, + "GCTATTTG": 5223, + "GAAATACA": 5224, + "TCTGTATT": 5225, + "TCTATGTT": 5226, + "GCTAAATT": 5227, + "GAAAACAAA": 5228, + "CCATGCCTG": 5229, + "CAGGGCTG": 5230, + 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5281, + "TAAATATAA": 5282, + "CTCTCCAA": 5283, + "TTAAGAA": 5284, + "GAGGGTG": 5285, + "TAAAATGAA": 5286, + "CTGCAGTGA": 5287, + "GACAGGAA": 5288, + "TTTTTGAA": 5289, + "CATTGATT": 5290, + "CGCTGAA": 5291, + "TCTATATT": 5292, + "CTCTCCTG": 5293, + "CTCCACTT": 5294, + "CAATATTA": 5295, + "GAGATGAA": 5296, + "TAGAGTA": 5297, + "TGTTGATT": 5298, + "TCCAGTGA": 5299, + "GAAATGTA": 5300, + "TTACTAA": 5301, + "GAAATTCA": 5302, + "TCGGGAA": 5303, + "TCTAGTA": 5304, + "GCTATCA": 5305, + "GAAGAATT": 5306, + "CCATAGG": 5307, + "GAAATGGG": 5308, + "TTTTTCAA": 5309, + "CACATGA": 5310, + "TGTTGTTTT": 5311, + "CTTTAAAAA": 5312, + "CCAACTA": 5313, + "TACAGTCA": 5314, + "GCTGGTGA": 5315, + "CTTAGTA": 5316, + "CGTCCTG": 5317, + "GTAAGCA": 5318, + "GAAAATTAA": 5319, + "CACATAA": 5320, + "GCTATTAA": 5321, + "GAAACAAAA": 5322, + "CACAGTCA": 5323, + "TCCACCTT": 5324, + "GTCTCGCTCTG": 5325, + "TTACATG": 5326, + "CACACACACACACACACACACACA": 5327, + "GGTAAAAA": 5328, + "GAAACGG": 5329, + "GAGACAAA": 5330, + "TCGACAA": 5331, + "GGAAGTGA": 5332, + "CTCGCA": 5333, + "CAGCTAA": 5334, + "GGTGTTTT": 5335, + "GTCTCCAA": 5336, + "CAGACC": 5337, + "TTAGTAA": 5338, + "GTTTGCC": 5339, + "TATTGCTT": 5340, + "CAGGCTGA": 5341, + "TGCAGCA": 5342, + "CCTCCCTG": 5343, + "TAGAGGA": 5344, + "GTGCATA": 5345, + "GGGGGAA": 5346, + "TTATAAAA": 5347, + "CCATCCAA": 5348, + "CAGATTCA": 5349, + "TATTCTGA": 5350, + "GCCGAA": 5351, + "TCAGCAAA": 5352, + "GATTGCA": 5353, + "TCTCTCTCA": 5354, + "GGCCCCA": 5355, + "GCCTCGAA": 5356, + "GCTTCAAA": 5357, + "GCTTCTGA": 5358, + "GATCGG": 5359, + "TCTCAAAAAAAAAAAAAAAA": 5360, + "CTACGA": 5361, + "TCTTCATG": 5362, + "CAACTAAA": 5363, + "TAAAACTT": 5364, + "CTCCTGCCTCAGCCTCCCGAGTAGCTG": 5365, + "TATTTATAA": 5366, + "GTGAGTA": 5367, + "CACTAAAA": 5368, + "GTCTCCTG": 5369, + "GGAATGA": 5370, + "GCCCAGCC": 5371, + "CTCCCTCA": 5372, + "CATTCTGA": 5373, + "CCCATCAA": 5374, + "CAAAATAAA": 5375, + "GACACCTG": 5376, + "TAAAAAAAAAA": 5377, + "TGAACAGA": 5378, + "GGTTAAAA": 5379, + "GAAATGGA": 5380, + "GAAACATG": 5381, + "GACACAAA": 5382, + "CGTTTTAA": 5383, + "CGCAATT": 5384, + "GTCCAGTG": 5385, + "CTGAAATT": 5386, + "CCCCCC": 5387, + "TCTCTATG": 5388, + "TCTACAAA": 5389, + "CTTTTAAAAA": 5390, + "CCAGGGAA": 5391, + "TATTTGAAA": 5392, + "CATTGTTA": 5393, + "TCCCTGAA": 5394, + "CTTTCTCC": 5395, + "GTTGCTA": 5396, + "GAGAGTA": 5397, + "TAAAATTTG": 5398, + "CTTTTTTAA": 5399, + "GTTTCTGA": 5400, + "GGAATTAA": 5401, + "GTTCTGTT": 5402, + "TCTTTGCA": 5403, + "GAGCTTTT": 5404, + "GAAATAGA": 5405, + "GGTGGGGG": 5406, + "TGTATGTG": 5407, + "TAACACAA": 5408, + "CATTATTA": 5409, + "CAGAGATG": 5410, + "TAACTGAA": 5411, + "GAAAGATT": 5412, + "CAGGCACA": 5413, + "TCTTTCCA": 5414, + "TGTTGCTG": 5415, + "CGCCATG": 5416, + "CCCTCAAA": 5417, + "TATTTAGG": 5418, + "CTGCAGAA": 5419, + "GAAACCAA": 5420, + "GCATAGG": 5421, + "TATTCATG": 5422, + "CGACCCCA": 5423, + "TGACACC": 5424, + "GCAATTAA": 5425, + "GCTTCTCA": 5426, + "GCACGTG": 5427, + "GGAGCTA": 5428, + "CTAGGTG": 5429, + "CTGGGCC": 5430, + "AAATGAAAA": 5431, + "TAAGTCC": 5432, + "GGACACC": 5433, + "CTCTTTTTT": 5434, + "GGCAGCC": 5435, + "TAAAAACTA": 5436, + "TCCTCTCA": 5437, + "TATTTTAAAA": 5438, + "GGAGGCAGA": 5439, + "TAAAAATAAA": 5440, + "GATAATAA": 5441, + "CAAĊ": 5442, + "TCTGGAGA": 5443, + "TTTTAGAA": 5444, + "GTTAATTA": 5445, + "TATTGTGA": 5446, + "GTTAAATT": 5447, + "CAGCTGAA": 5448, + "CCAATGAA": 5449, + "CTTCCTCC": 5450, + "TTCAGAA": 5451, + "TAACCTA": 5452, + "CTGGCTA": 5453, + "CAATAATT": 5454, + "CATTTACA": 5455, + "CCCACGG": 5456, + "CTCCACAA": 5457, + "TCCACTGA": 5458, + "TAAAATTAAA": 5459, + "CACAGCTT": 5460, + "CTCCTGGA": 5461, + "TATCTATA": 5462, + "TCGTTTG": 5463, + "GCACAGCA": 5464, + "CGCTTTG": 5465, + "TTTTACTT": 5466, + "TATCTGTG": 5467, + "TCAAATTTT": 5468, + "CACACGG": 5469, + "CATAAAAAA": 5470, + "TCAGTGTT": 5471, + "GCTCAGTG": 5472, + "GAGATTTG": 5473, + "CTCCTGCA": 5474, + "GCGATGA": 5475, + "CAACCCA": 5476, + "TCTTTAAAA": 5477, + "GCAATGAA": 5478, + "TAACCCA": 5479, + "CCTTTCTG": 5480, + "GTCACATG": 5481, + "TCCACATG": 5482, + "GTTĊ": 5483, + "TGATGCC": 5484, + "TTTTCTTA": 5485, + "TCCCCATT": 5486, + "GACACCA": 5487, + "CTCAGTGA": 5488, + "CTCCCAGG": 5489, + "GGCCAAAA": 5490, + "CGAGGTG": 5491, + "GTTTCTTA": 5492, + "GCTGCAGA": 5493, + "CTGTGCC": 5494, + "GTTACCA": 5495, + "TTTTTCTG": 5496, + "TCCGAA": 5497, + "TCAAGATT": 5498, + "TTTTATATA": 5499, + "GTCCCTGA": 5500, + "GTTTTTTTG": 5501, + "CACAGTAA": 5502, + "GTTTTTCA": 5503, + "GCCACCGCGCC": 5504, + "GGATAAAA": 5505, + "GCCATTTG": 5506, + "GTCTCTCC": 5507, + "CAATGAAAA": 5508, + "GTTGGAAA": 5509, + "CCTGGTA": 5510, + "TGTGTGTGTT": 5511, + "GAGAGTG": 5512, + "GCGCTCC": 5513, + "TCCCAGGA": 5514, + "GCAGTGAA": 5515, + "CAGGGGAA": 5516, + "CTCAGTCA": 5517, + "TAAACCAA": 5518, + "CACGGTGAAA": 5519, + "CATAGTA": 5520, + "CTAGCTA": 5521, + "CACATTCA": 5522, + "TCTCTATA": 5523, + "TGATGAAAA": 5524, + "TAGTTTTA": 5525, + "TAACATCA": 5526, + "TGATATTA": 5527, + "GTTAGTTA": 5528, + "TGTTCAAA": 5529, + "GTTTACC": 5530, + "GCGAGTG": 5531, + "TCAGCTA": 5532, + "CTTCTGTT": 5533, + "CAGATGGG": 5534, + "TTTGGAA": 5535, + "CTTAATTA": 5536, + "GGAGGCAGAGGTT": 5537, + "TATCTGAA": 5538, + "TAAGAATT": 5539, + "TAAAGCTT": 5540, + "TCCAGTCA": 5541, + "CTTTCTGA": 5542, + "GAGĊ": 5543, + "TGTGGATG": 5544, + "CAATTATT": 5545, + "TCTCCA": 5546, + "GCAGACC": 5547, + "CTGATTTA": 5548, + "TATCTCTG": 5549, + "GCCACCTG": 5550, + "CAAAATTTT": 5551, + "CCCAAAAA": 5552, + "GGTAGAA": 5553, + "GCTGCTCA": 5554, + "TCTCTGTGAGTT": 5555, + "CCCACAAA": 5556, + "GCAAGAAA": 5557, + "CTCCCAAA": 5558, + "GGGATTTT": 5559, + "CATTATTTT": 5560, + "TCCTACC": 5561, + "CCTTCATT": 5562, + "TGAATTAAA": 5563, + "TGCATGTG": 5564, + "CCTGTAGTCCCAGCTA": 5565, + "GCGCATG": 5566, + "GGAACCA": 5567, + "TATGCATT": 5568, + "TGAACTAA": 5569, + "GTGACTTA": 5570, + "CGGCGC": 5571, + "GTGGAGAA": 5572, + "CTCTCTTA": 5573, + "TCTTGTTTT": 5574, + "GCTGGAAA": 5575, + "TCCACCAA": 5576, + "GATAGAA": 5577, + "TGAAGAAAA": 5578, + "CTGACAAA": 5579, + "TAACATTTT": 5580, + "TCAATATA": 5581, + "CTCATTAA": 5582, + "GTCĊ": 5583, + "TATTGG": 5584, + "TAATGCC": 5585, + "GCACAGTG": 5586, + "GAGAGTT": 5587, + "CGAGATT": 5588, + "GGAGCTGA": 5589, + "TAAGACC": 5590, + "CTGGTTTT": 5591, + "TTAGCC": 5592, + "GCTGGGGG": 5593, + "CCCAGCTT": 5594, + "CTGAGGTT": 5595, + "CTTCCTCA": 5596, + "CAGGCC": 5597, + "CGCTCTG": 5598, + "GTTAATTTT": 5599, + "CCACTTTT": 5600, + "CTCGATG": 5601, + "GTTATGA": 5602, + "TCTAGAAA": 5603, + "CCCCTCTG": 5604, + "GTGGGAAA": 5605, + "CTCTACTT": 5606, + "TCCAGATG": 5607, + "TGTGTGGG": 5608, + "TCCAGAAAA": 5609, + "GCATTCTCA": 5610, + "TCTTTCTTTT": 5611, + "TCTCAAAAAA": 5612, + "CCTATCA": 5613, + "TAAATAGG": 5614, + "TGTTCTTA": 5615, + "TTTGTGA": 5616, + "GTCCCAA": 5617, + "TAGATATA": 5618, + "TCTTGTAA": 5619, + "CATTCCAA": 5620, + "GTCAGAGA": 5621, + "CTAAGTT": 5622, + "TAAAGTC": 5623, + "TTATCTG": 5624, + "GTGTCTGA": 5625, + "GTCCTCTG": 5626, + "TCTTGGCTCACTGCAA": 5627, + "CATTCTTTT": 5628, + "GAAAAAAAAAA": 5629, + "CTTGCTA": 5630, + "GGAGGAAAA": 5631, + "TCAGGAGATCGAGA": 5632, + "GTAGCCA": 5633, + "GGCCTCA": 5634, + "GCGCCAA": 5635, + "GGAAATGGCAACCCA": 5636, + "GCTCCATG": 5637, + "GCCGGGG": 5638, + "GAAGACC": 5639, + "CCTGCAGG": 5640, + "TCGTCTG": 5641, + "CTCAGTAA": 5642, + "CAAGCAAA": 5643, + "CAAATACA": 5644, + "TAAGAGAA": 5645, + "GGAĊ": 5646, + "TGAGGAAAA": 5647, + "GCCCCAAA": 5648, + "TTACAGG": 5649, + "GGAATGAA": 5650, + "CATAAATT": 5651, + "CTTTATAA": 5652, + "CCCCTTTT": 5653, + "TACTTTTA": 5654, + "CATTCTTA": 5655, + "GAGACTA": 5656, + "GCCAGGTG": 5657, + "TATGAGA": 5658, + "GTACAAAA": 5659, + "TCTTTGGG": 5660, + "TGTTCATG": 5661, + "CTCAGAGG": 5662, + "CATAGCA": 5663, + "TACCATG": 5664, + "CACACTGA": 5665, + "CTTTGGAA": 5666, + "CATTGTCA": 5667, + "CCTGGAAA": 5668, + "GGACAAAA": 5669, + "TGTTGCC": 5670, + "CAAAGA": 5671, + "CTCCTGACC": 5672, + "TAGGTGA": 5673, + "GCGATCA": 5674, + "CATACATG": 5675, + "TCACACC": 5676, + "GACAGTGA": 5677, + "CAAGGTA": 5678, + "CATAATTA": 5679, + "GCAGCAGCAGCA": 5680, + "TGTTTGGG": 5681, + "CAAACCAA": 5682, + "TAATATAA": 5683, + "TCTTATTA": 5684, + "GCGGGCGCC": 5685, + "CTTCAGTT": 5686, + "TATTCAGA": 5687, + "CATACTA": 5688, + "GTCCCAGG": 5689, + "TATTATTTA": 5690, + "TGTTCACA": 5691, + "TCTCTAAA": 5692, + "CGCGTG": 5693, + "TTTTTTTTAAA": 5694, + "TTTTTGCA": 5695, + "GTTAGGA": 5696, + "CTCATGA": 5697, + "GAACTGAA": 5698, + "CAATCAAA": 5699, + "GCCCAAAA": 5700, + "GCTAATAA": 5701, + "CATATGA": 5702, + "GTAGGGA": 5703, + "CCACGTG": 5704, + "GCGTCTT": 5705, + "GCCCAGAA": 5706, + "CAGĊ": 5707, + "GTTTCTTTT": 5708, + "GTCAGCTT": 5709, + "CTCCTGTG": 5710, + "CTTATATA": 5711, + "GGGCTTA": 5712, + "CCATACA": 5713, + "CTTĊ": 5714, + "GCACCCCA": 5715, + "GCACAGAA": 5716, + "GAAATGCA": 5717, + "CATATTAA": 5718, + "GGACCCA": 5719, + "TATGCATG": 5720, + "TGATTAAAA": 5721, + "GAACAGAA": 5722, + "GATGGAGG": 5723, + "TCTGCCCA": 5724, + "GGAAGATT": 5725, + "GCCACATG": 5726, + "CTTCTGAA": 5727, + "CTTCAGAA": 5728, + "CAGATTTA": 5729, + "GAAACTAA": 5730, + "TGAAGATT": 5731, + "TCTCCTCTCC": 5732, + "CTCCCCTT": 5733, + "TCATCATCATCA": 5734, + "CTTCCAAA": 5735, + "GTCCAGAA": 5736, + "GCCAGCAA": 5737, + "CATTGTAA": 5738, + "TGAATTCA": 5739, + "CCAACC": 5740, + "CCACGCC": 5741, + "TGAATGGA": 5742, + "GATGTGA": 5743, + "TGTGCATG": 5744, + "GCAGCAAA": 5745, + "GGAATGGG": 5746, + "CCAGACC": 5747, + "GTCCAGCA": 5748, + "CCTTCAAA": 5749, + "CTCCTTCA": 5750, + "TAACACC": 5751, + "TCAĊ": 5752, + "TCGGCAA": 5753, + "TAAATATTTT": 5754, + "TTTTACAA": 5755, + "CTTGGAAA": 5756, + "CACAGATT": 5757, + "CTGGAGAA": 5758, + "GCCTGAAA": 5759, + "GTCCCTA": 5760, + "CACAAAAAA": 5761, + "GCCAAATT": 5762, + "GGACTTTT": 5763, + "CACATACA": 5764, + "GACACTA": 5765, + "CCCCCGG": 5766, + "GGCCCGG": 5767, + "GTTTGATT": 5768, + "CTCCTCCA": 5769, + "GGCCCATT": 5770, + "TCCCAGTG": 5771, + "GTCTCTAA": 5772, + "TTTTTATG": 5773, + "TCACAGCA": 5774, + "GCCACAAA": 5775, + "TCGAGGA": 5776, + "TCTGCAGG": 5777, + "TCTTCAGG": 5778, + "CACAGA": 5779, + "GCCTGGAAAA": 5780, + "CAGACTGA": 5781, + "CAGAGTTA": 5782, + "GCAAGCAA": 5783, + "CATĊ": 5784, + "CTGGGAAA": 5785, + "TATATGCA": 5786, + "TCAGAATT": 5787, + "GTCTACC": 5788, + "TAAAAGAAA": 5789, + "CCTTCTC": 5790, + "CCTTGTGA": 5791, + "GATTTTAAA": 5792, + "TTTTAATTA": 5793, + "GCACAAAA": 5794, + "CTAAGTG": 5795, + "TAGAAAAAA": 5796, + "TGAACATG": 5797, + "CAGATGTT": 5798, + "TGAGTAAA": 5799, + "CATTGCTT": 5800, + "GTTTAGTT": 5801, + "GGATGAAA": 5802, + "AAAAAATTA": 5803, + "GATGCAA": 5804, + "GTACCTA": 5805, + "CCCTAAAA": 5806, + "GTAGGCA": 5807, + "TTAATTTG": 5808, + "GAGATAA": 5809, + "CCCCCAGG": 5810, + "TCAATGTG": 5811, + "GCCACTGA": 5812, + "TCGACTG": 5813, + "CTTCTCTG": 5814, + "GATGGAAA": 5815, + "TAGATAAA": 5816, + "GGAGTTTT": 5817, + "GAGGAGAGGA": 5818, + "TTTTGCA": 5819, + "GTGATGTT": 5820, + "GCTGTCTT": 5821, + "TCTAGTG": 5822, + "GGTTATG": 5823, + "GATGCTCA": 5824, + "GTTGTAAA": 5825, + "GTTCAATT": 5826, + "GGATACA": 5827, + "CGCCCGCCA": 5828, + "TCCTACAA": 5829, + "GGAAGGTG": 5830, + "GCGGAGG": 5831, + "TAAAATATG": 5832, + "CCTTGCA": 5833, + "GCTCTTAA": 5834, + "TCACCTA": 5835, + "GTGAGAGG": 5836, + "GAGAGTCC": 5837, + "TCAGTGAA": 5838, + "GCTGGCTT": 5839, + "GCCAGGATGGTCTCGA": 5840, + "TCTGGGTG": 5841, + "TGGAGAAA": 5842, + "TTTTGATT": 5843, + "GAAĊ": 5844, + "TAAATGGG": 5845, + "CGCTTAA": 5846, + "CCACCTA": 5847, + "CATTTGGG": 5848, + "TCCCTCTT": 5849, + "CTGGTCC": 5850, + "TCTGCTGA": 5851, + "TTTTTTTTTTTTTTTTTTTT": 5852, + "TCTATATG": 5853, + "TTTTTTTTA": 5854, + "GTTCCCC": 5855, + "GGAACACA": 5856, + "TGAATATG": 5857, + "GCAACTA": 5858, + "GCCAGCCA": 5859, + "GCTCCACA": 5860, + "CTGCTGAA": 5861, + "TAGACTT": 5862, + "CAGATTAA": 5863, + "GTCACACA": 5864, + "CATCTCTT": 5865, + "TATAGTTA": 5866, + "GACTTTTA": 5867, + "TCCCCCTT": 5868, + "CCCACCTG": 5869, + "TATATATATATATATATATATATA": 5870, + "TCCTCCAA": 5871, + "GAAACAGG": 5872, + "TCCCCCTG": 5873, + "TGAAGCTG": 5874, + "CGGGAGA": 5875, + "GCGACTG": 5876, + "GTTCCAAA": 5877, + "CAAAGATA": 5878, + "TTACTTTT": 5879, + "TTAATTAAA": 5880, + "GCTTCTC": 5881, + "TCGAATG": 5882, + "TACTTTTTT": 5883, + "TCGGTCA": 5884, + "AAAAAAAAAAAAAAAAA": 5885, + "GAAAGACA": 5886, + "CAGAGGCA": 5887, + "GGGACAGG": 5888, + "TATATTTG": 5889, + "TCGCATT": 5890, + "TCAAGG": 5891, + "TAAAACTG": 5892, + "CTTGGCC": 5893, + "CATCCATT": 5894, + "GTCAGTGA": 5895, + "CCACGTT": 5896, + "CCAACTGA": 5897, + "CTTACACA": 5898, + "CGATTTG": 5899, + "CTTCCTGA": 5900, + "CTAAATGA": 5901, + "GAAGCAAA": 5902, + "CCTTAAAAA": 5903, + "TCGAGTG": 5904, + "TGAAGCC": 5905, + "TCAAGCAA": 5906, + "CGTGTGTG": 5907, + "CTCAAATG": 5908, + "CATACATT": 5909, + "GACTCAAA": 5910, + "CTAATAAA": 5911, + "GCCAGCTG": 5912, + "TAGACCA": 5913, + "GATTTATA": 5914, + "CTTCAGCA": 5915, + "CTAACCA": 5916, + "TAAGTATT": 5917, + "TAACCAAA": 5918, + "TCAGGCC": 5919, + "TTAATTTA": 5920, + "CTCACCTT": 5921, + "GAAAGCTT": 5922, + "GAAAATGA": 5923, + "TCCGTT": 5924, + "GAGACTGA": 5925, + "GTCACC": 5926, + "GATGCTA": 5927, + "GTCCTGTG": 5928, + "CAAACC": 5929, + "TAAAAGTAA": 5930, + "CTCCCTC": 5931, + "GGCTTAAA": 5932, + "TCCTCATT": 5933, + "TGAAGTAA": 5934, + "TCAACC": 5935, + "GAAACC": 5936, + "CAGAGTAA": 5937, + "CTTTATCA": 5938, + "GGAGAGCA": 5939, + "TCCATACA": 5940, + "GCCCGCC": 5941, + "GATTTGCA": 5942, + "CAAGGCC": 5943, + "GAAGGTA": 5944, + "GCGATCC": 5945, + "TCTTCATA": 5946, + "TCAGTAAA": 5947, + "GTATATTA": 5948, + "CTGAATTA": 5949, + "TCTTGTTA": 5950, + "TAGACCAGGCTG": 5951, + "CCTTTAAA": 5952, + "CTGATGTT": 5953, + "CCCAGTCA": 5954, + "TCGACCA": 5955, + "CTCATTCA": 5956, + "AAAATATT": 5957, + "GCACTTTT": 5958, + "TAATTTATA": 5959, + "TCCCAGTT": 5960, + "GCCATGAA": 5961, + "TTTTAATTTT": 5962, + "CATGTATT": 5963, + "GCTTCATT": 5964, + "GAAGAAAAA": 5965, + "GTCTGATT": 5966, + "GCGTCAA": 5967, + "CTCGGGG": 5968, + "TCTTTCTC": 5969, + "GCTCCAGA": 5970, + "CGGTTTG": 5971, + "TGAGTGAA": 5972, + "CCTGCCAA": 5973, + "TCCĊ": 5974, + "CGCGTC": 5975, + "CTAAGGA": 5976, + "GTGATTAA": 5977, + "TGAGACGGA": 5978, + "GTGATGGG": 5979, + "CTTATATT": 5980, + "TAAAAGTT": 5981, + "GTTATATT": 5982, + "TATCTATT": 5983, + "GCGCCTT": 5984, + "GCTATGTT": 5985, + "GGTTGTT": 5986, + "CACCTGTG": 5987, + "GAGCTGTT": 5988, + "GCCCTTTT": 5989, + "GAAATATTA": 5990, + "CGCTCAA": 5991, + "TCTGTCTA": 5992, + "GTTTTCTG": 5993, + "GATACTT": 5994, + "GGTCGG": 5995, + "TGTATATT": 5996, + "TAAGTGTGTA": 5997, + "TTTTCCAA": 5998, + "GCTCTCAA": 5999, + "CAAGGTCA": 6000, + "GAAAGGAAA": 6001, + "GATATAA": 6002, + "GTCTCACTCTG": 6003, + "TGAAGCAA": 6004, + "TAGATATT": 6005, + "TGCACTG": 6006, + "GGTTTGA": 6007, + "TCTCCATA": 6008, + "TAATTGAA": 6009, + "TAATTTAAA": 6010, + "CCTAGCA": 6011, + "GACTAAAA": 6012, + "GCCTCTGA": 6013, + "CTCTAGAA": 6014, + "CAAAGCTT": 6015, + "GTCCCC": 6016, + "CCCATGA": 6017, + "CCTCCAGA": 6018, + "CGTCATG": 6019, + "GGATCAAA": 6020, + "CGACTA": 6021, + "TTTTCTAA": 6022, + "GTCGCTCA": 6023, + "TCTTGTGA": 6024, + "CAGCTGTT": 6025, + "GGATTGCTT": 6026, + "GATCGTG": 6027, + "TAACAGCA": 6028, + "CATTCC": 6029, + "CGAGGAA": 6030, + "GAGAGACA": 6031, + "TATCTCAA": 6032, + "GAGAGAGAA": 6033, + "TTTATCA": 6034, + "GACAAATT": 6035, + "TTTAGCA": 6036, + "CCAACCTG": 6037, + "GTTATATA": 6038, + "GCAGGCTG": 6039, + "TAAATCTG": 6040, + "GCTCACACCTG": 6041, + "TGATGATGATGA": 6042, + "GAAAGCC": 6043, + "GAAAATTTT": 6044, + "GAGCTCTG": 6045, + "TACAAATT": 6046, + "GGATCCA": 6047, + "CTTACATT": 6048, + "TCTTTCCTT": 6049, + "GATAAAAAA": 6050, + "TAAGTGAA": 6051, + "GAAATGAAA": 6052, + "GCTGCATA": 6053, + "TCTACACA": 6054, + "CTTCAGTG": 6055, + "CCTTTATT": 6056, + "CAAGAGAA": 6057, + "CTGGGTA": 6058, + "CCAGAGAA": 6059, + "CTCTGATG": 6060, + "GAGGTTTT": 6061, + "TATGCACA": 6062, + "GTCATTCA": 6063, + "GTCACAAA": 6064, + "CCTTTGAA": 6065, + "TAATTCTA": 6066, + "GTCTTGCTCTG": 6067, + "GTTTAAAAA": 6068, + "TCTCTCCTT": 6069, + "GAAGAGCA": 6070, + "TAAGAAAAA": 6071, + "CCATAAAA": 6072, + "CTAATCA": 6073, + "CGCCACA": 6074, + "TAACAATT": 6075, + "TCTCCACC": 6076, + "CCCCCTA": 6077, + "GTCCAATT": 6078, + "GCAACACA": 6079, + "TCCACGG": 6080, + "CGCGGTG": 6081, + "GGATTTTA": 6082, + "CCTCCCGG": 6083, + "CAGACGG": 6084, + "GTTAGTA": 6085, + "GCTGGGCA": 6086, + "CATGGCC": 6087, + "TCTAGCA": 6088, + "TTATGAAAA": 6089, + "CGTAAAAA": 6090, + "GTTACCTG": 6091, + "CCACAGAA": 6092, + "GCCACCAA": 6093, + "CATTGTTTT": 6094, + "GTGACTGA": 6095, + "TATTATTAA": 6096, + "TAGGCAA": 6097, + "GATATTTG": 6098, + "GCAAGCC": 6099, + "GCCATCTCA": 6100, + "CATAAATG": 6101, + "CTTACAAA": 6102, + "GTTACAAA": 6103, + "GGGAGTG": 6104, + "CAATGCAA": 6105, + "CATGGAGA": 6106, + "GTTTTAAAAA": 6107, + "TCACTTGAGG": 6108, + "TCTGCACA": 6109, + "TACACTGA": 6110, + "GAAACTCA": 6111, + "TAATCATT": 6112, + "TCACTGAA": 6113, + "GGACAGAA": 6114, + "TAAAATGTA": 6115, + "TAAAAAAATT": 6116, + "TCTGTCTGTCTGTCTG": 6117, + "GTGAGATG": 6118, + "GCTGCACA": 6119, + "TAATGATT": 6120, + "TCCCACTT": 6121, + "CTGCCTA": 6122, + "CAGAGGGG": 6123, + "GCTTTCAA": 6124, + "CTAATATT": 6125, + "TCCAGGTG": 6126, + "TAGTGTG": 6127, + "TACGCC": 6128, + "GACGTGA": 6129, + "CTTAAATT": 6130, + "GAAAATATT": 6131, + "TGTTCCTG": 6132, + "CTCAAGTGA": 6133, + "CCGGGC": 6134, + "TTTGCAA": 6135, + "TAAACCTT": 6136, + "GCGTTAA": 6137, + "GCCATTAA": 6138, + "GTTGCAAA": 6139, + "GGTTGAA": 6140, + "CATTCCTG": 6141, + "TCTCTACTAAAAATACAAAAATTA": 6142, + "TGTGGCC": 6143, + "GCTTGGTG": 6144, + "TCTGGAGG": 6145, + "CTGCAAAA": 6146, + "TCTTGGAA": 6147, + "GTAATATA": 6148, + "TGTGCCTG": 6149, + "CTGGGAAAA": 6150, + "GATATCA": 6151, + "TTTTATATT": 6152, + "TACATCAA": 6153, + "CAATACAA": 6154, + "CTCCCGG": 6155, + "GATAATTA": 6156, + "GTGACTCA": 6157, + "CTAAAAATACAAAAATTA": 6158, + "GTGTGCC": 6159, + "CTCCCCTG": 6160, + "TCTAATTA": 6161, + "TAGATGAA": 6162, + "GTATCCA": 6163, + "GCAACAAA": 6164, + "CTTAGAAA": 6165, + "TCTACATT": 6166, + "TCCCTGTG": 6167, + "GCAAGGAA": 6168, + "TATGGGA": 6169, + "TCTTGAGA": 6170, + "TTTGCTT": 6171, + "CTAGGGG": 6172, + "CAGGTGGG": 6173, + "TCTCTAGG": 6174, + "GTAATGAA": 6175, + "TAGCCTG": 6176, + "TGTGTTCA": 6177, + "CTTTTCTG": 6178, + "GAAAATGAA": 6179, + "TAAATTAAAA": 6180, + "TAGACAAA": 6181, + "CACGCTG": 6182, + "GCAĊ": 6183, + "GCCCAGTG": 6184, + "CTCTACTG": 6185, + "GCCGAGG": 6186, + "TGTGTATT": 6187, + "TGATAATT": 6188, + "GTGTCCTT": 6189, + "CTCGG": 6190, + "CTAACAAA": 6191, + "TGGACAA": 6192, + "CACAAATT": 6193, + "TTAACAA": 6194, + "GCCCCTCC": 6195, + "TAACTTAA": 6196, + "TCCCACCA": 6197, + "TGAGGCTG": 6198, + "GTATTTTTT": 6199, + "TCCCAGC": 6200, + "CATAGAAA": 6201, + "TCAAGATG": 6202, + "GATACTG": 6203, + "GCCAGCTT": 6204, + "CCTGCGG": 6205, + "CAGCATCA": 6206, + "CTTTTTCTT": 6207, + "TGTGGAGG": 6208, + "GTCAGATT": 6209, + "TAAATAATA": 6210, + "CTCCACTA": 6211, + "CGAAAATT": 6212, + "TGTGGGTG": 6213, + "GGAATTTA": 6214, + "CCTTCCAA": 6215, + "CGACTGAA": 6216, + "GAATTTCA": 6217, + "GCTAGGA": 6218, + "TGACCTA": 6219, + "TGTACAA": 6220, + "TGTGAGCCA": 6221, + "TGATCAAA": 6222, + "GGAAGCTG": 6223, + "CCACATTTT": 6224, + "TAAAGTTTT": 6225, + "TCAAGTAA": 6226, + "GCAGGGAA": 6227, + "TCGAAAAA": 6228, + "CAAAGAGG": 6229, + "GATGCC": 6230, + "CTGATGGG": 6231, + "CTACCTA": 6232, + "TAACGTT": 6233, + "TAAACTTTT": 6234, + "GTCTGAGA": 6235, + "GATTCATT": 6236, + "TAGGCTT": 6237, + "CACCCAA": 6238, + "GTATATAA": 6239, + "TGTTGGTT": 6240, + "TGTTCC": 6241, + "TTTTTAAAAA": 6242, + "GTTAGTG": 6243, + "GTTAGAAA": 6244, + "GCACTCC": 6245, + "CTGATTAA": 6246, + "CATGCAAA": 6247, + "TAGCTTTT": 6248, + "GCCAGTCA": 6249, + "GCTTTTATT": 6250, + "TCATCATT": 6251, + "GTCCACTG": 6252, + "TTTTCCTG": 6253, + "TGTGGCTG": 6254, + "CCAATTAA": 6255, + "TGTTGATG": 6256, + "CTTCCCCA": 6257, + "GCTAGTG": 6258, + "GCCAGGTT": 6259, + "GTTTTTCTT": 6260, + "TAAACAGG": 6261, + "TGTGCACA": 6262, + "TCTGTATA": 6263, + "GTTTACAA": 6264, + "CTTCCATT": 6265, + "CATGGCTT": 6266, + "GCTCAATT": 6267, + "GCCGCTG": 6268, + "CTCTCTCTT": 6269, + "TCCATATT": 6270, + "CATGCATT": 6271, + "CTTTCCAA": 6272, + "GAATATTA": 6273, + "GAAGAGGA": 6274, + "GTCCATCA": 6275, + "TGTTGGAA": 6276, + "TTACTCA": 6277, + "CTCTAATT": 6278, + "CTGAGCTT": 6279, + "TCACCCAGGCTGGAGTGCAGTG": 6280, + "TCACGTG": 6281, + "CTAGGCA": 6282, + "CCAAGATG": 6283, + "TCCACTCA": 6284, + "CCCACCCCA": 6285, + "CTGTCTC": 6286, + "TATTCAAAA": 6287, + "GCAAGATG": 6288, + "TCTATGAA": 6289, + "GCCTCTCC": 6290, + "TCTCTGTGTA": 6291, + "TCACTTAA": 6292, + "TCATATCA": 6293, + "TAGACATT": 6294, + "TATTTTTTTA": 6295, + "GTGATGAA": 6296, + "TATGTCTT": 6297, + "GCCTCTCA": 6298, + "TCTAGGTT": 6299, + "TCATGCC": 6300, + "GTTTACTT": 6301, + "GTGACACA": 6302, + "GTTATTAAA": 6303, + "TAGTAAAA": 6304, + "GTTTTTATT": 6305, + "GTCTCTTA": 6306, + "CAGATAAA": 6307, + "GTCAGAGG": 6308, + "TCCTCCTCCTCC": 6309, + "TCCCATCA": 6310, + "TGTGCAAA": 6311, + "CTTATTCA": 6312, + "TTTTCTCA": 6313, + "CTTCTAAA": 6314, + "CTTTTCCTT": 6315, + "GATĊ": 6316, + "TGATGATG": 6317, + "CTAACACA": 6318, + "GCTATAA": 6319, + "TTACCAA": 6320, + "CATTTCAAA": 6321, + "TCTAGGA": 6322, + "GTCCTGGA": 6323, + "GTCAGGAA": 6324, + "GATTCAAA": 6325, + "TAGGATG": 6326, + "CTTGGGAA": 6327, + "CATTTTATT": 6328, + "TTACATA": 6329, + "GCCCATA": 6330, + "GAAGGGAA": 6331, + "TATTCATA": 6332, + "TGAGAATT": 6333, + "TCCTTATT": 6334, + "TCTTTTAATT": 6335, + "TCGTGTG": 6336, + "CTTCACTT": 6337, + "TATTGATG": 6338, + "GCTGGGTT": 6339, + "CATGCCTT": 6340, + "GGTGGCA": 6341, + "AAAAAAAAAAAAAAAAAA": 6342, + "GTCCAGC": 6343, + "GAGGTGGA": 6344, + "CGGCAGG": 6345, + "CCCTTAAA": 6346, + "TGACAGC": 6347, + "GCTGCAAA": 6348, + "GCTTTGGA": 6349, + "CCAACACA": 6350, + "CCCCATCTCTA": 6351, + "GTTTCCAA": 6352, + "GTTATAAA": 6353, + "GTAATTAAA": 6354, + "CTCTCTTTT": 6355, + "GGATGCC": 6356, + "CAGAGAAAA": 6357, + "GTTGCTTA": 6358, + "CTTTTCTA": 6359, + "GCTTGCA": 6360, + "CAACAGCA": 6361, + "GTGATGGA": 6362, + "GCAATAAA": 6363, + "TAGACACA": 6364, + "TCAGACC": 6365, + "CATGTATA": 6366, + "TTTTCTCC": 6367, + "TCCCTCAA": 6368, + "CATGCTCA": 6369, + "TCGCTTA": 6370, + "GCAATTTA": 6371, + "TATĊ": 6372, + "TCACACCA": 6373, + "GTGAGGAA": 6374, + "TCCACTTA": 6375, + "GCCTCCTT": 6376, + "CGGTGTG": 6377, + "TCCTGGAACTCA": 6378, + "GGTGGAGG": 6379, + "TCCTGATT": 6380, + "CCTGTCTG": 6381, + "GAATTATT": 6382, + "TGTAGTA": 6383, + "CCTAGGA": 6384, + "TAGGAGG": 6385, + "TCACGTT": 6386, + "TGTTGTAA": 6387, + "CTCCAGTGTT": 6388, + "TCGCCCAGGCTGGAGTGCAGTG": 6389, + "GTCATGA": 6390, + "CGGGTT": 6391, + "CAAAGGTT": 6392, + "CAAATGGA": 6393, + "TCGCTCC": 6394, + "GAAAGGGG": 6395, + "GTGAGGGG": 6396, + "GTCCAGTT": 6397, + "CTTCACTG": 6398, + "TAATATATT": 6399, + "CATGGAGG": 6400, + "GGGTATG": 6401, + "CTCTATCA": 6402, + "CTTAGGA": 6403, + "GGCCCAGG": 6404, + "GAAAGTTA": 6405, + "CTCACAGAGA": 6406, + "CTGAGACA": 6407, + "CCCAGCCA": 6408, + "CAATATAA": 6409, + "TCTGCATA": 6410, + "CAAGACCA": 6411, + "GGGAGCC": 6412, + "GCGTATT": 6413, + "GCAGCTCA": 6414, + "TAGCTGTG": 6415, + "CGTTATG": 6416, + "GGGACTG": 6417, + "GCTGAAAAA": 6418, + "TTATTTTTT": 6419, + "TGTTTGCA": 6420, + "TCAATAAAA": 6421, + "CTTTGGGAGGCC": 6422, + "CAACCCC": 6423, + "CCAATTTA": 6424, + "CCACACCTG": 6425, + "CGAGATG": 6426, + "CACATGAA": 6427, + "TACCCAAA": 6428, + "CTTTACTT": 6429, + "CTGACTCA": 6430, + "TCTCATTTT": 6431, + "CAACAAAAA": 6432, + "CTACTCC": 6433, + "CTGACATT": 6434, + "TCTCCTTCA": 6435, + "TAATTATG": 6436, + "TTATACA": 6437, + "TGACTGAA": 6438, + "CTGTGATT": 6439, + "GTGATTCA": 6440, + "TCTACCTT": 6441, + "GATAAATG": 6442, + "CACATGCA": 6443, + "TGACAGCA": 6444, + "GCTTGG": 6445, + "GTAAGGA": 6446, + "GTGĊ": 6447, + "GGATTCTCCAGGCAAGAA": 6448, + "GCTTAAAAA": 6449, + "TGTTTACA": 6450, + "TTATAGA": 6451, + "TCAACATG": 6452, + "GAGCCAGA": 6453, + "TCCCTCTG": 6454, + "CAGGGTA": 6455, + "TCCCGTG": 6456, + "GCGACAA": 6457, + "TGACCTCA": 6458, + "TCGGATT": 6459, + "GTGCGTG": 6460, + "GTCTATTA": 6461, + "GCTGCTC": 6462, + "TTTTCC": 6463, + "GACAGCAA": 6464, + "GGGAGCA": 6465, + "TGTTGTCA": 6466, + "CCCACTCC": 6467, + "TCAACACA": 6468, + "GGTGATGGA": 6469, + "TCCCTGTT": 6470, + "GTTTCTAA": 6471, + "TTTATAAA": 6472, + "GTTTTCAA": 6473, + "GATCTCTT": 6474, + "GAGAGATT": 6475, + "TAAGAGTT": 6476, + "CATTTTTTTT": 6477, + "GACGAAA": 6478, + "TGTGGGAA": 6479, + "TCAATTAAA": 6480, + "GTTCTCTT": 6481, + "GGACACCA": 6482, + "TGTTTGGA": 6483, + "GTTAGCA": 6484, + "GTGTGAAA": 6485, + "CCACTGAA": 6486, + "GTCTATAA": 6487, + "CTAATGAA": 6488, + "GAGAGTGA": 6489, + "CAAAAAAAAA": 6490, + "CCATCTC": 6491, + "TCTCAGAA": 6492, + "TTTTTTATT": 6493, + "GCCATCTG": 6494, + "GAAAATATA": 6495, + "TAGACAGA": 6496, + "GCTACAGA": 6497, + "GATGTGTT": 6498, + "CCCCCATG": 6499, + "GCGACCA": 6500, + "AAAACTA": 6501, + "TCAATATG": 6502, + "CAAGGCTG": 6503, + "CATTGGA": 6504, + "TCGTTTTA": 6505, + "TCATAATT": 6506, + "CTCCAAAAA": 6507, + "CTGAGAGG": 6508, + "TGACGTT": 6509, + "TCAAGTCA": 6510, + "GGAAGTCA": 6511, + "CCCTCCTT": 6512, + "TCAGACTT": 6513, + "CAGCCAGA": 6514, + "CCCTATG": 6515, + "TCTGGATG": 6516, + "CCTTCATG": 6517, + "TAGTCTG": 6518, + "CGAGAAA": 6519, + "CAGCTCTT": 6520, + "CACAGGTG": 6521, + "GACTCATT": 6522, + "TAAAGCTG": 6523, + "GCATTATT": 6524, + "GTTACAGA": 6525, + "TCACAGTG": 6526, + "CCTACAAA": 6527, + "GGTCAGGCTG": 6528, + "GCCTCCTG": 6529, + "TCTGCAGA": 6530, + "GCTTCAGG": 6531, + "GAGAGGGA": 6532, + "CTAATATA": 6533, + "TAGAGTG": 6534, + "TAGGTACA": 6535, + "GTTGAGAA": 6536, + "CATTGAAA": 6537, + "TAGCATG": 6538, + "CTTAGTG": 6539, + "GTCCCATG": 6540, + "GGTTTTAA": 6541, + "GCTTGGTT": 6542, + "CACATATG": 6543, + "TAATCCCAGCACTTTGGGAGGCTGA": 6544, + "TCCCAGCC": 6545, + "CAGGTGCA": 6546, + "CAGGCCTG": 6547, + "CTCCCTGA": 6548, + "GCAAATTA": 6549, + "GTGATAA": 6550, + "TCTGAGTT": 6551, + "GCTACACAGAGAAA": 6552, + "CTTTACAA": 6553, + "GGGGCCA": 6554, + "TCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTT": 6555, + "GAACCAAA": 6556, + "GTATAATT": 6557, + "GCCAGATT": 6558, + "TAAAAGCA": 6559, + "GCAACC": 6560, + "TCTATGTG": 6561, + "GGGGTCA": 6562, + "GAGATTAA": 6563, + "TATGAGG": 6564, + "GCTTTTAAA": 6565, + "CAAAACTA": 6566, + "GTAACAAA": 6567, + "GAAAACTA": 6568, + "GTTTTCTA": 6569, + "TTTACTA": 6570, + "CTTGACTT": 6571, + "GCCGTT": 6572, + "TGTACTG": 6573, + "GTAGGAAA": 6574, + "AAAATGA": 6575, + "GAGGGGAA": 6576, + "GACTCTA": 6577, + "CTTTTGTT": 6578, + "TCTTGTCA": 6579, + "GTGACATT": 6580, + "CGGTCC": 6581, + "CGGTGAA": 6582, + "AAAATATA": 6583, + "CTCCATTA": 6584, + "TAGATCA": 6585, + "GCCCAGGCTGGAGTGCAGTG": 6586, + "CTTATTAAA": 6587, + "GGAGAGC": 6588, + "CCTTTGTG": 6589, + "TTAATATT": 6590, + "GGCATCA": 6591, + "CCCCTGTG": 6592, + "CTTATAAA": 6593, + "TATGTACA": 6594, + "GATCCAAA": 6595, + "GCAGAGTG": 6596, + "TCTGTGTGTG": 6597, + "CTCACAAA": 6598, + "CCGCTA": 6599, + "GAGACAGAA": 6600, + "TCAACCAA": 6601, + "GAGTCAGACA": 6602, + "TGAAGACA": 6603, + "CTCCCACC": 6604, + "GCCGTA": 6605, + "CCTTGG": 6606, + "TAAAAATAAAA": 6607, + "CTTTCTAA": 6608, + "GAGGCTA": 6609, + "TTAGATG": 6610, + "GCTGTCTG": 6611, + "TACTTTAA": 6612, + "CCATGCCCA": 6613, + "TAGGCATG": 6614, + "GTGTTATT": 6615, + "GGAGATCA": 6616, + "TAGGTTTG": 6617, + "CAGATATT": 6618, + "CATAGTT": 6619, + "GCTGGATT": 6620, + "CTCCCACA": 6621, + "CCTATGA": 6622, + "GCACGCA": 6623, + "TCAATTCA": 6624, + "GCCCGTG": 6625, + "CTCTCACA": 6626, + "CCTGCACA": 6627, + "TAATAATAATAATAATAATAATAA": 6628, + "CTCTGAGG": 6629, + "CGGTAA": 6630, + "CGGTGGG": 6631, + "GAAAAATTA": 6632, + "CCTGCTCA": 6633, + "CACAAATG": 6634, + "TATTGCAA": 6635, + "TATAGATA": 6636, + "CAGGCAAA": 6637, + "GTTACATT": 6638, + "TTTAGTT": 6639, + "CTCCGTCTCAAAA": 6640, + "TACAGCTT": 6641, + "GCCACTCA": 6642, + "GCTAGAAA": 6643, + "GCAGAGC": 6644, + "GAGAGCTT": 6645, + "CATGTAAA": 6646, + "TCCAGTTTT": 6647, + "GGTCTTTT": 6648, + "CATATGAA": 6649, + "CAACCCCA": 6650, + "GCTACAAA": 6651, + "CCATGAAA": 6652, + "CAAACAAACAAACAAA": 6653, + "GGAACAAA": 6654, + "GTGATATT": 6655, + "GCGATTA": 6656, + "CAATGCC": 6657, + "GCTCCATT": 6658, + "TAAGACAA": 6659, + "TTAGTCA": 6660, + "CCAGAGCA": 6661, + "GTGCCCC": 6662, + "GTGGTAAA": 6663, + "TGCAGGA": 6664, + "TGATATAA": 6665, + "TCACCTGAGGTCAGGA": 6666, + "GTGATGTG": 6667, + "GCTGAATG": 6668, + "GCACGC": 6669, + "GAAAAGTA": 6670, + "CAAAATATT": 6671, + "CTCCAGCCTG": 6672, + "GATCGAA": 6673, + "TAATTTTAAA": 6674, + "CCTCCATT": 6675, + "TGTTCCAA": 6676, + "CTGCAGTG": 6677, + "CTAGGGA": 6678, + "CTCGGTG": 6679, + "GATGAATT": 6680, + "CAGGCTCA": 6681, + "CCTTCCCC": 6682, + "TCAGAGCA": 6683, + "CTCCCCAA": 6684, + "TCTGATTTT": 6685, + "TCATTCTCA": 6686, + "TACTAAAA": 6687, + "TAAGAGCA": 6688, + "GGAATAAA": 6689, + "CGGGCTG": 6690, + "CTTTTTATT": 6691, + "GAACACC": 6692, + "CACATGGA": 6693, + "CAGAGGCAGG": 6694, + "CCTCCGCCTCC": 6695, + "TCTCAATG": 6696, + "GATGTTAA": 6697, + "TGCAGTA": 6698, + "TAATACAA": 6699, + "TTATTTATA": 6700, + "CCAGGCTT": 6701, + "TAAAAATTAA": 6702, + "CTTAATTTT": 6703, + "GTGAAATG": 6704, + "GCACACC": 6705, + "CTCCCA": 6706, + "GTCCACCA": 6707, + "GAGAGCC": 6708, + "GACACC": 6709, + "TGCAGAAA": 6710, + "GGGCTAA": 6711, + "GATGTGAA": 6712, + "CTGĊ": 6713, + "CTTCAGCC": 6714, + "TGTTCTAA": 6715, + "GGAGATGA": 6716, + "CTCCTTTA": 6717, + "TACATCTT": 6718, + "TCATTTTTT": 6719, + "CTCGTCA": 6720, + "TCTATTTTA": 6721, + "CAGACAGACAGACAGA": 6722, + "TAGAGGAA": 6723, + "GAGAGCAA": 6724, + "CGCACACA": 6725, + "GTTTATCA": 6726, + "TCTACC": 6727, + "GCACTGAA": 6728, + "TCAAGTTA": 6729, + "GATATAAA": 6730, + "CTGAGTTA": 6731, + "CATAGCAA": 6732, + "GAGATGTG": 6733, + "CCTGTCTT": 6734, + "CATATTTTA": 6735, + "GGCAGTA": 6736, + "CCTTTCTTTT": 6737, + "CTCCCAGA": 6738, + "TAAAACAAAA": 6739, + "CATATGTT": 6740, + "CTCCTAA": 6741, + "CTGCTGCTGCTG": 6742, + "GATCTGTT": 6743, + "CAAGACC": 6744, + "CTAATTTTTT": 6745, + "GAATGTTA": 6746, + "TGACAGTG": 6747, + "CTGCTGTT": 6748, + "TGGGGAAA": 6749, + "TATGTCTG": 6750, + "GAGGTAA": 6751, + "CTCCATGA": 6752, + "CCTTGCTT": 6753, + "GCATGCC": 6754, + "CAGCTCCA": 6755, + "CTCTATTA": 6756, + "GCGATAA": 6757, + "GAAGTAAA": 6758, + "GCCĊ": 6759, + "GTTTTTTTTTT": 6760, + "GGACAGCA": 6761, + "TGTGCATT": 6762, + "GAATGCC": 6763, + "CCAĊ": 6764, + "GAATCAAA": 6765, + "GCATAAAA": 6766, + "CCACACTG": 6767, + "TAATAGTA": 6768, + "TTCAGTT": 6769, + "TATATATATT": 6770, + "GAGGGTA": 6771, + "GAATAATT": 6772, + "CCACCCTG": 6773, + "TCTTGCTG": 6774, + "TGTTCTTTT": 6775, + "TATGGTAA": 6776, + "GGCAGCTG": 6777, + "GCCGTC": 6778, + "GCTTTAAA": 6779, + "CAGAAAAAA": 6780, + "TCCATATA": 6781, + "TAATCCCAGCACTTTGGGAGGCCAA": 6782, + "TACACCAA": 6783, + "GTGTAAAA": 6784, + "CAAACTAA": 6785, + "GAGGAATT": 6786, + "CATGGATT": 6787, + "GATCGA": 6788, + "TCATATTA": 6789, + "CTCTGAGA": 6790, + "CGATGAA": 6791, + "GAATACTG": 6792, + "GTCCCGG": 6793, + "CCCACTCA": 6794, + "GATACTA": 6795, + "CATATTTG": 6796, + "TATTGAAAA": 6797, + "GAGAGAAAA": 6798, + "GCGCAGG": 6799, + "GTTGTGTT": 6800, + "TCACATCA": 6801, + "CTTCATTA": 6802, + "GTCTTTTTT": 6803, + "CCACAGTG": 6804, + "CACACCTT": 6805, + "CTATTAAAA": 6806, + "CCACCCCA": 6807, + "TCCAGTAA": 6808, + "GGTTTCC": 6809, + "TCGCTAA": 6810, + "GTCACTGA": 6811, + "TGTTGTGA": 6812, + "TAGCAGG": 6813, + "GACATTAA": 6814, + "GAACTTTT": 6815, + "TATTCCTG": 6816, + "TGGGTTTG": 6817, + "GGCCTCCCAAAGTGCTGGGATTACAGG": 6818, + "GACGTCA": 6819, + "TAACAGTA": 6820, + "CAGATGGA": 6821, + "TAATTTCTT": 6822, + "CCTAGGAA": 6823, + "TGACCCCA": 6824, + "GTGGGCC": 6825, + "GCGCATT": 6826, + "CCACATGA": 6827, + "TACCCTG": 6828, + "TACACACACA": 6829, + "CTTGACC": 6830, + "CTTTAGAA": 6831, + "CTTTGCTG": 6832, + "TGATTTTTT": 6833, + "CAAAATGTG": 6834, + "GATCTGTG": 6835, + "GTTGAATT": 6836, + "GCAGCTGA": 6837, + "CTCAGATT": 6838, + "TTCAGTA": 6839, + "CTCAGCTG": 6840, + "GTCAGACA": 6841, + "TAAAACATT": 6842, + "GCAGACGC": 6843, + "TAGACGG": 6844, + "CATCTTTG": 6845, + "CTAGGTA": 6846, + "CCGAAAA": 6847, + "TCACTGTT": 6848, + "TTAGAAAA": 6849, + "TTATCTA": 6850, + "CAACATCA": 6851, + "CCTTTGGG": 6852, + "TCTTTAGG": 6853, + "CGAGTC": 6854, + "CCTĊ": 6855, + "CATGTGTG": 6856, + "CAATCCA": 6857, + "TCTGCCTCC": 6858, + "CTCTCTAA": 6859, + "GATGGGAA": 6860, + "GAGTCAAA": 6861, + "TCTGTCAA": 6862, + "CCAAGCAA": 6863, + "GAACTAAA": 6864, + "CAAGCTA": 6865, + "GCTCAAGCCTG": 6866, + "TTTTGTAA": 6867, + "TAATTGTA": 6868, + "CACATTAAA": 6869, + "GAAGCTA": 6870, + "CAAGTAAA": 6871, + "GTGACAAA": 6872, + "CTTTAGTT": 6873, + "GTTGCTCA": 6874, + "CTTCTCAA": 6875, + "TGCAGCCA": 6876, + "GAGGTTAA": 6877, + "TAATATAAA": 6878, + "CCTAGAAA": 6879, + "GCTAAATG": 6880, + "TAAAAATAA": 6881, + "TCAGATGA": 6882, + "CATATGTG": 6883, + "GATATGA": 6884, + "TCCAAATG": 6885, + "CCAGGCCA": 6886, + "CTTATTTTA": 6887, + "GTCCTTAA": 6888, + "GATTAATG": 6889, + "GGTGGTA": 6890, + "CTTTACTG": 6891, + "CATGTGTT": 6892, + "GCATGAAA": 6893, + "CTTTGATT": 6894, + "TGTTCTC": 6895, + "TTATTATTTT": 6896, + "CTTCATCA": 6897, + "GTTCAGTG": 6898, + "CAGGGTGA": 6899, + "CCCCCTCA": 6900, + "CCTACAGA": 6901, + "GACACTGA": 6902, + "TCTCCCTG": 6903, + "TCATGAAAA": 6904, + "GTTTAGAA": 6905, + "CCATCAAA": 6906, + "GATTACAGG": 6907, + "CATGTCTG": 6908, + "TCGCCTG": 6909, + "CATCCAGA": 6910, + "GTTCAGAA": 6911, + "TACAGCC": 6912, + "CGACAAA": 6913, + "TCCCCTCA": 6914, + "CTTGCATT": 6915, + "GCTCCGCCTCCCGG": 6916, + "CACATGGG": 6917, + "TATTTTTTAA": 6918, + "TAGCTCC": 6919, + "GTGGCTA": 6920, + "TGACATTTT": 6921, + "GAAAGGTT": 6922, + "GCTTGATG": 6923, + "CATCCAGG": 6924, + "CCAGAGGA": 6925, + "TCTGCCAA": 6926, + "CCAAATTA": 6927, + "TCAAATTAA": 6928, + "CTTCACAA": 6929, + "GGAGACTG": 6930, + "CCCAGTC": 6931, + "CATGCTAA": 6932, + "GGTTGGTT": 6933, + "TATATTATT": 6934, + "CCTTCTGA": 6935, + "GTTTTTAAA": 6936, + "TAATAATTA": 6937, + "TAATCTC": 6938, + "GTCCCAGA": 6939, + "CGAGCTG": 6940, + "TGACACAA": 6941, + "CCCAGGTG": 6942, + "TGAGATTA": 6943, + "GCGAGCC": 6944, + "GCAGATGA": 6945, + "TCTATCAA": 6946, + "TTTTGGAA": 6947, + "TCACACTG": 6948, + "GATTTATG": 6949, + "TCGAGTA": 6950, + "GTAGAGAA": 6951, + "GTGAGCC": 6952, + "GATATATT": 6953, + "GGAAATATCTT": 6954, + "CTCAGG": 6955, + "TAGGTGTG": 6956, + "TGAAGTTA": 6957, + "TTTTGG": 6958, + "GAAGGCC": 6959, + "GTTCTGAA": 6960, + "TAGAAATT": 6961, + "TTTTATAAA": 6962, + "TCTCTGAAA": 6963, + "TAACACTT": 6964, + "TAAGATAA": 6965, + "CTATATTA": 6966, + "GCCACCTT": 6967, + "TCTACCTG": 6968, + "TCGGAGG": 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7018, + "CATTCATG": 7019, + "TCTGGTGA": 7020, + "TCAATTTTA": 7021, + "CCACAGTT": 7022, + "GAAATCTT": 7023, + "CTCGCTG": 7024, + "CTTACCTT": 7025, + "TCTGTGAAA": 7026, + "TACGTC": 7027, + "TCAACTAA": 7028, + "CTTTTTAAA": 7029, + "GGTTCTA": 7030, + "TCATGTTA": 7031, + "CTTGCCTT": 7032, + "CTAAAAATACAAAAAATTA": 7033, + "TCACCAAA": 7034, + "GTCGGGG": 7035, + "TGAAAAATT": 7036, + "TCATATTTT": 7037, + "GATGGCTG": 7038, + "GACGAGG": 7039, + "TCCGTA": 7040, + "GGTACTG": 7041, + "TCTAATTTT": 7042, + "CGCAATG": 7043, + "CAAACTGA": 7044, + "CAGGTGGA": 7045, + "TACACTTA": 7046, + "TAATAATAA": 7047, + "TCATATAA": 7048, + "TAAAATTCA": 7049, + "GTTTTTGTT": 7050, + "GGAGGTGGG": 7051, + "TTTTATTAAA": 7052, + "TCTCCTTTG": 7053, + "TGTGCTTA": 7054, + "TATGGCAA": 7055, + "TGAAGAGG": 7056, + "CATGGCTG": 7057, + "GTGTGGTG": 7058, + "GATGTCTT": 7059, + "GGACCCC": 7060, + "GCGTGGG": 7061, + "CTTGTAAA": 7062, + "GTATGAAA": 7063, + "TCTATCTT": 7064, + "TATGAAATT": 7065, + "CAACATTA": 7066, + "CATGCC": 7067, + 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"TATGGTCA": 7117, + "CCAACATT": 7118, + "CGATGTT": 7119, + "CTCGGGAGGCTGAGGCAGGAGAATG": 7120, + "GCATCAAA": 7121, + "TCATAGTT": 7122, + "GCCCTGAA": 7123, + "CACAGAAAA": 7124, + "TCTGCTTA": 7125, + "GTAACTAA": 7126, + "TATATTTTTT": 7127, + "CTCAGCCA": 7128, + "TATAATTAA": 7129, + "GTCTAATT": 7130, + "GATTCTGA": 7131, + "TCCTTCTG": 7132, + "CTCCAGCTT": 7133, + "GTTGTGAA": 7134, + "GAAGACAA": 7135, + "TATCATTA": 7136, + "GTTTTTAA": 7137, + "TCTACTTA": 7138, + "GTCCTTCA": 7139, + "GGCCTGTG": 7140, + "GTCCTTTA": 7141, + "CCACTGCACTCCAGCCTGGG": 7142, + "CAGATATA": 7143, + "CCAAGG": 7144, + "CCTGGATG": 7145, + "TCACATGA": 7146, + "GCAGCATT": 7147, + "CAGGGTCA": 7148, + "CAGAGGGA": 7149, + "CCCGGGAGGCGGAGCTT": 7150, + "TGGGTTA": 7151, + "GTAGGCTG": 7152, + "TGAATGAAA": 7153, + "GAATGGAA": 7154, + "CTGTCAAA": 7155, + "CTCTGTTA": 7156, + "TTTTTATTTT": 7157, + "CTCAGGGG": 7158, + "CTCGTGA": 7159, + "CTGATGTG": 7160, + "CCAATAAA": 7161, + "TCACTGTG": 7162, + "TCTCTCTGA": 7163, + "TCTATCC": 7164, + 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"GAATATAA": 7215, + "CAAAGGCA": 7216, + "TACACAAAA": 7217, + "TCCCATTA": 7218, + "GAGAGGTG": 7219, + "TCTGTAAAA": 7220, + "CCTGGGGG": 7221, + "GTCACCTT": 7222, + "TAAAGGTG": 7223, + "CCCTAACCCTAA": 7224, + "GTGCTGAA": 7225, + "TATTCTC": 7226, + "TTTATGTG": 7227, + "TATAAATTA": 7228, + "TCAGCTCA": 7229, + "TGAATGCA": 7230, + "GATTTGTG": 7231, + "TATGTTAA": 7232, + "CACGTGA": 7233, + "CACTGATT": 7234, + "GACACGG": 7235, + "GATCTCTG": 7236, + "GTCCATTA": 7237, + "CAGATCTT": 7238, + "GTTACTTA": 7239, + "GAAATCTG": 7240, + "CAGGCGG": 7241, + "TCTAGGAA": 7242, + "GAAAGAGAA": 7243, + "GATACAAA": 7244, + "TAACGAA": 7245, + "TCAACTGA": 7246, + "CCTCAGCCTCCCAAA": 7247, + "CGCAAAAA": 7248, + "CTCGAGG": 7249, + "CCCATCCTAATT": 7250, + "TATCTCTT": 7251, + "GTTCACTG": 7252, + "GCAGGAAAA": 7253, + "CATGTTAA": 7254, + "CTAGGAAA": 7255, + "GTGACCTT": 7256, + "CCAAGTCA": 7257, + "TACAGCTG": 7258, + "CAGCCATT": 7259, + "CCAGCCTT": 7260, + "GTTTCTCC": 7261, + "TCAAGCC": 7262, + "CAATTCAA": 7263, + "GATTATTTT": 7264, + "CTCGCTT": 7265, + "TATCTAAA": 7266, + "CCTCCACCTCC": 7267, + "TACATAAAA": 7268, + "CCCATCTT": 7269, + "TATTGGTT": 7270, + "CAATGTTA": 7271, + "TTATTATTATTATTATTATTATTA": 7272, + "GCTAATTTTTGTATTTTTAGTAGAGA": 7273, + "GCCAGATG": 7274, + "GTAACATT": 7275, + "TAAGGCC": 7276, + "CTGACACA": 7277, + "CACCCACA": 7278, + "GTGACC": 7279, + "TGCCTCC": 7280, + "GACCTTTT": 7281, + "AAAAA": 7282, + "TCAACTCA": 7283, + "TCACCTGA": 7284, + "GCAGCCAA": 7285, + "CGATGTG": 7286, + "AAATGTTTT": 7287, + "TAGCAAAA": 7288, + "TAAGCCAA": 7289, + "TCATCTGA": 7290, + "TGATCTCA": 7291, + "TGAAGGTT": 7292, + "CAGATATG": 7293, + "GTGAGCTG": 7294, + "CTCTGAAAA": 7295, + "GAAGGGGA": 7296, + "TCATAGAA": 7297, + "GTAĊ": 7298, + "GTGAGG": 7299, + "GAGGCCAA": 7300, + "CACCACCA": 7301, + "CGGAATG": 7302, + "TCGACTA": 7303, + "CTCAAAATT": 7304, + "TATTTCTTA": 7305, + "CCTGCCCC": 7306, + "CAAACATA": 7307, + "CAAATGAAA": 7308, + "TCTATATA": 7309, + "CAACCAAA": 7310, + "CTACCAAA": 7311, + "CCTGGATT": 7312, + "GTAAGGAA": 7313, + "TCCTTCCTT": 7314, + "CTTCGTT": 7315, + "CATTTGAAA": 7316, + "CTCCTGCCTCA": 7317, + "GTTTTCCTT": 7318, + "CGTGCGTG": 7319, + "GTGGCTGA": 7320, + "GAGAGGTT": 7321, + "GTCAGCC": 7322, + "CTCTGCTG": 7323, + "TCCAGTTA": 7324, + "CTGATGGA": 7325, + "CGCTCTT": 7326, + "CACCAGGCTCC": 7327, + "TAATTTGA": 7328, + "GTCTAGAA": 7329, + "TCTCCAAAA": 7330, + "TGCCCAA": 7331, + "CAGTTATT": 7332, + "TAACTATT": 7333, + "CTTGGCCA": 7334, + "TGAATAAAA": 7335, + "TCTGGCAA": 7336, + "GATTGGA": 7337, + "GGGCTGTG": 7338, + "GGACACGACTGA": 7339, + "GCGTGCTG": 7340, + "TAATCTTA": 7341, + "GTTCTAAA": 7342, + "GAAAGATA": 7343, + "GAATACC": 7344, + "CTTAGCAA": 7345, + "TCCACGA": 7346, + "CAGATGCA": 7347, + "TAAAGGTT": 7348, + "TTTACTG": 7349, + "TGGGTGGG": 7350, + "GCCTTTTA": 7351, + "TAGGTAGG": 7352, + "TCCCTAAA": 7353, + "TATTGTAAA": 7354, + "CGGGGGG": 7355, + "TATGTGAA": 7356, + "GACGATG": 7357, + "GTTCCTCA": 7358, + "TTTAGTA": 7359, + "TCTGCTTTT": 7360, + "GAATTCCA": 7361, + "TCATGCATT": 7362, + "TATAGATG": 7363, + "TCTCTCTCTT": 7364, + "TAACAAAAA": 7365, + "GCCCACTG": 7366, + "TGTGTGTGTGTGTGTGTGTGTGTG": 7367, + "GTCGGTG": 7368, + "CTAAAAATT": 7369, + "GCGGATG": 7370, + "CTCATCTT": 7371, + "GTTATGTT": 7372, + "TACATAGA": 7373, + "CTCTATAA": 7374, + "GTGACAGG": 7375, + "CCCAGTAA": 7376, + "TCACCTCC": 7377, + "GGAAATTA": 7378, + "CCACACAA": 7379, + "GCCTCCAA": 7380, + "GCCACGA": 7381, + "TATTAATTA": 7382, + "CATGTGAA": 7383, + "TCTACATG": 7384, + "TATATATATG": 7385, + "GTGTGTGTG": 7386, + "CCTCTCAA": 7387, + "GAGACC": 7388, + "CAGACCTG": 7389, + "CAAAAGAAA": 7390, + "CAAATAGG": 7391, + "GTCCCCTT": 7392, + "TGTGGG": 7393, + "TGAATCC": 7394, + "GACACTTA": 7395, + "GTTTGGAAA": 7396, + "CTGCAGTT": 7397, + "TAAGATTA": 7398, + "GCCTAAAA": 7399, + "TCATTCTT": 7400, + "CAAGAGCA": 7401, + "TATTGATA": 7402, + "TATGGCTT": 7403, + "CTGTACC": 7404, + "GTGGGAGG": 7405, + "TCTACCAA": 7406, + "GGTAGGA": 7407, + "TGATATCA": 7408, + "GCCGGCC": 7409, + "CAAAATATA": 7410, + "CAGTCATT": 7411, + "CTTGCAAA": 7412, + "CCATTATT": 7413, + "CGGAAAAA": 7414, + "CATTGCTG": 7415, + "GTCACCAA": 7416, + "GACAGTAA": 7417, + "GCAGTGAGCC": 7418, + "CAGCTTTG": 7419, + "CTGCCCTG": 7420, + "GTTCGAA": 7421, + "CATTGG": 7422, + "TCTAGAGA": 7423, + "CAATATCA": 7424, + "CCATCTCC": 7425, + "TGTTGGTG": 7426, + "CTGTGTTTT": 7427, + "TCAAAAATT": 7428, + "TAAACGA": 7429, + "TGAATGGG": 7430, + "GCTTTGCA": 7431, + "CCCCCCAA": 7432, + "GATCTGAA": 7433, + "GTCCAGCC": 7434, + "TTAGCTG": 7435, + "GCCGGTG": 7436, + "GTTTTCAAA": 7437, + "TAATTGTT": 7438, + "GGAGACCA": 7439, + "CTATATAA": 7440, + "TCTGCTAA": 7441, + "CTTTGGGAGGCCAA": 7442, + "CATATATG": 7443, + "CTTCAGC": 7444, + "CTATACTA": 7445, + "TTTGGTA": 7446, + "GAACACCA": 7447, + "TAATTATTA": 7448, + "GGACCTCA": 7449, + "CATGTCC": 7450, + "TCTACTCA": 7451, + "GAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGA": 7452, + "TATGTGTG": 7453, + "CATTTAGA": 7454, + "CTCCTTAA": 7455, + "CTTGGCTT": 7456, + "TCCATATG": 7457, + "CTGTGAAAA": 7458, + "CTTTCCTG": 7459, + "CAAACAAAAA": 7460, + "CATTGCAA": 7461, + "TCAGGCTG": 7462, + "TCAGAGGA": 7463, + "GGAGACTT": 7464, + "CTTAGGAA": 7465, + "TCAGGAAAA": 7466, + "TCAGAGTT": 7467, + "CTTTCTCTT": 7468, + "CAAAATGA": 7469, + "GGGACAA": 7470, + "TCTAAAATT": 7471, + "GGAGAGTG": 7472, + "GTCTACTT": 7473, + "GAAATAGG": 7474, + "TCTTGATG": 7475, + "CATGCAGG": 7476, + "GTCGCA": 7477, + "CACATCTG": 7478, + "CACAGGTT": 7479, + "GACAGTCA": 7480, + "CTGAAGAA": 7481, + "GGGCCAGG": 7482, + "CATTTAGG": 7483, + "CCTTCAGG": 7484, + "GGCAGTGA": 7485, + "TGTAGTT": 7486, + "GATCGCA": 7487, + "TATTAAATT": 7488, + "CACACCAA": 7489, + "TCTTCCTA": 7490, + "GGAAGGTT": 7491, + "TCTACAGA": 7492, + "TTCAGTG": 7493, + "CTAATTAAA": 7494, + "GGCAGTT": 7495, + "GGACAGTG": 7496, + "TGAGGATT": 7497, + "GAAAGCTG": 7498, + "TATAGCAA": 7499, + "CTCACCTG": 7500, + "CCTTTCAA": 7501, + "GGGTTAAA": 7502, + "CTTCTTTG": 7503, + "GGAGAAAAA": 7504, + "GTAATGTT": 7505, + "GAATGTTTT": 7506, + "CCACCTCA": 7507, + "CTCATCC": 7508, + "TATGCTAA": 7509, + "TCAAAGAA": 7510, + "CTGAACAA": 7511, + "TCACATTA": 7512, + "TCTTTCTCA": 7513, + "TAATTCTG": 7514, + "CTCTCTCTG": 7515, + "GAAAATA": 7516, + "GACATTTG": 7517, + "CCAGTGAA": 7518, + "CAGACCAA": 7519, + "TTTTACTA": 7520, + "TATGTCAA": 7521, + "CTTATGAA": 7522, + "CCTCCCGA": 7523, + "GAAATGAAAA": 7524, + "GCAGGAGG": 7525, + "CTCAGGTT": 7526, + "TCAGTCTT": 7527, + "GCTAGTA": 7528, + "TCATTCCTT": 7529, + "TCACTCTT": 7530, + "GACGAGA": 7531, + "TAATCCCAGCTA": 7532, + "CTGGGGAGG": 7533, + "GCAAGAGG": 7534, + "GATCGC": 7535, + "GTCACTAA": 7536, + "GCCAGTAA": 7537, + "TAGACTGA": 7538, + "TCGCGA": 7539, + "TATTATTTG": 7540, + "CATTTAATT": 7541, + "GGAACTGA": 7542, + "TCTATCTG": 7543, + "GGGGAGGGG": 7544, + "CCTCCTCC": 7545, + "CACĊ": 7546, + "GCCCATCA": 7547, + "TAAAATCAA": 7548, + "GTCACAGG": 7549, + "TCTCTCTCTG": 7550, + "CATAGTGA": 7551, + "TTTTTTTTTTAA": 7552, + "GTTTTATTTT": 7553, + "GTTACC": 7554, + "TAAAGAGG": 7555, + "GACAAAAAA": 7556, + 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"GAGAAGAGAA": 7605, + "GATTTGAAA": 7606, + "TCCCGC": 7607, + "TAATTTCAA": 7608, + "GATGCTGA": 7609, + "GGCAGCAA": 7610, + "TGCCTGTG": 7611, + "GTGAGTCA": 7612, + "CCCATCTG": 7613, + "GGCACC": 7614, + "CCTTGGTG": 7615, + "TCTCTACA": 7616, + "TCACCTCA": 7617, + "GTAGAATT": 7618, + "CTGTGCTG": 7619, + "TGTTGAAAA": 7620, + "CATGAATT": 7621, + "GGAATATT": 7622, + "TGGGGTG": 7623, + "TAACTGTT": 7624, + "TCACAATT": 7625, + "TATACC": 7626, + "CTCACAGG": 7627, + "TCTACTGA": 7628, + "CAAAACCA": 7629, + "GGAGACAA": 7630, + "TATGCTGA": 7631, + "CTAAGCAA": 7632, + "TGGGGTTTCA": 7633, + "CACCTCTG": 7634, + "GGTGTGTG": 7635, + "GCTATAAA": 7636, + "TATAATTTA": 7637, + "TGACACTG": 7638, + "GTCACGG": 7639, + "TTATTGTT": 7640, + "TCAAGGTT": 7641, + "GCTGGGGA": 7642, + "GGCACCA": 7643, + "CTGAAATG": 7644, + "TCCTTCTA": 7645, + "GACCTGAA": 7646, + "CAATACC": 7647, + "CATTTTAAAA": 7648, + "CTTGAATT": 7649, + "GGAAGCTT": 7650, + "GGCGCTG": 7651, + "GTATCAAA": 7652, + "CACATATT": 7653, + "TGTTGACA": 7654, + 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8191, + "GAACACAA": 8192, + "GGTTGTA": 8193, + "GCCACGTG": 8194, + "TATCCATG": 8195, + "GTGAGGTG": 8196, + "TCCCCACC": 8197, + "GCAGTCCAA": 8198, + "CTCCATTTT": 8199, + "TCTAGCTT": 8200, + "TCTTCCATT": 8201, + "GATTTAAAA": 8202, + "CTAACAGA": 8203, + "GGTTGTG": 8204, + "GTCTCAGG": 8205, + "TGAAAAATG": 8206, + "CTTCCTAA": 8207, + "TGATAAAAA": 8208, + "TCCCGTT": 8209, + "CTGCCTCC": 8210, + "CTCATCTG": 8211, + "TGCACAA": 8212, + "CCCCCAGA": 8213, + "CTGTTGCTG": 8214, + "TATGATTA": 8215, + "GTCAAAAAA": 8216, + "AATG": 8217, + "CCTGCATT": 8218, + "CTATGAAAA": 8219, + "GCTTAATG": 8220, + "TCCCGAA": 8221, + "TGTTTATTTT": 8222, + "CAGGGACA": 8223, + "CTGATATT": 8224, + "GTCCCACA": 8225, + "CCAGCTCA": 8226, + "CAAAGGGA": 8227, + "TAAATAAATAAA": 8228, + "GTGGAGCA": 8229, + "GTCCACAA": 8230, + "GTCTGAGG": 8231, + "TCTTTAGA": 8232, + "TGTACTA": 8233, + "CAAATTAAAA": 8234, + "TCTTGCAA": 8235, + "CGATAAA": 8236, + "GTACAGTA": 8237, + "TAAAATAGA": 8238, + "TCGCTCTG": 8239, + "TCTGCTCC": 8240, + 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8290, + "GCTGGGTGTG": 8291, + "TGAGCTCA": 8292, + "GTAGCTCA": 8293, + "TATATGGA": 8294, + "GGCAGGTGGA": 8295, + "CTATCCA": 8296, + "CAAAATGAA": 8297, + "GCTACTGA": 8298, + "TGACAGAGTGA": 8299, + "GATGTTTG": 8300, + "TCACCATT": 8301, + "CTTAGTAA": 8302, + "TCTTGGGG": 8303, + "TGTTTGTTTG": 8304, + "CAACAGTG": 8305, + "GTGTCAAA": 8306, + "TCTTTCTCTT": 8307, + "CTCACTAA": 8308, + "GGTTTTTG": 8309, + "TTTTGCTT": 8310, + "TAAACAGAA": 8311, + "TGAGAAAAA": 8312, + "GAACCCGGGAGG": 8313, + "GACAGATT": 8314, + "GCTGGTCA": 8315, + "CTGAGGGG": 8316, + "GAAGAGGAA": 8317, + "GGGGGGGGGGGG": 8318, + "CCCGTCC": 8319, + "TGTTGAGG": 8320, + "CTAAGTCA": 8321, + "CTTGAGTT": 8322, + "GCCCCCTG": 8323, + "CCAGTGTT": 8324, + "TATATATAAA": 8325, + "TATGCCAA": 8326, + "CCTGTTTG": 8327, + "TAATCTAA": 8328, + "TGACCCTG": 8329, + "TATAGTCA": 8330, + "GGTTTCTG": 8331, + "CTCTAGGA": 8332, + "GAGACATG": 8333, + "GCAACAGA": 8334, + "TAAGATGA": 8335, + "TCAGTCAA": 8336, + "GTGTCCAA": 8337, + "GTTCAGCA": 8338, + 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"TTTTTTGTA": 8389, + "GCCCTCTT": 8390, + "GAGGCCTG": 8391, + "TAATGATG": 8392, + "TAAGGTCA": 8393, + "CACAGGGA": 8394, + "GCTTCTCC": 8395, + "TAATATTTTA": 8396, + "GCACCAAA": 8397, + "GAGATTAAA": 8398, + "GAGTCATT": 8399, + "TGATAGTT": 8400, + "TATGTAGA": 8401, + "TCCAGGAAA": 8402, + "CAACAATT": 8403, + "GGTGGGAA": 8404, + "TAAAAATTTT": 8405, + "GACGCTT": 8406, + "CTGTCATT": 8407, + "CTATACC": 8408, + "GCAACATG": 8409, + "GTCTAGTT": 8410, + "TATATAGG": 8411, + "TCTGGGGA": 8412, + "CTCACTC": 8413, + "GAAAAGGA": 8414, + "GGAAAGAA": 8415, + "TAGCCAGG": 8416, + "CTGCCAAA": 8417, + "TAATTTAAAA": 8418, + "CCACGC": 8419, + "GCAACGG": 8420, + "GCATTAAAA": 8421, + "GAGTGAAAA": 8422, + "TCAGTGTG": 8423, + "CTGAGGTA": 8424, + "TACATAGG": 8425, + "TGACGC": 8426, + "CCATCTGA": 8427, + "TATTTACAA": 8428, + "GTAACACA": 8429, + "GTCATGTG": 8430, + "TAAGTCAA": 8431, + "GCAGCGG": 8432, + "CATATAAAA": 8433, + "TAAGAGC": 8434, + "GTCTGATG": 8435, + "CTAAATTTT": 8436, + "GTGGCTCACACCTG": 8437, + "GTCCCAAA": 8438, + "GCAAAGAA": 8439, + "GCAAACTA": 8440, + "TGAAGGCA": 8441, + "TTCACTA": 8442, + "TGAGTCTT": 8443, + "TGAGTGTG": 8444, + "TTTGGAAA": 8445, + "TTTACAAA": 8446, + "GGAGGGAGA": 8447, + "GTTGTCTT": 8448, + "GGACCCTG": 8449, + "GTAAGTTA": 8450, + "CGGGGAA": 8451, + "TCCCGCC": 8452, + "GTGTCATT": 8453, + "GTCCCTCA": 8454, + "GCGAATG": 8455, + "TCCTCTAA": 8456, + "CTAAAAATG": 8457, + "GCTCTATA": 8458, + "CCTAACCTAA": 8459, + "CAAGTGAA": 8460, + "GTACAGTG": 8461, + "GAGATATT": 8462, + "GTTGGCTG": 8463, + "CCTTGCTG": 8464, + "CCTTTTTG": 8465, + "GAGTAATT": 8466, + "TCAGCCTT": 8467, + "TTAAAAATT": 8468, + "CGAATTA": 8469, + "CATTGGTT": 8470, + "TAGACATG": 8471, + "GTAAATTTT": 8472, + "TATGGTTA": 8473, + "CCTACCTA": 8474, + "CACCCCAA": 8475, + "TCTATTAAA": 8476, + "TCAGTTTG": 8477, + "TAAGGGAA": 8478, + "GGATGAAAA": 8479, + "GATGGGTG": 8480, + "TCAGACAA": 8481, + "TGTTGGGG": 8482, + "CACCCTA": 8483, + "TCTCTTAAA": 8484, + "TAGCATTA": 8485, + "GGACTAAA": 8486, + "GCTAATTA": 8487, + "TCAACTTA": 8488, + "TATACTTTT": 8489, + "CTGGACC": 8490, + "GTTGGCC": 8491, + "GTTCTATT": 8492, + "TGAATTTTA": 8493, + "TGCAGCC": 8494, + "TAATACTA": 8495, + "GTATTCAGA": 8496, + "CTGTGGTGTT": 8497, + "GTCCATTTT": 8498, + "GTAAGTGA": 8499, + "GCACAGCC": 8500, + "GGACGCA": 8501, + "CACAGCCA": 8502, + "GCAACTAA": 8503, + "CCAGCTCC": 8504, + "GGGATTACAGG": 8505, + "TGATATGA": 8506, + "CTGCCTCA": 8507, + "CTCTATGA": 8508, + "TTATTGAA": 8509, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 8510, + "TCTGGGCA": 8511, + "TCAGCCTCC": 8512, + "TCTTCCAAA": 8513, + "CAAAGCCA": 8514, + "GCTGCCAA": 8515, + "TCTCTTTTA": 8516, + "GTTTACTG": 8517, + "CCAGTCAA": 8518, + "CTGACCTG": 8519, + "GCTACATG": 8520, + "GGACCTA": 8521, + "TGTGTAAA": 8522, + "TATTTCCTT": 8523, + "CTCATCAA": 8524, + "TTAAGTA": 8525, + "TGACGTA": 8526, + "CACACACACACACACACACACACACACACACACACACACA": 8527, + "GTGCAGTT": 8528, + "GTTATCTT": 8529, + "CTTTACTA": 8530, + "CAAAACTG": 8531, + "CATAGATG": 8532, + 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"GTCTACTG": 8582, + "GTCATCC": 8583, + "CCTTAATG": 8584, + "CACTCTA": 8585, + "GAATATCA": 8586, + "CTGAGTTTT": 8587, + "TCTGACTCTTTG": 8588, + "TATTTAATA": 8589, + "GGAATATG": 8590, + "GCTTCATG": 8591, + "GTCCCCTG": 8592, + "GATAGAAA": 8593, + "CTTTTTTTTTTTTTT": 8594, + "GAAGTGAA": 8595, + "CTCGGGA": 8596, + "TATAGCTG": 8597, + "GTAACTTA": 8598, + "TTATTTCAA": 8599, + "GCCCGCA": 8600, + "GAGGGTGA": 8601, + "GAAAACCA": 8602, + "TGATACTG": 8603, + "TTTTACATT": 8604, + "GAAGAGTG": 8605, + "GAGGCTT": 8606, + "CTGGAGTT": 8607, + "GTCATCTG": 8608, + "CAAGGGAA": 8609, + "GTTCTCTG": 8610, + "GTCCTCTT": 8611, + "TCTTTTTTTTTT": 8612, + "TTAGTTTT": 8613, + "TATTGACTT": 8614, + "GTCCTGCC": 8615, + "GTTTAATA": 8616, + "GCTGCTAA": 8617, + "GGAGGGATT": 8618, + "TCACATAA": 8619, + "GCCGGGA": 8620, + "CTATCAAA": 8621, + "GATGTCC": 8622, + "TTTGGG": 8623, + "CCCCTCAA": 8624, + "TAACCTCA": 8625, + "CAAGAAAAA": 8626, + "GTCGAGA": 8627, + "GAAACCTT": 8628, + "TTTTATGTT": 8629, + "TCCAGCCA": 8630, + "CGACCTT": 8631, + "TCAGAGC": 8632, + "GTCTCGG": 8633, + "CCCCTGAA": 8634, + "GCAATTCA": 8635, + "TTATTTGAA": 8636, + "CACGTAA": 8637, + "TAAATACAA": 8638, + "TAATCCTAGCA": 8639, + "GTAAGAGA": 8640, + "TATGTAGG": 8641, + "GTCCCAGC": 8642, + "TATAATATA": 8643, + "CAAAATTAAA": 8644, + "CTAATACA": 8645, + "GGTAGTA": 8646, + "TGACAGGA": 8647, + "CTCTAGCA": 8648, + "GATTGAGA": 8649, + "CACGATT": 8650, + "TGATAATA": 8651, + "GTGGCAAA": 8652, + "CTTACCTG": 8653, + "TGTGTGGA": 8654, + "TCTTTATTA": 8655, + "GGAATGTT": 8656, + "GTCAGG": 8657, + "GAAAATTAAA": 8658, + "GAATAAAAA": 8659, + "GTGTAATT": 8660, + "GGAGTGGG": 8661, + "TAAAGCAAA": 8662, + "GGAATTCA": 8663, + "CTTTTTCC": 8664, + "GAAGAGTT": 8665, + "CTTAAATG": 8666, + "GCACACAA": 8667, + "GATGGATT": 8668, + "GGGACTCTCAA": 8669, + "TCTCCCTCC": 8670, + "CTCTTAAAA": 8671, + "CAGCAATG": 8672, + "GGAGTGTT": 8673, + "CCATACC": 8674, + "CACGCTT": 8675, + "GAAGACTG": 8676, + "CTACAGCA": 8677, + "GACCTCC": 8678, + "GCGCTTA": 8679, + "GTGGTTAA": 8680, + "GATGTAA": 8681, + "GATGGCAA": 8682, + "TCTCCTTTA": 8683, + "GAAGCCAA": 8684, + "GAAAGGTG": 8685, + "GATTCCAA": 8686, + "AAAATTAAAA": 8687, + "TATAGAAAA": 8688, + "GTGACGG": 8689, + "TATGTAAAA": 8690, + "TCAGGAGATCGAGACCATCCTGGCTAA": 8691, + "CTCCTCAA": 8692, + "CCACAGCC": 8693, + "TTTTAGTA": 8694, + "GGAGGCGG": 8695, + "TCAGGCTT": 8696, + "TTACGG": 8697, + "CTACAGTT": 8698, + "CATAGAGA": 8699, + "CTCTAAAAA": 8700, + "GTCAGGTG": 8701, + "TAGGTTTA": 8702, + "CCTACCTG": 8703, + "GATAATTTT": 8704, + "GCCCAGTT": 8705, + "TGTGTCAA": 8706, + "TATTCAGG": 8707, + "CATTTCATT": 8708, + "CTGTTCTT": 8709, + "TCACCATG": 8710, + "CAACATGA": 8711, + "CTGCACTG": 8712, + "GTTAAATG": 8713, + "TATGCAGA": 8714, + "TACACCTT": 8715, + "GTTTATGA": 8716, + "GTTGGCTT": 8717, + "CTATTTTTT": 8718, + "CACACACTG": 8719, + "TCAGAATG": 8720, + "TCTTTGAAA": 8721, + "GTGAGCAA": 8722, + "GTAATCAA": 8723, + "GAAGCCTG": 8724, + "TCCTGAGA": 8725, + "TCACCCAA": 8726, + "GAAGGCTG": 8727, + "GAATACTA": 8728, + "TGAACTGA": 8729, + "GGCCAGGCTGGTCTCGAA": 8730, + "TTATATTA": 8731, + "CTCTCATG": 8732, + "TCTGGGGG": 8733, + "TCCACTAA": 8734, + "TCATCCTT": 8735, + "CCACAGGA": 8736, + "GCCCGGA": 8737, + "GGAAGAGGA": 8738, + "GTCAGGGA": 8739, + "CTGTGCCTG": 8740, + "CTATTGTGAA": 8741, + "TCTAGTTA": 8742, + "TGTATGTT": 8743, + "TGCAGCAA": 8744, + "GGCACAAA": 8745, + "CACGGTG": 8746, + "GTGGTGAA": 8747, + "GTGTGTGTGTG": 8748, + "GTTTCTCTT": 8749, + "TATTTATGA": 8750, + "TAAAACGA": 8751, + "CAGTGATG": 8752, + "TAATAAATT": 8753, + "GCAGACTT": 8754, + "GCCCCCCA": 8755, + "GAAATCAAA": 8756, + "CTAGCCA": 8757, + "CCTCTTTG": 8758, + "GCCCCAGG": 8759, + "CTTGGTCA": 8760, + "TATTGTATT": 8761, + "TCTACTTTT": 8762, + "CTCCAGGGGATCTT": 8763, + "CTGCACTCCAGCCTGGGCGACAGA": 8764, + "CACTCATT": 8765, + "GTGGCTTA": 8766, + "GCCTAGAA": 8767, + "TATACCAA": 8768, + "TGAACTTA": 8769, + "CAACCTA": 8770, + "CCAGGTCA": 8771, + "TAAAATATTA": 8772, + "GCTTGCTG": 8773, + "TCTACAAAA": 8774, + "TCTCCTGGG": 8775, + "TGATCTGA": 8776, + "GTCGTCA": 8777, + "CTGACATG": 8778, + "TAATCCTT": 8779, + "GTTCGC": 8780, + "CTAGAGAA": 8781, + "TATATAAAAA": 8782, + "CCTTGAGA": 8783, + "GCCCCCAA": 8784, + "CGGCATT": 8785, + "CTCCAGTACTCTT": 8786, + "CCGTGTT": 8787, + "CGGCGCA": 8788, + "CGTTAAAA": 8789, + "TCTAGATG": 8790, + "CATCTATG": 8791, + "TCGGATG": 8792, + "TCGTCAAA": 8793, + "CTTGTGAA": 8794, + "TAAATACC": 8795, + "CAGAGTC": 8796, + "TCAAAAGACTACACACTTATTTCAAAAAAA": 8797, + "CTTCATAA": 8798, + "TTTGGTTTT": 8799, + "TCTAGTTTT": 8800, + "GACACCAA": 8801, + "GTTCATTTT": 8802, + "TCTTTACA": 8803, + "GTCTGAAAA": 8804, + "CTTCGTG": 8805, + "GGATCTC": 8806, + "GTAGCTGA": 8807, + "CTCCTCCTT": 8808, + "GATGGAGGA": 8809, + "CTGTTGCTT": 8810, + "TCGTAAAA": 8811, + "GATATATG": 8812, + "GGATTAAAA": 8813, + "GTCTGTTA": 8814, + "GACGATT": 8815, + "GTCAGGTT": 8816, + "TACACGA": 8817, + "GCCTCGG": 8818, + "CTTTTGAAA": 8819, + "TAAGTCTT": 8820, + "TATAGGAA": 8821, + "TATACAAAA": 8822, + "TGAGTCAA": 8823, + "CTCGTAA": 8824, + "GTCATATT": 8825, + "TTCAGCA": 8826, + "CTGAAGCA": 8827, + "GCAAACACA": 8828, + "CTCCCTCCCTCCCTCC": 8829, + "GGCACCTG": 8830, + "GTTCCAGGA": 8831, + "TCCCTTTG": 8832, + "CTTCAGTA": 8833, + "CCAGGATG": 8834, + "TCACGC": 8835, + "TCACAGTA": 8836, + "GCCTGAGG": 8837, + "TTTTTTGTTTT": 8838, + "AAAAAAAAATT": 8839, + "GCCTCATT": 8840, + "TTCACAA": 8841, + "CTTGCTCA": 8842, + "GGGGTGGGGG": 8843, + "GAGGGAGGGAGGGAGG": 8844, + "TCCTATTA": 8845, + "TCAGACTG": 8846, + "CTTTATGA": 8847, + "TAAATCCA": 8848, + "CACACTTA": 8849, + "GCTTCTAA": 8850, + "GAAAATCAA": 8851, + "TTTTTGAAAA": 8852, + "GACTCCAA": 8853, + "TAAATCATT": 8854, + "TTTTTTTTTTTTTTTTTTTTTT": 8855, + "TTTTCTGA": 8856, + "TCTCAGAAA": 8857, + "GTGAAAGTGAA": 8858, + "TCAGAGTG": 8859, + "CTGTAATT": 8860, + "GGATCATT": 8861, + "TCTTTCAAA": 8862, + "TAGGGAGA": 8863, + "CAACGAA": 8864, + "CCTTGGGG": 8865, + "TTAACTA": 8866, + "TACGTAA": 8867, + "TCATGATT": 8868, + "CACAGAGCA": 8869, + "TCTTGTC": 8870, + "GATACATG": 8871, + "GCAACAGG": 8872, + "CGAGTGA": 8873, + "TCCATCACCAACTCC": 8874, + "TCCGCCTG": 8875, + "CTGTCCTT": 8876, + "TCTTCCTCC": 8877, + "GCTTGTTTT": 8878, + "CGGCTGA": 8879, + "CGACGAA": 8880, + "TCATTTTAA": 8881, + "GATTGGAA": 8882, + "TCCCATAA": 8883, + "TATACCTT": 8884, + "GCGCCCC": 8885, + "CCATATAA": 8886, + "CCTGCAAA": 8887, + "TGTGCTGA": 8888, + "GTACTCC": 8889, + "TGTTTAGA": 8890, + "TCTGTCTC": 8891, + "GTAACTCA": 8892, + "CACGGAA": 8893, + "TGAGAATG": 8894, + "GTTCATTA": 8895, + "GAAATTAAAA": 8896, + "GCGCTAA": 8897, + "TGTGGTCA": 8898, + "CAGATGTA": 8899, + "GTATTTATT": 8900, + "TCACCCAGG": 8901, + "GTAAACAA": 8902, + "TATTTTAATT": 8903, + "GATCTCAA": 8904, + "GCAGTTTG": 8905, + "GTATCTCA": 8906, + "CACATCAA": 8907, + "GATTTGGA": 8908, + "TACTTAAAA": 8909, + "CCATGAAAA": 8910, + "GAACGAA": 8911, + "GAAGATCA": 8912, + "TAAACAATT": 8913, + "TGAAGCCA": 8914, + "GGAAGTTA": 8915, + "GAAAAGCAA": 8916, + "TTATAATT": 8917, + "GTGTATTA": 8918, + "GGATTTTCCAGGCAA": 8919, + "TCGTGGG": 8920, + "GATGAATG": 8921, + "GCATCATT": 8922, + "GCACACCA": 8923, + "CTCTCAGG": 8924, + "TGCACCTG": 8925, + "GTTTGAGG": 8926, + "TGAATACA": 8927, + "TGTTTATTA": 8928, + "GGAGATCCAA": 8929, + "GGGCGCGGTG": 8930, + "CCGTGAA": 8931, + "GTTCACAA": 8932, + "GTTACCTT": 8933, + "GGTCTCTT": 8934, + "CAGGGTTTCA": 8935, + "TCTGTTTTA": 8936, + "GCTTGAGG": 8937, + "TATGTGTT": 8938, + "CTCGGTT": 8939, + "GTAAGCAA": 8940, + "GAAAATGTA": 8941, + "TCTTTTAAAA": 8942, + "TCATCAAACCCATCCTAATT": 8943, + "GGTGCCTG": 8944, + "CAAAGGGG": 8945, + "CTGAATGTT": 8946, + "TGAGCATT": 8947, + "CTGGAGTGCAGTG": 8948, + "CTTATATG": 8949, + "GGAAAGAAA": 8950, + "TCCTTTCCTT": 8951, + "GTGTTTTTT": 8952, + "GTGAGTAAA": 8953, + "CCTTCTTTT": 8954, + "CATGGGTT": 8955, + "GGGCTCTG": 8956, + "TACGAAA": 8957, + "CAATCTC": 8958, + "GAGACATT": 8959, + "TCTTATTTA": 8960, + "CAGGGAGGCCTG": 8961, + "TGACGGA": 8962, + "CACAGTTA": 8963, + "CGGGATG": 8964, + "CGACCAA": 8965, + "CCCAGGAGG": 8966, + "GGAAACTGA": 8967, + "GAACATCA": 8968, + "CTTGTTAA": 8969, + "CTTTAATG": 8970, + "GAAAAGCA": 8971, + "TCTCACTG": 8972, + "TAATGAATT": 8973, + "CTTCGC": 8974, + "TCCAATTA": 8975, + "CTCCTGCCTCAGCCTCCCAAGTAGCTG": 8976, + "TCTTTAAAAA": 8977, + "CTTACTAA": 8978, + "CGATATA": 8979, + "CCTCCCAAA": 8980, + "CCTCGGCCTCCCAAA": 8981, + "GAGCAATT": 8982, + "TTTTGGGG": 8983, + "CTTTTTAAAA": 8984, + "CCAGAGTT": 8985, + "GTCTCATG": 8986, + "CAGATCAA": 8987, + "GTAATAAAA": 8988, + "CAAAACACA": 8989, + "TTATTCTG": 8990, + "GGCCAGGCTGGTCTTGAA": 8991, + "GACGTTA": 8992, + "CTCGGCC": 8993, + "GAAACATA": 8994, + "GTTACTGA": 8995, + "CTTAGCTT": 8996, + "GTGTACC": 8997, + "CTCATGTT": 8998, + "CTCCCCTCC": 8999, + "CTTTGCCTT": 9000, + "CAAGATGA": 9001, + "TTTTGGTT": 9002, + "TATTĊ": 9003, + "CAAATCAAA": 9004, + "CTCATGGG": 9005, + "GTCATCAA": 9006, + "CTCAGGAGGCTGA": 9007, + "CATCTATA": 9008, + "GTTCTGTG": 9009, + "GTTGAGTT": 9010, + "GTGATAAA": 9011, + "CGTGCTG": 9012, + "CAAATGAAAA": 9013, + "GGGTCGCAAA": 9014, + "TAAGGCTG": 9015, + "CTCTCTGTT": 9016, + "GCTACCTG": 9017, + "CATCAATT": 9018, + "CCAGACTG": 9019, + "TGTGGGGG": 9020, + "GAACTCAA": 9021, + "GAACACTT": 9022, + "TCCCCAGG": 9023, + "GTTGAGGA": 9024, + "CAGGGGTG": 9025, + "GGGACGG": 9026, + "GACTTATT": 9027, + "GATGAAAAA": 9028, + "TCTTGGCTCA": 9029, + "CTGTTGTG": 9030, + "CAGCAATT": 9031, + "CAAGAGGA": 9032, + "CCACAGC": 9033, + "GAAATAAAAA": 9034, + "GCCTGGAAAATCCCATG": 9035, + "TCTTTGCC": 9036, + "GCTTTATA": 9037, + "TTCACTG": 9038, + "TAAAAAAAAAAAA": 9039, + "TAAGAATG": 9040, + "TTTTTCTA": 9041, + "GGACATCA": 9042, + "TAACAAAAAA": 9043, + "CAAAGTATT": 9044, + "CTCAGTCC": 9045, + "GCAAGG": 9046, + "TAGAGAAAA": 9047, + "CCTGTGAA": 9048, + "CAGGCCAA": 9049, + "GTATGAAAA": 9050, + "TAATGAGA": 9051, + "GAAGAATG": 9052, + "GTATCTTA": 9053, + "GGTAGAAA": 9054, + "TAGAATTA": 9055, + "TGACTCTT": 9056, + "TCATTATG": 9057, + "CCACGCA": 9058, + "TATTCCCA": 9059, + "TATTGACA": 9060, + "GCTTGATT": 9061, + "CGACACA": 9062, + "GATACATT": 9063, + "TCCTGTTA": 9064, + "TCATATGA": 9065, + "GCATAATT": 9066, + "GCCCTCTG": 9067, + "TGTGGACA": 9068, + "CATGGTAA": 9069, + "CAAAATGTT": 9070, + "CTGGAAAAA": 9071, + "TATGTCAAA": 9072, + "TCTCTAAAA": 9073, + "GTAAATTAA": 9074, + "CCTTCTAA": 9075, + "CTTAGTGA": 9076, + "GTCTCTGTG": 9077, + "TCAAGGCTG": 9078, + "TCTGCAAGTGGA": 9079, + "CAGATAGA": 9080, + "GTTTTCATT": 9081, + "GTGCACTG": 9082, + "CCAGACTT": 9083, + "GTTACATG": 9084, + "GCTACATT": 9085, + "TGACTGTG": 9086, + "GAAGAGC": 9087, + "CAGGTGTT": 9088, + "GATTTCTA": 9089, + "CGACTC": 9090, + "GGATACC": 9091, + "CCTCCTGGG": 9092, + "TCGGCGG": 9093, + "GCCAGGTGTG": 9094, + "GTGCAGGA": 9095, + "TCTGTACA": 9096, + "TCTGGTCC": 9097, + "GGAACTCA": 9098, + "CCTTGGCCTCCCAAA": 9099, + "CCCACTTTT": 9100, + "GCTCACTGCAA": 9101, + "CAAGGCAA": 9102, + "GGAGGATG": 9103, + "TAAACTC": 9104, + "TCTGGTCA": 9105, + "TACTATTA": 9106, + "GTTCACGCCATT": 9107, + "CCTGGCAA": 9108, + "GCTTCAGA": 9109, + "CTTACTGA": 9110, + "GGAATATA": 9111, + "CCAGAGCC": 9112, + "TAAATATCA": 9113, + "TGTGGCAA": 9114, + "TAAAACACA": 9115, + "TACTCAAA": 9116, + "AAAAAAAAAAAAAAAAAAAA": 9117, + "GATCCAGG": 9118, + "TAATTTTTTTT": 9119, + "CTACTGTT": 9120, + "CAAAGAGAA": 9121, + "CAGGGCAA": 9122, + "CCAAGGTT": 9123, + "CTTATCAA": 9124, + "TATCTACA": 9125, + "CTGGAGCA": 9126, + "TAGGGTA": 9127, + "CTGAAGTA": 9128, + "CCCTTTTA": 9129, + "GTGCCCTG": 9130, + "CGAGTCA": 9131, + "CCGTAAA": 9132, + "GTGAGGGA": 9133, + "GTTAGTAA": 9134, + "CGTGAAAA": 9135, + "TACTGAAAA": 9136, + "GTGTGAAAA": 9137, + "CGTĊ": 9138, + "GAAAACACA": 9139, + "CCCCCATT": 9140, + "GGACACTT": 9141, + "CTAAGTAA": 9142, + "TCGCATG": 9143, + "TGATAATG": 9144, + "TGACAGTA": 9145, + "TATTCGG": 9146, + "GTGATGTA": 9147, + "GCGATTTT": 9148, + "CAACTCAA": 9149, + "CCACACTT": 9150, + "TATGGCC": 9151, + "GACGTAA": 9152, + "CAGAGCCAGG": 9153, + "TCATCTTA": 9154, + "CTTGCTGA": 9155, + "GCTTCACA": 9156, + "TCCCAGTA": 9157, + "GTCTATGA": 9158, + "TCGAAATT": 9159, + "TTTTCATG": 9160, + "GAACAGTG": 9161, + "TCTGTAGA": 9162, + "GCTTCCCTG": 9163, + "GTGAGGTT": 9164, + "CCTTCAGA": 9165, + "TATATACC": 9166, + "CAGGTGAA": 9167, + "GATATGAA": 9168, + "GAGTACTGGAGTG": 9169, + "CCCAAATT": 9170, + "CAATAGCA": 9171, + "GTTCCTGA": 9172, + "GCCTATCA": 9173, + "GTTAGGAA": 9174, + "CTTAGAGA": 9175, + "GTGTAGTG": 9176, + "CCCCTCCA": 9177, + "CTCTGTCTC": 9178, + "GTGACTAA": 9179, + "CCTTCCTCA": 9180, + "CTCCCCCA": 9181, + "CAATACTT": 9182, + "GTTGACTT": 9183, + "GTAGAGCA": 9184, + "GCCTACTT": 9185, + "CTCCTCTA": 9186, + "TTTTTTTATT": 9187, + "CACGTTA": 9188, + "TATAGACA": 9189, + "CCAAGAGG": 9190, + "CCCCCGCC": 9191, + "TCACTCAA": 9192, + "GGAGTGAA": 9193, + "TAAAGGGA": 9194, + "TCTGGTTTT": 9195, + "GATGTGTG": 9196, + "TTATGGG": 9197, + "CAAATTTTA": 9198, + "CGAGTTA": 9199, + "CAGCTCAA": 9200, + "TCCATCCATCCATCCA": 9201, + "GGGTAAAA": 9202, + "TGATACTT": 9203, + "TTATGCA": 9204, + "GCAGAAAAA": 9205, + "TAAAGGTA": 9206, + "CTGATCTT": 9207, + "GCAAGGCA": 9208, + "GCTATGTG": 9209, + "TCTTTTTCTT": 9210, + "CTAAATTAA": 9211, + "TTTTGGTA": 9212, + "GTTGGCAA": 9213, + "GATACACA": 9214, + "CTCCTGACCTCA": 9215, + "CTGAGGCAGGAGAATTGCTT": 9216, + "GTTAATTAA": 9217, + "TAGCTGTT": 9218, + "TCCTATCA": 9219, + "CCAGAATT": 9220, + "CTCTATCC": 9221, + "CAGCTATA": 9222, + "GTCGGTT": 9223, + "GCTTCTCTT": 9224, + "GAAAATTTA": 9225, + "CACCTGTT": 9226, + "GCTGTAGG": 9227, + "CCAAGCCA": 9228, + "GCTATATT": 9229, + "GAAGACTT": 9230, + "TAAAATACA": 9231, + "CCTTTATG": 9232, + "GAGGAGAGG": 9233, + "GTGGGAAAA": 9234, + "TCGCTTTT": 9235, + "CCAAGGTG": 9236, + "CTCCCATG": 9237, + "CCAGCCTGGCCAACATGGTGAAA": 9238, + "CAATGATT": 9239, + "CAATACCA": 9240, + "GTCATGGG": 9241, + "GATTCC": 9242, + "GAGGAATG": 9243, + "TGACAATG": 9244, + "CTATAATT": 9245, + "CAGGTAA": 9246, + "CTCCTGTA": 9247, + "GGAGGATCACTT": 9248, + "CATTATTTA": 9249, + "CTCATGAA": 9250, + "GATTCTTA": 9251, + "GCCAGTTTT": 9252, + "CTACAGTA": 9253, + "TAATATAATA": 9254, + "GGACGGA": 9255, + "GTATCTC": 9256, + "TCAAGTCAA": 9257, + "GAGGTGAA": 9258, + "GAAAATATG": 9259, + "GAGGAGAGA": 9260, + "GATCTATT": 9261, + "CTGTCTCA": 9262, + "CTGGAATT": 9263, + "GCACCTCA": 9264, + "GCTTGCC": 9265, + "GCTTTCTA": 9266, + "TCTCCTCC": 9267, + "CTTCTTAAA": 9268, + "TATTTATTTTA": 9269, + "GTGGAAGG": 9270, + "GGAAGGGAA": 9271, + "GGGGCCTG": 9272, + "CTCTGTTTT": 9273, + "CGTGAGCCA": 9274, + "TATTGGTG": 9275, + "TCTGCAAAA": 9276, + "CCTATGTT": 9277, + "CACAGGCA": 9278, + "TCGCAGG": 9279, + "GATCAATT": 9280, + "GACTCTGA": 9281, + "TCATACTT": 9282, + "GTTCCCAA": 9283, + "CCTGGAAAA": 9284, + "CTTTTTTCA": 9285, + "GGTTTCTT": 9286, + "GCTCCAAAA": 9287, + "TAAAATATAA": 9288, + "GCTGGCAA": 9289, + "CGGGAAA": 9290, + "CCCGCTG": 9291, + "TATAGCC": 9292, + "CCAATTGTG": 9293, + "GAGGTAGG": 9294, + "TTATGAAA": 9295, + "CCATCCTT": 9296, + "CGATCAA": 9297, + "TCAAATCAAA": 9298, + "CGGCTCC": 9299, + "GTAGTAAA": 9300, + "CTGAGGCC": 9301, + "TCCAATTTG": 9302, + "AAAAAAAAAAAAAAAAAAA": 9303, + "TAACCATT": 9304, + "CAAATAAAAA": 9305, + "GATAATAAA": 9306, + "CATATATATA": 9307, + "TGTTGAGA": 9308, + "CTTATCTT": 9309, + "CTTCATGA": 9310, + "GTGGCCTT": 9311, + "GTATTGGA": 9312, + "GCTTATTTT": 9313, + "GAACGTT": 9314, + "GCCCCCTT": 9315, + "CGGTGGA": 9316, + "GATTCTAA": 9317, + "CCTACATG": 9318, + "TCCCGCA": 9319, + "CTCCATCTC": 9320, + "CTTACAGA": 9321, + "CGCCAGA": 9322, + "CTGCACTT": 9323, + "GTTAGAGG": 9324, + "CGGCTCA": 9325, + "TTATTCAA": 9326, + "TCCTGTTTT": 9327, + "CCAGCCTGGCCAA": 9328, + "TCTGTCCTT": 9329, + "CCTGGGCA": 9330, + "GAACAGTT": 9331, + "GCCATAA": 9332, + "CTACAGTG": 9333, + "GTTTTTTTTTTTT": 9334, + "TCTAGAAAA": 9335, + "CTGTGGGA": 9336, + "TAAATGTTA": 9337, + "GACTCCTT": 9338, + "GCCTCAGG": 9339, + "TCCATA": 9340, + "GGTCAGGAA": 9341, + "CATACTCA": 9342, + "TCACGTA": 9343, + "GTGTAGTT": 9344, + "TTTTTTTCC": 9345, + "TCTCCTGCATT": 9346, + "CCTGTGTG": 9347, + "TATGGGTT": 9348, + "GATGCCTT": 9349, + "GGGAGGTG": 9350, + "CTACTAAA": 9351, + "GTTTTCTTTT": 9352, + "GCCAGTTA": 9353, + "TAGAGTCA": 9354, + "GACACACACA": 9355, + "TTAGAGG": 9356, + "CAACAGTT": 9357, + "GCATCTGA": 9358, + "CCTAGAGA": 9359, + "GTTGGGGA": 9360, + "GAAAGCAAA": 9361, + "CATTCTC": 9362, + "TGAACCAA": 9363, + "GGAGAGGAA": 9364, + "GTGCACCA": 9365, + "GTTGCCTT": 9366, + "TAAATAGAA": 9367, + "GCTTCCCA": 9368, + "CATACTTA": 9369, + "TCACGCA": 9370, + "TAAACCTG": 9371, + "TGAAGGTA": 9372, + "GATTGTAA": 9373, + "GTTTTCCA": 9374, + "CTGGGGAA": 9375, + "TAAAAGTTA": 9376, + "TATCCAGA": 9377, + "TCAGAAAAA": 9378, + "CAAGAGTT": 9379, + "CGGCACA": 9380, + "CTAATGTT": 9381, + "GTCCTGTA": 9382, + "TGAGTTTG": 9383, + "CAACCTCA": 9384, + "GTGGACTG": 9385, + "TCTTTATTTT": 9386, + "CTGCCCTT": 9387, + "GATATGTT": 9388, + "GTGAATTTT": 9389, + "GATCCCCTGGAGAA": 9390, + "CCACATCC": 9391, + "GGAGTAAA": 9392, + "GAGGGG": 9393, + "CGAAAAAAA": 9394, + "TCTGCTC": 9395, + "GTAATA": 9396, + "CAAAATCAA": 9397, + "GACATAA": 9398, + "GTGTTAAAA": 9399, + "CCAAGGGG": 9400, + "CTTAGG": 9401, + "GTTTTGATT": 9402, + "CCAATTAAA": 9403, + "TCGCAGCA": 9404, + "TCATAAAAA": 9405, + "CCACGGA": 9406, + "GAGATAGG": 9407, + "CATTTATAA": 9408, + "GATGCAGA": 9409, + "CAAAAAAATT": 9410, + "TGGAGGAA": 9411, + "GCGTGGTG": 9412, + "TCATCTCC": 9413, + "GAATAATA": 9414, + "GCACATCA": 9415, + "GTAAGTCA": 9416, + "GAGGGCTG": 9417, + "GTCCTGCA": 9418, + "TGATTGTA": 9419, + "CCAGCCTGGTCTA": 9420, + "CTTGGATT": 9421, + "GTCGTGA": 9422, + "TATATACAA": 9423, + "CAAATAATT": 9424, + "TAGACTAA": 9425, + "GATTTTTTTGAAA": 9426, + "GTGTATTTT": 9427, + "GTCATTAAA": 9428, + "CAAAGCAAA": 9429, + "GTTACTTTT": 9430, + "CTTATGTT": 9431, + "CGACATG": 9432, + "GGAGATAA": 9433, + "GCCCATCC": 9434, + "GGTGGTGA": 9435, + "TCATTCTA": 9436, + "TCATCCAA": 9437, + "TGATAGCA": 9438, + "GCAGACCA": 9439, + "GCAGCATG": 9440, + "CACATCTT": 9441, + "CCTTTGCA": 9442, + "TGATTTCTT": 9443, + "TTTTTGTTA": 9444, + "GCACCATT": 9445, + "CCTTTCCA": 9446, + "GGAGACAGA": 9447, + "GTTTTAATG": 9448, + "GTGCCATT": 9449, + "TACTGGAGTG": 9450, + "CTTGCTTA": 9451, + "GTCCCATT": 9452, + "TTTTAATTAA": 9453, + "GAAGATAA": 9454, + "GTGAAAAAA": 9455, + "GCAGATAA": 9456, + "CCAGAGC": 9457, + "CACAGGGG": 9458, + "TAAAATAATT": 9459, + "CATCTACA": 9460, + "CAGGTTTA": 9461, + "TCATTTAA": 9462, + "CTGCTGTG": 9463, + "CACATTTTA": 9464, + "CAAAAGCAA": 9465, + "TAGAGTAA": 9466, + "CCTCTATT": 9467, + "GTGGGCAAA": 9468, + "CTCCTGACCTCAAGTGA": 9469, + "TGACTCAA": 9470, + "CTGCAATT": 9471, + "GCATTTTTT": 9472, + "TATGGTTTT": 9473, + "CAGAATTA": 9474, + "TAAGTGTGTAGTCTTTT": 9475, + "GCACACTT": 9476, + "GAAAATATTA": 9477, + "TTATTTCC": 9478, + "GTCTTAAAA": 9479, + "GTACAGCA": 9480, + "GTCGTTA": 9481, + "GGAGGTTA": 9482, + "CTGGGATT": 9483, + "GAAAGGCA": 9484, + "CTGCACTCCAGCC": 9485, + "GCTCTGGATAA": 9486, + "GACAGATG": 9487, + "GTTCATGA": 9488, + "TGACTATT": 9489, + "TGACAGAGCAA": 9490, + "TACAGGAA": 9491, + "GAACCCC": 9492, + "GGTCTGTT": 9493, + "TAAAGTAAA": 9494, + "GGAACCTG": 9495, + "TATTTGTTA": 9496, + "GCTGGGCGTG": 9497, + "CAGTCCTT": 9498, + "GACGCCA": 9499, + "TCACTATT": 9500, + "TCATCATG": 9501, + "CAAGACTG": 9502, + "GTTTAGTG": 9503, + "GAAAAGGAAA": 9504, + "AAAAAAAAAAAAAAAAAAAAA": 9505, + "CCGGGCA": 9506, + "CTTCCTTTT": 9507, + "GGACAGTT": 9508, + "CCAGACAA": 9509, + "TCAAGCTG": 9510, + "GCTTTAGG": 9511, + "TATGGCTG": 9512, + "GGTGGCTGA": 9513, + "CGCGCGC": 9514, + "TATTTCTAA": 9515, + "TCTGTCCATG": 9516, + "CAGACTAA": 9517, + "GAGGCAAA": 9518, + "CTACCCC": 9519, + "GAGAATTTT": 9520, + "CCTCTATA": 9521, + "GTTCAGC": 9522, + "TTAATATA": 9523, + "TCTAGTGA": 9524, + "CGAGTAA": 9525, + "GCAATGTG": 9526, + "TAATCTTTT": 9527, + "TAAGGCAA": 9528, + "GTTCCCCA": 9529, + "GTTGACAA": 9530, + "TCTTGCTA": 9531, + "TCATTGTA": 9532, + "TCGTTTA": 9533, + "TCCCGGA": 9534, + "CATTCAGG": 9535, + "TCTGCCTA": 9536, + "TAAAATGAAA": 9537, + "TAGAGTTA": 9538, + "TGTACATG": 9539, + "GAGGTGTG": 9540, + "TCAATACA": 9541, + "TCCAAAAAA": 9542, + "GTGGGCTT": 9543, + "GATTGGTT": 9544, + "TCTCCAGCA": 9545, + "GTGCAGC": 9546, + "CCACATTA": 9547, + "TCTGGTTA": 9548, + "CCTGGGAGG": 9549, + "CTCCCCAAA": 9550, + "GAAAACTCA": 9551, + "TCAATGAAA": 9552, + "CATAGATT": 9553, + "CTTGCCTGGAGAATCCCAGG": 9554, + "TCCCTGCC": 9555, + "CTGTATAA": 9556, + "TATGCTCA": 9557, + "GGTGGAGA": 9558, + "GATACAGA": 9559, + "TAACAATG": 9560, + "GCCCGTT": 9561, + "GGAACAAAA": 9562, + "CTCCTAGG": 9563, + "TTTGCAAA": 9564, + "TCCCTTAA": 9565, + "CCACCATT": 9566, + "TCAATGCA": 9567, + "TCTCTACAA": 9568, + "TATCTTAA": 9569, + "TCGCGCC": 9570, + "GAGTCTA": 9571, + "GTCAGTTA": 9572, + "CCAAGTGA": 9573, + "CACCCAGG": 9574, + "GAACTGTG": 9575, + "GGTAGAGA": 9576, + "GCTCTATT": 9577, + "GGATTACAGGTG": 9578, + "CCATATTA": 9579, + "TATACTCA": 9580, + "GTAACTGA": 9581, + "TCCTGCCTT": 9582, + "CGGGGCC": 9583, + "TGGAGTA": 9584, + "TCAAAATTA": 9585, + "GGAGACAGG": 9586, + "GACAGAGGAGCCTG": 9587, + "GGGCAAAA": 9588, + "GTTGCATT": 9589, + "GGAGAGAAA": 9590, + "GTCAAATA": 9591, + "TATACAGTA": 9592, + "TGTTGCAA": 9593, + "CGGTTAA": 9594, + "TAGCCATG": 9595, + "GGCTTTTA": 9596, + "CAACAGC": 9597, + "GGACGTT": 9598, + "GTCTTTTAA": 9599, + "CGGCAGA": 9600, + "GTTTTATTA": 9601, + "CTCATTAAA": 9602, + "TAACACTA": 9603, + "TCAGCTCC": 9604, + "CCCACCTCC": 9605, + "CTCTAATG": 9606, + "GATTGAAA": 9607, + "CCAACAGA": 9608, + "CCCATGAA": 9609, + "CTGCTCTG": 9610, + "GTCCATAA": 9611, + "TATGGGAA": 9612, + "TATTTCTGA": 9613, + "GCCTTATT": 9614, + "CTCTCCTCC": 9615, + "TGTTGTTGTT": 9616, + "TAAAAGAAAA": 9617, + "TCCTGAGTT": 9618, + "GTGCGTT": 9619, + "GTGTGTTTT": 9620, + "GATGGATGGATGGATG": 9621, + "CCCĊ": 9622, + "TAAGGTAA": 9623, + "GTAGAGTT": 9624, + "CCTTGAGG": 9625, + "TGGATGA": 9626, + "TCATTTCTT": 9627, + "GCTCAATG": 9628, + "GTTTAATG": 9629, + "GAGATAGA": 9630, + "GGGGTAGG": 9631, + "TAATTCTT": 9632, + "GCATTGAA": 9633, + "CTGCGTG": 9634, + "GTTTTTCAA": 9635, + "CCTACATT": 9636, + "TGATTCTA": 9637, + "TAGCCACA": 9638, + "GGTTAGGTTA": 9639, + "GAACCCGG": 9640, + "CCGAGC": 9641, + "GCCCCTGA": 9642, + "TATATAATA": 9643, + "GCAAGTAA": 9644, + "CCCAGCCTG": 9645, + "TCATGGCTG": 9646, + "CAAAAAAAAAA": 9647, + "GCTATATG": 9648, + "TCCTACTG": 9649, + "CTGTATCA": 9650, + "GGGACACA": 9651, + "TATCTTTTA": 9652, + "GTGGCCAA": 9653, + "GCACATATA": 9654, + "GTATACC": 9655, + "CGGTATT": 9656, + "TATTTATTTTTT": 9657, + "CCTAAATT": 9658, + "TCATAGCA": 9659, + "GCTGCTTA": 9660, + "GGTGCTGA": 9661, + "CATATCTT": 9662, + "CATCACACA": 9663, + "CAAATCTCA": 9664, + "GTAACCAA": 9665, + "GTGGATAA": 9666, + "GTTAGTGA": 9667, + "GGATTATAGG": 9668, + "CTAGCAAA": 9669, + "TGTGCAGA": 9670, + "TAGCTCTT": 9671, + "CAAGAGC": 9672, + "CTCTAATA": 9673, + "CGGCGCC": 9674, + "CAAGTGTT": 9675, + "CAACATTTT": 9676, + "CTATATTTT": 9677, + "CAATAGCAAA": 9678, + "CAACGC": 9679, + "CTGTACTT": 9680, + "GGATATAA": 9681, + "CTTTGGGAGG": 9682, + "TACAATTTT": 9683, + "GTTGAGTG": 9684, + "GTTCATAA": 9685, + "CATGCCAA": 9686, + "CGGCAAA": 9687, + "CCTATAAA": 9688, + "GAGAATTA": 9689, + "GGCCAGGCTGGTCTCAAA": 9690, + "GGGGCTGA": 9691, + "GACATCAA": 9692, + "CAGTTTTTT": 9693, + "TAAAAGTG": 9694, + "CTAAATAAA": 9695, + "CCATACCA": 9696, + "CTGTACTG": 9697, + "GCACTGTT": 9698, + "CACCCAGA": 9699, + "GGTCTGTG": 9700, + "GATCTTAA": 9701, + "GGAAGGCA": 9702, + "GTAAGATG": 9703, + "GAGGCTCA": 9704, + "CTAATTTTA": 9705, + "TAGGGAAA": 9706, + "GCTATCTT": 9707, + "TCAAAAAAAA": 9708, + "GCATCACTGA": 9709, + "GCCGTCA": 9710, + "GCTCTATG": 9711, + "GTATACTT": 9712, + "GCAAGGTG": 9713, + "GACAGAAAA": 9714, + "TCCGGA": 9715, + "TCAAGATA": 9716, + "TAGAGCTG": 9717, + "GGTTTATT": 9718, + "GACACATG": 9719, + "GAAGTCAA": 9720, + "TGTTCAGA": 9721, + "CCCCCGA": 9722, + "GTACAGTT": 9723, + "CTAACATG": 9724, + "GAAGTTAA": 9725, + "TGAAGGGG": 9726, + "GAGGGGTG": 9727, + "GTCCCCCA": 9728, + "GCTAAAAAA": 9729, + "TCATGAGG": 9730, + "CAAAAAATT": 9731, + "GCCGCTT": 9732, + "CTGATGTA": 9733, + "TCTCCCCA": 9734, + "GTCCAAAAA": 9735, + "TCACAAGGTCA": 9736, + "TAACGCA": 9737, + "GTAAAAATG": 9738, + "TTTTAAATT": 9739, + "GAACGTG": 9740, + "CAAATATAA": 9741, + "CACCCATG": 9742, + "GAGTCCTT": 9743, + "TCCGGCC": 9744, + "GCACAGTA": 9745, + "TTAATGAA": 9746, + "TGGGGTT": 9747, + "CTCTTTCTT": 9748, + 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"GAATACTT": 9890, + "TCCTGAGG": 9891, + "TATTTAGAA": 9892, + "GAGACCAA": 9893, + "GGAGGGGAGG": 9894, + "GCTCTAAA": 9895, + "TCTCTGCTTTT": 9896, + "GGATAATT": 9897, + "GTCAGGGG": 9898, + "CATTTTTTA": 9899, + "CAAGTTAA": 9900, + "GAAATAGAA": 9901, + "CAAGGGCA": 9902, + "GTGCTTTG": 9903, + "CTTACTCA": 9904, + "TCCATAGG": 9905, + "CACACAAAA": 9906, + "CACAGATA": 9907, + "TAAAATGGG": 9908, + "GTTATCAA": 9909, + "TCTCCCTA": 9910, + "TGCCCAGG": 9911, + "TTATTATTAA": 9912, + "TTTTGATG": 9913, + "TCATACTG": 9914, + "GCAAATTTG": 9915, + "TAAGTATTTG": 9916, + "GACTCTTA": 9917, + "TCAGGATT": 9918, + "GGTGCTA": 9919, + "GTATTCCATG": 9920, + "CGTCAAAA": 9921, + "GTGTATAA": 9922, + "TATACACAA": 9923, + "CACGAGA": 9924, + "GCCACGTT": 9925, + "GAGTGCAATG": 9926, + "GAATGATT": 9927, + "GTGTCTGTT": 9928, + "GTTTCAAAA": 9929, + "GTAATGTG": 9930, + "CTCCTAAA": 9931, + "GGTAGAGG": 9932, + "TTTTGAGA": 9933, + "CTGCATCA": 9934, + "GTGTCCTG": 9935, + "GGTTGGGG": 9936, + "TATGTATGTATGTATG": 9937, + "CAGAGGAAA": 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+ "CCAGCATG": 10033, + "GTCAGAATG": 10034, + "TGGAGAAAA": 10035, + "CATCTAAA": 10036, + "GGGCTGTT": 10037, + "GTTTAGCA": 10038, + "GCACGAA": 10039, + "CCTCTGAAA": 10040, + "GGACCAAA": 10041, + "CACACACCA": 10042, + "TAATTTATG": 10043, + "CTTTCATG": 10044, + "GTACTGAA": 10045, + "GCCCATGA": 10046, + "CCTCCCCTCC": 10047, + "GGTCTGAA": 10048, + "TGACAAAAA": 10049, + "GGACCCCA": 10050, + "TCAATGGG": 10051, + "GTCATATG": 10052, + "GGGATATT": 10053, + "TCATCTAA": 10054, + "CAATCTCA": 10055, + "CTGTAGTT": 10056, + "CTTTTTCTG": 10057, + "CGATCTT": 10058, + "TCAAATTTG": 10059, + "CGCCCAGC": 10060, + "TCCTCATG": 10061, + "GTAGGCC": 10062, + "TAGGCAAA": 10063, + "GGTGGCC": 10064, + "GGGAGGTT": 10065, + "GTAGGCTA": 10066, + "CTGAGGCTG": 10067, + "GATTTCCA": 10068, + "GCGAGACTCCGTCTCAAAA": 10069, + "GTGCTCTT": 10070, + "CCAGTCTG": 10071, + "CCACTGGA": 10072, + "CATTTAAAAA": 10073, + "CCCAACCCAGG": 10074, + "GTATAGTT": 10075, + "GAAATTTTA": 10076, + "TGTTTATAA": 10077, + "GAGCGTCTG": 10078, + 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+ "GCATATTTT": 10399, + "TTTTACAAA": 10400, + "TGGACTA": 10401, + "CTTTATAAA": 10402, + "CTTTAGGA": 10403, + "GAGAGTAA": 10404, + "CATTTCTCA": 10405, + "CCAACTCA": 10406, + "GTAGTTTG": 10407, + "CATTTCTAA": 10408, + "CATAGTTA": 10409, + "TCAAATATTA": 10410, + "CTGCGC": 10411, + "CTTGATTA": 10412, + "GTTTATAAA": 10413, + "CACTAATT": 10414, + "CCTGCCCA": 10415, + "GCCACTAA": 10416, + "CCCCCCTCC": 10417, + "GAATTCTCA": 10418, + "GCATGTTA": 10419, + "TATTTGATT": 10420, + "TAAAAGATT": 10421, + "GCTGTAAA": 10422, + "CTTATGA": 10423, + "CTCTCGG": 10424, + "GGTTTGTT": 10425, + "CTACAGTAA": 10426, + "TCGCTCTT": 10427, + "TAACCCTT": 10428, + "CACACAGCA": 10429, + "GTGAGACC": 10430, + "TTTACTAA": 10431, + "CCAGCCTGACCAA": 10432, + "GATAAATA": 10433, + "GAAGACCA": 10434, + "CTGCACCA": 10435, + "TGTGGTTA": 10436, + "CAAATAATA": 10437, + "CCCAAGGA": 10438, + "CTACACTG": 10439, + "GCTTGCAA": 10440, + "GCTAGTAA": 10441, + "TCTTTTTCTTTT": 10442, + "GAAGAAGAAGAAGAAGAAGAAGAA": 10443, + "CTGGTTGGA": 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"CTGTCTTA": 10584, + "TAAACACC": 10585, + "GTCGTCC": 10586, + "TTAATCA": 10587, + "GAAAAGAAAAA": 10588, + "GGGTGAAA": 10589, + "GTTGGTGA": 10590, + "TAAATATAAA": 10591, + "GGAACTAA": 10592, + "GAGGGGGA": 10593, + "CGTCTTA": 10594, + "CGTAAATT": 10595, + "CCATCTTA": 10596, + "TAAAGCCA": 10597, + "GAGAGATA": 10598, + "CCTGGTCA": 10599, + "CTTCCTGTT": 10600, + "TGACCATT": 10601, + "TAAATAATG": 10602, + "TAAAATGCA": 10603, + "GATGCAAA": 10604, + "CTTTTTATG": 10605, + "TCGGAAAA": 10606, + "GAGAAAATT": 10607, + "CTGCTTTG": 10608, + "CTGGACTT": 10609, + "GCTGTCAA": 10610, + "GCTAGCTT": 10611, + "GCCCTCAA": 10612, + "GCCTCTAA": 10613, + "TGTACACA": 10614, + "CTGTTCTA": 10615, + "CATAAAATT": 10616, + "CCGTCTT": 10617, + "GAAAAAATT": 10618, + "CAATACTG": 10619, + "GGATACAA": 10620, + "GAAAATGAAA": 10621, + "TAGAGCTT": 10622, + "TCCTCCAAA": 10623, + "TAAGAAAAAA": 10624, + "GGTTATTA": 10625, + "GCGACTA": 10626, + "CTCTGTGTT": 10627, + "TTTTATGGA": 10628, + "GCACAATT": 10629, + "GATTCACA": 10630, + 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"TCAACAGG": 11045, + "TACAGTCC": 11046, + "TTTTTTTTTTA": 11047, + "ATTA": 11048, + "GAAGTATT": 11049, + "TAGGCTCA": 11050, + "CTGCAGCCTT": 11051, + "TCACAGCAA": 11052, + "GACTTTTTT": 11053, + "GAAGGATG": 11054, + "GTGGCAGG": 11055, + "GGAATGTA": 11056, + "CATGCACCA": 11057, + "CCTATGAA": 11058, + "CTGTTCTCA": 11059, + "CCAGGGAGG": 11060, + "CAATAGTT": 11061, + "TATTTACTA": 11062, + "GCTAGATG": 11063, + "CCACATCCA": 11064, + "CCCTAATT": 11065, + "GTTGGTAA": 11066, + "CCATAGAA": 11067, + "GTAGTGAA": 11068, + "TTTTATCA": 11069, + "TAATTCTAA": 11070, + "TCTGATTTA": 11071, + "TGCCCTCA": 11072, + "CTGGTAAA": 11073, + "TAAACTAAA": 11074, + "TAGGCTTA": 11075, + "GGGAGTGA": 11076, + "CAATTCTA": 11077, + "GGTATTAA": 11078, + "CAACTATT": 11079, + "CTGGACTG": 11080, + "GTATAATA": 11081, + "GCTATTTA": 11082, + "GGAGTCAA": 11083, + "GGAGATTTT": 11084, + "GCTTTAAAA": 11085, + "TTTGCCTT": 11086, + "GCAGAGGAA": 11087, + "GGCGAGA": 11088, + "TATATGTATA": 11089, + "TATAAAATT": 11090, + "TAAGCTGA": 11091, + "GGTGGAAA": 11092, + "CATATCTG": 11093, + "CATAGAAAA": 11094, + "TCATTTCC": 11095, + "TACCTGTT": 11096, + "CAGACAAAA": 11097, + "GCAAGGTT": 11098, + "GTTAGATT": 11099, + "GGGATGTG": 11100, + "TCGTTTCA": 11101, + "CCCCCCTT": 11102, + "TAATTGTTTT": 11103, + "CTCTGCCTT": 11104, + "GAGTCCTG": 11105, + "TCCGTCC": 11106, + "CTAAGAGA": 11107, + "TAATTATTAA": 11108, + "GGAGATCGAGA": 11109, + "CCTTATTA": 11110, + "GAGTCCAA": 11111, + "GCACATAA": 11112, + "GTATACTG": 11113, + "GTAATGGG": 11114, + "CTCCCGCC": 11115, + "TTCATGTG": 11116, + "CTGCACCTG": 11117, + "GGCĊ": 11118, + "TAAATAAAAAA": 11119, + "GTCGGAA": 11120, + "CAGAGCTGA": 11121, + "CATGTACA": 11122, + "CATGGACA": 11123, + "TAGTATTA": 11124, + "GTATGTTTT": 11125, + "GTGGTGGTGGTG": 11126, + "CCACCCCCA": 11127, + "CTCCAACACCACAGTT": 11128, + "GCGGAGGA": 11129, + "TAAGGATT": 11130, + "TCATACCA": 11131, + "CAAGATTA": 11132, + "GTTGAGC": 11133, + "CTAGTAAA": 11134, + "CGACTCA": 11135, + "GCGCTCTG": 11136, + "TCTCCAGTT": 11137, + "CGTGTTTT": 11138, + "TACCTGA": 11139, + "GGGCTCCA": 11140, + "GGGACTCTCAAGAGTCTT": 11141, + "CTGGTGTT": 11142, + "GCAGAAGA": 11143, + "TCTTTCTCC": 11144, + "GTGACTC": 11145, + "GTCATGGA": 11146, + "CGGCTAA": 11147, + "GGCCCCTG": 11148, + "GAGGAGGGG": 11149, + "GAAACTTTT": 11150, + "GCTAGTT": 11151, + "GCAAATTAA": 11152, + "TGCCTGA": 11153, + "GACCTGA": 11154, + "CTCTGCCTG": 11155, + "CAGCTTAA": 11156, + "TAACTAAAA": 11157, + "CAAATCTA": 11158, + "TCTGTCTCC": 11159, + "CGACTTA": 11160, + "GCTAGATT": 11161, + "TAGCTATT": 11162, + "GAACATTTT": 11163, + "CCACTTAA": 11164, + "TTTGTAAA": 11165, + "CTCCTCTGTCCATG": 11166, + "GCCTCTTA": 11167, + "CATTGACA": 11168, + "TGTAGAGA": 11169, + "GGAAATTAA": 11170, + "CACTCCAA": 11171, + "CAGTCCAA": 11172, + "TACGGAA": 11173, + "GAGAAAATG": 11174, + "GAAGAAGG": 11175, + "CTTAAAATT": 11176, + "CACACCCA": 11177, + "TCTTCTGAA": 11178, + "CACTCTTTTTGTA": 11179, + "GAACCCTG": 11180, + "GGAGCCTT": 11181, + "TCAAAGAAA": 11182, + "GGGACTA": 11183, + "CCTCGCCCGG": 11184, + "CAACAGCAA": 11185, + "CCCCAAAAA": 11186, + "TATTCCTA": 11187, + "TGTATGA": 11188, + "TACCTAAA": 11189, + "GCATGTTTT": 11190, + "TCGACGG": 11191, + "TCTTTCTTA": 11192, + "TCAATCTA": 11193, + "CCTTTTTTTT": 11194, + "TCAAGAGTT": 11195, + "GCTTGTGA": 11196, + "CCAAGGCA": 11197, + "GGCCCAA": 11198, + "GCCACCAGGGAA": 11199, + "GCCTCGAACTCACAGAGA": 11200, + "TAGCCTA": 11201, + "GTGCATTA": 11202, + "GTGTACTT": 11203, + "GGATAGAA": 11204, + "GATGCTTA": 11205, + "GCTATTTTA": 11206, + "GAAGAGTA": 11207, + "GTGCTCAA": 11208, + "CAAACAAAAAA": 11209, + "CTCCCAGCA": 11210, + "CACGGTT": 11211, + "TATTTACTG": 11212, + "GCTGATTTT": 11213, + "CAATCATT": 11214, + "GTGGTGTG": 11215, + "GTTTGATG": 11216, + "GTCCATC": 11217, + "GGAATCTG": 11218, + "CTGACCCA": 11219, + "GCTTTCTGA": 11220, + "TCAGCAAAA": 11221, + "GATGTATG": 11222, + "TCTCAGCCTCC": 11223, + "CGCGGGG": 11224, + "CTGTGGTA": 11225, + "CGCTGAAA": 11226, + "TAACAAACCTG": 11227, + "GGCTCTGA": 11228, + "GTTGGATT": 11229, + "GCAGTGGG": 11230, + "GGACTGTT": 11231, + "GCCGTCC": 11232, + "TATTTATTAA": 11233, + "TCAAAAATG": 11234, + "TACAGCCA": 11235, + "CCTACTAA": 11236, + "CTAGGCCA": 11237, + "TAGATCTT": 11238, + "CTACATAA": 11239, + "CATTCAAAA": 11240, + "GTTTTCTCA": 11241, + "CAAGAATG": 11242, + "CCATGATT": 11243, + "CTACATCA": 11244, + "GTAAAAAAAA": 11245, + "CGCATTA": 11246, + "CTCCTGGCCTCAA": 11247, + "CCTCAATT": 11248, + "GGATGATT": 11249, + "GGAGTCTG": 11250, + "GCCTACTG": 11251, + "CTACTCTT": 11252, + "GAAATACAA": 11253, + "GCGGCAGG": 11254, + "CCCTCCTG": 11255, + "GACGTCC": 11256, + "GCTAAAATT": 11257, + "CTGGATTA": 11258, + "TTTTACTTA": 11259, + "CACCCTCC": 11260, + "CAATTTATT": 11261, + "TCACTTTTCACTT": 11262, + "TCAGAGTA": 11263, + "TCCTCGG": 11264, + "GCATACAA": 11265, + "TGGGGTTTCACCATGTT": 11266, + "GCAAATTTT": 11267, + "GGTAATTA": 11268, + "CAACTTTG": 11269, + "CAAGTCTT": 11270, + "GGAAGCCA": 11271, + "TTTACTCA": 11272, + "CATACCTT": 11273, + "CTGCGTT": 11274, + "CTTTATC": 11275, + "GTAGATGA": 11276, + "GCCAAAATT": 11277, + "TCTTGGCA": 11278, + "TTATTCTAA": 11279, + "GGATGAGATG": 11280, + "GAACCTGA": 11281, + "GCATACTT": 11282, + "GGATACTT": 11283, + "TAAACAAAAA": 11284, + "TAGGCAGG": 11285, + "CCTAATAA": 11286, + "GAGTAAAAA": 11287, + "GTACACAA": 11288, + "AAAATGTT": 11289, + "TGTGTACA": 11290, + "GGAATGCA": 11291, + "CGGTGCC": 11292, + "TCTAGGTG": 11293, + "CTTTTTGGA": 11294, + "TCCGTC": 11295, + "TCTTCCCTT": 11296, + "GAGATGCA": 11297, + "TCTACCCA": 11298, + "CGACTAA": 11299, + "CAATTTAA": 11300, + "GGACACGACTGAA": 11301, + "TAGĊ": 11302, + "GTGTCATG": 11303, + "CCATCATG": 11304, + "CTATCTTA": 11305, + "TTTGTAA": 11306, + "GGGGGGTG": 11307, + "GTAGGGAA": 11308, + "CATGTAGA": 11309, + "CCAAGACA": 11310, + "CGGGGTTTCACCATGTT": 11311, + "CCATAATT": 11312, + "CTCTCAAAA": 11313, + "GATTCAGG": 11314, + "CCAATGTT": 11315, + "GATGTAAA": 11316, + "TTTATAAAA": 11317, + "CTGCAGTA": 11318, + "CCAGATCA": 11319, + "CACACCTA": 11320, + "TCTCCACTG": 11321, + "GTTTTTAAAA": 11322, + "CAGAGGAGCCTG": 11323, + "CCGACTT": 11324, + "GTTGACTG": 11325, + "GGATATGA": 11326, + "GAAACAGTG": 11327, + "GTGAGGTAA": 11328, + "CCAATCAA": 11329, + "TCCCTTCC": 11330, + "GCAGGATG": 11331, + "CACCCAAA": 11332, + "GCACCTCC": 11333, + "GTTATAGA": 11334, + "GAAAATTCA": 11335, + "CAGAATTTT": 11336, + "CCGACTG": 11337, + "TGATCATG": 11338, + "CAATTTCA": 11339, + "TGAGGTTTG": 11340, + "TCCGGTG": 11341, + "TGTGTTATT": 11342, + "CCTTCGA": 11343, + "TGGAGCTG": 11344, + "CAAGCCAA": 11345, + "GAGGGACA": 11346, + "TCTAATTAA": 11347, + "CATTTACATT": 11348, + "TCAACTTTT": 11349, + "CTTACAGG": 11350, + "TTTTTTCTT": 11351, + "GACTTGGAA": 11352, + "GTCATAGA": 11353, + "TTTTAGTG": 11354, + "CCTTATTTT": 11355, + "CACGTTTT": 11356, + "GCACTCAA": 11357, + "GTCTCTCTT": 11358, + "GTCTCTGTT": 11359, + "TATACCTG": 11360, + "GTTTTCTGTT": 11361, + "CATAGGAA": 11362, + "TCATTGATG": 11363, + "GAATAATG": 11364, + "GTAAACTA": 11365, + "TGGGTTTGATGAAA": 11366, + "TGAGAAGG": 11367, + "GTTTCTGTT": 11368, + "CTGATCAA": 11369, + "GCTGTATT": 11370, + "CCATTCCTT": 11371, + "GCAGGCAGA": 11372, + "GCATGGTGGTG": 11373, + "TCGCAAAA": 11374, + "GGAGACTA": 11375, + "GTACGTA": 11376, + "TCCACAAAA": 11377, + "TCTAGTTCTA": 11378, + "TAATTCTTA": 11379, + "GAACCCAA": 11380, + "GCCGAAA": 11381, + "GTGAGGCC": 11382, + "CCTCTATG": 11383, + "TCTCCAAAAA": 11384, + "CAATTTTAA": 11385, + "TCTGTTATT": 11386, + "TGTATTTTTT": 11387, + "TCCTTCTT": 11388, + "AAAAAAAATG": 11389, + "GATCCGGCCA": 11390, + "TAGATTAA": 11391, + "GTGCCCTT": 11392, + "TAATAGAAA": 11393, + "TAAGGCTT": 11394, + "GTACATTTT": 11395, + "TATTTAATG": 11396, + "TGTTCCCA": 11397, + "TAGGTCTT": 11398, + "TCGAATA": 11399, + "CAAAGGTA": 11400, + "CATTGAGA": 11401, + "GTGAGCTT": 11402, + "GATTTATTTT": 11403, + "GCTCATTA": 11404, + "GTCAATTA": 11405, + "AAAAGAAA": 11406, + "GCTGCAGCA": 11407, + "CCACACGG": 11408, + "CAACCCTG": 11409, + "CATTAAATT": 11410, + "GCTATCTG": 11411, + "TAATTTCC": 11412, + "GCAAAGTA": 11413, + "GCCTGCTG": 11414, + "CTTGTGTG": 11415, + "GTGGTCTG": 11416, + "TCAGGTTA": 11417, + "GCAGATCC": 11418, + "GGAAGATA": 11419, + "CTTTTCTAA": 11420, + "TAGACTTA": 11421, + "CAATTTAAA": 11422, + "TGGAATACTA": 11423, + "CAACAGCC": 11424, + "GGATCTTA": 11425, + "GTTAGATA": 11426, + "TAGATACA": 11427, + "CATAGAGG": 11428, + "CAGCTATG": 11429, + "TAATCCCAACA": 11430, + "CTTGAGGA": 11431, + "GTGGACAA": 11432, + "GTAATGGA": 11433, + "TGTATCA": 11434, + "CCATGTTTT": 11435, + "TACTAATT": 11436, + "GTGGATTA": 11437, + "GTTGGAGG": 11438, + "GTAGACTT": 11439, + "TGTTTCAAA": 11440, + "GAAAATCTA": 11441, + "CGAATTTT": 11442, + "TCCCACCAA": 11443, + "GAACACTGA": 11444, + "CCGTATT": 11445, + "TAACCTGA": 11446, + "GAACTTAA": 11447, + "TTTGTATA": 11448, + "TGCCCCTG": 11449, + "CGATTAAA": 11450, + "TACTTATTTT": 11451, + "TCGGAGA": 11452, + "GCCTGAGA": 11453, + "TAAAATAGG": 11454, + "GTGAGGAAA": 11455, + "CCTTGTTTT": 11456, + "GATTGATG": 11457, + "TGCATATG": 11458, + "TACCTCAA": 11459, + "CCCAAGGAA": 11460, + "CCGCTCC": 11461, + "GGTAATAA": 11462, + "CTTGAAAAA": 11463, + "TGTGGTTTT": 11464, + "CAGACTTA": 11465, + "CTTTTGCTT": 11466, + "GAATTCTG": 11467, + "GTGGTGGG": 11468, + "GCCTAGTT": 11469, + "TAAACCCA": 11470, + "TCTTTATCA": 11471, + "GCAACTC": 11472, + "CACCCCCA": 11473, + "GCTAAATA": 11474, + "CAACTGTT": 11475, + "CAATAATAA": 11476, + "CAAATCCA": 11477, + "GAATTTCAA": 11478, + "TGAGGGAGG": 11479, + "GCATTTTAA": 11480, + "GTACGCA": 11481, + "CTATCTC": 11482, + "GAAAGTTTT": 11483, + "TTTTTTCTTTT": 11484, + "CCGCTC": 11485, + "GTGCAGCC": 11486, + "GTGTGTGTT": 11487, + "GTTGCTTTT": 11488, + "TACATATTTT": 11489, + "TGTATGAA": 11490, + "GATTTGCATT": 11491, + "CACTCTCA": 11492, + "TGACGTCA": 11493, + "TAAAATTATT": 11494, + "CTGACTCC": 11495, + "TTAATTCA": 11496, + "TTTGGGAA": 11497, + "TCTATTATT": 11498, + "TGTGCCCA": 11499, + "TATGTTATT": 11500, + "TGGAGTCA": 11501, + "TAACTGTG": 11502, + "CAACTCTT": 11503, + "AAAAAAATT": 11504, + "TATTTGCC": 11505, + "TGTTAAATT": 11506, + "CATTTACTT": 11507, + "CTGAGGAAA": 11508, + "TTTTATCTT": 11509, + "CTGCCTGA": 11510, + "GTGTAGTA": 11511, + "GCCTCATG": 11512, + "CAAAGTAAA": 11513, + "CGGGCTT": 11514, + "CACCTGAA": 11515, + "TCGCTGTG": 11516, + "CTTCAGTC": 11517, + "GTGCTATT": 11518, + "TAAATATATT": 11519, + "CATTGGTG": 11520, + "CCTTCCCTT": 11521, + "CATAGTCA": 11522, + "GGTACTA": 11523, + "GGTAGATG": 11524, + "TAAATGTTTT": 11525, + "TCACAAAAA": 11526, + "TGACCTAA": 11527, + "GAAAAACC": 11528, + "GAATGCTGA": 11529, + "TCGCCATT": 11530, + "AAAATGAA": 11531, + "CAGAGGCTG": 11532, + "TAAAACTTA": 11533, + "CTTTTTTATT": 11534, + "TTTAGATT": 11535, + "GACATATG": 11536, + "CTGAATTTG": 11537, + "TCAAAAGACTACACACTTATT": 11538, + "TCAATATTTT": 11539, + "GTCCCTTA": 11540, + "GCTAGTTTT": 11541, + "GAATACACA": 11542, + "CTTCAGCTT": 11543, + "CCCGAGA": 11544, + "CTTCACTA": 11545, + "GTTGAGCA": 11546, + "GGAGTTAA": 11547, + "CAAAGTCAA": 11548, + "TGGGGAGA": 11549, + "TCAGCAGG": 11550, + "CAAAGGACA": 11551, + "GTGAAGTAA": 11552, + "TACCTGAA": 11553, + "GCTAATTTTA": 11554, + "CAAAAGTAA": 11555, + "GGCGCAAA": 11556, + "TTAGATA": 11557, + "AAAATATAA": 11558, + "TCAACTC": 11559, + "TACATAATT": 11560, + "CCATTGCCTT": 11561, + "GCCTTGAACTCA": 11562, + "GCTCAAAAA": 11563, + "CAACGCA": 11564, + "CTTTAGTA": 11565, + "CCTGCCCTG": 11566, + "GATGCTAA": 11567, + "CATAGCTT": 11568, + "TCATTTCTG": 11569, + "TCCGCCCGCC": 11570, + "GCCGGGCGTGGTG": 11571, + "GTGCATCA": 11572, + "TAAATTATG": 11573, + "GTGATGCA": 11574, + "CCAATATG": 11575, + "GGTTCCA": 11576, + "TGTATCTT": 11577, + "CGCTCTC": 11578, + "GCGCACC": 11579, + "TAAAAGGA": 11580, + "CAACAATG": 11581, + "CTATGATT": 11582, + "CATGTAGG": 11583, + "CGACCCC": 11584, + "CACTCTTTT": 11585, + "CCCGAAA": 11586, + "TAAATAATAA": 11587, + "CACATAGA": 11588, + "GTGCTGGGATTATAGG": 11589, + "TAAGCCTT": 11590, + "TCCGTAA": 11591, + "CCTTCCCTG": 11592, + "GTCACCCTG": 11593, + "TTTGTATG": 11594, + "CTGTAATA": 11595, + "GTTTAGGA": 11596, + "CCACTCAA": 11597, + "GGAATGAAA": 11598, + "GTTTACATT": 11599, + "TAAGCTCA": 11600, + "GTTTCCTGA": 11601, + "GCCGGCA": 11602, + "GCAACCTG": 11603, + "TTTTATGTG": 11604, + "GTTATGAAA": 11605, + "GGGGCTCA": 11606, + "GTGGAAAAA": 11607, + "CTGATATG": 11608, + "GCCATCCA": 11609, + "TTTATGA": 11610, + "CAGTATTTT": 11611, + "GTGGCACATG": 11612, + "TCCTCTCTT": 11613, + "GTGCGGA": 11614, + "TATAĊ": 11615, + "GTGAGGCA": 11616, + "GATGTATT": 11617, + "TCGAAGA": 11618, + "TCCAAATA": 11619, + "CTGGCCAA": 11620, + "CCATTTTTT": 11621, + "CTTAGAAAA": 11622, + "GGTAGCC": 11623, + "GGGCGGGG": 11624, + "GCGTTGG": 11625, + "TCATTTAAA": 11626, + "CTACCATT": 11627, + "CGAGAAAA": 11628, + "GGGATGTT": 11629, + "GGGACAAA": 11630, + "TTTTCTC": 11631, + "GTTTATATT": 11632, + "CCTTGTGAA": 11633, + "CTCCTCTC": 11634, + "CTTATGTA": 11635, + "CACCCTGA": 11636, + "CTCTGGAGG": 11637, + "GACCAGGCTG": 11638, + "GGGCCATG": 11639, + "TCTTTATAA": 11640, + "TGTTTACTT": 11641, + "CAGATGAAA": 11642, + "TCAAATATT": 11643, + "GAGCTAAA": 11644, + "CTGATGCTGAA": 11645, + "TCGTTGA": 11646, + "CCACCTGA": 11647, + "GTATATGA": 11648, + "CTACTTAA": 11649, + "CTACAATT": 11650, + "TAGACTCA": 11651, + "CTATTTATT": 11652, + "CCAGGATT": 11653, + "CCTGGGTA": 11654, + "CTAATGCA": 11655, + "TGCCTCTT": 11656, + "GATCGTT": 11657, + "GGATGAGG": 11658, + "CACATAAAA": 11659, + "TCAAATTAAA": 11660, + "TCTTCAAACCCATG": 11661, + "CAAGCTCA": 11662, + "CCTATCAA": 11663, + "CGGGGTTTCA": 11664, + "CCGCCTG": 11665, + "TAGTCAAA": 11666, + "CTTGAGTG": 11667, + "GCATAGCA": 11668, + "CGGCGTG": 11669, + "CCAAGTTA": 11670, + "GCTATCAA": 11671, + "GCTACTTTT": 11672, + "CAGCTACA": 11673, + "CTGCGCA": 11674, + "GTGCCCCA": 11675, + "GTCTAATG": 11676, + "GCAATGGA": 11677, + "GGTGCAGG": 11678, + "CCGCGCC": 11679, + "TGAGTCTG": 11680, + "CAAATATCA": 11681, + "CAAGAGTG": 11682, + "GTGCGAA": 11683, + "TAAAGTTAA": 11684, + "CAAATAAATT": 11685, + "GTCAGTTTT": 11686, + "GATGCTTTT": 11687, + "GACATTTTA": 11688, + "GCAGGCAGG": 11689, + "CCGATCA": 11690, + "CTGGTGAA": 11691, + "CTTGCTTTT": 11692, + "GTAGTGTT": 11693, + "GCTGCTCTT": 11694, + "GATGTCAA": 11695, + "CCCAGCTCC": 11696, + "TACCCAGG": 11697, + "CTCTGTCA": 11698, + "CCAGGCCTG": 11699, + "CAGAGACAGAGA": 11700, + "TTTTAAAAAA": 11701, + "TCCGTGA": 11702, + "GGATGATG": 11703, + "CTCGTTTT": 11704, + "GGAATAAAA": 11705, + "TGCCCACA": 11706, + "GCGGAGA": 11707, + "CTGCATCC": 11708, + "CTATTTTAA": 11709, + "GGAAGCAAA": 11710, + "TAGATCAA": 11711, + "TCTCCTCTA": 11712, + "CTAAATATA": 11713, + "GCAGGGCTG": 11714, + "TCCGCTT": 11715, + "GTAGCATT": 11716, + "TCTGCCCC": 11717, + "CCGTGTG": 11718, + "CCCGCCA": 11719, + "TAAGGTGA": 11720, + "GCCTATTA": 11721, + "GAAACAGGA": 11722, + "GAGATATA": 11723, + "TAAAACATA": 11724, + "GATTGCAA": 11725, + "CTTAGGTT": 11726, + "GACCTCAA": 11727, + "GTTTATTTTA": 11728, + "TCTAAAAAAA": 11729, + "GTAGAAAAA": 11730, + "TGAAGTC": 11731, + "CTGACGA": 11732, + "CCTTGTC": 11733, + "CTAACTCA": 11734, + "GACACGA": 11735, + "GGGACATG": 11736, + "CCTACTTTT": 11737, + "GTTCACTA": 11738, + "CCAGGAGG": 11739, + "GTACACTT": 11740, + "CACCCATT": 11741, + "TCCAAGAA": 11742, + "TCTAATTTG": 11743, + "CAGTAATG": 11744, + "CCGACAA": 11745, + "GCATAGTT": 11746, + "TATTCTCC": 11747, + "TATTCATAA": 11748, + "CAAACCAAA": 11749, + "CCAACAGG": 11750, + "CTATTTCTT": 11751, + "CCACTCTG": 11752, + "GGAAAAATG": 11753, + "TCTCCCGG": 11754, + "CATGATTA": 11755, + "TCCGG": 11756, + "CTGGTTAA": 11757, + "GTCACCTA": 11758, + "CAAAATGTA": 11759, + "GACCCTCA": 11760, + "CGGAGCC": 11761, + "GAGCAGGGG": 11762, + "GAAGATTA": 11763, + "GAAAGAGAAA": 11764, + "CCGGGTT": 11765, + "TCTTĊ": 11766, + "TACTATTTT": 11767, + "TATCCTTA": 11768, + "CTGCAGCTG": 11769, + "GTTTATC": 11770, + "CTCTAGC": 11771, + "CTGATCTG": 11772, + "GTCATTTTA": 11773, + "CAAAAGAAAA": 11774, + "GGGACCTG": 11775, + "GCAAAGAGG": 11776, + "CCCATTAA": 11777, + "GTGCGTA": 11778, + "GTTTTGCTT": 11779, + "TAGGTGGG": 11780, + "GATATCTG": 11781, + "GCGGCGA": 11782, + "TAACAGAAA": 11783, + "CAAGTTTG": 11784, + "CTGGAGC": 11785, + "GTGGAATG": 11786, + "CTAATGTG": 11787, + "CCCAGAAAA": 11788, + "GATATATA": 11789, + "TGAGCCTT": 11790, + "GAACTGTT": 11791, + "CTGCACAA": 11792, + "CCACGTA": 11793, + "CTACAGCC": 11794, + "CACACGTA": 11795, + "CCTTCGTT": 11796, + "GGAGGTTTT": 11797, + "TCTTTTCTTTT": 11798, + "GCGAAGA": 11799, + "TGAGCCTG": 11800, + "GCCCAGTA": 11801, + "CCAAGAAAA": 11802, + "GCCACTGTG": 11803, + "TATTTTTAGTAGAGA": 11804, + "GCAGGTTA": 11805, + "TCTTTCCCAGCA": 11806, + "CAAATACTT": 11807, + "GAGAGTTTT": 11808, + "CGGTCAA": 11809, + "AAAATTTA": 11810, + "TCTTGAATT": 11811, + "CTCAGGAAA": 11812, + "GAGGTGTT": 11813, + "TTTGGGA": 11814, + "TCGTTGG": 11815, + "GAAACTC": 11816, + "GAAAGGGAA": 11817, + "CATGAAATT": 11818, + "GCAAGCTT": 11819, + "GATTGTTA": 11820, + "GACAATTA": 11821, + "TAAACATTTT": 11822, + "TGTAGCTG": 11823, + "GGGTGGGGG": 11824, + "CACGCCA": 11825, + "CAAGGTGA": 11826, + "TGATTTCAA": 11827, + "GGGCTGAA": 11828, + "GCATAATG": 11829, + "GTACATAA": 11830, + "TGTGTCATT": 11831, + "CAGACATA": 11832, + "CTAATATG": 11833, + "GATGTCTG": 11834, + "TCTGAGGTT": 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"GATGGTCA": 11926, + "CAAAATTCA": 11927, + "CGTGTTTG": 11928, + "GGCCCCTT": 11929, + "GGCGGTG": 11930, + "TCGTTCC": 11931, + "GTAGATCA": 11932, + "TATAGGTT": 11933, + "CAAACTC": 11934, + "CAATTTCTT": 11935, + "TCATGATG": 11936, + "GTTCCTCTG": 11937, + "CTTATGTG": 11938, + "TCCTTCCTG": 11939, + "TTTTTATTTTA": 11940, + "GGTGTGAA": 11941, + "TCGĊ": 11942, + "CTGGAGCC": 11943, + "GTGCATGA": 11944, + "GTTTAAGA": 11945, + "GCATAAAAA": 11946, + "CAAACTCC": 11947, + "GCTGCCCC": 11948, + "GCTGGGTCA": 11949, + "CAGTAAAAA": 11950, + "CCGTTAA": 11951, + "TTTTGCAA": 11952, + "GCCTATAA": 11953, + "CTAGACC": 11954, + "TACAĊ": 11955, + "TACCTCTG": 11956, + "CATTACTTTG": 11957, + "GCCATACTG": 11958, + "TAAGTGTG": 11959, + "CTACACTA": 11960, + "TATATCCA": 11961, + "GAGATCAA": 11962, + "CTCAGTTTT": 11963, + "TCTTTTTTAA": 11964, + "GAACATGTG": 11965, + "TACAGATT": 11966, + "GGCTCCTT": 11967, + "TGATCTAA": 11968, + "TCCTAGTG": 11969, + "CAATAGTG": 11970, + "GTACTGTT": 11971, + "GAAATATTTT": 11972, + 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12111, + "GCAGCTAA": 12112, + "GCAAGAAAA": 12113, + "GCATATGA": 12114, + "TATTCGA": 12115, + "TCTCCACCA": 12116, + "TACACTTTT": 12117, + "GAACTTAAA": 12118, + "GGAGAGTA": 12119, + "CTCTCCCA": 12120, + "GTGAAGACA": 12121, + "CCACCATGCCCA": 12122, + "TCGAGAAA": 12123, + "CTCTCCATT": 12124, + "GTCTAATA": 12125, + "GAAAGCCA": 12126, + "GGGGGATG": 12127, + "GGACTACAGGTG": 12128, + "TCAGAGAAA": 12129, + "CTAATAAAA": 12130, + "GGCCTGAA": 12131, + "CAACAGAGTGA": 12132, + "CCCTCATT": 12133, + "TCGTTGTT": 12134, + "TCCCCTTTT": 12135, + "CTGATGAAA": 12136, + "CTCATAGA": 12137, + "CCAATGTG": 12138, + "GCACCATG": 12139, + "CTACATGA": 12140, + "TGATCCTG": 12141, + "TGATAGTG": 12142, + "CTCCCCTA": 12143, + "TAGCTATA": 12144, + "GGAGTTCGAGA": 12145, + "CCGGGGA": 12146, + "TAATTAAAAA": 12147, + "GAATACCA": 12148, + "CTGAGTCC": 12149, + "TATGGGTG": 12150, + "TCCACTTTT": 12151, + "TTCACAAA": 12152, + "TGTACTT": 12153, + "GCGCTTTT": 12154, + "TAAGCTTA": 12155, + "TAAGTATG": 12156, + "TCATTATTA": 12157, + 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"CATTGAGG": 12249, + "CCTGGTCC": 12250, + "TCTCTCCTG": 12251, + "TCTAGATA": 12252, + "CTGTGATG": 12253, + "TATACATAA": 12254, + "CCAACCTT": 12255, + "CTAATCTT": 12256, + "CTGAATTAA": 12257, + "GGTAGGAA": 12258, + "GAAGTAAAA": 12259, + "TGGGTGTG": 12260, + "GGTTCTCA": 12261, + "CACACACTCA": 12262, + "GTGGTCAA": 12263, + "GTATGATT": 12264, + "TATTCTGTT": 12265, + "TGTAGAGG": 12266, + "GGCACTA": 12267, + "GGTCGTG": 12268, + "GTTGAATG": 12269, + "GTTTATTAA": 12270, + "GTCAAAATT": 12271, + "GGTGTTTTA": 12272, + "GGTGCAGTG": 12273, + "TGCATGAA": 12274, + "TGATACTA": 12275, + "CTGTAGTG": 12276, + "GGACGCC": 12277, + "GTACCCC": 12278, + "TCTCAGCTT": 12279, + "GGTATGTG": 12280, + "GCTGGACA": 12281, + "GTAAGAAAA": 12282, + "GGGGGTGA": 12283, + "GAAAAAGAAAA": 12284, + "CCTCGGCC": 12285, + "TCTTGGGA": 12286, + "CCTTTAGG": 12287, + "TCCACCTA": 12288, + "CTTTTTCAA": 12289, + "TATATGAAAA": 12290, + "GACTCCGTCTCAAAA": 12291, + "CTAATACAA": 12292, + "GTAAATATA": 12293, + "CCATTTATT": 12294, + "GGCTGGGAA": 12295, + "GCGGGTG": 12296, + "CTTGGTTA": 12297, + "CCCTCTTTT": 12298, + "ATTTT": 12299, + "GCACTCCAGCCTGGG": 12300, + "CCATACTT": 12301, + "TAAAAGAGA": 12302, + "GGGGCTA": 12303, + "CTGCAGCTT": 12304, + "TGACTATG": 12305, + "TCCGGTT": 12306, + "TCTTCCCTG": 12307, + "CAGAGTTTT": 12308, + "GTCACGA": 12309, + "GTAATAGA": 12310, + "TCTCCCTCA": 12311, + "GCAAAAGAAA": 12312, + "TAAAGCTA": 12313, + "TAAATGCC": 12314, + "TCTTCAGTT": 12315, + "CAAACAGCA": 12316, + "CTTTTTTTTTTTTTTTT": 12317, + "CAATTTCAA": 12318, + "GCATTTATT": 12319, + "CCGAATT": 12320, + "GAAGGATT": 12321, + "CAATCTAA": 12322, + "GGACTCTG": 12323, + "CCACACTA": 12324, + "GCTCCTGGG": 12325, + "GAACTCTG": 12326, + "GCACCTGA": 12327, + "CCATAGTG": 12328, + "GCTGCGA": 12329, + "TATGTCTA": 12330, + "TCTCAAAAAAA": 12331, + "GATGCATG": 12332, + "TCCGAGG": 12333, + "GGAGAGCC": 12334, + "TACACGTG": 12335, + "GGGTCGCACAGA": 12336, + "TACGTGA": 12337, + "TCGTTCA": 12338, + "GACCTTAA": 12339, + "CAAGGTAA": 12340, + "TGTTCAAAA": 12341, + "TGTTTAATT": 12342, + "GCTGCCCA": 12343, + "CCCCGCC": 12344, + "CGCCACCACC": 12345, + "GGTGAATT": 12346, + "CATTCGG": 12347, + "GTCCTAGG": 12348, + "GATAGGAA": 12349, + "TGAGAAGA": 12350, + "TCCTCCCA": 12351, + "GGACAGTA": 12352, + "TCTGGACA": 12353, + "GGTTTCAA": 12354, + "GGGATGAA": 12355, + "TTTTGAGG": 12356, + "TCACCTAA": 12357, + "CAATACTA": 12358, + "GTACAAAAA": 12359, + "TAAAAGTGTT": 12360, + "GTTATGGG": 12361, + "CCTACGG": 12362, + "TAAAAATGAA": 12363, + "CGTGAATT": 12364, + "GCAGTGAGCCGAGATT": 12365, + "TCAGAAGG": 12366, + "CAACGTA": 12367, + "CCTGGTAA": 12368, + "TCCTTCTCC": 12369, + "CGCCACCA": 12370, + "CCCTTATG": 12371, + "GCGTGGA": 12372, + "TCCTCAGA": 12373, + "CTTTCTGTT": 12374, + "GCAGCTTA": 12375, + "CCTTCCCCA": 12376, + "CCCCCTC": 12377, + "CAGCAAAAA": 12378, + "GAGATGAAA": 12379, + "TCAACTCC": 12380, + "GGAATAGG": 12381, + "CCTGGCCA": 12382, + "TTTGGATT": 12383, + "GGTCCATT": 12384, + "CTTCGTA": 12385, + "CTTCCAGA": 12386, + "GCATCATG": 12387, + "TCTATCCA": 12388, + "TAAAAAGAAA": 12389, + "CTGTTCTTA": 12390, + "TGAGATCTT": 12391, + "TGAGATGAA": 12392, + "TAATAATTAA": 12393, + "TATATATATATATA": 12394, + "GTCCTTAAA": 12395, + "TCTTTTATG": 12396, + "TCGCTGTT": 12397, + "TAAGGGTT": 12398, + "GTGCATAA": 12399, + "GCCTAGCA": 12400, + "CATGTTTTA": 12401, + "CCCCTACC": 12402, + "TGCACTA": 12403, + "GTGTTTGTG": 12404, + "GAATCCAA": 12405, + "GTAAATGAA": 12406, + "CAGGTTAAA": 12407, + "TCGCGTG": 12408, + "CTTCTTGG": 12409, + "CCTGCCTCA": 12410, + "CAAAACAGA": 12411, + "CTGAAGAAA": 12412, + "CACTGTTA": 12413, + "CTTTATTTTA": 12414, + "GTTGGTTTT": 12415, + "CCCCATGGACTGTA": 12416, + "CTGATAGA": 12417, + "CCTGTATT": 12418, + "CGGATTA": 12419, + "TCAGGAATT": 12420, + "TAAACCAAA": 12421, + "TCTTTCACTT": 12422, + "CTAATGGG": 12423, + "GCTATATA": 12424, + "TCCTAATG": 12425, + "GTACGAA": 12426, + "GCCGGAA": 12427, + "GCCCCATG": 12428, + "TAAAGACAA": 12429, + "TGTTTCATT": 12430, + "GGGAGTTA": 12431, + "GACCCACA": 12432, + "TAGTAATT": 12433, + "TCGAACA": 12434, + "GAACCTCA": 12435, + "GAAAAAAAAAAA": 12436, + "CCATGATG": 12437, + "CTAGAGCA": 12438, + "CCTGCCTCC": 12439, + "GAAAAGTTA": 12440, + "GTTTTGTTA": 12441, + "GTCCTGCTG": 12442, + "TGCATATT": 12443, + "TGAGCATG": 12444, + "CAAGCCTT": 12445, + "CTGTCCTG": 12446, + "TTATTTCTG": 12447, + "TTCACTCA": 12448, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 12449, + "TAATATTTG": 12450, + "TTTTTTTTAA": 12451, + "GCTTTCAAA": 12452, + "TATGGCTA": 12453, + "CAAAATGAAA": 12454, + "TCTCCTGACCTT": 12455, + "TCTGATTTG": 12456, + "GCGCAGCA": 12457, + "CTACAAAAA": 12458, + "TGTTGGCAA": 12459, + "GGCCTGTT": 12460, + "GCCTGGTCTA": 12461, + "CCGGCGG": 12462, + "GTTGGATG": 12463, + "CCAGTTTG": 12464, + "TGTGGGGA": 12465, + "TATATATGA": 12466, + "CAAACACC": 12467, + "CATTGATA": 12468, + "CTCCCACTT": 12469, + "TAGATTTTA": 12470, + "CCTATTTTA": 12471, + "CAGTTTTAA": 12472, + "CTGGCTTA": 12473, + "TACATCCA": 12474, + "CGACGCA": 12475, + "TGCAGCTT": 12476, + "CTTTGGGAA": 12477, + "GCCTGATG": 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12567, + "CCATTTCTT": 12568, + "TCAAAAGACTA": 12569, + "GTAACAAAA": 12570, + "GAGGCAGAA": 12571, + "CTAACTGA": 12572, + "GGTTCAAA": 12573, + "TCATAGTA": 12574, + "GTGGAGTA": 12575, + "TGGGGCTG": 12576, + "GCTAGTCA": 12577, + "GGTAGATT": 12578, + "CTTCCCAAA": 12579, + "GTGGCTCC": 12580, + "GTTGGGCA": 12581, + "GTTAGTTTT": 12582, + "CTCCTCCTG": 12583, + "GGCATGTT": 12584, + "GGGAGCTT": 12585, + "CCGATGA": 12586, + "GTAGCCTT": 12587, + "TCTGGAGAA": 12588, + "CAGACCCA": 12589, + "TGGGCAGG": 12590, + "CTATGTTTT": 12591, + "GTGAAGAA": 12592, + "GGAGGAGAAGG": 12593, + "GTCATGTATGGATG": 12594, + "CTAGGATT": 12595, + "CTCTĊ": 12596, + "CATTCACC": 12597, + "TTTTACTTTT": 12598, + "GCCATACA": 12599, + "TTTACCTT": 12600, + "CTTTGGTA": 12601, + "CTTTGGATG": 12602, + "TCTTTTTTTG": 12603, + "TTATTTTTTA": 12604, + "TAAACATCA": 12605, + "GGAACTTA": 12606, + "GTGAGAAAA": 12607, + "GCAACGA": 12608, + "GTTACGG": 12609, + "GGTTGATT": 12610, + "TAATTTCTA": 12611, + "CTTTGGAAA": 12612, + "TAGCTATG": 12613, + 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"TCAGCTAA": 12660, + "CTAGAGTT": 12661, + "TAAATCATG": 12662, + "TATACATTTT": 12663, + "CTAACCTT": 12664, + "TTATTGATT": 12665, + "CAATGCCA": 12666, + "CTAGCCGGGC": 12667, + "CAGGTTTGTTACA": 12668, + "TCGAAGAA": 12669, + "CTTTATTAA": 12670, + "TGTGGCCTT": 12671, + "TAATGGAA": 12672, + "TGACAGTGA": 12673, + "TTTTTGAAA": 12674, + "CTTCGCA": 12675, + "TGTTCATCA": 12676, + "GGTTTCAAA": 12677, + "GATACTGA": 12678, + "GACCCCTT": 12679, + "TCGTGGA": 12680, + "GTATTATG": 12681, + "GTCGCAA": 12682, + "GATGGAAAA": 12683, + "GATGGACA": 12684, + "GGCACTCA": 12685, + "CAAAAACAAA": 12686, + "GCTCTGGATAAGAGCGTCTG": 12687, + "CACACGTG": 12688, + "GAAAGAGGA": 12689, + "CTTATACA": 12690, + "TCCTGGCTAA": 12691, + "CAAGAAAAAA": 12692, + "TCAGGATG": 12693, + "CAAGAGTA": 12694, + "CTCGCAA": 12695, + "TCTTGACA": 12696, + "CCAATGGG": 12697, + "GCCAACAAA": 12698, + "TAAAAGACA": 12699, + "GCTTGAGA": 12700, + "GTTAATTTG": 12701, + "CTCCTGCCTCAGCC": 12702, + "GTGCCTCC": 12703, + "CTAGGCAA": 12704, + "TCTTCACC": 12705, + "TCTACCTA": 12706, + "CGACGCC": 12707, + "GAAGACATT": 12708, + "GGTCCCC": 12709, + "TCATACTA": 12710, + "GAAGCAGG": 12711, + "TAAATGATT": 12712, + "CAGGTAGG": 12713, + "GTAAATTTG": 12714, + "GCTCCCACA": 12715, + "TAGTCTA": 12716, + "GAGCTGGG": 12717, + "CAACAGAAA": 12718, + "CTTTAAAAAA": 12719, + "CCTGGGCC": 12720, + "TCTCCAGTG": 12721, + "GAGCTAA": 12722, + "GTCCAACTCTCACA": 12723, + "TCATAGTG": 12724, + "CAACCCTT": 12725, + "TATTCTTAA": 12726, + "TATTCCATT": 12727, + "GCTGGTTA": 12728, + "GATTAAATT": 12729, + "TTTAGTTA": 12730, + "TCAAACAAA": 12731, + "TCGAGTC": 12732, + "GCGAATA": 12733, + "GCTGCATATG": 12734, + "GTTTTTCTG": 12735, + "GGTGTCTG": 12736, + "TAATTCAAA": 12737, + "CTTGGTTTT": 12738, + "CACCTTTG": 12739, + "GAATTATG": 12740, + "CAGGTAGA": 12741, + "CGGCAGC": 12742, + "TGTGATTTT": 12743, + "CCCGTCA": 12744, + "TGTTĊ": 12745, + "GCCATAGG": 12746, + "GGCATGAA": 12747, + "CGCTAAA": 12748, + "CGGACGGA": 12749, + "CCGCATT": 12750, + "TAACCCTA": 12751, + "CAACCATT": 12752, + "GGACAATG": 12753, + "GTAGTCTT": 12754, + "GACTTAAAA": 12755, + "TGAATAGG": 12756, + "CCTTGACA": 12757, + "CTTTTGTTA": 12758, + "GGGCCAGA": 12759, + "GTCTCACTCTGTT": 12760, + "GGAGTGTG": 12761, + "GAAATATCA": 12762, + "CTGATAGG": 12763, + "GTAATGAAA": 12764, + "CCTGCTAA": 12765, + "TCCCATGAA": 12766, + "GATTTGCATTTCTC": 12767, + "TAACGTAA": 12768, + "GAACAATG": 12769, + "CTATAAATT": 12770, + "CATTTCCAA": 12771, + "GTGATCTT": 12772, + "GCAACAGAA": 12773, + "GTTAGAATG": 12774, + "TATATTAAAA": 12775, + "CCATCCAGCCATCTCA": 12776, + "CTGCTTAA": 12777, + "GCATCTAA": 12778, + "CTACACCA": 12779, + "CATTTAGAA": 12780, + "GCTGTATA": 12781, + "GCAAAAATG": 12782, + "TACATACACA": 12783, + "GGGGAAAAAA": 12784, + "TTATTTGA": 12785, + "TATATGTTA": 12786, + "CCGTGTTA": 12787, + "TCGATTAA": 12788, + "CTGTAGTA": 12789, + "GAATTTGTT": 12790, + "GGTATGTT": 12791, + "AAATATAAAA": 12792, + "GGAATGAAAA": 12793, + "GCGACTTCACTT": 12794, + "CTCATATA": 12795, + "GTAACAGG": 12796, + "GAGGGAGAA": 12797, + "CCAATAAAA": 12798, + "CATAGCC": 12799, + "TCAAAGTA": 12800, + "GTAAATGTT": 12801, + "GGGTACATGTG": 12802, + "CTAGGCC": 12803, + "CATTĊ": 12804, + "GTAATATTA": 12805, + "TTTTATAGA": 12806, + "CCTAGAAAA": 12807, + "TCTCCTCTT": 12808, + "TACTCTA": 12809, + "GTTGAGTA": 12810, + "TCTTGATA": 12811, + "CGGCACC": 12812, + "CCTAGTGA": 12813, + "GTCTGGAA": 12814, + "TCGCCACA": 12815, + "TAACAATA": 12816, + "TTTTCAAAA": 12817, + "CAATATTAA": 12818, + "TCAAGGGA": 12819, + "GGAAGTTTT": 12820, + "CTCACTCC": 12821, + "CCTTGGCTT": 12822, + "TACAGTATT": 12823, + "TGATTTGTT": 12824, + "GGCTGCAGTGA": 12825, + "TAGTCATT": 12826, + "CAACCCAA": 12827, + "GCATTATG": 12828, + "TATTTCAAAA": 12829, + "CAGGGTTTT": 12830, + "GGAAGTC": 12831, + "GCTGTAGA": 12832, + "CCAACTAA": 12833, + "TTAATGTT": 12834, + "GTAAACAAA": 12835, + "CATTAATTA": 12836, + "CTGCAGAGA": 12837, + "TAACCTCC": 12838, + "CTAGTTAA": 12839, + "TAAACACTT": 12840, + "TATGGACA": 12841, + "CCTGCTC": 12842, + "CGATAGA": 12843, + "CTTCCCCTT": 12844, + "GAGTTTTTT": 12845, + "GGACTCAA": 12846, + "GTACTTTG": 12847, + "TCAAGGCA": 12848, + "TAAAAGTCA": 12849, + "CCTAGCTT": 12850, + "GCCTGTTA": 12851, + "GCCTCCAAA": 12852, + "GGCAGATT": 12853, + "GATCAAAAA": 12854, + "CTAGAAAAA": 12855, + "GAGATTTTAA": 12856, + "GATGTATA": 12857, + "CGTATTTG": 12858, + "TAACATCC": 12859, + "TCAGGAGG": 12860, + "GGAGGAGA": 12861, + "CGGTGCA": 12862, + "CCCATCTCA": 12863, + "GGTTGCTT": 12864, + "TGATTTATA": 12865, + "GGTATTTG": 12866, + "CTGTACTA": 12867, + "GTTTCCAAA": 12868, + "CTATATGA": 12869, + "TAATTAAATT": 12870, + "CTTTTGAACTGTGGTGTT": 12871, + "CATATTAAA": 12872, + "GACTGATG": 12873, + "TAAGGGTG": 12874, + "CTTGAATG": 12875, + "GGACTGTG": 12876, + "TATTTCCAA": 12877, + "CCTTCAGAA": 12878, + "GATCGCC": 12879, + "TAAGGGCA": 12880, + "GCACAATG": 12881, + "GATTCTTTT": 12882, + "CATATCC": 12883, + "GAAGACAAA": 12884, + "TCAGCATG": 12885, + "CTGCTATT": 12886, + "CTGTACCA": 12887, + "GTGGAGCC": 12888, + "GTCTCTTTG": 12889, + "CACAGGTA": 12890, + "CCTGCAAAA": 12891, + "TCATTTGA": 12892, + "GTTCATATT": 12893, + "TCCGCCCGTC": 12894, + "CCTATTAA": 12895, + "GCTGCGC": 12896, + "GCTGGGATTACAGG": 12897, + "TAGGTGGAA": 12898, + "CTGCAGTCCATG": 12899, + "CTCCTTAAA": 12900, + "TAACAGCC": 12901, + "GTGAAAATG": 12902, + "CACAGCTA": 12903, + "CCTTGTCC": 12904, + "CCTGTAATCC": 12905, + "TCTCCAGGA": 12906, + "GTGAACAGG": 12907, + "GTGGGTTA": 12908, + "GTTAAAATT": 12909, + "CTCTATC": 12910, + "TTTACTTTT": 12911, + "CAAGACTA": 12912, + "CTCTCTGAA": 12913, + "GTCTAGTA": 12914, + "TCAATAGG": 12915, + "GCTTTAGA": 12916, + "GCTTCCTGA": 12917, + "GGGATGA": 12918, + "CCTCATTA": 12919, + "GCCATGAAA": 12920, + "GCAGTGAGCTGAGATCCGGCCA": 12921, + "GGCGGAA": 12922, + "GGATCCTG": 12923, + "GCCCATTTT": 12924, + "CAAATACC": 12925, + "CAGGCAAAA": 12926, + "CCTGGACA": 12927, + "CCCATATT": 12928, + "TCAAATGAA": 12929, + "TCATTCTTTT": 12930, + "CACTATTA": 12931, + "GAAGCCTT": 12932, + "CACAGAGAAA": 12933, + "GTTAGAAAA": 12934, + "GCTATCC": 12935, + "GAGGTTAAA": 12936, + "GGGTTATT": 12937, + "GGCGCTT": 12938, + "GTGGCATG": 12939, + "CAGGTGTA": 12940, + "GTTCGTA": 12941, + "GATGTCTA": 12942, + "TGATGCCA": 12943, + "AAATATTTTA": 12944, + "TGTTTGTTTGTTTGTT": 12945, + "GGTGTTTG": 12946, + "GCGĊ": 12947, + "GTAGAGTG": 12948, + "CTCCAGGAA": 12949, + "GTCTCACAA": 12950, + "TATTAAATG": 12951, + "CGGGTCA": 12952, + "GTTTTCTAA": 12953, + "GGCAAAAAA": 12954, + "TAAGAGAAA": 12955, + "CTGCATTTT": 12956, + "CAGGCTCC": 12957, + "GGCATCC": 12958, + "TTCAGAGA": 12959, + "TCTCATTTA": 12960, + "TGGGTTTGAA": 12961, + "GTGCGCC": 12962, + "GCCCTGCC": 12963, + "TCTTCTAAA": 12964, + "CAATGGAAA": 12965, + "GCACAAAAA": 12966, + "GCACTATT": 12967, + "TAAACTTAA": 12968, + "GTCCCAGCA": 12969, + "TCTCCTGCA": 12970, + "GAATTCAA": 12971, + "CCATTCCATT": 12972, + "TGAGATGGAGTC": 12973, + "GCTCTCCTT": 12974, + "TTTTGAAAA": 12975, + "TTCAGCTT": 12976, + "CTGTTGTCA": 12977, + "CGTCTTTG": 12978, + "TAGTGTTA": 12979, + "GAGGTCC": 12980, + "GATGCTCAA": 12981, + "TCTACTCC": 12982, + "GTATTTGTT": 12983, + "TAATTTTAAAA": 12984, + "CCCAGATG": 12985, + "GACTCCTG": 12986, + "GTTCCTTTT": 12987, + "TATTGCTA": 12988, + "CAAAGACAA": 12989, + "TCTCTATAA": 12990, + "GTTAGGTT": 12991, + "TCCACGCA": 12992, + "GGTGTGA": 12993, + "GGAGGGGAA": 12994, + "CATCCTTA": 12995, + "TAGGAATT": 12996, + "GACACACA": 12997, + "CAACCTCC": 12998, + "CTGCATGA": 12999, + "CTCTTTTTG": 13000, + "CAGGTCTT": 13001, + "GCTGCGTG": 13002, + "GTTTTCTGA": 13003, + "TCATTTATA": 13004, + "GGTATTTTA": 13005, + "GCCTGAAAA": 13006, + "TGGAACAGAA": 13007, + "GCACCAGG": 13008, + "GGACAAAAA": 13009, + "CACACTCTT": 13010, + "GATTGAGG": 13011, + "TTATTCTTA": 13012, + "TAATATGTT": 13013, + "CACTCAAAA": 13014, + "CCCGTGA": 13015, + "TCACCTTA": 13016, + "TCAAGGGG": 13017, + "GCTGAAATT": 13018, + "GATACTTA": 13019, + "GTTAAGAA": 13020, + "TCGGTTTT": 13021, + "CTTTATCTT": 13022, + "TCCTCCCACC": 13023, + "CCCTGATG": 13024, + "TAGAGATT": 13025, + "TAAGAACA": 13026, + "TTAATAAAA": 13027, + "TAATTCCAA": 13028, + "GGCCCAGA": 13029, + "GCTCCTTA": 13030, + "GATTATATTTT": 13031, + "GGGCCACA": 13032, + "GCGCCATT": 13033, + "CTACTATT": 13034, + "TATTTAGGA": 13035, + "CCTTGCAA": 13036, + "TCTACGA": 13037, + "TTATTCATT": 13038, + "GGTACAAA": 13039, + "GCCTGGCTT": 13040, + "GCTCGCC": 13041, + "CTGGCTAA": 13042, + "TGTGAAATT": 13043, + "TATATACTT": 13044, + "CTCAGAAAA": 13045, + "GTTATATTTT": 13046, + "CCCACCTCA": 13047, + "GGGAGCAA": 13048, + "GAATTTAAA": 13049, + "TCCCTGGGTTGGGAA": 13050, + "CAAGGAGAA": 13051, + "CTGGACCA": 13052, + "CTCATAAA": 13053, + "CGGTGAAA": 13054, + "GATGAAATT": 13055, + "CCACCAGG": 13056, + "GTGTATGTG": 13057, + "CCCTGATT": 13058, + "GCCCAGTC": 13059, + "CTCTCCCCA": 13060, + "TATTCTATT": 13061, + "CAGATAAAA": 13062, + "GAGAAGAGA": 13063, + "TGATGGTGA": 13064, + "GAAATGTTA": 13065, + "GTTCTTCTT": 13066, + "CATTGTC": 13067, + "TGAAĊ": 13068, + "CTAACCAA": 13069, + "CCAAAGAA": 13070, + "CTCAATTTT": 13071, + "GTTGACTA": 13072, + "TATTTTTATTTT": 13073, + "GCGTTTA": 13074, + "TGATAGGA": 13075, + "GCTAGGTG": 13076, + "TCTTCCCCA": 13077, + "GAAAACCAA": 13078, + "TGTTAATTA": 13079, + "GGAATAGA": 13080, + "CTTAGAGG": 13081, + "GATATAAAA": 13082, + "GACCCAAA": 13083, + "CAAAAACAAAA": 13084, + "CCCTGTCTT": 13085, + "GGCGTCA": 13086, + "GACCAATT": 13087, + "GACAAAATT": 13088, + "GTAGACTG": 13089, + "GAGACGA": 13090, + "GTGACCCC": 13091, + "GAAAATCTG": 13092, + "CTTTTTGAA": 13093, + "TGTAGTTA": 13094, + "TCTGAGAAA": 13095, + "TAGTGAAAA": 13096, + "CCCAGGAAA": 13097, + "GTAGCTGGGACTACAGG": 13098, + "GGACAGCC": 13099, + "GTCACAAAA": 13100, + "GCCAATTA": 13101, + "TGAGAGAAA": 13102, + "TCCCTCATT": 13103, + "CTCAGGCA": 13104, + "CATGGGGG": 13105, + "GTCCCACC": 13106, + "CCTAGTAA": 13107, + "TCCTTTGAA": 13108, + "TCATGCAA": 13109, + "GTGTAGCA": 13110, + "CCAGATAA": 13111, + "GCTGTCTA": 13112, + "GCAATGAAA": 13113, + "CCCATATA": 13114, + "GAATTTCTG": 13115, + "TAATAGCCA": 13116, + "GCCTGGAGG": 13117, + "GCTATGAAA": 13118, + "GCGTGAACCCGGGAGGCGGAGCTT": 13119, + "GTTGACCA": 13120, + "GCATACTG": 13121, + "CAGCTAAA": 13122, + "GGCAGGCAGA": 13123, + "CACTCCTG": 13124, + "CCCGATT": 13125, + "CAAGGTTA": 13126, + "GTTAAATA": 13127, + "CCCAGCTA": 13128, + "GCGACGC": 13129, + "CCACCACACCTG": 13130, + "TAGTTTTTT": 13131, + "TCAAAAAATT": 13132, + "GTTGTTTTA": 13133, + "TGAATATTA": 13134, + "CATGGGCA": 13135, + "TACACTCC": 13136, + "TTTTTTTCA": 13137, + "TCCTTCTTA": 13138, + "GCATAATA": 13139, + "TAAATATTAA": 13140, + "GCAATGTA": 13141, + "CGTAAATG": 13142, + "GTTGGAAAA": 13143, + "GTAACGG": 13144, + "GTTTTTATG": 13145, + "CGCTCTA": 13146, + "TTTTTTAAATT": 13147, + "CTAAAGAA": 13148, + "CGCCTCC": 13149, + "CACCAATT": 13150, + "TGACATC": 13151, + "GTTTAAATT": 13152, + "GAGATGTA": 13153, + "GTGATGAAA": 13154, + "CTAAGAGG": 13155, + "TCCATAAAA": 13156, + "CCCACTC": 13157, + "GGGAAGGGG": 13158, + "GCAGGTGGG": 13159, + "GTCAGGCA": 13160, + "CCCCTAAA": 13161, + "TCAAACACA": 13162, + "TTTTTTCTA": 13163, + "GTGAATGGG": 13164, + "GCTGAGATTA": 13165, + "TTTTAATTTA": 13166, + "GCTCTTTTA": 13167, + "GCGACGG": 13168, + "GGACTTTG": 13169, + "CTATACCA": 13170, + "GTCTTTTTG": 13171, + "CCCCCTGA": 13172, + "CTGGGTATA": 13173, + "TCCCAGTCC": 13174, + "CTTAAATA": 13175, + "GGATTTTTG": 13176, + "TTCAGTGA": 13177, + "TGACCTCC": 13178, + "CAAGTGGA": 13179, + "TAAAGATAA": 13180, + "CTCCCTCTT": 13181, + "TTAACAAA": 13182, + "TAATACTAA": 13183, + "TAATCCCAGCACTTTGGGAGGCCGAGGCGG": 13184, + "GGCTAATT": 13185, + "CAAAAAATG": 13186, + "GCCTAGGA": 13187, + "CATGGAAAA": 13188, + "TTTGCTGA": 13189, + "CCCACATG": 13190, + "TAAGCATG": 13191, + "GTTGTCTG": 13192, + "GATAGTAA": 13193, + "TAAAAATGTT": 13194, + "GCTCĊ": 13195, + "CCGTTTG": 13196, + "CCGTCTG": 13197, + "CTGCCATG": 13198, + "CACAGAGTT": 13199, + "TGAACCTT": 13200, + "CGGTAGG": 13201, + "CTAAATTTA": 13202, + "TATATATGTG": 13203, + "TACGTTTT": 13204, + "TAACTATG": 13205, + "TCACTATG": 13206, + "CCCATTTG": 13207, + "GAAGTTTG": 13208, + "GCAAACAAA": 13209, + "CTCTGTGA": 13210, + "CTTCCTCTG": 13211, + "GTTCAAAAA": 13212, + "GAAATACC": 13213, + "TTCAGTAA": 13214, + "CGCGTA": 13215, + "TACTGATT": 13216, + "TAATGTTAA": 13217, + "GTGCCCAA": 13218, + "TATATCAAA": 13219, + "TCTTTACTT": 13220, + "CTTAGTCA": 13221, + "GATGGTAAA": 13222, + "TAGGTGTT": 13223, + "GTGTTCTT": 13224, + "CTTCCCAGG": 13225, + "TATTCCAAA": 13226, + "GAAACAAAAA": 13227, + "CAGACACC": 13228, + "TTTTTTTCTA": 13229, + "CTCTGCTTA": 13230, + "CTATCATT": 13231, + "CTCGACA": 13232, + "TCTTCTGTT": 13233, + "GCAAGGGA": 13234, + "GATGCACA": 13235, + "CGAGCCA": 13236, + "TCTCATTAA": 13237, + "TCGTTCTT": 13238, + "GAATCTTA": 13239, + "TCAATCTG": 13240, + "GAGGTAGA": 13241, + "CCTGTAAA": 13242, + "TCTCTAGTT": 13243, + "CAAAAGACA": 13244, + "TCGTATA": 13245, + "TAACATC": 13246, + "TCTCTTATT": 13247, + "CCCACGTG": 13248, + "CATAGCTG": 13249, + "GTCCTTCCAA": 13250, + "TAAGGATG": 13251, + "CAAGGGGA": 13252, + "CCATTTTAA": 13253, + "GCCCTTGG": 13254, + "GAGACTAA": 13255, + "CGGGGCTG": 13256, + "CCTGGTTA": 13257, + "CTCCGTC": 13258, + "GTTTTGGGG": 13259, + "CAAAACATG": 13260, + "TGGAGTGA": 13261, + "CTTCATCCA": 13262, + "GCTGTTAAA": 13263, + "GTGGACCA": 13264, + "GGACTGGG": 13265, + "TGATTTCTG": 13266, + "TACTGTTTT": 13267, + "TCAGGTGGCCAAA": 13268, + "GGTGTTAA": 13269, + "GTGTGAGA": 13270, + "GACATGAAA": 13271, + "TATACATATG": 13272, + "GTCATGCA": 13273, + "GAGAAGGAA": 13274, + "CCCCATCTC": 13275, + "TACCATTA": 13276, + "TATATATTTTA": 13277, + "TATATACCCA": 13278, + "TACGATT": 13279, + "GAAATACTT": 13280, + "TGGGTTCA": 13281, + "TCCTTTCC": 13282, + "TCTTGGTA": 13283, + "CAGGTAAA": 13284, + "CTAATATTA": 13285, + "GACATTTA": 13286, + "TCTTGTCTT": 13287, + "TCAAGGAAA": 13288, + "TAATATGAA": 13289, + "TGATTTGAA": 13290, + "CCTCĊ": 13291, + "GTGTCTAA": 13292, + "TGTGAAATG": 13293, + "GAAACTAAA": 13294, + "CCTTTAGA": 13295, + "CGCTATA": 13296, + "CTGTTGTA": 13297, + "GAAAAAGAAAAA": 13298, + "GGAGAGCTG": 13299, + "CTTCCCCAA": 13300, + "TCCTGAATG": 13301, + "CTTTAATAA": 13302, + "CACACACAAA": 13303, + "GTGAATGAA": 13304, + "CCCCACCCCA": 13305, + "GGGCTAATA": 13306, + "CTTCCTGTG": 13307, + "TCCTACTA": 13308, + "CAACATCC": 13309, + "CTTCAGAAA": 13310, + "AAAAAAAAAAAAA": 13311, + "TCTTTACC": 13312, + "TCTCGAA": 13313, + "GCCAĊ": 13314, + "TAGGTGAA": 13315, + "CGTTTATT": 13316, + "CTTAATTTG": 13317, + "GACCTGTT": 13318, + "CTCTGCCA": 13319, + "CCACATAAATTTT": 13320, + "TCACCACCA": 13321, + "TAAAACTCA": 13322, + "TGGGCATG": 13323, + "GTCGTTTT": 13324, + "GTCTAAAAA": 13325, + "CAGAGGGAA": 13326, + "CTTAATTTA": 13327, + "TCTTAGTTTT": 13328, + "TAATĊ": 13329, + "TTATATTTT": 13330, + "TCCGCA": 13331, + "CGGCGAA": 13332, + "GGGGTCTG": 13333, + "CCTCTACA": 13334, + "TGACGAAA": 13335, + "TCCGTCTG": 13336, + "GAAAGGTA": 13337, + "GGTTGCA": 13338, + "GGCACTTA": 13339, + "CAAAAATAAA": 13340, + "TCGACGA": 13341, + "GGGCCCTG": 13342, + "TGACAGAAA": 13343, + "CTTGCTCC": 13344, + "TCAAĊ": 13345, + "TAAAATACTG": 13346, + "GTGATTTTA": 13347, + "TGATTTAAA": 13348, + "GCCTGGCC": 13349, + "TATTATTAAA": 13350, + "GACTCATG": 13351, + "CTACGAA": 13352, + "CAGGGTAA": 13353, + "GCTTTGCC": 13354, + "CCTGCTTTT": 13355, + "TGGGGGAA": 13356, + "GGTGCTCA": 13357, + "GATTTTAAAA": 13358, + "CAGGTGTCTTTTA": 13359, + "TAGCAATT": 13360, + "CAGAGGCCA": 13361, + "GTGATAGA": 13362, + "GTCAGAAAA": 13363, + "TAGGGGAA": 13364, + "GGGACATT": 13365, + "CTCAAGGA": 13366, + "GGCGTTA": 13367, + "CAAGGATT": 13368, + "GTACCTCA": 13369, + "CCTTTGAAA": 13370, + "GGTTTAAA": 13371, + "TTCATATT": 13372, + "TCATTGTTTT": 13373, + "CCAAATTAA": 13374, + "CTATTCTATT": 13375, + "CTCCACCAA": 13376, + "CAAGAGGAA": 13377, + "CCGTCAA": 13378, + "GCGAACA": 13379, + "GAATTTTAA": 13380, + "CAATTATG": 13381, + "TCTCCAGC": 13382, + "TTTGCTTA": 13383, + "CAAAAGGAA": 13384, + "GGTTCTGA": 13385, + "CCCCAAATT": 13386, + "TTCAGGA": 13387, + "CCACGTC": 13388, + "GTAGGTAA": 13389, + "GATGGCTCA": 13390, + "TTATTTGTG": 13391, + "TATAATATT": 13392, + "AAAAAAAAAAAAAAAAAAAAAA": 13393, + "TCGTCATT": 13394, + "GATCAATG": 13395, + "TCCTAATA": 13396, + "CGGTAAA": 13397, + "CTTTTTTTTA": 13398, + "TGGGTGCA": 13399, + "TAGGGCTG": 13400, + "GGATGTTTT": 13401, + "TCACATTGTA": 13402, + "TATGTGTGTG": 13403, + "GCGCAGA": 13404, + "TAACATTAA": 13405, + "CTGCCTTA": 13406, + "GTATAATG": 13407, + "GTAGGCAA": 13408, + "TAGACATA": 13409, + "GGGACTGA": 13410, + "CTAAATATT": 13411, + "TATTATTTTA": 13412, + "CTCTTTCTG": 13413, + "TAAGTTTTA": 13414, + "GTCGGCA": 13415, + "CTTATGAAA": 13416, + "GGTTTATA": 13417, + "CTTTGGGA": 13418, + "GAAGAGAAAA": 13419, + "GGTGGCTGACTAAA": 13420, + "GGTCCAGG": 13421, + "GGCAGATG": 13422, + "CCATCTAA": 13423, + "GTACACCA": 13424, + "CATGTACTG": 13425, + "GAGGTCAA": 13426, + "CGGACGG": 13427, + "CTTTCTTTCTT": 13428, + "GTGTCTTTT": 13429, + "CTATGACAGA": 13430, + "TCTTGCCTT": 13431, + "TAAAAGTGA": 13432, + "CTAATAATT": 13433, + "CTAAATTTG": 13434, + "GAGAAGCAA": 13435, + "CTTCAGCAA": 13436, + "TGGGTTTGATGAAACCGAA": 13437, + "CAATGATG": 13438, + "GTACTATT": 13439, + "TATTGGTA": 13440, + "GTCACAGAA": 13441, + "TACATACC": 13442, + "CACCCCTT": 13443, + "TAATCTCC": 13444, + "GTTGTATG": 13445, + "GAGACAAAA": 13446, + "GGTTCTT": 13447, + "TAAAAATTTA": 13448, + "GAATTTATA": 13449, + "TCCTGCCTG": 13450, + "GAGCACTGA": 13451, + "CTACCTGA": 13452, + "TGTTCCCTT": 13453, + "CAGACACTG": 13454, + "CTCATGCA": 13455, + "GTCATGCTG": 13456, + "GCTATAAAA": 13457, + "TAAATACTA": 13458, + "CACAGGGAA": 13459, + "TCTCAGCA": 13460, + "GCCAAGAA": 13461, + "TTTTAAAAATT": 13462, + "GAATATGAA": 13463, + "GTTCAAGTGATT": 13464, + "CTACAGC": 13465, + "GCTTCCCTT": 13466, + "TCTCTATCA": 13467, + "TACATCAAA": 13468, + "GGGGTCC": 13469, + "TCGAAAAAA": 13470, + "CACACACGCA": 13471, + "GCGCCAGG": 13472, + "TTTGTCA": 13473, + "GTAAATTTA": 13474, + "TGCCCATG": 13475, + "GACCCATG": 13476, + "CTCAACATT": 13477, + "GAAAAATAAA": 13478, + "GTGGTGCATG": 13479, + "TGAGAAATG": 13480, + "GTTCCTC": 13481, + "GCCTCCACA": 13482, + "CAGGTATT": 13483, + "GAGGGAAAA": 13484, + "CTTAGTTTT": 13485, + "TTATTCTT": 13486, + "GTAGGTGTG": 13487, + "TCTTTTAAAAA": 13488, + "TGACGTC": 13489, + "CACATCCA": 13490, + "CCTTCCAAA": 13491, + "GCAACAGCA": 13492, + "GCCACAAAA": 13493, + "CGACTCC": 13494, + "GGTGGGAGGA": 13495, + "CTGAATTTA": 13496, + "TGTTTGTTA": 13497, + "GCCGACA": 13498, + "CTCTCCCTT": 13499, + "GTCTGTGTT": 13500, + "CTTTTTCTTTT": 13501, + "GCTATCTA": 13502, + "GAGGACAAA": 13503, + "CTTGCACAATT": 13504, + "TCCTATCC": 13505, + "TCTTCACTT": 13506, + "TGTTTCTTTT": 13507, + "GAAAATGCA": 13508, + "TTTTATTTATT": 13509, + "CTAGGGAA": 13510, + "CTAGTTTTTT": 13511, + "TGTGTCTA": 13512, + "TGATGAAAATTA": 13513, + "TACTCCAA": 13514, + "CTGCCCAA": 13515, + "GTTCAATG": 13516, + "GTTTATCC": 13517, + "TGAACGG": 13518, + "CTCATCCA": 13519, + "CTAAGCC": 13520, + "CTTATTTTTT": 13521, + "CAGAAGAA": 13522, + "GCCTGCCTG": 13523, + "CTCTAGCCTGGG": 13524, + "GGCTTATT": 13525, + "GGCGCCA": 13526, + "GCGTAAAA": 13527, + "TAATGAGG": 13528, + "TAACTGAAA": 13529, + "TTTTCATA": 13530, + "GTGGCTAA": 13531, + "TGTTGGCA": 13532, + "GCATTCTCAGAAA": 13533, + "CCGGTGA": 13534, + "TACTCCTT": 13535, + "TCAAATATA": 13536, + "TTAATTAAAA": 13537, + "TATTTTATG": 13538, + "GAATAGTA": 13539, + "CTCTGAGAA": 13540, + "CCAAAAATA": 13541, + "CTGTGGCTT": 13542, + "GATCATTA": 13543, + "CACTCTTA": 13544, + "GGGGAATT": 13545, + "GGCTCCTG": 13546, + "GTGTTATG": 13547, + "GTTGTTTA": 13548, + "GCATAGTA": 13549, + "GTAGCCAA": 13550, + "CTATAGTA": 13551, + "CGGGGCA": 13552, + "GAAGAGCAA": 13553, + "TCTCATTGTGGTTTT": 13554, + "GGTAAATG": 13555, + "TCTTCATTTT": 13556, + "TCTCTTTTTT": 13557, + "TACATTTTAA": 13558, + "GGTGCCA": 13559, + "GAAGGCAATG": 13560, + "TCCTCTGTG": 13561, + "TCTTCTTTG": 13562, + "CAGGCAGCA": 13563, + "TCTGGCCA": 13564, + "CTCAGAGAA": 13565, + "CACAAAATT": 13566, + "TAAGAAATT": 13567, + "TTTTTGGA": 13568, + "GATAGGA": 13569, + "GCAAAGAAA": 13570, + "GAGAGGCC": 13571, + "GCTGCTGCTG": 13572, + "GTAAGAGG": 13573, + "CTTTTCACA": 13574, + "CCATGCTG": 13575, + "CAAGGAAAA": 13576, + "CTTCATC": 13577, + "GTTTAATTA": 13578, + "CAAAGAATT": 13579, + "TGAACGA": 13580, + "CTCACGA": 13581, + "CCAACCCC": 13582, + "CTCAAGAAA": 13583, + "TAATTTTCATCA": 13584, + "TAAGTGGGAGAA": 13585, + "CCGGCAA": 13586, + "TAAAGTGAA": 13587, + "CCAAGGGA": 13588, + "CTCCTATG": 13589, + "CTAATGAAA": 13590, + "CGAAGCCA": 13591, + "TCGCGCCA": 13592, + "TAGATTTG": 13593, + "TCGCTGAA": 13594, + "TTTTTCAAA": 13595, + "TGAGCTTA": 13596, + "GTTAGCTT": 13597, + "GTTTTTCCA": 13598, + "GTCCTCTA": 13599, + "GGGGTGAA": 13600, + "TCCTTTACA": 13601, + "GAAGGCAGA": 13602, + "CTGCCTGTG": 13603, + "GCACCCTT": 13604, + "CTATCCAA": 13605, + "CTAGGCTG": 13606, + "CATTTACTG": 13607, + "GGAACCTT": 13608, + "GAGGGGTT": 13609, + "CCCATATG": 13610, + "CTTGGGAGGCTGA": 13611, + "GTTTCGG": 13612, + "GTTCAGTC": 13613, + "CAAAGACC": 13614, + "CATTGAAAA": 13615, + "GGAATCC": 13616, + "TAGGGGA": 13617, + "TAATATCAA": 13618, + "TAATAGCAA": 13619, + "GAAGGCAGG": 13620, + "CAGGTTCAAGCGATT": 13621, + "TAAGAATA": 13622, + "TAACTCCTT": 13623, + "CAAGGGTG": 13624, + "CTTTCTCTG": 13625, + "GAGATCTT": 13626, + "CTGAGTAAA": 13627, + "TCTCAAATG": 13628, + "GCCATAGA": 13629, + "TAATTTAAAAA": 13630, + "CGCTACA": 13631, + "CGTTTCTT": 13632, + "TACCCTCC": 13633, + "GAGAGAGAGAGAGAGAGAGAGAGA": 13634, + "GGGCTTTG": 13635, + "GCGCTGTT": 13636, + "CTGGATCC": 13637, + "GTGTCTGTG": 13638, + "TGTGTGAAA": 13639, + "CAAACACTT": 13640, + "TCTGGGCC": 13641, + "TAAAAGCTT": 13642, + "CCAACAAAA": 13643, + "CAATTCCTG": 13644, + "GTGCAGCTG": 13645, + "CTGTAATG": 13646, + "CTATCTCC": 13647, + "TATTCTGAA": 13648, + "GAAAGTGAA": 13649, + "CATTCTGTT": 13650, + "CAAAATGCTG": 13651, + "TAATTCTGA": 13652, + "GTTCCAGGACAGCCAGG": 13653, + "GAACCCCA": 13654, + "CTCTCCAAA": 13655, + "TCTGGGAAA": 13656, + "GCTTCAGTT": 13657, + "CATGCGG": 13658, + "TCTAGTC": 13659, + "CATACGA": 13660, + "TAATATCA": 13661, + "GCCTTGACTA": 13662, + "CTGCGAA": 13663, + "GGAATACCA": 13664, + "CTAACAGAA": 13665, + "TGCAGTTA": 13666, + "GTATTCCCA": 13667, + "TGATGTCAA": 13668, + "GTGCCCGG": 13669, + "TAATAAATA": 13670, + "TGAGCAGG": 13671, + "CTTCAATG": 13672, + "CTCTCAGA": 13673, + "TATACAGAA": 13674, + "TCCTGGAGG": 13675, + "TCCTGGATG": 13676, + "TACTTTCC": 13677, + "CCTCAAAAA": 13678, + "CCCGGTG": 13679, + "TAACCAAAA": 13680, + "GAATATAAA": 13681, + "CTGGATAA": 13682, + "GTGTAGGA": 13683, + "TAAAATACAA": 13684, + "CTGATCC": 13685, + "GTAATACA": 13686, + "TATGTCCA": 13687, + "TCAAATTCA": 13688, + "GGGAGTA": 13689, + "TCCTTCACA": 13690, + "GAATGGAAA": 13691, + "TATCTTATT": 13692, + "TTACAGTGA": 13693, + "GGGGAAAAA": 13694, + "GTTCGGA": 13695, + "GCATCTTTT": 13696, + "GCAGACTA": 13697, + "GACATACAA": 13698, + "GGTGGTCA": 13699, + "TGTAGATG": 13700, + "CTAAAGAAA": 13701, + "TAGCTAGG": 13702, + "GGATGTGGAGAAA": 13703, + "CAGGGTC": 13704, + "CCTTTAATCCCAGCA": 13705, + "TACCAGGCTCC": 13706, + "CCGCCCC": 13707, + "GGGCCCA": 13708, + "GTTTCTTAA": 13709, + "CTCACCCA": 13710, + "GTAACTTTT": 13711, + "CCAACGA": 13712, + "TGCCCTCC": 13713, + "GCTCAGGCTG": 13714, + "CGTCTCC": 13715, + "CCACAATGAGATA": 13716, + "CTTGACTA": 13717, + "GCAACATA": 13718, + "GTATTCTA": 13719, + "TAGTTCACC": 13720, + "TTTTATGA": 13721, + "GAGGTAAA": 13722, + "GAAAATCA": 13723, + "CCTAGCC": 13724, + "CCGCCAA": 13725, + "TTTTGTCC": 13726, + "CAATATAAA": 13727, + "GTCCAGAAA": 13728, + "GTTACAAAA": 13729, + "CATTATTTG": 13730, + "GCTCTACA": 13731, + "GCTCTTAAA": 13732, + "CAAGGGTT": 13733, + "GTGGATTTT": 13734, + "CAAATGATT": 13735, + "TATGGTGTT": 13736, + "CCTGCTTA": 13737, + "CCCAGCCTT": 13738, + "GGCAGCCA": 13739, + "TCGTAATT": 13740, + "GTGTCTCTT": 13741, + "CTCAGC": 13742, + "CGGGAGGA": 13743, + "CCCCCCCCCCCCCCCC": 13744, + "CCTGTATG": 13745, + "TCAAATGA": 13746, + "CGCAGCC": 13747, + "TGATGTCA": 13748, + "GTGGCCTG": 13749, + "GGACACTA": 13750, + "GTATCTAA": 13751, + "TGTGCTC": 13752, + "TATTTAGTA": 13753, + "TGTTTACTG": 13754, + "CATTCAGAA": 13755, + "CAGGCCTCTC": 13756, + "GTAAGTTTT": 13757, + "GTAATATTTT": 13758, + "GACATAGA": 13759, + "GTTCTTGG": 13760, + "GCAGGAGGA": 13761, + "GGAGCTCC": 13762, + "TCTGAAATG": 13763, + "GTAATGCA": 13764, + "CCTGCCCCA": 13765, + "GGGACCA": 13766, + "GCGGAAAA": 13767, + "CAGTTATG": 13768, + "TGTTTCTTA": 13769, + "TCTAGCC": 13770, + "GCGGTTA": 13771, + "GGATGAGATGGTT": 13772, + "CACCTTAA": 13773, + "CAACCTGA": 13774, + "GTATTTTTG": 13775, + "TCAATTATT": 13776, + "GTCAGGGAA": 13777, + "CTTAGATG": 13778, + "TCCAGCTA": 13779, + "GGGACTCA": 13780, + "TCTCCCAGG": 13781, + "CTGTTTCA": 13782, + "GGCACATT": 13783, + "CTTGAGTA": 13784, + "CTACTCAA": 13785, + "TCTTCCCAA": 13786, + "TCTGTCTCA": 13787, + "GGAACAGAA": 13788, + "TTTTATAAAA": 13789, + "CCAGGGATG": 13790, + "GTTTTGTTTTGTTTT": 13791, + "GCTAAAATG": 13792, + "GTTTCCATT": 13793, + "CAGATAGG": 13794, + "GCTGGGCTT": 13795, + "TCCATCTC": 13796, + "TTAAAAATG": 13797, + "GACAGTC": 13798, + "GGAGCTAA": 13799, + "GCCGAGC": 13800, + "TGTGGATCA": 13801, + "CATTCTCTT": 13802, + "CCTGTCAA": 13803, + "TCCAGAAAAA": 13804, + "CGCGCTT": 13805, + "CATCATTTT": 13806, + "CTCCAGAGA": 13807, + "TACTCATT": 13808, + "GTATCCTT": 13809, + "TGTGCATA": 13810, + "CATTGTCC": 13811, + "TGAATTATT": 13812, + "GCAAGAGAA": 13813, + "GTCAGAGAA": 13814, + "CGGCGTT": 13815, + "CCAAGTCC": 13816, + "CAGTGTTTT": 13817, + "CTTCAGCAAA": 13818, + "GAAGACTA": 13819, + "GGATTATG": 13820, + "CAGGTATG": 13821, + "GAAAATGGA": 13822, + "CAAATGTTA": 13823, + "TAACAGTAA": 13824, + "GAGTCGGACACGACTGA": 13825, + "TCGAGACAGGGTTTCTCTGTGTA": 13826, + "GATCTATG": 13827, + "GAACTATT": 13828, + "TCCTCTCTG": 13829, + "GCCCGTA": 13830, + "TCTGĊ": 13831, + "GCCATCTCTCCA": 13832, + "CGATTATT": 13833, + "TCAAACTCA": 13834, + "GGTACAGA": 13835, + "CATGTGTGTG": 13836, + "CTGGCCTG": 13837, + "TAAAGTATT": 13838, + "CACAGACC": 13839, + "TTAATGA": 13840, + "GACATCC": 13841, + "TGCCTCAA": 13842, + "CAGTCTTA": 13843, + "CAGGTTCAAGTGATT": 13844, + "CTTCTCCTT": 13845, + "TGTTTATCA": 13846, + "CAAATACCA": 13847, + "TAAAATAATA": 13848, + "GTTAGCTG": 13849, + "GAAAAGATG": 13850, + "CATGATTTT": 13851, + "CACATGAAAA": 13852, + "TACGAGA": 13853, + "TCTATATATA": 13854, + "GCGCCCA": 13855, + 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13899, + "CAGAAAATG": 13900, + "GTTCAAGG": 13901, + "GCCGCAA": 13902, + "TAGGGTTA": 13903, + "CAGTGTTTG": 13904, + "CTGCAGTCA": 13905, + "GAATAGCA": 13906, + "GAAAAAATG": 13907, + "GCCCCTC": 13908, + "CTACGTA": 13909, + "TAAACAGCA": 13910, + "TCTGTCTGA": 13911, + "TAAAAGATG": 13912, + "GCTTCCTCA": 13913, + "CTTTTCTGA": 13914, + "TAAAAATATG": 13915, + "TTTTAATTTG": 13916, + "GAGCTAGA": 13917, + "CCAGGCATG": 13918, + "CTCCCACTTAAAAA": 13919, + "GGCGCAA": 13920, + "CAATATATT": 13921, + "GTTCCCTA": 13922, + "CATTCTAAA": 13923, + "CTTATTATT": 13924, + "TTAATGTG": 13925, + "CCTAGTTA": 13926, + "GAATTTCTA": 13927, + "CTTTGTCTT": 13928, + "CTCTGTAGACCAGGCTG": 13929, + "GTGGAAAATAAGTATTTG": 13930, + "CTGTCATG": 13931, + "GCACCCAA": 13932, + "CTCAAAATG": 13933, + "GAGAGGGCA": 13934, + "GACATACA": 13935, + "GGCCAGGATG": 13936, + "TCAGAGCAA": 13937, + "CCAGCCTGGCCAACATG": 13938, + "GCGCTGAA": 13939, + "CTCTGAATT": 13940, + "TATATACTA": 13941, + "GCTGCCCTG": 13942, + "GATTGTCA": 13943, + "TGTATCTG": 13944, + "CAAGAAGG": 13945, + "GGATACTG": 13946, + "CTACCTCC": 13947, + "GTCCAGGCTG": 13948, + "TATAGGTA": 13949, + "GTGATATTTT": 13950, + "GCTGCAGGA": 13951, + "GATTTACAA": 13952, + "CTAACCTG": 13953, + "TGCAGAAAA": 13954, + "TTCATCA": 13955, + "CTGTTTGTT": 13956, + "GCAGGCGG": 13957, + "TAAAGACTT": 13958, + "TCAAAGATCA": 13959, + "TAATTTGTG": 13960, + "GGCAGTAA": 13961, + "GTGAATTAA": 13962, + "GTCTTGCTCTGTT": 13963, + "GCATTTGA": 13964, + "TTTTTTTTTTTG": 13965, + "TGGGTTCAAGCGATT": 13966, + "GGCAGGTT": 13967, + "CTTTCTAAA": 13968, + "GGATCCAA": 13969, + "CTCTCCAGG": 13970, + "CACACAGTG": 13971, + "CTTATCTA": 13972, + "TCAAAGAAAA": 13973, + "TTCATCTT": 13974, + "CGCATTTT": 13975, + "GCGGTGGG": 13976, + "CACTATTATT": 13977, + "GAGACTGGCTAGG": 13978, + "GGCTCTA": 13979, + "CTGGAATG": 13980, + "CACATACACA": 13981, + "CATGCCCA": 13982, + "GCAAGCCA": 13983, + "TGAAATTTG": 13984, + "CCAATTGTGCAAGTT": 13985, + "GCGGAGAA": 13986, + "GTGCCATG": 13987, + "GTTTĊ": 13988, + "TCTTCACAA": 13989, + "TCCATCTCA": 13990, + "CGATTTTA": 13991, + "TCATTTGTG": 13992, + "CCTTCTCCTCCTG": 13993, + "GCGAGAAA": 13994, + "TAATTTTATT": 13995, + "GAACAGCC": 13996, + "CAAGCCTG": 13997, + "GGATGAGA": 13998, + "CTAGTGTT": 13999, + "TATTCATTTT": 14000, + "TATGGTGAAA": 14001, + "GGGGGAAAA": 14002, + "GGCGGGTGGATCA": 14003, + "CAACCCTA": 14004, + "CTTTCACTT": 14005, + "CTGAGCTA": 14006, + "GCAAGGAAA": 14007, + "TCATTTCTA": 14008, + "TCATAGC": 14009, + "GAACGGA": 14010, + "GGACAGAAA": 14011, + "TATACCCA": 14012, + "TATATACTG": 14013, + "CAGATTTTA": 14014, + "GTTTTTGAA": 14015, + "GTATTCACA": 14016, + "TCCCTGCTG": 14017, + "CTTTĊ": 14018, + "GTAGGTTA": 14019, + "CTAGAGTG": 14020, + "CTAGATAA": 14021, + "TAAAAAAATA": 14022, + "CTTACGG": 14023, + "TACAAAAAATTA": 14024, + "CAAAAAATTA": 14025, + "GTGCTGGGATTACAGGCATGA": 14026, + "GTTCGTC": 14027, + "GCCTTTTTT": 14028, + "GCTGCAGTG": 14029, + "CAAAAGTGA": 14030, + "CACCTCTT": 14031, + "TACGCTT": 14032, + "GACTTTTAA": 14033, + "GTGTCTCTG": 14034, + "TGTGGAAAA": 14035, + "CAGAGCAAA": 14036, + "CTCAGGCC": 14037, + "CTCCTGAAA": 14038, + "GTATTTGAA": 14039, + "GATGATTA": 14040, + "CCAGGTTA": 14041, + "GCCAGTCTCCAGA": 14042, + "GGGAAAAAAA": 14043, + "GTGTTTTAA": 14044, + "GCAGAAATT": 14045, + "CTATATAAA": 14046, + "GATGGATA": 14047, + "TAGGCTGA": 14048, + "GCCAAGATT": 14049, + "CTCTGTCTT": 14050, + "TAATGTATG": 14051, + "CAGGACCTT": 14052, + "GAGCATGTG": 14053, + "TAAACTGAA": 14054, + "GTAATATAA": 14055, + "TCCATTATT": 14056, + "TTCAGTCA": 14057, + "TCTCCTGTT": 14058, + "TAAAAAGTA": 14059, + "CAGCTCCTT": 14060, + "CATAATTAA": 14061, + "GCTCCGCCTCCCGGGTTCACGCCATT": 14062, + "TAGTGATT": 14063, + "GGCGGTT": 14064, + "GTGCACTA": 14065, + "GGAACTGAA": 14066, + "GTGACATCA": 14067, + "GTAATTATT": 14068, + "GTTTTGGTT": 14069, + "CACTCCAAA": 14070, + "GGTGAAAAA": 14071, + "GGGAGATT": 14072, + "CAACGCC": 14073, + "TAAAATCC": 14074, + "GCTGGTCC": 14075, + "CCCAĊ": 14076, + "CCTATCTT": 14077, + "TCTCCTGTG": 14078, + "TACCCATG": 14079, + "GAAGGGAGA": 14080, + "TAAAAAAAAATT": 14081, + "TCTGGGTA": 14082, + "GCTTTCCTT": 14083, + "GGCAGAAAA": 14084, + "GCAAATATT": 14085, + "CTGTGCTCA": 14086, + "GCCTTAAAA": 14087, + "GGTCTTTG": 14088, + "TCAAAAATA": 14089, + "TACCTCC": 14090, + "GAGCATTA": 14091, + "TTTTCAAACCACATAAATTTT": 14092, + "TTTTGGTG": 14093, + "GAATCCTT": 14094, + "TCTCAGTT": 14095, + "CTCTGCTCA": 14096, + "CCCCATCTCTACTAAAAATA": 14097, + "GAGCTCCA": 14098, + "GGTCGCA": 14099, + "TAATGATA": 14100, + "CAAGATTTT": 14101, + "GGACCCAA": 14102, + "CTAGACTT": 14103, + "TGAACTCC": 14104, + "GCTGCAGAA": 14105, + "GTCCCTAA": 14106, + "GGGGTGA": 14107, + "CTCTTCACA": 14108, + "TCGCCAGG": 14109, + "GCTGGGAAA": 14110, + "TTCATATG": 14111, + "TACCCTGA": 14112, + "GCATTTCTT": 14113, + "CGTTTTAAA": 14114, + "GACAGGAAA": 14115, + "TATAGGGTT": 14116, + "CAGGCTTA": 14117, + "TCTGGATA": 14118, + "TCTGTACTT": 14119, + "TTTGCATA": 14120, + "GACATCTA": 14121, + "GACCTCTT": 14122, + "CAATTTATA": 14123, + "TAATGTAAA": 14124, + "TAATGGAAA": 14125, + "TAGCTCTTA": 14126, + "CCACCACACCCA": 14127, + "GCGAAGAA": 14128, + "TAACTTCTT": 14129, + "TTTTTGGG": 14130, + "CTTGGATG": 14131, + "GTGAGTCC": 14132, + "GCTGGAGTG": 14133, + "TTAAGATT": 14134, + "TTTGGCAA": 14135, + "CAGCTCTGA": 14136, + "GAAGGGAAA": 14137, + "GTATTATTA": 14138, + "CAAACTGAA": 14139, + "GCCACCATG": 14140, + "TATTTTTATT": 14141, + "TCCTGCATT": 14142, + "GCTCCTCC": 14143, + "TCAGAGTCTTTT": 14144, + "GCGTTCA": 14145, + "CTTGGAGG": 14146, + "GGATCCTT": 14147, + "CCTGGGAAA": 14148, + "GTTATACA": 14149, + "GTTATTTTTT": 14150, + "CCAATCTT": 14151, + "GCCACGC": 14152, + "TAGAGCATG": 14153, + "GGTTCGA": 14154, + "CACTGTTTT": 14155, + "GTAGTCAA": 14156, + "CATGTAAAA": 14157, + "GTCATGTA": 14158, + "CTCCTGCTG": 14159, + "GGTATATT": 14160, + "GTTTGGCTG": 14161, + "CAACACAAA": 14162, + "GCTGCATAGTATTCCATG": 14163, + "TAAAGTCC": 14164, + "GAAACACC": 14165, + "GCTTCAGAA": 14166, + "TGGGTGGA": 14167, + "TAGACAGCA": 14168, + "CTTAATCCA": 14169, + "TTACAGTAA": 14170, + "GTTCAAGCAATT": 14171, + "TGATCTCTG": 14172, + "CCACTATT": 14173, + "CCACTGTA": 14174, + "TATATAGAA": 14175, + "TCTGTGATT": 14176, + "CGGGGGA": 14177, + "CCAACTC": 14178, + "TTAATATG": 14179, + "TCATAGTTTT": 14180, + "TACTTATTTTCCACCA": 14181, + "TAACAAATT": 14182, + "GTGGGGGTG": 14183, + "TCTGCAGCA": 14184, + "GGGGGTA": 14185, + "CATAGTTTT": 14186, + "TGATGCTA": 14187, + "TGACAGCAA": 14188, + "TACGATG": 14189, + "GTCGGCC": 14190, + "CAAACTAAA": 14191, + "GTGATCAA": 14192, + "GGGGGCC": 14193, + "CACTAAAAA": 14194, + "TCAGTTTTA": 14195, + "GTGCTGCA": 14196, + "GCACATCC": 14197, + "TAAACAGTT": 14198, + "GTAAGACA": 14199, + "TCTATTTTTT": 14200, + "TAATGTATA": 14201, + "GTTTGGGAA": 14202, + "CCAGGCAGG": 14203, + "TAGCTAGA": 14204, + "GAGTACTGGAGTGGGGTG": 14205, + "GAGTCATG": 14206, + "GCGTTTCA": 14207, + "TAACGTC": 14208, + "TCATGAGA": 14209, + "GAACGTA": 14210, + "GTTATGCA": 14211, + "CTTTTTAAAAA": 14212, + "CAAAACTTA": 14213, + "CCTCAGCC": 14214, + "CAGTCCATGGGGTC": 14215, + "TCGTGCTG": 14216, + "TTTTTCCA": 14217, + "TGATATATT": 14218, + "TGTGTCCA": 14219, + "TGTTGTCC": 14220, + "CATTTCAAAA": 14221, + "CAGGTGCC": 14222, + "TTTGGGTT": 14223, + "TTTACACA": 14224, + "GCTCAGAGA": 14225, + "GTCTTCTA": 14226, + "CAATGCTA": 14227, + "GCAAAATTA": 14228, + "CTCCTAGA": 14229, + "CTAAGGTT": 14230, + "TAGGCACA": 14231, + "CTGAATGAA": 14232, + "GTAAAGAAA": 14233, + "TCGATTTG": 14234, + "CTGCGCC": 14235, + "TGTTCGG": 14236, + "CTGATTTTA": 14237, + "GTCACCCA": 14238, + "GTTTTTGTG": 14239, + "GTGTTGAA": 14240, + "TTTTTGTTTTTG": 14241, + "TCCCTTCTT": 14242, + "CTTCTGAAA": 14243, + "GTTTCTATT": 14244, + "GGAATCTT": 14245, + "TAAAATCTG": 14246, + "CCAAAAATG": 14247, + "TGGGGATT": 14248, + "CCCCCCCA": 14249, + "TATTAATTAA": 14250, + "CCCGTAA": 14251, + "TCGCCCA": 14252, + "TCCTCAAAA": 14253, + "CTTGAGCC": 14254, + "GTTCATCC": 14255, + "GTATCCAA": 14256, + "GTACTCTT": 14257, + "TATAGAGG": 14258, + "GTAATCTT": 14259, + "GCACCCGG": 14260, + "TCAGTATG": 14261, + "GTGACAAAA": 14262, + "GATGGAGCA": 14263, + "TAATTTACA": 14264, + "TGCAGTTTT": 14265, + "GGTATAAA": 14266, + "CGATTTATTA": 14267, + "CCTTTTATT": 14268, + "CTCAGTAAA": 14269, + "GATAGCTT": 14270, + "TCGGCGA": 14271, + "TCTTTGAAAA": 14272, + "TCCTCTGTCGTCC": 14273, + "CAGATGTGA": 14274, + "CTAACACAA": 14275, + "GTCCCTC": 14276, + "TTTAGAGA": 14277, + "GATAGCAA": 14278, + "CTTTCTGAA": 14279, + "GTGCCTTA": 14280, + "CATGGAGAA": 14281, + "GAAAATAGA": 14282, + "CTTTTTGTG": 14283, + "TAATTCCTT": 14284, + "TAATTTCAAA": 14285, + "CAGGTTCAAGCAATT": 14286, + "TCATAGGA": 14287, + "TCCTTTTAA": 14288, + "CCAGAAGG": 14289, + "CATTCCAAA": 14290, + "TTTGGAGA": 14291, + "TGGACAAA": 14292, + "TAAGCTTTT": 14293, + "TAAGTGAAA": 14294, + "GTGTAATG": 14295, + "GCTGGCCA": 14296, + "CTCACAGTT": 14297, + "CATAGTGAAA": 14298, + "GGGAGCCA": 14299, + "CCCTGAAAA": 14300, + "GGTCCAAA": 14301, + "TGATGAGG": 14302, + "CCAGCTAA": 14303, + "TAAACGTA": 14304, + "GATTGGTG": 14305, + "TTTAGTTTT": 14306, + "CTGAACTGA": 14307, + "CGCCTATAA": 14308, + "CATGTTTA": 14309, + "GAACCATT": 14310, + "CAATAAAAAA": 14311, + "CTAGATTA": 14312, + "CTACCCTC": 14313, + "TCTGCAGAA": 14314, + "CTAACAGG": 14315, + "GGCCTCTT": 14316, + "TGATGCTGA": 14317, + "CCTGGAGGA": 14318, + "TTTACCAA": 14319, + "GTGAAGAAA": 14320, + "GACTCTCC": 14321, + "CCATCATCA": 14322, + "GTACCATT": 14323, + "TAAATCTTA": 14324, + "CTCGACC": 14325, + "GAGAĊ": 14326, + "GCAATCCA": 14327, + "CTGTTGTTTT": 14328, + "CCTCCAAAA": 14329, + "TAGAATAGAA": 14330, + "TAGTGTTTT": 14331, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTG": 14332, + "TATTCAGGA": 14333, + "CAAAGAAAAA": 14334, + "CATTTGCC": 14335, + "CTGAAGTCA": 14336, + "GTCTTCCTT": 14337, + "GTGGGTTTT": 14338, + "CAACTCACAGA": 14339, + "TGATGTGTG": 14340, + "GTTGCTCC": 14341, + "GTAGCTAA": 14342, + "TAAACTCC": 14343, + "TGTGCGTG": 14344, + "CAAATGCC": 14345, + "CAAACATCA": 14346, + "CATGCCTA": 14347, + "TTTTATCAA": 14348, + "TCAAATACTT": 14349, + "TTCAGCAA": 14350, + "GGCTTCCCTG": 14351, + "CCTGAAAAA": 14352, + "TGATCTTTT": 14353, + "GAACACTA": 14354, + "CTGTGAGA": 14355, + "GGACGTA": 14356, + "GTCCAGGAA": 14357, + "TATACGG": 14358, + "GCTGĊ": 14359, + "CATGTGATT": 14360, + "GATGTAGA": 14361, + "GGTTCCTT": 14362, + "CATATTATT": 14363, + "GCCATTAAA": 14364, + "TGGGTTCAAGTGATT": 14365, + "TGACATCC": 14366, + "GAAGCTAA": 14367, + "CATGTCCA": 14368, + "GATGGTTA": 14369, + "CGACACC": 14370, + "TGGAGCC": 14371, + "GAGTTAAAA": 14372, + "GAAGACAGA": 14373, + "CCGGTCA": 14374, + "CTGTGAGG": 14375, + "GAAAGAATT": 14376, + "CATTTGATT": 14377, + "TCTGCCTCA": 14378, + "CCTTCCTA": 14379, + "CTAACAAAA": 14380, + "TTTAGAAAA": 14381, + "TAGGGATT": 14382, + "TGTAGAAAA": 14383, + "TACATTAAA": 14384, + "GGGCTGGG": 14385, + "TCCCAACC": 14386, + "CAAATAGAA": 14387, + "TGAAGTCC": 14388, + "CCCCTCTA": 14389, + "GATAGTGA": 14390, + "CCCTCTTA": 14391, + "CCGTCTATG": 14392, + "CCATCCAACCATCTCA": 14393, + "GGTCTAAA": 14394, + "GTACAGC": 14395, + "GCTTTCTCA": 14396, + "CTTTTTCTA": 14397, + "TTTACAGA": 14398, + "CCCACTCCAGTATT": 14399, + "TCCTTCTCA": 14400, + "GACCCTT": 14401, + "GCCTGTTTT": 14402, + "TCACCTCCA": 14403, + "GTTCCTCTT": 14404, + "CCGATAA": 14405, + "GGTGGGTT": 14406, + "CTGAAATA": 14407, + "GGACCATT": 14408, + "GCAATCTT": 14409, + "CTTACAGTT": 14410, + "GCCAGCAAA": 14411, + "TAGGTATT": 14412, + "GCTATTATT": 14413, + "GCATTTGTT": 14414, + "TAACCCTAA": 14415, + "TGATATAAA": 14416, + "CAAGAGAAA": 14417, + "GGATAATG": 14418, + "CTCTGACA": 14419, + "CATGCCATT": 14420, + "TCTCCATGA": 14421, + "GTCTGTCTG": 14422, + "GACTGTTA": 14423, + "GTATTTTTAGTA": 14424, + "CAGGGTCC": 14425, + "GAGGGTAA": 14426, + "TCATTCTAA": 14427, + "GGTAGCTG": 14428, + "CTGTGTCTG": 14429, + "GAGTTTGGA": 14430, + "GCAGTTTTGAAA": 14431, + "CACCAATG": 14432, + "GGTCCCCA": 14433, + "TCCGCTG": 14434, + "TAAACGAA": 14435, + "TTAAGTAA": 14436, + "TGTAGCTT": 14437, + "TAATATTATT": 14438, + "GCTCATTTG": 14439, + "TCCGGAA": 14440, + "GTTTCAGA": 14441, + "CATTCGA": 14442, + "GAGGGGAAA": 14443, + "GAAAACTT": 14444, + "GCTACGA": 14445, + "CTGAAGTG": 14446, + "TAATATCTT": 14447, + "CGTCTTTT": 14448, + "CTAAATTGCTCAAA": 14449, + "TAATCCCA": 14450, + "TCAGTGGG": 14451, + "GTCGAATT": 14452, + "GTCTCACTG": 14453, + "CACAGAGGA": 14454, + "TCAATCAAA": 14455, + "TCTCTAATT": 14456, + "GGCAGGAGGA": 14457, + "GTGTTGGGATTACAGG": 14458, + "GTTCAAGGCCA": 14459, + "TCGTTTTTT": 14460, + "TCAGCTTTT": 14461, + "GCCTACTA": 14462, + "GAGAGGGGA": 14463, + "CATTCCTA": 14464, + "GCAAGTTTT": 14465, + "CTTTGCAA": 14466, + "TACCCCAA": 14467, + "CATTTGTTTT": 14468, + "TCACAGTCA": 14469, + "GTGATGTTA": 14470, + "TAGAGATG": 14471, + "TATTCTCTT": 14472, + "CACAĊ": 14473, + "TGAAGAGAA": 14474, + "TAAAATATCA": 14475, + "CCTGCAGCA": 14476, + "TAGGCCA": 14477, + "GTTAATTTTA": 14478, + "CTCACAGAGTTGAA": 14479, + "GTTAAAATG": 14480, + "TAAATCTCA": 14481, + "CAGGGAGAA": 14482, + "GCTTCAGCA": 14483, + "GCAATAGA": 14484, + "CCGACAGA": 14485, + "GTTCGCC": 14486, + "CATTCACTT": 14487, + "GATGCGA": 14488, + "TCTACGG": 14489, + "TCATTCAGA": 14490, + "TCATTCTGA": 14491, + "CTGTGGGAA": 14492, + "CCTAATTTT": 14493, + "GTTCAGAAA": 14494, + "TATACGA": 14495, + "GTTAGTCA": 14496, + "TCCACCAAA": 14497, + "GGGGCAA": 14498, + "CCCCATCTCTACTAAAAATACAAAAATTA": 14499, + "TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC": 14500, + "GTAGGTGA": 14501, + "TAAACACTG": 14502, + "GTCTATCC": 14503, + "GAGAGATGA": 14504, + "TTTGCTTTT": 14505, + "GCTCAAGCCTGTAATCCCAGCACTTTGGGAGGCCGAGACGG": 14506, + "GTTGAAGG": 14507, + "CTGAGAGAA": 14508, + "CATGCTTTT": 14509, + "CGGTCTG": 14510, + "TCTCCACAA": 14511, + "TACACCTA": 14512, + "TTAAGAAAA": 14513, + "TCTAGGGA": 14514, + "TTCACTT": 14515, + "GGAAAGGAA": 14516, + "TAATGTATT": 14517, + "TCACTGGG": 14518, + "TCCCAATG": 14519, + "CCACCCTC": 14520, + "CTCTCCACA": 14521, + "TGTGGGCA": 14522, + "TCAATATCA": 14523, + "GAGGTCAGG": 14524, + "GTAAAGAATCTGCCTG": 14525, + "GGGTCAAA": 14526, + "GCTCTCATT": 14527, + "TGACTTCTT": 14528, + "GTTCAAGA": 14529, + "GAGACAGCA": 14530, + "GATTTGATT": 14531, + "CTCCCAGCC": 14532, + "CTTAGCC": 14533, + "GCGGGAGG": 14534, + "CCCTTATT": 14535, + "GACCAAAAA": 14536, + "GTATACTA": 14537, + "CTCAGATA": 14538, + "CCTTGAAAA": 14539, + "CCTGGAGCA": 14540, + "GTTACGTT": 14541, + "TTTAGGA": 14542, + "TAGACAGAA": 14543, + "CATACTTTT": 14544, + "TGGAGCA": 14545, + "TAAGTCTG": 14546, + "TTAĊ": 14547, + "CAATAAATT": 14548, + "CTGGGAGG": 14549, + "GTGGCTC": 14550, + "GAATTGTAA": 14551, + "CACCCTT": 14552, + "TAACATTTTA": 14553, + "TCCCTCCCAGCA": 14554, + "TAACTTAAA": 14555, + "CAACGGA": 14556, + "GTCTCCTGA": 14557, + "TAAAAATGTA": 14558, + "TATAATAAA": 14559, + "GGATGGATG": 14560, + "TATATGTATG": 14561, + "CACTCATG": 14562, + "CACCAAAAA": 14563, + "CCACCTC": 14564, + "GTATAGTG": 14565, + "TATTCATCA": 14566, + "TCTCTGAGTT": 14567, + "CCCACCCTG": 14568, + "TTATTGTTTT": 14569, + "CTGAAGTGA": 14570, + "CTGTTCCTT": 14571, + "TCAGGGAAATG": 14572, + "CTGCCCGG": 14573, + "GAAATTAGGA": 14574, + "TGTGCCACATTTT": 14575, + "TACAAAAATTA": 14576, + "GGAGTATT": 14577, + "GTAGGGGA": 14578, + "GCCCAGGTT": 14579, + "TCTTGAGTT": 14580, + "CTGAGGGAA": 14581, + "TTATTATTTA": 14582, + "GGGCTCAA": 14583, + "GTATTTAGTCAGCCA": 14584, + "GTTGCCATT": 14585, + "CTAGACCA": 14586, + "CATTGGGG": 14587, + "CATGGATA": 14588, + "TACACTC": 14589, + "TCCATCTCC": 14590, + "TTTTTTTCTTTT": 14591, + "GTGTTGGAA": 14592, + "TACCCTCA": 14593, + "CTACCCTG": 14594, + "GAAACTCC": 14595, + "GTGAGTTCCA": 14596, + "CAAAACTAA": 14597, + "CACACACATT": 14598, + "CTTCCTGAA": 14599, + "TGCTGGAGA": 14600, + "TCCATGAGCATG": 14601, + "TCATATAAA": 14602, + "GAACTTTG": 14603, + "CTGCGGA": 14604, + "GCACTATG": 14605, + "CACAGTTTT": 14606, + "GAAACAGAAA": 14607, + "GTCAGGGTT": 14608, + "GTCCCGCC": 14609, + "TGGATGTT": 14610, + "CTCTGCTTTT": 14611, + "GATGTTAAA": 14612, + "CACGCTA": 14613, + "CCCGTTA": 14614, + "CACATAGG": 14615, + "GTTATCTA": 14616, + "TTTAGGAA": 14617, + "CGCTAGG": 14618, + "TGGAAAAAA": 14619, + "GTACAGTAA": 14620, + "CTATAGCA": 14621, + "TATAGGGAA": 14622, + "TCTTCATTA": 14623, + "CTTTGCTGAA": 14624, + "CACCATTA": 14625, + "GGCGATT": 14626, + "TCGCAGA": 14627, + "GTGTACCA": 14628, + "CTCGGTA": 14629, + "CAGACTTTT": 14630, + "GATGCAAAAA": 14631, + "GAAAACGG": 14632, + "TTTGGCTT": 14633, + "TCTAĊ": 14634, + "TAGATAAAA": 14635, + "GGCATATA": 14636, + "TAATACTTA": 14637, + "CTGTTCATT": 14638, + "TACCCATT": 14639, + "GTCATTAAAA": 14640, + "TAAATATTTTA": 14641, + "TGATCTCC": 14642, + "GTTTAATAA": 14643, + "GGACATC": 14644, + "TAAATCAAAA": 14645, + "CCTGGGTGA": 14646, + "TGTACTCA": 14647, + "TATTATATT": 14648, + "CTCTGGCTG": 14649, + "CTATTCCTT": 14650, + "GAAGGTGAA": 14651, + "CACTTTTAA": 14652, + "GAACCCTT": 14653, + "CCAGCGG": 14654, + "GTGAGATA": 14655, + "TCTCTGAGA": 14656, + "GGGGGGGGGGGGGGGG": 14657, + "TCAGTAAAA": 14658, + "GTGGCTTTT": 14659, + "TGTTCGA": 14660, + "CAAACAATT": 14661, + "GTGACAGAA": 14662, + "GTAAGGGG": 14663, + "TCAGGTTTT": 14664, + "TCAGCAGCA": 14665, + "GTGATAGG": 14666, + "TACAGCTA": 14667, + "CGCGAA": 14668, + "TTTTTTTAATT": 14669, + "GGATTTCTT": 14670, + "CTCCAGCTG": 14671, + "CTCCCTTTT": 14672, + "TTCATGAA": 14673, + "CGCTCGG": 14674, + "CAGCATTTT": 14675, + "CAATATCC": 14676, + "GCCCAGAGA": 14677, + "CACAGTC": 14678, + "GAAATATGA": 14679, + "CATTTAGTT": 14680, + "CCCATAAA": 14681, + "GGGAGAATG": 14682, + "CTCTTCCA": 14683, + "TGAGAAAAAA": 14684, + "GTTCCCTC": 14685, + "TATTTACCA": 14686, + "TCTTTCTAA": 14687, + "CAGACTC": 14688, + "GTAAGCTT": 14689, + "GAAAAGAGG": 14690, + "GACACAAAA": 14691, + "TTTTTTTATA": 14692, + "GAACCCAGGAGGCAGAGGTT": 14693, + "GAGTGAAAAA": 14694, + "CTAGTATT": 14695, + "CTATCTAA": 14696, + "GAAACGAA": 14697, + "TGTTGGTA": 14698, + "GATTTCTTA": 14699, + "CTAAGTTTT": 14700, + "TACACTGAA": 14701, + "TACATAAAAA": 14702, + "CCCAGCCAA": 14703, + "GCTACAAAA": 14704, + "GGCCTATG": 14705, + "TGATTGGTG": 14706, + "GCATTCTTTG": 14707, + "TGTTTGAAAA": 14708, + "GTGATGTTTT": 14709, + "TCATATCC": 14710, + "TCAGAAGA": 14711, + "CTGGTCAA": 14712, + "CATTGTCTT": 14713, + "TCTCTATTA": 14714, + "CTTTGCTT": 14715, + "GTGTTGTG": 14716, + "TATCAAAAA": 14717, + "GCCGAGCA": 14718, + "CAGGTCTA": 14719, + "CCAACATCA": 14720, + "CTTAGCTG": 14721, + "GACAGCCA": 14722, + "GATAGAGG": 14723, + "TTTTTTTAAAA": 14724, + "CGTGCCTG": 14725, + "CAATTTGTT": 14726, + "CGCCCGCC": 14727, + "TAGCTCTA": 14728, + "GCCACCATGTG": 14729, + "CCATATCC": 14730, + "GCCTCTCTG": 14731, + "TGTGTGCC": 14732, + "CAAAGTGAA": 14733, + "CTCACAGCA": 14734, + "GAAAAGTCA": 14735, + "TTTGGCA": 14736, + "TGCATCTT": 14737, + "TGCATCTG": 14738, + "GGCCCTAA": 14739, + "CACCCTAA": 14740, + "GCTCAGGAA": 14741, + "CTGCTTCTG": 14742, + "TGTGCGCA": 14743, + "CAAACACTG": 14744, + "CATGGGGA": 14745, + "CCAACACCA": 14746, + "TGGAGTGGCCTA": 14747, + "GGTTTTTTTT": 14748, + "TCACGCCA": 14749, + "TGATTTTTG": 14750, + "TTATTTTAA": 14751, + "CATTAAAAAA": 14752, + "GTCATCCA": 14753, + "CCCCTCCCC": 14754, + "TCCTTTGTT": 14755, + "CCAGGCACA": 14756, + "CTGTAGCCTT": 14757, + "TCCCCAAAA": 14758, + "GGATAGTT": 14759, + "GGATAGCA": 14760, + "TATTGATTA": 14761, + "TAAAATCAAA": 14762, + "CCTTGGGA": 14763, + "GAAAAGTG": 14764, + "GGAAAGAGA": 14765, + "CATCTTTTA": 14766, + "TCGTGTA": 14767, + "GAATGAATT": 14768, + "TCCTAGTA": 14769, + "GGATCATG": 14770, + "CCACTGGG": 14771, + "CTCTCCCTG": 14772, + "GGAAGAGCA": 14773, + "CTCACACTG": 14774, + "TCTCTCTAA": 14775, + "CTCCTCTGA": 14776, + "GTTTTTTCA": 14777, + "GTCCCCAAA": 14778, + "GGCATCAA": 14779, + "GGCACGA": 14780, + "GCTCAGCTG": 14781, + "TAAAAACTAGACAGAA": 14782, + "GGTCCACA": 14783, + "TCGCCATG": 14784, + "TGATAATAA": 14785, + "GGACCCTT": 14786, + "CTACTTTG": 14787, + "GTCTĊ": 14788, + "CAGACAGTG": 14789, + "TATGAAATG": 14790, + "GATTCGG": 14791, + "CATACACACACA": 14792, + "TAATTGTG": 14793, + "TCCTTTCTG": 14794, + "CAAGGAGG": 14795, + "GTTGGCTA": 14796, + "CCATAATG": 14797, + "GCCTATGA": 14798, + "CTAGGCTT": 14799, + "CTCGGGAA": 14800, + "TCTCTACTG": 14801, + "TCTCTACCA": 14802, + "TTTGTTTTA": 14803, + "CCTACGA": 14804, + "GCTGACTG": 14805, + "GGATGGGGG": 14806, + "GGAGGAGAGG": 14807, + "TCGCACC": 14808, + "CCAGATTA": 14809, + "GATTTCTGA": 14810, + "TCCAGCAAA": 14811, + "TGGGGTATT": 14812, + "GGAAATCAA": 14813, + "GTGGGGATG": 14814, + "TCCTCCTGCC": 14815, + "TATTTGCAAATTATG": 14816, + "TACATTTA": 14817, + "TCATTTTTG": 14818, + "CAAAGGGAA": 14819, + "CATGTCTA": 14820, + "TATGTAGAA": 14821, + "CTGTGGAAA": 14822, + "CTCCATCTT": 14823, + "CTGGAGCTG": 14824, + "TAAGGTTAA": 14825, + "GTAATTAAAA": 14826, + "GACCAATG": 14827, + "GAGGCTTTT": 14828, + "CCTGTATA": 14829, + "GTCCTATT": 14830, + "CCATGTCCA": 14831, + "GTTTCTAAA": 14832, + "CATTTCATG": 14833, + "CAAAATAGA": 14834, + "GGTGCAA": 14835, + "CATATAAAAA": 14836, + "GTGTTTCTG": 14837, + "GTGTTGCTT": 14838, + "TGATGGAGG": 14839, + "GCGGTTTT": 14840, + "TCCCAGCTT": 14841, + "CTGGAGGAA": 14842, + "GGGTTTTTT": 14843, + "GAATAGGA": 14844, + "GCCCATAA": 14845, + "CTATTATTA": 14846, + "TGTGĊ": 14847, + "TGTTCTCTT": 14848, + "CTGATACA": 14849, + "TATGGGGG": 14850, + "GACATAGG": 14851, + "CCCCCGTG": 14852, + "GCCAAGATG": 14853, + "GATCACACCA": 14854, + "TCAGACTA": 14855, + "GTGGCTCATGCCTG": 14856, + "GCATACCA": 14857, + "GCACAGAAA": 14858, + "CTCGCTCA": 14859, + "TTTGCTAA": 14860, + "TTATTGTAA": 14861, + "TCATTGTCA": 14862, + "TGATTGGTT": 14863, + "CAAAACAAAACAAAA": 14864, + "GGCTCCAA": 14865, + "GTTGTAAAA": 14866, + "GTACTCAA": 14867, + "CTCGATA": 14868, + "CTCTCACC": 14869, + "TGTTTAGTT": 14870, + "GGCAGTTA": 14871, + "TGAAATAAA": 14872, + "TAATACCAA": 14873, + "GTTTGCTGA": 14874, + "GTGGGGAGA": 14875, + "AAAATGTTA": 14876, + "GCTCAAATT": 14877, + "TCACTTGG": 14878, + "TCCGATG": 14879, + "GTTGCCTG": 14880, + "CTCTATAAA": 14881, + "CTCATGTA": 14882, + "CGGTCTT": 14883, + "CCTGCATA": 14884, + "CCTGGGCTG": 14885, + "CTAAGATA": 14886, + "CAAAAGATT": 14887, + "CTGTGCCTT": 14888, + "CATCCAAAA": 14889, + "TAACAACA": 14890, + "CTATCCTT": 14891, + "TGTGGCCA": 14892, + "CCAAGATA": 14893, + "GGAAATTTT": 14894, + "GAATTCTT": 14895, + "CTCTGGAGA": 14896, + "CAGCTCTCA": 14897, + "GATAATTAA": 14898, + "TAAGTTTA": 14899, + "TCACTTTTA": 14900, + "TCCTAAAAA": 14901, + "GGATACTA": 14902, + "CTATCTGA": 14903, + "TCAACATCA": 14904, + "GCTGGGTA": 14905, + "CGGGACA": 14906, + "TCCATCCTT": 14907, + "CGATAAAA": 14908, + "CAGCTCAAA": 14909, + "TTTTTTTTTTTA": 14910, + "GCTTTTAAAA": 14911, + "CATTTGTTA": 14912, + "TCACCCAGGCTGGAGTG": 14913, + "TCTTCAAACCCATGCTAAATTGCTCAAA": 14914, + "GATCTACA": 14915, + "GCTCTCAAA": 14916, + "TGACCCTA": 14917, + "TCCGCCA": 14918, + "CTTTCCAAA": 14919, + "TAAATAGTA": 14920, + "CAAAGACTT": 14921, + "CAGATGCC": 14922, + "CTCCTCCAA": 14923, + "CTAATAGA": 14924, + "TACAGTTAA": 14925, + "TTATTTAATT": 14926, + "TTTTTTAAAAAA": 14927, + "TGATTGATT": 14928, + "GTTTGCTAA": 14929, + "GCGGCGCC": 14930, + "AAAATTTATG": 14931, + "TCACTGCAGATG": 14932, + "CGGTGGCTCACGCCTG": 14933, + "CCCCTTTG": 14934, + "GTCGATA": 14935, + "GAAACGTT": 14936, + "GAAACACTG": 14937, + "GTGACATA": 14938, + "TCTCCAGTA": 14939, + "TACATACTT": 14940, + "CTGAAGGAA": 14941, + "CAATTCTGA": 14942, + "CCTCCTTA": 14943, + "CTTCAGACGG": 14944, + "GGTCCAGA": 14945, + "GTGTCAGG": 14946, + "CTAGCTAA": 14947, + "CTCTCAATT": 14948, + "CTTATGCA": 14949, + "GAAAACAGAA": 14950, + "CTTTTTATTTT": 14951, + "TCATTTCCA": 14952, + "GCTGTGCAGAA": 14953, + "CAAGTCTG": 14954, + "CAGACGGA": 14955, + "GTCACATA": 14956, + "GATTTCTCA": 14957, + "CCCAGCAAA": 14958, + "TACCTATA": 14959, + "CTGTGGAA": 14960, + "GAGCAGAGA": 14961, + "GCTCTGCCTCCCGA": 14962, + "GCGCGCC": 14963, + "CCATATGAA": 14964, + "CCAATGAGTCAA": 14965, + "GGACCAGG": 14966, + "CTAGATGA": 14967, + "TAAAGGAAAA": 14968, + "GGAAGGTA": 14969, + "GTTATATAA": 14970, + "CCCAGCTCA": 14971, + "TAATTTTTTA": 14972, + "GCGACGA": 14973, + "CCCGATG": 14974, + "CCTGAATG": 14975, + "GGCCAATG": 14976, + "GGACTTGG": 14977, + "GTCGACA": 14978, + "TCCACTTCTG": 14979, + "TTTGTAGA": 14980, + "GTGCTAGGATTACAGG": 14981, + "CCCGCAA": 14982, + "CAAGATCC": 14983, + "TGTGGATA": 14984, + "TCTGCCCTG": 14985, + "GCTTTAGAA": 14986, + "TTTGGAGG": 14987, + "GTCCTAA": 14988, + "GGGGGCTT": 14989, + "TCCGTTA": 14990, + "GTTTAGC": 14991, + "CTAGCCTG": 14992, + "TAAATTTTAA": 14993, + "TGTGCAGTT": 14994, + "TATTTCTGTG": 14995, + "TATATAGTT": 14996, + "GAGACATA": 14997, + "GCTGGTTTT": 14998, + "GATTTAATT": 14999, + "CCCAGTTTT": 15000, + "CAAAATGCA": 15001, + "TGTACCA": 15002, + "GCTGAGGAA": 15003, + "TCAAAATTAA": 15004, + "GCATGCTG": 15005, + "CTGGGGCTG": 15006, + "CTCCACCTT": 15007, + "CTTTCCATT": 15008, + "GTAGCTTA": 15009, + "CTCACAGAA": 15010, + "TTTTAGTTTT": 15011, + "GAGGCAGCA": 15012, + "TTTGCAGA": 15013, + "GCCACCCCA": 15014, + "TGTATAAAA": 15015, + "CTGTTCCTG": 15016, + "CGCCAAAA": 15017, + "GAGCTATG": 15018, + "GCTCCCAGG": 15019, + "CCTGTAATCTCA": 15020, + "TACGCTG": 15021, + "TAGCTTTG": 15022, + "GCGTGAAA": 15023, + "TTAGGTG": 15024, + "GTATAGCA": 15025, + "CACAGACTT": 15026, + "TGTTGGAAA": 15027, + "TGTTGGCTT": 15028, + "CTGATCTGA": 15029, + "CTATTTCTA": 15030, + "GAGTATTTT": 15031, + "CGTCAACTATGACAGA": 15032, + "TCATCCAGG": 15033, + "CTTCTAAAA": 15034, + "GGATAAAAA": 15035, + "TATTGAGTG": 15036, + "GAAATAATG": 15037, + "CCTGGGCAA": 15038, + "CCCCCGCA": 15039, + "GTAATGTCTCTGCTTTT": 15040, + "CTTGTTTTA": 15041, + "TGTTCTTAA": 15042, + "CTGAĊ": 15043, + "GTCATCTA": 15044, + "GGTTTAA": 15045, + "CTATTTATA": 15046, + "GCCTATTTT": 15047, + "GCCTCAGTT": 15048, + "CTACCCCA": 15049, + "TGTTCAGTG": 15050, + "CAGAGTCAA": 15051, + "TCTCCATTTT": 15052, + "CAATTCTAA": 15053, + "GCATTTCTG": 15054, + "GATGAGAGAGGCCTG": 15055, + "TCGCCTCC": 15056, + "GGAGCTTA": 15057, + "GTAGTTTTA": 15058, + "CATGGAGAAA": 15059, + "TCTCTAGAA": 15060, + "CAAAAGAGA": 15061, + "TAGGTAGA": 15062, + "GCTAGCCA": 15063, + "CACCTCAA": 15064, + "CCTCCCAGG": 15065, + "GCAGGGAGA": 15066, + "TACAGGTAACGA": 15067, + "GGTATTTA": 15068, + "TCGCATA": 15069, + "CTTTGTGTT": 15070, + "GCACCTC": 15071, + "GGAGCATG": 15072, + "CCCAGCACC": 15073, + "TCTTTGGTCTT": 15074, + "GCCTCAGCCTCC": 15075, + "TCACAAGG": 15076, + "CAACCTAA": 15077, + "GTTTCCCCA": 15078, + "GCCGTAA": 15079, + "TATTCACAA": 15080, + "CAGACACCA": 15081, + "GATTGGGG": 15082, + "CTCCTACA": 15083, + "TGGGTTAA": 15084, + "CCTACCATT": 15085, + "TCATTGCAA": 15086, + "TCCCATCTG": 15087, + "TGATTATTA": 15088, + "GTAGGGTT": 15089, + "CTCTCTCAA": 15090, + "CTAATGGA": 15091, + "TGTAGTGA": 15092, + "GAAGGGGAA": 15093, + "GGAGAGTCA": 15094, + "CTACATATG": 15095, + "CCATCTCTT": 15096, + "TCTTCAGCA": 15097, + "GTTATGGA": 15098, + "TGCACTCA": 15099, + "GCTCTTTAGTT": 15100, + "TACCAATT": 15101, + "GGGATTAA": 15102, + "GCCCTGCTG": 15103, + "GTCGCGG": 15104, + "GTCTCCTCC": 15105, + "TATTGGCA": 15106, + "GAAACACTT": 15107, + "CAAACATAA": 15108, + "TCAATACAA": 15109, + "TACAGAAAAA": 15110, + "CCCAGAATT": 15111, + "TCTCATTCA": 15112, + "GCGCCATG": 15113, + "TAAAGACACATG": 15114, + "TGACTTGG": 15115, + "CTGCACTA": 15116, + "CTTCCAAAA": 15117, + "GTAGACTA": 15118, + "CACACTTTT": 15119, + "TGAAGTAAA": 15120, + "CAGACACTT": 15121, + "TACAGTGTT": 15122, + "GGTTGAGA": 15123, + 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15169, + "CATTTAGTG": 15170, + "CATGCACTG": 15171, + "GTAAGATA": 15172, + "CTCCTCCTCC": 15173, + "CTGGGTGGG": 15174, + "AAAAAAAAAAAAAAAAAAAAAAA": 15175, + "CCCCAATT": 15176, + "GGTCTCAA": 15177, + "TCCGAGA": 15178, + "GCCTCTTTT": 15179, + "GTCCATTTG": 15180, + "TGAATATAA": 15181, + "GCTTGAAAA": 15182, + "GCTGGAGGA": 15183, + "GCCTGCAGG": 15184, + "TCCCACCTG": 15185, + "CAAACAAACAAA": 15186, + "GCAGACGCTCTTATCCAGAGCGACTTA": 15187, + "GGTCTTAA": 15188, + "GATGTTTA": 15189, + "GCTCTCCTG": 15190, + "GTATACCA": 15191, + "GAAATGCC": 15192, + "TATACCTA": 15193, + "TAAAATACTT": 15194, + "TCATTCCA": 15195, + "TCATTGTTA": 15196, + "GCATTGCCTT": 15197, + "GAGCCTGGG": 15198, + "AAAATTAAAATT": 15199, + "TATATATATATATATATATATATATATATATATATATATA": 15200, + "CTATTGTGAATAGTG": 15201, + "TTTTGATA": 15202, + "CACAGGCTG": 15203, + "TGAATAATT": 15204, + "GCTAGCTAA": 15205, + "TTTTTTTCAA": 15206, + "CTCTGTCTG": 15207, + "CTCTGTAGA": 15208, + "GGACAGCAA": 15209, + 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"TGTTGCTA": 15297, + "CATGCGTG": 15298, + "GATGGGCA": 15299, + "GGTATCTG": 15300, + "TAATGCTG": 15301, + "GAACAGCAA": 15302, + "TCTGTCTGTCTG": 15303, + "GGGATTTG": 15304, + "TCGTTCTG": 15305, + "CTTGCATG": 15306, + "GTGCTTGG": 15307, + "CTAGGTCA": 15308, + "GTCGCCA": 15309, + "GAGGTTCA": 15310, + "CCCATAGG": 15311, + "CTGAAGAGA": 15312, + "TACCTCCA": 15313, + "TAGCTACA": 15314, + "CCCTCCAAA": 15315, + "TCGTAGG": 15316, + "GGACTATT": 15317, + "CTCTCATA": 15318, + "TGTTGTGTT": 15319, + "GCTGGAAAA": 15320, + "TCTGTAGATT": 15321, + "TCTCCTGACCTCA": 15322, + "CCAATTGTGCAAGTTCTCCCACTTAAAAA": 15323, + "TTATCCA": 15324, + "CAAGTGTG": 15325, + "GTTGTTATT": 15326, + "GGATACCA": 15327, + "CATTCCCC": 15328, + "TAAAAĊ": 15329, + "GTCCCAGGA": 15330, + "CTTTTCCTG": 15331, + "GGGGCGTG": 15332, + "CGACAGAA": 15333, + "GGTTCAGG": 15334, + "GATCATTTT": 15335, + "TATCAATG": 15336, + "CTTCTATG": 15337, + "GCAGTATG": 15338, + "GTATCTTTT": 15339, + "CACACTCC": 15340, + "GGTTGCTG": 15341, + "CCTATCTG": 15342, + "TCTCCCAAA": 15343, + "CTTTATCCA": 15344, + "TCGTCTA": 15345, + "GTACGCC": 15346, + "GTACTGTG": 15347, + "CTCAGGTGA": 15348, + "CCTTCCCAA": 15349, + "TCAAATAA": 15350, + "TAAAAATTAAA": 15351, + "GACATTAAA": 15352, + "CAATAACAA": 15353, + "GTGTAATA": 15354, + "CCAGCTC": 15355, + "GTAGGAGG": 15356, + "CTAGCTCA": 15357, + "CTAGAATG": 15358, + "CTCTGTGTG": 15359, + "CCTTTATCA": 15360, + "GTTATATTA": 15361, + "TACAATTAA": 15362, + "GGACACGG": 15363, + "GGTCCTGGAGTGGCCTA": 15364, + "TCCTCTCCAGCA": 15365, + "GAATGAGG": 15366, + "GCACGTC": 15367, + "CATTCCATT": 15368, + "GCTTCAAAA": 15369, + "GTCAGAGCA": 15370, + "GACAGTTTT": 15371, + "TACCCAGA": 15372, + "TAACACAAA": 15373, + "TGTGTGTGTGTGTGTGTG": 15374, + "CATCTTAAA": 15375, + "CGCCTGTAATCCCAGCTA": 15376, + "GGGCCAAA": 15377, + "GCGTGTA": 15378, + "TCCCTTGG": 15379, + "TATTGCCA": 15380, + "TGAATCTA": 15381, + "CTCATAA": 15382, + "CCTTGGAAA": 15383, + "CGACAGC": 15384, + "GGTTGTTA": 15385, + "GCAAAGAGA": 15386, + "GCCAAGACCAAA": 15387, + "GGTTATTTT": 15388, + "TCACAGTGA": 15389, + "TAAAATGAAAA": 15390, + "TCCCCAGA": 15391, + "GTACAATG": 15392, + "TCTGTAGAA": 15393, + "GCTGTGATT": 15394, + "TTTTAGAGA": 15395, + "GCCAAGGA": 15396, + "TGATGGCCA": 15397, + "TATCGTGA": 15398, + "TCCCCCTA": 15399, + "CAAGCGATT": 15400, + "GTTGCTGTT": 15401, + "GCCCAATT": 15402, + "TATTGGAAA": 15403, + "CACAGGCC": 15404, + "CCTTCAAAA": 15405, + "GCAAĊ": 15406, + "CCCCCTTA": 15407, + "CCTATCTA": 15408, + "GAATTGTGA": 15409, + "CGCCCACCA": 15410, + "TAATTATTTA": 15411, + "GTTTACAAA": 15412, + "GGAGAATCACTT": 15413, + "GCCCACCAGGCTCC": 15414, + "GAATCATG": 15415, + "CTAGTCTT": 15416, + "TGTTTGCC": 15417, + "TTTGAAATG": 15418, + "TAATGTCA": 15419, + "GAGGTGGAGGTT": 15420, + "TCTCCTGACCTTGTGA": 15421, + "GAGACTGGCTAGGCCACTC": 15422, + "TAGTCCA": 15423, + "GAAGCGG": 15424, + "CCATCAGA": 15425, + "CCCCCTAA": 15426, + "GACCCTAA": 15427, + "TGAATTAAAA": 15428, + "TCGATATA": 15429, + "TGACCTC": 15430, + "GTGAAAATT": 15431, + "TCTTGCCA": 15432, + "CAGGTGTGA": 15433, + "TCAATATAA": 15434, + "CTGACATA": 15435, + "GTAACCTG": 15436, + "CCCAGCTAA": 15437, + "TATGTGAAA": 15438, + "CGTTTTATT": 15439, + "GCTTCTGAA": 15440, + "TACAGAATT": 15441, + "GTCCTGGAA": 15442, + "GGTTTATG": 15443, + "GCTCAGAGG": 15444, + "CTCTGCATT": 15445, + "GAATATTATT": 15446, + "CACACTACGCC": 15447, + "TACGAGG": 15448, + "GACTAAAAA": 15449, + "GAACAGAAA": 15450, + "GCACACTA": 15451, + "CTATTATG": 15452, + "GTCTCAGAA": 15453, + "GCTGCTGTT": 15454, + "GATTTCATT": 15455, + "TACACGCA": 15456, + "TTTGTCAA": 15457, + "TTTGCGA": 15458, + "CTGTGGAGG": 15459, + "GTTTAGAAA": 15460, + "GCCCCCTCC": 15461, + "TCATTATA": 15462, + "TCCAAAATT": 15463, + "CTTGAAATT": 15464, + "CCAGCTTA": 15465, + "TAGGCTAA": 15466, + "TCAAAGCTG": 15467, + "CGTGGATG": 15468, + "TGCCCAAA": 15469, + "CGCCCAGCC": 15470, + "CTTCACCTT": 15471, + "CTCCAATGCATGAAAGTGAAAA": 15472, + "GTTTCCTCA": 15473, + "GTAGGGCA": 15474, + "GCCTCAAAA": 15475, + "GTTTATTTG": 15476, + "TATTTATTTA": 15477, + "TGAAGACAA": 15478, + "CAGATAATT": 15479, + "GAGGAGAAA": 15480, + "GAAGACAGG": 15481, + "CCACAGCAA": 15482, + "GACGTGTACCTA": 15483, + "TGAGCAAAA": 15484, + "GTTGTGGG": 15485, + "TCTTTAGAA": 15486, + "TGTTGTTAA": 15487, + "CTCACTTTT": 15488, + "GACAGTAAA": 15489, + "GGTGGCAA": 15490, + "GATAGTTTT": 15491, + "GCAAACAA": 15492, + "GCCTTGAA": 15493, + "GAAGGCCAA": 15494, + "GTGTAAAAA": 15495, + "TAAATAAATG": 15496, + "TATGĊ": 15497, + "GGGGGGA": 15498, + "GCGGGCTG": 15499, + "TGATACAAA": 15500, + "CCGATTA": 15501, + "TACGCAA": 15502, + "CTTTCTATT": 15503, + "GTTTCTGAA": 15504, + "CCAGATCC": 15505, + "GCCTCAGA": 15506, + "TATACTTAA": 15507, + "GTCACTTTT": 15508, + "GGGGCCAA": 15509, + "TGGGTTTA": 15510, + "GCAAACTG": 15511, + "GGCCCCCA": 15512, + "GCTCAGTC": 15513, + "CGAAGAA": 15514, + "CCCTCAAAA": 15515, + "TCTAAATTA": 15516, + "GCTGGCTGA": 15517, + "CTCACATA": 15518, + "GTAATAGG": 15519, + "CTCCĊ": 15520, + "TGGACAGG": 15521, + "TAATACACA": 15522, + "CTATTGTTTT": 15523, + "TACCATTTT": 15524, + "TAGACGGCAGCC": 15525, + "GTTTCTGTG": 15526, + "TAAACCCTT": 15527, + "TGTTTACAA": 15528, + "CATTCATTA": 15529, + "TCAACTGTG": 15530, + "TACATAGAA": 15531, + "TGGGGCA": 15532, + "CGGAGCTG": 15533, + "CTGCACTCCAGCCTG": 15534, + "GCTGGGTCTT": 15535, + "TGACCAACCTAGA": 15536, + "TAGCTTAA": 15537, + "TGTGCGA": 15538, + "TGAAAAATA": 15539, + "TCAATAGAA": 15540, + "TATGTACAA": 15541, + "TTAATATTA": 15542, + "TGGGGACA": 15543, + "GGTACTTA": 15544, + "CAAAAATATT": 15545, + "CGTATTTA": 15546, + "CCTCCTGA": 15547, + "GATGATTTT": 15548, + "GTACATATA": 15549, + "GTGCTGGGATTACAGGTGTGA": 15550, + "CTGTAAAAA": 15551, + "CCTTGACC": 15552, + "TAGATCTG": 15553, + "CCAAAGCAA": 15554, + "GAGAAGAA": 15555, + "TCGGAGGA": 15556, + "TCACGTGA": 15557, + "TAAAGATTA": 15558, + "GTCGGGA": 15559, + "TGAACTTTT": 15560, + "GGAAGGGGA": 15561, + "TTTTAGTAA": 15562, + "TTTGAAATT": 15563, + "CAATTGATT": 15564, + "GGAGGCCTG": 15565, + "GTTTGCATT": 15566, + "CTTCCCTGA": 15567, + "GCTGAAACTCCAA": 15568, + "GGGTCTCA": 15569, + "TCGCCCTG": 15570, + "GCGTTGA": 15571, + "CAAGCTCC": 15572, + "TGTGGGAAA": 15573, + "TATTGTGTT": 15574, + "TAAAAGTTTT": 15575, + "CCAATACAA": 15576, + "CATTTTTTAA": 15577, + "CTCCAGTGA": 15578, + "TCTTAATTA": 15579, + "TCAGACAGG": 15580, + "GTGCTGGGATT": 15581, + "GCTTTGCGCCTT": 15582, + "CCAAAAAAAA": 15583, + "GGTTTTATT": 15584, + "CATTCAGTT": 15585, + "CCAACAGGA": 15586, + "TGGGTGTT": 15587, + "GTCTTTCTT": 15588, + "CAGTGGAA": 15589, + "TAACATATT": 15590, + "GAACATCTT": 15591, + "GGCACATG": 15592, + "TCACATC": 15593, + "GGAGAAATT": 15594, + "GTCGGTA": 15595, + "GCTGCAGCC": 15596, + "CTCAGCAAA": 15597, + "TCTCCATTA": 15598, + "GTTAGGGG": 15599, + "TGCCCCTT": 15600, + "GTTCATATG": 15601, + "CCATAGCTT": 15602, + "GCGCCTCC": 15603, + "CCAGACTA": 15604, + "GTACCCTT": 15605, + "GTACCTGA": 15606, + "TGAATATTTT": 15607, + "CTAAGGCA": 15608, + "CAAAACAGG": 15609, + "TGTGTGTTTT": 15610, + "GTTATTAAAA": 15611, + "TATTCAGTT": 15612, + "TATTTCATG": 15613, + "CTGAGATTA": 15614, + "GATTGCC": 15615, + "TTAATTATT": 15616, + "CCCATCTA": 15617, + "GACAGTGTG": 15618, + "CCAAACAAA": 15619, + "TAATAGAGA": 15620, + "TGATTTAA": 15621, + "GCCAAGACAA": 15622, + "CGCACGG": 15623, + "GGAGGGAAA": 15624, + "TACCCGG": 15625, + "GAGTTTCAGCTT": 15626, + "GACTTTGTT": 15627, + "CTGGGCAGG": 15628, + "TGACAGAGA": 15629, + "TAAACATGA": 15630, + "TCAAGCCA": 15631, + "TCAACAGCA": 15632, + "GTGAGGCTG": 15633, + "GAGGCTTA": 15634, + "CCCCGGGG": 15635, + "CCAAATGAA": 15636, + "GGTAGTG": 15637, + "CGCCTTA": 15638, + "CCCTCTCC": 15639, + "CTGCCCAGG": 15640, + "GGGTCCTG": 15641, + "GTTCTATG": 15642, + "GTTGAAGA": 15643, + "GTAGATTTT": 15644, + "GCTGTCCTG": 15645, + "GGTGCTT": 15646, + "GCTAGAAAA": 15647, + "TCAAAGGAA": 15648, + "GCAAATGTT": 15649, + "CTGGGTTTG": 15650, + "TAGCTCTGA": 15651, + "CTTTACAAA": 15652, + "GGCGTCC": 15653, + "GCGCCACA": 15654, + "CAATCAAAA": 15655, + "CCACTATG": 15656, + "GTAGGATT": 15657, + "TAAACTATT": 15658, + "GGAAGAAAAA": 15659, + "TAGGCCTT": 15660, + "GGTGTATG": 15661, + "TCAAAGTCA": 15662, + "CTGAAGCAA": 15663, + "TATAAATATA": 15664, + "TCACGTC": 15665, + "CTGTCTAA": 15666, + "GTACCTTA": 15667, + "TCTGCGA": 15668, + "CAGACATGA": 15669, + "CTCATATG": 15670, + "CTAATATAA": 15671, + "CCCCTAA": 15672, + "TTCACTTA": 15673, + "CGCTCTCC": 15674, + "GGAAAGAAAA": 15675, + "GGTACACA": 15676, + "GCTGAGAA": 15677, + "GTTTGGTTTT": 15678, + "CCGGCTA": 15679, + "TCGTCCA": 15680, + "TAAACGTG": 15681, + "TATTCCCTT": 15682, + "CAGAGCCAA": 15683, + "GCTTGGGA": 15684, + "GATGGCCA": 15685, + "GAGTGTGTG": 15686, + "CATGATGAAA": 15687, + "GGAGACGG": 15688, + "TAGTCTCA": 15689, + "TCGGAATT": 15690, + "GTTCTGTA": 15691, + "GAAAGGAAAA": 15692, + "TCTGTATTTT": 15693, + "CCTTTATTTT": 15694, + "TCTAGTCC": 15695, + "TAGAGCC": 15696, + "GGTACAGG": 15697, + "GGTACATG": 15698, + "GTTAATAAA": 15699, + "GGGCAGAGG": 15700, + "TCCCAGAGA": 15701, + "GACGAAAA": 15702, + "TTTTTATAA": 15703, + "CTGTTTTAA": 15704, + "GTTCTCAAA": 15705, + "GTCTCCAGG": 15706, + "TCCATCCATG": 15707, + "TGGGGGA": 15708, + "GGTGTCA": 15709, + "TCAAAGAGA": 15710, + "TGATGGAGA": 15711, + "GCCTGGGA": 15712, + "GAATACAAA": 15713, + "TCCGTTTT": 15714, + "GTTTGAGAA": 15715, + "GCTGCAGC": 15716, + "CTCAGGCTG": 15717, + "CTTTTGGTT": 15718, + "GGGGTGTT": 15719, + "TTCATTTTA": 15720, + "CATAATTTA": 15721, + "TATAAAATG": 15722, + "GGCTCATG": 15723, + "GCTCTCTA": 15724, + "CTTTCAGA": 15725, + "TAAACCTA": 15726, + "TATTCACTT": 15727, + "TCAAGTCC": 15728, + "GCTTTATTA": 15729, + "TCTAGTCAA": 15730, + "TGATTCCATT": 15731, + "TGAGAGGAA": 15732, + "GGTCATTA": 15733, + "CCGGTAA": 15734, + "GTATCAAAA": 15735, + "GAAACCTA": 15736, + "CAGAGTGAA": 15737, + "TATGTATGTG": 15738, + "CCAACTTA": 15739, + "GATAGCA": 15740, + "GATACTCA": 15741, + "GGAAATGGAA": 15742, + "CATCAAATT": 15743, + "TTTTCATTTT": 15744, + "GTAGGCCA": 15745, + "TTTTATATAA": 15746, + "GAAAACAGG": 15747, + "CTTTTTTTTTTTTTTTTTTTT": 15748, + "CAGCCAAAA": 15749, + "TCAATTAAAA": 15750, + "TGGGTTCAAGCAATT": 15751, + "GGGAGAGAGA": 15752, + "GGAAAAAAAAA": 15753, + "CAGACTCC": 15754, + "TATGTAATT": 15755, + "CTTTTGTG": 15756, + "CTAAACAA": 15757, + "CTAAACCA": 15758, + "GGCAGGAAA": 15759, + "GTTGGGC": 15760, + "TAAATAGCA": 15761, + "TGTGTATTTT": 15762, + "TCTGCTTTG": 15763, + "GCTTCATA": 15764, + "TTTTAGTTA": 15765, + "GATGCATA": 15766, + "GGGACTC": 15767, + "GATACTAA": 15768, + "GAGAAGAGG": 15769, + "CGCCCGC": 15770, + "GCATTTGAA": 15771, + "CCCCATTA": 15772, + "TAGAATTAA": 15773, + "CTTCATGTT": 15774, + "GAAATAGTT": 15775, + "TCAATAATT": 15776, + "TAAAACCTT": 15777, + "GCTGTCCA": 15778, + "GTCAGTC": 15779, + "TAGGCCAA": 15780, + "TTTTTTGATT": 15781, + "GGAGATGAA": 15782, + "CCCTTTTTT": 15783, + "GAAGCTTA": 15784, + "CACACAGTA": 15785, + "TACATATATT": 15786, + "GATATTATT": 15787, + "CGCTCAAA": 15788, + "CTGAAGGA": 15789, + "TCTGAGATG": 15790, + "CTGTAAGTC": 15791, + "GTTTAAAAAA": 15792, + "CACATGCC": 15793, + "CACACATAA": 15794, + "TTTTATATTA": 15795, + "TGACCAAAA": 15796, + "GAGACAGAGA": 15797, + "GCTTTCCAA": 15798, + "GTCAGTCC": 15799, + "GATTCTCC": 15800, + "TTTTTTGGTT": 15801, + "CGTGGAA": 15802, + "GGACCTCTG": 15803, + "GAGAGGCTG": 15804, + "GCTTCCAAA": 15805, + "GCTGCAAAA": 15806, + "CGAĊ": 15807, + "CGGATCTCTGTGAGTTCGAGG": 15808, + "CCTCCTCTG": 15809, + "GGATGGAGG": 15810, + "TCTCTTGTGA": 15811, + "CCAGGTGTG": 15812, + "CTTCTCAAA": 15813, + "CTATATATT": 15814, + "TGTTTAAAAA": 15815, + "CCAATTTATTTT": 15816, + "GTTTTGTAA": 15817, + "CTTTTTGTA": 15818, + "GATACGG": 15819, + "AAAAAATAAA": 15820, + "CAATTTCTG": 15821, + "CAAGAGCAA": 15822, + "GGTTTGAAA": 15823, + "CCTCTGCCTCCCGG": 15824, + "TAGAAAATT": 15825, + "GGGTCCTT": 15826, + "TGATAAATT": 15827, + "CTTTCTCTA": 15828, + "TGTTGCCA": 15829, + "CTTACAGAA": 15830, + "GAAAATACA": 15831, + "TTTAGCAA": 15832, + "GGTGGTAA": 15833, + "GTGTTTGAA": 15834, + "TGTGTGTGAA": 15835, + "CCAGACATG": 15836, + "TAGGTTAAA": 15837, + "TACTTATTTTCCACCATAATTTG": 15838, + "CAAGCAAAA": 15839, + "CTGGATTTT": 15840, + "CTCCAGGTT": 15841, + "CAGAGCTCA": 15842, + "GGAAGTAAA": 15843, + "CTTACAAAA": 15844, + "TCTATCAAA": 15845, + "TGGATATG": 15846, + "TCATTGATT": 15847, + "CGTAGTGTGTTA": 15848, + "TGAGACTGA": 15849, + "CCAGGTTTG": 15850, + "GGCCAATT": 15851, + "TAATGATGA": 15852, + "TCCCTCTA": 15853, + "CAAGCTAA": 15854, + "GTTCTTCC": 15855, + "CTAGCTGA": 15856, + "CAAAGAGCA": 15857, + "GGAACATA": 15858, + "GATGCCAAA": 15859, + "TGGATATA": 15860, + "TCCTGTCTG": 15861, + "GAGCTAGG": 15862, + "CCACAGCTG": 15863, + "TCCCCGG": 15864, + "GTACAGAAA": 15865, + "TCTTTACAA": 15866, + "GGAACTTTT": 15867, + "CTCATCTCA": 15868, + "CATGGTTTT": 15869, + "CGATACC": 15870, + "TTTATCTG": 15871, + "GGTTGGGA": 15872, + "CGCĊ": 15873, + "TCATTTATG": 15874, + "GAATTCCTGA": 15875, + "GGCCGAGG": 15876, + "GTTTCATTA": 15877, + "GAATAGAAA": 15878, + "GCTAATTTTTGTA": 15879, + "GTCTCGCTCTGTCGCCCAGGCTGGAGTGCAGTG": 15880, + "GGTTAATG": 15881, + "GAACCCAGG": 15882, + "CAATATC": 15883, + "CTGCAATG": 15884, + "GTGGAGAAA": 15885, + "GTTTCCATG": 15886, + "CTACGCA": 15887, + "GAAAAGCTG": 15888, + "TCTATAATT": 15889, + "GTCCAAATT": 15890, + "TAGACCTT": 15891, + "GGTGGCTCA": 15892, + "GCCTTCCTT": 15893, + "GGGCTCTT": 15894, + "CCTGTAATCCCAGCTACTTGGGAGG": 15895, + "TGGGTTTGAAGAAATTAGGA": 15896, + "GGTGAATG": 15897, + "TGAGAAATT": 15898, + "CAACTGTG": 15899, + "CAGACACAA": 15900, + "GTAATAAAAA": 15901, + "TCCACTGAA": 15902, + "TTTACAAAA": 15903, + "GGTTGTCA": 15904, + "CCTATAAAA": 15905, + "GTAAATAA": 15906, + "CACCATGGAATACTA": 15907, + "GACTATTTT": 15908, + "CTCCACCCC": 15909, + "GAAGAGCTG": 15910, + "CTCATTGGAAAA": 15911, + "GAGATTTCAA": 15912, + "CTCGGGAGGCAGAGCCAGG": 15913, + "GAAGTCTG": 15914, + "CTTCTTTTA": 15915, + "GCAAATAAA": 15916, + "TCCTTGTA": 15917, + "GTATTTCA": 15918, + "GAGCTATA": 15919, + "GGAGAATCGCTT": 15920, + "TAACATATA": 15921, + "GGACACAAA": 15922, + "TGATATC": 15923, + "CTGCCTC": 15924, + "GCCGACTA": 15925, + "CTAGACTG": 15926, + "CAGGTCC": 15927, + "GAGGAAATG": 15928, + "TAATTGCA": 15929, + "GTAAATAAAA": 15930, + "CAACCCTGA": 15931, + "CAGCTGGG": 15932, + "TGAGTGGG": 15933, + "GAGAGCCA": 15934, + "CAGGGCCA": 15935, + "GCAAGACC": 15936, + "GAGGTATG": 15937, + "TCAAATATG": 15938, + "CGCAAATG": 15939, + "TCACCTGAA": 15940, + "TGATATTTTA": 15941, + "CGTCAATT": 15942, + "GAACCTGGGAGGCAGAGGTT": 15943, + "GAGTTATG": 15944, + "TTTTTACA": 15945, + "CTGCATC": 15946, + "GTTCTCCTT": 15947, + "GCTTGGCA": 15948, + "CATGCAAAA": 15949, + "GTTTTCCTG": 15950, + "CGTGCGG": 15951, + "TGATGGATG": 15952, + "CAGTTTCTT": 15953, + "CTCCAGTCA": 15954, + "CTTTATATGCC": 15955, + "GCCCAGGCTGGAGTG": 15956, + "CTCTCAGAA": 15957, + "CATTCATCA": 15958, + "TTTGTCTG": 15959, + "TCTAGAGAA": 15960, + "CTTTTCTCC": 15961, + "CGCTTTTA": 15962, + "CGCGATG": 15963, + "GCTGAGGA": 15964, + "CTGTGGCTG": 15965, + "TATCATTAA": 15966, + "TGATGAAAATTACAGGCCTCTC": 15967, + "CTGGTCTG": 15968, + "CTTCTCTGA": 15969, + "GCAGAAGAA": 15970, + "AAAATATG": 15971, + "GTGAĊ": 15972, + "GTTACAGAA": 15973, + "TCTTTTTATT": 15974, + "GCGAAATT": 15975, + "TTACCAAA": 15976, + "CTACAATG": 15977, + "GTCTGTGAA": 15978, + "CATTTAATA": 15979, + "TCAAGCTA": 15980, + "TCTCTAGTG": 15981, + "TAGGTTCA": 15982, + "GGGACTT": 15983, + "CGTGTGCA": 15984, + "TGATTGTAA": 15985, + "GAGCCTCTT": 15986, + "CATTGGCA": 15987, + "GTGAGAGAA": 15988, + "TCTCCTGGA": 15989, + "TTTTTTTCTG": 15990, + "CCTGAGGAA": 15991, + "GCAGACACA": 15992, + "TCGGCCTCCCAAAGTGCTGGGATTACAGGCGTGA": 15993, + "TCACTGAAA": 15994, + "TGTTTCTAA": 15995, + "CAAACTATT": 15996, + "GCTTCGA": 15997, + "CTCAGAGCA": 15998, + "CCCATAA": 15999, + "TAGGCCTG": 16000, + "GACCCTCC": 16001, + "TGAGATCTG": 16002, + "GAATAGCAA": 16003, + "TCATCATTA": 16004, + "TCCCATC": 16005, + "TAAAGGCC": 16006, + "TGTGCAGAA": 16007, + "TGTGTATGA": 16008, + "GTGAGCTA": 16009, + "GTTACGA": 16010, + "GAATTCTGA": 16011, + "TGATTCAGA": 16012, + "GAAAAAGAA": 16013, + "GAAGATATG": 16014, + "CAACAGTAA": 16015, + "CAACAAATG": 16016, + "CCAGCAGCA": 16017, + "GGGGCACA": 16018, + "CTCTTCTGA": 16019, + "CTCTGGGA": 16020, + "TGATGGTA": 16021, + "GGGAATTA": 16022, + "TACAAAATT": 16023, + "TCGAAATG": 16024, + "CAACTTAAA": 16025, + "CCACAGAAA": 16026, + "CCTTAAATG": 16027, + "GCCACCCTG": 16028, + "CTTTTCATG": 16029, + "CAGCCTTA": 16030, + "GAACATGAA": 16031, + "CTGGCATG": 16032, + "GCATCGG": 16033, + "GCACTTGG": 16034, + "GAAACATCA": 16035, + "GGAACTC": 16036, + "GCTTTACA": 16037, + "GAGGGCC": 16038, + "GAGGTCTT": 16039, + "TTTGCCTG": 16040, + "TAGAĊ": 16041, + "TGTACCTG": 16042, + "CCTACTCC": 16043, + "CGCACCAA": 16044, + "CAATTCAAA": 16045, + "TAGCTCCTT": 16046, + "GGGGAGGGA": 16047, + "CCACACTGA": 16048, + "GCGTTGTT": 16049, + "CTGTATCC": 16050, + "TATATTATTA": 16051, + "CGGGTCC": 16052, + "CATATCCA": 16053, + "TCAAATCAA": 16054, + "CTGTTTATT": 16055, + "TGATGGTTTCCA": 16056, + "GATATGAAA": 16057, + "GTTCCAAAA": 16058, + "TCTGCTCTT": 16059, + "TAAAATAAAAAA": 16060, + "GCTGTCTC": 16061, + "GAAAAGAGAA": 16062, + "GCCAGCTA": 16063, + "GGTACCTG": 16064, + "CAGTTGAA": 16065, + "TCATATTTTA": 16066, + "CCACACTCA": 16067, + "CTTAAAAAAA": 16068, + "TAAATACCA": 16069, + "GAAATCATT": 16070, + "TGTTGCCTT": 16071, + "CCTTTCTCC": 16072, + "TTATTTACA": 16073, + "GAATTCATT": 16074, + "GCCTGGAA": 16075, + "GATAAAATG": 16076, + "CTTTCTTAA": 16077, + "CCAATCC": 16078, + "CTAATATTTT": 16079, + "TTTGGTAA": 16080, + "CTAAACATG": 16081, + "CTAAAGCAA": 16082, + "TCCTGGGA": 16083, + "CAATATTTTA": 16084, + "CCTCTCCTT": 16085, + "GGTCGGA": 16086, + "TCGTAGA": 16087, + "TCACCTC": 16088, + "CAATAGGA": 16089, + "TCTTGGAAA": 16090, + "TCTTTAGTT": 16091, + "TAAAACTTTT": 16092, + "CTGACAGAA": 16093, + "TATGGGGA": 16094, + "GATTTACC": 16095, + "CTCCTATT": 16096, + "TCCAGCTCA": 16097, + "CCCCCCCCA": 16098, + "GATACCTT": 16099, + "GTAAAGATG": 16100, + "CTGTTCTAA": 16101, + "GTGTTCCTT": 16102, + "TGATGGTG": 16103, + "GTTTATGTT": 16104, + "AAAAAAAAAAAAAAAAAAAAAAAA": 16105, + "GACTCATTGGAAAA": 16106, + "CTGGCGG": 16107, + "GCACAGTC": 16108, + "CCACCTTA": 16109, + "CGGCTTA": 16110, + "TCCATGTGTT": 16111, + "GAAAATCAAA": 16112, + "GAAAACAATT": 16113, + "TAGATATTA": 16114, + "CTAAATGAA": 16115, + "TCTTTTTTTA": 16116, + "CGGAGGGG": 16117, + "CTCTGCAGG": 16118, + "TGATGGAAA": 16119, + "CCCTGTTA": 16120, + "GAGCTCTGA": 16121, + "TTATTTTAAAA": 16122, + "TCGCACA": 16123, + "TCACCCTC": 16124, + "CTGCAAAAA": 16125, + "CTTTAATTA": 16126, + "TAAAGTCAA": 16127, + "CAAACGTT": 16128, + "GCTTCCCAA": 16129, + "GTGACTTTT": 16130, + "CATGGCCA": 16131, + "GAGGGTTA": 16132, + "TCTCTATTTT": 16133, + "TCTCTAGCA": 16134, + "TTAAGGAA": 16135, + "TTTGGGGA": 16136, + "GTAAATATG": 16137, + "TAGAAGAA": 16138, + "CTGGGTGTG": 16139, + "CTTTAGAAA": 16140, + "GTAAĊ": 16141, + "GGTGCAAA": 16142, + "TGTAGCAA": 16143, + "GTTAACAAA": 16144, + "CACAATTTT": 16145, + "GATGTGAAA": 16146, + "GTCGCTA": 16147, + "CTTATATTA": 16148, + "CAAAAGGA": 16149, + "GAATTTAA": 16150, + "CTGGGTTTT": 16151, + "CAAGGCAGG": 16152, + "TTACAGTTA": 16153, + "CCTGTAATCCCAGCTACTCGGGAGG": 16154, + "GATAGGTT": 16155, + "CTGAAAATT": 16156, + "GTTCTGAAA": 16157, + "CCATCCCTG": 16158, + "GCCTAGTA": 16159, + "GCCCCAGA": 16160, + "GCAAGGCC": 16161, + "CCAAĊ": 16162, + "CTCCTACC": 16163, + "CCCCCTTTT": 16164, + "CGCGATT": 16165, + "GACCCATT": 16166, + "GAGGAGGA": 16167, + "CCTCCTTTT": 16168, + "GTATATTTTA": 16169, + "GCCCATCTG": 16170, + "GGCCTTTG": 16171, + "GCTCTCCAA": 16172, + "GTTTCTTTG": 16173, + "GCAGCTTTT": 16174, + "CTCTAAATT": 16175, + "CGGCGTC": 16176, + "GGGGCAGGA": 16177, + "TGTACAGA": 16178, + "TCTCCCCTT": 16179, + "GAAAACATA": 16180, + "TCTCAGGTG": 16181, + "GTGTGGACA": 16182, + "TAGCTCCA": 16183, + "CTTCAGTCA": 16184, + "CACACACCTG": 16185, + "GTTTGTGTT": 16186, + "GTTTCCACA": 16187, + "CTAGATCA": 16188, + "CTCTCACCA": 16189, + "TATTGTCC": 16190, + "CACAGTGAA": 16191, + "GCTGCACC": 16192, + "CCAACATA": 16193, + "TGGATAAAA": 16194, + "CTCTGGGAA": 16195, + "TACTGGGGCA": 16196, + "CTTTATGTT": 16197, + "CTTCTGTTA": 16198, + "CCGCGA": 16199, + "TAACTTTTTT": 16200, + "TCACCACC": 16201, + "GCTGCCCCA": 16202, + "CTAACTTTT": 16203, + "GAAAAGGGG": 16204, + "TCTAGGAAA": 16205, + "CTGTTGATT": 16206, + "GATAATTTA": 16207, + "GGAGAGTGA": 16208, + "TAACAGTC": 16209, + "GCCCAGGAA": 16210, + "GCTTGCTCA": 16211, + "GCAAAGGGG": 16212, + "CTCTGGAA": 16213, + "CTCTGCTA": 16214, + "GGGAAGAGA": 16215, + "TAATGACAAA": 16216, + "CTAATTTTTGTATTTTTAGTAGAGA": 16217, + "TCCTCACC": 16218, + "CAATAAATG": 16219, + "CTGCAGAAA": 16220, + "GCAGTAAAA": 16221, + "CAGGGGGG": 16222, + "CTAATAGG": 16223, + "GGCATCTT": 16224, + "TCATTGTG": 16225, + "TGAGGGAGA": 16226, + "TATCĊ": 16227, + "GAGTATTA": 16228, + "CAAGATCTCA": 16229, + "CAACATGAA": 16230, + "TGAGTATG": 16231, + "CTGGTGTG": 16232, + "GTTAAAAAAA": 16233, + "TCTGTACC": 16234, + "CAGACGTG": 16235, + "CTCAGCTA": 16236, + "CATGĊ": 16237, + "TCTCGACGTT": 16238, + "TAATTGTGA": 16239, + "GTCTTTGTT": 16240, + "CTATTTCA": 16241, + "GATTTTTTA": 16242, + "CAGTGATTTT": 16243, + "CAGAGAATT": 16244, + "GGAAGGCTT": 16245, + "CTGACCTCA": 16246, + "GTAACCTT": 16247, + "GAGGAAATT": 16248, + "TTAATGAAA": 16249, + "TAGAGCCA": 16250, + "GGTTGATG": 16251, + "TCTCAGGA": 16252, + "GGAGATCAA": 16253, + "CTTCATCTT": 16254, + "CTGTAGATT": 16255, + "CGGTTAAA": 16256, + "CGTTAAAAA": 16257, + "GGCTCTCA": 16258, + "CTTGTAAAA": 16259, + "TCTGGGCTG": 16260, + "CATGCTTTG": 16261, + "TGGGCTA": 16262, + "GGTTGTTTT": 16263, + "GGCACCAA": 16264, + "CCTACAAAA": 16265, + "TGATTCATT": 16266, + "CTGTTTGAA": 16267, + "TGAGGCCA": 16268, + "GTGGGCGG": 16269, + "TGTGGTCC": 16270, + "CAAACGCA": 16271, + "CAAACGAA": 16272, + "GTGACTGAA": 16273, + "GTCACTCC": 16274, + "GTCATTATT": 16275, + "CCTGGCCAA": 16276, + "TCTCTATGA": 16277, + "TTTGGATG": 16278, + "TGCACCAA": 16279, + "TAATAGATT": 16280, + "GCTCCTTTT": 16281, + "TAAAGAGTT": 16282, + "CATTCACAA": 16283, + "GCTTĊ": 16284, + "TCTAGGGG": 16285, + "TGCCCCAA": 16286, + "CTGTTTCC": 16287, + "CTGTTTATG": 16288, + "CTCAAGATG": 16289, + "GTCAAGGGG": 16290, + "GTTTTATTTA": 16291, + "CTTGATCTT": 16292, + "TAAGTTAAA": 16293, + "TCACAGTC": 16294, + "GAAGCATG": 16295, + "TAAAAGATA": 16296, + "CTCAGGTA": 16297, + "CTAACGA": 16298, + "TACATACTG": 16299, + "GCAAATGAA": 16300, + "TGATTTATG": 16301, + "TCATGCATTGGAGAAGGAAATGGCAACCCA": 16302, + "TCGCTTTG": 16303, + "GTTGCGG": 16304, + "TGTTTAGAA": 16305, + "GTGACAGCA": 16306, + "GCTGGAATCAA": 16307, + "CCCATAGA": 16308, + "GCAAAGGAA": 16309, + "CTCTGCTGA": 16310, + "CTAGCCGGGCGAGGTG": 16311, + "CCGATCC": 16312, + "TGAGCTC": 16313, + "TCCCAGAAA": 16314, + "GCACAGAGA": 16315, + "GGAGCTC": 16316, + "GTAGGAAAA": 16317, + "CACAGAGTG": 16318, + "TCTTGGCC": 16319, + "TGTTGGGA": 16320, + "CAGGGGCTG": 16321, + "GCAAGTC": 16322, + "CTAATTATT": 16323, + "TCCACGTT": 16324, + "GACAĊ": 16325, + "GCTAGGGG": 16326, + "TGCATTTTA": 16327, + "GGCATATG": 16328, + "GATACCTG": 16329, + "CAGCTAGG": 16330, + "CAGCTCCAA": 16331, + "TAGCATTTT": 16332, + "CAAGCAGG": 16333, + "GCATACTA": 16334, + "TAAATATC": 16335, + "GTGAGGTA": 16336, + "GATTTCCTT": 16337, + "CTCCCAGAA": 16338, + "GAAAATATTTT": 16339, + "TTTGCCAA": 16340, + "GCCCCCGG": 16341, + "CAACGTC": 16342, + "GGATCTAA": 16343, + "TAAAGTGTT": 16344, + "TGTTTCCAA": 16345, + "CATTCTTAA": 16346, + "TCAATAAAAA": 16347, + "TCCAGAATT": 16348, + "TGGGCAGA": 16349, + "GTATTTATG": 16350, + "TCAAAATTTT": 16351, + "TCACAGCTT": 16352, + "TGTATTTTTAGTA": 16353, + "CATAAAAAAA": 16354, + "GGCGTAA": 16355, + "GCGTCCA": 16356, + "GCAGCGA": 16357, + "CCTGGTC": 16358, + "TGGAGTTA": 16359, + "TCATTTAAAA": 16360, + "TGATTTGA": 16361, + "TGATTCATG": 16362, + "TCATCTTTTTAAGTGGGAGAA": 16363, + "TAGGAATG": 16364, + "GGTTTTAAA": 16365, + "TCCCTGGA": 16366, + "GAAACAGTT": 16367, + "TCCACCCCA": 16368, + "TGCACTT": 16369, + "TTTTTGTTTTTT": 16370, + "CTTTGTGATGTGTG": 16371, + "GGTATATG": 16372, + "TCGTATTA": 16373, + "GATCGGA": 16374, + "TGTTTCTC": 16375, + "CAGGCATGA": 16376, + "TCTGGCTTA": 16377, + "CTGAGACC": 16378, + "TCTCACTCA": 16379, + "GGAGGGATG": 16380, + "GACTATTA": 16381, + "GGAGATGTG": 16382, + "GGAGATATA": 16383, + "GTGCAGTGA": 16384, + "CTCATGTCCATT": 16385, + "CCGCGC": 16386, + "TAGTCTTA": 16387, + "TAAAAGGAAA": 16388, + "CTCCTATA": 16389, + "TCTAGCCA": 16390, + "TGGACTAA": 16391, + "TCTCAGTAA": 16392, + "CTGAAATTA": 16393, + "GCAGAGCTT": 16394, + "GAACATCTG": 16395, + "TCAGAATTA": 16396, + "GAAGTAGTGATT": 16397, + "CCTATATG": 16398, + "GAAGAAATT": 16399, + "CTGTAGC": 16400, + "GTGTACTA": 16401, + "GTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 16402, + "GTCTATAAA": 16403, + "TGTGCACCA": 16404, + "GCAATTTTA": 16405, + "CCAAGGCAA": 16406, + "TACACTAAA": 16407, + "TTCATATA": 16408, + "GCAAATGGG": 16409, + "GGCCTCAA": 16410, + "GAAGGCAAA": 16411, + "AAATACAAA": 16412, + "CCCTCATG": 16413, + "TAATGACA": 16414, + "TTTTATCAAA": 16415, + "TTAACTAA": 16416, + "TGTAGGAA": 16417, + "CCTACCCTG": 16418, + "GCAAACTAA": 16419, + "TCATTCAAA": 16420, + "TAAATTTATT": 16421, + "CAGTCGTGTCCGACTC": 16422, + "TCAGAAAAAA": 16423, + "TCACCAGG": 16424, + "GCCCCCTC": 16425, + "CACATCTTAAA": 16426, + "TCTTCTTCTT": 16427, + "GCCTGGGAA": 16428, + "TATATATATATATATATATATATATATA": 16429, + "TGACAGAGCGAGA": 16430, + "CATCTGAAA": 16431, + "GTCACGTG": 16432, + "CCCACCACCA": 16433, + "GGTGTATA": 16434, + "CGCGGTT": 16435, + "CTGAAGATG": 16436, + "CGTTCAA": 16437, + "CAATTTGAA": 16438, + "CTATTTGTT": 16439, + "CTTTACATT": 16440, + "TAACATAAA": 16441, + "CCGAAGG": 16442, + "TAAGGGGA": 16443, + "GTACCCTG": 16444, + "TCTGCAGTG": 16445, + "GATTCGA": 16446, + "CTTTTTATTA": 16447, + "GGTGCCTT": 16448, + "GGTACGG": 16449, + "CATGTGAAA": 16450, + "GCAGAGACATTACTTTG": 16451, + "GGATTTTAA": 16452, + "CCACCCTA": 16453, + "CACACCCC": 16454, + "TCAACGTT": 16455, + "TGTGTTTGTG": 16456, + "GAAAATGAAAA": 16457, + "CGAGAGAGA": 16458, + "GGTCGTT": 16459, + "TGACTGGG": 16460, + "CAACTAAAA": 16461, + "GTGTATATT": 16462, + "CTCGAAAA": 16463, + "GAGATAAAA": 16464, + "GAGAGAGTG": 16465, + "CCTGGCAAA": 16466, + "GTTACAGCA": 16467, + "TGGGGTCA": 16468, + "GAATTCCTT": 16469, + "CTAAAGAGCTT": 16470, + "GCTGAGATG": 16471, + "GCATTTCA": 16472, + "GAGCTATT": 16473, + "CTCCAGCCA": 16474, + "TCCCACCTT": 16475, + "CTTCAGTGA": 16476, + "GCACAGCTG": 16477, + "GGCGTGTG": 16478, + "GCGTAGG": 16479, + "GTGCTTCTT": 16480, + "GTTGCATG": 16481, + "GCAGCCATG": 16482, + "CTCTGATA": 16483, + "GGAATACA": 16484, + "CTCACACC": 16485, + "CCAATGAAA": 16486, + "GGGGCATG": 16487, + "TTTAGATG": 16488, + "GGTGTCC": 16489, + "TGCACGG": 16490, + "GATACGA": 16491, + "TCCTTGCTT": 16492, + "CTGCAGCAA": 16493, + "GCTCCAAAAA": 16494, + "GGGTCCA": 16495, + "GGACATCC": 16496, + "CAAACACCA": 16497, + "CATTGCTA": 16498, + "TGAATACTT": 16499, + "TGCATCA": 16500, + "TAATATGTG": 16501, + "GTGTTCTA": 16502, + "CAATTCCA": 16503, + "TCCTGCAGG": 16504, + "TCAGGCCTT": 16505, + "TATTTACTTA": 16506, + "TCGTATTTT": 16507, + "GTTCTAAAA": 16508, + "GTCAAAATG": 16509, + "CAGAGCGAGA": 16510, + "CCTGTCTA": 16511, + "TGTACTGA": 16512, + "CCTACCTCA": 16513, + "GAATATTTTA": 16514, + "CAATAGAAA": 16515, + "CGTCCAAA": 16516, + "TCAATGAAAA": 16517, + "CCCTAACCCTAACCCTAACCCTAA": 16518, + "GGTCCTA": 16519, + "GAATGTGAA": 16520, + "GTTGTTTTTT": 16521, + "TAAATCTGA": 16522, + "TAAAACAAAAA": 16523, + "CAAAATATAA": 16524, + "TCAAAGCAA": 16525, + "CCATTTCTG": 16526, + "CAGCCTTATT": 16527, + "CCAGGCTGA": 16528, + "CTTCCCTCC": 16529, + "GGATTCTCCAGGCAAGAACACTGGAGTGGGTTGCCATTTCCTT": 16530, + "GTAGACCA": 16531, + "GTCACAGCA": 16532, + "TACACGTA": 16533, + "TTATTTCATT": 16534, + "GTAAACACA": 16535, + "CTGTTCTGA": 16536, + "GCTAATTAA": 16537, + "GATAATTTG": 16538, + "TAACAGCAA": 16539, + "TAATTTTCATCATAGGTACA": 16540, + "TCCAAAATG": 16541, + "CTAGCCAA": 16542, + "TGTGGTC": 16543, + "TATACAAAAA": 16544, + "CAAATAGTT": 16545, + "TCAATATGA": 16546, + "CCAACAGTG": 16547, + "TTATTTCCA": 16548, + "TTCACTGA": 16549, + "GGCCTCCA": 16550, + "CTCAATTTG": 16551, + "GCCCAGCTG": 16552, + "TAACGCC": 16553, + "GCCCCACC": 16554, + "GTCAGGTA": 16555, + "CCTGCAGAA": 16556, + "GTTACTTAA": 16557, + "GGTTCATT": 16558, + "TGCATAAA": 16559, + "TAATACTT": 16560, + "TGATTCAA": 16561, + "TGATTCTAA": 16562, + 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16881, + "GTTTGAATT": 16882, + "CTAGGGCA": 16883, + "TATTGTCAA": 16884, + "TCAAATGAAA": 16885, + "CACCCGA": 16886, + "CTGTTTCTA": 16887, + "CTCTTTGTT": 16888, + "CCATTTCC": 16889, + "GGGAAGAGG": 16890, + "TACTCTTA": 16891, + "GAAGTGTG": 16892, + "CTACCCAA": 16893, + "CTACCTTA": 16894, + "CATTCGTT": 16895, + "TCTGCACTG": 16896, + "CCTGCCTA": 16897, + "TTTGCCA": 16898, + "TGGGCGG": 16899, + "GTGAAGAGG": 16900, + "CGGATTTT": 16901, + "CTGGGCTGA": 16902, + "GTTGTGGGTT": 16903, + "TAGTAATG": 16904, + "GGAGCTGAA": 16905, + "GAGAGAAAAA": 16906, + "CATGCTCC": 16907, + "GATTTATAA": 16908, + "GTCCTGAAA": 16909, + "CTTTTGGAA": 16910, + "TGGGCTGA": 16911, + "GATAGTG": 16912, + "GGTAGTTA": 16913, + "GTGTTCCA": 16914, + "CAGCĊ": 16915, + "GGCTCTTA": 16916, + "CAATTAATT": 16917, + "GTACCCAA": 16918, + "GCCCAAGG": 16919, + "CTACCATG": 16920, + "TATTGAATT": 16921, + "TATTCAGCA": 16922, + "CACACTGAA": 16923, + "TATAGGTG": 16924, + "GAGACACCA": 16925, + "CAGGCCAAA": 16926, + "CTTAGGTG": 16927, + 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"TCGCTATT": 17198, + "TCACCTTTT": 17199, + "TCCCAAAAA": 17200, + "GCATATAAA": 17201, + "GGAGCTCTT": 17202, + "GAAATATAAA": 17203, + "TTTTATATTTT": 17204, + "CGGCGGA": 17205, + "CTAAGGAAA": 17206, + "TAGATATTTT": 17207, + "CAATTTAAAA": 17208, + "CAGTTCTG": 17209, + "CCCTGTCTCTACTAAAAATA": 17210, + "GCCGGGCATG": 17211, + "GGTAAAAAA": 17212, + "TATTCTTTG": 17213, + "CTGAGCAAA": 17214, + "CATGGACTG": 17215, + "TAGGGACATG": 17216, + "CACCTATG": 17217, + "GTGTGTCTG": 17218, + "GGATGGAGA": 17219, + "GGAGGAGGGG": 17220, + "CCCGGTT": 17221, + "TAATATATG": 17222, + "GGACGTC": 17223, + "GGAGTATG": 17224, + "CTCTTACA": 17225, + "CACATACAA": 17226, + "TTAAGATG": 17227, + "TGCACTTA": 17228, + "CTCAACAAA": 17229, + "GAAGGTTTT": 17230, + "CTTCCTCCA": 17231, + "TTTTGGGAGG": 17232, + "TGATAGC": 17233, + "GTAAGGTG": 17234, + "CCAAGCAAA": 17235, + "CTCCCACTG": 17236, + "GAAAATAAAAA": 17237, + "CCCACCCCCA": 17238, + "AAAAAAAAATG": 17239, + "GAGCCAAAA": 17240, + "TCAGAGTCA": 17241, + "TCATATGAA": 17242, + 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17288, + "GGCCTTAA": 17289, + "TCATCTCTT": 17290, + "GTACCCCA": 17291, + "GCCTCCAGG": 17292, + "GCTGGACC": 17293, + "GCTGGGACTACAGG": 17294, + "GATGGGAAA": 17295, + "GGTAGCAA": 17296, + "GTCTTCAA": 17297, + "GAGGAGAA": 17298, + "CGTATGTA": 17299, + "GCGGCGCA": 17300, + "GAAGACACA": 17301, + "GCAGACAGG": 17302, + "GTGTATATGTGCCACATTTT": 17303, + "GGTCCCTT": 17304, + "CTTTATTTG": 17305, + "GCAGAGGTT": 17306, + "GGACCTTA": 17307, + "GAAAGAGTT": 17308, + "GAAAGATCA": 17309, + "GAAAATAATT": 17310, + "TTTGTATTTT": 17311, + "TTTATCC": 17312, + "TGGGCAA": 17313, + "GGTTCACA": 17314, + "TGGAGTT": 17315, + "TAAAAATATTTT": 17316, + "CTAAAGTA": 17317, + "TCCTGGCTT": 17318, + "GCCTTCCAA": 17319, + "TCGATGTG": 17320, + "TCGCTAAA": 17321, + "CATTCTGAA": 17322, + "GCCACCTCA": 17323, + "GACACTTTT": 17324, + "TTATTAAATT": 17325, + "CTAAATGTT": 17326, + "GGTACTGA": 17327, + "TGATTCCA": 17328, + "TGATTCTGA": 17329, + "CTCTTTCC": 17330, + "CGTATTAA": 17331, + "GTGGGTGTG": 17332, + "GGAGACCTG": 17333, + 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"TTTTATACA": 17380, + "TCATTCACA": 17381, + "TATCATTTG": 17382, + "CCCTTAAAA": 17383, + "GGACACATT": 17384, + "GTCTCCCA": 17385, + "GAAAGCCAA": 17386, + "GAAATACCA": 17387, + "CAGGCGA": 17388, + "TCTGGATGA": 17389, + "GATTTAAAAA": 17390, + "GTAAATGTA": 17391, + "TCTCCCTT": 17392, + "GTTTGTCTT": 17393, + "GCTAATTTG": 17394, + "CTTGAGCCA": 17395, + "GCCCAGGCTGGTCTCAAA": 17396, + "GCAGTGCTG": 17397, + "GAAACAATT": 17398, + "TCTGGAATT": 17399, + "CTGACAGCA": 17400, + "CCTTGACTT": 17401, + "GTTACAGAGA": 17402, + "GGTTGTAA": 17403, + "TCTGATTAA": 17404, + "CTGTTGTT": 17405, + "GTGTTTCA": 17406, + "CAATTTATG": 17407, + "TGAGGTGGG": 17408, + "GTTTTATTTG": 17409, + "TACTCTGA": 17410, + "TAAAAAAAATT": 17411, + "GTGCATCC": 17412, + "GCCCACTA": 17413, + "GTCTGAGAA": 17414, + "CAGATCTA": 17415, + "CGGGTAA": 17416, + "CGGGACC": 17417, + "CTATTCATT": 17418, + "CCATGTCA": 17419, + "GCATGTTTTA": 17420, + "TCAGAGTGA": 17421, + "CAATATCTT": 17422, + "TGACAGCTT": 17423, + "CGCTTAAA": 17424, + "TAACTTAAAA": 17425, + "GAAATACAAA": 17426, + "GCTGCACTG": 17427, + "GCTGGGGTG": 17428, + "GCCACCCCCA": 17429, + "GACATAAAA": 17430, + "TCAAAGTTA": 17431, + "GAGCGAAA": 17432, + "GAAGATATA": 17433, + "CTGTTTTTTCA": 17434, + "CCAGTCCATCC": 17435, + "TAAGAAGAA": 17436, + "TGACCTCTG": 17437, + "GCCGTTTT": 17438, + "CAAAGGCAA": 17439, + "CCTGTAGA": 17440, + "CTCCTCTCA": 17441, + "CTTATATAA": 17442, + "TTTATAATT": 17443, + "TCCTTGGGA": 17444, + "GGCCCATG": 17445, + "CTTTGTAAA": 17446, + "CGAATTAA": 17447, + "GACAACACA": 17448, + "GCTCCTTTG": 17449, + "GAAATGTTTT": 17450, + "CTGTATGTG": 17451, + "CAATACATT": 17452, + "GCCCCCAGG": 17453, + "TAGTTTTAA": 17454, + "GGAGCAGCA": 17455, + "CTAGGGAGG": 17456, + "TCTGCAGTT": 17457, + "GTGATACA": 17458, + "GTCACGTT": 17459, + "CCTGCGA": 17460, + "GTTATCC": 17461, + "CTTTTGGA": 17462, + "TTATTCAAA": 17463, + "TTTTTTCCTT": 17464, + "TCATTGTAA": 17465, + "GGCCCACA": 17466, + "GGCCATTA": 17467, + "TAAATTTAAA": 17468, + "CCATGGCAA": 17469, + "GCCCTGGCTGTCCTGGAACTCA": 17470, + "TAGCCCTG": 17471, + "GAGTTTTTG": 17472, + "TAACTTGG": 17473, + "TAAGTATA": 17474, + "TCATTATTTT": 17475, + "TGAGTTGG": 17476, + "GAACGCC": 17477, + "GCACCCTA": 17478, + "CTCTGAGTT": 17479, + "CTCTCACTG": 17480, + "TATTTAAAAAA": 17481, + "GAGAGGGTG": 17482, + "CTGACCCC": 17483, + "CCAACACC": 17484, + "TGTACCCA": 17485, + "GCAAATGCA": 17486, + "GCTGATGGAA": 17487, + "GTGTTCTG": 17488, + "TTATACATT": 17489, + "TGTGTGTGTGTGTGTGTGTGTG": 17490, + "GACGGGGGA": 17491, + "GTGGTGATG": 17492, + "GAAAGACTT": 17493, + "TATAGCCA": 17494, + "TCTGGCTA": 17495, + "CTTTTAAATT": 17496, + "GGTGGTCC": 17497, + "TCTCCTGAA": 17498, + "TCTGAGAGA": 17499, + "TGAAACCAA": 17500, + "TGATTGTTTT": 17501, + "CTCTTTCCA": 17502, + "TCAGAGCTG": 17503, + "TCGATGTT": 17504, + "CAATACACA": 17505, + "CCGCTTA": 17506, + "GCTCTCTCTC": 17507, + "TCACCCTA": 17508, + "TCAATACC": 17509, + "TTAACTG": 17510, + "TGTAGTCA": 17511, + "TCAAACATT": 17512, + "CGTGTTCA": 17513, + "CTGTGGTGA": 17514, + "GAGCCCAGG": 17515, + "GTTTAGATT": 17516, + "GGACACGA": 17517, + "CTAAAAATACAAAAAA": 17518, + "GAAGTGGATGCTCA": 17519, + "GAGGCAGGTGGA": 17520, + "CTGAGGCCA": 17521, + "TCTCAGAGA": 17522, + "TAATATAATATT": 17523, + "GTGTTCATT": 17524, + "TCCTGGGAA": 17525, + "GAGGATTA": 17526, + "GATTATTTA": 17527, + "CTCCAGTCC": 17528, + "GTTTCATTTT": 17529, + "TCATAGCTCA": 17530, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 17531, + "GTGTCGG": 17532, + "CCACCTAA": 17533, + "GCAAGCAAA": 17534, + "CCTGGTTTT": 17535, + "CTAAGGTG": 17536, + "TACACAAAAA": 17537, + "CGACAAAA": 17538, + "TAGGGTAA": 17539, + "CCAAACAA": 17540, + "GAATTTCC": 17541, + "TAATAGCTGA": 17542, + "CAGGATTA": 17543, + "CACATTAAAA": 17544, + "CAAGATGAA": 17545, + "CCGGAGG": 17546, + "GAAAGCTA": 17547, + "GAAAGTATT": 17548, + "CAGGTATA": 17549, + "GCTTCCTA": 17550, + "TATGCCCA": 17551, + "CGGTATA": 17552, + "TCTAGAACTA": 17553, + "TAATTCACA": 17554, + "TGCACCTT": 17555, + "GGGATCC": 17556, + "GTTTGGAGA": 17557, + "TGAGAGCTG": 17558, + "GAGCTCTA": 17559, + "GCTCCCGG": 17560, + "TCGGATA": 17561, + "GCATAGGA": 17562, + "CTACATCC": 17563, + "TATTTATTTG": 17564, + "CAAAGCCAA": 17565, + "CTGACACC": 17566, + "CTGATGCTG": 17567, + "CATGGTC": 17568, + "GTCAGGATG": 17569, + "GAGGCTGAA": 17570, + "CCTGGTCAA": 17571, + "CCTGGGCTT": 17572, + "GAAAACCTT": 17573, + "GACACGTG": 17574, + "GCAAATTTA": 17575, + "GCTGAGGTG": 17576, + "CCTCCTCCTCC": 17577, + "TCATCACTG": 17578, + "TCCAGGGGATCTT": 17579, + "CCACCCACA": 17580, + "TTTTAAATTA": 17581, + "CAGCTTAAA": 17582, + "GTTGCAAAA": 17583, + "GAAACATAA": 17584, + "CAGAGGCAA": 17585, + "TCAACACC": 17586, + "TTAAGAGA": 17587, + "CCTACCCA": 17588, + "TAAAAAGGAA": 17589, + "TAAAAATTTG": 17590, + "CGGATCACGAGG": 17591, + "TTTTTTTTTTTTA": 17592, + "GGTAATTTA": 17593, + "CGTGAAAAA": 17594, + "GAAGCAAAA": 17595, + "GTACGGA": 17596, + "TCTTTGATT": 17597, + "TCTTCAATT": 17598, + "TCTGGGTCA": 17599, + "CCAAGTTTT": 17600, + "GGGGGAGTG": 17601, + "CGAGTTTT": 17602, + "TGTAGCC": 17603, + "TATTTTTAAA": 17604, + "CTGTGCCCA": 17605, + "GCCTGCCAGG": 17606, + "GCCTGGCCAACATGGTGAAA": 17607, + "GAATGCTA": 17608, + "GAAGACATG": 17609, + "TAACACTAA": 17610, + "TAAGATGTGAA": 17611, + "GTCCACGG": 17612, + "GACTAATG": 17613, + "GCACATC": 17614, + "GGATAGTG": 17615, + "TGTGTAAAA": 17616, + "TATTGTGAA": 17617, + "CACAGAGCC": 17618, + "CAGGAAATG": 17619, + "CTGAGAGCA": 17620, + "GCTTTACAA": 17621, + "CCAATTTTA": 17622, + "GCCATCTC": 17623, + "GGGGCTT": 17624, + "GTGTTTCC": 17625, + "TAATGGATT": 17626, + "TGAGATGTT": 17627, + "GGATTCTCCAGG": 17628, + "GAAGGTGGG": 17629, + "TTATTATTTG": 17630, + "TCTCTGCAGG": 17631, + "CCACAGTCA": 17632, + "CGTGAATG": 17633, + "TGAGGTGGCCAAA": 17634, + "CACTTATG": 17635, + "GGTCCTCA": 17636, + "GCGTTCCA": 17637, + "CTGCCAGG": 17638, + "GTGCCGG": 17639, + "CCAGATTTT": 17640, + "CTCTCTATT": 17641, + "CTCTCTAAA": 17642, + "GAAATGATT": 17643, + "CCTTTCTC": 17644, + "TCCAGCAGCA": 17645, + "GGAAATGAA": 17646, + "TATTATATG": 17647, + "CACAACACA": 17648, + "TAATAATTTA": 17649, + "GTTTCACTCTT": 17650, + "GCAGAGTCA": 17651, + "GGATTACAGGCGTGA": 17652, + "TATCTCAAA": 17653, + "GCGAAAAAA": 17654, + "CTCTAAGA": 17655, + "GTCTCAGTT": 17656, + "GCTTTCTTTT": 17657, + "CCTGCCCTT": 17658, + "CTCCCCCTT": 17659, + "CTTTTTGA": 17660, + "GGCACTTAA": 17661, + "TAAAAAGAAAA": 17662, + "GGAAACAA": 17663, + "CCATTTCAA": 17664, + "GCTTTTTTA": 17665, + "CTGCATGTG": 17666, + "CAAGACAAA": 17667, + "TCCTGAGTGAGGTAA": 17668, + "GTAATGAAAA": 17669, + "CAAAACAAAACAAAACAAAA": 17670, + "TCGTGATT": 17671, + "CCAGTCCATT": 17672, + "GCCCCCTA": 17673, + "TCTTAAAAAA": 17674, + "TCTGTCATT": 17675, + "GCAAGGCTG": 17676, + "CTTAGAGAA": 17677, + "CAGCACAA": 17678, + "GTGTTGATG": 17679, + "CCCTCTTCA": 17680, + "TATATATATATATATATATA": 17681, + "CAAAATGAAAA": 17682, + "CGCCAGGCCTCCCTG": 17683, + "GGTCCATG": 17684, + "TATTCAATT": 17685, + "GAAAGAGTA": 17686, + "CCAACCCA": 17687, + "CTCCTGGAA": 17688, + "CTTAĊ": 17689, + "GGTTGAGAA": 17690, + "TCAAATGCA": 17691, + "GCGTTATT": 17692, + "TGATCCCTG": 17693, + "CTTGATCC": 17694, + "TCTTCTTAA": 17695, + "CATTTACTA": 17696, + "TGAATAGAA": 17697, + "GGAAGGAGG": 17698, + "CCTTTAATT": 17699, + "CCTTTACC": 17700, + "CTTATGGG": 17701, + "GATGTACCA": 17702, + "GTTTTTTTTA": 17703, + "GTCCCCTA": 17704, + "GGGGCCCC": 17705, + "GTTAATATA": 17706, + "GTATTTCTA": 17707, + "GAGGAGATG": 17708, + "GCCTGCCTT": 17709, + "TACTGTATA": 17710, + "TTATATAAAA": 17711, + "TAGAGGTT": 17712, + "GTACACTA": 17713, + "TAAACAGTG": 17714, + "TACATACTA": 17715, + "GAAAAGCAAA": 17716, + "GAATGCAAA": 17717, + "TCACACCTG": 17718, + "GTTCTTTTA": 17719, + "CCATGAGG": 17720, + "GTACCTAA": 17721, + "TCTGGACC": 17722, + "TCTGGGGAA": 17723, + "GTAATCC": 17724, + "TTTAGGTT": 17725, + "GGGACCAA": 17726, + "CGCGGGC": 17727, + "GGAGATCAAA": 17728, + "TAACATGTT": 17729, + "TCCCCCAAA": 17730, + "TATTTCTTCACTA": 17731, + "GACTCTGTCTC": 17732, + "GTTGAGAAA": 17733, + "CTCAGTGAA": 17734, + "GAGGCCAGGA": 17735, + "GATTTCTTTT": 17736, + "CTCCAAATT": 17737, + "TACATATGA": 17738, + "CAAAAGTCA": 17739, + "TAGACCTG": 17740, + "GGCTGAAAA": 17741, + "GTTAAATAAA": 17742, + "TGTGTGTGTGTGTGTGTGTG": 17743, + "TACTTATG": 17744, + "GGCTGATG": 17745, + "GAAGAGCC": 17746, + "CTTTAAATG": 17747, + "TATATGCC": 17748, + "TGAAGAGTT": 17749, + "GCTATGATT": 17750, + "GGAAAGGA": 17751, + "CTAAACTA": 17752, + "GTTAATATT": 17753, + "TGAGAGAAAA": 17754, + "TCAGATGTG": 17755, + "TCCCACGG": 17756, + "CTTCACTGA": 17757, + "TCCACATTTT": 17758, + "GTGGAGGAA": 17759, + "GAGTCAAAA": 17760, + "CCGCTAA": 17761, + "GGGCAATT": 17762, + "GAATAAATT": 17763, + "CTTGCAGG": 17764, + "CACAGATGA": 17765, + "TTATTTCAAA": 17766, + "CGCGGCA": 17767, + "TAGCTTTTA": 17768, + "CTATTCACAATAGCAAA": 17769, + "TAAGATCC": 17770, + "GAAGTGGG": 17771, + "CTGCCTAA": 17772, + "GGAGCACA": 17773, + "CAAATACTG": 17774, + "CCTTCGCC": 17775, + "CCTGTCTC": 17776, + "GGGGGATT": 17777, + "CAAAACTGA": 17778, + "GAAAAATAAAA": 17779, + "TATCATTTA": 17780, + "TCTTAATTTT": 17781, + "GACCTGAAA": 17782, + "GCCGGGCGCGGTG": 17783, + "GGCGCAAAGCTACACAGAGAAACCCTGTCTC": 17784, + "TATGTTTA": 17785, + "GATCGTC": 17786, + "CAAGCTTA": 17787, + "GTGGTATG": 17788, + "TGTTCAGTT": 17789, + "CATTGGGA": 17790, + "CAGGGATA": 17791, + "GTGAGTGAA": 17792, + "GCAAGATA": 17793, + "GTTACATTA": 17794, + "TTAATAATT": 17795, + "GCCACGGA": 17796, + "CGACGGA": 17797, + "CCTAGCTG": 17798, + "CTCTTTCA": 17799, + "TATTAATTTA": 17800, + "CAGCCCCAAA": 17801, + "GAACACTTA": 17802, + "GGACACTGA": 17803, + "CCATAGCCTTGACTA": 17804, + "GCTAGAGAAAGTA": 17805, + "CAATAATTA": 17806, + "GTTGCAGG": 17807, + "CACAGAATT": 17808, + "TCTGTCTTA": 17809, + "CTCAGGGAA": 17810, + "CTCACACAA": 17811, + "GTCAĊ": 17812, + "TATGTATGA": 17813, + "GTTTGGTTTG": 17814, + "GAGCTTTTA": 17815, + "GGAGATCTT": 17816, + "GAGGAATGGG": 17817, + "TCTGTAGTT": 17818, + "CTCAGACC": 17819, + "TGAAATAAAA": 17820, + "GTGTTTATT": 17821, + "GCATTTGTG": 17822, + "TGACAATGA": 17823, + "GGAGGAGGAGGAGGAGGAGGAGGA": 17824, + "GGTCGCC": 17825, + "TAAGCCTG": 17826, + "TGAGTTTA": 17827, + "TTAGGGA": 17828, + "CCATCAGG": 17829, + "CTAGAGTA": 17830, + "TCAAGTTAA": 17831, + "TGGGCCTG": 17832, + "GGTACATT": 17833, + "TGATGGAA": 17834, + "GGTCAGGA": 17835, + "TAAAAATTGATTTTTT": 17836, + "GCGTTCTT": 17837, + "GCGGGGTG": 17838, + "TGACTAAAA": 17839, + "GGATCTTTT": 17840, + "GTAGGGTG": 17841, + "TGTGTACTT": 17842, + "CAGGCCCA": 17843, + "TCTGTAATG": 17844, + "CTCATGAAA": 17845, + "GATTCATA": 17846, + "CTTTTTCAAA": 17847, + "TGGGCAAAA": 17848, + "TAGGTCA": 17849, + "TTATTGAAA": 17850, + "GCCTTCTGA": 17851, + "TGAGACACA": 17852, + "TGACAGTAA": 17853, + "CACGAAAA": 17854, + "GATCTGAAA": 17855, + "CAACTTTTA": 17856, + "GTACTTTTA": 17857, + "TAAACAATG": 17858, + "TATAGTAAA": 17859, + "CATTCCATG": 17860, + "TCTAGGCA": 17861, + "GCCAGCTCA": 17862, + "GTCCTAGA": 17863, + "CTGTTGATG": 17864, + "GGAAAAAAAAAA": 17865, + "CTTCATGAA": 17866, + "CAACAGTGA": 17867, + "TCGTGAAAAAA": 17868, + "CTGGAGAAA": 17869, + "GTGGTAAAA": 17870, + "GTCTGTTTG": 17871, + "GTCTCCAGA": 17872, + "TATGCGA": 17873, + "GGGGTTTA": 17874, + "TCTCCCACA": 17875, + "GGGCAGGGG": 17876, + "GTGCTGAGATTACAGG": 17877, + "CCGTGGG": 17878, + "TCGCAGAA": 17879, + "TGAGTTTTA": 17880, + "CAACTTCTT": 17881, + "CTGCTTGG": 17882, + "TATTTAATAA": 17883, + "GAAACGTG": 17884, + "CAGGGAGCA": 17885, + "GCTTTATTTT": 17886, + "TCCACCCTG": 17887, + "TGTACCTT": 17888, + "TAATTCATA": 17889, + "GTGAAAGAGGA": 17890, + "TTATAGTAA": 17891, + "TGACCAGG": 17892, + "CTGTTATG": 17893, + "GTGGATCC": 17894, + "CTACTGTG": 17895, + "CTATAATG": 17896, + "GCTTGGCTT": 17897, + "CCTGGCTCA": 17898, + "TCTCGTGA": 17899, + "TCCAGCTCC": 17900, + "GTTTTGCAA": 17901, + "GGGGTCTT": 17902, + "CAAAACCAAA": 17903, + "GGTTGCC": 17904, + "GGTTCAGA": 17905, + "CGCAAATT": 17906, + "CTTTGCTTA": 17907, + "CAAAAATTTT": 17908, + "CAATAGTGA": 17909, + "GTTGGTTTG": 17910, + "GCACAGTGA": 17911, + "GTTGAAATT": 17912, + "GTAGAAGG": 17913, + "GTAGGGTA": 17914, + "TATGTACTT": 17915, + "GAGGĊ": 17916, + "GATTCAGAA": 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"TATATCTCA": 18274, + "TATACAGTT": 18275, + "GAGAGACC": 18276, + "TGAATCAAA": 18277, + "CAGGTACA": 18278, + "GATGCAGAA": 18279, + "TTTAGCTT": 18280, + "TTTATCCA": 18281, + "TAGGAAATT": 18282, + "GACAGTGAA": 18283, + "CATATAATT": 18284, + "TGAAATGAAA": 18285, + "TGATTCCTG": 18286, + "CCTTTTAAAA": 18287, + "TGACAGCTG": 18288, + "CAAGAGTGA": 18289, + "GAGAGATGGCTCA": 18290, + "GTGAAAGTGAAGTCGCTCA": 18291, + "CCGAAAAA": 18292, + "TAGTCCAA": 18293, + "GCAGCCAGG": 18294, + "CACAGGCTT": 18295, + "GAAACTCAA": 18296, + "TCTGCCCTT": 18297, + "TCAATCCA": 18298, + "TATGTAGCA": 18299, + "GTTAGCTA": 18300, + "GGTGGTT": 18301, + "GTAAAGTA": 18302, + "TCTCCTCA": 18303, + "TCCTTCCTCA": 18304, + "CTAAACAAA": 18305, + "GCTGAGGCA": 18306, + "TCACCCTCA": 18307, + "CTGCCCTCA": 18308, + "GGGGTTAAA": 18309, + "CAGCCATGAAATTAAAA": 18310, + "TGACTTAAA": 18311, + "CTCTCCAGA": 18312, + "CATGCACAA": 18313, + "GGTGCACA": 18314, + "TCCTTCAGG": 18315, + "GCATTTAAA": 18316, + "GTGTGGGA": 18317, + "CAAGGGTTTT": 18318, + "TTATACAAA": 18319, + "CTTTTGAACTGTGGTGTTGGAGAAGACTCTT": 18320, + "TACGTCC": 18321, + "GATAAATAAA": 18322, + "GCGTTAAA": 18323, + "TAAGTAATT": 18324, + "CTTCTGCTG": 18325, + "CTCTCACTT": 18326, + "TGTTCTGTT": 18327, + "CTGAGGCTT": 18328, + "GCTTCCCCA": 18329, + "GGGGGAGGA": 18330, + "TTATTCTGA": 18331, + "TTATTGTCA": 18332, + "GGGATCTG": 18333, + "GGTATCAA": 18334, + "TCTTAGAA": 18335, + "CTTTGGAGA": 18336, + "GAGCATTTT": 18337, + "TCTCTTTCTT": 18338, + "TCCAAATGCA": 18339, + "CTGGAGTTA": 18340, + "GTCATGTATGGATGTGAGAGTTGGA": 18341, + "TCGTTCAA": 18342, + "TCGCTTAA": 18343, + "TAATCAGG": 18344, + "CAATTATTA": 18345, + "CTGTCTCTG": 18346, + "GCACCTTA": 18347, + "CCATAGGA": 18348, + "CTCGGCTG": 18349, + "GAGATACTG": 18350, + "CAGAGGGCA": 18351, + "GTGATAAAA": 18352, + "CCTGGCTA": 18353, + "GAAAAGGTT": 18354, + "TAGGTCCA": 18355, + "GAATTTAAAA": 18356, + "GTGGGGCTG": 18357, + "GTTTATGAA": 18358, + "GTGGCAGGTG": 18359, + "CCATGAGA": 18360, + "GTCCAGAGA": 18361, + "TATTGATGA": 18362, + "CATTGTTAA": 18363, + "GCTGCTGTG": 18364, + "CTCCCCTCA": 18365, + "TCAAATAGA": 18366, + "CCATGTCTG": 18367, + "GACCATTA": 18368, + "CTTGGTTTG": 18369, + "CAATATGAA": 18370, + "CTTTCATA": 18371, + "GTGCAAAAA": 18372, + "GCATATATT": 18373, + "CCACAGTC": 18374, + "TATTGTCTT": 18375, + "TATACTAAA": 18376, + "CAGAGAAGG": 18377, + "GTCACAGTG": 18378, + "GTAAGCTG": 18379, + "TGGGCATT": 18380, + "GTAAAGGAA": 18381, + "GCTGAGAGG": 18382, + "CTTTGCTCA": 18383, + "GAATGGCTT": 18384, + "CTAATTAAAA": 18385, + "TCGCTGGGCAA": 18386, + "TCTCCTTGCAGTCCAAGGGACTCTCAAGAGTCTT": 18387, + "CCGCATG": 18388, + "TACCTTTG": 18389, + "GGCGTTTT": 18390, + "TCGGAGAA": 18391, + "CAAGAAGAA": 18392, + "CCACAAATG": 18393, + "TCTTCTCAA": 18394, + "GAGACAGTG": 18395, + "CAGACCAAA": 18396, + "TATGGGAAA": 18397, + "TCTCTGATG": 18398, + "GATGĊ": 18399, + "CCCACGCC": 18400, + "GGCACGTG": 18401, + "TCTGACTTA": 18402, + "GAATTCACA": 18403, + "CTATTCCAA": 18404, + "GATTTTTTTG": 18405, + "GCAGTGAGCTGAGATT": 18406, + "CTGAACTGAACTGAA": 18407, + "TAAGGAGG": 18408, + "GTGCTATG": 18409, + "GTCTGAATT": 18410, + "GTCTCCATG": 18411, + "GAGACACAA": 18412, + "GAGAGAGC": 18413, + "TCAACACCA": 18414, + "CCTTCACTT": 18415, + "TTTTATGCA": 18416, + "TACAGTATG": 18417, + "CTTTTGAAAA": 18418, + "CCTAACCTAACCTAACCTAA": 18419, + "GCAGĊ": 18420, + "GAAAGTCAA": 18421, + "CATTAAATG": 18422, + "CGGTTTTA": 18423, + "TACACTTAA": 18424, + "GTGTTGTA": 18425, + "CCTCTAAAA": 18426, + "GGAGGAGGAA": 18427, + "TGACATGAA": 18428, + "GCGTGATT": 18429, + "TAAACACTA": 18430, + "TATACAGCA": 18431, + "CAAAGATGA": 18432, + "TATGAAAAAA": 18433, + "CCTGGATA": 18434, + "CCAACAGCA": 18435, + "GTTTTTGAAA": 18436, + "GACATCCA": 18437, + "GGTGCGA": 18438, + "GAGAATTTG": 18439, + "TATTTTCTA": 18440, + "CCTCAGATATG": 18441, + "GTTCAACCATT": 18442, + "GAGTCGGACATGA": 18443, + "GGTCCTTTG": 18444, + "TAAGCTCC": 18445, + "CTCTAATTA": 18446, + "TGTGCAAAA": 18447, + "TGTGGCAAA": 18448, + "GTCATGTGA": 18449, + "GTAAGGGA": 18450, + "TTAATGTA": 18451, + "TTCATCTG": 18452, + "TCTCACATG": 18453, + "CCCATTAAA": 18454, + "TAATCTAAA": 18455, + "TGATGAGAA": 18456, + "TCCCAGGAA": 18457, + "GTCGAGC": 18458, + "CACACGTT": 18459, + "CTGAGGTCA": 18460, + "GCTGCCCTT": 18461, + "GAGGTATT": 18462, + "GAAAAGTT": 18463, + "CGTGTTTA": 18464, + "GTGAACTCC": 18465, + "CTTTGGATT": 18466, + "GTTTGGCTT": 18467, + "TCCCACCCC": 18468, + "TGTGATTTTCTG": 18469, + "TAACGAAA": 18470, + "TAAATATTTG": 18471, + "TCTGCCCGCC": 18472, + "CAGATACTT": 18473, + "CTGACGTT": 18474, + "CCAACCCCA": 18475, + "TTTGCAGG": 18476, + "CATATTTTAA": 18477, + "TCAAATGTG": 18478, + "GTCTTCAAA": 18479, + "CATTAATTTT": 18480, + "GAAGACGG": 18481, + "GCAGACAAA": 18482, + "AAAATGTTTT": 18483, + "TAAATTAATT": 18484, + "CAGACTGAA": 18485, + "CTGACTC": 18486, + "CCTTGCCA": 18487, + "TATGGAGAA": 18488, + "CGTGCATG": 18489, + "GGCCCAAA": 18490, + "CTCTTTCAA": 18491, + "CAATTCATG": 18492, + "TGAGAGTGA": 18493, + "GTGGAGCTG": 18494, + "CAGATGACACCA": 18495, + "TGATGTGTT": 18496, + "GTGCATC": 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"GTCAGCAAA": 18543, + "CTTAGGTA": 18544, + "CCCATCCAA": 18545, + "CAAAATTTTA": 18546, + "TTCACCTT": 18547, + "CTCTGCATG": 18548, + "CTTTTATTTA": 18549, + "TGTGAATTA": 18550, + "CTTCAGTTA": 18551, + "TGACATTTTA": 18552, + "GTGGCACACA": 18553, + "CGTTAATG": 18554, + "GAAGGGGACGACAGA": 18555, + "GGGTCTGA": 18556, + "CCACTACA": 18557, + "GTACTATG": 18558, + "CTAGGTGA": 18559, + "TATGTATCA": 18560, + "GTCCCCTCC": 18561, + "GGTTCTCC": 18562, + "CGCGCTC": 18563, + "TCTCCTGCC": 18564, + "TCCTTCATT": 18565, + "TATTGACTTGAAATTTG": 18566, + "TGATCTCAA": 18567, + "CTGCTATG": 18568, + "GAAACCCTG": 18569, + "TGTTCCAAA": 18570, + "GGAACACC": 18571, + "GCTTTATAA": 18572, + "CCTTTCCAA": 18573, + "GTCAGAGGA": 18574, + "GCCAGGATT": 18575, + "TGGGGCC": 18576, + "TAGGTGGA": 18577, + "GCTGAGGGG": 18578, + "TGAGATGTG": 18579, + "CCACCCTCA": 18580, + "GAAGCTTTT": 18581, + "GAAGTGAAA": 18582, + "GTGGAAGAA": 18583, + "GTTTATGAAA": 18584, + "GGAGCCAGG": 18585, + "CCAGCCAGG": 18586, + "TCCATCATT": 18587, + "TCCACTCTT": 18588, + "GTATTCTT": 18589, + "CCCTCCACC": 18590, + "CAAATGTTTT": 18591, + "TGAGCAATTTAGCA": 18592, + "TAATGTTTG": 18593, + "CACACCAAA": 18594, + "GAAAGTGTT": 18595, + "GAAATAGCA": 18596, + "TATGTTTTTT": 18597, + "GAGGAAAAAA": 18598, + "CTAATCTG": 18599, + "TTAACATG": 18600, + "CGATAGG": 18601, + "TTATTCTTTT": 18602, + "CGCGGCGG": 18603, + "GAGAAAAAAAA": 18604, + "CAGCGCC": 18605, + "TACAATATA": 18606, + "GCACAGCAA": 18607, + "GTACATGTG": 18608, + "GATCTCAAA": 18609, + "TCACTTTA": 18610, + "CTATCTTTT": 18611, + "CTCTCTTAA": 18612, + "TCTTCCTTTT": 18613, + "CAAATATGA": 18614, + "GGCATATT": 18615, + "TGATGGCTT": 18616, + "GCAGGTTTGAAA": 18617, + "GCATCTCTT": 18618, + "GGACAGGAA": 18619, + "GCCTCAATT": 18620, + "AAAACATT": 18621, + "CTCTCCATG": 18622, + "TGTTTCCTG": 18623, + "CGTGGAGA": 18624, + "TCGGTCTG": 18625, + "GGGGACAGGA": 18626, + "GAATAGGAA": 18627, + "TGAGAACA": 18628, + "GAATCCTG": 18629, + "CAGGGACC": 18630, + "GCAACAGTT": 18631, + "GTCACATGA": 18632, + "CGGĊ": 18633, + "CGCTAGA": 18634, + "TGAGGCTA": 18635, + "CTGGGCCTG": 18636, + "CTGCACTGA": 18637, + "TGACCTTTT": 18638, + "TCCTCACCA": 18639, + "CTGGCAGG": 18640, + "GGACAAGG": 18641, + "CCATCTCTG": 18642, + "GTCGAAGG": 18643, + "CACATAAAAA": 18644, + "CACAGCTCA": 18645, + "TCTTTACCA": 18646, + "CAGAGAAAAA": 18647, + "CCTTTGCTG": 18648, + "CCAAGGCCA": 18649, + "CTGAAAAATT": 18650, + "GAATTGTTTT": 18651, + "GAATGGCC": 18652, + "GCTCCTCTG": 18653, + "GTTTATGTG": 18654, + "TAACAGTTA": 18655, + "TCACAGCTCA": 18656, + "TGACACTGA": 18657, + "GGACAGTCA": 18658, + "CACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACA": 18659, + "TGAGTAGCTGGGATTACAGG": 18660, + "TGATCAGG": 18661, + "AAAATGTA": 18662, + "GAAATAGAAA": 18663, + "GGAAGATGA": 18664, + "CCTTCCTTA": 18665, + "GTCATACA": 18666, + "TATGTACC": 18667, + "CTCCTCAAA": 18668, + "CGCGTT": 18669, + "CAGCATTTG": 18670, + "CAAGACCAA": 18671, + "CCTATTTCTCCACA": 18672, + "GCTCATGCCTA": 18673, + "GGATATC": 18674, + "TATTCATGA": 18675, + 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"GATTAAAAAA": 18720, + "TCCACAATG": 18721, + "TAATTCATG": 18722, + "CGTTTGTG": 18723, + "CTGTGCATG": 18724, + "CCCACAACCCTG": 18725, + "TCGTCTCA": 18726, + "TTAGGTA": 18727, + "CTGCGTC": 18728, + "TATTCAGTG": 18729, + "TATTGCATT": 18730, + "TCTCTCTGTT": 18731, + "GTTAGGTG": 18732, + "GTCCTAAA": 18733, + "TGGAGTTTT": 18734, + "TTCAGCTG": 18735, + "CAGGAGGGA": 18736, + "TAATAATAAA": 18737, + "GTGTGGCTG": 18738, + "CCAAAACAGCATG": 18739, + "TTATAGTTA": 18740, + "AAAATTTATGTGAGGTT": 18741, + "CATAAATAAA": 18742, + "TCGCAATT": 18743, + "TCAGTCCTT": 18744, + "CAATCTCC": 18745, + "GTTTGATA": 18746, + "GTAGGATG": 18747, + "CATTCACTG": 18748, + "CAGGGCTGA": 18749, + "CCTTTATAA": 18750, + "GTCATAAAA": 18751, + "GATGCTC": 18752, + "CTAAAGTAA": 18753, + "CGCACACC": 18754, + "GCTCTTATT": 18755, + "GCGGGCA": 18756, + "TAGGATTTT": 18757, + "GAACTAAAA": 18758, + "GAAACATGA": 18759, + "TCTTTATGA": 18760, + "CATTCATTTT": 18761, + "CATACC": 18762, + "CTAAATTAAA": 18763, + "CTGTTTATA": 18764, + "GGAGGCTAA": 18765, + "CAACATGGTGAAA": 18766, + "TCCCCACA": 18767, + "GTTTAATTTT": 18768, + "CCACATC": 18769, + "CTAGAGAAA": 18770, + "TGAAGACC": 18771, + "GCTTTGGAA": 18772, + "TCTCGCC": 18773, + "CAAAACTT": 18774, + "CGTTGTGA": 18775, + "CTAAATAAAA": 18776, + "CAATTAAATT": 18777, + "CTCAAATTA": 18778, + "CTTCAGCTG": 18779, + "GAATCAAAA": 18780, + "TCCTTTTTG": 18781, + "CAACAGAGA": 18782, + "TATATAATG": 18783, + "CAGGCCCCA": 18784, + "CAGGCCCTG": 18785, + "CGGTGTA": 18786, + "TCAAACATG": 18787, + "TGGACTGA": 18788, + "GTCTTGA": 18789, + "CGCCTGTA": 18790, + "TCCTGCTCA": 18791, + "CTGTGGCC": 18792, + "GGCCAGGTG": 18793, + "TATTTATTATT": 18794, + "GGACATGAA": 18795, + "CTCTTACCTG": 18796, + "CCAAATTTCAAGTCAA": 18797, + "CAAGAGCC": 18798, + "GCAGGAGA": 18799, + "GGAGCAGTG": 18800, + "CTACTCTG": 18801, + "GGAACAGCA": 18802, + "TCTCGGTT": 18803, + "CCAACACAA": 18804, + "CCAAGACCA": 18805, + "GATGTAAAA": 18806, + "GATGTAGAA": 18807, + "TGGGCACA": 18808, + "CCTAĊ": 18809, + "TGATTCTT": 18810, + "GAAAAACAAAA": 18811, + "TATCTAATT": 18812, + "CTCCATGTT": 18813, + "CAGTCTTTT": 18814, + "GGACAGTGA": 18815, + "GCACCCTATT": 18816, + "CACACATACA": 18817, + "TGAGTGTGTG": 18818, + "TATAGAGAA": 18819, + "TCTGTCTCTG": 18820, + "GTAATAGAA": 18821, + "TCTCTACTA": 18822, + "CTTTTCCCCA": 18823, + "TAAAAATAGA": 18824, + "GGTAGCA": 18825, + "TGATTGAA": 18826, + "GAAGATAAA": 18827, + "TCCCAGTGA": 18828, + "CAAGGCTA": 18829, + "GATTTGAAAA": 18830, + "CGCCTTGGCCTCCCAAA": 18831, + "CAATAGC": 18832, + "TGTGTATTA": 18833, + "TATATAGCA": 18834, + "CATTCAGAAAA": 18835, + "TCTGCTGAA": 18836, + "GCTTCTGTT": 18837, + "GTAACACAA": 18838, + "CCTGCTCTT": 18839, + "GGGGAAATG": 18840, + "CCCCĊ": 18841, + "CGCGTCA": 18842, + "GTAAATTCA": 18843, + "CGTTGTA": 18844, + "TAATAGTCA": 18845, + "GAGAAAGAGA": 18846, + "TGATGATGATGATGATGATGATGA": 18847, + "TCACAAGGTCAGGAGATCGAGA": 18848, + "TATATACCCAGTAATG": 18849, + "TAAGTGATT": 18850, + "TATTCTCAA": 18851, + "GAAAGGCTT": 18852, + "GCTTGCCA": 18853, + "CCTTAAAAAA": 18854, + "CCAACACTT": 18855, + "TCCAGTGTT": 18856, + "TCCATACC": 18857, + "CCTAGTC": 18858, + "TCTTATTTTA": 18859, + "GTCAAGTG": 18860, + "GTAAAATTAA": 18861, + "CTTCCCGG": 18862, + "GAACAGAAAA": 18863, + "TCTCTCACTGTT": 18864, + "TCACTGTA": 18865, + "TATTGAGAA": 18866, + "TGAACACTG": 18867, + "TCAACATTTT": 18868, + "CCTTGCCTT": 18869, + "CCTGTTTTA": 18870, + "GTTACAGTT": 18871, + "CTAAGTCGCTT": 18872, + "TACATAGTT": 18873, + "TAATTGTATT": 18874, + "TGATTTCC": 18875, + "GGGAATTTT": 18876, + "CTTCCCTCA": 18877, + "TATATGTTTT": 18878, + "TAATGATTA": 18879, + "GTTGCCCAGGCTGGAGTGCAATG": 18880, + "TAATTTTCATCATAGGTACACGTCAACTATGACAGA": 18881, + "CCCGTTTT": 18882, + "GAACATCC": 18883, + "CACATGGAA": 18884, + "TGTTCAGCA": 18885, + "GAGACTTTT": 18886, + "CATTCTCAA": 18887, + "CAGAGCCTT": 18888, + "GCCAGCATG": 18889, + "CAAAACATTA": 18890, + "TCCTTTGTG": 18891, + "TCCTTCTGA": 18892, + "TGATTCTCA": 18893, + "TCCAATATG": 18894, + "TCTTTTCTT": 18895, + "GTCAAGAAA": 18896, + "TCCTGCTCC": 18897, + "GAAAAACAA": 18898, + "TAGAAGAAAA": 18899, + "CATCTAAAA": 18900, + "TATATGCCCA": 18901, + "GCTTTGTTTT": 18902, + "CTCGAGGA": 18903, + "CTCTAAGAA": 18904, + "TGTGGAATT": 18905, + "TATGGAGTT": 18906, + "GTTAGACA": 18907, + "TAAAAATAAAAAA": 18908, + "TAATACCA": 18909, + "GTCTTCTGA": 18910, + "CGCCCTG": 18911, + "CAATGTCA": 18912, + "TCAGGGAGG": 18913, + "GTGAAATAA": 18914, + "TAACAGTGA": 18915, + "GTTATTTTCAATT": 18916, + "CTCCTGCCTCAGCCTCCCGAGTAGCTGGGACTACAGGCGCCCGCCA": 18917, + "TTACATTA": 18918, + "GAAGATTTT": 18919, + "TGTGGAGTT": 18920, + "CAAATAATG": 18921, + "CAAAGACCA": 18922, + "CCTGCAGTG": 18923, + "TTTGGAAAA": 18924, + "CCCCTAGG": 18925, + "GGAGGCTGAA": 18926, + "TTTTTTTTTTTTAA": 18927, + "GGATGCTGA": 18928, + "GAAAGAAAGAAA": 18929, + "TGACAAATT": 18930, + "CTGGACTA": 18931, + "GGACCCAGA": 18932, + "CAAATACTA": 18933, + "CAGATACC": 18934, + "CAGATGGAA": 18935, + "TCAAGACC": 18936, + "CCTTCAGCA": 18937, + "CATGTACC": 18938, + "TATGTAGTA": 18939, + "CGGCCCA": 18940, + "GATGGTGAA": 18941, + "GTCCTATA": 18942, + "GGTGTCTT": 18943, + "GAATTCAGG": 18944, + "CGTTTCTG": 18945, + "CAGAAGCAA": 18946, + "GTCTTCCA": 18947, + "GGAGGCGGA": 18948, + "TACTTTGTT": 18949, + "GTGTGGATG": 18950, + "CGTCGAGATTTTAA": 18951, + "TAACAGAAAA": 18952, + "TCATAATTA": 18953, + "GGACCATG": 18954, + "GGACCTC": 18955, + "TATTGTTTTA": 18956, + "CACAGACTG": 18957, + "CATTGGTA": 18958, + "CATTCCCCA": 18959, + "CAGATACAA": 18960, + "CAGACATCA": 18961, + "TCAACATAA": 18962, + "GGAAGGCTG": 18963, + "GTGACTCC": 18964, + "CCTGCAGCC": 18965, + "CTTTTGACCA": 18966, + "GGGGGTCA": 18967, + "CGTTCGG": 18968, + "GCTGAGCAA": 18969, + "TCTTACTA": 18970, + "GAAGATGTT": 18971, + "TCACATGAA": 18972, + "CTTTTTTAAAA": 18973, + "CCATCTCACACCA": 18974, + "TCATAATAA": 18975, + "GTCTCCCC": 18976, + "GTCCACAAA": 18977, + "TATTGACAA": 18978, + "CACACACTA": 18979, + "CAGACGCA": 18980, + "TCAACAGTT": 18981, + "GATTTGGAA": 18982, + "CCAAGACC": 18983, + "GTTTTGAGG": 18984, + "CGTGGAGG": 18985, + "TAATAGCA": 18986, + "CAATTTCC": 18987, + "TATAAACAAA": 18988, + "CTGCCTCTT": 18989, + "CAGGTCTGTGAGA": 18990, + "TAATAAAATT": 18991, + "GTTCGATT": 18992, + "GTAGCATG": 18993, + "TCAACAGGA": 18994, + "GCTTCTTTG": 18995, + "GCTTCACTG": 18996, + "GGTTCTAA": 18997, + "GCATTCTTA": 18998, + "TCATGGAA": 18999, + "GCAATTAAAA": 19000, + "GCACAGTCA": 19001, + "GGGAGGGAGG": 19002, + "TAAAATGTTA": 19003, + "GGGATTAAA": 19004, + "TCCAGAAAATCACATTGTA": 19005, + "TAAGTTATT": 19006, + "TCCCTCTCC": 19007, + "GTACCATG": 19008, + "TAAACACCA": 19009, + "TATATCTCC": 19010, + "GAGATCCTG": 19011, + "GCTTTAATT": 19012, + "GCTGGGCCA": 19013, + "CTCCCATCC": 19014, + "CTTTTGTGA": 19015, + "CAGCAGCA": 19016, + "TCTTAGTTA": 19017, + "CGTCCATC": 19018, + "GCAGATGAA": 19019, + "CACTCCAGCCTGGG": 19020, + "CTCCTGACCTCGTGA": 19021, + "GTGCTGGGATTACAGGCATGAGCCA": 19022, + "CAAGTGAAA": 19023, + "TAAATGATG": 19024, + "GCTGGAGTT": 19025, + "CCTTCACCA": 19026, + "CCTTCCTTTT": 19027, + "GTCAGACC": 19028, + "GATTTACTT": 19029, + "CCAAGAGTT": 19030, + "GGTTGTGA": 19031, + "CATATCAAA": 19032, + "GGGAGTCC": 19033, + "CCTAGTT": 19034, + "GTAAACAAAA": 19035, + "TCCAATTAA": 19036, + "GGGTTGTTTT": 19037, + "GAATATGTG": 19038, + "TAAGAGTAA": 19039, + "GCGTCCTT": 19040, + "TCATATTAA": 19041, + "GTATATTAA": 19042, + "TACATATAAA": 19043, + "CTTATATTTT": 19044, + "CGTTCCTG": 19045, + "GGCCTAGG": 19046, + "TAATACTGA": 19047, + "TGCCCGCCA": 19048, + "GAGAAGGGG": 19049, + "GTCAACATG": 19050, + "CCATTCAA": 19051, + "TGAGGTAGG": 19052, + "CTGCAGTAA": 19053, + "CAACAACAAAA": 19054, + "TAGGGGTT": 19055, + "GCGTTCTG": 19056, + "TAATCTCTT": 19057, + "TGATTATA": 19058, + "GCCCTGGA": 19059, + "GTCGTCTT": 19060, + "CACAGACACA": 19061, + "GAAAGGCTG": 19062, + "GAGACCCTG": 19063, + "CAGGCAGTG": 19064, + "GCTTTACC": 19065, + "GCTTTACTT": 19066, + "TTTGGTCA": 19067, + "GCAAACATT": 19068, + "CCAAAGCCTT": 19069, + "CTTAAGAA": 19070, + "CTTTGGAAAA": 19071, + "CAAAAATAAAA": 19072, + "CTGTGGCAA": 19073, + "GAGGAGGGA": 19074, + "TTATGCTAA": 19075, + "TATCTTTTTT": 19076, + "CTGGGGATG": 19077, + "GAGCCTGGA": 19078, + "CTCCATGAA": 19079, + "CTTCAGTTTT": 19080, + "CAGATGGTTGTGA": 19081, + "TAGCCCC": 19082, + "GCGTATA": 19083, + "TAACTCAAA": 19084, + "TCACAGAGA": 19085, + "TCCGCAA": 19086, + "CCACAGAGA": 19087, + "GTATTTTATT": 19088, + "GCCTCAGTG": 19089, + "CTGATGATG": 19090, + "TTTGGGTG": 19091, + "TGCACGCA": 19092, + "TCTCACTCTG": 19093, + "CTGAACTCA": 19094, + "TGCCCTCAA": 19095, + "CACCCTCTT": 19096, + "GGAGGTGAA": 19097, + "CCCTGCCCC": 19098, + "CCCTTCCCC": 19099, + "CCAAAACATCA": 19100, + "GTTTATCAA": 19101, + "CTGCCTGTT": 19102, + "CTTGCCTGGAGAATCCCATG": 19103, + "CAACAGTC": 19104, + "CTGCCCTC": 19105, + "CACATATAA": 19106, + "GCTTGGAAA": 19107, + "CGGCAGGA": 19108, + "CCAACTTTT": 19109, + "TTAATCTT": 19110, + "GATACTTTT": 19111, + "TCCTTGTTTT": 19112, + "GAGAAGGA": 19113, + "CTTTGTAGA": 19114, + "GGGGATTA": 19115, + "GTTCATGTT": 19116, + "CACACATATA": 19117, + "CTTAAAATG": 19118, + "GTTGTCCTT": 19119, + "GCACATTAA": 19120, + "CTAGGGTT": 19121, + "CATTTGGGTT": 19122, + "CAGAGGCTT": 19123, + "CTTAGGGG": 19124, + "GGCACTAA": 19125, + "TTCACCCA": 19126, + "TAATAGAAAA": 19127, + "TAATACTTTT": 19128, + "TGCCTATG": 19129, + "CTGTTGTTA": 19130, + "GGGAAGGAA": 19131, + "GACTGTTTT": 19132, + "AAATATGAA": 19133, + "TGATGAAAATTACAGGCCTCTCTCATCTTTTTAAGTGGGAGAA": 19134, + "TCATGACA": 19135, + "TCCCTCCTT": 19136, + "TCCCCTCTG": 19137, + "GCAGTTCTT": 19138, + "CCATCAAAA": 19139, + "CTCGCCAA": 19140, + "CTCTCTTTG": 19141, + "CAGAGGTTA": 19142, + "GCTTCCTCC": 19143, + "TAGGTCTA": 19144, + "TCAAAGATT": 19145, + "TCTCACTA": 19146, + "TCCTTCCCC": 19147, + "TGCCCTCTT": 19148, + "CACCTCTA": 19149, + "TCCAATTTA": 19150, + "CGAAGCC": 19151, + "GGGAAGAAA": 19152, + "GCAGAAAGTGAA": 19153, + "TCGGACA": 19154, + "TGATTATTTT": 19155, + "CTTTGAATT": 19156, + "CTTTCCCTT": 19157, + "GCATCAAAA": 19158, + "GGACCTCTT": 19159, + "CCATACTA": 19160, + "GCCTCACC": 19161, + "TGTGCGACCCCA": 19162, + "TGAAGATAA": 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"GTGGATGAA": 19207, + "GTACTGGAGTTTCAGCTT": 19208, + "GCGGGCGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGCAGGAGAATG": 19209, + "CTTTCTTTG": 19210, + "CTCGGCTT": 19211, + "GTGATGGAA": 19212, + "TTAACTTA": 19213, + "GCCAGCTGA": 19214, + "GCCAGCATT": 19215, + "TAGGGACA": 19216, + "TCTCATGTT": 19217, + "GCAAAGTAA": 19218, + "GCCTTCTCC": 19219, + "TGAGACTA": 19220, + "CCCAATTA": 19221, + "TCAGAGTAA": 19222, + "CTTCCTCAA": 19223, + "CTGGACATG": 19224, + "GTTCTATTTT": 19225, + "TAAAGAAAGCTGA": 19226, + "TCATCATCATCATCATCATCATCA": 19227, + "GACCCTGATGCTGGGAAAGATT": 19228, + "GACGCTA": 19229, + "CTTTGTGAA": 19230, + "GGAGCCAGA": 19231, + "TCTTCACCA": 19232, + "TCTTGATTA": 19233, + "GGAAGCTA": 19234, + "CTCAGCTCA": 19235, + "GCAATACAA": 19236, + "GATTCAAAA": 19237, + "CTTACATTA": 19238, + "TAGAGGAGA": 19239, + "TCAAAGTG": 19240, + "TGGATCAA": 19241, + "TAATTTCTTA": 19242, + "CTTAATATA": 19243, + "CAGGATTTT": 19244, + "CACTTGTAA": 19245, + "TATTAATATA": 19246, + "TCAGGCATG": 19247, + "TTTTATTTTATTTTATTTTA": 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"TGACAGTCA": 19382, + "TAAATACAAA": 19383, + "TGAAAAATTA": 19384, + "CAACAGGAA": 19385, + "CAACTATG": 19386, + "CTTGAGAAA": 19387, + "CTTGTTAAA": 19388, + "GGAGTTTTA": 19389, + "TAAACTCTT": 19390, + "TCAACCCA": 19391, + "TCAAGATGA": 19392, + "TGCATGA": 19393, + "GCAAATGTG": 19394, + "GTCTTCCTG": 19395, + "CAATTGTA": 19396, + "CCCCATGAA": 19397, + "GTCTGTCC": 19398, + "GCGAGCTG": 19399, + "GCTAATTTTTTGTATTTTTAGTAGAGA": 19400, + "GGTAGGTT": 19401, + "GTGTATC": 19402, + "GCCTCTCTT": 19403, + "CTCTTATTA": 19404, + "GAGACATGA": 19405, + "CTCCCAGTT": 19406, + "TCCATATCA": 19407, + "GCCACCTCC": 19408, + "TGTACAAAA": 19409, + "CGTGTTAA": 19410, + "GGCCGGGCGCGGTG": 19411, + "GCTCAGCTT": 19412, + "TAATGTTTTA": 19413, + "CATCATTAA": 19414, + "GCAGGGTGA": 19415, + "TAATTATTTG": 19416, + "TGTTTGCATTCAA": 19417, + "GCAAAAAAAAA": 19418, + "GCGTCTCC": 19419, + "TTTTCTTCTA": 19420, + "TATTCCATG": 19421, + "TGAACACTT": 19422, + "TCTGGAGGA": 19423, + "CAGAGGGTG": 19424, + "GGAAGAGTG": 19425, + 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"GAATTCTCCAGG": 19515, + "TCCAAGGAA": 19516, + "CGGAGAA": 19517, + "TAATGCATG": 19518, + "TAATGGTAAA": 19519, + "CGTATATA": 19520, + "TAAGTGGATATTA": 19521, + "GCGTTTAA": 19522, + "TTAGCTA": 19523, + "CTTCTCCA": 19524, + "CAAAGTTAA": 19525, + "TCAACACTG": 19526, + "CGGTGTTTG": 19527, + "GAAAAGATA": 19528, + "GTCCCAGTG": 19529, + "TCTCAGCTG": 19530, + "CGCGTGA": 19531, + "TAATACAAAA": 19532, + "GCCAAGGAA": 19533, + "GACCCCCA": 19534, + "CCCTGTTTG": 19535, + "GCTCGGGG": 19536, + "CCCAATTTT": 19537, + "CTTTACTTA": 19538, + "GCGCAGAA": 19539, + "GCGAGATT": 19540, + "TGAGAATA": 19541, + "TCCCTTCTG": 19542, + "GTGCTTCA": 19543, + "GCACACATT": 19544, + "GCAGCGCC": 19545, + "GGAGCAGGA": 19546, + "CAAACAGTG": 19547, + "CAGGTAGAA": 19548, + "TCTGGATGTG": 19549, + "TAAAATAATAA": 19550, + "CCTTTCATT": 19551, + "CATGCAGCA": 19552, + "GTTACCAAA": 19553, + "GTTTTGAGA": 19554, + "GCCAGAACTT": 19555, + "CTTTTCTCTT": 19556, + "TGGACTT": 19557, + "TAAGACCAA": 19558, + "TCATAGCTT": 19559, + "GTCATGCTGAAA": 19560, + "TGACATTAA": 19561, + "GTGTATCC": 19562, + "GTTTAAGG": 19563, + "GCCTCTGAA": 19564, + "TAAATCTC": 19565, + "TAAAGAATG": 19566, + "CTCTTTTTTTT": 19567, + "TGTGGCTA": 19568, + "TATTCGAA": 19569, + "CAGGGGCC": 19570, + "CAGATTATT": 19571, + "GATTTCATG": 19572, + "TTAAGCAA": 19573, + "CGTGGGTG": 19574, + "CGTTCTG": 19575, + "TGCCTCTA": 19576, + "GCAGGCCTG": 19577, + "CCTGATTTG": 19578, + "GGAATTGAACTCA": 19579, + "CTTTTCTTTTCTTTTCTTTT": 19580, + "CACAAAATG": 19581, + "CCCACACA": 19582, + "TGACTTTCA": 19583, + "GTTGATCC": 19584, + "GTTTGAGTT": 19585, + "TCTTGAGAA": 19586, + "CCAACACTG": 19587, + "TAGGTGTA": 19588, + "CGTGGTA": 19589, + "TAAAAATTCA": 19590, + "TAAAAATATTA": 19591, + "CTTTGCCA": 19592, + "CAAAAATGAA": 19593, + "GCATTTATA": 19594, + "TATCAAAAAA": 19595, + "TCAGATATA": 19596, + "GTGGCCAGG": 19597, + "GTTCAAATT": 19598, + "GAAACAGTA": 19599, + "TCTTCATGA": 19600, + "GCTGCCTA": 19601, + "CTCACAAAA": 19602, + "GCAAGGCCA": 19603, + "CGGCCTCA": 19604, + "TGGAGTAA": 19605, + "TCTCCCGA": 19606, + "CTCTGGTCA": 19607, + "CTATTCTAA": 19608, + "GTGTGGTA": 19609, + "CTCCAGTCTGGG": 19610, + "TTATATTATT": 19611, + "GACTCTGATGCTG": 19612, + "CTTTTGTAATATTTT": 19613, + "TCGAGATT": 19614, + "TAATCTGTT": 19615, + "TAATATATA": 19616, + "CTCTGGGTT": 19617, + "CATGTTATT": 19618, + "TTTTATTTTAA": 19619, + "CTTTTGCTG": 19620, + "TAGAGAATT": 19621, + "TCTCACCTT": 19622, + "CTGTTGTAA": 19623, + "GGATTCTA": 19624, + "GCGGGCGG": 19625, + "TATGATTAA": 19626, + "TATTTGTATA": 19627, + "GGAGAGGAGG": 19628, + "CCCTAAAAA": 19629, + "TAATCTCAA": 19630, + "TCAGTTCA": 19631, + "GTGCAGAAA": 19632, + "GCACCGG": 19633, + "TAAAGTTTG": 19634, + "TATTGTTTG": 19635, + "CAAATATAAA": 19636, + "CAGAGTTGAA": 19637, + "GGAAGGGTG": 19638, + "CTGAGCTCA": 19639, + "CGACCCA": 19640, + "TTTAĊ": 19641, + "GAGAAGGAAA": 19642, + "CAGAAGAGG": 19643, + "TCATGCCTG": 19644, + "TATTAAGAA": 19645, + "GCTCCCTG": 19646, + "CTTTAGTAA": 19647, + "CCAGACTCA": 19648, + "GACTCAAAA": 19649, + "CTTGCAAAA": 19650, + "CTTTCTCAA": 19651, + "GCCTCTGTT": 19652, + "TAAACAGGA": 19653, + "CTCTCGA": 19654, + "CACAGCCTT": 19655, + "TCAAGAAAAA": 19656, + "CTTACCTA": 19657, + "TCCAGTCTT": 19658, + "GACACGTT": 19659, + "CCCCTCTC": 19660, + "TCAAACTGA": 19661, + "GGGATCTT": 19662, + "GGTAGTCA": 19663, + "GAGAAGCTAA": 19664, + "TCATATCCA": 19665, + "CTTGCTCTG": 19666, + "GCCGATA": 19667, + "GAAATCCAA": 19668, + "GAGATTTTA": 19669, + "CAGGCACTG": 19670, + "CGTGTGTGTGTG": 19671, + "GTGAATGTG": 19672, + "CTTTTATTTG": 19673, + "TCTATATTA": 19674, + "TCACTACTTCCA": 19675, + "GTCTCGCTCTGTT": 19676, + "GAGGTGGGTGGA": 19677, + "GAGAGTCCCTTGGACTGCAAGGAGATCCAA": 19678, + "TAATCGG": 19679, + "GCAGAAATG": 19680, + "GGATCAGG": 19681, + "GGAGTTGAA": 19682, + "GCCCATC": 19683, + "GCAATGCC": 19684, + "CCTGTACA": 19685, + "TAGGGAAAA": 19686, + "CTGAATATT": 19687, + "TCCTTTATT": 19688, + "GAATTCCAA": 19689, + "CAGTGGCTCA": 19690, + "TAATGCCA": 19691, + "TGTTTGTTTGTT": 19692, + "TACTGATG": 19693, + "TAACACATT": 19694, + "TCAGTTTA": 19695, + "GAAGTGGA": 19696, + "CAACCATG": 19697, + "CAAGAATA": 19698, + "CTCTAAGG": 19699, + "GTCCAGGTT": 19700, + "GTCTGTTGTT": 19701, + "TATTGGGTG": 19702, + "CACATATTA": 19703, + "CATTGAGAA": 19704, + "GCTGTACA": 19705, + "GCTGCGTT": 19706, + "CCTGGACTG": 19707, + "GATGGAGTT": 19708, + "GGGGTTCA": 19709, + "CATACCCA": 19710, + "CAGCAGCTG": 19711, + "GTATTGTA": 19712, + "CAATTGTAA": 19713, + "TACCCTTA": 19714, + "CAGCTCTC": 19715, + "GCTCCTGCC": 19716, + "CCTTATATA": 19717, + "TCTTTTTTAAA": 19718, + "GAAATATTACAACTAAA": 19719, + "CATCTCCTT": 19720, + "TACTCTCA": 19721, + "TCATTAAAAA": 19722, + "CAATTTTTTTT": 19723, + "CTTCAAATT": 19724, + "GAAAGACTG": 19725, + "TGTTCAGTA": 19726, + "CATTCCTCA": 19727, + "GCTTTAGCA": 19728, + "CCTTAAATT": 19729, + "GGGGTATG": 19730, + "TAGGTCTG": 19731, + "TAGGGCAA": 19732, + "TCAAAGGA": 19733, + "TGCACTAA": 19734, + "GAATTTCCA": 19735, + "TACCCTAA": 19736, + "GAATGGATT": 19737, + "GTGTGGAGG": 19738, + "CCTTTTTTA": 19739, + "CTTCCCTTTT": 19740, + "CTGTAGTTA": 19741, + "CAATAGTAA": 19742, + "TGACATGTT": 19743, + "TGAATGTTA": 19744, + "TAATTTATTTT": 19745, + "TCCTCTGTCATCC": 19746, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 19747, + "CCTAAAATT": 19748, + "TAATCCATT": 19749, + "GAACTGAAA": 19750, + "TCCGGGA": 19751, + "GCATTTTTG": 19752, + "TCTTCATAA": 19753, + "TATGGCTGA": 19754, + "GAAAAGCTT": 19755, + "GTTTTAAATT": 19756, + "TCTACCTCC": 19757, + "TGTACAGG": 19758, + "CTGAACTA": 19759, + "TTTTTTTAAAAA": 19760, + "CTTAAAAATG": 19761, + "TCCTGCCA": 19762, + "CTCTGCAAA": 19763, + "CTGTGCCA": 19764, + "CTGTGGAGA": 19765, + "CCTCCGCCTCCCGG": 19766, + "GTGTGCATG": 19767, + "GCACATGTG": 19768, + "CAGATGAAAA": 19769, + "TGAGCAATTTAGCATGGGTTTGAAGAAATTAGGA": 19770, + "TCAGCTGTT": 19771, + "CAATCTTTT": 19772, + "TAAAACTAAA": 19773, + "TACATAATA": 19774, + "TTAATATAA": 19775, + "TTTGGTGA": 19776, + "CCCACGGA": 19777, + "TCTCACTGA": 19778, + "TCTCACTTA": 19779, + "GCAGGCTGA": 19780, + "CTTCCCATT": 19781, + "TGACATCTG": 19782, + "TAATGAAAAA": 19783, + "CTACGCC": 19784, + "GAAACACCA": 19785, + "TATAGGCA": 19786, + "CCTTGCAAA": 19787, + "CCAAGGTCA": 19788, + "GAAAACGA": 19789, + "CGAGCTA": 19790, + "CAATTCTTA": 19791, + "GTGGGGAAA": 19792, + "TATAAATTTT": 19793, + "TCAGATGAA": 19794, + "CAATAGCAA": 19795, + "TAATAAATTA": 19796, + "CAAAATTAAAA": 19797, + "GACTCCATCTC": 19798, + "CCGCCAGA": 19799, + "CCGAGTC": 19800, + "TAGCAAAAA": 19801, + "GAACATC": 19802, + "CTTGCTC": 19803, + "TAAAGTAAAA": 19804, + "CAAACTCAA": 19805, + "GCAACTTTT": 19806, + "TTTTAGGA": 19807, + "TATGCATTA": 19808, + "GATGTTATT": 19809, + "CAAAATAAAAA": 19810, + "CGTGTGGG": 19811, + "GAGGAGGTG": 19812, + "TCAGATATT": 19813, + "TCAGACATG": 19814, + "GCCAGGCATGGTG": 19815, + "TAAGACATT": 19816, + "GCCCACCTG": 19817, + "GTGTGAGATGGTA": 19818, + "TCATĊ": 19819, + "TCAGAATA": 19820, + "GCATCTGTT": 19821, + "GTATATCC": 19822, + "TAAACGC": 19823, + "CTCTGTTTG": 19824, + "GAAATAAATT": 19825, + "GCTTCTCTG": 19826, + "CTCAGAGTT": 19827, + "GTTACAGTA": 19828, + "TCTATCATT": 19829, + "GCCATTTTAA": 19830, + "CGACCCTG": 19831, + "CGTTTGAA": 19832, + "CACCCTTA": 19833, + "CTCTTCTTTT": 19834, + "CGCAGAGG": 19835, + "GTGTTTGA": 19836, + "TAATGTCTT": 19837, + "CCTCACTG": 19838, + "GGATTTCAA": 19839, + "AAATGTGA": 19840, + "CTTCAGTAA": 19841, + "GCTGGGCATGGTG": 19842, + "CTCCAAAGATTTT": 19843, + "CAAGTATG": 19844, + "CAATGAATT": 19845, + "TAAAGCTGA": 19846, + "CTCTCAGGA": 19847, + "TGTTCATAA": 19848, + "TGTTTCCATT": 19849, + "GCTTGACA": 19850, + "GTTACATAA": 19851, + "CGACATCA": 19852, + "TCATTGGTT": 19853, + "GTGTTGGGG": 19854, + "GTCTTCTCA": 19855, + "CAATTCACA": 19856, + "GCCTTCTG": 19857, + "GAAGAGTAA": 19858, + "CTGCATATG": 19859, + "CAAGGGATT": 19860, + "TCATCAGACCA": 19861, + "GCCCCTGTG": 19862, + "TAAATATAAAA": 19863, + "GTTCCTGTT": 19864, + "CAGTTTTTTAACGA": 19865, + "CTTGGGAGGCTGAGGCA": 19866, + "TCTTTCCCAGCATCAGGGTCTTTT": 19867, + "TACTAATG": 19868, + "TCGTCCAA": 19869, + "GAGTĊ": 19870, + "CTTTGTTAA": 19871, + "GTTTAAATG": 19872, + "CCAGCACC": 19873, + "CTATGATG": 19874, + "GTCTATATT": 19875, + "GTCGTGTCC": 19876, + "TATACACTG": 19877, + "TCTTTACTA": 19878, + "GAGAGCTA": 19879, + "GAGATAGAA": 19880, + "CATTTATATT": 19881, + "CAGGGGAAA": 19882, + "TCTGTCTAA": 19883, + "CTGAGATCA": 19884, + "GAAAAGGTCA": 19885, + "TCCTGCCCC": 19886, + "GAGGATTTT": 19887, + "CTGCAGCCTACCAGGCTCC": 19888, + "TACAGGTG": 19889, + "TAAGATTAA": 19890, + "TCCTCCATT": 19891, + "CTTCTCTA": 19892, + "GTACTTGAA": 19893, + "TAAACTGTT": 19894, + "TATAGGGA": 19895, + "CCTTTAATAA": 19896, + "GAGGGATA": 19897, + "CCAACCCTG": 19898, + "TCCAGTGAA": 19899, + "GATGGAGAAA": 19900, + "TTAAGGA": 19901, + "TTAAAAATA": 19902, + "TCAAAGTGA": 19903, + "GCTCAGCC": 19904, + "GTTTGGAGG": 19905, + "TAGCTAAAA": 19906, + "TTACATATT": 19907, + "TACGCCA": 19908, + "TCATCTCAA": 19909, + "TGATAATGA": 19910, + "GAACTTGG": 19911, + "CAACCAAAA": 19912, + "GTTCGAGG": 19913, + "GCACTGAAA": 19914, + "CAAACACAAA": 19915, + "GGAACCCA": 19916, + "CCTTCACTG": 19917, + "TACAGTCAA": 19918, + "GTTTTCTCC": 19919, + "CGACTTTT": 19920, + "CAAAAGCAAA": 19921, + "TGATTGTG": 19922, + "TGATTCTTA": 19923, + "CCTCCTCCA": 19924, + "TCCCAGTCA": 19925, + "CAACAGCTG": 19926, + "GTATAGTAA": 19927, + "GTAGGGATG": 19928, + "GGTATTTAGTA": 19929, + "TAAAATAAAATAAAATAAAA": 19930, + "GACAAAAAAA": 19931, + "TCCTCACA": 19932, + "GTTCCAGAA": 19933, + "GTAGTATG": 19934, + "GTCGACC": 19935, + "GAAAGGCC": 19936, + "TGTTGTATT": 19937, + "GCTTTCTC": 19938, + "CTCATTATT": 19939, + "CCTTCTGTT": 19940, + "CCTTTACTT": 19941, + "GCAACGTT": 19942, + "GATTGGAAA": 19943, + "TTAATAGA": 19944, + "TTCAGCCA": 19945, + "TCCTTGAAA": 19946, + "CTGTGCAGG": 19947, + "GCAGACGG": 19948, + "GGTGGTGGCGCACGCCTTTAATCCCAGCA": 19949, + "CAAAAAAAAAAA": 19950, + "GTTTCCAGG": 19951, + "CCAGCCTCC": 19952, + "CTCTTATA": 19953, + "TCAAGGCC": 19954, + "CATGTATAA": 19955, + "GATGCTGAA": 19956, + "GTCTTCCAA": 19957, + "CCATTCCAA": 19958, + "GAAAAATATA": 19959, + 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"GTCGGGTG": 20005, + "TCTGCCAAA": 20006, + "CAGATATTTT": 20007, + "TCAAGATCA": 20008, + "TTTTAGGTT": 20009, + "GATTGAAAA": 20010, + "CCAAGATAA": 20011, + "GTTTTTGA": 20012, + "GTTTTCATG": 20013, + "GCCAGTGTT": 20014, + "GGGGCTGGG": 20015, + "GACACGGA": 20016, + "CTGAATTCA": 20017, + "CAGAAATTA": 20018, + "CTCCATGTG": 20019, + "TAAGAGCAA": 20020, + "GCGCGCGC": 20021, + "GAGGTGGAAA": 20022, + "TCAAATTGCCAA": 20023, + "CCTAGGTT": 20024, + "TACGAAAA": 20025, + "GGTCAATG": 20026, + "TCCGGCA": 20027, + "GCCTATCC": 20028, + "CTATTATTTT": 20029, + "TCTGTATAA": 20030, + "TTTTACCA": 20031, + "CCTGGAGTT": 20032, + "TCCACTGTT": 20033, + "CAAAAGCTT": 20034, + "TGGACGG": 20035, + "TGCATACA": 20036, + "TTTTTTGAGA": 20037, + "GTCTTGAAA": 20038, + "CAATTCCCA": 20039, + "CTATTGTAAA": 20040, + "CCAGACACA": 20041, + "TATTTATTTATTTATTTATTTATTTATTTATT": 20042, + "GCGAAATG": 20043, + "GAAGGAGGA": 20044, + "TCCTCATCA": 20045, + "TGTTCTAAA": 20046, + "CAAACCCC": 20047, + "CTGAGCTGA": 20048, + "CTCATAAAA": 20049, + "CATGCATTA": 20050, + "TCTCCACTA": 20051, + "CCAAGGTA": 20052, + "CTAAGTGAA": 20053, + "TAAAAATACA": 20054, + "TGATTGCTT": 20055, + "GTCTTTATT": 20056, + "CTGTGGATG": 20057, + "CTGTGGCTCA": 20058, + "CACTGCTG": 20059, + "CAATGCCAAA": 20060, + "TAAGGCAGG": 20061, + "TCAGAAGAA": 20062, + "CTTCAGAGA": 20063, + "GTGCGTC": 20064, + "GTTCTGCTG": 20065, + "GCACAAGG": 20066, + "GTACCTC": 20067, + "TGTGGAGAA": 20068, + "TCTTTACTG": 20069, + "GAGAGGTGA": 20070, + "TCAATGGAA": 20071, + "GCAAGCTGA": 20072, + "GTTATTATTA": 20073, + "TTTAGTGA": 20074, + "TCTGATGCA": 20075, + "GGAAAGAGG": 20076, + "CTTAATGAA": 20077, + "TACCTCTA": 20078, + "TCCTGGAAA": 20079, + "TAAAAAATAAA": 20080, + "GGAGACATG": 20081, + "GTTGTTGTTTT": 20082, + "CTGGAGTCA": 20083, + "TCACTTCTT": 20084, + "CAATGAGG": 20085, + "GTGGCACA": 20086, + "GGAGTTGG": 20087, + "CACACAGTT": 20088, + "CAGGCATA": 20089, + "GAGGCTCC": 20090, + "CCTGTCTCA": 20091, + "CTAAGGGA": 20092, + "CGTGTGTT": 20093, + "CGTGCGC": 20094, + "TAAAAACTG": 20095, + "TGATTCCTT": 20096, + "CTCTTGCTT": 20097, + "CTTTGTATT": 20098, + "CTGTGTGA": 20099, + "TGATGGGAA": 20100, + "GTTTAGTTA": 20101, + "CTGTACATT": 20102, + "CCTTTGTTTT": 20103, + "CACCTGAAA": 20104, + "GTAGTATAGTT": 20105, + "CAGACAGACAGA": 20106, + "TAAACTTCTGA": 20107, + "TCGCTCAA": 20108, + "TAACCAGG": 20109, + "TAATCAGCA": 20110, + "GAACTGGG": 20111, + "CTAGTTTG": 20112, + "GTCGGGCA": 20113, + "CTGATAAAA": 20114, + "TTAACCA": 20115, + "TTTGGTTA": 20116, + "TCATTGCTG": 20117, + "GTATTCTG": 20118, + "GGGTGAAAA": 20119, + "CTTTATGTG": 20120, + "CAATATCAA": 20121, + "GTCAAATTA": 20122, + "GGAGTGGCTT": 20123, + "GTGGTTTTA": 20124, + "GCAGCCCA": 20125, + "TCTTGATGA": 20126, + "GAGAGGAGG": 20127, + "GAGAGGGAGA": 20128, + "CCTTTGGAA": 20129, + "CCCCGTCC": 20130, + "TGATTGTTA": 20131, + "GTCAATGAA": 20132, + "CCTGAGGTT": 20133, + "TCGGCGC": 20134, + "GTGAAATTTT": 20135, + "CCCTCTCTG": 20136, + "TATTTACAAA": 20137, + "CTATAGTAA": 20138, + "TCTATATTTT": 20139, + "CCATCAGCAA": 20140, + "GACTTATG": 20141, + "TAATCATCA": 20142, + "TAATCCAGG": 20143, + "TCCCTTTTA": 20144, + "CTTCAAGA": 20145, + "CAGATATTA": 20146, + "CGGCAAAA": 20147, + "CCAAACCAA": 20148, + "CTCAATAAA": 20149, + "CACTGCAA": 20150, + "GGATGGCTT": 20151, + "TGATAGAAA": 20152, + "TTACACAAA": 20153, + "GTATATGTG": 20154, + "TCATGTTAA": 20155, + "GCCGGCCA": 20156, + "CACATTATT": 20157, + "TATAGGGG": 20158, + "GAGAGACAA": 20159, + "CAGATACTG": 20160, + "GTCATCTGA": 20161, + "GTCAGAGTT": 20162, + "GTAACTGAA": 20163, + "CTTTTGGGG": 20164, + "CTTTTAAAAAA": 20165, + "GTAAAGCAA": 20166, + "CAATTTCAAA": 20167, + "CTCTGGAAA": 20168, + "CCAGGCGG": 20169, + "TCTCTGTATA": 20170, + "GCCAGGCTG": 20171, + "CGTAAAAAA": 20172, + "TAAGGATA": 20173, + "TCCTGTGTT": 20174, + "GTGGCAGCA": 20175, + "GTATAGGA": 20176, + "CTCTAATAA": 20177, + "CTCTGAATG": 20178, + "CATTTAGCA": 20179, + "CATGCGCA": 20180, + "GTCCTGTGA": 20181, + "TAGGGTTTT": 20182, + "TGAAATATT": 20183, + "CACCCGCCA": 20184, + "CTTAACTA": 20185, + "TATAACTA": 20186, + "TACAATGTT": 20187, + "TAACATATG": 20188, + "CTCATTAAAA": 20189, + "GTTCTTGTGA": 20190, + "GTCATGTCTGA": 20191, + "GTAAATTTGTT": 20192, + "TCCGCCTGCCTCTG": 20193, + "CATCTCTA": 20194, + "TAGAAAAAAA": 20195, + "TAAATTATA": 20196, + "GTCTCACC": 20197, + "TATATCTAA": 20198, + "TCTTGCTCTG": 20199, + "CATTTCTCC": 20200, + "TAAAAGAAAAA": 20201, + "GAGGTGCA": 20202, + "CTTTTCACTT": 20203, + "CGATACA": 20204, + "TAGGAAATG": 20205, + "CGTGCAGG": 20206, + "TCCTGGCC": 20207, + "GAGTGCTG": 20208, + "CATGACTTA": 20209, + "GCGGCTC": 20210, + "GAAGAGCTT": 20211, + "CTTCCTCTA": 20212, + "CCCTCTGGG": 20213, + "GTTCACTGA": 20214, + "GTGCACATGTA": 20215, + "TCGTAGAA": 20216, + "TAACTTGA": 20217, + "TAATATATTA": 20218, + "GCAGAGATT": 20219, + "TATTCCTTA": 20220, + "CATTTAATG": 20221, + "GTCATACACA": 20222, + "TTTTAGGAA": 20223, + "GATTCCCA": 20224, + "TTAATATTTT": 20225, + "GAAAATCTT": 20226, + "TAATTGGAA": 20227, + "CTTTGTCTCTG": 20228, + "CAATGCAAA": 20229, + "GAAGACCCA": 20230, + "TAAATATGAA": 20231, + "TTAGGCA": 20232, + "CTTTAATTTT": 20233, + "CTTTGAGAA": 20234, + "GTCTCGC": 20235, + "CACAGGCAA": 20236, + "GAGATGCC": 20237, + "GAGAGGCCA": 20238, + "TAAAACATTTT": 20239, + "TTTTAGCTT": 20240, + "GATGGGGAA": 20241, + "CATACCTA": 20242, + "TCATTGTGA": 20243, + "TCCTTGGTT": 20244, + "CTAAAGGAA": 20245, + "GCTCACTG": 20246, + "CTTTATTTTTT": 20247, + "TAATCCCAGC": 20248, + "GGACCACAGG": 20249, + "GAAAATGTGGCACATATA": 20250, + "TAGCCTCC": 20251, + "GCGGGGTT": 20252, + "TTTTCTGTT": 20253, + "TGATATTTG": 20254, + "GTACTTGG": 20255, + "CTCTTATTTT": 20256, + "TGTTCGTT": 20257, + "GAGACACC": 20258, + "TCTGGCCTT": 20259, + "TCAACACTT": 20260, + "CGGCCGG": 20261, + "GAAAACAAAAA": 20262, + "GGGGGTTA": 20263, + "TCTCAGTCA": 20264, + "CTGAAGAGG": 20265, + "TCCTTGTCTT": 20266, + "GTTTGCCA": 20267, + "CTATAGTCA": 20268, + "CAGGTTTGTTACATATGTATA": 20269, + "GGGAAAATT": 20270, + "TCGAGGTT": 20271, + "TCCCTTTCA": 20272, + "GTGCTTTTA": 20273, + "GCCTCCAGA": 20274, + "GTCTCAGCA": 20275, + "GTCTCCTA": 20276, + "GAAATAGTA": 20277, + "TATATTTTTG": 20278, + "TCTTGGTCA": 20279, + "CAAAGCAAAA": 20280, + "GAGACGC": 20281, + "TCAATAGTT": 20282, + "CTCACTAAA": 20283, + "CTCAGGCTT": 20284, + "GTAAGGCAA": 20285, + "CTAAGCCA": 20286, + "TAGGTCAA": 20287, + "TCAAACCA": 20288, + "TGGACGA": 20289, + "TAATATTTTAA": 20290, + "TACAATATT": 20291, + "TGAGATAGA": 20292, + "GAAGGTTTG": 20293, + "GTGGGGTGGG": 20294, + "TGTGTGAAAA": 20295, + "GTTCAGTCA": 20296, + "CCGTGCC": 20297, + "TAACAAGA": 20298, + "TTAGGGG": 20299, + "GAACCTTA": 20300, + "GTGCCTTTT": 20301, + "GCAGCTGAA": 20302, + "TGTTCCCC": 20303, + "TGTTCACTT": 20304, + "TTTTAGTT": 20305, + "GATTGCATT": 20306, + "CTCCCCCCA": 20307, + "CTTTTGTCA": 20308, + "GCTACCTA": 20309, + "TATAACACA": 20310, + "GAGCACAGG": 20311, + "CTGGGCACA": 20312, + "TCGGTTTG": 20313, + "CTTCATATT": 20314, + "TCGCGGCC": 20315, + "GCCGGGTGTG": 20316, + "ATTAA": 20317, + "TCGCACTG": 20318, + "GGACCGG": 20319, + "GTAGCTCC": 20320, + "GAAATCTCA": 20321, + "CAAACTTAA": 20322, + "GGAAGGGGAA": 20323, + "TAAAACATAA": 20324, + "CTCAGACTG": 20325, + "CCTTCCTGA": 20326, + "CGGTATG": 20327, + "GGGAĊ": 20328, + "GCAAAGATG": 20329, + "GGATTGACTG": 20330, + "CAATGGTTGAA": 20331, + "GAACAGTAA": 20332, + "GCCATTCCCTT": 20333, + "TCGTCAAACCCATG": 20334, + "TTTTCTCTT": 20335, + "CTGCTTTTA": 20336, + "GGAGCAAAA": 20337, + "GCCCCAAAA": 20338, + "CTCTAACC": 20339, + "CTCGTTACCTG": 20340, + "TCTTCTGTG": 20341, + "GAGAGCTGA": 20342, + "CATTCATGA": 20343, + "CAGACCCC": 20344, + "CCTTGATA": 20345, + "GATTCTTAA": 20346, + "GTCCCATA": 20347, + "CGAGTTGA": 20348, + "GGTTCATA": 20349, + "TGTAĊ": 20350, + "TTATTGCAA": 20351, + "CCAAAGGAA": 20352, + "CGCCTGTG": 20353, + "GAGGAGGAGGA": 20354, + "GCTCGTG": 20355, + "TACTATTTA": 20356, + "CTGGGGATT": 20357, + "CTCCACCTG": 20358, + "GAAGAGTGA": 20359, + "CTTCCCCTG": 20360, + "CTTCATATG": 20361, + "GCGTAGA": 20362, + "CTTCTGCA": 20363, + "GTACCTCC": 20364, + "GTCGACTT": 20365, + "GAGACAGC": 20366, + "CTGACATCA": 20367, + "GAAAATAGAA": 20368, + "GAAAAAAATA": 20369, + "CAAAAGGAAA": 20370, + "TGTACC": 20371, + "TTTTTTGTT": 20372, + "CGCAGCTG": 20373, + "CCATGGAA": 20374, + "TAAAAAATTAA": 20375, + "CCCTCTGCC": 20376, + "TGATACATT": 20377, + "CTGGATGAA": 20378, + "CTTCTGCCTCAGCCTCC": 20379, + "GGATGACAGA": 20380, + "CACCAACAGTG": 20381, + "TCCATCACCAACTCCCGGA": 20382, + "TGAGCTTTT": 20383, + "TTAGCCA": 20384, + "TCCTCATA": 20385, + "CTCTCAGTG": 20386, + "GATTGATGA": 20387, + "CTAAGGGG": 20388, + "GCCAGCCCGG": 20389, + "CATAGTAAA": 20390, + "CGCAGGTG": 20391, + "CGAATAA": 20392, + "GCGGTTAA": 20393, + "TCACACTGA": 20394, + "GGAAATGGCAA": 20395, + "TAATAAATAAA": 20396, + "GTATGAGA": 20397, + "CTACGTC": 20398, + "GAAACAAAATT": 20399, + "TCTTCTCTG": 20400, + "TGAACGTA": 20401, + "TCTGCATTTT": 20402, + "TCAAGCATT": 20403, + "GAGGTGAAA": 20404, + "CCTGCAGC": 20405, + "TAGAGAAAAA": 20406, + "GATATAATT": 20407, + "GTAAATTAAA": 20408, + "TCTGAGATT": 20409, + "TCATTTCAAA": 20410, + "CCAAACACA": 20411, + "CAGAAGGAA": 20412, + "TGATGCGG": 20413, + "TACAAGAA": 20414, + "CAGCCCGG": 20415, + "CCAGGTGGG": 20416, + "CTACAGTCA": 20417, + "GGGGTGGAAA": 20418, + "CCCATGAAA": 20419, + "CCATAAGGGTG": 20420, + "CAGAGTACATCA": 20421, + "TCAGTTCTT": 20422, + "CAAGCAGA": 20423, + "CAAAGCATT": 20424, + "GTAACATTA": 20425, + "CTAATAATA": 20426, + "GAAAAGGGA": 20427, + "CTTTTTGCA": 20428, + "TGAAATATG": 20429, + "GAGAATGAA": 20430, + "CTCTTCTTA": 20431, + "CGAAGTCA": 20432, + "TGATGGATT": 20433, + "GGCTGTGGG": 20434, + "GCGGCTCC": 20435, + "GATCAGATCA": 20436, + "CCTTTGAAAA": 20437, + "GTCATGTTTT": 20438, + "GCGTCATT": 20439, + "GAAAACAGCA": 20440, + "TTTAGAGG": 20441, + "GTTTGCTCA": 20442, + "CCATGGCC": 20443, + "GCAGGTTTG": 20444, + "GGAGACTCA": 20445, + "TGTGTGTATA": 20446, + "TCGATAAA": 20447, + "TTATACTTA": 20448, + "CCACATCTT": 20449, + "GGATGAGATGGTTGGATG": 20450, + "ATTTG": 20451, + "CAGCAAATT": 20452, + "GGTCTACA": 20453, + "TTACGTT": 20454, + "TCCCTGAAA": 20455, + "CATTCTATT": 20456, + "CAGATATCA": 20457, + "GCTGTCTCA": 20458, + "GCTGCTCTG": 20459, + "CGACACCA": 20460, + "TGGACCA": 20461, + "TCTGACTCA": 20462, + "GGTACTT": 20463, + "CTTCACTCA": 20464, + "GAACACGG": 20465, + "TATTTGAACAGTGA": 20466, + "TCCCAAGG": 20467, + "GCACAGGAA": 20468, + "CATTGACTT": 20469, + "TCAACTCAA": 20470, + "GCAAGAGCA": 20471, + "GATGTACA": 20472, + "GCCAGCCAA": 20473, + "TTATTGGTT": 20474, + "TCCAACAAA": 20475, + "GCTGAGGGAA": 20476, + "TAATGCTGA": 20477, + "TCTCTGAAAA": 20478, + "CGGGGTTTCACCATGTTA": 20479, + "GATCTTTTA": 20480, + "GTGTĊ": 20481, + "GGATATCC": 20482, + "TGTTGATGA": 20483, + "TCTGGAGCA": 20484, + "GGAAGCGG": 20485, + "GGAAGGTGA": 20486, + "GCTGGAGC": 20487, + "CATGAAATG": 20488, + "TCTCTAGTA": 20489, + "CTTAGAATG": 20490, + "TCCATTTTTT": 20491, + "CTTTTTTTTAA": 20492, + "CAAAACAGAAA": 20493, + "CATAGCTA": 20494, + "CGGAGAGA": 20495, + "TTATGTATA": 20496, + "GGATGCAGG": 20497, + "GCATGCATG": 20498, + "GACTAGAGA": 20499, + "CTGCAGTGAA": 20500, + "CGTAATTA": 20501, + "TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC": 20502, + "GCTCAAATG": 20503, + "TAAGGAATT": 20504, + "TTTTCCCC": 20505, + "GTGAAAAAAA": 20506, + "CCAGCCCC": 20507, + "GTACTCTG": 20508, + "GGAAGATCA": 20509, + "GCTTGGCTG": 20510, + "GCAATATTA": 20511, + "CCAATCCA": 20512, + "CTAACACTT": 20513, + "CCCACCAAA": 20514, + "TAGGCGA": 20515, + "TAATTGGTT": 20516, + "CCTAGGGG": 20517, + "TGCCCTTA": 20518, + "TCTTTTGCTGTGCAGAA": 20519, + "TACAATTTA": 20520, + "GAGTTTAGTTGTAA": 20521, + "GGGCTATG": 20522, + "CCACCACCATG": 20523, + "GCAGTGAGCCAAGATT": 20524, + "TACGGTG": 20525, + "GGGTCTCC": 20526, + "TAACATTTG": 20527, + "GCATCATCA": 20528, + "CCAGCCCTG": 20529, + "TCTTGTATT": 20530, + "CATTCAGTG": 20531, + "TGAACAGCA": 20532, + "GGAAGACTT": 20533, + "GATTCTGAA": 20534, + "CTTTTGATG": 20535, + "CTTTTGACTG": 20536, + "TAGGCAAAA": 20537, + "TCATTCCAA": 20538, + "AAATTTAAA": 20539, + "GCAGGCATG": 20540, + "GCTTATTTG": 20541, + "GAATAGCTT": 20542, + "CAAGTAGCTGGGATTACAGG": 20543, + "GGGCTAATATCCAGAA": 20544, + "TAACTGTA": 20545, + "TTTTGCTG": 20546, + "GTGTGTGAA": 20547, + "GTAGCGG": 20548, + "TATATAATTA": 20549, + "TGTTCATTTT": 20550, + "CCTTCATCA": 20551, + "TATGTATATT": 20552, + "CCTGCTGAA": 20553, + "GGCAGCGG": 20554, + "GTCTTGGTT": 20555, + "CGAACGAA": 20556, + "TCTAAGAAA": 20557, + "TAATGCTAA": 20558, + "GGCTGGAGG": 20559, + "GTCTGCTG": 20560, + "TATCTAAAAA": 20561, + "TCCCACTGA": 20562, + "TAAGATATT": 20563, + "GTCCAGTGA": 20564, + "CCAATTAAAA": 20565, + "TCACTTCC": 20566, + "CCATCTATG": 20567, + "CTACAGTC": 20568, + "TAAACCTCA": 20569, + "CTCCACATT": 20570, + "TGAAGGCC": 20571, + "TGAAGTGAA": 20572, + "CGAGTTAA": 20573, + "CCTAGTTTT": 20574, + "TCTCACACA": 20575, + "GCAAAGCTG": 20576, + "TCTGAGTCA": 20577, + "TCTGAGAGG": 20578, + "CCAAATATA": 20579, + "TGCCCTAA": 20580, + "CTCTTCCAA": 20581, + "GTTTGTATT": 20582, + "TGAGAGCAA": 20583, + "TAAATTTAAAA": 20584, + "GAACATGTT": 20585, + "CAACAGCTT": 20586, + "GAGGTGGAGG": 20587, + "GGCTCTGGG": 20588, + "TCTTTATAGCAGCA": 20589, + "GTCCTTCCAATGAATATT": 20590, + "GGGTCCAA": 20591, + "GACGTAAA": 20592, + "GAACTATG": 20593, + "GTTTAAGAA": 20594, + "TAAATAACAA": 20595, + "CTCCATTTG": 20596, + "CACAGATTTT": 20597, + "GAAACCCC": 20598, + "TACACTGTT": 20599, + "TCCAGCTGA": 20600, + "GCTACTGAA": 20601, + "GTAAATGCA": 20602, + "CTTTGCCAA": 20603, + "TCATGCTG": 20604, + "GTGTGTATA": 20605, + "GAAGAAGTTA": 20606, + "GGAGACCAA": 20607, + "CTTTATCAA": 20608, + "TGATACCTT": 20609, + "CCAGAAGAGG": 20610, + "TCCCTAAAA": 20611, + "CTTCTCATT": 20612, + "GTTCTATA": 20613, + "CACACCCCA": 20614, + "GAAAGAATG": 20615, + "CTCACTGTT": 20616, + "TATGTATATATA": 20617, + "CTCCCATCA": 20618, + "TACAGCATT": 20619, + "TCTACTGAA": 20620, + "GTCCCAGCC": 20621, + "CAAAATGGG": 20622, + "GACATAGAA": 20623, + "TCCTTTCAA": 20624, + "CTAAACTAA": 20625, + "TAATAĊ": 20626, + "CGCATTAA": 20627, + "GAAGAGTCA": 20628, + "GGACTATAGG": 20629, + "TCTGGAAGTGGA": 20630, + "GACCCTGTCTC": 20631, + "GTGCTGGGATTAAAGGCGTGCGCCACCACCGCCCGG": 20632, + "CATCTCATT": 20633, + "CCGTTTTA": 20634, + "CCCCAATG": 20635, + "GATTTTATG": 20636, + "GACTTTTTG": 20637, + "GTACATCC": 20638, + "GCCTĊ": 20639, + "TATTTATCC": 20640, + "CTCAGCCAA": 20641, + "CTCACCCTT": 20642, + "CATGGATGAA": 20643, + "CGGCATCA": 20644, + "GATGCAGTG": 20645, + "TTCATCC": 20646, + "TCTTAGAAA": 20647, + "CTCAAGAGA": 20648, + "CTCTGGTG": 20649, + "GGGAAGAA": 20650, + "GGATTGCTG": 20651, + "TAGTATTTG": 20652, + "GGGTTAAAA": 20653, + "GTGTACTTA": 20654, + "CCATAGCAA": 20655, + "CAAAATCAGCAGG": 20656, + "TCGGATTA": 20657, + "TCTAAAAATT": 20658, + "TAACCTC": 20659, + "TCAGĊ": 20660, + "GTAACTC": 20661, + "CGACCTCA": 20662, + "GGCAGGAGG": 20663, + "GGGACCAGGA": 20664, + "TCTCATATT": 20665, + "TAAAAAGTTA": 20666, + "GAATTTGA": 20667, + "GAAAAATTTT": 20668, + "GGCTGCTG": 20669, + "CAGGAGGA": 20670, + "GAATGGGAA": 20671, + "GAATAGATT": 20672, + "TTAAAAATTA": 20673, + "CCTGTTAAA": 20674, + "CTTGGGAGGCAGAGGCAGG": 20675, + "CAATTTTTG": 20676, + "CCAGCCCCA": 20677, + "CAAATAAATG": 20678, + "CCAAGAGCA": 20679, + "TTATTGTGA": 20680, + "CTTTGCAGA": 20681, + "TCCTGCTA": 20682, + "GTTTGGGA": 20683, + "TGATGCCAA": 20684, + "TCATGGAAA": 20685, + "GACCAGAGA": 20686, + "GATTTTTTAA": 20687, + "GGGTTGGGG": 20688, + "CAACAACAAAAA": 20689, + "TTATAGATT": 20690, + "GTGGTGCACA": 20691, + "CAACACGG": 20692, + "TCATAGCAA": 20693, + "GATTTGTTTT": 20694, + "CTTGGGAGGCTGAGGCAGGAGAATG": 20695, + "CCTCTCTCTC": 20696, + "TCCCAAGA": 20697, + "GCACTGCTG": 20698, + "GCCCCTCTG": 20699, + "TAAAGTCTT": 20700, + "TCAACCCC": 20701, + "GATTCCCTG": 20702, + "GGTGGACA": 20703, + "TTATTCCA": 20704, + "GGGACTCC": 20705, + "GATAĊ": 20706, + "GTAAATGTG": 20707, + "GTAAAGGAAA": 20708, + "CTCTTTCTA": 20709, + "CTCAAGAGG": 20710, + "CCTGACATT": 20711, + "TAAAAAAAAAAAAAAA": 20712, + "TCAGATATG": 20713, + "TCCAGGCCCGTC": 20714, + "TAACTGAAAA": 20715, + "CTGGGTATATACCCAAA": 20716, + "CAAGTTTTA": 20717, + "GTTTCCCTG": 20718, + "TCTTAAATG": 20719, + "TGTTTACCA": 20720, + "CAAACCCCA": 20721, + "CATTGATTA": 20722, + "TGAACACAA": 20723, + "CAGGGTCTT": 20724, + "CTGACAAAA": 20725, + "CATGTATTTT": 20726, + "TCCAGTGTG": 20727, + "GTCCTCAAA": 20728, + "CGCGCGCA": 20729, + "TACCCCTT": 20730, + "CCATTCTCA": 20731, + "TCTAATATA": 20732, + "GGATTTCTG": 20733, + "GAATGGAGA": 20734, + "TCATGCATG": 20735, + "GGCCAGGTT": 20736, + "AAAAAAAAAAAAAAAAAAAAAAAAA": 20737, + "CCTCTGCCTCCCAA": 20738, + "CCACTGCACTCCAGCC": 20739, + "GTTTCGA": 20740, + "GCAGTTTA": 20741, + "GCAGCCAGA": 20742, + "GTCGTGTT": 20743, + "CAAATCTTA": 20744, + "TCAAGATTA": 20745, + "TAAAATAAATT": 20746, + "GTGATCCA": 20747, + "TCTCTCCATT": 20748, + "CGTTCATT": 20749, + "GGTACCTT": 20750, + "CTCTTGAA": 20751, + "CGCATTTG": 20752, + "GTATTGATT": 20753, + "GTCAATTTA": 20754, + "GAAAAATATT": 20755, + "TCAGACAGA": 20756, + "CCAGAGTAA": 20757, + "CCACCCTCC": 20758, + "TTACAGCAA": 20759, + "CTGATGAAAA": 20760, + "GATCTTAAA": 20761, + "TATATATATATATATATATATATATATATATATATATATATATATATATATATATA": 20762, + "TCCGCCTGGCTCTGCCTCCCGA": 20763, + "TCGGAGCA": 20764, + "TCAAAAAAAAA": 20765, + "TAATCTGAA": 20766, + "CTGTGAGCA": 20767, + "CTTTCAATT": 20768, + "GCCTCAGCA": 20769, + "CTATCAAAA": 20770, + "CTACTTTTA": 20771, + "TAAACAAATT": 20772, + "CTCGGGTT": 20773, + "TCAACTAAA": 20774, + "TCAAGGTCA": 20775, + "CTGAGACTT": 20776, + "GCTTCAGTG": 20777, + "CCTTTCCAAA": 20778, + "CATGTAGAA": 20779, + "GATGCAAAA": 20780, + "GGTTGGAAA": 20781, + "GAATTAAATT": 20782, + "CAGAATTAA": 20783, + "CAATTGAA": 20784, + "CGGAGGAA": 20785, + "GCATTTCTA": 20786, + "TGAGGCAGA": 20787, + "GCGGCCTT": 20788, + "TATAAATTAA": 20789, + "CTATAGTTA": 20790, + "CATGTGCCACCA": 20791, + "TGACTCCAA": 20792, + "CACAGCCAA": 20793, + "CACAGGCCA": 20794, + "GAGACCTA": 20795, + "GTGAGTGCA": 20796, + "CATGTCCTTTG": 20797, + "GATGAAATG": 20798, + "GATAGCTG": 20799, + "CGTTTACTCTG": 20800, + "CTCTTTATT": 20801, + "GTATTGGAA": 20802, + "CTGTGGTTTT": 20803, + "CGTATGTG": 20804, + "CTTTACCTT": 20805, + "CCCTCTCTCA": 20806, + "GTTTTTTAAAA": 20807, + "CCCAGGTCTCC": 20808, + "CGTCAAAAA": 20809, + "GGATCGG": 20810, + "GTATAAATT": 20811, + "CATTCCTTTT": 20812, + "TATGTAGTT": 20813, + "GTTATAATT": 20814, + "CTTTTGGGA": 20815, + "TAGATTATT": 20816, + "GCTCAGGTG": 20817, + "GTGTTCTTA": 20818, + "CTCAAGGAA": 20819, + "TTTTAATGAGG": 20820, + "CATGAGCCACCA": 20821, + "GAAGGCAA": 20822, + "CCTAATTTA": 20823, + "CTCCACCCA": 20824, + "CGGTGGCTCACACCTG": 20825, + "CCACATGGTGGCTCACAACCA": 20826, + "CCTGTAATCCCAGCTACTCAGGAGG": 20827, + "GATCAAATT": 20828, + "TTTTTACAA": 20829, + "GTGTCAAAA": 20830, + "GGAGCTGTG": 20831, + "CACACTAAA": 20832, + "CACAGAGC": 20833, + "CACAGATAA": 20834, + "GAAATACTA": 20835, + "TCAACTTAA": 20836, + "GCTGCCTCA": 20837, + "TTTTAGAAAA": 20838, + "TATGCACTCA": 20839, + "TCCAGTGATG": 20840, + "GCTACTC": 20841, + "TGTACCAA": 20842, + "TCTCCCCTG": 20843, + "AAAAAATAAAAA": 20844, + "GGATGGGAA": 20845, + "CCTCGGCCTCCCAAAGTGCTGGGATTACAGG": 20846, + "GACCACTG": 20847, + "TAACATACA": 20848, + "GTTCAGTGA": 20849, + "TCCCCTGTG": 20850, + "CGTTATTA": 20851, + "GGCAGGCGGA": 20852, + "GTCTTTGCTATTGTGAATAGTG": 20853, + "GAGGAGAGGAGAGGAGAGGA": 20854, + "CACGTGAA": 20855, + "TCACTGGA": 20856, + "TGAGCAGTG": 20857, + "CTTTCCACA": 20858, + "TAAACCCC": 20859, + "CAAAGGAAAA": 20860, + "CATTGCATT": 20861, + "TGAAGTCAA": 20862, + "TCTGCATTA": 20863, + "TAAAATAGTT": 20864, + "CTGACCTA": 20865, + "CTGATCTCA": 20866, + "TCCATATAA": 20867, + "CAAAATCAAA": 20868, + "GCCTGGCCTG": 20869, + "TGAGACGG": 20870, + "GTTTAGTCTT": 20871, + "GAATACATG": 20872, + "TAACAGTCA": 20873, + "CCGACGG": 20874, + "GCGTGATG": 20875, + "CACACCCTG": 20876, + "TCTTGCAAA": 20877, + "CTGACCTAA": 20878, + "GCTTTGTGA": 20879, + "GTAAGCAAA": 20880, + "GTTAGCC": 20881, + "CTAAGTAAA": 20882, + "GAAAATTATT": 20883, + "CGCGCCA": 20884, + "CGCGAGC": 20885, + "CACCTCTCA": 20886, + "GAGAATTAA": 20887, + "GTTAAGATT": 20888, + "TCTTATATA": 20889, + "GACCTCTA": 20890, + "CTATTTATG": 20891, + "TATATATATATATT": 20892, + "CCAGGCTCA": 20893, + "GAGCCCGG": 20894, + "TAAGAGGAA": 20895, + "GTTCCCAGG": 20896, + "TTAAAAAGTCAGGAAA": 20897, + "CAACCTACAGAATG": 20898, + "GAACCTGGGAGGTGGAGGTT": 20899, + "ATTTA": 20900, + "GCGGACA": 20901, + "TTATCAAA": 20902, + "TGTGTCTGA": 20903, + "TGTTGTCTT": 20904, + "CAAAGATTA": 20905, + "TCTGCAGCC": 20906, + "CTGAGACTG": 20907, + "TTTTATCCAAA": 20908, + "CAAAACCTT": 20909, + "TTATTCCAA": 20910, + "CGTTTATG": 20911, + "GTGTTGTT": 20912, + "CAGTGCCTG": 20913, + "GTCTGTCTT": 20914, + "TCACACTCA": 20915, + "CGGTGGCTCATGCCTG": 20916, + "GCCTGACCAA": 20917, + "GGTGGGCGGA": 20918, + "TCTGCAACCCTA": 20919, + "TAAATATCC": 20920, + "GTCTGACA": 20921, + "GAGACTCC": 20922, + "GCTTCCATG": 20923, + "GTGATGCC": 20924, + "GCTGGGATT": 20925, + "GCTGGACTG": 20926, + "GCAACACAA": 20927, + "CTAATATCA": 20928, + "GATGGCCTA": 20929, + "TCCTTGTGA": 20930, + "GGTAGCCTT": 20931, + "TCCTGCAGA": 20932, + "GTTTGCCAA": 20933, + "TGAGGTCA": 20934, + "TCGGTCTGTCTG": 20935, + "TAACACTTA": 20936, + "TTAATGAAAA": 20937, + "CTCAATGGACATGAGTT": 20938, + "GGAGGGATTGGGGGCA": 20939, + "GCGTCTA": 20940, + "TGATĊ": 20941, + "GCAGCCCTG": 20942, + "CTCCAGCCAGG": 20943, + "CACATCAAA": 20944, + "GAAACGTC": 20945, + "TATAGATTA": 20946, + "CAAAGGCTG": 20947, + "TGAACTGAA": 20948, + "TCAATGATT": 20949, + "GGAACCAAA": 20950, + "CTGAGATGA": 20951, + "GTCACCAAA": 20952, + "GAGGGGAGG": 20953, + "TCTCTCATG": 20954, + "CTTACCTATT": 20955, + "CAAAAAAATA": 20956, + "CATATTATG": 20957, + "TGAAATTTA": 20958, + "GGTATAAAA": 20959, + "TCTTACATG": 20960, + "CGAAGAGTA": 20961, + "GAGGACATT": 20962, + "CCTCCCCA": 20963, + "GGTGATTTT": 20964, + "TCGATATT": 20965, + "TAAGATAAA": 20966, + "CTGATTTCCTTTA": 20967, + "TGACCCAGG": 20968, + "GCTTTGAAAA": 20969, + "GCCCAGGCTGGTCTTGAA": 20970, + "GAACCCGGGAGGCAGAGGTT": 20971, + "CCGTATG": 20972, + "TCCGACA": 20973, + "CTTGAACC": 20974, + "GTGTCTATT": 20975, + "GTGTTTTAAA": 20976, + "CTCTCATTA": 20977, + "GTCTAAATT": 20978, + "TATACTGAA": 20979, + "CAAACCTA": 20980, + "TGAATATCA": 20981, + "TAAAAGGTT": 20982, + "TATGCAGCA": 20983, + "TCATTGTT": 20984, + "CGTTGCC": 20985, + "CTTTGTAGACCAGGCTG": 20986, + "GTATTGTTA": 20987, + "GAGCAGAA": 20988, + "CCACCTGTG": 20989, + "TAAGTTTCA": 20990, + "TCCTCAGAA": 20991, + "CAACTTTA": 20992, + "CTTTCCCCA": 20993, + "GTAGTAAAA": 20994, + "GAAACTGTT": 20995, + "CATTGTGAA": 20996, + "CTGACACCA": 20997, + "CATGTCCTT": 20998, + "CTTTTGAGA": 20999, + "TAATTĊ": 21000, + "TTTTTTCAA": 21001, + "GCTCAGTGGTA": 21002, + "GAGAAGATG": 21003, + "CTCTGCCCA": 21004, + "CTGTGGAAAATT": 21005, + "CCTCCCCTT": 21006, + "TATTAATTTG": 21007, + "CCTCTGCCTCTG": 21008, + "GTGTATGTT": 21009, + "CAAGAAACACA": 21010, + "TTTTTTTTTTTTTTTTTTTTTTTTTTTT": 21011, + "CCGCCTCC": 21012, + "TTATATCA": 21013, + "GTCGGACACGACTGAA": 21014, + "CAAACGTA": 21015, + "GGAATAGTT": 21016, + "GTCACCCAGG": 21017, + "CGGTAGA": 21018, + "TAGGGCA": 21019, + "TCTCAGATT": 21020, + "GGAGGGACA": 21021, + "TCCTGGATT": 21022, + "CCTCACACCA": 21023, + "GCCTGGGGG": 21024, + "TACAATTTG": 21025, + "GCGGTTTG": 21026, + "GTTGGGAGA": 21027, + "TGACCCAGA": 21028, + "TAGTTATG": 21029, + "GACGTGAA": 21030, + "TAATCGAA": 21031, + "TTTTCGA": 21032, + "CTTGCTGTG": 21033, + "CTTCTCTCA": 21034, + "GTATTAATT": 21035, + "CTATATC": 21036, + "GTCGGCTG": 21037, + "GTCTTATTTT": 21038, + "CACATGCTG": 21039, + "GAAATCAAAA": 21040, + "GATTGGGA": 21041, + "CTTAGACA": 21042, + "TCCATAGAA": 21043, + "TCTATCTCA": 21044, + "GCCAGCCTT": 21045, + "CTTTTTAGA": 21046, + "GCTACTCC": 21047, + "CGCGCAA": 21048, + "TCTGACAGA": 21049, + "GGTACTCA": 21050, + "GTGTTGGTG": 21051, + "CTGTGGGGG": 21052, + "TAATGTAGA": 21053, + "GCAGGGCTT": 21054, + "GTGGGGAAAA": 21055, + "TATCCCCTT": 21056, + "TAAGACAGA": 21057, + "TCTTATTAAA": 21058, + "CAAACAAGCAA": 21059, + "TGATGACA": 21060, + "TCCCTTTA": 21061, + "TCCCAACA": 21062, + "CTCTCTCCTT": 21063, + "GAAATTTTAA": 21064, + "TATAGTTAA": 21065, + "TGTTGAGAA": 21066, + "GTGACATTA": 21067, + "TATGCATTTT": 21068, + "GTTATTTTAA": 21069, + "CTTAGAATT": 21070, + "CCCAGTGTG": 21071, + "GTAAAGTTA": 21072, + "TCTGAGCTG": 21073, + "TGCCCGA": 21074, + "TCCAATGGG": 21075, + "CGCAGTG": 21076, + "GTATTGTAA": 21077, + "CGGATTTG": 21078, + "TAGAATTTG": 21079, + "TAGCATGAA": 21080, + "CCTTATTTG": 21081, + "GTTTACCTT": 21082, + "GAATATCAA": 21083, + "CCAGAGCAA": 21084, + "GCCCATGTT": 21085, + "GCCCAGTGA": 21086, + "GAGGTTTCTG": 21087, + "TCGAAAATT": 21088, + "TCCGGGTG": 21089, + "CTTTAAGAA": 21090, + "GTTTCCAGA": 21091, + "CACAGACCA": 21092, + "CTGATACC": 21093, + "GAGGCTGGG": 21094, + "CTTAGCAAA": 21095, + "GGGACTAA": 21096, + "GCAAACAGG": 21097, + "TAATATAAAAA": 21098, + "GTATTTGATCC": 21099, + "GAAGAATTA": 21100, + "CTGTACATG": 21101, + "TAAGACAAAAA": 21102, + "TGATAGTGA": 21103, + "CTGGAGCTT": 21104, + "GTAGAATAATA": 21105, + "GTGCATGTG": 21106, + "TAAGTTAAAA": 21107, + "GAACCTGGGAGGCGGAGGTT": 21108, + "GCGTAATT": 21109, + "GCGGATA": 21110, + "TGAGCTGAA": 21111, + "TCCCAAATG": 21112, + "CTTTGGGTT": 21113, + "TAAAGAGGA": 21114, + "TATTGAGTA": 21115, + "GAAATTATG": 21116, + "GAAAGATTA": 21117, + "TCTGCAAAAA": 21118, + "GAGGCAGTG": 21119, + "GGCAGCTCA": 21120, + "GTAAAGATT": 21121, + "TCTGATGAA": 21122, + "GTGAAGGTG": 21123, + "CGCCCGA": 21124, + "TAATGGCTT": 21125, + "TAATGGAAAA": 21126, + "GAAGAGTTA": 21127, + "GGGTATATAA": 21128, + "GTTTTTTGTTTT": 21129, + "TTTTTATTATT": 21130, + "CTGGAATGCA": 21131, + "TCCCTACAAAGGACA": 21132, + "GGCCAAAAA": 21133, + "TCGTCTTA": 21134, + "TCACTGCA": 21135, + "TCAGAAATG": 21136, + "TCAGCAGA": 21137, + "CCACTGCTG": 21138, + "GCCCTGAAA": 21139, + "CTAGGTTA": 21140, + "GAAATCTTA": 21141, + "GAAACGCA": 21142, + "TCTTTCCTG": 21143, + "GTAAAAAAAAA": 21144, + "TACACAATG": 21145, + "TTCAGAAAA": 21146, + "GATATTTTTT": 21147, + "GTAAATATTTT": 21148, + "TCATTGCA": 21149, + "GAATTCAGA": 21150, + "CACCTATA": 21151, + "CTTTGCCTG": 21152, + "GTATTCATT": 21153, + "GTCTTGTATG": 21154, + "CCATTTGAA": 21155, + "GCAGGGGAA": 21156, + "TAGTATTTA": 21157, + "CCCTATTA": 21158, + "AAATATGTT": 21159, + "CAGTCGCTCA": 21160, + "CTTCATCTG": 21161, + "TCATACAAA": 21162, + "TTACATGTT": 21163, + "CTACATGATT": 21164, + "GAACCCAGGAGGCGGAGGTT": 21165, + "CTCCTGGGCTCAAGTGA": 21166, + "GCCATCCCATTA": 21167, + "GCGGATCACGAGGTCAGGAGATCGAGACCATCCTGGCTAA": 21168, + "GCTGAAACTCCAATACTTTG": 21169, + "CCAGTGGG": 21170, + "TAAAAGAGG": 21171, + "CTCAGCCTG": 21172, + "CTCACACTT": 21173, + "CATGGTCC": 21174, + "CCTGGCTAA": 21175, + "TTAACTGA": 21176, + "CTTTTGTTATT": 21177, + "TAGGTAAGCA": 21178, + "TCAAATTTTA": 21179, + "TCAAATGTA": 21180, + "CCTACTTAA": 21181, + "TCTCACAA": 21182, + "GCAAATATG": 21183, + "TCATTTAATT": 21184, + "TGAAATCAA": 21185, + "GGCCTCTA": 21186, + "GGCCGGGG": 21187, + "GTCTTTAATT": 21188, + "CCATTTCTA": 21189, + "GTCTGGAGA": 21190, + "CAGCCTCTG": 21191, + "CCACACAAA": 21192, + "CTTAAAATCTCGACGTT": 21193, + "TAAAAATTAAAA": 21194, + "GCCACCGCGCCTG": 21195, + "TCTTCTTTTGAGAA": 21196, + "TCCCCACCA": 21197, + "GTGGTGAAA": 21198, + "GTTTGGGTT": 21199, + "CTAGAGC": 21200, + "TAAATGAGA": 21201, + "TGTGGGTAA": 21202, + "CAGGCTGAA": 21203, + "CAGGTGCTG": 21204, + "GTGACTAAA": 21205, + "GATTCCAAA": 21206, + "GCCAGGAGG": 21207, + "TAGGAAAAAA": 21208, + "GGCAĊ": 21209, + "CCTAGTCC": 21210, + "GCAAATGTA": 21211, + "CTTAACCAA": 21212, + "GTGTTGGTT": 21213, + "TAATGTCAA": 21214, + "GTTTGCTTTT": 21215, + "GAAGGCGG": 21216, + "CAAGACACA": 21217, + "GGATTACAGA": 21218, + "CTGCCCCCA": 21219, + "GGACACCAA": 21220, + "TAATCATGTG": 21221, + "TAACTGGG": 21222, + "TCCCTCCA": 21223, + "TCCTCCAGA": 21224, + "CTTTCTATA": 21225, + "GGAGCTAAA": 21226, + "CCAGAAGAA": 21227, + "GAAAGAAAAAA": 21228, + "CAAACCTCA": 21229, + "TCTCTTATG": 21230, + "CTAATAAAAA": 21231, + "GACACCCA": 21232, + "TAAAAATGTG": 21233, + "CGTTCCTT": 21234, + "GGCCTATA": 21235, + "GCTGAGGTT": 21236, + "TATTTTTATA": 21237, + "GCCAAGAAA": 21238, + "GACCCGA": 21239, + "CAACATATA": 21240, + "CTCTCCCTCC": 21241, + "CTCAAAATAAATTGGTT": 21242, + "CCTGAGGTCAGGA": 21243, + "GCGACTTAGCAGCAGCAGCA": 21244, + "GAAGCTCC": 21245, + "CTGCCAAAA": 21246, + "GTTTCCTTA": 21247, + "GCAGCTGTT": 21248, + "CCAGCTGTG": 21249, + "GTCTCATCA": 21250, + "GAAACAATG": 21251, + "CAAAGAGAAA": 21252, + "CAGGCTTTT": 21253, + "TAAAACCAAA": 21254, + "CCTGGGTCA": 21255, + "GATGCAGCA": 21256, + "TTAAGTG": 21257, + "TTAATTTTTT": 21258, + "GGTGGTTA": 21259, + "GGTGCGTG": 21260, + "TGATGGAATT": 21261, + "TCACATCTG": 21262, + "CCCCCAATCCCTCCCAGCA": 21263, + "TGAGĊ": 21264, + "CTTGTGAAA": 21265, + "GTTGCTGTG": 21266, + "GCCTTTTAA": 21267, + "TATTTCTAAA": 21268, + "GCTGGGTGA": 21269, + "GCAACACTG": 21270, + "GATTTCCAA": 21271, + "TCCATATGA": 21272, + "GGCATAGA": 21273, + "TCTCAGATG": 21274, + "CTGAACAAA": 21275, + "TAATACCTT": 21276, + "GAAAAACTA": 21277, + "GTTTGCCTT": 21278, + "GCATGGAGG": 21279, + "CACTCTGATG": 21280, + "TGATCATTA": 21281, + "CTTGTTCTT": 21282, + "GTTTCAATT": 21283, + "GCATCCCTG": 21284, + "TGTGCAGTG": 21285, + "CAGGCTC": 21286, + "GTCACAGGA": 21287, + "TTTTATCTA": 21288, + "TCTCCCAGA": 21289, + "TTTTTTGAA": 21290, + "GCAAACTGA": 21291, + "GGCCTCTGA": 21292, + "CTTTGGTGA": 21293, + "CTTTGTATG": 21294, + "TATAAAAAAAA": 21295, + "CCTCAGTGA": 21296, + "TACTATTAA": 21297, + "TAAAAAATAAAA": 21298, + "CTTTAAGAGG": 21299, + "CACTCTGTTTG": 21300, + "GTTTCCTTTG": 21301, + "CATATTTATT": 21302, + "TACCAAAAA": 21303, + "GCGAACAA": 21304, + "TCCTCGAA": 21305, + "CAAGCCACA": 21306, + "CAATGACC": 21307, + "CTGTCAAAA": 21308, + "TGTTTAGCA": 21309, + "CAGGGAGTA": 21310, + "CCTTCTAAA": 21311, + "GATTCAGTT": 21312, + "TCTCTATCC": 21313, + "CTCCTGTGA": 21314, + "GCCATCTGA": 21315, + "CAAAATCTG": 21316, + "CTTTGCATG": 21317, + "CCATTGTG": 21318, + "GGCTGGGTG": 21319, + "CAGTATTTA": 21320, + "TAAGATATA": 21321, + "TCATCTCTG": 21322, + "TCCTCACTG": 21323, + "CTGTTTTTG": 21324, + "CTTCAGGAA": 21325, + "CATTGATCA": 21326, + "CAGGCTGGG": 21327, + "TCAACCTA": 21328, + "GGAATACAA": 21329, + "GCTGCACTT": 21330, + "GTAAGTGAA": 21331, + "CTAACATCA": 21332, + "TTAAGTCA": 21333, + "GTTTTGGA": 21334, + "GTTTTGATG": 21335, + "CCCATACC": 21336, + "TAGACATAA": 21337, + "TAATTCAAAA": 21338, + "CGTGGCTT": 21339, + "TCTAATATT": 21340, + "TAATGGTTA": 21341, + "CGTCTCTG": 21342, + "GTGGGCAGA": 21343, + "TCTGAATTA": 21344, + "CTGTAGATG": 21345, + "CAAGACAGA": 21346, + "GGTGGGAAA": 21347, + "TAGTGACACTA": 21348, + "GACGGAGGAGCCTG": 21349, + "TCTGTGTGTGTG": 21350, + "TAATCAGA": 21351, + "TGAGTTAAA": 21352, + "CTTTATATA": 21353, + "GTATTATTTT": 21354, + "GAAAGACAAA": 21355, + "CGGCCCCA": 21356, + "TTAAGTGA": 21357, + "GTCCTACC": 21358, + "CTTTTTGGG": 21359, + "GGGGCTTA": 21360, + "TCAAAGCA": 21361, + "TAATACAGA": 21362, + "TGCCCCCA": 21363, + "CGAATTTA": 21364, + "GGAGAGCTT": 21365, + "CGTCAATG": 21366, + "GAGTCAGACACGACTGA": 21367, + "TATCTGAAA": 21368, + "GACAAAATG": 21369, + "TGACAAGAA": 21370, + "TGTTAAAAAA": 21371, + "TGAACTC": 21372, + "CTGATGGAA": 21373, + "GTGAGGTCA": 21374, + "GCAACAGTTA": 21375, + "TTTTAĊ": 21376, + "TATGCTAAA": 21377, + "TTAACAAAA": 21378, + "GCCAGTGAA": 21379, + "GCCAGCAGG": 21380, + "CTTTTGCA": 21381, + "GGGGTGTA": 21382, + "GGTGGAGGA": 21383, + "TAATTGATG": 21384, + "CTCTGCCAA": 21385, + "CATCAGGGA": 21386, + "TTATGTATT": 21387, + "CCAGGGCTG": 21388, + "TCCCACTCA": 21389, + "GTTTATAAAA": 21390, + "GGATTTTCCAGGCAAGAGTACTGGAGTGGGGTG": 21391, + "CATTTTAAAAA": 21392, + "TCGCCAGA": 21393, + "TCAGCTGTG": 21394, + "GTTCAGAGA": 21395, + "GGATATAAA": 21396, + "GAAACGGA": 21397, + "TGTTCACC": 21398, + "CAAATAGCA": 21399, + "TCTGTAGTG": 21400, + "TCCAGAATG": 21401, + "GGTTCGG": 21402, + "TCAAAGACA": 21403, + "TTTTTTTGTG": 21404, + "TCCTTTCCCCATT": 21405, + "TAATAGGAA": 21406, + "TCTTACAA": 21407, + "TCTTTTCCTT": 21408, + "GCATTGTCA": 21409, + "TATCACTTA": 21410, + "CACCATTTT": 21411, + "GCTCCTGGA": 21412, + "CAATAGCCAA": 21413, + "GCCATTCTGA": 21414, + "GGTCAAAAA": 21415, + "TAAGTTCTT": 21416, + "TAACTTTCA": 21417, + "TTATAGAA": 21418, + "GTTCGAAA": 21419, + "GGACTGGA": 21420, + "GCCGTGTT": 21421, + "CTAGCATG": 21422, + "GAAAGGGCA": 21423, + "CAGGCTCAA": 21424, + "CTCCCTGAA": 21425, + "CTAAGACAA": 21426, + "TCTGAGTGA": 21427, + "GGTAGAAAA": 21428, + "GCCAATATCA": 21429, + "CTGTGCTT": 21430, + "GTTTGTTTTA": 21431, + "CTATTGATT": 21432, + "GATGAGAA": 21433, + "CTCAAACTCA": 21434, + "CAGATTAAAA": 21435, + "TAAGGGATT": 21436, + "CAAGAATACTG": 21437, + "TATATATAAAA": 21438, + "CCCCCACCCC": 21439, + "CAGTTTTTG": 21440, + "TAAGCAGCA": 21441, + "TAATCCAAA": 21442, + "TCAGCTCTG": 21443, + "TCACGTCA": 21444, + "GTCGGTTA": 21445, + "CAAAGAGGA": 21446, + "GAGATCTA": 21447, + "CAGACACACACA": 21448, + "GCTTCTAAA": 21449, + "CATGGCTA": 21450, + "TAATTCAGA": 21451, + "GTGAAGTCA": 21452, + "TGATTTCCA": 21453, + "TATTACAAA": 21454, + "TCTAATAAA": 21455, + "GTTTGGTA": 21456, + "CGTACGG": 21457, + "TAATAATTTG": 21458, + "TCTCTTAAAA": 21459, + "CTATGGAA": 21460, + "CTTGGCCTT": 21461, + "GAGAGAGAGAGAAA": 21462, + "GTTTACTAA": 21463, + "TGATATGTG": 21464, + "GTTTAACCTTTCTTTT": 21465, + "GCACCACTGCACTCCAGCCTGGG": 21466, + "CTTCAAACTATACTA": 21467, + "CCTGTTTA": 21468, + "GTTGAGCC": 21469, + "GCCCGAGG": 21470, + "CTCGCTA": 21471, + "GTCGAGAA": 21472, + "TATTGACC": 21473, + "CCAATCTA": 21474, + "TCTACAATTTT": 21475, + "GTCCĊ": 21476, + "TCTCACAAA": 21477, + "TATTTTTTTTA": 21478, + "CGCCTTTT": 21479, + "TAATGGGAA": 21480, + "CTAAAATTAA": 21481, + "TCTGTGTTA": 21482, + "GATCCTTA": 21483, + "GTGCCTGTG": 21484, + "CACTCTTTTTGTG": 21485, + "CTGGTTCCAAA": 21486, + "TAAGCATA": 21487, + "TCATCAGA": 21488, + "TCAGCCAGG": 21489, + "TGAACGTG": 21490, + "GTAATCAAA": 21491, + "CTCCCAAAA": 21492, + "TCTACCATT": 21493, + "CCCACCCAA": 21494, + "GTAAACATG": 21495, + "CCAAACATT": 21496, + "GCTGAGTGA": 21497, + "GTTAATGAA": 21498, + "GTCAATTTG": 21499, + "CAATATAAAA": 21500, + "TCACATCAA": 21501, + "TCTCAACCCCA": 21502, + "CACTGAATT": 21503, + "TGATAAATG": 21504, + "CCAGCGTG": 21505, + "GAAAGCATG": 21506, + "GAAAGGGGA": 21507, + "CAAACTCTT": 21508, + "GCTTCATCA": 21509, + "CTCAGTCTT": 21510, + "CCTGCCACCA": 21511, + "TACACAATT": 21512, + "TACAGCCTT": 21513, + "TCCAGGATG": 21514, + "GCCACCCAA": 21515, + "GGGGGAGAA": 21516, + "TTTAGATA": 21517, + "GGTGCTAA": 21518, + "GGTGCTTA": 21519, + "TGCAGCAAA": 21520, + "TTCACTAA": 21521, + "TCTCCCCTCC": 21522, + "GAATTCATA": 21523, + "GGCTGTTA": 21524, + "TTATGGAA": 21525, + "GTATGTATA": 21526, + "GCGACCTT": 21527, + "CAATATTATT": 21528, + "TCACATATA": 21529, + "TGACATCAA": 21530, + "GGACATAGGCATG": 21531, + "GCCCAGCAA": 21532, + "GGGCAATG": 21533, + "TAAGTGGA": 21534, + "TGACATTTG": 21535, + "CTTTCCTA": 21536, + "GTGCCCTA": 21537, + "GTTTGTTAA": 21538, + "GTATTTTAAA": 21539, + "CTAGGAAAA": 21540, + "TAAACGGA": 21541, + "TAAATTATTTT": 21542, + "TATTGAAAAA": 21543, + "TGAATACAA": 21544, + "CAGGCACCA": 21545, + "CAGGCGTGA": 21546, + "CTGACAGTG": 21547, + "CCTGGGGCA": 21548, + "GAAAATATGA": 21549, + "GTTTTCACA": 21550, + "CCCAGTGTT": 21551, + "CAAAAGAATTGAAAA": 21552, + "GCAAAGATT": 21553, + "TGAAATATA": 21554, + "CAGTACAGTA": 21555, + "TAGTATTAA": 21556, + "TAAATAAATAAAA": 21557, + "TCGCCCAGGCTG": 21558, + "GCTAGGGAGG": 21559, + "CAGTAAATT": 21560, + "TCCCTGCA": 21561, + "CTTTCATCA": 21562, + "GTAGCTTTT": 21563, + "TAAAGCCAA": 21564, + "TCTGCAGGA": 21565, + "TCAAGAATT": 21566, + "GCTGCTCAA": 21567, + "GCAACGAA": 21568, + "GGTTTAAAA": 21569, + "GGTGTATT": 21570, + "TGCACGTG": 21571, + "CCAAACCA": 21572, + "CGCCTGCC": 21573, + "CCATTCCTG": 21574, + "TAATGCTTA": 21575, + "CCCCATGTGA": 21576, + "CTCCACTCC": 21577, + "GTTTACTTTT": 21578, + "CTTCCTCCAA": 21579, + "GAACAGTCA": 21580, + "CTGCCTCCA": 21581, + "TCACTGTTA": 21582, + "TGATGAATT": 21583, + "GCACCACTG": 21584, + "GGAGCAGAA": 21585, + "CTCCACTATG": 21586, + "TGTTTAATA": 21587, + "TCTGTAGCA": 21588, + "GTGACACC": 21589, + "TTTTACCAA": 21590, + "TATGCATAA": 21591, + "TTTACCTG": 21592, + "TGGACTGTA": 21593, + "GGGAGGATT": 21594, + "GTAAATCAA": 21595, + "GCAAAGTCA": 21596, + "TCCTTCATG": 21597, + "CCAAACTA": 21598, + "CCAAATTCA": 21599, + "GCTCATTAA": 21600, + "CTGTGCTTA": 21601, + "TGATGCTAA": 21602, + "TGAGGTGTG": 21603, + "TCATGCTA": 21604, + "CCCTTTCTT": 21605, + "CAATGGTG": 21606, + "TCGGCCTG": 21607, + "CTGCACCCATCAA": 21608, + "CTTTAGTTA": 21609, + "TAATTTATTA": 21610, + "TGTTGAATAGGA": 21611, + "GCTAATTTTTGTATTTTTAGTA": 21612, + "TCCTATAAA": 21613, + "TCCTCCACA": 21614, + "GGACAAGA": 21615, + "CCAGCCTCA": 21616, + "GTAGCAGG": 21617, + "TAAATAAATA": 21618, + "CTCTAATGA": 21619, + "GTCTCAGTG": 21620, + "CAAACATGA": 21621, + "CAGACAGTA": 21622, + "GCTGGCTA": 21623, + "TCAAACTAA": 21624, + "CTTAATATT": 21625, + "CTTTGCAGG": 21626, + "GACCTCCA": 21627, + "GTATAGTAACGA": 21628, + "GTGGATCACGAGG": 21629, + "CTTCAGACGGGCCTGGA": 21630, + "CCTATTTA": 21631, + "GGCTAATG": 21632, + "GTGGGAGA": 21633, + "GTTTCCCC": 21634, + "CAAAGCCTT": 21635, + "CAAACAGTA": 21636, + "GAGAGATCA": 21637, + "CTCAGGGCA": 21638, + "TTTTATTTTATTTT": 21639, + "GAGGTATA": 21640, + "TAGAGCTA": 21641, + "GTATTTAAAA": 21642, + "GTATTGCTT": 21643, + "CTCTGCGG": 21644, + "TCAAAACAA": 21645, + "GTGTGGGGG": 21646, + "TCGGTTAA": 21647, + "TAAGACATG": 21648, + "TCTATCTATCTA": 21649, + "CTTTATATGCCCCAGTA": 21650, + "TCTCTCACTGTTCAAATAAA": 21651, + "TAGCCCCA": 21652, + "TAAAGGTAA": 21653, + "CACACAGCC": 21654, + "TCTTCCTAA": 21655, + "CAAATCTAA": 21656, + "TGAATGCC": 21657, + "CAGATCCTT": 21658, + "CTCATGCTG": 21659, + "TATGGGTA": 21660, + "TCTCTTTTAA": 21661, + "TACACCCC": 21662, + "GATGTCCA": 21663, + "TTTGAAAAAA": 21664, + "GCCATTATT": 21665, + "CGCTGACA": 21666, + "CTGTTTCTCA": 21667, + "GTGTTTCTA": 21668, + "GTCAATTAAA": 21669, + "CAAAAACTA": 21670, + "GTTTGGTTA": 21671, + "CTATTCCA": 21672, + "TGATGCCTT": 21673, + "TATCACATT": 21674, + "TATCACAATT": 21675, + "TCATGTCCAA": 21676, + "GCTTATCCA": 21677, + "TCGGTGTG": 21678, + "GTGGTGTGTG": 21679, + "CCGGCGA": 21680, + "GCCCAGCTT": 21681, + "TCCTAATAATT": 21682, + "GCTCTGATG": 21683, + "CGCCAATT": 21684, + "GACAAATGGGA": 21685, + "TGATGTGAA": 21686, + "CTTTAAGG": 21687, + "GCCTAATA": 21688, + "CTCGCCTT": 21689, + "GTCTGTTAA": 21690, + "TGTTTATATT": 21691, + "CTGAGACAA": 21692, + "GTGAGACTG": 21693, + "GTTTTTTATT": 21694, + "TGGGGCCATG": 21695, + "TGCAGCTA": 21696, + "GGCATCCA": 21697, + "TTTTTTCCCC": 21698, + "TAAAAATAATT": 21699, + "CTCTTCTAA": 21700, + "GTCAAGGA": 21701, + "CCAGGCTA": 21702, + "GGTGAGAAGG": 21703, + "CTTTACTCA": 21704, + "GTTTACATG": 21705, + "GTTGGTGAA": 21706, + "GTTCCCCTG": 21707, + "CTTTCCAAAA": 21708, + "CCGACTC": 21709, + "TGAGGAGGA": 21710, + "GTATCTGTT": 21711, + "GTCTCCCCA": 21712, + "TCTTTAATA": 21713, + "TCTTCAGGA": 21714, + "CAAAGATCA": 21715, + "TCTGGACTT": 21716, + "GTGACGTT": 21717, + "CCTTGGTA": 21718, + "TATGGTAAA": 21719, + "CTTATCCA": 21720, + "CCCACGTT": 21721, + "TAATTGCAAA": 21722, + "CCAAAGTCA": 21723, + "CTAAACATT": 21724, + "GTTAAGTAA": 21725, + "CAATTTGTG": 21726, + "CCATTCTGA": 21727, + "GCCTGCTCA": 21728, + "TGAGGTTAA": 21729, + "CATTTTTTTG": 21730, + "GTTTATTATT": 21731, + "TCTATTAAAA": 21732, + "GTCAAAGCTATG": 21733, + "TATTAAATTA": 21734, + "GAAATAAGTATT": 21735, + "TCTCATTGTGGTTTTGATTTGCATTTCTC": 21736, + "TGCTGGAGAGGATGTGGAGAAA": 21737, + "GCGTTTTTT": 21738, + "TCAGAACA": 21739, + "TGACTGTA": 21740, + "GAAGCCAGA": 21741, + "CTACATC": 21742, + "GAGACAGTT": 21743, + "GGAAGACTG": 21744, + "GTGATATTA": 21745, + "CCTGCGTG": 21746, + "GTTTTTAAAAA": 21747, + "CTTTTGCAA": 21748, + "CCCACCCTT": 21749, + "CATATCCTTTG": 21750, + "TATTTTTAAAA": 21751, + "CTTTGCTAA": 21752, + "TCTTTTTTTTTTTTTTTTTTTT": 21753, + "TACCCAAAA": 21754, + "TACCTAGG": 21755, + "TCCTGCCAA": 21756, + "CTGTGTATG": 21757, + "GCATGGCAA": 21758, + "GTGCTGGGA": 21759, + "GTGCTGGAATTACAGG": 21760, + "GTTCCATAAACAA": 21761, + "GTCCTGGTCC": 21762, + "CAGAGGAATGGA": 21763, + "CCTCGCCCGGCTAGTTTTTT": 21764, + "GCGAGGTG": 21765, + "TAACGTCA": 21766, + "CTTGTGGG": 21767, + "GCATTATTA": 21768, + "GTATAATTA": 21769, + "GCCTCAGAA": 21770, + "CTAGCTGTG": 21771, + "CAAAGGCTT": 21772, + "CATTCTTAAA": 21773, + "TCTGCACTT": 21774, + "CTCAGAAGG": 21775, + "GATTCAGCA": 21776, + "GTTATCAAA": 21777, + "CACAATTAA": 21778, + "CTGCAGTTA": 21779, + "GTTTATCTA": 21780, + "CAGTCTTTG": 21781, + "CTCTACCAACTGA": 21782, + "GTGTCATCTGCATA": 21783, + "CTTCAAGAA": 21784, + "CTCCATTAA": 21785, + "GAAAGAGTG": 21786, + "TGAACACC": 21787, + "CAGATATAA": 21788, + "GGAATAGAA": 21789, + "GCTGCCAAA": 21790, + "CTCAGCTGA": 21791, + "GCCAGAATT": 21792, + "CCCCAAAAAA": 21793, + "GGGACTTA": 21794, + "GTAAACAGA": 21795, + "GCTCAGGGG": 21796, + "GACCTCAAA": 21797, + "CCATTTAAA": 21798, + "CTATTGTAA": 21799, + "TCATGTCATCTG": 21800, + "GCATCACCAA": 21801, + "GAACAAAGCTGGAGG": 21802, + "CACTĊ": 21803, + "TACCTGAAA": 21804, + "GCTAAATAAA": 21805, + "TTTTGCC": 21806, + "TCACAGGAA": 21807, + "TGAGCAGCA": 21808, + "GAACCAGG": 21809, + "TCCTGTGAA": 21810, + "CAACAAATT": 21811, + "GTTTGTGAA": 21812, + "GCTGTAGTT": 21813, + "CGGCCTCC": 21814, + "GATTCTGTT": 21815, + "CCCCTCTGA": 21816, + "CCCCCCCAA": 21817, + "TTATTTCTGA": 21818, + "GGCAGTTTT": 21819, + "TCTCATCTG": 21820, + "CGTTTAAA": 21821, + "CTTTGCTCC": 21822, + "GACCCTTA": 21823, + "CGGACGA": 21824, + "CTATTTAAAA": 21825, + "AAATTTATT": 21826, + "TGAGGCCAA": 21827, + "GCAGGGCC": 21828, + "CTTTATTATT": 21829, + "GAATAGAGA": 21830, + "GTGGTGGGTG": 21831, + "TCAATGTTA": 21832, + "CCGACGA": 21833, + "CTTGCTGTT": 21834, + "GTGGCAAAA": 21835, + "GCAGCTCTT": 21836, + "CTCTCTACA": 21837, + "GTCGAGGA": 21838, + "GTCTCAATT": 21839, + "TATACACTT": 21840, + "TCTTGGGTT": 21841, + "CAAATATATT": 21842, + "GGAACACTG": 21843, + "GCTGGGGCA": 21844, + "GAGGCAAAA": 21845, + "TCTAACAA": 21846, + "TTATTATTATTTT": 21847, + "TCAGATAGG": 21848, + "CTTTACTAA": 21849, + "TTATAGATG": 21850, + "GTTCAGTTA": 21851, + "CTCCTGTCTCAGCCTCC": 21852, + "GAACCCAGGAGGTGGAGGTT": 21853, + "GAAGTAGTGATTCCTTGGGCA": 21854, + "GGTCTCAAA": 21855, + "GTGGCGTG": 21856, + "CTACTGCTG": 21857, + "CTACCATCA": 21858, + "GTCTCGTT": 21859, + "TGAAGCTA": 21860, + "GGAATATTA": 21861, + "GGAAAAATA": 21862, + "GCAACTCC": 21863, + "GTCACATCA": 21864, + "CTTTTTAATT": 21865, + "CCCCTATG": 21866, + "TCCTTTCCA": 21867, + "CTCTTCATT": 21868, + "GTGTTGGGA": 21869, + "CAGTGCTG": 21870, + "CAGTGTTAA": 21871, + "CCTCAGGGA": 21872, + "TAAGATGTG": 21873, + "TTTTGTAAA": 21874, + "TCCTGACC": 21875, + "TCCCTTCCA": 21876, + "GGATGAATT": 21877, + "CACAGAAAAA": 21878, + "CATTCCAGG": 21879, + "CAGGGCTA": 21880, + "TCTGGGCCA": 21881, + "GTGACAATATA": 21882, + "CATGCTGAA": 21883, + "TATGGTTTTA": 21884, + "CCCCAAATG": 21885, + "TGCATAAAA": 21886, + "CTGAAGCTG": 21887, + "GCAAAGAAAA": 21888, + "CTTAAGTGA": 21889, + "GCCAAGCTT": 21890, + "GTCTGGAGG": 21891, + "GACTTGAAA": 21892, + "CCCTTCCCCA": 21893, + "CATGAGCCACTG": 21894, + "CGTCTTTA": 21895, + "GAGAAATTA": 21896, + "TCTGTGTTTT": 21897, + "CAACAATATGAA": 21898, + "GTTTACAGA": 21899, + "GTCCAGCTT": 21900, + "GTGGCAGGCA": 21901, + "GGTCGTA": 21902, + "TCGTCTGA": 21903, + "TCAGTTATT": 21904, + "CTCGAGC": 21905, + "CTCGGGGA": 21906, + "CTGACTGAA": 21907, + "GCAAGGGAA": 21908, + "CCCATAAAA": 21909, + "CAATTGCTT": 21910, + "GCCTTTGTT": 21911, + "GAGCAGGAA": 21912, + "TGAGGCGG": 21913, + "GTGTGCGG": 21914, + "CCTCTTATT": 21915, + "CCAAAATACCA": 21916, + "CCTCAAGGATCTA": 21917, + "GATTCTTCCTA": 21918, + "TTTTTCTTA": 21919, + "TCATTTTAAA": 21920, + "GAAGGAGAA": 21921, + "CTCAGAATT": 21922, + "TTTTATGAAA": 21923, + "GTTACACAA": 21924, + "GCCATACCA": 21925, + "TGGATAGA": 21926, + "TCTCAGGTT": 21927, + "GTAAAGAAAA": 21928, + "GGAAATTTG": 21929, + "TCCTTCTAA": 21930, + "CTAAACAAAA": 21931, + "GGCCCGA": 21932, + "CACCTACA": 21933, + "CAGAATTTG": 21934, + "GTATTTGATACA": 21935, + "CAATTGTTTT": 21936, + "CGAACGA": 21937, + "CTCTGTCTCAAAAAAAAAAAAAAAA": 21938, + "GCATTTATG": 21939, + "TGAGGCCTTCGTT": 21940, + "GAATGCAA": 21941, + "CAATGCTG": 21942, + "GGGCCTGGG": 21943, + "TGATGTTAA": 21944, + "TCCCTTAAA": 21945, + "GGAGCAGA": 21946, + "AAAAGGAA": 21947, + "GAGACGCA": 21948, + "TCAAGCCAA": 21949, + "CTGAGTGAA": 21950, + "GATTTATCA": 21951, + "CCAATATCA": 21952, + "CCTACGTT": 21953, + "TCTGAGACA": 21954, + "TCCTTCCCTT": 21955, + "CCAAATGTT": 21956, + "GTATTGTTTT": 21957, + "GTGTTGTTTT": 21958, + "TGATGTATT": 21959, + "TATGATTTA": 21960, + "CCCAACACA": 21961, + "TATCGTCA": 21962, + "TATCGATT": 21963, + "GGAAAATTA": 21964, + "TCAGAGCTT": 21965, + "TCCCACTCC": 21966, + "CTTTACATG": 21967, + "GTTTACACA": 21968, + "CCACAGTAA": 21969, + "GAGACTGGCTAGGCCACTCCAGGACCTT": 21970, + "GCGAAGCA": 21971, + "GCGTCTGA": 21972, + "TTTTTATCA": 21973, + "GCACGCTG": 21974, + "GGACCCTGA": 21975, + "TAAAGCTCA": 21976, + "CTCTATTAA": 21977, + "GTCTCATA": 21978, + "TGTTGAGTT": 21979, + "CATTCCCTG": 21980, + "GTGAGAGCA": 21981, + "GCAATTATT": 21982, + "TCTCTCAGG": 21983, + "CCAACCTA": 21984, + "CTTAACAAA": 21985, + "CTCTTCCCA": 21986, + "GTCAACAAA": 21987, + "CTGTGCTAA": 21988, + "TGATGGTAA": 21989, + "GTCTGCCTG": 21990, + "GAATGCATT": 21991, + "GCTCGCTG": 21992, + "CCTCGGGG": 21993, + "CTGCACTCA": 21994, + "TTACAGTTTT": 21995, + "GAACCCATG": 21996, + "GAAAATTAAAA": 21997, + "TCCCAAACACA": 21998, + "TCATCGA": 21999, + "GAAGAAATG": 22000, + "CTTCTACA": 22001, + "CCAGCCACA": 22002, + "TAAACAGC": 22003, + "CTCTCGTT": 22004, + "CACATACCA": 22005, + "CAGGGCTCC": 22006, + "TCAATGCC": 22007, + "TTTTAGCA": 22008, + "CGGGAAAA": 22009, + "TCTCTCCCC": 22010, + "TCCACCTCCA": 22011, + "TAATTGAGA": 22012, + "TCTCACAGA": 22013, + "CTTAATGTT": 22014, + "TCTTACAGA": 22015, + "CTATTGTTA": 22016, + "CAGGAGGAA": 22017, + "TCAGACCTT": 22018, + "TCACATGTG": 22019, + "CAACACCAA": 22020, + "GCACAGCTT": 22021, + "TCAGTATAAAA": 22022, + "TAGCCAAAA": 22023, + "CTAAAGAGCTTCTGCACA": 22024, + "GCAAAAAATT": 22025, + "TGACAGGAA": 22026, + "TGACAAGG": 22027, + "GAACCTAA": 22028, + "GTGTCACTG": 22029, + "GTTGTGAAA": 22030, + "CCATCGG": 22031, + "CCATCACC": 22032, + "GCCAAAATG": 22033, + "TATTTĊ": 22034, + "TATATATTAA": 22035, + "TCTTGACC": 22036, + "TGAACAGTT": 22037, + "TGAAGATTTT": 22038, + "CAAAATTATT": 22039, + "CGAACAA": 22040, + "GGGAAGGGA": 22041, + "CCTCCTTTG": 22042, + "CAGTAGAGA": 22043, + "TCTGAAGACA": 22044, + "GAGGTTTAATT": 22045, + "GCTGGGCGTGGTG": 22046, + "TCACATTAA": 22047, + "TTACAGCA": 22048, + "GAATCTTTT": 22049, + "CCACGTGA": 22050, + "CTATAATAA": 22051, + "GAAATAGTG": 22052, + "CAAACGGA": 22053, + "CAAAGACTG": 22054, + "CATTTCCTG": 22055, + "TGAATAAAAA": 22056, + "TCTGTACAA": 22057, + "TCAAGATAA": 22058, + "GGAAGGCCA": 22059, + "TAAAATGGAA": 22060, + "GTGATAAAAA": 22061, + "GCAACTAAA": 22062, + "GTCAGAGTG": 22063, + "TCTCTGTGTG": 22064, + "TACAGCTCA": 22065, + "GTTTTCAGA": 22066, + "TGCATAGG": 22067, + "CGTTGATT": 22068, + "CGCAGCTT": 22069, + "CCAGGGCC": 22070, + "TAAGAGAAAA": 22071, + "GTTCAAGACCAGCCTGGCCAACATGGTGAAA": 22072, + "TTTTAAATG": 22073, + "TGAGCCAGG": 22074, + "CTTCATTAA": 22075, + "GTCTGACTG": 22076, + "CACAGCATT": 22077, + "CACACATGA": 22078, + "CTGATATTA": 22079, + "GTTATAGTT": 22080, + "GATGCTCC": 22081, + "GCCATCAAA": 22082, + "GACACCCC": 22083, + "CGTGCTCA": 22084, + "GAATTCATG": 22085, + "TCTTACTG": 22086, + "TACCTTTTA": 22087, + "GTTTGCATG": 22088, + "GAATGGTA": 22089, + "CTATGGAAAA": 22090, + "CAGAGATATA": 22091, + "CTTCACCTG": 22092, + "TGATATGTT": 22093, + "CTAAAAAATACAAAAAA": 22094, + "GAATGGACTGGTTGGA": 22095, + "TCTGATCTTTGACAAA": 22096, + "GGGTCATG": 22097, + "GACTĊ": 22098, + "GCGTTGAA": 22099, + "TAAGĊ": 22100, + "TGAGTGCTG": 22101, + "TGAGTTTCA": 22102, + "TGAGCAGA": 22103, + "TCCTCAGCA": 22104, + "GTTGAAGAA": 22105, + "CATTCACCA": 22106, + "CTGATCCA": 22107, + "GTGACACTG": 22108, + "GCTGGACTT": 22109, + "GCTGGAGTA": 22110, + "CCTTGGCCA": 22111, + "GTCAGTGAA": 22112, + "CGACAGTG": 22113, + "GACATTATT": 22114, + "TCTGACATT": 22115, + "TCATTCCTG": 22116, + "GTATTCTAA": 22117, + "GTATTTCCA": 22118, + "CGCCTTTG": 22119, + "CTGTGGATT": 22120, + "TACTTTATT": 22121, + "GCTCCTTCA": 22122, + "GTTCATGAA": 22123, + "GCGCGGTG": 22124, + "GAGAGAGAGAGAGAGAGAGAGAGAGAGA": 22125, + "CCATGCTGTTTTGGTTA": 22126, + "TAGAAAATG": 22127, + "GGGTAAAAA": 22128, + "GCGAGATG": 22129, + "TCAGCTCTT": 22130, + "TTACATAA": 22131, + 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22177, + "TAAGCCAAA": 22178, + "TCCTGAGAA": 22179, + "GTTTCCTA": 22180, + "CCATCCCCA": 22181, + "TAAAGGCTT": 22182, + "CTCGTGAA": 22183, + "CAAATACAAA": 22184, + "GTGACAGTT": 22185, + "GTAAGCC": 22186, + "GTAAGGCA": 22187, + "GTAATAATA": 22188, + "TATGCCTA": 22189, + "CGGGGGTG": 22190, + "GATTTAGTT": 22191, + "GTTATACTT": 22192, + "TAGATACTG": 22193, + "GAATTGTTA": 22194, + "CGCACTG": 22195, + "TCCTGCATG": 22196, + "CCTCATTTG": 22197, + "GCCTGCATG": 22198, + "TGAGGCTCA": 22199, + "GCTCCTGTG": 22200, + "CTTCATCAA": 22201, + "TCACATTTTA": 22202, + "TAATCCTTTGGG": 22203, + "GTATTTAGTCAGCCACCAATTGTGCAAGTTCTCCCACTTAAAAAGATGAGAGAGGCCTG": 22204, + "TAGTCATG": 22205, + "GCGTATTA": 22206, + "TCATCTGAA": 22207, + "GAACAGAGA": 22208, + "GAATTAATT": 22209, + "CTATAATTA": 22210, + "CTACCCTA": 22211, + "CTCGGGCA": 22212, + "GTCTGACC": 22213, + "GCTGTTATT": 22214, + "GCAAGACTT": 22215, + "CTAACTCC": 22216, + "GTTTTGCTCTT": 22217, + "CAAAAGGCA": 22218, + "CCTACCCCA": 22219, + "CTGAATGTG": 22220, + 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22265, + "TGAATATGA": 22266, + "TCAATAATA": 22267, + "CCTTTCCTG": 22268, + "CCTTGGCTG": 22269, + "GTCACTTAA": 22270, + "GTAATACAA": 22271, + "CGGGGAAA": 22272, + "TACAGCTGA": 22273, + "CTTAGCCA": 22274, + "TCAAACCAA": 22275, + "GCTCAGGTT": 22276, + "GTCTTGGCTG": 22277, + "GTATGGCAA": 22278, + "GAATAGAATT": 22279, + "GATACATATG": 22280, + "GCAGTGAGCCGAGATCGCGCCA": 22281, + "TTTTTAGTT": 22282, + "TCAGTCATT": 22283, + "CTGCTTCTT": 22284, + "GTCTGATA": 22285, + "GAGAGTGAA": 22286, + "GAGACGAA": 22287, + "GGAAGATAA": 22288, + "GGAAGGGAGG": 22289, + "TAAAATACTA": 22290, + "TTTTAGATG": 22291, + "GAGGTGGAA": 22292, + "CCTGGATGA": 22293, + "CCTGCCTTA": 22294, + "GTTACATTTT": 22295, + "CTCCTGACTT": 22296, + "TTTTTTCATT": 22297, + "TCCTTCAAA": 22298, + "GCCTTCTCA": 22299, + "TGATGGTTA": 22300, + "GCAGGCAA": 22301, + "GCCCCCAAA": 22302, + "GCCCCCGCC": 22303, + "TGACTACTGGAAAAA": 22304, + "GTCTCGATCTCCTGACCTCGTGA": 22305, + "TCAGAGTCTTTTCCAATGAGTCAA": 22306, + "GAAGTGGATGCTCACAGTCA": 22307, + "TAGCCTCA": 22308, + "TTTTGAGTT": 22309, + "GTGCCCTC": 22310, + "GTGGCTCTT": 22311, + "TAAAGACTG": 22312, + "GTCTATTAA": 22313, + "TGTTGGCTG": 22314, + "GAGACAGAAA": 22315, + "CTCATAGG": 22316, + "CCTGCATCA": 22317, + "GATGTACAA": 22318, + "CTTTGGCA": 22319, + "TCTCTTCAA": 22320, + "GAAGATATT": 22321, + "CTTATTTAACTTA": 22322, + "TGACATATT": 22323, + "CCACCTATAGGGTT": 22324, + "TCGTCATG": 22325, + "TCATTTTATT": 22326, + "GTAGAAGAA": 22327, + "CTCGGTGA": 22328, + "TATATATC": 22329, + "TCTTGTGTT": 22330, + "TGTTGGTGA": 22331, + "GTGACCCA": 22332, + "GCAACCCC": 22333, + "CGGGTTTT": 22334, + "CCAAGGGAA": 22335, + "CTTACACAA": 22336, + "GATGCCCA": 22337, + "GGGGGGGA": 22338, + "CCTATTATT": 22339, + "CTGAAGACA": 22340, + "GCAAACCA": 22341, + "TAATGCCAA": 22342, + "GTTAAATAAGCAGG": 22343, + "GCAGAAAAAA": 22344, + "TCAGTGTTA": 22345, + "TCTCCTCTCCTCTCCTCTCC": 22346, + "TAAGTCAAA": 22347, + "TCCCGAGG": 22348, + "CTATATTAA": 22349, + "TATATAGTG": 22350, + "TGAATGCTG": 22351, + "CAGGTTTTA": 22352, + "GCTTTATCA": 22353, + "GTAACACTT": 22354, + "TACAGCTAA": 22355, + "TACATTTTTG": 22356, + "GGTTGCCATT": 22357, + "CCAAACTG": 22358, + "GCTGAGTCA": 22359, + "TATAATGAA": 22360, + "CTATTCTTA": 22361, + "TGATGTCTT": 22362, + "GTAAAATTTT": 22363, + "GACCACAGTG": 22364, + "TCAGGTGTG": 22365, + "GATAAATTA": 22366, + "GGCAGGAAAA": 22367, + "CGCCTGTAATCC": 22368, + "GGCACACA": 22369, + "GCGCAATT": 22370, + "TAATCTTAA": 22371, + "TGACAAAATT": 22372, + "GAAGCTGTT": 22373, + "GTGGCAGTG": 22374, + "GTTGTTTCA": 22375, + "CTCTGAAATT": 22376, + "GTCTGATGA": 22377, + "TATATCATT": 22378, + "CAGAGCCTG": 22379, + "GCTTTAAAAA": 22380, + "CTCAGGCCA": 22381, + "CATGCAGTT": 22382, + "CATGGGCCA": 22383, + "TTTTATAGG": 22384, + "TATGTAATA": 22385, + "GAAAATACC": 22386, + "GTAAATAGA": 22387, + "GTCTTGCTG": 22388, + "CTCAAGCCA": 22389, + "CAAAAATCACAA": 22390, + "CTGTGCATT": 22391, + "CATTATTTTA": 22392, + "CTTTACACA": 22393, + "TAACACAAAA": 22394, + "CTGTAGCTG": 22395, + "TGAATGTTTT": 22396, + "GTGATGAAAA": 22397, + "CAAACAAACAAAAAA": 22398, + "TGGGGAAAGGATT": 22399, + "TCGGGGTT": 22400, + "GATACATA": 22401, + "CTTTCCAGA": 22402, + "GTGGAAATG": 22403, + "TAAATCTTTT": 22404, + "CATTGATTTT": 22405, + "TCTGTAGTA": 22406, + "CATGTACTT": 22407, + "GTTATGACCAACCTAGA": 22408, + "GACATCAAA": 22409, + "GGTTCCCA": 22410, + "TTTTTTCTTA": 22411, + "CTCTTCAAA": 22412, + "TATTTTTTATT": 22413, + "GACCGAGG": 22414, + "CAATTCCAA": 22415, + "CAAAAAGAAA": 22416, + "CCTCCCCCA": 22417, + "GTCTGCCTT": 22418, + "GTTTAGTTTT": 22419, + "TCACCCTCC": 22420, + "GTTGGCCAA": 22421, + "GGCAGGAGAATCGCTT": 22422, + "TAAGTGGG": 22423, + "CCATTTTTG": 22424, + "CCAGTATG": 22425, + "CACAGAGGAA": 22426, + "CAAATTATG": 22427, + "CAGGCGCA": 22428, + "CAGACAGAGA": 22429, + "CTGAGGGCA": 22430, + "GCTTGATA": 22431, + "GTCACACC": 22432, + "GTTACAGTG": 22433, + "CCAATGAGTCA": 22434, + "TCCACGTA": 22435, + "TCTACCAAA": 22436, + "CGATAAAAA": 22437, + "CCCCTCCCCA": 22438, + "CTGAAGCC": 22439, + "TTTTTTTGAA": 22440, + "GCAAAGTGA": 22441, + "GGTACTAA": 22442, + "TCCAACAA": 22443, + "CTGTTCAA": 22444, + "CTCAACATCA": 22445, + "GAATACAGA": 22446, + "TTTTAAATTTT": 22447, + "CCAGCCTGGTCTACAGAGTGA": 22448, + "CAGTGATTTTGGAGCC": 22449, + "TAAGTATTA": 22450, + "TCATAATTTT": 22451, + "CTGTCATCA": 22452, + "CTTGAAGAA": 22453, + "CTTGTGCCA": 22454, + "GTTGGAAAAA": 22455, + "CCACCTCTG": 22456, + "GTCGATGA": 22457, + "TATTTCTGTT": 22458, + "CAGACAGC": 22459, + "GGAAGTCAA": 22460, + "GGAAGTGTG": 22461, + "CTGATTATT": 22462, + "GCTTAAATT": 22463, + "GTAACAGCA": 22464, + "GTTAGAGAA": 22465, + "GTTATAGAA": 22466, + "CTTACAGCA": 22467, + "TCCAGCATT": 22468, + "GATGGATGA": 22469, + "GGGACGA": 22470, + "AAAAAAGAAA": 22471, + "GAGGAGAGAA": 22472, + "GGATTGTCTT": 22473, + "TAACATCTG": 22474, + "GGCAAAGTAATGTCTCTGCTTTT": 22475, + "GCTAAAAAAA": 22476, + "TAATCAATT": 22477, + "CTACTGGG": 22478, + "GTCGGGAA": 22479, + "GTCGAGTT": 22480, + "TGTGGAGTG": 22481, + "CTGACACTG": 22482, + "CTGAGAGTT": 22483, + "GTAATATCA": 22484, + "GATTGTAAA": 22485, + "GCTACTGTT": 22486, + "TGCACCTGA": 22487, + "TCATTAAATT": 22488, + "TGATTGCATT": 22489, + "TCCAACTG": 22490, + "TCTTATATT": 22491, + "CTTTGGCAA": 22492, + "CGAAGAAA": 22493, + "TACCCCTG": 22494, + "CTGTGTCCA": 22495, + "TAACAGAGA": 22496, + "CTGTCTCTT": 22497, + "CTTCTAAAAA": 22498, + "GTTCTCCAA": 22499, + "GGAGCTTTT": 22500, + "CCATATAAA": 22501, + "CAGGGACTT": 22502, + "TATGGTTTG": 22503, + "TCTCCATC": 22504, + "GACATATTA": 22505, + "GCAAATATA": 22506, + "GGTATCTA": 22507, + "TCTTACCTT": 22508, + "GAGCGCC": 22509, + "GATCATTAA": 22510, + "GAGAGAGAGAGG": 22511, + "TCAGATGTT": 22512, + "TTATATCTT": 22513, + "TTTTTAGAAA": 22514, + "TCACTACATC": 22515, + "GTGTGACTGTT": 22516, + "CAGAAAACCAAA": 22517, + "TGAAGTCAGGTA": 22518, + "CATAGGAAA": 22519, + "CAACATTAA": 22520, + "GGACCCTA": 22521, + "CCAGGAGAA": 22522, + "TAAATAAGA": 22523, + "GCTTCTTAA": 22524, + "GTGAGATGA": 22525, + "CTCAGACTT": 22526, + "GTCAGATGAAA": 22527, + "TCTCAAATA": 22528, + "CTCCAAAATT": 22529, + "TACATAGCA": 22530, + "GAAAACCTG": 22531, + "TTATTCACAA": 22532, + "TTTTTTGTAGAGA": 22533, + "CGTGTGTA": 22534, + "CGTGCACCA": 22535, + "CGTTGCTT": 22536, + "CTAAACACA": 22537, + "CTCTTGCTG": 22538, + "TATTTTTATTA": 22539, + "GGCCATAGTG": 22540, + "CTAAAAGTA": 22541, + "CTAAAACTA": 22542, + "TGTGTGCATG": 22543, + "TTATAGCAA": 22544, + "TGATATCTG": 22545, + "TCATATGTT": 22546, + "GGACATCTA": 22547, + "TATATACATG": 22548, + "TAAATACATT": 22549, + "CTACATCTT": 22550, + "TAACTATTA": 22551, + "CGACAGAGCAA": 22552, + "GAATAATTA": 22553, + "GTTTCGTT": 22554, + "CCAGAAATT": 22555, + "GCCTGTGTT": 22556, + "GCCGGGGA": 22557, + "CAAATAGTA": 22558, + "CTTTTTTTTTTTTTTTTTT": 22559, + "TAGATATCA": 22560, + "CATACAAAAA": 22561, + "CCAAAGAGA": 22562, + "GTATTCCTT": 22563, + "GTGTTTCCA": 22564, + "TTATACTAA": 22565, + "CTTCAGAAAA": 22566, + "CTGCCTGAA": 22567, + "GGAATTAAAA": 22568, + "GGCCTGAAA": 22569, + "CCTATGTCCTGAATG": 22570, + "TCTGAGGTTATTGATATT": 22571, + "CCGCGTG": 22572, + "TGAGGAGA": 22573, + "GAATAGC": 22574, + "CTTCCACA": 22575, + "GTGGTTTA": 22576, + "CTCGGGAGA": 22577, + "TATTGCTTA": 22578, + "CAAAGCTGA": 22579, + "CAGACATTTT": 22580, + "CTCACGGA": 22581, + "CTTACTGAA": 22582, + "GAAAATGGAA": 22583, + "CAAAAGAGG": 22584, + "TAGATACAA": 22585, + "TCAAACTTA": 22586, + "TGGACCAA": 22587, + "CGTTTCAA": 22588, + "CTAAATACCAA": 22589, + "CTTAATTCA": 22590, + "CTGTGCACA": 22591, + "TAATAATAAAA": 22592, + "TATGATTTG": 22593, + "CTGCATCTG": 22594, + "GCCAGGCGTGGTG": 22595, + "TTATAGTTTT": 22596, + "AAAAAAATTAA": 22597, + "TGATATCAA": 22598, + "GGCTCCAAAA": 22599, + "TGAGTAGCTGGGACTACAGG": 22600, + "GCTGCATAGTATTCCATGGTGTATATGTGCCACATTTT": 22601, + "GCGTCATG": 22602, + "CTTGTCAAA": 22603, + "CTTGCTCTT": 22604, + "GTAGAGCC": 22605, + "CTCCAGATT": 22606, + "GTCTCAAAAAAA": 22607, + "CACACGGA": 22608, + "TCTTGTTAA": 22609, + "CAAAGGCCA": 22610, + "TGAACTAAA": 22611, + "GCTTTCCTG": 22612, + "CTCATAGAA": 22613, + "CCCCCCTCA": 22614, + "TGTACCACATTTT": 22615, + "GCAAATGAAA": 22616, + "TCTGACCTT": 22617, + "TCCTTCCCA": 22618, + "CGTTCTCA": 22619, + "TAATAGTT": 22620, + "TAATAGTGA": 22621, + "GGTACCAAA": 22622, + "GAGAATTTTA": 22623, + "GCCAACAA": 22624, + "GACCTAAA": 22625, + "CCCTGCCCTG": 22626, + "GATCCCATTTG": 22627, + "CCGGGAGG": 22628, + "GCCCACCCC": 22629, + "CTGTAAATT": 22630, + "GCATCACC": 22631, + "GCAGTTGG": 22632, + "GCAGCTCCA": 22633, + "CACAGAGTC": 22634, + "TGTTGATTA": 22635, + "GAGAGAGGAA": 22636, + "CAGAGGAGG": 22637, + "GGAAGGATT": 22638, + "CTGACACTT": 22639, + "CGGGCCA": 22640, + "TACAGTATA": 22641, + "CAAAATAGG": 22642, + "GGTGGATCA": 22643, + "TCCTTGCTG": 22644, + "TAATAGTAAA": 22645, + "GCTCAGAAA": 22646, + "TCCAAGTGA": 22647, + "CGCACGC": 22648, + "GCAGGTGAA": 22649, + "GATCCAAAA": 22650, + "TAACAAGTTCAAA": 22651, + "TTACATATG": 22652, + "CGAGTAGCTGGGATTACAGG": 22653, + "CCTCACTCAGGATGA": 22654, + "GCGACTTCACTTTCACTTTTCACTT": 22655, + "GATAGGAAA": 22656, + "TTTTCCAAA": 22657, + "CTGGCTTTT": 22658, + "CTTCAACAA": 22659, + "GCCCCAGCA": 22660, + "TATTTAGC": 22661, + "CACAGGTGA": 22662, + "GAGAGAAGG": 22663, + "TAAAAGCATT": 22664, + "TAATATAATAA": 22665, + "TGATTGATG": 22666, + "AAAAAATATA": 22667, + "GCCAACAGA": 22668, + "TAATGGCAA": 22669, + "CAGCCCAGG": 22670, + "CCCTTCTCC": 22671, + "GCTCCCTCC": 22672, + "GAAGACTCA": 22673, + "CTTTAGATT": 22674, + "GTGGACGG": 22675, + "GTTGATGTT": 22676, + "CAGATGTTA": 22677, + "TAAGTGAAAA": 22678, + "TACACAATGGAGTACTA": 22679, + "GCCACCGCGCCCGG": 22680, + "TATCAAATT": 22681, + "GTAGCAGCA": 22682, + "CACACAGAGA": 22683, + "TCTTGTTTG": 22684, + "TGTTCTCATT": 22685, + "CATTCAGTA": 22686, + "CTGAGAGGA": 22687, + "CATGTACAA": 22688, + "GAAAAGGTG": 22689, + "GTCCCACCA": 22690, + "CGAGCTGA": 22691, + "TGGGGCAA": 22692, + "TAATTCACTG": 22693, + "GGCAGCTA": 22694, + "CGCGGAA": 22695, + "TCTGAGCCA": 22696, + "TCCTTGAGAA": 22697, + "CTAAAGTTA": 22698, + "CTGTTGGAA": 22699, + "CATGATTTG": 22700, + "GCGGGGA": 22701, + "GAAATTTCTT": 22702, + "GAGAAAGAAA": 22703, + "CCACCCAGG": 22704, + "TTACATGAA": 22705, + "CTCACCCTCATG": 22706, + "GAAGTTAAA": 22707, + "TCTTGCTTA": 22708, + "TGTTCACAA": 22709, + "CAGACCCTG": 22710, + "GTGATAGAA": 22711, + "TCTCGGTG": 22712, + "CTTTTCATA": 22713, + "CGATCAAA": 22714, + "CCCAGTGAA": 22715, + "TGGGTAGG": 22716, + "GACACTCC": 22717, + "TGTACGG": 22718, + "TCAAACAAAA": 22719, + "TCCTTCCTCC": 22720, + "GGCCTATT": 22721, + "CTGTGTATT": 22722, + "GTGTGTGA": 22723, + "CTGGGGAAA": 22724, + "CTCCACTCA": 22725, + "CAAGGCTACAGTAA": 22726, + "CAACATAAA": 22727, + "CCAATGAAAA": 22728, + "TCTTTTAATG": 22729, + "CTCGGGAGGCAGAGGCAGG": 22730, + "TACTCCTG": 22731, + "GACGTGTT": 22732, + "TCAGTGCTG": 22733, + "CAAGCTCTG": 22734, + "CTTGCTGAA": 22735, + "GCCCTTCC": 22736, + "CTCTGAAAAA": 22737, + "TGTTCTGAA": 22738, + "CCTTCAGTG": 22739, + "GCAACATCA": 22740, + "TTTTAGCAA": 22741, + "TCCATACTA": 22742, + "GATGGCAAA": 22743, + "TTTAGTC": 22744, + "TAGACATTA": 22745, + "TCAAATGGA": 22746, + "TCTCCCCAA": 22747, + "TCTGAGGCTG": 22748, + "CACCTATT": 22749, + "GTTAAAAATG": 22750, + "CAGTGAAATG": 22751, + "GTTTGTATA": 22752, + "TGATGGGA": 22753, + "CAATGCCTG": 22754, + "CATCCTTTT": 22755, + "TGACATGTG": 22756, + "CCAGAGTCA": 22757, + "TCAATGTTTT": 22758, + "CTAGGCAAA": 22759, + "GAGCTGTCAAA": 22760, + "TCAGCCCAGAA": 22761, + "CCTGCTGATTTTGTA": 22762, + "GGTATATA": 22763, + "TAAAAAAAAAAAAA": 22764, + "TTACACAA": 22765, + "GTGGGAGAA": 22766, + "GTTCTCATT": 22767, + "GTATCTATA": 22768, + "GCCGGGCC": 22769, + "GAGAGATAA": 22770, + "GAGAGACAGA": 22771, + "TGAAGGGAA": 22772, + "CTGAGACCA": 22773, + "GTGACAGGA": 22774, + "TTTTACAAAA": 22775, + "GATTGGTA": 22776, + "CTTATAGAA": 22777, + "GTTTTTCTTTT": 22778, + "TCTAGAATT": 22779, + "TAATTGAGG": 22780, + "TCTCAGAAAA": 22781, + "TCTCCTGTA": 22782, + "TCTGATGTT": 22783, + "TGATTCACA": 22784, + "GTTAAGAAA": 22785, + "CTCTGTATG": 22786, + "GTTTGTATG": 22787, + "GCCTGCACA": 22788, + "TGAGATAGG": 22789, + "TTTTTTTTTTTTTG": 22790, + "CATAATATA": 22791, + "GCATGGTGA": 22792, + "CGTCGCC": 22793, + "TCCCATGTT": 22794, + "GTACAGTCA": 22795, + "TCAAAAAATG": 22796, + "CTGTATC": 22797, + "CTTTAAGA": 22798, + "CTTCTTTTTT": 22799, + "GTTTCGAA": 22800, + "GTATĊ": 22801, + "CTACCTC": 22802, + "TGAAGAGCA": 22803, + "CAGACATAA": 22804, + "GTGACCTCA": 22805, + "CATGGACC": 22806, + "GTAACC": 22807, + "GTAACATCA": 22808, + "TCTCTGTGTT": 22809, + "TCTAGCATT": 22810, + "GACACGCA": 22811, + "GATATCCA": 22812, + "CGCTAAAA": 22813, + "GCAAATTCA": 22814, + "GAATTGCAA": 22815, + "CGAACGG": 22816, + "CTCTGTATT": 22817, + "CCTCCTGCC": 22818, + "TCATGCCA": 22819, + "CAGCCTTTT": 22820, + "TCAGGCAGG": 22821, + "TCAGACATT": 22822, + "GAATACTTA": 22823, + "GAACACCAA": 22824, + "GTGGTGGGCA": 22825, + "CACATGTATA": 22826, + "CAAACAAACAAAAA": 22827, + "CTTCTCCAA": 22828, + "CTAGCTTA": 22829, + "GAAAGATGAA": 22830, + "CAAATCATT": 22831, + "CAAACAGC": 22832, + "TGAAGACTT": 22833, + "CAGGGACAA": 22834, + "TTTGTAGG": 22835, + "GTTTTTATAA": 22836, + "GGGGCCCTG": 22837, + "CCCCGGCC": 22838, + "TAATTTCTTTT": 22839, + "TCCTTCTTTT": 22840, + "GAGCGAGG": 22841, + "TCATGTCTG": 22842, + "GAATACCCA": 22843, + "CAAGACATG": 22844, + "TATTTATTTATTTATTTT": 22845, + "TAGAAAATCTTTGGAGG": 22846, + "CACAAAAATTA": 22847, + "GAGTTAATT": 22848, + "TAATCTCTG": 22849, + "TCATTTTTA": 22850, + "TTACATTTT": 22851, + "TCCGTCCATG": 22852, + "CTGGAAGAA": 22853, + "CTGCCCTA": 22854, + "GTTCTTCA": 22855, + "GCCTAAAAA": 22856, + "GCCCAAGAA": 22857, + "CTAGAAGG": 22858, + "TGTGCATGTG": 22859, + "TATTGAATG": 22860, + "TCTTGCATT": 22861, + "CAGGAAATA": 22862, + "GTTATCCA": 22863, + "TCTACTC": 22864, + "GTCCGTCC": 22865, + "TTATTGCTT": 22866, + "TCTCATATG": 22867, + "CTGAAGCTT": 22868, + "GTAAATGAAA": 22869, + "GCAAAGGA": 22870, + "TAATATCTG": 22871, + "GCTCACAGAA": 22872, + "GTGTTGCTG": 22873, + "CGCCCAGG": 22874, + "CAGTGTCTG": 22875, + "GGCTGTTTT": 22876, + "GAGAGAGATG": 22877, + "CAATACAAAA": 22878, + "TCACACGG": 22879, + "TTTTTTTTTTTTTTTG": 22880, + "CTCGGGAGGCTGA": 22881, + "GACCCCAAA": 22882, + "TGACAACAA": 22883, + "TCCGACC": 22884, + "CTTCTGTA": 22885, + "TAAAGCTAA": 22886, + "TATTCCTAA": 22887, + "TATTTCTATT": 22888, + "GAGAGACTT": 22889, + "TGAACATCA": 22890, + "TCAACGAA": 22891, + "GGAATCGAA": 22892, + "CTGACCCTG": 22893, + "CCTGGACTT": 22894, + "GTTTTCGA": 22895, + "GTTTTGGTA": 22896, + "TGGGTCTG": 22897, + "CGCTCCTG": 22898, + "CGTGCACA": 22899, + "GGAGGTTAA": 22900, + "TGAGATCTGA": 22901, + "GGCCAGGCA": 22902, + "GCAGGGAAA": 22903, + "CTGGGTTAA": 22904, + "CTGCACCCA": 22905, + "CTTTACCAA": 22906, + "CCACAGCCA": 22907, + "GCGCACTG": 22908, + "TCAGTTAAA": 22909, + "CTTCACATT": 22910, + "GCATCAATT": 22911, + "CTCTCTCTCTCTCTCTCTC": 22912, + "GTCTCACCA": 22913, + "TGTGGAAAAA": 22914, + "CACATACAAA": 22915, + "TAAAACACTT": 22916, + "GCTGGGGGA": 22917, + "CTCACTCTT": 22918, + "CTCCTAAAA": 22919, + "GAAAAGGCA": 22920, + "GAAAATACTT": 22921, + "TTATTTAAAAA": 22922, + "CCTACTC": 22923, + "GTAAAGAGA": 22924, + "TTTTTTGGGG": 22925, + "GCAAACAGA": 22926, + "TCTTATCTT": 22927, + "CTGTGGCA": 22928, + "GAGGACAGG": 22929, + "TGAGAGCCA": 22930, + "GTTCTTAAAA": 22931, + "TTACATAAA": 22932, + "CAGACCGACAGA": 22933, + "GGTCTTTTA": 22934, + "TAACTTCA": 22935, + "TAATCAGAA": 22936, + "TCATGAATT": 22937, + "TTACATCA": 22938, + "CTTGCGG": 22939, + "GTGGĊ": 22940, + "GTAGTGGG": 22941, + "TAAACCTAA": 22942, + "GTCTCTCAA": 22943, + "CATTGTCAA": 22944, + "TGAACATGA": 22945, + "TCAAGGGAA": 22946, + "GGAATCCA": 22947, + "GTGATGTGA": 22948, + "CCTGGCCTG": 22949, + "TTTGGCCA": 22950, + "TCTATTTTAA": 22951, + "GCAAACTCA": 22952, + "TCATTCCCA": 22953, + "CTGTTCCCA": 22954, + "GTTAATTCA": 22955, + "TCTAATGAA": 22956, + "TGAGGGAAA": 22957, + "GTTCCCTTTT": 22958, + "GTCCATCCTT": 22959, + "TACAAAATG": 22960, + "GGGAAAATG": 22961, + "GAGTAAATT": 22962, + "TAACAAGG": 22963, + "TCACAAATT": 22964, + "TCCTCAGTG": 22965, + "CTTGTTTCA": 22966, + "CTTTGATA": 22967, + "TAAAGGTCA": 22968, + "GTGAGCCAA": 22969, + "CCTTTAAAAA": 22970, + "GAGGCCCTG": 22971, + "GTTTTCAAAA": 22972, + "TCTGACATG": 22973, + "CGTTCAAA": 22974, + "CTAAATGTA": 22975, + "CGCCTGCA": 22976, + "TGAGATATT": 22977, + "GAGCTAAAA": 22978, + "CATTATATA": 22979, + "GAGTGGAGG": 22980, + "GGATTTGTT": 22981, + "TACTGTTAA": 22982, + "TTATATTTTTT": 22983, + "GTGATGGGACCA": 22984, + "GTCATGTCCGACTC": 22985, + "GTTGCATCCAAA": 22986, + "TCAAATATTATATTATTAAA": 22987, + "TGACTTCA": 22988, + "GGATATTAA": 22989, + "CTCGTCATT": 22990, + "GTCGGGGA": 22991, + "CACATACTT": 22992, + "GCAACGTG": 22993, + "CCAAGACAA": 22994, + "CTTACTTAA": 22995, + "GATGCAGTT": 22996, + "GACAGAAAAA": 22997, + "CCCCTCTCA": 22998, + "TCAAATCTA": 22999, + "GTAAATATTA": 23000, + "TCTCCCATG": 23001, + "CCAAAGGA": 23002, + "CGTTCTA": 23003, + "GCTGAGTAA": 23004, + "CAATTAAAAAA": 23005, + "CCATTGACTT": 23006, + "TACTACAGA": 23007, + "TCATACATT": 23008, + "GCACCCACA": 23009, + "CGGGGTTTCACCA": 23010, + "GATCCTTGAGGAA": 23011, + "TGCCTCCAGCTTTGTT": 23012, + "GGGTCTTTT": 23013, + "TTTTTGCC": 23014, + "GTGCTAAAA": 23015, + "GTTTCCCTT": 23016, + "CCACAAGG": 23017, + "GCCTAGC": 23018, + "CTCGGCGG": 23019, + "GTCGCTGA": 23020, + "TGTTGGCC": 23021, + "CTGACCCCA": 23022, + "CATGCATCA": 23023, + "TATGGAATCA": 23024, + "CTTACACTT": 23025, + "GAAAATACAA": 23026, + "CTTTTTGAAA": 23027, + "GGGGGGCA": 23028, + "TCTGAGGTG": 23029, + "GGAAACTG": 23030, + "TCCTTCCCTCA": 23031, + "GGTAGCCA": 23032, + "TGATTCCAA": 23033, + "TCCAAGCA": 23034, + "GGAGGCAA": 23035, + "TAATGGACA": 23036, + "TAAAAAATAAAAA": 23037, + "GGGTTTGGG": 23038, + "GGGCGTGGTG": 23039, + "TCATAGAAA": 23040, + "CTGCCCTCC": 23041, + "CAACAGAGCGAGA": 23042, + "CGAGTAGCTGGGACTACAGG": 23043, + "TTACCCA": 23044, + "GAACGTC": 23045, + "TCCTCGA": 23046, + "GTGTTAATT": 23047, + "GTCTGAGGA": 23048, + "TGTTCTATT": 23049, + "CAAACAGAGA": 23050, + "TCAACAGTG": 23051, + "GTCATATTA": 23052, + "TATGCTTAA": 23053, + "TCTCTAAAAA": 23054, + "TTAAGAGG": 23055, + "TTAACATA": 23056, + "TCAAATACA": 23057, + "TAATTTCTAA": 23058, + "GAATTGGAA": 23059, + "CTTTGTCTG": 23060, + "GTTTGGTGA": 23061, + "GAGCAGAGCA": 23062, + "TAAATTTATA": 23063, + "TCAGGGATG": 23064, + "GATCTTATT": 23065, + "TCCTTAATT": 23066, + "GTGCCACTG": 23067, + "GCATCTATT": 23068, + "GGACAGTC": 23069, + "GTACAGAGA": 23070, + "CTCGACTG": 23071, + "CTCTAACA": 23072, + "TGAACGTT": 23073, + "CGGCAGAA": 23074, + "CCTGCAGTT": 23075, + "CCTGGACCA": 23076, + "TCTCTCCCTC": 23077, + "TACAGTGTG": 23078, + "CTTACATTTT": 23079, + "TTTGGGAAA": 23080, + "TCTGACAA": 23081, + "TAAATTCTA": 23082, + "TTTTTGTATT": 23083, + "AAATAGAAA": 23084, + "TCACATCTT": 23085, + "CATTTGGAGCGCTT": 23086, + "TACTCTTTT": 23087, + "TGAGCTCTG": 23088, + "TTATACTT": 23089, + "GAAGCAGCA": 23090, + "CTGTGTGTG": 23091, + "CTGAAAATG": 23092, + "GTATCGG": 23093, + "GCCTATTTGAA": 23094, + "TATTGGCCA": 23095, + "CATTGCCTT": 23096, + "CAGGGCTCA": 23097, + "TCAAGGCCA": 23098, + "GCTTTGAGG": 23099, + "GAAAATTTTA": 23100, + "CTTTTCCCA": 23101, + "GGGGCGA": 23102, + "TTTATAATA": 23103, + "GACACTGTT": 23104, + "GCTACTAAA": 23105, + "GGAAATGGG": 23106, + "GTATTCAAA": 23107, + "CGTACATA": 23108, + "CAGGAGTGA": 23109, + "GAGCTCAAA": 23110, + "TAGTTCTA": 23111, + "GCGGTGTG": 23112, + "GCGGCTCA": 23113, + "CTCCATCTCAAAAAAAAAAAAAAAA": 23114, + "GTTTATTTTTT": 23115, + "TAACATCTT": 23116, + "TGACACGA": 23117, + "GTGCAGTCA": 23118, + "CAGCTTCTA": 23119, + "GGTGTGTGTG": 23120, + "TAATCACAA": 23121, + "TTATCTTA": 23122, + "CTTGTAATT": 23123, + "GGACTGAAA": 23124, + "GAGACGTT": 23125, + "TGAATAATG": 23126, + "GCAACAGC": 23127, + "CTCCCATGA": 23128, + "TACAGTCTT": 23129, + "TCCATTTTAA": 23130, + "TCCATACTG": 23131, + "TCTAGCTAA": 23132, + "GTCCCCCTG": 23133, + "CAAAATATCA": 23134, + "CCCCGTGGG": 23135, + "CCCCTCCTCC": 23136, + "TCATTTCTTTT": 23137, + "GCATTCACA": 23138, + "CCCCATTTT": 23139, + "GAACATTTTTT": 23140, + "GCGATATG": 23141, + "TGACATAAA": 23142, + "CAACATCTG": 23143, + "GTTGAGGAA": 23144, + "CTTGGTAGCCCAGGCTG": 23145, + "GGAAACGGGATTTCTT": 23146, + "GGAAATGGCAACCCACTCCAGTATT": 23147, + "TGACAAATG": 23148, + "TCCTCTTTG": 23149, + "TATTGCCAA": 23150, + "TAAAAGAGAA": 23151, + "GATTAAATG": 23152, + "CTAACCCA": 23153, + "TCTAGATTA": 23154, + "CTTTTCAGA": 23155, + "GGGGGTT": 23156, + "GCTACGTT": 23157, + "TGTATTTTAA": 23158, + "CGCGGCTG": 23159, + "TAAAAATGCA": 23160, + "TCCTTTAAA": 23161, + "CATCCTGGG": 23162, + "GCGTGGAGCC": 23163, + "GATGTGGGGTA": 23164, + "TTTTAATGGTAGGTT": 23165, + "GTCGTGTCTGA": 23166, + "TGAGTTCTT": 23167, + "GCAGCCCCA": 23168, + "GAAACTATT": 23169, + "TATACCAAA": 23170, + "TGTTCCTCA": 23171, + "GCTTTCTAA": 23172, + "CGGGGCCA": 23173, + "GATTTATGA": 23174, + "TCCACGCC": 23175, + "TCTACTTAA": 23176, + "TGGAGCTGA": 23177, + "CTTTGTATA": 23178, + "TCCTGGGGG": 23179, + "CAGCTCTAA": 23180, + "GAGGAGCTG": 23181, + "GCTCCTGCA": 23182, + "GCGGCACA": 23183, + "CCCTCTCTT": 23184, + "CCCTCTTTG": 23185, + "TCACCTAGG": 23186, + "TCTCAATAGATG": 23187, + "GCACATAAA": 23188, + "CAAAATCAGCAGGGGATCAAA": 23189, + "CATAGGTG": 23190, + "GGCTTATG": 23191, + "TAATATTAAA": 23192, + "TCATGATTA": 23193, + "TCCCTTGA": 23194, + "CAACTGGG": 23195, + "GTGGCGCA": 23196, + "GTCTGGGTT": 23197, + "TATTGATCA": 23198, + "TCTTGGTGA": 23199, + "GAGACCAAA": 23200, + "CATGGAGCA": 23201, + "TTTTACTCA": 23202, + "GTCCTCTCC": 23203, + "GTCCCGGA": 23204, + "TTTATTATTA": 23205, + "GGTTCTTTT": 23206, + "GTAAAGCA": 23207, + "GGAAACAGA": 23208, + "GACCTAGA": 23209, + "CAATTCTCA": 23210, + "CGAATGTT": 23211, + "CTCTGTCAA": 23212, + "CCTCGGTG": 23213, + "TCAGGGATT": 23214, + "CTTCCTCCTT": 23215, + "GGAGGAGGGA": 23216, + "TAAAGAAAATGTG": 23217, + "GTCATTTAGCAGACGCTCTTATCCAGAGCGACTTA": 23218, + "GAAGCAGAA": 23219, + "CTTGTTATT": 23220, + "GTACATC": 23221, + "CTCGGTCA": 23222, + "GAAAGAACC": 23223, + "GTGAGGGTG": 23224, + "GCAATATCA": 23225, + "CCAACATGA": 23226, + "CTTACAGTA": 23227, + "TGGACAAAA": 23228, + "TAATTGCTG": 23229, + "CCTAGCTA": 23230, + "TCCTTGCC": 23231, + "GTGAACTA": 23232, + "TATTTTTTCA": 23233, + "GTCTTGTTTT": 23234, + "CCATTTATTAAA": 23235, + "CCTCCCTA": 23236, + "CGTCTGTA": 23237, + "GAGAGAGATT": 23238, + "TCTCTCTCTCTCC": 23239, + "GTTTATCTG": 23240, + "CCCTCCATCA": 23241, + "GTTGGTAGAGCATG": 23242, + "TATTTATGAA": 23243, + "GATTAATTA": 23244, + "TAATCCCAGAA": 23245, + "GAAAGAGAGGCCCATT": 23246, + "GAAAAGAAAAGAAAAGAAAA": 23247, + "TGAACTAACCAGTA": 23248, + "GATTTGCATTTCCCTGA": 23249, + "CAGTTAATT": 23250, + "GAGCAAATT": 23251, + "TGACACATT": 23252, + "GAATTATTA": 23253, + "GAAGCAGA": 23254, + "GTGGCCACA": 23255, + "GTTTAACAA": 23256, + "TGTGCACTT": 23257, + "GTGATGATG": 23258, + "GTCATCAAA": 23259, + "CTAAGAGAA": 23260, + "TGAAATGTT": 23261, + "CTTAAGTA": 23262, + "CTTAATTAAA": 23263, + "TCTTATAAA": 23264, + "GTGTTGGA": 23265, + "GGGAAGGTG": 23266, + "CATTACATT": 23267, + "GTCTGGCTT": 23268, + "GAATGGGA": 23269, + "CAGCCTTTG": 23270, + "GAATAGTAA": 23271, + "TGACAGTTA": 23272, + "TAAATAAGGCTG": 23273, + "TACACTAGCAA": 23274, + "CCTCACATAAATTTTCAAACCACATAAATTTTCGTTCGGTTTCATCAAACCCATCCTAATT": 23275, + "TTAGATAA": 23276, + "TCCTAAATT": 23277, + "CAACTGAAA": 23278, + "CAAGAACA": 23279, + "GAGAGGGAGG": 23280, + "CCTTGCTCA": 23281, + "CATGCACC": 23282, + "GTCAGGGCA": 23283, + "GAGGTACTG": 23284, + "GTTAGGTA": 23285, + "TAGACACAA": 23286, + "GCAAAGACA": 23287, + "TGATTTCAAA": 23288, + "TGATTTCATT": 23289, + "AAAAAATTAAA": 23290, + "GCCAATTAA": 23291, + "CAATTTCCA": 23292, + "CAATTCAATT": 23293, + "GGAGGGCTG": 23294, + "TCTAACTA": 23295, + "GAAAAAGCAA": 23296, + "TGAGAGTAA": 23297, + "CCAGGGAAA": 23298, + "TCATATCAA": 23299, + "TATTCAACATA": 23300, + "GCTGGAATCAAGATT": 23301, + "GGCAAAATT": 23302, + "TCATCTAAA": 23303, + "TCAGTGGA": 23304, + "TGACTGGA": 23305, + "CAATĊ": 23306, + "CTTCAGATT": 23307, + "CTTTCCCTG": 23308, + "CCATATC": 23309, + "CCACTTCTT": 23310, + "CTCGAGAA": 23311, + "CAAACCCACA": 23312, + "CATTTATTAA": 23313, + "TGAAGCTGA": 23314, + "TGAAGACTG": 23315, + "TCTGCACCA": 23316, + "CAGACGTT": 23317, + "TTTTATTTTATT": 23318, + "TATGGGCA": 23319, + "TTTGGCTG": 23320, + "TCTATAAAAA": 23321, + "GCCACGAA": 23322, + "GTCCCTGAA": 23323, + "CAAAATACAA": 23324, + "TGGGGTAA": 23325, + "GCTACCAAA": 23326, + "TCCAAGAAA": 23327, + "TCCAACTCA": 23328, + "TGATGCAAA": 23329, + "CCCTCCAGG": 23330, + "CAATATGTT": 23331, + "GGACAGCTG": 23332, + "TAAATATGTT": 23333, + "GACCTTTGTT": 23334, + "GGTAGGTG": 23335, + "TTACAGTT": 23336, + "GTTTAACTT": 23337, + "CCATĊ": 23338, + "CCACTAAAA": 23339, + "CCAGCTCTT": 23340, + "CTCGCTGA": 23341, + "CTCGCGG": 23342, + "CACATACTG": 23343, + "TGAAGGCAA": 23344, + "GGAAGAAGG": 23345, + "GATGGGTA": 23346, + "TCTACCTCA": 23347, + "GCCATACC": 23348, + "CATACTAAA": 23349, + "GAATTGAATT": 23350, + "GTGAAAAATT": 23351, + "TCCAAGATT": 23352, + "GCCTGGAAA": 23353, + "GTGCCACTGCA": 23354, + "TCCTACAATGTGATTTT": 23355, + "TCACCTGAGGTCAGGAGTTTGAGA": 23356, + "TATAAAATA": 23357, + "CTTTCCCA": 23358, + "GTTCTGCC": 23359, + "GCACTGGA": 23360, + "GTCTTATTA": 23361, + "CTGATACAA": 23362, + "CTGACAGTT": 23363, + "CATGCAGTG": 23364, + "TATGCAGTG": 23365, + "GAGGGGGAA": 23366, + "CCTGCATTA": 23367, + "CCTGGCTCC": 23368, + "GAAAATCC": 23369, + "CTTTTGATA": 23370, + "TGCAĊ": 23371, + "GCAAAGTG": 23372, + "TGAAATTAAA": 23373, + "CCAAATAGG": 23374, + "GTGAATAAA": 23375, + "CGCCCTCC": 23376, + "GCCTTTCTT": 23377, + "GTTTGTAAA": 23378, + "TAGAATTTA": 23379, + "GCAGGCACA": 23380, + "GACCATTTT": 23381, + "TGACACCAA": 23382, + "GTCCAGTCA": 23383, + "TAGGTGACCAAA": 23384, + "TCTGTCTGTCTGTCTGTCTGTCTGTCTGTCTG": 23385, + "TCATCTGTG": 23386, + "GAAGCTGAA": 23387, + "TCCTCACTT": 23388, + "GTTTCCTCC": 23389, + "GTACTGAAA": 23390, + "CTATGAGG": 23391, + "TGTTGTGTG": 23392, + "TGTTCTTGG": 23393, + "CATGCACTT": 23394, + "GCAACAGGA": 23395, + "GAGGGGCC": 23396, + "TACATCTTA": 23397, + "GATGGTCTT": 23398, + "GGGATCAA": 23399, + "TAAAAAGACA": 23400, + "TGAAATTCA": 23401, + "CCATTCCCTT": 23402, + "TGTTAATTAA": 23403, + "GAGCCTTA": 23404, + "GTTCACCAA": 23405, + "TTAGGGTTA": 23406, + "CACTTTTTG": 23407, + "TATCTCATT": 23408, + "TCGAAATA": 23409, + "TAAGTAGA": 23410, + "TCACTTCA": 23411, + "GAACAAATT": 23412, + "GTTCTTATT": 23413, + "GTATGTGAA": 23414, + "TAAAGATTTT": 23415, + "CTCGAGCA": 23416, + "TGTGGGTGA": 23417, + "CATTTATAAA": 23418, + "TGAATACC": 23419, + "CAGGAAAAAA": 23420, + "CAGAGCTCC": 23421, + "TAAAAGGCA": 23422, + "TCTCCATCTT": 23423, + "CCAATCAAAA": 23424, + "CTAACACTG": 23425, + "CCCAGCATT": 23426, + "TAGACC": 23427, + "GAGAAGGGA": 23428, + "GAAAAACATT": 23429, + "GAGCGGGG": 23430, + "GGATGGAAA": 23431, + "CAGGAATTTT": 23432, + "CAACACCTG": 23433, + "CCACCCCAA": 23434, + "GTTATGAAAA": 23435, + "TCTGTCATAGTT": 23436, + "TGAATGAATGAATGAA": 23437, + "CCGCTGCCCAAGGAA": 23438, + "GAGAAATGCAAATCAAAACCACAATGAGATA": 23439, + "TAATCCATG": 23440, + "TAAGTACA": 23441, + "TGACCCCATGGA": 23442, + "GTAGCAAAA": 23443, + "CTCTCTCTCC": 23444, + "GTCTCAAAAA": 23445, + "TATTCCTGA": 23446, + "TCTTGGCCA": 23447, + "TCTTGACTG": 23448, + "TGTTGATTTT": 23449, + "TGAACGAA": 23450, + "TTTTATGGG": 23451, + "CTAACAGCA": 23452, + "TCCATCCATT": 23453, + "CGATATTTT": 23454, + "TTTACGG": 23455, + "GGTGAAATT": 23456, + "GGAAACAGG": 23457, + "TCCTTGGGG": 23458, + "GTGAAGGGG": 23459, + "GTTAATTAAA": 23460, + "GAAAAAGGAA": 23461, + "TAATGGCTG": 23462, + "CCCTGTTTT": 23463, + "CTCCAGTAA": 23464, + "CCATAGATT": 23465, + "TAAAATGCAAA": 23466, + "CTGTGAAGAAA": 23467, + "CGTAAAATT": 23468, + "GATCTCATT": 23469, + "GCCACACACA": 23470, + "TAAGCAGAA": 23471, + "CAACTCAAA": 23472, + "GCAGTTCTG": 23473, + "GGATATATT": 23474, + "GCCCGCTG": 23475, + "TGTGCCTCA": 23476, + "TGTTTCTCC": 23477, + "GAGAGCAGG": 23478, + "CTGAGATAA": 23479, + "TTTTATAATT": 23480, + "CTAAGACC": 23481, + "CTTACATAA": 23482, + "CGATCTA": 23483, + "CCCACGC": 23484, + "GACAGTGTT": 23485, + "CATAGCAAA": 23486, + "CATACTTAA": 23487, + "TTATTCATG": 23488, + "TTATTAAAAAA": 23489, + "TCCTTCTCTT": 23490, + "CAAAAAGAA": 23491, + "TCAAAAGAAA": 23492, + "CCCTGCTG": 23493, + "TAGTGTTTA": 23494, + "TAAGACACA": 23495, + "GGATTTTTAATGAATT": 23496, + "TCAATTTTGGCTTTT": 23497, + "CCTATTTCTCCACATCCTCTCCAGCA": 23498, + "CCTCCAATT": 23499, + "TACCAATG": 23500, + "GCATTAATT": 23501, + "TCTTTCTGTG": 23502, + "TGTTCTCTG": 23503, + "CAGGTCATT": 23504, + "TCTGCAGC": 23505, + "TAAAATTTTAA": 23506, + "TTAACCAA": 23507, + "GTTTTGCTG": 23508, + "GTCCCCCC": 23509, + "TAATTAAATG": 23510, + "TCTCATGGA": 23511, + "CGTGTGCATGTG": 23512, + "GCAAATGGA": 23513, + "TCCTTTCTA": 23514, + "GGCCGGGCA": 23515, + "CAGAACTG": 23516, + "TCTTACAAA": 23517, + "CTTTGCACA": 23518, + "GACCCGTG": 23519, + "GTATTCTGTT": 23520, + "TGATGTGA": 23521, + "GAACAGCTG": 23522, + "CTTCTGTTTT": 23523, + "CTCCTGCCTCAGCCTCCTGAGTA": 23524, + "TAGACGGCAGCCCACCAGGCTCC": 23525, + "TACTCATG": 23526, + "GACCAAATT": 23527, + "CTTCTGGA": 23528, + "GTGGAGATT": 23529, + "GTTGCCATG": 23530, + "GCACAAGAA": 23531, + "CTAGATCC": 23532, + "GTCTAAGG": 23533, + "GTCTCCCTT": 23534, + "TCTTGAAAAA": 23535, + "TGTTGGGAA": 23536, + "GAGACCTCA": 23537, + "GTAATAGTT": 23538, + "GAGGCTGCAGTGA": 23539, + "CTTACTAAA": 23540, + "CGACATC": 23541, + "GCTCAGATG": 23542, + "GTCTTCATG": 23543, + "CGCCCACC": 23544, + "TATAACAAA": 23545, + "CTGTGCAAA": 23546, + "GGGAAGGA": 23547, + "GAATGCAAAA": 23548, + "TCATGGTCA": 23549, + "GTGTGGAA": 23550, + "GGCTTCATCC": 23551, + "TAAAAAATTTT": 23552, + "TTATATAAAAA": 23553, + "CTTTAACAA": 23554, + "CTTTATTTTAA": 23555, + "GCCTTTTTG": 23556, + "TCTGGGTTTG": 23557, + "CAGATAGAA": 23558, + "GTGAGATCA": 23559, + "TATGCAGTT": 23560, + "CCTGTAGTT": 23561, + "GCCATCTCC": 23562, + "TTCACCAAA": 23563, + "TCCTTGTCA": 23564, + "GTGAAGCTG": 23565, + "CGCAGCAA": 23566, + "CGCACACACACA": 23567, + "GTGTTGTGTT": 23568, + "CTCTGGCAA": 23569, + "CCATGGAGG": 23570, + "CCCTCTGCA": 23571, + "TGAGAATTA": 23572, + "GTTCCTGTG": 23573, + "CTATACAAA": 23574, + "TATTTGAACAGTGAGAGACAGAA": 23575, + "TAATCTATA": 23576, + "TCACAATA": 23577, + "CTTGĊ": 23578, + "CTTTAACA": 23579, + "GCCGAGGA": 23580, + "CTCTCACAA": 23581, + "CTCACCTA": 23582, + "CATGTACCA": 23583, + "TTTTATTATTA": 23584, + "CTTACCAAA": 23585, + "CTTAGAGCA": 23586, + "TCCATAATT": 23587, + "GATGCCCTT": 23588, + "GGAAATGTT": 23589, + "TCCTTGGTG": 23590, + "GAATTGTT": 23591, + "CGTTGAA": 23592, + "CGTTGTCA": 23593, + "TCTTACATT": 23594, + "CAATTGTTA": 23595, + "CAGTGCAGTG": 23596, + "GCATTTCC": 23597, + "CCCTCTGTG": 23598, + "GCACACAAA": 23599, + "GAATAAGGCTG": 23600, + "TTAGTAGAAAATATTA": 23601, + "TAAGCAGA": 23602, + "CAATAATGA": 23603, + "GTGTCTTTG": 23604, + "GTCGGTCA": 23605, + "GTGAGAGGA": 23606, + "CATGAAAAAA": 23607, + "CATGGGCTT": 23608, + "CCTGAAATG": 23609, + "CTTTTGGCA": 23610, + "CGTTTATA": 23611, + "GCTCATCTG": 23612, + "GTTAAGTTA": 23613, + "CTTTGGTAA": 23614, + "GCCTTTCTG": 23615, + "GTCTGGAAA": 23616, + "AAAAAAAATTA": 23617, + "TCAGGGCTG": 23618, + "GGCTATTA": 23619, + "TATATGTATATATA": 23620, + "GCACAGTTA": 23621, + "GGACAGCCA": 23622, + "TGACTGTTA": 23623, + "CACGGAAA": 23624, + "GGGCTGAAA": 23625, + "CTACCATCAGA": 23626, + "AGAA": 23627, + "TAACGATT": 23628, + "CTTTCCAGG": 23629, + "GCAGTGGA": 23630, + "GTACTGTA": 23631, + "GCCGAGAA": 23632, + "TAAAGACTA": 23633, + "TGTGTACAA": 23634, + "CATTGCTGA": 23635, + "CAGGCCTCA": 23636, + "CAGGGAGCC": 23637, + "TCAATTTTAA": 23638, + "TCAAGGCAA": 23639, + "GCTTTGATT": 23640, + "CCTTGCTTA": 23641, + "CTAAGATCA": 23642, + "GGGGCAGAA": 23643, + "TTTAGGGG": 23644, + "TAGACATTTT": 23645, + "GACATTTTAA": 23646, + "CTGTTGTGA": 23647, + "TCCTGCAA": 23648, + "TCCTGCTGA": 23649, + "CAGTTCTGA": 23650, + "TCAGGGAAA": 23651, + "TAAATGTATA": 23652, + "TTACAGCTT": 23653, + "GTGGCATGCA": 23654, + "TTATTATTATTATTATTA": 23655, + "GGAGAAAATTTTTG": 23656, + "TGATGTACTCTG": 23657, + "TCCGCCCGTCTCGGCCTCCCAAAGTGCTGGGATTACAGGCTTGAGCCACCGCGCC": 23658, + "GCGTCTCA": 23659, + "TTTTTCTC": 23660, + "CTGTĊ": 23661, + "CTGGCTCTG": 23662, + "GTATTTTTTTT": 23663, + "GAGACCCC": 23664, + "CAGATCTCA": 23665, + "TCAACGTG": 23666, + "TAAAAGCTA": 23667, + "GTGACGTCA": 23668, + "GCTGCCTGA": 23669, + "CATGTCTAA": 23670, + "GAGGGGGGA": 23671, + "GATTCCTA": 23672, + "CTTAGTAAA": 23673, + "TCCATATTTT": 23674, + "TAGGGTT": 23675, + "TCCTTTGA": 23676, + "CGTTGTTTT": 23677, + "CGTTTGGG": 23678, + "TGCCTATT": 23679, + "GTGTTCAA": 23680, + "CAAAAATTTA": 23681, + "CTGTGTATA": 23682, + "GATGAGGAA": 23683, + "CCATGGTAA": 23684, + "TCAGACTCC": 23685, + "TCATAGTAA": 23686, + "GAAGTTTTA": 23687, + "GGAGCCCC": 23688, + "CCAGGAGA": 23689, + "CACATATTTT": 23690, + "CACATATCA": 23691, + "GTGACGAA": 23692, + "GTGACAGTA": 23693, + "TATGGAGGA": 23694, + "CCTGCCAAA": 23695, + "CCTGGGGGA": 23696, + "GTTTTTCAAA": 23697, + "TCATTCGA": 23698, + "TGATTGAAA": 23699, + "TCCAATGAA": 23700, + "CAGAAAAATG": 23701, + "CTCAACCAA": 23702, + "GGAGGGTAA": 23703, + "CTGTGTCC": 23704, + "TAATAAAAAAAA": 23705, + "GCAGGTAAGA": 23706, + "TACTATATA": 23707, + "CAACAATTA": 23708, + "GGGTCTGAATA": 23709, + "CGCCAATG": 23710, + "GTGCTGGGATTACAGGTGTGAGCCA": 23711, + "CACGCAAA": 23712, + "GTGGCAGAA": 23713, + "GTCGGTAA": 23714, + "CACATCTCA": 23715, + "CACACCTCA": 23716, + "CACAGGTCA": 23717, + "TCTTCAGC": 23718, + "TCAACTGAA": 23719, + "CATGTAATT": 23720, + "TATGTACCA": 23721, + "CGGGAGAA": 23722, + "GCCACCCTA": 23723, + "TAGAGCAAA": 23724, + "TTATTTCTTA": 23725, + "GATAGGGG": 23726, + "CGTGGTGA": 23727, + "CTCAACAA": 23728, + "GGAGGCTCA": 23729, + "GCCTGGATG": 23730, + "CGTATTCA": 23731, + "GAGTGGAGA": 23732, + "CAAGGCAAAA": 23733, + "TTATATGTG": 23734, + "TAAGAGTTA": 23735, + "GTTCAGCTG": 23736, + "CAGGTGCCATTAA": 23737, + "GTCTGAGAGA": 23738, + "GCTCTCACAGA": 23739, + "GAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGA": 23740, + "GTCCCATCACTTCATG": 23741, + "CCAAAACAGCATGGTACTG": 23742, + "CACCAAATT": 23743, + "TAAGCGG": 23744, + "TTTTCTAAA": 23745, + "CCAGTAAAA": 23746, + "GCCTGAGAA": 23747, + "CTAGTAAAA": 23748, + "AAAATGAAA": 23749, + "GTCGATCA": 23750, + "CATTTAGTA": 23751, + "CAGGCAGTT": 23752, + "TCTGGGATG": 23753, + "GTCAGCTA": 23754, + "GAGGCGC": 23755, + "GAGGCAGTT": 23756, + "TCTCGATT": 23757, + "CTAACATAA": 23758, + "CTTATATCA": 23759, + "TCTACAATT": 23760, + "CGACACAA": 23761, + "TGGAĊ": 23762, + "TCTCCTCAA": 23763, + "TCCTTTCTCA": 23764, + "GTATTGTG": 23765, + "GTATTTACA": 23766, + "GCCTGCAA": 23767, + "TCATGCAAA": 23768, + "CAAGGGATG": 23769, + "GTTCTTGTA": 23770, + "CTACATATA": 23771, + "CCTTTGTTA": 23772, + "TAGGAACACTTTTA": 23773, + "GGATGGACTGGTTGGA": 23774, + "TCACTTGAGGCCAGGA": 23775, + "CCCATTTA": 23776, + "CTACTGTTA": 23777, + "TAAAGCAAAA": 23778, + "CTCGTAAA": 23779, + "CACAGTTAA": 23780, + "TCTTCATCC": 23781, + "CTCACACCA": 23782, + "CTTATCAAA": 23783, + "TTAACTCA": 23784, + "CTTTTGAGG": 23785, + "CGATATAA": 23786, + "TAGGCATA": 23787, + "CGCTGATT": 23788, + "TCATTGAAA": 23789, + "TCATTTAAAAA": 23790, + "TCCTTGTCC": 23791, + "CGGATCC": 23792, + "GAAGGTATA": 23793, + "TCTCTGCCTCC": 23794, + "GGAGATGTT": 23795, + "TCAGATCTCA": 23796, + "TCATTATTTA": 23797, + "GTATTTTTTAGTAGAGA": 23798, + "GTCTTTTGATTTTTTTGAAA": 23799, + "TACCTACTGTGAA": 23800, + "GCGTCTTA": 23801, + "TGATCTCTT": 23802, + "GCCCTATG": 23803, + "TGTTGCAAA": 23804, + "TCTGAAAAAA": 23805, + "TCAAGTGAA": 23806, + "CTAACC": 23807, + "TTAATTAATT": 23808, + "TCTAGACC": 23809, + "TCATTCTCC": 23810, + "CTAAAGATA": 23811, + "CTCTTGGTG": 23812, + "GGAGGTTTG": 23813, + "CTCTGTAGG": 23814, + "CTCTGCCCC": 23815, + "TGAGGGTG": 23816, + "CATTTTTGGG": 23817, + "GGCTTTGGG": 23818, + "CTCCACAAA": 23819, + "CCCTAGAACTTAAA": 23820, + "CAAGGCAAA": 23821, + "GAACATATG": 23822, + "GAGGGAGGGAGG": 23823, + "CTCAGCTATTAAAAA": 23824, + "TACAAAAAAA": 23825, + "TACAGGAAA": 23826, + "CAATATTTG": 23827, + "GTTTCTATG": 23828, + "TAAATTAAAAA": 23829, + "CACATAGAA": 23830, + "TGTTGCATT": 23831, + "TCAATACCA": 23832, + "CTGACATTTT": 23833, + "CTCAGCATT": 23834, + "CCTGGAGC": 23835, + "CTCCCTCCTT": 23836, + "TTTGTAAAA": 23837, + "CAAAACGG": 23838, + "TAGAGTAAA": 23839, + "TGGACTGAA": 23840, + "TCCTTTATA": 23841, + "CCAAATATT": 23842, + "CTGTGCCAA": 23843, + "GCCTTCCAAA": 23844, + "TGATGGCAA": 23845, + "TGAGAGTCA": 23846, + "CATGATTTTA": 23847, + "CTTCTTAAAA": 23848, + "TGATAGTAA": 23849, + "CAACATATG": 23850, + "TTACAGAAAA": 23851, + "CTTGAGGAA": 23852, + "CCTTAATTA": 23853, + "CTCCTGCCTCAGCCTCCTGAGTAGCTGGGATTACAGG": 23854, + "GTGGAACAA": 23855, + "GCAGCCAAA": 23856, + "GCAGGGGTT": 23857, + "CTAGAAATT": 23858, + "CAAACGC": 23859, + "TGAATAGTT": 23860, + "CAGGGAGTG": 23861, + "CCTTGATGA": 23862, + "GATTCTAAA": 23863, + "GTTATAGCA": 23864, + "TACATACATG": 23865, + "TCCAGTATT": 23866, + "CCCAGCAGG": 23867, + "TAGACCTA": 23868, + "CAGAATGAA": 23869, + "GTGTTGTGA": 23870, + "CAATTGCAA": 23871, + "GAGGAGGCA": 23872, + "GGATTTCCCAGG": 23873, + "TCATGGTA": 23874, + "GATAAGAA": 23875, + "CGTCTGTT": 23876, + "TCAGACTGA": 23877, + "GTCCAGCTG": 23878, + "AAAAAAAAAAAAAAAAAAAAAAAAAA": 23879, + "TCACTGATT": 23880, + "GAACAGGAA": 23881, + "GTTTGTGTG": 23882, + "TCTGCCTGA": 23883, + "CCTTGAGAA": 23884, + "CCTTGCTGA": 23885, + "GTCAGAATT": 23886, + "TCTACAAAAA": 23887, + "GGTGCC": 23888, + "CTGAAGTAA": 23889, + "TCATTTCCAA": 23890, + "CGTTTGACC": 23891, + "CTCAATGGA": 23892, + "TACCTGATG": 23893, + "TCCCCGGGA": 23894, + "GCGCCCGG": 23895, + "GCAGCCTTATTTA": 23896, + "GTATTCCCATCTCTT": 23897, + "CGTCGAGATTTTAAGTTTGA": 23898, + "GAACCTCC": 23899, + "GGAGCTCTG": 23900, + "TAAACATAAA": 23901, + "TATTTAATTTT": 23902, + "TCTTCAGCC": 23903, + "TAAAACC": 23904, + "GTGATATGA": 23905, + "CCTTTACAA": 23906, + "CATGTCTCA": 23907, + "CATGGTAAA": 23908, + "CATGGCAAAA": 23909, + "TTTTATGTTA": 23910, + "TCTCGGCC": 23911, + "CTCCCTAAA": 23912, + "TACAGCCAA": 23913, + "TTTGGTG": 23914, + "CTTTTCCCC": 23915, + "GGTGTCAA": 23916, + "TTATTĊ": 23917, + "GTAAACTGA": 23918, + "TCATTTCTGA": 23919, + "TATTACATT": 23920, + "TCTTACTCA": 23921, + "TCTTAGCAA": 23922, + "TTTTAAAAATG": 23923, + "TAATGGAGA": 23924, + "TTTTTTTTTTCA": 23925, + "GTGTGGTGA": 23926, + "CCTTATTAA": 23927, + "GAAGACGA": 23928, + "CTGCACGG": 23929, + "TTTTATTGAGGA": 23930, + "GCACACTGA": 23931, + "GGACACCTG": 23932, + "TTTTTATGTT": 23933, + "TCTGACAAAGGGCTAATATCCAGAA": 23934, + "GGTGTTTA": 23935, + "GAATCGG": 23936, + "GTTCATTAA": 23937, + "AAAAAAATG": 23938, + "CAGAGTTAA": 23939, + "CTCAGCCTCC": 23940, + "CCTTGAGTT": 23941, + "TATGTACTG": 23942, + "CTAATACTT": 23943, + "GACACCCTG": 23944, + "GCTATCCA": 23945, + "TCAAACCTG": 23946, + "TGCATAGA": 23947, + "GTAAACTG": 23948, + "GGTAGTTTT": 23949, + "CCATTGCTT": 23950, + "TGATGCATT": 23951, + "GGCTGCAGG": 23952, + "TCCCATGTG": 23953, + "CTTTAGAGA": 23954, + "GGACATCAA": 23955, + "GCGCGGGG": 23956, + "CACACACTAAA": 23957, + "CCTGTGTTA": 23958, + "TACTTTAAGTTCTA": 23959, + "GATAGGTG": 23960, + "GACTCCAAA": 23961, + "GCGGAATT": 23962, + "TAAGCTGAA": 23963, + "TGATCCAGAA": 23964, + "TCCCTTCA": 23965, + "CTTTAATGA": 23966, + "CTAGCAAAA": 23967, + "GAAACCTCA": 23968, + "GTCACTAAA": 23969, + "GATTCAGTG": 23970, + "GTTTTGCA": 23971, + "GTCCCACTT": 23972, + "TAATTCCCA": 23973, + "GGCATTTTA": 23974, + "GCAAATATTTT": 23975, + "CTCTTGGTT": 23976, + "TCTTATATG": 23977, + "GTCAAAAATT": 23978, + "GCCTGTCTG": 23979, + "CAATGCCAA": 23980, + "AAAAAAAAAAATT": 23981, + "CGTCCAGG": 23982, + "GCACACCTG": 23983, + "TCCCTGTCTT": 23984, + "TGAGGTTGTGGA": 23985, + "CCAGCCTGGGCTACCAA": 23986, + "GACGTGTACCTATGATGAAAATTACAGGCCTCTCTCATCTTTTTAAGTGGGAGAA": 23987, + "GGCATTTA": 23988, + "TGACGTGA": 23989, + "GCAGAACA": 23990, + "GTACTGGG": 23991, + "GAAAGGATT": 23992, + "GAGATCCA": 23993, + "GTGACCCCA": 23994, + "CCTTGGGAA": 23995, + "GTCAGCTGA": 23996, + "GTCAGACAA": 23997, + "GAGGTGCTG": 23998, + "GTTACAGGA": 23999, + "TACATTATG": 24000, + "TTTAGCC": 24001, + "GACAGCTGA": 24002, + "GGTGTAGG": 24003, + "TAATTGATA": 24004, + "TGAAAGGAA": 24005, + "GGTATCA": 24006, + "CTGTGCAA": 24007, + "GCCTTTGTG": 24008, + "GGCTGGGCA": 24009, + "CCTCCCCTG": 24010, + "GCGGCGTG": 24011, + "CCATGCTCA": 24012, + "TATTTGTATT": 24013, + "CTGCTGCTG": 24014, + "TCACATATT": 24015, + "TGACCTCAA": 24016, + "GAACCTGTG": 24017, + "GACGGAAA": 24018, + "CAAAATGTGGAAAAA": 24019, + "GCGCTGGG": 24020, + "TAACTCTA": 24021, + "TAAGTATTTT": 24022, + "CTTGAAGA": 24023, + "CTCGAGTT": 24024, + "CACACTTATTTCAAAAAAA": 24025, + "GAAAGGCCA": 24026, + "TATATCTGA": 24027, + "CAAACTGTT": 24028, + "CAGACCTCA": 24029, + "GGAACATCA": 24030, + "GCTGCGCC": 24031, + "GTCAGGCC": 24032, + "GATTTACTG": 24033, + "CTAATAGAA": 24034, + "GAAAACACAA": 24035, + "GGGATAGA": 24036, + "GATATTTTAA": 24037, + "CTAAACAGG": 24038, + "TACCTAGA": 24039, + "CCATTTCCA": 24040, + "TAATGGGA": 24041, + "CATAATATT": 24042, + "CAAGGTTAA": 24043, + "CTATATTTTA": 24044, + "CCACTGGAAAAA": 24045, + "TGGGTGTTTT": 24046, + "TAAAATACCTAGGAA": 24047, + "CTTGCCTGGAGAATT": 24048, + "GACTTAATT": 24049, + "TAAGTTGTT": 24050, + "TAATTTGAAA": 24051, + "GAAGCTC": 24052, + "CTTCĊ": 24053, + "CTTGTTCA": 24054, + "CTTGAAAAAA": 24055, + "GTGGAGGTG": 24056, + "TAAAGGCCA": 24057, + "TAAACCCAA": 24058, + "TAAAGAGTA": 24059, + "AAAAGTA": 24060, + "TCTGGATCA": 24061, + "TCTGGAGTA": 24062, + "GGAATTATT": 24063, + "GTGATCTA": 24064, + "CCTTTCTAA": 24065, + "GAGGCAGC": 24066, + "GTTACTGAA": 24067, + "CCAAGCTA": 24068, + "CTTATACAA": 24069, + "TTTGTTTTTT": 24070, + "GTCCCAGTT": 24071, + "GGTATGA": 24072, + "CTGTTGGTT": 24073, + "CAGAAGAAAA": 24074, + "TATTTTTGTT": 24075, + "CTTTGTCA": 24076, + "TCTAACATT": 24077, + "CCTAATTAA": 24078, + "GGGCTACA": 24079, + "CAAGGGAGA": 24080, + "CAATATGTG": 24081, + "TCATAGATG": 24082, + "GCACACTCA": 24083, + "CCGGAGGA": 24084, + "TAAGTTTAA": 24085, + "TAAGTCTCA": 24086, + "TTTTCCTA": 24087, + "GTGGAAATT": 24088, + "GTGGCTGTT": 24089, + "GTTCGCTG": 24090, + "GCATĊ": 24091, + "GTACGTC": 24092, + "CAAAGATTTT": 24093, + "GAGAGAATT": 24094, + "TCTGGCTCA": 24095, + "TAAAACTGAA": 24096, + "GCAAGAGC": 24097, + "GAGGTACAA": 24098, + "CGGGGCGG": 24099, + "CTCCTACTT": 24100, + "GCAAATTAAA": 24101, + "CGTTTACTT": 24102, + "TAATATCC": 24103, + "GTATTCAA": 24104, + "TCATGGCTT": 24105, + "GCGGCCCC": 24106, + "TATATTTATT": 24107, + "CAGTCTGTG": 24108, + "CCACCCCCACCCCA": 24109, + "CTCCTCTGTCCTCCA": 24110, + "GGTTTCATCTGA": 24111, + "GCCTCGAACTCAGAAA": 24112, + "GAAGGCAATGGCACCCCA": 24113, + "TACTCAAAA": 24114, + "GATTTTAATT": 24115, + "GACGTCAA": 24116, + "TTTTCAGTT": 24117, + "CTTTAACTT": 24118, + "GTATTATA": 24119, + "TAAAGTTTTA": 24120, + "AAAAGTAA": 24121, + "GTCTGAGTG": 24122, + "TATAGAATT": 24123, + "TCAAGACTG": 24124, + "CTCACCCCA": 24125, + "CCTTCTTAA": 24126, + "TTTTACAGG": 24127, + "GTTACATCA": 24128, + "CTTACGTT": 24129, + "CAAAAGATA": 24130, + "TTATTGATG": 24131, + "GGGATATA": 24132, + "GGGACCCA": 24133, + "TCATTGACA": 24134, + "GGAAATGTG": 24135, + "TGAAACTA": 24136, + "GCTGAGCC": 24137, + "TCTTATGTT": 24138, + "GCCAAGACC": 24139, + "GTGTTCCTG": 24140, + "CAATTGAAA": 24141, + "CAATTTAAAAA": 24142, + "CTCTGGGGG": 24143, + "CTATTTCC": 24144, + "CTATTGCTG": 24145, + "CAATGGCTT": 24146, + "CAATGGCAA": 24147, + "CATAACAA": 24148, + "GGATGGGA": 24149, + "AAATATCTT": 24150, + "CCGGCGC": 24151, + "TATATACTTA": 24152, + "GTAGACCTGCACCA": 24153, + "GGCAGGCAGATT": 24154, + "TGACCTGCAGAGA": 24155, + "CAACCTCAAACAGTCA": 24156, + "TTATACAA": 24157, + "GAATCTCC": 24158, + "GTTGĊ": 24159, + "CCATCCAAA": 24160, + "GCCCGAGA": 24161, + "GTCGCTC": 24162, + "TGTTCCCCA": 24163, + "GAGAGAATG": 24164, + "CATTGAGCA": 24165, + "TCTGTACTG": 24166, + "CAGACTAAA": 24167, + "GGAAGAGAAA": 24168, + "CGGCGTA": 24169, + "CCAATAGAA": 24170, + "TCCACCCTT": 24171, + "GAAAAGAAAAAA": 24172, + "GCCATACAA": 24173, + "GTCCCAAAA": 24174, + "GGTTGCCATTTCCTT": 24175, + "TCTCAGTTA": 24176, + "GTAAATGA": 24177, + "TCTGATGGG": 24178, + "GGAAAGGGG": 24179, + "GCATTTCTAA": 24180, + "CATCACTGA": 24181, + "CACAATGTG": 24182, + "TAGTTTCTT": 24183, + "GCTTTTGGTGTTTTA": 24184, + "CTTTACTTTT": 24185, + "CAAGACATT": 24186, + "CAACATATT": 24187, + "CGTTATTTT": 24188, + "CACATGCACA": 24189, + "GACGCTTA": 24190, + "CGTTTACTCTGGAACC": 24191, + "TAGTCCTG": 24192, + "TAGAGGAAA": 24193, + "TGAGCCAAA": 24194, + "CCAGCCTA": 24195, + "TAAACCATT": 24196, + "TGAACATTTT": 24197, + "TGAATGTGA": 24198, + "GCTGGTCTT": 24199, + "CATGCTGTT": 24200, + "GTAACAGTT": 24201, + "GATTGAGAA": 24202, + "CTCCCTGTT": 24203, + "GATGGTTTTA": 24204, + "TTTGCACA": 24205, + "GCAAAGCAAA": 24206, + "TAAAAAGCAA": 24207, + "GTGTTGAAA": 24208, + "GGCTTGGTG": 24209, + "GGAGAACTG": 24210, + "TCACATATG": 24211, + "TTTTTACTTTT": 24212, + "CAACCTCCA": 24213, + "GAGGATTGGGAGAATG": 24214, + "CCACATCCAGTGGAA": 24215, + "TTAGAGAA": 24216, + "GGAGTAAAA": 24217, + "CCAGGAGGA": 24218, + "GTATAGC": 24219, + "GTCCACATT": 24220, + "GCTTCTCAA": 24221, + "CGGGAGC": 24222, + "GATTGAGTT": 24223, + "TTAATCCATT": 24224, + "CCCATAAAACGA": 24225, + "CCCCTACAA": 24226, + "CATAGAAAAA": 24227, + "TTTTTTTCCA": 24228, + "CTTAAGAAA": 24229, + "GACCTAGG": 24230, + "CGGAGAAA": 24231, + "TATCAGAA": 24232, + "TAAATTCAA": 24233, + "GATAATGAA": 24234, + "GGATGGTGA": 24235, + "GAGCCTGTG": 24236, + "GAATATAAAA": 24237, + "CAAGAACAGTA": 24238, + "CAACCACTCTG": 24239, + "CAAAGCCTACCA": 24240, + "TACTGGGGCATATAAA": 24241, + "TGATTTTATT": 24242, + "GTGCAGAGA": 24243, + "GTTGCCAGG": 24244, + "TATACGTT": 24245, + "TGTTGTGAA": 24246, + "CAAACGTG": 24247, + "CAGGTAAAA": 24248, + "CAGACGAA": 24249, + "TCAAGACTT": 24250, + "GCAACGC": 24251, + "GTCACACTT": 24252, + "GAGGCACTG": 24253, + "CTCCCCATT": 24254, + "CTCCCTCTG": 24255, + "TAGATACTA": 24256, + "GCTATAATT": 24257, + "TCAAAGCTT": 24258, + "TTCATAGA": 24259, + "CAAAAATTTG": 24260, + "TAATGCACA": 24261, + "GGCTGGGGG": 24262, + "GAGCAGCTG": 24263, + "GTCTGGCTG": 24264, + "GTGGGCATG": 24265, + "TATATTGTCACCCTG": 24266, + "CTAATGAAAA": 24267, + "CTCGGGAGGCAGAGCCAGGCGGATCTCTGTGAGTTCGAGG": 24268, + "CAGTGAATT": 24269, + "TGAGTCCCTG": 24270, + "TTACACTG": 24271, + "GGAGCCAAA": 24272, + "GCCTCGTG": 24273, + "TAAAGGTTA": 24274, + "GTCGAAAA": 24275, + "TCTTGCTCA": 24276, + "GTCACAGTT": 24277, + "CCCAGCTCTG": 24278, + "GGCAGTCC": 24279, + "TTCATAAAA": 24280, + "GGAAATTAAA": 24281, + "CTGTGCAAAA": 24282, + "GCATTTCCA": 24283, + "CATAATGTT": 24284, + "TAACAGCTG": 24285, + "CTTTGTTACTTTG": 24286, + "CAGTAAATG": 24287, + "TCCGATA": 24288, + "CTGTCTGTT": 24289, + "CTTGTTGG": 24290, + "CTTTGACTT": 24291, + "GGATGTGAA": 24292, + "GCCGGTA": 24293, + "CTCTCTATA": 24294, + "CACAGGGTG": 24295, + "TCTGCCCCA": 24296, + "GCTGTAATT": 24297, + "GCTGTCCAA": 24298, + "GTAATATGA": 24299, + "CGGTCCA": 24300, + "GATTCGTT": 24301, + "CCTGTCTGA": 24302, + "CTCCCGC": 24303, + "TAAAAATCA": 24304, + "GAATTGAGG": 24305, + "CTAAATGTG": 24306, + "CTCTTGAGA": 24307, + "CTTTGGTGAA": 24308, + "CAGCTAAAA": 24309, + "CTATTGAA": 24310, + "CTATTTGTG": 24311, + "TTTTTTTTTTTTTTTTTTTTTTTTTT": 24312, + "GCTAATTTA": 24313, + "CAGTATTAA": 24314, + "TGACATCTT": 24315, + "GTTCACAAA": 24316, + "TTACAGTCA": 24317, + "CTACAGCAA": 24318, + "CGGTTCCAGAGTAAA": 24319, + "GGTCCCCCTG": 24320, + "GAAGTGAAAA": 24321, + "CGACAGAGTGA": 24322, + "CACCCCAAA": 24323, + "TCGTTTTAA": 24324, + "GCGTCCTG": 24325, + "TTTTCTTAA": 24326, + "TCAGTGTA": 24327, + "CAAGCTTTT": 24328, + "GTGGCAGA": 24329, + "GTGTCCAGA": 24330, + "GTTCTCTGA": 24331, + "CCAGCAAAA": 24332, + "GTATGTGTT": 24333, + "TGTTTGTGTT": 24334, + "TGAAGAGTG": 24335, + "CAGATATGA": 24336, + "CTTTTCAGG": 24337, + "GGGGAAATT": 24338, + "CAAAACAAAAAA": 24339, + "CCCCGTGTG": 24340, + "TCAAATCTT": 24341, + "TCTCACAGG": 24342, + "TTTTTTGAAA": 24343, + "TGAAACATG": 24344, + "TCCTTGAGG": 24345, + "GGCCTCGG": 24346, + "TGATTTACA": 24347, + "CTCAATTTA": 24348, + "TCAAAATATT": 24349, + "CATTAATTAA": 24350, + "CTTCCCATG": 24351, + "CAATACATG": 24352, + "CAACATCTT": 24353, + "GAGTGAATT": 24354, + "TAAGCTC": 24355, + "CAAGCTC": 24356, + "CTGTCAGG": 24357, + "TATAGTATT": 24358, + "CAAACACACACA": 24359, + "TGAAGAGGA": 24360, + "TCTGTATGA": 24361, + "CAGACTCAA": 24362, + "TCAATGTGA": 24363, + "CGGGGGC": 24364, + "CGATCCCTG": 24365, + "TGTAGCTA": 24366, + "GGCATCTA": 24367, + "GGGACCCC": 24368, + "GGGAGAAAAA": 24369, + "TTTTTTCAAA": 24370, + "CTCTTGACC": 24371, + "CAGAATTTA": 24372, + "GCCAACATT": 24373, + "CGCAGAGA": 24374, + "TATAAGAAA": 24375, + "TAATGCCTT": 24376, + "GCCTGCTGA": 24377, + "GAATGGATG": 24378, + "TATTAAAAATT": 24379, + "GGATGGCAA": 24380, + "CCCAAGAGA": 24381, + "TCCCATCTT": 24382, + "CTTCACGGTT": 24383, + "TAACACCAA": 24384, + "CTGTACTCCAGCCTGGG": 24385, + "TCACAGTTA": 24386, + "CCACATGTG": 24387, + "GTTCCCTCA": 24388, + "GCACCTGTG": 24389, + "GTCATGTCTGACTCTTTG": 24390, + "GTAGTACTCCATT": 24391, + "AAAATTATAGACTGA": 24392, + "TTTTCTGTAAAATTA": 24393, + "TCCCTCATTAAAATG": 24394, + "TAAGTCATT": 24395, + "GTTCTCCA": 24396, + "GTATGAGG": 24397, + "GTAGAAATT": 24398, + "GCCGCTA": 24399, + "CTATCCTG": 24400, + "CTAGCTCC": 24401, + "CTAGGGGA": 24402, + "TATTGGCTG": 24403, + "TGTTCATGA": 24404, + "CAGACCTA": 24405, + "CAGAGTTTG": 24406, + "CTAAAAAAAAA": 24407, + "CATAGCCA": 24408, + "CTGAAGTTA": 24409, + "GGAAATGAAA": 24410, + "CTGTTCTGTT": 24411, + "TATTAGGAA": 24412, + "GTCTTTCAA": 24413, + "CCATTTGTG": 24414, + "CTGTGGTTA": 24415, + "TGATGTATA": 24416, + "TGATGGTCA": 24417, + "CAATGCAGG": 24418, + "CGTCTGCC": 24419, + "GAAGAAGGAA": 24420, + "CTGCATGAA": 24421, + "TAATTTTTTTA": 24422, + "CCACATGAA": 24423, + "GTTCCCTGTG": 24424, + "GTATTTTAAAA": 24425, + "TAAAATGTTTT": 24426, + "TCATCTTCAA": 24427, + "CTATGGGGTTGAGA": 24428, + "TAATATAATATTTGAAA": 24429, + "CACGTCTT": 24430, + "CATTTTTTAAA": 24431, + "TCAGTTGTT": 24432, + "TCAGCGA": 24433, + "GAACCATG": 24434, + "GAAGGAAAAA": 24435, + "GTGTATAAA": 24436, + "GTGGCTCTG": 24437, + "GGAGGAATG": 24438, + "GGACAACCA": 24439, + "TAAATGTTAA": 24440, + "CTCGAGTG": 24441, + "TGTTTCAAAA": 24442, + "TGTTTAGTA": 24443, + "GCTGTAAAA": 24444, + "GCTGTATTA": 24445, + "GCAATGGAA": 24446, + "GATTGATTA": 24447, + "CGACCAAA": 24448, + "TAGACACTG": 24449, + "CATACGTT": 24450, + "GTAAATACA": 24451, + "CAGCAGATG": 24452, + "GTGTTGTCA": 24453, + "GCATTGCTT": 24454, + "TGAGACTCA": 24455, + "CACTTCTCAAAA": 24456, + "GTCTGCCAA": 24457, + "GGTGATTA": 24458, + "TCTGTTGGCTG": 24459, + "CTGTAGAGA": 24460, + "GAACAGTTTT": 24461, + "CTCCTTCGTT": 24462, + "CAACAGTTA": 24463, + "TAAGGTTTG": 24464, + "CCACCCGG": 24465, + "GCACAGTAA": 24466, + "GCACACCAA": 24467, + "GCCCATGTG": 24468, + "GTAGAATTCA": 24469, + "CCTCCATGCAGA": 24470, + "GGATGAGATGGCTGGATGGCATCACTGA": 24471, + "GATATTTATT": 24472, + "TCATCCATT": 24473, + "TCCTAGC": 24474, + "CTAGTTTTA": 24475, + "CTCTTTTCTT": 24476, + "TATTGACTG": 24477, + "TGTTCCATT": 24478, + "GGAACAGTT": 24479, + "GCAACATTA": 24480, + "TTTTAGATA": 24481, + "CTCCGGGG": 24482, + "TCTATAATA": 24483, + "TCTATTTTTG": 24484, + "GGGGAAAAAAA": 24485, + "CCCATCTGA": 24486, + "TAGAGTCC": 24487, + "CGCTCTTA": 24488, + "TCCAACTTA": 24489, + "GCTGAGGCTG": 24490, + "GCCAAGAGG": 24491, + "GCCAAGGCTG": 24492, + "CAATTCCCAGCAA": 24493, + "GGGAATTTG": 24494, + "TACTTTAAAA": 24495, + "GTCTGGCCA": 24496, + "GCGGCCTG": 24497, + "CCATGGAAA": 24498, + "GAATACTGAA": 24499, + "CAAGACCTT": 24500, + "CCACACCAA": 24501, + "CTGCCCTGA": 24502, + "GTTCAGTTTT": 24503, + "GCCCAGTCC": 24504, + "GAGATGTTA": 24505, + "GAACTGAAAA": 24506, + "CTGCTATAAAGACACATG": 24507, + "CCCACAACCCTGGCGTT": 24508, + "CACGTAAA": 24509, + "CACGTTTG": 24510, + "TAACĊ": 24511, + "TAATCATTTT": 24512, + "TCATTACA": 24513, + "GCACTTTTA": 24514, + "GCATGTTTG": 24515, + "CTCGATCA": 24516, + "CTCTTAAAAA": 24517, + "TCAAGTATT": 24518, + "GTAATAGTA": 24519, + "GATTGTCC": 24520, + "TCTAGTAAA": 24521, + "TGGGAAATG": 24522, + "CATACCAAA": 24523, + "TGGATACA": 24524, + "CAGCAGCTT": 24525, + "TAATGTGA": 24526, + "TAATGTAGG": 24527, + "GGATTGGGG": 24528, + "CCTGAGAGG": 24529, + "CTGGGGCTT": 24530, + "TAGTGTTTG": 24531, + "TCATACACA": 24532, + "CTCTAGAGA": 24533, + "TTAGGTTTA": 24534, + "TCCTTTTTTATG": 24535, + "TAGATAGATAGA": 24536, + "CTCTCAACCAGCTT": 24537, + "CTCCACTATGTTCATA": 24538, + "GGCAGGAGAATCACTT": 24539, + "GCATATTAA": 24540, + "GCATCGA": 24541, + "GCCTCAGGA": 24542, + "TAAATCCTT": 24543, + "CTCTTTTAAA": 24544, + "TCTTCACTA": 24545, + "TGTTTATAAA": 24546, + "CAAACAGAAA": 24547, + "CATTCCTGA": 24548, + "CCTGGAATT": 24549, + "GATGCACAA": 24550, + "GCCATCATT": 24551, + "GGAAAGGTG": 24552, + "CCAAACTCA": 24553, + "TCCAAGTA": 24554, + "CTCTTCCCC": 24555, + "CTCTTCCTCC": 24556, + "GTTAATATG": 24557, + "GTCTTGTA": 24558, + "GGATTGGAA": 24559, + "CCAGGGTGA": 24560, + "TCTCTGTTTT": 24561, + "TCAGAGAAAA": 24562, + "TCCCACTTA": 24563, + "GAATATCTG": 24564, + "TTATACATG": 24565, + "TCCAGGAAAA": 24566, + "TGATACATG": 24567, + "GTAGGGAGG": 24568, + "GGCAGGAGAATTGCTT": 24569, + "CTGAATGTTGAGCTT": 24570, + "TCGTCTTTT": 24571, + "TCCCĊ": 24572, + "CAAGAAATG": 24573, + "TAAAATCCA": 24574, + "CTGACATTA": 24575, + "CGGGACAA": 24576, + "CTAAGAGTT": 24577, + "CAAAATAATA": 24578, + "TAGGGATA": 24579, + "TAAAAACATG": 24580, + "TCTGAGCTT": 24581, + "AAAAAAGAAAA": 24582, + "CTGTTCTTTT": 24583, + "CTTAACATT": 24584, + "CTCAATGAA": 24585, + "GAGCATTTG": 24586, + "GACCACTGATAA": 24587, + "TCTCTTCCTT": 24588, + "CTGGGTCTG": 24589, + "CCAGGCTCC": 24590, + "CAAGGGAGG": 24591, + "TCCAAATTA": 24592, + "CAAGAGAAAA": 24593, + "TAAATACTTA": 24594, + "CTATAGATT": 24595, + "CCTGTTGTTTCCTGA": 24596, + "TCTTGCTTGTTTG": 24597, + "CCAACCCAAATG": 24598, + "GGCTGGGAAGACTGAA": 24599, + "GCTTTTAATT": 24600, + "TCACTCAAA": 24601, + "GAACACATT": 24602, + "CAACCTC": 24603, + "GGAGAGAGAGA": 24604, + "GAAATAATAA": 24605, + "GAGACGGAA": 24606, + "CTGATAGAA": 24607, + "GTAACCAAA": 24608, + "GTAAGGTA": 24609, + "CGGGCTC": 24610, + "CTAAGGTA": 24611, + "GTTTTTAATT": 24612, + "GGTGGAGAA": 24613, + "TGGACCTT": 24614, + "TCTCACCTG": 24615, + "TTTTTTCCA": 24616, + "TCTGATGA": 24617, + "CTAAATCAA": 24618, + "CTCTTCCAAA": 24619, + "TCTAATATG": 24620, + "TGATGGCTG": 24621, + "CGTAGAGG": 24622, + "GAGCAGGGA": 24623, + "CTGGGCTCA": 24624, + "GAAGAAGAAA": 24625, + "TCACCTCTT": 24626, + "CCCCTGAAA": 24627, + "GTCCAACTCTCACATCCATACA": 24628, + "GACGAATT": 24629, + "TAAGTTGA": 24630, + "TCATCCACA": 24631, + "GTGTTATTA": 24632, + "CTCGATGA": 24633, + "CCTTCATTTT": 24634, + "GCAAGTGAA": 24635, + "TCTCTCCCTT": 24636, + "CGATATCA": 24637, + "TGGACCTG": 24638, + "TAATTGGA": 24639, + "CGCTCCTT": 24640, + "CTGAAGGAAA": 24641, + "TGATTAAATT": 24642, + "CTGTTCCAA": 24643, + "GTATTTCC": 24644, + "GTCAAGAGA": 24645, + "GAAAAATATG": 24646, + "TCATGGGGG": 24647, + "CCTTATTTA": 24648, + "TCAGGTTTA": 24649, + "TGTGTTAAAA": 24650, + "GTATGTATG": 24651, + "TTATAGAAAA": 24652, + "GAACACTAA": 24653, + "GCACCCCCA": 24654, + "GGATACAAA": 24655, + "TCTACATTTT": 24656, + "TGTTTGAACTTGTTA": 24657, + "GGGCAAAAA": 24658, + "TCACTGCC": 24659, + "GCCTGACC": 24660, + "TAAAGCTTA": 24661, + "CACACTTATT": 24662, + "TGAAGTTAA": 24663, + "TCTGCAGTA": 24664, + "GGAACATTA": 24665, + "CATGGACTT": 24666, + "GCAAGACTG": 24667, + "GATTCCCTT": 24668, + "GGGGCAGTG": 24669, + "CAAAACGA": 24670, + "TGTACGA": 24671, + "TGCATTATT": 24672, + "GGAAAGAATGAA": 24673, + "GTGTTTCAA": 24674, + "GTGTTCTGA": 24675, + "CGAATCC": 24676, + "CTCTGTCCA": 24677, + "GAGGATGGG": 24678, + "CAGGAGAGA": 24679, + "CAGGAGATG": 24680, + "CCTCCCTCA": 24681, + "CACCATGTT": 24682, + "GTGTGGAGA": 24683, + "GACCACCAA": 24684, + "CGTCATTTA": 24685, + "GAACATCAA": 24686, + "TGACACCTG": 24687, + "TGTGATGAAA": 24688, + "GTTCCCCAA": 24689, + "GGGAAAGGA": 24690, + "CCTAGGTATTTTATT": 24691, + "GCAGATTCAGTCTT": 24692, + "GGAGCAATTATTA": 24693, + "CCCCGTCTCTACTAAAAATACAAAAAATTA": 24694, + "TAATCACTT": 24695, + "TAACTTCTG": 24696, + "TCAGTTGG": 24697, + "GTGTCTGAA": 24698, + "GTTCAGGAA": 24699, + "GTCTGAGTT": 24700, + "TATTCCCAA": 24701, + "TATAGAGTT": 24702, + "CATTCAATT": 24703, + "TGAAGGCTT": 24704, + "GGAACTCC": 24705, + "GCTTCGTG": 24706, + "GTGAGAGTG": 24707, + "TACATCATT": 24708, + "TCTAGCAAA": 24709, + "TGTATAATT": 24710, + "TAATATCAAA": 24711, + "CTGTTCTCC": 24712, + "TCTTAGAGA": 24713, + "CTTTGGTCA": 24714, + "CAAAAATTAAA": 24715, + "CAATGCTGA": 24716, + "CCAGGGAGA": 24717, + "CTTGGGCTT": 24718, + "GAATAGATG": 24719, + "TCCACACAGTCAAA": 24720, + "CTGATTAAAA": 24721, + "GCGTGTGTG": 24722, + "CTCCCAATCCTCTT": 24723, + "TGAGGAATT": 24724, + "GTGCCACCA": 24725, + "CCAGAGATT": 24726, + "CTACACAAA": 24727, + "TAAAGAAAAAA": 24728, + "TATTCCCTG": 24729, + "TATTGCTAA": 24730, + "CACATCCAA": 24731, + "TCTTGGAGG": 24732, + "GCTTTAGTT": 24733, + "CTCAGGTCA": 24734, + "GATTGTATT": 24735, + "GGTGGCCAAA": 24736, + "GATATCAAA": 24737, + "CGCGAGTG": 24738, + "CCAAACTGA": 24739, + "CTAAAGAGA": 24740, + "CAGAATGTG": 24741, + "CCATTGTTA": 24742, + "GTTTGTAGA": 24743, + "CCTCAGCTG": 24744, + "CATCAGTCA": 24745, + "CTCCAGCCTT": 24746, + "CTTTATAAAA": 24747, + "GTTTACTCA": 24748, + "GCAGATGTT": 24749, + "GCTAAAGGGA": 24750, + "CACACAATACCCCA": 24751, + "GCTCTTTAGTTTAATTA": 24752, + "TCGTAAAAA": 24753, + "TAATCACTG": 24754, + "TTTTCATTA": 24755, + "CCAGTTTTA": 24756, + "GCCGCTCC": 24757, + "TAAATAAGG": 24758, + "GTCTCTATA": 24759, + "TATTCACTA": 24760, + "CAGGGGTA": 24761, + "GCTGTATCA": 24762, + "CCTTGCTA": 24763, + "GTAACACTG": 24764, + "GAAAAGTAAA": 24765, + "GCTACTTGGGAGG": 24766, + "TGTATCCA": 24767, + "TCTCACCA": 24768, + "CAGCACTG": 24769, + "CTCTTCAGA": 24770, + "CTCAAGTCA": 24771, + "TTTTAATTTTAA": 24772, + "GAGGAGGAAA": 24773, + "TGAGGGATG": 24774, + "TTATAGCTG": 24775, + "CTGGAGTGA": 24776, + "TCAGCATGACAATGA": 24777, + "TCATTTTTTA": 24778, + "GCACCACTGCA": 24779, + "TATTAATGGCACC": 24780, + "GAGAAGAGAAGAGAAGAGAA": 24781, + "TCAGCCAGA": 24782, + "CAACTTGG": 24783, + "CTTTGTTTG": 24784, + "CTTGAAGGA": 24785, + "GTTGTAGA": 24786, + "GCAGTGATT": 24787, + "GGAGAGATG": 24788, + "GTACTTAAA": 24789, + "TAAATAATTTT": 24790, + "TATTTCATTA": 24791, + "TCTTTCATCA": 24792, + "CATTGACAA": 24793, + "TCTGCCATT": 24794, + "GGAAGGCAA": 24795, + "GCTTCCTAA": 24796, + "CATGGGGTT": 24797, + "GTTAGGGTT": 24798, + "CCAACCCAAA": 24799, + "CTAAGCAAA": 24800, + "CTAATTATG": 24801, + "CTTAGCTA": 24802, + "TTAAĊ": 24803, + "CCCAGTAAA": 24804, + "TAGGTGAAA": 24805, + "CGCTCTAA": 24806, + "TCTCCCACC": 24807, + "CTAAACAGAA": 24808, + "TAATGGTGA": 24809, + "GTTTGTGA": 24810, + "GCCTGCAGA": 24811, + "CACCACACA": 24812, + "CATTTTCTG": 24813, + "GCAGGGTCA": 24814, + "GTAAAATAAA": 24815, + "TCGGGAGG": 24816, + "TCCATTAAAA": 24817, + "GAATACTGA": 24818, + "CCCTCTGAA": 24819, + "CTTCATGTG": 24820, + "CAACACAAAA": 24821, + "GCGCGGCC": 24822, + "TAGGAAGAATCAA": 24823, + "TAATAGCCAGAAGCTG": 24824, + "TACACACACACACA": 24825, + "TCGTGATG": 24826, + "TCGCAAAAA": 24827, + "TAACTAATT": 24828, + "TTTTGCTA": 24829, + "GCACCACC": 24830, + "GCCCCTTTT": 24831, + "GCCCAAGA": 24832, + "TAAACAAATG": 24833, + "CTCGGGC": 24834, + "TATTGAAATT": 24835, + "TCTTGGGAA": 24836, + "TCTTTAGGA": 24837, + "TGAACCAAA": 24838, + "GGAACACTT": 24839, + "CATGGGCTG": 24840, + "CATGGACAA": 24841, + "GATGCAGCTG": 24842, + "TTAATGGA": 24843, + "TAGATCC": 24844, + "TTATTGTAAA": 24845, + "TCTGACACA": 24846, + "CTAAACATA": 24847, + "CTGTTCAAA": 24848, + "CTTTGTCAA": 24849, + "CTGTGCTGTG": 24850, + "GGATTCTGA": 24851, + "GGCCATCTTTT": 24852, + "GCGGGCACCTG": 24853, + "GAATATAATT": 24854, + "CAACAGTCA": 24855, + "GCAGTGGCCACA": 24856, + "CCTGTAACTTCTT": 24857, + "TTTTTTTGTATTTTTAGTAGAGA": 24858, + "GTTCAAGACCAGCCTGGCCAA": 24859, + "TAATGAATG": 24860, + "TTTTCACTT": 24861, + "TCACĊ": 24862, + "GGATGAGAA": 24863, + "TAAATAGGA": 24864, + "GTCGTGTCCGACTC": 24865, + "TATACATCA": 24866, + "TCTCGCTT": 24867, + "GTCCTCCAA": 24868, + "GACATATAA": 24869, + "TCTCAGGGG": 24870, + "TTTTTTGTAA": 24871, + "CGTGTCC": 24872, + "CGTGGGTT": 24873, + "GAATTCAAAA": 24874, + "CTAAATATAA": 24875, + "CAGCAGGAA": 24876, + "CGAAAAATT": 24877, + "GTCAAGAA": 24878, + "GTCAACTTA": 24879, + "GAAAAATGTT": 24880, + "GGATTTCTTTG": 24881, + "GAATGGCTG": 24882, + "GAATGCCTT": 24883, + "GGGTGTTTG": 24884, + "TGACATATG": 24885, + "CCAGAGTGA": 24886, + "CTTCTGAAAA": 24887, + "GAACCCAGA": 24888, + "CTGATGGTAGG": 24889, + "TCGCCCAGGCTGGAGTG": 24890, + "TGTGTATGTGTGTG": 24891, + "TCTTGATTCCAGCTT": 24892, + "GTGAGTTCCAGGAAA": 24893, + "CTGCTGAAA": 24894, + "CCAGCAGTG": 24895, + "CTACTGAAA": 24896, + "GTCTAAGA": 24897, + "TATAGCAAA": 24898, + "TCTTTCATG": 24899, + "CATTTAAAAAA": 24900, + "CTCAGCTCC": 24901, + "CCTTCAGC": 24902, + "CCAAGGCTT": 24903, + "GCCACCCTT": 24904, + "CGAGCGA": 24905, + "CAAAATGTTA": 24906, + "TGGGAAATT": 24907, + "TAGACATCA": 24908, + "TTATTGTG": 24909, + "TCTGAGTA": 24910, + "CTAAAGAAAA": 24911, + "GCTCAGAA": 24912, + "GAGAACAAA": 24913, + "CAATTCTT": 24914, + "GCATTCCTG": 24915, + "TACAATGAA": 24916, + "GGGAAGATG": 24917, + "GAAGAĊ": 24918, + "TAACACTCA": 24919, + "CTGTACCTG": 24920, + "TAATTAATTA": 24921, + "GATCĊ": 24922, + "GCCACCACGCC": 24923, + "CACTCCTCCATT": 24924, + "CTGGAGTGCAATG": 24925, + "GCTCCCACAGTTGATT": 24926, + "GAAATACCATTTGACCCA": 24927, + "CCATCTCACACCAGTTAGAATG": 24928, + "GTTGCATCCAAAGAACACCA": 24929, + "CAGCAAATG": 24930, + "TCGTTATG": 24931, + "TAATGAGTT": 24932, + "TCATCCATG": 24933, + "TGACGAGA": 24934, + "GTTCTGATT": 24935, + "CTAGCTTTT": 24936, + "CTAGGGC": 24937, + "TAAATAGC": 24938, + "TGTGTCAAA": 24939, + "GAAAGACCA": 24940, + "CGGAAATT": 24941, + "CCAATATAA": 24942, + "CTTACGTA": 24943, + "TAGGTTTTA": 24944, + "GGCAGCTGA": 24945, + "TCTGAGTAA": 24946, + "GGAAATAAAA": 24947, + "TCCTTGTG": 24948, + "CGTTGGTG": 24949, + "CGTTTGAAA": 24950, + "TAATAGCTA": 24951, + "CTCAAGTA": 24952, + "TCCTGCTT": 24953, + "TAATGCAGA": 24954, + "TAATGTAAAA": 24955, + "CCTCCTTCA": 24956, + "GGCAAGAGAA": 24957, + "TCTCTCTCTCTG": 24958, + "CCCCAATTTA": 24959, + "AAAAAATGCTCA": 24960, + "TCTTCAAACCAAGCTG": 24961, + "TCGTAATG": 24962, + "GCGTATTTT": 24963, + "GCGATTTG": 24964, + "TCATCTATT": 24965, + "TGACGTAA": 24966, + "GCACTAAAA": 24967, + "CCAGĊ": 24968, + "CTCTCGTG": 24969, + "TGAACATAA": 24970, + "TCTGGATTTT": 24971, + "CAGATACCA": 24972, + "CTGAGCTCC": 24973, + "GTCAGTGTG": 24974, + "GAGGTAGAA": 24975, + "GTTATATCA": 24976, + "TCCAGTCAA": 24977, + "CCCCCGGA": 24978, + "TGTATGTGTGTG": 24979, + "GGCACTCC": 24980, + "CCTACCAAA": 24981, + "CGCGTTA": 24982, + "GTGAATTCA": 24983, + "CACCTAGA": 24984, + "CAAAAATAGA": 24985, + "CCTCAGATAAA": 24986, + "CCTGAGCCA": 24987, + "CGAGGAAAA": 24988, + "TGATAGATT": 24989, + "GCCACAGTCA": 24990, + "CATTTATTTTA": 24991, + "GTCTGATGAAA": 24992, + "GTTTAATGA": 24993, + "CTCGCTC": 24994, + "GTCTCATTA": 24995, + "TCAAGTCTT": 24996, + "GGAATGCC": 24997, + "CCTGCTGTT": 24998, + "CCAATGAGAA": 24999, + "CTAAGATCATG": 25000, + "TTTGTCAAA": 25001, + "GTTTTĊ": 25002, + "TCCTTTATG": 25003, + "CGTTCCA": 25004, + "CCATTGTAA": 25005, + "CGGATTTA": 25006, + "CTGTGGTAA": 25007, + "GCCTTGGGG": 25008, + "TGAGAGTG": 25009, + "GTCTGTTTTA": 25010, + "GAATGTAAA": 25011, + "CATTTTGAAA": 25012, + "TTATGTATG": 25013, + "CTTTATCTG": 25014, + "CTTTAGAAAA": 25015, + "CAAGGCAGA": 25016, + "TGACACCTT": 25017, + "GTGGACTGA": 25018, + "TCAGGAGATCAAGACCA": 25019, + "CCGCTCTG": 25020, + "GCGAGGTT": 25021, + "TAAGCACA": 25022, + "TGACTTGA": 25023, + "GAAGGAGA": 25024, + "TCCTGACTT": 25025, + "CTTGTTGTT": 25026, + "CTTGTATA": 25027, + "GTCGTAAA": 25028, + "GTCATGCC": 25029, + "TGGGGATGA": 25030, + "CGCGGCCA": 25031, + "CTGAATATG": 25032, + "TAAAAACATA": 25033, + "GGAAATATG": 25034, + "TCCTTCCCCA": 25035, + "CAATTGCA": 25036, + "GGAGGTAGG": 25037, + "CTGTGGAAAA": 25038, + "GCATTGTA": 25039, + "GTTTGCAGA": 25040, + "TGATGCATG": 25041, + "TCAAAACTATGAAA": 25042, + "TCATGCCTT": 25043, + "TATTAATATT": 25044, + "GCTCGTTA": 25045, + "CATCGCC": 25046, + "CTGCATGTT": 25047, + "GTTTAGTAA": 25048, + "TCACCCTGA": 25049, + "TGACAGAAAA": 25050, + "GTTCTAACTGTTGCTT": 25051, + "CTAACTGGTGTGAGATGGTA": 25052, + "TGAGCTGTG": 25053, + "CTGTAACC": 25054, + "GGAGTTGGA": 25055, + "GTATCTCTT": 25056, + "CAAACCCTG": 25057, + "CAAAGGGCA": 25058, + "GAGATACAA": 25059, + "GTGATTATT": 25060, + "CATGCCAAA": 25061, + "CATGCTAAA": 25062, + "GAGGGGGTG": 25063, + "CTCCTAGAA": 25064, + "GCCACTGCACTCCAGCCTGGG": 25065, + "CTTTTTATAA": 25066, + "CAAAAGCTG": 25067, + "GGGAGAAGG": 25068, + "CGTGGGGG": 25069, + "TAAAAACACA": 25070, + "GGCCTCAAA": 25071, + "GTGAATATA": 25072, + "CAATTTACA": 25073, + "CCTCCCACC": 25074, + "GTCTGCCA": 25075, + "GTGTGCAGG": 25076, + "GATCATTTG": 25077, + "AAATACACA": 25078, + "TCACACCAA": 25079, + "GCCCACCTT": 25080, + "GAGTCCAGGA": 25081, + "TCTTTTATTTT": 25082, + "GGCAGAGGCAGA": 25083, + "GGCAGGAGAATG": 25084, + "GAAACCATCATTCTCA": 25085, + "TGACAAGA": 25086, + "GTGGGAATG": 25087, + "GTTGGAGAA": 25088, + "GTTGTCAAA": 25089, + "GTTCCGG": 25090, + "GCATCAGA": 25091, + "CTATTTTAAA": 25092, + "TAAAATAAAATT": 25093, + "GCTGTCAAA": 25094, + "CTCACGCA": 25095, + "TCCACGGA": 25096, + "TTAATGCA": 25097, + "GGTGĊ": 25098, + "GGTGGTTTT": 25099, + "CGCTCGA": 25100, + "TTTTTTAAATG": 25101, + "TCCTTGAGTG": 25102, + "GGTATCTC": 25103, + "GCTCACACA": 25104, + "GGATTCTG": 25105, + "GCAGGTGTT": 25106, + "GGATGGAA": 25107, + "CCTAACAGAA": 25108, + "TCTGTGAAAA": 25109, + "CTTCCCTTA": 25110, + "GCAGACATT": 25111, + "CTGTATGAA": 25112, + "TGATGATTA": 25113, + "CCAGATGTG": 25114, + "CTACATATT": 25115, + "CCTGTTCCTG": 25116, + "TGTTTGTTTGTTTG": 25117, + "TTTTCCACAGTT": 25118, + "TCGATGTAGTGACACTA": 25119, + "CTTCACAGTAGG": 25120, + "CAGGGAAGCCTG": 25121, + "TACCTCAGCCTCC": 25122, + "GATCTCCTT": 25123, + "TCAGTTCTG": 25124, + "GTTGTCATT": 25125, + "GTTGTATA": 25126, + "GCACCAGCA": 25127, + "GAAACGTA": 25128, + "GAAAGCAAAA": 25129, + "TAAAACCTG": 25130, + "GCTGGGGTT": 25131, + "CTCAGACAA": 25132, + "GTCACATTA": 25133, + "GAGGGTCC": 25134, + "GATGCTGTT": 25135, + "CTTTTGACC": 25136, + "TTTATAAAAA": 25137, + "GACATCATT": 25138, + "CATATCTGA": 25139, + "TTATTCCAAA": 25140, + "GGCACCCA": 25141, + "TCTCACAGTT": 25142, + "CTGAACATG": 25143, + "CAGCAGAA": 25144, + "TCTTTTTTCA": 25145, + "TGATGCAGG": 25146, + "TATCATATG": 25147, + "GAGTGTTTG": 25148, + "TGAGGGTGAGTAAA": 25149, + "GAGTTCTA": 25150, + "CTGGGGACA": 25151, + "TCCCATCTA": 25152, + "GGAGGAGAAGGGGACGACAGA": 25153, + "GAATATCTCA": 25154, + "CAAGGCCAA": 25155, + "TAAGATGTT": 25156, + "GCTGTGTTA": 25157, + "GCTCATGCCTGTAATCCCAGCA": 25158, + "TCCTCCTCCTCCTCCTCCTCCTCC": 25159, + "CAAATAAATTCATTAAAAA": 25160, + "GAAATAAGTATTTGACCCC": 25161, + "CCGAAATT": 25162, + "GATCAAATG": 25163, + "GCGCTTAA": 25164, + "TAACTCATT": 25165, + "CAACGTCA": 25166, + "GTGCCCAGG": 25167, + "TAAAGGGCA": 25168, + "GAAAGAAGG": 25169, + "TCTGCCTTA": 25170, + "CAGAGGGCC": 25171, + "CATGCCCC": 25172, + "GTCAGGGTG": 25173, + "TCTCTCAAAA": 25174, + "CTTACATCA": 25175, + "CGACACTG": 25176, + "TGGATCTG": 25177, + "TAATTGAAAA": 25178, + "TCTCATGTG": 25179, + "TCTCCCTTTT": 25180, + "GGAAAGCAA": 25181, + "GAATTTCAAA": 25182, + "GTCTTCTCTT": 25183, + "GGAGGCCA": 25184, + "TCTAATCTA": 25185, + "TGAGGGGAA": 25186, + "GAAGACCTG": 25187, + "TAACATTTTTT": 25188, + "TATTTATGTT": 25189, + "GAAAAAAAAAAAAAAA": 25190, + "GTACAGCCA": 25191, + "CACGTGTG": 25192, + "CCGTAAAA": 25193, + "CCGCACC": 25194, + "CCCGCTA": 25195, + "TCACAAGA": 25196, + "TGACTGCTG": 25197, + "CAACGAAA": 25198, + "GTTGAATTA": 25199, + "GGACTTGA": 25200, + "CTCGCTAA": 25201, + "CTCTCGAA": 25202, + "GCAAGTAAA": 25203, + "GTCAGACTT": 25204, + "TATGCACAA": 25205, + "CTCCCCCTG": 25206, + "GTCCTACA": 25207, + "GTCCCACAA": 25208, + "TCCTTCTTCTT": 25209, + "TATTATATAA": 25210, + "GTTAACAA": 25211, + "TCTTAGATT": 25212, + "CGTACTG": 25213, + "TGAGACCA": 25214, + "GAATGGCTA": 25215, + "GTGGGGGAA": 25216, + "TCAGGCCTG": 25217, + "CTCAAACAA": 25218, + "TTTTTAGTTTT": 25219, + "CTCTCCAAAA": 25220, + 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25353, + "GTAGGTGTT": 25354, + "TCACAATTA": 25355, + "TGACAATTA": 25356, + "GACCTTAAA": 25357, + "CTTAAGCCAGTACATG": 25358, + "CCTCAAATT": 25359, + "GATTTTATTTT": 25360, + "GACGGAGG": 25361, + "TAATATATATA": 25362, + "TAAGGAGAA": 25363, + "TTTTCTATT": 25364, + "TCAGTTGA": 25365, + "CTGTGTGAA": 25366, + "CTTGCTATT": 25367, + "CCAGTGAAA": 25368, + "TGTGCTTCTT": 25369, + "TATTCACCA": 25370, + "GAGAGGTCA": 25371, + "CAGGGACCA": 25372, + "TCAAGAGCA": 25373, + "GCTGTACC": 25374, + "CTCAGATGA": 25375, + "GCAACAAAAA": 25376, + "CGGAAATG": 25377, + "TCTCTĊ": 25378, + "TCCACAACCTCAAACAGTCA": 25379, + "TCTAGGGTT": 25380, + "TAGACGTT": 25381, + "GGGATCAAA": 25382, + "CCTAGCCA": 25383, + "CTCTTCTCAA": 25384, + "TAAATTTGTT": 25385, + "GCAGAATTA": 25386, + "GCTTAAGCAGGGG": 25387, + "CTGAGGCAGGAGGA": 25388, + "TCTGCAATAGGTAAGCA": 25389, + "TACGTAAA": 25390, + "TGAGGACA": 25391, + "GTGGAAGA": 25392, + "GTGGTTGTT": 25393, + "GTGCCTCTG": 25394, + "GCCCACAAA": 25395, + "GAAAGTCTT": 25396, + "TCTTTATCC": 25397, + "GGAAGACCA": 25398, + "CTCACGTG": 25399, + "GAGGGTTTT": 25400, + "CGGTACA": 25401, + "CGGCAGTG": 25402, + "GTTACTAAA": 25403, + "CTAATCAAA": 25404, + "GGGGGAAAAA": 25405, + "CTGAAGGTG": 25406, + "TTTTTTTCAAA": 25407, + "TCTGATGTG": 25408, + "GTGAATATG": 25409, + "TGCCTAGG": 25410, + "CACTGTTAA": 25411, + "TCATGGTAA": 25412, + "GCGGTCC": 25413, + "GCAGGGACA": 25414, + "GAAGGCCTG": 25415, + "CCAGGCAGA": 25416, + "GAAATGCTTCTTA": 25417, + "GTTTACCAA": 25418, + "TAATTTTTTTG": 25419, + "GAACACCTG": 25420, + "CTCTACAAA": 25421, + "GTTCCTGAA": 25422, + "CCCGGAAA": 25423, + "CGTTTGCCCACTGA": 25424, + "CTATCCCAAAA": 25425, + "CCAAGAACACCA": 25426, + "CATCATGCTTTGGG": 25427, + "TGCCCTTGCTGATGGAA": 25428, + "CTTTGGGAGGCCGAGGCGG": 25429, + "TCACACATT": 25430, + "TGACTAGG": 25431, + "GAATAAAAAA": 25432, + "CAATCCCTG": 25433, + "GTGTCCACA": 25434, + "GCCGGCGG": 25435, + "GAAAGGTAA": 25436, + "TCAACCTCA": 25437, + "GCTTCATTA": 25438, + "TACATAATG": 25439, + "GATGGTCAA": 25440, + "GAAAACTAAA": 25441, + "CGAGCAAA": 25442, + "CAAAATACTA": 25443, + "TTCAGTAAA": 25444, + "GCAAAGTTA": 25445, + "TCCTTGATT": 25446, + "TCCTTTCTTTT": 25447, + "TAATACTCA": 25448, + "TATTACTTA": 25449, + "CTTTGCCCA": 25450, + "CGCCTCTG": 25451, + "CCATTTAAAA": 25452, + "AAATTTCAA": 25453, + "GCGGCATG": 25454, + "TCCCATATT": 25455, + "TAAGATTTTA": 25456, + "TGTGTGTGTGTGTGTGTGTGTGTGTG": 25457, + "GTATTTTGGCCCATT": 25458, + "GTGGGCAAACGTA": 25459, + "GCATTCTTTGTCCTCCAAA": 25460, + "GTGTGAGTG": 25461, + "GGATTTTTTTT": 25462, + "CCACCGG": 25463, + "CTACAGAGA": 25464, + "CTAGGGTA": 25465, + "CACACGAA": 25466, + "TCTTTCAAAA": 25467, + "CAGATCAAA": 25468, + "CTGAGTCAA": 25469, + "CTGACACAA": 25470, + "CCTTTAGAA": 25471, + "TATGTCTTA": 25472, + "CTAAAAAATG": 25473, + "GGGGCCCA": 25474, + "TAGATACTT": 25475, + "CGTGCAA": 25476, + "TCTGAGCAA": 25477, + "TCATTTCTCA": 25478, + "TCCAACAATT": 25479, + "GCTGAGACA": 25480, + "GTGTTGTTA": 25481, + "CGCCCCCC": 25482, + "CTCTGTCTCC": 25483, + "TTTTAATAAA": 25484, + "TAATGGATG": 25485, + "GGCTGTGTG": 25486, + "TGAGGGCTG": 25487, + "GATAATATT": 25488, + "CTTTAGTTTT": 25489, + "TCGAGGAA": 25490, + "GGAGCAGGTT": 25491, + "CTGTAAGTCTTCA": 25492, + "GACTACACACTTATTTCAAAAAAA": 25493, + "CTTTGGATGCAACTCA": 25494, + "TCGAGACAGGGTTTCTCTGTGTAGCTTTGCGCCTT": 25495, + "GCGAGAGAGA": 25496, + "TGATTTTAAA": 25497, + "CTTCATTTG": 25498, + "CTTCTCTCC": 25499, + "GTGGTTTCA": 25500, + "CTACAAGA": 25501, + "CTCTAACTT": 25502, + "GAAAGGAGG": 25503, + "GAAACAGC": 25504, + "GAGATCAAA": 25505, + "CATTCTTAAAA": 25506, + "TCTGGGGCA": 25507, + "TAAAATATTAA": 25508, + "TAAAATGTGA": 25509, + "GCAATAATT": 25510, + "CGGGGCTT": 25511, + "TACACTCAA": 25512, + "TCCAGGATT": 25513, + "TCCAGGAGAA": 25514, + "TTTAGACA": 25515, + "TGGATCTT": 25516, + "TGAAATGCA": 25517, + "TGATTGCAA": 25518, + "GCTGATGGG": 25519, + "TACCTGAGA": 25520, + "TAATGGCA": 25521, + "GTCTGTCATAGTT": 25522, + "TCATGCCAA": 25523, + "GCTCCCTCA": 25524, + "CTGAAATTTT": 25525, + "CTGTAGTAA": 25526, + "GTGGTGCGTG": 25527, + "GAAAAAAAAATT": 25528, + "CACACACACACACACACACACA": 25529, + "GGCGCGGTG": 25530, + "CCTATAATCCCAGCTA": 25531, + "CAGAGGAGCCTCTTAAA": 25532, + "TAGTTTTTG": 25533, + "GGGTTATG": 25534, + "TCGGGGTG": 25535, + "TGAGTTATT": 25536, + "TTACCTA": 25537, + "GTTGGAGGA": 25538, + "GCCTAGAAA": 25539, + "TATTTCAATT": 25540, + "TATTAAAAAAA": 25541, + "CAAAGGTCA": 25542, + "TGAAGACCA": 25543, + "TCTGGACTG": 25544, + "CTGAGAATT": 25545, + "CTGAGGGTG": 25546, + "GCTTGAGAA": 25547, + "GTGACTTAA": 25548, + "CATGTCAAA": 25549, + "GTCATCTCC": 25550, + "GTCAGATAA": 25551, + "TATGCTCC": 25552, + "TATGGCCA": 25553, + "GTTTTCTTGG": 25554, + "GTCCCTCTT": 25555, + "CGACCCCATGGA": 25556, + "TGGAGGAGG": 25557, + "TTCACAAAA": 25558, + "GTAAACATA": 25559, + "GCAAAGAAAAA": 25560, + "GCCAACAGG": 25561, + "GTGTTGAGG": 25562, + "CTCTGGATG": 25563, + "GCCTTGGTG": 25564, + "CAGGAGACA": 25565, + "GAGCTCTCA": 25566, + "GATGAGATT": 25567, + "GTGGGGTGA": 25568, + "AAATATCAA": 25569, + "AAATACTTA": 25570, + "TCAGAGCCTG": 25571, + "GGCTATTTT": 25572, + "GTTCAGTAA": 25573, + "CCACCCCTT": 25574, + "TGACCCCAA": 25575, + "CGCCAGGGTTGTGGGTT": 25576, + "CCCAGCCTGGTGCAGG": 25577, + "TGACCTGCAGAGAGCTGGGA": 25578, + "TAACTCCAA": 25579, + "TGATCTGTT": 25580, + "CTTTCCATG": 25581, + "GCATCTGTG": 25582, + "GCACCACCA": 25583, + "TAAACTATG": 25584, + "TATATAATATA": 25585, + "TCTTCCCTA": 25586, + "TAAAAGGTA": 25587, + "CTCAGGAGA": 25588, + "CATGCTCTT": 25589, + "GTCAGACTG": 25590, + "TCTACCCTG": 25591, + "GCCACCTCTT": 25592, + "CTTTTTACA": 25593, + "GATATAAAAA": 25594, + "TCCTTGGA": 25595, + "GTGAACACA": 25596, + "CTGTTTCCCCA": 25597, + "GCCAAGGTT": 25598, + "GTCAAGCTG": 25599, + "GCATTCAAA": 25600, + "GCCTTCAA": 25601, + "TGAGACCTT": 25602, + "GAGCAGGTG": 25603, + "CTTCATAAA": 25604, + "TATATACAAA": 25605, + "TCTATGAAAA": 25606, + "GTAATGTTA": 25607, + "GTGCCATTTG": 25608, + "GGTGTGAAA": 25609, + "GCCACCCTTTGCCTT": 25610, + "CCATCAGCAAGGGCATT": 25611, + "CCGTTTCA": 25612, + "TATTTTTTAAA": 25613, + "CTGTATAAA": 25614, + "GTTTAACA": 25615, + "GCACCTTTT": 25616, + "CTACCAAAA": 25617, + "TGTGTACC": 25618, + "TCTTGGATT": 25619, + "GAGAGGCAA": 25620, + "TCTGGCAAA": 25621, + "CTCACATCA": 25622, + "CTCAGACCA": 25623, + "CTCAGGGTT": 25624, + "GCAACCAAA": 25625, + "CCAATCAAA": 25626, + "CTAATAGTT": 25627, + "TACATACAAA": 25628, + "GAAAATACTG": 25629, + "GCCAGTAAA": 25630, + "GGGGĊ": 25631, + "GGGGTCAA": 25632, + "GCTATCAAA": 25633, + "CGTGTGAA": 25634, + "GCAAAGAGTT": 25635, + "TAAAAATACAA": 25636, + "TGAAACTG": 25637, + "TAATACAGG": 25638, + "CGCAGAA": 25639, + "TCTTTTTGTA": 25640, + "TACTATTTG": 25641, + "TATTTGCATA": 25642, + "CTTTAGCTT": 25643, + "GCGACGAA": 25644, + "TGATAGGAA": 25645, + "TCACTGAAAA": 25646, + "GTAGATGAA": 25647, + "GCAAAAATCCCAGAA": 25648, + "TCCCTCTGGG": 25649, + "CAACATAGTGA": 25650, + "GGAAATGGAAGACATACAA": 25651, + "GTCAGATGAAACCAAAA": 25652, + "TAACCTCTT": 25653, + "TAAGAAATA": 25654, + "TGACAATA": 25655, + "CAAGTTAAA": 25656, + "GTGTAAATT": 25657, + "GTTGGCTTAAA": 25658, + "CCATCTGTG": 25659, + "GTACAAGGA": 25660, + "TGTGCACAA": 25661, + "GAGACGTG": 25662, + "GAGATCTGA": 25663, + "TGAACATTA": 25664, + "CTCACCTCC": 25665, + "TATGTAAAAA": 25666, + "TATGTCATT": 25667, + "GAGGTCAGGA": 25668, + "CGGCTGAA": 25669, + "CCAACCTCA": 25670, + "TTTGĊ": 25671, + "CAAAATCC": 25672, + "GGGATAGTT": 25673, + "CCAAATGCA": 25674, + "TAATATCCA": 25675, + "TGCCTTTTA": 25676, + "TCCAACCTGA": 25677, + "TCTAACAAA": 25678, + "CGTACACA": 25679, + "TCATGTCTT": 25680, + "CAACACTAA": 25681, + "CAACAGAAAA": 25682, + "CTGCCCCTT": 25683, + "GTTGATATT": 25684, + "GGCAAAGGGTG": 25685, + "CTTTCTTAAA": 25686, + "CTCCTGACCTTGTGA": 25687, + "GCCCAGGCTGGAGTGCAATG": 25688, + "GATTTGCATTTTAATGAGG": 25689, + "GTTTGCTAGAGTG": 25690, + "GATGGAGGAGCCTG": 25691, + "TCAGTAATT": 25692, + "TGAGTGGA": 25693, + "GAATCACTT": 25694, + "CTTCAATA": 25695, + "GTGCTTCTG": 25696, + "GAAATAAAAAA": 25697, + "CAGAGTCTT": 25698, + "GCTGCCATT": 25699, + "CATGCTCAA": 25700, + "CATGGAGTT": 25701, + "GCAACGCA": 25702, + "GTAAGAGTT": 25703, + "CGGCATA": 25704, + "GATGCACC": 25705, + "TGGGTATA": 25706, + "TGGACTTA": 25707, + "TAATTGGTG": 25708, + "CCTATAATT": 25709, + "GCAAAGAGAA": 25710, + "GGCCCGC": 25711, + "GCTCACAGA": 25712, + "TCCAAAAATT": 25713, + "GAGAAGTGA": 25714, + "GTATTGTATT": 25715, + "TCTTTTCTA": 25716, + "TACCTTATT": 25717, + "CTCTGCACA": 25718, + "GCTAACAAA": 25719, + "GTGTGTATT": 25720, + "GCATGCAGG": 25721, + "TAAGACTGA": 25722, + "TATTTATAAAA": 25723, + "GCCATGCCCTCC": 25724, + "TGACTGTGTGGATCA": 25725, + "GTAATGTTTT": 25726, + "CTCCTGCCTCAGCCTCCCGA": 25727, + "CAAAAATGAAAATT": 25728, + "CAGACAGACAGACAGACAGACAGACAGACAGA": 25729, + "GAAGCATTTCAA": 25730, + "CACTAAATT": 25731, + "CTTCTGATT": 25732, + "GTGGTTGG": 25733, + "GTTGCAGCA": 25734, + "GGAGCTGTT": 25735, + "GTACCCTA": 25736, + "TGTGTTTTTT": 25737, + "TCTTGATTTT": 25738, + "GTGATCTGA": 25739, + "GCTGCACAA": 25740, + "TCTACTCTT": 25741, + "GGTTTACA": 25742, + "GGAAAGATG": 25743, + "GCTCAGTTTG": 25744, + "TATTATGTA": 25745, + "CTTAACAA": 25746, + "CAGAAGAGA": 25747, + "CGCCCCTG": 25748, + "GATTATCTT": 25749, + "TCGGGTTA": 25750, + "TCAGAGTTA": 25751, + "GGTAATTTG": 25752, + "GGAATTGAACAA": 25753, + "TAACAATTTT": 25754, + "CAAAAAACAAAAAA": 25755, + "GTCTGATGGG": 25756, + "TGAAATTAAAA": 25757, + "CAGATCACGAGG": 25758, + "GCCCAGCGACAGCC": 25759, + "GTTCATTTG": 25760, + "GTTCĊ": 25761, + "GTTGCTCTT": 25762, + "CCAGCCAGA": 25763, + "GTATCTATT": 25764, + "CTACTAAAA": 25765, + "TCTTCGTG": 25766, + "TGTTGTCAA": 25767, + "CATTGTAAAA": 25768, + "TCAATAGCA": 25769, + "GGAAGGTCA": 25770, + "GAGGCACC": 25771, + "TCTCGCTCTG": 25772, + "CTCCGCC": 25773, + "CTTTTGTAAA": 25774, + "GGGGTGCTG": 25775, + "CCCATCCCTG": 25776, + "TAGGĊ": 25777, + "TGAAAGAAAA": 25778, + "TACCCGA": 25779, + "CCATTCTCC": 25780, + "GAAAAAGAGA": 25781, + "GTGTGGGAA": 25782, + "TTATGGAAA": 25783, + "TTATGGAAAA": 25784, + "CCAGGTGAA": 25785, + "GAACAGAAAACCAAA": 25786, + "GCGAGTGA": 25787, + "TCACATAAA": 25788, + "TTACACTGA": 25789, + "TTTTTACATT": 25790, + "TGATCACAAGAA": 25791, + "GCCTCGAACTCACAGAGATCCGCCTGGCTCTGCCTCCCGA": 25792, + "GAGCAGGGGGACCTT": 25793, + "GAAAATGTGGCACATATACACCATGGAATACTA": 25794, + "CAAACAAGCAAGATTTCTG": 25795, + "CAAAGCCTACCATCAGTAA": 25796, + "GATCTCTA": 25797, + "GCGAATTA": 25798, + "TGATAAGAA": 25799, + "CCAGGAATT": 25800, + "GCTGGTTTTA": 25801, + "CCTTCCTCTT": 25802, + "GTAATACTT": 25803, + "CCAATACCA": 25804, + "GTTTTTGGG": 25805, + "GTTTTCATA": 25806, + "CGATACAA": 25807, + "TCTGATATG": 25808, + "TCATTTCCTT": 25809, + "CGAATGAA": 25810, + "CAGTGGTA": 25811, + "CTGTGCAGA": 25812, + "CTATTCTCA": 25813, + "TCATGGCTA": 25814, + "GTGTGGCTT": 25815, + "CAATGTAAA": 25816, + "GGATGCAGA": 25817, + "TCAGGGTCA": 25818, + "TCGATAAAA": 25819, + "GTTTAGTCA": 25820, + "CTGTATTTTA": 25821, + "GCACAGCCA": 25822, + "GACTCTTTA": 25823, + "TCTCTACTAAAAA": 25824, + "GGCTAATTTT": 25825, + "CCCTATTTAATAAATG": 25826, + "GGTCAGGCTGGTCTCAAA": 25827, + "TCGGAATG": 25828, + "TAACTGGAA": 25829, + "TTTTGTGTT": 25830, + "TTATAATA": 25831, + "CAAGTGTGA": 25832, + "GCATCAGCA": 25833, + "CTCGTCTT": 25834, + "TCTGTTTTTT": 25835, + "CATGGAATT": 25836, + "CCTGGGGCC": 25837, + "GATGGGGATT": 25838, + "TTATTCACA": 25839, + "CCTATCAAA": 25840, + "CTAAAGGA": 25841, + "TGATTGCTG": 25842, + "TCTTTTCCAA": 25843, + "CACAATATT": 25844, + "CATTTTTTTA": 25845, + "TACTGTTTG": 25846, + "CAATGGCTG": 25847, + "TCTGTGTTTG": 25848, + "CTTCTTCCTG": 25849, + "CAACAAGAGTGAAA": 25850, + "GGTAATTAA": 25851, + "TCACCCCAA": 25852, + "TAAGACTAA": 25853, + "CTCCTTGGAAGGAAA": 25854, + "CTGGATGTT": 25855, + "GCCCAGTCA": 25856, + "GCTTGGTTTGAAGAAA": 25857, + "TCTCCTCTAGAGCA": 25858, + "GACGGGGGAGCCTGGTGGGCTG": 25859, + "TAGAGGTG": 25860, + "TAATCTTTG": 25861, + "TCAGCAATT": 25862, + "TCCTTATTA": 25863, + "CTGGCTC": 25864, + "GCATCCACA": 25865, + "CTAGTGTG": 25866, + "TATGGACAA": 25867, + "GATTGAGCA": 25868, + "CCTGCTCTG": 25869, + "CTAAAAAATA": 25870, + "GATGGTGTT": 25871, + "CGTGGGA": 25872, + 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"GTATTCAGACCCCTT": 25960, + "GTCCCAGCTCTCTGCAGG": 25961, + "CCACAGGAA": 25962, + "TAAACTAAAA": 25963, + "TAAATAATAAA": 25964, + "TATATTATTTT": 25965, + "TGTTTATTAA": 25966, + "CAAACCATT": 25967, + "CAGGTCCA": 25968, + "TCTGGCTCTA": 25969, + "CATATAATA": 25970, + "GGCATAGG": 25971, + "TGCCTAAA": 25972, + "CACCCAAAA": 25973, + "GCTGAGCTT": 25974, + "GTTAAGAAAA": 25975, + "GTCTTTCTA": 25976, + "CTCAAGGTCA": 25977, + "CGGAGATG": 25978, + "TAATGTACA": 25979, + "CCCTGTGGG": 25980, + "GAATGTATT": 25981, + "GGCCATTTT": 25982, + "GCGGGGAA": 25983, + "TAAGACAGG": 25984, + "GAACAGTTA": 25985, + "CCACCACGCC": 25986, + "CAAGAGCTT": 25987, + "CAACATGTG": 25988, + "TGACCTGTG": 25989, + "TGTTTATTTTA": 25990, + "GAAGTTAAAA": 25991, + "CTTTGCAGAAAAA": 25992, + "TCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTT": 25993, + "CCGATC": 25994, + "GGTTTATTTT": 25995, + "GACGTTAA": 25996, + "TGATTTTTA": 25997, + "TGATTAAAAA": 25998, + "GTTGCTGAA": 25999, + "CCAGTTTCA": 26000, + "GTACACAAA": 26001, + "CTAGGATG": 26002, + "CTAGAGCC": 26003, + "GTCTGACTT": 26004, + "TATTTCCAAA": 26005, + "GAAACCCCA": 26006, + "TATACGAA": 26007, + "CAAATTTTAA": 26008, + "CAGGGAGTT": 26009, + "CAGATGATT": 26010, + "GGAAGAGGAA": 26011, + "TAAAAGTATT": 26012, + "CTGATATAA": 26013, + "GCAATATAA": 26014, + "GCAATAGAA": 26015, + "CCTGGGGTG": 26016, + "GTTTTCCCA": 26017, + "TTATTCCTG": 26018, + "CCTAGAATT": 26019, + "CCAAATATG": 26020, + "GGCCTAGA": 26021, + "TCTAAAAAAAA": 26022, + "GCCTTGGTT": 26023, + "GTGTGGAAA": 26024, + "CCTCGTCC": 26025, + "CATCGTCA": 26026, + "TCTCTCTCTCTCTT": 26027, + "TGTGTGCGTG": 26028, + "TCAGATAAA": 26029, + "TTATAGTCA": 26030, + "TAAGGTTTA": 26031, + "TGATGAGCATTTT": 26032, + "GGGTATTTT": 26033, + "TCATTTTAAAA": 26034, + "GTAAAAACTCAA": 26035, + "GAGAGAGAGAGAGAGAGAGA": 26036, + "CTCTTCTCTTCTCTTCTCTT": 26037, + "GTCTCAGGATACAAAA": 26038, + "CACTCCAAACTCCACTATG": 26039, + "TCCACTTCTGTGTTTG": 26040, + "TAATCTGTG": 26041, + "TAACCTTAA": 26042, + "TCATGTGAA": 26043, + "TGATCGA": 26044, + "GAAGTCAAA": 26045, + "CAATGTGAA": 26046, + "GTGTGAATT": 26047, + "GTTCTGCA": 26048, + "TATATAATAA": 26049, + "CATTCTTTG": 26050, + "CAGAGGAAAA": 26051, + "GGAATATCA": 26052, + "GCTGGAGTGCAGTG": 26053, + "CTCACGTA": 26054, + "CCTTTACCA": 26055, + "CATGGAGGA": 26056, + "GCAAGCTA": 26057, + "GATTCCATT": 26058, + "GATTTCCTG": 26059, + "CTAATCCA": 26060, + "GACAGGATT": 26061, + "TCAAAĊ": 26062, + "CCTATCCA": 26063, + "TCATTTCTAA": 26064, + "CTAAAGCCA": 26065, + "CGCAGATG": 26066, + "GTATTCTTA": 26067, + "GTGTTGTAA": 26068, + "CATCATTTA": 26069, + "GTGTGTAGG": 26070, + "GCTCCCTGA": 26071, + "CGTCCATT": 26072, + "CTGTACTTA": 26073, + "TATTTATCAA": 26074, + "TAAGAATGTT": 26075, + "GTTCTGGGATTTTTG": 26076, + "GCCTGGGAAA": 26077, + "GGGGCTGTCGCTGGGCAA": 26078, + "GTCATGCTGAAAGACCCA": 26079, + "CCCTĊ": 26080, + "TACCAAATT": 26081, + "TCATGTGTT": 26082, + "TCATCCCC": 26083, + "TGAGGAGAA": 26084, + "TGACTCAAA": 26085, + "GTTGCTCTG": 26086, + "CCAGCTGAA": 26087, + "CTCCAGGTG": 26088, + "GGAATAATT": 26089, + "GCAAGAGGA": 26090, + "GTTTTCCATT": 26091, + "TCTATCTTA": 26092, + "TAGACAGTA": 26093, + "TGCATCCA": 26094, + "GATAGTAAA": 26095, + "CGTTTGCA": 26096, + "TATTATGAA": 26097, + "CTTAACTG": 26098, + "GACAATTTG": 26099, + "CCATGCTCC": 26100, + "GCAGGGCA": 26101, + "CCCTAGAAGAAAA": 26102, + "CTTCCGACTT": 26103, + "CAAGGCTGA": 26104, + "TCACCCACA": 26105, + "TCATCATTTT": 26106, + "TCGTTAAAA": 26107, + "GACTCTGTA": 26108, + "TAATATGCTGTCTA": 26109, + "GCAACAGTGTGTGAAAA": 26110, + "GCTCTGGATAAGAGCGTCTGCTAAATGACTAAAATG": 26111, + "GCTGAAACTCCAGTACTTTGGCCA": 26112, + "CCCAAAATT": 26113, + "TAATCCACA": 26114, + "TTACTGAA": 26115, + "CTGTCTATG": 26116, + "GTGTATATG": 26117, + "GCAGCCATT": 26118, + "GGAGATTAA": 26119, + "CCAGGACA": 26120, + "TATAGAAAAA": 26121, + "GCTTGAGTT": 26122, + "CTCAGAGCC": 26123, + "GTCAGGCTT": 26124, + "TTTTAGTGA": 26125, + "TTAACCTT": 26126, + "CTTTTTAGG": 26127, + "TGGGGACC": 26128, + "CCTACCCAA": 26129, + "CGCGTAA": 26130, + "CTGAAGGGG": 26131, + "TCTGATCTGA": 26132, + "CCAAATGTA": 26133, + "CAGCAGAGA": 26134, + "GTATTGGAGCTT": 26135, + "CAAAAACATG": 26136, + "TAATGTTATT": 26137, + "TGAGACCAA": 26138, + "TTATGTTTTA": 26139, + "TAGCTCAAA": 26140, + "GGAGACATT": 26141, + "TTATAGAGA": 26142, + "CAACACCTT": 26143, + "CTGCCCATG": 26144, + "CCAGACAGA": 26145, + "TTACATGTG": 26146, + "GTCCATGTG": 26147, + "GAGATTATCAGTG": 26148, + "CATCTCCAA": 26149, + "TGACTTCTG": 26150, + "GTTCAAGAA": 26151, + "GTACAGTC": 26152, + "CTCTATTTG": 26153, + "TCTTCAAAAA": 26154, + "TCTGCACAA": 26155, + "TCTGCCCAA": 26156, + "GGAATATTTT": 26157, + "CGGCACCA": 26158, + "TAGATCCA": 26159, + "GGTTTCTC": 26160, + "GGGAGTTTT": 26161, + "GTAAATATAA": 26162, + "GAGAATACTA": 26163, + "TATTACAA": 26164, + "CAGCAGAAA": 26165, + "CAGTGCTGA": 26166, + "GGGAAAAAAAA": 26167, + "GAGCAGAAA": 26168, + "TTATGTAATG": 26169, + "CTGTACAAA": 26170, + "TCATAGTTA": 26171, + "TAAGGCAAA": 26172, + "CCACTGAAAA": 26173, + "GAACTTAAAA": 26174, + "GCAGAGACATTACTTTGCCAACAAA": 26175, + "TCATCTTTG": 26176, + "TTACGTA": 26177, + "TCCGTGTAAA": 26178, + "GTTGAATA": 26179, + "GTTGCATTA": 26180, + "GGATCACTT": 26181, + "GCCGAGTG": 26182, + "GTCTGAGCA": 26183, + "TATTGGCTT": 26184, + "TATAGTCAA": 26185, + "TATATTTTAAA": 26186, + "GGAACATGA": 26187, + "TATGCCAAA": 26188, + "GATTTCTC": 26189, + "CCTGTAAAA": 26190, + "CCTGCACTT": 26191, + "GGTGGAAAA": 26192, + "CGTGGTCA": 26193, + "GCAAAGCTT": 26194, + "GTGAACTG": 26195, + "CACCTCTGA": 26196, + "CAGCAGTGA": 26197, + "CAAAAATGTG": 26198, + "CAGCTCTTA": 26199, + "CATCACTA": 26200, + "CAGGAGGGG": 26201, + "TACTTCTA": 26202, + "GTCTGCTA": 26203, + "TCATGGAGG": 26204, + "GCGGCAGA": 26205, + "TCGGTGGG": 26206, + "TCCCACAAA": 26207, + "GAACACTCA": 26208, + "TCTAAATTTT": 26209, + "CCACCATGCCCGG": 26210, + "TCTCAATTTG": 26211, + "TTAATTCAAGATG": 26212, + "GTTCAGCAA": 26213, + "CTATATGAA": 26214, + "GTACAGTGA": 26215, + "CATCTTGTTCC": 26216, + "GGGGTTGGGG": 26217, + "CTGCTCTAGAGGAGA": 26218, + "CAATCCCAGCA": 26219, + "CTATGAACATAGTG": 26220, + "TCTCAGCTCACTGCAACCTCTGCCTCC": 26221, + "CAAGTAGCTGGGACTACAGG": 26222, + "AGAGA": 26223, + "TCAGTCAAA": 26224, + "TCACTCTGA": 26225, + "GTGCCAAAA": 26226, + "GCACTGCA": 26227, + "CCATATTAA": 26228, + "CCATGTGTT": 26229, + "TAAAGGCTG": 26230, + "GTCGAGTG": 26231, + "TATTGCTGA": 26232, + "GAAAGTAAAA": 26233, + "TCTTGCTGA": 26234, + "CAGGGACATG": 26235, + "TCTGGTCTT": 26236, + "GCTGGACCA": 26237, + "CATGTCTAAAA": 26238, + "TATGTGATT": 26239, + "TATGGATGA": 26240, + "GACAGCTCA": 26241, + "GGTTTCCTT": 26242, + "TTTTTTCTAA": 26243, + "GAATTGGA": 26244, + "GAGAACACA": 26245, + "CGCAGGGG": 26246, + "CAAAAAGTA": 26247, + "CAGCTCTCC": 26248, + "TTTTAATTAAA": 26249, + "GCCTTGCC": 26250, + "TGAGGCCTT": 26251, + "TTATGCATT": 26252, + "GGTCATTTG": 26253, + "TTATAGCTT": 26254, + "TAAGAATTTT": 26255, + "CTTGACCTT": 26256, + "CTAGGCCCC": 26257, + "CTCCTGGCCTCAAGTGA": 26258, + "GTATTTGATCCCCTGCTGATTTTGTA": 26259, + "GGTTTCATCTGACCATATGA": 26260, + "TAAGTTGAA": 26261, + "TAAGCTATT": 26262, + "TCCCTCAAA": 26263, + "GTGTAATAA": 26264, + "GCAGTTGTT": 26265, + "CCAGTGCTG": 26266, + "GTAGAAATG": 26267, + "TATTTCCATT": 26268, + "GAAAGTGAAA": 26269, + "GAGAGGGTT": 26270, + "CATTCCTCC": 26271, + "CAGGCACTT": 26272, + "GTAACTAAA": 26273, + "TATGTAGTG": 26274, + "GAGGGACTG": 26275, + "CCTGGGGTT": 26276, + "CCAAGTCTT": 26277, + "CCAATACC": 26278, + "TACATCTGA": 26279, + "TACATCCAA": 26280, + "CAAAATACCA": 26281, + "TCTCCTAAGCAA": 26282, + "CCAAATGTG": 26283, + "CCAAAGAAAA": 26284, + "GGCCCGGTT": 26285, + "GTGAACAAA": 26286, + "GCATTGTGA": 26287, + "GAGCGTGA": 26288, + "GGATTTAAA": 26289, + "TCATGTCAA": 26290, + "GATGATGAA": 26291, + "CTGGGCGG": 26292, + "GAGTATTTG": 26293, + "TAGCTCTAA": 26294, + "GTTGGCCTT": 26295, + "CTTTTTTCTTTT": 26296, + "TAAATAAATAAAAA": 26297, + "TACTGATAACAAGTTCAAA": 26298, + "GAGTCAGACATGACTGA": 26299, + "CTGGATCATCCAAATGCA": 26300, + "GAGCTGTCAAAGGACACCA": 26301, + "TACTCTCC": 26302, + "TAACTACA": 26303, + "CTGAAAAAAA": 26304, + "GCACCCTC": 26305, + "CCATCACA": 26306, + "TGTGTCTC": 26307, + "GAAATCTAA": 26308, + "CATGCCCCA": 26309, + "CATGTATGA": 26310, + "GTCACCCC": 26311, + "TATGGTC": 26312, + "CTCCCTCAA": 26313, + "TAGACAGTG": 26314, + "CCCCCTCTT": 26315, + "CATAGTTAA": 26316, + "TTCAGTCC": 26317, + "CGCTCATT": 26318, + "GAGAAGGTG": 26319, + "CGCACCTG": 26320, + "GGAGGCTA": 26321, + "AAATTCTA": 26322, + "TCATGCTGA": 26323, + "CAATGCATT": 26324, + "GACCACGA": 26325, + "GGCAATTTT": 26326, + "TAAGATCAA": 26327, + "TTATTTTTTTA": 26328, + "CAAGACAAAA": 26329, + "TCATTATTAA": 26330, + "GTAGGGGAGG": 26331, + "TGACCCTGA": 26332, + "GTCCATAGG": 26333, + "TAAGTAGGAAAA": 26334, + "GTTGTGTTA": 26335, + "GTCATGAAAA": 26336, + "TCGCACCAA": 26337, + "TGAGATGGAGTCTT": 26338, + "TCTCCCTCGATCTG": 26339, + "GGGTTAGGGTTAGGGTTAGGGTTA": 26340, + "TAGAGAGAGA": 26341, + "TCTGTGTGTGTGTG": 26342, + "TAACCCTC": 26343, + "TCATCACC": 26344, + "TGACAGATT": 26345, + "GAATTTTTG": 26346, + "CTTGCTTTG": 26347, + "GTTGTATTTT": 26348, + "CCACGCCCGG": 26349, + "CTCGCTCC": 26350, + "CACAGCAAAA": 26351, + "TCTGTAGGA": 26352, + "TATGCGTT": 26353, + "CGGCTTTT": 26354, + "GATGGGGTT": 26355, + "GGTTĊ": 26356, + "GCTACCTCA": 26357, + "TAATTTCTCA": 26358, + "TTATTTCTAA": 26359, + "TCTCATGTA": 26360, + "CGCTCCAA": 26361, + "CTCAACTG": 26362, + "CAGCTGATT": 26363, + "TTTTAATTTTA": 26364, + "GGATTCTCA": 26365, + "CCCTTCTCA": 26366, + "GGATGGATT": 26367, + "GCATGGCTG": 26368, + "CCAGGCATT": 26369, + "CCAGGGCTT": 26370, + "TCGGTGTT": 26371, + "AAATAGGAA": 26372, + "TCGATCAA": 26373, + "CAATAGTTA": 26374, + "TCCAGAGTGGTT": 26375, + "GTAAAGAATCCACCTG": 26376, + "CCTCTCTA": 26377, + "TCATAAATG": 26378, + "TTACACC": 26379, + "GAACCCTA": 26380, + "CTGGCAAAA": 26381, + "GTTCTGGA": 26382, + "GCATCAGTT": 26383, + "GTCCAGATT": 26384, + "TCTGGATTA": 26385, + "CTGATACTT": 26386, + "CATGGCTGA": 26387, + "CATGTATCA": 26388, + "GTCACTGTT": 26389, + "GTAATGGAA": 26390, + "GAGGGAAGG": 26391, + "TCTCGTTA": 26392, + "TCCATCCTG": 26393, + "TGCATCTA": 26394, + "GGGACTGAA": 26395, + "GGAAATATT": 26396, + "TGCCTCTGA": 26397, + "CAGCAGCC": 26398, + "TCTTAGGTCA": 26399, + "GTTTGTCAA": 26400, + "TGAGAGTA": 26401, + "GTCTGCAGG": 26402, + "CAATGGGAA": 26403, + "GCATGGCTT": 26404, + "CTAAAATTTT": 26405, + "CCAGGGGA": 26406, + "TATTTGCATT": 26407, + "CAACAACAAAAAA": 26408, + "CTTTATCCAA": 26409, + "TAACACTTTT": 26410, + "CAAGATATT": 26411, + "GTTGGTTTA": 26412, + "GATTAATTTT": 26413, + "TCCCCTCTT": 26414, + "CCAGCATGACAA": 26415, + "GAAGGAGAGA": 26416, + "GTTTGATTTT": 26417, + "TTAGGGTTAA": 26418, + "CAAACAACCCCA": 26419, + "GAACCCGGGAGGCGGAGGTT": 26420, + "CGGGGTTTCACCGTGTTAGCCAGGATGGTCTCGATCTCCTGACCTCGTGA": 26421, + "CAATGACAA": 26422, + "GCATGTGTG": 26423, + "CACAGAATG": 26424, + "TCTTTAGCA": 26425, + "TCTGGGGTG": 26426, + "GTGAGGCAA": 26427, + "CTCAGATAA": 26428, + "GTCACCATT": 26429, + "GTCATACCA": 26430, + "GTAAGAGCA": 26431, + "GTAACATTTT": 26432, + "TCTCGCTG": 26433, + "CTCCCCCAA": 26434, + "GGGGGCCA": 26435, + "CCCACTCTT": 26436, + "GGTGGCCA": 26437, + "CGCGGGA": 26438, + "GACCTATG": 26439, + "CTCTGTCTA": 26440, + "GCCTTCTA": 26441, + "CCTCAGGA": 26442, + "GCTCCTGAA": 26443, + "GACTATGCC": 26444, + "TCTCTCTCTCTCTG": 26445, + "GGGGAGAGA": 26446, + "CAAGGTTTG": 26447, + "TAATTTTTTTTTT": 26448, + "CAAGACAGG": 26449, + "CTTGAGCTT": 26450, + "GTTCCAGAAAAA": 26451, + "TCTCTTAATT": 26452, + "CTGTAGCCTTGTAGTATAGTT": 26453, + "CAGCCCCAAAGCATGA": 26454, + "GTACTGGAGTTTCAGCTTTAGCA": 26455, + "GTGTGACTGTTTGAGGTTGTGGA": 26456, + "CCGAGGAA": 26457, + "TCTAAAAATG": 26458, + "GACGCAAA": 26459, + "TAACTGATT": 26460, + "TTACAATT": 26461, + "TCCTCAGTT": 26462, + "CTTGTCATT": 26463, + "CTTGTTTAA": 26464, + "TAAAGAGTG": 26465, + "TAAACAGCTT": 26466, + "CACATAGTA": 26467, + "GAAACTCTT": 26468, + "GAAACACTA": 26469, + "CATTCAGGA": 26470, + "TCTGCATAA": 26471, + "CCTTCATTA": 26472, + "TCTCTCTGAA": 26473, + "TCCATACATGA": 26474, + "TAGACTGAA": 26475, + "GGTTTCTGA": 26476, + "CGCGGTA": 26477, + "GTAAATTTTA": 26478, + "TAAAAACTAA": 26479, + "GGAAACCAA": 26480, + "TAATAGTG": 26481, + "CTCTTGCC": 26482, + "CAGTTTGTG": 26483, + "GGATTTGTG": 26484, + "TGAGGCAA": 26485, + "CAATGTCAA": 26486, + "GCAGGCCAA": 26487, + "GATTTTTTTA": 26488, + "CATCCTTTG": 26489, + "TCAGGTGGG": 26490, + "TAGGAGAGG": 26491, + "TAATTATTTTA": 26492, + "CTGCAGTCC": 26493, + "TCGATGAA": 26494, + "CAAATGGAAGAA": 26495, + "CTCAAACTA": 26496, + "CAATACCAA": 26497, + "TGACATATA": 26498, + "TGACCTCTT": 26499, + "CTCATGGATT": 26500, + "CCGCTTTT": 26501, + "TAGGGGTG": 26502, + "TCGCAGGA": 26503, + "CCAGCAGA": 26504, + "TGTGTATCA": 26505, + "TATTGTATG": 26506, + "CAAATATTAA": 26507, + "CAAACCCAA": 26508, + "CATTGGCC": 26509, + "TCTGTCTCTT": 26510, + "TCTGGGGTT": 26511, + "CAGACGCC": 26512, + "TCAACAGCC": 26513, + "GCAACACTT": 26514, + "CCTGGAGCC": 26515, + "CCCACGAA": 26516, + "CATATCATT": 26517, + "CGCGCTA": 26518, + "TCTCCCTTA": 26519, + "CGTGCTGA": 26520, + "GCAAACATG": 26521, + "GTGAATATT": 26522, + "GCCTGCCAA": 26523, + "CTGGGGCC": 26524, + "TCGACGTT": 26525, + "GTTTAGGAA": 26526, + "CAAGATGTT": 26527, + "GCCCCGCC": 26528, + "GTGTATTTTA": 26529, + "TCACAACAA": 26530, + "GAACGAAA": 26531, + "GCAGTTGTG": 26532, + "CCACTTTCA": 26533, + "GAAAGGTGA": 26534, + "TATACATGTG": 26535, + "CAAAGTCTT": 26536, + "GAGACACTT": 26537, + "GTGACACTT": 26538, + "GCTGGCTCA": 26539, + "TATGTCCTT": 26540, + "TCTACCCTT": 26541, + "CTTTTTAATGA": 26542, + "CGAGTAAA": 26543, + "GACACCTA": 26544, + "CATACGTG": 26545, + "TAATTTAAATT": 26546, + "GATAGCC": 26547, + "CCAAACCTG": 26548, + "GCCAAGAGAA": 26549, + "GGAGGGCTT": 26550, + "GCTCGGCA": 26551, + "GATGATTTG": 26552, + "GCAGGGGA": 26553, + "GGCAAGAGAAA": 26554, + "CTTGGCTA": 26555, + "CTTGGTTAA": 26556, + "GTTTAGAGA": 26557, + "CTCTACCTT": 26558, + "CTCTACCTG": 26559, + "CCGGTTTG": 26560, + "TAAATACATG": 26561, + "GTAGGCAGA": 26562, + "CGTATTAAA": 26563, + "GAGTCCAAA": 26564, + "GCTCTCATG": 26565, + "CACGTTAA": 26566, + "TAACTTCC": 26567, + "TAACGTTTT": 26568, + "TCATCCAAA": 26569, + "TGACTATA": 26570, + "TCCTATC": 26571, + "TCCCTCTTA": 26572, + "CTTTCCTTA": 26573, + "GTTCACATT": 26574, + "GCAGTTAAA": 26575, + "CCATTATTA": 26576, + "CAAAGCTCA": 26577, + "TAAAAGGTG": 26578, + "GTGACATGA": 26579, + "CCTTTACTG": 26580, + "GAGGCGAA": 26581, + "TCTCTGAATT": 26582, + "TCCATAGCA": 26583, + "TTTGCTCTT": 26584, + "GCCAGTCAA": 26585, + "CGCGAAA": 26586, + "GGAAACTAA": 26587, + "GTGAAGATT": 26588, + "GTGAAGTTA": 26589, + "GTGAATTAAA": 26590, + "CAGCATTAA": 26591, + "GTTAATATTA": 26592, + "GACCĊ": 26593, + "TCTTTTTATA": 26594, + "CTCTGGCC": 26595, + "GCCTTTGAA": 26596, + "TAATGCATA": 26597, + "CCTCATTAA": 26598, + "CCCTTTGTTGGCAA": 26599, + "CAATGTATA": 26600, + "CAATGTCTG": 26601, + "CCATGTCTT": 26602, + "GCAGGGATT": 26603, + "TCCCAGAGATT": 26604, + "TAAGAGCTT": 26605, + "TAAGACAAAA": 26606, + "CAAGATAAA": 26607, + "CAAGAGTCA": 26608, + "CAACACTCA": 26609, + "TAAATACACA": 26610, + "TCAGTGAAAA": 26611, + "CGCAAGATCTCA": 26612, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 26613, + "GCTCAACATTCAGAAAA": 26614, + "GCTCTAGGAA": 26615, + "CCCCAAATTTA": 26616, + "TGACACAGGTCAAA": 26617, + "TGAGATCTTGCATGGA": 26618, + "GAGGAATGGGCCAAAATACCA": 26619, + "TCTTGCTTGTTTGTAGGTGACCAAA": 26620, + "GATTTTAAAAA": 26621, + "TCACGCTG": 26622, + "TCAGCAGAA": 26623, + "TCCTCCATG": 26624, + "TCCTGAGTG": 26625, + "GTTTATTTA": 26626, + "CCAGTTCTT": 26627, + "GCCTCACAA": 26628, + "CTCGACAA": 26629, + "TATTGGCC": 26630, + "CATTAAAATT": 26631, + "CATTCTAATT": 26632, + "TCTGGACAA": 26633, + "TCTGGACCA": 26634, + "CAGAGGTAA": 26635, + "TCAAGTGTT": 26636, + "TAAAACTTAA": 26637, + "CTGAGTCTT": 26638, + "CTAACCCC": 26639, + "GTTTTAAAAAA": 26640, + "GGTTGTCC": 26641, + "GGTGTCCA": 26642, + "CGTGGTGAAA": 26643, + "GGAAACACA": 26644, + "TGAAACAA": 26645, + "CGAAGGAA": 26646, + "TCTTTTCTTCTT": 26647, + "CCCTGGAGA": 26648, + "GTCTGTAGA": 26649, + "GCTAAGGTG": 26650, + "TATGAGTGA": 26651, + "GATCAGGAA": 26652, + "GTTTTATTTTA": 26653, + "CTTGGCAGG": 26654, + "TCAGACTCA": 26655, + "CAATACGAA": 26656, + "TGATATTATT": 26657, + "CCGGGATT": 26658, + "TCCCCCTCA": 26659, + "GTTCTGTTTT": 26660, + "GCTATTAAAA": 26661, + "GAGGTGGGGG": 26662, + "TAATGCTATCCCTCC": 26663, + "CACTCTGATGGTAGTT": 26664, + "CTATGGGGTTGAGATCTG": 26665, + "CATCTGATT": 26666, + "GAATCAGG": 26667, + "TCCGGTA": 26668, + "CAAGAAGA": 26669, + "CTGCTTTCA": 26670, + "GTCTTACA": 26671, + "TATTCCTTTT": 26672, + "CACAGTGTT": 26673, + "TGAATCTCA": 26674, + "TGAATTTTTT": 26675, + "TAAAATAGTA": 26676, + "GTAAGTGTT": 26677, + "GTTACAATT": 26678, + "TACATCTC": 26679, + "CTTACAGTG": 26680, + "GGTTGCCA": 26681, + "TCTCCTCCTCC": 26682, + "CGTGGATT": 26683, + "TCCTTGGAA": 26684, + "GAATTGTG": 26685, + "GAATTGAGA": 26686, + "CTGTTTAAA": 26687, + "GTTAACTA": 26688, + "GACCCTGAA": 26689, + "CGCACATG": 26690, + "GTATTCGA": 26691, + "CGGAGATT": 26692, + "CAAAAATATG": 26693, + "CTCTGGTGA": 26694, + "CAGGAGGTG": 26695, + "GAGTGTATG": 26696, + "TAGGATTTG": 26697, + "TCTGTTAAAA": 26698, + "GAATACTAA": 26699, + "CTTCATTTTA": 26700, + "TGATAGATG": 26701, + "GTGATTAAAA": 26702, + "CCGGCGCC": 26703, + "CTTGAAGAGA": 26704, + "TGATCAGTCTATAA": 26705, + "GGCTAAAAA": 26706, + "TCAGATTAA": 26707, + "CTGTCTGTG": 26708, + "GTTTGATTA": 26709, + "GCATAAATT": 26710, + "CCACCTTTT": 26711, + "GTACTAAAA": 26712, + "GTAGTTTTTT": 26713, + "TGAAGACTA": 26714, + "TGAAGGAAAA": 26715, + "TAAAAGCC": 26716, + "TAAAAGACAA": 26717, + "GCTGTCATT": 26718, + "GAAAACCTA": 26719, + "GTCCGCC": 26720, + "CTTTTĊ": 26721, + "GACAGTCAA": 26722, + "GGCAGTC": 26723, + "CTCTTGATT": 26724, + "TAATAATAATT": 26725, + "TATGATATA": 26726, + "GGGCTCTA": 26727, + "GAATAGAAAA": 26728, + "GTCTCCCAAA": 26729, + "CCAGTACCATGCTGTTTTGGTTA": 26730, + "TCCTTTGTAGGGACATG": 26731, + "AGAAA": 26732, + "CCGCAGCC": 26733, + "GGATAAATT": 26734, + "GGACTGCAA": 26735, + "GGACTTCCA": 26736, + "CCAGAAGA": 26737, + "CCACTTCC": 26738, + "TCTTGGGCA": 26739, + "TGTTCTCAA": 26740, + "CATTTAAATT": 26741, + "TCTGTCTGTG": 26742, + "CAGAGCTC": 26743, + "GCTTTACTG": 26744, + "CATGCGCCA": 26745, + "GCAAGATGA": 26746, + "GTTATCTGA": 26747, + "GCCAGAATG": 26748, + "GCAAAGGCA": 26749, + "TAAAAATTATT": 26750, + "TAATAGCTG": 26751, + "GGTACAAAA": 26752, + "TGATTGTCA": 26753, + "GCTCATTTA": 26754, + "GCTGATAAGCAA": 26755, + "GTTAATGGG": 26756, + "TCTTTTTAAA": 26757, + "TATAATTATT": 26758, + "CTCTGGGAGTT": 26759, + "TGATGTAGG": 26760, + "TGATGGTGTT": 26761, + "CAGGACAGG": 26762, + "TATCCTTAA": 26763, + "GGGCTAGA": 26764, + "TCGACCTG": 26765, + "GAATACAAAA": 26766, + "GCGAGGAA": 26767, + "CTATATCTA": 26768, + "TAACTATTTT": 26769, + "GATATTAAAA": 26770, + "CCTTCCTTCCTT": 26771, + "CGAATTAAA": 26772, + "GAAATCAGTCTG": 26773, + "CTCCTGCCTCAGCCTCCCAAGTA": 26774, + "TCAGCATGACAATGATCCCAAACACA": 26775, + "CCCTTTTAA": 26776, + "GGGTTTTAA": 26777, + "TAACGGAA": 26778, + "TAATCTATG": 26779, + "TCAGCAGTG": 26780, + "CTGCCAGA": 26781, + "CTTTGACA": 26782, + "CTTTGATTTT": 26783, + "GTTTCCCA": 26784, + "GGACTTTTA": 26785, + "GTCGCTAA": 26786, + "TGTGTAGAA": 26787, + "TGAAGATCA": 26788, + "TCTGGTAAA": 26789, + "CTGAGAGTG": 26790, + "CCTTGGTCA": 26791, + "TCTCGAAAA": 26792, + "GTTAGGGTTA": 26793, + "GCCAGTCTT": 26794, + "GCCAGTGTG": 26795, + "TTTTTTGTTA": 26796, + "GTGAAGTGA": 26797, + "CACCTAAA": 26798, + "TCCAAGTCA": 26799, + "CAGAAGATG": 26800, + "GTTAAGAGA": 26801, + "TCTTTTCAAA": 26802, + "CAGCGCTG": 26803, + "GGCTGGAAAA": 26804, + "CGTATGTT": 26805, + "TCAAAATAAA": 26806, + "CCTCCCACCA": 26807, + "TTATGTAAA": 26808, + "GCGGGTGCCTG": 26809, + "TAGCATTTA": 26810, + "TATCCTTTA": 26811, + "GTTGGGGAA": 26812, + "CTCTAGTGAA": 26813, + "CCGGTTTT": 26814, + "GTTCCAGACCA": 26815, + "CATATTAAAA": 26816, + "CTCATGCCTG": 26817, + "GATGGATGGATG": 26818, + "GTATTGGAGTTTCAGCTT": 26819, + "GTTATTCTGTCTCTCACTGTTCAAATAAA": 26820, + "TAGTCAAAA": 26821, + "GACGCCAA": 26822, + "GCGAACTG": 26823, + "GCTCCAATT": 26824, + "TGAGATTAA": 26825, + "TCCTGTTTG": 26826, + "GTGCAAGG": 26827, + "GGAGTTAAA": 26828, + "GTACAAATT": 26829, + "GTCTTTTTA": 26830, + "CATTCTCCCAATCCTCTT": 26831, + "CTCACCAAA": 26832, + "CTCAGAGC": 26833, + "GTAACATAA": 26834, + "GTTACCCA": 26835, + "CTAACGCA": 26836, + "CTAATACTG": 26837, + "TACATCTCA": 26838, + "TACACGC": 26839, + "CTTACCCC": 26840, + "GTCCTCCTT": 26841, + "GGGGCGGA": 26842, + "CCCACCTGA": 26843, + "TAGATATGA": 26844, + "TGGATTTTA": 26845, + "CGTGCGA": 26846, + "TAAAAAGATA": 26847, + "GGAAACATT": 26848, + "TATTAGAAA": 26849, + "CAATTTAATT": 26850, + "CAAAAATGTT": 26851, + "TTTTAATATT": 26852, + "CATTTTTTTTTT": 26853, + "GCTCGGCC": 26854, + "GCATGCCTG": 26855, + "TGTGAATTTT": 26856, + "TAAGAGCTG": 26857, + "TCCCAGGGCTGA": 26858, + "CTTTTTCACTCTCC": 26859, + "GCTAATTTTACAGAAAATT": 26860, + "GGTGATGGACAGGGAGGCCTGGCGTGCTG": 26861, + "CAGGTCTGTGAGAGCCAGAAA": 26862, + "CCTAAAATG": 26863, + "TGACĊ": 26864, + "TGAGCTCTT": 26865, + "TATTGGTCA": 26866, + "TGTTGGCTA": 26867, + "TGAAGCTTA": 26868, + "CAGGCGCC": 26869, + "CCCCGAGG": 26870, + "GTAAACCAA": 26871, + "GGAAACTA": 26872, + "GGAAAGTGA": 26873, + "GCCTTGCTT": 26874, + "GGCTGGATG": 26875, + "TGAGATGAGTG": 26876, + "TTTTTTTTTAAA": 26877, + "GAGCAGTGA": 26878, + "GGATTTCA": 26879, + "AAATGTATG": 26880, + "CTGGGCTA": 26881, + "AAATAGATG": 26882, + "CTTCATATA": 26883, + "CTGTAGAAA": 26884, + "CTGGACAGG": 26885, + "TCAGAATTTT": 26886, + "TCGCTCTC": 26887, + "GGCGGCGG": 26888, + "TCCCATGGACAGA": 26889, + "GAGATGGGGTTTCA": 26890, + "GCTGCATATGTATCAAAA": 26891, + "TACCTACTGTGAAGCATG": 26892, + "CCTATTAAA": 26893, + "TCGAGGTG": 26894, + "CAACACATT": 26895, + "CTTCCTTAA": 26896, + "CTTTCCTCA": 26897, + "GTGTCCCCA": 26898, + "GCACTGCC": 26899, + "GGACCCTCA": 26900, + "CCATATATT": 26901, + "CTCGTGTT": 26902, + "TGTGTGATT": 26903, + "CAAAGAATG": 26904, + "TAAAACGTT": 26905, + "GCAATGCTG": 26906, + "TTTTATAGTT": 26907, + "TGGGTAAA": 26908, + "TGTATGTGTG": 26909, + "TCAAATATCA": 26910, + "GGGAGCTGA": 26911, + "TAAAAACTTA": 26912, + "CTCTTGTAA": 26913, + "TACAATTCA": 26914, + "TCATGTATT": 26915, + "CTTGGGAAA": 26916, + "CTGCATCTT": 26917, + "CCCTATTTT": 26918, + "AAATACAAAA": 26919, + "TCATAGCAAA": 26920, + "TCCCCTCTCC": 26921, + "CTTGACATT": 26922, + "TCTATACCTT": 26923, + "CCACTGGACCA": 26924, + "TCTATTTGCCATGAA": 26925, + "CCATCCAGCCATCTCATCCTCTGTCGTCC": 26926, + "TCTTCAAACCAAGCTGCTTACCTATT": 26927, + "TAACGTGA": 26928, + "TAAGTAGG": 26929, + "CAACCCCATGGA": 26930, + "CTTCAAATG": 26931, + "GTGGCAGGA": 26932, + "GCATCTGAA": 26933, + "GCTGCATCC": 26934, + "CCAAGCTCA": 26935, + "GTCCTAGAA": 26936, + "CCCCGGTG": 26937, + "CACCGCATGTT": 26938, + "TCTTAGCTT": 26939, + "TCTTACTTA": 26940, + "CTTTGTAGG": 26941, + "CAGTGTTTA": 26942, + "CCCTTCCTG": 26943, + "GCTAACTA": 26944, + "CAATGTATG": 26945, + "TATATATATATG": 26946, + "GATGACCTG": 26947, + "TAGCATTAA": 26948, + "CTTTTATTTTA": 26949, + "GAAGACCTT": 26950, + "CCTTTTTTTG": 26951, + "TAACATAGA": 26952, + "CAATACTAA": 26953, + "CTCTAGATT": 26954, + "GTGCAGCCA": 26955, + "TGTTTACATT": 26956, + "TCCGCCCACC": 26957, + "GCCAGGATGGTCTCAA": 26958, + "TAGTCTGA": 26959, + "TACTGAATT": 26960, + "GAATGTGTT": 26961, + "GAAGĊ": 26962, + "CTTGGAATT": 26963, + "CTTTCCCAA": 26964, + "CTCTCATTTT": 26965, + "TGTGCCTA": 26966, + "TCTTAAAATT": 26967, + "GCTGGGCAA": 26968, + "GTCATATTTT": 26969, + "TACATAGTG": 26970, + "CTTACACTG": 26971, + "CTTACAGGA": 26972, + "CTTAGACTT": 26973, + "CTTTTTCATT": 26974, + "TGGGCGA": 26975, + "TGCAGCTCA": 26976, + "TCTCATTTTA": 26977, + "CGCACGA": 26978, + "GTGTTCTAA": 26979, + "TCTTTTTGTT": 26980, + "CCATTCACA": 26981, + "GTTTGGCC": 26982, + "GGGCTATA": 26983, + "CTTTAGTGA": 26984, + "TCACCTCAA": 26985, + "TTATATAATT": 26986, + "CCAGACGG": 26987, + "GTTCCCAGA": 26988, + "GCTTTGTTA": 26989, + "GCCCACTGA": 26990, + "CGTTTTTTA": 26991, + "GAAATAGAAAA": 26992, + "GCGTCTTTTAATT": 26993, + "TCATATGGTCAGATGAAACCAAAA": 26994, + "GTCACAGAGTTGAA": 26995, + "ACAAA": 26996, + "CTTCTAATT": 26997, + "CCAGTTGTG": 26998, + "GTAGTGGTT": 26999, + "TATACTATT": 27000, + "TGAAGGCTG": 27001, + "TCTGCCTCTG": 27002, + "CTGAGCCTT": 27003, + "GCTGTCTGA": 27004, + "GCTGTACTT": 27005, + "CCTTGGTGA": 27006, + "CATGGTGAA": 27007, + "TCTCTCCCCA": 27008, + "CTAAGTCACTT": 27009, + "TCTACTCAA": 27010, + "GGGGTAAA": 27011, + "TGGGGAGAA": 27012, + "GACAGAATT": 27013, + "GGTGCATCC": 27014, + "CATAGTCC": 27015, + "CGTTCAACTCACAGA": 27016, + "GCTGATTAA": 27017, + "GTTAATGCA": 27018, + "GTCTTCAGG": 27019, + "GGAGGGTCA": 27020, + "TATAATGTA": 27021, + "CAAAAATCA": 27022, + "TTTTAAGAAA": 27023, + "TCATGCAGA": 27024, + "GCATGCACA": 27025, + "TTATACTTTT": 27026, + "GTTAAATTTT": 27027, + "CTGGACACA": 27028, + "GCATATTTTA": 27029, + "CTGATGTTTT": 27030, + "TAACTGTTA": 27031, + "CAGTCACTCA": 27032, + "CAACAACAACAACAACAACAACAA": 27033, + "CCGTGGTG": 27034, + "GGGTAATG": 27035, + "GAACTCAAA": 27036, + "GTTGCTATT": 27037, + "GCACGAGG": 27038, + "CCATCCACA": 27039, + "GTCTCTATG": 27040, + "TCTTGATCA": 27041, + "TCAACAGTA": 27042, + "GCTTGAGCC": 27043, + "GCTGGAGCC": 27044, + "GCAATACC": 27045, + "GAGGCCCA": 27046, + "CGGGGTA": 27047, + "TCCACGC": 27048, + "TGGAGTCC": 27049, + "TAATTGTTTATG": 27050, + "TCTCAGTTTT": 27051, + "GATACTGAA": 27052, + "GTAAATACAA": 27053, + "GTGTTGATT": 27054, + "CAATTCGA": 27055, + "TCTAAGTG": 27056, + "CGGACAGA": 27057, + "CTATTGCTT": 27058, + "CCTCAGAA": 27059, + "CCTCCTGCA": 27060, + "GCTCCCAAA": 27061, + "GCGGCGGA": 27062, + "CCTCGACC": 27063, + "CACTATTTA": 27064, + "TAATCCAAAA": 27065, + "TTACAGCTG": 27066, + "TATATACATT": 27067, + "GAGATTAAAA": 27068, + "GTCATGTCCAA": 27069, + "GAGCTTAAA": 27070, + "GTTTCTGGTGTCCTT": 27071, + "CTGGGTATATACCCAAAGGATTA": 27072, + "TGTTGAATAGGAGTGGTGA": 27073, + "GGCCATAGTGGAGTTTGGA": 27074, + "CTGATGGTAGGCTTTGTTACTTTG": 27075, + "GTCCCAGCTCTCTGCAGGTCATTCA": 27076, + "CCGCGCA": 27077, + "TGACTCATT": 27078, + "CTTCCACCA": 27079, + "GTTTGAGGA": 27080, + "CTCGGCAA": 27081, + "GTCTCAAATT": 27082, + "TGTGGACTT": 27083, + "TATTTGATG": 27084, + "TATTCGTA": 27085, + "CAAAGTTTGGA": 27086, + "TGAACTTAA": 27087, + "TCTGGGTTA": 27088, + "TCAAGTTGTA": 27089, + "GTGAGGGGA": 27090, + "CTCATACTT": 27091, + "CATGCATGTG": 27092, + "GTAAGACTT": 27093, + "TATGTTATG": 27094, + "GAGGCCAAA": 27095, + "CGGGAGCA": 27096, + "TCTACGTT": 27097, + "GACACGAA": 27098, + "CATACGCA": 27099, + "CTCTTCCCTT": 27100, + "CTCAACCTT": 27101, + "CAGCGGTG": 27102, + "CCCTGGTGGCTCA": 27103, + "GGCCATTTG": 27104, + "CATCAAAAAA": 27105, + "TCAGGGTAA": 27106, + "CTTTAGCAA": 27107, + "GCCAGGGACTCAAA": 27108, + "GAACATAAA": 27109, + "GTCCATGTT": 27110, + "GAGATGGAGA": 27111, + "CTCATGTCCA": 27112, + "GAGTGGGTTGCCATT": 27113, + "GGTCGTC": 27114, + "TCGCAATG": 27115, + "TAACTTTTAA": 27116, + "CTGTGTGTT": 27117, + "CTTTATATG": 27118, + "GTGGAGGTT": 27119, + "CTCTCTATG": 27120, + "TATTGGTTA": 27121, + "TATACACC": 27122, + "CAAATAATAA": 27123, + "TCTGGAGTG": 27124, + "TCAAGCTCA": 27125, + "GGAATACC": 27126, + "TTTTACATTTT": 27127, + "GTTATAGTA": 27128, + "CCAATAATT": 27129, + "TACATGATT": 27130, + "TCTAGTTAA": 27131, + "TAGGTGTGA": 27132, + "GGTTGTAAA": 27133, + "TGTACCTA": 27134, + "TTCACTTTT": 27135, + "CGTGGAAA": 27136, + "GGCCĊ": 27137, + "TGCCTAGA": 27138, + "TATTAGTAA": 27139, + "GTGTTTAAA": 27140, + "GCCTGGTCC": 27141, + "TGAGAGCTT": 27142, + "TATCAGTAA": 27143, + "GAAGATCAA": 27144, + "AAATACATG": 27145, + "TCCCACCCA": 27146, + "CAAGGGCAA": 27147, + "GAACACGA": 27148, + "CAACATGTT": 27149, + "CCACATGTT": 27150, + "CCAGATCGAGG": 27151, + "GTAGATGTT": 27152, + "CTTCTATTTT": 27153, + "CCGTGCCTG": 27154, + "GTTTGTTTGTT": 27155, + "GCACATGTATACATATG": 27156, + "GTGGTCCAGTG": 27157, + "TCAGTATAAAAGACACCTG": 27158, + "CTGTAAGTCTTCACAAGGTTTT": 27159, + "CCCACACACA": 27160, + "GCGTAAAAA": 27161, + "TCACTGCAA": 27162, + "TGAGCAGAA": 27163, + "CAAGCGCC": 27164, + "GCATCAGAA": 27165, + "GTACAGGAA": 27166, + "GTCTCATTTT": 27167, + "TGTGCATCA": 27168, + "TATTCGC": 27169, + "CACACTGTT": 27170, + "TCAATCATT": 27171, + "TCAAGCTGA": 27172, + "TCAACAATT": 27173, + "GTGACCAAA": 27174, + "GCAATATGA": 27175, + "GATGCCCTG": 27176, + "GTTTTGCC": 27177, + "TTTACCCA": 27178, + "TAGATTTTTT": 27179, + "CCCCTAGTG": 27180, + "TGGAGCCA": 27181, + "GTAAACTAA": 27182, + "TCCTTGTTAA": 27183, + "TCCAACATT": 27184, + "GCCTGGTGA": 27185, + "CAGTTTGAA": 27186, + "TGTGTGTGTGTT": 27187, + "CGTCTTAA": 27188, + "GCAGCAGCAA": 27189, + "GTTTAGCTT": 27190, + "CTTCACTAA": 27191, + "TCACTTAAAA": 27192, + "CTCTACTGA": 27193, + "CCACTGCATT": 27194, + "GTTTAATTAA": 27195, + "CAACTGAAAA": 27196, + "GGAGGTGAAA": 27197, + "GTCGGCCATCACTG": 27198, + "GTAGACCTGCACCAGGCTGGGAAGACTGAA": 27199, + "CACGTCAA": 27200, + "GGCTTTTAA": 27201, + "GACAGGCTG": 27202, + "TAACTCAAAA": 27203, + "TCACGCTT": 27204, + "CTTTCGA": 27205, + "GTGGCCAAA": 27206, + "GTGGAAGGA": 27207, + "GTTTCCCAA": 27208, + "GCACTTAAA": 27209, + "CTCTAACAA": 27210, + "GTCGTTAA": 27211, + "GTCGACAA": 27212, + "GTCGACTA": 27213, + "GTCAAAAAAA": 27214, + "GTCTCCTTA": 27215, + "GAAAGGGTA": 27216, + "TATATGATT": 27217, + "CAAAGGTAA": 27218, + "GGAATGCTG": 27219, + "TAAAATCAAAA": 27220, + "CTGAGCCAA": 27221, + "CTGAGATCTT": 27222, + "GCTGTCCTT": 27223, + "CATGCATGA": 27224, + "CGGTCGG": 27225, + "GATTTGCTG": 27226, + "CCAAGACTT": 27227, + "CCAAGATCA": 27228, + "GTTTTGAAAA": 27229, + "GACAGCATT": 27230, + "TCTCACATT": 27231, + "CTGAAGAAAA": 27232, + "GGAAATGCA": 27233, + "CAGCACAGG": 27234, + "GTATTCCATT": 27235, + "TATAATGTT": 27236, + "GCATTGATT": 27237, + "GCATTGTTTT": 27238, + "GTCTGGGGG": 27239, + "TGAGGGTAA": 27240, + "GTGTGGTCA": 27241, + "GCGGGAGA": 27242, + "TCTCTGCAGCA": 27243, + "GGAGATAGA": 27244, + "TGTGTGCAAA": 27245, + "CTTCCCGA": 27246, + "TAACACCTT": 27247, + "CAAGAATTA": 27248, + "GTACACATATG": 27249, + "GCGTCTGCTAAATG": 27250, + "GAATGCTGAGTTGCATCCAAAGAACACCA": 27251, + "CCAAAGTAACAAAGCCTACCATCAGTAA": 27252, + "CATTCTGTAGGTTGCCTGTT": 27253, + "CACGTCTG": 27254, + "GGCGCTA": 27255, + "CAACCAGG": 27256, + "GCATTTTAAA": 27257, + "CTGAGAATG": 27258, + "GCTTGACC": 27259, + "TATGGTCC": 27260, + "GTTATAAAAA": 27261, + "GATGGGCTG": 27262, + "TTTGTAATT": 27263, + "TTATTTCTTTT": 27264, + "TTTTTTCGTT": 27265, + "TCCTTTCAAA": 27266, + "CAGAAGATA": 27267, + "CAGCTCATT": 27268, + "TACAATGCA": 27269, + "CAGTTCATG": 27270, + "CACAACGTG": 27271, + "GGATTCGAA": 27272, + "TCATGGTGA": 27273, + "TAGTTTATT": 27274, + "TCGGGGA": 27275, + "GGAGACCTT": 27276, + "GTTTACGCCATT": 27277, + "CAAGGGAAA": 27278, + "CTTGAGTCA": 27279, + "TAACCCAAA": 27280, + "CTTCACAGTAGGTATGGTGTT": 27281, + "CCGGAAAA": 27282, + "CCGAACC": 27283, + "TCACGAAA": 27284, + "TCAGCCATT": 27285, + "GTGTGTTTG": 27286, + "GGAGTAGG": 27287, + "GCCCAAAAA": 27288, + "TGTGCTTAA": 27289, + "TCTTTATAAA": 27290, + "CATTGCTCA": 27291, + "TCTGCATGA": 27292, + "TAAAACAATT": 27293, + "GCTGGCCTT": 27294, + "CTCACCTCA": 27295, + "GCAAGATCA": 27296, + "TATGTATAAA": 27297, + "GCCATCCAA": 27298, + "GGTTCCCC": 27299, + "CTAAATATGTCAAA": 27300, + "TGCCĊ": 27301, + "TGATTGTT": 27302, + "TCTTACCTG": 27303, + "CGCAGTCA": 27304, + "CTCTGCTCTG": 27305, + "CTATTCCTG": 27306, + "GCCTGGCCAACATG": 27307, + "GTCTGGATG": 27308, + "CTCCATCTA": 27309, + "GCGACAGA": 27310, + "TGACACGG": 27311, + "CTGGACAAA": 27312, + "TTACATACA": 27313, + "TTACATAGA": 27314, + "GTTCCCCTT": 27315, + "CCTCCTTCCATCAGCAAGGGCATT": 27316, + "CAAAGCTGTCATCAA": 27317, + "TACGTGAA": 27318, + "TAAGTAAAAA": 27319, + "TCAGCTGAA": 27320, + "TGATAACAA": 27321, + "TCCCACATT": 27322, + "CTTTCCTGA": 27323, + "TGAAGAATG": 27324, + "CTGAGGAGTG": 27325, + "CTCACAGGA": 27326, + "CCTTTAATCC": 27327, + "GCAAGGCTT": 27328, + "CCTGTCCCTG": 27329, + "TCTCCAGCTT": 27330, + "CTTAGAGTT": 27331, + "TTAATATCA": 27332, + "TCTAGACTT": 27333, + "GGGGGGAAA": 27334, + "CGAGCAAAA": 27335, + "CAAAAGAATT": 27336, + "TTCATCCA": 27337, + "CAGCAGGTG": 27338, + "GCTGATTTA": 27339, + "CTTTGGTCTT": 27340, + "TCTAAGTA": 27341, + "CTATTGTG": 27342, + "TGAGATAAA": 27343, + "GATGACGG": 27344, + "GAAGGCATT": 27345, + "GCATGGAGA": 27346, + "GGCAAGATA": 27347, + "GAGAGAGAGAGAA": 27348, + "GCAGAGTAA": 27349, + "CTGTAGTCA": 27350, + "CAAGATGTG": 27351, + "CAACATTTTA": 27352, + "TCATAGTCA": 27353, + "TATTTAGAAA": 27354, + "TATTTACTGA": 27355, + "CTTGACACA": 27356, + "GTTCAAAAAA": 27357, + "TACCTTAAA": 27358, + "CCCCATCTCTACTAAAAA": 27359, + "TAAAAATTGATTTTTTGATAATAAA": 27360, + "TATTAATGGCACCTGTTTGAACTTGTTA": 27361, + "CCTAGGTG": 27362, + "TTATGATT": 27363, + "CAATTATA": 27364, + "CTGTAATTTT": 27365, + "GTGGTTAAA": 27366, + "GCCCCACCA": 27367, + "CTATTAATT": 27368, + "TATTGACTA": 27369, + "CAGGCAGCC": 27370, + "CAGGTGGAA": 27371, + "TCAAGAGTA": 27372, + "TCAAAAATGTTA": 27373, + "GTGAGTGTG": 27374, + "GCTGGAATT": 27375, + "CTCACAGCC": 27376, + "TATGGAATT": 27377, + "TACAGCTTA": 27378, + "TGGAGAAATG": 27379, + "TGCACTAAA": 27380, + "CTCTTGTA": 27381, + "GTATTCCAA": 27382, + "TACCTAATG": 27383, + "CGGATCAGTC": 27384, + "AAATTTAAAA": 27385, + "CAGGAGAAA": 27386, + "TGAGGTTTA": 27387, + "CAATGCCTT": 27388, + "TATCGTAA": 27389, + "CAATACTTAA": 27390, + "TCACAGCCA": 27391, + "GTATACAAA": 27392, + "CGACAGAGCGAGA": 27393, + "CCAGGCCTCCCTG": 27394, + "CCCGTCCCTG": 27395, + "GTTCAACCATTGTGGAA": 27396, + "TCATTTTTTTT": 27397, + "TCATGATGA": 27398, + "TCACTCTCC": 27399, + "TCATAATCA": 27400, + "TCCTCACAA": 27401, + "GTTGTAATT": 27402, + "GCACAAATT": 27403, + "CTATAGC": 27404, + "CTAGAGCTG": 27405, + "CTGATATGA": 27406, + "CTCATCCTT": 27407, + "TTTTACCTG": 27408, + "GTTAGGCA": 27409, + "TCTAGAGCA": 27410, + "CAAAATACTT": 27411, + "CCCCTCAAA": 27412, + "CGCGAGA": 27413, + "CTGAATGCA": 27414, + "TCCAAGTAA": 27415, + "TCTTACTAA": 27416, + "CGAAGGTT": 27417, + "TATAACTTTT": 27418, + "CTGTGTCTA": 27419, + "CCCCAGAGG": 27420, + "CATAAGAA": 27421, + "GAAGAAGAA": 27422, + "GTTTAGAAAA": 27423, + "CAAATGCTG": 27424, + "TGATATCTT": 27425, + "TCATTATTTG": 27426, + "GCTATACAGAGAAA": 27427, + "GTGGTTAAAA": 27428, + "TCTCTGTGAGTTCAAGGCCA": 27429, + "TCAACTGTGGGAGCAATTATTA": 27430, + "CTCTTACCTGCTTGATT": 27431, + "CACTTAATT": 27432, + "CCGTGCTG": 27433, + "TATCTCTA": 27434, + "GGCAAAATG": 27435, + "GATAAAATA": 27436, + "GCGAAGTT": 27437, + "TCCCGCTG": 27438, + "CAACGCTG": 27439, + "CTTGGAGGA": 27440, + "CTTTGAAAAA": 27441, + "GTTGAACA": 27442, + "GCATGTGAA": 27443, + "GCAGTTCA": 27444, + "CTATĊ": 27445, + "TAAAGAGC": 27446, + "CTCTTTTATT": 27447, + "TGTTGACAA": 27448, + "GAGACAGTA": 27449, + "GAGATAGAAA": 27450, + "GCTTCGTT": 27451, + "GCTGCTAAA": 27452, + "CTAACCAGTA": 27453, + "GATGGGGCA": 27454, + "GGTGGAGTG": 27455, + "GGTGGTGGTG": 27456, + "CCTACCCTT": 27457, + "TCTCAGGGA": 27458, + "CCAAACAAAA": 27459, + "AAAAAACAAAA": 27460, + "GAGAATTTA": 27461, + "GTCTTGGAA": 27462, + "CCATTCCCA": 27463, + "TCTAAGGAA": 27464, + "TTTTAATATA": 27465, + "TATTAATATTA": 27466, + "CATAATAAAA": 27467, + "TAGCACTC": 27468, + "CTGGGCAGA": 27469, + "TCAGGCTCC": 27470, + "TAAAAAATATA": 27471, + "GGGTGTTA": 27472, + "CTTTAGTAAA": 27473, + "TCATACATG": 27474, + "GCACATGAA": 27475, + "CTGAGAATGATGGTTTCCA": 27476, + "CAAAGTATTGAGAAA": 27477, + "GTGTGTGTGTGTGTG": 27478, + "GTTAAAAAAAA": 27479, + "CCAGTCCA": 27480, + "GCCGACTT": 27481, + "CAAACCCTT": 27482, + "CTGACGTG": 27483, + "GCTGTAGAA": 27484, + "GCAAGAGTG": 27485, + "GTAAGCCA": 27486, + "TATGCACTT": 27487, + "GAGGTCAAA": 27488, + "CCTGCTGTG": 27489, + "TACACCATG": 27490, + "TACATATGTG": 27491, + "GGTGTAGA": 27492, + "TGTAGCCA": 27493, + "TTATTGTTAA": 27494, + "TTCATCTA": 27495, + "GCAAAGGTG": 27496, + "TCATTĊ": 27497, + "GGAAATTTA": 27498, + "GCTCAGTCC": 27499, + "CTCTTCAGG": 27500, + "CAGAATATA": 27501, + "GTTAATAAAA": 27502, + "GCCAAGAGA": 27503, + "CAATTGAGA": 27504, + "TGATGCCTG": 27505, + "GCTCTAATT": 27506, + "GATGATTAA": 27507, + "GTGGGGACA": 27508, + "TCGGGGAA": 27509, + "GGGGATGGG": 27510, + "GGGCTCTGA": 27511, + "TCAGATCTT": 27512, + "GAAATGGAA": 27513, + "TCTTTGGGATT": 27514, + "TCGACCAA": 27515, + "TAACAGTTTT": 27516, + "GTTGGCAAA": 27517, + "CCACAGTTA": 27518, + "TATATATGTT": 27519, + "TCCCCATCAA": 27520, + "TAATGATTTT": 27521, + "GCGCCACTGCACTCCAGCCTGGG": 27522, + "CCTCTCCTG": 27523, + "TGATCTGTG": 27524, + "TCCTCTAAA": 27525, + "CTTCTCTTA": 27526, + "GTTGCAGA": 27527, + "GTTCAGATT": 27528, + "GCATAATAA": 27529, + "GCAGCGCA": 27530, + "CCATCTGTT": 27531, + "TATTCTAAAA": 27532, + "CTGACTGTT": 27533, + "CCTTGGCAA": 27534, + "TATGCGTG": 27535, + "TCTCTAATG": 27536, + "CAAAAGGTT": 27537, + "TAGGCTTTT": 27538, + "GCTACGTG": 27539, + "TTATTGTGAA": 27540, + "GCAAACCAA": 27541, + "TCCTTGTTA": 27542, + "TCTTATGAA": 27543, + "CGAAGGA": 27544, + "TATAAGTAA": 27545, + "CCATTAAAAAA": 27546, + "TCTAACTTA": 27547, + "CAAAAAGAAAA": 27548, + "GTTTGCGG": 27549, + "GTCTGGGA": 27550, + "TGAGGGTCA": 27551, + "TTATGCAA": 27552, + "GAAGGTGTG": 27553, + "GACCAGGTT": 27554, + "TTATTATTTTA": 27555, + "TCTGTTTCTCA": 27556, + "GTTCTGTTA": 27557, + "GTTGAATCACATG": 27558, + "GTGGCACGTG": 27559, + "TGTGTTTGGGATCATT": 27560, + "TACTTTTTTCCCTCA": 27561, + "CCTTGTGAAGACTTACAGAAAA": 27562, + "TGTGTCATTGCCAACAAA": 27563, + "GTTTCTCAATACTTTGTTATATA": 27564, + "CATAAAATA": 27565, + "CCGGAGA": 27566, + "TCGAAATTA": 27567, + "TCTATTAATT": 27568, + "TAACTGGA": 27569, + "TGACTCTGA": 27570, + "TCCCGGAA": 27571, + "TCCCAGGTT": 27572, + "GTTTGAAAAA": 27573, + "GCCCTAAAA": 27574, + "TGTGAAAAAA": 27575, + "CATTGACC": 27576, + "CATTGTTTTA": 27577, + "TCAACTGTT": 27578, + "TCAAGGCTT": 27579, + "CATGCCTCA": 27580, + "GCAAGGCAA": 27581, + "TATGGATTA": 27582, + "GATTCACC": 27583, + "TCTACGAA": 27584, + "GGGGCACCA": 27585, + "CAAAATCTC": 27586, + "TAGGTTTTTT": 27587, + "TAAAAACTCA": 27588, + "CTAAATACTT": 27589, + "TGATTCTTTT": 27590, + "TATAAGAA": 27591, + "CACTGGAA": 27592, + "CAGGAGCTG": 27593, + "TTTTTTTTAAAA": 27594, + "GGCCAGACTG": 27595, + "CCATGGACA": 27596, + "CAAGGTCAA": 27597, + "TGATAGAGA": 27598, + "GTACATGAA": 27599, + "GCGGATCACAAGGTCAGGAGATCGAGA": 27600, + "TTTTATGTTTT": 27601, + "CCGTTCC": 27602, + "TAAGCCACA": 27603, + "GTTGGAGA": 27604, + "GTTCTCTCA": 27605, + "GCAGTAGG": 27606, + "CCATAAATT": 27607, + "GCCTCCATT": 27608, + "CTCTGGGTG": 27609, + "TCTTGATAA": 27610, + "GCTTGCTA": 27611, + "CATGGTCTT": 27612, + "CATGGGGAA": 27613, + "CTAAGTTAA": 27614, + "GATGTCCTT": 27615, + "GTTTTCAGG": 27616, + "GTCCCGTG": 27617, + "CGATTTTTT": 27618, + "TCATTCAGG": 27619, + "GAGAAAAATT": 27620, + "GCATTCCAA": 27621, + "GTCTGGTCTG": 27622, + "GCGGGTGA": 27623, + "CATCCTTCA": 27624, + "TAATTATATA": 27625, + "CTCCAĊ": 27626, + "TCCATTTCTTCTA": 27627, + "CAATTACTTTT": 27628, + "TTTTTTATTTA": 27629, + "TAAGCAGAGTT": 27630, + "TATTTTATTTT": 27631, + "CTGTTTTTTCACGATTTATTA": 27632, + "CCTCTCAAA": 27633, + "TAGATGAAA": 27634, + "TCGAGATG": 27635, + "TCAGCGTG": 27636, + "TTACACCA": 27637, + "CTGCAGGAA": 27638, + "CTTGTTGAA": 27639, + "GTGCTTCC": 27640, + "GTGTCCCTG": 27641, + "GTTCCATA": 27642, + "GCAGAATA": 27643, + "GCACTTTCA": 27644, + "GCACCCAGG": 27645, + "GTAGTTAAA": 27646, + "CTATCTCTT": 27647, + "TAAATATATATA": 27648, + "TAAACCTGA": 27649, + "GTCTAATTA": 27650, + "CACATTTTTT": 27651, + "CACACATTTT": 27652, + "CATTGATAA": 27653, + "CAGGGGCCA": 27654, + "GCTTCCATT": 27655, + "GTCATGGAA": 27656, + "GTAACCCA": 27657, + "TCTCGGGG": 27658, + "CTCCCTGTG": 27659, + "CTAATCCAA": 27660, + "CTAAGAAAAA": 27661, + "TCCACCATT": 27662, + "GATGGACATT": 27663, + "TTAATACA": 27664, + "GTTTTTGCA": 27665, + "CTTTTGACA": 27666, + "CGAGTGAA": 27667, + "TAATTTAATA": 27668, + "CGCTGATG": 27669, + "GGTATTATT": 27670, + "AAAAAATTTA": 27671, + "GAGAAGCTG": 27672, + "CTGTTGAAA": 27673, + "CAGAAGTGA": 27674, + "TATTTTTATG": 27675, + "GTGTTCATG": 27676, + "CTCAAGATT": 27677, + "GGAGGCGA": 27678, + "TACCTAAAA": 27679, + "TACCCCCA": 27680, + "TAGTTTGTT": 27681, + "CAATGTATT": 27682, + "CTGCACAAA": 27683, + "AAATATTATT": 27684, + "TAACATTTTAA": 27685, + "CTCCTGCCTCAGCCTCCCAAGTAGCTGGGATTACAGG": 27686, + "GCCTTGAACTCACAGAGA": 27687, + "GCGCTGCTG": 27688, + "GCTTTTAAAAA": 27689, + "TAACCACA": 27690, + "TCACTCATT": 27691, + "TGAGTTCA": 27692, + "TCCTTATTTT": 27693, + "GCAGCGTT": 27694, + "GTAGAGATG": 27695, + "CTACTGTA": 27696, + "GTCGGAAA": 27697, + "GTCGTCAA": 27698, + "TGTGTGAATT": 27699, + "CACAGAGTA": 27700, + "TCTTCATC": 27701, + "TCTTTCCATT": 27702, + "TCTGCGCA": 27703, + "TCTGCAATT": 27704, + "GCTTGGGAA": 27705, + "CTAAGACTT": 27706, + "CTAATGGAA": 27707, + "TCTAGATAA": 27708, + "GTCCTTTTA": 27709, + "CAAAATAGAA": 27710, + "TGGGTGAAA": 27711, + "TAGATAGTA": 27712, + "GCTCACAGG": 27713, + "CAGCACCA": 27714, + "CTCTTTAAA": 27715, + "GACCTATA": 27716, + "GTATTGTCA": 27717, + "GTCAAGATT": 27718, + "GCATTGTAA": 27719, + "CCTCAGAAAATT": 27720, + "GAGTTTGAA": 27721, + "GCTAAGAA": 27722, + "TACTGTATG": 27723, + "GAAGGTTAA": 27724, + "GGTCACACA": 27725, + "GTGGGTGTT": 27726, + "GACTATTAA": 27727, + "TCACAGAAAA": 27728, + "TCCTCCTGCCTCA": 27729, + "GTGGAGTGA": 27730, + "GTATATTATT": 27731, + "CTGATGTTA": 27732, + "GATTTGTTA": 27733, + "CATAAATTA": 27734, + "GTGCTGAGATTA": 27735, + "GCTCTCCAAA": 27736, + "GCTGTTTTTCTGCAAA": 27737, + "CTCCTGCCTCAGCCTCCCGAGTAGCTGGGATTACAGG": 27738, + "TCGTTTTATGGGTTTT": 27739, + "CTGCACTCCAGCCTGGGCGACAGAGCGAGA": 27740, + "CCGCAGCA": 27741, + "GCGAACTT": 27742, + "TAACGAGA": 27743, + "TAAGTTGG": 27744, + "TCACGAGA": 27745, + "CAACCAGA": 27746, + "GCCGAGCC": 27747, + "GCCGCGC": 27748, + "CTACTTGG": 27749, + "CTCGATTTT": 27750, + "CTCTCGCC": 27751, + "GTCGGGC": 27752, + "GTCTTAAAAA": 27753, + "GAAATAGGA": 27754, + "GAAAGGGAAA": 27755, + "TGTTTCGG": 27756, + "GGAACACCA": 27757, + "GCTTTCTCC": 27758, + "CTCAGGCAA": 27759, + "CCTTGAGCA": 27760, + "CCTGGAATG": 27761, + "GTTAGTAAA": 27762, + "GTTATATGA": 27763, + "GATGTCAAA": 27764, + "GTTTTTGGA": 27765, + "GTCCTAAAA": 27766, + "CGAGCCAA": 27767, + "CCCAGCATG": 27768, + "TGGGTAGA": 27769, + "GCTATAAAAA": 27770, + "TCTCAGGTGA": 27771, + "GGTAĊ": 27772, + "CTCTTGTGA": 27773, + "GTGTTGAGA": 27774, + "TCTAAGATT": 27775, + "CAAAAATGTA": 27776, + "CTATTGCAA": 27777, + "TCAAAATATG": 27778, + "CCATGGCTG": 27779, + "GAAGGGATT": 27780, + "TGATACTGA": 27781, + "TAACCATTA": 27782, + "TCACTATGTT": 27783, + "GAATTTGCAAGTG": 27784, + "TTTATAACATTTT": 27785, + "CATTTGGATGATCCAGAA": 27786, + "GAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAA": 27787, + "GGCAGAGGAACCAGAGA": 27788, + "TTTTCTCAA": 27789, + "TCACATTTG": 27790, + "CTGGCTGTG": 27791, + "CTGGATTAA": 27792, + "GCACGTGA": 27793, + "GTACCAGG": 27794, + "GCCTCCCA": 27795, + "GCCTGAGGA": 27796, + "TAAAGGGGA": 27797, + "CTCTTTTTA": 27798, + "CACATACTA": 27799, + "CAAAGGATT": 27800, + "CAAAGGTGA": 27801, + "CATTGACTG": 27802, + "CATTCCCAA": 27803, + "TCTGGGGGA": 27804, + "CCTTCACAA": 27805, + "GTTTTCCAAA": 27806, + "GGCACAAAA": 27807, + "CGCGACA": 27808, + "GAATTGGTT": 27809, + "AAAAAAGAA": 27810, + "GCTCAGATT": 27811, + "GGAGGGTA": 27812, + "TACCCAAGATA": 27813, + "TCTAATCAA": 27814, + "TCCTGGTGA": 27815, + "GGCTGGGCTG": 27816, + "GAGGACAGA": 27817, + "CCCCAGGA": 27818, + "TACTTTCAA": 27819, + "GCTCCTTAA": 27820, + "GCAGGCTTCTT": 27821, + "TCGGCGCC": 27822, + "TCTGTGTCTGTG": 27823, + "CCTTTTTTTTTT": 27824, + "GTTAAAGTACATA": 27825, + "TCGAATTTT": 27826, + "CTTGATGAA": 27827, + "TAATGACCA": 27828, + "TCTCCTTAAA": 27829, + "GAACCAGTCTGTTGTT": 27830, + "CTACTCTACTG": 27831, + "CGATTAATT": 27832, + "CCTGGGAGGCGGAGCTT": 27833, + "GCAATCTACAGATT": 27834, + "TCTCACGATACATG": 27835, + "CCGTGGA": 27836, + "TAACAGATT": 27837, + "TGACCCTC": 27838, + "TGAGTCAAA": 27839, + "CTTGCAGA": 27840, + "CTTCTATA": 27841, + "GTGTAATTA": 27842, + "GTGTCCCC": 27843, + "GCACTTTA": 27844, + "GCAGTCCTT": 27845, + "TAAACAGAAA": 27846, + "GTCTCGTG": 27847, + "TATACGTG": 27848, + "TATATCAAAA": 27849, + "TCTTCTTGG": 27850, + "CAAACCAAAA": 27851, + "GAGATGAGG": 27852, + "CAGGCATAA": 27853, + "CATGCATAA": 27854, + "CGGCATC": 27855, + "TCTCTGTGAA": 27856, + "CCAAGTAAA": 27857, + "CTAATGCC": 27858, + "GGGGTAAATTTG": 27859, + "CATAGTATT": 27860, + "GCTACTCTT": 27861, + "TGAAATGTA": 27862, + "TGATTGGA": 27863, + "GCCTGCTCC": 27864, + "TATCATATA": 27865, + "GTCTGCAA": 27866, + "TCATGGTTA": 27867, + "GTGTGTAGA": 27868, + "CCATGGTA": 27869, + "CCTGAGATG": 27870, + "CTGGGCTCC": 27871, + "CTCCACGCA": 27872, + "AAATAGTAA": 27873, + "TCAGACCTG": 27874, + "TAAGATATG": 27875, + "GCGACGCA": 27876, + "CAAGACTGA": 27877, + "TATTTACTTTT": 27878, + "CCACCCTGA": 27879, + "GAAGGAGTTACAGAGA": 27880, + "CATTTACTTA": 27881, + "CTTATGAAAA": 27882, + "GACCTGAAAA": 27883, + "GACTCTGTCTCAAAAAAAAAAAAAAAA": 27884, + "TATCGTGAGATTTT": 27885, + "CTGCTCTAGAGGAGATCTGCATG": 27886, + "CCTCTCATT": 27887, + "TGAGTGTA": 27888, + "TCCTAATTA": 27889, + "TCCTCTATT": 27890, + "GTTCAACAA": 27891, + "GCATAATTA": 27892, + "CCACĊ": 27893, + "CACAGTCTT": 27894, + "TATACTGTT": 27895, + "GGAACGCA": 27896, + "GCAACCCTG": 27897, + "GTCATCTCA": 27898, + "CGGTGGAA": 27899, + "TCCATAGTA": 27900, + "TCAAAGATAA": 27901, + "TAATTTCCAA": 27902, + "TTCATTATT": 27903, + "TTCACAATT": 27904, + "CAGAACAA": 27905, + "CTTTGGCTG": 27906, + "GTCAACAA": 27907, + "GCATTGTG": 27908, + "GAGCTCCTT": 27909, + "GACTTTGGG": 27910, + "GTGTGCTGA": 27911, + "CATGACTGA": 27912, + "GGCTTCCTG": 27913, + "TCTCTTGACC": 27914, + "GTGGGGGA": 27915, + "TATTTGTTTTA": 27916, + "AAATAGAAAA": 27917, + "CAGTCTTCA": 27918, + "GCAGACTGA": 27919, + "TAACATCAAA": 27920, + "TCTCAATTA": 27921, + "CTACAGCTG": 27922, + "GCCATGAAAA": 27923, + "GGCTCTTTG": 27924, + "GGAAATATCTTCGTA": 27925, + "GTCACCTACAAACAAGCAAGATTTCTG": 27926, + "TATATACCCAGTAATGGGATG": 27927, + "CACGTATT": 27928, + "TAGATTAAA": 27929, + "TAAGGACA": 27930, + "TTTTGACA": 27931, + "TGATAATTTT": 27932, + "GAACCAAAA": 27933, + "CTGCAATA": 27934, + "CTTGTTGA": 27935, + "GTGCTTTA": 27936, + "GTTTCAAAAA": 27937, + "GCCCTGCA": 27938, + "CTCGATTA": 27939, + "TGTTGCTTA": 27940, + "CAAATATCC": 27941, + "CATTTAAATG": 27942, + "TCTGTGAGG": 27943, + "TCTGTACCA": 27944, + "CAGATAGTT": 27945, + "GGAATAGGA": 27946, + "TAAAAGGGG": 27947, + "CATGGATGA": 27948, + "TTTTACATTA": 27949, + "TACATACATT": 27950, + "TTTAGGTG": 27951, + "TCTCATCAA": 27952, + "CGTGGCTGGGTCTT": 27953, + "GTGAAGGAAA": 27954, + "TGATTCAATTA": 27955, + "GCATTGCAA": 27956, + "CAGTTCTA": 27957, + "TGAGAGTTA": 27958, + "GTCTGCTGA": 27959, + "GAATGGAGG": 27960, + "TATTAATAAA": 27961, + "TTATGGTAA": 27962, + "TAAATTAAATT": 27963, + "GTGGGGTGGGGG": 27964, + "GTATGTATT": 27965, + "CTCCAGTTA": 27966, + "TCACCCGG": 27967, + "CCACCTCTT": 27968, + "CAGGTGGATT": 27969, + "GTATTATTAA": 27970, + "TTACCAGAAAACAA": 27971, + "GTGTTTGGAGGACAAA": 27972, + "TGAGGATTTCGTTGGAAACGG": 27973, + "CCGCTGTG": 27974, + "TCATGTTTG": 27975, + "CTTCTCCTG": 27976, + "CTTCCTTTG": 27977, + "GCCGCTGA": 27978, + "GCCCTTCTG": 27979, + "CTCTAATCA": 27980, + "TATTTGTTAA": 27981, + "CAGACCTGA": 27982, + "CTCACGTT": 27983, + "TCTCGTA": 27984, + "CTCCCGCA": 27985, + "CTCCCCCTCC": 27986, + "CTTATAATA": 27987, + "TCTAGACAA": 27988, + "TAGACACCA": 27989, + "CCCCTCCTG": 27990, + "GGTGTACA": 27991, + "CATAGGGG": 27992, + "TAATTTCCTT": 27993, + "GGGACCCTG": 27994, + "TCTCCCTCTT": 27995, + "GCAAACGG": 27996, + "CCAAACGA": 27997, + "TATTAGTTA": 27998, + "CAGCATGGG": 27999, + "GTTAACTTA": 28000, + "GGAGGCACA": 28001, + "CTGTGTAGG": 28002, + "GCCTTCTTTG": 28003, + "TGATGTCATG": 28004, + "GGATTAAATG": 28005, + "CTGGGGTCA": 28006, + "TCGCTCAAA": 28007, + "GTTTGAGACCAGCCTGGG": 28008, + "CCTGGCTGGAGTCA": 28009, + "ACACA": 28010, + "CCTCTCCAA": 28011, + "CAAGGAGGA": 28012, + "CTTGAAATG": 28013, + "CCACAAGAA": 28014, + "GCCCTCCTT": 28015, + "GTCTTATA": 28016, + "TGTGTAGTG": 28017, + "TCTTCGAA": 28018, + "TCTTGGTTA": 28019, + "CAGGCGTG": 28020, + "TCAATAAATG": 28021, + "GGAACTAAA": 28022, + "GCTTGTCTT": 28023, + "GCTTCAGTA": 28024, + "GTGACACCA": 28025, + "CTCATCATT": 28026, + "GCAATGTGA": 28027, + "GTCATACAA": 28028, + "CTCCTCATT": 28029, + "CTAAGCTA": 28030, + "GTCCCGTT": 28031, + "CTTTTTCCTT": 28032, + "CCAAATTAAA": 28033, + "GAGAACTG": 28034, + "CTGTGCGG": 28035, + "GCCTTGGCTG": 28036, + "TGATGCTCA": 28037, + "GGGAACAAAA": 28038, + "CCATGCCTT": 28039, + "TAGCTCATT": 28040, + "CTCTAGAAA": 28041, + "TGACCCAAAA": 28042, + "TGACTGAAAA": 28043, + "GATCAAAAATT": 28044, + "GACCTGGAGG": 28045, + "CTTTTTGGTAAAAACTCAA": 28046, + "CCCACTTTTTGATGGG": 28047, + "GCTCTCACAGACCTGTAACTTCTT": 28048, + "TCATCTTCAATGCCCTTGCTGATGGAA": 28049, + "TAAGCCAGG": 28050, + "CACACTGCTT": 28051, + "TCTTCTTTTA": 28052, + "GAGAGACCA": 28053, + "GAGAGAGTA": 28054, + "TCTGCTTAA": 28055, + "TCTGCATCC": 28056, + "TCTGGGTGA": 28057, + "CAGATCTGA": 28058, + "GGAACGAA": 28059, + "CATGGCTCA": 28060, + "GAAAACACTG": 28061, + "GTTTTTAGG": 28062, + "CGATATC": 28063, + "GCTATTTTTT": 28064, + "GTAAATGGG": 28065, + "TATTTTCCTT": 28066, + "CGCATTTA": 28067, + "GTGTTCCAA": 28068, + "CGGACGC": 28069, + "GCTAATTTTTG": 28070, + "GAAGACAAAA": 28071, + "GAATACAGG": 28072, + "GAACATATT": 28073, + "CCACACCCC": 28074, + "GTACAGTTA": 28075, + "GCTGATTAAA": 28076, + "GGAAGCACAGTT": 28077, + "GTACCTTCAGG": 28078, + "CGACCCCATGAA": 28079, + "TCAAAAAATCAATTTTTACACAA": 28080, + "TAAGCTAAA": 28081, + "TCATATATG": 28082, + "TCCTGTGTG": 28083, + "GTTCAATA": 28084, + "GCACTTCTT": 28085, + "GTCTCTACA": 28086, + "TGTGTCCTT": 28087, + "TGTGTAGTT": 28088, + "TATATCCAA": 28089, + "TCTTTAATG": 28090, + "TCTTGACAA": 28091, + "CATTTAGGA": 28092, + "CAGAGGCAGA": 28093, + "GAAAATGCC": 28094, + "TTTATCAAA": 28095, + "TTCAĊ": 28096, + "CTGAACCTG": 28097, + "GCAAACAGAA": 28098, + "TCTTAGGAA": 28099, + "GTATTGAAA": 28100, + "CTATTCAAA": 28101, + "CTATTTCAAA": 28102, + "GGATTTATG": 28103, + "TGAGGTGAA": 28104, + "GAATGCAGA": 28105, + "TATATATATTA": 28106, + "CCATGGATG": 28107, + "GGGTGCAGTG": 28108, + "TCGACATG": 28109, + "CAAGAGCTG": 28110, + "CCACCTCAA": 28111, + "GCATATGAA": 28112, + "TACAAATTA": 28113, + "GTATTTTGGCCCATTCCTCCATGCAGA": 28114, + "TAATGTGTG": 28115, + "TCATAAAAAA": 28116, + "GAAGTATA": 28117, + "CTTGCCTA": 28118, + "GTTGCCAAA": 28119, + "GGAGTGAAA": 28120, + "CCATGTGAA": 28121, + "GTAGCTC": 28122, + "CTAGTCTG": 28123, + "CACAGATTA": 28124, + "TGTTGATAA": 28125, + "TAAAATGCC": 28126, + "GCAATAAAAA": 28127, + "TACAGCATG": 28128, + "CTTACTGTT": 28129, + "CTTACCTCA": 28130, + "CTTATAGTT": 28131, + "TCTACTTTG": 28132, + "CAAAAGTG": 28133, + "TCAAAGAGG": 28134, + "TAATTCCAAA": 28135, + "GGCACGTT": 28136, + "GAATTCTTTT": 28137, + "TGATTTCTTTT": 28138, + "GAGAAGACA": 28139, + "CAATTCAAAA": 28140, + "CTCAATATT": 28141, + "CAAAAATAGAA": 28142, + "GCATTCTAA": 28143, + "GTTTGCAGG": 28144, + "CCCCAGAGA": 28145, + "GGATGCCAA": 28146, + "TTTTTGGGGG": 28147, + "GCAGAAGGAA": 28148, + "TTTTTATGAA": 28149, + "TGGGTTTGACGAAA": 28150, + "TCCTGAGGCC": 28151, + "GACTAAATT": 28152, + "GCGTTCTA": 28153, + "TTTTTAGAA": 28154, + "TCATGAGAA": 28155, + "TCACGTTA": 28156, + "CTGTAATTA": 28157, + "CTTCGAAA": 28158, + "CTTGTTTTTT": 28159, + "GCAGTCATT": 28160, + "GGACGCAA": 28161, + "GGACCTGTG": 28162, + "CCACAAATT": 28163, + "CCAGCTCCA": 28164, + "GTATCAGG": 28165, + "GCCGGGAA": 28166, + "GCCTGAGTT": 28167, + "GCCCCAGAA": 28168, + "GCCTCGCC": 28169, + "TAAAGGGTT": 28170, + "CTCGTCAA": 28171, + "GTCTAAGAA": 28172, + "TCTTTAAAAAA": 28173, + "TGTTCCTTTT": 28174, + "TCAATGCTG": 28175, + "GTCACGTA": 28176, + "GTAACTTAA": 28177, + "GGTTGATA": 28178, + "TAATTTAAAAAA": 28179, + "TCTGACAGG": 28180, + "CCATTGAA": 28181, + "CTGTGGACA": 28182, + "TTTTAAGAA": 28183, + "GAAGGGCTG": 28184, + "CATCTTTTTT": 28185, + "CAACAAGAGCAAAA": 28186, + "TCGACGC": 28187, + "CTTCCCCCA": 28188, + "GAATAGTTA": 28189, + "TGACTTAAAA": 28190, + "GGCTCCAAA": 28191, + "CCATGCCCGG": 28192, + "CCTGCTCCCCGCA": 28193, + "CTTTCTCTAGCTCCTCCATT": 28194, + "TTTTAAAATT": 28195, + "TGACTTTTTT": 28196, + "TTAGCAAA": 28197, + "CTTTGATTA": 28198, + "GTTCTCCTG": 28199, + "CCATAGC": 28200, + "GCCTCTAAA": 28201, + "GTCGATTA": 28202, + "GAAACAGCC": 28203, + "GAAATAATTA": 28204, + "TCTGCGTT": 28205, + "TAAAACAGCA": 28206, + "TAAAAGTTAA": 28207, + "GTAACACC": 28208, + "GTAAGACAA": 28209, + "TATGGTATT": 28210, + "CCTGGAGTG": 28211, + "GATGGAGTC": 28212, + "GGTTCTGTT": 28213, + "TGCACGA": 28214, + "GATACCAAA": 28215, + "CGTGGGAA": 28216, + "TGAAAGCAA": 28217, + "GCTGAGCCA": 28218, + "GACCCTGTT": 28219, + "GGCTGCCTG": 28220, + "CGTACAA": 28221, + "CCTCCTGGA": 28222, + "GAGTTGGTG": 28223, + "CAATGCACA": 28224, + "GCGGCTGA": 28225, + "CCATGCCAA": 28226, + "CTATGCCTG": 28227, + "GAGCCTTTG": 28228, + "CAAGGTGGG": 28229, + "TTATACTGA": 28230, + "GCTGTGAAAA": 28231, + "CGCTGCTG": 28232, + "CTCTACCAA": 28233, + "CCAGATGAA": 28234, + "GTTTTAATTTG": 28235, + "TATATTTTAAAA": 28236, + "CTCTGTGCGACCCCA": 28237, + "TGTTCTTCCATT": 28238, + "TACAGGTAACGAGTGGAGGA": 28239, + "TTTTGGAAA": 28240, + "TCAGGGGTT": 28241, + "TTATCATT": 28242, + "TCCGACTG": 28243, + "GTTGTGGTG": 28244, + "GTTTGAATG": 28245, + "GTAGATCC": 28246, + "GCCTGAATT": 28247, + "GCCTCACTT": 28248, + "TATATATCC": 28249, + "CAGGGCAAA": 28250, + "CTGACATGA": 28251, + "CTCACTCAA": 28252, + "CTCACATAA": 28253, + "CCTTGTGTT": 28254, + "GAGGTGCC": 28255, + "GAGGCATTTT": 28256, + "CTAACTGAA": 28257, + "CTTACTCTT": 28258, + "TCCATCTTA": 28259, + "TCCATCCTCC": 28260, + "TCTAGTCTT": 28261, + "GTCCCCTCA": 28262, + "GTCCAAATG": 28263, + "TCTCCCCCA": 28264, + "CTAAACTGA": 28265, + "CAAAAACACA": 28266, + "GAAAAATCAA": 28267, + "TAATGCTTTT": 28268, + "TCAAAATTTG": 28269, + "CACAACACAA": 28270, + "TACTGGAA": 28271, + "CTGGGTAATTTA": 28272, + "CTGCACCTT": 28273, + "GTTTAGCAA": 28274, + "CCACCACGCCCGG": 28275, + "CGTAAGAAGCATTTCAA": 28276, + "CAACACGA": 28277, + "GTGCCTGAA": 28278, + "GGCTCTGTG": 28279, + "CAGTTTTAAA": 28280, + "CCTACCATTAAAATTATAGACTGA": 28281, + "TCTTCCATTTCCTAATAATTGCTCCCACAGTTGATT": 28282, + "GGAAATGGAAGACATACAAGACCACTGATAA": 28283, + "CACGGAGG": 28284, + "GCGTAGAA": 28285, + "TGACGATG": 28286, + "TTACACTT": 28287, + "CTTCGATT": 28288, + "TAAACTTTG": 28289, + "CTCGGGTG": 28290, + "TATTTGAGA": 28291, + "CAAATCTC": 28292, + "GAGACTGCCA": 28293, + "TGAACAATT": 28294, + "TCTGGCTGA": 28295, + "GGAAGCTCA": 28296, + "GTGACCTA": 28297, + "GTGATATCA": 28298, + "CCTTCCAGAA": 28299, + "GTAAGTCC": 28300, + "TATGCCATT": 28301, + "CGGCGATG": 28302, + "CCAAGATGA": 28303, + "GAAAATGTGA": 28304, + "CAAAACTGAA": 28305, + "GGTTAAATT": 28306, + "CCCCTTTTA": 28307, + "TAATTCATAA": 28308, + "TCTGACCAA": 28309, + "TCCTTCCCTG": 28310, + "CTAAAGTCA": 28311, + "CTAAAGATT": 28312, + "GGTACCTA": 28313, + "TGATTTAAAAA": 28314, + "TCTTTTCTG": 28315, + "GCATTGAATCTA": 28316, + "TCAAAATATA": 28317, + "CAGTTTCAA": 28318, + "GAGCAGCAA": 28319, + "GAGTGGATT": 28320, + "GCTCCTCTT": 28321, + "CATGATAAAA": 28322, + "TACCACTA": 28323, + "CGTCTCTA": 28324, + "TATCGTA": 28325, + "TCTCTGTTTTGGTA": 28326, + "TAACAĊ": 28327, + "TGATAGTCC": 28328, + "GTCAAATTTT": 28329, + "TAAGGGAAA": 28330, + "CCGGCTCC": 28331, + "GTGGAGTCA": 28332, + "TCCCCCGA": 28333, + "TTTTTAGTAAA": 28334, + "TCAAAAAGTGGGCAAA": 28335, + "TCGTCAAACCCATGCTAAATATGTCAAA": 28336, + "GACGCCTT": 28337, + "GCGCAATG": 28338, + "TCACTTATT": 28339, + "TTACGAA": 28340, + "TCCGGCCA": 28341, + "CTGTATTAA": 28342, + "GTTGAAATG": 28343, + "GTTTATTAAA": 28344, + "GCACAACAA": 28345, + "GCATGACAA": 28346, + "CCACTTTTA": 28347, + "TGTGGGGAA": 28348, + "CACAGTCAA": 28349, + "CACACAGAAA": 28350, + "GAAAGGATG": 28351, + "TGTTGCTAA": 28352, + "CAAAGGAGTG": 28353, + "GAGAGGCTT": 28354, + "CAGGCTGTT": 28355, + "TAAAATAATTA": 28356, + "CTCATCTC": 28357, + "TTAATAAAAA": 28358, + "CAAAACCCA": 28359, + "TTATTCATA": 28360, + "CTGAATCAA": 28361, + "TTTTTTĊ": 28362, + "CCAAATGGA": 28363, + "TAATATTATTA": 28364, + "GAGAACAA": 28365, + "GCTGAGGCTT": 28366, + "TATTTTTGTA": 28367, + "GTGTTCGA": 28368, + "TCCTGGCAA": 28369, + "GAGCAGATG": 28370, + "TGAGGCAAA": 28371, + "TGAGGTCAA": 28372, + "CAACAGTAAA": 28373, + "TCATAGATT": 28374, + "TATTTAGTTA": 28375, + "GCCATTCTGACTG": 28376, + "GTGGACATG": 28377, + "GTAGGGAAAA": 28378, + "TTAGGTCCA": 28379, + "CTCGGGAGGCTGAGGCA": 28380, + "TCTCGGCTCACTGCAACCTCTGCCTCC": 28381, + "GCCAAAATTGACAAATGGGA": 28382, + "GAAATATCAATAACCTCAGATATG": 28383, + "CTTTTGTTATTGACCAAA": 28384, + "CAGAGGAATGGATACAGAAAA": 28385, + "CATCTCCTG": 28386, + "GGCGAAAA": 28387, + "GGGATGAAA": 28388, + "TTTTTCCAA": 28389, + "TGATAAAAAA": 28390, + "TGAGTTGTT": 28391, + "TGATCATCA": 28392, + "TCCCGTGA": 28393, + "GTGGCTTAA": 28394, + "GTTGCAGAA": 28395, + "GGACGTGA": 28396, + "GGACAAGGA": 28397, + "GCCGGTGA": 28398, + "TAAAGACACA": 28399, + "CTCGACCA": 28400, + "GTCTATTTG": 28401, + "TGTGGTAAA": 28402, + "TATACAATG": 28403, + "TCTTTAGTA": 28404, + "CATTCGAA": 28405, + "CAGAGCAGG": 28406, + "CCAAGGGCA": 28407, + "GACAGCATG": 28408, + "TCAAAGGAAA": 28409, + "GGGACC": 28410, + "GCAAACCAAA": 28411, + "GAATTCTCAGTAA": 28412, + "GAGAACTA": 28413, + "CTGTTGTTCTG": 28414, + "CTGTGTAAA": 28415, + "CATCACTAA": 28416, + "GAGCAGCTT": 28417, + "GGATTCCTG": 28418, + "GAATGGTGA": 28419, + "TAATAATATT": 28420, + "CAGCCTGCA": 28421, + "TTATGGATT": 28422, + "TAGCAGAGA": 28423, + "GCGATGAA": 28424, + "CCGGCCCC": 28425, + "GCAATTCGA": 28426, + "GCACTGCAGGATT": 28427, + "CTCACCGTTCTTGTGA": 28428, + "GTTCCTCTGCCTTTT": 28429, + "GGTAAATTA": 28430, + "GATAAAAATT": 28431, + "TAACCTGAAA": 28432, + "TTTTTATGA": 28433, + "TTAGTAAA": 28434, + "GAATGACTT": 28435, + "GTGCTGATT": 28436, + "GTTCCAGCA": 28437, + "CCACTTCTG": 28438, + "TCTTTATATT": 28439, + "TGTTGACTG": 28440, + "CATTTCTGTT": 28441, + "GTCATTTTAA": 28442, + "TCTCTAATA": 28443, + "TACATCTAA": 28444, + "GCCACTAAA": 28445, + "CGAGTTGATG": 28446, + "CAAAATCTA": 28447, + "GCTACAATT": 28448, + "GGAAATGA": 28449, + "GGAAATGTA": 28450, + "TCCTTGATG": 28451, + "GGCCCTTA": 28452, + "TATTATAAAA": 28453, + "GTGTTCACA": 28454, + "TCCTGGTCA": 28455, + "CAGCGAGG": 28456, + "GTTTGGAAAA": 28457, + "CATTATATT": 28458, + "TACTTCAA": 28459, + "TAGTTTCAGAA": 28460, + "CAACAGTTTT": 28461, + "CCACACCTT": 28462, + "TCATGACATTCTAA": 28463, + "GCCCGGGCGG": 28464, + "TAAAAAAAATG": 28465, + "TGAGACGGAGTCTCGCTCTG": 28466, + "GTTTTTACCAAAAA": 28467, + "TCCAGGCCCGTCTGAA": 28468, + "CACTCCAAACTCCACTATGGCCAAGACCAAA": 28469, + "TAGTGAATT": 28470, + "GCTTTTTTTTTTTT": 28471, + "TCACAAATG": 28472, + "CTTGTTCAA": 28473, + "GTGCAGGAA": 28474, + "GGACAAGAA": 28475, + "GTAGCCAGG": 28476, + "TAAACATATA": 28477, + "TATATCTC": 28478, + "GAGATGTGA": 28479, + "TGAAGATTA": 28480, + "CAGGCCTGA": 28481, + "CTCAGATCA": 28482, + "CTCCCGTT": 28483, + "TACAGTAAAA": 28484, + "CGACACTT": 28485, + "GGCAGTGAA": 28486, + "TAATAGCAAA": 28487, + "CTCTTGTTTT": 28488, + "GTATTTCAAA": 28489, + "GGAGGTAGA": 28490, + "CAAAAAGTTA": 28491, + "GAAAAATTTG": 28492, + "CATTTTTATT": 28493, + "TCAGGGCTT": 28494, + "GTATGGTA": 28495, + "GGAGATCTG": 28496, + "CCCTCCACA": 28497, + "TCACCCCCA": 28498, + "TAAGAGTCA": 28499, + "TCACAGTTTT": 28500, + "CACACACGG": 28501, + "GTTTCCTTTT": 28502, + "GGATTACAGGCATGAGCCA": 28503, + "CAGCATCAGTCCTT": 28504, + "GCAGGCAGATTCTTTACCA": 28505, + "TCCCAACCGTGAA": 28506, + "GGGACCAGGACGACTGA": 28507, + "CTTTAAGAGGCTCCTCTGTCCTCCA": 28508, + "TCTGCAATAGGTAAGCAGCTTGGTTTGAAGAAA": 28509, + "TGATCACAAGAACGGTGA": 28510, + "GTCATGCTGAAAGACCCAGCCACGTT": 28511, + "TGGGTTTGACGAAATTAGGA": 28512, + "GCGAAATA": 28513, + "TAATGTTTA": 28514, + "TCCTTATA": 28515, + "TCCCTCCTG": 28516, + "CTTCTGCAA": 28517, + "GTTCTAATT": 28518, + "GTTGCTAAA": 28519, + "GGATĊ": 28520, + "TAAAGAAGG": 28521, + "TGTGGATTA": 28522, + "GAAAGAGC": 28523, + "CTGAGGTTA": 28524, + "GCTGTACCA": 28525, + "TTTTAGGTG": 28526, + "GAGGGGGCA": 28527, + "GTTATACC": 28528, + "CTCCTGATT": 28529, + "TCCAGTCTG": 28530, + "GATGTATTA": 28531, + "CCCCCCAAAA": 28532, + "CATAGAATT": 28533, + "GCTACGAA": 28534, + "CGCTCGCC": 28535, + "TTTTTTGCTT": 28536, + "TTTTTTGAAAA": 28537, + "TAAAAATGGA": 28538, + "TGAAAGAGA": 28539, + "CCATTCAAA": 28540, + "CCATTGTCA": 28541, + "GCATTCAA": 28542, + "GCATTTAAAA": 28543, + "GGCTGCGG": 28544, + "GGCTGTTTG": 28545, + "TTTTTTTTTTTTTTA": 28546, + "TGAGGGCAA": 28547, + "TATATATATATAA": 28548, + "GACTGTAGCC": 28549, + "CCTCGGTT": 28550, + "CTCCATCTCC": 28551, + "CTTCACGG": 28552, + "TAACATCTA": 28553, + "CTGTACACA": 28554, + "GCGATGTT": 28555, + "CAATAGATT": 28556, + "CCCAGGCTGGAGTGCAGTG": 28557, + "GAGTCGGACACAA": 28558, + "GTGCAGCTT": 28559, + "TGATTTTAAAA": 28560, + "CCTCCACCTCCCGG": 28561, + "TCTTCACAGAATT": 28562, + "CTATCTAGGTTGGTCA": 28563, + "GAGACGGAGTCTCGCTCTG": 28564, + "GCAGACGCTTTTATCCAAA": 28565, + "TCTTTCACTTTCATCAA": 28566, + "TAGGAACACTTTTACACTGTTGGTG": 28567, + "CATCTCTGA": 28568, + "TAATTTTATG": 28569, + "TGATCTGAA": 28570, + "TTAGGGAAA": 28571, + "GAAGGAATT": 28572, + "TCCAAAAAAA": 28573, + "TCCTCGTG": 28574, + "GGACCCAGG": 28575, + "CACATCTGA": 28576, + "CAGGTGATT": 28577, + "GTGAGGTGA": 28578, + "CTCATATAA": 28579, + "CCTTCAGCC": 28580, + "TATGCTGAA": 28581, + "TATGAAATA": 28582, + "GAGGTGTA": 28583, + "CCAACATTA": 28584, + "TACATATATA": 28585, + "CTTACGAA": 28586, + "TCCACCTCTG": 28587, + "GGGGTGCC": 28588, + "TGGGCTCC": 28589, + "CCCCCGAA": 28590, + "TAATTCAGAA": 28591, + "TTCATACA": 28592, + "GATACAATT": 28593, + "TTTTTTTAAATT": 28594, + "GCAAACTGAA": 28595, + "GCTCACTGA": 28596, + "GAGAATGTG": 28597, + "CTTAAGCAA": 28598, + "GTTTGGCAA": 28599, + "TGATGCTGCCA": 28600, + "CAATGGAGA": 28601, + "CAGTATGATATT": 28602, + "CCATGGTG": 28603, + "GGGCĊ": 28604, + "CTTTACAAAA": 28605, + "CTTCCTAGA": 28606, + "CTTCACTTTT": 28607, + "TCCTCCAAAA": 28608, + "CAAGATCAA": 28609, + "TAAGGCAGA": 28610, + "GTTGACATT": 28611, + "GCCCAGCCTG": 28612, + "CACACATGTG": 28613, + "CTCTTAACCACTGA": 28614, + "GCTATTATAAATAAGGCTG": 28615, + "TACTTTAACGAGA": 28616, + "GCAGATTCAGTCTTCCCAGCCTGGTGCAGG": 28617, + "CCGAGAAA": 28618, + "CCGCTCAA": 28619, + "TTTTTCATT": 28620, + "TGACGCTG": 28621, + "TGAGGATA": 28622, + "GAATAAATG": 28623, + "CAAGTTATT": 28624, + "GTTCTTTTTT": 28625, + "CTCGTATT": 28626, + "GTCCATTAA": 28627, + "TCTTGGCAA": 28628, + "TGTTCGTA": 28629, + "CAAAGACAAA": 28630, + "CATTCCTTA": 28631, + "GCTGTACTG": 28632, + "GTAACAGTA": 28633, + "CTAAGATAA": 28634, + "GCCATATCA": 28635, + "CGATAGAA": 28636, + "TGAAATGTG": 28637, + "GTGTTTCTCA": 28638, + "GTCTTTAAA": 28639, + "TATAAGAATG": 28640, + "TCTAAGTTA": 28641, + "CTCTGTAAA": 28642, + "GTTTGGTCA": 28643, + "TGATGCACA": 28644, + "TGAGAĊ": 28645, + "GAGCGGTT": 28646, + "GACTTCTCA": 28647, + "GATGAGAAA": 28648, + "CCAGGCCCA": 28649, + "TATAAATTTA": 28650, + "TCGACATT": 28651, + "CAAGGCCTG": 28652, + "CAAGGCCCA": 28653, + "TCACCCCTG": 28654, + "TGACACTCA": 28655, + "TGACACTAA": 28656, + "CTCCTTCCTT": 28657, + "CCCCCACCCCA": 28658, + "CAACTCCGTTTATGAAA": 28659, + "CCAAAAAATG": 28660, + "TACTCTAA": 28661, + "TAAGAGATT": 28662, + "TAACCACAA": 28663, + "TTTTTTAAAAA": 28664, + "TCAGTATA": 28665, + "GAATGAGAA": 28666, + "GTGTCAGA": 28667, + "GCACTTGA": 28668, + "GGATAGC": 28669, + "CTATAAATG": 28670, + "CTCTTAATG": 28671, + "GAAAGGTCA": 28672, + "TGAACAGTG": 28673, + "CCTTGAGGA": 28674, + "CATGCAGTA": 28675, + "GCAACCTA": 28676, + "GTAAGAATT": 28677, + "TATGCAAATT": 28678, + "CCAAGGCTA": 28679, + "TCTACTGTT": 28680, + "GCCACGTA": 28681, + "GGGGGTGGGGG": 28682, + "CATACTGTT": 28683, + "GGCACTGAA": 28684, + "GATATTTTCTT": 28685, + "TCTGATATT": 28686, + "TCTGATGGA": 28687, + "TCATTGGTG": 28688, + "GGAAAGGGAA": 28689, + "CGTTCGA": 28690, + "GTGAATGGA": 28691, + "CACCCGCC": 28692, + "TGATTGCCATT": 28693, + "GCTCACCTG": 28694, + "GACCCAAAA": 28695, + "CAGTTTGTT": 28696, + "CCCCACCTG": 28697, + "CCCTGGGAA": 28698, + "CCTCCCTGA": 28699, + "TGAGGGCTT": 28700, + "CAATGGTA": 28701, + "CCTCTTTTTT": 28702, + "TCGGCTCC": 28703, + "TCAGGTTAA": 28704, + "TCTGTGGAGG": 28705, + "GTGCTGGAGAA": 28706, + "TCATACAGG": 28707, + "CCGGCCTG": 28708, + "CTACAGTTA": 28709, + "CTCTCCTTA": 28710, + "GTTTCTCTGTGTA": 28711, + "CACATCTTAAACCAATTTATTTT": 28712, + "TAAAAAAAAAAAAAA": 28713, + "TCCGAAAA": 28714, + "CAATAAGAA": 28715, + "CTTGAATA": 28716, + "CTTTCTATG": 28717, + "GCAGTTATT": 28718, + "GCATTATA": 28719, + "GCACĊ": 28720, + "CCAGTCTGTG": 28721, + "CTATTTTTA": 28722, + "GTCGACTG": 28723, + "TCTTTGAGTT": 28724, + "GTGAGGCCA": 28725, + "TCTCTCCCTCC": 28726, + "GTTTTTTTTTTTTTT": 28727, + "TCTAGGCC": 28728, + "TCTATCTAA": 28729, + "GCCACCATT": 28730, + "CTTTTGAAAAA": 28731, + "GACAGAATG": 28732, + "GGTGCCCA": 28733, + "TAATTGTAAA": 28734, + "GGCAGCCTT": 28735, + "CCAAAGCTT": 28736, + "GCTCACGG": 28737, + "CAGCATATTAAAAA": 28738, + "GTGTTCAAA": 28739, + "CAGTGCCTT": 28740, + "TCCTGCGG": 28741, + "CTCTGTATA": 28742, + "GCATTGGTT": 28743, + "TAATGGAGG": 28744, + "GTTTGAAAATTTATGTGAGGTT": 28745, + "AAATTTATA": 28746, + "TCAAAATGAA": 28747, + "CACTGGGA": 28748, + "CACCATTTG": 28749, + "TAGTTTATA": 28750, + "TTATGGTCA": 28751, + "CTTGGTTTA": 28752, + "TCATATCTT": 28753, + "GCACAGCTA": 28754, + "GTTCTGAAAA": 28755, + "GACTCTGCA": 28756, + "GGCGGAAA": 28757, + "CACCTCAGCCTCCCAAA": 28758, + "CTCAAAATTCTCCAA": 28759, + "GTTCATATTGTTGTT": 28760, + "CCCGAAAA": 28761, + "TAACTGCTG": 28762, + "TTTTGAATT": 28763, + "TCAGTCTA": 28764, + "TGACAACA": 28765, + "GAACAAAAAA": 28766, + "TCCTATATT": 28767, + "GGAGTGGTG": 28768, + "GTAGTTGTT": 28769, + "CACAGGGTT": 28770, + "CAGGTACTT": 28771, + "CTCACAGTA": 28772, + "GCAATCAAA": 28773, + "GTTTTTAGA": 28774, + "CGAGCGG": 28775, + "CAAAATACC": 28776, + "GGTTGAAAA": 28777, + "GGGACAAAA": 28778, + "CGCTCGC": 28779, + "GCAAAGCC": 28780, + "TCATTCATA": 28781, + "CGTTCAGCA": 28782, + "GGCCGGCC": 28783, + "CAGAACCAAA": 28784, + "GAAAAATTAAA": 28785, + "GTTTGCCTG": 28786, + "TGATGGAAAA": 28787, + "CACTGTAGCTG": 28788, + "GAGTTTAGA": 28789, + "CAGCCCATG": 28790, + "GCTTAGTGA": 28791, + "TCTCTGCCTT": 28792, + "TAGGAGGAA": 28793, + "GGAGAĊ": 28794, + "GGAGAGTAA": 28795, + "CTTTACCTG": 28796, + "TCACCCATG": 28797, + "CCACATCAA": 28798, + "GTTCATCAA": 28799, + "TCCATGTAGTT": 28800, + "GCACACGCA": 28801, + "GAACCAGAAA": 28802, + "CACTTAAAAA": 28803, + "TGACTTATT": 28804, + "TTATAAAAA": 28805, + "CTGTCCCCA": 28806, + "TGAATATC": 28807, + "TGAAGGTGA": 28808, + "GGAAGGGAAA": 28809, + "GTGAGACAA": 28810, + "GCTGGACAA": 28811, + "TTTTACAGAA": 28812, + "TATGCATCA": 28813, + "GATTTCAAAA": 28814, + "CTAACAGTG": 28815, + "CTAAGGGAA": 28816, + "GCCATATTA": 28817, + "GGGGTCCA": 28818, + "TGTACTTAA": 28819, + "TCTCATGGG": 28820, + "CGTGCTA": 28821, + "TCCTTCCTGA": 28822, + "CTAAACTAAA": 28823, + "TGCCTACA": 28824, + "TGATTGCCAA": 28825, + "CAGCAGATGCTG": 28826, + "TCTTATCTG": 28827, + "GACCTCTGA": 28828, + "CTCTGCTGTT": 28829, + "TGATGTAGA": 28830, + "TACAAAAAAAA": 28831, + "TACTTTAAA": 28832, + "GATAATATA": 28833, + "GAAGGCGA": 28834, + "GAAGGTAGA": 28835, + "TTTTTGTTTTA": 28836, + "CTGCATCAA": 28837, + "TCCATTTGTT": 28838, + "CAGTCTGGG": 28839, + "CGTCATTA": 28840, + "CAATACTTTT": 28841, + "TGACAGTTTT": 28842, + "TGATACAGA": 28843, + "CTATACATT": 28844, + "CTACATCTG": 28845, + "GGTTTTAAAA": 28846, + "GGCAGGGATG": 28847, + "CTTTGAGAGG": 28848, + "TATCCACTTA": 28849, + "TATCCATTCCTCTGTT": 28850, + "CGCCTATAATCCCAGCA": 28851, + "GTGATGTTTTGGGGCTGTCGCTGGGCAA": 28852, + "TTTTGGCA": 28853, + "GAACAAGAA": 28854, + "CAACCTTTT": 28855, + "CTTTCGTT": 28856, + "GCAGAGGTG": 28857, + "GCCTGGGTT": 28858, + "GTCTAATAA": 28859, + "GTCTTTTAAA": 28860, + "TGTGGCCAA": 28861, + "GAAATATC": 28862, + "TCTTGAGTA": 28863, + "GAGATATAA": 28864, + "CAGAGGGTT": 28865, + "TACATTAGG": 28866, + "TTAACACAA": 28867, + "CGATAATT": 28868, + "CCCACAATT": 28869, + "GACATAATT": 28870, + "TCTCAGTCC": 28871, + "TCTGAGCCACCAGGGAA": 28872, + "GGAAATAGG": 28873, + "CGTTGATG": 28874, + "GTGAAAAATG": 28875, + "CTCTTGGA": 28876, + "GTATTCATA": 28877, + "CTCTGTCTCAAAA": 28878, + "TGAGGGATT": 28879, + "TTATGCTG": 28880, + "GAAGGCTA": 28881, + "CGTCCATG": 28882, + "GTGGGGTG": 28883, + "CCTAACTCA": 28884, + "TATAAATAAA": 28885, + "CAAGGTGAA": 28886, + "GGAGAAGGAGAA": 28887, + "CTCATTTCAA": 28888, + "CCAGACAAA": 28889, + "GTAGATATT": 28890, + "GAGTCGGTGATG": 28891, + "GTGCCCGA": 28892, + "CAACAGGTGCTGGAGAGGATGTGGAGAAA": 28893, + "CTTTTTTTTTTTTTTTTTTTTTTTT": 28894, + "CCCACCAACAGTG": 28895, + "TCATAACAA": 28896, + "TGAGTAGG": 28897, + "GTTGCATA": 28898, + "GCATCACTG": 28899, + "GGAGTTTGA": 28900, + "CCATCACTT": 28901, + "CCATCCCC": 28902, + "GCCCĊ": 28903, + "TATTTAAATG": 28904, + "GAGAGGGTA": 28905, + "TGAATACTG": 28906, + "TAAAACATCA": 28907, + "CATGTCTGA": 28908, + "GTAATACC": 28909, + "TTTTATTTTTTA": 28910, + "CGGCACTT": 28911, + "GTTACTGTT": 28912, + "CTCCTACCA": 28913, + "CTCCCCAGG": 28914, + "TTAAGCTT": 28915, + "GCTAGTAAA": 28916, + "GGCACGCA": 28917, + "TCTCACTAA": 28918, + "CGCAAAAAA": 28919, + "TCTCCTGGAA": 28920, + "GGAAATGGA": 28921, + "TCCTTTCTTA": 28922, + "CTCTTTATG": 28923, + "GCTGACATT": 28924, + "TCTTACTTTT": 28925, + "CGCCTGAA": 28926, + "CACCAGAGA": 28927, + "GTGTGGTTTT": 28928, + "CATCTAAAAA": 28929, + "CATCCCTG": 28930, + "CTTGGGATG": 28931, + "CAAATTTCTG": 28932, + "CTCCATATG": 28933, + "CAAGGCACA": 28934, + "GAACACCTT": 28935, + "GCGACGCC": 28936, + "TATTTATCTT": 28937, + "GTCCAGTCC": 28938, + "GGGCCTTTG": 28939, + "GGGTCTGGG": 28940, + "TCCACCTGCCTCTGCCTCC": 28941, + "TCAGGTGGTCTG": 28942, + "GTCGTGTCTGACTCTTTG": 28943, + "GTAAAAAAATATTTT": 28944, + "CCGGGCGCGGTG": 28945, + "GGTCAGGCTGGTCTTGAA": 28946, + "GAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAA": 28947, + "GGAGGTTTTCACTCAAAA": 28948, + "GAACAGGTT": 28949, + "CTTGTACA": 28950, + "CTTGTGTTA": 28951, + "CCACTTCCA": 28952, + "GCCGCTC": 28953, + "CTCGAATT": 28954, + "TGTGCCCC": 28955, + "CACAGCCTG": 28956, + "CAAACACTA": 28957, + "CAGGGGTCA": 28958, + "GTGAGATAA": 28959, + "CCTTGACAA": 28960, + "GCAATCTCA": 28961, + "GAGGCTGTT": 28962, + "GAGGCTCTTTA": 28963, + "GATTCCATG": 28964, + "CTCCCGTG": 28965, + "GAAAACGTT": 28966, + "GCCATCTTA": 28967, + "TAGGCAGCA": 28968, + "GACATATTTT": 28969, + "CTGAATGTA": 28970, + "TGAAATGGA": 28971, + "CACCCGTT": 28972, + "TATTATCAA": 28973, + "CAGAACAAA": 28974, + "TGAGATCAA": 28975, + "GAGCACTG": 28976, + "CACACACACATA": 28977, + "TACTTTCTT": 28978, + "TTATGGTTA": 28979, + "TAAATTGTA": 28980, + "GAAGGGCTT": 28981, + "GCATGGAAA": 28982, + "CTTGGTGGG": 28983, + "GGAGACGA": 28984, + "CTTCTTCCTT": 28985, + "GGGCGAGG": 28986, + "TCCCATAGA": 28987, + "CTCAAACAAATTTA": 28988, + "TTACATCAA": 28989, + "CTTTCATTTA": 28990, + "GCAAAAGAAACTACCATCAGA": 28991, + "TCATGGCTGCAGTCACCATCTG": 28992, + "TATAAAAATT": 28993, + "TCACAAGAA": 28994, + "TTACTAAA": 28995, + "GTTGAGATT": 28996, + "GTTTGACTT": 28997, + "GCAGGAATT": 28998, + "GGATGAGGA": 28999, + "GCCTGTGTG": 29000, + "TAAATATATG": 29001, + "CACATTTTAA": 29002, + "CATTCACTA": 29003, + "TGAAGTATT": 29004, + "CAGGCGTT": 29005, + "GGAATGTGA": 29006, + "TAAAACCTA": 29007, + "CCTTCTCAA": 29008, + "GAAAACATTA": 29009, + "CCCACCCTA": 29010, + "CTCTTTATA": 29011, + "GTTAAGATG": 29012, + "TCTTAGTA": 29013, + "CGCAGGTT": 29014, + "CAATTGTCA": 29015, + "TCCTGCAAA": 29016, + "GGATTGTTTT": 29017, + "GAGTTGGGG": 29018, + "TTATGGCAA": 29019, + "GCTCCTTTA": 29020, + "TAAATTAAAAAA": 29021, + "GGATGCGG": 29022, + "TAGCTCTCA": 29023, + "GAAGATCTCA": 29024, + "TCAGATCCA": 29025, + "CTTTAGTCA": 29026, + "GGTTATTTG": 29027, + "TCACCCAGA": 29028, + "GCGATGTG": 29029, + "TCATAGCTG": 29030, + "CCAGACCTG": 29031, + "TCCATGAAAA": 29032, + "CACATGCTCCACTATGTTCATA": 29033, + "CAAGATCTCACCTC": 29034, + "CGGTTCCAGAGTAAACGTGCGCA": 29035, + "TTACCTCA": 29036, + "CTGTAATAA": 29037, + "CTTGGATA": 29038, + "CTTTAAAAAAA": 29039, + "GGACGAGG": 29040, + "TAAACAGCC": 29041, + "CTCGATAA": 29042, + "GTCTTTTTTTT": 29043, + "GAGATAGTT": 29044, + "GCTTTATGA": 29045, + "GAGGTAGGA": 29046, + "GATTCATCA": 29047, + "CTAAGCATT": 29048, + "GCCATACTT": 29049, + "TAGAGTTAA": 29050, + "GGTTGGGGA": 29051, + "GGCACCCC": 29052, + "GGAAAGGGA": 29053, + "GGAAATTCA": 29054, + "GCTGATGAA": 29055, + "TCTTATCTA": 29056, + "CTTTGTTTTTT": 29057, + "GTATTCTGA": 29058, + "CGAACAAA": 29059, + "TCCTGCTTA": 29060, + "CTATTGAAA": 29061, + "GCCTGCTA": 29062, + "GCCTGGCAA": 29063, + "TGAGGCCTG": 29064, + "GAATGTCTG": 29065, + "TCATGGATG": 29066, + "GTGTGCACA": 29067, + "CAATGGTAA": 29068, + "GAGTATTTA": 29069, + "CCAGGCCTT": 29070, + "GAGAAAGAAAAA": 29071, + "TATATTTATG": 29072, + "TCGACAAA": 29073, + "GCCAGGAAAA": 29074, + "TCCAAATTTT": 29075, + "GAACATATA": 29076, + "TGATACCAA": 29077, + "TATTTACATG": 29078, + "CAAGAATTTG": 29079, + "GCATGACATAA": 29080, + "CTTGTGATTTTTG": 29081, + "CATCCATGTCCCTACAAAGGACA": 29082, + "CTTTGGATGCAACTCAGCATTCTTTGTCCTCCAAA": 29083, + "TAACCAGAA": 29084, + "CAATGTTAA": 29085, + "CTGCTTCC": 29086, + "GTTCAAATG": 29087, + "GCATGAGTT": 29088, + "GGACCAAAA": 29089, + "CCACGTCA": 29090, + "CCACGATG": 29091, + "CTATTATA": 29092, + "TATTCTGTG": 29093, + "TCTGTCCTG": 29094, + "CTGACATAA": 29095, + "GTGATACAA": 29096, + "GTGAGACTT": 29097, + "CTCACATGA": 29098, + "CCTTTAGTT": 29099, + "GCAAGGGCA": 29100, + "GTCAGGATT": 29101, + "CGGTCAAA": 29102, + "CGGGATA": 29103, + "CGGCCTC": 29104, + "GATTGGCA": 29105, + "GTTATACTG": 29106, + "TACACCCTG": 29107, + "CAAAACCTG": 29108, + "TGCCCTGAA": 29109, + "CACCCGTG": 29110, + "GAGAAGGGAA": 29111, + "TCTTAGTAA": 29112, + "GTATTTCTAGTTCTA": 29113, + "TGATGCTTA": 29114, + "GAGCTCTC": 29115, + "GTCTGCATT": 29116, + "GAATGTCTT": 29117, + "GAATGTCAA": 29118, + "TATTTGGAA": 29119, + "CTTGGCATT": 29120, + "GTGGAGCTT": 29121, + "GTTCCTCAA": 29122, + "CTATAGAAA": 29123, + "TCAGCATTTT": 29124, + "GTCTCACTATGTT": 29125, + "CTGAGGTGGGAGGA": 29126, + "GTGAACAGGCAACCTACAGAATG": 29127, + "TAACTGCA": 29128, + "TCATAATGA": 29129, + "TCAGTAGA": 29130, + "TGATAAAATT": 29131, + "TCCTAACC": 29132, + "CTGTCGG": 29133, + "GCATTATTTT": 29134, + "GCATCCTCA": 29135, + "CCATCTGAA": 29136, + "CCATCACTG": 29137, + "GTAGTTTCA": 29138, + "GTAGAATA": 29139, + "TAAACCTTA": 29140, + "GTCGATTTT": 29141, + "GTCTGTTTA": 29142, + "TGTGCCCTG": 29143, + "TATTTCTTAA": 29144, + "TGTTTCATG": 29145, + "TGAAGTCTT": 29146, + "CAGGCGC": 29147, + "CTGAGGTGA": 29148, + "GCAACATTTT": 29149, + "GTCACCTCA": 29150, + "TATGCTGTT": 29151, + "TCCATCTAA": 29152, + "GAAAACTGAA": 29153, + "GTTTTGATA": 29154, + "CGATACTG": 29155, + "GACATACTT": 29156, + "CATACCATG": 29157, + "GCTACTTAA": 29158, + "TTATTCATTA": 29159, + "CTAAACCAA": 29160, + "GTGAATGGAA": 29161, + "GTTAAGGAA": 29162, + "CAATTCTTTT": 29163, + "TCCTGGTG": 29164, + "CTCTGCGA": 29165, + "CTATTCAA": 29166, + "CAGGATTTG": 29167, + "GAATGTATA": 29168, + "GAATGCTTA": 29169, + "CAATGCTAA": 29170, + "GCTCGAGG": 29171, + "GTGGGCGCCTGTAGTCCCAGCTA": 29172, + "GCAAAATTAA": 29173, + "CTCCATCAA": 29174, + "CGTGAGCCACTG": 29175, + "CTGCTGGATT": 29176, + "CAAGGGGAA": 29177, + "CCAGACATT": 29178, + "CAGTCCAAA": 29179, + "GTTGAGAGAATGCCAA": 29180, + "GGAAAGAATGAATGGGGCCATG": 29181, + "GACGTATT": 29182, + "TGATCCATT": 29183, + "GTATATATG": 29184, + "CAAATTTTTT": 29185, + "TCTGGGCAA": 29186, + "GGAAGCATT": 29187, + "GTGACACAA": 29188, + "GATTCATTA": 29189, + "CCAAGACTG": 29190, + "TCCATAGTG": 29191, + "GAAAAGCC": 29192, + "GTTTTCTAAA": 29193, + "CATACTGAA": 29194, + "TGGATAGG": 29195, + "GTATTTCATT": 29196, + "CCATTTATG": 29197, + "CAGCGTCA": 29198, + "GGATTTATA": 29199, + "TGAGGTAGA": 29200, + "TAATAATAAAAA": 29201, + "GGCCAGAGG": 29202, + "GCAGGCTCA": 29203, + "TAAGACTTA": 29204, + "CAATATCTA": 29205, + "TAAATATGTG": 29206, + "CTATAGATG": 29207, + "CCATATGTG": 29208, + "GGATAGAAA": 29209, + "GGCTCCAGG": 29210, + "GTCACATGGGGTAA": 29211, + "TACTGATAACAAGTTCAAACAGGTGCCATTAA": 29212, + "TAAGTTCTA": 29213, + "TCAGGAGGA": 29214, + "GCACCTCTG": 29215, + "CCAGCCCA": 29216, + "CTCTAGGTT": 29217, + "GTCCAGGTG": 29218, + "CACACTCAA": 29219, + "TGTTTAGGA": 29220, + "GAGAGCTCA": 29221, + "CATGGTAAAA": 29222, + "GTCATAGAA": 29223, + "TCTCTCGG": 29224, + "TCCACCTGA": 29225, + "TAGACATGA": 29226, + "TGGAGAAAAA": 29227, + "GAATTGTCA": 29228, + "CTTAAGTAA": 29229, + "GTATTGATG": 29230, + "GGAGGTGA": 29231, + "CCATTCTTA": 29232, + "CTCTGCTAA": 29233, + "CTATTCTGA": 29234, + "GCCTGGTG": 29235, + "TACAACAAA": 29236, + "GTCTGCATG": 29237, + "CAATGCAGA": 29238, + "CCATGCTA": 29239, + "GATCAGAAA": 29240, + "GACCATCTG": 29241, + "GGGTGGAGG": 29242, + "GCAGCACTCTT": 29243, + "CTTCCTATG": 29244, + "GCAGAGCCA": 29245, + "CAAGATATG": 29246, + "CTCTACATT": 29247, + "CATACATTTT": 29248, + "GGTCTGAAA": 29249, + "TTACAGTA": 29250, + "GAATTAAAAA": 29251, + "TCCCGTCC": 29252, + "GTTTCTTTTA": 29253, + "GCAGTTGA": 29254, + "TATTCCCCA": 29255, + "TATATATTTG": 29256, + "TCTTTCTGTT": 29257, + "GAGAGATTA": 29258, + "TCTGGTTTG": 29259, + "GCTGCGCA": 29260, + "CTCATCTGA": 29261, + "TTTTATATCA": 29262, + "CGGGGTCA": 29263, + "GTTATCATT": 29264, + "CCAATATCC": 29265, + "TACAGCAAAA": 29266, + "TACATTTTTA": 29267, + "GGGGCACC": 29268, + "CCCAGCCCC": 29269, + "CAAAAGGTG": 29270, + "GACATCTCA": 29271, + "TCAAACCCATCCTAATT": 29272, + "GGAAATATCC": 29273, + "TCCTTCAGA": 29274, + "CGTTTCCA": 29275, + "GTGAACATT": 29276, + "CTTTGTCTA": 29277, + "GTATTCTCA": 29278, + "TCTTTTCATT": 29279, + "TATAATGAAA": 29280, + "CGGATGAA": 29281, + "CTGTGCTTTT": 29282, + "GAGGAGGAGGAA": 29283, + "AAATTAAAAAA": 29284, + "CAGTTCCTT": 29285, + "CCTCCCCCC": 29286, + "CCCAAGAA": 29287, + "CCCAACAA": 29288, + "GGCTTTGTG": 29289, + "GTATGGCCATTTT": 29290, + "TCAGACTTA": 29291, + "TCGACGTG": 29292, + "TCGACGAA": 29293, + "CTTATTTATT": 29294, + "TTATATCTG": 29295, + "TGATACACA": 29296, + "TCATATGTG": 29297, + "GTATACTTA": 29298, + "GGTCCTGCC": 29299, + "GAGTTTTAAA": 29300, + "TCTTTGTCAGTGGGCAAACGTA": 29301, + "GCCCAGCGACAGCCCCAAAACATCA": 29302, + "GGCTĊ": 29303, + "GTGGAAAAAA": 29304, + "GTTCAGGTT": 29305, + "GCAGTCAAA": 29306, + "GTACCTTTT": 29307, + "GTACCTCTG": 29308, + "GCCTATC": 29309, + "CTCGAGCC": 29310, + "GAAACACAAA": 29311, + "GCTTAAAAAA": 29312, + "GCTTCCTTA": 29313, + "GTGATAGTT": 29314, + "GTTAGTTAA": 29315, + "CTCCCGAA": 29316, + "GAAAAGACAA": 29317, + "TTTGTATTA": 29318, + "TCTAGTGTT": 29319, + "GCCAGCTTTT": 29320, + "GTCCCGC": 29321, + "CTTTTCATTTT": 29322, + "CGATACTT": 29323, + "GACATACC": 29324, + "GGTTGGCA": 29325, + "CATACAATT": 29326, + "GGCAGCTCC": 29327, + "TTTTTTTCTTA": 29328, + "CTAAATGCA": 29329, + "CACCTCCAA": 29330, + "TGATTTAATT": 29331, + "GTCAAGATG": 29332, + "CAAAAACAAAAAA": 29333, + "GCATTCCCA": 29334, + "GCATTTCACTG": 29335, + "GCCTGTAGG": 29336, + "AAATTTGAA": 29337, + "TGAGATATA": 29338, + "CATTACAA": 29339, + "GGATTGATT": 29340, + "CTTGGGGAA": 29341, + "AAATACAAAAATTA": 29342, + "TCCCATCAA": 29343, + "GTTGATCTTTT": 29344, + "GATCGATT": 29345, + "GCTCTTATG": 29346, + "GGAAAGGAAAGGAAAGGAAA": 29347, + "TGGGGTAGAGGATT": 29348, + "TAATCAATG": 29349, + "TAAGTTTGA": 29350, + "TCACAGATT": 29351, + "TCACAACA": 29352, + "TCATAAGG": 29353, + "TCAGCCCTG": 29354, + "GAACCTC": 29355, + "GAATCTGAA": 29356, + "CAATTTTATT": 29357, + "CAAGTTCTT": 29358, + "CTTTCACC": 29359, + "GTGGCCAGA": 29360, + "GTTGGAATT": 29361, + "GCATTACA": 29362, + "GCCCTCAAA": 29363, + "GCCTCACTG": 29364, + "TAAAGCCTT": 29365, + "TAAAGATAAA": 29366, + "GTCTCGGA": 29367, + "CAGGCATCA": 29368, + "TCAATCAAAA": 29369, + "GCTTTATGAA": 29370, + "GTGACGCA": 29371, + "GTGAGTTTG": 29372, + "CTCAGTGAAA": 29373, + "TTTTAGAGG": 29374, + "CTCCCCTGA": 29375, + "CTAACAGTT": 29376, + "GACAGCCAA": 29377, + "GGTGCCCC": 29378, + "GCTAGCCAA": 29379, + "TCTCAGACA": 29380, + "TCCTTGCCTT": 29381, + "CACCTAGG": 29382, + "CAGCACACA": 29383, + "GTTAAGCAA": 29384, + "TAATGCCTG": 29385, + "CATAATAAA": 29386, + "TCAGGCTGA": 29387, + "GGAGATAAA": 29388, + "GGAGACGGA": 29389, + "GTTTAĊ": 29390, + "TGACAGAATT": 29391, + "CTGGATATA": 29392, + "CCAGATGTT": 29393, + "CTGCAAAAAA": 29394, + "GTCTACAAA": 29395, + "CCTCTTGAGAAA": 29396, + "GTCCACCTG": 29397, + "GATGAAGCTG": 29398, + "GACAGGAAAA": 29399, + "TTTTATGAAAA": 29400, + "TAATCCCAGCACTTTGGGAGGCCGAGGTGGG": 29401, + "TCCTCTTTTATT": 29402, + "GCAGTGAGCCGAGATCGTG": 29403, + "TGACACAGGTCAAACGTTTT": 29404, + "CAGTTTTATT": 29405, + "GGTATTAAA": 29406, + "TCCTGAGGA": 29407, + "CTTTAAAATT": 29408, + "GTTTATATG": 29409, + "GCCGATGA": 29410, + "GCCCCACTG": 29411, + "TGTGGACC": 29412, + "TATTGTAAAA": 29413, + "TATTGGCAA": 29414, + "GAGAGGAAAA": 29415, + "GGAACAGTG": 29416, + "CCTTTATGA": 29417, + "CCTTTCTCTT": 29418, + "GCAAGACTAA": 29419, + "GTAATTTTAA": 29420, + "GATTTACTA": 29421, + "CCAACGTG": 29422, + "CCAAGTGTT": 29423, + "TTTGTTATT": 29424, + "CTTTTCTAAA": 29425, + "CGTTGCTG": 29426, + "GGTACGTT": 29427, + "CTCTTGTCA": 29428, + "CTCTTCCCCA": 29429, + "GTCTTCAGA": 29430, + "CTCAACTA": 29431, + "GTCAAGGAA": 29432, + "CCATTGATT": 29433, + "TAATGTCTA": 29434, + "GAGCATTTA": 29435, + "TCATGTATG": 29436, + "TAAATTCTAA": 29437, + "AAATGTATA": 29438, + "TCAGGCATT": 29439, + "CTTCTTGAGTT": 29440, + "GCCAGGTGTGGTG": 29441, + "TGATACTAA": 29442, + "GTGGACACA": 29443, + "TCTATATCTATA": 29444, + "GTACATATG": 29445, + "GAACCTGAA": 29446, + "GATGAAAGTGAAAGAGGA": 29447, + "CAAGATGAAAATATAA": 29448, + "GTTCACTCAGACTCA": 29449, + "TGAGTCCCTGGCGG": 29450, + "TCATCTTAA": 29451, + "TCACTACA": 29452, + "TGATCCAAA": 29453, + "TGAGCGTG": 29454, + "GAACCGG": 29455, + "CTTCAACA": 29456, + "CCAGCTGTT": 29457, + "CTGATATTTT": 29458, + "GTGAGGGCA": 29459, + "GCTGGGAGG": 29460, + "GTCAGGTCA": 29461, + "TTTTACAGTT": 29462, + "CCTGCGCA": 29463, + "TCCACTAAA": 29464, + "TTAAGATA": 29465, + "CGACATAA": 29466, + "CAAAATACTG": 29467, + "TGTAGGGG": 29468, + "TGCACCCA": 29469, + "GTAAAGATA": 29470, + "GCAAATACA": 29471, + "TCCTTGCAA": 29472, + "CAGAACAGAA": 29473, + "GTTAAGTA": 29474, + "CAGCGGCA": 29475, + "TAATGCTCA": 29476, + "CTATTCACA": 29477, + "GAGCATCTTTT": 29478, + "TATCAGAAA": 29479, + "TACTTCCAAA": 29480, + "GAATGGGGG": 29481, + "GCAGGCAAA": 29482, + "TGTGTTTGTT": 29483, + "GAGTCTTTG": 29484, + "GGGCTCCACGCAAGATCTCA": 29485, + "TCAGCATGACAATGATCCCAAACACACCGCC": 29486, + "CATAAAAATT": 29487, + "TTTTCCATT": 29488, + "TGACTCCTT": 29489, + "CAATTTTAAA": 29490, + "CAAGCATA": 29491, + "CTTTCAAAAA": 29492, + "GCACTTCC": 29493, + "CCACTTTAA": 29494, + "GAGATGATT": 29495, + "GAGATATTA": 29496, + "TAAAACAGTT": 29497, + "CATGTAGTT": 29498, + "GTAAGATAA": 29499, + "TATGTATATG": 29500, + "GATTCAATT": 29501, + "GATTTGTGA": 29502, + "TCTCTCTTTG": 29503, + "TCTCTAGAAA": 29504, + "CTCCTACTG": 29505, + "TCCATCACC": 29506, + "GAAAACACTT": 29507, + "GAAAATATATT": 29508, + "TCTACGTA": 29509, + "GCCATATC": 29510, + "CTTTTCCAAA": 29511, + "CGATACCA": 29512, + "CGATCGG": 29513, + "GGTTTAATT": 29514, + "TTTTTTCTG": 29515, + "CGTTCTTA": 29516, + "CTCTTGGAA": 29517, + "TATTTTCTTA": 29518, + "GCATTCAGA": 29519, + "GCCTTGAAA": 29520, + "GCCTTGGCC": 29521, + "GACTTTGAA": 29522, + "TTATGTTATG": 29523, + "TAAATTTCAA": 29524, + "CCATGGTGA": 29525, + "CCTGAGTTA": 29526, + "GATTATTTTA": 29527, + "TGACACTTA": 29528, + "GTTGGTAGG": 29529, + "GTTTTAATTA": 29530, + "GCTCAAGGGCA": 29531, + "TCTACATTA": 29532, + "GTAATGGGGGTG": 29533, + "GGTTTGTTTT": 29534, + "TCTTTACCTA": 29535, + "CATTCTTTCCTTTACA": 29536, + "TAAAAGTGTTCCTATTTCTCCACATCCTCTCCAGCA": 29537, + "CCGTCTATGGGGTCGCACAGA": 29538, + "TGATAAGG": 29539, + "TGAGCACA": 29540, + "GAAGTGATT": 29541, + "TCCCTCTCA": 29542, + "GTGCAATA": 29543, + "GTGCACAAA": 29544, + "GTATGAATT": 29545, + "TATTCAATA": 29546, + "TCTTGGCTA": 29547, + "CTCATGGAA": 29548, + "CCTTGTAAA": 29549, + "CATGCCCTT": 29550, + "GTAAGACC": 29551, + "CCAATTATT": 29552, + "CTAAGTCC": 29553, + "TCCATCCATTTG": 29554, + "GAAAAGCTA": 29555, + "GTTTTTACTT": 29556, + "CCCAGGATT": 29557, + "TAGACAGTT": 29558, + "GACACTAAA": 29559, + "TTCAGCAAA": 29560, + "GGAAATATA": 29561, + "GGCCTACA": 29562, + "GCTGACGG": 29563, + "GTTAACTAA": 29564, + "GCATTGTTA": 29565, + "TGATGCAGA": 29566, + "TACAATAAA": 29567, + "GAGCAGGTT": 29568, + "GCTCGGA": 29569, + "GCAGGGCAA": 29570, + "CTTGGCAAA": 29571, + "CTTTAGATG": 29572, + "TCGAGCAA": 29573, + "GTTGGCAGA": 29574, + "GCCCAGGAGG": 29575, + "GTTGAATTTT": 29576, + "TATAAAAGACACCTGTCCACA": 29577, + "TCAGTCCTGAA": 29578, + "CAGGCAGGCAGGCAGG": 29579, + "TCAAATACTTATG": 29580, + "GGTAGGAAA": 29581, + "TAAGCGA": 29582, + "TGACTGCA": 29583, + "GAAAAAAAAAAAAA": 29584, + "TCCTATTAA": 29585, + "CCAGTTTA": 29586, + "GTATATTTG": 29587, + "CTATCCCTT": 29588, + "CTCTAATTTT": 29589, + "GTCTGAATG": 29590, + "TGTGCTGTT": 29591, + "TGTGCACTG": 29592, + "TATTGGATT": 29593, + "TCAATACTG": 29594, + "TCTCGGA": 29595, + "CCAACCCTT": 29596, + "TCCACTCAA": 29597, + "GCCACGCTG": 29598, + "CCCATCCTT": 29599, + "TGGGGTCC": 29600, + "TAGATACC": 29601, + "TAATTCTTAA": 29602, + "CGTTCGTT": 29603, + "GGCCCGTG": 29604, + "GTGAATGTA": 29605, + "GTATTCTTTT": 29606, + "GTCTTCTTA": 29607, + "TCTTTTGATTTT": 29608, + "TAATGTCAAA": 29609, + "GGGAAGGGTA": 29610, + "GAGCTCCAA": 29611, + "CTATGCTA": 29612, + "GAGAAATTAA": 29613, + "CTTGGCACA": 29614, + "TTTTTGGTAGAGA": 29615, + "GAAGAATGAA": 29616, + "GTTGTTAAAA": 29617, + "TTTTTAGATT": 29618, + "GAAAAAAATTA": 29619, + "CATATACATATA": 29620, + "GCCTGACCAACATG": 29621, + "GGTGAGATCTT": 29622, + "CTCCTGACCTCAGGTGATCCACCCA": 29623, + "GTAGAATAATAGTGAAGACA": 29624, + "TAACCCAGG": 29625, + "TCAGCCCC": 29626, + "GAAGTTCTT": 29627, + "TCCGTAAA": 29628, + "TCCCTGTA": 29629, + "TCCCGAAA": 29630, + "CTTGCCAAA": 29631, + "CTTCGGTG": 29632, + "GCATCAGTG": 29633, + "CCATTTTAAA": 29634, + "GCCTCTCAA": 29635, + "GCCCGCTT": 29636, + "GCCTACAAA": 29637, + "CTCGCCTG": 29638, + "TGTGCATTTT": 29639, + "TATATAAATT": 29640, + "CAAAGAAATA": 29641, + "CTGAGTCTG": 29642, + "GCTTGACAA": 29643, + "GTGATACTG": 29644, + "GCTGCAGTA": 29645, + "GCAAGGTCA": 29646, + "CGGGCTA": 29647, + "GATTCTCTT": 29648, + "GTTAGGGAA": 29649, + "TACAGTGGG": 29650, + "TCCAGTTTG": 29651, + "GCCATCACAAA": 29652, + "CCCAGCAGTG": 29653, + "TAGACGTA": 29654, + "GATACCTA": 29655, + "TCTCCCCCC": 29656, + "CGTGTCCCCCTG": 29657, + "TGATTTCTTA": 29658, + "GCTCAGTA": 29659, + "TCTTTTTTTTTTTTTTTTTT": 29660, + "CGGAGACA": 29661, + "GAAAAAGAGG": 29662, + "TTTTAATGTT": 29663, + "GCCTTGGAA": 29664, + "GGCTGTGGGTTTG": 29665, + "GAGGAGACA": 29666, + "CAATGGTGTCA": 29667, + "TCAGGTGTT": 29668, + "CTATGGAAA": 29669, + "TAGCTTATT": 29670, + "CTCCAGAAAA": 29671, + "CTCCACGA": 29672, + "TCGATCTT": 29673, + "CTATACTTA": 29674, + "GTGTACATG": 29675, + "GTAGACTCA": 29676, + "TCCCTTGTAAGTT": 29677, + "GGCGGGCGGA": 29678, + "GTTGGAGACCA": 29679, + "TATATATATATATATATATATATATATATATATATATATATATA": 29680, + "GACTCCATCTCAAAAAAAAAAAAAAAA": 29681, + "GCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGGCGG": 29682, + "GAGATTATCAGTGGTCTTGTATG": 29683, + "CCGĊ": 29684, + "TAAGCTGTT": 29685, + "TAATCAGTT": 29686, + "TCACCACA": 29687, + "TCACGATG": 29688, + "TCACTCTGTT": 29689, + "TGATAATAAA": 29690, + "TCCTTTTCTT": 29691, + "CAAGCCAAA": 29692, + "CTTCAAAAAA": 29693, + "CCAGCACA": 29694, + "GCCTATAAA": 29695, + "TAAACATACA": 29696, + "CTCTTTGAAA": 29697, + "TATTCATATT": 29698, + "CAAATAAAAAA": 29699, + "GAGACTAAA": 29700, + "TCTGCTGTG": 29701, + "TCAAGGTGA": 29702, + "GTAAGTC": 29703, + "TTTTATACTT": 29704, + "GGTGGCAAAA": 29705, + 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+ "GACAGCA": 3741, + "GAATGAAA": 3742, + "GGTCTGA": 3743, + "CAGAGCTG": 3744, + "GGAAAATT": 3745, + "TGAGTAGCTG": 3746, + "CATACAA": 3747, + "TAGTATT": 3748, + "GGAGGAAA": 3749, + "GAAATGTG": 3750, + "CTCACCA": 3751, + "TAAĊ": 3752, + "CCTTTCA": 3753, + "TCAACTA": 3754, + "TCCCCCC": 3755, + "TATTTATTA": 3756, + "GTAACAA": 3757, + "CTTGCCA": 3758, + "TCAGGAGA": 3759, + "GGGGCGG": 3760, + "TACGAA": 3761, + "TCCACTA": 3762, + "CATTGCA": 3763, + "CCGGGA": 3764, + "TCTCAGCTCACTGCAA": 3765, + "GAGGTTA": 3766, + "CTCCAGGA": 3767, + "CCTCAAAA": 3768, + "GTGCTCC": 3769, + "GTAATTAA": 3770, + "TCTTTGAA": 3771, + "TCGGCCTCCCAAA": 3772, + "GGATTCA": 3773, + "CCTGTAGTCC": 3774, + "TCCATTTG": 3775, + "TTTTTATA": 3776, + "TAGTGAA": 3777, + "CACATCA": 3778, + "GTCCTCA": 3779, + "TATGGTA": 3780, + "TAATCAAA": 3781, + "GGATCAA": 3782, + "TAAATAAATAAATAAA": 3783, + "GACTGAAA": 3784, + "TGTTTCTG": 3785, + "CAGCTCTG": 3786, + "CACAGCAA": 3787, + "GCCTATA": 3788, + "CGGAGCTT": 3789, + "TACTTAAA": 3790, + 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+ "GCTTTCTG": 4856, + "TCGATAA": 4857, + "TTTTATAA": 4858, + "GAGGGAAA": 4859, + "GGAAGATG": 4860, + "CATCTGTG": 4861, + "CATATCA": 4862, + "TTTTCTTTT": 4863, + "TATGGAAA": 4864, + "TCTTCTAA": 4865, + "GATACAA": 4866, + "GCCTACA": 4867, + "CATTTCTC": 4868, + "CAAAATAA": 4869, + "TAAAACAAA": 4870, + "GCGCTGA": 4871, + "CATTCTAA": 4872, + "GTAATAAA": 4873, + "CCCACTA": 4874, + "TACTGTG": 4875, + "TGGAGTG": 4876, + "GGGGTTA": 4877, + "TCCAAATT": 4878, + "CATCCACA": 4879, + "GGGGATT": 4880, + "GTGCTTTT": 4881, + "TCTTGGTT": 4882, + "GGAAGAAAA": 4883, + "CTCTCATT": 4884, + "GAATTAAAA": 4885, + "TAAACAGA": 4886, + "TCGCCAA": 4887, + "GCGCTCA": 4888, + "GGACACAA": 4889, + "GTCCTTTG": 4890, + "TATAGATT": 4891, + "CCATGTTA": 4892, + "CCACAGCA": 4893, + "TGTAGAA": 4894, + "TAAAATCA": 4895, + "GTGACAGA": 4896, + "GGACTCC": 4897, + "GTAATGA": 4898, + "CGGCTA": 4899, + "GGGGGTG": 4900, + "TATTCACA": 4901, + "CGCGCA": 4902, + "GCAAGTA": 4903, + "TGATGTTA": 4904, + "TCCACAAA": 4905, + "GATTAAAAA": 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5107, + "GTGGAGGA": 5108, + "CACATATA": 5109, + "CTGAGATT": 5110, + "GTAAGAAA": 5111, + "CAAAATCA": 5112, + "GTCTTATT": 5113, + "CATCTGAA": 5114, + "CTAATGA": 5115, + "CGACCA": 5116, + "CCTTTTAA": 5117, + "GGGTCAA": 5118, + "CCTGGAGA": 5119, + "CGCCAAA": 5120, + "GTTTTTTAA": 5121, + "TAACTAAA": 5122, + "TTAGATT": 5123, + "TCTATAAA": 5124, + "GGCAAAAA": 5125, + "TGTGTATA": 5126, + "TAACATAA": 5127, + "CTAAGAAA": 5128, + "TGGATTTT": 5129, + "TCTTAATG": 5130, + "TCGATCA": 5131, + "TCTGTAAA": 5132, + "GTCAGCCA": 5133, + "GACATGAA": 5134, + "TCTTCCAA": 5135, + "GATGCCA": 5136, + "GCAGAGCA": 5137, + "TAATGTTTT": 5138, + "GGAGAAGG": 5139, + "GAGGTGA": 5140, + "CAGGCATG": 5141, + "GTCAGTA": 5142, + "TAGACTA": 5143, + "GAGCTGTG": 5144, + "TTAGCAA": 5145, + "GCATACA": 5146, + "TCTGGCTG": 5147, + "TTTTTTTCTT": 5148, + "GAAGTTTT": 5149, + "CTCCTCTT": 5150, + "GATTGTA": 5151, + "CAACACC": 5152, + "CTCAGCAA": 5153, + "GACAGTT": 5154, + "TTAGTTA": 5155, + "TGTTGTTA": 5156, + "GGTCCAA": 5157, + 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5258, + "TTATGTA": 5259, + "CACATGTG": 5260, + "TTATTTAA": 5261, + "TCGCCTT": 5262, + "GGAGAAGA": 5263, + "TAGATAA": 5264, + "GAATGAAAA": 5265, + "TCAATGTT": 5266, + "CCAGTTTT": 5267, + "CGATAA": 5268, + "GTTTATAA": 5269, + "TCTGGGAA": 5270, + "CTTTCTTA": 5271, + "TCTGGCTT": 5272, + "GTCAAATG": 5273, + "TCTTCTTA": 5274, + "CAGGTCA": 5275, + "CCCCCAAA": 5276, + "CTCTTCTG": 5277, + "TAAACTGA": 5278, + "CCTTGGA": 5279, + "CCTCTGAA": 5280, + "CATAATAA": 5281, + "TAAATATAA": 5282, + "CTCTCCAA": 5283, + "TTAAGAA": 5284, + "GAGGGTG": 5285, + "TAAAATGAA": 5286, + "CTGCAGTGA": 5287, + "GACAGGAA": 5288, + "TTTTTGAA": 5289, + "CATTGATT": 5290, + "CGCTGAA": 5291, + "TCTATATT": 5292, + "CTCTCCTG": 5293, + "CTCCACTT": 5294, + "CAATATTA": 5295, + "GAGATGAA": 5296, + "TAGAGTA": 5297, + "TGTTGATT": 5298, + "TCCAGTGA": 5299, + "GAAATGTA": 5300, + "TTACTAA": 5301, + "GAAATTCA": 5302, + "TCGGGAA": 5303, + "TCTAGTA": 5304, + "GCTATCA": 5305, + "GAAGAATT": 5306, + "CCATAGG": 5307, + "GAAATGGG": 5308, + 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"GATCGG": 5359, + "TCTCAAAAAAAAAAAAAAAA": 5360, + "CTACGA": 5361, + "TCTTCATG": 5362, + "CAACTAAA": 5363, + "TAAAACTT": 5364, + "CTCCTGCCTCAGCCTCCCGAGTAGCTG": 5365, + "TATTTATAA": 5366, + "GTGAGTA": 5367, + "CACTAAAA": 5368, + "GTCTCCTG": 5369, + "GGAATGA": 5370, + "GCCCAGCC": 5371, + "CTCCCTCA": 5372, + "CATTCTGA": 5373, + "CCCATCAA": 5374, + "CAAAATAAA": 5375, + "GACACCTG": 5376, + "TAAAAAAAAAA": 5377, + "TGAACAGA": 5378, + "GGTTAAAA": 5379, + "GAAATGGA": 5380, + "GAAACATG": 5381, + "GACACAAA": 5382, + "CGTTTTAA": 5383, + "CGCAATT": 5384, + "GTCCAGTG": 5385, + "CTGAAATT": 5386, + "CCCCCC": 5387, + "TCTCTATG": 5388, + "TCTACAAA": 5389, + "CTTTTAAAAA": 5390, + "CCAGGGAA": 5391, + "TATTTGAAA": 5392, + "CATTGTTA": 5393, + "TCCCTGAA": 5394, + "CTTTCTCC": 5395, + "GTTGCTA": 5396, + "GAGAGTA": 5397, + "TAAAATTTG": 5398, + "CTTTTTTAA": 5399, + "GTTTCTGA": 5400, + "GGAATTAA": 5401, + "GTTCTGTT": 5402, + "TCTTTGCA": 5403, + "GAGCTTTT": 5404, + "GAAATAGA": 5405, + "GGTGGGGG": 5406, + "TGTATGTG": 5407, + "TAACACAA": 5408, + "CATTATTA": 5409, + "CAGAGATG": 5410, + "TAACTGAA": 5411, + "GAAAGATT": 5412, + "CAGGCACA": 5413, + "TCTTTCCA": 5414, + "TGTTGCTG": 5415, + "CGCCATG": 5416, + "CCCTCAAA": 5417, + "TATTTAGG": 5418, + "CTGCAGAA": 5419, + "GAAACCAA": 5420, + "GCATAGG": 5421, + "TATTCATG": 5422, + "CGACCCCA": 5423, + "TGACACC": 5424, + "GCAATTAA": 5425, + "GCTTCTCA": 5426, + "GCACGTG": 5427, + "GGAGCTA": 5428, + "CTAGGTG": 5429, + "CTGGGCC": 5430, + "AAATGAAAA": 5431, + "TAAGTCC": 5432, + "GGACACC": 5433, + "CTCTTTTTT": 5434, + "GGCAGCC": 5435, + "TAAAAACTA": 5436, + "TCCTCTCA": 5437, + "TATTTTAAAA": 5438, + "GGAGGCAGA": 5439, + "TAAAAATAAA": 5440, + "GATAATAA": 5441, + "CAAĊ": 5442, + "TCTGGAGA": 5443, + "TTTTAGAA": 5444, + "GTTAATTA": 5445, + "TATTGTGA": 5446, + "GTTAAATT": 5447, + "CAGCTGAA": 5448, + "CCAATGAA": 5449, + "CTTCCTCC": 5450, + "TTCAGAA": 5451, + "TAACCTA": 5452, + "CTGGCTA": 5453, + "CAATAATT": 5454, + "CATTTACA": 5455, + "CCCACGG": 5456, + "CTCCACAA": 5457, + "TCCACTGA": 5458, + "TAAAATTAAA": 5459, + "CACAGCTT": 5460, + "CTCCTGGA": 5461, + "TATCTATA": 5462, + "TCGTTTG": 5463, + "GCACAGCA": 5464, + "CGCTTTG": 5465, + "TTTTACTT": 5466, + "TATCTGTG": 5467, + "TCAAATTTT": 5468, + "CACACGG": 5469, + "CATAAAAAA": 5470, + "TCAGTGTT": 5471, + "GCTCAGTG": 5472, + "GAGATTTG": 5473, + "CTCCTGCA": 5474, + "GCGATGA": 5475, + "CAACCCA": 5476, + "TCTTTAAAA": 5477, + "GCAATGAA": 5478, + "TAACCCA": 5479, + "CCTTTCTG": 5480, + "GTCACATG": 5481, + "TCCACATG": 5482, + "GTTĊ": 5483, + "TGATGCC": 5484, + "TTTTCTTA": 5485, + "TCCCCATT": 5486, + "GACACCA": 5487, + "CTCAGTGA": 5488, + "CTCCCAGG": 5489, + "GGCCAAAA": 5490, + "CGAGGTG": 5491, + "GTTTCTTA": 5492, + "GCTGCAGA": 5493, + "CTGTGCC": 5494, + "GTTACCA": 5495, + "TTTTTCTG": 5496, + "TCCGAA": 5497, + "TCAAGATT": 5498, + "TTTTATATA": 5499, + "GTCCCTGA": 5500, + "GTTTTTTTG": 5501, + "CACAGTAA": 5502, + "GTTTTTCA": 5503, + "GCCACCGCGCC": 5504, + "GGATAAAA": 5505, + "GCCATTTG": 5506, + "GTCTCTCC": 5507, + "CAATGAAAA": 5508, + "GTTGGAAA": 5509, + "CCTGGTA": 5510, + "TGTGTGTGTT": 5511, + "GAGAGTG": 5512, + "GCGCTCC": 5513, + "TCCCAGGA": 5514, + "GCAGTGAA": 5515, + "CAGGGGAA": 5516, + "CTCAGTCA": 5517, + "TAAACCAA": 5518, + "CACGGTGAAA": 5519, + "CATAGTA": 5520, + "CTAGCTA": 5521, + "CACATTCA": 5522, + "TCTCTATA": 5523, + "TGATGAAAA": 5524, + "TAGTTTTA": 5525, + "TAACATCA": 5526, + "TGATATTA": 5527, + "GTTAGTTA": 5528, + "TGTTCAAA": 5529, + "GTTTACC": 5530, + "GCGAGTG": 5531, + "TCAGCTA": 5532, + "CTTCTGTT": 5533, + "CAGATGGG": 5534, + "TTTGGAA": 5535, + "CTTAATTA": 5536, + "GGAGGCAGAGGTT": 5537, + "TATCTGAA": 5538, + "TAAGAATT": 5539, + "TAAAGCTT": 5540, + "TCCAGTCA": 5541, + "CTTTCTGA": 5542, + "GAGĊ": 5543, + "TGTGGATG": 5544, + "CAATTATT": 5545, + "TCTCCA": 5546, + "GCAGACC": 5547, + "CTGATTTA": 5548, + "TATCTCTG": 5549, + "GCCACCTG": 5550, + "CAAAATTTT": 5551, + "CCCAAAAA": 5552, + "GGTAGAA": 5553, + "GCTGCTCA": 5554, + "TCTCTGTGAGTT": 5555, + "CCCACAAA": 5556, + "GCAAGAAA": 5557, + "CTCCCAAA": 5558, + "GGGATTTT": 5559, + "CATTATTTT": 5560, + "TCCTACC": 5561, + "CCTTCATT": 5562, + "TGAATTAAA": 5563, + "TGCATGTG": 5564, + "CCTGTAGTCCCAGCTA": 5565, + "GCGCATG": 5566, + "GGAACCA": 5567, + "TATGCATT": 5568, + "TGAACTAA": 5569, + "GTGACTTA": 5570, + "CGGCGC": 5571, + "GTGGAGAA": 5572, + "CTCTCTTA": 5573, + "TCTTGTTTT": 5574, + "GCTGGAAA": 5575, + "TCCACCAA": 5576, + "GATAGAA": 5577, + "TGAAGAAAA": 5578, + "CTGACAAA": 5579, + "TAACATTTT": 5580, + "TCAATATA": 5581, + "CTCATTAA": 5582, + "GTCĊ": 5583, + "TATTGG": 5584, + "TAATGCC": 5585, + "GCACAGTG": 5586, + "GAGAGTT": 5587, + "CGAGATT": 5588, + "GGAGCTGA": 5589, + "TAAGACC": 5590, + "CTGGTTTT": 5591, + "TTAGCC": 5592, + "GCTGGGGG": 5593, + "CCCAGCTT": 5594, + "CTGAGGTT": 5595, + "CTTCCTCA": 5596, + "CAGGCC": 5597, + "CGCTCTG": 5598, + "GTTAATTTT": 5599, + "CCACTTTT": 5600, + "CTCGATG": 5601, + "GTTATGA": 5602, + "TCTAGAAA": 5603, + "CCCCTCTG": 5604, + "GTGGGAAA": 5605, + "CTCTACTT": 5606, + "TCCAGATG": 5607, + "TGTGTGGG": 5608, + "TCCAGAAAA": 5609, + "GCATTCTCA": 5610, + "TCTTTCTTTT": 5611, + "TCTCAAAAAA": 5612, + "CCTATCA": 5613, + "TAAATAGG": 5614, + "TGTTCTTA": 5615, + "TTTGTGA": 5616, + "GTCCCAA": 5617, + "TAGATATA": 5618, + "TCTTGTAA": 5619, + "CATTCCAA": 5620, + "GTCAGAGA": 5621, + "CTAAGTT": 5622, + "TAAAGTC": 5623, + "TTATCTG": 5624, + "GTGTCTGA": 5625, + "GTCCTCTG": 5626, + "TCTTGGCTCACTGCAA": 5627, + "CATTCTTTT": 5628, + "GAAAAAAAAAA": 5629, + "CTTGCTA": 5630, + "GGAGGAAAA": 5631, + "TCAGGAGATCGAGA": 5632, + "GTAGCCA": 5633, + "GGCCTCA": 5634, + "GCGCCAA": 5635, + "GGAAATGGCAACCCA": 5636, + "GCTCCATG": 5637, + "GCCGGGG": 5638, + "GAAGACC": 5639, + "CCTGCAGG": 5640, + "TCGTCTG": 5641, + "CTCAGTAA": 5642, + "CAAGCAAA": 5643, + "CAAATACA": 5644, + "TAAGAGAA": 5645, + "GGAĊ": 5646, + "TGAGGAAAA": 5647, + "GCCCCAAA": 5648, + "TTACAGG": 5649, + "GGAATGAA": 5650, + "CATAAATT": 5651, + "CTTTATAA": 5652, + "CCCCTTTT": 5653, + "TACTTTTA": 5654, + "CATTCTTA": 5655, + "GAGACTA": 5656, + "GCCAGGTG": 5657, + "TATGAGA": 5658, + "GTACAAAA": 5659, + "TCTTTGGG": 5660, + "TGTTCATG": 5661, + "CTCAGAGG": 5662, + "CATAGCA": 5663, + "TACCATG": 5664, + "CACACTGA": 5665, + "CTTTGGAA": 5666, + "CATTGTCA": 5667, + "CCTGGAAA": 5668, + "GGACAAAA": 5669, + "TGTTGCC": 5670, + "CAAAGA": 5671, + "CTCCTGACC": 5672, + "TAGGTGA": 5673, + "GCGATCA": 5674, + "CATACATG": 5675, + "TCACACC": 5676, + "GACAGTGA": 5677, + "CAAGGTA": 5678, + "CATAATTA": 5679, + "GCAGCAGCAGCA": 5680, + "TGTTTGGG": 5681, + "CAAACCAA": 5682, + "TAATATAA": 5683, + "TCTTATTA": 5684, + "GCGGGCGCC": 5685, + "CTTCAGTT": 5686, + "TATTCAGA": 5687, + "CATACTA": 5688, + "GTCCCAGG": 5689, + "TATTATTTA": 5690, + "TGTTCACA": 5691, + "TCTCTAAA": 5692, + "CGCGTG": 5693, + "TTTTTTTTAAA": 5694, + "TTTTTGCA": 5695, + "GTTAGGA": 5696, + "CTCATGA": 5697, + "GAACTGAA": 5698, + "CAATCAAA": 5699, + "GCCCAAAA": 5700, + "GCTAATAA": 5701, + "CATATGA": 5702, + "GTAGGGA": 5703, + "CCACGTG": 5704, + "GCGTCTT": 5705, + 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5756, + "CACAGATT": 5757, + "CTGGAGAA": 5758, + "GCCTGAAA": 5759, + "GTCCCTA": 5760, + "CACAAAAAA": 5761, + "GCCAAATT": 5762, + "GGACTTTT": 5763, + "CACATACA": 5764, + "GACACTA": 5765, + "CCCCCGG": 5766, + "GGCCCGG": 5767, + "GTTTGATT": 5768, + "CTCCTCCA": 5769, + "GGCCCATT": 5770, + "TCCCAGTG": 5771, + "GTCTCTAA": 5772, + "TTTTTATG": 5773, + "TCACAGCA": 5774, + "GCCACAAA": 5775, + "TCGAGGA": 5776, + "TCTGCAGG": 5777, + "TCTTCAGG": 5778, + "CACAGA": 5779, + "GCCTGGAAAA": 5780, + "CAGACTGA": 5781, + "CAGAGTTA": 5782, + "GCAAGCAA": 5783, + "CATĊ": 5784, + "CTGGGAAA": 5785, + "TATATGCA": 5786, + "TCAGAATT": 5787, + "GTCTACC": 5788, + "TAAAAGAAA": 5789, + "CCTTCTC": 5790, + "CCTTGTGA": 5791, + "GATTTTAAA": 5792, + "TTTTAATTA": 5793, + "GCACAAAA": 5794, + "CTAAGTG": 5795, + "TAGAAAAAA": 5796, + "TGAACATG": 5797, + "CAGATGTT": 5798, + "TGAGTAAA": 5799, + "CATTGCTT": 5800, + "GTTTAGTT": 5801, + "GGATGAAA": 5802, + "AAAAAATTA": 5803, + "GATGCAA": 5804, + "GTACCTA": 5805, + "CCCTAAAA": 5806, + "GTAGGCA": 5807, + "TTAATTTG": 5808, + "GAGATAA": 5809, + "CCCCCAGG": 5810, + "TCAATGTG": 5811, + "GCCACTGA": 5812, + "TCGACTG": 5813, + "CTTCTCTG": 5814, + "GATGGAAA": 5815, + "TAGATAAA": 5816, + "GGAGTTTT": 5817, + "GAGGAGAGGA": 5818, + "TTTTGCA": 5819, + "GTGATGTT": 5820, + "GCTGTCTT": 5821, + "TCTAGTG": 5822, + "GGTTATG": 5823, + "GATGCTCA": 5824, + "GTTGTAAA": 5825, + "GTTCAATT": 5826, + "GGATACA": 5827, + "CGCCCGCCA": 5828, + "TCCTACAA": 5829, + "GGAAGGTG": 5830, + "GCGGAGG": 5831, + "TAAAATATG": 5832, + "CCTTGCA": 5833, + "GCTCTTAA": 5834, + "TCACCTA": 5835, + "GTGAGAGG": 5836, + "GAGAGTCC": 5837, + "TCAGTGAA": 5838, + "GCTGGCTT": 5839, + "GCCAGGATGGTCTCGA": 5840, + "TCTGGGTG": 5841, + "TGGAGAAA": 5842, + "TTTTGATT": 5843, + "GAAĊ": 5844, + "TAAATGGG": 5845, + "CGCTTAA": 5846, + "CCACCTA": 5847, + "CATTTGGG": 5848, + "TCCCTCTT": 5849, + "CTGGTCC": 5850, + "TCTGCTGA": 5851, + "TTTTTTTTTTTTTTTTTTTT": 5852, + "TCTATATG": 5853, + "TTTTTTTTA": 5854, + "GTTCCCC": 5855, + "GGAACACA": 5856, + "TGAATATG": 5857, + "GCAACTA": 5858, + "GCCAGCCA": 5859, + "GCTCCACA": 5860, + "CTGCTGAA": 5861, + "TAGACTT": 5862, + "CAGATTAA": 5863, + "GTCACACA": 5864, + "CATCTCTT": 5865, + "TATAGTTA": 5866, + "GACTTTTA": 5867, + "TCCCCCTT": 5868, + "CCCACCTG": 5869, + "TATATATATATATATATATATATA": 5870, + "TCCTCCAA": 5871, + "GAAACAGG": 5872, + "TCCCCCTG": 5873, + "TGAAGCTG": 5874, + "CGGGAGA": 5875, + "GCGACTG": 5876, + "GTTCCAAA": 5877, + "CAAAGATA": 5878, + "TTACTTTT": 5879, + "TTAATTAAA": 5880, + "GCTTCTC": 5881, + "TCGAATG": 5882, + "TACTTTTTT": 5883, + "TCGGTCA": 5884, + "AAAAAAAAAAAAAAAAA": 5885, + "GAAAGACA": 5886, + "CAGAGGCA": 5887, + "GGGACAGG": 5888, + "TATATTTG": 5889, + "TCGCATT": 5890, + "TCAAGG": 5891, + "TAAAACTG": 5892, + "CTTGGCC": 5893, + "CATCCATT": 5894, + "GTCAGTGA": 5895, + "CCACGTT": 5896, + "CCAACTGA": 5897, + "CTTACACA": 5898, + "CGATTTG": 5899, + "CTTCCTGA": 5900, + "CTAAATGA": 5901, + "GAAGCAAA": 5902, + "CCTTAAAAA": 5903, + "TCGAGTG": 5904, + "TGAAGCC": 5905, + "TCAAGCAA": 5906, + "CGTGTGTG": 5907, + "CTCAAATG": 5908, + "CATACATT": 5909, + "GACTCAAA": 5910, + "CTAATAAA": 5911, + "GCCAGCTG": 5912, + "TAGACCA": 5913, + "GATTTATA": 5914, + "CTTCAGCA": 5915, + "CTAACCA": 5916, + "TAAGTATT": 5917, + "TAACCAAA": 5918, + "TCAGGCC": 5919, + "TTAATTTA": 5920, + "CTCACCTT": 5921, + "GAAAGCTT": 5922, + "GAAAATGA": 5923, + "TCCGTT": 5924, + "GAGACTGA": 5925, + "GTCACC": 5926, + "GATGCTA": 5927, + "GTCCTGTG": 5928, + "CAAACC": 5929, + "TAAAAGTAA": 5930, + "CTCCCTC": 5931, + "GGCTTAAA": 5932, + "TCCTCATT": 5933, + "TGAAGTAA": 5934, + "TCAACC": 5935, + "GAAACC": 5936, + "CAGAGTAA": 5937, + "CTTTATCA": 5938, + "GGAGAGCA": 5939, + "TCCATACA": 5940, + "GCCCGCC": 5941, + "GATTTGCA": 5942, + "CAAGGCC": 5943, + "GAAGGTA": 5944, + "GCGATCC": 5945, + "TCTTCATA": 5946, + "TCAGTAAA": 5947, + "GTATATTA": 5948, + "CTGAATTA": 5949, + "TCTTGTTA": 5950, + "TAGACCAGGCTG": 5951, + "CCTTTAAA": 5952, + "CTGATGTT": 5953, + "CCCAGTCA": 5954, + "TCGACCA": 5955, + "CTCATTCA": 5956, + "AAAATATT": 5957, + "GCACTTTT": 5958, + "TAATTTATA": 5959, + "TCCCAGTT": 5960, + "GCCATGAA": 5961, + "TTTTAATTTT": 5962, + "CATGTATT": 5963, + "GCTTCATT": 5964, + "GAAGAAAAA": 5965, + "GTCTGATT": 5966, + "GCGTCAA": 5967, + "CTCGGGG": 5968, + "TCTTTCTC": 5969, + "GCTCCAGA": 5970, + "CGGTTTG": 5971, + "TGAGTGAA": 5972, + "CCTGCCAA": 5973, + "TCCĊ": 5974, + "CGCGTC": 5975, + "CTAAGGA": 5976, + "GTGATTAA": 5977, + "TGAGACGGA": 5978, + "GTGATGGG": 5979, + "CTTATATT": 5980, + "TAAAAGTT": 5981, + "GTTATATT": 5982, + "TATCTATT": 5983, + "GCGCCTT": 5984, + "GCTATGTT": 5985, + "GGTTGTT": 5986, + "CACCTGTG": 5987, + "GAGCTGTT": 5988, + "GCCCTTTT": 5989, + "GAAATATTA": 5990, + "CGCTCAA": 5991, + "TCTGTCTA": 5992, + "GTTTTCTG": 5993, + "GATACTT": 5994, + "GGTCGG": 5995, + "TGTATATT": 5996, + "TAAGTGTGTA": 5997, + "TTTTCCAA": 5998, + "GCTCTCAA": 5999, + "CAAGGTCA": 6000, + "GAAAGGAAA": 6001, + "GATATAA": 6002, + "GTCTCACTCTG": 6003, + "TGAAGCAA": 6004, + "TAGATATT": 6005, + "TGCACTG": 6006, + "GGTTTGA": 6007, + "TCTCCATA": 6008, + "TAATTGAA": 6009, + "TAATTTAAA": 6010, + "CCTAGCA": 6011, + "GACTAAAA": 6012, + "GCCTCTGA": 6013, + "CTCTAGAA": 6014, + "CAAAGCTT": 6015, + "GTCCCC": 6016, + "CCCATGA": 6017, + "CCTCCAGA": 6018, + "CGTCATG": 6019, + "GGATCAAA": 6020, + "CGACTA": 6021, + "TTTTCTAA": 6022, + "GTCGCTCA": 6023, + "TCTTGTGA": 6024, + "CAGCTGTT": 6025, + "GGATTGCTT": 6026, + "GATCGTG": 6027, + "TAACAGCA": 6028, + "CATTCC": 6029, + "CGAGGAA": 6030, + "GAGAGACA": 6031, + "TATCTCAA": 6032, + "GAGAGAGAA": 6033, + "TTTATCA": 6034, + "GACAAATT": 6035, + "TTTAGCA": 6036, + "CCAACCTG": 6037, + "GTTATATA": 6038, + "GCAGGCTG": 6039, + "TAAATCTG": 6040, + "GCTCACACCTG": 6041, + "TGATGATGATGA": 6042, + "GAAAGCC": 6043, + "GAAAATTTT": 6044, + "GAGCTCTG": 6045, + "TACAAATT": 6046, + "GGATCCA": 6047, + "CTTACATT": 6048, + "TCTTTCCTT": 6049, + "GATAAAAAA": 6050, + "TAAGTGAA": 6051, + "GAAATGAAA": 6052, + "GCTGCATA": 6053, + "TCTACACA": 6054, + "CTTCAGTG": 6055, + "CCTTTATT": 6056, + "CAAGAGAA": 6057, + "CTGGGTA": 6058, + "CCAGAGAA": 6059, + "CTCTGATG": 6060, + "GAGGTTTT": 6061, + "TATGCACA": 6062, + "GTCATTCA": 6063, + "GTCACAAA": 6064, + "CCTTTGAA": 6065, + "TAATTCTA": 6066, + "GTCTTGCTCTG": 6067, + "GTTTAAAAA": 6068, + "TCTCTCCTT": 6069, + "GAAGAGCA": 6070, + "TAAGAAAAA": 6071, + "CCATAAAA": 6072, + "CTAATCA": 6073, + "CGCCACA": 6074, + "TAACAATT": 6075, + "TCTCCACC": 6076, + "CCCCCTA": 6077, + "GTCCAATT": 6078, + "GCAACACA": 6079, + "TCCACGG": 6080, + "CGCGGTG": 6081, + "GGATTTTA": 6082, + "CCTCCCGG": 6083, + "CAGACGG": 6084, + "GTTAGTA": 6085, + "GCTGGGCA": 6086, + "CATGGCC": 6087, + "TCTAGCA": 6088, + "TTATGAAAA": 6089, + "CGTAAAAA": 6090, + "GTTACCTG": 6091, + "CCACAGAA": 6092, + "GCCACCAA": 6093, + "CATTGTTTT": 6094, + "GTGACTGA": 6095, + "TATTATTAA": 6096, + "TAGGCAA": 6097, + "GATATTTG": 6098, + "GCAAGCC": 6099, + "GCCATCTCA": 6100, + "CATAAATG": 6101, + "CTTACAAA": 6102, + "GTTACAAA": 6103, + "GGGAGTG": 6104, + "CAATGCAA": 6105, + "CATGGAGA": 6106, + "GTTTTAAAAA": 6107, + "TCACTTGAGG": 6108, + "TCTGCACA": 6109, + "TACACTGA": 6110, + "GAAACTCA": 6111, + "TAATCATT": 6112, + "TCACTGAA": 6113, + "GGACAGAA": 6114, + "TAAAATGTA": 6115, + "TAAAAAAATT": 6116, + "TCTGTCTGTCTGTCTG": 6117, + "GTGAGATG": 6118, + "GCTGCACA": 6119, + "TAATGATT": 6120, + "TCCCACTT": 6121, + "CTGCCTA": 6122, + "CAGAGGGG": 6123, + "GCTTTCAA": 6124, + "CTAATATT": 6125, + "TCCAGGTG": 6126, + "TAGTGTG": 6127, + "TACGCC": 6128, + "GACGTGA": 6129, + "CTTAAATT": 6130, + "GAAAATATT": 6131, + "TGTTCCTG": 6132, + "CTCAAGTGA": 6133, + "CCGGGC": 6134, + "TTTGCAA": 6135, + "TAAACCTT": 6136, + "GCGTTAA": 6137, + "GCCATTAA": 6138, + "GTTGCAAA": 6139, + "GGTTGAA": 6140, + "CATTCCTG": 6141, + "TCTCTACTAAAAATACAAAAATTA": 6142, + "TGTGGCC": 6143, + "GCTTGGTG": 6144, + "TCTGGAGG": 6145, + "CTGCAAAA": 6146, + "TCTTGGAA": 6147, + "GTAATATA": 6148, + "TGTGCCTG": 6149, + "CTGGGAAAA": 6150, + "GATATCA": 6151, + "TTTTATATT": 6152, + "TACATCAA": 6153, + "CAATACAA": 6154, + "CTCCCGG": 6155, + "GATAATTA": 6156, + "GTGACTCA": 6157, + "CTAAAAATACAAAAATTA": 6158, + "GTGTGCC": 6159, + "CTCCCCTG": 6160, + "TCTAATTA": 6161, + "TAGATGAA": 6162, + "GTATCCA": 6163, + "GCAACAAA": 6164, + "CTTAGAAA": 6165, + "TCTACATT": 6166, + "TCCCTGTG": 6167, + "GCAAGGAA": 6168, + "TATGGGA": 6169, + "TCTTGAGA": 6170, + "TTTGCTT": 6171, + "CTAGGGG": 6172, + "CAGGTGGG": 6173, + "TCTCTAGG": 6174, + "GTAATGAA": 6175, + "TAGCCTG": 6176, + "TGTGTTCA": 6177, + "CTTTTCTG": 6178, + "GAAAATGAA": 6179, + "TAAATTAAAA": 6180, + "TAGACAAA": 6181, + "CACGCTG": 6182, + "GCAĊ": 6183, + "GCCCAGTG": 6184, + "CTCTACTG": 6185, + "GCCGAGG": 6186, + "TGTGTATT": 6187, + "TGATAATT": 6188, + "GTGTCCTT": 6189, + "CTCGG": 6190, + "CTAACAAA": 6191, + "TGGACAA": 6192, + "CACAAATT": 6193, + "TTAACAA": 6194, + "GCCCCTCC": 6195, + "TAACTTAA": 6196, + "TCCCACCA": 6197, + "TGAGGCTG": 6198, + "GTATTTTTT": 6199, + "TCCCAGC": 6200, + "CATAGAAA": 6201, + "TCAAGATG": 6202, + "GATACTG": 6203, + "GCCAGCTT": 6204, + "CCTGCGG": 6205, + "CAGCATCA": 6206, + "CTTTTTCTT": 6207, + "TGTGGAGG": 6208, + "GTCAGATT": 6209, + "TAAATAATA": 6210, + "CTCCACTA": 6211, + "CGAAAATT": 6212, + "TGTGGGTG": 6213, + "GGAATTTA": 6214, + "CCTTCCAA": 6215, + "CGACTGAA": 6216, + "GAATTTCA": 6217, + "GCTAGGA": 6218, + "TGACCTA": 6219, + "TGTACAA": 6220, + "TGTGAGCCA": 6221, + "TGATCAAA": 6222, + "GGAAGCTG": 6223, + "CCACATTTT": 6224, + "TAAAGTTTT": 6225, + "TCAAGTAA": 6226, + "GCAGGGAA": 6227, + "TCGAAAAA": 6228, + "CAAAGAGG": 6229, + "GATGCC": 6230, + "CTGATGGG": 6231, + "CTACCTA": 6232, + "TAACGTT": 6233, + "TAAACTTTT": 6234, + "GTCTGAGA": 6235, + "GATTCATT": 6236, + "TAGGCTT": 6237, + "CACCCAA": 6238, + "GTATATAA": 6239, + "TGTTGGTT": 6240, + "TGTTCC": 6241, + "TTTTTAAAAA": 6242, + "GTTAGTG": 6243, + "GTTAGAAA": 6244, + "GCACTCC": 6245, + "CTGATTAA": 6246, + "CATGCAAA": 6247, + "TAGCTTTT": 6248, + "GCCAGTCA": 6249, + "GCTTTTATT": 6250, + "TCATCATT": 6251, + "GTCCACTG": 6252, + "TTTTCCTG": 6253, + "TGTGGCTG": 6254, + "CCAATTAA": 6255, + "TGTTGATG": 6256, + "CTTCCCCA": 6257, + "GCTAGTG": 6258, + "GCCAGGTT": 6259, + "GTTTTTCTT": 6260, + "TAAACAGG": 6261, + "TGTGCACA": 6262, + "TCTGTATA": 6263, + "GTTTACAA": 6264, + "CTTCCATT": 6265, + "CATGGCTT": 6266, + "GCTCAATT": 6267, + "GCCGCTG": 6268, + "CTCTCTCTT": 6269, + "TCCATATT": 6270, + "CATGCATT": 6271, + "CTTTCCAA": 6272, + "GAATATTA": 6273, + "GAAGAGGA": 6274, + "GTCCATCA": 6275, + "TGTTGGAA": 6276, + "TTACTCA": 6277, + "CTCTAATT": 6278, + "CTGAGCTT": 6279, + "TCACCCAGGCTGGAGTGCAGTG": 6280, + "TCACGTG": 6281, + "CTAGGCA": 6282, + "CCAAGATG": 6283, + "TCCACTCA": 6284, + "CCCACCCCA": 6285, + "CTGTCTC": 6286, + "TATTCAAAA": 6287, + "GCAAGATG": 6288, + "TCTATGAA": 6289, + "GCCTCTCC": 6290, + "TCTCTGTGTA": 6291, + "TCACTTAA": 6292, + "TCATATCA": 6293, + "TAGACATT": 6294, + "TATTTTTTTA": 6295, + "GTGATGAA": 6296, + "TATGTCTT": 6297, + "GCCTCTCA": 6298, + "TCTAGGTT": 6299, + "TCATGCC": 6300, + "GTTTACTT": 6301, + "GTGACACA": 6302, + "GTTATTAAA": 6303, + "TAGTAAAA": 6304, + "GTTTTTATT": 6305, + "GTCTCTTA": 6306, + "CAGATAAA": 6307, + "GTCAGAGG": 6308, + "TCCTCCTCCTCC": 6309, + "TCCCATCA": 6310, + "TGTGCAAA": 6311, + "CTTATTCA": 6312, + "TTTTCTCA": 6313, + "CTTCTAAA": 6314, + "CTTTTCCTT": 6315, + "GATĊ": 6316, + "TGATGATG": 6317, + "CTAACACA": 6318, + "GCTATAA": 6319, + "TTACCAA": 6320, + "CATTTCAAA": 6321, + "TCTAGGA": 6322, + "GTCCTGGA": 6323, + "GTCAGGAA": 6324, + "GATTCAAA": 6325, + "TAGGATG": 6326, + "CTTGGGAA": 6327, + "CATTTTATT": 6328, + "TTACATA": 6329, + "GCCCATA": 6330, + "GAAGGGAA": 6331, + "TATTCATA": 6332, + "TGAGAATT": 6333, + "TCCTTATT": 6334, + "TCTTTTAATT": 6335, + "TCGTGTG": 6336, + "CTTCACTT": 6337, + "TATTGATG": 6338, + "GCTGGGTT": 6339, + "CATGCCTT": 6340, + "GGTGGCA": 6341, + "AAAAAAAAAAAAAAAAAA": 6342, + "GTCCAGC": 6343, + "GAGGTGGA": 6344, + "CGGCAGG": 6345, + "CCCTTAAA": 6346, + "TGACAGC": 6347, + "GCTGCAAA": 6348, + "GCTTTGGA": 6349, + "CCAACACA": 6350, + "CCCCATCTCTA": 6351, + "GTTTCCAA": 6352, + "GTTATAAA": 6353, + "GTAATTAAA": 6354, + "CTCTCTTTT": 6355, + "GGATGCC": 6356, + "CAGAGAAAA": 6357, + "GTTGCTTA": 6358, + "CTTTTCTA": 6359, + "GCTTGCA": 6360, + "CAACAGCA": 6361, + "GTGATGGA": 6362, + "GCAATAAA": 6363, + "TAGACACA": 6364, + "TCAGACC": 6365, + "CATGTATA": 6366, + "TTTTCTCC": 6367, + "TCCCTCAA": 6368, + "CATGCTCA": 6369, + "TCGCTTA": 6370, + "GCAATTTA": 6371, + "TATĊ": 6372, + "TCACACCA": 6373, + "GTGAGGAA": 6374, + "TCCACTTA": 6375, + "GCCTCCTT": 6376, + "CGGTGTG": 6377, + "TCCTGGAACTCA": 6378, + "GGTGGAGG": 6379, + "TCCTGATT": 6380, + "CCTGTCTG": 6381, + "GAATTATT": 6382, + "TGTAGTA": 6383, + "CCTAGGA": 6384, + "TAGGAGG": 6385, + "TCACGTT": 6386, + "TGTTGTAA": 6387, + "CTCCAGTGTT": 6388, + "TCGCCCAGGCTGGAGTGCAGTG": 6389, + "GTCATGA": 6390, + "CGGGTT": 6391, + "CAAAGGTT": 6392, + "CAAATGGA": 6393, + "TCGCTCC": 6394, + "GAAAGGGG": 6395, + "GTGAGGGG": 6396, + "GTCCAGTT": 6397, + "CTTCACTG": 6398, + "TAATATATT": 6399, + "CATGGAGG": 6400, + "GGGTATG": 6401, + "CTCTATCA": 6402, + "CTTAGGA": 6403, + 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6453, + "TCCCTCTG": 6454, + "CAGGGTA": 6455, + "TCCCGTG": 6456, + "GCGACAA": 6457, + "TGACCTCA": 6458, + "TCGGATT": 6459, + "GTGCGTG": 6460, + "GTCTATTA": 6461, + "GCTGCTC": 6462, + "TTTTCC": 6463, + "GACAGCAA": 6464, + "GGGAGCA": 6465, + "TGTTGTCA": 6466, + "CCCACTCC": 6467, + "TCAACACA": 6468, + "GGTGATGGA": 6469, + "TCCCTGTT": 6470, + "GTTTCTAA": 6471, + "TTTATAAA": 6472, + "GTTTTCAA": 6473, + "GATCTCTT": 6474, + "GAGAGATT": 6475, + "TAAGAGTT": 6476, + "CATTTTTTTT": 6477, + "GACGAAA": 6478, + "TGTGGGAA": 6479, + "TCAATTAAA": 6480, + "GTTCTCTT": 6481, + "GGACACCA": 6482, + "TGTTTGGA": 6483, + "GTTAGCA": 6484, + "GTGTGAAA": 6485, + "CCACTGAA": 6486, + "GTCTATAA": 6487, + "CTAATGAA": 6488, + "GAGAGTGA": 6489, + "CAAAAAAAAA": 6490, + "CCATCTC": 6491, + "TCTCAGAA": 6492, + "TTTTTTATT": 6493, + "GCCATCTG": 6494, + "GAAAATATA": 6495, + "TAGACAGA": 6496, + "GCTACAGA": 6497, + "GATGTGTT": 6498, + "CCCCCATG": 6499, + "GCGACCA": 6500, + "AAAACTA": 6501, + "TCAATATG": 6502, + "CAAGGCTG": 6503, + "CATTGGA": 6504, + "TCGTTTTA": 6505, + "TCATAATT": 6506, + "CTCCAAAAA": 6507, + "CTGAGAGG": 6508, + "TGACGTT": 6509, + "TCAAGTCA": 6510, + "GGAAGTCA": 6511, + "CCCTCCTT": 6512, + "TCAGACTT": 6513, + "CAGCCAGA": 6514, + "CCCTATG": 6515, + "TCTGGATG": 6516, + "CCTTCATG": 6517, + "TAGTCTG": 6518, + "CGAGAAA": 6519, + "CAGCTCTT": 6520, + "CACAGGTG": 6521, + "GACTCATT": 6522, + "TAAAGCTG": 6523, + "GCATTATT": 6524, + "GTTACAGA": 6525, + "TCACAGTG": 6526, + "CCTACAAA": 6527, + "GGTCAGGCTG": 6528, + "GCCTCCTG": 6529, + "TCTGCAGA": 6530, + "GCTTCAGG": 6531, + "GAGAGGGA": 6532, + "CTAATATA": 6533, + "TAGAGTG": 6534, + "TAGGTACA": 6535, + "GTTGAGAA": 6536, + "CATTGAAA": 6537, + "TAGCATG": 6538, + "CTTAGTG": 6539, + "GTCCCATG": 6540, + "GGTTTTAA": 6541, + "GCTTGGTT": 6542, + "CACATATG": 6543, + "TAATCCCAGCACTTTGGGAGGCTGA": 6544, + "TCCCAGCC": 6545, + "CAGGTGCA": 6546, + "CAGGCCTG": 6547, + "CTCCCTGA": 6548, + "GCAAATTA": 6549, + "GTGATAA": 6550, + "TCTGAGTT": 6551, + "GCTACACAGAGAAA": 6552, + 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+ "GAGTCAGACA": 6602, + "TGAAGACA": 6603, + "CTCCCACC": 6604, + "GCCGTA": 6605, + "CCTTGG": 6606, + "TAAAAATAAAA": 6607, + "CTTTCTAA": 6608, + "GAGGCTA": 6609, + "TTAGATG": 6610, + "GCTGTCTG": 6611, + "TACTTTAA": 6612, + "CCATGCCCA": 6613, + "TAGGCATG": 6614, + "GTGTTATT": 6615, + "GGAGATCA": 6616, + "TAGGTTTG": 6617, + "CAGATATT": 6618, + "CATAGTT": 6619, + "GCTGGATT": 6620, + "CTCCCACA": 6621, + "CCTATGA": 6622, + "GCACGCA": 6623, + "TCAATTCA": 6624, + "GCCCGTG": 6625, + "CTCTCACA": 6626, + "CCTGCACA": 6627, + "TAATAATAATAATAATAATAATAA": 6628, + "CTCTGAGG": 6629, + "CGGTAA": 6630, + "CGGTGGG": 6631, + "GAAAAATTA": 6632, + "CCTGCTCA": 6633, + "CACAAATG": 6634, + "TATTGCAA": 6635, + "TATAGATA": 6636, + "CAGGCAAA": 6637, + "GTTACATT": 6638, + "TTTAGTT": 6639, + "CTCCGTCTCAAAA": 6640, + "TACAGCTT": 6641, + "GCCACTCA": 6642, + "GCTAGAAA": 6643, + "GCAGAGC": 6644, + "GAGAGCTT": 6645, + "CATGTAAA": 6646, + "TCCAGTTTT": 6647, + "GGTCTTTT": 6648, + "CATATGAA": 6649, + "CAACCCCA": 6650, + 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6749, + "TATGTCTG": 6750, + "GAGGTAA": 6751, + "CTCCATGA": 6752, + "CCTTGCTT": 6753, + "GCATGCC": 6754, + "CAGCTCCA": 6755, + "CTCTATTA": 6756, + "GCGATAA": 6757, + "GAAGTAAA": 6758, + "GCCĊ": 6759, + "GTTTTTTTTTT": 6760, + "GGACAGCA": 6761, + "TGTGCATT": 6762, + "GAATGCC": 6763, + "CCAĊ": 6764, + "GAATCAAA": 6765, + "GCATAAAA": 6766, + "CCACACTG": 6767, + "TAATAGTA": 6768, + "TTCAGTT": 6769, + "TATATATATT": 6770, + "GAGGGTA": 6771, + "GAATAATT": 6772, + "CCACCCTG": 6773, + "TCTTGCTG": 6774, + "TGTTCTTTT": 6775, + "TATGGTAA": 6776, + "GGCAGCTG": 6777, + "GCCGTC": 6778, + "GCTTTAAA": 6779, + "CAGAAAAAA": 6780, + "TCCATATA": 6781, + "TAATCCCAGCACTTTGGGAGGCCAA": 6782, + "TACACCAA": 6783, + "GTGTAAAA": 6784, + "CAAACTAA": 6785, + "GAGGAATT": 6786, + "CATGGATT": 6787, + "GATCGA": 6788, + "TCATATTA": 6789, + "CTCTGAGA": 6790, + "CGATGAA": 6791, + "GAATACTG": 6792, + "GTCCCGG": 6793, + "CCCACTCA": 6794, + "GATACTA": 6795, + "CATATTTG": 6796, + "TATTGAAAA": 6797, + "GAGAGAAAA": 6798, + "GCGCAGG": 6799, + "GTTGTGTT": 6800, + "TCACATCA": 6801, + "CTTCATTA": 6802, + "GTCTTTTTT": 6803, + "CCACAGTG": 6804, + "CACACCTT": 6805, + "CTATTAAAA": 6806, + "CCACCCCA": 6807, + "TCCAGTAA": 6808, + "GGTTTCC": 6809, + "TCGCTAA": 6810, + "GTCACTGA": 6811, + "TGTTGTGA": 6812, + "TAGCAGG": 6813, + "GACATTAA": 6814, + "GAACTTTT": 6815, + "TATTCCTG": 6816, + "TGGGTTTG": 6817, + "GGCCTCCCAAAGTGCTGGGATTACAGG": 6818, + "GACGTCA": 6819, + "TAACAGTA": 6820, + "CAGATGGA": 6821, + "TAATTTCTT": 6822, + "CCTAGGAA": 6823, + "TGACCCCA": 6824, + "GTGGGCC": 6825, + "GCGCATT": 6826, + "CCACATGA": 6827, + "TACCCTG": 6828, + "TACACACACA": 6829, + "CTTGACC": 6830, + "CTTTAGAA": 6831, + "CTTTGCTG": 6832, + "TGATTTTTT": 6833, + "CAAAATGTG": 6834, + "GATCTGTG": 6835, + "GTTGAATT": 6836, + "GCAGCTGA": 6837, + "CTCAGATT": 6838, + "TTCAGTA": 6839, + "CTCAGCTG": 6840, + "GTCAGACA": 6841, + "TAAAACATT": 6842, + "GCAGACGC": 6843, + "TAGACGG": 6844, + "CATCTTTG": 6845, + "CTAGGTA": 6846, + "CCGAAAA": 6847, + 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"GTTCAGTG": 6898, + "CAGGGTGA": 6899, + "CCCCCTCA": 6900, + "CCTACAGA": 6901, + "GACACTGA": 6902, + "TCTCCCTG": 6903, + "TCATGAAAA": 6904, + "GTTTAGAA": 6905, + "CCATCAAA": 6906, + "GATTACAGG": 6907, + "CATGTCTG": 6908, + "TCGCCTG": 6909, + "CATCCAGA": 6910, + "GTTCAGAA": 6911, + "TACAGCC": 6912, + "CGACAAA": 6913, + "TCCCCTCA": 6914, + "CTTGCATT": 6915, + "GCTCCGCCTCCCGG": 6916, + "CACATGGG": 6917, + "TATTTTTTAA": 6918, + "TAGCTCC": 6919, + "GTGGCTA": 6920, + "TGACATTTT": 6921, + "GAAAGGTT": 6922, + "GCTTGATG": 6923, + "CATCCAGG": 6924, + "CCAGAGGA": 6925, + "TCTGCCAA": 6926, + "CCAAATTA": 6927, + "TCAAATTAA": 6928, + "CTTCACAA": 6929, + "GGAGACTG": 6930, + "CCCAGTC": 6931, + "CATGCTAA": 6932, + "GGTTGGTT": 6933, + "TATATTATT": 6934, + "CCTTCTGA": 6935, + "GTTTTTAAA": 6936, + "TAATAATTA": 6937, + "TAATCTC": 6938, + "GTCCCAGA": 6939, + "CGAGCTG": 6940, + "TGACACAA": 6941, + "CCCAGGTG": 6942, + "TGAGATTA": 6943, + "GCGAGCC": 6944, + "GCAGATGA": 6945, + "TCTATCAA": 6946, + "TTTTGGAA": 6947, + "TCACACTG": 6948, + "GATTTATG": 6949, + "TCGAGTA": 6950, + "GTAGAGAA": 6951, + "GTGAGCC": 6952, + "GATATATT": 6953, + "GGAAATATCTT": 6954, + "CTCAGG": 6955, + "TAGGTGTG": 6956, + "TGAAGTTA": 6957, + "TTTTGG": 6958, + "GAAGGCC": 6959, + "GTTCTGAA": 6960, + "TAGAAATT": 6961, + "TTTTATAAA": 6962, + "TCTCTGAAA": 6963, + "TAACACTT": 6964, + "TAAGATAA": 6965, + "CTATATTA": 6966, + "GCCACCTT": 6967, + "TCTACCTG": 6968, + "TCGGAGG": 6969, + "TGATACC": 6970, + "GCGAATT": 6971, + "CTTCTATT": 6972, + "TAACACTG": 6973, + "GGAGCAAA": 6974, + "TCAGGGAA": 6975, + "GTCCTGTT": 6976, + "TAGATGTG": 6977, + "TCCAGTC": 6978, + "CTGACAGA": 6979, + "GTTCTCAA": 6980, + "CAGGGCTT": 6981, + "CTCTAGTT": 6982, + "TCTTTGGA": 6983, + "TGAATGTA": 6984, + "GAGGCC": 6985, + "GTTTTGAAA": 6986, + "GGACAGGA": 6987, + "TATATCC": 6988, + "GTTTGGAA": 6989, + "TATTGCC": 6990, + "TATTTCATT": 6991, + "TATTTACC": 6992, + "CTGCTCTT": 6993, + "CGTAATG": 6994, + "GACGCTG": 6995, + "CATGGCAA": 6996, + "CCCCCACA": 6997, + "GCACCTA": 6998, + "TATTGCTG": 6999, + "CCTGGGTG": 7000, + "TGATGATT": 7001, + "TGACATTA": 7002, + "GCTTCCAA": 7003, + "CGGGTA": 7004, + "TCATCTTTT": 7005, + "CTACAGAA": 7006, + "CATTGTGA": 7007, + "CTCCTGTT": 7008, + "TACACC": 7009, + "GAAAGAAAAA": 7010, + "GTTACAGG": 7011, + "CAATGGAA": 7012, + "GAGAGCTG": 7013, + "GGCACAA": 7014, + "GAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAA": 7015, + "CAAAAACAAAAA": 7016, + "GTTCAGGA": 7017, + "TGAAGTGA": 7018, + "CATTCATG": 7019, + "TCTGGTGA": 7020, + "TCAATTTTA": 7021, + "CCACAGTT": 7022, + "GAAATCTT": 7023, + "CTCGCTG": 7024, + "CTTACCTT": 7025, + "TCTGTGAAA": 7026, + "TACGTC": 7027, + "TCAACTAA": 7028, + "CTTTTTAAA": 7029, + "GGTTCTA": 7030, + "TCATGTTA": 7031, + "CTTGCCTT": 7032, + "CTAAAAATACAAAAAATTA": 7033, + "TCACCAAA": 7034, + "GTCGGGG": 7035, + "TGAAAAATT": 7036, + "TCATATTTT": 7037, + "GATGGCTG": 7038, + "GACGAGG": 7039, + "TCCGTA": 7040, + "GGTACTG": 7041, + "TCTAATTTT": 7042, + "CGCAATG": 7043, + "CAAACTGA": 7044, + "CAGGTGGA": 7045, + 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+ "CAGATATA": 7143, + "CCAAGG": 7144, + "CCTGGATG": 7145, + "TCACATGA": 7146, + "GCAGCATT": 7147, + "CAGGGTCA": 7148, + "CAGAGGGA": 7149, + "CCCGGGAGGCGGAGCTT": 7150, + "TGGGTTA": 7151, + "GTAGGCTG": 7152, + "TGAATGAAA": 7153, + "GAATGGAA": 7154, + "CTGTCAAA": 7155, + "CTCTGTTA": 7156, + "TTTTTATTTT": 7157, + "CTCAGGGG": 7158, + "CTCGTGA": 7159, + "CTGATGTG": 7160, + "CCAATAAA": 7161, + "TCACTGTG": 7162, + "TCTCTCTGA": 7163, + "TCTATCC": 7164, + "CTTTTGTA": 7165, + "TAAGTTAA": 7166, + "GATTGCTT": 7167, + "CTTTTTTTA": 7168, + "CTTCCTTA": 7169, + "TGTTTAAAA": 7170, + "CAATATTTT": 7171, + "CTACCCA": 7172, + "GCAGTGTT": 7173, + "CGGGGTTTCACC": 7174, + "GTTATTCA": 7175, + "TCAGCTGA": 7176, + "CAAGACAA": 7177, + "CCTGGGGA": 7178, + "TATTTAAAAA": 7179, + "GCCAGGCATG": 7180, + "TAACAGTT": 7181, + "GGCATGTG": 7182, + "CGACGC": 7183, + "TGATCATT": 7184, + "TGCAGTT": 7185, + "GTGCCTA": 7186, + "GCATATTA": 7187, + "TATGGATT": 7188, + "CTTCTGTG": 7189, + "CTCACATT": 7190, + "GTGCAAAA": 7191, + 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"TCTAGGAA": 7242, + "GAAAGAGAA": 7243, + "GATACAAA": 7244, + "TAACGAA": 7245, + "TCAACTGA": 7246, + "CCTCAGCCTCCCAAA": 7247, + "CGCAAAAA": 7248, + "CTCGAGG": 7249, + "CCCATCCTAATT": 7250, + "TATCTCTT": 7251, + "GTTCACTG": 7252, + "GCAGGAAAA": 7253, + "CATGTTAA": 7254, + "CTAGGAAA": 7255, + "GTGACCTT": 7256, + "CCAAGTCA": 7257, + "TACAGCTG": 7258, + "CAGCCATT": 7259, + "CCAGCCTT": 7260, + "GTTTCTCC": 7261, + "TCAAGCC": 7262, + "CAATTCAA": 7263, + "GATTATTTT": 7264, + "CTCGCTT": 7265, + "TATCTAAA": 7266, + "CCTCCACCTCC": 7267, + "TACATAAAA": 7268, + "CCCATCTT": 7269, + "TATTGGTT": 7270, + "CAATGTTA": 7271, + "TTATTATTATTATTATTATTATTA": 7272, + "GCTAATTTTTGTATTTTTAGTAGAGA": 7273, + "GCCAGATG": 7274, + "GTAACATT": 7275, + "TAAGGCC": 7276, + "CTGACACA": 7277, + "CACCCACA": 7278, + "GTGACC": 7279, + "TGCCTCC": 7280, + "GACCTTTT": 7281, + "AAAAA": 7282, + "TCAACTCA": 7283, + "TCACCTGA": 7284, + "GCAGCCAA": 7285, + "CGATGTG": 7286, + "AAATGTTTT": 7287, + "TAGCAAAA": 7288, + "TAAGCCAA": 7289, + "TCATCTGA": 7290, + "TGATCTCA": 7291, + "TGAAGGTT": 7292, + "CAGATATG": 7293, + "GTGAGCTG": 7294, + "CTCTGAAAA": 7295, + "GAAGGGGA": 7296, + "TCATAGAA": 7297, + "GTAĊ": 7298, + "GTGAGG": 7299, + "GAGGCCAA": 7300, + "CACCACCA": 7301, + "CGGAATG": 7302, + "TCGACTA": 7303, + "CTCAAAATT": 7304, + "TATTTCTTA": 7305, + "CCTGCCCC": 7306, + "CAAACATA": 7307, + "CAAATGAAA": 7308, + "TCTATATA": 7309, + "CAACCAAA": 7310, + "CTACCAAA": 7311, + "CCTGGATT": 7312, + "GTAAGGAA": 7313, + "TCCTTCCTT": 7314, + "CTTCGTT": 7315, + "CATTTGAAA": 7316, + "CTCCTGCCTCA": 7317, + "GTTTTCCTT": 7318, + "CGTGCGTG": 7319, + "GTGGCTGA": 7320, + "GAGAGGTT": 7321, + "GTCAGCC": 7322, + "CTCTGCTG": 7323, + "TCCAGTTA": 7324, + "CTGATGGA": 7325, + "CGCTCTT": 7326, + "CACCAGGCTCC": 7327, + "TAATTTGA": 7328, + "GTCTAGAA": 7329, + "TCTCCAAAA": 7330, + "TGCCCAA": 7331, + "CAGTTATT": 7332, + "TAACTATT": 7333, + "CTTGGCCA": 7334, + "TGAATAAAA": 7335, + "TCTGGCAA": 7336, + "GATTGGA": 7337, + "GGGCTGTG": 7338, + "GGACACGACTGA": 7339, + "GCGTGCTG": 7340, + "TAATCTTA": 7341, + "GTTCTAAA": 7342, + "GAAAGATA": 7343, + "GAATACC": 7344, + "CTTAGCAA": 7345, + "TCCACGA": 7346, + "CAGATGCA": 7347, + "TAAAGGTT": 7348, + "TTTACTG": 7349, + "TGGGTGGG": 7350, + "GCCTTTTA": 7351, + "TAGGTAGG": 7352, + "TCCCTAAA": 7353, + "TATTGTAAA": 7354, + "CGGGGGG": 7355, + "TATGTGAA": 7356, + "GACGATG": 7357, + "GTTCCTCA": 7358, + "TTTAGTA": 7359, + "TCTGCTTTT": 7360, + "GAATTCCA": 7361, + "TCATGCATT": 7362, + "TATAGATG": 7363, + "TCTCTCTCTT": 7364, + "TAACAAAAA": 7365, + "GCCCACTG": 7366, + "TGTGTGTGTGTGTGTGTGTGTGTG": 7367, + "GTCGGTG": 7368, + "CTAAAAATT": 7369, + "GCGGATG": 7370, + "CTCATCTT": 7371, + "GTTATGTT": 7372, + "TACATAGA": 7373, + "CTCTATAA": 7374, + "GTGACAGG": 7375, + "CCCAGTAA": 7376, + "TCACCTCC": 7377, + "GGAAATTA": 7378, + "CCACACAA": 7379, + "GCCTCCAA": 7380, + "GCCACGA": 7381, + "TATTAATTA": 7382, + "CATGTGAA": 7383, + "TCTACATG": 7384, + "TATATATATG": 7385, + "GTGTGTGTG": 7386, + "CCTCTCAA": 7387, + "GAGACC": 7388, + "CAGACCTG": 7389, + "CAAAAGAAA": 7390, + "CAAATAGG": 7391, + "GTCCCCTT": 7392, + "TGTGGG": 7393, + "TGAATCC": 7394, + "GACACTTA": 7395, + "GTTTGGAAA": 7396, + "CTGCAGTT": 7397, + "TAAGATTA": 7398, + "GCCTAAAA": 7399, + "TCATTCTT": 7400, + "CAAGAGCA": 7401, + "TATTGATA": 7402, + "TATGGCTT": 7403, + "CTGTACC": 7404, + "GTGGGAGG": 7405, + "TCTACCAA": 7406, + "GGTAGGA": 7407, + "TGATATCA": 7408, + "GCCGGCC": 7409, + "CAAAATATA": 7410, + "CAGTCATT": 7411, + "CTTGCAAA": 7412, + "CCATTATT": 7413, + "CGGAAAAA": 7414, + "CATTGCTG": 7415, + "GTCACCAA": 7416, + "GACAGTAA": 7417, + "GCAGTGAGCC": 7418, + "CAGCTTTG": 7419, + "CTGCCCTG": 7420, + "GTTCGAA": 7421, + "CATTGG": 7422, + "TCTAGAGA": 7423, + "CAATATCA": 7424, + "CCATCTCC": 7425, + "TGTTGGTG": 7426, + "CTGTGTTTT": 7427, + "TCAAAAATT": 7428, + "TAAACGA": 7429, + "TGAATGGG": 7430, + "GCTTTGCA": 7431, + "CCCCCCAA": 7432, + "GATCTGAA": 7433, + "GTCCAGCC": 7434, + "TTAGCTG": 7435, + "GCCGGTG": 7436, + "GTTTTCAAA": 7437, + "TAATTGTT": 7438, + "GGAGACCA": 7439, + "CTATATAA": 7440, + "TCTGCTAA": 7441, + "CTTTGGGAGGCCAA": 7442, + "CATATATG": 7443, + "CTTCAGC": 7444, + "CTATACTA": 7445, + "TTTGGTA": 7446, + "GAACACCA": 7447, + "TAATTATTA": 7448, + "GGACCTCA": 7449, + "CATGTCC": 7450, + "TCTACTCA": 7451, + "GAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGA": 7452, + "TATGTGTG": 7453, + "CATTTAGA": 7454, + "CTCCTTAA": 7455, + "CTTGGCTT": 7456, + "TCCATATG": 7457, + "CTGTGAAAA": 7458, + "CTTTCCTG": 7459, + "CAAACAAAAA": 7460, + "CATTGCAA": 7461, + "TCAGGCTG": 7462, + "TCAGAGGA": 7463, + "GGAGACTT": 7464, + "CTTAGGAA": 7465, + "TCAGGAAAA": 7466, + "TCAGAGTT": 7467, + "CTTTCTCTT": 7468, + "CAAAATGA": 7469, + "GGGACAA": 7470, + "TCTAAAATT": 7471, + "GGAGAGTG": 7472, + "GTCTACTT": 7473, + "GAAATAGG": 7474, + "TCTTGATG": 7475, + "CATGCAGG": 7476, + "GTCGCA": 7477, + "CACATCTG": 7478, + "CACAGGTT": 7479, + "GACAGTCA": 7480, + "CTGAAGAA": 7481, + "GGGCCAGG": 7482, + "CATTTAGG": 7483, + "CCTTCAGG": 7484, + "GGCAGTGA": 7485, + "TGTAGTT": 7486, + "GATCGCA": 7487, + "TATTAAATT": 7488, + "CACACCAA": 7489, + "TCTTCCTA": 7490, + "GGAAGGTT": 7491, + "TCTACAGA": 7492, + "TTCAGTG": 7493, + "CTAATTAAA": 7494, + "GGCAGTT": 7495, + "GGACAGTG": 7496, + "TGAGGATT": 7497, + "GAAAGCTG": 7498, + "TATAGCAA": 7499, + "CTCACCTG": 7500, + "CCTTTCAA": 7501, + "GGGTTAAA": 7502, + "CTTCTTTG": 7503, + "GGAGAAAAA": 7504, + "GTAATGTT": 7505, + "GAATGTTTT": 7506, + "CCACCTCA": 7507, + "CTCATCC": 7508, + "TATGCTAA": 7509, + "TCAAAGAA": 7510, + "CTGAACAA": 7511, + "TCACATTA": 7512, + "TCTTTCTCA": 7513, + "TAATTCTG": 7514, + "CTCTCTCTG": 7515, + "GAAAATA": 7516, + "GACATTTG": 7517, + "CCAGTGAA": 7518, + "CAGACCAA": 7519, + "TTTTACTA": 7520, + "TATGTCAA": 7521, + "CTTATGAA": 7522, + "CCTCCCGA": 7523, + "GAAATGAAAA": 7524, + "GCAGGAGG": 7525, + "CTCAGGTT": 7526, + "TCAGTCTT": 7527, + "GCTAGTA": 7528, + "TCATTCCTT": 7529, + "TCACTCTT": 7530, + "GACGAGA": 7531, + "TAATCCCAGCTA": 7532, + "CTGGGGAGG": 7533, + "GCAAGAGG": 7534, + "GATCGC": 7535, + "GTCACTAA": 7536, + "GCCAGTAA": 7537, + "TAGACTGA": 7538, + "TCGCGA": 7539, + "TATTATTTG": 7540, + "CATTTAATT": 7541, + "GGAACTGA": 7542, + "TCTATCTG": 7543, + "GGGGAGGGG": 7544, + "CCTCCTCC": 7545, + "CACĊ": 7546, + "GCCCATCA": 7547, + "TAAAATCAA": 7548, + "GTCACAGG": 7549, + "TCTCTCTCTG": 7550, + "CATAGTGA": 7551, + "TTTTTTTTTTAA": 7552, + "GTTTTATTTT": 7553, + "GTTACC": 7554, + "TAAAGAGG": 7555, + "GACAAAAAA": 7556, + "TGACTAAA": 7557, + "GTGCGCA": 7558, + "TAACCCC": 7559, + "TAATCCAA": 7560, + "CATTCAGA": 7561, + "TCTGCGG": 7562, + "TCATTTATT": 7563, + "TCAGATCA": 7564, + "CTGAGGCAGGAGAATCGCTT": 7565, + "GGGAAATT": 7566, + "CTTGTCTT": 7567, + "TATGTTTTA": 7568, + "GTGTCCGACTC": 7569, + "CTGGATCA": 7570, + "TGGGGAGG": 7571, + "CTTGTGTT": 7572, + "CCATATGA": 7573, + "GATTCTCA": 7574, + "GGATGGCA": 7575, + "TCGGCCTCCCAAAGTGCTGGGATTACAGG": 7576, + "TCACTCTG": 7577, + "CTGCAGC": 7578, + "CACATAAA": 7579, + "CATTGATG": 7580, + "TACAATTA": 7581, + "GGAATGTG": 7582, + "GTTTTTTTA": 7583, + "GAAACCTG": 7584, + "CCTTGGGCA": 7585, + "CACCTTTT": 7586, + "TCTTGGTG": 7587, + "GCAGTAAA": 7588, + "TGTTCTGA": 7589, + "TATGCATA": 7590, + "TGAGGGAA": 7591, + "TAGGGTG": 7592, + "CAAAAATTAA": 7593, + "CGACAGG": 7594, + "GGCCTTTT": 7595, + "CAACACCA": 7596, + "GGGGCC": 7597, + "TCTCCTTTT": 7598, + "GTTTTTTAAA": 7599, + "CAATAGAA": 7600, + "TGAGCTGA": 7601, + "CTTAAAAAA": 7602, + "GGACGC": 7603, + "GCTTCGG": 7604, + "GAGAAGAGAA": 7605, + "GATTTGAAA": 7606, + "TCCCGC": 7607, + "TAATTTCAA": 7608, + "GATGCTGA": 7609, + "GGCAGCAA": 7610, + "TGCCTGTG": 7611, + "GTGAGTCA": 7612, + "CCCATCTG": 7613, + "GGCACC": 7614, + "CCTTGGTG": 7615, + "TCTCTACA": 7616, + "TCACCTCA": 7617, + "GTAGAATT": 7618, + "CTGTGCTG": 7619, + "TGTTGAAAA": 7620, + "CATGAATT": 7621, + "GGAATATT": 7622, + "TGGGGTG": 7623, + "TAACTGTT": 7624, + "TCACAATT": 7625, + "TATACC": 7626, + "CTCACAGG": 7627, + "TCTACTGA": 7628, + "CAAAACCA": 7629, + "GGAGACAA": 7630, + "TATGCTGA": 7631, + "CTAAGCAA": 7632, + "TGGGGTTTCA": 7633, + "CACCTCTG": 7634, + "GGTGTGTG": 7635, + "GCTATAAA": 7636, + "TATAATTTA": 7637, + "TGACACTG": 7638, + "GTCACGG": 7639, + "TTATTGTT": 7640, + "TCAAGGTT": 7641, + "GCTGGGGA": 7642, + "GGCACCA": 7643, + "CTGAAATG": 7644, + "TCCTTCTA": 7645, + "GACCTGAA": 7646, + "CAATACC": 7647, + "CATTTTAAAA": 7648, + "CTTGAATT": 7649, + "GGAAGCTT": 7650, + "GGCGCTG": 7651, + "GTATCAAA": 7652, + "CACATATT": 7653, + "TGTTGACA": 7654, + "CATTTATTTT": 7655, + "GGAGCAGG": 7656, + "TAAAATGTG": 7657, + "GTCATGAA": 7658, + "GATACCA": 7659, + "TAGATATG": 7660, + "TGATCTC": 7661, + "TTATTTTAAA": 7662, + "GAAACTGAA": 7663, + "TGATCTTA": 7664, + "TCTTGAGG": 7665, + "GGAACAGA": 7666, + "CGCACGCA": 7667, + "GTGGCTCACGCCTG": 7668, + "TCCCCCCA": 7669, + "CATTGCC": 7670, + "CTCTTCTA": 7671, + "TAACTCAA": 7672, + "CATACCA": 7673, + "CTTTTGTAA": 7674, + "CACTTATT": 7675, + "TACACATT": 7676, + "CATGCTGA": 7677, + "CTTTTCAA": 7678, + "TAATAGAA": 7679, + "TGACGAA": 7680, + "CGGGGTG": 7681, + "TACATTAA": 7682, + "GAAAAAAATT": 7683, + "TCTCCTTA": 7684, + "GAGTTATT": 7685, + "TCCATCTA": 7686, + "TATACAGG": 7687, + "GGAAGTAA": 7688, + "TCTCTACC": 7689, + "TTATTTAAA": 7690, + "CTATACTT": 7691, + "TCACACAA": 7692, + "CATAGTAA": 7693, + "CATTTATTA": 7694, + "TGTGCC": 7695, + "GTGAGATT": 7696, + "GGAAAGATT": 7697, + "CGTTGTG": 7698, + "GTTATGAA": 7699, + "TCCTGAAAA": 7700, + "CAGGTTCA": 7701, + "TAACTTTG": 7702, + "TGACTTAA": 7703, + "GTCCACTT": 7704, + "CAAAATTTG": 7705, + "GACCCTA": 7706, + "CTCCATAA": 7707, + "TATGCCTT": 7708, + "CACGAAA": 7709, + "CCTAGTA": 7710, + "GGTAAATT": 7711, + "CACATCC": 7712, + "CCAAGCTT": 7713, + "TTATGGA": 7714, + "TAAATCTA": 7715, + "GAAAACTG": 7716, + "GACAGCTT": 7717, + "TAAGTGTT": 7718, + "GCAAGATT": 7719, + "GGATATTA": 7720, + "GTGATCC": 7721, + "CATGCAGA": 7722, + "CTTCTTAA": 7723, + "GGAGAATT": 7724, + "GTCTATCA": 7725, + "TATTTCTCA": 7726, + "GAGGTCTG": 7727, + "GGGGTTTG": 7728, + "TTATTGTTA": 7729, + "TCTCCCTC": 7730, + "CCTTTTAAA": 7731, + "CAGTAATT": 7732, + "TTTTATTCA": 7733, + "TACACTAA": 7734, + "CACCCTCA": 7735, + "CAAATAGA": 7736, + "TATAGAGA": 7737, + "CTTTTTTAAA": 7738, + "CAGAGAGAGA": 7739, + "CATGTCTT": 7740, + "TCGAGCC": 7741, + "TATGCTTA": 7742, + "GCTACTCA": 7743, + "GGAAAGGAAA": 7744, + "TAAGACTT": 7745, + "GTCAGTAA": 7746, + "GAAGCTGA": 7747, + "GTGCAGAA": 7748, + "GGAGCTCA": 7749, + "TGTGGGTT": 7750, + "TCTGGTAA": 7751, + "GCAACAAAA": 7752, + "CCAAGAGA": 7753, + "CTTTTCATT": 7754, + "TCCTTTTTT": 7755, + "GGACGTG": 7756, + "GCCCTTTG": 7757, + "CCTACCTT": 7758, + "TGAGCCAA": 7759, + "CAGCTATT": 7760, + "GTGTCTCA": 7761, + "CCACTGTT": 7762, + "CAAACTTA": 7763, + "GATAGTT": 7764, + "CTTTTATTTT": 7765, + "GTCTGTTTT": 7766, + "TGTGTTAAA": 7767, + "CTTCCCAA": 7768, + "TGTGCAGG": 7769, + "GCAGCCTT": 7770, + "TGTGGTGA": 7771, + "TATACTAA": 7772, + "GGAGGCGGAGGTT": 7773, + "TCTTTTTTA": 7774, + "TATTTGTTTT": 7775, + "TAGAAATG": 7776, + "TAAATCAAA": 7777, + "GTCGAGG": 7778, + "GTCATGTT": 7779, + "TAACACCA": 7780, + "TCCACTCC": 7781, + "TGAGATTTT": 7782, + "TGAGTTAA": 7783, + "CACACACACACACACACACACACACACACACACACACACACACACACA": 7784, + "CCACCAAA": 7785, + "TTATTCTA": 7786, + "CAGGGTTTCTCTGTGTA": 7787, + "CTGTATTA": 7788, + "GTGGTGTT": 7789, + "GCATCTC": 7790, + "CCAGCAAA": 7791, + "CTCAGTTA": 7792, + "CGGCCTG": 7793, + "GCTATGAA": 7794, + "GAACGC": 7795, + "GTGGACC": 7796, + "CAGACGA": 7797, + "TCAAGAGG": 7798, + "TAGCTCTG": 7799, + "TCAGCATT": 7800, + "TTACCTG": 7801, + "CCACACCA": 7802, + "GCCATATT": 7803, + "TAGATGTT": 7804, + "GCAATGTT": 7805, + "CATCTATT": 7806, + "TCAGTTAA": 7807, + "TGACGTG": 7808, + "TGTGGATT": 7809, + "CCTACCAA": 7810, + "GAAGCATT": 7811, + "GCAGCTCC": 7812, + "TCAACAAAA": 7813, + "CCTACTTA": 7814, + "TCATGTTTT": 7815, + "GCAGCAGG": 7816, + "GTCGATG": 7817, + "GTAAGATT": 7818, + "CTCTTCTCTT": 7819, + "CAAATCTG": 7820, + "CCTTGAAA": 7821, + "TATGGAGA": 7822, + "CCAGGAAAA": 7823, + "CCCTCCA": 7824, + "TAATATTAA": 7825, + "GTTCTTAA": 7826, + "CTAAGGAA": 7827, + "GCAGACTG": 7828, + "CAAATCTT": 7829, + "CCTTGATT": 7830, + "TCCACCCC": 7831, + "GGCAGCTT": 7832, + "CAGTTCAGTT": 7833, + "TTTTTTTTTTTTTTTTTTTTTTTT": 7834, + "CGGTGTT": 7835, + "TCTGATTA": 7836, + "GTAGACC": 7837, + "GTTCAAGACCAGCCTG": 7838, + "GGACACTG": 7839, + "GAGCTCTT": 7840, + "CAGTGATT": 7841, + "TAACATGA": 7842, + "TGAGAGGA": 7843, + "GCTGCATT": 7844, + "GCAAGTGA": 7845, + "CGCCTTTAATCCCAGCA": 7846, + "CTGTCTCC": 7847, + "TTTTATTTTTT": 7848, + "TAATTAATT": 7849, + "CTTGGCTG": 7850, + "GCACGTT": 7851, + "CTCGAGA": 7852, + "TGGAGCTT": 7853, + "TAATTTGTT": 7854, + "CATATATA": 7855, + "TGATACAA": 7856, + "CCTTCACA": 7857, + "GCAACTTA": 7858, + "GAAAAGAGA": 7859, + "TGCAGAGA": 7860, + "CCATTAAAA": 7861, + "GATTTTATT": 7862, + "CTTCACCA": 7863, + "GTTCGTT": 7864, + "CTCAAAAAA": 7865, + "TGTGCTCA": 7866, + "TTTGCTA": 7867, + "TGAAGCTT": 7868, + "CCTGGTGA": 7869, + "TCGAAGG": 7870, + "CTTCAATT": 7871, + "TCAATGTA": 7872, + "CTGATTCA": 7873, + "CCACACCCA": 7874, + "TCAGTATT": 7875, + "CGGCCAA": 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+ "GGGTTTTA": 7927, + "GAATAGAA": 7928, + "GCAGCCTG": 7929, + "CTCAGATG": 7930, + "CAGATTAAA": 7931, + "CTCTATTTT": 7932, + "GTCTATTTT": 7933, + "GTAACGA": 7934, + "CGCTCCA": 7935, + "GAGGAAAAA": 7936, + "TCCCCCAA": 7937, + "CTCACCAA": 7938, + "CAGTCATG": 7939, + "GACGGAA": 7940, + "CTGACC": 7941, + "CACACACAA": 7942, + "GCATGAAAA": 7943, + "CTTTAATA": 7944, + "GTCTAGTG": 7945, + "GCCAAATG": 7946, + "TCATTCATT": 7947, + "CTTAGATT": 7948, + "GTCGCTG": 7949, + "GACCCAGG": 7950, + "TCACTTTG": 7951, + "GTGGAGTG": 7952, + "GTGGTTTG": 7953, + "TCAAGCTT": 7954, + "TAAAAGCAA": 7955, + "TGAGGTTA": 7956, + "TCTACTAA": 7957, + "TAGGTATA": 7958, + "GAACCTA": 7959, + "CTCCATC": 7960, + "GTCAGATG": 7961, + "TCCAATTTT": 7962, + "CACGATG": 7963, + "TTAATAAA": 7964, + "GAGATTTA": 7965, + "CATCATTA": 7966, + "CAAAATTTA": 7967, + "GTTCCCTT": 7968, + "CTCATGGA": 7969, + "TGTAGTAA": 7970, + "GGCGAGG": 7971, + "GCAGACAA": 7972, + "TCCTGCTG": 7973, + "GAGAGAGGA": 7974, + "GAAAAGGAA": 7975, + "TTTGCTG": 7976, + "TGACATAA": 7977, + "CTAĊ": 7978, + "TCCTTCCA": 7979, + "CTCCAACACCA": 7980, + "CTTGCCTG": 7981, + "CTCATTTTA": 7982, + "GATCCATT": 7983, + "TCAGACCA": 7984, + "CGGGCAA": 7985, + "TCCAGTCC": 7986, + "GTTTTGTA": 7987, + "GCTACAGG": 7988, + "GCCATCAA": 7989, + "GTTACTAA": 7990, + "GTCCAATG": 7991, + "GCACACTG": 7992, + "GCCGTGA": 7993, + "GCGAAGG": 7994, + "GCATCTCA": 7995, + "TACATCC": 7996, + "GAATTGATG": 7997, + "GAATATTTT": 7998, + "CACACACC": 7999, + "CATGCTTA": 8000, + "CGGCCTT": 8001, + "GTCCATGA": 8002, + "TGAATCAA": 8003, + "TTATTTGTT": 8004, + "TAATTATTTT": 8005, + "GTTCCCTG": 8006, + "GAGTGGGTTGCCATTTCCTT": 8007, + "TCATACC": 8008, + "CAACACTG": 8009, + "GTCAGGAAA": 8010, + "CTTTGGGAGGCTGA": 8011, + "GATTATTA": 8012, + "GGAGAATG": 8013, + "GGAATGGA": 8014, + "TAGAGCAA": 8015, + "CCCTGTCTCTA": 8016, + "TAACAGTG": 8017, + "CTAACATT": 8018, + "CAAAATAAAA": 8019, + "GATATTTTA": 8020, + "CAACTGAA": 8021, + "CTTGAGAA": 8022, + "GAAATAATT": 8023, + "GTGGGTGGG": 8024, + "GCACTGTG": 8025, + "CCTACC": 8026, + "TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC": 8027, + "GGGGAGA": 8028, + "TAAAGGCA": 8029, + "CAAAGGTG": 8030, + "TGATAGAA": 8031, + "TAAAĊ": 8032, + "GAAACAGAA": 8033, + "GAGCCATG": 8034, + "TCACACTT": 8035, + "TAAAGAAAAA": 8036, + "GTAATTCA": 8037, + "GAAAACATT": 8038, + "CTTGTTTG": 8039, + "GGAACC": 8040, + "GTGGCGG": 8041, + "CGGCCCC": 8042, + "GTCACATT": 8043, + "TCCTCTTA": 8044, + "GTTCATCA": 8045, + "TCTTCCCC": 8046, + "CAGGCCTT": 8047, + "TAGCTC": 8048, + "TAAGCATT": 8049, + "CTCCTGCCTCAGCCTCCTGAGTAGCTG": 8050, + "CTTCATTTT": 8051, + "GGACTGAA": 8052, + "CAACACTT": 8053, + "TGTGGAGA": 8054, + "TCTTCTCTT": 8055, + "CACTCCA": 8056, + "GTGCAGCA": 8057, + "CTTTTCCA": 8058, + "GAATTCTA": 8059, + "TATTTGAAAA": 8060, + "TCACTAAA": 8061, + "TCAATGGA": 8062, + "CTCTCTCTCTC": 8063, + "TATGCC": 8064, + "TGTATTTTA": 8065, + "TCCTACTT": 8066, + "TGTTTAGG": 8067, + "CAAAGGAAA": 8068, + "GGAGGCAGG": 8069, + "TCTGTCCA": 8070, + "CTCCCGA": 8071, + "GGTTGGGAA": 8072, + "GCTAGAGA": 8073, + "TGTGCCTT": 8074, + "TCAAGACCA": 8075, + "GTCACCTG": 8076, + "CTGAGGCAGGAGAATCACTT": 8077, + "TCCCATCC": 8078, + "CTCACTTA": 8079, + "GGGAGCTG": 8080, + "TAACGTG": 8081, + "TTATTGTA": 8082, + "GTGGGGAGG": 8083, + "TATTTACTT": 8084, + "GTCACTTA": 8085, + "GATTCCTT": 8086, + "CAAAAGTA": 8087, + "GGCCTGA": 8088, + "TATTATATA": 8089, + "GCCCCTCA": 8090, + "TACAGTTTT": 8091, + "CATACAGA": 8092, + "CAATGTTTT": 8093, + "TAATCTGA": 8094, + "TAATAAATG": 8095, + "TCTGTAGG": 8096, + "GAACACTG": 8097, + "GTGGCATT": 8098, + "CACTCAAA": 8099, + "TCGCGC": 8100, + "CTACTGAA": 8101, + "TCAAGGTG": 8102, + "GGAAGAGAA": 8103, + "TTTTACTG": 8104, + "GCCCCATT": 8105, + "CTCTACTA": 8106, + "TGCCCCA": 8107, + "CACGTCA": 8108, + "GCCCTGTG": 8109, + "CATTCCCA": 8110, + "GTTAGAGA": 8111, + "TACACCTG": 8112, + "GAAAATAAAA": 8113, + "CTCAAGCAA": 8114, + "TGAAGGTG": 8115, + "GTTACACA": 8116, + "GTGTGATT": 8117, + "GCAGAGCC": 8118, + "GTCAGCAA": 8119, + "TCTCGGCTCA": 8120, + "GTTCTTTG": 8121, + "GCAGTCTT": 8122, + "TCACAGGA": 8123, + "GTGGAGTT": 8124, + "GCTGCATG": 8125, + "CCTTCCCA": 8126, + "CTGACAGG": 8127, + "TAAATACTT": 8128, + "CTAACTAA": 8129, + "GTCCCCAA": 8130, + "CTTGCCTGGA": 8131, + "TGACATGA": 8132, + "GAAAATGTT": 8133, + "TGGGCTG": 8134, + "TCACATTTT": 8135, + "CATAATTTT": 8136, + "TCACGAA": 8137, + "GTCCATCC": 8138, + "TATACAGA": 8139, + "TTTTAGATT": 8140, + "TGGGGTA": 8141, + "TAAGATCA": 8142, + "TCTATAAAA": 8143, + "GTTGTATT": 8144, + "CTGAGTAA": 8145, + "TCCACCTCC": 8146, + "CTCGGAA": 8147, + "TAGCTGAA": 8148, + "GATCTAAA": 8149, + "CTCAGGGA": 8150, + "GGGAAATG": 8151, + "GCAGTTAA": 8152, + "GGAGGCTG": 8153, + "TATTTGCAAA": 8154, + "TCAAGACA": 8155, + "GTTATATG": 8156, + "GGCAGGAGAA": 8157, + "GTTTGCTA": 8158, + "CTTGGAAAA": 8159, + "TAACGTA": 8160, + "GCCTGATT": 8161, + "TTTATGTT": 8162, + "GAGTCCA": 8163, + "GAGGGATG": 8164, + "GACĊ": 8165, + "GTCATCTT": 8166, + "TCTCTAGA": 8167, + "GATAGTA": 8168, + "GCAATTAAA": 8169, + "CCACATCA": 8170, + "TATTGGAA": 8171, + "CATGTTTG": 8172, + "GTGGAATT": 8173, + "GCCTACCA": 8174, + "CAAACCTT": 8175, + "CCTGTGTT": 8176, + "TGACACCA": 8177, + "CCTGCATG": 8178, + "TTTAGAAA": 8179, + "CATCTTAA": 8180, + "TATATAATT": 8181, + "CCTACACA": 8182, + "CCAGACCA": 8183, + "TCTTTTTTTTTTTT": 8184, + "CAACGTT": 8185, + "TATAGGA": 8186, + "TCTAGTAA": 8187, + "TAATAGTAA": 8188, + "GCAGTGTG": 8189, + "CTCACATG": 8190, + "TAACCCCA": 8191, + "GAACACAA": 8192, + "GGTTGTA": 8193, + "GCCACGTG": 8194, + "TATCCATG": 8195, + "GTGAGGTG": 8196, + "TCCCCACC": 8197, + "GCAGTCCAA": 8198, + "CTCCATTTT": 8199, + "TCTAGCTT": 8200, + "TCTTCCATT": 8201, + "GATTTAAAA": 8202, + "CTAACAGA": 8203, + "GGTTGTG": 8204, + "GTCTCAGG": 8205, + "TGAAAAATG": 8206, + "CTTCCTAA": 8207, + "TGATAAAAA": 8208, + "TCCCGTT": 8209, + "CTGCCTCC": 8210, + "CTCATCTG": 8211, + "TGCACAA": 8212, + "CCCCCAGA": 8213, + "CTGTTGCTG": 8214, + "TATGATTA": 8215, + "GTCAAAAAA": 8216, + "AATG": 8217, + "CCTGCATT": 8218, + "CTATGAAAA": 8219, + "GCTTAATG": 8220, + "TCCCGAA": 8221, + "TGTTTATTTT": 8222, + "CAGGGACA": 8223, + "CTGATATT": 8224, + "GTCCCACA": 8225, + "CCAGCTCA": 8226, + "CAAAGGGA": 8227, + "TAAATAAATAAA": 8228, + "GTGGAGCA": 8229, + "GTCCACAA": 8230, + "GTCTGAGG": 8231, + "TCTTTAGA": 8232, + "TGTACTA": 8233, + "CAAATTAAAA": 8234, + "TCTTGCAA": 8235, + "CGATAAA": 8236, + "GTACAGTA": 8237, + "TAAAATAGA": 8238, + "TCGCTCTG": 8239, + "TCTGCTCC": 8240, + "CTGAATTTT": 8241, + "GAGATAAA": 8242, + "CTAAGATT": 8243, + "CTCGATT": 8244, + "TAACGC": 8245, + "TCAGATAA": 8246, + "TCCTAATT": 8247, + "CCCATGTG": 8248, + "CGTATTA": 8249, + "GTGCTGTG": 8250, + "GTAGTATT": 8251, + "GTGACATG": 8252, + "TAGCGG": 8253, + "GCCCACTT": 8254, + "GTCTACCA": 8255, + "CAAACCTG": 8256, + "GCTGCTCC": 8257, + "CATATCAA": 8258, + "CTGTTAAAA": 8259, + "GGAGGGAA": 8260, + "GAAGAAAAAA": 8261, + "GACCCAA": 8262, + "GTTCACTT": 8263, + "TATCCAGG": 8264, + "CAAATGGG": 8265, + "CCAAGATT": 8266, + "CCCACCTA": 8267, + "TAAATGAAAA": 8268, + "GGACTACAGGCGCCCGCCA": 8269, + "TGACAATT": 8270, + "GGCACGG": 8271, + "TCTGAGAA": 8272, + "TCAGCCTG": 8273, + "GTTTCCTG": 8274, + "GTAGTCC": 8275, + "GGATTTGAA": 8276, + "TGACACTT": 8277, + "GCACAGGA": 8278, + "CTTACC": 8279, + "CAGAAAATT": 8280, + "TAACTCTT": 8281, + "GCACAGTT": 8282, + "GCAATATT": 8283, + "TACTTATT": 8284, + "CTTGACTG": 8285, + "GCTTTGTA": 8286, + "GCCACTGCA": 8287, + "CGACATT": 8288, + "GATAGAGA": 8289, + "CTGTTATT": 8290, + "GCTGGGTGTG": 8291, + "TGAGCTCA": 8292, + "GTAGCTCA": 8293, + "TATATGGA": 8294, + "GGCAGGTGGA": 8295, + "CTATCCA": 8296, + "CAAAATGAA": 8297, + "GCTACTGA": 8298, + "TGACAGAGTGA": 8299, + "GATGTTTG": 8300, + "TCACCATT": 8301, + "CTTAGTAA": 8302, + "TCTTGGGG": 8303, + "TGTTTGTTTG": 8304, + "CAACAGTG": 8305, + "GTGTCAAA": 8306, + "TCTTTCTCTT": 8307, + "CTCACTAA": 8308, + "GGTTTTTG": 8309, + "TTTTGCTT": 8310, + "TAAACAGAA": 8311, + "TGAGAAAAA": 8312, + "GAACCCGGGAGG": 8313, + "GACAGATT": 8314, + "GCTGGTCA": 8315, + "CTGAGGGG": 8316, + "GAAGAGGAA": 8317, + "GGGGGGGGGGGG": 8318, + "CCCGTCC": 8319, + "TGTTGAGG": 8320, + "CTAAGTCA": 8321, + "CTTGAGTT": 8322, + "GCCCCCTG": 8323, + "CCAGTGTT": 8324, + "TATATATAAA": 8325, + "TATGCCAA": 8326, + "CCTGTTTG": 8327, + "TAATCTAA": 8328, + "TGACCCTG": 8329, + "TATAGTCA": 8330, + "GGTTTCTG": 8331, + "CTCTAGGA": 8332, + "GAGACATG": 8333, + "GCAACAGA": 8334, + "TAAGATGA": 8335, + "TCAGTCAA": 8336, + "GTGTCCAA": 8337, + "GTTCAGCA": 8338, + "CACGAGG": 8339, + "TGAGTGTT": 8340, + "GCTTCTTA": 8341, + "CCCAGTCC": 8342, + "TCAGCCAA": 8343, + "TAAACGG": 8344, + "CTTCAGGA": 8345, + "TATTTCTC": 8346, + "TGCAGTGA": 8347, + "TATCATTTT": 8348, + "CTCAGGTG": 8349, + "GTTCCATT": 8350, + "TATATCTG": 8351, + "TCAACATA": 8352, + "TAAAACTAA": 8353, + "TACATTTG": 8354, + "CCGCGG": 8355, + "GGAATTAAA": 8356, + "CAGGGATT": 8357, + "TCCTTAAAA": 8358, + "CAAAGAGTT": 8359, + "GAAAACAAAA": 8360, + "GAAGATGA": 8361, + "CAAGAATT": 8362, + "GCACAGC": 8363, + "TCAACCTT": 8364, + "TACATATTA": 8365, + "GAAAACCAAA": 8366, + "GCGAGTA": 8367, + "CTCGTTA": 8368, + "CACATGTT": 8369, + "GATCCCA": 8370, + "GAGGGGGG": 8371, + "TCCATTTTA": 8372, + "TTTGCATT": 8373, + "GTAAATAAA": 8374, + "TGAAGATA": 8375, + "GCCACTCC": 8376, + "CCCACGA": 8377, + "GGGCTTTT": 8378, + "GACTGATT": 8379, + "TGACTTTG": 8380, + "TTAGGTT": 8381, + "GTGCTGTT": 8382, + "GTTGTCAA": 8383, + "GATTTGTA": 8384, + "TCGTATG": 8385, + "TCCCTGGG": 8386, + "GAAACTTA": 8387, + "CCAATGCA": 8388, + "TTTTTTGTA": 8389, + "GCCCTCTT": 8390, + "GAGGCCTG": 8391, + "TAATGATG": 8392, + "TAAGGTCA": 8393, + "CACAGGGA": 8394, + "GCTTCTCC": 8395, + "TAATATTTTA": 8396, + "GCACCAAA": 8397, + "GAGATTAAA": 8398, + "GAGTCATT": 8399, + "TGATAGTT": 8400, + "TATGTAGA": 8401, + "TCCAGGAAA": 8402, + "CAACAATT": 8403, + "GGTGGGAA": 8404, + "TAAAAATTTT": 8405, + "GACGCTT": 8406, + "CTGTCATT": 8407, + "CTATACC": 8408, + "GCAACATG": 8409, + "GTCTAGTT": 8410, + "TATATAGG": 8411, + "TCTGGGGA": 8412, + "CTCACTC": 8413, + "GAAAAGGA": 8414, + "GGAAAGAA": 8415, + "TAGCCAGG": 8416, + "CTGCCAAA": 8417, + "TAATTTAAAA": 8418, + "CCACGC": 8419, + "GCAACGG": 8420, + "GCATTAAAA": 8421, + "GAGTGAAAA": 8422, + "TCAGTGTG": 8423, + "CTGAGGTA": 8424, + "TACATAGG": 8425, + "TGACGC": 8426, + "CCATCTGA": 8427, + "TATTTACAA": 8428, + "GTAACACA": 8429, + "GTCATGTG": 8430, + "TAAGTCAA": 8431, + "GCAGCGG": 8432, + "CATATAAAA": 8433, + "TAAGAGC": 8434, + "GTCTGATG": 8435, + "CTAAATTTT": 8436, + "GTGGCTCACACCTG": 8437, + "GTCCCAAA": 8438, + "GCAAAGAA": 8439, + "GCAAACTA": 8440, + "TGAAGGCA": 8441, + "TTCACTA": 8442, + "TGAGTCTT": 8443, + "TGAGTGTG": 8444, + "TTTGGAAA": 8445, + "TTTACAAA": 8446, + "GGAGGGAGA": 8447, + "GTTGTCTT": 8448, + "GGACCCTG": 8449, + "GTAAGTTA": 8450, + "CGGGGAA": 8451, + "TCCCGCC": 8452, + "GTGTCATT": 8453, + "GTCCCTCA": 8454, + "GCGAATG": 8455, + "TCCTCTAA": 8456, + "CTAAAAATG": 8457, + "GCTCTATA": 8458, + "CCTAACCTAA": 8459, + "CAAGTGAA": 8460, + "GTACAGTG": 8461, + "GAGATATT": 8462, + "GTTGGCTG": 8463, + "CCTTGCTG": 8464, + "CCTTTTTG": 8465, + "GAGTAATT": 8466, + "TCAGCCTT": 8467, + "TTAAAAATT": 8468, + "CGAATTA": 8469, + "CATTGGTT": 8470, + "TAGACATG": 8471, + "GTAAATTTT": 8472, + "TATGGTTA": 8473, + "CCTACCTA": 8474, + "CACCCCAA": 8475, + "TCTATTAAA": 8476, + "TCAGTTTG": 8477, + "TAAGGGAA": 8478, + "GGATGAAAA": 8479, + "GATGGGTG": 8480, + "TCAGACAA": 8481, + "TGTTGGGG": 8482, + "CACCCTA": 8483, + "TCTCTTAAA": 8484, + "TAGCATTA": 8485, + "GGACTAAA": 8486, + "GCTAATTA": 8487, + "TCAACTTA": 8488, + "TATACTTTT": 8489, + "CTGGACC": 8490, + "GTTGGCC": 8491, + "GTTCTATT": 8492, + "TGAATTTTA": 8493, + "TGCAGCC": 8494, + "TAATACTA": 8495, + "GTATTCAGA": 8496, + "CTGTGGTGTT": 8497, + "GTCCATTTT": 8498, + "GTAAGTGA": 8499, + "GCACAGCC": 8500, + "GGACGCA": 8501, + "CACAGCCA": 8502, + "GCAACTAA": 8503, + "CCAGCTCC": 8504, + "GGGATTACAGG": 8505, + "TGATATGA": 8506, + "CTGCCTCA": 8507, + "CTCTATGA": 8508, + "TTATTGAA": 8509, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 8510, + "TCTGGGCA": 8511, + "TCAGCCTCC": 8512, + "TCTTCCAAA": 8513, + "CAAAGCCA": 8514, + "GCTGCCAA": 8515, + "TCTCTTTTA": 8516, + "GTTTACTG": 8517, + "CCAGTCAA": 8518, + "CTGACCTG": 8519, + "GCTACATG": 8520, + "GGACCTA": 8521, + "TGTGTAAA": 8522, + "TATTTCCTT": 8523, + "CTCATCAA": 8524, + "TTAAGTA": 8525, + "TGACGTA": 8526, + "CACACACACACACACACACACACACACACACACACACACA": 8527, + "GTGCAGTT": 8528, + "GTTATCTT": 8529, + "CTTTACTA": 8530, + "CAAAACTG": 8531, + "CATAGATG": 8532, + "GCGGGCGCCTGTAGTCCCAGCTA": 8533, + "GTTGCTGA": 8534, + "GGAACATT": 8535, + "TAAAACAGA": 8536, + "CCTTGGCC": 8537, + "GTCAGCTG": 8538, + "TATGAATT": 8539, + "TCACAGC": 8540, + "TGTTTGAAA": 8541, + "TCACCCTG": 8542, + "TATAGTTTT": 8543, + "CATGTCAA": 8544, + "TACACGG": 8545, + "TACAAATG": 8546, + "GCGTTCC": 8547, + "GTCTCTTTT": 8548, + "TGGGTGAA": 8549, + "GGAGAGTT": 8550, + "GCCCTGTT": 8551, + "GGGGGAGA": 8552, + "GCCCTGGCTG": 8553, + "CAACGTG": 8554, + "GGATCTCA": 8555, + "CTTTTTGTT": 8556, + "TGTAGTG": 8557, + "GCAGGCCA": 8558, + "GGATCTGA": 8559, + "TATGCAGG": 8560, + 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+ "CTAGCCA": 8757, + "CCTCTTTG": 8758, + "GCCCCAGG": 8759, + "CTTGGTCA": 8760, + "TATTGTATT": 8761, + "TCTACTTTT": 8762, + "CTCCAGGGGATCTT": 8763, + "CTGCACTCCAGCCTGGGCGACAGA": 8764, + "CACTCATT": 8765, + "GTGGCTTA": 8766, + "GCCTAGAA": 8767, + "TATACCAA": 8768, + "TGAACTTA": 8769, + "CAACCTA": 8770, + "CCAGGTCA": 8771, + "TAAAATATTA": 8772, + "GCTTGCTG": 8773, + "TCTACAAAA": 8774, + "TCTCCTGGG": 8775, + "TGATCTGA": 8776, + "GTCGTCA": 8777, + "CTGACATG": 8778, + "TAATCCTT": 8779, + "GTTCGC": 8780, + "CTAGAGAA": 8781, + "TATATAAAAA": 8782, + "CCTTGAGA": 8783, + "GCCCCCAA": 8784, + "CGGCATT": 8785, + "CTCCAGTACTCTT": 8786, + "CCGTGTT": 8787, + "CGGCGCA": 8788, + "CGTTAAAA": 8789, + "TCTAGATG": 8790, + "CATCTATG": 8791, + "TCGGATG": 8792, + "TCGTCAAA": 8793, + "CTTGTGAA": 8794, + "TAAATACC": 8795, + "CAGAGTC": 8796, + "TCAAAAGACTACACACTTATTTCAAAAAAA": 8797, + "CTTCATAA": 8798, + "TTTGGTTTT": 8799, + "TCTAGTTTT": 8800, + "GACACCAA": 8801, + "GTTCATTTT": 8802, + "TCTTTACA": 8803, + 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8852, + "GACTCCAA": 8853, + "TAAATCATT": 8854, + "TTTTTTTTTTTTTTTTTTTTTT": 8855, + "TTTTCTGA": 8856, + "TCTCAGAAA": 8857, + "GTGAAAGTGAA": 8858, + "TCAGAGTG": 8859, + "CTGTAATT": 8860, + "GGATCATT": 8861, + "TCTTTCAAA": 8862, + "TAGGGAGA": 8863, + "CAACGAA": 8864, + "CCTTGGGG": 8865, + "TTAACTA": 8866, + "TACGTAA": 8867, + "TCATGATT": 8868, + "CACAGAGCA": 8869, + "TCTTGTC": 8870, + "GATACATG": 8871, + "GCAACAGG": 8872, + "CGAGTGA": 8873, + "TCCATCACCAACTCC": 8874, + "TCCGCCTG": 8875, + "CTGTCCTT": 8876, + "TCTTCCTCC": 8877, + "GCTTGTTTT": 8878, + "CGGCTGA": 8879, + "CGACGAA": 8880, + "TCATTTTAA": 8881, + "GATTGGAA": 8882, + "TCCCATAA": 8883, + "TATACCTT": 8884, + "GCGCCCC": 8885, + "CCATATAA": 8886, + "CCTGCAAA": 8887, + "TGTGCTGA": 8888, + "GTACTCC": 8889, + "TGTTTAGA": 8890, + "TCTGTCTC": 8891, + "GTAACTCA": 8892, + "CACGGAA": 8893, + "TGAGAATG": 8894, + "GTTCATTA": 8895, + "GAAATTAAAA": 8896, + "GCGCTAA": 8897, + "TGTGGTCA": 8898, + "CAGATGTA": 8899, + "GTATTTATT": 8900, + "TCACCCAGG": 8901, + "GTAAACAA": 8902, + "TATTTTAATT": 8903, + "GATCTCAA": 8904, + "GCAGTTTG": 8905, + "GTATCTCA": 8906, + "CACATCAA": 8907, + "GATTTGGA": 8908, + "TACTTAAAA": 8909, + "CCATGAAAA": 8910, + "GAACGAA": 8911, + "GAAGATCA": 8912, + "TAAACAATT": 8913, + "TGAAGCCA": 8914, + "GGAAGTTA": 8915, + "GAAAAGCAA": 8916, + "TTATAATT": 8917, + "GTGTATTA": 8918, + "GGATTTTCCAGGCAA": 8919, + "TCGTGGG": 8920, + "GATGAATG": 8921, + "GCATCATT": 8922, + "GCACACCA": 8923, + "CTCTCAGG": 8924, + "TGCACCTG": 8925, + "GTTTGAGG": 8926, + "TGAATACA": 8927, + "TGTTTATTA": 8928, + "GGAGATCCAA": 8929, + "GGGCGCGGTG": 8930, + "CCGTGAA": 8931, + "GTTCACAA": 8932, + "GTTACCTT": 8933, + "GGTCTCTT": 8934, + "CAGGGTTTCA": 8935, + "TCTGTTTTA": 8936, + "GCTTGAGG": 8937, + "TATGTGTT": 8938, + "CTCGGTT": 8939, + "GTAAGCAA": 8940, + "GAAAATGTA": 8941, + "TCTTTTAAAA": 8942, + "TCATCAAACCCATCCTAATT": 8943, + "GGTGCCTG": 8944, + "CAAAGGGG": 8945, + "CTGAATGTT": 8946, + "TGAGCATT": 8947, + "CTGGAGTGCAGTG": 8948, + 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8996, + "GTGTACC": 8997, + "CTCATGTT": 8998, + "CTCCCCTCC": 8999, + "CTTTGCCTT": 9000, + "CAAGATGA": 9001, + "TTTTGGTT": 9002, + "TATTĊ": 9003, + "CAAATCAAA": 9004, + "CTCATGGG": 9005, + "GTCATCAA": 9006, + "CTCAGGAGGCTGA": 9007, + "CATCTATA": 9008, + "GTTCTGTG": 9009, + "GTTGAGTT": 9010, + "GTGATAAA": 9011, + "CGTGCTG": 9012, + "CAAATGAAAA": 9013, + "GGGTCGCAAA": 9014, + "TAAGGCTG": 9015, + "CTCTCTGTT": 9016, + "GCTACCTG": 9017, + "CATCAATT": 9018, + "CCAGACTG": 9019, + "TGTGGGGG": 9020, + "GAACTCAA": 9021, + "GAACACTT": 9022, + "TCCCCAGG": 9023, + "GTTGAGGA": 9024, + "CAGGGGTG": 9025, + "GGGACGG": 9026, + "GACTTATT": 9027, + "GATGAAAAA": 9028, + "TCTTGGCTCA": 9029, + "CTGTTGTG": 9030, + "CAGCAATT": 9031, + "CAAGAGGA": 9032, + "CCACAGC": 9033, + "GAAATAAAAA": 9034, + "GCCTGGAAAATCCCATG": 9035, + "TCTTTGCC": 9036, + "GCTTTATA": 9037, + "TTCACTG": 9038, + "TAAAAAAAAAAAA": 9039, + "TAAGAATG": 9040, + "TTTTTCTA": 9041, + "GGACATCA": 9042, + "TAACAAAAAA": 9043, + "CAAAGTATT": 9044, + "CTCAGTCC": 9045, + "GCAAGG": 9046, + "TAGAGAAAA": 9047, + "CCTGTGAA": 9048, + "CAGGCCAA": 9049, + "GTATGAAAA": 9050, + "TAATGAGA": 9051, + "GAAGAATG": 9052, + "GTATCTTA": 9053, + "GGTAGAAA": 9054, + "TAGAATTA": 9055, + "TGACTCTT": 9056, + "TCATTATG": 9057, + "CCACGCA": 9058, + "TATTCCCA": 9059, + "TATTGACA": 9060, + "GCTTGATT": 9061, + "CGACACA": 9062, + "GATACATT": 9063, + "TCCTGTTA": 9064, + "TCATATGA": 9065, + "GCATAATT": 9066, + "GCCCTCTG": 9067, + "TGTGGACA": 9068, + "CATGGTAA": 9069, + "CAAAATGTT": 9070, + "CTGGAAAAA": 9071, + "TATGTCAAA": 9072, + "TCTCTAAAA": 9073, + "GTAAATTAA": 9074, + "CCTTCTAA": 9075, + "CTTAGTGA": 9076, + "GTCTCTGTG": 9077, + "TCAAGGCTG": 9078, + "TCTGCAAGTGGA": 9079, + "CAGATAGA": 9080, + "GTTTTCATT": 9081, + "GTGCACTG": 9082, + "CCAGACTT": 9083, + "GTTACATG": 9084, + "GCTACATT": 9085, + "TGACTGTG": 9086, + "GAAGAGC": 9087, + "CAGGTGTT": 9088, + "GATTTCTA": 9089, + "CGACTC": 9090, + "GGATACC": 9091, + "CCTCCTGGG": 9092, + "TCGGCGG": 9093, + "GCCAGGTGTG": 9094, + "GTGCAGGA": 9095, + "TCTGTACA": 9096, + "TCTGGTCC": 9097, + "GGAACTCA": 9098, + "CCTTGGCCTCCCAAA": 9099, + "CCCACTTTT": 9100, + "GCTCACTGCAA": 9101, + "CAAGGCAA": 9102, + "GGAGGATG": 9103, + "TAAACTC": 9104, + "TCTGGTCA": 9105, + "TACTATTA": 9106, + "GTTCACGCCATT": 9107, + "CCTGGCAA": 9108, + "GCTTCAGA": 9109, + "CTTACTGA": 9110, + "GGAATATA": 9111, + "CCAGAGCC": 9112, + "TAAATATCA": 9113, + "TGTGGCAA": 9114, + "TAAAACACA": 9115, + "TACTCAAA": 9116, + "AAAAAAAAAAAAAAAAAAAA": 9117, + "GATCCAGG": 9118, + "TAATTTTTTTT": 9119, + "CTACTGTT": 9120, + "CAAAGAGAA": 9121, + "CAGGGCAA": 9122, + "CCAAGGTT": 9123, + "CTTATCAA": 9124, + "TATCTACA": 9125, + "CTGGAGCA": 9126, + "TAGGGTA": 9127, + "CTGAAGTA": 9128, + "CCCTTTTA": 9129, + "GTGCCCTG": 9130, + "CGAGTCA": 9131, + "CCGTAAA": 9132, + "GTGAGGGA": 9133, + "GTTAGTAA": 9134, + "CGTGAAAA": 9135, + "TACTGAAAA": 9136, + "GTGTGAAAA": 9137, + "CGTĊ": 9138, + "GAAAACACA": 9139, + "CCCCCATT": 9140, + "GGACACTT": 9141, + "CTAAGTAA": 9142, + "TCGCATG": 9143, + "TGATAATG": 9144, + "TGACAGTA": 9145, + "TATTCGG": 9146, + "GTGATGTA": 9147, + "GCGATTTT": 9148, + "CAACTCAA": 9149, + "CCACACTT": 9150, + "TATGGCC": 9151, + "GACGTAA": 9152, + "CAGAGCCAGG": 9153, + "TCATCTTA": 9154, + "CTTGCTGA": 9155, + "GCTTCACA": 9156, + "TCCCAGTA": 9157, + "GTCTATGA": 9158, + "TCGAAATT": 9159, + "TTTTCATG": 9160, + "GAACAGTG": 9161, + "TCTGTAGA": 9162, + "GCTTCCCTG": 9163, + "GTGAGGTT": 9164, + "CCTTCAGA": 9165, + "TATATACC": 9166, + "CAGGTGAA": 9167, + "GATATGAA": 9168, + "GAGTACTGGAGTG": 9169, + "CCCAAATT": 9170, + "CAATAGCA": 9171, + "GTTCCTGA": 9172, + "GCCTATCA": 9173, + "GTTAGGAA": 9174, + "CTTAGAGA": 9175, + "GTGTAGTG": 9176, + "CCCCTCCA": 9177, + "CTCTGTCTC": 9178, + "GTGACTAA": 9179, + "CCTTCCTCA": 9180, + "CTCCCCCA": 9181, + "CAATACTT": 9182, + "GTTGACTT": 9183, + "GTAGAGCA": 9184, + "GCCTACTT": 9185, + "CTCCTCTA": 9186, + "TTTTTTTATT": 9187, + "CACGTTA": 9188, + "TATAGACA": 9189, + "CCAAGAGG": 9190, + "CCCCCGCC": 9191, + "TCACTCAA": 9192, + "GGAGTGAA": 9193, + "TAAAGGGA": 9194, + "TCTGGTTTT": 9195, + "GATGTGTG": 9196, + "TTATGGG": 9197, + "CAAATTTTA": 9198, + "CGAGTTA": 9199, + "CAGCTCAA": 9200, + "TCCATCCATCCATCCA": 9201, + "GGGTAAAA": 9202, + "TGATACTT": 9203, + "TTATGCA": 9204, + "GCAGAAAAA": 9205, + "TAAAGGTA": 9206, + "CTGATCTT": 9207, + "GCAAGGCA": 9208, + "GCTATGTG": 9209, + "TCTTTTTCTT": 9210, + "CTAAATTAA": 9211, + "TTTTGGTA": 9212, + "GTTGGCAA": 9213, + "GATACACA": 9214, + "CTCCTGACCTCA": 9215, + "CTGAGGCAGGAGAATTGCTT": 9216, + "GTTAATTAA": 9217, + "TAGCTGTT": 9218, + "TCCTATCA": 9219, + "CCAGAATT": 9220, + "CTCTATCC": 9221, + "CAGCTATA": 9222, + "GTCGGTT": 9223, + "GCTTCTCTT": 9224, + "GAAAATTTA": 9225, + "CACCTGTT": 9226, + "GCTGTAGG": 9227, + "CCAAGCCA": 9228, + "GCTATATT": 9229, + "GAAGACTT": 9230, + "TAAAATACA": 9231, + "CCTTTATG": 9232, + "GAGGAGAGG": 9233, + "GTGGGAAAA": 9234, + "TCGCTTTT": 9235, + "CCAAGGTG": 9236, + "CTCCCATG": 9237, + "CCAGCCTGGCCAACATGGTGAAA": 9238, + "CAATGATT": 9239, + "CAATACCA": 9240, + "GTCATGGG": 9241, + "GATTCC": 9242, + "GAGGAATG": 9243, + "TGACAATG": 9244, + "CTATAATT": 9245, + "CAGGTAA": 9246, + "CTCCTGTA": 9247, + "GGAGGATCACTT": 9248, + "CATTATTTA": 9249, + "CTCATGAA": 9250, + "GATTCTTA": 9251, + "GCCAGTTTT": 9252, + "CTACAGTA": 9253, + "TAATATAATA": 9254, + "GGACGGA": 9255, + "GTATCTC": 9256, + "TCAAGTCAA": 9257, + "GAGGTGAA": 9258, + "GAAAATATG": 9259, + "GAGGAGAGA": 9260, + "GATCTATT": 9261, + "CTGTCTCA": 9262, + "CTGGAATT": 9263, + "GCACCTCA": 9264, + "GCTTGCC": 9265, + "GCTTTCTA": 9266, + "TCTCCTCC": 9267, + "CTTCTTAAA": 9268, + "TATTTATTTTA": 9269, + "GTGGAAGG": 9270, + "GGAAGGGAA": 9271, + "GGGGCCTG": 9272, + "CTCTGTTTT": 9273, + "CGTGAGCCA": 9274, + "TATTGGTG": 9275, + "TCTGCAAAA": 9276, + "CCTATGTT": 9277, + "CACAGGCA": 9278, + "TCGCAGG": 9279, + "GATCAATT": 9280, + "GACTCTGA": 9281, + "TCATACTT": 9282, + "GTTCCCAA": 9283, + "CCTGGAAAA": 9284, + "CTTTTTTCA": 9285, + "GGTTTCTT": 9286, + "GCTCCAAAA": 9287, + "TAAAATATAA": 9288, + "GCTGGCAA": 9289, + "CGGGAAA": 9290, + "CCCGCTG": 9291, + "TATAGCC": 9292, + "CCAATTGTG": 9293, + "GAGGTAGG": 9294, + "TTATGAAA": 9295, + "CCATCCTT": 9296, + "CGATCAA": 9297, + "TCAAATCAAA": 9298, + "CGGCTCC": 9299, + "GTAGTAAA": 9300, + "CTGAGGCC": 9301, + "TCCAATTTG": 9302, + "AAAAAAAAAAAAAAAAAAA": 9303, + "TAACCATT": 9304, + "CAAATAAAAA": 9305, + "GATAATAAA": 9306, + "CATATATATA": 9307, + "TGTTGAGA": 9308, + "CTTATCTT": 9309, + "CTTCATGA": 9310, + "GTGGCCTT": 9311, + "GTATTGGA": 9312, + "GCTTATTTT": 9313, + "GAACGTT": 9314, + "GCCCCCTT": 9315, + "CGGTGGA": 9316, + "GATTCTAA": 9317, + "CCTACATG": 9318, + "TCCCGCA": 9319, + "CTCCATCTC": 9320, + "CTTACAGA": 9321, + "CGCCAGA": 9322, + "CTGCACTT": 9323, + "GTTAGAGG": 9324, + "CGGCTCA": 9325, + "TTATTCAA": 9326, + "TCCTGTTTT": 9327, + "CCAGCCTGGCCAA": 9328, + "TCTGTCCTT": 9329, + "CCTGGGCA": 9330, + "GAACAGTT": 9331, + "GCCATAA": 9332, + "CTACAGTG": 9333, + "GTTTTTTTTTTTT": 9334, + "TCTAGAAAA": 9335, + "CTGTGGGA": 9336, + "TAAATGTTA": 9337, + "GACTCCTT": 9338, + "GCCTCAGG": 9339, + "TCCATA": 9340, + "GGTCAGGAA": 9341, + "CATACTCA": 9342, + "TCACGTA": 9343, + "GTGTAGTT": 9344, + "TTTTTTTCC": 9345, + "TCTCCTGCATT": 9346, + "CCTGTGTG": 9347, + "TATGGGTT": 9348, + "GATGCCTT": 9349, + "GGGAGGTG": 9350, + "CTACTAAA": 9351, + "GTTTTCTTTT": 9352, + "GCCAGTTA": 9353, + "TAGAGTCA": 9354, + "GACACACACA": 9355, + "TTAGAGG": 9356, + "CAACAGTT": 9357, + "GCATCTGA": 9358, + "CCTAGAGA": 9359, + "GTTGGGGA": 9360, + "GAAAGCAAA": 9361, + "CATTCTC": 9362, + "TGAACCAA": 9363, + "GGAGAGGAA": 9364, + "GTGCACCA": 9365, + "GTTGCCTT": 9366, + "TAAATAGAA": 9367, + "GCTTCCCA": 9368, + "CATACTTA": 9369, + "TCACGCA": 9370, + "TAAACCTG": 9371, + "TGAAGGTA": 9372, + "GATTGTAA": 9373, + "GTTTTCCA": 9374, + "CTGGGGAA": 9375, + "TAAAAGTTA": 9376, + "TATCCAGA": 9377, + "TCAGAAAAA": 9378, + "CAAGAGTT": 9379, + "CGGCACA": 9380, + "CTAATGTT": 9381, + "GTCCTGTA": 9382, + "TGAGTTTG": 9383, + "CAACCTCA": 9384, + "GTGGACTG": 9385, + "TCTTTATTTT": 9386, + "CTGCCCTT": 9387, + "GATATGTT": 9388, + "GTGAATTTT": 9389, + "GATCCCCTGGAGAA": 9390, + "CCACATCC": 9391, + "GGAGTAAA": 9392, + "GAGGGG": 9393, + "CGAAAAAAA": 9394, + "TCTGCTC": 9395, + "GTAATA": 9396, + "CAAAATCAA": 9397, + "GACATAA": 9398, + "GTGTTAAAA": 9399, + "CCAAGGGG": 9400, + "CTTAGG": 9401, + "GTTTTGATT": 9402, + "CCAATTAAA": 9403, + "TCGCAGCA": 9404, + "TCATAAAAA": 9405, + "CCACGGA": 9406, + "GAGATAGG": 9407, + "CATTTATAA": 9408, + "GATGCAGA": 9409, + "CAAAAAAATT": 9410, + "TGGAGGAA": 9411, + "GCGTGGTG": 9412, + "TCATCTCC": 9413, + "GAATAATA": 9414, + "GCACATCA": 9415, + "GTAAGTCA": 9416, + "GAGGGCTG": 9417, + "GTCCTGCA": 9418, + "TGATTGTA": 9419, + "CCAGCCTGGTCTA": 9420, + "CTTGGATT": 9421, + "GTCGTGA": 9422, + "TATATACAA": 9423, + "CAAATAATT": 9424, + "TAGACTAA": 9425, + "GATTTTTTTGAAA": 9426, + "GTGTATTTT": 9427, + "GTCATTAAA": 9428, + "CAAAGCAAA": 9429, + "GTTACTTTT": 9430, + "CTTATGTT": 9431, + "CGACATG": 9432, + "GGAGATAA": 9433, + "GCCCATCC": 9434, + "GGTGGTGA": 9435, + "TCATTCTA": 9436, + "TCATCCAA": 9437, + "TGATAGCA": 9438, + "GCAGACCA": 9439, + "GCAGCATG": 9440, + "CACATCTT": 9441, + "CCTTTGCA": 9442, + "TGATTTCTT": 9443, + "TTTTTGTTA": 9444, + "GCACCATT": 9445, + "CCTTTCCA": 9446, + "GGAGACAGA": 9447, + "GTTTTAATG": 9448, + "GTGCCATT": 9449, + "TACTGGAGTG": 9450, + "CTTGCTTA": 9451, + "GTCCCATT": 9452, + "TTTTAATTAA": 9453, + "GAAGATAA": 9454, + "GTGAAAAAA": 9455, + "GCAGATAA": 9456, + "CCAGAGC": 9457, + "CACAGGGG": 9458, + "TAAAATAATT": 9459, + "CATCTACA": 9460, + "CAGGTTTA": 9461, + "TCATTTAA": 9462, + "CTGCTGTG": 9463, + "CACATTTTA": 9464, + "CAAAAGCAA": 9465, + "TAGAGTAA": 9466, + "CCTCTATT": 9467, + "GTGGGCAAA": 9468, + "CTCCTGACCTCAAGTGA": 9469, + "TGACTCAA": 9470, + "CTGCAATT": 9471, + "GCATTTTTT": 9472, + "TATGGTTTT": 9473, + "CAGAATTA": 9474, + "TAAGTGTGTAGTCTTTT": 9475, + "GCACACTT": 9476, + "GAAAATATTA": 9477, + "TTATTTCC": 9478, + "GTCTTAAAA": 9479, + "GTACAGCA": 9480, + "GTCGTTA": 9481, + "GGAGGTTA": 9482, + "CTGGGATT": 9483, + "GAAAGGCA": 9484, + "CTGCACTCCAGCC": 9485, + "GCTCTGGATAA": 9486, + "GACAGATG": 9487, + "GTTCATGA": 9488, + "TGACTATT": 9489, + "TGACAGAGCAA": 9490, + "TACAGGAA": 9491, + "GAACCCC": 9492, + "GGTCTGTT": 9493, + "TAAAGTAAA": 9494, + "GGAACCTG": 9495, + "TATTTGTTA": 9496, + "GCTGGGCGTG": 9497, + "CAGTCCTT": 9498, + "GACGCCA": 9499, + "TCACTATT": 9500, + "TCATCATG": 9501, + "CAAGACTG": 9502, + "GTTTAGTG": 9503, + "GAAAAGGAAA": 9504, + "AAAAAAAAAAAAAAAAAAAAA": 9505, + "CCGGGCA": 9506, + "CTTCCTTTT": 9507, + "GGACAGTT": 9508, + "CCAGACAA": 9509, + "TCAAGCTG": 9510, + "GCTTTAGG": 9511, + "TATGGCTG": 9512, + "GGTGGCTGA": 9513, + "CGCGCGC": 9514, + "TATTTCTAA": 9515, + "TCTGTCCATG": 9516, + "CAGACTAA": 9517, + "GAGGCAAA": 9518, + "CTACCCC": 9519, + "GAGAATTTT": 9520, + "CCTCTATA": 9521, + "GTTCAGC": 9522, + "TTAATATA": 9523, + "TCTAGTGA": 9524, + "CGAGTAA": 9525, + "GCAATGTG": 9526, + "TAATCTTTT": 9527, + "TAAGGCAA": 9528, + "GTTCCCCA": 9529, + "GTTGACAA": 9530, + "TCTTGCTA": 9531, + "TCATTGTA": 9532, + "TCGTTTA": 9533, + "TCCCGGA": 9534, + "CATTCAGG": 9535, + "TCTGCCTA": 9536, + "TAAAATGAAA": 9537, + "TAGAGTTA": 9538, + "TGTACATG": 9539, + "GAGGTGTG": 9540, + "TCAATACA": 9541, + "TCCAAAAAA": 9542, + "GTGGGCTT": 9543, + "GATTGGTT": 9544, + "TCTCCAGCA": 9545, + "GTGCAGC": 9546, + "CCACATTA": 9547, + "TCTGGTTA": 9548, + "CCTGGGAGG": 9549, + "CTCCCCAAA": 9550, + "GAAAACTCA": 9551, + "TCAATGAAA": 9552, + "CATAGATT": 9553, + "CTTGCCTGGAGAATCCCAGG": 9554, + "TCCCTGCC": 9555, + "CTGTATAA": 9556, + "TATGCTCA": 9557, + "GGTGGAGA": 9558, + "GATACAGA": 9559, + "TAACAATG": 9560, + "GCCCGTT": 9561, + "GGAACAAAA": 9562, + "CTCCTAGG": 9563, + "TTTGCAAA": 9564, + "TCCCTTAA": 9565, + "CCACCATT": 9566, + "TCAATGCA": 9567, + "TCTCTACAA": 9568, + "TATCTTAA": 9569, + "TCGCGCC": 9570, + "GAGTCTA": 9571, + "GTCAGTTA": 9572, + "CCAAGTGA": 9573, + "CACCCAGG": 9574, + "GAACTGTG": 9575, + "GGTAGAGA": 9576, + "GCTCTATT": 9577, + "GGATTACAGGTG": 9578, + "CCATATTA": 9579, + "TATACTCA": 9580, + "GTAACTGA": 9581, + "TCCTGCCTT": 9582, + "CGGGGCC": 9583, + "TGGAGTA": 9584, + "TCAAAATTA": 9585, + "GGAGACAGG": 9586, + "GACAGAGGAGCCTG": 9587, + "GGGCAAAA": 9588, + "GTTGCATT": 9589, + "GGAGAGAAA": 9590, + "GTCAAATA": 9591, + "TATACAGTA": 9592, + "TGTTGCAA": 9593, + "CGGTTAA": 9594, + "TAGCCATG": 9595, + "GGCTTTTA": 9596, + "CAACAGC": 9597, + "GGACGTT": 9598, + "GTCTTTTAA": 9599, + "CGGCAGA": 9600, + "GTTTTATTA": 9601, + "CTCATTAAA": 9602, + "TAACACTA": 9603, + "TCAGCTCC": 9604, + "CCCACCTCC": 9605, + "CTCTAATG": 9606, + "GATTGAAA": 9607, + "CCAACAGA": 9608, + "CCCATGAA": 9609, + "CTGCTCTG": 9610, + "GTCCATAA": 9611, + "TATGGGAA": 9612, + "TATTTCTGA": 9613, + "GCCTTATT": 9614, + "CTCTCCTCC": 9615, + "TGTTGTTGTT": 9616, + "TAAAAGAAAA": 9617, + "TCCTGAGTT": 9618, + "GTGCGTT": 9619, + "GTGTGTTTT": 9620, + "GATGGATGGATGGATG": 9621, + "CCCĊ": 9622, + "TAAGGTAA": 9623, + "GTAGAGTT": 9624, + "CCTTGAGG": 9625, + "TGGATGA": 9626, + "TCATTTCTT": 9627, + "GCTCAATG": 9628, + "GTTTAATG": 9629, + "GAGATAGA": 9630, + "GGGGTAGG": 9631, + "TAATTCTT": 9632, + "GCATTGAA": 9633, + "CTGCGTG": 9634, + "GTTTTTCAA": 9635, + "CCTACATT": 9636, + "TGATTCTA": 9637, + "TAGCCACA": 9638, + "GGTTAGGTTA": 9639, + "GAACCCGG": 9640, + "CCGAGC": 9641, + "GCCCCTGA": 9642, + "TATATAATA": 9643, + "GCAAGTAA": 9644, + "CCCAGCCTG": 9645, + "TCATGGCTG": 9646, + "CAAAAAAAAAA": 9647, + "GCTATATG": 9648, + "TCCTACTG": 9649, + "CTGTATCA": 9650, + "GGGACACA": 9651, + "TATCTTTTA": 9652, + "GTGGCCAA": 9653, + "GCACATATA": 9654, + "GTATACC": 9655, + "CGGTATT": 9656, + "TATTTATTTTTT": 9657, + "CCTAAATT": 9658, + "TCATAGCA": 9659, + "GCTGCTTA": 9660, + "GGTGCTGA": 9661, + "CATATCTT": 9662, + "CATCACACA": 9663, + "CAAATCTCA": 9664, + "GTAACCAA": 9665, + "GTGGATAA": 9666, + "GTTAGTGA": 9667, + "GGATTATAGG": 9668, + "CTAGCAAA": 9669, + "TGTGCAGA": 9670, + "TAGCTCTT": 9671, + "CAAGAGC": 9672, + "CTCTAATA": 9673, + "CGGCGCC": 9674, + "CAAGTGTT": 9675, + "CAACATTTT": 9676, + "CTATATTTT": 9677, + "CAATAGCAAA": 9678, + "CAACGC": 9679, + "CTGTACTT": 9680, + "GGATATAA": 9681, + "CTTTGGGAGG": 9682, + "TACAATTTT": 9683, + "GTTGAGTG": 9684, + "GTTCATAA": 9685, + "CATGCCAA": 9686, + "CGGCAAA": 9687, + "CCTATAAA": 9688, + "GAGAATTA": 9689, + "GGCCAGGCTGGTCTCAAA": 9690, + "GGGGCTGA": 9691, + "GACATCAA": 9692, + "CAGTTTTTT": 9693, + "TAAAAGTG": 9694, + "CTAAATAAA": 9695, + "CCATACCA": 9696, + "CTGTACTG": 9697, + "GCACTGTT": 9698, + "CACCCAGA": 9699, + "GGTCTGTG": 9700, + "GATCTTAA": 9701, + "GGAAGGCA": 9702, + "GTAAGATG": 9703, + "GAGGCTCA": 9704, + "CTAATTTTA": 9705, + "TAGGGAAA": 9706, + "GCTATCTT": 9707, + "TCAAAAAAAA": 9708, + "GCATCACTGA": 9709, + "GCCGTCA": 9710, + "GCTCTATG": 9711, + "GTATACTT": 9712, + "GCAAGGTG": 9713, + "GACAGAAAA": 9714, + "TCCGGA": 9715, + "TCAAGATA": 9716, + "TAGAGCTG": 9717, + "GGTTTATT": 9718, + "GACACATG": 9719, + "GAAGTCAA": 9720, + "TGTTCAGA": 9721, + "CCCCCGA": 9722, + "GTACAGTT": 9723, + "CTAACATG": 9724, + "GAAGTTAA": 9725, + "TGAAGGGG": 9726, + "GAGGGGTG": 9727, + "GTCCCCCA": 9728, + "GCTAAAAAA": 9729, + "TCATGAGG": 9730, + "CAAAAAATT": 9731, + "GCCGCTT": 9732, + "CTGATGTA": 9733, + "TCTCCCCA": 9734, + "GTCCAAAAA": 9735, + "TCACAAGGTCA": 9736, + "TAACGCA": 9737, + "GTAAAAATG": 9738, + "TTTTAAATT": 9739, + "GAACGTG": 9740, + "CAAATATAA": 9741, + "CACCCATG": 9742, + "GAGTCCTT": 9743, + "TCCGGCC": 9744, + "GCACAGTA": 9745, + "TTAATGAA": 9746, + "TGGGGTT": 9747, + "CTCTTTCTT": 9748, + "GTAAAATTA": 9749, + "TAAGATTTT": 9750, + "GCATATAA": 9751, + "GTAGCAAA": 9752, + "TAAACATTA": 9753, + "TCAATAGA": 9754, + "CCCAAAAAA": 9755, + "GTAGTAGTAGTA": 9756, + "TCAGGTCA": 9757, + "TGACCTTA": 9758, + "TCCGATT": 9759, + "GCCAAATA": 9760, + "TAAACATAA": 9761, + "TCAACCTG": 9762, + "CCTTGTCA": 9763, + "GCTAGCTG": 9764, + "TAATTTGAA": 9765, + "CGTTCGGTT": 9766, + "GGAGGATT": 9767, + "GCTTTATG": 9768, + "GTATTTTAA": 9769, + "CTATGCC": 9770, + "CCTGAATT": 9771, + "GTCGAAA": 9772, + "CTCATGTG": 9773, + "CCTAGCAA": 9774, + "TGTTCAGG": 9775, + "TTTATCTT": 9776, + "CACTGAAAA": 9777, + "GGAAGGAAGGAAGGAAGGAAGGAAGGAAGGAA": 9778, + "TTTGTCTT": 9779, + "TTTATTTTTT": 9780, + "GGGACAGA": 9781, + "GCGGCGC": 9782, + "TCCTCTTTT": 9783, + "GGAACCAA": 9784, + "GCCACCCC": 9785, + "GCATCCTT": 9786, + "GCCGATG": 9787, + "TATACTGA": 9788, + "TTTGGGGG": 9789, + "TCTAGCAA": 9790, + "TCTGACAAA": 9791, + "GGATTATT": 9792, + "TAATTTTTG": 9793, + "CTGTACAA": 9794, + "CTTTTCCAA": 9795, + "CTATGTTA": 9796, + "AAAATGAAAA": 9797, + "GGTCTCTG": 9798, + "GATCTTTG": 9799, + "TGTACATT": 9800, + "CCTAGTG": 9801, + "TCTATGAAA": 9802, + "GGGTTAGGGTTA": 9803, + "CTTGAGCA": 9804, + "GTCCAGTA": 9805, + "GCTTGAAA": 9806, + "GTTTTGTG": 9807, + "TCCCCTGA": 9808, + "GTTTCATG": 9809, + "TCTTCAAAA": 9810, + "GCTAATTTTTGTATTTT": 9811, + "TTTGTATT": 9812, + "CACCTGA": 9813, + "TATTATATTA": 9814, + "TACTGTTA": 9815, + "TCCTGATG": 9816, + "GTAAATATT": 9817, + "CAAGATAA": 9818, + "TCAACAGAA": 9819, + "CTGATTAAA": 9820, + "CAATATGA": 9821, + "GGAGTTTG": 9822, + "GTAACAGA": 9823, + "TGGACATT": 9824, + "GGCCCTGA": 9825, + "TCAGAGCC": 9826, + "TCTCCACTT": 9827, + "CAAGCATT": 9828, + "TTTATGAA": 9829, + "GACATGTT": 9830, + "TATATATATATATATATATATATATATATATATATATATATATATATATATATATATATATATA": 9831, + "TCACCCCA": 9832, + "GGAACATG": 9833, + "GCTTTA": 9834, + "GTAACATG": 9835, + "TCCACCCGCC": 9836, + "TCAGGGTCTTTT": 9837, + "TAACAGGA": 9838, + "GTTCAGTA": 9839, + "GGGGTGTG": 9840, + "GAATTGAA": 9841, + "CCTAATTA": 9842, + "TCACAGCC": 9843, + "GCACCCTG": 9844, + "TCTGGAAAA": 9845, + "TCCCAATT": 9846, + "CTTCATCC": 9847, + "CCTTTCTA": 9848, + "TTTTAGAAA": 9849, + "CGCTATT": 9850, + "CTACTACTACTA": 9851, + "TAACTCTG": 9852, + "TTTTTAGG": 9853, + "CTTGTCTG": 9854, + "GCTCAAGCCTGTAATCCCAGCACTTTGGGAGGCC": 9855, + "TAAAAATATA": 9856, + "TCCTTTCTT": 9857, + "CTAAAATTA": 9858, + "GTACGTT": 9859, + "CTTACATG": 9860, + "CCCTCCAA": 9861, + "CTTATAAAA": 9862, + "GGGĊ": 9863, + "GTAGAGGA": 9864, + "TGGACACA": 9865, + "TCCTTCCAA": 9866, + "CCAAATTTG": 9867, + "CATTCATA": 9868, + "TCTGAAATT": 9869, + "CTCAGACA": 9870, + "GCAAGACA": 9871, + "GGGGGCTG": 9872, + "TAAGCAGG": 9873, + "CCATCATT": 9874, + "CCACTCTT": 9875, + "GATGCAGG": 9876, + "GAAAACTGA": 9877, + "CGAGTCC": 9878, + "TGATTCTG": 9879, + "GCAGAGTA": 9880, + "CAACAAAAAA": 9881, + "CACACACACACACACACACACACACACA": 9882, + "TGTTGTC": 9883, + "CCTTTGTA": 9884, + "CCTGTA": 9885, + "GTAATATG": 9886, + "TGCAGCTG": 9887, + "CCAAAGAAA": 9888, + "GCATGGTG": 9889, + "GAATACTT": 9890, + "TCCTGAGG": 9891, + "TATTTAGAA": 9892, + "GAGACCAA": 9893, + "GGAGGGGAGG": 9894, + "GCTCTAAA": 9895, + "TCTCTGCTTTT": 9896, + "GGATAATT": 9897, + "GTCAGGGG": 9898, + "CATTTTTTA": 9899, + "CAAGTTAA": 9900, + "GAAATAGAA": 9901, + "CAAGGGCA": 9902, + "GTGCTTTG": 9903, + "CTTACTCA": 9904, + "TCCATAGG": 9905, + "CACACAAAA": 9906, + "CACAGATA": 9907, + "TAAAATGGG": 9908, + "GTTATCAA": 9909, + "TCTCCCTA": 9910, + "TGCCCAGG": 9911, + "TTATTATTAA": 9912, + "TTTTGATG": 9913, + "TCATACTG": 9914, + "GCAAATTTG": 9915, + "TAAGTATTTG": 9916, + "GACTCTTA": 9917, + 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+ "CCCGAGG": 10379, + "CACACTAA": 10380, + "TCTGAGGA": 10381, + "TAGCCAGA": 10382, + "CTGCCATT": 10383, + "CTGCATTA": 10384, + "GTATTTCTG": 10385, + "GTCTTATG": 10386, + "TATTGGGG": 10387, + "GCTTGTAA": 10388, + "GCAACTGA": 10389, + "TTTTATGAA": 10390, + "GGTGCGG": 10391, + "TGAAATTAA": 10392, + "TCCCACTA": 10393, + "CTCCCTAA": 10394, + "TACAGCAAA": 10395, + "CAAGGTTTT": 10396, + "TAAGGAAAA": 10397, + "GTTCCTTA": 10398, + "GCATATTTT": 10399, + "TTTTACAAA": 10400, + "TGGACTA": 10401, + "CTTTATAAA": 10402, + "CTTTAGGA": 10403, + "GAGAGTAA": 10404, + "CATTTCTCA": 10405, + "CCAACTCA": 10406, + "GTAGTTTG": 10407, + "CATTTCTAA": 10408, + "CATAGTTA": 10409, + "TCAAATATTA": 10410, + "CTGCGC": 10411, + "CTTGATTA": 10412, + "GTTTATAAA": 10413, + "CACTAATT": 10414, + "CCTGCCCA": 10415, + "GCCACTAA": 10416, + "CCCCCCTCC": 10417, + "GAATTCTCA": 10418, + "GCATGTTA": 10419, + "TATTTGATT": 10420, + "TAAAAGATT": 10421, + "GCTGTAAA": 10422, + "CTTATGA": 10423, + "CTCTCGG": 10424, + "GGTTTGTT": 10425, + 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10564, + "TCGGTTA": 10565, + "CTTGCTAA": 10566, + "GTTTCAGG": 10567, + "TGAAGTTTT": 10568, + "CCTTGTAA": 10569, + "TCTAGTCA": 10570, + "CACTGATG": 10571, + "GATCCACA": 10572, + "GAGTGATT": 10573, + "CTTTGATG": 10574, + "CCTTTATA": 10575, + "GAGGGAGGA": 10576, + "TAATTTCTG": 10577, + "GGAGAAGACTCTT": 10578, + "CCGGAAA": 10579, + "CAAGGCTT": 10580, + "TAGTTAAAA": 10581, + "GCAGGCTA": 10582, + "CCTAAAAAA": 10583, + "CTGTCTTA": 10584, + "TAAACACC": 10585, + "GTCGTCC": 10586, + "TTAATCA": 10587, + "GAAAAGAAAAA": 10588, + "GGGTGAAA": 10589, + "GTTGGTGA": 10590, + "TAAATATAAA": 10591, + "GGAACTAA": 10592, + "GAGGGGGA": 10593, + "CGTCTTA": 10594, + "CGTAAATT": 10595, + "CCATCTTA": 10596, + "TAAAGCCA": 10597, + "GAGAGATA": 10598, + "CCTGGTCA": 10599, + "CTTCCTGTT": 10600, + "TGACCATT": 10601, + "TAAATAATG": 10602, + "TAAAATGCA": 10603, + "GATGCAAA": 10604, + "CTTTTTATG": 10605, + "TCGGAAAA": 10606, + "GAGAAAATT": 10607, + "CTGCTTTG": 10608, + "CTGGACTT": 10609, + "GCTGTCAA": 10610, + "GCTAGCTT": 10611, + "GCCCTCAA": 10612, + "GCCTCTAA": 10613, + "TGTACACA": 10614, + "CTGTTCTA": 10615, + "CATAAAATT": 10616, + "CCGTCTT": 10617, + "GAAAAAATT": 10618, + "CAATACTG": 10619, + "GGATACAA": 10620, + "GAAAATGAAA": 10621, + "TAGAGCTT": 10622, + "TCCTCCAAA": 10623, + "TAAGAAAAAA": 10624, + "GGTTATTA": 10625, + "GCGACTA": 10626, + "CTCTGTGTT": 10627, + "TTTTATGGA": 10628, + "GCACAATT": 10629, + "GATTCACA": 10630, + "TTTGCATG": 10631, + "TGACAGCC": 10632, + "CTTTAGC": 10633, + "GAGGTTTA": 10634, + "GTTACTCA": 10635, + "TTAACATT": 10636, + "CCCCACCCC": 10637, + "GACAGGTG": 10638, + "TGTTCTCC": 10639, + "TACATTATT": 10640, + "GATGGAGAA": 10641, + "TCAAATTTA": 10642, + "CCACCATGCCTG": 10643, + "GTGTCTCC": 10644, + "TAGTATTTT": 10645, + "TGACCTGA": 10646, + "CTCTCTGTG": 10647, + "CAAAGTTTT": 10648, + "GAGAGGCA": 10649, + "TCTAGCTG": 10650, + "CAAGCTGA": 10651, + "GAGAGAGCA": 10652, + "CCTTTAAAA": 10653, + "CACACACACACACA": 10654, + "TCATCCTG": 10655, + "GAACAGTA": 10656, + "CCAGAAAAA": 10657, + "GCCCGAA": 10658, + "CTACAGGA": 10659, + "CAAACGA": 10660, + "GTTATAAAA": 10661, + "GTACTAAA": 10662, + "CTCAAATA": 10663, + "CACATGAAA": 10664, + "CCAAAAATT": 10665, + "TCCAACTCTTTG": 10666, + "TATCCAAAA": 10667, + "GTGTACAA": 10668, + "GTGCAATG": 10669, + "TGCCCATT": 10670, + "GTTGGTCA": 10671, + "GTAGAGTA": 10672, + "CAAACCCA": 10673, + "GTGACCTG": 10674, + "GGGTCATT": 10675, + "GACAGGTT": 10676, + "CTGGCCTT": 10677, + "GTTCGTG": 10678, + "TCCATTAAA": 10679, + "TCAGCCTCCCAAA": 10680, + "TGAGTATT": 10681, + "CTAGTGAA": 10682, + "TGTGCGG": 10683, + "CACAGCAAA": 10684, + "GGTTCCAA": 10685, + "CAGCTAGA": 10686, + "TCTCTGTCTCTG": 10687, + "GCCAGGAAA": 10688, + "TGATCCAA": 10689, + "GAACATGA": 10690, + "CTAGACAA": 10691, + "GTCGTAA": 10692, + "GAGACTTA": 10693, + "TATGGATA": 10694, + "TCTAGAGG": 10695, + "CCTATATT": 10696, + "GAGCTCAA": 10697, + "TCTCCTTGCAGTCCAA": 10698, + "CACGCAA": 10699, + "TGAAATTTT": 10700, + "CACGGTGAAACCCCGTCTCTA": 10701, + "GCACGCC": 10702, + "CTGACGG": 10703, + "TAATTCCA": 10704, + "CTATATCA": 10705, + "CCTTTA": 10706, + "GTGAGTAA": 10707, + "CATGGTTA": 10708, + "TATATATATATATATATATATATATATATATATATATATATATATATA": 10709, + "CCCACATT": 10710, + "TCCTATTTT": 10711, + "CTTCAAAAA": 10712, + "CCCACTAA": 10713, + "TTTTTTTGTT": 10714, + "TCATTTGAA": 10715, + "GACCCAGA": 10716, + "CTTCCATG": 10717, + "TCAACGA": 10718, + "TTAACGA": 10719, + "GACATGTG": 10720, + "GACCTCTG": 10721, + "GCAAAAAAAA": 10722, + "GCTGCTTTT": 10723, + "TCTCTGATT": 10724, + "GTCTACTA": 10725, + "CATAGGA": 10726, + "CCTCTAAA": 10727, + "TAAGGCCA": 10728, + "CTACGTT": 10729, + "CAAACACAA": 10730, + "CATACAGG": 10731, + "GAAAAAAAAAAAA": 10732, + "CAATAATG": 10733, + "CTCTCCCC": 10734, + "GACATATA": 10735, + "GGTTTGAA": 10736, + "GAACGCA": 10737, + "GTGCAATT": 10738, + "GTATAGTA": 10739, + "CTCTTATG": 10740, + "CAAAGCTA": 10741, + "GCTGGGCC": 10742, + "GGTGGGTG": 10743, + "CAAAAGCATCAATT": 10744, + "GCACGTA": 10745, + "GAGACTCA": 10746, + "GACACTAA": 10747, + "GGAAAAATT": 10748, + "CCTGGAGAA": 10749, + "TCTCĊ": 10750, + "TACAGAATG": 10751, + "TCTCATTTG": 10752, + "CACTTAAAA": 10753, + "CTTGATAA": 10754, + "GTCTAGGA": 10755, + "CGACGTT": 10756, + "TAGGTATG": 10757, + "CAGTATTA": 10758, + "GTAGCTGGGATTACAGG": 10759, + "TAACGGA": 10760, + "TCAGATTTT": 10761, + "TCTTTCCAA": 10762, + "TCTCTCTTTT": 10763, + "GCTACCTT": 10764, + "TCAGAGGAA": 10765, + "GGTCGA": 10766, + "TCTTCATCA": 10767, + "GAGATCTG": 10768, + "GATATGTG": 10769, + "CCGAGGA": 10770, + "GTGGATGA": 10771, + "GTACTTAA": 10772, + "TTTTATTATT": 10773, + "TAATTCATT": 10774, + "CCAAATTTT": 10775, + "GGCAGGTG": 10776, + "TCCGAAA": 10777, + "CAATCTTA": 10778, + "GTTGGGTT": 10779, + "GTCGGCCA": 10780, + "GGGAGAAAA": 10781, + "CACGTCC": 10782, + "CCACCCTT": 10783, + "GTGACCAA": 10784, + "GCTGGGCTG": 10785, + "TTTTATATG": 10786, + "CTGTTCCATT": 10787, + "GGTTAATT": 10788, + "GCTAGGTT": 10789, + "TGATGAGA": 10790, + "TGTTTCTCA": 10791, + "CATACTGA": 10792, + "CATACCTG": 10793, + "GTGGTCTT": 10794, + "TATATGGG": 10795, + "CAGGGGTT": 10796, + "GGGAGTCA": 10797, + "CCTCCCTCC": 10798, + "TCCGTCA": 10799, + "TATTGAGG": 10800, + "GATTCAGA": 10801, + "TATAATTTG": 10802, + "TACCTGTG": 10803, + "GTAGATAA": 10804, + "TACATACATACATACA": 10805, + "GCAGGATT": 10806, + "CTATACTG": 10807, + "CACACATCA": 10808, + "CCTACTCA": 10809, + "GTGTGCTG": 10810, + "GAAATCTA": 10811, + "TAAAATCTT": 10812, + "TAATTGTTA": 10813, + "GGGAGGAGG": 10814, + "GTTGATTA": 10815, + "CGGTTTA": 10816, + "CCCAGTTA": 10817, + "CTTTGGCCA": 10818, + "CTCTTTTAA": 10819, + "TGATTATG": 10820, + "CAATAGTA": 10821, + "CTTTAGTG": 10822, + "GCCCACAA": 10823, + "CATTCTCC": 10824, + "GGGGCAGA": 10825, + "CCTAGAGG": 10826, + "GTATGTTA": 10827, + "TATGTTTG": 10828, + "GCCGGTT": 10829, + "CGGTTCA": 10830, + "CAAAATATG": 10831, + "GTCAATTTT": 10832, + "TCCTCAGG": 10833, + "GTTGCCAA": 10834, + "GCATCTCC": 10835, + "TATTCATTA": 10836, + "CTGATGCA": 10837, + "CAAAATATTA": 10838, + "CCATATTTT": 10839, + "CCATACTG": 10840, + "TGTTTCCTT": 10841, + "CATACTAA": 10842, + "GTTCACTCA": 10843, + "CCGCTCA": 10844, + "CCTATTTG": 10845, + "TACACACA": 10846, + "GGAATCAA": 10847, + "CGTTCGGTTTCATCAAACCCATCCTAATT": 10848, + "CTGCTCAA": 10849, + "TATTTAGCA": 10850, + "CAGCATTA": 10851, + "GAAAAACAAA": 10852, + "GGGTTGCAAA": 10853, + "GTACAATT": 10854, + "GTCCTCAA": 10855, + "TGATCCTT": 10856, + "GCACATGA": 10857, + "TATCTAAAA": 10858, + "TCTTCCTCA": 10859, + "GTTACCAA": 10860, + "TTTATTATT": 10861, + "CCCCCCTG": 10862, + "GCTAGCAA": 10863, + "CCTATGTG": 10864, + "TATCTTAAA": 10865, + "GTTGTGTG": 10866, + "GCCTACAA": 10867, + "GAAAGTAAA": 10868, + "GAGATCC": 10869, + "CGGGTGA": 10870, + "CGATCTG": 10871, + "GTTTGCAA": 10872, + "GACCCTGATGCTG": 10873, + "CCGCAAA": 10874, + "GACTAATT": 10875, + "TAACAACAA": 10876, + "CCACAATG": 10877, + "CTTCTGGG": 10878, + "TCTTCAGAA": 10879, + "GATTGTGA": 10880, + "TAGGCAGA": 10881, + "CAGCTCTA": 10882, + "CCCAGGTT": 10883, + "CTTGACAA": 10884, + "GCATATCA": 10885, + "GAGGGCAA": 10886, + "GTCCCATCA": 10887, + "TGATTTCA": 10888, + "GCCCCGG": 10889, + "TTTTACAGA": 10890, + "TACAGTAAA": 10891, + "TCCCAGCAA": 10892, + "GTGCTGAAA": 10893, + "CCGAGAA": 10894, + "TCCCCCTC": 10895, + "GCCAGGCGTG": 10896, + "GGCTGATT": 10897, + "GATAAAATT": 10898, + "GACCTTTG": 10899, + "CTGGACAA": 10900, + "CTGGGCAA": 10901, + "GAGGCACA": 10902, + "TACATACAA": 10903, + "GAAAACATG": 10904, + "TTTTTATTTTTT": 10905, + "TGTGCTAA": 10906, + "GAGGGGCA": 10907, + "GTTATTATT": 10908, + "GTGAAGGA": 10909, + "TAATAATTTT": 10910, + "GTTGTTAAA": 10911, + "GATCTATA": 10912, + "GAATGAGA": 10913, + "GTGGGCCA": 10914, + "GCTTTCCA": 10915, + "GATTGCTG": 10916, + "GTAGATTA": 10917, + "TATTTATATT": 10918, + "CTTACCAA": 10919, + "GCTAGGAA": 10920, + "TAATTAATG": 10921, + "CATGAATG": 10922, + "GTGGACTT": 10923, + "CTATAGTT": 10924, + "GAATTGAAAA": 10925, + "GGTACCA": 10926, + "CGCCTGTAGTCCCAGCTA": 10927, + "TATATACACA": 10928, + "CTGGGTCA": 10929, + "CTGACTAA": 10930, + "CACCTCAGCCTCC": 10931, + "GAGGCGA": 10932, + "TGGACAGA": 10933, + "TCCTTCAA": 10934, + "TCCTGGAA": 10935, + "TACAGATG": 10936, + "TAAAAACAA": 10937, + "CTTGGACTGCAA": 10938, + "GACGGTT": 10939, + "GTATGCC": 10940, + "TCAAACTA": 10941, + "CGAAAATG": 10942, + "TCACGCC": 10943, + "GTGCTCTG": 10944, + "GTGAGCCA": 10945, + "GTAATGTA": 10946, + "GTTAGCAA": 10947, + "TACATCTA": 10948, + "CTTAATTAA": 10949, + "GGGAAGGGAA": 10950, + "TAGTCCCAGCTA": 10951, + "TAAGTTTG": 10952, + "GGATATTTT": 10953, + "GATTTAGG": 10954, + "CCGGCCA": 10955, + "TAGCCAAA": 10956, + "GTGCACTT": 10957, + "GTTGGTTA": 10958, + "GCCCAGAAA": 10959, + "GGTGGCTG": 10960, + "CATTATTAA": 10961, + "CCTCTTAA": 10962, + "GGCAAATG": 10963, + "TTATATAA": 10964, + "GTGTGATG": 10965, + "TATTTAGTT": 10966, + "GTCATATA": 10967, + "CTCCCCCC": 10968, + "CTAAGCTT": 10969, + "GTCCTCCA": 10970, + "CTTTTCAAA": 10971, + "TGACCATG": 10972, + "CTTCCTC": 10973, + "CACACGA": 10974, + "CTCTTCCTG": 10975, + "TCTTTGTTTT": 10976, + "CAGATCTG": 10977, + "CTGACTTA": 10978, + "GTGAGACA": 10979, + "GCCATAAA": 10980, + "CTGTTTCTT": 10981, + "GGAGAAGGAAATGGCAACCCA": 10982, + "CCGCCTT": 10983, + "GTTATGTA": 10984, + "CTATTCTA": 10985, + "GTGATATG": 10986, + "GTTGATTTT": 10987, + "CAGACAGAA": 10988, + "TATGAATG": 10989, + "TTTACCA": 10990, + "CTGTTGGTG": 10991, + "CCTAAATG": 10992, + "TAACTTTTA": 10993, + "GAATCATT": 10994, + "GCCAAAAAA": 10995, + "TGTGTAGG": 10996, + "CTTTTTATA": 10997, + "GCGAAAAA": 10998, + "GAATAGTT": 10999, + "TAAATAGTT": 11000, + "TATTGTTAA": 11001, + "GAATTTGAA": 11002, + "GTACACTG": 11003, + "GCAATATG": 11004, + "GGTTGAGG": 11005, + "GTACACC": 11006, + "GATACAGG": 11007, + "TCGTTAAA": 11008, + "GAAGGAGG": 11009, + "GTCCAGTC": 11010, + "CCAATATA": 11011, + "TGCATATA": 11012, + "CCCAGATT": 11013, + "GAAGTCTT": 11014, + "CTGGTCTT": 11015, + "CTGGCATT": 11016, + "TCAAGTTTT": 11017, + "CCCATCCA": 11018, + "TCTCCTTAA": 11019, + "GATTTACA": 11020, + "TCTCTCAAA": 11021, + "CTGTTTCCA": 11022, + "TGACCCTT": 11023, + "CTGGGATG": 11024, + "TGTGTTTTA": 11025, + "TCAACGG": 11026, + "GTTAGATG": 11027, + "CACACACATG": 11028, + "CTAAGATG": 11029, + "GATATCTT": 11030, + "GGACCTGA": 11031, + "GAGACCTT": 11032, + "GAATTTCTT": 11033, + "TGATTTATT": 11034, + "GCTCCGCCTCC": 11035, + "GCCGGGCA": 11036, + "GAGCAATG": 11037, + "CTTGATTTT": 11038, + "TCAATCC": 11039, + "GCTGGTAA": 11040, + "CACCCCTG": 11041, + "TAACCTAA": 11042, + "GCAGATTA": 11043, + "CATTTCTTTT": 11044, + "TCAACAGG": 11045, + "TACAGTCC": 11046, + "TTTTTTTTTTA": 11047, + "ATTA": 11048, + "GAAGTATT": 11049, + "TAGGCTCA": 11050, + "CTGCAGCCTT": 11051, + "TCACAGCAA": 11052, + "GACTTTTTT": 11053, + "GAAGGATG": 11054, + "GTGGCAGG": 11055, + "GGAATGTA": 11056, + "CATGCACCA": 11057, + "CCTATGAA": 11058, + "CTGTTCTCA": 11059, + "CCAGGGAGG": 11060, + "CAATAGTT": 11061, + "TATTTACTA": 11062, + "GCTAGATG": 11063, + "CCACATCCA": 11064, + "CCCTAATT": 11065, + "GTTGGTAA": 11066, + "CCATAGAA": 11067, + "GTAGTGAA": 11068, + "TTTTATCA": 11069, + "TAATTCTAA": 11070, + "TCTGATTTA": 11071, + "TGCCCTCA": 11072, + "CTGGTAAA": 11073, + "TAAACTAAA": 11074, + "TAGGCTTA": 11075, + "GGGAGTGA": 11076, + "CAATTCTA": 11077, + "GGTATTAA": 11078, + "CAACTATT": 11079, + "CTGGACTG": 11080, + "GTATAATA": 11081, + "GCTATTTA": 11082, + "GGAGTCAA": 11083, + "GGAGATTTT": 11084, + "GCTTTAAAA": 11085, + "TTTGCCTT": 11086, + "GCAGAGGAA": 11087, + "GGCGAGA": 11088, + "TATATGTATA": 11089, + "TATAAAATT": 11090, + "TAAGCTGA": 11091, + "GGTGGAAA": 11092, + "CATATCTG": 11093, + "CATAGAAAA": 11094, + "TCATTTCC": 11095, + "TACCTGTT": 11096, + "CAGACAAAA": 11097, + "GCAAGGTT": 11098, + "GTTAGATT": 11099, + "GGGATGTG": 11100, + "TCGTTTCA": 11101, + "CCCCCCTT": 11102, + "TAATTGTTTT": 11103, + "CTCTGCCTT": 11104, + "GAGTCCTG": 11105, + "TCCGTCC": 11106, + "CTAAGAGA": 11107, + "TAATTATTAA": 11108, + "GGAGATCGAGA": 11109, + "CCTTATTA": 11110, + "GAGTCCAA": 11111, + "GCACATAA": 11112, + "GTATACTG": 11113, + "GTAATGGG": 11114, + "CTCCCGCC": 11115, + "TTCATGTG": 11116, + "CTGCACCTG": 11117, + "GGCĊ": 11118, + "TAAATAAAAAA": 11119, + "GTCGGAA": 11120, + "CAGAGCTGA": 11121, + "CATGTACA": 11122, + "CATGGACA": 11123, + "TAGTATTA": 11124, + "GTATGTTTT": 11125, + "GTGGTGGTGGTG": 11126, + "CCACCCCCA": 11127, + "CTCCAACACCACAGTT": 11128, + "GCGGAGGA": 11129, + "TAAGGATT": 11130, + "TCATACCA": 11131, + "CAAGATTA": 11132, + "GTTGAGC": 11133, + "CTAGTAAA": 11134, + "CGACTCA": 11135, + "GCGCTCTG": 11136, + "TCTCCAGTT": 11137, + "CGTGTTTT": 11138, + "TACCTGA": 11139, + "GGGCTCCA": 11140, + "GGGACTCTCAAGAGTCTT": 11141, + "CTGGTGTT": 11142, + "GCAGAAGA": 11143, + "TCTTTCTCC": 11144, + "GTGACTC": 11145, + "GTCATGGA": 11146, + "CGGCTAA": 11147, + "GGCCCCTG": 11148, + "GAGGAGGGG": 11149, + "GAAACTTTT": 11150, + "GCTAGTT": 11151, + "GCAAATTAA": 11152, + "TGCCTGA": 11153, + "GACCTGA": 11154, + "CTCTGCCTG": 11155, + "CAGCTTAA": 11156, + "TAACTAAAA": 11157, + "CAAATCTA": 11158, + "TCTGTCTCC": 11159, + "CGACTTA": 11160, + "GCTAGATT": 11161, + "TAGCTATT": 11162, + "GAACATTTT": 11163, + "CCACTTAA": 11164, + "TTTGTAAA": 11165, + "CTCCTCTGTCCATG": 11166, + "GCCTCTTA": 11167, + "CATTGACA": 11168, + "TGTAGAGA": 11169, + "GGAAATTAA": 11170, + "CACTCCAA": 11171, + "CAGTCCAA": 11172, + "TACGGAA": 11173, + "GAGAAAATG": 11174, + "GAAGAAGG": 11175, + "CTTAAAATT": 11176, + "CACACCCA": 11177, + "TCTTCTGAA": 11178, + "CACTCTTTTTGTA": 11179, + "GAACCCTG": 11180, + "GGAGCCTT": 11181, + "TCAAAGAAA": 11182, + "GGGACTA": 11183, + "CCTCGCCCGG": 11184, + "CAACAGCAA": 11185, + "CCCCAAAAA": 11186, + "TATTCCTA": 11187, + "TGTATGA": 11188, + "TACCTAAA": 11189, + "GCATGTTTT": 11190, + "TCGACGG": 11191, + "TCTTTCTTA": 11192, + "TCAATCTA": 11193, + "CCTTTTTTTT": 11194, + "TCAAGAGTT": 11195, + "GCTTGTGA": 11196, + "CCAAGGCA": 11197, + "GGCCCAA": 11198, + "GCCACCAGGGAA": 11199, + "GCCTCGAACTCACAGAGA": 11200, + "TAGCCTA": 11201, + "GTGCATTA": 11202, + "GTGTACTT": 11203, + "GGATAGAA": 11204, + "GATGCTTA": 11205, + "GCTATTTTA": 11206, + "GAAGAGTA": 11207, + "GTGCTCAA": 11208, + "CAAACAAAAAA": 11209, + "CTCCCAGCA": 11210, + "CACGGTT": 11211, + "TATTTACTG": 11212, + "GCTGATTTT": 11213, + "CAATCATT": 11214, + "GTGGTGTG": 11215, + "GTTTGATG": 11216, + "GTCCATC": 11217, + "GGAATCTG": 11218, + "CTGACCCA": 11219, + "GCTTTCTGA": 11220, + "TCAGCAAAA": 11221, + "GATGTATG": 11222, + "TCTCAGCCTCC": 11223, + "CGCGGGG": 11224, + "CTGTGGTA": 11225, + "CGCTGAAA": 11226, + "TAACAAACCTG": 11227, + "GGCTCTGA": 11228, + "GTTGGATT": 11229, + "GCAGTGGG": 11230, + "GGACTGTT": 11231, + "GCCGTCC": 11232, + "TATTTATTAA": 11233, + "TCAAAAATG": 11234, + "TACAGCCA": 11235, + "CCTACTAA": 11236, + "CTAGGCCA": 11237, + "TAGATCTT": 11238, + "CTACATAA": 11239, + "CATTCAAAA": 11240, + "GTTTTCTCA": 11241, + "CAAGAATG": 11242, + "CCATGATT": 11243, + "CTACATCA": 11244, + "GTAAAAAAAA": 11245, + "CGCATTA": 11246, + "CTCCTGGCCTCAA": 11247, + "CCTCAATT": 11248, + "GGATGATT": 11249, + "GGAGTCTG": 11250, + "GCCTACTG": 11251, + "CTACTCTT": 11252, + "GAAATACAA": 11253, + "GCGGCAGG": 11254, + "CCCTCCTG": 11255, + "GACGTCC": 11256, + "GCTAAAATT": 11257, + "CTGGATTA": 11258, + "TTTTACTTA": 11259, + "CACCCTCC": 11260, + "CAATTTATT": 11261, + "TCACTTTTCACTT": 11262, + "TCAGAGTA": 11263, + "TCCTCGG": 11264, + "GCATACAA": 11265, + "TGGGGTTTCACCATGTT": 11266, + "GCAAATTTT": 11267, + "GGTAATTA": 11268, + "CAACTTTG": 11269, + "CAAGTCTT": 11270, + "GGAAGCCA": 11271, + "TTTACTCA": 11272, + "CATACCTT": 11273, + "CTGCGTT": 11274, + "CTTTATC": 11275, + "GTAGATGA": 11276, + "GCCAAAATT": 11277, + "TCTTGGCA": 11278, + "TTATTCTAA": 11279, + "GGATGAGATG": 11280, + "GAACCTGA": 11281, + "GCATACTT": 11282, + "GGATACTT": 11283, + "TAAACAAAAA": 11284, + "TAGGCAGG": 11285, + "CCTAATAA": 11286, + "GAGTAAAAA": 11287, + "GTACACAA": 11288, + "AAAATGTT": 11289, + "TGTGTACA": 11290, + "GGAATGCA": 11291, + "CGGTGCC": 11292, + "TCTAGGTG": 11293, + "CTTTTTGGA": 11294, + "TCCGTC": 11295, + "TCTTCCCTT": 11296, + "GAGATGCA": 11297, + "TCTACCCA": 11298, + "CGACTAA": 11299, + "CAATTTAA": 11300, + "GGACACGACTGAA": 11301, + "TAGĊ": 11302, + "GTGTCATG": 11303, + "CCATCATG": 11304, + "CTATCTTA": 11305, + "TTTGTAA": 11306, + "GGGGGGTG": 11307, + "GTAGGGAA": 11308, + "CATGTAGA": 11309, + "CCAAGACA": 11310, + "CGGGGTTTCACCATGTT": 11311, + "CCATAATT": 11312, + "CTCTCAAAA": 11313, + "GATTCAGG": 11314, + "CCAATGTT": 11315, + "GATGTAAA": 11316, + "TTTATAAAA": 11317, + "CTGCAGTA": 11318, + "CCAGATCA": 11319, + "CACACCTA": 11320, + "TCTCCACTG": 11321, + "GTTTTTAAAA": 11322, + "CAGAGGAGCCTG": 11323, + "CCGACTT": 11324, + "GTTGACTG": 11325, + "GGATATGA": 11326, + "GAAACAGTG": 11327, + "GTGAGGTAA": 11328, + "CCAATCAA": 11329, + "TCCCTTCC": 11330, + "GCAGGATG": 11331, + "CACCCAAA": 11332, + "GCACCTCC": 11333, + "GTTATAGA": 11334, + "GAAAATTCA": 11335, + "CAGAATTTT": 11336, + "CCGACTG": 11337, + "TGATCATG": 11338, + "CAATTTCA": 11339, + "TGAGGTTTG": 11340, + "TCCGGTG": 11341, + "TGTGTTATT": 11342, + "CCTTCGA": 11343, + "TGGAGCTG": 11344, + "CAAGCCAA": 11345, + "GAGGGACA": 11346, + "TCTAATTAA": 11347, + "CATTTACATT": 11348, + "TCAACTTTT": 11349, + "CTTACAGG": 11350, + "TTTTTTCTT": 11351, + "GACTTGGAA": 11352, + "GTCATAGA": 11353, + "TTTTAGTG": 11354, + "CCTTATTTT": 11355, + "CACGTTTT": 11356, + "GCACTCAA": 11357, + "GTCTCTCTT": 11358, + "GTCTCTGTT": 11359, + "TATACCTG": 11360, + "GTTTTCTGTT": 11361, + "CATAGGAA": 11362, + "TCATTGATG": 11363, + "GAATAATG": 11364, + "GTAAACTA": 11365, + "TGGGTTTGATGAAA": 11366, + "TGAGAAGG": 11367, + "GTTTCTGTT": 11368, + "CTGATCAA": 11369, + "GCTGTATT": 11370, + "CCATTCCTT": 11371, + "GCAGGCAGA": 11372, + "GCATGGTGGTG": 11373, + "TCGCAAAA": 11374, + "GGAGACTA": 11375, + "GTACGTA": 11376, + "TCCACAAAA": 11377, + "TCTAGTTCTA": 11378, + "TAATTCTTA": 11379, + "GAACCCAA": 11380, + "GCCGAAA": 11381, + "GTGAGGCC": 11382, + "CCTCTATG": 11383, + "TCTCCAAAAA": 11384, + "CAATTTTAA": 11385, + "TCTGTTATT": 11386, + "TGTATTTTTT": 11387, + "TCCTTCTT": 11388, + "AAAAAAAATG": 11389, + "GATCCGGCCA": 11390, + "TAGATTAA": 11391, + "GTGCCCTT": 11392, + "TAATAGAAA": 11393, + "TAAGGCTT": 11394, + "GTACATTTT": 11395, + "TATTTAATG": 11396, + "TGTTCCCA": 11397, + "TAGGTCTT": 11398, + "TCGAATA": 11399, + "CAAAGGTA": 11400, + "CATTGAGA": 11401, + "GTGAGCTT": 11402, + "GATTTATTTT": 11403, + "GCTCATTA": 11404, + "GTCAATTA": 11405, + "AAAAGAAA": 11406, + "GCTGCAGCA": 11407, + "CCACACGG": 11408, + "CAACCCTG": 11409, + "CATTAAATT": 11410, + "GCTATCTG": 11411, + "TAATTTCC": 11412, + "GCAAAGTA": 11413, + "GCCTGCTG": 11414, + "CTTGTGTG": 11415, + "GTGGTCTG": 11416, + "TCAGGTTA": 11417, + "GCAGATCC": 11418, + "GGAAGATA": 11419, + "CTTTTCTAA": 11420, + "TAGACTTA": 11421, + "CAATTTAAA": 11422, + "TGGAATACTA": 11423, + "CAACAGCC": 11424, + "GGATCTTA": 11425, + "GTTAGATA": 11426, + "TAGATACA": 11427, + "CATAGAGG": 11428, + "CAGCTATG": 11429, + "TAATCCCAACA": 11430, + "CTTGAGGA": 11431, + "GTGGACAA": 11432, + "GTAATGGA": 11433, + "TGTATCA": 11434, + "CCATGTTTT": 11435, + "TACTAATT": 11436, + "GTGGATTA": 11437, + "GTTGGAGG": 11438, + "GTAGACTT": 11439, + "TGTTTCAAA": 11440, + "GAAAATCTA": 11441, + "CGAATTTT": 11442, + "TCCCACCAA": 11443, + "GAACACTGA": 11444, + "CCGTATT": 11445, + "TAACCTGA": 11446, + "GAACTTAA": 11447, + "TTTGTATA": 11448, + "TGCCCCTG": 11449, + "CGATTAAA": 11450, + "TACTTATTTT": 11451, + "TCGGAGA": 11452, + "GCCTGAGA": 11453, + "TAAAATAGG": 11454, + "GTGAGGAAA": 11455, + "CCTTGTTTT": 11456, + "GATTGATG": 11457, + "TGCATATG": 11458, + "TACCTCAA": 11459, + "CCCAAGGAA": 11460, + "CCGCTCC": 11461, + "GGTAATAA": 11462, + "CTTGAAAAA": 11463, + "TGTGGTTTT": 11464, + "CAGACTTA": 11465, + "CTTTTGCTT": 11466, + "GAATTCTG": 11467, + "GTGGTGGG": 11468, + "GCCTAGTT": 11469, + "TAAACCCA": 11470, + "TCTTTATCA": 11471, + "GCAACTC": 11472, + "CACCCCCA": 11473, + "GCTAAATA": 11474, + "CAACTGTT": 11475, + "CAATAATAA": 11476, + "CAAATCCA": 11477, + "GAATTTCAA": 11478, + "TGAGGGAGG": 11479, + "GCATTTTAA": 11480, + "GTACGCA": 11481, + "CTATCTC": 11482, + "GAAAGTTTT": 11483, + "TTTTTTCTTTT": 11484, + "CCGCTC": 11485, + "GTGCAGCC": 11486, + "GTGTGTGTT": 11487, + "GTTGCTTTT": 11488, + "TACATATTTT": 11489, + "TGTATGAA": 11490, + "GATTTGCATT": 11491, + "CACTCTCA": 11492, + "TGACGTCA": 11493, + "TAAAATTATT": 11494, + "CTGACTCC": 11495, + "TTAATTCA": 11496, + "TTTGGGAA": 11497, + "TCTATTATT": 11498, + "TGTGCCCA": 11499, + "TATGTTATT": 11500, + "TGGAGTCA": 11501, + "TAACTGTG": 11502, + "CAACTCTT": 11503, + "AAAAAAATT": 11504, + "TATTTGCC": 11505, + "TGTTAAATT": 11506, + "CATTTACTT": 11507, + "CTGAGGAAA": 11508, + "TTTTATCTT": 11509, + "CTGCCTGA": 11510, + "GTGTAGTA": 11511, + "GCCTCATG": 11512, + "CAAAGTAAA": 11513, + "CGGGCTT": 11514, + "CACCTGAA": 11515, + "TCGCTGTG": 11516, + "CTTCAGTC": 11517, + "GTGCTATT": 11518, + "TAAATATATT": 11519, + "CATTGGTG": 11520, + "CCTTCCCTT": 11521, + "CATAGTCA": 11522, + "GGTACTA": 11523, + "GGTAGATG": 11524, + "TAAATGTTTT": 11525, + "TCACAAAAA": 11526, + "TGACCTAA": 11527, + "GAAAAACC": 11528, + "GAATGCTGA": 11529, + "TCGCCATT": 11530, + "AAAATGAA": 11531, + "CAGAGGCTG": 11532, + "TAAAACTTA": 11533, + "CTTTTTTATT": 11534, + "TTTAGATT": 11535, + "GACATATG": 11536, + "CTGAATTTG": 11537, + "TCAAAAGACTACACACTTATT": 11538, + "TCAATATTTT": 11539, + "GTCCCTTA": 11540, + "GCTAGTTTT": 11541, + "GAATACACA": 11542, + "CTTCAGCTT": 11543, + "CCCGAGA": 11544, + "CTTCACTA": 11545, + "GTTGAGCA": 11546, + "GGAGTTAA": 11547, + "CAAAGTCAA": 11548, + "TGGGGAGA": 11549, + "TCAGCAGG": 11550, + "CAAAGGACA": 11551, + "GTGAAGTAA": 11552, + "TACCTGAA": 11553, + "GCTAATTTTA": 11554, + "CAAAAGTAA": 11555, + "GGCGCAAA": 11556, + "TTAGATA": 11557, + "AAAATATAA": 11558, + "TCAACTC": 11559, + "TACATAATT": 11560, + "CCATTGCCTT": 11561, + "GCCTTGAACTCA": 11562, + "GCTCAAAAA": 11563, + "CAACGCA": 11564, + "CTTTAGTA": 11565, + "CCTGCCCTG": 11566, + "GATGCTAA": 11567, + "CATAGCTT": 11568, + "TCATTTCTG": 11569, + "TCCGCCCGCC": 11570, + "GCCGGGCGTGGTG": 11571, + "GTGCATCA": 11572, + "TAAATTATG": 11573, + "GTGATGCA": 11574, + "CCAATATG": 11575, + "GGTTCCA": 11576, + "TGTATCTT": 11577, + "CGCTCTC": 11578, + "GCGCACC": 11579, + "TAAAAGGA": 11580, + "CAACAATG": 11581, + "CTATGATT": 11582, + "CATGTAGG": 11583, + "CGACCCC": 11584, + "CACTCTTTT": 11585, + "CCCGAAA": 11586, + "TAAATAATAA": 11587, + "CACATAGA": 11588, + "GTGCTGGGATTATAGG": 11589, + "TAAGCCTT": 11590, + "TCCGTAA": 11591, + "CCTTCCCTG": 11592, + "GTCACCCTG": 11593, + "TTTGTATG": 11594, + "CTGTAATA": 11595, + "GTTTAGGA": 11596, + "CCACTCAA": 11597, + "GGAATGAAA": 11598, + "GTTTACATT": 11599, + "TAAGCTCA": 11600, + "GTTTCCTGA": 11601, + "GCCGGCA": 11602, + "GCAACCTG": 11603, + "TTTTATGTG": 11604, + "GTTATGAAA": 11605, + "GGGGCTCA": 11606, + "GTGGAAAAA": 11607, + "CTGATATG": 11608, + "GCCATCCA": 11609, + "TTTATGA": 11610, + "CAGTATTTT": 11611, + "GTGGCACATG": 11612, + "TCCTCTCTT": 11613, + "GTGCGGA": 11614, + "TATAĊ": 11615, + "GTGAGGCA": 11616, + "GATGTATT": 11617, + "TCGAAGA": 11618, + "TCCAAATA": 11619, + "CTGGCCAA": 11620, + "CCATTTTTT": 11621, + "CTTAGAAAA": 11622, + "GGTAGCC": 11623, + "GGGCGGGG": 11624, + "GCGTTGG": 11625, + "TCATTTAAA": 11626, + "CTACCATT": 11627, + "CGAGAAAA": 11628, + "GGGATGTT": 11629, + "GGGACAAA": 11630, + "TTTTCTC": 11631, + "GTTTATATT": 11632, + "CCTTGTGAA": 11633, + "CTCCTCTC": 11634, + "CTTATGTA": 11635, + "CACCCTGA": 11636, + "CTCTGGAGG": 11637, + "GACCAGGCTG": 11638, + "GGGCCATG": 11639, + "TCTTTATAA": 11640, + "TGTTTACTT": 11641, + "CAGATGAAA": 11642, + "TCAAATATT": 11643, + "GAGCTAAA": 11644, + "CTGATGCTGAA": 11645, + "TCGTTGA": 11646, + "CCACCTGA": 11647, + "GTATATGA": 11648, + "CTACTTAA": 11649, + "CTACAATT": 11650, + "TAGACTCA": 11651, + "CTATTTATT": 11652, + "CCAGGATT": 11653, + "CCTGGGTA": 11654, + "CTAATGCA": 11655, + "TGCCTCTT": 11656, + "GATCGTT": 11657, + "GGATGAGG": 11658, + "CACATAAAA": 11659, + "TCAAATTAAA": 11660, + "TCTTCAAACCCATG": 11661, + "CAAGCTCA": 11662, + "CCTATCAA": 11663, + "CGGGGTTTCA": 11664, + "CCGCCTG": 11665, + "TAGTCAAA": 11666, + "CTTGAGTG": 11667, + "GCATAGCA": 11668, + "CGGCGTG": 11669, + "CCAAGTTA": 11670, + "GCTATCAA": 11671, + "GCTACTTTT": 11672, + "CAGCTACA": 11673, + "CTGCGCA": 11674, + "GTGCCCCA": 11675, + "GTCTAATG": 11676, + "GCAATGGA": 11677, + "GGTGCAGG": 11678, + "CCGCGCC": 11679, + "TGAGTCTG": 11680, + "CAAATATCA": 11681, + "CAAGAGTG": 11682, + "GTGCGAA": 11683, + "TAAAGTTAA": 11684, + "CAAATAAATT": 11685, + "GTCAGTTTT": 11686, + "GATGCTTTT": 11687, + "GACATTTTA": 11688, + "GCAGGCAGG": 11689, + "CCGATCA": 11690, + "CTGGTGAA": 11691, + "CTTGCTTTT": 11692, + "GTAGTGTT": 11693, + "GCTGCTCTT": 11694, + "GATGTCAA": 11695, + "CCCAGCTCC": 11696, + "TACCCAGG": 11697, + "CTCTGTCA": 11698, + "CCAGGCCTG": 11699, + "CAGAGACAGAGA": 11700, + "TTTTAAAAAA": 11701, + "TCCGTGA": 11702, + "GGATGATG": 11703, + "CTCGTTTT": 11704, + "GGAATAAAA": 11705, + "TGCCCACA": 11706, + "GCGGAGA": 11707, + "CTGCATCC": 11708, + "CTATTTTAA": 11709, + "GGAAGCAAA": 11710, + "TAGATCAA": 11711, + "TCTCCTCTA": 11712, + "CTAAATATA": 11713, + "GCAGGGCTG": 11714, + "TCCGCTT": 11715, + "GTAGCATT": 11716, + "TCTGCCCC": 11717, + "CCGTGTG": 11718, + "CCCGCCA": 11719, + "TAAGGTGA": 11720, + "GCCTATTA": 11721, + "GAAACAGGA": 11722, + "GAGATATA": 11723, + "TAAAACATA": 11724, + "GATTGCAA": 11725, + "CTTAGGTT": 11726, + "GACCTCAA": 11727, + "GTTTATTTTA": 11728, + "TCTAAAAAAA": 11729, + "GTAGAAAAA": 11730, + "TGAAGTC": 11731, + "CTGACGA": 11732, + "CCTTGTC": 11733, + "CTAACTCA": 11734, + "GACACGA": 11735, + "GGGACATG": 11736, + "CCTACTTTT": 11737, + "GTTCACTA": 11738, + "CCAGGAGG": 11739, + "GTACACTT": 11740, + "CACCCATT": 11741, + "TCCAAGAA": 11742, + "TCTAATTTG": 11743, + "CAGTAATG": 11744, + "CCGACAA": 11745, + "GCATAGTT": 11746, + "TATTCTCC": 11747, + "TATTCATAA": 11748, + "CAAACCAAA": 11749, + "CCAACAGG": 11750, + "CTATTTCTT": 11751, + "CCACTCTG": 11752, + "GGAAAAATG": 11753, + "TCTCCCGG": 11754, + "CATGATTA": 11755, + "TCCGG": 11756, + "CTGGTTAA": 11757, + "GTCACCTA": 11758, + "CAAAATGTA": 11759, + "GACCCTCA": 11760, + "CGGAGCC": 11761, + "GAGCAGGGG": 11762, + "GAAGATTA": 11763, + "GAAAGAGAAA": 11764, + "CCGGGTT": 11765, + "TCTTĊ": 11766, + "TACTATTTT": 11767, + "TATCCTTA": 11768, + "CTGCAGCTG": 11769, + "GTTTATC": 11770, + "CTCTAGC": 11771, + "CTGATCTG": 11772, + "GTCATTTTA": 11773, + "CAAAAGAAAA": 11774, + "GGGACCTG": 11775, + "GCAAAGAGG": 11776, + "CCCATTAA": 11777, + "GTGCGTA": 11778, + "GTTTTGCTT": 11779, + "TAGGTGGG": 11780, + "GATATCTG": 11781, + "GCGGCGA": 11782, + "TAACAGAAA": 11783, + "CAAGTTTG": 11784, + "CTGGAGC": 11785, + "GTGGAATG": 11786, + "CTAATGTG": 11787, + "CCCAGAAAA": 11788, + "GATATATA": 11789, + "TGAGCCTT": 11790, + "GAACTGTT": 11791, + "CTGCACAA": 11792, + "CCACGTA": 11793, + "CTACAGCC": 11794, + "CACACGTA": 11795, + "CCTTCGTT": 11796, + "GGAGGTTTT": 11797, + "TCTTTTCTTTT": 11798, + "GCGAAGA": 11799, + "TGAGCCTG": 11800, + "GCCCAGTA": 11801, + "CCAAGAAAA": 11802, + "GCCACTGTG": 11803, + "TATTTTTAGTAGAGA": 11804, + "GCAGGTTA": 11805, + "TCTTTCCCAGCA": 11806, + "CAAATACTT": 11807, + "GAGAGTTTT": 11808, + "CGGTCAA": 11809, + "AAAATTTA": 11810, + "TCTTGAATT": 11811, + "CTCAGGAAA": 11812, + "GAGGTGTT": 11813, + "TTTGGGA": 11814, + "TCGTTGG": 11815, + "GAAACTC": 11816, + "GAAAGGGAA": 11817, + "CATGAAATT": 11818, + "GCAAGCTT": 11819, + "GATTGTTA": 11820, + "GACAATTA": 11821, + "TAAACATTTT": 11822, + "TGTAGCTG": 11823, + "GGGTGGGGG": 11824, + "CACGCCA": 11825, + "CAAGGTGA": 11826, + "TGATTTCAA": 11827, + "GGGCTGAA": 11828, + "GCATAATG": 11829, + "GTACATAA": 11830, + "TGTGTCATT": 11831, + "CAGACATA": 11832, + "CTAATATG": 11833, + "GATGTCTG": 11834, + "TCTGAGGTT": 11835, + "GTTTCAGCTT": 11836, + "CCATTGCACTCCAGCCTGGG": 11837, + "CCATTATG": 11838, + "TATTTCTCC": 11839, + "GAAAGTCC": 11840, + "TCTTTAATT": 11841, + "CATTTACAA": 11842, + "GACACCTT": 11843, + "CCCCTGTT": 11844, + "TACTGGAGTGGGTT": 11845, + "TAAGGCTA": 11846, + "CTACGTG": 11847, + "GAAGGCTGA": 11848, + "TAAATTTTTT": 11849, + "GATTTCAAA": 11850, + "CTTAGTTA": 11851, + "TAAAAACAAA": 11852, + "CAGTTAAAA": 11853, + "CTCTCCCTCTCC": 11854, + "GTGTATGA": 11855, + "CCAGTGTG": 11856, + "GTACCAAAA": 11857, + "CTATCTCA": 11858, + "GAAACACAA": 11859, + "TATATAGTA": 11860, + "CTAATCAA": 11861, + "CAAAAGTTA": 11862, + "TAGAGTTTT": 11863, + "CGCCGCC": 11864, + "CCGAGTG": 11865, + "GACTCTCA": 11866, + "CTTCCAGG": 11867, + "GGACTTAA": 11868, + "CATTTACC": 11869, + "GCTTTGAAA": 11870, + "TGCATGTT": 11871, + "GGCAGTCA": 11872, + "TAGTTGTAA": 11873, + "GGTCCCA": 11874, + "TATTCTAAA": 11875, + "GCAACCTT": 11876, + "GAGGGTCA": 11877, + "GATGCGG": 11878, + "CGCTATG": 11879, + "CCTCATTTT": 11880, + "GTTTTTGTTTT": 11881, + "GCAGAGAAA": 11882, + "TCTTGTCC": 11883, + "GCTGGTC": 11884, + "TCGTGAAA": 11885, + "TAAAAAAAAAAA": 11886, + "GGATTTTTT": 11887, + "CCAGAATG": 11888, + "TAAAACCAA": 11889, + "CACAGTCC": 11890, + "TCTCTCATT": 11891, + "GCCATATA": 11892, + "TCAGGAGTTTGAGA": 11893, + "TACĊ": 11894, + "GCAGCTC": 11895, + "CTAAGTGA": 11896, + "CATACCAA": 11897, + "GCCTAATT": 11898, + "CATACGG": 11899, + "GGCATGA": 11900, + "TATTTTTTTG": 11901, + "GGAGGGGA": 11902, + "CACACACACACACACACACA": 11903, + "TATTTATTTATT": 11904, + "TAAGACTA": 11905, + "CTGTATGA": 11906, + "GTTCCAGG": 11907, + "GAAAGCTGA": 11908, + "TCTCCAGCC": 11909, + "CAGTCAAAA": 11910, + "GACGTTTT": 11911, + "CTGTTTTTT": 11912, + "GTGCACAA": 11913, + "GTAAGGAAA": 11914, + "GAGCTGAAA": 11915, + "CTTCTTCTTCTTCTTCTTCTTCTT": 11916, + "TAAGAAGG": 11917, + "CAATCCAA": 11918, + "GTACAGGA": 11919, + "CACTTTCTG": 11920, + "TAAATTGGTT": 11921, + "CCGAGCC": 11922, + "GTCTCCAAA": 11923, + "GAGGGCTT": 11924, + "TCTCCATGTT": 11925, + "GATGGTCA": 11926, + "CAAAATTCA": 11927, + "CGTGTTTG": 11928, + "GGCCCCTT": 11929, + "GGCGGTG": 11930, + "TCGTTCC": 11931, + "GTAGATCA": 11932, + "TATAGGTT": 11933, + "CAAACTC": 11934, + "CAATTTCTT": 11935, + "TCATGATG": 11936, + "GTTCCTCTG": 11937, + "CTTATGTG": 11938, + "TCCTTCCTG": 11939, + "TTTTTATTTTA": 11940, + "GGTGTGAA": 11941, + "TCGĊ": 11942, + "CTGGAGCC": 11943, + "GTGCATGA": 11944, + "GTTTAAGA": 11945, + "GCATAAAAA": 11946, + "CAAACTCC": 11947, + "GCTGCCCC": 11948, + "GCTGGGTCA": 11949, + "CAGTAAAAA": 11950, + "CCGTTAA": 11951, + "TTTTGCAA": 11952, + "GCCTATAA": 11953, + "CTAGACC": 11954, + "TACAĊ": 11955, + "TACCTCTG": 11956, + "CATTACTTTG": 11957, + "GCCATACTG": 11958, + "TAAGTGTG": 11959, + "CTACACTA": 11960, + "TATATCCA": 11961, + "GAGATCAA": 11962, + "CTCAGTTTT": 11963, + "TCTTTTTTAA": 11964, + "GAACATGTG": 11965, + "TACAGATT": 11966, + "GGCTCCTT": 11967, + "TGATCTAA": 11968, + "TCCTAGTG": 11969, + "CAATAGTG": 11970, + "GTACTGTT": 11971, + "GAAATATTTT": 11972, + "TAATTGCTT": 11973, + "CTGGGTTA": 11974, + "TCACATCC": 11975, + "GCAGTCTG": 11976, + "GGACATTTT": 11977, + "CCACCCAA": 11978, + "TAAAAAAATG": 11979, + "GAGAAATGCAAATCAAAA": 11980, + "TTTTCAGA": 11981, + "GAACAGC": 11982, + "GTGGGCAA": 11983, + "CCAGTTAA": 11984, + "CCTTGTTA": 11985, + "CCTTCTCTT": 11986, + "CTTTTGATT": 11987, + "TAGGCATT": 11988, + "GGTTGGTG": 11989, + "TGCCCAGA": 11990, + "GGCTGGAGTGCAGTG": 11991, + "TCTAATTAAA": 11992, + "TATCTTTG": 11993, + "CTTCGCC": 11994, + "TGTTGGCCA": 11995, + "GAGAGGGAA": 11996, + "GCAGACGCTCTTATCCAGA": 11997, + "TTTTCAGG": 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12044, + "GTACGC": 12045, + "GTACATCA": 12046, + "GTAGACAA": 12047, + "AAAATTTG": 12048, + "CATTCCCTT": 12049, + "GTGACGA": 12050, + "CTCCTCTCC": 12051, + "CTCCCACCA": 12052, + "GGCCCTCA": 12053, + "GGCGAAA": 12054, + "GTAGAATG": 12055, + "GAGAGTTA": 12056, + "TTAATTTTA": 12057, + "GACAGTTA": 12058, + "CCCCTCTCC": 12059, + "CCCGCTT": 12060, + "TAACCTTA": 12061, + "TAAATACTG": 12062, + "CTAATGTA": 12063, + "GGCATCTG": 12064, + "TACCCACA": 12065, + "TATCAATT": 12066, + "TCGTGGTG": 12067, + "CCACATAA": 12068, + "GTATATATT": 12069, + "GCTGGAGCA": 12070, + "GTTTTCTTA": 12071, + "TTTACATG": 12072, + "GATACCAA": 12073, + "GCTCATTTT": 12074, + "TAATGCAA": 12075, + "CCGACCA": 12076, + "GGGCCCC": 12077, + "GATCCATG": 12078, + "TAAATAAATT": 12079, + "GAAACCCA": 12080, + "CATGAAAGTGAAAA": 12081, + "TACGTTA": 12082, + "GGCATTAA": 12083, + "GGCATTTG": 12084, + "CAAGCAATT": 12085, + "GCATGATG": 12086, + "GCAATAAAA": 12087, + "TCTCCATCC": 12088, + "GATGCCTG": 12089, + "GGCACTT": 12090, + "TCGTTATT": 12091, + "GTCTCTGAA": 12092, + "TAGAATTTT": 12093, + "TAATCCTG": 12094, + "TAAGCTAA": 12095, + "GTTTCTCAA": 12096, + "CTACATTTT": 12097, + "CTCCAGAAA": 12098, + "GCAATCTG": 12099, + "GATTCATG": 12100, + "TCTCTCCAA": 12101, + "GGGGTTAA": 12102, + "CACCTCCA": 12103, + "CACTCTCC": 12104, + "GCGCACA": 12105, + "TTACGA": 12106, + "TCCCGTC": 12107, + "GCAACAGTG": 12108, + "CCTAGTCA": 12109, + "GACCCCAA": 12110, + "TCCTATGA": 12111, + "GCAGCTAA": 12112, + "GCAAGAAAA": 12113, + "GCATATGA": 12114, + "TATTCGA": 12115, + "TCTCCACCA": 12116, + "TACACTTTT": 12117, + "GAACTTAAA": 12118, + "GGAGAGTA": 12119, + "CTCTCCCA": 12120, + "GTGAAGACA": 12121, + "CCACCATGCCCA": 12122, + "TCGAGAAA": 12123, + "CTCTCCATT": 12124, + "GTCTAATA": 12125, + "GAAAGCCA": 12126, + "GGGGGATG": 12127, + "GGACTACAGGTG": 12128, + "TCAGAGAAA": 12129, + "CTAATAAAA": 12130, + "GGCCTGAA": 12131, + "CAACAGAGTGA": 12132, + "CCCTCATT": 12133, + "TCGTTGTT": 12134, + "TCCCCTTTT": 12135, + "CTGATGAAA": 12136, + "CTCATAGA": 12137, + "CCAATGTG": 12138, + "GCACCATG": 12139, + "CTACATGA": 12140, + "TGATCCTG": 12141, + "TGATAGTG": 12142, + "CTCCCCTA": 12143, + "TAGCTATA": 12144, + "GGAGTTCGAGA": 12145, + "CCGGGGA": 12146, + "TAATTAAAAA": 12147, + "GAATACCA": 12148, + "CTGAGTCC": 12149, + "TATGGGTG": 12150, + "TCCACTTTT": 12151, + "TTCACAAA": 12152, + "TGTACTT": 12153, + "GCGCTTTT": 12154, + "TAAGCTTA": 12155, + "TAAGTATG": 12156, + "TCATTATTA": 12157, + "CAAGTGATT": 12158, + "GGATGAGTT": 12159, + "TAAAGAATT": 12160, + "TGTAGATT": 12161, + "GGGTCACAAA": 12162, + "GCACTGGG": 12163, + "TATACATTA": 12164, + "GCTGGATCA": 12165, + "CTAACTTA": 12166, + "AAAAAATTAA": 12167, + "GAAAAATTAA": 12168, + "GTTCAAGCGATT": 12169, + "TCCCGTA": 12170, + "GCCCTTAA": 12171, + "GAAACAGCA": 12172, + "CTCATATT": 12173, + "CAAAACCAA": 12174, + "CTCTTTGAA": 12175, + "GCTGATTA": 12176, + "GCTGAGATT": 12177, + "GACCCCTG": 12178, + "CCTGTTAA": 12179, + "GTTGGGTG": 12180, + "GGATAGGA": 12181, + "GTCCACTA": 12182, + "CAGGTCAA": 12183, + "CAGATACA": 12184, + "CGACGTG": 12185, + "TGGATAAA": 12186, + "TCACCCAGGCTG": 12187, + "CAGAAAAAAA": 12188, + "GAGTGATG": 12189, + "CCCACTTA": 12190, + "CATATCTA": 12191, + "TGCCCTGA": 12192, + "GAATGCCAA": 12193, + "TACGTCA": 12194, + "TTTTCACA": 12195, + "TCCTTATG": 12196, + "TCCTAGGA": 12197, + "CAATCTGA": 12198, + "GCACTCTG": 12199, + "GGACATAA": 12200, + "TATGCTTTT": 12201, + "CCATTGTA": 12202, + "CAGTCCTG": 12203, + "TCATAAATT": 12204, + "CTACCTCA": 12205, + "TTTAGTAA": 12206, + "CTCTTCTT": 12207, + "GAAGGGAGG": 12208, + "GCGCACGCCTTTAATCCCAGCA": 12209, + "GGAGTCTT": 12210, + "CCATAGTT": 12211, + "GTCTCAGGA": 12212, + "GAAATAATA": 12213, + "TATATTTTAA": 12214, + "GCCATTTTA": 12215, + "TAATCCCAGCACTTTGGGAGG": 12216, + "GTCTCTCTG": 12217, + "TATGTATAA": 12218, + "TGCAGTAA": 12219, + "TACACATG": 12220, + "GCTATTAAA": 12221, + "GAAACCAAA": 12222, + "GCAATCAA": 12223, + "CTAAGAAAA": 12224, + "CATACAAAA": 12225, + "TGCACAAA": 12226, + "CGGATTCA": 12227, + "GCCTGGCCAA": 12228, + "GGAGACTGA": 12229, + "TATTTATTTATTTT": 12230, + "GAATCTAA": 12231, + "GTTGATCA": 12232, + "GAAGATGAA": 12233, + "CTTGACCA": 12234, + "GTTCTGGG": 12235, + "CTACACTT": 12236, + "TAAACGTT": 12237, + "GCTGGAGAA": 12238, + "CCCATTTTA": 12239, + "TTATTTAAAA": 12240, + "GGATGTTA": 12241, + "TATGTATTTT": 12242, + "GATTTATTA": 12243, + "CATCAATG": 12244, + "CCGGCTT": 12245, + "TTTTTAGA": 12246, + "CTGGATGA": 12247, + "AAAACAAA": 12248, + "CATTGAGG": 12249, + "CCTGGTCC": 12250, + "TCTCTCCTG": 12251, + "TCTAGATA": 12252, + "CTGTGATG": 12253, + "TATACATAA": 12254, + "CCAACCTT": 12255, + "CTAATCTT": 12256, + "CTGAATTAA": 12257, + "GGTAGGAA": 12258, + "GAAGTAAAA": 12259, + "TGGGTGTG": 12260, + "GGTTCTCA": 12261, + "CACACACTCA": 12262, + "GTGGTCAA": 12263, + "GTATGATT": 12264, + "TATTCTGTT": 12265, + "TGTAGAGG": 12266, + "GGCACTA": 12267, + "GGTCGTG": 12268, + "GTTGAATG": 12269, + "GTTTATTAA": 12270, + "GTCAAAATT": 12271, + "GGTGTTTTA": 12272, + "GGTGCAGTG": 12273, + "TGCATGAA": 12274, + "TGATACTA": 12275, + "CTGTAGTG": 12276, + "GGACGCC": 12277, + "GTACCCC": 12278, + "TCTCAGCTT": 12279, + "GGTATGTG": 12280, + "GCTGGACA": 12281, + "GTAAGAAAA": 12282, + "GGGGGTGA": 12283, + "GAAAAAGAAAA": 12284, + "CCTCGGCC": 12285, + "TCTTGGGA": 12286, + "CCTTTAGG": 12287, + "TCCACCTA": 12288, + "CTTTTTCAA": 12289, + "TATATGAAAA": 12290, + "GACTCCGTCTCAAAA": 12291, + "CTAATACAA": 12292, + "GTAAATATA": 12293, + "CCATTTATT": 12294, + "GGCTGGGAA": 12295, + "GCGGGTG": 12296, + "CTTGGTTA": 12297, + "CCCTCTTTT": 12298, + "ATTTT": 12299, + "GCACTCCAGCCTGGG": 12300, + "CCATACTT": 12301, + "TAAAAGAGA": 12302, + "GGGGCTA": 12303, + "CTGCAGCTT": 12304, + "TGACTATG": 12305, + "TCCGGTT": 12306, + "TCTTCCCTG": 12307, + "CAGAGTTTT": 12308, + "GTCACGA": 12309, + "GTAATAGA": 12310, + "TCTCCCTCA": 12311, + "GCAAAAGAAA": 12312, + "TAAAGCTA": 12313, + "TAAATGCC": 12314, + "TCTTCAGTT": 12315, + "CAAACAGCA": 12316, + "CTTTTTTTTTTTTTTTT": 12317, + "CAATTTCAA": 12318, + "GCATTTATT": 12319, + "CCGAATT": 12320, + "GAAGGATT": 12321, + "CAATCTAA": 12322, + "GGACTCTG": 12323, + "CCACACTA": 12324, + "GCTCCTGGG": 12325, + "GAACTCTG": 12326, + "GCACCTGA": 12327, + "CCATAGTG": 12328, + "GCTGCGA": 12329, + "TATGTCTA": 12330, + "TCTCAAAAAAA": 12331, + "GATGCATG": 12332, + "TCCGAGG": 12333, + "GGAGAGCC": 12334, + "TACACGTG": 12335, + "GGGTCGCACAGA": 12336, + "TACGTGA": 12337, + "TCGTTCA": 12338, + "GACCTTAA": 12339, + "CAAGGTAA": 12340, + "TGTTCAAAA": 12341, + "TGTTTAATT": 12342, + "GCTGCCCA": 12343, + "CCCCGCC": 12344, + "CGCCACCACC": 12345, + "GGTGAATT": 12346, + "CATTCGG": 12347, + "GTCCTAGG": 12348, + "GATAGGAA": 12349, + "TGAGAAGA": 12350, + "TCCTCCCA": 12351, + "GGACAGTA": 12352, + "TCTGGACA": 12353, + "GGTTTCAA": 12354, + "GGGATGAA": 12355, + "TTTTGAGG": 12356, + "TCACCTAA": 12357, + "CAATACTA": 12358, + "GTACAAAAA": 12359, + "TAAAAGTGTT": 12360, + "GTTATGGG": 12361, + "CCTACGG": 12362, + "TAAAAATGAA": 12363, + "CGTGAATT": 12364, + "GCAGTGAGCCGAGATT": 12365, + "TCAGAAGG": 12366, + "CAACGTA": 12367, + "CCTGGTAA": 12368, + "TCCTTCTCC": 12369, + "CGCCACCA": 12370, + "CCCTTATG": 12371, + "GCGTGGA": 12372, + "TCCTCAGA": 12373, + "CTTTCTGTT": 12374, + "GCAGCTTA": 12375, + "CCTTCCCCA": 12376, + "CCCCCTC": 12377, + "CAGCAAAAA": 12378, + "GAGATGAAA": 12379, + "TCAACTCC": 12380, + "GGAATAGG": 12381, + "CCTGGCCA": 12382, + "TTTGGATT": 12383, + "GGTCCATT": 12384, + "CTTCGTA": 12385, + "CTTCCAGA": 12386, + "GCATCATG": 12387, + "TCTATCCA": 12388, + "TAAAAAGAAA": 12389, + "CTGTTCTTA": 12390, + "TGAGATCTT": 12391, + "TGAGATGAA": 12392, + "TAATAATTAA": 12393, + "TATATATATATATA": 12394, + "GTCCTTAAA": 12395, + "TCTTTTATG": 12396, + "TCGCTGTT": 12397, + "TAAGGGTT": 12398, + "GTGCATAA": 12399, + "GCCTAGCA": 12400, + "CATGTTTTA": 12401, + "CCCCTACC": 12402, + "TGCACTA": 12403, + "GTGTTTGTG": 12404, + "GAATCCAA": 12405, + "GTAAATGAA": 12406, + "CAGGTTAAA": 12407, + "TCGCGTG": 12408, + "CTTCTTGG": 12409, + "CCTGCCTCA": 12410, + "CAAAACAGA": 12411, + "CTGAAGAAA": 12412, + "CACTGTTA": 12413, + "CTTTATTTTA": 12414, + "GTTGGTTTT": 12415, + "CCCCATGGACTGTA": 12416, + "CTGATAGA": 12417, + "CCTGTATT": 12418, + "CGGATTA": 12419, + "TCAGGAATT": 12420, + "TAAACCAAA": 12421, + "TCTTTCACTT": 12422, + "CTAATGGG": 12423, + "GCTATATA": 12424, + "TCCTAATG": 12425, + "GTACGAA": 12426, + "GCCGGAA": 12427, + "GCCCCATG": 12428, + "TAAAGACAA": 12429, + "TGTTTCATT": 12430, + "GGGAGTTA": 12431, + "GACCCACA": 12432, + "TAGTAATT": 12433, + "TCGAACA": 12434, + "GAACCTCA": 12435, + "GAAAAAAAAAAA": 12436, + "CCATGATG": 12437, + "CTAGAGCA": 12438, + "CCTGCCTCC": 12439, + "GAAAAGTTA": 12440, + "GTTTTGTTA": 12441, + "GTCCTGCTG": 12442, + "TGCATATT": 12443, + "TGAGCATG": 12444, + "CAAGCCTT": 12445, + "CTGTCCTG": 12446, + "TTATTTCTG": 12447, + "TTCACTCA": 12448, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 12449, + "TAATATTTG": 12450, + "TTTTTTTTAA": 12451, + "GCTTTCAAA": 12452, + "TATGGCTA": 12453, + "CAAAATGAAA": 12454, + "TCTCCTGACCTT": 12455, + "TCTGATTTG": 12456, + "GCGCAGCA": 12457, + "CTACAAAAA": 12458, + "TGTTGGCAA": 12459, + "GGCCTGTT": 12460, + "GCCTGGTCTA": 12461, + "CCGGCGG": 12462, + "GTTGGATG": 12463, + "CCAGTTTG": 12464, + "TGTGGGGA": 12465, + "TATATATGA": 12466, + "CAAACACC": 12467, + "CATTGATA": 12468, + "CTCCCACTT": 12469, + "TAGATTTTA": 12470, + "CCTATTTTA": 12471, + "CAGTTTTAA": 12472, + "CTGGCTTA": 12473, + "TACATCCA": 12474, + "CGACGCA": 12475, + "TGCAGCTT": 12476, + "CTTTGGGAA": 12477, + "GCCTGATG": 12478, + "CGTGCGCA": 12479, + "TGCCTGAA": 12480, + "GAGAAGAAA": 12481, + "TATGAAAAA": 12482, + "GGCTCATT": 12483, + "TGAGCTAA": 12484, + "CAAGGATG": 12485, + "GTGTGAGG": 12486, + "CAGGCAGAA": 12487, + "TCTAGACA": 12488, + "GGAGACACA": 12489, + "CAACAGAGCAA": 12490, + "TGATAGTA": 12491, + "TAAAGGGAA": 12492, + "TCAAGGTA": 12493, + "TCATTCTTA": 12494, + "CTCCTGGGCTCAA": 12495, + "GATATTAAA": 12496, + "TCAGCTTA": 12497, + "CTGCATAA": 12498, + "TATTTATAAA": 12499, + "TGTACAAA": 12500, + "GTGAATTTG": 12501, + "GTTAATTTA": 12502, + "GTATTTCTT": 12503, + "GGGAGGAAA": 12504, + "TACTCCA": 12505, + "TAACCATG": 12506, + "TCAGCGG": 12507, + "GCCGCGG": 12508, + "CATTTCTTA": 12509, + "CCTTGGCA": 12510, + "GCCACGCC": 12511, + "TGTATCAA": 12512, + "CCAAAGTAA": 12513, + "CGTTTCC": 12514, + "CTCCAGTGTTCTTGCCTGGAGAATCCCAGG": 12515, + "TGAGCTCC": 12516, + "GAATGATG": 12517, + "GCCGATT": 12518, + "GCTCACTGCAGCC": 12519, + "TACCTCTT": 12520, + "GAGCGAGA": 12521, + "TAAGCAAAA": 12522, + "CACACACACACACACACACACACACACACACACACACACACACA": 12523, + "GGTGATGGACAGGGAGGCCTG": 12524, + "CCAGCAGG": 12525, + "GTACATGA": 12526, + "GCCCATTA": 12527, + "CCACAGTGA": 12528, + "GGACTCTT": 12529, + "GTATAATAA": 12530, + "CATTTATCA": 12531, + "GCTGTATG": 12532, + "CGGGTC": 12533, + "CTTACTTTT": 12534, + "TGGATGTG": 12535, + "TAATTGATT": 12536, + "GTTCAGCC": 12537, + "GGAGAAGAA": 12538, + "CTATAATA": 12539, + "TAACTTTA": 12540, + "TCACAGAAA": 12541, + "GCCCGTC": 12542, + "CTCTCATCA": 12543, + "CAGACAGCA": 12544, + "TACAGTGAA": 12545, + "TTCATGTT": 12546, + "CTGTTTCTG": 12547, + "CCTGTAGG": 12548, + "TCCAĊ": 12549, + "TGGACATG": 12550, + "TCTCCTCTG": 12551, + "GTAGAGC": 12552, + "TATGGAAAA": 12553, + "CCTGCACCA": 12554, + "GTTATAGG": 12555, + "GCCAATTTT": 12556, + "GAAGGTTA": 12557, + "CTTTAAATT": 12558, + "TCTCTACTT": 12559, + "TCCACGTG": 12560, + "TGAGGTTTT": 12561, + "TAGCTAAA": 12562, + "TCCCCTC": 12563, + "AAAATAAAA": 12564, + "TTTTACCTT": 12565, + "TCTCTCTTA": 12566, + "TTATTTCTA": 12567, + "CCATTTCTT": 12568, + "TCAAAAGACTA": 12569, + "GTAACAAAA": 12570, + "GAGGCAGAA": 12571, + "CTAACTGA": 12572, + "GGTTCAAA": 12573, + "TCATAGTA": 12574, + "GTGGAGTA": 12575, + "TGGGGCTG": 12576, + "GCTAGTCA": 12577, + "GGTAGATT": 12578, + "CTTCCCAAA": 12579, + "GTGGCTCC": 12580, + "GTTGGGCA": 12581, + "GTTAGTTTT": 12582, + "CTCCTCCTG": 12583, + "GGCATGTT": 12584, + "GGGAGCTT": 12585, + "CCGATGA": 12586, + "GTAGCCTT": 12587, + "TCTGGAGAA": 12588, + "CAGACCCA": 12589, + "TGGGCAGG": 12590, + "CTATGTTTT": 12591, + "GTGAAGAA": 12592, + "GGAGGAGAAGG": 12593, + "GTCATGTATGGATG": 12594, + "CTAGGATT": 12595, + "CTCTĊ": 12596, + "CATTCACC": 12597, + "TTTTACTTTT": 12598, + "GCCATACA": 12599, + "TTTACCTT": 12600, + "CTTTGGTA": 12601, + "CTTTGGATG": 12602, + "TCTTTTTTTG": 12603, + "TTATTTTTTA": 12604, + "TAAACATCA": 12605, + "GGAACTTA": 12606, + "GTGAGAAAA": 12607, + "GCAACGA": 12608, + "GTTACGG": 12609, + "GGTTGATT": 12610, + "TAATTTCTA": 12611, + "CTTTGGAAA": 12612, + "TAGCTATG": 12613, + "GAGCAAAAA": 12614, + "CTGCTGGG": 12615, + "CACAGGAAA": 12616, + "GCAATAGG": 12617, + "GATGTAGG": 12618, + "TTTACTGA": 12619, + "TAATTGAAA": 12620, + "GCGTTTTA": 12621, + "GCTCTGATT": 12622, + "CCACAAAAA": 12623, + "TAAAACTGA": 12624, + "CTGTGCTA": 12625, + "GAATCCCATG": 12626, + "GGTCTATA": 12627, + "GGGAGATG": 12628, + "GTGGGATG": 12629, + "GTTCGCA": 12630, + "GCACTCTT": 12631, + "TATACAGTG": 12632, + "GGTGGCTT": 12633, + "CGCGGA": 12634, + "CTGAAAAAA": 12635, + "CTTCGGA": 12636, + "CTTTCAGG": 12637, + "CTTTATATT": 12638, + "GTTCCATG": 12639, + "GTTTCTCTG": 12640, + "GTGTTTGTT": 12641, + "AAAAAAATTA": 12642, + "CAACTCTG": 12643, + "CTGTCCAA": 12644, + "GTGGACTA": 12645, + "GCATAGTG": 12646, + "CTCGCCA": 12647, + "CAGAĊ": 12648, + "TCAACCTCC": 12649, + "GAGGCATG": 12650, + "CCAACGG": 12651, + "GCCTAGTG": 12652, + "TCAACATTA": 12653, + "TTTTATGTA": 12654, + "GTTACATA": 12655, + "CCAAACCAAA": 12656, + "TAATATAAAA": 12657, + "GCCGGATCTCAGCTCACTGCAA": 12658, + "TCATCAAAA": 12659, + "TCAGCTAA": 12660, + "CTAGAGTT": 12661, + "TAAATCATG": 12662, + "TATACATTTT": 12663, + "CTAACCTT": 12664, + "TTATTGATT": 12665, + "CAATGCCA": 12666, + "CTAGCCGGGC": 12667, + "CAGGTTTGTTACA": 12668, + "TCGAAGAA": 12669, + "CTTTATTAA": 12670, + "TGTGGCCTT": 12671, + "TAATGGAA": 12672, + "TGACAGTGA": 12673, + "TTTTTGAAA": 12674, + "CTTCGCA": 12675, + "TGTTCATCA": 12676, + "GGTTTCAAA": 12677, + "GATACTGA": 12678, + "GACCCCTT": 12679, + "TCGTGGA": 12680, + "GTATTATG": 12681, + "GTCGCAA": 12682, + "GATGGAAAA": 12683, + "GATGGACA": 12684, + "GGCACTCA": 12685, + "CAAAAACAAA": 12686, + "GCTCTGGATAAGAGCGTCTG": 12687, + 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13147, + "CTAAAGAA": 13148, + "CGCCTCC": 13149, + "CACCAATT": 13150, + "TGACATC": 13151, + "GTTTAAATT": 13152, + "GAGATGTA": 13153, + "GTGATGAAA": 13154, + "CTAAGAGG": 13155, + "TCCATAAAA": 13156, + "CCCACTC": 13157, + "GGGAAGGGG": 13158, + "GCAGGTGGG": 13159, + "GTCAGGCA": 13160, + "CCCCTAAA": 13161, + "TCAAACACA": 13162, + "TTTTTTCTA": 13163, + "GTGAATGGG": 13164, + "GCTGAGATTA": 13165, + "TTTTAATTTA": 13166, + "GCTCTTTTA": 13167, + "GCGACGG": 13168, + "GGACTTTG": 13169, + "CTATACCA": 13170, + "GTCTTTTTG": 13171, + "CCCCCTGA": 13172, + "CTGGGTATA": 13173, + "TCCCAGTCC": 13174, + "CTTAAATA": 13175, + "GGATTTTTG": 13176, + "TTCAGTGA": 13177, + "TGACCTCC": 13178, + "CAAGTGGA": 13179, + "TAAAGATAA": 13180, + "CTCCCTCTT": 13181, + "TTAACAAA": 13182, + "TAATACTAA": 13183, + "TAATCCCAGCACTTTGGGAGGCCGAGGCGG": 13184, + "GGCTAATT": 13185, + "CAAAAAATG": 13186, + "GCCTAGGA": 13187, + "CATGGAAAA": 13188, + "TTTGCTGA": 13189, + "CCCACATG": 13190, + "TAAGCATG": 13191, + "GTTGTCTG": 13192, + 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"CGGCGAA": 13332, + "GGGGTCTG": 13333, + "CCTCTACA": 13334, + "TGACGAAA": 13335, + "TCCGTCTG": 13336, + "GAAAGGTA": 13337, + "GGTTGCA": 13338, + "GGCACTTA": 13339, + "CAAAAATAAA": 13340, + "TCGACGA": 13341, + "GGGCCCTG": 13342, + "TGACAGAAA": 13343, + "CTTGCTCC": 13344, + "TCAAĊ": 13345, + "TAAAATACTG": 13346, + "GTGATTTTA": 13347, + "TGATTTAAA": 13348, + "GCCTGGCC": 13349, + "TATTATTAAA": 13350, + "GACTCATG": 13351, + "CTACGAA": 13352, + "CAGGGTAA": 13353, + "GCTTTGCC": 13354, + "CCTGCTTTT": 13355, + "TGGGGGAA": 13356, + "GGTGCTCA": 13357, + "GATTTTAAAA": 13358, + "CAGGTGTCTTTTA": 13359, + "TAGCAATT": 13360, + "CAGAGGCCA": 13361, + "GTGATAGA": 13362, + "GTCAGAAAA": 13363, + "TAGGGGAA": 13364, + "GGGACATT": 13365, + "CTCAAGGA": 13366, + "GGCGTTA": 13367, + "CAAGGATT": 13368, + "GTACCTCA": 13369, + "CCTTTGAAA": 13370, + "GGTTTAAA": 13371, + "TTCATATT": 13372, + "TCATTGTTTT": 13373, + "CCAAATTAA": 13374, + "CTATTCTATT": 13375, + "CTCCACCAA": 13376, + "CAAGAGGAA": 13377, + "CCGTCAA": 13378, + "GCGAACA": 13379, + "GAATTTTAA": 13380, + "CAATTATG": 13381, + "TCTCCAGC": 13382, + "TTTGCTTA": 13383, + "CAAAAGGAA": 13384, + "GGTTCTGA": 13385, + "CCCCAAATT": 13386, + "TTCAGGA": 13387, + "CCACGTC": 13388, + "GTAGGTAA": 13389, + "GATGGCTCA": 13390, + "TTATTTGTG": 13391, + "TATAATATT": 13392, + "AAAAAAAAAAAAAAAAAAAAAA": 13393, + "TCGTCATT": 13394, + "GATCAATG": 13395, + "TCCTAATA": 13396, + "CGGTAAA": 13397, + "CTTTTTTTTA": 13398, + "TGGGTGCA": 13399, + "TAGGGCTG": 13400, + "GGATGTTTT": 13401, + "TCACATTGTA": 13402, + "TATGTGTGTG": 13403, + "GCGCAGA": 13404, + "TAACATTAA": 13405, + "CTGCCTTA": 13406, + "GTATAATG": 13407, + "GTAGGCAA": 13408, + "TAGACATA": 13409, + "GGGACTGA": 13410, + "CTAAATATT": 13411, + "TATTATTTTA": 13412, + "CTCTTTCTG": 13413, + "TAAGTTTTA": 13414, + "GTCGGCA": 13415, + "CTTATGAAA": 13416, + "GGTTTATA": 13417, + "CTTTGGGA": 13418, + "GAAGAGAAAA": 13419, + "GGTGGCTGACTAAA": 13420, + "GGTCCAGG": 13421, + "GGCAGATG": 13422, + "CCATCTAA": 13423, + "GTACACCA": 13424, + "CATGTACTG": 13425, + "GAGGTCAA": 13426, + "CGGACGG": 13427, + "CTTTCTTTCTT": 13428, + "GTGTCTTTT": 13429, + "CTATGACAGA": 13430, + "TCTTGCCTT": 13431, + "TAAAAGTGA": 13432, + "CTAATAATT": 13433, + "CTAAATTTG": 13434, + "GAGAAGCAA": 13435, + "CTTCAGCAA": 13436, + "TGGGTTTGATGAAACCGAA": 13437, + "CAATGATG": 13438, + "GTACTATT": 13439, + "TATTGGTA": 13440, + "GTCACAGAA": 13441, + "TACATACC": 13442, + "CACCCCTT": 13443, + "TAATCTCC": 13444, + "GTTGTATG": 13445, + "GAGACAAAA": 13446, + "GGTTCTT": 13447, + "TAAAAATTTA": 13448, + "GAATTTATA": 13449, + "TCCTGCCTG": 13450, + "GAGCACTGA": 13451, + "CTACCTGA": 13452, + "TGTTCCCTT": 13453, + "CAGACACTG": 13454, + "CTCATGCA": 13455, + "GTCATGCTG": 13456, + "GCTATAAAA": 13457, + "TAAATACTA": 13458, + "CACAGGGAA": 13459, + "TCTCAGCA": 13460, + "GCCAAGAA": 13461, + "TTTTAAAAATT": 13462, + "GAATATGAA": 13463, + "GTTCAAGTGATT": 13464, + "CTACAGC": 13465, + "GCTTCCCTT": 13466, + "TCTCTATCA": 13467, + "TACATCAAA": 13468, + "GGGGTCC": 13469, + "TCGAAAAAA": 13470, + "CACACACGCA": 13471, + "GCGCCAGG": 13472, + "TTTGTCA": 13473, + "GTAAATTTA": 13474, + "TGCCCATG": 13475, + "GACCCATG": 13476, + "CTCAACATT": 13477, + "GAAAAATAAA": 13478, + "GTGGTGCATG": 13479, + "TGAGAAATG": 13480, + "GTTCCTC": 13481, + "GCCTCCACA": 13482, + "CAGGTATT": 13483, + "GAGGGAAAA": 13484, + "CTTAGTTTT": 13485, + "TTATTCTT": 13486, + "GTAGGTGTG": 13487, + "TCTTTTAAAAA": 13488, + "TGACGTC": 13489, + "CACATCCA": 13490, + "CCTTCCAAA": 13491, + "GCAACAGCA": 13492, + "GCCACAAAA": 13493, + "CGACTCC": 13494, + "GGTGGGAGGA": 13495, + "CTGAATTTA": 13496, + "TGTTTGTTA": 13497, + "GCCGACA": 13498, + "CTCTCCCTT": 13499, + "GTCTGTGTT": 13500, + "CTTTTTCTTTT": 13501, + "GCTATCTA": 13502, + "GAGGACAAA": 13503, + "CTTGCACAATT": 13504, + "TCCTATCC": 13505, + "TCTTCACTT": 13506, + "TGTTTCTTTT": 13507, + "GAAAATGCA": 13508, + "TTTTATTTATT": 13509, + "CTAGGGAA": 13510, + "CTAGTTTTTT": 13511, + "TGTGTCTA": 13512, + "TGATGAAAATTA": 13513, + "TACTCCAA": 13514, + "CTGCCCAA": 13515, + "GTTCAATG": 13516, + "GTTTATCC": 13517, + "TGAACGG": 13518, + "CTCATCCA": 13519, + "CTAAGCC": 13520, + "CTTATTTTTT": 13521, + "CAGAAGAA": 13522, + "GCCTGCCTG": 13523, + "CTCTAGCCTGGG": 13524, + "GGCTTATT": 13525, + "GGCGCCA": 13526, + "GCGTAAAA": 13527, + "TAATGAGG": 13528, + "TAACTGAAA": 13529, + "TTTTCATA": 13530, + "GTGGCTAA": 13531, + "TGTTGGCA": 13532, + "GCATTCTCAGAAA": 13533, + "CCGGTGA": 13534, + "TACTCCTT": 13535, + "TCAAATATA": 13536, + "TTAATTAAAA": 13537, + "TATTTTATG": 13538, + "GAATAGTA": 13539, + "CTCTGAGAA": 13540, + "CCAAAAATA": 13541, + "CTGTGGCTT": 13542, + "GATCATTA": 13543, + "CACTCTTA": 13544, + "GGGGAATT": 13545, + "GGCTCCTG": 13546, + "GTGTTATG": 13547, + "GTTGTTTA": 13548, + "GCATAGTA": 13549, + "GTAGCCAA": 13550, + "CTATAGTA": 13551, + "CGGGGCA": 13552, + "GAAGAGCAA": 13553, + "TCTCATTGTGGTTTT": 13554, + "GGTAAATG": 13555, + "TCTTCATTTT": 13556, + "TCTCTTTTTT": 13557, + "TACATTTTAA": 13558, + "GGTGCCA": 13559, + "GAAGGCAATG": 13560, + "TCCTCTGTG": 13561, + "TCTTCTTTG": 13562, + "CAGGCAGCA": 13563, + "TCTGGCCA": 13564, + "CTCAGAGAA": 13565, + "CACAAAATT": 13566, + "TAAGAAATT": 13567, + "TTTTTGGA": 13568, + "GATAGGA": 13569, + "GCAAAGAAA": 13570, + "GAGAGGCC": 13571, + "GCTGCTGCTG": 13572, + "GTAAGAGG": 13573, + "CTTTTCACA": 13574, + "CCATGCTG": 13575, + "CAAGGAAAA": 13576, + "CTTCATC": 13577, + "GTTTAATTA": 13578, + "CAAAGAATT": 13579, + "TGAACGA": 13580, + "CTCACGA": 13581, + "CCAACCCC": 13582, + "CTCAAGAAA": 13583, + "TAATTTTCATCA": 13584, + "TAAGTGGGAGAA": 13585, + "CCGGCAA": 13586, + "TAAAGTGAA": 13587, + "CCAAGGGA": 13588, + "CTCCTATG": 13589, + "CTAATGAAA": 13590, + "CGAAGCCA": 13591, + "TCGCGCCA": 13592, + "TAGATTTG": 13593, + "TCGCTGAA": 13594, + "TTTTTCAAA": 13595, + "TGAGCTTA": 13596, + "GTTAGCTT": 13597, + "GTTTTTCCA": 13598, + "GTCCTCTA": 13599, + "GGGGTGAA": 13600, + "TCCTTTACA": 13601, + "GAAGGCAGA": 13602, + "CTGCCTGTG": 13603, + "GCACCCTT": 13604, + "CTATCCAA": 13605, + "CTAGGCTG": 13606, + "CATTTACTG": 13607, + "GGAACCTT": 13608, + "GAGGGGTT": 13609, + "CCCATATG": 13610, + "CTTGGGAGGCTGA": 13611, + "GTTTCGG": 13612, + "GTTCAGTC": 13613, + "CAAAGACC": 13614, + "CATTGAAAA": 13615, + "GGAATCC": 13616, + "TAGGGGA": 13617, + "TAATATCAA": 13618, + "TAATAGCAA": 13619, + "GAAGGCAGG": 13620, + "CAGGTTCAAGCGATT": 13621, + "TAAGAATA": 13622, + "TAACTCCTT": 13623, + "CAAGGGTG": 13624, + "CTTTCTCTG": 13625, + "GAGATCTT": 13626, + "CTGAGTAAA": 13627, + "TCTCAAATG": 13628, + "GCCATAGA": 13629, + "TAATTTAAAAA": 13630, + "CGCTACA": 13631, + "CGTTTCTT": 13632, + "TACCCTCC": 13633, + "GAGAGAGAGAGAGAGAGAGAGAGA": 13634, + "GGGCTTTG": 13635, + "GCGCTGTT": 13636, + "CTGGATCC": 13637, + "GTGTCTGTG": 13638, + "TGTGTGAAA": 13639, + "CAAACACTT": 13640, + "TCTGGGCC": 13641, + "TAAAAGCTT": 13642, + "CCAACAAAA": 13643, + "CAATTCCTG": 13644, + "GTGCAGCTG": 13645, + "CTGTAATG": 13646, + "CTATCTCC": 13647, + "TATTCTGAA": 13648, + "GAAAGTGAA": 13649, + "CATTCTGTT": 13650, + "CAAAATGCTG": 13651, + "TAATTCTGA": 13652, + "GTTCCAGGACAGCCAGG": 13653, + "GAACCCCA": 13654, + "CTCTCCAAA": 13655, + "TCTGGGAAA": 13656, + "GCTTCAGTT": 13657, + "CATGCGG": 13658, + "TCTAGTC": 13659, + "CATACGA": 13660, + "TAATATCA": 13661, + "GCCTTGACTA": 13662, + "CTGCGAA": 13663, + "GGAATACCA": 13664, + "CTAACAGAA": 13665, + "TGCAGTTA": 13666, + "GTATTCCCA": 13667, + "TGATGTCAA": 13668, + "GTGCCCGG": 13669, + "TAATAAATA": 13670, + "TGAGCAGG": 13671, + "CTTCAATG": 13672, + "CTCTCAGA": 13673, + "TATACAGAA": 13674, + "TCCTGGAGG": 13675, + "TCCTGGATG": 13676, + "TACTTTCC": 13677, + "CCTCAAAAA": 13678, + "CCCGGTG": 13679, + "TAACCAAAA": 13680, + "GAATATAAA": 13681, + "CTGGATAA": 13682, + "GTGTAGGA": 13683, + "TAAAATACAA": 13684, + "CTGATCC": 13685, + "GTAATACA": 13686, + "TATGTCCA": 13687, + "TCAAATTCA": 13688, + "GGGAGTA": 13689, + "TCCTTCACA": 13690, + "GAATGGAAA": 13691, + "TATCTTATT": 13692, + "TTACAGTGA": 13693, + "GGGGAAAAA": 13694, + "GTTCGGA": 13695, + "GCATCTTTT": 13696, + "GCAGACTA": 13697, + "GACATACAA": 13698, + "GGTGGTCA": 13699, + "TGTAGATG": 13700, + "CTAAAGAAA": 13701, + "TAGCTAGG": 13702, + "GGATGTGGAGAAA": 13703, + "CAGGGTC": 13704, + "CCTTTAATCCCAGCA": 13705, + "TACCAGGCTCC": 13706, + "CCGCCCC": 13707, + "GGGCCCA": 13708, + "GTTTCTTAA": 13709, + "CTCACCCA": 13710, + "GTAACTTTT": 13711, + "CCAACGA": 13712, + "TGCCCTCC": 13713, + "GCTCAGGCTG": 13714, + "CGTCTCC": 13715, + "CCACAATGAGATA": 13716, + "CTTGACTA": 13717, + "GCAACATA": 13718, + "GTATTCTA": 13719, + "TAGTTCACC": 13720, + "TTTTATGA": 13721, + "GAGGTAAA": 13722, + "GAAAATCA": 13723, + "CCTAGCC": 13724, + "CCGCCAA": 13725, + "TTTTGTCC": 13726, + "CAATATAAA": 13727, + "GTCCAGAAA": 13728, + "GTTACAAAA": 13729, + "CATTATTTG": 13730, + "GCTCTACA": 13731, + "GCTCTTAAA": 13732, + "CAAGGGTT": 13733, + "GTGGATTTT": 13734, + "CAAATGATT": 13735, + "TATGGTGTT": 13736, + "CCTGCTTA": 13737, + "CCCAGCCTT": 13738, + "GGCAGCCA": 13739, + "TCGTAATT": 13740, + "GTGTCTCTT": 13741, + "CTCAGC": 13742, + "CGGGAGGA": 13743, + "CCCCCCCCCCCCCCCC": 13744, + "CCTGTATG": 13745, + "TCAAATGA": 13746, + "CGCAGCC": 13747, + "TGATGTCA": 13748, + "GTGGCCTG": 13749, + "GGACACTA": 13750, + "GTATCTAA": 13751, + "TGTGCTC": 13752, + "TATTTAGTA": 13753, + "TGTTTACTG": 13754, + "CATTCAGAA": 13755, + "CAGGCCTCTC": 13756, + "GTAAGTTTT": 13757, + "GTAATATTTT": 13758, + "GACATAGA": 13759, + "GTTCTTGG": 13760, + "GCAGGAGGA": 13761, + "GGAGCTCC": 13762, + "TCTGAAATG": 13763, + "GTAATGCA": 13764, + "CCTGCCCCA": 13765, + "GGGACCA": 13766, + "GCGGAAAA": 13767, + "CAGTTATG": 13768, + "TGTTTCTTA": 13769, + "TCTAGCC": 13770, + "GCGGTTA": 13771, + "GGATGAGATGGTT": 13772, + "CACCTTAA": 13773, + "CAACCTGA": 13774, + "GTATTTTTG": 13775, + "TCAATTATT": 13776, + "GTCAGGGAA": 13777, + "CTTAGATG": 13778, + "TCCAGCTA": 13779, + "GGGACTCA": 13780, + "TCTCCCAGG": 13781, + "CTGTTTCA": 13782, + "GGCACATT": 13783, + "CTTGAGTA": 13784, + "CTACTCAA": 13785, + "TCTTCCCAA": 13786, + "TCTGTCTCA": 13787, + "GGAACAGAA": 13788, + "TTTTATAAAA": 13789, + "CCAGGGATG": 13790, + "GTTTTGTTTTGTTTT": 13791, + "GCTAAAATG": 13792, + "GTTTCCATT": 13793, + "CAGATAGG": 13794, + "GCTGGGCTT": 13795, + "TCCATCTC": 13796, + "TTAAAAATG": 13797, + "GACAGTC": 13798, + "GGAGCTAA": 13799, + "GCCGAGC": 13800, + "TGTGGATCA": 13801, + "CATTCTCTT": 13802, + "CCTGTCAA": 13803, + "TCCAGAAAAA": 13804, + "CGCGCTT": 13805, + "CATCATTTT": 13806, + "CTCCAGAGA": 13807, + "TACTCATT": 13808, + "GTATCCTT": 13809, + "TGTGCATA": 13810, + "CATTGTCC": 13811, + "TGAATTATT": 13812, + "GCAAGAGAA": 13813, + "GTCAGAGAA": 13814, + "CGGCGTT": 13815, + "CCAAGTCC": 13816, + "CAGTGTTTT": 13817, + "CTTCAGCAAA": 13818, + "GAAGACTA": 13819, + "GGATTATG": 13820, + "CAGGTATG": 13821, + "GAAAATGGA": 13822, + "CAAATGTTA": 13823, + "TAACAGTAA": 13824, + "GAGTCGGACACGACTGA": 13825, + "TCGAGACAGGGTTTCTCTGTGTA": 13826, + "GATCTATG": 13827, + "GAACTATT": 13828, + "TCCTCTCTG": 13829, + "GCCCGTA": 13830, + "TCTGĊ": 13831, + "GCCATCTCTCCA": 13832, + "CGATTATT": 13833, + "TCAAACTCA": 13834, + "GGTACAGA": 13835, + "CATGTGTGTG": 13836, + "CTGGCCTG": 13837, + "TAAAGTATT": 13838, + "CACAGACC": 13839, + "TTAATGA": 13840, + "GACATCC": 13841, + "TGCCTCAA": 13842, + "CAGTCTTA": 13843, + "CAGGTTCAAGTGATT": 13844, + "CTTCTCCTT": 13845, + "TGTTTATCA": 13846, + "CAAATACCA": 13847, + "TAAAATAATA": 13848, + "GTTAGCTG": 13849, + "GAAAAGATG": 13850, + "CATGATTTT": 13851, + "CACATGAAAA": 13852, + "TACGAGA": 13853, + "TCTATATATA": 13854, + "GCGCCCA": 13855, + "CAAGCATG": 13856, + "CAGGGTTTCACCATGTT": 13857, + "GGAAĊ": 13858, + "GTGAGTTTT": 13859, + "GTCAGATA": 13860, + "GAGGCTAA": 13861, + "CCCACAAAA": 13862, + "TAGATAGG": 13863, + "TGTAGCA": 13864, + "TAATTCTCA": 13865, + "TATTATTATTA": 13866, + "TGAATGAAAA": 13867, + "TAATAATAATAATAATAA": 13868, + "CAATGAGA": 13869, + "CACACACTT": 13870, + "CATTGAATT": 13871, + "GCCACCTGA": 13872, + "CTCTTCTCA": 13873, + "TGAGGCAGG": 13874, + "GTAGGTCA": 13875, + "TATTCAGAA": 13876, + "GAAAGAGCA": 13877, + "GTAACATA": 13878, + "CCAAGGAAA": 13879, + "TGTGTGTATG": 13880, + "TAAGGTTTT": 13881, + "TCCCCTAA": 13882, + "CAATCCTT": 13883, + "CTGGCTCC": 13884, + "CTGTAGGA": 13885, + "GTATATAAA": 13886, + "GCCTCTGTG": 13887, + "CTATAGGA": 13888, + "CTAACATA": 13889, + "CTCCTGCCTCAGCCTCCCGAGTAGCTGGGACTACAGG": 13890, + "GAGAGTCCCTTGGACTGCAA": 13891, + "GGTCCCTG": 13892, + "CTGTCTTTT": 13893, + "GCCGGGTG": 13894, + "GAAAGACAA": 13895, + "TGTTTCTGA": 13896, + "GTAAGGTT": 13897, + "TTATATGAA": 13898, + "GAACACAAA": 13899, + "CAGAAAATG": 13900, + "GTTCAAGG": 13901, + "GCCGCAA": 13902, + "TAGGGTTA": 13903, + "CAGTGTTTG": 13904, + "CTGCAGTCA": 13905, + "GAATAGCA": 13906, + "GAAAAAATG": 13907, + "GCCCCTC": 13908, + "CTACGTA": 13909, + "TAAACAGCA": 13910, + "TCTGTCTGA": 13911, + "TAAAAGATG": 13912, + "GCTTCCTCA": 13913, + "CTTTTCTGA": 13914, + "TAAAAATATG": 13915, + "TTTTAATTTG": 13916, + "GAGCTAGA": 13917, + "CCAGGCATG": 13918, + "CTCCCACTTAAAAA": 13919, + "GGCGCAA": 13920, + "CAATATATT": 13921, + "GTTCCCTA": 13922, + "CATTCTAAA": 13923, + "CTTATTATT": 13924, + "TTAATGTG": 13925, + "CCTAGTTA": 13926, + "GAATTTCTA": 13927, + "CTTTGTCTT": 13928, + "CTCTGTAGACCAGGCTG": 13929, + "GTGGAAAATAAGTATTTG": 13930, + "CTGTCATG": 13931, + "GCACCCAA": 13932, + "CTCAAAATG": 13933, + "GAGAGGGCA": 13934, + "GACATACA": 13935, + "GGCCAGGATG": 13936, + "TCAGAGCAA": 13937, + "CCAGCCTGGCCAACATG": 13938, + "GCGCTGAA": 13939, + "CTCTGAATT": 13940, + "TATATACTA": 13941, + "GCTGCCCTG": 13942, + "GATTGTCA": 13943, + "TGTATCTG": 13944, + "CAAGAAGG": 13945, + "GGATACTG": 13946, + "CTACCTCC": 13947, + "GTCCAGGCTG": 13948, + "TATAGGTA": 13949, + "GTGATATTTT": 13950, + "GCTGCAGGA": 13951, + "GATTTACAA": 13952, + "CTAACCTG": 13953, + "TGCAGAAAA": 13954, + "TTCATCA": 13955, + "CTGTTTGTT": 13956, + "GCAGGCGG": 13957, + "TAAAGACTT": 13958, + "TCAAAGATCA": 13959, + "TAATTTGTG": 13960, + "GGCAGTAA": 13961, + "GTGAATTAA": 13962, + "GTCTTGCTCTGTT": 13963, + "GCATTTGA": 13964, + "TTTTTTTTTTTG": 13965, + "TGGGTTCAAGCGATT": 13966, + "GGCAGGTT": 13967, + "CTTTCTAAA": 13968, + "GGATCCAA": 13969, + "CTCTCCAGG": 13970, + "CACACAGTG": 13971, + "CTTATCTA": 13972, + "TCAAAGAAAA": 13973, + "TTCATCTT": 13974, + "CGCATTTT": 13975, + "GCGGTGGG": 13976, + "CACTATTATT": 13977, + "GAGACTGGCTAGG": 13978, + "GGCTCTA": 13979, + "CTGGAATG": 13980, + "CACATACACA": 13981, + "CATGCCCA": 13982, + "GCAAGCCA": 13983, + "TGAAATTTG": 13984, + "CCAATTGTGCAAGTT": 13985, + "GCGGAGAA": 13986, + "GTGCCATG": 13987, + "GTTTĊ": 13988, + "TCTTCACAA": 13989, + "TCCATCTCA": 13990, + "CGATTTTA": 13991, + "TCATTTGTG": 13992, + "CCTTCTCCTCCTG": 13993, + "GCGAGAAA": 13994, + "TAATTTTATT": 13995, + "GAACAGCC": 13996, + "CAAGCCTG": 13997, + "GGATGAGA": 13998, + "CTAGTGTT": 13999, + "TATTCATTTT": 14000, + "TATGGTGAAA": 14001, + "GGGGGAAAA": 14002, + "GGCGGGTGGATCA": 14003, + "CAACCCTA": 14004, + "CTTTCACTT": 14005, + "CTGAGCTA": 14006, + "GCAAGGAAA": 14007, + "TCATTTCTA": 14008, + "TCATAGC": 14009, + "GAACGGA": 14010, + "GGACAGAAA": 14011, + "TATACCCA": 14012, + "TATATACTG": 14013, + "CAGATTTTA": 14014, + "GTTTTTGAA": 14015, + "GTATTCACA": 14016, + "TCCCTGCTG": 14017, + "CTTTĊ": 14018, + "GTAGGTTA": 14019, + "CTAGAGTG": 14020, + "CTAGATAA": 14021, + "TAAAAAAATA": 14022, + "CTTACGG": 14023, + "TACAAAAAATTA": 14024, + "CAAAAAATTA": 14025, + "GTGCTGGGATTACAGGCATGA": 14026, + "GTTCGTC": 14027, + "GCCTTTTTT": 14028, + "GCTGCAGTG": 14029, + "CAAAAGTGA": 14030, + "CACCTCTT": 14031, + "TACGCTT": 14032, + "GACTTTTAA": 14033, + "GTGTCTCTG": 14034, + "TGTGGAAAA": 14035, + "CAGAGCAAA": 14036, + "CTCAGGCC": 14037, + "CTCCTGAAA": 14038, + "GTATTTGAA": 14039, + "GATGATTA": 14040, + "CCAGGTTA": 14041, + "GCCAGTCTCCAGA": 14042, + "GGGAAAAAAA": 14043, + "GTGTTTTAA": 14044, + "GCAGAAATT": 14045, + "CTATATAAA": 14046, + "GATGGATA": 14047, + "TAGGCTGA": 14048, + "GCCAAGATT": 14049, + "CTCTGTCTT": 14050, + "TAATGTATG": 14051, + "CAGGACCTT": 14052, + "GAGCATGTG": 14053, + "TAAACTGAA": 14054, + "GTAATATAA": 14055, + "TCCATTATT": 14056, + "TTCAGTCA": 14057, + "TCTCCTGTT": 14058, + "TAAAAAGTA": 14059, + "CAGCTCCTT": 14060, + "CATAATTAA": 14061, + "GCTCCGCCTCCCGGGTTCACGCCATT": 14062, + "TAGTGATT": 14063, + "GGCGGTT": 14064, + "GTGCACTA": 14065, + "GGAACTGAA": 14066, + "GTGACATCA": 14067, + "GTAATTATT": 14068, + "GTTTTGGTT": 14069, + "CACTCCAAA": 14070, + "GGTGAAAAA": 14071, + "GGGAGATT": 14072, + "CAACGCC": 14073, + "TAAAATCC": 14074, + "GCTGGTCC": 14075, + "CCCAĊ": 14076, + "CCTATCTT": 14077, + "TCTCCTGTG": 14078, + "TACCCATG": 14079, + "GAAGGGAGA": 14080, + "TAAAAAAAAATT": 14081, + "TCTGGGTA": 14082, + "GCTTTCCTT": 14083, + "GGCAGAAAA": 14084, + "GCAAATATT": 14085, + "CTGTGCTCA": 14086, + "GCCTTAAAA": 14087, + "GGTCTTTG": 14088, + "TCAAAAATA": 14089, + "TACCTCC": 14090, + "GAGCATTA": 14091, + "TTTTCAAACCACATAAATTTT": 14092, + "TTTTGGTG": 14093, + "GAATCCTT": 14094, + "TCTCAGTT": 14095, + "CTCTGCTCA": 14096, + "CCCCATCTCTACTAAAAATA": 14097, + "GAGCTCCA": 14098, + "GGTCGCA": 14099, + "TAATGATA": 14100, + "CAAGATTTT": 14101, + "GGACCCAA": 14102, + "CTAGACTT": 14103, + "TGAACTCC": 14104, + "GCTGCAGAA": 14105, + "GTCCCTAA": 14106, + "GGGGTGA": 14107, + "CTCTTCACA": 14108, + "TCGCCAGG": 14109, + "GCTGGGAAA": 14110, + "TTCATATG": 14111, + "TACCCTGA": 14112, + "GCATTTCTT": 14113, + "CGTTTTAAA": 14114, + "GACAGGAAA": 14115, + "TATAGGGTT": 14116, + "CAGGCTTA": 14117, + "TCTGGATA": 14118, + "TCTGTACTT": 14119, + "TTTGCATA": 14120, + "GACATCTA": 14121, + "GACCTCTT": 14122, + "CAATTTATA": 14123, + "TAATGTAAA": 14124, + "TAATGGAAA": 14125, + "TAGCTCTTA": 14126, + "CCACCACACCCA": 14127, + "GCGAAGAA": 14128, + "TAACTTCTT": 14129, + "TTTTTGGG": 14130, + "CTTGGATG": 14131, + "GTGAGTCC": 14132, + "GCTGGAGTG": 14133, + "TTAAGATT": 14134, + "TTTGGCAA": 14135, + "CAGCTCTGA": 14136, + "GAAGGGAAA": 14137, + "GTATTATTA": 14138, + "CAAACTGAA": 14139, + "GCCACCATG": 14140, + "TATTTTTATT": 14141, + "TCCTGCATT": 14142, + "GCTCCTCC": 14143, + "TCAGAGTCTTTT": 14144, + "GCGTTCA": 14145, + "CTTGGAGG": 14146, + "GGATCCTT": 14147, + "CCTGGGAAA": 14148, + "GTTATACA": 14149, + "GTTATTTTTT": 14150, + "CCAATCTT": 14151, + "GCCACGC": 14152, + "TAGAGCATG": 14153, + "GGTTCGA": 14154, + "CACTGTTTT": 14155, + "GTAGTCAA": 14156, + "CATGTAAAA": 14157, + "GTCATGTA": 14158, + "CTCCTGCTG": 14159, + "GGTATATT": 14160, + "GTTTGGCTG": 14161, + "CAACACAAA": 14162, + "GCTGCATAGTATTCCATG": 14163, + "TAAAGTCC": 14164, + "GAAACACC": 14165, + "GCTTCAGAA": 14166, + "TGGGTGGA": 14167, + "TAGACAGCA": 14168, + "CTTAATCCA": 14169, + "TTACAGTAA": 14170, + "GTTCAAGCAATT": 14171, + "TGATCTCTG": 14172, + "CCACTATT": 14173, + "CCACTGTA": 14174, + "TATATAGAA": 14175, + "TCTGTGATT": 14176, + "CGGGGGA": 14177, + "CCAACTC": 14178, + "TTAATATG": 14179, + "TCATAGTTTT": 14180, + "TACTTATTTTCCACCA": 14181, + "TAACAAATT": 14182, + "GTGGGGGTG": 14183, + "TCTGCAGCA": 14184, + "GGGGGTA": 14185, + "CATAGTTTT": 14186, + "TGATGCTA": 14187, + "TGACAGCAA": 14188, + "TACGATG": 14189, + "GTCGGCC": 14190, + "CAAACTAAA": 14191, + "GTGATCAA": 14192, + "GGGGGCC": 14193, + "CACTAAAAA": 14194, + "TCAGTTTTA": 14195, + "GTGCTGCA": 14196, + "GCACATCC": 14197, + "TAAACAGTT": 14198, + "GTAAGACA": 14199, + "TCTATTTTTT": 14200, + "TAATGTATA": 14201, + "GTTTGGGAA": 14202, + "CCAGGCAGG": 14203, + "TAGCTAGA": 14204, + "GAGTACTGGAGTGGGGTG": 14205, + "GAGTCATG": 14206, + "GCGTTTCA": 14207, + "TAACGTC": 14208, + "TCATGAGA": 14209, + "GAACGTA": 14210, + "GTTATGCA": 14211, + "CTTTTTAAAAA": 14212, + "CAAAACTTA": 14213, + "CCTCAGCC": 14214, + "CAGTCCATGGGGTC": 14215, + "TCGTGCTG": 14216, + "TTTTTCCA": 14217, + "TGATATATT": 14218, + "TGTGTCCA": 14219, + "TGTTGTCC": 14220, + "CATTTCAAAA": 14221, + "CAGGTGCC": 14222, + "TTTGGGTT": 14223, + "TTTACACA": 14224, + "GCTCAGAGA": 14225, + "GTCTTCTA": 14226, + "CAATGCTA": 14227, + "GCAAAATTA": 14228, + "CTCCTAGA": 14229, + "CTAAGGTT": 14230, + "TAGGCACA": 14231, + "CTGAATGAA": 14232, + "GTAAAGAAA": 14233, + "TCGATTTG": 14234, + "CTGCGCC": 14235, + "TGTTCGG": 14236, + "CTGATTTTA": 14237, + "GTCACCCA": 14238, + "GTTTTTGTG": 14239, + "GTGTTGAA": 14240, + "TTTTTGTTTTTG": 14241, + "TCCCTTCTT": 14242, + "CTTCTGAAA": 14243, + "GTTTCTATT": 14244, + "GGAATCTT": 14245, + "TAAAATCTG": 14246, + "CCAAAAATG": 14247, + "TGGGGATT": 14248, + "CCCCCCCA": 14249, + "TATTAATTAA": 14250, + "CCCGTAA": 14251, + "TCGCCCA": 14252, + "TCCTCAAAA": 14253, + "CTTGAGCC": 14254, + "GTTCATCC": 14255, + "GTATCCAA": 14256, + "GTACTCTT": 14257, + "TATAGAGG": 14258, + "GTAATCTT": 14259, + "GCACCCGG": 14260, + "TCAGTATG": 14261, + "GTGACAAAA": 14262, + "GATGGAGCA": 14263, + "TAATTTACA": 14264, + "TGCAGTTTT": 14265, + "GGTATAAA": 14266, + "CGATTTATTA": 14267, + "CCTTTTATT": 14268, + "CTCAGTAAA": 14269, + "GATAGCTT": 14270, + "TCGGCGA": 14271, + "TCTTTGAAAA": 14272, + "TCCTCTGTCGTCC": 14273, + "CAGATGTGA": 14274, + "CTAACACAA": 14275, + "GTCCCTC": 14276, + "TTTAGAGA": 14277, + "GATAGCAA": 14278, + "CTTTCTGAA": 14279, + "GTGCCTTA": 14280, + "CATGGAGAA": 14281, + "GAAAATAGA": 14282, + "CTTTTTGTG": 14283, + "TAATTCCTT": 14284, + "TAATTTCAAA": 14285, + "CAGGTTCAAGCAATT": 14286, + "TCATAGGA": 14287, + "TCCTTTTAA": 14288, + "CCAGAAGG": 14289, + "CATTCCAAA": 14290, + "TTTGGAGA": 14291, + "TGGACAAA": 14292, + "TAAGCTTTT": 14293, + "TAAGTGAAA": 14294, + "GTGTAATG": 14295, + "GCTGGCCA": 14296, + "CTCACAGTT": 14297, + "CATAGTGAAA": 14298, + "GGGAGCCA": 14299, + "CCCTGAAAA": 14300, + "GGTCCAAA": 14301, + "TGATGAGG": 14302, + "CCAGCTAA": 14303, + "TAAACGTA": 14304, + "GATTGGTG": 14305, + "TTTAGTTTT": 14306, + "CTGAACTGA": 14307, + "CGCCTATAA": 14308, + "CATGTTTA": 14309, + "GAACCATT": 14310, + "CAATAAAAAA": 14311, + "CTAGATTA": 14312, + "CTACCCTC": 14313, + "TCTGCAGAA": 14314, + "CTAACAGG": 14315, + "GGCCTCTT": 14316, + "TGATGCTGA": 14317, + "CCTGGAGGA": 14318, + "TTTACCAA": 14319, + "GTGAAGAAA": 14320, + "GACTCTCC": 14321, + "CCATCATCA": 14322, + "GTACCATT": 14323, + "TAAATCTTA": 14324, + "CTCGACC": 14325, + "GAGAĊ": 14326, + "GCAATCCA": 14327, + "CTGTTGTTTT": 14328, + "CCTCCAAAA": 14329, + "TAGAATAGAA": 14330, + "TAGTGTTTT": 14331, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTG": 14332, + "TATTCAGGA": 14333, + "CAAAGAAAAA": 14334, + "CATTTGCC": 14335, + "CTGAAGTCA": 14336, + "GTCTTCCTT": 14337, + "GTGGGTTTT": 14338, + "CAACTCACAGA": 14339, + "TGATGTGTG": 14340, + "GTTGCTCC": 14341, + "GTAGCTAA": 14342, + "TAAACTCC": 14343, + "TGTGCGTG": 14344, + "CAAATGCC": 14345, + "CAAACATCA": 14346, + "CATGCCTA": 14347, + "TTTTATCAA": 14348, + "TCAAATACTT": 14349, + "TTCAGCAA": 14350, + "GGCTTCCCTG": 14351, + "CCTGAAAAA": 14352, + "TGATCTTTT": 14353, + "GAACACTA": 14354, + "CTGTGAGA": 14355, + "GGACGTA": 14356, + "GTCCAGGAA": 14357, + "TATACGG": 14358, + "GCTGĊ": 14359, + "CATGTGATT": 14360, + "GATGTAGA": 14361, + "GGTTCCTT": 14362, + "CATATTATT": 14363, + "GCCATTAAA": 14364, + "TGGGTTCAAGTGATT": 14365, + "TGACATCC": 14366, + "GAAGCTAA": 14367, + "CATGTCCA": 14368, + "GATGGTTA": 14369, + "CGACACC": 14370, + "TGGAGCC": 14371, + "GAGTTAAAA": 14372, + "GAAGACAGA": 14373, + "CCGGTCA": 14374, + "CTGTGAGG": 14375, + "GAAAGAATT": 14376, + "CATTTGATT": 14377, + "TCTGCCTCA": 14378, + "CCTTCCTA": 14379, + "CTAACAAAA": 14380, + "TTTAGAAAA": 14381, + "TAGGGATT": 14382, + "TGTAGAAAA": 14383, + "TACATTAAA": 14384, + "GGGCTGGG": 14385, + "TCCCAACC": 14386, + "CAAATAGAA": 14387, + "TGAAGTCC": 14388, + "CCCCTCTA": 14389, + "GATAGTGA": 14390, + "CCCTCTTA": 14391, + "CCGTCTATG": 14392, + "CCATCCAACCATCTCA": 14393, + "GGTCTAAA": 14394, + "GTACAGC": 14395, + "GCTTTCTCA": 14396, + "CTTTTTCTA": 14397, + "TTTACAGA": 14398, + "CCCACTCCAGTATT": 14399, + "TCCTTCTCA": 14400, + "GACCCTT": 14401, + "GCCTGTTTT": 14402, + "TCACCTCCA": 14403, + "GTTCCTCTT": 14404, + "CCGATAA": 14405, + "GGTGGGTT": 14406, + "CTGAAATA": 14407, + "GGACCATT": 14408, + "GCAATCTT": 14409, + "CTTACAGTT": 14410, + "GCCAGCAAA": 14411, + "TAGGTATT": 14412, + "GCTATTATT": 14413, + "GCATTTGTT": 14414, + "TAACCCTAA": 14415, + "TGATATAAA": 14416, + "CAAGAGAAA": 14417, + "GGATAATG": 14418, + "CTCTGACA": 14419, + "CATGCCATT": 14420, + "TCTCCATGA": 14421, + "GTCTGTCTG": 14422, + "GACTGTTA": 14423, + "GTATTTTTAGTA": 14424, + "CAGGGTCC": 14425, + "GAGGGTAA": 14426, + "TCATTCTAA": 14427, + "GGTAGCTG": 14428, + "CTGTGTCTG": 14429, + "GAGTTTGGA": 14430, + "GCAGTTTTGAAA": 14431, + "CACCAATG": 14432, + "GGTCCCCA": 14433, + "TCCGCTG": 14434, + "TAAACGAA": 14435, + "TTAAGTAA": 14436, + "TGTAGCTT": 14437, + "TAATATTATT": 14438, + "GCTCATTTG": 14439, + "TCCGGAA": 14440, + "GTTTCAGA": 14441, + "CATTCGA": 14442, + "GAGGGGAAA": 14443, + "GAAAACTT": 14444, + "GCTACGA": 14445, + "CTGAAGTG": 14446, + "TAATATCTT": 14447, + "CGTCTTTT": 14448, + "CTAAATTGCTCAAA": 14449, + "TAATCCCA": 14450, + "TCAGTGGG": 14451, + "GTCGAATT": 14452, + "GTCTCACTG": 14453, + "CACAGAGGA": 14454, + "TCAATCAAA": 14455, + "TCTCTAATT": 14456, + "GGCAGGAGGA": 14457, + "GTGTTGGGATTACAGG": 14458, + "GTTCAAGGCCA": 14459, + "TCGTTTTTT": 14460, + "TCAGCTTTT": 14461, + "GCCTACTA": 14462, + "GAGAGGGGA": 14463, + "CATTCCTA": 14464, + "GCAAGTTTT": 14465, + "CTTTGCAA": 14466, + "TACCCCAA": 14467, + "CATTTGTTTT": 14468, + "TCACAGTCA": 14469, + "GTGATGTTA": 14470, + "TAGAGATG": 14471, + "TATTCTCTT": 14472, + "CACAĊ": 14473, + "TGAAGAGAA": 14474, + "TAAAATATCA": 14475, + "CCTGCAGCA": 14476, + "TAGGCCA": 14477, + "GTTAATTTTA": 14478, + "CTCACAGAGTTGAA": 14479, + "GTTAAAATG": 14480, + "TAAATCTCA": 14481, + "CAGGGAGAA": 14482, + "GCTTCAGCA": 14483, + "GCAATAGA": 14484, + "CCGACAGA": 14485, + "GTTCGCC": 14486, + "CATTCACTT": 14487, + "GATGCGA": 14488, + "TCTACGG": 14489, + "TCATTCAGA": 14490, + "TCATTCTGA": 14491, + "CTGTGGGAA": 14492, + "CCTAATTTT": 14493, + "GTTCAGAAA": 14494, + "TATACGA": 14495, + "GTTAGTCA": 14496, + "TCCACCAAA": 14497, + "GGGGCAA": 14498, + "CCCCATCTCTACTAAAAATACAAAAATTA": 14499, + "TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC": 14500, + "GTAGGTGA": 14501, + "TAAACACTG": 14502, + "GTCTATCC": 14503, + "GAGAGATGA": 14504, + "TTTGCTTTT": 14505, + "GCTCAAGCCTGTAATCCCAGCACTTTGGGAGGCCGAGACGG": 14506, + "GTTGAAGG": 14507, + "CTGAGAGAA": 14508, + "CATGCTTTT": 14509, + "CGGTCTG": 14510, + "TCTCCACAA": 14511, + "TACACCTA": 14512, + "TTAAGAAAA": 14513, + "TCTAGGGA": 14514, + "TTCACTT": 14515, + "GGAAAGGAA": 14516, + "TAATGTATT": 14517, + "TCACTGGG": 14518, + "TCCCAATG": 14519, + "CCACCCTC": 14520, + "CTCTCCACA": 14521, + "TGTGGGCA": 14522, + "TCAATATCA": 14523, + "GAGGTCAGG": 14524, + "GTAAAGAATCTGCCTG": 14525, + "GGGTCAAA": 14526, + "GCTCTCATT": 14527, + "TGACTTCTT": 14528, + "GTTCAAGA": 14529, + "GAGACAGCA": 14530, + "GATTTGATT": 14531, + "CTCCCAGCC": 14532, + "CTTAGCC": 14533, + "GCGGGAGG": 14534, + "CCCTTATT": 14535, + "GACCAAAAA": 14536, + "GTATACTA": 14537, + "CTCAGATA": 14538, + "CCTTGAAAA": 14539, + "CCTGGAGCA": 14540, + "GTTACGTT": 14541, + "TTTAGGA": 14542, + "TAGACAGAA": 14543, + "CATACTTTT": 14544, + "TGGAGCA": 14545, + "TAAGTCTG": 14546, + "TTAĊ": 14547, + "CAATAAATT": 14548, + "CTGGGAGG": 14549, + "GTGGCTC": 14550, + "GAATTGTAA": 14551, + "CACCCTT": 14552, + "TAACATTTTA": 14553, + "TCCCTCCCAGCA": 14554, + "TAACTTAAA": 14555, + "CAACGGA": 14556, + "GTCTCCTGA": 14557, + "TAAAAATGTA": 14558, + "TATAATAAA": 14559, + "GGATGGATG": 14560, + "TATATGTATG": 14561, + "CACTCATG": 14562, + "CACCAAAAA": 14563, + "CCACCTC": 14564, + "GTATAGTG": 14565, + "TATTCATCA": 14566, + "TCTCTGAGTT": 14567, + "CCCACCCTG": 14568, + "TTATTGTTTT": 14569, + "CTGAAGTGA": 14570, + "CTGTTCCTT": 14571, + "TCAGGGAAATG": 14572, + "CTGCCCGG": 14573, + "GAAATTAGGA": 14574, + "TGTGCCACATTTT": 14575, + "TACAAAAATTA": 14576, + "GGAGTATT": 14577, + "GTAGGGGA": 14578, + "GCCCAGGTT": 14579, + "TCTTGAGTT": 14580, + "CTGAGGGAA": 14581, + "TTATTATTTA": 14582, + "GGGCTCAA": 14583, + "GTATTTAGTCAGCCA": 14584, + "GTTGCCATT": 14585, + "CTAGACCA": 14586, + "CATTGGGG": 14587, + "CATGGATA": 14588, + "TACACTC": 14589, + "TCCATCTCC": 14590, + "TTTTTTTCTTTT": 14591, + "GTGTTGGAA": 14592, + "TACCCTCA": 14593, + "CTACCCTG": 14594, + "GAAACTCC": 14595, + "GTGAGTTCCA": 14596, + "CAAAACTAA": 14597, + "CACACACATT": 14598, + "CTTCCTGAA": 14599, + "TGCTGGAGA": 14600, + "TCCATGAGCATG": 14601, + "TCATATAAA": 14602, + "GAACTTTG": 14603, + "CTGCGGA": 14604, + "GCACTATG": 14605, + "CACAGTTTT": 14606, + "GAAACAGAAA": 14607, + "GTCAGGGTT": 14608, + "GTCCCGCC": 14609, + "TGGATGTT": 14610, + "CTCTGCTTTT": 14611, + "GATGTTAAA": 14612, + "CACGCTA": 14613, + "CCCGTTA": 14614, + "CACATAGG": 14615, + "GTTATCTA": 14616, + "TTTAGGAA": 14617, + "CGCTAGG": 14618, + "TGGAAAAAA": 14619, + "GTACAGTAA": 14620, + "CTATAGCA": 14621, + "TATAGGGAA": 14622, + "TCTTCATTA": 14623, + "CTTTGCTGAA": 14624, + "CACCATTA": 14625, + "GGCGATT": 14626, + "TCGCAGA": 14627, + "GTGTACCA": 14628, + "CTCGGTA": 14629, + "CAGACTTTT": 14630, + "GATGCAAAAA": 14631, + "GAAAACGG": 14632, + "TTTGGCTT": 14633, + "TCTAĊ": 14634, + "TAGATAAAA": 14635, + "GGCATATA": 14636, + "TAATACTTA": 14637, + "CTGTTCATT": 14638, + "TACCCATT": 14639, + "GTCATTAAAA": 14640, + "TAAATATTTTA": 14641, + "TGATCTCC": 14642, + "GTTTAATAA": 14643, + "GGACATC": 14644, + "TAAATCAAAA": 14645, + "CCTGGGTGA": 14646, + "TGTACTCA": 14647, + "TATTATATT": 14648, + "CTCTGGCTG": 14649, + "CTATTCCTT": 14650, + "GAAGGTGAA": 14651, + "CACTTTTAA": 14652, + "GAACCCTT": 14653, + "CCAGCGG": 14654, + "GTGAGATA": 14655, + "TCTCTGAGA": 14656, + "GGGGGGGGGGGGGGGG": 14657, + "TCAGTAAAA": 14658, + "GTGGCTTTT": 14659, + "TGTTCGA": 14660, + "CAAACAATT": 14661, + "GTGACAGAA": 14662, + "GTAAGGGG": 14663, + "TCAGGTTTT": 14664, + "TCAGCAGCA": 14665, + "GTGATAGG": 14666, + "TACAGCTA": 14667, + "CGCGAA": 14668, + "TTTTTTTAATT": 14669, + "GGATTTCTT": 14670, + "CTCCAGCTG": 14671, + "CTCCCTTTT": 14672, + "TTCATGAA": 14673, + "CGCTCGG": 14674, + "CAGCATTTT": 14675, + "CAATATCC": 14676, + "GCCCAGAGA": 14677, + "CACAGTC": 14678, + "GAAATATGA": 14679, + "CATTTAGTT": 14680, + "CCCATAAA": 14681, + "GGGAGAATG": 14682, + "CTCTTCCA": 14683, + "TGAGAAAAAA": 14684, + "GTTCCCTC": 14685, + "TATTTACCA": 14686, + "TCTTTCTAA": 14687, + "CAGACTC": 14688, + "GTAAGCTT": 14689, + "GAAAAGAGG": 14690, + "GACACAAAA": 14691, + "TTTTTTTATA": 14692, + "GAACCCAGGAGGCAGAGGTT": 14693, + "GAGTGAAAAA": 14694, + "CTAGTATT": 14695, + "CTATCTAA": 14696, + "GAAACGAA": 14697, + "TGTTGGTA": 14698, + "GATTTCTTA": 14699, + "CTAAGTTTT": 14700, + "TACACTGAA": 14701, + "TACATAAAAA": 14702, + "CCCAGCCAA": 14703, + "GCTACAAAA": 14704, + "GGCCTATG": 14705, + "TGATTGGTG": 14706, + "GCATTCTTTG": 14707, + "TGTTTGAAAA": 14708, + "GTGATGTTTT": 14709, + "TCATATCC": 14710, + "TCAGAAGA": 14711, + "CTGGTCAA": 14712, + "CATTGTCTT": 14713, + "TCTCTATTA": 14714, + "CTTTGCTT": 14715, + "GTGTTGTG": 14716, + "TATCAAAAA": 14717, + "GCCGAGCA": 14718, + "CAGGTCTA": 14719, + "CCAACATCA": 14720, + "CTTAGCTG": 14721, + "GACAGCCA": 14722, + "GATAGAGG": 14723, + "TTTTTTTAAAA": 14724, + "CGTGCCTG": 14725, + "CAATTTGTT": 14726, + "CGCCCGCC": 14727, + "TAGCTCTA": 14728, + "GCCACCATGTG": 14729, + "CCATATCC": 14730, + "GCCTCTCTG": 14731, + "TGTGTGCC": 14732, + "CAAAGTGAA": 14733, + "CTCACAGCA": 14734, + "GAAAAGTCA": 14735, + "TTTGGCA": 14736, + "TGCATCTT": 14737, + "TGCATCTG": 14738, + "GGCCCTAA": 14739, + 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"CAACAGCAAA": 15150, + "CCAGAGGAA": 15151, + "CGGTGGCTCA": 15152, + "CTTGCACAATTGGTGGCTGACTAAA": 15153, + "GCAGATTTT": 15154, + "GCTTGGCC": 15155, + "CCTTTCAAA": 15156, + "TAATTTCCA": 15157, + "CGCGTCC": 15158, + "TCATTCAA": 15159, + "GGTAGTGA": 15160, + "TACCTATG": 15161, + "CTATTTCAA": 15162, + "GGAGAAAAAA": 15163, + "CCGCAGG": 15164, + "GAGTAATG": 15165, + "GTTCATC": 15166, + "GCCGTTA": 15167, + "CTATGAGA": 15168, + "CTAGACTA": 15169, + "CATTTAGTG": 15170, + "CATGCACTG": 15171, + "GTAAGATA": 15172, + "CTCCTCCTCC": 15173, + "CTGGGTGGG": 15174, + "AAAAAAAAAAAAAAAAAAAAAAA": 15175, + "CCCCAATT": 15176, + "GGTCTCAA": 15177, + "TCCGAGA": 15178, + "GCCTCTTTT": 15179, + "GTCCATTTG": 15180, + "TGAATATAA": 15181, + "GCTTGAAAA": 15182, + "GCTGGAGGA": 15183, + "GCCTGCAGG": 15184, + "TCCCACCTG": 15185, + "CAAACAAACAAA": 15186, + "GCAGACGCTCTTATCCAGAGCGACTTA": 15187, + "GGTCTTAA": 15188, + "GATGTTTA": 15189, + "GCTCTCCTG": 15190, + "GTATACCA": 15191, + "GAAATGCC": 15192, + "TATACCTA": 15193, + 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15235, + "CCGGATT": 15236, + "CCGATTTT": 15237, + "TCGTACA": 15238, + "GCCCCTTA": 15239, + "TGAACAGAA": 15240, + "GTCATAA": 15241, + "CAAAATGGA": 15242, + "TAATACTG": 15243, + "GCAGAGTGA": 15244, + "TGACACAAA": 15245, + "GTGCCAGG": 15246, + "GCAGCAAAA": 15247, + "CGATCCA": 15248, + "CCCCCCAAA": 15249, + "TCAAATGTT": 15250, + "GAATTCAAA": 15251, + "TAATACATG": 15252, + "GTCAATAAA": 15253, + "TTTTCAAACCACATAAATTTTCGTTCGGTTTCATCAAACCCATCCTAATT": 15254, + "TGATATTAA": 15255, + "GAATAGTG": 15256, + "TGTGCTCC": 15257, + "TCTTCAGTG": 15258, + "TGAAGTTTG": 15259, + "TCTGTAATT": 15260, + "GCAAGTCC": 15261, + "CCAAGGCC": 15262, + "TGTAGTTTT": 15263, + "CCGGGCTG": 15264, + "TAAAAAAATTA": 15265, + "TGAGTAAAA": 15266, + "TTAGACA": 15267, + "GCTGCACCA": 15268, + "TATGCACC": 15269, + "CCTGTCCA": 15270, + "TTAATCAA": 15271, + "GTCTTTCTG": 15272, + "TCTTTGGAGG": 15273, + "GTGGCTCACAACCA": 15274, + "CTCCAGTACTCTTGCCTGGAAAATCCCATG": 15275, + "TCATATC": 15276, + "TCATATATT": 15277, + "GAATATATT": 15278, + "CTACCCTT": 15279, + "TAAAGATGA": 15280, + "TAAATAATTA": 15281, + "GCTTTCTTA": 15282, + "GTAAGTAAA": 15283, + "CAAAATACA": 15284, + "GGTTGGAA": 15285, + "GTTTGGGGG": 15286, + "TAGGAAAAA": 15287, + "GCATGAGG": 15288, + "TATACGTA": 15289, + "CCAATGGA": 15290, + "GTCCTCTGA": 15291, + "GGTGGATT": 15292, + "GCGTCCAA": 15293, + "TGACGCC": 15294, + "CTACGC": 15295, + "TAAATCTAA": 15296, + "TGTTGCTA": 15297, + "CATGCGTG": 15298, + "GATGGGCA": 15299, + "GGTATCTG": 15300, + "TAATGCTG": 15301, + "GAACAGCAA": 15302, + "TCTGTCTGTCTG": 15303, + "GGGATTTG": 15304, + "TCGTTCTG": 15305, + "CTTGCATG": 15306, + "GTGCTTGG": 15307, + "CTAGGTCA": 15308, + "GTCGCCA": 15309, + "GAGGTTCA": 15310, + "CCCATAGG": 15311, + "CTGAAGAGA": 15312, + "TACCTCCA": 15313, + "TAGCTACA": 15314, + "CCCTCCAAA": 15315, + "TCGTAGG": 15316, + "GGACTATT": 15317, + "CTCTCATA": 15318, + "TGTTGTGTT": 15319, + "GCTGGAAAA": 15320, + "TCTGTAGATT": 15321, + "TCTCCTGACCTCA": 15322, + "CCAATTGTGCAAGTTCTCCCACTTAAAAA": 15323, + "TTATCCA": 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+ "GACAGTTTT": 15371, + "TACCCAGA": 15372, + "TAACACAAA": 15373, + "TGTGTGTGTGTGTGTGTG": 15374, + "CATCTTAAA": 15375, + "CGCCTGTAATCCCAGCTA": 15376, + "GGGCCAAA": 15377, + "GCGTGTA": 15378, + "TCCCTTGG": 15379, + "TATTGCCA": 15380, + "TGAATCTA": 15381, + "CTCATAA": 15382, + "CCTTGGAAA": 15383, + "CGACAGC": 15384, + "GGTTGTTA": 15385, + "GCAAAGAGA": 15386, + "GCCAAGACCAAA": 15387, + "GGTTATTTT": 15388, + "TCACAGTGA": 15389, + "TAAAATGAAAA": 15390, + "TCCCCAGA": 15391, + "GTACAATG": 15392, + "TCTGTAGAA": 15393, + "GCTGTGATT": 15394, + "TTTTAGAGA": 15395, + "GCCAAGGA": 15396, + "TGATGGCCA": 15397, + "TATCGTGA": 15398, + "TCCCCCTA": 15399, + "CAAGCGATT": 15400, + "GTTGCTGTT": 15401, + "GCCCAATT": 15402, + "TATTGGAAA": 15403, + "CACAGGCC": 15404, + "CCTTCAAAA": 15405, + "GCAAĊ": 15406, + "CCCCCTTA": 15407, + "CCTATCTA": 15408, + "GAATTGTGA": 15409, + "CGCCCACCA": 15410, + "TAATTATTTA": 15411, + "GTTTACAAA": 15412, + "GGAGAATCACTT": 15413, + "GCCCACCAGGCTCC": 15414, + "GAATCATG": 15415, + "CTAGTCTT": 15416, + "TGTTTGCC": 15417, + "TTTGAAATG": 15418, + "TAATGTCA": 15419, + "GAGGTGGAGGTT": 15420, + "TCTCCTGACCTTGTGA": 15421, + "GAGACTGGCTAGGCCACTC": 15422, + "TAGTCCA": 15423, + "GAAGCGG": 15424, + "CCATCAGA": 15425, + "CCCCCTAA": 15426, + "GACCCTAA": 15427, + "TGAATTAAAA": 15428, + "TCGATATA": 15429, + "TGACCTC": 15430, + "GTGAAAATT": 15431, + "TCTTGCCA": 15432, + "CAGGTGTGA": 15433, + "TCAATATAA": 15434, + "CTGACATA": 15435, + "GTAACCTG": 15436, + "CCCAGCTAA": 15437, + "TATGTGAAA": 15438, + "CGTTTTATT": 15439, + "GCTTCTGAA": 15440, + "TACAGAATT": 15441, + "GTCCTGGAA": 15442, + "GGTTTATG": 15443, + "GCTCAGAGG": 15444, + "CTCTGCATT": 15445, + "GAATATTATT": 15446, + "CACACTACGCC": 15447, + "TACGAGG": 15448, + "GACTAAAAA": 15449, + "GAACAGAAA": 15450, + "GCACACTA": 15451, + "CTATTATG": 15452, + "GTCTCAGAA": 15453, + "GCTGCTGTT": 15454, + "GATTTCATT": 15455, + "TACACGCA": 15456, + "TTTGTCAA": 15457, + "TTTGCGA": 15458, + "CTGTGGAGG": 15459, + "GTTTAGAAA": 15460, + 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15506, + "TATACTTAA": 15507, + "GTCACTTTT": 15508, + "GGGGCCAA": 15509, + "TGGGTTTA": 15510, + "GCAAACTG": 15511, + "GGCCCCCA": 15512, + "GCTCAGTC": 15513, + "CGAAGAA": 15514, + "CCCTCAAAA": 15515, + "TCTAAATTA": 15516, + "GCTGGCTGA": 15517, + "CTCACATA": 15518, + "GTAATAGG": 15519, + "CTCCĊ": 15520, + "TGGACAGG": 15521, + "TAATACACA": 15522, + "CTATTGTTTT": 15523, + "TACCATTTT": 15524, + "TAGACGGCAGCC": 15525, + "GTTTCTGTG": 15526, + "TAAACCCTT": 15527, + "TGTTTACAA": 15528, + "CATTCATTA": 15529, + "TCAACTGTG": 15530, + "TACATAGAA": 15531, + "TGGGGCA": 15532, + "CGGAGCTG": 15533, + "CTGCACTCCAGCCTG": 15534, + "GCTGGGTCTT": 15535, + "TGACCAACCTAGA": 15536, + "TAGCTTAA": 15537, + "TGTGCGA": 15538, + "TGAAAAATA": 15539, + "TCAATAGAA": 15540, + "TATGTACAA": 15541, + "TTAATATTA": 15542, + "TGGGGACA": 15543, + "GGTACTTA": 15544, + "CAAAAATATT": 15545, + "CGTATTTA": 15546, + "CCTCCTGA": 15547, + "GATGATTTT": 15548, + "GTACATATA": 15549, + "GTGCTGGGATTACAGGTGTGA": 15550, + "CTGTAAAAA": 15551, + "CCTTGACC": 15552, + "TAGATCTG": 15553, + "CCAAAGCAA": 15554, + "GAGAAGAA": 15555, + "TCGGAGGA": 15556, + "TCACGTGA": 15557, + "TAAAGATTA": 15558, + "GTCGGGA": 15559, + "TGAACTTTT": 15560, + "GGAAGGGGA": 15561, + "TTTTAGTAA": 15562, + "TTTGAAATT": 15563, + "CAATTGATT": 15564, + "GGAGGCCTG": 15565, + "GTTTGCATT": 15566, + "CTTCCCTGA": 15567, + "GCTGAAACTCCAA": 15568, + "GGGTCTCA": 15569, + "TCGCCCTG": 15570, + "GCGTTGA": 15571, + "CAAGCTCC": 15572, + "TGTGGGAAA": 15573, + "TATTGTGTT": 15574, + "TAAAAGTTTT": 15575, + "CCAATACAA": 15576, + "CATTTTTTAA": 15577, + "CTCCAGTGA": 15578, + "TCTTAATTA": 15579, + "TCAGACAGG": 15580, + "GTGCTGGGATT": 15581, + "GCTTTGCGCCTT": 15582, + "CCAAAAAAAA": 15583, + "GGTTTTATT": 15584, + "CATTCAGTT": 15585, + "CCAACAGGA": 15586, + "TGGGTGTT": 15587, + "GTCTTTCTT": 15588, + "CAGTGGAA": 15589, + "TAACATATT": 15590, + "GAACATCTT": 15591, + "GGCACATG": 15592, + "TCACATC": 15593, + "GGAGAAATT": 15594, + "GTCGGTA": 15595, + "GCTGCAGCC": 15596, + "CTCAGCAAA": 15597, + "TCTCCATTA": 15598, + "GTTAGGGG": 15599, + "TGCCCCTT": 15600, + "GTTCATATG": 15601, + "CCATAGCTT": 15602, + "GCGCCTCC": 15603, + "CCAGACTA": 15604, + "GTACCCTT": 15605, + "GTACCTGA": 15606, + "TGAATATTTT": 15607, + "CTAAGGCA": 15608, + "CAAAACAGG": 15609, + "TGTGTGTTTT": 15610, + "GTTATTAAAA": 15611, + "TATTCAGTT": 15612, + "TATTTCATG": 15613, + "CTGAGATTA": 15614, + "GATTGCC": 15615, + "TTAATTATT": 15616, + "CCCATCTA": 15617, + "GACAGTGTG": 15618, + "CCAAACAAA": 15619, + "TAATAGAGA": 15620, + "TGATTTAA": 15621, + "GCCAAGACAA": 15622, + "CGCACGG": 15623, + "GGAGGGAAA": 15624, + "TACCCGG": 15625, + "GAGTTTCAGCTT": 15626, + "GACTTTGTT": 15627, + "CTGGGCAGG": 15628, + "TGACAGAGA": 15629, + "TAAACATGA": 15630, + "TCAAGCCA": 15631, + "TCAACAGCA": 15632, + "GTGAGGCTG": 15633, + "GAGGCTTA": 15634, + "CCCCGGGG": 15635, + "CCAAATGAA": 15636, + "GGTAGTG": 15637, + "CGCCTTA": 15638, + "CCCTCTCC": 15639, + "CTGCCCAGG": 15640, + "GGGTCCTG": 15641, + "GTTCTATG": 15642, + "GTTGAAGA": 15643, + "GTAGATTTT": 15644, + "GCTGTCCTG": 15645, + "GGTGCTT": 15646, + "GCTAGAAAA": 15647, + "TCAAAGGAA": 15648, + "GCAAATGTT": 15649, + "CTGGGTTTG": 15650, + "TAGCTCTGA": 15651, + "CTTTACAAA": 15652, + "GGCGTCC": 15653, + "GCGCCACA": 15654, + "CAATCAAAA": 15655, + "CCACTATG": 15656, + "GTAGGATT": 15657, + "TAAACTATT": 15658, + "GGAAGAAAAA": 15659, + "TAGGCCTT": 15660, + "GGTGTATG": 15661, + "TCAAAGTCA": 15662, + "CTGAAGCAA": 15663, + "TATAAATATA": 15664, + "TCACGTC": 15665, + "CTGTCTAA": 15666, + "GTACCTTA": 15667, + "TCTGCGA": 15668, + "CAGACATGA": 15669, + "CTCATATG": 15670, + "CTAATATAA": 15671, + "CCCCTAA": 15672, + "TTCACTTA": 15673, + "CGCTCTCC": 15674, + "GGAAAGAAAA": 15675, + "GGTACACA": 15676, + "GCTGAGAA": 15677, + "GTTTGGTTTT": 15678, + "CCGGCTA": 15679, + "TCGTCCA": 15680, + "TAAACGTG": 15681, + "TATTCCCTT": 15682, + "CAGAGCCAA": 15683, + "GCTTGGGA": 15684, + "GATGGCCA": 15685, + "GAGTGTGTG": 15686, + "CATGATGAAA": 15687, + "GGAGACGG": 15688, + "TAGTCTCA": 15689, + "TCGGAATT": 15690, + "GTTCTGTA": 15691, + "GAAAGGAAAA": 15692, + "TCTGTATTTT": 15693, + "CCTTTATTTT": 15694, + "TCTAGTCC": 15695, + "TAGAGCC": 15696, + "GGTACAGG": 15697, + "GGTACATG": 15698, + "GTTAATAAA": 15699, + "GGGCAGAGG": 15700, + "TCCCAGAGA": 15701, + "GACGAAAA": 15702, + "TTTTTATAA": 15703, + "CTGTTTTAA": 15704, + "GTTCTCAAA": 15705, + "GTCTCCAGG": 15706, + "TCCATCCATG": 15707, + "TGGGGGA": 15708, + "GGTGTCA": 15709, + "TCAAAGAGA": 15710, + "TGATGGAGA": 15711, + "GCCTGGGA": 15712, + "GAATACAAA": 15713, + "TCCGTTTT": 15714, + "GTTTGAGAA": 15715, + "GCTGCAGC": 15716, + "CTCAGGCTG": 15717, + "CTTTTGGTT": 15718, + "GGGGTGTT": 15719, + "TTCATTTTA": 15720, + "CATAATTTA": 15721, + "TATAAAATG": 15722, + "GGCTCATG": 15723, + "GCTCTCTA": 15724, + "CTTTCAGA": 15725, + "TAAACCTA": 15726, + "TATTCACTT": 15727, + "TCAAGTCC": 15728, + "GCTTTATTA": 15729, + "TCTAGTCAA": 15730, + "TGATTCCATT": 15731, + "TGAGAGGAA": 15732, + "GGTCATTA": 15733, + "CCGGTAA": 15734, + "GTATCAAAA": 15735, + "GAAACCTA": 15736, + "CAGAGTGAA": 15737, + "TATGTATGTG": 15738, + "CCAACTTA": 15739, + "GATAGCA": 15740, + "GATACTCA": 15741, + "GGAAATGGAA": 15742, + "CATCAAATT": 15743, + "TTTTCATTTT": 15744, + "GTAGGCCA": 15745, + "TTTTATATAA": 15746, + "GAAAACAGG": 15747, + "CTTTTTTTTTTTTTTTTTTTT": 15748, + "CAGCCAAAA": 15749, + "TCAATTAAAA": 15750, + "TGGGTTCAAGCAATT": 15751, + "GGGAGAGAGA": 15752, + "GGAAAAAAAAA": 15753, + "CAGACTCC": 15754, + "TATGTAATT": 15755, + "CTTTTGTG": 15756, + "CTAAACAA": 15757, + "CTAAACCA": 15758, + "GGCAGGAAA": 15759, + "GTTGGGC": 15760, + "TAAATAGCA": 15761, + "TGTGTATTTT": 15762, + "TCTGCTTTG": 15763, + "GCTTCATA": 15764, + "TTTTAGTTA": 15765, + "GATGCATA": 15766, + "GGGACTC": 15767, + "GATACTAA": 15768, + "GAGAAGAGG": 15769, + "CGCCCGC": 15770, + "GCATTTGAA": 15771, + "CCCCATTA": 15772, + "TAGAATTAA": 15773, + "CTTCATGTT": 15774, + "GAAATAGTT": 15775, + "TCAATAATT": 15776, + "TAAAACCTT": 15777, + "GCTGTCCA": 15778, + "GTCAGTC": 15779, + "TAGGCCAA": 15780, + "TTTTTTGATT": 15781, + "GGAGATGAA": 15782, + "CCCTTTTTT": 15783, + "GAAGCTTA": 15784, + "CACACAGTA": 15785, + "TACATATATT": 15786, + "GATATTATT": 15787, + "CGCTCAAA": 15788, + "CTGAAGGA": 15789, + "TCTGAGATG": 15790, + "CTGTAAGTC": 15791, + "GTTTAAAAAA": 15792, + "CACATGCC": 15793, + "CACACATAA": 15794, + "TTTTATATTA": 15795, + "TGACCAAAA": 15796, + "GAGACAGAGA": 15797, + "GCTTTCCAA": 15798, + "GTCAGTCC": 15799, + "GATTCTCC": 15800, + "TTTTTTGGTT": 15801, + "CGTGGAA": 15802, + "GGACCTCTG": 15803, + "GAGAGGCTG": 15804, + "GCTTCCAAA": 15805, + "GCTGCAAAA": 15806, + "CGAĊ": 15807, + "CGGATCTCTGTGAGTTCGAGG": 15808, + "CCTCCTCTG": 15809, + "GGATGGAGG": 15810, + "TCTCTTGTGA": 15811, + "CCAGGTGTG": 15812, + "CTTCTCAAA": 15813, + "CTATATATT": 15814, + "TGTTTAAAAA": 15815, + "CCAATTTATTTT": 15816, + "GTTTTGTAA": 15817, + "CTTTTTGTA": 15818, + "GATACGG": 15819, + "AAAAAATAAA": 15820, + "CAATTTCTG": 15821, + "CAAGAGCAA": 15822, + "GGTTTGAAA": 15823, + "CCTCTGCCTCCCGG": 15824, + "TAGAAAATT": 15825, + "GGGTCCTT": 15826, + "TGATAAATT": 15827, + "CTTTCTCTA": 15828, + "TGTTGCCA": 15829, + "CTTACAGAA": 15830, + "GAAAATACA": 15831, + "TTTAGCAA": 15832, + "GGTGGTAA": 15833, + "GTGTTTGAA": 15834, + "TGTGTGTGAA": 15835, + "CCAGACATG": 15836, + "TAGGTTAAA": 15837, + "TACTTATTTTCCACCATAATTTG": 15838, + "CAAGCAAAA": 15839, + "CTGGATTTT": 15840, + "CTCCAGGTT": 15841, + "CAGAGCTCA": 15842, + "GGAAGTAAA": 15843, + "CTTACAAAA": 15844, + "TCTATCAAA": 15845, + "TGGATATG": 15846, + "TCATTGATT": 15847, + "CGTAGTGTGTTA": 15848, + "TGAGACTGA": 15849, + "CCAGGTTTG": 15850, + "GGCCAATT": 15851, + "TAATGATGA": 15852, + "TCCCTCTA": 15853, + "CAAGCTAA": 15854, + "GTTCTTCC": 15855, + "CTAGCTGA": 15856, + "CAAAGAGCA": 15857, + "GGAACATA": 15858, + "GATGCCAAA": 15859, + "TGGATATA": 15860, + "TCCTGTCTG": 15861, + "GAGCTAGG": 15862, + "CCACAGCTG": 15863, + "TCCCCGG": 15864, + "GTACAGAAA": 15865, + "TCTTTACAA": 15866, + "GGAACTTTT": 15867, + "CTCATCTCA": 15868, + "CATGGTTTT": 15869, + "CGATACC": 15870, + "TTTATCTG": 15871, + "GGTTGGGA": 15872, + "CGCĊ": 15873, + "TCATTTATG": 15874, + "GAATTCCTGA": 15875, + "GGCCGAGG": 15876, + "GTTTCATTA": 15877, + "GAATAGAAA": 15878, + "GCTAATTTTTGTA": 15879, + "GTCTCGCTCTGTCGCCCAGGCTGGAGTGCAGTG": 15880, + "GGTTAATG": 15881, + "GAACCCAGG": 15882, + "CAATATC": 15883, + "CTGCAATG": 15884, + "GTGGAGAAA": 15885, + "GTTTCCATG": 15886, + "CTACGCA": 15887, + "GAAAAGCTG": 15888, + "TCTATAATT": 15889, + "GTCCAAATT": 15890, + "TAGACCTT": 15891, + "GGTGGCTCA": 15892, + "GCCTTCCTT": 15893, + "GGGCTCTT": 15894, + "CCTGTAATCCCAGCTACTTGGGAGG": 15895, + "TGGGTTTGAAGAAATTAGGA": 15896, + "GGTGAATG": 15897, + "TGAGAAATT": 15898, + "CAACTGTG": 15899, + "CAGACACAA": 15900, + "GTAATAAAAA": 15901, + "TCCACTGAA": 15902, + "TTTACAAAA": 15903, + "GGTTGTCA": 15904, + "CCTATAAAA": 15905, + "GTAAATAA": 15906, + "CACCATGGAATACTA": 15907, + "GACTATTTT": 15908, + "CTCCACCCC": 15909, + "GAAGAGCTG": 15910, + "CTCATTGGAAAA": 15911, + "GAGATTTCAA": 15912, + "CTCGGGAGGCAGAGCCAGG": 15913, + "GAAGTCTG": 15914, + "CTTCTTTTA": 15915, + "GCAAATAAA": 15916, + "TCCTTGTA": 15917, + "GTATTTCA": 15918, + "GAGCTATA": 15919, + "GGAGAATCGCTT": 15920, + "TAACATATA": 15921, + "GGACACAAA": 15922, + "TGATATC": 15923, + "CTGCCTC": 15924, + "GCCGACTA": 15925, + "CTAGACTG": 15926, + "CAGGTCC": 15927, + "GAGGAAATG": 15928, + "TAATTGCA": 15929, + "GTAAATAAAA": 15930, + "CAACCCTGA": 15931, + "CAGCTGGG": 15932, + "TGAGTGGG": 15933, + "GAGAGCCA": 15934, + "CAGGGCCA": 15935, + "GCAAGACC": 15936, + "GAGGTATG": 15937, + "TCAAATATG": 15938, + "CGCAAATG": 15939, + "TCACCTGAA": 15940, + "TGATATTTTA": 15941, + "CGTCAATT": 15942, + "GAACCTGGGAGGCAGAGGTT": 15943, + "GAGTTATG": 15944, + "TTTTTACA": 15945, + "CTGCATC": 15946, + "GTTCTCCTT": 15947, + "GCTTGGCA": 15948, + "CATGCAAAA": 15949, + "GTTTTCCTG": 15950, + "CGTGCGG": 15951, + "TGATGGATG": 15952, + "CAGTTTCTT": 15953, + "CTCCAGTCA": 15954, + "CTTTATATGCC": 15955, + "GCCCAGGCTGGAGTG": 15956, + "CTCTCAGAA": 15957, + "CATTCATCA": 15958, + "TTTGTCTG": 15959, + "TCTAGAGAA": 15960, + "CTTTTCTCC": 15961, + "CGCTTTTA": 15962, + "CGCGATG": 15963, + "GCTGAGGA": 15964, + "CTGTGGCTG": 15965, + "TATCATTAA": 15966, + "TGATGAAAATTACAGGCCTCTC": 15967, + "CTGGTCTG": 15968, + "CTTCTCTGA": 15969, + "GCAGAAGAA": 15970, + "AAAATATG": 15971, + "GTGAĊ": 15972, + "GTTACAGAA": 15973, + "TCTTTTTATT": 15974, + "GCGAAATT": 15975, + "TTACCAAA": 15976, + "CTACAATG": 15977, + "GTCTGTGAA": 15978, + "CATTTAATA": 15979, + "TCAAGCTA": 15980, + "TCTCTAGTG": 15981, + "TAGGTTCA": 15982, + "GGGACTT": 15983, + "CGTGTGCA": 15984, + "TGATTGTAA": 15985, + "GAGCCTCTT": 15986, + "CATTGGCA": 15987, + "GTGAGAGAA": 15988, + "TCTCCTGGA": 15989, + "TTTTTTTCTG": 15990, + "CCTGAGGAA": 15991, + "GCAGACACA": 15992, + "TCGGCCTCCCAAAGTGCTGGGATTACAGGCGTGA": 15993, + "TCACTGAAA": 15994, + "TGTTTCTAA": 15995, + "CAAACTATT": 15996, + "GCTTCGA": 15997, + "CTCAGAGCA": 15998, + "CCCATAA": 15999, + "TAGGCCTG": 16000, + "GACCCTCC": 16001, + "TGAGATCTG": 16002, + "GAATAGCAA": 16003, + "TCATCATTA": 16004, + "TCCCATC": 16005, + "TAAAGGCC": 16006, + "TGTGCAGAA": 16007, + "TGTGTATGA": 16008, + "GTGAGCTA": 16009, + "GTTACGA": 16010, + "GAATTCTGA": 16011, + "TGATTCAGA": 16012, + "GAAAAAGAA": 16013, + "GAAGATATG": 16014, + "CAACAGTAA": 16015, + "CAACAAATG": 16016, + "CCAGCAGCA": 16017, + "GGGGCACA": 16018, + "CTCTTCTGA": 16019, + "CTCTGGGA": 16020, + "TGATGGTA": 16021, + "GGGAATTA": 16022, + "TACAAAATT": 16023, + "TCGAAATG": 16024, + "CAACTTAAA": 16025, + "CCACAGAAA": 16026, + "CCTTAAATG": 16027, + "GCCACCCTG": 16028, + "CTTTTCATG": 16029, + "CAGCCTTA": 16030, + "GAACATGAA": 16031, + "CTGGCATG": 16032, + "GCATCGG": 16033, + "GCACTTGG": 16034, + "GAAACATCA": 16035, + "GGAACTC": 16036, + "GCTTTACA": 16037, + "GAGGGCC": 16038, + "GAGGTCTT": 16039, + "TTTGCCTG": 16040, + "TAGAĊ": 16041, + "TGTACCTG": 16042, + "CCTACTCC": 16043, + "CGCACCAA": 16044, + "CAATTCAAA": 16045, + "TAGCTCCTT": 16046, + "GGGGAGGGA": 16047, + "CCACACTGA": 16048, + "GCGTTGTT": 16049, + "CTGTATCC": 16050, + "TATATTATTA": 16051, + "CGGGTCC": 16052, + "CATATCCA": 16053, + "TCAAATCAA": 16054, + "CTGTTTATT": 16055, + "TGATGGTTTCCA": 16056, + "GATATGAAA": 16057, + "GTTCCAAAA": 16058, + "TCTGCTCTT": 16059, + "TAAAATAAAAAA": 16060, + "GCTGTCTC": 16061, + "GAAAAGAGAA": 16062, + "GCCAGCTA": 16063, + "GGTACCTG": 16064, + "CAGTTGAA": 16065, + "TCATATTTTA": 16066, + "CCACACTCA": 16067, + "CTTAAAAAAA": 16068, + "TAAATACCA": 16069, + "GAAATCATT": 16070, + "TGTTGCCTT": 16071, + "CCTTTCTCC": 16072, + "TTATTTACA": 16073, + "GAATTCATT": 16074, + "GCCTGGAA": 16075, + "GATAAAATG": 16076, + "CTTTCTTAA": 16077, + "CCAATCC": 16078, + "CTAATATTTT": 16079, + "TTTGGTAA": 16080, + "CTAAACATG": 16081, + "CTAAAGCAA": 16082, + "TCCTGGGA": 16083, + "CAATATTTTA": 16084, + "CCTCTCCTT": 16085, + "GGTCGGA": 16086, + "TCGTAGA": 16087, + "TCACCTC": 16088, + "CAATAGGA": 16089, + "TCTTGGAAA": 16090, + "TCTTTAGTT": 16091, + "TAAAACTTTT": 16092, + "CTGACAGAA": 16093, + "TATGGGGA": 16094, + "GATTTACC": 16095, + "CTCCTATT": 16096, + "TCCAGCTCA": 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+ "TGTAGCAA": 16143, + "GTTAACAAA": 16144, + "CACAATTTT": 16145, + "GATGTGAAA": 16146, + "GTCGCTA": 16147, + "CTTATATTA": 16148, + "CAAAAGGA": 16149, + "GAATTTAA": 16150, + "CTGGGTTTT": 16151, + "CAAGGCAGG": 16152, + "TTACAGTTA": 16153, + "CCTGTAATCCCAGCTACTCGGGAGG": 16154, + "GATAGGTT": 16155, + "CTGAAAATT": 16156, + "GTTCTGAAA": 16157, + "CCATCCCTG": 16158, + "GCCTAGTA": 16159, + "GCCCCAGA": 16160, + "GCAAGGCC": 16161, + "CCAAĊ": 16162, + "CTCCTACC": 16163, + "CCCCCTTTT": 16164, + "CGCGATT": 16165, + "GACCCATT": 16166, + "GAGGAGGA": 16167, + "CCTCCTTTT": 16168, + "GTATATTTTA": 16169, + "GCCCATCTG": 16170, + "GGCCTTTG": 16171, + "GCTCTCCAA": 16172, + "GTTTCTTTG": 16173, + "GCAGCTTTT": 16174, + "CTCTAAATT": 16175, + "CGGCGTC": 16176, + "GGGGCAGGA": 16177, + "TGTACAGA": 16178, + "TCTCCCCTT": 16179, + "GAAAACATA": 16180, + "TCTCAGGTG": 16181, + "GTGTGGACA": 16182, + "TAGCTCCA": 16183, + "CTTCAGTCA": 16184, + "CACACACCTG": 16185, + "GTTTGTGTT": 16186, + "GTTTCCACA": 16187, + "CTAGATCA": 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16370, + "CTTTGTGATGTGTG": 16371, + "GGTATATG": 16372, + "TCGTATTA": 16373, + "GATCGGA": 16374, + "TGTTTCTC": 16375, + "CAGGCATGA": 16376, + "TCTGGCTTA": 16377, + "CTGAGACC": 16378, + "TCTCACTCA": 16379, + "GGAGGGATG": 16380, + "GACTATTA": 16381, + "GGAGATGTG": 16382, + "GGAGATATA": 16383, + "GTGCAGTGA": 16384, + "CTCATGTCCATT": 16385, + "CCGCGC": 16386, + "TAGTCTTA": 16387, + "TAAAAGGAAA": 16388, + "CTCCTATA": 16389, + "TCTAGCCA": 16390, + "TGGACTAA": 16391, + "TCTCAGTAA": 16392, + "CTGAAATTA": 16393, + "GCAGAGCTT": 16394, + "GAACATCTG": 16395, + "TCAGAATTA": 16396, + "GAAGTAGTGATT": 16397, + "CCTATATG": 16398, + "GAAGAAATT": 16399, + "CTGTAGC": 16400, + "GTGTACTA": 16401, + "GTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 16402, + "GTCTATAAA": 16403, + "TGTGCACCA": 16404, + "GCAATTTTA": 16405, + "CCAAGGCAA": 16406, + "TACACTAAA": 16407, + "TTCATATA": 16408, + "GCAAATGGG": 16409, + "GGCCTCAA": 16410, + "GAAGGCAAA": 16411, + "AAATACAAA": 16412, + "CCCTCATG": 16413, + "TAATGACA": 16414, + "TTTTATCAAA": 16415, + "TTAACTAA": 16416, + "TGTAGGAA": 16417, + "CCTACCCTG": 16418, + "GCAAACTAA": 16419, + "TCATTCAAA": 16420, + "TAAATTTATT": 16421, + "CAGTCGTGTCCGACTC": 16422, + "TCAGAAAAAA": 16423, + "TCACCAGG": 16424, + "GCCCCCTC": 16425, + "CACATCTTAAA": 16426, + "TCTTCTTCTT": 16427, + "GCCTGGGAA": 16428, + "TATATATATATATATATATATATATATA": 16429, + "TGACAGAGCGAGA": 16430, + "CATCTGAAA": 16431, + "GTCACGTG": 16432, + "CCCACCACCA": 16433, + "GGTGTATA": 16434, + "CGCGGTT": 16435, + "CTGAAGATG": 16436, + "CGTTCAA": 16437, + "CAATTTGAA": 16438, + "CTATTTGTT": 16439, + "CTTTACATT": 16440, + "TAACATAAA": 16441, + "CCGAAGG": 16442, + "TAAGGGGA": 16443, + "GTACCCTG": 16444, + "TCTGCAGTG": 16445, + "GATTCGA": 16446, + "CTTTTTATTA": 16447, + "GGTGCCTT": 16448, + "GGTACGG": 16449, + "CATGTGAAA": 16450, + "GCAGAGACATTACTTTG": 16451, + "GGATTTTAA": 16452, + "CCACCCTA": 16453, + "CACACCCC": 16454, + "TCAACGTT": 16455, + "TGTGTTTGTG": 16456, + "GAAAATGAAAA": 16457, + "CGAGAGAGA": 16458, + "GGTCGTT": 16459, + "TGACTGGG": 16460, + "CAACTAAAA": 16461, + "GTGTATATT": 16462, + "CTCGAAAA": 16463, + "GAGATAAAA": 16464, + "GAGAGAGTG": 16465, + "CCTGGCAAA": 16466, + "GTTACAGCA": 16467, + "TGGGGTCA": 16468, + "GAATTCCTT": 16469, + "CTAAAGAGCTT": 16470, + "GCTGAGATG": 16471, + "GCATTTCA": 16472, + "GAGCTATT": 16473, + "CTCCAGCCA": 16474, + "TCCCACCTT": 16475, + "CTTCAGTGA": 16476, + "GCACAGCTG": 16477, + "GGCGTGTG": 16478, + "GCGTAGG": 16479, + "GTGCTTCTT": 16480, + "GTTGCATG": 16481, + "GCAGCCATG": 16482, + "CTCTGATA": 16483, + "GGAATACA": 16484, + "CTCACACC": 16485, + "CCAATGAAA": 16486, + "GGGGCATG": 16487, + "TTTAGATG": 16488, + "GGTGTCC": 16489, + "TGCACGG": 16490, + "GATACGA": 16491, + "TCCTTGCTT": 16492, + "CTGCAGCAA": 16493, + "GCTCCAAAAA": 16494, + "GGGTCCA": 16495, + "GGACATCC": 16496, + "CAAACACCA": 16497, + "CATTGCTA": 16498, + "TGAATACTT": 16499, + "TGCATCA": 16500, + "TAATATGTG": 16501, + "GTGTTCTA": 16502, + "CAATTCCA": 16503, + "TCCTGCAGG": 16504, + "TCAGGCCTT": 16505, + "TATTTACTTA": 16506, + "TCGTATTTT": 16507, + "GTTCTAAAA": 16508, + "GTCAAAATG": 16509, + "CAGAGCGAGA": 16510, + "CCTGTCTA": 16511, + "TGTACTGA": 16512, + "CCTACCTCA": 16513, + "GAATATTTTA": 16514, + "CAATAGAAA": 16515, + "CGTCCAAA": 16516, + "TCAATGAAAA": 16517, + "CCCTAACCCTAACCCTAACCCTAA": 16518, + "GGTCCTA": 16519, + "GAATGTGAA": 16520, + "GTTGTTTTTT": 16521, + "TAAATCTGA": 16522, + "TAAAACAAAAA": 16523, + "CAAAATATAA": 16524, + "TCAAAGCAA": 16525, + "CCATTTCTG": 16526, + "CAGCCTTATT": 16527, + "CCAGGCTGA": 16528, + "CTTCCCTCC": 16529, + "GGATTCTCCAGGCAAGAACACTGGAGTGGGTTGCCATTTCCTT": 16530, + "GTAGACCA": 16531, + "GTCACAGCA": 16532, + "TACACGTA": 16533, + "TTATTTCATT": 16534, + "GTAAACACA": 16535, + "CTGTTCTGA": 16536, + "GCTAATTAA": 16537, + "GATAATTTG": 16538, + "TAACAGCAA": 16539, + "TAATTTTCATCATAGGTACA": 16540, + "TCCAAAATG": 16541, + "CTAGCCAA": 16542, + "TGTGGTC": 16543, + "TATACAAAAA": 16544, + "CAAATAGTT": 16545, + "TCAATATGA": 16546, + "CCAACAGTG": 16547, + "TTATTTCCA": 16548, + "TTCACTGA": 16549, + "GGCCTCCA": 16550, + "CTCAATTTG": 16551, + "GCCCAGCTG": 16552, + "TAACGCC": 16553, + "GCCCCACC": 16554, + "GTCAGGTA": 16555, + "CCTGCAGAA": 16556, + "GTTACTTAA": 16557, + "GGTTCATT": 16558, + "TGCATAAA": 16559, + "TAATACTT": 16560, + "TGATTCAA": 16561, + "TGATTCTAA": 16562, + "CTCTTCTCC": 16563, + "TCATTAATT": 16564, + "CTGCCTTTT": 16565, + "CCTGGACC": 16566, + "TACACCCA": 16567, + "CTTACGA": 16568, + "GTATTTAAA": 16569, + "TTATATGTT": 16570, + "GTTAAATTA": 16571, + "TGAGAAGAA": 16572, + "GAATAATAA": 16573, + "CAAACATTTT": 16574, + "CATTGCCA": 16575, + "GCTGGGGAGG": 16576, + "GCAAGGTA": 16577, + "CTCCCAGGA": 16578, + "CCCCTAGA": 16579, + "TCTCAGTA": 16580, + "GTGAAGAGA": 16581, + "GCTGAGAAA": 16582, + "GTGGGCAGG": 16583, + "CTCCACTGA": 16584, + "CACTGTTGGTG": 16585, + "TCAGCTTAA": 16586, + "GCAGCCCC": 16587, + "CTCTGTTAA": 16588, + "TCTTGTCAA": 16589, + "GCTGAAATG": 16590, + "GATTĊ": 16591, + "TTAACTT": 16592, + "TAGGGTGA": 16593, + "GGGATAAA": 16594, + "GCTCAGCAA": 16595, + "TATGTTAAA": 16596, + "GCGCAAAA": 16597, + "TCCTCCAGG": 16598, + "CTGGAAGG": 16599, + "GCATCAGG": 16600, + "TATTCACC": 16601, + "GTTACCTA": 16602, + "TAGGCGG": 16603, + "TGAGGGTGA": 16604, + "TCTTTGTTA": 16605, + "CCTGTGAAA": 16606, + "GGGAGAGGGAGA": 16607, + "TAGTAAAAA": 16608, + "TGATCAAAA": 16609, + "TATTCAGTA": 16610, + "CACACCATT": 16611, + "GTCAGTGTT": 16612, + "GTAACAGAA": 16613, + "CCTGCACC": 16614, + "CCAATCTG": 16615, + "GGCACCTT": 16616, + "CAATTCCTT": 16617, + "CTTCACTTA": 16618, + "CATAAAATG": 16619, + "TCATCAGG": 16620, + "GAAACATTA": 16621, + "CATTTATGA": 16622, + "CAGACGC": 16623, + "GCAACACC": 16624, + "TTTTACATG": 16625, + "CTTAATAAA": 16626, + "TGAGACAAA": 16627, + "CTGTAGCC": 16628, + "GGTCAATT": 16629, + "TAATCATA": 16630, + "CTGCCTCTG": 16631, + "TGTTTACC": 16632, + "CATTGTATT": 16633, + "CAGAGAATG": 16634, + "TCAAGACAA": 16635, + "TTTTATCC": 16636, + "TTTTACTAA": 16637, + "GCCACGCA": 16638, + "GGGGCCTA": 16639, + "TAGACGA": 16640, + "CATAĊ": 16641, + "TCTCAGTG": 16642, + "CTGAACACA": 16643, + "GCCTGGAGA": 16644, + "CATAATTTG": 16645, + "CATATGAAA": 16646, + "TACCTTAA": 16647, + "TAACCTTTT": 16648, + "GCAGCTCTG": 16649, + "GCAGCCACA": 16650, + "CCAGAGAAA": 16651, + "CTAGCATT": 16652, + "CAGATGCTG": 16653, + "TCAAGTAAA": 16654, + "CTGAGTC": 16655, + "CTCACCCC": 16656, + "GATTCGAA": 16657, + "CCAACCAAA": 16658, + "CTTATGGA": 16659, + "CGCGACC": 16660, + "TGAAATGAA": 16661, + "GGCCGCC": 16662, + "GCTCAGGA": 16663, + "CAAAAAAAAAAAA": 16664, + "TACTTTTAA": 16665, + "TGATTTTTTTT": 16666, + "CAAGTAAAA": 16667, + "GCTTGTCC": 16668, + "CTCAĊ": 16669, + "GTCAGGCTG": 16670, + "TCTCCATAA": 16671, + "GGAGGGTGA": 16672, + "CAGTGTGTG": 16673, + "GCCTGGCTG": 16674, + "GTGAAATTA": 16675, + "TATTTGCAAATTATGGTGGAAAATAAGTATTTG": 16676, + "TAACGTTA": 16677, + "TCACTAAAA": 16678, + "TGACAGTC": 16679, + "TCCCTATG": 16680, + "CAATCCTG": 16681, + "CTAGTCAA": 16682, + "TATTAAATA": 16683, + "CAGGCACC": 16684, + "GTAAAAATA": 16685, + "GGGGGCAA": 16686, + "TAGACAAAA": 16687, + "TCTCAGGAA": 16688, + "GGCCCTCC": 16689, + "CTGTTCATG": 16690, + "CTTTGCTGA": 16691, + "TAACATGAA": 16692, + "TCTGCAAGTGGATATTTGGA": 16693, + "GTGGGAGGA": 16694, + "GGACAGAGA": 16695, + "CCATCTTTT": 16696, + "CACACAGGA": 16697, + "CAGGCAGGA": 16698, + "CCTGCAGGA": 16699, + "GTTTTCCAA": 16700, + "GTTTTTGTA": 16701, + "GGTTGAAA": 16702, + "TCTCATCTT": 16703, + "GATAGAAAA": 16704, + "GCATTCTGA": 16705, + "TCCCATTTG": 16706, + "GCAGCTGTG": 16707, + "GTCTCCCAA": 16708, + "TATTTCCTG": 16709, + "GAGAGGTA": 16710, + "GGAACACAA": 16711, + "CCTGĊ": 16712, + "TCCATCAAA": 16713, + "GAATTGATT": 16714, + "GCTGATTTG": 16715, + "GGGAAGCC": 16716, + "CACAAAAAAA": 16717, + "GGATAATA": 16718, + "TAAATCCAA": 16719, + "CAGGAAATT": 16720, + "CCTGAAATT": 16721, + "GTCCCGA": 16722, + "TGGGGCTT": 16723, + "GATAGTTA": 16724, + "GGAAACAAA": 16725, + "GTTTGGATT": 16726, + "TGAGATGA": 16727, + "GACAATTTT": 16728, + "CCCTCTCCA": 16729, + "TATTTTAATG": 16730, + "GCAGCGTG": 16731, + "TCTTCGTT": 16732, + "CATTGTGTT": 16733, + "CGGGCGG": 16734, + "TAGACTTTT": 16735, + "GGGATAGG": 16736, + "GTATTTGTG": 16737, + "CCATTCTA": 16738, + "TAATGCAAA": 16739, + "CCTGACCCAGG": 16740, + "TTATACACA": 16741, + "CAATACAAA": 16742, + "TAATCCCAGTA": 16743, + "CGCCACCACCGCCCGG": 16744, + "TAGCCCA": 16745, + "GCGCCCTG": 16746, + "TAACAGGAA": 16747, + "TCAGAAATT": 16748, + "TAAAGACC": 16749, + "CTCTATATT": 16750, + "GTTTTTATTA": 16751, + "CCCAGCTGA": 16752, + "TGAAAGAAA": 16753, + "TAATATCTA": 16754, + "CTGTTGAA": 16755, + "CTTTGGTG": 16756, + "TGAGGCTGA": 16757, + "CCATGCTGTTTT": 16758, + "GCAGCATCATG": 16759, + "CCACATCTG": 16760, + "GGTCCTGGAGTGGCCTAGCCAGTCTCCAGA": 16761, + "CCCCTTAA": 16762, + "GAACTTTAA": 16763, + "CCAGCTCTG": 16764, + "CATTCAGCA": 16765, + "TATGCGG": 16766, + "TCCATACAA": 16767, + "GGTGCAGA": 16768, + "TGTATTATT": 16769, + "GTGAATGTT": 16770, + "CTCTGGCTT": 16771, + "GAAAAATGAA": 16772, + "CTGTGCTGA": 16773, + "CTGTATGTT": 16774, + 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16820, + "TCAGCTC": 16821, + "CACAGTAAA": 16822, + "CAGGCCCC": 16823, + "TCCACCTCA": 16824, + "TGGGCAAA": 16825, + "GGTATGAA": 16826, + "GGATTGCAA": 16827, + "CGTCTTCA": 16828, + "GGAGAGAAAA": 16829, + "TGACTTCC": 16830, + "CTTTCTGTG": 16831, + "GTGGCTGAA": 16832, + "GCCGGCTG": 16833, + "TATTTAAATT": 16834, + "TAATTAAAAAA": 16835, + "GCAAAGCAA": 16836, + "GTATTTCAA": 16837, + "GTGTTTATA": 16838, + "GTTTGCTG": 16839, + "CATTTTATG": 16840, + "GCTTTTATTTT": 16841, + "CTCGGAAA": 16842, + "TCTTCACTG": 16843, + "CAGGCTAA": 16844, + "CAGACAGGA": 16845, + "GGAATCAAA": 16846, + "GCTTGTC": 16847, + "GTGATGCTG": 16848, + "CGCGCGG": 16849, + "CTCTTCAA": 16850, + "CTTTGCAAA": 16851, + "TACCTAA": 16852, + "CGGAGGTG": 16853, + "GGAGACAAA": 16854, + "TCCTCCCACCTCAGCCTCC": 16855, + "CCGAGTA": 16856, + "GTGTTTTTG": 16857, + "GGAAGAATT": 16858, + "GTAACTCC": 16859, + "TAAAAAGAA": 16860, + "CACCAGGCC": 16861, + "CAACCTTA": 16862, + "GTTCTTAAA": 16863, + "GCATGAGA": 16864, + "GGAACGA": 16865, + "TACATATCA": 16866, + "GTTTTTCTA": 16867, + "CCCATCTC": 16868, + "TAGGCCTA": 16869, + "TCTCACGA": 16870, + "GATACAAAA": 16871, + "TCCTTGAGA": 16872, + "CAGAAGAAA": 16873, + "GATTTTTTTTTT": 16874, + "TTATAGAAA": 16875, + "TCTCCAATG": 16876, + "CACTAATG": 16877, + "CCGGTTA": 16878, + "TAGTGATG": 16879, + "GGGTGATT": 16880, + "TTAGGAAA": 16881, + "GTTTGAATT": 16882, + "CTAGGGCA": 16883, + "TATTGTCAA": 16884, + "TCAAATGAAA": 16885, + "CACCCGA": 16886, + "CTGTTTCTA": 16887, + "CTCTTTGTT": 16888, + "CCATTTCC": 16889, + "GGGAAGAGG": 16890, + "TACTCTTA": 16891, + "GAAGTGTG": 16892, + "CTACCCAA": 16893, + "CTACCTTA": 16894, + "CATTCGTT": 16895, + "TCTGCACTG": 16896, + "CCTGCCTA": 16897, + "TTTGCCA": 16898, + "TGGGCGG": 16899, + "GTGAAGAGG": 16900, + "CGGATTTT": 16901, + "CTGGGCTGA": 16902, + "GTTGTGGGTT": 16903, + "TAGTAATG": 16904, + "GGAGCTGAA": 16905, + "GAGAGAAAAA": 16906, + "CATGCTCC": 16907, + "GATTTATAA": 16908, + "GTCCTGAAA": 16909, + "CTTTTGGAA": 16910, + "TGGGCTGA": 16911, + "GATAGTG": 16912, + "GGTAGTTA": 16913, + "GTGTTCCA": 16914, + "CAGCĊ": 16915, + "GGCTCTTA": 16916, + "CAATTAATT": 16917, + "GTACCCAA": 16918, + "GCCCAAGG": 16919, + "CTACCATG": 16920, + "TATTGAATT": 16921, + "TATTCAGCA": 16922, + "CACACTGAA": 16923, + "TATAGGTG": 16924, + "GAGACACCA": 16925, + "CAGGCCAAA": 16926, + "CTTAGGTG": 16927, + "GTTTTGTCA": 16928, + "CATATTTTTT": 16929, + "TGTAGGA": 16930, + "TCAAACAGA": 16931, + "TGATTCAAA": 16932, + "CAACAACAA": 16933, + "TCTTGGCTT": 16934, + "CTAAĊ": 16935, + "GGGGGTGGG": 16936, + "TGGGGAAAA": 16937, + "TCCAAGATG": 16938, + "CTCTGCTT": 16939, + "CTGCAGCCA": 16940, + "TCCTGATA": 16941, + "GCCTCGA": 16942, + "TATTGGGA": 16943, + "CTGAGGCAA": 16944, + "TTCACGG": 16945, + "CAATTCTG": 16946, + "CCATTCCA": 16947, + "CGGAGGA": 16948, + "TCATGTCA": 16949, + "GAATACATT": 16950, + "GTAGGTTTT": 16951, + "TTAGTAGAGA": 16952, + "CTCTTAATT": 16953, + "GAAAGCATT": 16954, + "CAGAGTAAA": 16955, + "GCTGCAGTT": 16956, + "CCAACAGAA": 16957, + "TACACGTT": 16958, + "TACATATATATA": 16959, + "GTTTTTATCA": 16960, + "CGACGTC": 16961, + "CCAAATTTA": 16962, + "TGATTTCTA": 16963, + "CTGTGGTCA": 16964, + "GCAGGGATG": 16965, + "GAGTCTTA": 16966, + "GGAGTGCAGTG": 16967, + "CATAGGTT": 16968, + "CCTTĊ": 16969, + "CCTTGTCTT": 16970, + "TTTGCTCA": 16971, + "GACAGCAAA": 16972, + "TGGAGCAA": 16973, + "TCTGAGTTA": 16974, + "CTTAAGCCA": 16975, + "GTTAAGAGCA": 16976, + "TCCTGGAGA": 16977, + "CTTTATGAA": 16978, + "GAAATACCATT": 16979, + "CAAGAAATT": 16980, + "GGACCTCC": 16981, + "CTAGGGTG": 16982, + "CCTGGGGAA": 16983, + "CCAAGAGAA": 16984, + "CGTGGCC": 16985, + "TCATTGCTT": 16986, + "TCTTTTTTTTTTTTTT": 16987, + "TAAGACAAA": 16988, + "TAACAATTA": 16989, + "GTTCAACA": 16990, + "TGTGGGTA": 16991, + "GAAATACTG": 16992, + "CCTTGAATT": 16993, + "CTAATCTA": 16994, + "CTTAGGGA": 16995, + "CTTTGGCTT": 16996, + "TCTTTTTTTTTTTTTTTT": 16997, + "TAATGTCTG": 16998, + "CTATTTCTG": 16999, + "GCTCTAAAA": 17000, + "GCGCTCTT": 17001, + "TCCTĊ": 17002, + "CTTGAAGG": 17003, + "GCACCAAAA": 17004, + "GTATGATG": 17005, + "CATTTACCA": 17006, + "TTAACACA": 17007, + "GAAAAGTGA": 17008, + "TCAAACTG": 17009, + "TATTACTA": 17010, + "CCATTTCCTT": 17011, + "CTGTGGTG": 17012, + "GAATGGAATG": 17013, + "GGATGCTG": 17014, + "CACTATTTT": 17015, + "TCACAGTAA": 17016, + "GCGCGATCTCGGCTCACTGCAA": 17017, + "GCATTCTCAGAAACTT": 17018, + "TGATTAATT": 17019, + "CCACGCTG": 17020, + "GCCTGAATG": 17021, + "CTAGACAGAA": 17022, + "CATGGCAAA": 17023, + "GTCAGTAAA": 17024, + "GCCACCTA": 17025, + "CTTTTTCCA": 17026, + "CTTTTGGTG": 17027, + "TGCATCAA": 17028, + "TCTCCCTGA": 17029, + "TATAATTTTA": 17030, + "TACCTATT": 17031, + "GGTCGC": 17032, + "TCCTGACTG": 17033, + "TGTTGTAAA": 17034, + "GGAAGACAA": 17035, + "GCTTTGCTG": 17036, + "GCTGCATCA": 17037, + "GCTAGCC": 17038, + "TCAAAGTAA": 17039, + "TCTCAGTGA": 17040, + "AAAAAATAAAA": 17041, + "GCAACCCACTCCAGTATT": 17042, + "GGATGAGATGGCTGGATG": 17043, + "GCTCTGGATAAGAGCGTCTGCTAAATGA": 17044, + "CTCGGCCA": 17045, + "CTCTCAGCA": 17046, + "GGGGCAGCA": 17047, + "CCCATCTCC": 17048, + "TGGATATT": 17049, + "CGATTCATG": 17050, + "GTTATGTTA": 17051, + "CTAATTTTTTGTATTTTTAGTAGAGA": 17052, + "CCTCAATG": 17053, + "GCAGTGGTG": 17054, + "GTACAGCC": 17055, + "TGTGTATAA": 17056, + "TATATACCA": 17057, + "TCTGGTC": 17058, + "GGTTTCCA": 17059, + "TGGAGAATT": 17060, + "TAATAGTTA": 17061, + "TACCCTT": 17062, + "GCCTTCCTG": 17063, + "CTATTTAAA": 17064, + "TTATGCAAA": 17065, + "CTTTTATTAA": 17066, + "GAATATGTT": 17067, + "GGAGAATTTT": 17068, + "CAATGAGAA": 17069, + "CACAGGGCA": 17070, + "TTCAGTTTT": 17071, + "TATTATTATT": 17072, + "GCTGAGCTG": 17073, + "GCATTCTA": 17074, + "CTCCAGCAA": 17075, + "TCACAGCTG": 17076, + "CACAGACAA": 17077, + "TCAAGAGAA": 17078, + "TTTGGTT": 17079, + "TCTAGGTA": 17080, + "TGGACTCA": 17081, + "GGGATCCA": 17082, + "GTCTTCTG": 17083, + "GCCTTGCA": 17084, + "GTTCATCTT": 17085, + "TGTTCAGAA": 17086, + "TCTGCTGTT": 17087, + "TCAACACAA": 17088, + "TAAAATAGAA": 17089, + "CCCACTGAA": 17090, + "CAAAACTCA": 17091, + "CTCTGCAGA": 17092, + "AAATTAAATT": 17093, + "CTTGGGAGA": 17094, + "GGGGAGAGG": 17095, + "GAACAGCTT": 17096, + "CCGAATG": 17097, + "GGTTTTTA": 17098, + "CCATAGTA": 17099, + "GTAGTGTG": 17100, + "TGAAGAATT": 17101, + "TTAAGTTA": 17102, + "GGGGCTCC": 17103, + "CCTACTGAA": 17104, + "TCTCAGAGG": 17105, + "GATTATTTG": 17106, + "GCAGACAGA": 17107, + "GAACATTTTA": 17108, + "CTTTCATTTT": 17109, + "GTGCCAGA": 17110, + "GTATCTCC": 17111, + "CACACATTA": 17112, + "CGACGTA": 17113, + "CAAAACAGAA": 17114, + "GCTAĊ": 17115, + "CCTACCCC": 17116, + "GCAAAGGAAA": 17117, + "TCATTGAA": 17118, + "CGTTTGTT": 17119, + "GCTGAGAGA": 17120, + "CAGGAGAA": 17121, + "CTCCATCCA": 17122, + "TCGAGTCA": 17123, + "CTTAAATTA": 17124, + "GCTGTGTGTG": 17125, + "TGAGCGA": 17126, + "GAAAGATGA": 17127, + "GCTTCACC": 17128, + "TGTATCC": 17129, + "GGGATATG": 17130, + "TCTCAGCC": 17131, + "GGCAATTA": 17132, + "GAACAACAGA": 17133, + "GCTAGGAAA": 17134, + "TGGGTTTGATGAAACCGAACGAAAA": 17135, + "CCAGCTTTT": 17136, + "GTATCCTG": 17137, + "TGTGTATGTG": 17138, + "CTAAGCTG": 17139, + "TGCAGTC": 17140, + "TAAAAACAAAA": 17141, + "TCATTTACA": 17142, + "TCCTTTCA": 17143, + "TAATAATATA": 17144, + "GTTTTATTAA": 17145, + "CATCTTATT": 17146, + "TCTCATTAAA": 17147, + "TACATGAAA": 17148, + "TACTAAAAA": 17149, + "GGGTAATT": 17150, + "GGTCGAA": 17151, + "GATATATTA": 17152, + "CTGTATATT": 17153, + "CTATAGTG": 17154, + "TGTTCTTTG": 17155, + "CATTGCAAA": 17156, + "CTGATGCC": 17157, + "CTCATGCC": 17158, + "GATTTAGAA": 17159, + "GATGCGCA": 17160, + "CGCGAGG": 17161, + "CCTGATTA": 17162, + "TATCCTTTT": 17163, + "GTTTACTTA": 17164, + "GGTCCTCC": 17165, + "CCATAATA": 17166, + "CTCAAAAAAA": 17167, + "CAAAGATAA": 17168, + "GGAAGACC": 17169, + "TAAAACAGG": 17170, + "GCAAGAGTT": 17171, + "TATGCAGAA": 17172, + "TCTCTCCTCC": 17173, + "GTTAĊ": 17174, + "GTTTTGTGA": 17175, + "TTTAGTG": 17176, + "TAGGGCTT": 17177, + "TAATGCATT": 17178, + "GAGCTACA": 17179, + "GCTCGGTG": 17180, + "TTATACAGA": 17181, + "CTGCCCACA": 17182, + "GATCTAAAA": 17183, + "GCGCAGGA": 17184, + "CAAGGGC": 17185, + "TATTGAGTT": 17186, + "CACACTTAA": 17187, + "TCTGTATTA": 17188, + "CTCAGAGGA": 17189, + "GTCACACTG": 17190, + "CCAAGGCTG": 17191, + "CCCACGCA": 17192, + "TTCATCAA": 17193, + "CTTATTAAAA": 17194, + "CATGTTAAA": 17195, + "GGTGGGCAGA": 17196, + "TCGTCCTT": 17197, + "TCGCTATT": 17198, + "TCACCTTTT": 17199, + "TCCCAAAAA": 17200, + "GCATATAAA": 17201, + "GGAGCTCTT": 17202, + "GAAATATAAA": 17203, + "TTTTATATTTT": 17204, + "CGGCGGA": 17205, + "CTAAGGAAA": 17206, + "TAGATATTTT": 17207, + "CAATTTAAAA": 17208, + "CAGTTCTG": 17209, + "CCCTGTCTCTACTAAAAATA": 17210, + "GCCGGGCATG": 17211, + "GGTAAAAAA": 17212, + "TATTCTTTG": 17213, + "CTGAGCAAA": 17214, + "CATGGACTG": 17215, + "TAGGGACATG": 17216, + "CACCTATG": 17217, + "GTGTGTCTG": 17218, + "GGATGGAGA": 17219, + "GGAGGAGGGG": 17220, + "CCCGGTT": 17221, + "TAATATATG": 17222, + "GGACGTC": 17223, + "GGAGTATG": 17224, + "CTCTTACA": 17225, + "CACATACAA": 17226, + "TTAAGATG": 17227, + "TGCACTTA": 17228, + "CTCAACAAA": 17229, + "GAAGGTTTT": 17230, + "CTTCCTCCA": 17231, + "TTTTGGGAGG": 17232, + "TGATAGC": 17233, + "GTAAGGTG": 17234, + "CCAAGCAAA": 17235, + "CTCCCACTG": 17236, + "GAAAATAAAAA": 17237, + "CCCACCCCCA": 17238, + "AAAAAAAAATG": 17239, + "GAGCCAAAA": 17240, + "TCAGAGTCA": 17241, + "TCATATGAA": 17242, + "TCCCCCAGG": 17243, + "GCCGACC": 17244, + "TAAAGCATT": 17245, + "CAGATCCA": 17246, + "GGAATGGAA": 17247, + "TAAAAGCTG": 17248, + "GCAACTGAA": 17249, + "CGGGTTA": 17250, + "CCATTTGTT": 17251, + "GTTTGCTT": 17252, + "GAATGCTG": 17253, + "CCTCTTTTA": 17254, + "CCAGGTTTT": 17255, + "TAGGATTA": 17256, + "TGTGTGTTA": 17257, + "GAAGAAATGGA": 17258, + "GAGATACA": 17259, + "TCTGGGCTT": 17260, + "CAGACATTA": 17261, + "GGAATTTTA": 17262, + "CCTTTACA": 17263, + "TCTCTCTGTG": 17264, + "CCAACACTA": 17265, + "GCCACTC": 17266, + "TGGAGTACTA": 17267, + "TAATTCTTTT": 17268, + "TGCACCA": 17269, + "TCTCCTCCA": 17270, + "TGAAACAAA": 17271, + "CCAAAGGTG": 17272, + "GGTATCTT": 17273, + "TCCTGGCTG": 17274, + "GAAGATGTG": 17275, + "TATATATTTTTT": 17276, + "GGCAGAGGAA": 17277, + "TGACTGAAA": 17278, + "CTACCTAA": 17279, + "TATACATACA": 17280, + "CCAATGTA": 17281, + "GGTTCTTA": 17282, + "CCCCCGC": 17283, + "TCATTTCATT": 17284, + "TGATTGTGA": 17285, + "TCCTGTCTT": 17286, + "TGTTTGTTTTTT": 17287, + "TAGCAATG": 17288, + "GGCCTTAA": 17289, + "TCATCTCTT": 17290, + "GTACCCCA": 17291, + "GCCTCCAGG": 17292, + "GCTGGACC": 17293, + "GCTGGGACTACAGG": 17294, + "GATGGGAAA": 17295, + "GGTAGCAA": 17296, + "GTCTTCAA": 17297, + "GAGGAGAA": 17298, + "CGTATGTA": 17299, + "GCGGCGCA": 17300, + "GAAGACACA": 17301, + "GCAGACAGG": 17302, + "GTGTATATGTGCCACATTTT": 17303, + "GGTCCCTT": 17304, + "CTTTATTTG": 17305, + "GCAGAGGTT": 17306, + "GGACCTTA": 17307, + "GAAAGAGTT": 17308, + "GAAAGATCA": 17309, + "GAAAATAATT": 17310, + "TTTGTATTTT": 17311, + "TTTATCC": 17312, + "TGGGCAA": 17313, + "GGTTCACA": 17314, + "TGGAGTT": 17315, + "TAAAAATATTTT": 17316, + "CTAAAGTA": 17317, + "TCCTGGCTT": 17318, + "GCCTTCCAA": 17319, + "TCGATGTG": 17320, + "TCGCTAAA": 17321, + "CATTCTGAA": 17322, + "GCCACCTCA": 17323, + "GACACTTTT": 17324, + "TTATTAAATT": 17325, + "CTAAATGTT": 17326, + "GGTACTGA": 17327, + "TGATTCCA": 17328, + "TGATTCTGA": 17329, + "CTCTTTCC": 17330, + "CGTATTAA": 17331, + "GTGGGTGTG": 17332, + "GGAGACCTG": 17333, + "GGAGACCTGGGTT": 17334, + "GAACCCAGGA": 17335, + "GTTCTTTA": 17336, + "CTAGGTCC": 17337, + "CATGGGAAA": 17338, + "GATTTGCC": 17339, + "TAGGGTCA": 17340, + "GGGACCTT": 17341, + "CTTTGGTTTT": 17342, + "GGAGGTGTG": 17343, + "TGTTAATTTT": 17344, + "GGACTTAAA": 17345, + "TATAGCTA": 17346, + "TCAACCAAA": 17347, + "GGAAGAGTT": 17348, + "CGGCAGCC": 17349, + "GCCACTGAA": 17350, + "GACACTGAA": 17351, + "GATATCTA": 17352, + "GTGTTGTGTG": 17353, + "GCGCCAAA": 17354, + "TAATGTGAA": 17355, + "TGATCGG": 17356, + "GCATAGC": 17357, + "TGTTCCTA": 17358, + "TGAATCCA": 17359, + "CTAATCC": 17360, + "GAAAATATAA": 17361, + "GTCCCAGAA": 17362, + "TAGATCTA": 17363, + "GGTTTCTA": 17364, + "TCTCCCAA": 17365, + "TCCTTGAA": 17366, + "GGTAGCTT": 17367, + "GTATTGAA": 17368, + "GTTTGCAAA": 17369, + "TAAGAATTA": 17370, + "CAGCTGAAA": 17371, + "CTCAGGAGGCTGAGGCA": 17372, + "GGGCCTCC": 17373, + "GTATATC": 17374, + "CTATTTTTG": 17375, + "CTCTCCTA": 17376, + "CTGAGCGA": 17377, + "CATGTATTA": 17378, + "GTAATCTA": 17379, + "TTTTATACA": 17380, + "TCATTCACA": 17381, + "TATCATTTG": 17382, + "CCCTTAAAA": 17383, + "GGACACATT": 17384, + "GTCTCCCA": 17385, + "GAAAGCCAA": 17386, + "GAAATACCA": 17387, + "CAGGCGA": 17388, + "TCTGGATGA": 17389, + "GATTTAAAAA": 17390, + "GTAAATGTA": 17391, + "TCTCCCTT": 17392, + "GTTTGTCTT": 17393, + "GCTAATTTG": 17394, + "CTTGAGCCA": 17395, + "GCCCAGGCTGGTCTCAAA": 17396, + "GCAGTGCTG": 17397, + "GAAACAATT": 17398, + "TCTGGAATT": 17399, + "CTGACAGCA": 17400, + "CCTTGACTT": 17401, + "GTTACAGAGA": 17402, + "GGTTGTAA": 17403, + "TCTGATTAA": 17404, + "CTGTTGTT": 17405, + "GTGTTTCA": 17406, + "CAATTTATG": 17407, + "TGAGGTGGG": 17408, + "GTTTTATTTG": 17409, + "TACTCTGA": 17410, + "TAAAAAAAATT": 17411, + "GTGCATCC": 17412, + "GCCCACTA": 17413, + "GTCTGAGAA": 17414, + "CAGATCTA": 17415, + "CGGGTAA": 17416, + "CGGGACC": 17417, + "CTATTCATT": 17418, + "CCATGTCA": 17419, + "GCATGTTTTA": 17420, + "TCAGAGTGA": 17421, + "CAATATCTT": 17422, + "TGACAGCTT": 17423, + "CGCTTAAA": 17424, + "TAACTTAAAA": 17425, + "GAAATACAAA": 17426, + "GCTGCACTG": 17427, + "GCTGGGGTG": 17428, + "GCCACCCCCA": 17429, + "GACATAAAA": 17430, + "TCAAAGTTA": 17431, + "GAGCGAAA": 17432, + "GAAGATATA": 17433, + "CTGTTTTTTCA": 17434, + "CCAGTCCATCC": 17435, + "TAAGAAGAA": 17436, + "TGACCTCTG": 17437, + "GCCGTTTT": 17438, + "CAAAGGCAA": 17439, + "CCTGTAGA": 17440, + "CTCCTCTCA": 17441, + "CTTATATAA": 17442, + "TTTATAATT": 17443, + "TCCTTGGGA": 17444, + "GGCCCATG": 17445, + "CTTTGTAAA": 17446, + "CGAATTAA": 17447, + "GACAACACA": 17448, + "GCTCCTTTG": 17449, + "GAAATGTTTT": 17450, + "CTGTATGTG": 17451, + "CAATACATT": 17452, + "GCCCCCAGG": 17453, + "TAGTTTTAA": 17454, + "GGAGCAGCA": 17455, + 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"TGATTGTTTT": 17501, + "CTCTTTCCA": 17502, + "TCAGAGCTG": 17503, + "TCGATGTT": 17504, + "CAATACACA": 17505, + "CCGCTTA": 17506, + "GCTCTCTCTC": 17507, + "TCACCCTA": 17508, + "TCAATACC": 17509, + "TTAACTG": 17510, + "TGTAGTCA": 17511, + "TCAAACATT": 17512, + "CGTGTTCA": 17513, + "CTGTGGTGA": 17514, + "GAGCCCAGG": 17515, + "GTTTAGATT": 17516, + "GGACACGA": 17517, + "CTAAAAATACAAAAAA": 17518, + "GAAGTGGATGCTCA": 17519, + "GAGGCAGGTGGA": 17520, + "CTGAGGCCA": 17521, + "TCTCAGAGA": 17522, + "TAATATAATATT": 17523, + "GTGTTCATT": 17524, + "TCCTGGGAA": 17525, + "GAGGATTA": 17526, + "GATTATTTA": 17527, + "CTCCAGTCC": 17528, + "GTTTCATTTT": 17529, + "TCATAGCTCA": 17530, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 17531, + "GTGTCGG": 17532, + "CCACCTAA": 17533, + "GCAAGCAAA": 17534, + "CCTGGTTTT": 17535, + "CTAAGGTG": 17536, + "TACACAAAAA": 17537, + "CGACAAAA": 17538, + "TAGGGTAA": 17539, + "CCAAACAA": 17540, + "GAATTTCC": 17541, + "TAATAGCTGA": 17542, + "CAGGATTA": 17543, + "CACATTAAAA": 17544, + "CAAGATGAA": 17545, + "CCGGAGG": 17546, + "GAAAGCTA": 17547, + "GAAAGTATT": 17548, + "CAGGTATA": 17549, + "GCTTCCTA": 17550, + "TATGCCCA": 17551, + "CGGTATA": 17552, + "TCTAGAACTA": 17553, + "TAATTCACA": 17554, + "TGCACCTT": 17555, + "GGGATCC": 17556, + "GTTTGGAGA": 17557, + "TGAGAGCTG": 17558, + "GAGCTCTA": 17559, + "GCTCCCGG": 17560, + "TCGGATA": 17561, + "GCATAGGA": 17562, + "CTACATCC": 17563, + "TATTTATTTG": 17564, + "CAAAGCCAA": 17565, + "CTGACACC": 17566, + "CTGATGCTG": 17567, + "CATGGTC": 17568, + "GTCAGGATG": 17569, + "GAGGCTGAA": 17570, + "CCTGGTCAA": 17571, + "CCTGGGCTT": 17572, + "GAAAACCTT": 17573, + "GACACGTG": 17574, + "GCAAATTTA": 17575, + "GCTGAGGTG": 17576, + "CCTCCTCCTCC": 17577, + "TCATCACTG": 17578, + "TCCAGGGGATCTT": 17579, + "CCACCCACA": 17580, + "TTTTAAATTA": 17581, + "CAGCTTAAA": 17582, + "GTTGCAAAA": 17583, + "GAAACATAA": 17584, + "CAGAGGCAA": 17585, + "TCAACACC": 17586, + "TTAAGAGA": 17587, + "CCTACCCA": 17588, + "TAAAAAGGAA": 17589, + "TAAAAATTTG": 17590, + "CGGATCACGAGG": 17591, + "TTTTTTTTTTTTA": 17592, + "GGTAATTTA": 17593, + "CGTGAAAAA": 17594, + "GAAGCAAAA": 17595, + "GTACGGA": 17596, + "TCTTTGATT": 17597, + "TCTTCAATT": 17598, + "TCTGGGTCA": 17599, + "CCAAGTTTT": 17600, + "GGGGGAGTG": 17601, + "CGAGTTTT": 17602, + "TGTAGCC": 17603, + "TATTTTTAAA": 17604, + "CTGTGCCCA": 17605, + "GCCTGCCAGG": 17606, + "GCCTGGCCAACATGGTGAAA": 17607, + "GAATGCTA": 17608, + "GAAGACATG": 17609, + "TAACACTAA": 17610, + "TAAGATGTGAA": 17611, + "GTCCACGG": 17612, + "GACTAATG": 17613, + "GCACATC": 17614, + "GGATAGTG": 17615, + "TGTGTAAAA": 17616, + "TATTGTGAA": 17617, + "CACAGAGCC": 17618, + "CAGGAAATG": 17619, + "CTGAGAGCA": 17620, + "GCTTTACAA": 17621, + "CCAATTTTA": 17622, + "GCCATCTC": 17623, + "GGGGCTT": 17624, + "GTGTTTCC": 17625, + "TAATGGATT": 17626, + "TGAGATGTT": 17627, + "GGATTCTCCAGG": 17628, + "GAAGGTGGG": 17629, + "TTATTATTTG": 17630, + "TCTCTGCAGG": 17631, + "CCACAGTCA": 17632, + "CGTGAATG": 17633, + 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"CAGCACAA": 17678, + "GTGTTGATG": 17679, + "CCCTCTTCA": 17680, + "TATATATATATATATATATA": 17681, + "CAAAATGAAAA": 17682, + "CGCCAGGCCTCCCTG": 17683, + "GGTCCATG": 17684, + "TATTCAATT": 17685, + "GAAAGAGTA": 17686, + "CCAACCCA": 17687, + "CTCCTGGAA": 17688, + "CTTAĊ": 17689, + "GGTTGAGAA": 17690, + "TCAAATGCA": 17691, + "GCGTTATT": 17692, + "TGATCCCTG": 17693, + "CTTGATCC": 17694, + "TCTTCTTAA": 17695, + "CATTTACTA": 17696, + "TGAATAGAA": 17697, + "GGAAGGAGG": 17698, + "CCTTTAATT": 17699, + "CCTTTACC": 17700, + "CTTATGGG": 17701, + "GATGTACCA": 17702, + "GTTTTTTTTA": 17703, + "GTCCCCTA": 17704, + "GGGGCCCC": 17705, + "GTTAATATA": 17706, + "GTATTTCTA": 17707, + "GAGGAGATG": 17708, + "GCCTGCCTT": 17709, + "TACTGTATA": 17710, + "TTATATAAAA": 17711, + "TAGAGGTT": 17712, + "GTACACTA": 17713, + "TAAACAGTG": 17714, + "TACATACTA": 17715, + "GAAAAGCAAA": 17716, + "GAATGCAAA": 17717, + "TCACACCTG": 17718, + "GTTCTTTTA": 17719, + "CCATGAGG": 17720, + "GTACCTAA": 17721, + "TCTGGACC": 17722, + "TCTGGGGAA": 17723, + "GTAATCC": 17724, + "TTTAGGTT": 17725, + "GGGACCAA": 17726, + "CGCGGGC": 17727, + "GGAGATCAAA": 17728, + "TAACATGTT": 17729, + "TCCCCCAAA": 17730, + "TATTTCTTCACTA": 17731, + "GACTCTGTCTC": 17732, + "GTTGAGAAA": 17733, + "CTCAGTGAA": 17734, + "GAGGCCAGGA": 17735, + "GATTTCTTTT": 17736, + "CTCCAAATT": 17737, + "TACATATGA": 17738, + "CAAAAGTCA": 17739, + "TAGACCTG": 17740, + "GGCTGAAAA": 17741, + "GTTAAATAAA": 17742, + "TGTGTGTGTGTGTGTGTGTG": 17743, + "TACTTATG": 17744, + "GGCTGATG": 17745, + "GAAGAGCC": 17746, + "CTTTAAATG": 17747, + "TATATGCC": 17748, + "TGAAGAGTT": 17749, + "GCTATGATT": 17750, + "GGAAAGGA": 17751, + "CTAAACTA": 17752, + "GTTAATATT": 17753, + "TGAGAGAAAA": 17754, + "TCAGATGTG": 17755, + "TCCCACGG": 17756, + "CTTCACTGA": 17757, + "TCCACATTTT": 17758, + "GTGGAGGAA": 17759, + "GAGTCAAAA": 17760, + "CCGCTAA": 17761, + "GGGCAATT": 17762, + "GAATAAATT": 17763, + "CTTGCAGG": 17764, + "CACAGATGA": 17765, + "TTATTTCAAA": 17766, + "CGCGGCA": 17767, + "TAGCTTTTA": 17768, + "CTATTCACAATAGCAAA": 17769, + "TAAGATCC": 17770, + "GAAGTGGG": 17771, + "CTGCCTAA": 17772, + "GGAGCACA": 17773, + "CAAATACTG": 17774, + "CCTTCGCC": 17775, + "CCTGTCTC": 17776, + "GGGGGATT": 17777, + "CAAAACTGA": 17778, + "GAAAAATAAAA": 17779, + "TATCATTTA": 17780, + "TCTTAATTTT": 17781, + "GACCTGAAA": 17782, + "GCCGGGCGCGGTG": 17783, + "GGCGCAAAGCTACACAGAGAAACCCTGTCTC": 17784, + "TATGTTTA": 17785, + "GATCGTC": 17786, + "CAAGCTTA": 17787, + "GTGGTATG": 17788, + "TGTTCAGTT": 17789, + "CATTGGGA": 17790, + "CAGGGATA": 17791, + "GTGAGTGAA": 17792, + "GCAAGATA": 17793, + "GTTACATTA": 17794, + "TTAATAATT": 17795, + "GCCACGGA": 17796, + "CGACGGA": 17797, + "CCTAGCTG": 17798, + "CTCTTTCA": 17799, + "TATTAATTTA": 17800, + "CAGCCCCAAA": 17801, + "GAACACTTA": 17802, + "GGACACTGA": 17803, + "CCATAGCCTTGACTA": 17804, + "GCTAGAGAAAGTA": 17805, + "CAATAATTA": 17806, + "GTTGCAGG": 17807, + "CACAGAATT": 17808, + "TCTGTCTTA": 17809, + "CTCAGGGAA": 17810, + "CTCACACAA": 17811, + "GTCAĊ": 17812, + "TATGTATGA": 17813, + "GTTTGGTTTG": 17814, + "GAGCTTTTA": 17815, + "GGAGATCTT": 17816, + "GAGGAATGGG": 17817, + "TCTGTAGTT": 17818, + "CTCAGACC": 17819, + "TGAAATAAAA": 17820, + "GTGTTTATT": 17821, + "GCATTTGTG": 17822, + "TGACAATGA": 17823, + "GGAGGAGGAGGAGGAGGAGGAGGA": 17824, + "GGTCGCC": 17825, + "TAAGCCTG": 17826, + "TGAGTTTA": 17827, + "TTAGGGA": 17828, + "CCATCAGG": 17829, + "CTAGAGTA": 17830, + "TCAAGTTAA": 17831, + "TGGGCCTG": 17832, + "GGTACATT": 17833, + "TGATGGAA": 17834, + "GGTCAGGA": 17835, + "TAAAAATTGATTTTTT": 17836, + "GCGTTCTT": 17837, + "GCGGGGTG": 17838, + "TGACTAAAA": 17839, + "GGATCTTTT": 17840, + "GTAGGGTG": 17841, + "TGTGTACTT": 17842, + "CAGGCCCA": 17843, + "TCTGTAATG": 17844, + "CTCATGAAA": 17845, + "GATTCATA": 17846, + "CTTTTTCAAA": 17847, + "TGGGCAAAA": 17848, + "TAGGTCA": 17849, + "TTATTGAAA": 17850, + "GCCTTCTGA": 17851, + "TGAGACACA": 17852, + "TGACAGTAA": 17853, + "CACGAAAA": 17854, + "GATCTGAAA": 17855, + "CAACTTTTA": 17856, + "GTACTTTTA": 17857, + "TAAACAATG": 17858, + "TATAGTAAA": 17859, + "CATTCCATG": 17860, + "TCTAGGCA": 17861, + "GCCAGCTCA": 17862, + "GTCCTAGA": 17863, + "CTGTTGATG": 17864, + "GGAAAAAAAAAA": 17865, + "CTTCATGAA": 17866, + "CAACAGTGA": 17867, + "TCGTGAAAAAA": 17868, + "CTGGAGAAA": 17869, + "GTGGTAAAA": 17870, + "GTCTGTTTG": 17871, + "GTCTCCAGA": 17872, + "TATGCGA": 17873, + "GGGGTTTA": 17874, + "TCTCCCACA": 17875, + "GGGCAGGGG": 17876, + "GTGCTGAGATTACAGG": 17877, + "CCGTGGG": 17878, + "TCGCAGAA": 17879, + "TGAGTTTTA": 17880, + "CAACTTCTT": 17881, + "CTGCTTGG": 17882, + "TATTTAATAA": 17883, + "GAAACGTG": 17884, + "CAGGGAGCA": 17885, + "GCTTTATTTT": 17886, + "TCCACCCTG": 17887, + "TGTACCTT": 17888, + "TAATTCATA": 17889, + "GTGAAAGAGGA": 17890, + "TTATAGTAA": 17891, + "TGACCAGG": 17892, + "CTGTTATG": 17893, + "GTGGATCC": 17894, + "CTACTGTG": 17895, + "CTATAATG": 17896, + "GCTTGGCTT": 17897, + "CCTGGCTCA": 17898, + "TCTCGTGA": 17899, + "TCCAGCTCC": 17900, + "GTTTTGCAA": 17901, + "GGGGTCTT": 17902, + "CAAAACCAAA": 17903, + "GGTTGCC": 17904, + "GGTTCAGA": 17905, + "CGCAAATT": 17906, + "CTTTGCTTA": 17907, + "CAAAAATTTT": 17908, + "CAATAGTGA": 17909, + "GTTGGTTTG": 17910, + "GCACAGTGA": 17911, + "GTTGAAATT": 17912, + "GTAGAAGG": 17913, + "GTAGGGTA": 17914, + "TATGTACTT": 17915, + "GAGGĊ": 17916, + "GATTCAGAA": 17917, + "TCCAGCCAA": 17918, + "CCCAGTCTT": 17919, + "TTATTCCTT": 17920, + "GTGAAGATG": 17921, + "CTGTTCCA": 17922, + "CTTTGCATT": 17923, + "TATATTTATA": 17924, + "TTTTATTAAAA": 17925, + "GTTGGTTAA": 17926, + "GAAGTTTACATACA": 17927, + "TCTGAGTTCGAGG": 17928, + "TACGGTT": 17929, + "TATAAAAAAA": 17930, + "TAAGAAATG": 17931, + "GTTCTCTA": 17932, + "GTTCCAGA": 17933, + "GCATATC": 17934, + "TGAATGGAA": 17935, + "TCTGCACC": 17936, + "CAGACAGTT": 17937, + "CTGACGAA": 17938, + "GTCATGAAA": 17939, + "TTTTACTGA": 17940, + "GATTTCTAA": 17941, + "CCTGTCTCC": 17942, + "TCTCTAGGA": 17943, + "CTAACGG": 17944, + "GAATTGAAA": 17945, + "CAATTCATT": 17946, + "GTTTGTTTTTT": 17947, + "GATTATTAA": 17948, + "TAAATAAATAAATAAATAAATAAATAAATAAA": 17949, + "TAATGTGTT": 17950, + "CTTTCGG": 17951, + "CTTTCACA": 17952, + "GCAGGAGAA": 17953, + "TGTTGACTT": 17954, + "GCTGGATA": 17955, + "CTTTTCAAAA": 17956, + "GGGGCCTT": 17957, + "GTCTTTGAA": 17958, + "CCCTGTCTCTACTAAAAATACAAAAATTA": 17959, + "CTCCACGG": 17960, + "CCCCGTCTCTACTAAAAATA": 17961, + "GGTAAATA": 17962, + "TAATGAGAA": 17963, + "GTGCCTAA": 17964, + "GTGGTGCTG": 17965, + "TAAATTATTA": 17966, + "TCCATCCAA": 17967, + "GAAAACTTA": 17968, + "TCTACCCC": 17969, + "TCAAAGATG": 17970, + "CTTTGGAGG": 17971, + "CCTCAGCTT": 17972, + "TTACATATA": 17973, + "CACGTGTT": 17974, + "GCCCTATT": 17975, + "CTATATCC": 17976, + "GTCTGTGTG": 17977, + "TCTTCTATT": 17978, + "TGTTCATTA": 17979, + "GAGAGAGTT": 17980, + "TGAAGCAAA": 17981, + "GTCCTATG": 17982, + "TCTCCCATT": 17983, + "GTGTTCTCA": 17984, + "CTCTGTCC": 17985, + "GCGGGCC": 17986, + "TAGTATATA": 17987, + "TATTCCCC": 17988, + "GAGAGCAAA": 17989, + "TAAAATATAAA": 17990, + "CCCATACA": 17991, + "GTAAAGTAA": 17992, + "TCTGAGGTTATT": 17993, + "CTCTTCGCA": 17994, + "CCATTTCA": 17995, + "TCTAAGAA": 17996, + "CCTGATTTT": 17997, + "GAACAGTGA": 17998, + "TCATGGCTGCAGTCA": 17999, + "TAAGAGCC": 18000, + "TAATATTTA": 18001, + "GTATCATG": 18002, + "CTCTGATGA": 18003, + "CAGGGGGA": 18004, + "GCTTTCATT": 18005, + "GTCATTATG": 18006, + "GTCCCACTG": 18007, + "TTTACGA": 18008, + "CCAAACATG": 18009, + "GAATTCTTA": 18010, + "AAAAAATTTT": 18011, + "GCTGAGCTA": 18012, + "CATCGGTA": 18013, + "TCAGACACA": 18014, + "TCCTCCTGCCTCAGCCTCC": 18015, + "GCAATGAAAA": 18016, + "CTGGTGAAA": 18017, + "GTCTCCACA": 18018, + "TATTGATAA": 18019, + "CCTGTAATT": 18020, + "TCCATCTGA": 18021, + "GTTTTGGAA": 18022, + "CAAAATATTTT": 18023, + "GAATTTATG": 18024, + "CTAAATATG": 18025, + "GAATGTCA": 18026, + "CCACAGCTT": 18027, + "TAGATGGGGAAA": 18028, + "GTGGCGCATG": 18029, + "CCGACTA": 18030, + "TAAGAAGA": 18031, + "GCATATCC": 18032, + "GGATCTCC": 18033, + "GTCGGTGA": 18034, + "TCTGTCAAA": 18035, + 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"GCCACCAAA": 18080, + "CGACATA": 18081, + "CAAAATAATT": 18082, + "GCTCCTCCATT": 18083, + "CCTCAAAAAA": 18084, + "CTTCACAAA": 18085, + "GCTTTTATG": 18086, + "GCAGTGAAA": 18087, + "GTCTTAATT": 18088, + "TATTCACTG": 18089, + "TATTTAATTA": 18090, + "TCTGCGTG": 18091, + "GCTTCACTT": 18092, + "CCTGCACTG": 18093, + "CCTGCACAA": 18094, + "CTAACATTA": 18095, + "TCTAGCTA": 18096, + "GCCAGCTCC": 18097, + "CAAAACATA": 18098, + "TTCACCAA": 18099, + "TGAGACAA": 18100, + "GGTAATTTT": 18101, + "CAACCTCAAA": 18102, + "TATTTATC": 18103, + "TGTTCACTG": 18104, + "GGAAGCCAA": 18105, + "CTCACTGAA": 18106, + "CTTATAGG": 18107, + "GAAAAGGGAA": 18108, + "CAAAAAAATG": 18109, + "CGCTCTGA": 18110, + "GGCCGGGC": 18111, + "CTTAAAAATT": 18112, + "CCATTCATT": 18113, + "CAGCTTTTA": 18114, + "CCCTTCCTT": 18115, + "GTATTTAGTCAGCCACCAATTGTGCAAGTTCTCCCACTTAAAAA": 18116, + "GGGTCTA": 18117, + "TCCTCTGAA": 18118, + "CTTTCAGAA": 18119, + "TATAGTCC": 18120, + "TGTTTATGA": 18121, + "GAGGCGGA": 18122, + "CTTACCCA": 18123, + 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+ "CTGCCCCTG": 18170, + "GTTCAGCTT": 18171, + "TAGTCCTT": 18172, + "TTTTĊ": 18173, + "GTAGCCTG": 18174, + "GCCTAATG": 18175, + "TCTTGGCTG": 18176, + "TAAAACACAA": 18177, + "GTCACTGAA": 18178, + "GTAATCCA": 18179, + "CCAATACA": 18180, + "CGAAGAGTT": 18181, + "TATTAAAAATA": 18182, + "CCAGAGCTG": 18183, + "GCACACGG": 18184, + "CTATAGTTTT": 18185, + "TGAGTGAGAACATG": 18186, + "CTTGCACAATTGGTGGCTGACTAAATACTTTTTT": 18187, + "TAATCGA": 18188, + "CAACTGTA": 18189, + "GTGCCTC": 18190, + "GGAATATAA": 18191, + "GGGGGGTT": 18192, + "CGCTCTCA": 18193, + "CAGCAGAGG": 18194, + "GTCTTTGTG": 18195, + "TACCTACA": 18196, + "CCATTCTG": 18197, + "TTTTTACTT": 18198, + "GCAGTTTTA": 18199, + "CAGGĊ": 18200, + "CAGATGACA": 18201, + "GGAAGGCC": 18202, + "CATGCAGAA": 18203, + "CTCCTTTTA": 18204, + "GTTTTTCC": 18205, + "GGTGCCAA": 18206, + "TGATTTGTG": 18207, + "TGTGTGTGTGTA": 18208, + "GAGATGAAAA": 18209, + "GCCCCTAA": 18210, + "GAAAGTTAA": 18211, + "TAAAAGCAAA": 18212, + "CTCACCCAA": 18213, + "CCTTCTCTG": 18214, + "CAAAATCTT": 18215, + "TCTTTGTCA": 18216, + "CAACACTGA": 18217, + "TGGGTTAAA": 18218, + "TAACAAATG": 18219, + "TAATCATTA": 18220, + "GAATATTAA": 18221, + "GTGCAGTC": 18222, + "GCACCTAA": 18223, + "CTACGGA": 18224, + "CTTAGATA": 18225, + "GGGGCAAA": 18226, + "TGGGTATG": 18227, + "GTAAACATT": 18228, + "TAAAAATATAA": 18229, + "GATCACGCCA": 18230, + "TCCCAGTTA": 18231, + "GCCCACGG": 18232, + "GAAGATGAAA": 18233, + "CCACTGAAA": 18234, + "CCATCACCA": 18235, + "CTAGCCTT": 18236, + "CTAGATTTT": 18237, + "CTAGGTAA": 18238, + "GTCTATC": 18239, + "GAAACGC": 18240, + "CAGAGGTCA": 18241, + "GTAAGAGAA": 18242, + "CCTGGCCTT": 18243, + "GTTAGGGA": 18244, + "GGTTGCAA": 18245, + "TATAATCAA": 18246, + "GTCTGGGAA": 18247, + "GTTCAAGACCAGCCTGGG": 18248, + "GCGTCAAA": 18249, + "GCGTTGTG": 18250, + "TAAAGATCA": 18251, + "TATATTATG": 18252, + "CAGAGGAGCC": 18253, + "CATGCGA": 18254, + "GTAATAATT": 18255, + "CTCCCATA": 18256, + "GATGGTTTT": 18257, + "GATGGATCA": 18258, + "TTATTTCCTT": 18259, + 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"AAAATGTTTT": 18483, + "TAAATTAATT": 18484, + "CAGACTGAA": 18485, + "CTGACTC": 18486, + "CCTTGCCA": 18487, + "TATGGAGAA": 18488, + "CGTGCATG": 18489, + "GGCCCAAA": 18490, + "CTCTTTCAA": 18491, + "CAATTCATG": 18492, + "TGAGAGTGA": 18493, + "GTGGAGCTG": 18494, + "CAGATGACACCA": 18495, + "TGATGTGTT": 18496, + "GTGCATC": 18497, + "TAAACGCA": 18498, + "TATTAAAATT": 18499, + "CGGCCCTG": 18500, + "TCTCTGAGG": 18501, + "CTCCCAGC": 18502, + "TCCACGAA": 18503, + "TCCAGCCTG": 18504, + "GAAAAGACA": 18505, + "GAAAATATCA": 18506, + "TTTACAGG": 18507, + "TCAAATCA": 18508, + "TAATTGCAA": 18509, + "TTCACCTG": 18510, + "CTGTTTCAA": 18511, + "GTCAATTAA": 18512, + "CTCTGCAA": 18513, + "CCAAAATTA": 18514, + "GCGACCCCATGGA": 18515, + "CTCACTCATAGGTG": 18516, + "GGGTCTTA": 18517, + "TGAGCGG": 18518, + "TGAGCTGTT": 18519, + "CTGGATATTA": 18520, + "TAAAGACCA": 18521, + "GAAAGGCAA": 18522, + "TATACTC": 18523, + "CTCATACA": 18524, + "GAGGGGCTG": 18525, + "GATTGACA": 18526, + "GTCCCTTTT": 18527, + "GGTGCATG": 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18573, + "GTCAGAGGA": 18574, + "GCCAGGATT": 18575, + "TGGGGCC": 18576, + "TAGGTGGA": 18577, + "GCTGAGGGG": 18578, + "TGAGATGTG": 18579, + "CCACCCTCA": 18580, + "GAAGCTTTT": 18581, + "GAAGTGAAA": 18582, + "GTGGAAGAA": 18583, + "GTTTATGAAA": 18584, + "GGAGCCAGG": 18585, + "CCAGCCAGG": 18586, + "TCCATCATT": 18587, + "TCCACTCTT": 18588, + "GTATTCTT": 18589, + "CCCTCCACC": 18590, + "CAAATGTTTT": 18591, + "TGAGCAATTTAGCA": 18592, + "TAATGTTTG": 18593, + "CACACCAAA": 18594, + "GAAAGTGTT": 18595, + "GAAATAGCA": 18596, + "TATGTTTTTT": 18597, + "GAGGAAAAAA": 18598, + "CTAATCTG": 18599, + "TTAACATG": 18600, + "CGATAGG": 18601, + "TTATTCTTTT": 18602, + "CGCGGCGG": 18603, + "GAGAAAAAAAA": 18604, + "CAGCGCC": 18605, + "TACAATATA": 18606, + "GCACAGCAA": 18607, + "GTACATGTG": 18608, + "GATCTCAAA": 18609, + "TCACTTTA": 18610, + "CTATCTTTT": 18611, + "CTCTCTTAA": 18612, + "TCTTCCTTTT": 18613, + "CAAATATGA": 18614, + "GGCATATT": 18615, + "TGATGGCTT": 18616, + "GCAGGTTTGAAA": 18617, + "GCATCTCTT": 18618, + "GGACAGGAA": 18619, + "GCCTCAATT": 18620, + "AAAACATT": 18621, + "CTCTCCATG": 18622, + "TGTTTCCTG": 18623, + "CGTGGAGA": 18624, + "TCGGTCTG": 18625, + "GGGGACAGGA": 18626, + "GAATAGGAA": 18627, + "TGAGAACA": 18628, + "GAATCCTG": 18629, + "CAGGGACC": 18630, + "GCAACAGTT": 18631, + "GTCACATGA": 18632, + "CGGĊ": 18633, + "CGCTAGA": 18634, + "TGAGGCTA": 18635, + "CTGGGCCTG": 18636, + "CTGCACTGA": 18637, + "TGACCTTTT": 18638, + "TCCTCACCA": 18639, + "CTGGCAGG": 18640, + "GGACAAGG": 18641, + "CCATCTCTG": 18642, + "GTCGAAGG": 18643, + "CACATAAAAA": 18644, + "CACAGCTCA": 18645, + "TCTTTACCA": 18646, + "CAGAGAAAAA": 18647, + "CCTTTGCTG": 18648, + "CCAAGGCCA": 18649, + "CTGAAAAATT": 18650, + "GAATTGTTTT": 18651, + "GAATGGCC": 18652, + "GCTCCTCTG": 18653, + "GTTTATGTG": 18654, + "TAACAGTTA": 18655, + "TCACAGCTCA": 18656, + "TGACACTGA": 18657, + "GGACAGTCA": 18658, + "CACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACA": 18659, + "TGAGTAGCTGGGATTACAGG": 18660, + "TGATCAGG": 18661, + "AAAATGTA": 18662, + "GAAATAGAAA": 18663, + "GGAAGATGA": 18664, + "CCTTCCTTA": 18665, + "GTCATACA": 18666, + "TATGTACC": 18667, + "CTCCTCAAA": 18668, + "CGCGTT": 18669, + "CAGCATTTG": 18670, + "CAAGACCAA": 18671, + "CCTATTTCTCCACA": 18672, + "GCTCATGCCTA": 18673, + "GGATATC": 18674, + "TATTCATGA": 18675, + "CACACAGC": 18676, + "TATAGGAAA": 18677, + "TCTTGTAAA": 18678, + "CATTGGAAA": 18679, + "CAGGGACTG": 18680, + "GGAAGGAAAA": 18681, + "GAAAATAGG": 18682, + "CGACGGAA": 18683, + "TAAGTGTGTAGTCTTTTGATTTTTTTGAAA": 18684, + "TCCTGGGTT": 18685, + "CTTTCCCC": 18686, + "GTGGCTGTG": 18687, + "GTCTAGC": 18688, + "TGTTAAATG": 18689, + "CAGGGAGGA": 18690, + "CCAATAGA": 18691, + "CTTATTTTAA": 18692, + "GATGCCCCA": 18693, + "TTTATCTA": 18694, + "GGCATAAA": 18695, + "CTGTGTCTT": 18696, + "CTATTTGAA": 18697, + "TGAGATATG": 18698, + "CGCTTGTAA": 18699, + "TCGCCATGTT": 18700, + "TGCAGCCATAAAAAA": 18701, + "GTCGCAAA": 18702, + "GAAATCTGA": 18703, + "GAAACATTTT": 18704, + "TTTATTTTAA": 18705, + 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"CCAAATTTCAAGTCAA": 18797, + "CAAGAGCC": 18798, + "GCAGGAGA": 18799, + "GGAGCAGTG": 18800, + "CTACTCTG": 18801, + "GGAACAGCA": 18802, + "TCTCGGTT": 18803, + "CCAACACAA": 18804, + "CCAAGACCA": 18805, + "GATGTAAAA": 18806, + "GATGTAGAA": 18807, + "TGGGCACA": 18808, + "CCTAĊ": 18809, + "TGATTCTT": 18810, + "GAAAAACAAAA": 18811, + "TATCTAATT": 18812, + "CTCCATGTT": 18813, + "CAGTCTTTT": 18814, + "GGACAGTGA": 18815, + "GCACCCTATT": 18816, + "CACACATACA": 18817, + "TGAGTGTGTG": 18818, + "TATAGAGAA": 18819, + "TCTGTCTCTG": 18820, + "GTAATAGAA": 18821, + "TCTCTACTA": 18822, + "CTTTTCCCCA": 18823, + "TAAAAATAGA": 18824, + "GGTAGCA": 18825, + "TGATTGAA": 18826, + "GAAGATAAA": 18827, + "TCCCAGTGA": 18828, + "CAAGGCTA": 18829, + "GATTTGAAAA": 18830, + "CGCCTTGGCCTCCCAAA": 18831, + "CAATAGC": 18832, + "TGTGTATTA": 18833, + "TATATAGCA": 18834, + "CATTCAGAAAA": 18835, + "TCTGCTGAA": 18836, + "GCTTCTGTT": 18837, + "GTAACACAA": 18838, + "CCTGCTCTT": 18839, + "GGGGAAATG": 18840, + "CCCCĊ": 18841, + 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"CACATGGAA": 18884, + "TGTTCAGCA": 18885, + "GAGACTTTT": 18886, + "CATTCTCAA": 18887, + "CAGAGCCTT": 18888, + "GCCAGCATG": 18889, + "CAAAACATTA": 18890, + "TCCTTTGTG": 18891, + "TCCTTCTGA": 18892, + "TGATTCTCA": 18893, + "TCCAATATG": 18894, + "TCTTTTCTT": 18895, + "GTCAAGAAA": 18896, + "TCCTGCTCC": 18897, + "GAAAAACAA": 18898, + "TAGAAGAAAA": 18899, + "CATCTAAAA": 18900, + "TATATGCCCA": 18901, + "GCTTTGTTTT": 18902, + "CTCGAGGA": 18903, + "CTCTAAGAA": 18904, + "TGTGGAATT": 18905, + "TATGGAGTT": 18906, + "GTTAGACA": 18907, + "TAAAAATAAAAAA": 18908, + "TAATACCA": 18909, + "GTCTTCTGA": 18910, + "CGCCCTG": 18911, + "CAATGTCA": 18912, + "TCAGGGAGG": 18913, + "GTGAAATAA": 18914, + "TAACAGTGA": 18915, + "GTTATTTTCAATT": 18916, + "CTCCTGCCTCAGCCTCCCGAGTAGCTGGGACTACAGGCGCCCGCCA": 18917, + "TTACATTA": 18918, + "GAAGATTTT": 18919, + "TGTGGAGTT": 18920, + "CAAATAATG": 18921, + "CAAAGACCA": 18922, + "CCTGCAGTG": 18923, + "TTTGGAAAA": 18924, + "CCCCTAGG": 18925, + "GGAGGCTGAA": 18926, + 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18972, + "CTTTTTTAAAA": 18973, + "CCATCTCACACCA": 18974, + "TCATAATAA": 18975, + "GTCTCCCC": 18976, + "GTCCACAAA": 18977, + "TATTGACAA": 18978, + "CACACACTA": 18979, + "CAGACGCA": 18980, + "TCAACAGTT": 18981, + "GATTTGGAA": 18982, + "CCAAGACC": 18983, + "GTTTTGAGG": 18984, + "CGTGGAGG": 18985, + "TAATAGCA": 18986, + "CAATTTCC": 18987, + "TATAAACAAA": 18988, + "CTGCCTCTT": 18989, + "CAGGTCTGTGAGA": 18990, + "TAATAAAATT": 18991, + "GTTCGATT": 18992, + "GTAGCATG": 18993, + "TCAACAGGA": 18994, + "GCTTCTTTG": 18995, + "GCTTCACTG": 18996, + "GGTTCTAA": 18997, + "GCATTCTTA": 18998, + "TCATGGAA": 18999, + "GCAATTAAAA": 19000, + "GCACAGTCA": 19001, + "GGGAGGGAGG": 19002, + "TAAAATGTTA": 19003, + "GGGATTAAA": 19004, + "TCCAGAAAATCACATTGTA": 19005, + "TAAGTTATT": 19006, + "TCCCTCTCC": 19007, + "GTACCATG": 19008, + "TAAACACCA": 19009, + "TATATCTCC": 19010, + "GAGATCCTG": 19011, + "GCTTTAATT": 19012, + "GCTGGGCCA": 19013, + "CTCCCATCC": 19014, + "CTTTTGTGA": 19015, + "CAGCAGCA": 19016, + 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19061, + "GAAAGGCTG": 19062, + "GAGACCCTG": 19063, + "CAGGCAGTG": 19064, + "GCTTTACC": 19065, + "GCTTTACTT": 19066, + "TTTGGTCA": 19067, + "GCAAACATT": 19068, + "CCAAAGCCTT": 19069, + "CTTAAGAA": 19070, + "CTTTGGAAAA": 19071, + "CAAAAATAAAA": 19072, + "CTGTGGCAA": 19073, + "GAGGAGGGA": 19074, + "TTATGCTAA": 19075, + "TATCTTTTTT": 19076, + "CTGGGGATG": 19077, + "GAGCCTGGA": 19078, + "CTCCATGAA": 19079, + "CTTCAGTTTT": 19080, + "CAGATGGTTGTGA": 19081, + "TAGCCCC": 19082, + "GCGTATA": 19083, + "TAACTCAAA": 19084, + "TCACAGAGA": 19085, + "TCCGCAA": 19086, + "CCACAGAGA": 19087, + "GTATTTTATT": 19088, + "GCCTCAGTG": 19089, + "CTGATGATG": 19090, + "TTTGGGTG": 19091, + "TGCACGCA": 19092, + "TCTCACTCTG": 19093, + "CTGAACTCA": 19094, + "TGCCCTCAA": 19095, + "CACCCTCTT": 19096, + "GGAGGTGAA": 19097, + "CCCTGCCCC": 19098, + "CCCTTCCCC": 19099, + "CCAAAACATCA": 19100, + "GTTTATCAA": 19101, + "CTGCCTGTT": 19102, + "CTTGCCTGGAGAATCCCATG": 19103, + "CAACAGTC": 19104, + "CTGCCCTC": 19105, + "CACATATAA": 19106, + "GCTTGGAAA": 19107, + "CGGCAGGA": 19108, + "CCAACTTTT": 19109, + "TTAATCTT": 19110, + "GATACTTTT": 19111, + "TCCTTGTTTT": 19112, + "GAGAAGGA": 19113, + "CTTTGTAGA": 19114, + "GGGGATTA": 19115, + "GTTCATGTT": 19116, + "CACACATATA": 19117, + "CTTAAAATG": 19118, + "GTTGTCCTT": 19119, + "GCACATTAA": 19120, + "CTAGGGTT": 19121, + "CATTTGGGTT": 19122, + "CAGAGGCTT": 19123, + "CTTAGGGG": 19124, + "GGCACTAA": 19125, + "TTCACCCA": 19126, + "TAATAGAAAA": 19127, + "TAATACTTTT": 19128, + "TGCCTATG": 19129, + "CTGTTGTTA": 19130, + "GGGAAGGAA": 19131, + "GACTGTTTT": 19132, + "AAATATGAA": 19133, + "TGATGAAAATTACAGGCCTCTCTCATCTTTTTAAGTGGGAGAA": 19134, + "TCATGACA": 19135, + "TCCCTCCTT": 19136, + "TCCCCTCTG": 19137, + "GCAGTTCTT": 19138, + "CCATCAAAA": 19139, + "CTCGCCAA": 19140, + "CTCTCTTTG": 19141, + "CAGAGGTTA": 19142, + "GCTTCCTCC": 19143, + "TAGGTCTA": 19144, + "TCAAAGATT": 19145, + "TCTCACTA": 19146, + "TCCTTCCCC": 19147, + "TGCCCTCTT": 19148, + "CACCTCTA": 19149, + "TCCAATTTA": 19150, + "CGAAGCC": 19151, + "GGGAAGAAA": 19152, + "GCAGAAAGTGAA": 19153, + "TCGGACA": 19154, + "TGATTATTTT": 19155, + "CTTTGAATT": 19156, + "CTTTCCCTT": 19157, + "GCATCAAAA": 19158, + "GGACCTCTT": 19159, + "CCATACTA": 19160, + "GCCTCACC": 19161, + "TGTGCGACCCCA": 19162, + "TGAAGATAA": 19163, + "TAAAATAATG": 19164, + "TAAAACATTA": 19165, + "GCTTCAGGA": 19166, + "CCTGTACC": 19167, + "GTTAGGAAA": 19168, + "CTCCCAGTG": 19169, + "CTTAGGAAA": 19170, + "TGGGCAAAAA": 19171, + "TAGGCAGAA": 19172, + "GTGAAGGAA": 19173, + "TAATAGATG": 19174, + "GCCTTCATT": 19175, + "TGAGACTG": 19176, + "CAGCCTCAA": 19177, + "TGGGTTTGATGAAACCGAACGAAAATTTATGTG": 19178, + "GCGCATA": 19179, + "TCCTGAATT": 19180, + "GGAGCACC": 19181, + "TAAAGGCAA": 19182, + "TCTGCATCA": 19183, + "TCTGTATCA": 19184, + "CAGAGGTGA": 19185, + "TAATATTTTTT": 19186, + "CCATTTATA": 19187, + "CTCTGCTCC": 19188, + "CAGTATTTG": 19189, + "GTGCCACTGCACTCCAGCCTGGG": 19190, + "GTAGTGCACTA": 19191, + "GATATATATA": 19192, + "GCGATTAA": 19193, + "CTTCAAGG": 19194, + "CCACTTGG": 19195, + "CACAGCTGA": 19196, + "GAAAGAAATT": 19197, + "CAAACAGTT": 19198, + "CAAACAGGA": 19199, + "GGAATCTA": 19200, + "GCTGTTTTA": 19201, + "TAAAAATCAA": 19202, + "TGAAAGAA": 19203, + "TCATGTTTTA": 19204, + "GCAGGTGTG": 19205, + "TTATATCTA": 19206, + "GTGGATGAA": 19207, + "GTACTGGAGTTTCAGCTT": 19208, + "GCGGGCGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGCAGGAGAATG": 19209, + "CTTTCTTTG": 19210, + "CTCGGCTT": 19211, + "GTGATGGAA": 19212, + "TTAACTTA": 19213, + "GCCAGCTGA": 19214, + "GCCAGCATT": 19215, + "TAGGGACA": 19216, + "TCTCATGTT": 19217, + "GCAAAGTAA": 19218, + "GCCTTCTCC": 19219, + "TGAGACTA": 19220, + "CCCAATTA": 19221, + "TCAGAGTAA": 19222, + "CTTCCTCAA": 19223, + "CTGGACATG": 19224, + "GTTCTATTTT": 19225, + "TAAAGAAAGCTGA": 19226, + "TCATCATCATCATCATCATCATCA": 19227, + "GACCCTGATGCTGGGAAAGATT": 19228, + "GACGCTA": 19229, + "CTTTGTGAA": 19230, + "GGAGCCAGA": 19231, + "TCTTCACCA": 19232, + "TCTTGATTA": 19233, + "GGAAGCTA": 19234, + "CTCAGCTCA": 19235, + "GCAATACAA": 19236, + "GATTCAAAA": 19237, + "CTTACATTA": 19238, + "TAGAGGAGA": 19239, + "TCAAAGTG": 19240, + "TGGATCAA": 19241, + "TAATTTCTTA": 19242, + "CTTAATATA": 19243, + "CAGGATTTT": 19244, + "CACTTGTAA": 19245, + "TATTAATATA": 19246, + "TCAGGCATG": 19247, + "TTTTATTTTATTTTATTTTA": 19248, + "GATTCTTTACCA": 19249, + "TAATCTATT": 19250, + "GCAGCGC": 19251, + "TAAATAGTG": 19252, + "GAGGGAGCA": 19253, + "CGATCTC": 19254, + "CAAAACACAA": 19255, + "TGGGGTTA": 19256, + "TAAAAATGA": 19257, + "CGAATTTG": 19258, + "CCTCGCC": 19259, + "CTTCCCTAA": 19260, + "TAACATCAA": 19261, + "CCACCTCCA": 19262, + "CTTGATATG": 19263, + "GGTCTATG": 19264, + "GCGCTTTG": 19265, + "TAACTATA": 19266, + "TCACCAAAA": 19267, + "AAAATGTG": 19268, + "GTCTCACTT": 19269, + "CACACAAAAA": 19270, + "CCCCTACA": 19271, + "TAATTGTCA": 19272, + "TAAAAACATT": 19273, + "CCAAATAAA": 19274, + "CTTTGCTTTT": 19275, + "TATAAGTA": 19276, + "TATAATATG": 19277, + "CTATTTCCA": 19278, + "GCAGATGTG": 19279, + "GCAGACATG": 19280, + "TAAGATGAA": 19281, + "CTTTCATTA": 19282, + "GAACCAAAAAA": 19283, + "CCACCAAAA": 19284, + "CACACGC": 19285, + "TCTTTCTCTG": 19286, + "TAAAATATGA": 19287, + "GCTGGTAAA": 19288, + "GATTGTCTT": 19289, + "CCAATGCC": 19290, + "CTCCCGGA": 19291, + "GAAAAĊ": 19292, + "GGTGCAGCA": 19293, + "TTATTCTCA": 19294, + "CTGAATATA": 19295, + "GAATTCCTG": 19296, + "GTGTTTATG": 19297, + "TCGATCC": 19298, + "CTCCTTCTCCTT": 19299, + "CTCTAGCAAA": 19300, + "CTCCTGAGCTCAA": 19301, + "TATTCATTGGAAGGA": 19302, + "TGATGATA": 19303, + "GAAGTATG": 19304, + "CAATTAAAAA": 19305, + "CTTTCCTCC": 19306, + "TAAAGAGCA": 19307, + "TGTTCCCTG": 19308, + "TGTTTACTA": 19309, + "CAGGTGAAA": 19310, + "CAGACCCCA": 19311, + "TAAAATACC": 19312, + "TAAAATATATT": 19313, + "CTGATCTA": 19314, + "CCTTCAGTT": 19315, + "TCTGAGGAA": 19316, + "CTCTTTGTG": 19317, + "CTCCATCTG": 19318, + "GTTTATCTT": 19319, + "GTAGGTTTG": 19320, + "GCCACCATGTGGTTGCTG": 19321, + "GGCTTTTTT": 19322, + "TCGTCTCC": 19323, + "CTTCTTATT": 19324, + "GAGATGCTG": 19325, + "TCAATACTT": 19326, + "CTGACCTTTT": 19327, + "GCCATAAAA": 19328, + "GATAGTCA": 19329, + "CGTGTGTGTG": 19330, + "TCACCTCTG": 19331, + "CCGCCACA": 19332, + "TCAGTGAAA": 19333, + "TTTTACATA": 19334, + "TATGGCAAA": 19335, + "TCCATATTA": 19336, + "TGGGCAGAA": 19337, + "TAATTTCATT": 19338, + "CCAAAGTA": 19339, + "CGCCTGGG": 19340, + "GCATTCTG": 19341, + "GGTCATTTT": 19342, + "GTTGGGAGG": 19343, + "CTTTTTGTTTT": 19344, + "CATTCTGTAGGTT": 19345, + "GTGCTGGGATTACAGGCGTGAGCCA": 19346, + "TCACCTGAGGTCAGGAGTTCGAGA": 19347, + "TAGTCTTTT": 19348, + "TCATCGG": 19349, + "TCCCAAATT": 19350, + "TATTGAGCA": 19351, + "GCTGCCTCC": 19352, + "CTCACTCTG": 19353, + "TTTTACACA": 19354, + "CCAATATTA": 19355, + "GGGGTAGA": 19356, + "CAAAAGATG": 19357, + "TAAAAAGTAA": 19358, + "GCTGAGACTA": 19359, + "GTTAAAAATT": 19360, + "GAAAAACTAA": 19361, + "GGCGGGCAGATCA": 19362, + "CAGTĊ": 19363, + "CCCTAATG": 19364, + "GCTATATTA": 19365, + "GGATAGTA": 19366, + "CTCTCAGTT": 19367, + "GAGACACTG": 19368, + 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+ "CATCATTAA": 19414, + "GCAGGGTGA": 19415, + "TAATTATTTG": 19416, + "TGTTTGCATTCAA": 19417, + "GCAAAAAAAAA": 19418, + "GCGTCTCC": 19419, + "TTTTCTTCTA": 19420, + "TATTCCATG": 19421, + "TGAACACTT": 19422, + "TCTGGAGGA": 19423, + "CAGAGGGTG": 19424, + "GGAAGAGTG": 19425, + "GATTGATA": 19426, + "TACACCAAA": 19427, + "TAGATAATT": 19428, + "TAGGTGCA": 19429, + "TTCACCCAA": 19430, + "CTGAAGATT": 19431, + "GCTCAGTCA": 19432, + "GCTTATTAA": 19433, + "GAAATATCAATAA": 19434, + "CGCCCAGCTAATTTT": 19435, + "AAAACTG": 19436, + "GTCTCTATT": 19437, + "TATTGATTTT": 19438, + "GTAATTTTTT": 19439, + "TCCATAGTT": 19440, + "GTCCCAGCTG": 19441, + "GACAGAGAGAGA": 19442, + "GGTGCATT": 19443, + "GAATTGCTG": 19444, + "GTTTGGTG": 19445, + "GCTGGGTCAAATG": 19446, + "TCAAAAAATCAATTTT": 19447, + "GCGAACC": 19448, + "GCGAAGGA": 19449, + "TCATCTGTT": 19450, + "TGTTGAGCA": 19451, + "CAAAGTGTT": 19452, + "CTCAGTGTT": 19453, + "CCTGCCTGA": 19454, + "TCTATCTC": 19455, + "CGATATTA": 19456, + "CCCCTATA": 19457, + 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19502, + "CTGTAGCTT": 19503, + "GCTGAAACTCCAGTA": 19504, + "TAACTTATT": 19505, + "GCATCTCTG": 19506, + "TCTTGACTT": 19507, + "GAGACATCA": 19508, + "CCTTTCTGA": 19509, + "CCTGGCTGA": 19510, + "CCAATAGG": 19511, + "TCCAGCCTT": 19512, + "GCCAGCCTG": 19513, + "TAGATATAA": 19514, + "GAATTCTCCAGG": 19515, + "TCCAAGGAA": 19516, + "CGGAGAA": 19517, + "TAATGCATG": 19518, + "TAATGGTAAA": 19519, + "CGTATATA": 19520, + "TAAGTGGATATTA": 19521, + "GCGTTTAA": 19522, + "TTAGCTA": 19523, + "CTTCTCCA": 19524, + "CAAAGTTAA": 19525, + "TCAACACTG": 19526, + "CGGTGTTTG": 19527, + "GAAAAGATA": 19528, + "GTCCCAGTG": 19529, + "TCTCAGCTG": 19530, + "CGCGTGA": 19531, + "TAATACAAAA": 19532, + "GCCAAGGAA": 19533, + "GACCCCCA": 19534, + "CCCTGTTTG": 19535, + "GCTCGGGG": 19536, + "CCCAATTTT": 19537, + "CTTTACTTA": 19538, + "GCGCAGAA": 19539, + "GCGAGATT": 19540, + "TGAGAATA": 19541, + "TCCCTTCTG": 19542, + "GTGCTTCA": 19543, + "GCACACATT": 19544, + "GCAGCGCC": 19545, + "GGAGCAGGA": 19546, + "CAAACAGTG": 19547, + 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19682, + "GCCCATC": 19683, + "GCAATGCC": 19684, + "CCTGTACA": 19685, + "TAGGGAAAA": 19686, + "CTGAATATT": 19687, + "TCCTTTATT": 19688, + "GAATTCCAA": 19689, + "CAGTGGCTCA": 19690, + "TAATGCCA": 19691, + "TGTTTGTTTGTT": 19692, + "TACTGATG": 19693, + "TAACACATT": 19694, + "TCAGTTTA": 19695, + "GAAGTGGA": 19696, + "CAACCATG": 19697, + "CAAGAATA": 19698, + "CTCTAAGG": 19699, + "GTCCAGGTT": 19700, + "GTCTGTTGTT": 19701, + "TATTGGGTG": 19702, + "CACATATTA": 19703, + "CATTGAGAA": 19704, + "GCTGTACA": 19705, + "GCTGCGTT": 19706, + "CCTGGACTG": 19707, + "GATGGAGTT": 19708, + "GGGGTTCA": 19709, + "CATACCCA": 19710, + "CAGCAGCTG": 19711, + "GTATTGTA": 19712, + "CAATTGTAA": 19713, + "TACCCTTA": 19714, + "CAGCTCTC": 19715, + "GCTCCTGCC": 19716, + "CCTTATATA": 19717, + "TCTTTTTTAAA": 19718, + "GAAATATTACAACTAAA": 19719, + "CATCTCCTT": 19720, + "TACTCTCA": 19721, + "TCATTAAAAA": 19722, + "CAATTTTTTTT": 19723, + "CTTCAAATT": 19724, + "GAAAGACTG": 19725, + "TGTTCAGTA": 19726, + "CATTCCTCA": 19727, + "GCTTTAGCA": 19728, + "CCTTAAATT": 19729, + "GGGGTATG": 19730, + "TAGGTCTG": 19731, + "TAGGGCAA": 19732, + "TCAAAGGA": 19733, + "TGCACTAA": 19734, + "GAATTTCCA": 19735, + "TACCCTAA": 19736, + "GAATGGATT": 19737, + "GTGTGGAGG": 19738, + "CCTTTTTTA": 19739, + "CTTCCCTTTT": 19740, + "CTGTAGTTA": 19741, + "CAATAGTAA": 19742, + "TGACATGTT": 19743, + "TGAATGTTA": 19744, + "TAATTTATTTT": 19745, + "TCCTCTGTCATCC": 19746, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 19747, + "CCTAAAATT": 19748, + "TAATCCATT": 19749, + "GAACTGAAA": 19750, + "TCCGGGA": 19751, + "GCATTTTTG": 19752, + "TCTTCATAA": 19753, + "TATGGCTGA": 19754, + "GAAAAGCTT": 19755, + "GTTTTAAATT": 19756, + "TCTACCTCC": 19757, + "TGTACAGG": 19758, + "CTGAACTA": 19759, + "TTTTTTTAAAAA": 19760, + "CTTAAAAATG": 19761, + "TCCTGCCA": 19762, + "CTCTGCAAA": 19763, + "CTGTGCCA": 19764, + "CTGTGGAGA": 19765, + "CCTCCGCCTCCCGG": 19766, + "GTGTGCATG": 19767, + "GCACATGTG": 19768, + "CAGATGAAAA": 19769, + "TGAGCAATTTAGCATGGGTTTGAAGAAATTAGGA": 19770, + "TCAGCTGTT": 19771, + "CAATCTTTT": 19772, + "TAAAACTAAA": 19773, + "TACATAATA": 19774, + "TTAATATAA": 19775, + "TTTGGTGA": 19776, + "CCCACGGA": 19777, + "TCTCACTGA": 19778, + "TCTCACTTA": 19779, + "GCAGGCTGA": 19780, + "CTTCCCATT": 19781, + "TGACATCTG": 19782, + "TAATGAAAAA": 19783, + "CTACGCC": 19784, + "GAAACACCA": 19785, + "TATAGGCA": 19786, + "CCTTGCAAA": 19787, + "CCAAGGTCA": 19788, + "GAAAACGA": 19789, + "CGAGCTA": 19790, + "CAATTCTTA": 19791, + "GTGGGGAAA": 19792, + "TATAAATTTT": 19793, + "TCAGATGAA": 19794, + "CAATAGCAA": 19795, + "TAATAAATTA": 19796, + "CAAAATTAAAA": 19797, + "GACTCCATCTC": 19798, + "CCGCCAGA": 19799, + "CCGAGTC": 19800, + "TAGCAAAAA": 19801, + "GAACATC": 19802, + "CTTGCTC": 19803, + "TAAAGTAAAA": 19804, + "CAAACTCAA": 19805, + "GCAACTTTT": 19806, + "TTTTAGGA": 19807, + "TATGCATTA": 19808, + "GATGTTATT": 19809, + "CAAAATAAAAA": 19810, + "CGTGTGGG": 19811, + "GAGGAGGTG": 19812, + "TCAGATATT": 19813, + "TCAGACATG": 19814, + "GCCAGGCATGGTG": 19815, + "TAAGACATT": 19816, + "GCCCACCTG": 19817, + "GTGTGAGATGGTA": 19818, + "TCATĊ": 19819, + "TCAGAATA": 19820, + "GCATCTGTT": 19821, + "GTATATCC": 19822, + "TAAACGC": 19823, + "CTCTGTTTG": 19824, + "GAAATAAATT": 19825, + "GCTTCTCTG": 19826, + "CTCAGAGTT": 19827, + "GTTACAGTA": 19828, + "TCTATCATT": 19829, + "GCCATTTTAA": 19830, + "CGACCCTG": 19831, + "CGTTTGAA": 19832, + "CACCCTTA": 19833, + "CTCTTCTTTT": 19834, + "CGCAGAGG": 19835, + "GTGTTTGA": 19836, + "TAATGTCTT": 19837, + "CCTCACTG": 19838, + "GGATTTCAA": 19839, + "AAATGTGA": 19840, + "CTTCAGTAA": 19841, + "GCTGGGCATGGTG": 19842, + "CTCCAAAGATTTT": 19843, + "CAAGTATG": 19844, + "CAATGAATT": 19845, + "TAAAGCTGA": 19846, + "CTCTCAGGA": 19847, + "TGTTCATAA": 19848, + "TGTTTCCATT": 19849, + "GCTTGACA": 19850, + "GTTACATAA": 19851, + "CGACATCA": 19852, + "TCATTGGTT": 19853, + "GTGTTGGGG": 19854, + "GTCTTCTCA": 19855, + "CAATTCACA": 19856, + "GCCTTCTG": 19857, + "GAAGAGTAA": 19858, + "CTGCATATG": 19859, + "CAAGGGATT": 19860, + "TCATCAGACCA": 19861, + "GCCCCTGTG": 19862, + "TAAATATAAAA": 19863, + "GTTCCTGTT": 19864, + "CAGTTTTTTAACGA": 19865, + "CTTGGGAGGCTGAGGCA": 19866, + "TCTTTCCCAGCATCAGGGTCTTTT": 19867, + "TACTAATG": 19868, + "TCGTCCAA": 19869, + "GAGTĊ": 19870, + "CTTTGTTAA": 19871, + "GTTTAAATG": 19872, + "CCAGCACC": 19873, + "CTATGATG": 19874, + "GTCTATATT": 19875, + "GTCGTGTCC": 19876, + "TATACACTG": 19877, + "TCTTTACTA": 19878, + "GAGAGCTA": 19879, + "GAGATAGAA": 19880, + "CATTTATATT": 19881, + "CAGGGGAAA": 19882, + "TCTGTCTAA": 19883, + "CTGAGATCA": 19884, + "GAAAAGGTCA": 19885, + "TCCTGCCCC": 19886, + "GAGGATTTT": 19887, + "CTGCAGCCTACCAGGCTCC": 19888, + "TACAGGTG": 19889, + "TAAGATTAA": 19890, + "TCCTCCATT": 19891, + "CTTCTCTA": 19892, + "GTACTTGAA": 19893, + "TAAACTGTT": 19894, + "TATAGGGA": 19895, + "CCTTTAATAA": 19896, + "GAGGGATA": 19897, + "CCAACCCTG": 19898, + "TCCAGTGAA": 19899, + "GATGGAGAAA": 19900, + "TTAAGGA": 19901, + "TTAAAAATA": 19902, + "TCAAAGTGA": 19903, + "GCTCAGCC": 19904, + "GTTTGGAGG": 19905, + "TAGCTAAAA": 19906, + "TTACATATT": 19907, + "TACGCCA": 19908, + "TCATCTCAA": 19909, + "TGATAATGA": 19910, + "GAACTTGG": 19911, + "CAACCAAAA": 19912, + "GTTCGAGG": 19913, + "GCACTGAAA": 19914, + "CAAACACAAA": 19915, + "GGAACCCA": 19916, + "CCTTCACTG": 19917, + "TACAGTCAA": 19918, + "GTTTTCTCC": 19919, + "CGACTTTT": 19920, + "CAAAAGCAAA": 19921, + "TGATTGTG": 19922, + "TGATTCTTA": 19923, + "CCTCCTCCA": 19924, + "TCCCAGTCA": 19925, + "CAACAGCTG": 19926, + "GTATAGTAA": 19927, + "GTAGGGATG": 19928, + "GGTATTTAGTA": 19929, + "TAAAATAAAATAAAATAAAA": 19930, + "GACAAAAAAA": 19931, + "TCCTCACA": 19932, + "GTTCCAGAA": 19933, + "GTAGTATG": 19934, + "GTCGACC": 19935, + "GAAAGGCC": 19936, + "TGTTGTATT": 19937, + "GCTTTCTC": 19938, + "CTCATTATT": 19939, + "CCTTCTGTT": 19940, + "CCTTTACTT": 19941, + "GCAACGTT": 19942, + "GATTGGAAA": 19943, + "TTAATAGA": 19944, + "TTCAGCCA": 19945, + "TCCTTGAAA": 19946, + "CTGTGCAGG": 19947, + "GCAGACGG": 19948, + "GGTGGTGGCGCACGCCTTTAATCCCAGCA": 19949, + "CAAAAAAAAAAA": 19950, + "GTTTCCAGG": 19951, + "CCAGCCTCC": 19952, + "CTCTTATA": 19953, + "TCAAGGCC": 19954, + "CATGTATAA": 19955, + "GATGCTGAA": 19956, + "GTCTTCCAA": 19957, + "CCATTCCAA": 19958, + "GAAAAATATA": 19959, + "CAGCCCTG": 19960, + "TCTCTGTTA": 19961, + "GCGTACA": 19962, + "GCGTTTGA": 19963, + "CAATTATTTT": 19964, + "GTAGTCTG": 19965, + "TATAGATAA": 19966, + "TGTTGAATT": 19967, + "CAAATCCAA": 19968, + "GGAAGTGAA": 19969, + "GGAACAGGA": 19970, + "GCTTGGTA": 19971, + "GTGAGCTGA": 19972, + "TTTTATCTG": 19973, + "TCCAGTAAA": 19974, + "CAAAAĊ": 19975, + "GACAGCTA": 19976, + "CTGAATAAA": 19977, + "TCTGATTCA": 19978, + "TGATTTAAAA": 19979, + "GCATTCATT": 19980, + "GGCTGGAGTG": 19981, + "CATCATTTG": 19982, + "CCCTGGAGG": 19983, + "GTTTACTGA": 19984, + "CTTCCCAGA": 19985, + "AAAATACAAAAATTA": 19986, + "GCCCAATG": 19987, + "GTCTCACA": 19988, + "TCTTCAGTA": 19989, + "CTCCAAATG": 19990, + "CATACGTA": 19991, + "TCAAACGA": 19992, + "CGTGGCTG": 19993, + "GCTCAGCA": 19994, + "GCATTCCTT": 19995, + "GCTTATTTA": 19996, + "CGAAATTA": 19997, + "GGACAGCTT": 19998, + "GGTTAGGTTAGGTTAGGTTA": 19999, + "TCCACACACA": 20000, + "TCGTCCTG": 20001, + "TTTTCCCA": 20002, + "CTTGGAGAA": 20003, + "AAAATTCA": 20004, + "GTCGGGTG": 20005, + "TCTGCCAAA": 20006, + "CAGATATTTT": 20007, + "TCAAGATCA": 20008, + "TTTTAGGTT": 20009, + "GATTGAAAA": 20010, + "CCAAGATAA": 20011, + "GTTTTTGA": 20012, + "GTTTTCATG": 20013, + "GCCAGTGTT": 20014, + "GGGGCTGGG": 20015, + "GACACGGA": 20016, + "CTGAATTCA": 20017, + "CAGAAATTA": 20018, + "CTCCATGTG": 20019, + "TAAGAGCAA": 20020, + "GCGCGCGC": 20021, + "GAGGTGGAAA": 20022, + "TCAAATTGCCAA": 20023, + "CCTAGGTT": 20024, + "TACGAAAA": 20025, + "GGTCAATG": 20026, + "TCCGGCA": 20027, + "GCCTATCC": 20028, + "CTATTATTTT": 20029, + "TCTGTATAA": 20030, + "TTTTACCA": 20031, + "CCTGGAGTT": 20032, + "TCCACTGTT": 20033, + "CAAAAGCTT": 20034, + "TGGACGG": 20035, + "TGCATACA": 20036, + "TTTTTTGAGA": 20037, + "GTCTTGAAA": 20038, + 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"CTGGAGTCA": 20083, + "TCACTTCTT": 20084, + "CAATGAGG": 20085, + "GTGGCACA": 20086, + "GGAGTTGG": 20087, + "CACACAGTT": 20088, + "CAGGCATA": 20089, + "GAGGCTCC": 20090, + "CCTGTCTCA": 20091, + "CTAAGGGA": 20092, + "CGTGTGTT": 20093, + "CGTGCGC": 20094, + "TAAAAACTG": 20095, + "TGATTCCTT": 20096, + "CTCTTGCTT": 20097, + "CTTTGTATT": 20098, + "CTGTGTGA": 20099, + "TGATGGGAA": 20100, + "GTTTAGTTA": 20101, + "CTGTACATT": 20102, + "CCTTTGTTTT": 20103, + "CACCTGAAA": 20104, + "GTAGTATAGTT": 20105, + "CAGACAGACAGA": 20106, + "TAAACTTCTGA": 20107, + "TCGCTCAA": 20108, + "TAACCAGG": 20109, + "TAATCAGCA": 20110, + "GAACTGGG": 20111, + "CTAGTTTG": 20112, + "GTCGGGCA": 20113, + "CTGATAAAA": 20114, + "TTAACCA": 20115, + "TTTGGTTA": 20116, + "TCATTGCTG": 20117, + "GTATTCTG": 20118, + "GGGTGAAAA": 20119, + "CTTTATGTG": 20120, + "CAATATCAA": 20121, + "GTCAAATTA": 20122, + "GGAGTGGCTT": 20123, + "GTGGTTTTA": 20124, + "GCAGCCCA": 20125, + "TCTTGATGA": 20126, + "GAGAGGAGG": 20127, + "GAGAGGGAGA": 20128, + "CCTTTGGAA": 20129, + "CCCCGTCC": 20130, + "TGATTGTTA": 20131, + "GTCAATGAA": 20132, + "CCTGAGGTT": 20133, + "TCGGCGC": 20134, + "GTGAAATTTT": 20135, + "CCCTCTCTG": 20136, + "TATTTACAAA": 20137, + "CTATAGTAA": 20138, + "TCTATATTTT": 20139, + "CCATCAGCAA": 20140, + "GACTTATG": 20141, + "TAATCATCA": 20142, + "TAATCCAGG": 20143, + "TCCCTTTTA": 20144, + "CTTCAAGA": 20145, + "CAGATATTA": 20146, + "CGGCAAAA": 20147, + "CCAAACCAA": 20148, + "CTCAATAAA": 20149, + "CACTGCAA": 20150, + "GGATGGCTT": 20151, + "TGATAGAAA": 20152, + "TTACACAAA": 20153, + "GTATATGTG": 20154, + "TCATGTTAA": 20155, + "GCCGGCCA": 20156, + "CACATTATT": 20157, + "TATAGGGG": 20158, + "GAGAGACAA": 20159, + "CAGATACTG": 20160, + "GTCATCTGA": 20161, + "GTCAGAGTT": 20162, + "GTAACTGAA": 20163, + "CTTTTGGGG": 20164, + "CTTTTAAAAAA": 20165, + "GTAAAGCAA": 20166, + "CAATTTCAAA": 20167, + "CTCTGGAAA": 20168, + "CCAGGCGG": 20169, + "TCTCTGTATA": 20170, + "GCCAGGCTG": 20171, + "CGTAAAAAA": 20172, + "TAAGGATA": 20173, + 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"GCAGAGATT": 20219, + "TATTCCTTA": 20220, + "CATTTAATG": 20221, + "GTCATACACA": 20222, + "TTTTAGGAA": 20223, + "GATTCCCA": 20224, + "TTAATATTTT": 20225, + "GAAAATCTT": 20226, + "TAATTGGAA": 20227, + "CTTTGTCTCTG": 20228, + "CAATGCAAA": 20229, + "GAAGACCCA": 20230, + "TAAATATGAA": 20231, + "TTAGGCA": 20232, + "CTTTAATTTT": 20233, + "CTTTGAGAA": 20234, + "GTCTCGC": 20235, + "CACAGGCAA": 20236, + "GAGATGCC": 20237, + "GAGAGGCCA": 20238, + "TAAAACATTTT": 20239, + "TTTTAGCTT": 20240, + "GATGGGGAA": 20241, + "CATACCTA": 20242, + "TCATTGTGA": 20243, + "TCCTTGGTT": 20244, + "CTAAAGGAA": 20245, + "GCTCACTG": 20246, + "CTTTATTTTTT": 20247, + "TAATCCCAGC": 20248, + "GGACCACAGG": 20249, + "GAAAATGTGGCACATATA": 20250, + "TAGCCTCC": 20251, + "GCGGGGTT": 20252, + "TTTTCTGTT": 20253, + "TGATATTTG": 20254, + "GTACTTGG": 20255, + "CTCTTATTTT": 20256, + "TGTTCGTT": 20257, + "GAGACACC": 20258, + "TCTGGCCTT": 20259, + "TCAACACTT": 20260, + "CGGCCGG": 20261, + "GAAAACAAAAA": 20262, + "GGGGGTTA": 20263, + 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"GCTACCTA": 20309, + "TATAACACA": 20310, + "GAGCACAGG": 20311, + "CTGGGCACA": 20312, + "TCGGTTTG": 20313, + "CTTCATATT": 20314, + "TCGCGGCC": 20315, + "GCCGGGTGTG": 20316, + "ATTAA": 20317, + "TCGCACTG": 20318, + "GGACCGG": 20319, + "GTAGCTCC": 20320, + "GAAATCTCA": 20321, + "CAAACTTAA": 20322, + "GGAAGGGGAA": 20323, + "TAAAACATAA": 20324, + "CTCAGACTG": 20325, + "CCTTCCTGA": 20326, + "CGGTATG": 20327, + "GGGAĊ": 20328, + "GCAAAGATG": 20329, + "GGATTGACTG": 20330, + "CAATGGTTGAA": 20331, + "GAACAGTAA": 20332, + "GCCATTCCCTT": 20333, + "TCGTCAAACCCATG": 20334, + "TTTTCTCTT": 20335, + "CTGCTTTTA": 20336, + "GGAGCAAAA": 20337, + "GCCCCAAAA": 20338, + "CTCTAACC": 20339, + "CTCGTTACCTG": 20340, + "TCTTCTGTG": 20341, + "GAGAGCTGA": 20342, + "CATTCATGA": 20343, + "CAGACCCC": 20344, + "CCTTGATA": 20345, + "GATTCTTAA": 20346, + "GTCCCATA": 20347, + "CGAGTTGA": 20348, + "GGTTCATA": 20349, + "TGTAĊ": 20350, + "TTATTGCAA": 20351, + "CCAAAGGAA": 20352, + "CGCCTGTG": 20353, + "GAGGAGGAGGA": 20354, + "GCTCGTG": 20355, + "TACTATTTA": 20356, + "CTGGGGATT": 20357, + "CTCCACCTG": 20358, + "GAAGAGTGA": 20359, + "CTTCCCCTG": 20360, + "CTTCATATG": 20361, + "GCGTAGA": 20362, + "CTTCTGCA": 20363, + "GTACCTCC": 20364, + "GTCGACTT": 20365, + "GAGACAGC": 20366, + "CTGACATCA": 20367, + "GAAAATAGAA": 20368, + "GAAAAAAATA": 20369, + "CAAAAGGAAA": 20370, + "TGTACC": 20371, + "TTTTTTGTT": 20372, + "CGCAGCTG": 20373, + "CCATGGAA": 20374, + "TAAAAAATTAA": 20375, + "CCCTCTGCC": 20376, + "TGATACATT": 20377, + "CTGGATGAA": 20378, + "CTTCTGCCTCAGCCTCC": 20379, + "GGATGACAGA": 20380, + "CACCAACAGTG": 20381, + "TCCATCACCAACTCCCGGA": 20382, + "TGAGCTTTT": 20383, + "TTAGCCA": 20384, + "TCCTCATA": 20385, + "CTCTCAGTG": 20386, + "GATTGATGA": 20387, + "CTAAGGGG": 20388, + "GCCAGCCCGG": 20389, + "CATAGTAAA": 20390, + "CGCAGGTG": 20391, + "CGAATAA": 20392, + "GCGGTTAA": 20393, + "TCACACTGA": 20394, + "GGAAATGGCAA": 20395, + "TAATAAATAAA": 20396, + "GTATGAGA": 20397, + "CTACGTC": 20398, + "GAAACAAAATT": 20399, + "TCTTCTCTG": 20400, + "TGAACGTA": 20401, + "TCTGCATTTT": 20402, + "TCAAGCATT": 20403, + "GAGGTGAAA": 20404, + "CCTGCAGC": 20405, + "TAGAGAAAAA": 20406, + "GATATAATT": 20407, + "GTAAATTAAA": 20408, + "TCTGAGATT": 20409, + "TCATTTCAAA": 20410, + "CCAAACACA": 20411, + "CAGAAGGAA": 20412, + "TGATGCGG": 20413, + "TACAAGAA": 20414, + "CAGCCCGG": 20415, + "CCAGGTGGG": 20416, + "CTACAGTCA": 20417, + "GGGGTGGAAA": 20418, + "CCCATGAAA": 20419, + "CCATAAGGGTG": 20420, + "CAGAGTACATCA": 20421, + "TCAGTTCTT": 20422, + "CAAGCAGA": 20423, + "CAAAGCATT": 20424, + "GTAACATTA": 20425, + "CTAATAATA": 20426, + "GAAAAGGGA": 20427, + "CTTTTTGCA": 20428, + "TGAAATATG": 20429, + "GAGAATGAA": 20430, + "CTCTTCTTA": 20431, + "CGAAGTCA": 20432, + "TGATGGATT": 20433, + "GGCTGTGGG": 20434, + "GCGGCTCC": 20435, + "GATCAGATCA": 20436, + "CCTTTGAAAA": 20437, + "GTCATGTTTT": 20438, + "GCGTCATT": 20439, + "GAAAACAGCA": 20440, + "TTTAGAGG": 20441, + "GTTTGCTCA": 20442, + "CCATGGCC": 20443, + "GCAGGTTTG": 20444, + "GGAGACTCA": 20445, + "TGTGTGTATA": 20446, + "TCGATAAA": 20447, + "TTATACTTA": 20448, + "CCACATCTT": 20449, + "GGATGAGATGGTTGGATG": 20450, + "ATTTG": 20451, + "CAGCAAATT": 20452, + "GGTCTACA": 20453, + "TTACGTT": 20454, + "TCCCTGAAA": 20455, + "CATTCTATT": 20456, + "CAGATATCA": 20457, + "GCTGTCTCA": 20458, + "GCTGCTCTG": 20459, + "CGACACCA": 20460, + "TGGACCA": 20461, + "TCTGACTCA": 20462, + "GGTACTT": 20463, + "CTTCACTCA": 20464, + "GAACACGG": 20465, + "TATTTGAACAGTGA": 20466, + "TCCCAAGG": 20467, + "GCACAGGAA": 20468, + "CATTGACTT": 20469, + "TCAACTCAA": 20470, + "GCAAGAGCA": 20471, + "GATGTACA": 20472, + "GCCAGCCAA": 20473, + "TTATTGGTT": 20474, + "TCCAACAAA": 20475, + "GCTGAGGGAA": 20476, + "TAATGCTGA": 20477, + "TCTCTGAAAA": 20478, + "CGGGGTTTCACCATGTTA": 20479, + "GATCTTTTA": 20480, + "GTGTĊ": 20481, + "GGATATCC": 20482, + "TGTTGATGA": 20483, + "TCTGGAGCA": 20484, + "GGAAGCGG": 20485, + "GGAAGGTGA": 20486, + "GCTGGAGC": 20487, + "CATGAAATG": 20488, + "TCTCTAGTA": 20489, + "CTTAGAATG": 20490, + "TCCATTTTTT": 20491, + "CTTTTTTTTAA": 20492, + "CAAAACAGAAA": 20493, + "CATAGCTA": 20494, + "CGGAGAGA": 20495, + "TTATGTATA": 20496, + "GGATGCAGG": 20497, + "GCATGCATG": 20498, + "GACTAGAGA": 20499, + "CTGCAGTGAA": 20500, + "CGTAATTA": 20501, + "TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC": 20502, + "GCTCAAATG": 20503, + "TAAGGAATT": 20504, + "TTTTCCCC": 20505, + "GTGAAAAAAA": 20506, + "CCAGCCCC": 20507, + "GTACTCTG": 20508, + "GGAAGATCA": 20509, + "GCTTGGCTG": 20510, + "GCAATATTA": 20511, + "CCAATCCA": 20512, + "CTAACACTT": 20513, + "CCCACCAAA": 20514, + "TAGGCGA": 20515, + "TAATTGGTT": 20516, + "CCTAGGGG": 20517, + "TGCCCTTA": 20518, + "TCTTTTGCTGTGCAGAA": 20519, + "TACAATTTA": 20520, + "GAGTTTAGTTGTAA": 20521, + "GGGCTATG": 20522, + "CCACCACCATG": 20523, + "GCAGTGAGCCAAGATT": 20524, + "TACGGTG": 20525, + "GGGTCTCC": 20526, + "TAACATTTG": 20527, + "GCATCATCA": 20528, + "CCAGCCCTG": 20529, + "TCTTGTATT": 20530, + "CATTCAGTG": 20531, + "TGAACAGCA": 20532, + "GGAAGACTT": 20533, + "GATTCTGAA": 20534, + "CTTTTGATG": 20535, + "CTTTTGACTG": 20536, + "TAGGCAAAA": 20537, + "TCATTCCAA": 20538, + "AAATTTAAA": 20539, + "GCAGGCATG": 20540, + "GCTTATTTG": 20541, + "GAATAGCTT": 20542, + "CAAGTAGCTGGGATTACAGG": 20543, + "GGGCTAATATCCAGAA": 20544, + "TAACTGTA": 20545, + "TTTTGCTG": 20546, + "GTGTGTGAA": 20547, + "GTAGCGG": 20548, + "TATATAATTA": 20549, + "TGTTCATTTT": 20550, + "CCTTCATCA": 20551, + "TATGTATATT": 20552, + "CCTGCTGAA": 20553, + "GGCAGCGG": 20554, + "GTCTTGGTT": 20555, + "CGAACGAA": 20556, + "TCTAAGAAA": 20557, + "TAATGCTAA": 20558, + "GGCTGGAGG": 20559, + "GTCTGCTG": 20560, + "TATCTAAAAA": 20561, + "TCCCACTGA": 20562, + "TAAGATATT": 20563, + "GTCCAGTGA": 20564, + "CCAATTAAAA": 20565, + "TCACTTCC": 20566, + "CCATCTATG": 20567, + "CTACAGTC": 20568, + "TAAACCTCA": 20569, + "CTCCACATT": 20570, + "TGAAGGCC": 20571, + "TGAAGTGAA": 20572, + "CGAGTTAA": 20573, + "CCTAGTTTT": 20574, + "TCTCACACA": 20575, + "GCAAAGCTG": 20576, + "TCTGAGTCA": 20577, + "TCTGAGAGG": 20578, + "CCAAATATA": 20579, + "TGCCCTAA": 20580, + "CTCTTCCAA": 20581, + "GTTTGTATT": 20582, + "TGAGAGCAA": 20583, + "TAAATTTAAAA": 20584, + "GAACATGTT": 20585, + "CAACAGCTT": 20586, + "GAGGTGGAGG": 20587, + "GGCTCTGGG": 20588, + "TCTTTATAGCAGCA": 20589, + "GTCCTTCCAATGAATATT": 20590, + "GGGTCCAA": 20591, + "GACGTAAA": 20592, + "GAACTATG": 20593, + "GTTTAAGAA": 20594, + "TAAATAACAA": 20595, + "CTCCATTTG": 20596, + "CACAGATTTT": 20597, + "GAAACCCC": 20598, + "TACACTGTT": 20599, + "TCCAGCTGA": 20600, + "GCTACTGAA": 20601, + "GTAAATGCA": 20602, + "CTTTGCCAA": 20603, + "TCATGCTG": 20604, + "GTGTGTATA": 20605, + "GAAGAAGTTA": 20606, + "GGAGACCAA": 20607, + "CTTTATCAA": 20608, + "TGATACCTT": 20609, + "CCAGAAGAGG": 20610, + "TCCCTAAAA": 20611, + "CTTCTCATT": 20612, + "GTTCTATA": 20613, + "CACACCCCA": 20614, + "GAAAGAATG": 20615, + "CTCACTGTT": 20616, + "TATGTATATATA": 20617, + "CTCCCATCA": 20618, + "TACAGCATT": 20619, + "TCTACTGAA": 20620, + "GTCCCAGCC": 20621, + "CAAAATGGG": 20622, + "GACATAGAA": 20623, + "TCCTTTCAA": 20624, + "CTAAACTAA": 20625, + "TAATAĊ": 20626, + "CGCATTAA": 20627, + "GAAGAGTCA": 20628, + "GGACTATAGG": 20629, + "TCTGGAAGTGGA": 20630, + "GACCCTGTCTC": 20631, + "GTGCTGGGATTAAAGGCGTGCGCCACCACCGCCCGG": 20632, + "CATCTCATT": 20633, + "CCGTTTTA": 20634, + "CCCCAATG": 20635, + "GATTTTATG": 20636, + "GACTTTTTG": 20637, + "GTACATCC": 20638, + "GCCTĊ": 20639, + "TATTTATCC": 20640, + "CTCAGCCAA": 20641, + "CTCACCCTT": 20642, + "CATGGATGAA": 20643, + "CGGCATCA": 20644, + "GATGCAGTG": 20645, + "TTCATCC": 20646, + "TCTTAGAAA": 20647, + "CTCAAGAGA": 20648, + "CTCTGGTG": 20649, + "GGGAAGAA": 20650, + "GGATTGCTG": 20651, + "TAGTATTTG": 20652, + "GGGTTAAAA": 20653, + "GTGTACTTA": 20654, + "CCATAGCAA": 20655, + "CAAAATCAGCAGG": 20656, + "TCGGATTA": 20657, + "TCTAAAAATT": 20658, + "TAACCTC": 20659, + "TCAGĊ": 20660, + "GTAACTC": 20661, + "CGACCTCA": 20662, + "GGCAGGAGG": 20663, + "GGGACCAGGA": 20664, + "TCTCATATT": 20665, + "TAAAAAGTTA": 20666, + "GAATTTGA": 20667, + "GAAAAATTTT": 20668, + "GGCTGCTG": 20669, + "CAGGAGGA": 20670, + "GAATGGGAA": 20671, + "GAATAGATT": 20672, + "TTAAAAATTA": 20673, + "CCTGTTAAA": 20674, + "CTTGGGAGGCAGAGGCAGG": 20675, + "CAATTTTTG": 20676, + "CCAGCCCCA": 20677, + "CAAATAAATG": 20678, + "CCAAGAGCA": 20679, + "TTATTGTGA": 20680, + "CTTTGCAGA": 20681, + "TCCTGCTA": 20682, + "GTTTGGGA": 20683, + "TGATGCCAA": 20684, + "TCATGGAAA": 20685, + "GACCAGAGA": 20686, + "GATTTTTTAA": 20687, + "GGGTTGGGG": 20688, + "CAACAACAAAAA": 20689, + "TTATAGATT": 20690, + "GTGGTGCACA": 20691, + "CAACACGG": 20692, + "TCATAGCAA": 20693, + "GATTTGTTTT": 20694, + "CTTGGGAGGCTGAGGCAGGAGAATG": 20695, + "CCTCTCTCTC": 20696, + "TCCCAAGA": 20697, + "GCACTGCTG": 20698, + "GCCCCTCTG": 20699, + "TAAAGTCTT": 20700, + "TCAACCCC": 20701, + "GATTCCCTG": 20702, + "GGTGGACA": 20703, + "TTATTCCA": 20704, + "GGGACTCC": 20705, + "GATAĊ": 20706, + "GTAAATGTG": 20707, + "GTAAAGGAAA": 20708, + "CTCTTTCTA": 20709, + "CTCAAGAGG": 20710, + "CCTGACATT": 20711, + "TAAAAAAAAAAAAAAA": 20712, + "TCAGATATG": 20713, + "TCCAGGCCCGTC": 20714, + "TAACTGAAAA": 20715, + "CTGGGTATATACCCAAA": 20716, + "CAAGTTTTA": 20717, + "GTTTCCCTG": 20718, + "TCTTAAATG": 20719, + "TGTTTACCA": 20720, + "CAAACCCCA": 20721, + "CATTGATTA": 20722, + "TGAACACAA": 20723, + "CAGGGTCTT": 20724, + "CTGACAAAA": 20725, + "CATGTATTTT": 20726, + "TCCAGTGTG": 20727, + "GTCCTCAAA": 20728, + "CGCGCGCA": 20729, + "TACCCCTT": 20730, + "CCATTCTCA": 20731, + "TCTAATATA": 20732, + "GGATTTCTG": 20733, + "GAATGGAGA": 20734, + "TCATGCATG": 20735, + "GGCCAGGTT": 20736, + "AAAAAAAAAAAAAAAAAAAAAAAAA": 20737, + "CCTCTGCCTCCCAA": 20738, + "CCACTGCACTCCAGCC": 20739, + "GTTTCGA": 20740, + "GCAGTTTA": 20741, + "GCAGCCAGA": 20742, + "GTCGTGTT": 20743, + "CAAATCTTA": 20744, + "TCAAGATTA": 20745, + "TAAAATAAATT": 20746, + "GTGATCCA": 20747, + "TCTCTCCATT": 20748, + "CGTTCATT": 20749, + "GGTACCTT": 20750, + "CTCTTGAA": 20751, + "CGCATTTG": 20752, + "GTATTGATT": 20753, + "GTCAATTTA": 20754, + "GAAAAATATT": 20755, + "TCAGACAGA": 20756, + "CCAGAGTAA": 20757, + "CCACCCTCC": 20758, + "TTACAGCAA": 20759, + "CTGATGAAAA": 20760, + "GATCTTAAA": 20761, + "TATATATATATATATATATATATATATATATATATATATATATATATATATATATA": 20762, + "TCCGCCTGGCTCTGCCTCCCGA": 20763, + "TCGGAGCA": 20764, + "TCAAAAAAAAA": 20765, + "TAATCTGAA": 20766, + "CTGTGAGCA": 20767, + "CTTTCAATT": 20768, + "GCCTCAGCA": 20769, + "CTATCAAAA": 20770, + "CTACTTTTA": 20771, + "TAAACAAATT": 20772, + "CTCGGGTT": 20773, + "TCAACTAAA": 20774, + "TCAAGGTCA": 20775, + "CTGAGACTT": 20776, + "GCTTCAGTG": 20777, + "CCTTTCCAAA": 20778, + "CATGTAGAA": 20779, + "GATGCAAAA": 20780, + "GGTTGGAAA": 20781, + "GAATTAAATT": 20782, + "CAGAATTAA": 20783, + "CAATTGAA": 20784, + "CGGAGGAA": 20785, + "GCATTTCTA": 20786, + "TGAGGCAGA": 20787, + "GCGGCCTT": 20788, + "TATAAATTAA": 20789, + "CTATAGTTA": 20790, + "CATGTGCCACCA": 20791, + "TGACTCCAA": 20792, + "CACAGCCAA": 20793, + "CACAGGCCA": 20794, + "GAGACCTA": 20795, + "GTGAGTGCA": 20796, + 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21061, + "TCCCAACA": 21062, + "CTCTCTCCTT": 21063, + "GAAATTTTAA": 21064, + "TATAGTTAA": 21065, + "TGTTGAGAA": 21066, + "GTGACATTA": 21067, + "TATGCATTTT": 21068, + "GTTATTTTAA": 21069, + "CTTAGAATT": 21070, + "CCCAGTGTG": 21071, + "GTAAAGTTA": 21072, + "TCTGAGCTG": 21073, + "TGCCCGA": 21074, + "TCCAATGGG": 21075, + "CGCAGTG": 21076, + "GTATTGTAA": 21077, + "CGGATTTG": 21078, + "TAGAATTTG": 21079, + "TAGCATGAA": 21080, + "CCTTATTTG": 21081, + "GTTTACCTT": 21082, + "GAATATCAA": 21083, + "CCAGAGCAA": 21084, + "GCCCATGTT": 21085, + "GCCCAGTGA": 21086, + "GAGGTTTCTG": 21087, + "TCGAAAATT": 21088, + "TCCGGGTG": 21089, + "CTTTAAGAA": 21090, + "GTTTCCAGA": 21091, + "CACAGACCA": 21092, + "CTGATACC": 21093, + "GAGGCTGGG": 21094, + "CTTAGCAAA": 21095, + "GGGACTAA": 21096, + "GCAAACAGG": 21097, + "TAATATAAAAA": 21098, + "GTATTTGATCC": 21099, + "GAAGAATTA": 21100, + "CTGTACATG": 21101, + "TAAGACAAAAA": 21102, + "TGATAGTGA": 21103, + "CTGGAGCTT": 21104, + "GTAGAATAATA": 21105, + "GTGCATGTG": 21106, + "TAAGTTAAAA": 21107, + "GAACCTGGGAGGCGGAGGTT": 21108, + "GCGTAATT": 21109, + "GCGGATA": 21110, + "TGAGCTGAA": 21111, + "TCCCAAATG": 21112, + "CTTTGGGTT": 21113, + "TAAAGAGGA": 21114, + "TATTGAGTA": 21115, + "GAAATTATG": 21116, + "GAAAGATTA": 21117, + "TCTGCAAAAA": 21118, + "GAGGCAGTG": 21119, + "GGCAGCTCA": 21120, + "GTAAAGATT": 21121, + "TCTGATGAA": 21122, + "GTGAAGGTG": 21123, + "CGCCCGA": 21124, + "TAATGGCTT": 21125, + "TAATGGAAAA": 21126, + "GAAGAGTTA": 21127, + "GGGTATATAA": 21128, + "GTTTTTTGTTTT": 21129, + "TTTTTATTATT": 21130, + "CTGGAATGCA": 21131, + "TCCCTACAAAGGACA": 21132, + "GGCCAAAAA": 21133, + "TCGTCTTA": 21134, + "TCACTGCA": 21135, + "TCAGAAATG": 21136, + "TCAGCAGA": 21137, + "CCACTGCTG": 21138, + "GCCCTGAAA": 21139, + "CTAGGTTA": 21140, + "GAAATCTTA": 21141, + "GAAACGCA": 21142, + "TCTTTCCTG": 21143, + "GTAAAAAAAAA": 21144, + "TACACAATG": 21145, + "TTCAGAAAA": 21146, + "GATATTTTTT": 21147, + "GTAAATATTTT": 21148, + "TCATTGCA": 21149, + "GAATTCAGA": 21150, + "CACCTATA": 21151, + "CTTTGCCTG": 21152, + "GTATTCATT": 21153, + "GTCTTGTATG": 21154, + "CCATTTGAA": 21155, + "GCAGGGGAA": 21156, + "TAGTATTTA": 21157, + "CCCTATTA": 21158, + "AAATATGTT": 21159, + "CAGTCGCTCA": 21160, + "CTTCATCTG": 21161, + "TCATACAAA": 21162, + "TTACATGTT": 21163, + "CTACATGATT": 21164, + "GAACCCAGGAGGCGGAGGTT": 21165, + "CTCCTGGGCTCAAGTGA": 21166, + "GCCATCCCATTA": 21167, + "GCGGATCACGAGGTCAGGAGATCGAGACCATCCTGGCTAA": 21168, + "GCTGAAACTCCAATACTTTG": 21169, + "CCAGTGGG": 21170, + "TAAAAGAGG": 21171, + "CTCAGCCTG": 21172, + "CTCACACTT": 21173, + "CATGGTCC": 21174, + "CCTGGCTAA": 21175, + "TTAACTGA": 21176, + "CTTTTGTTATT": 21177, + "TAGGTAAGCA": 21178, + "TCAAATTTTA": 21179, + "TCAAATGTA": 21180, + "CCTACTTAA": 21181, + "TCTCACAA": 21182, + "GCAAATATG": 21183, + "TCATTTAATT": 21184, + "TGAAATCAA": 21185, + "GGCCTCTA": 21186, + "GGCCGGGG": 21187, + "GTCTTTAATT": 21188, + "CCATTTCTA": 21189, + "GTCTGGAGA": 21190, + "CAGCCTCTG": 21191, + "CCACACAAA": 21192, + "CTTAAAATCTCGACGTT": 21193, + "TAAAAATTAAAA": 21194, + "GCCACCGCGCCTG": 21195, + "TCTTCTTTTGAGAA": 21196, + "TCCCCACCA": 21197, + "GTGGTGAAA": 21198, + "GTTTGGGTT": 21199, + "CTAGAGC": 21200, + "TAAATGAGA": 21201, + "TGTGGGTAA": 21202, + "CAGGCTGAA": 21203, + "CAGGTGCTG": 21204, + "GTGACTAAA": 21205, + "GATTCCAAA": 21206, + "GCCAGGAGG": 21207, + "TAGGAAAAAA": 21208, + "GGCAĊ": 21209, + "CCTAGTCC": 21210, + "GCAAATGTA": 21211, + "CTTAACCAA": 21212, + "GTGTTGGTT": 21213, + "TAATGTCAA": 21214, + "GTTTGCTTTT": 21215, + "GAAGGCGG": 21216, + "CAAGACACA": 21217, + "GGATTACAGA": 21218, + "CTGCCCCCA": 21219, + "GGACACCAA": 21220, + "TAATCATGTG": 21221, + "TAACTGGG": 21222, + "TCCCTCCA": 21223, + "TCCTCCAGA": 21224, + "CTTTCTATA": 21225, + "GGAGCTAAA": 21226, + "CCAGAAGAA": 21227, + "GAAAGAAAAAA": 21228, + "CAAACCTCA": 21229, + "TCTCTTATG": 21230, + "CTAATAAAAA": 21231, + "GACACCCA": 21232, + "TAAAAATGTG": 21233, + "CGTTCCTT": 21234, + "GGCCTATA": 21235, + "GCTGAGGTT": 21236, + "TATTTTTATA": 21237, + "GCCAAGAAA": 21238, + "GACCCGA": 21239, + "CAACATATA": 21240, + "CTCTCCCTCC": 21241, + "CTCAAAATAAATTGGTT": 21242, + "CCTGAGGTCAGGA": 21243, + "GCGACTTAGCAGCAGCAGCA": 21244, + "GAAGCTCC": 21245, + "CTGCCAAAA": 21246, + "GTTTCCTTA": 21247, + "GCAGCTGTT": 21248, + "CCAGCTGTG": 21249, + "GTCTCATCA": 21250, + "GAAACAATG": 21251, + "CAAAGAGAAA": 21252, + "CAGGCTTTT": 21253, + "TAAAACCAAA": 21254, + "CCTGGGTCA": 21255, + "GATGCAGCA": 21256, + "TTAAGTG": 21257, + "TTAATTTTTT": 21258, + "GGTGGTTA": 21259, + "GGTGCGTG": 21260, + "TGATGGAATT": 21261, + "TCACATCTG": 21262, + "CCCCCAATCCCTCCCAGCA": 21263, + "TGAGĊ": 21264, + "CTTGTGAAA": 21265, + "GTTGCTGTG": 21266, + "GCCTTTTAA": 21267, + "TATTTCTAAA": 21268, + "GCTGGGTGA": 21269, + "GCAACACTG": 21270, + "GATTTCCAA": 21271, + "TCCATATGA": 21272, + "GGCATAGA": 21273, + "TCTCAGATG": 21274, + "CTGAACAAA": 21275, + "TAATACCTT": 21276, + "GAAAAACTA": 21277, + "GTTTGCCTT": 21278, + "GCATGGAGG": 21279, + "CACTCTGATG": 21280, + "TGATCATTA": 21281, + "CTTGTTCTT": 21282, + "GTTTCAATT": 21283, + "GCATCCCTG": 21284, + "TGTGCAGTG": 21285, + "CAGGCTC": 21286, + "GTCACAGGA": 21287, + "TTTTATCTA": 21288, + "TCTCCCAGA": 21289, + "TTTTTTGAA": 21290, + "GCAAACTGA": 21291, + "GGCCTCTGA": 21292, + "CTTTGGTGA": 21293, + "CTTTGTATG": 21294, + "TATAAAAAAAA": 21295, + "CCTCAGTGA": 21296, + "TACTATTAA": 21297, + "TAAAAAATAAAA": 21298, + "CTTTAAGAGG": 21299, + "CACTCTGTTTG": 21300, + "GTTTCCTTTG": 21301, + "CATATTTATT": 21302, + "TACCAAAAA": 21303, + "GCGAACAA": 21304, + "TCCTCGAA": 21305, + "CAAGCCACA": 21306, + "CAATGACC": 21307, + "CTGTCAAAA": 21308, + "TGTTTAGCA": 21309, + "CAGGGAGTA": 21310, + "CCTTCTAAA": 21311, + "GATTCAGTT": 21312, + "TCTCTATCC": 21313, + "CTCCTGTGA": 21314, + "GCCATCTGA": 21315, + "CAAAATCTG": 21316, + "CTTTGCATG": 21317, + "CCATTGTG": 21318, + "GGCTGGGTG": 21319, + "CAGTATTTA": 21320, + "TAAGATATA": 21321, + "TCATCTCTG": 21322, + "TCCTCACTG": 21323, + "CTGTTTTTG": 21324, + "CTTCAGGAA": 21325, + "CATTGATCA": 21326, + "CAGGCTGGG": 21327, + "TCAACCTA": 21328, + "GGAATACAA": 21329, + "GCTGCACTT": 21330, + "GTAAGTGAA": 21331, + "CTAACATCA": 21332, + "TTAAGTCA": 21333, + "GTTTTGGA": 21334, + "GTTTTGATG": 21335, + "CCCATACC": 21336, + "TAGACATAA": 21337, + "TAATTCAAAA": 21338, + "CGTGGCTT": 21339, + "TCTAATATT": 21340, + "TAATGGTTA": 21341, + "CGTCTCTG": 21342, + "GTGGGCAGA": 21343, + "TCTGAATTA": 21344, + "CTGTAGATG": 21345, + "CAAGACAGA": 21346, + "GGTGGGAAA": 21347, + "TAGTGACACTA": 21348, + "GACGGAGGAGCCTG": 21349, + "TCTGTGTGTGTG": 21350, + "TAATCAGA": 21351, + "TGAGTTAAA": 21352, + "CTTTATATA": 21353, + "GTATTATTTT": 21354, + "GAAAGACAAA": 21355, + "CGGCCCCA": 21356, + "TTAAGTGA": 21357, + "GTCCTACC": 21358, + "CTTTTTGGG": 21359, + "GGGGCTTA": 21360, + "TCAAAGCA": 21361, + "TAATACAGA": 21362, + "TGCCCCCA": 21363, + "CGAATTTA": 21364, + "GGAGAGCTT": 21365, + "CGTCAATG": 21366, + "GAGTCAGACACGACTGA": 21367, + "TATCTGAAA": 21368, + "GACAAAATG": 21369, + "TGACAAGAA": 21370, + "TGTTAAAAAA": 21371, + 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"TAAGTTCTT": 21416, + "TAACTTTCA": 21417, + "TTATAGAA": 21418, + "GTTCGAAA": 21419, + "GGACTGGA": 21420, + "GCCGTGTT": 21421, + "CTAGCATG": 21422, + "GAAAGGGCA": 21423, + "CAGGCTCAA": 21424, + "CTCCCTGAA": 21425, + "CTAAGACAA": 21426, + "TCTGAGTGA": 21427, + "GGTAGAAAA": 21428, + "GCCAATATCA": 21429, + "CTGTGCTT": 21430, + "GTTTGTTTTA": 21431, + "CTATTGATT": 21432, + "GATGAGAA": 21433, + "CTCAAACTCA": 21434, + "CAGATTAAAA": 21435, + "TAAGGGATT": 21436, + "CAAGAATACTG": 21437, + "TATATATAAAA": 21438, + "CCCCCACCCC": 21439, + "CAGTTTTTG": 21440, + "TAAGCAGCA": 21441, + "TAATCCAAA": 21442, + "TCAGCTCTG": 21443, + "TCACGTCA": 21444, + "GTCGGTTA": 21445, + "CAAAGAGGA": 21446, + "GAGATCTA": 21447, + "CAGACACACACA": 21448, + "GCTTCTAAA": 21449, + "CATGGCTA": 21450, + "TAATTCAGA": 21451, + "GTGAAGTCA": 21452, + "TGATTTCCA": 21453, + "TATTACAAA": 21454, + "TCTAATAAA": 21455, + "GTTTGGTA": 21456, + "CGTACGG": 21457, + "TAATAATTTG": 21458, + "TCTCTTAAAA": 21459, + "CTATGGAA": 21460, + "CTTGGCCTT": 21461, + "GAGAGAGAGAGAAA": 21462, + "GTTTACTAA": 21463, + "TGATATGTG": 21464, + "GTTTAACCTTTCTTTT": 21465, + "GCACCACTGCACTCCAGCCTGGG": 21466, + "CTTCAAACTATACTA": 21467, + "CCTGTTTA": 21468, + "GTTGAGCC": 21469, + "GCCCGAGG": 21470, + "CTCGCTA": 21471, + "GTCGAGAA": 21472, + "TATTGACC": 21473, + "CCAATCTA": 21474, + "TCTACAATTTT": 21475, + "GTCCĊ": 21476, + "TCTCACAAA": 21477, + "TATTTTTTTTA": 21478, + "CGCCTTTT": 21479, + "TAATGGGAA": 21480, + "CTAAAATTAA": 21481, + "TCTGTGTTA": 21482, + "GATCCTTA": 21483, + "GTGCCTGTG": 21484, + "CACTCTTTTTGTG": 21485, + "CTGGTTCCAAA": 21486, + "TAAGCATA": 21487, + "TCATCAGA": 21488, + "TCAGCCAGG": 21489, + "TGAACGTG": 21490, + "GTAATCAAA": 21491, + "CTCCCAAAA": 21492, + "TCTACCATT": 21493, + "CCCACCCAA": 21494, + "GTAAACATG": 21495, + "CCAAACATT": 21496, + "GCTGAGTGA": 21497, + "GTTAATGAA": 21498, + "GTCAATTTG": 21499, + "CAATATAAAA": 21500, + "TCACATCAA": 21501, + "TCTCAACCCCA": 21502, + "CACTGAATT": 21503, + "TGATAAATG": 21504, + "CCAGCGTG": 21505, + "GAAAGCATG": 21506, + "GAAAGGGGA": 21507, + "CAAACTCTT": 21508, + "GCTTCATCA": 21509, + "CTCAGTCTT": 21510, + "CCTGCCACCA": 21511, + "TACACAATT": 21512, + "TACAGCCTT": 21513, + "TCCAGGATG": 21514, + "GCCACCCAA": 21515, + "GGGGGAGAA": 21516, + "TTTAGATA": 21517, + "GGTGCTAA": 21518, + "GGTGCTTA": 21519, + "TGCAGCAAA": 21520, + "TTCACTAA": 21521, + "TCTCCCCTCC": 21522, + "GAATTCATA": 21523, + "GGCTGTTA": 21524, + "TTATGGAA": 21525, + "GTATGTATA": 21526, + "GCGACCTT": 21527, + "CAATATTATT": 21528, + "TCACATATA": 21529, + "TGACATCAA": 21530, + "GGACATAGGCATG": 21531, + "GCCCAGCAA": 21532, + "GGGCAATG": 21533, + "TAAGTGGA": 21534, + "TGACATTTG": 21535, + "CTTTCCTA": 21536, + "GTGCCCTA": 21537, + "GTTTGTTAA": 21538, + "GTATTTTAAA": 21539, + "CTAGGAAAA": 21540, + "TAAACGGA": 21541, + "TAAATTATTTT": 21542, + "TATTGAAAAA": 21543, + "TGAATACAA": 21544, + "CAGGCACCA": 21545, + "CAGGCGTGA": 21546, + "CTGACAGTG": 21547, + "CCTGGGGCA": 21548, + "GAAAATATGA": 21549, + "GTTTTCACA": 21550, + "CCCAGTGTT": 21551, + "CAAAAGAATTGAAAA": 21552, + "GCAAAGATT": 21553, + "TGAAATATA": 21554, + "CAGTACAGTA": 21555, + "TAGTATTAA": 21556, + "TAAATAAATAAAA": 21557, + "TCGCCCAGGCTG": 21558, + "GCTAGGGAGG": 21559, + "CAGTAAATT": 21560, + "TCCCTGCA": 21561, + "CTTTCATCA": 21562, + "GTAGCTTTT": 21563, + "TAAAGCCAA": 21564, + "TCTGCAGGA": 21565, + "TCAAGAATT": 21566, + "GCTGCTCAA": 21567, + "GCAACGAA": 21568, + "GGTTTAAAA": 21569, + "GGTGTATT": 21570, + "TGCACGTG": 21571, + "CCAAACCA": 21572, + "CGCCTGCC": 21573, + "CCATTCCTG": 21574, + "TAATGCTTA": 21575, + "CCCCATGTGA": 21576, + "CTCCACTCC": 21577, + "GTTTACTTTT": 21578, + "CTTCCTCCAA": 21579, + "GAACAGTCA": 21580, + "CTGCCTCCA": 21581, + "TCACTGTTA": 21582, + "TGATGAATT": 21583, + "GCACCACTG": 21584, + "GGAGCAGAA": 21585, + "CTCCACTATG": 21586, + "TGTTTAATA": 21587, + "TCTGTAGCA": 21588, + "GTGACACC": 21589, + "TTTTACCAA": 21590, + "TATGCATAA": 21591, + "TTTACCTG": 21592, + "TGGACTGTA": 21593, + "GGGAGGATT": 21594, + "GTAAATCAA": 21595, + "GCAAAGTCA": 21596, + "TCCTTCATG": 21597, + "CCAAACTA": 21598, + "CCAAATTCA": 21599, + "GCTCATTAA": 21600, + "CTGTGCTTA": 21601, + "TGATGCTAA": 21602, + "TGAGGTGTG": 21603, + "TCATGCTA": 21604, + "CCCTTTCTT": 21605, + "CAATGGTG": 21606, + "TCGGCCTG": 21607, + "CTGCACCCATCAA": 21608, + "CTTTAGTTA": 21609, + "TAATTTATTA": 21610, + "TGTTGAATAGGA": 21611, + "GCTAATTTTTGTATTTTTAGTA": 21612, + "TCCTATAAA": 21613, + "TCCTCCACA": 21614, + "GGACAAGA": 21615, + "CCAGCCTCA": 21616, + "GTAGCAGG": 21617, + "TAAATAAATA": 21618, + "CTCTAATGA": 21619, + "GTCTCAGTG": 21620, + "CAAACATGA": 21621, + "CAGACAGTA": 21622, + "GCTGGCTA": 21623, + "TCAAACTAA": 21624, + "CTTAATATT": 21625, + "CTTTGCAGG": 21626, + "GACCTCCA": 21627, + "GTATAGTAACGA": 21628, + "GTGGATCACGAGG": 21629, + "CTTCAGACGGGCCTGGA": 21630, + "CCTATTTA": 21631, + "GGCTAATG": 21632, + "GTGGGAGA": 21633, + "GTTTCCCC": 21634, + "CAAAGCCTT": 21635, + "CAAACAGTA": 21636, + "GAGAGATCA": 21637, + "CTCAGGGCA": 21638, + "TTTTATTTTATTTT": 21639, + "GAGGTATA": 21640, + "TAGAGCTA": 21641, + "GTATTTAAAA": 21642, + "GTATTGCTT": 21643, + "CTCTGCGG": 21644, + "TCAAAACAA": 21645, + "GTGTGGGGG": 21646, + "TCGGTTAA": 21647, + "TAAGACATG": 21648, + "TCTATCTATCTA": 21649, + "CTTTATATGCCCCAGTA": 21650, + "TCTCTCACTGTTCAAATAAA": 21651, + "TAGCCCCA": 21652, + "TAAAGGTAA": 21653, + "CACACAGCC": 21654, + "TCTTCCTAA": 21655, + "CAAATCTAA": 21656, + "TGAATGCC": 21657, + "CAGATCCTT": 21658, + "CTCATGCTG": 21659, + "TATGGGTA": 21660, + "TCTCTTTTAA": 21661, + "TACACCCC": 21662, + "GATGTCCA": 21663, + "TTTGAAAAAA": 21664, + "GCCATTATT": 21665, + "CGCTGACA": 21666, + "CTGTTTCTCA": 21667, + "GTGTTTCTA": 21668, + "GTCAATTAAA": 21669, + "CAAAAACTA": 21670, + "GTTTGGTTA": 21671, + "CTATTCCA": 21672, + "TGATGCCTT": 21673, + "TATCACATT": 21674, + "TATCACAATT": 21675, + "TCATGTCCAA": 21676, + "GCTTATCCA": 21677, + "TCGGTGTG": 21678, + "GTGGTGTGTG": 21679, + "CCGGCGA": 21680, + "GCCCAGCTT": 21681, + "TCCTAATAATT": 21682, + "GCTCTGATG": 21683, + "CGCCAATT": 21684, + "GACAAATGGGA": 21685, + "TGATGTGAA": 21686, + "CTTTAAGG": 21687, + "GCCTAATA": 21688, + "CTCGCCTT": 21689, + "GTCTGTTAA": 21690, + "TGTTTATATT": 21691, + "CTGAGACAA": 21692, + "GTGAGACTG": 21693, + "GTTTTTTATT": 21694, + "TGGGGCCATG": 21695, + "TGCAGCTA": 21696, + "GGCATCCA": 21697, + "TTTTTTCCCC": 21698, + "TAAAAATAATT": 21699, + "CTCTTCTAA": 21700, + "GTCAAGGA": 21701, + "CCAGGCTA": 21702, + "GGTGAGAAGG": 21703, + "CTTTACTCA": 21704, + "GTTTACATG": 21705, + "GTTGGTGAA": 21706, + "GTTCCCCTG": 21707, + "CTTTCCAAAA": 21708, + "CCGACTC": 21709, + "TGAGGAGGA": 21710, + "GTATCTGTT": 21711, + "GTCTCCCCA": 21712, + "TCTTTAATA": 21713, + "TCTTCAGGA": 21714, + "CAAAGATCA": 21715, + "TCTGGACTT": 21716, + "GTGACGTT": 21717, + "CCTTGGTA": 21718, + "TATGGTAAA": 21719, + "CTTATCCA": 21720, + "CCCACGTT": 21721, + "TAATTGCAAA": 21722, + "CCAAAGTCA": 21723, + "CTAAACATT": 21724, + "GTTAAGTAA": 21725, + "CAATTTGTG": 21726, + "CCATTCTGA": 21727, + "GCCTGCTCA": 21728, + "TGAGGTTAA": 21729, + "CATTTTTTTG": 21730, + "GTTTATTATT": 21731, + "TCTATTAAAA": 21732, + "GTCAAAGCTATG": 21733, + "TATTAAATTA": 21734, + "GAAATAAGTATT": 21735, + "TCTCATTGTGGTTTTGATTTGCATTTCTC": 21736, + "TGCTGGAGAGGATGTGGAGAAA": 21737, + "GCGTTTTTT": 21738, + "TCAGAACA": 21739, + "TGACTGTA": 21740, + "GAAGCCAGA": 21741, + "CTACATC": 21742, + "GAGACAGTT": 21743, + "GGAAGACTG": 21744, + "GTGATATTA": 21745, + "CCTGCGTG": 21746, + "GTTTTTAAAAA": 21747, + "CTTTTGCAA": 21748, + "CCCACCCTT": 21749, + "CATATCCTTTG": 21750, + "TATTTTTAAAA": 21751, + "CTTTGCTAA": 21752, + "TCTTTTTTTTTTTTTTTTTTTT": 21753, + "TACCCAAAA": 21754, + "TACCTAGG": 21755, + "TCCTGCCAA": 21756, + "CTGTGTATG": 21757, + "GCATGGCAA": 21758, + "GTGCTGGGA": 21759, + "GTGCTGGAATTACAGG": 21760, + "GTTCCATAAACAA": 21761, + "GTCCTGGTCC": 21762, + "CAGAGGAATGGA": 21763, + "CCTCGCCCGGCTAGTTTTTT": 21764, + "GCGAGGTG": 21765, + "TAACGTCA": 21766, + "CTTGTGGG": 21767, + "GCATTATTA": 21768, + "GTATAATTA": 21769, + "GCCTCAGAA": 21770, + "CTAGCTGTG": 21771, + "CAAAGGCTT": 21772, + "CATTCTTAAA": 21773, + "TCTGCACTT": 21774, + "CTCAGAAGG": 21775, + "GATTCAGCA": 21776, + "GTTATCAAA": 21777, + "CACAATTAA": 21778, + "CTGCAGTTA": 21779, + "GTTTATCTA": 21780, + "CAGTCTTTG": 21781, + "CTCTACCAACTGA": 21782, + "GTGTCATCTGCATA": 21783, + "CTTCAAGAA": 21784, + "CTCCATTAA": 21785, + "GAAAGAGTG": 21786, + "TGAACACC": 21787, + "CAGATATAA": 21788, + "GGAATAGAA": 21789, + "GCTGCCAAA": 21790, + "CTCAGCTGA": 21791, + "GCCAGAATT": 21792, + "CCCCAAAAAA": 21793, + "GGGACTTA": 21794, + "GTAAACAGA": 21795, + "GCTCAGGGG": 21796, + "GACCTCAAA": 21797, + "CCATTTAAA": 21798, + "CTATTGTAA": 21799, + "TCATGTCATCTG": 21800, + "GCATCACCAA": 21801, + "GAACAAAGCTGGAGG": 21802, + "CACTĊ": 21803, + "TACCTGAAA": 21804, + "GCTAAATAAA": 21805, + "TTTTGCC": 21806, + "TCACAGGAA": 21807, + "TGAGCAGCA": 21808, + "GAACCAGG": 21809, + "TCCTGTGAA": 21810, + "CAACAAATT": 21811, + "GTTTGTGAA": 21812, + "GCTGTAGTT": 21813, + "CGGCCTCC": 21814, + "GATTCTGTT": 21815, + "CCCCTCTGA": 21816, + "CCCCCCCAA": 21817, + "TTATTTCTGA": 21818, + "GGCAGTTTT": 21819, + "TCTCATCTG": 21820, + "CGTTTAAA": 21821, + "CTTTGCTCC": 21822, + "GACCCTTA": 21823, + "CGGACGA": 21824, + "CTATTTAAAA": 21825, + "AAATTTATT": 21826, + "TGAGGCCAA": 21827, + "GCAGGGCC": 21828, + "CTTTATTATT": 21829, + "GAATAGAGA": 21830, + "GTGGTGGGTG": 21831, + "TCAATGTTA": 21832, + "CCGACGA": 21833, + "CTTGCTGTT": 21834, + "GTGGCAAAA": 21835, + "GCAGCTCTT": 21836, + "CTCTCTACA": 21837, + "GTCGAGGA": 21838, + "GTCTCAATT": 21839, + "TATACACTT": 21840, + "TCTTGGGTT": 21841, + "CAAATATATT": 21842, + "GGAACACTG": 21843, + "GCTGGGGCA": 21844, + "GAGGCAAAA": 21845, + "TCTAACAA": 21846, + "TTATTATTATTTT": 21847, + "TCAGATAGG": 21848, + "CTTTACTAA": 21849, + "TTATAGATG": 21850, + "GTTCAGTTA": 21851, + "CTCCTGTCTCAGCCTCC": 21852, + "GAACCCAGGAGGTGGAGGTT": 21853, + "GAAGTAGTGATTCCTTGGGCA": 21854, + "GGTCTCAAA": 21855, + "GTGGCGTG": 21856, + "CTACTGCTG": 21857, + "CTACCATCA": 21858, + "GTCTCGTT": 21859, + "TGAAGCTA": 21860, + "GGAATATTA": 21861, + "GGAAAAATA": 21862, + "GCAACTCC": 21863, + "GTCACATCA": 21864, + "CTTTTTAATT": 21865, + "CCCCTATG": 21866, + "TCCTTTCCA": 21867, + "CTCTTCATT": 21868, + "GTGTTGGGA": 21869, + "CAGTGCTG": 21870, + "CAGTGTTAA": 21871, + "CCTCAGGGA": 21872, + "TAAGATGTG": 21873, + "TTTTGTAAA": 21874, + "TCCTGACC": 21875, + "TCCCTTCCA": 21876, + "GGATGAATT": 21877, + "CACAGAAAAA": 21878, + "CATTCCAGG": 21879, + "CAGGGCTA": 21880, + "TCTGGGCCA": 21881, + "GTGACAATATA": 21882, + "CATGCTGAA": 21883, + "TATGGTTTTA": 21884, + "CCCCAAATG": 21885, + "TGCATAAAA": 21886, + "CTGAAGCTG": 21887, + "GCAAAGAAAA": 21888, + "CTTAAGTGA": 21889, + "GCCAAGCTT": 21890, + "GTCTGGAGG": 21891, + "GACTTGAAA": 21892, + "CCCTTCCCCA": 21893, + "CATGAGCCACTG": 21894, + "CGTCTTTA": 21895, + "GAGAAATTA": 21896, + "TCTGTGTTTT": 21897, + "CAACAATATGAA": 21898, + "GTTTACAGA": 21899, + "GTCCAGCTT": 21900, + "GTGGCAGGCA": 21901, + "GGTCGTA": 21902, + "TCGTCTGA": 21903, + "TCAGTTATT": 21904, + "CTCGAGC": 21905, + "CTCGGGGA": 21906, + "CTGACTGAA": 21907, + "GCAAGGGAA": 21908, + "CCCATAAAA": 21909, + "CAATTGCTT": 21910, + "GCCTTTGTT": 21911, + "GAGCAGGAA": 21912, + "TGAGGCGG": 21913, + "GTGTGCGG": 21914, + "CCTCTTATT": 21915, + "CCAAAATACCA": 21916, + "CCTCAAGGATCTA": 21917, + "GATTCTTCCTA": 21918, + "TTTTTCTTA": 21919, + "TCATTTTAAA": 21920, + "GAAGGAGAA": 21921, + "CTCAGAATT": 21922, + "TTTTATGAAA": 21923, + "GTTACACAA": 21924, + "GCCATACCA": 21925, + "TGGATAGA": 21926, + "TCTCAGGTT": 21927, + "GTAAAGAAAA": 21928, + "GGAAATTTG": 21929, + "TCCTTCTAA": 21930, + "CTAAACAAAA": 21931, + "GGCCCGA": 21932, + "CACCTACA": 21933, + "CAGAATTTG": 21934, + "GTATTTGATACA": 21935, + "CAATTGTTTT": 21936, + "CGAACGA": 21937, + "CTCTGTCTCAAAAAAAAAAAAAAAA": 21938, + "GCATTTATG": 21939, + "TGAGGCCTTCGTT": 21940, + "GAATGCAA": 21941, + "CAATGCTG": 21942, + "GGGCCTGGG": 21943, + "TGATGTTAA": 21944, + "TCCCTTAAA": 21945, + "GGAGCAGA": 21946, + "AAAAGGAA": 21947, + "GAGACGCA": 21948, + "TCAAGCCAA": 21949, + "CTGAGTGAA": 21950, + "GATTTATCA": 21951, + "CCAATATCA": 21952, + "CCTACGTT": 21953, + "TCTGAGACA": 21954, + "TCCTTCCCTT": 21955, + "CCAAATGTT": 21956, + "GTATTGTTTT": 21957, + "GTGTTGTTTT": 21958, + "TGATGTATT": 21959, + "TATGATTTA": 21960, + "CCCAACACA": 21961, + "TATCGTCA": 21962, + "TATCGATT": 21963, + "GGAAAATTA": 21964, + "TCAGAGCTT": 21965, + "TCCCACTCC": 21966, + "CTTTACATG": 21967, + "GTTTACACA": 21968, + "CCACAGTAA": 21969, + "GAGACTGGCTAGGCCACTCCAGGACCTT": 21970, + "GCGAAGCA": 21971, + "GCGTCTGA": 21972, + "TTTTTATCA": 21973, + "GCACGCTG": 21974, + "GGACCCTGA": 21975, + "TAAAGCTCA": 21976, + "CTCTATTAA": 21977, + "GTCTCATA": 21978, + "TGTTGAGTT": 21979, + "CATTCCCTG": 21980, + "GTGAGAGCA": 21981, + "GCAATTATT": 21982, + "TCTCTCAGG": 21983, + "CCAACCTA": 21984, + "CTTAACAAA": 21985, + "CTCTTCCCA": 21986, + "GTCAACAAA": 21987, + "CTGTGCTAA": 21988, + "TGATGGTAA": 21989, + "GTCTGCCTG": 21990, + "GAATGCATT": 21991, + "GCTCGCTG": 21992, + "CCTCGGGG": 21993, + "CTGCACTCA": 21994, + "TTACAGTTTT": 21995, + "GAACCCATG": 21996, + "GAAAATTAAAA": 21997, + "TCCCAAACACA": 21998, + "TCATCGA": 21999, + "GAAGAAATG": 22000, + "CTTCTACA": 22001, + "CCAGCCACA": 22002, + "TAAACAGC": 22003, + "CTCTCGTT": 22004, + "CACATACCA": 22005, + "CAGGGCTCC": 22006, + "TCAATGCC": 22007, + "TTTTAGCA": 22008, + "CGGGAAAA": 22009, + "TCTCTCCCC": 22010, + "TCCACCTCCA": 22011, + "TAATTGAGA": 22012, + "TCTCACAGA": 22013, + "CTTAATGTT": 22014, + "TCTTACAGA": 22015, + "CTATTGTTA": 22016, + "CAGGAGGAA": 22017, + "TCAGACCTT": 22018, + "TCACATGTG": 22019, + "CAACACCAA": 22020, + "GCACAGCTT": 22021, + "TCAGTATAAAA": 22022, + "TAGCCAAAA": 22023, + "CTAAAGAGCTTCTGCACA": 22024, + "GCAAAAAATT": 22025, + "TGACAGGAA": 22026, + "TGACAAGG": 22027, + "GAACCTAA": 22028, + "GTGTCACTG": 22029, + "GTTGTGAAA": 22030, + "CCATCGG": 22031, + "CCATCACC": 22032, + "GCCAAAATG": 22033, + "TATTTĊ": 22034, + "TATATATTAA": 22035, + "TCTTGACC": 22036, + "TGAACAGTT": 22037, + "TGAAGATTTT": 22038, + "CAAAATTATT": 22039, + "CGAACAA": 22040, + "GGGAAGGGA": 22041, + "CCTCCTTTG": 22042, + "CAGTAGAGA": 22043, + "TCTGAAGACA": 22044, + "GAGGTTTAATT": 22045, + "GCTGGGCGTGGTG": 22046, + "TCACATTAA": 22047, + "TTACAGCA": 22048, + "GAATCTTTT": 22049, + "CCACGTGA": 22050, + "CTATAATAA": 22051, + "GAAATAGTG": 22052, + "CAAACGGA": 22053, + "CAAAGACTG": 22054, + "CATTTCCTG": 22055, + "TGAATAAAAA": 22056, + "TCTGTACAA": 22057, + "TCAAGATAA": 22058, + "GGAAGGCCA": 22059, + "TAAAATGGAA": 22060, + "GTGATAAAAA": 22061, + "GCAACTAAA": 22062, + "GTCAGAGTG": 22063, + "TCTCTGTGTG": 22064, + "TACAGCTCA": 22065, + "GTTTTCAGA": 22066, + "TGCATAGG": 22067, + "CGTTGATT": 22068, + "CGCAGCTT": 22069, + "CCAGGGCC": 22070, + "TAAGAGAAAA": 22071, + "GTTCAAGACCAGCCTGGCCAACATGGTGAAA": 22072, + "TTTTAAATG": 22073, + "TGAGCCAGG": 22074, + "CTTCATTAA": 22075, + "GTCTGACTG": 22076, + "CACAGCATT": 22077, + "CACACATGA": 22078, + "CTGATATTA": 22079, + "GTTATAGTT": 22080, + "GATGCTCC": 22081, + "GCCATCAAA": 22082, + "GACACCCC": 22083, + "CGTGCTCA": 22084, + "GAATTCATG": 22085, + "TCTTACTG": 22086, + "TACCTTTTA": 22087, + "GTTTGCATG": 22088, + "GAATGGTA": 22089, + "CTATGGAAAA": 22090, + "CAGAGATATA": 22091, + "CTTCACCTG": 22092, + "TGATATGTT": 22093, + "CTAAAAAATACAAAAAA": 22094, + "GAATGGACTGGTTGGA": 22095, + "TCTGATCTTTGACAAA": 22096, + "GGGTCATG": 22097, + "GACTĊ": 22098, + "GCGTTGAA": 22099, + "TAAGĊ": 22100, + "TGAGTGCTG": 22101, + "TGAGTTTCA": 22102, + "TGAGCAGA": 22103, + "TCCTCAGCA": 22104, + "GTTGAAGAA": 22105, + "CATTCACCA": 22106, + "CTGATCCA": 22107, + "GTGACACTG": 22108, + "GCTGGACTT": 22109, + "GCTGGAGTA": 22110, + "CCTTGGCCA": 22111, + "GTCAGTGAA": 22112, + "CGACAGTG": 22113, + "GACATTATT": 22114, + "TCTGACATT": 22115, + "TCATTCCTG": 22116, + "GTATTCTAA": 22117, + "GTATTTCCA": 22118, + "CGCCTTTG": 22119, + "CTGTGGATT": 22120, + "TACTTTATT": 22121, + "GCTCCTTCA": 22122, + "GTTCATGAA": 22123, + "GCGCGGTG": 22124, + "GAGAGAGAGAGAGAGAGAGAGAGAGAGA": 22125, + "CCATGCTGTTTTGGTTA": 22126, + "TAGAAAATG": 22127, + "GGGTAAAAA": 22128, + "GCGAGATG": 22129, + "TCAGCTCTT": 22130, + "TTACATAA": 22131, + "CCACTGCC": 22132, + "TAAAGGATT": 22133, + "CCTTCCATT": 22134, + "GTTATACAA": 22135, + "CTTACTCC": 22136, + "TAGGCC": 22137, + "GACACCAAA": 22138, + "TTATTTCCACA": 22139, + "CGTGTCTG": 22140, + "GCAAAGCA": 22141, + "TATTTTTCTT": 22142, + "GTCTTGATT": 22143, + "TATAATAAAA": 22144, + "TCTAATTTTA": 22145, + "TGATGTATG": 22146, + "CAGTTGGTG": 22147, + "CCCTGCCTG": 22148, + "TCATGGCTCA": 22149, + "TCCCAGCCA": 22150, + "CTTTACAGA": 22151, + "TAACATAAAA": 22152, + "GTATAGAGG": 22153, + "TGATGTTCCCCTT": 22154, + "TACGCTA": 22155, + "GGTAAAATT": 22156, + "TAAGTGTA": 22157, + "GCAGCCTCC": 22158, + "GCAGTTCC": 22159, + "GGACTATG": 22160, + "GTCGAGCA": 22161, + "GAAATATATT": 22162, + "CAGGGCAGG": 22163, + "CAGGTATGA": 22164, + "GCAACCCCA": 22165, + "GTAATGCC": 22166, + "CTAACACC": 22167, + "GCCACTGTT": 22168, + "TGGGGCTCA": 22169, + "CGCATGTG": 22170, + 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22214, + "GCAAGACTT": 22215, + "CTAACTCC": 22216, + "GTTTTGCTCTT": 22217, + "CAAAAGGCA": 22218, + "CCTACCCCA": 22219, + "CTGAATGTG": 22220, + "GCAAACAAAA": 22221, + "TCATTGACAA": 22222, + "GAGAAGAAAA": 22223, + "TATTATGTT": 22224, + "CTCAATTAA": 22225, + "CAAAAATCAA": 22226, + "CTCTGGATT": 22227, + "GTTTGCATA": 22228, + "CTATTGTA": 22229, + "GCCTGCGG": 22230, + "CCTCCTCTT": 22231, + "TCATGGTG": 22232, + "CAGCCTGTG": 22233, + "TATCCTTTG": 22234, + "CTTCAGTATT": 22235, + "CTTTCCTTTT": 22236, + "TCCGCCTGCC": 22237, + "CATTTTAATT": 22238, + "TAATAAAAAAA": 22239, + "CAAGCGG": 22240, + "CTTTATTTA": 22241, + "GTTTCTATA": 22242, + "TATACAATT": 22243, + "GTGACATTTT": 22244, + "CTTATAATT": 22245, + "TCTAGAGTT": 22246, + "TGCACAAAA": 22247, + "CTGAACATT": 22248, + "CGTGTATG": 22249, + "CTAAATGAAA": 22250, + "CAGGAGAGG": 22251, + "GAAGGGATG": 22252, + "CACTATTAA": 22253, + "GATCCTGGA": 22254, + "TCACCTGTG": 22255, + "TAACATGTG": 22256, + "GAACCCACA": 22257, + "GACTCTTTG": 22258, + "GTGGCATGTG": 22259, + "TCAGGGAAATGCAAATCAAAA": 22260, + "GACGTCTT": 22261, + "TAAGCAATT": 22262, + "CACAGATCA": 22263, + "GAGAGTAAA": 22264, + "GAGACAAGG": 22265, + "TGAATATGA": 22266, + "TCAATAATA": 22267, + "CCTTTCCTG": 22268, + "CCTTGGCTG": 22269, + "GTCACTTAA": 22270, + "GTAATACAA": 22271, + "CGGGGAAA": 22272, + "TACAGCTGA": 22273, + "CTTAGCCA": 22274, + "TCAAACCAA": 22275, + "GCTCAGGTT": 22276, + "GTCTTGGCTG": 22277, + "GTATGGCAA": 22278, + "GAATAGAATT": 22279, + "GATACATATG": 22280, + "GCAGTGAGCCGAGATCGCGCCA": 22281, + "TTTTTAGTT": 22282, + "TCAGTCATT": 22283, + "CTGCTTCTT": 22284, + "GTCTGATA": 22285, + "GAGAGTGAA": 22286, + "GAGACGAA": 22287, + "GGAAGATAA": 22288, + "GGAAGGGAGG": 22289, + "TAAAATACTA": 22290, + "TTTTAGATG": 22291, + "GAGGTGGAA": 22292, + "CCTGGATGA": 22293, + "CCTGCCTTA": 22294, + "GTTACATTTT": 22295, + "CTCCTGACTT": 22296, + "TTTTTTCATT": 22297, + "TCCTTCAAA": 22298, + "GCCTTCTCA": 22299, + "TGATGGTTA": 22300, + "GCAGGCAA": 22301, + "GCCCCCAAA": 22302, + "GCCCCCGCC": 22303, + "TGACTACTGGAAAAA": 22304, + "GTCTCGATCTCCTGACCTCGTGA": 22305, + "TCAGAGTCTTTTCCAATGAGTCAA": 22306, + "GAAGTGGATGCTCACAGTCA": 22307, + "TAGCCTCA": 22308, + "TTTTGAGTT": 22309, + "GTGCCCTC": 22310, + "GTGGCTCTT": 22311, + "TAAAGACTG": 22312, + "GTCTATTAA": 22313, + "TGTTGGCTG": 22314, + "GAGACAGAAA": 22315, + "CTCATAGG": 22316, + "CCTGCATCA": 22317, + "GATGTACAA": 22318, + "CTTTGGCA": 22319, + "TCTCTTCAA": 22320, + "GAAGATATT": 22321, + "CTTATTTAACTTA": 22322, + "TGACATATT": 22323, + "CCACCTATAGGGTT": 22324, + "TCGTCATG": 22325, + "TCATTTTATT": 22326, + "GTAGAAGAA": 22327, + "CTCGGTGA": 22328, + "TATATATC": 22329, + "TCTTGTGTT": 22330, + "TGTTGGTGA": 22331, + "GTGACCCA": 22332, + "GCAACCCC": 22333, + "CGGGTTTT": 22334, + "CCAAGGGAA": 22335, + "CTTACACAA": 22336, + "GATGCCCA": 22337, + "GGGGGGGA": 22338, + "CCTATTATT": 22339, + "CTGAAGACA": 22340, + "GCAAACCA": 22341, + "TAATGCCAA": 22342, + "GTTAAATAAGCAGG": 22343, + "GCAGAAAAAA": 22344, + "TCAGTGTTA": 22345, + "TCTCCTCTCCTCTCCTCTCC": 22346, + "TAAGTCAAA": 22347, + "TCCCGAGG": 22348, + "CTATATTAA": 22349, + "TATATAGTG": 22350, + "TGAATGCTG": 22351, + "CAGGTTTTA": 22352, + "GCTTTATCA": 22353, + "GTAACACTT": 22354, + "TACAGCTAA": 22355, + "TACATTTTTG": 22356, + "GGTTGCCATT": 22357, + "CCAAACTG": 22358, + "GCTGAGTCA": 22359, + "TATAATGAA": 22360, + "CTATTCTTA": 22361, + "TGATGTCTT": 22362, + "GTAAAATTTT": 22363, + "GACCACAGTG": 22364, + "TCAGGTGTG": 22365, + "GATAAATTA": 22366, + "GGCAGGAAAA": 22367, + "CGCCTGTAATCC": 22368, + "GGCACACA": 22369, + "GCGCAATT": 22370, + "TAATCTTAA": 22371, + "TGACAAAATT": 22372, + "GAAGCTGTT": 22373, + "GTGGCAGTG": 22374, + "GTTGTTTCA": 22375, + "CTCTGAAATT": 22376, + "GTCTGATGA": 22377, + "TATATCATT": 22378, + "CAGAGCCTG": 22379, + "GCTTTAAAAA": 22380, + "CTCAGGCCA": 22381, + "CATGCAGTT": 22382, + "CATGGGCCA": 22383, + "TTTTATAGG": 22384, + "TATGTAATA": 22385, + "GAAAATACC": 22386, + "GTAAATAGA": 22387, + "GTCTTGCTG": 22388, + "CTCAAGCCA": 22389, + "CAAAAATCACAA": 22390, + "CTGTGCATT": 22391, + "CATTATTTTA": 22392, + "CTTTACACA": 22393, + "TAACACAAAA": 22394, + "CTGTAGCTG": 22395, + "TGAATGTTTT": 22396, + "GTGATGAAAA": 22397, + "CAAACAAACAAAAAA": 22398, + "TGGGGAAAGGATT": 22399, + "TCGGGGTT": 22400, + "GATACATA": 22401, + "CTTTCCAGA": 22402, + "GTGGAAATG": 22403, + "TAAATCTTTT": 22404, + "CATTGATTTT": 22405, + "TCTGTAGTA": 22406, + "CATGTACTT": 22407, + "GTTATGACCAACCTAGA": 22408, + "GACATCAAA": 22409, + "GGTTCCCA": 22410, + "TTTTTTCTTA": 22411, + "CTCTTCAAA": 22412, + "TATTTTTTATT": 22413, + "GACCGAGG": 22414, + "CAATTCCAA": 22415, + "CAAAAAGAAA": 22416, + "CCTCCCCCA": 22417, + "GTCTGCCTT": 22418, + "GTTTAGTTTT": 22419, + "TCACCCTCC": 22420, + "GTTGGCCAA": 22421, + "GGCAGGAGAATCGCTT": 22422, + "TAAGTGGG": 22423, + "CCATTTTTG": 22424, + "CCAGTATG": 22425, + "CACAGAGGAA": 22426, + "CAAATTATG": 22427, + "CAGGCGCA": 22428, + "CAGACAGAGA": 22429, + "CTGAGGGCA": 22430, + "GCTTGATA": 22431, + "GTCACACC": 22432, + "GTTACAGTG": 22433, + "CCAATGAGTCA": 22434, + "TCCACGTA": 22435, + "TCTACCAAA": 22436, + "CGATAAAAA": 22437, + "CCCCTCCCCA": 22438, + "CTGAAGCC": 22439, + "TTTTTTTGAA": 22440, + "GCAAAGTGA": 22441, + "GGTACTAA": 22442, + "TCCAACAA": 22443, + "CTGTTCAA": 22444, + "CTCAACATCA": 22445, + "GAATACAGA": 22446, + "TTTTAAATTTT": 22447, + "CCAGCCTGGTCTACAGAGTGA": 22448, + "CAGTGATTTTGGAGCC": 22449, + "TAAGTATTA": 22450, + "TCATAATTTT": 22451, + "CTGTCATCA": 22452, + "CTTGAAGAA": 22453, + "CTTGTGCCA": 22454, + "GTTGGAAAAA": 22455, + "CCACCTCTG": 22456, + "GTCGATGA": 22457, + "TATTTCTGTT": 22458, + "CAGACAGC": 22459, + "GGAAGTCAA": 22460, + "GGAAGTGTG": 22461, + "CTGATTATT": 22462, + "GCTTAAATT": 22463, + "GTAACAGCA": 22464, + "GTTAGAGAA": 22465, + "GTTATAGAA": 22466, + "CTTACAGCA": 22467, + "TCCAGCATT": 22468, + "GATGGATGA": 22469, + "GGGACGA": 22470, + "AAAAAAGAAA": 22471, + "GAGGAGAGAA": 22472, + "GGATTGTCTT": 22473, + "TAACATCTG": 22474, + "GGCAAAGTAATGTCTCTGCTTTT": 22475, + "GCTAAAAAAA": 22476, + "TAATCAATT": 22477, + "CTACTGGG": 22478, + "GTCGGGAA": 22479, + "GTCGAGTT": 22480, + "TGTGGAGTG": 22481, + "CTGACACTG": 22482, + "CTGAGAGTT": 22483, + "GTAATATCA": 22484, + "GATTGTAAA": 22485, + "GCTACTGTT": 22486, + "TGCACCTGA": 22487, + "TCATTAAATT": 22488, + "TGATTGCATT": 22489, + "TCCAACTG": 22490, + "TCTTATATT": 22491, + "CTTTGGCAA": 22492, + "CGAAGAAA": 22493, + "TACCCCTG": 22494, + "CTGTGTCCA": 22495, + "TAACAGAGA": 22496, + "CTGTCTCTT": 22497, + "CTTCTAAAAA": 22498, + "GTTCTCCAA": 22499, + "GGAGCTTTT": 22500, + "CCATATAAA": 22501, + "CAGGGACTT": 22502, + "TATGGTTTG": 22503, + "TCTCCATC": 22504, + "GACATATTA": 22505, + "GCAAATATA": 22506, + "GGTATCTA": 22507, + "TCTTACCTT": 22508, + "GAGCGCC": 22509, + "GATCATTAA": 22510, + "GAGAGAGAGAGG": 22511, + "TCAGATGTT": 22512, + "TTATATCTT": 22513, + "TTTTTAGAAA": 22514, + "TCACTACATC": 22515, + "GTGTGACTGTT": 22516, + "CAGAAAACCAAA": 22517, + "TGAAGTCAGGTA": 22518, + "CATAGGAAA": 22519, + "CAACATTAA": 22520, + "GGACCCTA": 22521, + "CCAGGAGAA": 22522, + "TAAATAAGA": 22523, + "GCTTCTTAA": 22524, + "GTGAGATGA": 22525, + "CTCAGACTT": 22526, + "GTCAGATGAAA": 22527, + "TCTCAAATA": 22528, + "CTCCAAAATT": 22529, + "TACATAGCA": 22530, + "GAAAACCTG": 22531, + "TTATTCACAA": 22532, + "TTTTTTGTAGAGA": 22533, + "CGTGTGTA": 22534, + "CGTGCACCA": 22535, + "CGTTGCTT": 22536, + "CTAAACACA": 22537, + "CTCTTGCTG": 22538, + "TATTTTTATTA": 22539, + "GGCCATAGTG": 22540, + "CTAAAAGTA": 22541, + "CTAAAACTA": 22542, + "TGTGTGCATG": 22543, + "TTATAGCAA": 22544, + "TGATATCTG": 22545, + "TCATATGTT": 22546, + "GGACATCTA": 22547, + "TATATACATG": 22548, + "TAAATACATT": 22549, + "CTACATCTT": 22550, + "TAACTATTA": 22551, + "CGACAGAGCAA": 22552, + "GAATAATTA": 22553, + "GTTTCGTT": 22554, + "CCAGAAATT": 22555, + "GCCTGTGTT": 22556, + "GCCGGGGA": 22557, + "CAAATAGTA": 22558, + "CTTTTTTTTTTTTTTTTTT": 22559, + "TAGATATCA": 22560, + "CATACAAAAA": 22561, + "CCAAAGAGA": 22562, + "GTATTCCTT": 22563, + "GTGTTTCCA": 22564, + "TTATACTAA": 22565, + "CTTCAGAAAA": 22566, + "CTGCCTGAA": 22567, + "GGAATTAAAA": 22568, + "GGCCTGAAA": 22569, + "CCTATGTCCTGAATG": 22570, + "TCTGAGGTTATTGATATT": 22571, + "CCGCGTG": 22572, + "TGAGGAGA": 22573, + "GAATAGC": 22574, + "CTTCCACA": 22575, + "GTGGTTTA": 22576, + "CTCGGGAGA": 22577, + "TATTGCTTA": 22578, + "CAAAGCTGA": 22579, + "CAGACATTTT": 22580, + "CTCACGGA": 22581, + "CTTACTGAA": 22582, + "GAAAATGGAA": 22583, + "CAAAAGAGG": 22584, + "TAGATACAA": 22585, + "TCAAACTTA": 22586, + "TGGACCAA": 22587, + "CGTTTCAA": 22588, + "CTAAATACCAA": 22589, + "CTTAATTCA": 22590, + "CTGTGCACA": 22591, + "TAATAATAAAA": 22592, + "TATGATTTG": 22593, + "CTGCATCTG": 22594, + "GCCAGGCGTGGTG": 22595, + "TTATAGTTTT": 22596, + "AAAAAAATTAA": 22597, + "TGATATCAA": 22598, + "GGCTCCAAAA": 22599, + "TGAGTAGCTGGGACTACAGG": 22600, + "GCTGCATAGTATTCCATGGTGTATATGTGCCACATTTT": 22601, + "GCGTCATG": 22602, + "CTTGTCAAA": 22603, + "CTTGCTCTT": 22604, + "GTAGAGCC": 22605, + "CTCCAGATT": 22606, + "GTCTCAAAAAAA": 22607, + "CACACGGA": 22608, + "TCTTGTTAA": 22609, + "CAAAGGCCA": 22610, + "TGAACTAAA": 22611, + "GCTTTCCTG": 22612, + "CTCATAGAA": 22613, + "CCCCCCTCA": 22614, + "TGTACCACATTTT": 22615, + "GCAAATGAAA": 22616, + "TCTGACCTT": 22617, + "TCCTTCCCA": 22618, + "CGTTCTCA": 22619, + "TAATAGTT": 22620, + "TAATAGTGA": 22621, + "GGTACCAAA": 22622, + "GAGAATTTTA": 22623, + "GCCAACAA": 22624, + "GACCTAAA": 22625, + "CCCTGCCCTG": 22626, + "GATCCCATTTG": 22627, + "CCGGGAGG": 22628, + "GCCCACCCC": 22629, + "CTGTAAATT": 22630, + "GCATCACC": 22631, + "GCAGTTGG": 22632, + "GCAGCTCCA": 22633, + "CACAGAGTC": 22634, + "TGTTGATTA": 22635, + "GAGAGAGGAA": 22636, + "CAGAGGAGG": 22637, + "GGAAGGATT": 22638, + "CTGACACTT": 22639, + "CGGGCCA": 22640, + "TACAGTATA": 22641, + "CAAAATAGG": 22642, + "GGTGGATCA": 22643, + "TCCTTGCTG": 22644, + "TAATAGTAAA": 22645, + "GCTCAGAAA": 22646, + "TCCAAGTGA": 22647, + "CGCACGC": 22648, + "GCAGGTGAA": 22649, + "GATCCAAAA": 22650, + "TAACAAGTTCAAA": 22651, + "TTACATATG": 22652, + "CGAGTAGCTGGGATTACAGG": 22653, + "CCTCACTCAGGATGA": 22654, + "GCGACTTCACTTTCACTTTTCACTT": 22655, + "GATAGGAAA": 22656, + "TTTTCCAAA": 22657, + "CTGGCTTTT": 22658, + "CTTCAACAA": 22659, + "GCCCCAGCA": 22660, + "TATTTAGC": 22661, + "CACAGGTGA": 22662, + "GAGAGAAGG": 22663, + "TAAAAGCATT": 22664, + "TAATATAATAA": 22665, + "TGATTGATG": 22666, + "AAAAAATATA": 22667, + "GCCAACAGA": 22668, + "TAATGGCAA": 22669, + "CAGCCCAGG": 22670, + "CCCTTCTCC": 22671, + "GCTCCCTCC": 22672, + "GAAGACTCA": 22673, + "CTTTAGATT": 22674, + "GTGGACGG": 22675, + "GTTGATGTT": 22676, + "CAGATGTTA": 22677, + "TAAGTGAAAA": 22678, + "TACACAATGGAGTACTA": 22679, + "GCCACCGCGCCCGG": 22680, + "TATCAAATT": 22681, + "GTAGCAGCA": 22682, + "CACACAGAGA": 22683, + "TCTTGTTTG": 22684, + "TGTTCTCATT": 22685, + "CATTCAGTA": 22686, + "CTGAGAGGA": 22687, + "CATGTACAA": 22688, + "GAAAAGGTG": 22689, + "GTCCCACCA": 22690, + "CGAGCTGA": 22691, + "TGGGGCAA": 22692, + "TAATTCACTG": 22693, + "GGCAGCTA": 22694, + "CGCGGAA": 22695, + "TCTGAGCCA": 22696, + "TCCTTGAGAA": 22697, + "CTAAAGTTA": 22698, + "CTGTTGGAA": 22699, + "CATGATTTG": 22700, + "GCGGGGA": 22701, + "GAAATTTCTT": 22702, + "GAGAAAGAAA": 22703, + "CCACCCAGG": 22704, + "TTACATGAA": 22705, + "CTCACCCTCATG": 22706, + "GAAGTTAAA": 22707, + "TCTTGCTTA": 22708, + "TGTTCACAA": 22709, + "CAGACCCTG": 22710, + "GTGATAGAA": 22711, + "TCTCGGTG": 22712, + "CTTTTCATA": 22713, + "CGATCAAA": 22714, + "CCCAGTGAA": 22715, + "TGGGTAGG": 22716, + "GACACTCC": 22717, + "TGTACGG": 22718, + "TCAAACAAAA": 22719, + "TCCTTCCTCC": 22720, + "GGCCTATT": 22721, + "CTGTGTATT": 22722, + "GTGTGTGA": 22723, + "CTGGGGAAA": 22724, + "CTCCACTCA": 22725, + "CAAGGCTACAGTAA": 22726, + "CAACATAAA": 22727, + "CCAATGAAAA": 22728, + "TCTTTTAATG": 22729, + "CTCGGGAGGCAGAGGCAGG": 22730, + "TACTCCTG": 22731, + "GACGTGTT": 22732, + "TCAGTGCTG": 22733, + "CAAGCTCTG": 22734, + "CTTGCTGAA": 22735, + "GCCCTTCC": 22736, + "CTCTGAAAAA": 22737, + "TGTTCTGAA": 22738, + "CCTTCAGTG": 22739, + "GCAACATCA": 22740, + "TTTTAGCAA": 22741, + "TCCATACTA": 22742, + "GATGGCAAA": 22743, + "TTTAGTC": 22744, + "TAGACATTA": 22745, + "TCAAATGGA": 22746, + "TCTCCCCAA": 22747, + "TCTGAGGCTG": 22748, + "CACCTATT": 22749, + "GTTAAAAATG": 22750, + "CAGTGAAATG": 22751, + "GTTTGTATA": 22752, + "TGATGGGA": 22753, + "CAATGCCTG": 22754, + "CATCCTTTT": 22755, + "TGACATGTG": 22756, + "CCAGAGTCA": 22757, + "TCAATGTTTT": 22758, + "CTAGGCAAA": 22759, + "GAGCTGTCAAA": 22760, + "TCAGCCCAGAA": 22761, + "CCTGCTGATTTTGTA": 22762, + "GGTATATA": 22763, + "TAAAAAAAAAAAAA": 22764, + "TTACACAA": 22765, + "GTGGGAGAA": 22766, + "GTTCTCATT": 22767, + "GTATCTATA": 22768, + "GCCGGGCC": 22769, + "GAGAGATAA": 22770, + "GAGAGACAGA": 22771, + "TGAAGGGAA": 22772, + "CTGAGACCA": 22773, + "GTGACAGGA": 22774, + "TTTTACAAAA": 22775, + "GATTGGTA": 22776, + "CTTATAGAA": 22777, + "GTTTTTCTTTT": 22778, + "TCTAGAATT": 22779, + "TAATTGAGG": 22780, + "TCTCAGAAAA": 22781, + "TCTCCTGTA": 22782, + "TCTGATGTT": 22783, + "TGATTCACA": 22784, + "GTTAAGAAA": 22785, + "CTCTGTATG": 22786, + "GTTTGTATG": 22787, + "GCCTGCACA": 22788, + "TGAGATAGG": 22789, + "TTTTTTTTTTTTTG": 22790, + "CATAATATA": 22791, + "GCATGGTGA": 22792, + "CGTCGCC": 22793, + "TCCCATGTT": 22794, + "GTACAGTCA": 22795, + "TCAAAAAATG": 22796, + "CTGTATC": 22797, + "CTTTAAGA": 22798, + "CTTCTTTTTT": 22799, + "GTTTCGAA": 22800, + "GTATĊ": 22801, + "CTACCTC": 22802, + "TGAAGAGCA": 22803, + "CAGACATAA": 22804, + "GTGACCTCA": 22805, + "CATGGACC": 22806, + "GTAACC": 22807, + "GTAACATCA": 22808, + "TCTCTGTGTT": 22809, + "TCTAGCATT": 22810, + "GACACGCA": 22811, + "GATATCCA": 22812, + "CGCTAAAA": 22813, + "GCAAATTCA": 22814, + "GAATTGCAA": 22815, + "CGAACGG": 22816, + "CTCTGTATT": 22817, + "CCTCCTGCC": 22818, + "TCATGCCA": 22819, + "CAGCCTTTT": 22820, + "TCAGGCAGG": 22821, + "TCAGACATT": 22822, + "GAATACTTA": 22823, + "GAACACCAA": 22824, + "GTGGTGGGCA": 22825, + "CACATGTATA": 22826, + "CAAACAAACAAAAA": 22827, + "CTTCTCCAA": 22828, + "CTAGCTTA": 22829, + "GAAAGATGAA": 22830, + "CAAATCATT": 22831, + "CAAACAGC": 22832, + "TGAAGACTT": 22833, + "CAGGGACAA": 22834, + "TTTGTAGG": 22835, + "GTTTTTATAA": 22836, + "GGGGCCCTG": 22837, + "CCCCGGCC": 22838, + "TAATTTCTTTT": 22839, + "TCCTTCTTTT": 22840, + "GAGCGAGG": 22841, + "TCATGTCTG": 22842, + "GAATACCCA": 22843, + "CAAGACATG": 22844, + "TATTTATTTATTTATTTT": 22845, + "TAGAAAATCTTTGGAGG": 22846, + "CACAAAAATTA": 22847, + "GAGTTAATT": 22848, + "TAATCTCTG": 22849, + "TCATTTTTA": 22850, + "TTACATTTT": 22851, + "TCCGTCCATG": 22852, + "CTGGAAGAA": 22853, + "CTGCCCTA": 22854, + "GTTCTTCA": 22855, + "GCCTAAAAA": 22856, + "GCCCAAGAA": 22857, + "CTAGAAGG": 22858, + "TGTGCATGTG": 22859, + "TATTGAATG": 22860, + "TCTTGCATT": 22861, + "CAGGAAATA": 22862, + "GTTATCCA": 22863, + "TCTACTC": 22864, + "GTCCGTCC": 22865, + "TTATTGCTT": 22866, + "TCTCATATG": 22867, + "CTGAAGCTT": 22868, + "GTAAATGAAA": 22869, + "GCAAAGGA": 22870, + "TAATATCTG": 22871, + "GCTCACAGAA": 22872, + "GTGTTGCTG": 22873, + "CGCCCAGG": 22874, + "CAGTGTCTG": 22875, + "GGCTGTTTT": 22876, + "GAGAGAGATG": 22877, + "CAATACAAAA": 22878, + "TCACACGG": 22879, + "TTTTTTTTTTTTTTTG": 22880, + "CTCGGGAGGCTGA": 22881, + "GACCCCAAA": 22882, + "TGACAACAA": 22883, + "TCCGACC": 22884, + "CTTCTGTA": 22885, + "TAAAGCTAA": 22886, + "TATTCCTAA": 22887, + "TATTTCTATT": 22888, + "GAGAGACTT": 22889, + "TGAACATCA": 22890, + "TCAACGAA": 22891, + "GGAATCGAA": 22892, + "CTGACCCTG": 22893, + "CCTGGACTT": 22894, + "GTTTTCGA": 22895, + "GTTTTGGTA": 22896, + "TGGGTCTG": 22897, + "CGCTCCTG": 22898, + "CGTGCACA": 22899, + "GGAGGTTAA": 22900, + "TGAGATCTGA": 22901, + "GGCCAGGCA": 22902, + "GCAGGGAAA": 22903, + "CTGGGTTAA": 22904, + "CTGCACCCA": 22905, + "CTTTACCAA": 22906, + "CCACAGCCA": 22907, + "GCGCACTG": 22908, + "TCAGTTAAA": 22909, + "CTTCACATT": 22910, + "GCATCAATT": 22911, + "CTCTCTCTCTCTCTCTCTC": 22912, + "GTCTCACCA": 22913, + "TGTGGAAAAA": 22914, + "CACATACAAA": 22915, + "TAAAACACTT": 22916, + "GCTGGGGGA": 22917, + "CTCACTCTT": 22918, + "CTCCTAAAA": 22919, + "GAAAAGGCA": 22920, + "GAAAATACTT": 22921, + "TTATTTAAAAA": 22922, + "CCTACTC": 22923, + "GTAAAGAGA": 22924, + "TTTTTTGGGG": 22925, + "GCAAACAGA": 22926, + "TCTTATCTT": 22927, + "CTGTGGCA": 22928, + "GAGGACAGG": 22929, + "TGAGAGCCA": 22930, + "GTTCTTAAAA": 22931, + "TTACATAAA": 22932, + "CAGACCGACAGA": 22933, + "GGTCTTTTA": 22934, + "TAACTTCA": 22935, + "TAATCAGAA": 22936, + "TCATGAATT": 22937, + "TTACATCA": 22938, + "CTTGCGG": 22939, + "GTGGĊ": 22940, + "GTAGTGGG": 22941, + "TAAACCTAA": 22942, + "GTCTCTCAA": 22943, + "CATTGTCAA": 22944, + "TGAACATGA": 22945, + "TCAAGGGAA": 22946, + "GGAATCCA": 22947, + "GTGATGTGA": 22948, + "CCTGGCCTG": 22949, + "TTTGGCCA": 22950, + "TCTATTTTAA": 22951, + "GCAAACTCA": 22952, + "TCATTCCCA": 22953, + "CTGTTCCCA": 22954, + "GTTAATTCA": 22955, + "TCTAATGAA": 22956, + "TGAGGGAAA": 22957, + "GTTCCCTTTT": 22958, + "GTCCATCCTT": 22959, + "TACAAAATG": 22960, + "GGGAAAATG": 22961, + "GAGTAAATT": 22962, + "TAACAAGG": 22963, + "TCACAAATT": 22964, + "TCCTCAGTG": 22965, + "CTTGTTTCA": 22966, + "CTTTGATA": 22967, + "TAAAGGTCA": 22968, + "GTGAGCCAA": 22969, + "CCTTTAAAAA": 22970, + "GAGGCCCTG": 22971, + "GTTTTCAAAA": 22972, + "TCTGACATG": 22973, + "CGTTCAAA": 22974, + "CTAAATGTA": 22975, + "CGCCTGCA": 22976, + "TGAGATATT": 22977, + "GAGCTAAAA": 22978, + "CATTATATA": 22979, + "GAGTGGAGG": 22980, + "GGATTTGTT": 22981, + "TACTGTTAA": 22982, + "TTATATTTTTT": 22983, + "GTGATGGGACCA": 22984, + "GTCATGTCCGACTC": 22985, + "GTTGCATCCAAA": 22986, + "TCAAATATTATATTATTAAA": 22987, + "TGACTTCA": 22988, + "GGATATTAA": 22989, + "CTCGTCATT": 22990, + "GTCGGGGA": 22991, + "CACATACTT": 22992, + "GCAACGTG": 22993, + "CCAAGACAA": 22994, + "CTTACTTAA": 22995, + "GATGCAGTT": 22996, + "GACAGAAAAA": 22997, + "CCCCTCTCA": 22998, + "TCAAATCTA": 22999, + "GTAAATATTA": 23000, + "TCTCCCATG": 23001, + "CCAAAGGA": 23002, + "CGTTCTA": 23003, + "GCTGAGTAA": 23004, + "CAATTAAAAAA": 23005, + "CCATTGACTT": 23006, + "TACTACAGA": 23007, + "TCATACATT": 23008, + "GCACCCACA": 23009, + "CGGGGTTTCACCA": 23010, + "GATCCTTGAGGAA": 23011, + "TGCCTCCAGCTTTGTT": 23012, + "GGGTCTTTT": 23013, + "TTTTTGCC": 23014, + "GTGCTAAAA": 23015, + "GTTTCCCTT": 23016, + "CCACAAGG": 23017, + "GCCTAGC": 23018, + "CTCGGCGG": 23019, + "GTCGCTGA": 23020, + "TGTTGGCC": 23021, + "CTGACCCCA": 23022, + "CATGCATCA": 23023, + "TATGGAATCA": 23024, + "CTTACACTT": 23025, + "GAAAATACAA": 23026, + "CTTTTTGAAA": 23027, + "GGGGGGCA": 23028, + "TCTGAGGTG": 23029, + "GGAAACTG": 23030, + "TCCTTCCCTCA": 23031, + "GGTAGCCA": 23032, + "TGATTCCAA": 23033, + "TCCAAGCA": 23034, + "GGAGGCAA": 23035, + "TAATGGACA": 23036, + "TAAAAAATAAAAA": 23037, + "GGGTTTGGG": 23038, + "GGGCGTGGTG": 23039, + "TCATAGAAA": 23040, + "CTGCCCTCC": 23041, + "CAACAGAGCGAGA": 23042, + "CGAGTAGCTGGGACTACAGG": 23043, + "TTACCCA": 23044, + "GAACGTC": 23045, + "TCCTCGA": 23046, + "GTGTTAATT": 23047, + "GTCTGAGGA": 23048, + "TGTTCTATT": 23049, + "CAAACAGAGA": 23050, + "TCAACAGTG": 23051, + "GTCATATTA": 23052, + "TATGCTTAA": 23053, + "TCTCTAAAAA": 23054, + "TTAAGAGG": 23055, + "TTAACATA": 23056, + "TCAAATACA": 23057, + "TAATTTCTAA": 23058, + "GAATTGGAA": 23059, + "CTTTGTCTG": 23060, + "GTTTGGTGA": 23061, + "GAGCAGAGCA": 23062, + "TAAATTTATA": 23063, + "TCAGGGATG": 23064, + "GATCTTATT": 23065, + "TCCTTAATT": 23066, + "GTGCCACTG": 23067, + "GCATCTATT": 23068, + "GGACAGTC": 23069, + "GTACAGAGA": 23070, + "CTCGACTG": 23071, + "CTCTAACA": 23072, + "TGAACGTT": 23073, + "CGGCAGAA": 23074, + "CCTGCAGTT": 23075, + "CCTGGACCA": 23076, + "TCTCTCCCTC": 23077, + "TACAGTGTG": 23078, + "CTTACATTTT": 23079, + "TTTGGGAAA": 23080, + "TCTGACAA": 23081, + "TAAATTCTA": 23082, + "TTTTTGTATT": 23083, + "AAATAGAAA": 23084, + "TCACATCTT": 23085, + "CATTTGGAGCGCTT": 23086, + "TACTCTTTT": 23087, + "TGAGCTCTG": 23088, + "TTATACTT": 23089, + "GAAGCAGCA": 23090, + "CTGTGTGTG": 23091, + "CTGAAAATG": 23092, + "GTATCGG": 23093, + "GCCTATTTGAA": 23094, + "TATTGGCCA": 23095, + "CATTGCCTT": 23096, + "CAGGGCTCA": 23097, + "TCAAGGCCA": 23098, + "GCTTTGAGG": 23099, + "GAAAATTTTA": 23100, + "CTTTTCCCA": 23101, + "GGGGCGA": 23102, + "TTTATAATA": 23103, + "GACACTGTT": 23104, + "GCTACTAAA": 23105, + "GGAAATGGG": 23106, + "GTATTCAAA": 23107, + "CGTACATA": 23108, + "CAGGAGTGA": 23109, + "GAGCTCAAA": 23110, + "TAGTTCTA": 23111, + "GCGGTGTG": 23112, + "GCGGCTCA": 23113, + "CTCCATCTCAAAAAAAAAAAAAAAA": 23114, + "GTTTATTTTTT": 23115, + "TAACATCTT": 23116, + "TGACACGA": 23117, + "GTGCAGTCA": 23118, + "CAGCTTCTA": 23119, + "GGTGTGTGTG": 23120, + "TAATCACAA": 23121, + "TTATCTTA": 23122, + "CTTGTAATT": 23123, + "GGACTGAAA": 23124, + "GAGACGTT": 23125, + "TGAATAATG": 23126, + "GCAACAGC": 23127, + "CTCCCATGA": 23128, + "TACAGTCTT": 23129, + "TCCATTTTAA": 23130, + "TCCATACTG": 23131, + "TCTAGCTAA": 23132, + "GTCCCCCTG": 23133, + "CAAAATATCA": 23134, + "CCCCGTGGG": 23135, + "CCCCTCCTCC": 23136, + "TCATTTCTTTT": 23137, + "GCATTCACA": 23138, + "CCCCATTTT": 23139, + "GAACATTTTTT": 23140, + "GCGATATG": 23141, + "TGACATAAA": 23142, + "CAACATCTG": 23143, + "GTTGAGGAA": 23144, + "CTTGGTAGCCCAGGCTG": 23145, + "GGAAACGGGATTTCTT": 23146, + "GGAAATGGCAACCCACTCCAGTATT": 23147, + "TGACAAATG": 23148, + "TCCTCTTTG": 23149, + "TATTGCCAA": 23150, + "TAAAAGAGAA": 23151, + "GATTAAATG": 23152, + "CTAACCCA": 23153, + "TCTAGATTA": 23154, + "CTTTTCAGA": 23155, + "GGGGGTT": 23156, + "GCTACGTT": 23157, + "TGTATTTTAA": 23158, + "CGCGGCTG": 23159, + "TAAAAATGCA": 23160, + "TCCTTTAAA": 23161, + "CATCCTGGG": 23162, + "GCGTGGAGCC": 23163, + "GATGTGGGGTA": 23164, + "TTTTAATGGTAGGTT": 23165, + "GTCGTGTCTGA": 23166, + "TGAGTTCTT": 23167, + "GCAGCCCCA": 23168, + "GAAACTATT": 23169, + "TATACCAAA": 23170, + "TGTTCCTCA": 23171, + "GCTTTCTAA": 23172, + "CGGGGCCA": 23173, + "GATTTATGA": 23174, + "TCCACGCC": 23175, + "TCTACTTAA": 23176, + "TGGAGCTGA": 23177, + "CTTTGTATA": 23178, + "TCCTGGGGG": 23179, + "CAGCTCTAA": 23180, + "GAGGAGCTG": 23181, + "GCTCCTGCA": 23182, + "GCGGCACA": 23183, + "CCCTCTCTT": 23184, + "CCCTCTTTG": 23185, + "TCACCTAGG": 23186, + "TCTCAATAGATG": 23187, + "GCACATAAA": 23188, + "CAAAATCAGCAGGGGATCAAA": 23189, + "CATAGGTG": 23190, + "GGCTTATG": 23191, + "TAATATTAAA": 23192, + "TCATGATTA": 23193, + "TCCCTTGA": 23194, + "CAACTGGG": 23195, + "GTGGCGCA": 23196, + "GTCTGGGTT": 23197, + "TATTGATCA": 23198, + "TCTTGGTGA": 23199, + "GAGACCAAA": 23200, + "CATGGAGCA": 23201, + "TTTTACTCA": 23202, + "GTCCTCTCC": 23203, + "GTCCCGGA": 23204, + "TTTATTATTA": 23205, + "GGTTCTTTT": 23206, + "GTAAAGCA": 23207, + "GGAAACAGA": 23208, + "GACCTAGA": 23209, + "CAATTCTCA": 23210, + "CGAATGTT": 23211, + "CTCTGTCAA": 23212, + "CCTCGGTG": 23213, + "TCAGGGATT": 23214, + "CTTCCTCCTT": 23215, + "GGAGGAGGGA": 23216, + "TAAAGAAAATGTG": 23217, + "GTCATTTAGCAGACGCTCTTATCCAGAGCGACTTA": 23218, + "GAAGCAGAA": 23219, + "CTTGTTATT": 23220, + "GTACATC": 23221, + "CTCGGTCA": 23222, + "GAAAGAACC": 23223, + "GTGAGGGTG": 23224, + "GCAATATCA": 23225, + "CCAACATGA": 23226, + "CTTACAGTA": 23227, + "TGGACAAAA": 23228, + "TAATTGCTG": 23229, + "CCTAGCTA": 23230, + "TCCTTGCC": 23231, + "GTGAACTA": 23232, + "TATTTTTTCA": 23233, + "GTCTTGTTTT": 23234, + "CCATTTATTAAA": 23235, + "CCTCCCTA": 23236, + "CGTCTGTA": 23237, + "GAGAGAGATT": 23238, + "TCTCTCTCTCTCC": 23239, + "GTTTATCTG": 23240, + "CCCTCCATCA": 23241, + "GTTGGTAGAGCATG": 23242, + "TATTTATGAA": 23243, + "GATTAATTA": 23244, + "TAATCCCAGAA": 23245, + "GAAAGAGAGGCCCATT": 23246, + "GAAAAGAAAAGAAAAGAAAA": 23247, + "TGAACTAACCAGTA": 23248, + "GATTTGCATTTCCCTGA": 23249, + "CAGTTAATT": 23250, + "GAGCAAATT": 23251, + "TGACACATT": 23252, + "GAATTATTA": 23253, + "GAAGCAGA": 23254, + "GTGGCCACA": 23255, + "GTTTAACAA": 23256, + "TGTGCACTT": 23257, + "GTGATGATG": 23258, + "GTCATCAAA": 23259, + "CTAAGAGAA": 23260, + "TGAAATGTT": 23261, + "CTTAAGTA": 23262, + "CTTAATTAAA": 23263, + "TCTTATAAA": 23264, + "GTGTTGGA": 23265, + "GGGAAGGTG": 23266, + "CATTACATT": 23267, + "GTCTGGCTT": 23268, + "GAATGGGA": 23269, + "CAGCCTTTG": 23270, + "GAATAGTAA": 23271, + "TGACAGTTA": 23272, + "TAAATAAGGCTG": 23273, + "TACACTAGCAA": 23274, + "CCTCACATAAATTTTCAAACCACATAAATTTTCGTTCGGTTTCATCAAACCCATCCTAATT": 23275, + "TTAGATAA": 23276, + "TCCTAAATT": 23277, + "CAACTGAAA": 23278, + "CAAGAACA": 23279, + "GAGAGGGAGG": 23280, + "CCTTGCTCA": 23281, + "CATGCACC": 23282, + "GTCAGGGCA": 23283, + "GAGGTACTG": 23284, + "GTTAGGTA": 23285, + "TAGACACAA": 23286, + "GCAAAGACA": 23287, + "TGATTTCAAA": 23288, + "TGATTTCATT": 23289, + "AAAAAATTAAA": 23290, + "GCCAATTAA": 23291, + "CAATTTCCA": 23292, + "CAATTCAATT": 23293, + "GGAGGGCTG": 23294, + "TCTAACTA": 23295, + "GAAAAAGCAA": 23296, + "TGAGAGTAA": 23297, + "CCAGGGAAA": 23298, + "TCATATCAA": 23299, + "TATTCAACATA": 23300, + "GCTGGAATCAAGATT": 23301, + "GGCAAAATT": 23302, + "TCATCTAAA": 23303, + "TCAGTGGA": 23304, + "TGACTGGA": 23305, + "CAATĊ": 23306, + "CTTCAGATT": 23307, + "CTTTCCCTG": 23308, + "CCATATC": 23309, + "CCACTTCTT": 23310, + "CTCGAGAA": 23311, + "CAAACCCACA": 23312, + "CATTTATTAA": 23313, + "TGAAGCTGA": 23314, + "TGAAGACTG": 23315, + "TCTGCACCA": 23316, + "CAGACGTT": 23317, + "TTTTATTTTATT": 23318, + "TATGGGCA": 23319, + "TTTGGCTG": 23320, + "TCTATAAAAA": 23321, + "GCCACGAA": 23322, + "GTCCCTGAA": 23323, + "CAAAATACAA": 23324, + "TGGGGTAA": 23325, + "GCTACCAAA": 23326, + "TCCAAGAAA": 23327, + "TCCAACTCA": 23328, + "TGATGCAAA": 23329, + "CCCTCCAGG": 23330, + "CAATATGTT": 23331, + "GGACAGCTG": 23332, + "TAAATATGTT": 23333, + "GACCTTTGTT": 23334, + "GGTAGGTG": 23335, + "TTACAGTT": 23336, + "GTTTAACTT": 23337, + "CCATĊ": 23338, + "CCACTAAAA": 23339, + "CCAGCTCTT": 23340, + "CTCGCTGA": 23341, + "CTCGCGG": 23342, + "CACATACTG": 23343, + "TGAAGGCAA": 23344, + "GGAAGAAGG": 23345, + "GATGGGTA": 23346, + "TCTACCTCA": 23347, + "GCCATACC": 23348, + "CATACTAAA": 23349, + "GAATTGAATT": 23350, + "GTGAAAAATT": 23351, + "TCCAAGATT": 23352, + "GCCTGGAAA": 23353, + "GTGCCACTGCA": 23354, + "TCCTACAATGTGATTTT": 23355, + "TCACCTGAGGTCAGGAGTTTGAGA": 23356, + "TATAAAATA": 23357, + "CTTTCCCA": 23358, + "GTTCTGCC": 23359, + "GCACTGGA": 23360, + "GTCTTATTA": 23361, + "CTGATACAA": 23362, + "CTGACAGTT": 23363, + "CATGCAGTG": 23364, + "TATGCAGTG": 23365, + "GAGGGGGAA": 23366, + "CCTGCATTA": 23367, + "CCTGGCTCC": 23368, + "GAAAATCC": 23369, + "CTTTTGATA": 23370, + "TGCAĊ": 23371, + "GCAAAGTG": 23372, + "TGAAATTAAA": 23373, + "CCAAATAGG": 23374, + "GTGAATAAA": 23375, + "CGCCCTCC": 23376, + "GCCTTTCTT": 23377, + "GTTTGTAAA": 23378, + "TAGAATTTA": 23379, + "GCAGGCACA": 23380, + "GACCATTTT": 23381, + "TGACACCAA": 23382, + "GTCCAGTCA": 23383, + "TAGGTGACCAAA": 23384, + "TCTGTCTGTCTGTCTGTCTGTCTGTCTGTCTG": 23385, + "TCATCTGTG": 23386, + "GAAGCTGAA": 23387, + "TCCTCACTT": 23388, + "GTTTCCTCC": 23389, + "GTACTGAAA": 23390, + "CTATGAGG": 23391, + "TGTTGTGTG": 23392, + "TGTTCTTGG": 23393, + "CATGCACTT": 23394, + "GCAACAGGA": 23395, + "GAGGGGCC": 23396, + "TACATCTTA": 23397, + "GATGGTCTT": 23398, + "GGGATCAA": 23399, + "TAAAAAGACA": 23400, + "TGAAATTCA": 23401, + "CCATTCCCTT": 23402, + "TGTTAATTAA": 23403, + "GAGCCTTA": 23404, + "GTTCACCAA": 23405, + "TTAGGGTTA": 23406, + "CACTTTTTG": 23407, + "TATCTCATT": 23408, + "TCGAAATA": 23409, + "TAAGTAGA": 23410, + "TCACTTCA": 23411, + "GAACAAATT": 23412, + "GTTCTTATT": 23413, + "GTATGTGAA": 23414, + "TAAAGATTTT": 23415, + "CTCGAGCA": 23416, + "TGTGGGTGA": 23417, + "CATTTATAAA": 23418, + "TGAATACC": 23419, + "CAGGAAAAAA": 23420, + "CAGAGCTCC": 23421, + "TAAAAGGCA": 23422, + "TCTCCATCTT": 23423, + "CCAATCAAAA": 23424, + "CTAACACTG": 23425, + "CCCAGCATT": 23426, + "TAGACC": 23427, + "GAGAAGGGA": 23428, + "GAAAAACATT": 23429, + "GAGCGGGG": 23430, + "GGATGGAAA": 23431, + "CAGGAATTTT": 23432, + "CAACACCTG": 23433, + "CCACCCCAA": 23434, + "GTTATGAAAA": 23435, + "TCTGTCATAGTT": 23436, + "TGAATGAATGAATGAA": 23437, + "CCGCTGCCCAAGGAA": 23438, + "GAGAAATGCAAATCAAAACCACAATGAGATA": 23439, + "TAATCCATG": 23440, + "TAAGTACA": 23441, + "TGACCCCATGGA": 23442, + "GTAGCAAAA": 23443, + "CTCTCTCTCC": 23444, + "GTCTCAAAAA": 23445, + "TATTCCTGA": 23446, + "TCTTGGCCA": 23447, + "TCTTGACTG": 23448, + "TGTTGATTTT": 23449, + "TGAACGAA": 23450, + "TTTTATGGG": 23451, + "CTAACAGCA": 23452, + "TCCATCCATT": 23453, + "CGATATTTT": 23454, + "TTTACGG": 23455, + "GGTGAAATT": 23456, + "GGAAACAGG": 23457, + "TCCTTGGGG": 23458, + "GTGAAGGGG": 23459, + "GTTAATTAAA": 23460, + "GAAAAAGGAA": 23461, + "TAATGGCTG": 23462, + "CCCTGTTTT": 23463, + "CTCCAGTAA": 23464, + "CCATAGATT": 23465, + "TAAAATGCAAA": 23466, + "CTGTGAAGAAA": 23467, + "CGTAAAATT": 23468, + "GATCTCATT": 23469, + "GCCACACACA": 23470, + "TAAGCAGAA": 23471, + "CAACTCAAA": 23472, + "GCAGTTCTG": 23473, + "GGATATATT": 23474, + "GCCCGCTG": 23475, + "TGTGCCTCA": 23476, + "TGTTTCTCC": 23477, + "GAGAGCAGG": 23478, + "CTGAGATAA": 23479, + "TTTTATAATT": 23480, + "CTAAGACC": 23481, + "CTTACATAA": 23482, + "CGATCTA": 23483, + "CCCACGC": 23484, + "GACAGTGTT": 23485, + "CATAGCAAA": 23486, + "CATACTTAA": 23487, + "TTATTCATG": 23488, + "TTATTAAAAAA": 23489, + "TCCTTCTCTT": 23490, + "CAAAAAGAA": 23491, + "TCAAAAGAAA": 23492, + "CCCTGCTG": 23493, + "TAGTGTTTA": 23494, + "TAAGACACA": 23495, + "GGATTTTTAATGAATT": 23496, + "TCAATTTTGGCTTTT": 23497, + "CCTATTTCTCCACATCCTCTCCAGCA": 23498, + "CCTCCAATT": 23499, + "TACCAATG": 23500, + "GCATTAATT": 23501, + "TCTTTCTGTG": 23502, + "TGTTCTCTG": 23503, + "CAGGTCATT": 23504, + "TCTGCAGC": 23505, + "TAAAATTTTAA": 23506, + "TTAACCAA": 23507, + "GTTTTGCTG": 23508, + "GTCCCCCC": 23509, + "TAATTAAATG": 23510, + "TCTCATGGA": 23511, + "CGTGTGCATGTG": 23512, + "GCAAATGGA": 23513, + "TCCTTTCTA": 23514, + "GGCCGGGCA": 23515, + "CAGAACTG": 23516, + "TCTTACAAA": 23517, + "CTTTGCACA": 23518, + "GACCCGTG": 23519, + "GTATTCTGTT": 23520, + "TGATGTGA": 23521, + "GAACAGCTG": 23522, + "CTTCTGTTTT": 23523, + "CTCCTGCCTCAGCCTCCTGAGTA": 23524, + "TAGACGGCAGCCCACCAGGCTCC": 23525, + "TACTCATG": 23526, + "GACCAAATT": 23527, + "CTTCTGGA": 23528, + "GTGGAGATT": 23529, + "GTTGCCATG": 23530, + "GCACAAGAA": 23531, + "CTAGATCC": 23532, + "GTCTAAGG": 23533, + "GTCTCCCTT": 23534, + "TCTTGAAAAA": 23535, + "TGTTGGGAA": 23536, + "GAGACCTCA": 23537, + "GTAATAGTT": 23538, + "GAGGCTGCAGTGA": 23539, + "CTTACTAAA": 23540, + "CGACATC": 23541, + "GCTCAGATG": 23542, + "GTCTTCATG": 23543, + "CGCCCACC": 23544, + "TATAACAAA": 23545, + "CTGTGCAAA": 23546, + "GGGAAGGA": 23547, + "GAATGCAAAA": 23548, + "TCATGGTCA": 23549, + "GTGTGGAA": 23550, + "GGCTTCATCC": 23551, + "TAAAAAATTTT": 23552, + "TTATATAAAAA": 23553, + "CTTTAACAA": 23554, + "CTTTATTTTAA": 23555, + "GCCTTTTTG": 23556, + "TCTGGGTTTG": 23557, + "CAGATAGAA": 23558, + "GTGAGATCA": 23559, + "TATGCAGTT": 23560, + "CCTGTAGTT": 23561, + "GCCATCTCC": 23562, + "TTCACCAAA": 23563, + "TCCTTGTCA": 23564, + "GTGAAGCTG": 23565, + "CGCAGCAA": 23566, + "CGCACACACACA": 23567, + "GTGTTGTGTT": 23568, + "CTCTGGCAA": 23569, + "CCATGGAGG": 23570, + "CCCTCTGCA": 23571, + "TGAGAATTA": 23572, + "GTTCCTGTG": 23573, + "CTATACAAA": 23574, + "TATTTGAACAGTGAGAGACAGAA": 23575, + "TAATCTATA": 23576, + "TCACAATA": 23577, + "CTTGĊ": 23578, + "CTTTAACA": 23579, + "GCCGAGGA": 23580, + "CTCTCACAA": 23581, + "CTCACCTA": 23582, + 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"TAACGATT": 23628, + "CTTTCCAGG": 23629, + "GCAGTGGA": 23630, + "GTACTGTA": 23631, + "GCCGAGAA": 23632, + "TAAAGACTA": 23633, + "TGTGTACAA": 23634, + "CATTGCTGA": 23635, + "CAGGCCTCA": 23636, + "CAGGGAGCC": 23637, + "TCAATTTTAA": 23638, + "TCAAGGCAA": 23639, + "GCTTTGATT": 23640, + "CCTTGCTTA": 23641, + "CTAAGATCA": 23642, + "GGGGCAGAA": 23643, + "TTTAGGGG": 23644, + "TAGACATTTT": 23645, + "GACATTTTAA": 23646, + "CTGTTGTGA": 23647, + "TCCTGCAA": 23648, + "TCCTGCTGA": 23649, + "CAGTTCTGA": 23650, + "TCAGGGAAA": 23651, + "TAAATGTATA": 23652, + "TTACAGCTT": 23653, + "GTGGCATGCA": 23654, + "TTATTATTATTATTATTA": 23655, + "GGAGAAAATTTTTG": 23656, + "TGATGTACTCTG": 23657, + "TCCGCCCGTCTCGGCCTCCCAAAGTGCTGGGATTACAGGCTTGAGCCACCGCGCC": 23658, + "GCGTCTCA": 23659, + "TTTTTCTC": 23660, + "CTGTĊ": 23661, + "CTGGCTCTG": 23662, + "GTATTTTTTTT": 23663, + "GAGACCCC": 23664, + "CAGATCTCA": 23665, + "TCAACGTG": 23666, + "TAAAAGCTA": 23667, + "GTGACGTCA": 23668, + "GCTGCCTGA": 23669, + "CATGTCTAA": 23670, + "GAGGGGGGA": 23671, + "GATTCCTA": 23672, + "CTTAGTAAA": 23673, + "TCCATATTTT": 23674, + "TAGGGTT": 23675, + "TCCTTTGA": 23676, + "CGTTGTTTT": 23677, + "CGTTTGGG": 23678, + "TGCCTATT": 23679, + "GTGTTCAA": 23680, + "CAAAAATTTA": 23681, + "CTGTGTATA": 23682, + "GATGAGGAA": 23683, + "CCATGGTAA": 23684, + "TCAGACTCC": 23685, + "TCATAGTAA": 23686, + "GAAGTTTTA": 23687, + "GGAGCCCC": 23688, + "CCAGGAGA": 23689, + "CACATATTTT": 23690, + "CACATATCA": 23691, + "GTGACGAA": 23692, + "GTGACAGTA": 23693, + "TATGGAGGA": 23694, + "CCTGCCAAA": 23695, + "CCTGGGGGA": 23696, + "GTTTTTCAAA": 23697, + "TCATTCGA": 23698, + "TGATTGAAA": 23699, + "TCCAATGAA": 23700, + "CAGAAAAATG": 23701, + "CTCAACCAA": 23702, + "GGAGGGTAA": 23703, + "CTGTGTCC": 23704, + "TAATAAAAAAAA": 23705, + "GCAGGTAAGA": 23706, + "TACTATATA": 23707, + "CAACAATTA": 23708, + "GGGTCTGAATA": 23709, + "CGCCAATG": 23710, + "GTGCTGGGATTACAGGTGTGAGCCA": 23711, + "CACGCAAA": 23712, + "GTGGCAGAA": 23713, + "GTCGGTAA": 23714, + "CACATCTCA": 23715, + "CACACCTCA": 23716, + "CACAGGTCA": 23717, + "TCTTCAGC": 23718, + "TCAACTGAA": 23719, + "CATGTAATT": 23720, + "TATGTACCA": 23721, + "CGGGAGAA": 23722, + "GCCACCCTA": 23723, + "TAGAGCAAA": 23724, + "TTATTTCTTA": 23725, + "GATAGGGG": 23726, + "CGTGGTGA": 23727, + "CTCAACAA": 23728, + "GGAGGCTCA": 23729, + "GCCTGGATG": 23730, + "CGTATTCA": 23731, + "GAGTGGAGA": 23732, + "CAAGGCAAAA": 23733, + "TTATATGTG": 23734, + "TAAGAGTTA": 23735, + "GTTCAGCTG": 23736, + "CAGGTGCCATTAA": 23737, + "GTCTGAGAGA": 23738, + "GCTCTCACAGA": 23739, + "GAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGAGA": 23740, + "GTCCCATCACTTCATG": 23741, + "CCAAAACAGCATGGTACTG": 23742, + "CACCAAATT": 23743, + "TAAGCGG": 23744, + "TTTTCTAAA": 23745, + "CCAGTAAAA": 23746, + "GCCTGAGAA": 23747, + "CTAGTAAAA": 23748, + "AAAATGAAA": 23749, + "GTCGATCA": 23750, + "CATTTAGTA": 23751, + "CAGGCAGTT": 23752, + "TCTGGGATG": 23753, + "GTCAGCTA": 23754, + "GAGGCGC": 23755, + "GAGGCAGTT": 23756, + "TCTCGATT": 23757, + "CTAACATAA": 23758, + "CTTATATCA": 23759, + "TCTACAATT": 23760, + "CGACACAA": 23761, + "TGGAĊ": 23762, + "TCTCCTCAA": 23763, + "TCCTTTCTCA": 23764, + "GTATTGTG": 23765, + "GTATTTACA": 23766, + "GCCTGCAA": 23767, + "TCATGCAAA": 23768, + "CAAGGGATG": 23769, + "GTTCTTGTA": 23770, + "CTACATATA": 23771, + "CCTTTGTTA": 23772, + "TAGGAACACTTTTA": 23773, + "GGATGGACTGGTTGGA": 23774, + "TCACTTGAGGCCAGGA": 23775, + "CCCATTTA": 23776, + "CTACTGTTA": 23777, + "TAAAGCAAAA": 23778, + "CTCGTAAA": 23779, + "CACAGTTAA": 23780, + "TCTTCATCC": 23781, + "CTCACACCA": 23782, + "CTTATCAAA": 23783, + "TTAACTCA": 23784, + "CTTTTGAGG": 23785, + "CGATATAA": 23786, + "TAGGCATA": 23787, + "CGCTGATT": 23788, + "TCATTGAAA": 23789, + "TCATTTAAAAA": 23790, + "TCCTTGTCC": 23791, + "CGGATCC": 23792, + "GAAGGTATA": 23793, + "TCTCTGCCTCC": 23794, + "GGAGATGTT": 23795, + "TCAGATCTCA": 23796, + "TCATTATTTA": 23797, + "GTATTTTTTAGTAGAGA": 23798, + "GTCTTTTGATTTTTTTGAAA": 23799, + "TACCTACTGTGAA": 23800, + "GCGTCTTA": 23801, + "TGATCTCTT": 23802, + "GCCCTATG": 23803, + "TGTTGCAAA": 23804, + "TCTGAAAAAA": 23805, + "TCAAGTGAA": 23806, + "CTAACC": 23807, + "TTAATTAATT": 23808, + "TCTAGACC": 23809, + "TCATTCTCC": 23810, + "CTAAAGATA": 23811, + "CTCTTGGTG": 23812, + "GGAGGTTTG": 23813, + "CTCTGTAGG": 23814, + "CTCTGCCCC": 23815, + "TGAGGGTG": 23816, + "CATTTTTGGG": 23817, + "GGCTTTGGG": 23818, + "CTCCACAAA": 23819, + "CCCTAGAACTTAAA": 23820, + "CAAGGCAAA": 23821, + "GAACATATG": 23822, + "GAGGGAGGGAGG": 23823, + "CTCAGCTATTAAAAA": 23824, + "TACAAAAAAA": 23825, + "TACAGGAAA": 23826, + "CAATATTTG": 23827, + "GTTTCTATG": 23828, + "TAAATTAAAAA": 23829, + "CACATAGAA": 23830, + "TGTTGCATT": 23831, + "TCAATACCA": 23832, + "CTGACATTTT": 23833, + "CTCAGCATT": 23834, + "CCTGGAGC": 23835, + "CTCCCTCCTT": 23836, + "TTTGTAAAA": 23837, + "CAAAACGG": 23838, + "TAGAGTAAA": 23839, + "TGGACTGAA": 23840, + "TCCTTTATA": 23841, + "CCAAATATT": 23842, + "CTGTGCCAA": 23843, + "GCCTTCCAAA": 23844, + "TGATGGCAA": 23845, + "TGAGAGTCA": 23846, + "CATGATTTTA": 23847, + "CTTCTTAAAA": 23848, + "TGATAGTAA": 23849, + "CAACATATG": 23850, + "TTACAGAAAA": 23851, + "CTTGAGGAA": 23852, + "CCTTAATTA": 23853, + "CTCCTGCCTCAGCCTCCTGAGTAGCTGGGATTACAGG": 23854, + "GTGGAACAA": 23855, + "GCAGCCAAA": 23856, + "GCAGGGGTT": 23857, + "CTAGAAATT": 23858, + "CAAACGC": 23859, + "TGAATAGTT": 23860, + "CAGGGAGTG": 23861, + "CCTTGATGA": 23862, + "GATTCTAAA": 23863, + "GTTATAGCA": 23864, + "TACATACATG": 23865, + "TCCAGTATT": 23866, + "CCCAGCAGG": 23867, + "TAGACCTA": 23868, + "CAGAATGAA": 23869, + "GTGTTGTGA": 23870, + "CAATTGCAA": 23871, + "GAGGAGGCA": 23872, + "GGATTTCCCAGG": 23873, + "TCATGGTA": 23874, + "GATAAGAA": 23875, + "CGTCTGTT": 23876, + "TCAGACTGA": 23877, + "GTCCAGCTG": 23878, + "AAAAAAAAAAAAAAAAAAAAAAAAAA": 23879, + "TCACTGATT": 23880, + "GAACAGGAA": 23881, + "GTTTGTGTG": 23882, + "TCTGCCTGA": 23883, + "CCTTGAGAA": 23884, + "CCTTGCTGA": 23885, + "GTCAGAATT": 23886, + "TCTACAAAAA": 23887, + "GGTGCC": 23888, + "CTGAAGTAA": 23889, + "TCATTTCCAA": 23890, + "CGTTTGACC": 23891, + "CTCAATGGA": 23892, + "TACCTGATG": 23893, + "TCCCCGGGA": 23894, + "GCGCCCGG": 23895, + "GCAGCCTTATTTA": 23896, + "GTATTCCCATCTCTT": 23897, + "CGTCGAGATTTTAAGTTTGA": 23898, + "GAACCTCC": 23899, + "GGAGCTCTG": 23900, + "TAAACATAAA": 23901, + "TATTTAATTTT": 23902, + "TCTTCAGCC": 23903, + "TAAAACC": 23904, + "GTGATATGA": 23905, + "CCTTTACAA": 23906, + "CATGTCTCA": 23907, + "CATGGTAAA": 23908, + "CATGGCAAAA": 23909, + "TTTTATGTTA": 23910, + "TCTCGGCC": 23911, + "CTCCCTAAA": 23912, + "TACAGCCAA": 23913, + "TTTGGTG": 23914, + "CTTTTCCCC": 23915, + "GGTGTCAA": 23916, + "TTATTĊ": 23917, + "GTAAACTGA": 23918, + "TCATTTCTGA": 23919, + "TATTACATT": 23920, + "TCTTACTCA": 23921, + "TCTTAGCAA": 23922, + "TTTTAAAAATG": 23923, + "TAATGGAGA": 23924, + "TTTTTTTTTTCA": 23925, + "GTGTGGTGA": 23926, + "CCTTATTAA": 23927, + "GAAGACGA": 23928, + "CTGCACGG": 23929, + "TTTTATTGAGGA": 23930, + "GCACACTGA": 23931, + "GGACACCTG": 23932, + "TTTTTATGTT": 23933, + "TCTGACAAAGGGCTAATATCCAGAA": 23934, + "GGTGTTTA": 23935, + "GAATCGG": 23936, + "GTTCATTAA": 23937, + "AAAAAAATG": 23938, + "CAGAGTTAA": 23939, + "CTCAGCCTCC": 23940, + "CCTTGAGTT": 23941, + "TATGTACTG": 23942, + "CTAATACTT": 23943, + "GACACCCTG": 23944, + "GCTATCCA": 23945, + "TCAAACCTG": 23946, + "TGCATAGA": 23947, + "GTAAACTG": 23948, + "GGTAGTTTT": 23949, + "CCATTGCTT": 23950, + "TGATGCATT": 23951, + "GGCTGCAGG": 23952, + "TCCCATGTG": 23953, + "CTTTAGAGA": 23954, + "GGACATCAA": 23955, + "GCGCGGGG": 23956, + "CACACACTAAA": 23957, + "CCTGTGTTA": 23958, + "TACTTTAAGTTCTA": 23959, + "GATAGGTG": 23960, + "GACTCCAAA": 23961, + "GCGGAATT": 23962, + "TAAGCTGAA": 23963, + "TGATCCAGAA": 23964, + "TCCCTTCA": 23965, + "CTTTAATGA": 23966, + "CTAGCAAAA": 23967, + "GAAACCTCA": 23968, + "GTCACTAAA": 23969, + "GATTCAGTG": 23970, + "GTTTTGCA": 23971, + "GTCCCACTT": 23972, + "TAATTCCCA": 23973, + "GGCATTTTA": 23974, + "GCAAATATTTT": 23975, + "CTCTTGGTT": 23976, + "TCTTATATG": 23977, + "GTCAAAAATT": 23978, + "GCCTGTCTG": 23979, + "CAATGCCAA": 23980, + "AAAAAAAAAAATT": 23981, + "CGTCCAGG": 23982, + "GCACACCTG": 23983, + "TCCCTGTCTT": 23984, + "TGAGGTTGTGGA": 23985, + "CCAGCCTGGGCTACCAA": 23986, + "GACGTGTACCTATGATGAAAATTACAGGCCTCTCTCATCTTTTTAAGTGGGAGAA": 23987, + "GGCATTTA": 23988, + "TGACGTGA": 23989, + "GCAGAACA": 23990, + "GTACTGGG": 23991, + "GAAAGGATT": 23992, + "GAGATCCA": 23993, + "GTGACCCCA": 23994, + "CCTTGGGAA": 23995, + "GTCAGCTGA": 23996, + "GTCAGACAA": 23997, + "GAGGTGCTG": 23998, + "GTTACAGGA": 23999, + "TACATTATG": 24000, + "TTTAGCC": 24001, + "GACAGCTGA": 24002, + "GGTGTAGG": 24003, + "TAATTGATA": 24004, + "TGAAAGGAA": 24005, + "GGTATCA": 24006, + "CTGTGCAA": 24007, + "GCCTTTGTG": 24008, + "GGCTGGGCA": 24009, + "CCTCCCCTG": 24010, + "GCGGCGTG": 24011, + "CCATGCTCA": 24012, + "TATTTGTATT": 24013, + "CTGCTGCTG": 24014, + "TCACATATT": 24015, + "TGACCTCAA": 24016, + "GAACCTGTG": 24017, + "GACGGAAA": 24018, + "CAAAATGTGGAAAAA": 24019, + "GCGCTGGG": 24020, + "TAACTCTA": 24021, + "TAAGTATTTT": 24022, + "CTTGAAGA": 24023, + "CTCGAGTT": 24024, + "CACACTTATTTCAAAAAAA": 24025, + "GAAAGGCCA": 24026, + "TATATCTGA": 24027, + "CAAACTGTT": 24028, + "CAGACCTCA": 24029, + "GGAACATCA": 24030, + "GCTGCGCC": 24031, + "GTCAGGCC": 24032, + "GATTTACTG": 24033, + "CTAATAGAA": 24034, + "GAAAACACAA": 24035, + "GGGATAGA": 24036, + "GATATTTTAA": 24037, + "CTAAACAGG": 24038, + "TACCTAGA": 24039, + "CCATTTCCA": 24040, + "TAATGGGA": 24041, + "CATAATATT": 24042, + "CAAGGTTAA": 24043, + "CTATATTTTA": 24044, + "CCACTGGAAAAA": 24045, + "TGGGTGTTTT": 24046, + "TAAAATACCTAGGAA": 24047, + "CTTGCCTGGAGAATT": 24048, + "GACTTAATT": 24049, + "TAAGTTGTT": 24050, + "TAATTTGAAA": 24051, + "GAAGCTC": 24052, + "CTTCĊ": 24053, + "CTTGTTCA": 24054, + "CTTGAAAAAA": 24055, + "GTGGAGGTG": 24056, + "TAAAGGCCA": 24057, + "TAAACCCAA": 24058, + "TAAAGAGTA": 24059, + "AAAAGTA": 24060, + "TCTGGATCA": 24061, + "TCTGGAGTA": 24062, + "GGAATTATT": 24063, + "GTGATCTA": 24064, + "CCTTTCTAA": 24065, + "GAGGCAGC": 24066, + "GTTACTGAA": 24067, + "CCAAGCTA": 24068, + "CTTATACAA": 24069, + "TTTGTTTTTT": 24070, + "GTCCCAGTT": 24071, + "GGTATGA": 24072, + "CTGTTGGTT": 24073, + "CAGAAGAAAA": 24074, + "TATTTTTGTT": 24075, + "CTTTGTCA": 24076, + "TCTAACATT": 24077, + "CCTAATTAA": 24078, + "GGGCTACA": 24079, + "CAAGGGAGA": 24080, + "CAATATGTG": 24081, + "TCATAGATG": 24082, + "GCACACTCA": 24083, + "CCGGAGGA": 24084, + "TAAGTTTAA": 24085, + "TAAGTCTCA": 24086, + "TTTTCCTA": 24087, + "GTGGAAATT": 24088, + "GTGGCTGTT": 24089, + "GTTCGCTG": 24090, + "GCATĊ": 24091, + "GTACGTC": 24092, + "CAAAGATTTT": 24093, + "GAGAGAATT": 24094, + "TCTGGCTCA": 24095, + "TAAAACTGAA": 24096, + "GCAAGAGC": 24097, + "GAGGTACAA": 24098, + "CGGGGCGG": 24099, + "CTCCTACTT": 24100, + "GCAAATTAAA": 24101, + "CGTTTACTT": 24102, + "TAATATCC": 24103, + "GTATTCAA": 24104, + "TCATGGCTT": 24105, + "GCGGCCCC": 24106, + "TATATTTATT": 24107, + "CAGTCTGTG": 24108, + "CCACCCCCACCCCA": 24109, + "CTCCTCTGTCCTCCA": 24110, + "GGTTTCATCTGA": 24111, + "GCCTCGAACTCAGAAA": 24112, + "GAAGGCAATGGCACCCCA": 24113, + "TACTCAAAA": 24114, + "GATTTTAATT": 24115, + "GACGTCAA": 24116, + "TTTTCAGTT": 24117, + "CTTTAACTT": 24118, + "GTATTATA": 24119, + "TAAAGTTTTA": 24120, + "AAAAGTAA": 24121, + "GTCTGAGTG": 24122, + "TATAGAATT": 24123, + "TCAAGACTG": 24124, + "CTCACCCCA": 24125, + "CCTTCTTAA": 24126, + "TTTTACAGG": 24127, + "GTTACATCA": 24128, + "CTTACGTT": 24129, + "CAAAAGATA": 24130, + "TTATTGATG": 24131, + "GGGATATA": 24132, + "GGGACCCA": 24133, + "TCATTGACA": 24134, + "GGAAATGTG": 24135, + "TGAAACTA": 24136, + "GCTGAGCC": 24137, + "TCTTATGTT": 24138, + "GCCAAGACC": 24139, + "GTGTTCCTG": 24140, + "CAATTGAAA": 24141, + "CAATTTAAAAA": 24142, + "CTCTGGGGG": 24143, + "CTATTTCC": 24144, + "CTATTGCTG": 24145, + "CAATGGCTT": 24146, + "CAATGGCAA": 24147, + "CATAACAA": 24148, + "GGATGGGA": 24149, + "AAATATCTT": 24150, + "CCGGCGC": 24151, + "TATATACTTA": 24152, + "GTAGACCTGCACCA": 24153, + "GGCAGGCAGATT": 24154, + "TGACCTGCAGAGA": 24155, + "CAACCTCAAACAGTCA": 24156, + "TTATACAA": 24157, + "GAATCTCC": 24158, + "GTTGĊ": 24159, + "CCATCCAAA": 24160, + "GCCCGAGA": 24161, + "GTCGCTC": 24162, + "TGTTCCCCA": 24163, + "GAGAGAATG": 24164, + "CATTGAGCA": 24165, + "TCTGTACTG": 24166, + "CAGACTAAA": 24167, + "GGAAGAGAAA": 24168, + "CGGCGTA": 24169, + "CCAATAGAA": 24170, + "TCCACCCTT": 24171, + "GAAAAGAAAAAA": 24172, + "GCCATACAA": 24173, + "GTCCCAAAA": 24174, + "GGTTGCCATTTCCTT": 24175, + "TCTCAGTTA": 24176, + "GTAAATGA": 24177, + "TCTGATGGG": 24178, + "GGAAAGGGG": 24179, + "GCATTTCTAA": 24180, + "CATCACTGA": 24181, + "CACAATGTG": 24182, + "TAGTTTCTT": 24183, + "GCTTTTGGTGTTTTA": 24184, + "CTTTACTTTT": 24185, + "CAAGACATT": 24186, + "CAACATATT": 24187, + "CGTTATTTT": 24188, + "CACATGCACA": 24189, + "GACGCTTA": 24190, + "CGTTTACTCTGGAACC": 24191, + "TAGTCCTG": 24192, + "TAGAGGAAA": 24193, + "TGAGCCAAA": 24194, + "CCAGCCTA": 24195, + "TAAACCATT": 24196, + "TGAACATTTT": 24197, + "TGAATGTGA": 24198, + "GCTGGTCTT": 24199, + "CATGCTGTT": 24200, + "GTAACAGTT": 24201, + "GATTGAGAA": 24202, + "CTCCCTGTT": 24203, + "GATGGTTTTA": 24204, + "TTTGCACA": 24205, + "GCAAAGCAAA": 24206, + "TAAAAAGCAA": 24207, + "GTGTTGAAA": 24208, + "GGCTTGGTG": 24209, + "GGAGAACTG": 24210, + "TCACATATG": 24211, + "TTTTTACTTTT": 24212, + "CAACCTCCA": 24213, + "GAGGATTGGGAGAATG": 24214, + "CCACATCCAGTGGAA": 24215, + "TTAGAGAA": 24216, + "GGAGTAAAA": 24217, + "CCAGGAGGA": 24218, + "GTATAGC": 24219, + "GTCCACATT": 24220, + "GCTTCTCAA": 24221, + "CGGGAGC": 24222, + "GATTGAGTT": 24223, + "TTAATCCATT": 24224, + "CCCATAAAACGA": 24225, + "CCCCTACAA": 24226, + "CATAGAAAAA": 24227, + "TTTTTTTCCA": 24228, + "CTTAAGAAA": 24229, + "GACCTAGG": 24230, + "CGGAGAAA": 24231, + "TATCAGAA": 24232, + "TAAATTCAA": 24233, + "GATAATGAA": 24234, + "GGATGGTGA": 24235, + "GAGCCTGTG": 24236, + "GAATATAAAA": 24237, + "CAAGAACAGTA": 24238, + "CAACCACTCTG": 24239, + "CAAAGCCTACCA": 24240, + "TACTGGGGCATATAAA": 24241, + "TGATTTTATT": 24242, + "GTGCAGAGA": 24243, + "GTTGCCAGG": 24244, + "TATACGTT": 24245, + "TGTTGTGAA": 24246, + "CAAACGTG": 24247, + "CAGGTAAAA": 24248, + "CAGACGAA": 24249, + "TCAAGACTT": 24250, + "GCAACGC": 24251, + "GTCACACTT": 24252, + "GAGGCACTG": 24253, + "CTCCCCATT": 24254, + "CTCCCTCTG": 24255, + "TAGATACTA": 24256, + "GCTATAATT": 24257, + "TCAAAGCTT": 24258, + "TTCATAGA": 24259, + "CAAAAATTTG": 24260, + "TAATGCACA": 24261, + "GGCTGGGGG": 24262, + "GAGCAGCTG": 24263, + "GTCTGGCTG": 24264, + "GTGGGCATG": 24265, + "TATATTGTCACCCTG": 24266, + "CTAATGAAAA": 24267, + "CTCGGGAGGCAGAGCCAGGCGGATCTCTGTGAGTTCGAGG": 24268, + "CAGTGAATT": 24269, + "TGAGTCCCTG": 24270, + "TTACACTG": 24271, + "GGAGCCAAA": 24272, + "GCCTCGTG": 24273, + "TAAAGGTTA": 24274, + "GTCGAAAA": 24275, + "TCTTGCTCA": 24276, + "GTCACAGTT": 24277, + "CCCAGCTCTG": 24278, + "GGCAGTCC": 24279, + "TTCATAAAA": 24280, + "GGAAATTAAA": 24281, + "CTGTGCAAAA": 24282, + "GCATTTCCA": 24283, + "CATAATGTT": 24284, + "TAACAGCTG": 24285, + "CTTTGTTACTTTG": 24286, + "CAGTAAATG": 24287, + "TCCGATA": 24288, + "CTGTCTGTT": 24289, + "CTTGTTGG": 24290, + "CTTTGACTT": 24291, + "GGATGTGAA": 24292, + "GCCGGTA": 24293, + "CTCTCTATA": 24294, + "CACAGGGTG": 24295, + "TCTGCCCCA": 24296, + "GCTGTAATT": 24297, + "GCTGTCCAA": 24298, + "GTAATATGA": 24299, + "CGGTCCA": 24300, + "GATTCGTT": 24301, + "CCTGTCTGA": 24302, + "CTCCCGC": 24303, + "TAAAAATCA": 24304, + "GAATTGAGG": 24305, + "CTAAATGTG": 24306, + "CTCTTGAGA": 24307, + "CTTTGGTGAA": 24308, + "CAGCTAAAA": 24309, + "CTATTGAA": 24310, + "CTATTTGTG": 24311, + "TTTTTTTTTTTTTTTTTTTTTTTTTT": 24312, + "GCTAATTTA": 24313, + "CAGTATTAA": 24314, + "TGACATCTT": 24315, + "GTTCACAAA": 24316, + "TTACAGTCA": 24317, + "CTACAGCAA": 24318, + "CGGTTCCAGAGTAAA": 24319, + "GGTCCCCCTG": 24320, + "GAAGTGAAAA": 24321, + "CGACAGAGTGA": 24322, + "CACCCCAAA": 24323, + "TCGTTTTAA": 24324, + "GCGTCCTG": 24325, + "TTTTCTTAA": 24326, + "TCAGTGTA": 24327, + "CAAGCTTTT": 24328, + "GTGGCAGA": 24329, + "GTGTCCAGA": 24330, + "GTTCTCTGA": 24331, + "CCAGCAAAA": 24332, + "GTATGTGTT": 24333, + "TGTTTGTGTT": 24334, + "TGAAGAGTG": 24335, + "CAGATATGA": 24336, + "CTTTTCAGG": 24337, + "GGGGAAATT": 24338, + "CAAAACAAAAAA": 24339, + "CCCCGTGTG": 24340, + "TCAAATCTT": 24341, + "TCTCACAGG": 24342, + "TTTTTTGAAA": 24343, + "TGAAACATG": 24344, + "TCCTTGAGG": 24345, + "GGCCTCGG": 24346, + "TGATTTACA": 24347, + "CTCAATTTA": 24348, + "TCAAAATATT": 24349, + "CATTAATTAA": 24350, + "CTTCCCATG": 24351, + "CAATACATG": 24352, + "CAACATCTT": 24353, + "GAGTGAATT": 24354, + "TAAGCTC": 24355, + "CAAGCTC": 24356, + "CTGTCAGG": 24357, + "TATAGTATT": 24358, + "CAAACACACACA": 24359, + "TGAAGAGGA": 24360, + "TCTGTATGA": 24361, + "CAGACTCAA": 24362, + "TCAATGTGA": 24363, + "CGGGGGC": 24364, + "CGATCCCTG": 24365, + "TGTAGCTA": 24366, + "GGCATCTA": 24367, + "GGGACCCC": 24368, + "GGGAGAAAAA": 24369, + "TTTTTTCAAA": 24370, + "CTCTTGACC": 24371, + "CAGAATTTA": 24372, + "GCCAACATT": 24373, + "CGCAGAGA": 24374, + "TATAAGAAA": 24375, + "TAATGCCTT": 24376, + "GCCTGCTGA": 24377, + "GAATGGATG": 24378, + "TATTAAAAATT": 24379, + "GGATGGCAA": 24380, + "CCCAAGAGA": 24381, + "TCCCATCTT": 24382, + "CTTCACGGTT": 24383, + "TAACACCAA": 24384, + "CTGTACTCCAGCCTGGG": 24385, + "TCACAGTTA": 24386, + "CCACATGTG": 24387, + "GTTCCCTCA": 24388, + "GCACCTGTG": 24389, + "GTCATGTCTGACTCTTTG": 24390, + "GTAGTACTCCATT": 24391, + "AAAATTATAGACTGA": 24392, + "TTTTCTGTAAAATTA": 24393, + "TCCCTCATTAAAATG": 24394, + "TAAGTCATT": 24395, + "GTTCTCCA": 24396, + "GTATGAGG": 24397, + "GTAGAAATT": 24398, + "GCCGCTA": 24399, + "CTATCCTG": 24400, + "CTAGCTCC": 24401, + "CTAGGGGA": 24402, + "TATTGGCTG": 24403, + "TGTTCATGA": 24404, + "CAGACCTA": 24405, + "CAGAGTTTG": 24406, + "CTAAAAAAAAA": 24407, + "CATAGCCA": 24408, + "CTGAAGTTA": 24409, + "GGAAATGAAA": 24410, + "CTGTTCTGTT": 24411, + "TATTAGGAA": 24412, + "GTCTTTCAA": 24413, + "CCATTTGTG": 24414, + "CTGTGGTTA": 24415, + "TGATGTATA": 24416, + "TGATGGTCA": 24417, + "CAATGCAGG": 24418, + "CGTCTGCC": 24419, + "GAAGAAGGAA": 24420, + "CTGCATGAA": 24421, + "TAATTTTTTTA": 24422, + "CCACATGAA": 24423, + "GTTCCCTGTG": 24424, + "GTATTTTAAAA": 24425, + "TAAAATGTTTT": 24426, + "TCATCTTCAA": 24427, + "CTATGGGGTTGAGA": 24428, + "TAATATAATATTTGAAA": 24429, + "CACGTCTT": 24430, + "CATTTTTTAAA": 24431, + "TCAGTTGTT": 24432, + "TCAGCGA": 24433, + "GAACCATG": 24434, + "GAAGGAAAAA": 24435, + "GTGTATAAA": 24436, + "GTGGCTCTG": 24437, + "GGAGGAATG": 24438, + "GGACAACCA": 24439, + "TAAATGTTAA": 24440, + "CTCGAGTG": 24441, + "TGTTTCAAAA": 24442, + "TGTTTAGTA": 24443, + "GCTGTAAAA": 24444, + "GCTGTATTA": 24445, + "GCAATGGAA": 24446, + "GATTGATTA": 24447, + "CGACCAAA": 24448, + "TAGACACTG": 24449, + "CATACGTT": 24450, + "GTAAATACA": 24451, + "CAGCAGATG": 24452, + "GTGTTGTCA": 24453, + "GCATTGCTT": 24454, + "TGAGACTCA": 24455, + "CACTTCTCAAAA": 24456, + "GTCTGCCAA": 24457, + "GGTGATTA": 24458, + "TCTGTTGGCTG": 24459, + "CTGTAGAGA": 24460, + "GAACAGTTTT": 24461, + "CTCCTTCGTT": 24462, + "CAACAGTTA": 24463, + "TAAGGTTTG": 24464, + "CCACCCGG": 24465, + "GCACAGTAA": 24466, + "GCACACCAA": 24467, + "GCCCATGTG": 24468, + "GTAGAATTCA": 24469, + "CCTCCATGCAGA": 24470, + "GGATGAGATGGCTGGATGGCATCACTGA": 24471, + "GATATTTATT": 24472, + "TCATCCATT": 24473, + "TCCTAGC": 24474, + "CTAGTTTTA": 24475, + "CTCTTTTCTT": 24476, + "TATTGACTG": 24477, + "TGTTCCATT": 24478, + "GGAACAGTT": 24479, + "GCAACATTA": 24480, + "TTTTAGATA": 24481, + "CTCCGGGG": 24482, + "TCTATAATA": 24483, + "TCTATTTTTG": 24484, + "GGGGAAAAAAA": 24485, + "CCCATCTGA": 24486, + "TAGAGTCC": 24487, + "CGCTCTTA": 24488, + "TCCAACTTA": 24489, + "GCTGAGGCTG": 24490, + "GCCAAGAGG": 24491, + "GCCAAGGCTG": 24492, + "CAATTCCCAGCAA": 24493, + "GGGAATTTG": 24494, + "TACTTTAAAA": 24495, + "GTCTGGCCA": 24496, + "GCGGCCTG": 24497, + "CCATGGAAA": 24498, + "GAATACTGAA": 24499, + "CAAGACCTT": 24500, + "CCACACCAA": 24501, + "CTGCCCTGA": 24502, + "GTTCAGTTTT": 24503, + "GCCCAGTCC": 24504, + "GAGATGTTA": 24505, + "GAACTGAAAA": 24506, + "CTGCTATAAAGACACATG": 24507, + "CCCACAACCCTGGCGTT": 24508, + "CACGTAAA": 24509, + "CACGTTTG": 24510, + "TAACĊ": 24511, + "TAATCATTTT": 24512, + "TCATTACA": 24513, + "GCACTTTTA": 24514, + "GCATGTTTG": 24515, + "CTCGATCA": 24516, + "CTCTTAAAAA": 24517, + "TCAAGTATT": 24518, + "GTAATAGTA": 24519, + "GATTGTCC": 24520, + "TCTAGTAAA": 24521, + "TGGGAAATG": 24522, + "CATACCAAA": 24523, + "TGGATACA": 24524, + "CAGCAGCTT": 24525, + "TAATGTGA": 24526, + "TAATGTAGG": 24527, + "GGATTGGGG": 24528, + "CCTGAGAGG": 24529, + "CTGGGGCTT": 24530, + "TAGTGTTTG": 24531, + "TCATACACA": 24532, + "CTCTAGAGA": 24533, + "TTAGGTTTA": 24534, + "TCCTTTTTTATG": 24535, + "TAGATAGATAGA": 24536, + "CTCTCAACCAGCTT": 24537, + "CTCCACTATGTTCATA": 24538, + "GGCAGGAGAATCACTT": 24539, + "GCATATTAA": 24540, + "GCATCGA": 24541, + "GCCTCAGGA": 24542, + "TAAATCCTT": 24543, + "CTCTTTTAAA": 24544, + "TCTTCACTA": 24545, + "TGTTTATAAA": 24546, + "CAAACAGAAA": 24547, + "CATTCCTGA": 24548, + "CCTGGAATT": 24549, + "GATGCACAA": 24550, + "GCCATCATT": 24551, + "GGAAAGGTG": 24552, + "CCAAACTCA": 24553, + "TCCAAGTA": 24554, + "CTCTTCCCC": 24555, + "CTCTTCCTCC": 24556, + "GTTAATATG": 24557, + "GTCTTGTA": 24558, + "GGATTGGAA": 24559, + "CCAGGGTGA": 24560, + "TCTCTGTTTT": 24561, + "TCAGAGAAAA": 24562, + "TCCCACTTA": 24563, + "GAATATCTG": 24564, + "TTATACATG": 24565, + "TCCAGGAAAA": 24566, + "TGATACATG": 24567, + "GTAGGGAGG": 24568, + "GGCAGGAGAATTGCTT": 24569, + "CTGAATGTTGAGCTT": 24570, + "TCGTCTTTT": 24571, + "TCCCĊ": 24572, + "CAAGAAATG": 24573, + "TAAAATCCA": 24574, + "CTGACATTA": 24575, + "CGGGACAA": 24576, + "CTAAGAGTT": 24577, + "CAAAATAATA": 24578, + "TAGGGATA": 24579, + "TAAAAACATG": 24580, + "TCTGAGCTT": 24581, + "AAAAAAGAAAA": 24582, + "CTGTTCTTTT": 24583, + "CTTAACATT": 24584, + "CTCAATGAA": 24585, + "GAGCATTTG": 24586, + "GACCACTGATAA": 24587, + "TCTCTTCCTT": 24588, + "CTGGGTCTG": 24589, + "CCAGGCTCC": 24590, + "CAAGGGAGG": 24591, + "TCCAAATTA": 24592, + "CAAGAGAAAA": 24593, + "TAAATACTTA": 24594, + "CTATAGATT": 24595, + "CCTGTTGTTTCCTGA": 24596, + "TCTTGCTTGTTTG": 24597, + "CCAACCCAAATG": 24598, + "GGCTGGGAAGACTGAA": 24599, + "GCTTTTAATT": 24600, + "TCACTCAAA": 24601, + "GAACACATT": 24602, + "CAACCTC": 24603, + "GGAGAGAGAGA": 24604, + "GAAATAATAA": 24605, + "GAGACGGAA": 24606, + "CTGATAGAA": 24607, + "GTAACCAAA": 24608, + "GTAAGGTA": 24609, + "CGGGCTC": 24610, + "CTAAGGTA": 24611, + "GTTTTTAATT": 24612, + "GGTGGAGAA": 24613, + "TGGACCTT": 24614, + "TCTCACCTG": 24615, + "TTTTTTCCA": 24616, + "TCTGATGA": 24617, + "CTAAATCAA": 24618, + "CTCTTCCAAA": 24619, + "TCTAATATG": 24620, + "TGATGGCTG": 24621, + "CGTAGAGG": 24622, + "GAGCAGGGA": 24623, + "CTGGGCTCA": 24624, + "GAAGAAGAAA": 24625, + "TCACCTCTT": 24626, + "CCCCTGAAA": 24627, + "GTCCAACTCTCACATCCATACA": 24628, + "GACGAATT": 24629, + "TAAGTTGA": 24630, + "TCATCCACA": 24631, + "GTGTTATTA": 24632, + "CTCGATGA": 24633, + "CCTTCATTTT": 24634, + "GCAAGTGAA": 24635, + "TCTCTCCCTT": 24636, + "CGATATCA": 24637, + "TGGACCTG": 24638, + "TAATTGGA": 24639, + "CGCTCCTT": 24640, + "CTGAAGGAAA": 24641, + "TGATTAAATT": 24642, + "CTGTTCCAA": 24643, + "GTATTTCC": 24644, + "GTCAAGAGA": 24645, + "GAAAAATATG": 24646, + "TCATGGGGG": 24647, + "CCTTATTTA": 24648, + "TCAGGTTTA": 24649, + "TGTGTTAAAA": 24650, + "GTATGTATG": 24651, + "TTATAGAAAA": 24652, + "GAACACTAA": 24653, + "GCACCCCCA": 24654, + "GGATACAAA": 24655, + "TCTACATTTT": 24656, + "TGTTTGAACTTGTTA": 24657, + "GGGCAAAAA": 24658, + "TCACTGCC": 24659, + "GCCTGACC": 24660, + "TAAAGCTTA": 24661, + "CACACTTATT": 24662, + "TGAAGTTAA": 24663, + "TCTGCAGTA": 24664, + "GGAACATTA": 24665, + "CATGGACTT": 24666, + "GCAAGACTG": 24667, + "GATTCCCTT": 24668, + "GGGGCAGTG": 24669, + "CAAAACGA": 24670, + "TGTACGA": 24671, + "TGCATTATT": 24672, + "GGAAAGAATGAA": 24673, + "GTGTTTCAA": 24674, + "GTGTTCTGA": 24675, + "CGAATCC": 24676, + "CTCTGTCCA": 24677, + "GAGGATGGG": 24678, + "CAGGAGAGA": 24679, + "CAGGAGATG": 24680, + "CCTCCCTCA": 24681, + "CACCATGTT": 24682, + "GTGTGGAGA": 24683, + "GACCACCAA": 24684, + "CGTCATTTA": 24685, + "GAACATCAA": 24686, + "TGACACCTG": 24687, + "TGTGATGAAA": 24688, + "GTTCCCCAA": 24689, + "GGGAAAGGA": 24690, + "CCTAGGTATTTTATT": 24691, + "GCAGATTCAGTCTT": 24692, + "GGAGCAATTATTA": 24693, + "CCCCGTCTCTACTAAAAATACAAAAAATTA": 24694, + "TAATCACTT": 24695, + "TAACTTCTG": 24696, + "TCAGTTGG": 24697, + "GTGTCTGAA": 24698, + "GTTCAGGAA": 24699, + "GTCTGAGTT": 24700, + "TATTCCCAA": 24701, + "TATAGAGTT": 24702, + "CATTCAATT": 24703, + "TGAAGGCTT": 24704, + "GGAACTCC": 24705, + "GCTTCGTG": 24706, + "GTGAGAGTG": 24707, + "TACATCATT": 24708, + "TCTAGCAAA": 24709, + "TGTATAATT": 24710, + "TAATATCAAA": 24711, + "CTGTTCTCC": 24712, + "TCTTAGAGA": 24713, + "CTTTGGTCA": 24714, + "CAAAAATTAAA": 24715, + "CAATGCTGA": 24716, + "CCAGGGAGA": 24717, + "CTTGGGCTT": 24718, + "GAATAGATG": 24719, + "TCCACACAGTCAAA": 24720, + "CTGATTAAAA": 24721, + "GCGTGTGTG": 24722, + "CTCCCAATCCTCTT": 24723, + "TGAGGAATT": 24724, + "GTGCCACCA": 24725, + "CCAGAGATT": 24726, + "CTACACAAA": 24727, + "TAAAGAAAAAA": 24728, + "TATTCCCTG": 24729, + "TATTGCTAA": 24730, + "CACATCCAA": 24731, + "TCTTGGAGG": 24732, + "GCTTTAGTT": 24733, + "CTCAGGTCA": 24734, + "GATTGTATT": 24735, + "GGTGGCCAAA": 24736, + "GATATCAAA": 24737, + "CGCGAGTG": 24738, + "CCAAACTGA": 24739, + "CTAAAGAGA": 24740, + "CAGAATGTG": 24741, + "CCATTGTTA": 24742, + "GTTTGTAGA": 24743, + "CCTCAGCTG": 24744, + "CATCAGTCA": 24745, + "CTCCAGCCTT": 24746, + "CTTTATAAAA": 24747, + "GTTTACTCA": 24748, + "GCAGATGTT": 24749, + "GCTAAAGGGA": 24750, + "CACACAATACCCCA": 24751, + "GCTCTTTAGTTTAATTA": 24752, + "TCGTAAAAA": 24753, + "TAATCACTG": 24754, + "TTTTCATTA": 24755, + "CCAGTTTTA": 24756, + "GCCGCTCC": 24757, + "TAAATAAGG": 24758, + "GTCTCTATA": 24759, + "TATTCACTA": 24760, + "CAGGGGTA": 24761, + "GCTGTATCA": 24762, + "CCTTGCTA": 24763, + "GTAACACTG": 24764, + "GAAAAGTAAA": 24765, + "GCTACTTGGGAGG": 24766, + "TGTATCCA": 24767, + "TCTCACCA": 24768, + "CAGCACTG": 24769, + "CTCTTCAGA": 24770, + "CTCAAGTCA": 24771, + "TTTTAATTTTAA": 24772, + "GAGGAGGAAA": 24773, + "TGAGGGATG": 24774, + 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"CCCTCTGAA": 24819, + "CTTCATGTG": 24820, + "CAACACAAAA": 24821, + "GCGCGGCC": 24822, + "TAGGAAGAATCAA": 24823, + "TAATAGCCAGAAGCTG": 24824, + "TACACACACACACA": 24825, + "TCGTGATG": 24826, + "TCGCAAAAA": 24827, + "TAACTAATT": 24828, + "TTTTGCTA": 24829, + "GCACCACC": 24830, + "GCCCCTTTT": 24831, + "GCCCAAGA": 24832, + "TAAACAAATG": 24833, + "CTCGGGC": 24834, + "TATTGAAATT": 24835, + "TCTTGGGAA": 24836, + "TCTTTAGGA": 24837, + "TGAACCAAA": 24838, + "GGAACACTT": 24839, + "CATGGGCTG": 24840, + "CATGGACAA": 24841, + "GATGCAGCTG": 24842, + "TTAATGGA": 24843, + "TAGATCC": 24844, + "TTATTGTAAA": 24845, + "TCTGACACA": 24846, + "CTAAACATA": 24847, + "CTGTTCAAA": 24848, + "CTTTGTCAA": 24849, + "CTGTGCTGTG": 24850, + "GGATTCTGA": 24851, + "GGCCATCTTTT": 24852, + "GCGGGCACCTG": 24853, + "GAATATAATT": 24854, + "CAACAGTCA": 24855, + "GCAGTGGCCACA": 24856, + "CCTGTAACTTCTT": 24857, + "TTTTTTTGTATTTTTAGTAGAGA": 24858, + "GTTCAAGACCAGCCTGGCCAA": 24859, + "TAATGAATG": 24860, + "TTTTCACTT": 24861, + "TCACĊ": 24862, + "GGATGAGAA": 24863, + "TAAATAGGA": 24864, + "GTCGTGTCCGACTC": 24865, + "TATACATCA": 24866, + "TCTCGCTT": 24867, + "GTCCTCCAA": 24868, + "GACATATAA": 24869, + "TCTCAGGGG": 24870, + "TTTTTTGTAA": 24871, + "CGTGTCC": 24872, + "CGTGGGTT": 24873, + "GAATTCAAAA": 24874, + "CTAAATATAA": 24875, + "CAGCAGGAA": 24876, + "CGAAAAATT": 24877, + "GTCAAGAA": 24878, + "GTCAACTTA": 24879, + "GAAAAATGTT": 24880, + "GGATTTCTTTG": 24881, + "GAATGGCTG": 24882, + "GAATGCCTT": 24883, + "GGGTGTTTG": 24884, + "TGACATATG": 24885, + "CCAGAGTGA": 24886, + "CTTCTGAAAA": 24887, + "GAACCCAGA": 24888, + "CTGATGGTAGG": 24889, + "TCGCCCAGGCTGGAGTG": 24890, + "TGTGTATGTGTGTG": 24891, + "TCTTGATTCCAGCTT": 24892, + "GTGAGTTCCAGGAAA": 24893, + "CTGCTGAAA": 24894, + "CCAGCAGTG": 24895, + "CTACTGAAA": 24896, + "GTCTAAGA": 24897, + "TATAGCAAA": 24898, + "TCTTTCATG": 24899, + "CATTTAAAAAA": 24900, + "CTCAGCTCC": 24901, + "CCTTCAGC": 24902, + "CCAAGGCTT": 24903, + "GCCACCCTT": 24904, + "CGAGCGA": 24905, + "CAAAATGTTA": 24906, + "TGGGAAATT": 24907, + "TAGACATCA": 24908, + "TTATTGTG": 24909, + "TCTGAGTA": 24910, + "CTAAAGAAAA": 24911, + "GCTCAGAA": 24912, + "GAGAACAAA": 24913, + "CAATTCTT": 24914, + "GCATTCCTG": 24915, + "TACAATGAA": 24916, + "GGGAAGATG": 24917, + "GAAGAĊ": 24918, + "TAACACTCA": 24919, + "CTGTACCTG": 24920, + "TAATTAATTA": 24921, + "GATCĊ": 24922, + "GCCACCACGCC": 24923, + "CACTCCTCCATT": 24924, + "CTGGAGTGCAATG": 24925, + "GCTCCCACAGTTGATT": 24926, + "GAAATACCATTTGACCCA": 24927, + "CCATCTCACACCAGTTAGAATG": 24928, + "GTTGCATCCAAAGAACACCA": 24929, + "CAGCAAATG": 24930, + "TCGTTATG": 24931, + "TAATGAGTT": 24932, + "TCATCCATG": 24933, + "TGACGAGA": 24934, + "GTTCTGATT": 24935, + "CTAGCTTTT": 24936, + "CTAGGGC": 24937, + "TAAATAGC": 24938, + "TGTGTCAAA": 24939, + "GAAAGACCA": 24940, + "CGGAAATT": 24941, + "CCAATATAA": 24942, + "CTTACGTA": 24943, + "TAGGTTTTA": 24944, + "GGCAGCTGA": 24945, + "TCTGAGTAA": 24946, + "GGAAATAAAA": 24947, + "TCCTTGTG": 24948, + "CGTTGGTG": 24949, + "CGTTTGAAA": 24950, + "TAATAGCTA": 24951, + "CTCAAGTA": 24952, + "TCCTGCTT": 24953, + "TAATGCAGA": 24954, + "TAATGTAAAA": 24955, + "CCTCCTTCA": 24956, + "GGCAAGAGAA": 24957, + "TCTCTCTCTCTG": 24958, + "CCCCAATTTA": 24959, + "AAAAAATGCTCA": 24960, + "TCTTCAAACCAAGCTG": 24961, + "TCGTAATG": 24962, + "GCGTATTTT": 24963, + "GCGATTTG": 24964, + "TCATCTATT": 24965, + "TGACGTAA": 24966, + "GCACTAAAA": 24967, + "CCAGĊ": 24968, + "CTCTCGTG": 24969, + "TGAACATAA": 24970, + "TCTGGATTTT": 24971, + "CAGATACCA": 24972, + "CTGAGCTCC": 24973, + "GTCAGTGTG": 24974, + "GAGGTAGAA": 24975, + "GTTATATCA": 24976, + "TCCAGTCAA": 24977, + "CCCCCGGA": 24978, + "TGTATGTGTGTG": 24979, + "GGCACTCC": 24980, + "CCTACCAAA": 24981, + "CGCGTTA": 24982, + "GTGAATTCA": 24983, + "CACCTAGA": 24984, + "CAAAAATAGA": 24985, + "CCTCAGATAAA": 24986, + "CCTGAGCCA": 24987, + "CGAGGAAAA": 24988, + "TGATAGATT": 24989, + "GCCACAGTCA": 24990, + "CATTTATTTTA": 24991, + "GTCTGATGAAA": 24992, + "GTTTAATGA": 24993, + "CTCGCTC": 24994, + "GTCTCATTA": 24995, + "TCAAGTCTT": 24996, + "GGAATGCC": 24997, + "CCTGCTGTT": 24998, + "CCAATGAGAA": 24999, + "CTAAGATCATG": 25000, + "TTTGTCAAA": 25001, + "GTTTTĊ": 25002, + "TCCTTTATG": 25003, + "CGTTCCA": 25004, + "CCATTGTAA": 25005, + "CGGATTTA": 25006, + "CTGTGGTAA": 25007, + "GCCTTGGGG": 25008, + "TGAGAGTG": 25009, + "GTCTGTTTTA": 25010, + "GAATGTAAA": 25011, + "CATTTTGAAA": 25012, + "TTATGTATG": 25013, + "CTTTATCTG": 25014, + "CTTTAGAAAA": 25015, + "CAAGGCAGA": 25016, + "TGACACCTT": 25017, + "GTGGACTGA": 25018, + "TCAGGAGATCAAGACCA": 25019, + "CCGCTCTG": 25020, + "GCGAGGTT": 25021, + "TAAGCACA": 25022, + "TGACTTGA": 25023, + "GAAGGAGA": 25024, + "TCCTGACTT": 25025, + "CTTGTTGTT": 25026, + "CTTGTATA": 25027, + "GTCGTAAA": 25028, + "GTCATGCC": 25029, + "TGGGGATGA": 25030, + "CGCGGCCA": 25031, + "CTGAATATG": 25032, + "TAAAAACATA": 25033, + "GGAAATATG": 25034, + "TCCTTCCCCA": 25035, + "CAATTGCA": 25036, + "GGAGGTAGG": 25037, + "CTGTGGAAAA": 25038, + "GCATTGTA": 25039, + "GTTTGCAGA": 25040, + "TGATGCATG": 25041, + "TCAAAACTATGAAA": 25042, + "TCATGCCTT": 25043, + "TATTAATATT": 25044, + "GCTCGTTA": 25045, + "CATCGCC": 25046, + "CTGCATGTT": 25047, + "GTTTAGTAA": 25048, + "TCACCCTGA": 25049, + "TGACAGAAAA": 25050, + "GTTCTAACTGTTGCTT": 25051, + "CTAACTGGTGTGAGATGGTA": 25052, + "TGAGCTGTG": 25053, + "CTGTAACC": 25054, + "GGAGTTGGA": 25055, + "GTATCTCTT": 25056, + "CAAACCCTG": 25057, + "CAAAGGGCA": 25058, + "GAGATACAA": 25059, + "GTGATTATT": 25060, + "CATGCCAAA": 25061, + "CATGCTAAA": 25062, + "GAGGGGGTG": 25063, + "CTCCTAGAA": 25064, + "GCCACTGCACTCCAGCCTGGG": 25065, + "CTTTTTATAA": 25066, + "CAAAAGCTG": 25067, + "GGGAGAAGG": 25068, + "CGTGGGGG": 25069, + "TAAAAACACA": 25070, + "GGCCTCAAA": 25071, + "GTGAATATA": 25072, + "CAATTTACA": 25073, + "CCTCCCACC": 25074, + "GTCTGCCA": 25075, + "GTGTGCAGG": 25076, + "GATCATTTG": 25077, + "AAATACACA": 25078, + "TCACACCAA": 25079, + "GCCCACCTT": 25080, + "GAGTCCAGGA": 25081, + "TCTTTTATTTT": 25082, + "GGCAGAGGCAGA": 25083, + "GGCAGGAGAATG": 25084, + "GAAACCATCATTCTCA": 25085, + "TGACAAGA": 25086, + "GTGGGAATG": 25087, + "GTTGGAGAA": 25088, + "GTTGTCAAA": 25089, + "GTTCCGG": 25090, + "GCATCAGA": 25091, + "CTATTTTAAA": 25092, + "TAAAATAAAATT": 25093, + "GCTGTCAAA": 25094, + "CTCACGCA": 25095, + "TCCACGGA": 25096, + "TTAATGCA": 25097, + "GGTGĊ": 25098, + "GGTGGTTTT": 25099, + "CGCTCGA": 25100, + "TTTTTTAAATG": 25101, + "TCCTTGAGTG": 25102, + "GGTATCTC": 25103, + "GCTCACACA": 25104, + "GGATTCTG": 25105, + "GCAGGTGTT": 25106, + "GGATGGAA": 25107, + "CCTAACAGAA": 25108, + "TCTGTGAAAA": 25109, + "CTTCCCTTA": 25110, + "GCAGACATT": 25111, + "CTGTATGAA": 25112, + "TGATGATTA": 25113, + "CCAGATGTG": 25114, + "CTACATATT": 25115, + "CCTGTTCCTG": 25116, + "TGTTTGTTTGTTTG": 25117, + "TTTTCCACAGTT": 25118, + "TCGATGTAGTGACACTA": 25119, + "CTTCACAGTAGG": 25120, + "CAGGGAAGCCTG": 25121, + "TACCTCAGCCTCC": 25122, + "GATCTCCTT": 25123, + "TCAGTTCTG": 25124, + "GTTGTCATT": 25125, + "GTTGTATA": 25126, + "GCACCAGCA": 25127, + "GAAACGTA": 25128, + "GAAAGCAAAA": 25129, + "TAAAACCTG": 25130, + "GCTGGGGTT": 25131, + "CTCAGACAA": 25132, + "GTCACATTA": 25133, + "GAGGGTCC": 25134, + "GATGCTGTT": 25135, + "CTTTTGACC": 25136, + "TTTATAAAAA": 25137, + "GACATCATT": 25138, + "CATATCTGA": 25139, + "TTATTCCAAA": 25140, + "GGCACCCA": 25141, + "TCTCACAGTT": 25142, + "CTGAACATG": 25143, + "CAGCAGAA": 25144, + "TCTTTTTTCA": 25145, + "TGATGCAGG": 25146, + "TATCATATG": 25147, + "GAGTGTTTG": 25148, + "TGAGGGTGAGTAAA": 25149, + "GAGTTCTA": 25150, + "CTGGGGACA": 25151, + "TCCCATCTA": 25152, + "GGAGGAGAAGGGGACGACAGA": 25153, + "GAATATCTCA": 25154, + "CAAGGCCAA": 25155, + "TAAGATGTT": 25156, + "GCTGTGTTA": 25157, + "GCTCATGCCTGTAATCCCAGCA": 25158, + "TCCTCCTCCTCCTCCTCCTCCTCC": 25159, + "CAAATAAATTCATTAAAAA": 25160, + "GAAATAAGTATTTGACCCC": 25161, + "CCGAAATT": 25162, + "GATCAAATG": 25163, + "GCGCTTAA": 25164, + "TAACTCATT": 25165, + "CAACGTCA": 25166, + "GTGCCCAGG": 25167, + "TAAAGGGCA": 25168, + "GAAAGAAGG": 25169, + "TCTGCCTTA": 25170, + "CAGAGGGCC": 25171, + "CATGCCCC": 25172, + "GTCAGGGTG": 25173, + "TCTCTCAAAA": 25174, + "CTTACATCA": 25175, + "CGACACTG": 25176, + "TGGATCTG": 25177, + "TAATTGAAAA": 25178, + "TCTCATGTG": 25179, + "TCTCCCTTTT": 25180, + "GGAAAGCAA": 25181, + "GAATTTCAAA": 25182, + "GTCTTCTCTT": 25183, + "GGAGGCCA": 25184, + "TCTAATCTA": 25185, + "TGAGGGGAA": 25186, + "GAAGACCTG": 25187, + "TAACATTTTTT": 25188, + "TATTTATGTT": 25189, + "GAAAAAAAAAAAAAAA": 25190, + "GTACAGCCA": 25191, + "CACGTGTG": 25192, + "CCGTAAAA": 25193, + "CCGCACC": 25194, + "CCCGCTA": 25195, + "TCACAAGA": 25196, + "TGACTGCTG": 25197, + "CAACGAAA": 25198, + "GTTGAATTA": 25199, + "GGACTTGA": 25200, + "CTCGCTAA": 25201, + "CTCTCGAA": 25202, + "GCAAGTAAA": 25203, + "GTCAGACTT": 25204, + "TATGCACAA": 25205, + "CTCCCCCTG": 25206, + "GTCCTACA": 25207, + "GTCCCACAA": 25208, + "TCCTTCTTCTT": 25209, + "TATTATATAA": 25210, + "GTTAACAA": 25211, + "TCTTAGATT": 25212, + "CGTACTG": 25213, + "TGAGACCA": 25214, + "GAATGGCTA": 25215, + "GTGGGGGAA": 25216, + "TCAGGCCTG": 25217, + "CTCAAACAA": 25218, + "TTTTTAGTTTT": 25219, + "CTCTCCAAAA": 25220, + "GCTGGGTGTGGTG": 25221, + "TAATCTCAGCACTTTG": 25222, + "TCATATTTG": 25223, + "TGAGCCACA": 25224, + "TCCTCTCAA": 25225, + "CTTTGAGTT": 25226, + "GTAGAGGAA": 25227, + "CTAGGTTTT": 25228, + "GTCTCCTCA": 25229, + "GAAACCCAA": 25230, + "TATACATGA": 25231, + "TCTTGTGAA": 25232, + "CATTGATGA": 25233, + "TGAACTGTT": 25234, + "TCTCAAAATT": 25235, + "GTTACATGA": 25236, + "CTTATACTT": 25237, + "CTTTTCTGTT": 25238, + "TAATTCCTG": 25239, + "TCTCATGAA": 25240, + "CTCTTGGGA": 25241, + "GCCAAGGTG": 25242, + "GTTTGGTAA": 25243, + "GCCTGGATT": 25244, + "GCTCCCCTG": 25245, + "TAGCATTTG": 25246, + "CCTTAGTTA": 25247, + "GAAGACCAA": 25248, + "CTTTAGGAA": 25249, + "CTTTATCTA": 25250, + "GCCACCACGCCTG": 25251, + "TGACAGCTCTTTGGTCTT": 25252, + "TCAGCCACA": 25253, + "GAACAGTC": 25254, + "GCAGAGATG": 25255, + "CTACTATG": 25256, + "CTCGACTT": 25257, + "CTCTAAATG": 25258, + "GTCGTGAA": 25259, + "TATTTCTCTT": 25260, + "TATAGACAA": 25261, + "TCTTTAGTG": 25262, + "TGTTTAGTG": 25263, + "CAAAGAGTA": 25264, + "TCTGTCCAA": 25265, + "CAGATGAGA": 25266, + "GTGAGCAAA": 25267, + "GTGACGTA": 25268, + "CGGTACC": 25269, + "GAAAAGAAATT": 25270, + "TCTGACCA": 25271, + "CACCĊ": 25272, + "GCTGAGGGA": 25273, + "GTCAATAACAAAA": 25274, + "CGGATGGG": 25275, + "CCCTGCCTT": 25276, + "CACTTCCTG": 25277, + "TTATGTCAA": 25278, + "CCATGGGA": 25279, + "GGGCGGTG": 25280, + "CCCTCTCTCC": 25281, + "GTTCATGTG": 25282, + "CTACAGCTT": 25283, + "GACTTACAGAAAA": 25284, + "GTGACTGCAGCCATGAAATTAAAA": 25285, + "TACTTTGTTATATA": 25286, + "TCTTCCATTTCCTAATAATT": 25287, + "GAATTATTTT": 25288, + "GTTGTGTA": 25289, + "TAAAGAAATT": 25290, + "GAAACCATT": 25291, + "TCTTGCTTTT": 25292, + "CAGGCAGC": 25293, + "CAGACCCTT": 25294, + "GCTGGTGAA": 25295, + "CGGCAGTT": 25296, + "CTAACTAAA": 25297, + "TCTGACTGA": 25298, + "GGCCCGTT": 25299, + "GCTGATTCA": 25300, + "GTCTTGCTT": 25301, + "CTCAAGTAA": 25302, + "TATAACAA": 25303, + "CCTCCTTTA": 25304, + "GAGCTCCTG": 25305, + "CGTCTGCA": 25306, + "TTTTTGGTTTT": 25307, + "CAATACTTA": 25308, + "TATTTATGTG": 25309, + "CCAGAGCTT": 25310, + "TATATATGAA": 25311, + "GAGATGGGAATACCA": 25312, + "TCATTTTGACC": 25313, + "CTTGCCTGGGAAA": 25314, + "CATTCATTCATTCATT": 25315, + "GACTGAATT": 25316, + "TAAGCCATT": 25317, + "TAACCATA": 25318, + "TTTTCGTT": 25319, + "TTTTGTTAA": 25320, + "TGATCTAAA": 25321, + "GAATĊ": 25322, + "GAACAACAA": 25323, + "GGACAAATT": 25324, + "GCCTGTGAA": 25325, + "GCCCTTCTT": 25326, + "GTCGGCTT": 25327, + "GTCGGCGG": 25328, + "CACATGTGA": 25329, + "GAGATAATT": 25330, + "CATTTCTCTT": 25331, + "GTGACGTG": 25332, + "CCTTCAGGA": 25333, + "GCAATATTTT": 25334, + "GCAACACCA": 25335, + "TTTTAGTCA": 25336, + "TCTCTCCCTG": 25337, + "TCCATACTT": 25338, + "TTAACTTTT": 25339, + "TTAAGGTT": 25340, + "GTCCCTGGG": 25341, + "TTTACTTAA": 25342, + "TCTCATATA": 25343, + "CTGAATTAAA": 25344, + "CTTTGGTTA": 25345, + "CCTCAGGAAA": 25346, + "CCTCCTGTG": 25347, + "CAGCCTCC": 25348, + "CTTCACCAA": 25349, + "CTGTATCTG": 25350, + "TCACATGTT": 25351, + "GTGGATGTG": 25352, + "GTTCCCAAA": 25353, + "GTAGGTGTT": 25354, + "TCACAATTA": 25355, + "TGACAATTA": 25356, + "GACCTTAAA": 25357, + "CTTAAGCCAGTACATG": 25358, + "CCTCAAATT": 25359, + "GATTTTATTTT": 25360, + "GACGGAGG": 25361, + "TAATATATATA": 25362, + "TAAGGAGAA": 25363, + "TTTTCTATT": 25364, + "TCAGTTGA": 25365, + "CTGTGTGAA": 25366, + "CTTGCTATT": 25367, + "CCAGTGAAA": 25368, + "TGTGCTTCTT": 25369, + "TATTCACCA": 25370, + "GAGAGGTCA": 25371, + "CAGGGACCA": 25372, + "TCAAGAGCA": 25373, + "GCTGTACC": 25374, + "CTCAGATGA": 25375, + "GCAACAAAAA": 25376, + "CGGAAATG": 25377, + "TCTCTĊ": 25378, + "TCCACAACCTCAAACAGTCA": 25379, + "TCTAGGGTT": 25380, + "TAGACGTT": 25381, + "GGGATCAAA": 25382, + "CCTAGCCA": 25383, + "CTCTTCTCAA": 25384, + "TAAATTTGTT": 25385, + "GCAGAATTA": 25386, + "GCTTAAGCAGGGG": 25387, + "CTGAGGCAGGAGGA": 25388, + "TCTGCAATAGGTAAGCA": 25389, + "TACGTAAA": 25390, + "TGAGGACA": 25391, + "GTGGAAGA": 25392, + "GTGGTTGTT": 25393, + "GTGCCTCTG": 25394, + "GCCCACAAA": 25395, + "GAAAGTCTT": 25396, + "TCTTTATCC": 25397, + "GGAAGACCA": 25398, + "CTCACGTG": 25399, + "GAGGGTTTT": 25400, + "CGGTACA": 25401, + "CGGCAGTG": 25402, + "GTTACTAAA": 25403, + "CTAATCAAA": 25404, + "GGGGGAAAAA": 25405, + "CTGAAGGTG": 25406, + "TTTTTTTCAAA": 25407, + "TCTGATGTG": 25408, + "GTGAATATG": 25409, + "TGCCTAGG": 25410, + "CACTGTTAA": 25411, + "TCATGGTAA": 25412, + "GCGGTCC": 25413, + "GCAGGGACA": 25414, + "GAAGGCCTG": 25415, + "CCAGGCAGA": 25416, + "GAAATGCTTCTTA": 25417, + "GTTTACCAA": 25418, + "TAATTTTTTTG": 25419, + "GAACACCTG": 25420, + "CTCTACAAA": 25421, + "GTTCCTGAA": 25422, + "CCCGGAAA": 25423, + "CGTTTGCCCACTGA": 25424, + "CTATCCCAAAA": 25425, + "CCAAGAACACCA": 25426, + "CATCATGCTTTGGG": 25427, + "TGCCCTTGCTGATGGAA": 25428, + "CTTTGGGAGGCCGAGGCGG": 25429, + "TCACACATT": 25430, + "TGACTAGG": 25431, + "GAATAAAAAA": 25432, + "CAATCCCTG": 25433, + "GTGTCCACA": 25434, + "GCCGGCGG": 25435, + "GAAAGGTAA": 25436, + "TCAACCTCA": 25437, + "GCTTCATTA": 25438, + "TACATAATG": 25439, + "GATGGTCAA": 25440, + "GAAAACTAAA": 25441, + "CGAGCAAA": 25442, + "CAAAATACTA": 25443, + "TTCAGTAAA": 25444, + "GCAAAGTTA": 25445, + "TCCTTGATT": 25446, + "TCCTTTCTTTT": 25447, + "TAATACTCA": 25448, + "TATTACTTA": 25449, + "CTTTGCCCA": 25450, + "CGCCTCTG": 25451, + "CCATTTAAAA": 25452, + "AAATTTCAA": 25453, + "GCGGCATG": 25454, + "TCCCATATT": 25455, + "TAAGATTTTA": 25456, + "TGTGTGTGTGTGTGTGTGTGTGTGTG": 25457, + "GTATTTTGGCCCATT": 25458, + "GTGGGCAAACGTA": 25459, + "GCATTCTTTGTCCTCCAAA": 25460, + "GTGTGAGTG": 25461, + "GGATTTTTTTT": 25462, + "CCACCGG": 25463, + "CTACAGAGA": 25464, + "CTAGGGTA": 25465, + "CACACGAA": 25466, + "TCTTTCAAAA": 25467, + "CAGATCAAA": 25468, + "CTGAGTCAA": 25469, + "CTGACACAA": 25470, + "CCTTTAGAA": 25471, + "TATGTCTTA": 25472, + "CTAAAAAATG": 25473, + "GGGGCCCA": 25474, + "TAGATACTT": 25475, + "CGTGCAA": 25476, + "TCTGAGCAA": 25477, + "TCATTTCTCA": 25478, + "TCCAACAATT": 25479, + "GCTGAGACA": 25480, + "GTGTTGTTA": 25481, + "CGCCCCCC": 25482, + "CTCTGTCTCC": 25483, + "TTTTAATAAA": 25484, + "TAATGGATG": 25485, + "GGCTGTGTG": 25486, + "TGAGGGCTG": 25487, + "GATAATATT": 25488, + "CTTTAGTTTT": 25489, + "TCGAGGAA": 25490, + "GGAGCAGGTT": 25491, + "CTGTAAGTCTTCA": 25492, + "GACTACACACTTATTTCAAAAAAA": 25493, + "CTTTGGATGCAACTCA": 25494, + "TCGAGACAGGGTTTCTCTGTGTAGCTTTGCGCCTT": 25495, + "GCGAGAGAGA": 25496, + "TGATTTTAAA": 25497, + "CTTCATTTG": 25498, + "CTTCTCTCC": 25499, + "GTGGTTTCA": 25500, + "CTACAAGA": 25501, + "CTCTAACTT": 25502, + "GAAAGGAGG": 25503, + "GAAACAGC": 25504, + "GAGATCAAA": 25505, + "CATTCTTAAAA": 25506, + "TCTGGGGCA": 25507, + "TAAAATATTAA": 25508, + "TAAAATGTGA": 25509, + "GCAATAATT": 25510, + "CGGGGCTT": 25511, + "TACACTCAA": 25512, + "TCCAGGATT": 25513, + "TCCAGGAGAA": 25514, + "TTTAGACA": 25515, + "TGGATCTT": 25516, + "TGAAATGCA": 25517, + "TGATTGCAA": 25518, + "GCTGATGGG": 25519, + "TACCTGAGA": 25520, + "TAATGGCA": 25521, + "GTCTGTCATAGTT": 25522, + "TCATGCCAA": 25523, + "GCTCCCTCA": 25524, + "CTGAAATTTT": 25525, + "CTGTAGTAA": 25526, + "GTGGTGCGTG": 25527, + "GAAAAAAAAATT": 25528, + "CACACACACACACACACACACA": 25529, + "GGCGCGGTG": 25530, + "CCTATAATCCCAGCTA": 25531, + "CAGAGGAGCCTCTTAAA": 25532, + "TAGTTTTTG": 25533, + "GGGTTATG": 25534, + "TCGGGGTG": 25535, + "TGAGTTATT": 25536, + "TTACCTA": 25537, + "GTTGGAGGA": 25538, + "GCCTAGAAA": 25539, + "TATTTCAATT": 25540, + "TATTAAAAAAA": 25541, + "CAAAGGTCA": 25542, + "TGAAGACCA": 25543, + "TCTGGACTG": 25544, + "CTGAGAATT": 25545, + "CTGAGGGTG": 25546, + "GCTTGAGAA": 25547, + "GTGACTTAA": 25548, + "CATGTCAAA": 25549, + "GTCATCTCC": 25550, + "GTCAGATAA": 25551, + "TATGCTCC": 25552, + "TATGGCCA": 25553, + "GTTTTCTTGG": 25554, + "GTCCCTCTT": 25555, + "CGACCCCATGGA": 25556, + "TGGAGGAGG": 25557, + "TTCACAAAA": 25558, + "GTAAACATA": 25559, + "GCAAAGAAAAA": 25560, + "GCCAACAGG": 25561, + "GTGTTGAGG": 25562, + "CTCTGGATG": 25563, + "GCCTTGGTG": 25564, + "CAGGAGACA": 25565, + 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+ "GCCACCCTTTGCCTT": 25610, + "CCATCAGCAAGGGCATT": 25611, + "CCGTTTCA": 25612, + "TATTTTTTAAA": 25613, + "CTGTATAAA": 25614, + "GTTTAACA": 25615, + "GCACCTTTT": 25616, + "CTACCAAAA": 25617, + "TGTGTACC": 25618, + "TCTTGGATT": 25619, + "GAGAGGCAA": 25620, + "TCTGGCAAA": 25621, + "CTCACATCA": 25622, + "CTCAGACCA": 25623, + "CTCAGGGTT": 25624, + "GCAACCAAA": 25625, + "CCAATCAAA": 25626, + "CTAATAGTT": 25627, + "TACATACAAA": 25628, + "GAAAATACTG": 25629, + "GCCAGTAAA": 25630, + "GGGGĊ": 25631, + "GGGGTCAA": 25632, + "GCTATCAAA": 25633, + "CGTGTGAA": 25634, + "GCAAAGAGTT": 25635, + "TAAAAATACAA": 25636, + "TGAAACTG": 25637, + "TAATACAGG": 25638, + "CGCAGAA": 25639, + "TCTTTTTGTA": 25640, + "TACTATTTG": 25641, + "TATTTGCATA": 25642, + "CTTTAGCTT": 25643, + "GCGACGAA": 25644, + "TGATAGGAA": 25645, + "TCACTGAAAA": 25646, + "GTAGATGAA": 25647, + "GCAAAAATCCCAGAA": 25648, + "TCCCTCTGGG": 25649, + "CAACATAGTGA": 25650, + "GGAAATGGAAGACATACAA": 25651, + "GTCAGATGAAACCAAAA": 25652, + "TAACCTCTT": 25653, + "TAAGAAATA": 25654, + "TGACAATA": 25655, + "CAAGTTAAA": 25656, + "GTGTAAATT": 25657, + "GTTGGCTTAAA": 25658, + "CCATCTGTG": 25659, + "GTACAAGGA": 25660, + "TGTGCACAA": 25661, + "GAGACGTG": 25662, + "GAGATCTGA": 25663, + "TGAACATTA": 25664, + "CTCACCTCC": 25665, + "TATGTAAAAA": 25666, + "TATGTCATT": 25667, + "GAGGTCAGGA": 25668, + "CGGCTGAA": 25669, + "CCAACCTCA": 25670, + "TTTGĊ": 25671, + "CAAAATCC": 25672, + "GGGATAGTT": 25673, + "CCAAATGCA": 25674, + "TAATATCCA": 25675, + "TGCCTTTTA": 25676, + "TCCAACCTGA": 25677, + "TCTAACAAA": 25678, + "CGTACACA": 25679, + "TCATGTCTT": 25680, + "CAACACTAA": 25681, + "CAACAGAAAA": 25682, + "CTGCCCCTT": 25683, + "GTTGATATT": 25684, + "GGCAAAGGGTG": 25685, + "CTTTCTTAAA": 25686, + "CTCCTGACCTTGTGA": 25687, + "GCCCAGGCTGGAGTGCAATG": 25688, + "GATTTGCATTTTAATGAGG": 25689, + "GTTTGCTAGAGTG": 25690, + "GATGGAGGAGCCTG": 25691, + "TCAGTAATT": 25692, + "TGAGTGGA": 25693, + "GAATCACTT": 25694, + "CTTCAATA": 25695, + "GTGCTTCTG": 25696, + "GAAATAAAAAA": 25697, + "CAGAGTCTT": 25698, + "GCTGCCATT": 25699, + "CATGCTCAA": 25700, + "CATGGAGTT": 25701, + "GCAACGCA": 25702, + "GTAAGAGTT": 25703, + "CGGCATA": 25704, + "GATGCACC": 25705, + "TGGGTATA": 25706, + "TGGACTTA": 25707, + "TAATTGGTG": 25708, + "CCTATAATT": 25709, + "GCAAAGAGAA": 25710, + "GGCCCGC": 25711, + "GCTCACAGA": 25712, + "TCCAAAAATT": 25713, + "GAGAAGTGA": 25714, + "GTATTGTATT": 25715, + "TCTTTTCTA": 25716, + "TACCTTATT": 25717, + "CTCTGCACA": 25718, + "GCTAACAAA": 25719, + "GTGTGTATT": 25720, + "GCATGCAGG": 25721, + "TAAGACTGA": 25722, + "TATTTATAAAA": 25723, + "GCCATGCCCTCC": 25724, + "TGACTGTGTGGATCA": 25725, + "GTAATGTTTT": 25726, + "CTCCTGCCTCAGCCTCCCGA": 25727, + "CAAAAATGAAAATT": 25728, + "CAGACAGACAGACAGACAGACAGACAGACAGA": 25729, + "GAAGCATTTCAA": 25730, + "CACTAAATT": 25731, + "CTTCTGATT": 25732, + "GTGGTTGG": 25733, + "GTTGCAGCA": 25734, + "GGAGCTGTT": 25735, + "GTACCCTA": 25736, + "TGTGTTTTTT": 25737, + "TCTTGATTTT": 25738, + "GTGATCTGA": 25739, + "GCTGCACAA": 25740, + "TCTACTCTT": 25741, + "GGTTTACA": 25742, + "GGAAAGATG": 25743, + "GCTCAGTTTG": 25744, + "TATTATGTA": 25745, + "CTTAACAA": 25746, + "CAGAAGAGA": 25747, + "CGCCCCTG": 25748, + "GATTATCTT": 25749, + "TCGGGTTA": 25750, + "TCAGAGTTA": 25751, + "GGTAATTTG": 25752, + "GGAATTGAACAA": 25753, + "TAACAATTTT": 25754, + "CAAAAAACAAAAAA": 25755, + "GTCTGATGGG": 25756, + "TGAAATTAAAA": 25757, + "CAGATCACGAGG": 25758, + "GCCCAGCGACAGCC": 25759, + "GTTCATTTG": 25760, + "GTTCĊ": 25761, + "GTTGCTCTT": 25762, + "CCAGCCAGA": 25763, + "GTATCTATT": 25764, + "CTACTAAAA": 25765, + "TCTTCGTG": 25766, + "TGTTGTCAA": 25767, + "CATTGTAAAA": 25768, + "TCAATAGCA": 25769, + "GGAAGGTCA": 25770, + "GAGGCACC": 25771, + "TCTCGCTCTG": 25772, + "CTCCGCC": 25773, + "CTTTTGTAAA": 25774, + "GGGGTGCTG": 25775, + "CCCATCCCTG": 25776, + "TAGGĊ": 25777, + "TGAAAGAAAA": 25778, + "TACCCGA": 25779, + "CCATTCTCC": 25780, + "GAAAAAGAGA": 25781, + "GTGTGGGAA": 25782, + "TTATGGAAA": 25783, + "TTATGGAAAA": 25784, + "CCAGGTGAA": 25785, + "GAACAGAAAACCAAA": 25786, + "GCGAGTGA": 25787, + "TCACATAAA": 25788, + "TTACACTGA": 25789, + "TTTTTACATT": 25790, + "TGATCACAAGAA": 25791, + "GCCTCGAACTCACAGAGATCCGCCTGGCTCTGCCTCCCGA": 25792, + "GAGCAGGGGGACCTT": 25793, + "GAAAATGTGGCACATATACACCATGGAATACTA": 25794, + "CAAACAAGCAAGATTTCTG": 25795, + "CAAAGCCTACCATCAGTAA": 25796, + "GATCTCTA": 25797, + "GCGAATTA": 25798, + "TGATAAGAA": 25799, + "CCAGGAATT": 25800, + "GCTGGTTTTA": 25801, + "CCTTCCTCTT": 25802, + "GTAATACTT": 25803, + "CCAATACCA": 25804, + "GTTTTTGGG": 25805, + "GTTTTCATA": 25806, + "CGATACAA": 25807, + "TCTGATATG": 25808, + "TCATTTCCTT": 25809, + "CGAATGAA": 25810, + "CAGTGGTA": 25811, + "CTGTGCAGA": 25812, + "CTATTCTCA": 25813, + "TCATGGCTA": 25814, + "GTGTGGCTT": 25815, + "CAATGTAAA": 25816, + "GGATGCAGA": 25817, + "TCAGGGTCA": 25818, + "TCGATAAAA": 25819, + "GTTTAGTCA": 25820, + "CTGTATTTTA": 25821, + "GCACAGCCA": 25822, + "GACTCTTTA": 25823, + "TCTCTACTAAAAA": 25824, + "GGCTAATTTT": 25825, + "CCCTATTTAATAAATG": 25826, + "GGTCAGGCTGGTCTCAAA": 25827, + "TCGGAATG": 25828, + "TAACTGGAA": 25829, + "TTTTGTGTT": 25830, + "TTATAATA": 25831, + "CAAGTGTGA": 25832, + "GCATCAGCA": 25833, + "CTCGTCTT": 25834, + "TCTGTTTTTT": 25835, + "CATGGAATT": 25836, + "CCTGGGGCC": 25837, + "GATGGGGATT": 25838, + "TTATTCACA": 25839, + "CCTATCAAA": 25840, + "CTAAAGGA": 25841, + "TGATTGCTG": 25842, + "TCTTTTCCAA": 25843, + "CACAATATT": 25844, + "CATTTTTTTA": 25845, + "TACTGTTTG": 25846, + "CAATGGCTG": 25847, + "TCTGTGTTTG": 25848, + "CTTCTTCCTG": 25849, + "CAACAAGAGTGAAA": 25850, + "GGTAATTAA": 25851, + "TCACCCCAA": 25852, + "TAAGACTAA": 25853, + "CTCCTTGGAAGGAAA": 25854, + "CTGGATGTT": 25855, + "GCCCAGTCA": 25856, + "GCTTGGTTTGAAGAAA": 25857, + "TCTCCTCTAGAGCA": 25858, + "GACGGGGGAGCCTGGTGGGCTG": 25859, + "TAGAGGTG": 25860, + "TAATCTTTG": 25861, + "TCAGCAATT": 25862, + "TCCTTATTA": 25863, + "CTGGCTC": 25864, + "GCATCCACA": 25865, + "CTAGTGTG": 25866, + "TATGGACAA": 25867, + "GATTGAGCA": 25868, + "CCTGCTCTG": 25869, + "CTAAAAAATA": 25870, + "GATGGTGTT": 25871, + "CGTGGGA": 25872, + "GCTGATGTG": 25873, + "GCTGACTCA": 25874, + "TATTTTTTTTTT": 25875, + "GGAGGCAAA": 25876, + "TCTAAGTAA": 25877, + "GCCTTTCC": 25878, + "GTCTGGATT": 25879, + "TATGACCCA": 25880, + "TCAGGGAGA": 25881, + "GCAGAGAAAA": 25882, + "CTGTATCTT": 25883, + "GTATATGAA": 25884, + "GTTTCCAAAA": 25885, + "CTACAAACCACTG": 25886, + "GACACCTGTCCACA": 25887, + "CCGCGGA": 25888, + "TTTTCCCTT": 25889, + "CAAGĊ": 25890, + "CTGTTATTA": 25891, + "CTGGAAATT": 25892, + "CCATGTTTG": 25893, + "GCCTCCATG": 25894, + "TAAAGAGAAA": 25895, + "TAAACACAAA": 25896, + "GAAATGAGA": 25897, + "TATACTCC": 25898, + "TGTTGTTATT": 25899, + "CAAATCTGA": 25900, + "GTGATATAA": 25901, + "CATGGATTA": 25902, + "GTCACACCA": 25903, + "TATGTCTC": 25904, + "GAGGCCCC": 25905, + "GTTAGCCA": 25906, + "CTCCTCTCTT": 25907, + "CTAAGTGAAA": 25908, + "TACAGTTTG": 25909, + "CTTTTTACTT": 25910, + "GCTACCCC": 25911, + "GGCAGCAAA": 25912, + "TCATTGAGAA": 25913, + "CCAAAGATG": 25914, + "CTAAATATTA": 25915, + "GCTCAGGGA": 25916, + "TGATGGACA": 25917, + "CCTCAGAGA": 25918, + "CACAATTTA": 25919, + "GGATTTATT": 25920, + "TCATGGGAA": 25921, + "TCATGGATT": 25922, + "TTATGCTA": 25923, + "CCATGGAGA": 25924, + "CCATGGCTT": 25925, + "GGTCAGAAGTTTACATACA": 25926, + "TCATACAGA": 25927, + "TTACATCTG": 25928, + "GTTGATGAA": 25929, + "TCTTTCTTTCTT": 25930, + "TTAATGTTA": 25931, + "GGGCTCCATG": 25932, + "GGAAATATCTTCCCA": 25933, + "CCCAGGAGGCGGAGCTT": 25934, + "GAACTGGACATG": 25935, + "TGACTTCCA": 25936, + "CTTGTGTA": 25937, + "GCACCTCTT": 25938, + "GGAGTTCAA": 25939, + "TATACACTA": 25940, + "GCTGTCTCC": 25941, + "GCAAGACAA": 25942, + "CGGCACTG": 25943, + "CTTAATTTTA": 25944, + "GTTAACATT": 25945, + "TCTTACTGA": 25946, + "GTATTGTTAA": 25947, + "CAGTGGAAA": 25948, + "TAATGGTG": 25949, + "CACACACACACAAA": 25950, + "GACTTCCTT": 25951, + "CATCTAATT": 25952, + "GTGGGTGAA": 25953, + "AAATATTTTTT": 25954, + "TAACTTTCCTTCCAA": 25955, + "TGACCTGAA": 25956, + "CACACACTGTTGCTG": 25957, + "GGATTACAGGTGTGA": 25958, + "CAGTCCATGGGGTCA": 25959, + "GTATTCAGACCCCTT": 25960, + "GTCCCAGCTCTCTGCAGG": 25961, + "CCACAGGAA": 25962, + "TAAACTAAAA": 25963, + "TAAATAATAAA": 25964, + "TATATTATTTT": 25965, + "TGTTTATTAA": 25966, + "CAAACCATT": 25967, + "CAGGTCCA": 25968, + "TCTGGCTCTA": 25969, + "CATATAATA": 25970, + "GGCATAGG": 25971, + "TGCCTAAA": 25972, + "CACCCAAAA": 25973, + "GCTGAGCTT": 25974, + "GTTAAGAAAA": 25975, + "GTCTTTCTA": 25976, + "CTCAAGGTCA": 25977, + "CGGAGATG": 25978, + "TAATGTACA": 25979, + "CCCTGTGGG": 25980, + "GAATGTATT": 25981, + "GGCCATTTT": 25982, + "GCGGGGAA": 25983, + "TAAGACAGG": 25984, + "GAACAGTTA": 25985, + "CCACCACGCC": 25986, + "CAAGAGCTT": 25987, + "CAACATGTG": 25988, + "TGACCTGTG": 25989, + "TGTTTATTTTA": 25990, + "GAAGTTAAAA": 25991, + "CTTTGCAGAAAAA": 25992, + "TCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTTTCTT": 25993, + "CCGATC": 25994, + "GGTTTATTTT": 25995, + "GACGTTAA": 25996, + "TGATTTTTA": 25997, + 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26040, + "TAATCTGTG": 26041, + "TAACCTTAA": 26042, + "TCATGTGAA": 26043, + "TGATCGA": 26044, + "GAAGTCAAA": 26045, + "CAATGTGAA": 26046, + "GTGTGAATT": 26047, + "GTTCTGCA": 26048, + "TATATAATAA": 26049, + "CATTCTTTG": 26050, + "CAGAGGAAAA": 26051, + "GGAATATCA": 26052, + "GCTGGAGTGCAGTG": 26053, + "CTCACGTA": 26054, + "CCTTTACCA": 26055, + "CATGGAGGA": 26056, + "GCAAGCTA": 26057, + "GATTCCATT": 26058, + "GATTTCCTG": 26059, + "CTAATCCA": 26060, + "GACAGGATT": 26061, + "TCAAAĊ": 26062, + "CCTATCCA": 26063, + "TCATTTCTAA": 26064, + "CTAAAGCCA": 26065, + "CGCAGATG": 26066, + "GTATTCTTA": 26067, + "GTGTTGTAA": 26068, + "CATCATTTA": 26069, + "GTGTGTAGG": 26070, + "GCTCCCTGA": 26071, + "CGTCCATT": 26072, + "CTGTACTTA": 26073, + "TATTTATCAA": 26074, + "TAAGAATGTT": 26075, + "GTTCTGGGATTTTTG": 26076, + "GCCTGGGAAA": 26077, + "GGGGCTGTCGCTGGGCAA": 26078, + "GTCATGCTGAAAGACCCA": 26079, + "CCCTĊ": 26080, + "TACCAAATT": 26081, + "TCATGTGTT": 26082, + "TCATCCCC": 26083, + "TGAGGAGAA": 26084, + "TGACTCAAA": 26085, + "GTTGCTCTG": 26086, + "CCAGCTGAA": 26087, + "CTCCAGGTG": 26088, + "GGAATAATT": 26089, + "GCAAGAGGA": 26090, + "GTTTTCCATT": 26091, + "TCTATCTTA": 26092, + "TAGACAGTA": 26093, + "TGCATCCA": 26094, + "GATAGTAAA": 26095, + "CGTTTGCA": 26096, + "TATTATGAA": 26097, + "CTTAACTG": 26098, + "GACAATTTG": 26099, + "CCATGCTCC": 26100, + "GCAGGGCA": 26101, + "CCCTAGAAGAAAA": 26102, + "CTTCCGACTT": 26103, + "CAAGGCTGA": 26104, + "TCACCCACA": 26105, + "TCATCATTTT": 26106, + "TCGTTAAAA": 26107, + "GACTCTGTA": 26108, + "TAATATGCTGTCTA": 26109, + "GCAACAGTGTGTGAAAA": 26110, + "GCTCTGGATAAGAGCGTCTGCTAAATGACTAAAATG": 26111, + "GCTGAAACTCCAGTACTTTGGCCA": 26112, + "CCCAAAATT": 26113, + "TAATCCACA": 26114, + "TTACTGAA": 26115, + "CTGTCTATG": 26116, + "GTGTATATG": 26117, + "GCAGCCATT": 26118, + "GGAGATTAA": 26119, + "CCAGGACA": 26120, + "TATAGAAAAA": 26121, + "GCTTGAGTT": 26122, + "CTCAGAGCC": 26123, + "GTCAGGCTT": 26124, + "TTTTAGTGA": 26125, + "TTAACCTT": 26126, + "CTTTTTAGG": 26127, + "TGGGGACC": 26128, + "CCTACCCAA": 26129, + "CGCGTAA": 26130, + "CTGAAGGGG": 26131, + "TCTGATCTGA": 26132, + "CCAAATGTA": 26133, + "CAGCAGAGA": 26134, + "GTATTGGAGCTT": 26135, + "CAAAAACATG": 26136, + "TAATGTTATT": 26137, + "TGAGACCAA": 26138, + "TTATGTTTTA": 26139, + "TAGCTCAAA": 26140, + "GGAGACATT": 26141, + "TTATAGAGA": 26142, + "CAACACCTT": 26143, + "CTGCCCATG": 26144, + "CCAGACAGA": 26145, + "TTACATGTG": 26146, + "GTCCATGTG": 26147, + "GAGATTATCAGTG": 26148, + "CATCTCCAA": 26149, + "TGACTTCTG": 26150, + "GTTCAAGAA": 26151, + "GTACAGTC": 26152, + "CTCTATTTG": 26153, + "TCTTCAAAAA": 26154, + "TCTGCACAA": 26155, + "TCTGCCCAA": 26156, + "GGAATATTTT": 26157, + "CGGCACCA": 26158, + "TAGATCCA": 26159, + "GGTTTCTC": 26160, + "GGGAGTTTT": 26161, + "GTAAATATAA": 26162, + "GAGAATACTA": 26163, + "TATTACAA": 26164, + "CAGCAGAAA": 26165, + "CAGTGCTGA": 26166, + "GGGAAAAAAAA": 26167, + "GAGCAGAAA": 26168, + "TTATGTAATG": 26169, + "CTGTACAAA": 26170, + "TCATAGTTA": 26171, + "TAAGGCAAA": 26172, + "CCACTGAAAA": 26173, + "GAACTTAAAA": 26174, + "GCAGAGACATTACTTTGCCAACAAA": 26175, + "TCATCTTTG": 26176, + "TTACGTA": 26177, + "TCCGTGTAAA": 26178, + "GTTGAATA": 26179, + "GTTGCATTA": 26180, + "GGATCACTT": 26181, + "GCCGAGTG": 26182, + "GTCTGAGCA": 26183, + "TATTGGCTT": 26184, + "TATAGTCAA": 26185, + "TATATTTTAAA": 26186, + "GGAACATGA": 26187, + "TATGCCAAA": 26188, + "GATTTCTC": 26189, + "CCTGTAAAA": 26190, + "CCTGCACTT": 26191, + "GGTGGAAAA": 26192, + "CGTGGTCA": 26193, + "GCAAAGCTT": 26194, + "GTGAACTG": 26195, + "CACCTCTGA": 26196, + "CAGCAGTGA": 26197, + "CAAAAATGTG": 26198, + "CAGCTCTTA": 26199, + "CATCACTA": 26200, + "CAGGAGGGG": 26201, + "TACTTCTA": 26202, + "GTCTGCTA": 26203, + "TCATGGAGG": 26204, + "GCGGCAGA": 26205, + "TCGGTGGG": 26206, + "TCCCACAAA": 26207, + "GAACACTCA": 26208, + "TCTAAATTTT": 26209, + "CCACCATGCCCGG": 26210, + "TCTCAATTTG": 26211, + "TTAATTCAAGATG": 26212, + "GTTCAGCAA": 26213, + "CTATATGAA": 26214, + "GTACAGTGA": 26215, + "CATCTTGTTCC": 26216, + "GGGGTTGGGG": 26217, + "CTGCTCTAGAGGAGA": 26218, + "CAATCCCAGCA": 26219, + "CTATGAACATAGTG": 26220, + "TCTCAGCTCACTGCAACCTCTGCCTCC": 26221, + "CAAGTAGCTGGGACTACAGG": 26222, + "AGAGA": 26223, + "TCAGTCAAA": 26224, + "TCACTCTGA": 26225, + "GTGCCAAAA": 26226, + "GCACTGCA": 26227, + "CCATATTAA": 26228, + "CCATGTGTT": 26229, + "TAAAGGCTG": 26230, + "GTCGAGTG": 26231, + "TATTGCTGA": 26232, + "GAAAGTAAAA": 26233, + "TCTTGCTGA": 26234, + "CAGGGACATG": 26235, + "TCTGGTCTT": 26236, + "GCTGGACCA": 26237, + "CATGTCTAAAA": 26238, + "TATGTGATT": 26239, + "TATGGATGA": 26240, + "GACAGCTCA": 26241, + "GGTTTCCTT": 26242, + "TTTTTTCTAA": 26243, + "GAATTGGA": 26244, + "GAGAACACA": 26245, + "CGCAGGGG": 26246, + "CAAAAAGTA": 26247, + "CAGCTCTCC": 26248, + "TTTTAATTAAA": 26249, + "GCCTTGCC": 26250, + "TGAGGCCTT": 26251, + "TTATGCATT": 26252, + "GGTCATTTG": 26253, + "TTATAGCTT": 26254, + "TAAGAATTTT": 26255, + "CTTGACCTT": 26256, + "CTAGGCCCC": 26257, + "CTCCTGGCCTCAAGTGA": 26258, + "GTATTTGATCCCCTGCTGATTTTGTA": 26259, + "GGTTTCATCTGACCATATGA": 26260, + "TAAGTTGAA": 26261, + "TAAGCTATT": 26262, + "TCCCTCAAA": 26263, + "GTGTAATAA": 26264, + "GCAGTTGTT": 26265, + "CCAGTGCTG": 26266, + "GTAGAAATG": 26267, + "TATTTCCATT": 26268, + "GAAAGTGAAA": 26269, + "GAGAGGGTT": 26270, + "CATTCCTCC": 26271, + "CAGGCACTT": 26272, + "GTAACTAAA": 26273, + "TATGTAGTG": 26274, + "GAGGGACTG": 26275, + "CCTGGGGTT": 26276, + "CCAAGTCTT": 26277, + "CCAATACC": 26278, + "TACATCTGA": 26279, + "TACATCCAA": 26280, + "CAAAATACCA": 26281, + "TCTCCTAAGCAA": 26282, + "CCAAATGTG": 26283, + "CCAAAGAAAA": 26284, + "GGCCCGGTT": 26285, + "GTGAACAAA": 26286, + "GCATTGTGA": 26287, + "GAGCGTGA": 26288, + "GGATTTAAA": 26289, + "TCATGTCAA": 26290, + "GATGATGAA": 26291, + "CTGGGCGG": 26292, + "GAGTATTTG": 26293, + "TAGCTCTAA": 26294, + "GTTGGCCTT": 26295, + "CTTTTTTCTTTT": 26296, + "TAAATAAATAAAAA": 26297, + "TACTGATAACAAGTTCAAA": 26298, + "GAGTCAGACATGACTGA": 26299, + "CTGGATCATCCAAATGCA": 26300, + "GAGCTGTCAAAGGACACCA": 26301, + "TACTCTCC": 26302, + "TAACTACA": 26303, + "CTGAAAAAAA": 26304, + "GCACCCTC": 26305, + "CCATCACA": 26306, + "TGTGTCTC": 26307, + "GAAATCTAA": 26308, + "CATGCCCCA": 26309, + "CATGTATGA": 26310, + "GTCACCCC": 26311, + "TATGGTC": 26312, + "CTCCCTCAA": 26313, + "TAGACAGTG": 26314, + "CCCCCTCTT": 26315, + "CATAGTTAA": 26316, + "TTCAGTCC": 26317, + "CGCTCATT": 26318, + "GAGAAGGTG": 26319, + "CGCACCTG": 26320, + "GGAGGCTA": 26321, + "AAATTCTA": 26322, + "TCATGCTGA": 26323, + "CAATGCATT": 26324, + "GACCACGA": 26325, + "GGCAATTTT": 26326, + "TAAGATCAA": 26327, + "TTATTTTTTTA": 26328, + "CAAGACAAAA": 26329, + "TCATTATTAA": 26330, + "GTAGGGGAGG": 26331, + "TGACCCTGA": 26332, + "GTCCATAGG": 26333, + "TAAGTAGGAAAA": 26334, + "GTTGTGTTA": 26335, + "GTCATGAAAA": 26336, + "TCGCACCAA": 26337, + "TGAGATGGAGTCTT": 26338, + "TCTCCCTCGATCTG": 26339, + "GGGTTAGGGTTAGGGTTAGGGTTA": 26340, + "TAGAGAGAGA": 26341, + "TCTGTGTGTGTGTG": 26342, + "TAACCCTC": 26343, + "TCATCACC": 26344, + "TGACAGATT": 26345, + "GAATTTTTG": 26346, + "CTTGCTTTG": 26347, + "GTTGTATTTT": 26348, + "CCACGCCCGG": 26349, + "CTCGCTCC": 26350, + "CACAGCAAAA": 26351, + "TCTGTAGGA": 26352, + "TATGCGTT": 26353, + "CGGCTTTT": 26354, + "GATGGGGTT": 26355, + "GGTTĊ": 26356, + "GCTACCTCA": 26357, + "TAATTTCTCA": 26358, + "TTATTTCTAA": 26359, + "TCTCATGTA": 26360, + "CGCTCCAA": 26361, + "CTCAACTG": 26362, + "CAGCTGATT": 26363, + "TTTTAATTTTA": 26364, + "GGATTCTCA": 26365, + "CCCTTCTCA": 26366, + "GGATGGATT": 26367, + "GCATGGCTG": 26368, + "CCAGGCATT": 26369, + "CCAGGGCTT": 26370, + "TCGGTGTT": 26371, + "AAATAGGAA": 26372, + "TCGATCAA": 26373, + "CAATAGTTA": 26374, + "TCCAGAGTGGTT": 26375, + "GTAAAGAATCCACCTG": 26376, + "CCTCTCTA": 26377, + "TCATAAATG": 26378, + "TTACACC": 26379, + "GAACCCTA": 26380, + "CTGGCAAAA": 26381, + "GTTCTGGA": 26382, + "GCATCAGTT": 26383, + "GTCCAGATT": 26384, + "TCTGGATTA": 26385, + "CTGATACTT": 26386, + "CATGGCTGA": 26387, + "CATGTATCA": 26388, + "GTCACTGTT": 26389, + "GTAATGGAA": 26390, + "GAGGGAAGG": 26391, + "TCTCGTTA": 26392, + "TCCATCCTG": 26393, + "TGCATCTA": 26394, + "GGGACTGAA": 26395, + "GGAAATATT": 26396, + "TGCCTCTGA": 26397, + "CAGCAGCC": 26398, + "TCTTAGGTCA": 26399, + "GTTTGTCAA": 26400, + "TGAGAGTA": 26401, + "GTCTGCAGG": 26402, + "CAATGGGAA": 26403, + "GCATGGCTT": 26404, + "CTAAAATTTT": 26405, + "CCAGGGGA": 26406, + "TATTTGCATT": 26407, + "CAACAACAAAAAA": 26408, + "CTTTATCCAA": 26409, + "TAACACTTTT": 26410, + "CAAGATATT": 26411, + "GTTGGTTTA": 26412, + "GATTAATTTT": 26413, + "TCCCCTCTT": 26414, + "CCAGCATGACAA": 26415, + "GAAGGAGAGA": 26416, + "GTTTGATTTT": 26417, + "TTAGGGTTAA": 26418, + "CAAACAACCCCA": 26419, + "GAACCCGGGAGGCGGAGGTT": 26420, + "CGGGGTTTCACCGTGTTAGCCAGGATGGTCTCGATCTCCTGACCTCGTGA": 26421, + "CAATGACAA": 26422, + "GCATGTGTG": 26423, + "CACAGAATG": 26424, + "TCTTTAGCA": 26425, + "TCTGGGGTG": 26426, + "GTGAGGCAA": 26427, + "CTCAGATAA": 26428, + "GTCACCATT": 26429, + "GTCATACCA": 26430, + "GTAAGAGCA": 26431, + "GTAACATTTT": 26432, + "TCTCGCTG": 26433, + "CTCCCCCAA": 26434, + "GGGGGCCA": 26435, + "CCCACTCTT": 26436, + "GGTGGCCA": 26437, + "CGCGGGA": 26438, + "GACCTATG": 26439, + "CTCTGTCTA": 26440, + "GCCTTCTA": 26441, + "CCTCAGGA": 26442, + "GCTCCTGAA": 26443, + "GACTATGCC": 26444, + "TCTCTCTCTCTCTG": 26445, + "GGGGAGAGA": 26446, + "CAAGGTTTG": 26447, + "TAATTTTTTTTTT": 26448, + "CAAGACAGG": 26449, + "CTTGAGCTT": 26450, + "GTTCCAGAAAAA": 26451, + "TCTCTTAATT": 26452, + "CTGTAGCCTTGTAGTATAGTT": 26453, + "CAGCCCCAAAGCATGA": 26454, + "GTACTGGAGTTTCAGCTTTAGCA": 26455, + "GTGTGACTGTTTGAGGTTGTGGA": 26456, + "CCGAGGAA": 26457, + "TCTAAAAATG": 26458, + "GACGCAAA": 26459, + "TAACTGATT": 26460, + "TTACAATT": 26461, + "TCCTCAGTT": 26462, + "CTTGTCATT": 26463, + "CTTGTTTAA": 26464, + "TAAAGAGTG": 26465, + "TAAACAGCTT": 26466, + "CACATAGTA": 26467, + "GAAACTCTT": 26468, + "GAAACACTA": 26469, + "CATTCAGGA": 26470, + "TCTGCATAA": 26471, + "CCTTCATTA": 26472, + "TCTCTCTGAA": 26473, + "TCCATACATGA": 26474, + "TAGACTGAA": 26475, + "GGTTTCTGA": 26476, + "CGCGGTA": 26477, + "GTAAATTTTA": 26478, + "TAAAAACTAA": 26479, + "GGAAACCAA": 26480, + "TAATAGTG": 26481, + "CTCTTGCC": 26482, + "CAGTTTGTG": 26483, + "GGATTTGTG": 26484, + "TGAGGCAA": 26485, + "CAATGTCAA": 26486, + "GCAGGCCAA": 26487, + "GATTTTTTTA": 26488, + "CATCCTTTG": 26489, + "TCAGGTGGG": 26490, + "TAGGAGAGG": 26491, + "TAATTATTTTA": 26492, + "CTGCAGTCC": 26493, + "TCGATGAA": 26494, + "CAAATGGAAGAA": 26495, + "CTCAAACTA": 26496, + "CAATACCAA": 26497, + "TGACATATA": 26498, + "TGACCTCTT": 26499, + "CTCATGGATT": 26500, + "CCGCTTTT": 26501, + "TAGGGGTG": 26502, + "TCGCAGGA": 26503, + "CCAGCAGA": 26504, + "TGTGTATCA": 26505, + "TATTGTATG": 26506, + "CAAATATTAA": 26507, + "CAAACCCAA": 26508, + "CATTGGCC": 26509, + "TCTGTCTCTT": 26510, + "TCTGGGGTT": 26511, + "CAGACGCC": 26512, + "TCAACAGCC": 26513, + "GCAACACTT": 26514, + "CCTGGAGCC": 26515, + "CCCACGAA": 26516, + "CATATCATT": 26517, + "CGCGCTA": 26518, + "TCTCCCTTA": 26519, + "CGTGCTGA": 26520, + "GCAAACATG": 26521, + "GTGAATATT": 26522, + "GCCTGCCAA": 26523, + "CTGGGGCC": 26524, + "TCGACGTT": 26525, + "GTTTAGGAA": 26526, + "CAAGATGTT": 26527, + "GCCCCGCC": 26528, + "GTGTATTTTA": 26529, + "TCACAACAA": 26530, + "GAACGAAA": 26531, + "GCAGTTGTG": 26532, + "CCACTTTCA": 26533, + "GAAAGGTGA": 26534, + "TATACATGTG": 26535, + "CAAAGTCTT": 26536, + "GAGACACTT": 26537, + "GTGACACTT": 26538, + "GCTGGCTCA": 26539, + "TATGTCCTT": 26540, + "TCTACCCTT": 26541, + "CTTTTTAATGA": 26542, + "CGAGTAAA": 26543, + "GACACCTA": 26544, + "CATACGTG": 26545, + "TAATTTAAATT": 26546, + "GATAGCC": 26547, + "CCAAACCTG": 26548, + "GCCAAGAGAA": 26549, + "GGAGGGCTT": 26550, + "GCTCGGCA": 26551, + "GATGATTTG": 26552, + "GCAGGGGA": 26553, + "GGCAAGAGAAA": 26554, + "CTTGGCTA": 26555, + "CTTGGTTAA": 26556, + "GTTTAGAGA": 26557, + "CTCTACCTT": 26558, + "CTCTACCTG": 26559, + "CCGGTTTG": 26560, + "TAAATACATG": 26561, + "GTAGGCAGA": 26562, + "CGTATTAAA": 26563, + "GAGTCCAAA": 26564, + "GCTCTCATG": 26565, + "CACGTTAA": 26566, + "TAACTTCC": 26567, + "TAACGTTTT": 26568, + "TCATCCAAA": 26569, + "TGACTATA": 26570, + "TCCTATC": 26571, + "TCCCTCTTA": 26572, + "CTTTCCTTA": 26573, + "GTTCACATT": 26574, + "GCAGTTAAA": 26575, + "CCATTATTA": 26576, + "CAAAGCTCA": 26577, + "TAAAAGGTG": 26578, + "GTGACATGA": 26579, + "CCTTTACTG": 26580, + "GAGGCGAA": 26581, + "TCTCTGAATT": 26582, + "TCCATAGCA": 26583, + "TTTGCTCTT": 26584, + "GCCAGTCAA": 26585, + "CGCGAAA": 26586, + "GGAAACTAA": 26587, + "GTGAAGATT": 26588, + "GTGAAGTTA": 26589, + "GTGAATTAAA": 26590, + "CAGCATTAA": 26591, + "GTTAATATTA": 26592, + "GACCĊ": 26593, + "TCTTTTTATA": 26594, + "CTCTGGCC": 26595, + "GCCTTTGAA": 26596, + "TAATGCATA": 26597, + "CCTCATTAA": 26598, + "CCCTTTGTTGGCAA": 26599, + "CAATGTATA": 26600, + "CAATGTCTG": 26601, + "CCATGTCTT": 26602, + "GCAGGGATT": 26603, + "TCCCAGAGATT": 26604, + "TAAGAGCTT": 26605, + "TAAGACAAAA": 26606, + "CAAGATAAA": 26607, + "CAAGAGTCA": 26608, + "CAACACTCA": 26609, + "TAAATACACA": 26610, + "TCAGTGAAAA": 26611, + "CGCAAGATCTCA": 26612, + "TGTGTGTGTGTGTGTGTGTGTGTGTGTGTG": 26613, + "GCTCAACATTCAGAAAA": 26614, + "GCTCTAGGAA": 26615, + "CCCCAAATTTA": 26616, + "TGACACAGGTCAAA": 26617, + "TGAGATCTTGCATGGA": 26618, + "GAGGAATGGGCCAAAATACCA": 26619, + "TCTTGCTTGTTTGTAGGTGACCAAA": 26620, + "GATTTTAAAAA": 26621, + "TCACGCTG": 26622, + "TCAGCAGAA": 26623, + "TCCTCCATG": 26624, + "TCCTGAGTG": 26625, + "GTTTATTTA": 26626, + "CCAGTTCTT": 26627, + "GCCTCACAA": 26628, + "CTCGACAA": 26629, + "TATTGGCC": 26630, + "CATTAAAATT": 26631, + "CATTCTAATT": 26632, + "TCTGGACAA": 26633, + "TCTGGACCA": 26634, + "CAGAGGTAA": 26635, + "TCAAGTGTT": 26636, + "TAAAACTTAA": 26637, + "CTGAGTCTT": 26638, + "CTAACCCC": 26639, + "GTTTTAAAAAA": 26640, + "GGTTGTCC": 26641, + "GGTGTCCA": 26642, + "CGTGGTGAAA": 26643, + "GGAAACACA": 26644, + "TGAAACAA": 26645, + "CGAAGGAA": 26646, + "TCTTTTCTTCTT": 26647, + "CCCTGGAGA": 26648, + "GTCTGTAGA": 26649, + "GCTAAGGTG": 26650, + "TATGAGTGA": 26651, + "GATCAGGAA": 26652, + "GTTTTATTTTA": 26653, + "CTTGGCAGG": 26654, + "TCAGACTCA": 26655, + "CAATACGAA": 26656, + "TGATATTATT": 26657, + "CCGGGATT": 26658, + "TCCCCCTCA": 26659, + "GTTCTGTTTT": 26660, + "GCTATTAAAA": 26661, + "GAGGTGGGGG": 26662, + "TAATGCTATCCCTCC": 26663, + "CACTCTGATGGTAGTT": 26664, + "CTATGGGGTTGAGATCTG": 26665, + "CATCTGATT": 26666, + "GAATCAGG": 26667, + "TCCGGTA": 26668, + "CAAGAAGA": 26669, + "CTGCTTTCA": 26670, + "GTCTTACA": 26671, + "TATTCCTTTT": 26672, + "CACAGTGTT": 26673, + "TGAATCTCA": 26674, + "TGAATTTTTT": 26675, + "TAAAATAGTA": 26676, + "GTAAGTGTT": 26677, + "GTTACAATT": 26678, + "TACATCTC": 26679, + "CTTACAGTG": 26680, + "GGTTGCCA": 26681, + "TCTCCTCCTCC": 26682, + "CGTGGATT": 26683, + "TCCTTGGAA": 26684, + "GAATTGTG": 26685, + "GAATTGAGA": 26686, + "CTGTTTAAA": 26687, + "GTTAACTA": 26688, + "GACCCTGAA": 26689, + "CGCACATG": 26690, + "GTATTCGA": 26691, + "CGGAGATT": 26692, + "CAAAAATATG": 26693, + "CTCTGGTGA": 26694, + "CAGGAGGTG": 26695, + "GAGTGTATG": 26696, + "TAGGATTTG": 26697, + "TCTGTTAAAA": 26698, + "GAATACTAA": 26699, + "CTTCATTTTA": 26700, + "TGATAGATG": 26701, + "GTGATTAAAA": 26702, + "CCGGCGCC": 26703, + "CTTGAAGAGA": 26704, + "TGATCAGTCTATAA": 26705, + "GGCTAAAAA": 26706, + "TCAGATTAA": 26707, + "CTGTCTGTG": 26708, + "GTTTGATTA": 26709, + "GCATAAATT": 26710, + "CCACCTTTT": 26711, + "GTACTAAAA": 26712, + "GTAGTTTTTT": 26713, + "TGAAGACTA": 26714, + "TGAAGGAAAA": 26715, + "TAAAAGCC": 26716, + "TAAAAGACAA": 26717, + "GCTGTCATT": 26718, + "GAAAACCTA": 26719, + "GTCCGCC": 26720, + "CTTTTĊ": 26721, + "GACAGTCAA": 26722, + "GGCAGTC": 26723, + "CTCTTGATT": 26724, + "TAATAATAATT": 26725, + "TATGATATA": 26726, + "GGGCTCTA": 26727, + "GAATAGAAAA": 26728, + "GTCTCCCAAA": 26729, + "CCAGTACCATGCTGTTTTGGTTA": 26730, + "TCCTTTGTAGGGACATG": 26731, + "AGAAA": 26732, + "CCGCAGCC": 26733, + "GGATAAATT": 26734, + "GGACTGCAA": 26735, + "GGACTTCCA": 26736, + "CCAGAAGA": 26737, + "CCACTTCC": 26738, + "TCTTGGGCA": 26739, + "TGTTCTCAA": 26740, + 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26827, + "GGAGTTAAA": 26828, + "GTACAAATT": 26829, + "GTCTTTTTA": 26830, + "CATTCTCCCAATCCTCTT": 26831, + "CTCACCAAA": 26832, + "CTCAGAGC": 26833, + "GTAACATAA": 26834, + "GTTACCCA": 26835, + "CTAACGCA": 26836, + "CTAATACTG": 26837, + "TACATCTCA": 26838, + "TACACGC": 26839, + "CTTACCCC": 26840, + "GTCCTCCTT": 26841, + "GGGGCGGA": 26842, + "CCCACCTGA": 26843, + "TAGATATGA": 26844, + "TGGATTTTA": 26845, + "CGTGCGA": 26846, + "TAAAAAGATA": 26847, + "GGAAACATT": 26848, + "TATTAGAAA": 26849, + "CAATTTAATT": 26850, + "CAAAAATGTT": 26851, + "TTTTAATATT": 26852, + "CATTTTTTTTTT": 26853, + "GCTCGGCC": 26854, + "GCATGCCTG": 26855, + "TGTGAATTTT": 26856, + "TAAGAGCTG": 26857, + "TCCCAGGGCTGA": 26858, + "CTTTTTCACTCTCC": 26859, + "GCTAATTTTACAGAAAATT": 26860, + "GGTGATGGACAGGGAGGCCTGGCGTGCTG": 26861, + "CAGGTCTGTGAGAGCCAGAAA": 26862, + "CCTAAAATG": 26863, + "TGACĊ": 26864, + "TGAGCTCTT": 26865, + "TATTGGTCA": 26866, + "TGTTGGCTA": 26867, + "TGAAGCTTA": 26868, + "CAGGCGCC": 26869, + "CCCCGAGG": 26870, + "GTAAACCAA": 26871, + "GGAAACTA": 26872, + "GGAAAGTGA": 26873, + "GCCTTGCTT": 26874, + "GGCTGGATG": 26875, + "TGAGATGAGTG": 26876, + "TTTTTTTTTAAA": 26877, + "GAGCAGTGA": 26878, + "GGATTTCA": 26879, + "AAATGTATG": 26880, + "CTGGGCTA": 26881, + "AAATAGATG": 26882, + "CTTCATATA": 26883, + "CTGTAGAAA": 26884, + "CTGGACAGG": 26885, + "TCAGAATTTT": 26886, + "TCGCTCTC": 26887, + "GGCGGCGG": 26888, + "TCCCATGGACAGA": 26889, + "GAGATGGGGTTTCA": 26890, + "GCTGCATATGTATCAAAA": 26891, + "TACCTACTGTGAAGCATG": 26892, + "CCTATTAAA": 26893, + "TCGAGGTG": 26894, + "CAACACATT": 26895, + "CTTCCTTAA": 26896, + "CTTTCCTCA": 26897, + "GTGTCCCCA": 26898, + "GCACTGCC": 26899, + "GGACCCTCA": 26900, + "CCATATATT": 26901, + "CTCGTGTT": 26902, + "TGTGTGATT": 26903, + "CAAAGAATG": 26904, + "TAAAACGTT": 26905, + "GCAATGCTG": 26906, + "TTTTATAGTT": 26907, + "TGGGTAAA": 26908, + "TGTATGTGTG": 26909, + "TCAAATATCA": 26910, + "GGGAGCTGA": 26911, + "TAAAAACTTA": 26912, + "CTCTTGTAA": 26913, + "TACAATTCA": 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"TGAAGGCTG": 27001, + "TCTGCCTCTG": 27002, + "CTGAGCCTT": 27003, + "GCTGTCTGA": 27004, + "GCTGTACTT": 27005, + "CCTTGGTGA": 27006, + "CATGGTGAA": 27007, + "TCTCTCCCCA": 27008, + "CTAAGTCACTT": 27009, + "TCTACTCAA": 27010, + "GGGGTAAA": 27011, + "TGGGGAGAA": 27012, + "GACAGAATT": 27013, + "GGTGCATCC": 27014, + "CATAGTCC": 27015, + "CGTTCAACTCACAGA": 27016, + "GCTGATTAA": 27017, + "GTTAATGCA": 27018, + "GTCTTCAGG": 27019, + "GGAGGGTCA": 27020, + "TATAATGTA": 27021, + "CAAAAATCA": 27022, + "TTTTAAGAAA": 27023, + "TCATGCAGA": 27024, + "GCATGCACA": 27025, + "TTATACTTTT": 27026, + "GTTAAATTTT": 27027, + "CTGGACACA": 27028, + "GCATATTTTA": 27029, + "CTGATGTTTT": 27030, + "TAACTGTTA": 27031, + "CAGTCACTCA": 27032, + "CAACAACAACAACAACAACAACAA": 27033, + "CCGTGGTG": 27034, + "GGGTAATG": 27035, + "GAACTCAAA": 27036, + "GTTGCTATT": 27037, + "GCACGAGG": 27038, + "CCATCCACA": 27039, + "GTCTCTATG": 27040, + "TCTTGATCA": 27041, + "TCAACAGTA": 27042, + "GCTTGAGCC": 27043, + "GCTGGAGCC": 27044, + "GCAATACC": 27045, + "GAGGCCCA": 27046, + "CGGGGTA": 27047, + "TCCACGC": 27048, + "TGGAGTCC": 27049, + "TAATTGTTTATG": 27050, + "TCTCAGTTTT": 27051, + "GATACTGAA": 27052, + "GTAAATACAA": 27053, + "GTGTTGATT": 27054, + "CAATTCGA": 27055, + "TCTAAGTG": 27056, + "CGGACAGA": 27057, + "CTATTGCTT": 27058, + "CCTCAGAA": 27059, + "CCTCCTGCA": 27060, + "GCTCCCAAA": 27061, + "GCGGCGGA": 27062, + "CCTCGACC": 27063, + "CACTATTTA": 27064, + "TAATCCAAAA": 27065, + "TTACAGCTG": 27066, + "TATATACATT": 27067, + "GAGATTAAAA": 27068, + "GTCATGTCCAA": 27069, + "GAGCTTAAA": 27070, + "GTTTCTGGTGTCCTT": 27071, + "CTGGGTATATACCCAAAGGATTA": 27072, + "TGTTGAATAGGAGTGGTGA": 27073, + "GGCCATAGTGGAGTTTGGA": 27074, + "CTGATGGTAGGCTTTGTTACTTTG": 27075, + "GTCCCAGCTCTCTGCAGGTCATTCA": 27076, + "CCGCGCA": 27077, + "TGACTCATT": 27078, + "CTTCCACCA": 27079, + "GTTTGAGGA": 27080, + "CTCGGCAA": 27081, + "GTCTCAAATT": 27082, + "TGTGGACTT": 27083, + "TATTTGATG": 27084, + "TATTCGTA": 27085, + "CAAAGTTTGGA": 27086, + "TGAACTTAA": 27087, + "TCTGGGTTA": 27088, + "TCAAGTTGTA": 27089, + "GTGAGGGGA": 27090, + "CTCATACTT": 27091, + "CATGCATGTG": 27092, + "GTAAGACTT": 27093, + "TATGTTATG": 27094, + "GAGGCCAAA": 27095, + "CGGGAGCA": 27096, + "TCTACGTT": 27097, + "GACACGAA": 27098, + "CATACGCA": 27099, + "CTCTTCCCTT": 27100, + "CTCAACCTT": 27101, + "CAGCGGTG": 27102, + "CCCTGGTGGCTCA": 27103, + "GGCCATTTG": 27104, + "CATCAAAAAA": 27105, + "TCAGGGTAA": 27106, + "CTTTAGCAA": 27107, + "GCCAGGGACTCAAA": 27108, + "GAACATAAA": 27109, + "GTCCATGTT": 27110, + "GAGATGGAGA": 27111, + "CTCATGTCCA": 27112, + "GAGTGGGTTGCCATT": 27113, + "GGTCGTC": 27114, + "TCGCAATG": 27115, + "TAACTTTTAA": 27116, + "CTGTGTGTT": 27117, + "CTTTATATG": 27118, + "GTGGAGGTT": 27119, + "CTCTCTATG": 27120, + "TATTGGTTA": 27121, + "TATACACC": 27122, + "CAAATAATAA": 27123, + "TCTGGAGTG": 27124, + "TCAAGCTCA": 27125, + "GGAATACC": 27126, + "TTTTACATTTT": 27127, + "GTTATAGTA": 27128, + "CCAATAATT": 27129, + "TACATGATT": 27130, + "TCTAGTTAA": 27131, + "TAGGTGTGA": 27132, + "GGTTGTAAA": 27133, + "TGTACCTA": 27134, + "TTCACTTTT": 27135, + "CGTGGAAA": 27136, + "GGCCĊ": 27137, + "TGCCTAGA": 27138, + "TATTAGTAA": 27139, + "GTGTTTAAA": 27140, + "GCCTGGTCC": 27141, + "TGAGAGCTT": 27142, + "TATCAGTAA": 27143, + "GAAGATCAA": 27144, + "AAATACATG": 27145, + "TCCCACCCA": 27146, + "CAAGGGCAA": 27147, + "GAACACGA": 27148, + "CAACATGTT": 27149, + "CCACATGTT": 27150, + "CCAGATCGAGG": 27151, + "GTAGATGTT": 27152, + "CTTCTATTTT": 27153, + "CCGTGCCTG": 27154, + "GTTTGTTTGTT": 27155, + "GCACATGTATACATATG": 27156, + "GTGGTCCAGTG": 27157, + "TCAGTATAAAAGACACCTG": 27158, + "CTGTAAGTCTTCACAAGGTTTT": 27159, + "CCCACACACA": 27160, + "GCGTAAAAA": 27161, + "TCACTGCAA": 27162, + "TGAGCAGAA": 27163, + "CAAGCGCC": 27164, + "GCATCAGAA": 27165, + "GTACAGGAA": 27166, + "GTCTCATTTT": 27167, + "TGTGCATCA": 27168, + "TATTCGC": 27169, + "CACACTGTT": 27170, + "TCAATCATT": 27171, + "TCAAGCTGA": 27172, + "TCAACAATT": 27173, + "GTGACCAAA": 27174, + "GCAATATGA": 27175, + "GATGCCCTG": 27176, + "GTTTTGCC": 27177, + "TTTACCCA": 27178, + "TAGATTTTTT": 27179, + "CCCCTAGTG": 27180, + "TGGAGCCA": 27181, + "GTAAACTAA": 27182, + "TCCTTGTTAA": 27183, + "TCCAACATT": 27184, + "GCCTGGTGA": 27185, + "CAGTTTGAA": 27186, + "TGTGTGTGTGTT": 27187, + "CGTCTTAA": 27188, + "GCAGCAGCAA": 27189, + "GTTTAGCTT": 27190, + "CTTCACTAA": 27191, + "TCACTTAAAA": 27192, + "CTCTACTGA": 27193, + "CCACTGCATT": 27194, + "GTTTAATTAA": 27195, + "CAACTGAAAA": 27196, + "GGAGGTGAAA": 27197, + "GTCGGCCATCACTG": 27198, + "GTAGACCTGCACCAGGCTGGGAAGACTGAA": 27199, + "CACGTCAA": 27200, + "GGCTTTTAA": 27201, + "GACAGGCTG": 27202, + "TAACTCAAAA": 27203, + "TCACGCTT": 27204, + "CTTTCGA": 27205, + "GTGGCCAAA": 27206, + "GTGGAAGGA": 27207, + "GTTTCCCAA": 27208, + "GCACTTAAA": 27209, + "CTCTAACAA": 27210, + "GTCGTTAA": 27211, + "GTCGACAA": 27212, + "GTCGACTA": 27213, + "GTCAAAAAAA": 27214, + "GTCTCCTTA": 27215, + "GAAAGGGTA": 27216, + "TATATGATT": 27217, + "CAAAGGTAA": 27218, + "GGAATGCTG": 27219, + "TAAAATCAAAA": 27220, + "CTGAGCCAA": 27221, + "CTGAGATCTT": 27222, + "GCTGTCCTT": 27223, + "CATGCATGA": 27224, + "CGGTCGG": 27225, + "GATTTGCTG": 27226, + "CCAAGACTT": 27227, + "CCAAGATCA": 27228, + "GTTTTGAAAA": 27229, + "GACAGCATT": 27230, + "TCTCACATT": 27231, + "CTGAAGAAAA": 27232, + "GGAAATGCA": 27233, + "CAGCACAGG": 27234, + "GTATTCCATT": 27235, + "TATAATGTT": 27236, + "GCATTGATT": 27237, + "GCATTGTTTT": 27238, + "GTCTGGGGG": 27239, + "TGAGGGTAA": 27240, + "GTGTGGTCA": 27241, + "GCGGGAGA": 27242, + "TCTCTGCAGCA": 27243, + "GGAGATAGA": 27244, + "TGTGTGCAAA": 27245, + "CTTCCCGA": 27246, + "TAACACCTT": 27247, + "CAAGAATTA": 27248, + "GTACACATATG": 27249, + "GCGTCTGCTAAATG": 27250, + "GAATGCTGAGTTGCATCCAAAGAACACCA": 27251, + "CCAAAGTAACAAAGCCTACCATCAGTAA": 27252, + "CATTCTGTAGGTTGCCTGTT": 27253, + "CACGTCTG": 27254, + "GGCGCTA": 27255, + "CAACCAGG": 27256, + "GCATTTTAAA": 27257, + "CTGAGAATG": 27258, + "GCTTGACC": 27259, + "TATGGTCC": 27260, + "GTTATAAAAA": 27261, + "GATGGGCTG": 27262, + "TTTGTAATT": 27263, + "TTATTTCTTTT": 27264, + "TTTTTTCGTT": 27265, + "TCCTTTCAAA": 27266, + "CAGAAGATA": 27267, + "CAGCTCATT": 27268, + "TACAATGCA": 27269, + "CAGTTCATG": 27270, + "CACAACGTG": 27271, + "GGATTCGAA": 27272, + "TCATGGTGA": 27273, + "TAGTTTATT": 27274, + "TCGGGGA": 27275, + "GGAGACCTT": 27276, + "GTTTACGCCATT": 27277, + "CAAGGGAAA": 27278, + "CTTGAGTCA": 27279, + "TAACCCAAA": 27280, + "CTTCACAGTAGGTATGGTGTT": 27281, + "CCGGAAAA": 27282, + "CCGAACC": 27283, + "TCACGAAA": 27284, + "TCAGCCATT": 27285, + "GTGTGTTTG": 27286, + "GGAGTAGG": 27287, + "GCCCAAAAA": 27288, + "TGTGCTTAA": 27289, + "TCTTTATAAA": 27290, + "CATTGCTCA": 27291, + "TCTGCATGA": 27292, + "TAAAACAATT": 27293, + "GCTGGCCTT": 27294, + "CTCACCTCA": 27295, + "GCAAGATCA": 27296, + "TATGTATAAA": 27297, + "GCCATCCAA": 27298, + "GGTTCCCC": 27299, + "CTAAATATGTCAAA": 27300, + "TGCCĊ": 27301, + "TGATTGTT": 27302, + "TCTTACCTG": 27303, + "CGCAGTCA": 27304, + "CTCTGCTCTG": 27305, + "CTATTCCTG": 27306, + "GCCTGGCCAACATG": 27307, + "GTCTGGATG": 27308, + "CTCCATCTA": 27309, + "GCGACAGA": 27310, + "TGACACGG": 27311, + "CTGGACAAA": 27312, + "TTACATACA": 27313, + "TTACATAGA": 27314, + "GTTCCCCTT": 27315, + "CCTCCTTCCATCAGCAAGGGCATT": 27316, + "CAAAGCTGTCATCAA": 27317, + "TACGTGAA": 27318, + "TAAGTAAAAA": 27319, + "TCAGCTGAA": 27320, + "TGATAACAA": 27321, + "TCCCACATT": 27322, + "CTTTCCTGA": 27323, + "TGAAGAATG": 27324, + "CTGAGGAGTG": 27325, + "CTCACAGGA": 27326, + "CCTTTAATCC": 27327, + "GCAAGGCTT": 27328, + "CCTGTCCCTG": 27329, + "TCTCCAGCTT": 27330, + "CTTAGAGTT": 27331, + "TTAATATCA": 27332, + "TCTAGACTT": 27333, + "GGGGGGAAA": 27334, + "CGAGCAAAA": 27335, + "CAAAAGAATT": 27336, + "TTCATCCA": 27337, + "CAGCAGGTG": 27338, + "GCTGATTTA": 27339, + "CTTTGGTCTT": 27340, + "TCTAAGTA": 27341, + "CTATTGTG": 27342, + "TGAGATAAA": 27343, + "GATGACGG": 27344, + "GAAGGCATT": 27345, + "GCATGGAGA": 27346, + "GGCAAGATA": 27347, + "GAGAGAGAGAGAA": 27348, + "GCAGAGTAA": 27349, + "CTGTAGTCA": 27350, + "CAAGATGTG": 27351, + "CAACATTTTA": 27352, + "TCATAGTCA": 27353, + "TATTTAGAAA": 27354, + "TATTTACTGA": 27355, + "CTTGACACA": 27356, + "GTTCAAAAAA": 27357, + "TACCTTAAA": 27358, + "CCCCATCTCTACTAAAAA": 27359, + "TAAAAATTGATTTTTTGATAATAAA": 27360, + "TATTAATGGCACCTGTTTGAACTTGTTA": 27361, + "CCTAGGTG": 27362, + "TTATGATT": 27363, + "CAATTATA": 27364, + "CTGTAATTTT": 27365, + "GTGGTTAAA": 27366, + "GCCCCACCA": 27367, + "CTATTAATT": 27368, + "TATTGACTA": 27369, + "CAGGCAGCC": 27370, + "CAGGTGGAA": 27371, + "TCAAGAGTA": 27372, + "TCAAAAATGTTA": 27373, + "GTGAGTGTG": 27374, + "GCTGGAATT": 27375, + "CTCACAGCC": 27376, + "TATGGAATT": 27377, + "TACAGCTTA": 27378, + "TGGAGAAATG": 27379, + "TGCACTAAA": 27380, + "CTCTTGTA": 27381, + "GTATTCCAA": 27382, + "TACCTAATG": 27383, + "CGGATCAGTC": 27384, + "AAATTTAAAA": 27385, + "CAGGAGAAA": 27386, + "TGAGGTTTA": 27387, + "CAATGCCTT": 27388, + "TATCGTAA": 27389, + "CAATACTTAA": 27390, + "TCACAGCCA": 27391, + "GTATACAAA": 27392, + "CGACAGAGCGAGA": 27393, + "CCAGGCCTCCCTG": 27394, + "CCCGTCCCTG": 27395, + "GTTCAACCATTGTGGAA": 27396, + "TCATTTTTTTT": 27397, + "TCATGATGA": 27398, + "TCACTCTCC": 27399, + "TCATAATCA": 27400, + "TCCTCACAA": 27401, + "GTTGTAATT": 27402, + "GCACAAATT": 27403, + "CTATAGC": 27404, + "CTAGAGCTG": 27405, + "CTGATATGA": 27406, + "CTCATCCTT": 27407, + "TTTTACCTG": 27408, + "GTTAGGCA": 27409, + "TCTAGAGCA": 27410, + "CAAAATACTT": 27411, + "CCCCTCAAA": 27412, + "CGCGAGA": 27413, + "CTGAATGCA": 27414, + "TCCAAGTAA": 27415, + "TCTTACTAA": 27416, + "CGAAGGTT": 27417, + "TATAACTTTT": 27418, + "CTGTGTCTA": 27419, + "CCCCAGAGG": 27420, + "CATAAGAA": 27421, + "GAAGAAGAA": 27422, + "GTTTAGAAAA": 27423, + "CAAATGCTG": 27424, + "TGATATCTT": 27425, + "TCATTATTTG": 27426, + "GCTATACAGAGAAA": 27427, + "GTGGTTAAAA": 27428, + "TCTCTGTGAGTTCAAGGCCA": 27429, + "TCAACTGTGGGAGCAATTATTA": 27430, + "CTCTTACCTGCTTGATT": 27431, + "CACTTAATT": 27432, + "CCGTGCTG": 27433, + "TATCTCTA": 27434, + "GGCAAAATG": 27435, + "GATAAAATA": 27436, + "GCGAAGTT": 27437, + "TCCCGCTG": 27438, + "CAACGCTG": 27439, + "CTTGGAGGA": 27440, + "CTTTGAAAAA": 27441, + "GTTGAACA": 27442, + "GCATGTGAA": 27443, + "GCAGTTCA": 27444, + "CTATĊ": 27445, + "TAAAGAGC": 27446, + "CTCTTTTATT": 27447, + "TGTTGACAA": 27448, + "GAGACAGTA": 27449, + "GAGATAGAAA": 27450, + "GCTTCGTT": 27451, + "GCTGCTAAA": 27452, + "CTAACCAGTA": 27453, + "GATGGGGCA": 27454, + "GGTGGAGTG": 27455, + "GGTGGTGGTG": 27456, + "CCTACCCTT": 27457, + "TCTCAGGGA": 27458, + "CCAAACAAAA": 27459, + "AAAAAACAAAA": 27460, + "GAGAATTTA": 27461, + "GTCTTGGAA": 27462, + "CCATTCCCA": 27463, + "TCTAAGGAA": 27464, + "TTTTAATATA": 27465, + "TATTAATATTA": 27466, + "CATAATAAAA": 27467, + "TAGCACTC": 27468, + "CTGGGCAGA": 27469, + "TCAGGCTCC": 27470, + "TAAAAAATATA": 27471, + "GGGTGTTA": 27472, + "CTTTAGTAAA": 27473, + "TCATACATG": 27474, + "GCACATGAA": 27475, + "CTGAGAATGATGGTTTCCA": 27476, + "CAAAGTATTGAGAAA": 27477, + "GTGTGTGTGTGTGTG": 27478, + "GTTAAAAAAAA": 27479, + "CCAGTCCA": 27480, + "GCCGACTT": 27481, + "CAAACCCTT": 27482, + "CTGACGTG": 27483, + "GCTGTAGAA": 27484, + "GCAAGAGTG": 27485, + "GTAAGCCA": 27486, + "TATGCACTT": 27487, + "GAGGTCAAA": 27488, + "CCTGCTGTG": 27489, + "TACACCATG": 27490, + "TACATATGTG": 27491, + "GGTGTAGA": 27492, + "TGTAGCCA": 27493, + "TTATTGTTAA": 27494, + "TTCATCTA": 27495, + "GCAAAGGTG": 27496, + "TCATTĊ": 27497, + "GGAAATTTA": 27498, + "GCTCAGTCC": 27499, + "CTCTTCAGG": 27500, + "CAGAATATA": 27501, + "GTTAATAAAA": 27502, + "GCCAAGAGA": 27503, + "CAATTGAGA": 27504, + "TGATGCCTG": 27505, + "GCTCTAATT": 27506, + "GATGATTAA": 27507, + "GTGGGGACA": 27508, + "TCGGGGAA": 27509, + "GGGGATGGG": 27510, + "GGGCTCTGA": 27511, + "TCAGATCTT": 27512, + "GAAATGGAA": 27513, + "TCTTTGGGATT": 27514, + "TCGACCAA": 27515, + "TAACAGTTTT": 27516, + "GTTGGCAAA": 27517, + "CCACAGTTA": 27518, + "TATATATGTT": 27519, + "TCCCCATCAA": 27520, + "TAATGATTTT": 27521, + "GCGCCACTGCACTCCAGCCTGGG": 27522, + "CCTCTCCTG": 27523, + "TGATCTGTG": 27524, + "TCCTCTAAA": 27525, + "CTTCTCTTA": 27526, + "GTTGCAGA": 27527, + "GTTCAGATT": 27528, + "GCATAATAA": 27529, + "GCAGCGCA": 27530, + "CCATCTGTT": 27531, + "TATTCTAAAA": 27532, + "CTGACTGTT": 27533, + "CCTTGGCAA": 27534, + "TATGCGTG": 27535, + "TCTCTAATG": 27536, + "CAAAAGGTT": 27537, + "TAGGCTTTT": 27538, + "GCTACGTG": 27539, + "TTATTGTGAA": 27540, + "GCAAACCAA": 27541, + "TCCTTGTTA": 27542, + "TCTTATGAA": 27543, + "CGAAGGA": 27544, + "TATAAGTAA": 27545, + "CCATTAAAAAA": 27546, + "TCTAACTTA": 27547, + "CAAAAAGAAAA": 27548, + "GTTTGCGG": 27549, + "GTCTGGGA": 27550, + "TGAGGGTCA": 27551, + "TTATGCAA": 27552, + "GAAGGTGTG": 27553, + "GACCAGGTT": 27554, + "TTATTATTTTA": 27555, + "TCTGTTTCTCA": 27556, + "GTTCTGTTA": 27557, + "GTTGAATCACATG": 27558, + "GTGGCACGTG": 27559, + "TGTGTTTGGGATCATT": 27560, + "TACTTTTTTCCCTCA": 27561, + "CCTTGTGAAGACTTACAGAAAA": 27562, + "TGTGTCATTGCCAACAAA": 27563, + "GTTTCTCAATACTTTGTTATATA": 27564, + "CATAAAATA": 27565, + "CCGGAGA": 27566, + "TCGAAATTA": 27567, + "TCTATTAATT": 27568, + "TAACTGGA": 27569, + "TGACTCTGA": 27570, + "TCCCGGAA": 27571, + "TCCCAGGTT": 27572, + "GTTTGAAAAA": 27573, + "GCCCTAAAA": 27574, + "TGTGAAAAAA": 27575, + "CATTGACC": 27576, + "CATTGTTTTA": 27577, + "TCAACTGTT": 27578, + "TCAAGGCTT": 27579, + "CATGCCTCA": 27580, + "GCAAGGCAA": 27581, + "TATGGATTA": 27582, + "GATTCACC": 27583, + "TCTACGAA": 27584, + "GGGGCACCA": 27585, + "CAAAATCTC": 27586, + "TAGGTTTTTT": 27587, + "TAAAAACTCA": 27588, + "CTAAATACTT": 27589, + "TGATTCTTTT": 27590, + "TATAAGAA": 27591, + "CACTGGAA": 27592, + "CAGGAGCTG": 27593, + "TTTTTTTTAAAA": 27594, + "GGCCAGACTG": 27595, + "CCATGGACA": 27596, + "CAAGGTCAA": 27597, + "TGATAGAGA": 27598, + "GTACATGAA": 27599, + "GCGGATCACAAGGTCAGGAGATCGAGA": 27600, + "TTTTATGTTTT": 27601, + "CCGTTCC": 27602, + "TAAGCCACA": 27603, + "GTTGGAGA": 27604, + "GTTCTCTCA": 27605, + "GCAGTAGG": 27606, + "CCATAAATT": 27607, + "GCCTCCATT": 27608, + "CTCTGGGTG": 27609, + "TCTTGATAA": 27610, + "GCTTGCTA": 27611, + "CATGGTCTT": 27612, + "CATGGGGAA": 27613, + "CTAAGTTAA": 27614, + "GATGTCCTT": 27615, + "GTTTTCAGG": 27616, + "GTCCCGTG": 27617, + "CGATTTTTT": 27618, + "TCATTCAGG": 27619, + "GAGAAAAATT": 27620, + "GCATTCCAA": 27621, + "GTCTGGTCTG": 27622, + "GCGGGTGA": 27623, + "CATCCTTCA": 27624, + "TAATTATATA": 27625, + "CTCCAĊ": 27626, + "TCCATTTCTTCTA": 27627, + "CAATTACTTTT": 27628, + "TTTTTTATTTA": 27629, + "TAAGCAGAGTT": 27630, + "TATTTTATTTT": 27631, + "CTGTTTTTTCACGATTTATTA": 27632, + "CCTCTCAAA": 27633, + "TAGATGAAA": 27634, + "TCGAGATG": 27635, + "TCAGCGTG": 27636, + "TTACACCA": 27637, + "CTGCAGGAA": 27638, + "CTTGTTGAA": 27639, + "GTGCTTCC": 27640, + "GTGTCCCTG": 27641, + "GTTCCATA": 27642, + "GCAGAATA": 27643, + "GCACTTTCA": 27644, + "GCACCCAGG": 27645, + "GTAGTTAAA": 27646, + "CTATCTCTT": 27647, + "TAAATATATATA": 27648, + "TAAACCTGA": 27649, + "GTCTAATTA": 27650, + "CACATTTTTT": 27651, + "CACACATTTT": 27652, + "CATTGATAA": 27653, + "CAGGGGCCA": 27654, + "GCTTCCATT": 27655, + "GTCATGGAA": 27656, + "GTAACCCA": 27657, + "TCTCGGGG": 27658, + "CTCCCTGTG": 27659, + "CTAATCCAA": 27660, + "CTAAGAAAAA": 27661, + "TCCACCATT": 27662, + "GATGGACATT": 27663, + "TTAATACA": 27664, + "GTTTTTGCA": 27665, + "CTTTTGACA": 27666, + "CGAGTGAA": 27667, + "TAATTTAATA": 27668, + "CGCTGATG": 27669, + "GGTATTATT": 27670, + "AAAAAATTTA": 27671, + "GAGAAGCTG": 27672, + "CTGTTGAAA": 27673, + "CAGAAGTGA": 27674, + "TATTTTTATG": 27675, + "GTGTTCATG": 27676, + "CTCAAGATT": 27677, + "GGAGGCGA": 27678, + "TACCTAAAA": 27679, + "TACCCCCA": 27680, + "TAGTTTGTT": 27681, + "CAATGTATT": 27682, + "CTGCACAAA": 27683, + "AAATATTATT": 27684, + "TAACATTTTAA": 27685, + "CTCCTGCCTCAGCCTCCCAAGTAGCTGGGATTACAGG": 27686, + "GCCTTGAACTCACAGAGA": 27687, + "GCGCTGCTG": 27688, + "GCTTTTAAAAA": 27689, + "TAACCACA": 27690, + "TCACTCATT": 27691, + "TGAGTTCA": 27692, + "TCCTTATTTT": 27693, + "GCAGCGTT": 27694, + "GTAGAGATG": 27695, + "CTACTGTA": 27696, + "GTCGGAAA": 27697, + "GTCGTCAA": 27698, + "TGTGTGAATT": 27699, + "CACAGAGTA": 27700, + "TCTTCATC": 27701, + "TCTTTCCATT": 27702, + "TCTGCGCA": 27703, + "TCTGCAATT": 27704, + "GCTTGGGAA": 27705, + "CTAAGACTT": 27706, + "CTAATGGAA": 27707, + "TCTAGATAA": 27708, + "GTCCTTTTA": 27709, + "CAAAATAGAA": 27710, + "TGGGTGAAA": 27711, + "TAGATAGTA": 27712, + "GCTCACAGG": 27713, + "CAGCACCA": 27714, + "CTCTTTAAA": 27715, + "GACCTATA": 27716, + "GTATTGTCA": 27717, + "GTCAAGATT": 27718, + "GCATTGTAA": 27719, + "CCTCAGAAAATT": 27720, + "GAGTTTGAA": 27721, + "GCTAAGAA": 27722, + "TACTGTATG": 27723, + "GAAGGTTAA": 27724, + "GGTCACACA": 27725, + "GTGGGTGTT": 27726, + "GACTATTAA": 27727, + "TCACAGAAAA": 27728, + "TCCTCCTGCCTCA": 27729, + "GTGGAGTGA": 27730, + "GTATATTATT": 27731, + "CTGATGTTA": 27732, + "GATTTGTTA": 27733, + "CATAAATTA": 27734, + "GTGCTGAGATTA": 27735, + "GCTCTCCAAA": 27736, + "GCTGTTTTTCTGCAAA": 27737, + "CTCCTGCCTCAGCCTCCCGAGTAGCTGGGATTACAGG": 27738, + "TCGTTTTATGGGTTTT": 27739, + "CTGCACTCCAGCCTGGGCGACAGAGCGAGA": 27740, + "CCGCAGCA": 27741, + "GCGAACTT": 27742, + "TAACGAGA": 27743, + "TAAGTTGG": 27744, + "TCACGAGA": 27745, + "CAACCAGA": 27746, + "GCCGAGCC": 27747, + "GCCGCGC": 27748, + "CTACTTGG": 27749, + "CTCGATTTT": 27750, + "CTCTCGCC": 27751, + "GTCGGGC": 27752, + "GTCTTAAAAA": 27753, + "GAAATAGGA": 27754, + "GAAAGGGAAA": 27755, + "TGTTTCGG": 27756, + "GGAACACCA": 27757, + "GCTTTCTCC": 27758, + "CTCAGGCAA": 27759, + "CCTTGAGCA": 27760, + "CCTGGAATG": 27761, + "GTTAGTAAA": 27762, + "GTTATATGA": 27763, + "GATGTCAAA": 27764, + "GTTTTTGGA": 27765, + "GTCCTAAAA": 27766, + "CGAGCCAA": 27767, + "CCCAGCATG": 27768, + "TGGGTAGA": 27769, + "GCTATAAAAA": 27770, + "TCTCAGGTGA": 27771, + "GGTAĊ": 27772, + "CTCTTGTGA": 27773, + "GTGTTGAGA": 27774, + "TCTAAGATT": 27775, + "CAAAAATGTA": 27776, + "CTATTGCAA": 27777, + "TCAAAATATG": 27778, + "CCATGGCTG": 27779, + "GAAGGGATT": 27780, + "TGATACTGA": 27781, + "TAACCATTA": 27782, + "TCACTATGTT": 27783, + "GAATTTGCAAGTG": 27784, + "TTTATAACATTTT": 27785, + "CATTTGGATGATCCAGAA": 27786, + "GAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAAGAAA": 27787, + "GGCAGAGGAACCAGAGA": 27788, + "TTTTCTCAA": 27789, + "TCACATTTG": 27790, + "CTGGCTGTG": 27791, + "CTGGATTAA": 27792, + "GCACGTGA": 27793, + "GTACCAGG": 27794, + "GCCTCCCA": 27795, + "GCCTGAGGA": 27796, + "TAAAGGGGA": 27797, + "CTCTTTTTA": 27798, + "CACATACTA": 27799, + "CAAAGGATT": 27800, + "CAAAGGTGA": 27801, + "CATTGACTG": 27802, + "CATTCCCAA": 27803, + "TCTGGGGGA": 27804, + "CCTTCACAA": 27805, + "GTTTTCCAAA": 27806, + "GGCACAAAA": 27807, + "CGCGACA": 27808, + "GAATTGGTT": 27809, + "AAAAAAGAA": 27810, + "GCTCAGATT": 27811, + "GGAGGGTA": 27812, + "TACCCAAGATA": 27813, + "TCTAATCAA": 27814, + "TCCTGGTGA": 27815, + "GGCTGGGCTG": 27816, + "GAGGACAGA": 27817, + "CCCCAGGA": 27818, + "TACTTTCAA": 27819, + "GCTCCTTAA": 27820, + "GCAGGCTTCTT": 27821, + "TCGGCGCC": 27822, + "TCTGTGTCTGTG": 27823, + "CCTTTTTTTTTT": 27824, + "GTTAAAGTACATA": 27825, + "TCGAATTTT": 27826, + "CTTGATGAA": 27827, + "TAATGACCA": 27828, + "TCTCCTTAAA": 27829, + "GAACCAGTCTGTTGTT": 27830, + "CTACTCTACTG": 27831, + "CGATTAATT": 27832, + "CCTGGGAGGCGGAGCTT": 27833, + "GCAATCTACAGATT": 27834, + "TCTCACGATACATG": 27835, + "CCGTGGA": 27836, + "TAACAGATT": 27837, + "TGACCCTC": 27838, + "TGAGTCAAA": 27839, + "CTTGCAGA": 27840, + "CTTCTATA": 27841, + "GTGTAATTA": 27842, + "GTGTCCCC": 27843, + "GCACTTTA": 27844, + "GCAGTCCTT": 27845, + "TAAACAGAAA": 27846, + "GTCTCGTG": 27847, + "TATACGTG": 27848, + "TATATCAAAA": 27849, + "TCTTCTTGG": 27850, + "CAAACCAAAA": 27851, + "GAGATGAGG": 27852, + "CAGGCATAA": 27853, + "CATGCATAA": 27854, + "CGGCATC": 27855, + "TCTCTGTGAA": 27856, + "CCAAGTAAA": 27857, + "CTAATGCC": 27858, + "GGGGTAAATTTG": 27859, + "CATAGTATT": 27860, + "GCTACTCTT": 27861, + "TGAAATGTA": 27862, + "TGATTGGA": 27863, + "GCCTGCTCC": 27864, + "TATCATATA": 27865, + "GTCTGCAA": 27866, + "TCATGGTTA": 27867, + "GTGTGTAGA": 27868, + "CCATGGTA": 27869, + "CCTGAGATG": 27870, + "CTGGGCTCC": 27871, + "CTCCACGCA": 27872, + "AAATAGTAA": 27873, + "TCAGACCTG": 27874, + "TAAGATATG": 27875, + "GCGACGCA": 27876, + "CAAGACTGA": 27877, + "TATTTACTTTT": 27878, + "CCACCCTGA": 27879, + "GAAGGAGTTACAGAGA": 27880, + "CATTTACTTA": 27881, + "CTTATGAAAA": 27882, + "GACCTGAAAA": 27883, + "GACTCTGTCTCAAAAAAAAAAAAAAAA": 27884, + "TATCGTGAGATTTT": 27885, + "CTGCTCTAGAGGAGATCTGCATG": 27886, + "CCTCTCATT": 27887, + "TGAGTGTA": 27888, + "TCCTAATTA": 27889, + "TCCTCTATT": 27890, + "GTTCAACAA": 27891, + "GCATAATTA": 27892, + "CCACĊ": 27893, + "CACAGTCTT": 27894, + "TATACTGTT": 27895, + "GGAACGCA": 27896, + "GCAACCCTG": 27897, + "GTCATCTCA": 27898, + "CGGTGGAA": 27899, + "TCCATAGTA": 27900, + "TCAAAGATAA": 27901, + "TAATTTCCAA": 27902, + "TTCATTATT": 27903, + "TTCACAATT": 27904, + "CAGAACAA": 27905, + "CTTTGGCTG": 27906, + "GTCAACAA": 27907, + "GCATTGTG": 27908, + "GAGCTCCTT": 27909, + "GACTTTGGG": 27910, + "GTGTGCTGA": 27911, + "CATGACTGA": 27912, + "GGCTTCCTG": 27913, + "TCTCTTGACC": 27914, + "GTGGGGGA": 27915, + "TATTTGTTTTA": 27916, + "AAATAGAAAA": 27917, + "CAGTCTTCA": 27918, + "GCAGACTGA": 27919, + "TAACATCAAA": 27920, + "TCTCAATTA": 27921, + "CTACAGCTG": 27922, + "GCCATGAAAA": 27923, + "GGCTCTTTG": 27924, + "GGAAATATCTTCGTA": 27925, + "GTCACCTACAAACAAGCAAGATTTCTG": 27926, + "TATATACCCAGTAATGGGATG": 27927, + "CACGTATT": 27928, + "TAGATTAAA": 27929, + "TAAGGACA": 27930, + "TTTTGACA": 27931, + "TGATAATTTT": 27932, + "GAACCAAAA": 27933, + "CTGCAATA": 27934, + "CTTGTTGA": 27935, + "GTGCTTTA": 27936, + "GTTTCAAAAA": 27937, + "GCCCTGCA": 27938, + "CTCGATTA": 27939, + "TGTTGCTTA": 27940, + "CAAATATCC": 27941, + "CATTTAAATG": 27942, + "TCTGTGAGG": 27943, + "TCTGTACCA": 27944, + "CAGATAGTT": 27945, + "GGAATAGGA": 27946, + "TAAAAGGGG": 27947, + "CATGGATGA": 27948, + "TTTTACATTA": 27949, + "TACATACATT": 27950, + "TTTAGGTG": 27951, + "TCTCATCAA": 27952, + "CGTGGCTGGGTCTT": 27953, + "GTGAAGGAAA": 27954, + "TGATTCAATTA": 27955, + "GCATTGCAA": 27956, + "CAGTTCTA": 27957, + "TGAGAGTTA": 27958, + "GTCTGCTGA": 27959, + "GAATGGAGG": 27960, + "TATTAATAAA": 27961, + "TTATGGTAA": 27962, + "TAAATTAAATT": 27963, + "GTGGGGTGGGGG": 27964, + "GTATGTATT": 27965, + "CTCCAGTTA": 27966, + "TCACCCGG": 27967, + "CCACCTCTT": 27968, + "CAGGTGGATT": 27969, + "GTATTATTAA": 27970, + "TTACCAGAAAACAA": 27971, + "GTGTTTGGAGGACAAA": 27972, + "TGAGGATTTCGTTGGAAACGG": 27973, + "CCGCTGTG": 27974, + "TCATGTTTG": 27975, + "CTTCTCCTG": 27976, + "CTTCCTTTG": 27977, + "GCCGCTGA": 27978, + "GCCCTTCTG": 27979, + "CTCTAATCA": 27980, + "TATTTGTTAA": 27981, + "CAGACCTGA": 27982, + "CTCACGTT": 27983, + "TCTCGTA": 27984, + "CTCCCGCA": 27985, + "CTCCCCCTCC": 27986, + "CTTATAATA": 27987, + "TCTAGACAA": 27988, + "TAGACACCA": 27989, + "CCCCTCCTG": 27990, + "GGTGTACA": 27991, + "CATAGGGG": 27992, + "TAATTTCCTT": 27993, + "GGGACCCTG": 27994, + "TCTCCCTCTT": 27995, + "GCAAACGG": 27996, + "CCAAACGA": 27997, + "TATTAGTTA": 27998, + "CAGCATGGG": 27999, + "GTTAACTTA": 28000, + "GGAGGCACA": 28001, + "CTGTGTAGG": 28002, + "GCCTTCTTTG": 28003, + "TGATGTCATG": 28004, + "GGATTAAATG": 28005, + "CTGGGGTCA": 28006, + "TCGCTCAAA": 28007, + "GTTTGAGACCAGCCTGGG": 28008, + "CCTGGCTGGAGTCA": 28009, + "ACACA": 28010, + "CCTCTCCAA": 28011, + "CAAGGAGGA": 28012, + "CTTGAAATG": 28013, + "CCACAAGAA": 28014, + "GCCCTCCTT": 28015, + "GTCTTATA": 28016, + "TGTGTAGTG": 28017, + "TCTTCGAA": 28018, + "TCTTGGTTA": 28019, + "CAGGCGTG": 28020, + "TCAATAAATG": 28021, + "GGAACTAAA": 28022, + "GCTTGTCTT": 28023, + "GCTTCAGTA": 28024, + "GTGACACCA": 28025, + "CTCATCATT": 28026, + "GCAATGTGA": 28027, + "GTCATACAA": 28028, + "CTCCTCATT": 28029, + "CTAAGCTA": 28030, + "GTCCCGTT": 28031, + "CTTTTTCCTT": 28032, + "CCAAATTAAA": 28033, + "GAGAACTG": 28034, + "CTGTGCGG": 28035, + "GCCTTGGCTG": 28036, + "TGATGCTCA": 28037, + "GGGAACAAAA": 28038, + "CCATGCCTT": 28039, + "TAGCTCATT": 28040, + "CTCTAGAAA": 28041, + "TGACCCAAAA": 28042, + "TGACTGAAAA": 28043, + "GATCAAAAATT": 28044, + "GACCTGGAGG": 28045, + "CTTTTTGGTAAAAACTCAA": 28046, + "CCCACTTTTTGATGGG": 28047, + "GCTCTCACAGACCTGTAACTTCTT": 28048, + "TCATCTTCAATGCCCTTGCTGATGGAA": 28049, + "TAAGCCAGG": 28050, + "CACACTGCTT": 28051, + "TCTTCTTTTA": 28052, + "GAGAGACCA": 28053, + "GAGAGAGTA": 28054, + "TCTGCTTAA": 28055, + "TCTGCATCC": 28056, + "TCTGGGTGA": 28057, + "CAGATCTGA": 28058, + "GGAACGAA": 28059, + "CATGGCTCA": 28060, + "GAAAACACTG": 28061, + "GTTTTTAGG": 28062, + "CGATATC": 28063, + "GCTATTTTTT": 28064, + "GTAAATGGG": 28065, + "TATTTTCCTT": 28066, + "CGCATTTA": 28067, + "GTGTTCCAA": 28068, + "CGGACGC": 28069, + "GCTAATTTTTG": 28070, + "GAAGACAAAA": 28071, + "GAATACAGG": 28072, + "GAACATATT": 28073, + "CCACACCCC": 28074, + "GTACAGTTA": 28075, + "GCTGATTAAA": 28076, + "GGAAGCACAGTT": 28077, + "GTACCTTCAGG": 28078, + "CGACCCCATGAA": 28079, + "TCAAAAAATCAATTTTTACACAA": 28080, + "TAAGCTAAA": 28081, + "TCATATATG": 28082, + "TCCTGTGTG": 28083, + "GTTCAATA": 28084, + "GCACTTCTT": 28085, + "GTCTCTACA": 28086, + "TGTGTCCTT": 28087, + "TGTGTAGTT": 28088, + "TATATCCAA": 28089, + "TCTTTAATG": 28090, + "TCTTGACAA": 28091, + "CATTTAGGA": 28092, + "CAGAGGCAGA": 28093, + "GAAAATGCC": 28094, + "TTTATCAAA": 28095, + "TTCAĊ": 28096, + "CTGAACCTG": 28097, + "GCAAACAGAA": 28098, + "TCTTAGGAA": 28099, + "GTATTGAAA": 28100, + "CTATTCAAA": 28101, + "CTATTTCAAA": 28102, + "GGATTTATG": 28103, + "TGAGGTGAA": 28104, + "GAATGCAGA": 28105, + "TATATATATTA": 28106, + "CCATGGATG": 28107, + "GGGTGCAGTG": 28108, + "TCGACATG": 28109, + "CAAGAGCTG": 28110, + "CCACCTCAA": 28111, + "GCATATGAA": 28112, + "TACAAATTA": 28113, + "GTATTTTGGCCCATTCCTCCATGCAGA": 28114, + "TAATGTGTG": 28115, + "TCATAAAAAA": 28116, + "GAAGTATA": 28117, + "CTTGCCTA": 28118, + "GTTGCCAAA": 28119, + "GGAGTGAAA": 28120, + "CCATGTGAA": 28121, + "GTAGCTC": 28122, + "CTAGTCTG": 28123, + "CACAGATTA": 28124, + "TGTTGATAA": 28125, + "TAAAATGCC": 28126, + "GCAATAAAAA": 28127, + "TACAGCATG": 28128, + "CTTACTGTT": 28129, + "CTTACCTCA": 28130, + "CTTATAGTT": 28131, + "TCTACTTTG": 28132, + "CAAAAGTG": 28133, + "TCAAAGAGG": 28134, + "TAATTCCAAA": 28135, + "GGCACGTT": 28136, + "GAATTCTTTT": 28137, + "TGATTTCTTTT": 28138, + "GAGAAGACA": 28139, + "CAATTCAAAA": 28140, + "CTCAATATT": 28141, + "CAAAAATAGAA": 28142, + "GCATTCTAA": 28143, + "GTTTGCAGG": 28144, + "CCCCAGAGA": 28145, + "GGATGCCAA": 28146, + "TTTTTGGGGG": 28147, + "GCAGAAGGAA": 28148, + "TTTTTATGAA": 28149, + "TGGGTTTGACGAAA": 28150, + "TCCTGAGGCC": 28151, + "GACTAAATT": 28152, + "GCGTTCTA": 28153, + "TTTTTAGAA": 28154, + "TCATGAGAA": 28155, + "TCACGTTA": 28156, + "CTGTAATTA": 28157, + "CTTCGAAA": 28158, + "CTTGTTTTTT": 28159, + "GCAGTCATT": 28160, + "GGACGCAA": 28161, + "GGACCTGTG": 28162, + "CCACAAATT": 28163, + "CCAGCTCCA": 28164, + "GTATCAGG": 28165, + "GCCGGGAA": 28166, + "GCCTGAGTT": 28167, + "GCCCCAGAA": 28168, + "GCCTCGCC": 28169, + "TAAAGGGTT": 28170, + "CTCGTCAA": 28171, + "GTCTAAGAA": 28172, + "TCTTTAAAAAA": 28173, + "TGTTCCTTTT": 28174, + "TCAATGCTG": 28175, + "GTCACGTA": 28176, + "GTAACTTAA": 28177, + "GGTTGATA": 28178, + "TAATTTAAAAAA": 28179, + "TCTGACAGG": 28180, + "CCATTGAA": 28181, + "CTGTGGACA": 28182, + "TTTTAAGAA": 28183, + "GAAGGGCTG": 28184, + "CATCTTTTTT": 28185, + "CAACAAGAGCAAAA": 28186, + "TCGACGC": 28187, + "CTTCCCCCA": 28188, + "GAATAGTTA": 28189, + "TGACTTAAAA": 28190, + "GGCTCCAAA": 28191, + "CCATGCCCGG": 28192, + "CCTGCTCCCCGCA": 28193, + "CTTTCTCTAGCTCCTCCATT": 28194, + "TTTTAAAATT": 28195, + "TGACTTTTTT": 28196, + "TTAGCAAA": 28197, + "CTTTGATTA": 28198, + "GTTCTCCTG": 28199, + "CCATAGC": 28200, + "GCCTCTAAA": 28201, + "GTCGATTA": 28202, + "GAAACAGCC": 28203, + "GAAATAATTA": 28204, + "TCTGCGTT": 28205, + "TAAAACAGCA": 28206, + "TAAAAGTTAA": 28207, + "GTAACACC": 28208, + "GTAAGACAA": 28209, + "TATGGTATT": 28210, + "CCTGGAGTG": 28211, + "GATGGAGTC": 28212, + "GGTTCTGTT": 28213, + "TGCACGA": 28214, + "GATACCAAA": 28215, + "CGTGGGAA": 28216, + "TGAAAGCAA": 28217, + "GCTGAGCCA": 28218, + "GACCCTGTT": 28219, + "GGCTGCCTG": 28220, + "CGTACAA": 28221, + "CCTCCTGGA": 28222, + "GAGTTGGTG": 28223, + "CAATGCACA": 28224, + "GCGGCTGA": 28225, + "CCATGCCAA": 28226, + "CTATGCCTG": 28227, + "GAGCCTTTG": 28228, + "CAAGGTGGG": 28229, + "TTATACTGA": 28230, + "GCTGTGAAAA": 28231, + "CGCTGCTG": 28232, + "CTCTACCAA": 28233, + "CCAGATGAA": 28234, + "GTTTTAATTTG": 28235, + "TATATTTTAAAA": 28236, + "CTCTGTGCGACCCCA": 28237, + "TGTTCTTCCATT": 28238, + "TACAGGTAACGAGTGGAGGA": 28239, + "TTTTGGAAA": 28240, + "TCAGGGGTT": 28241, + "TTATCATT": 28242, + "TCCGACTG": 28243, + "GTTGTGGTG": 28244, + "GTTTGAATG": 28245, + "GTAGATCC": 28246, + "GCCTGAATT": 28247, + "GCCTCACTT": 28248, + "TATATATCC": 28249, + "CAGGGCAAA": 28250, + "CTGACATGA": 28251, + "CTCACTCAA": 28252, + "CTCACATAA": 28253, + "CCTTGTGTT": 28254, + "GAGGTGCC": 28255, + "GAGGCATTTT": 28256, + "CTAACTGAA": 28257, + "CTTACTCTT": 28258, + "TCCATCTTA": 28259, + "TCCATCCTCC": 28260, + "TCTAGTCTT": 28261, + "GTCCCCTCA": 28262, + "GTCCAAATG": 28263, + "TCTCCCCCA": 28264, + "CTAAACTGA": 28265, + "CAAAAACACA": 28266, + "GAAAAATCAA": 28267, + "TAATGCTTTT": 28268, + "TCAAAATTTG": 28269, + "CACAACACAA": 28270, + "TACTGGAA": 28271, + "CTGGGTAATTTA": 28272, + "CTGCACCTT": 28273, + "GTTTAGCAA": 28274, + "CCACCACGCCCGG": 28275, + "CGTAAGAAGCATTTCAA": 28276, + "CAACACGA": 28277, + "GTGCCTGAA": 28278, + "GGCTCTGTG": 28279, + "CAGTTTTAAA": 28280, + "CCTACCATTAAAATTATAGACTGA": 28281, + "TCTTCCATTTCCTAATAATTGCTCCCACAGTTGATT": 28282, + "GGAAATGGAAGACATACAAGACCACTGATAA": 28283, + "CACGGAGG": 28284, + "GCGTAGAA": 28285, + "TGACGATG": 28286, + "TTACACTT": 28287, + "CTTCGATT": 28288, + "TAAACTTTG": 28289, + "CTCGGGTG": 28290, + "TATTTGAGA": 28291, + "CAAATCTC": 28292, + "GAGACTGCCA": 28293, + "TGAACAATT": 28294, + "TCTGGCTGA": 28295, + "GGAAGCTCA": 28296, + "GTGACCTA": 28297, + "GTGATATCA": 28298, + "CCTTCCAGAA": 28299, + "GTAAGTCC": 28300, + "TATGCCATT": 28301, + "CGGCGATG": 28302, + "CCAAGATGA": 28303, + "GAAAATGTGA": 28304, + "CAAAACTGAA": 28305, + "GGTTAAATT": 28306, + "CCCCTTTTA": 28307, + "TAATTCATAA": 28308, + "TCTGACCAA": 28309, + "TCCTTCCCTG": 28310, + "CTAAAGTCA": 28311, + "CTAAAGATT": 28312, + "GGTACCTA": 28313, + "TGATTTAAAAA": 28314, + "TCTTTTCTG": 28315, + "GCATTGAATCTA": 28316, + "TCAAAATATA": 28317, + "CAGTTTCAA": 28318, + "GAGCAGCAA": 28319, + "GAGTGGATT": 28320, + "GCTCCTCTT": 28321, + "CATGATAAAA": 28322, + "TACCACTA": 28323, + "CGTCTCTA": 28324, + "TATCGTA": 28325, + "TCTCTGTTTTGGTA": 28326, + "TAACAĊ": 28327, + "TGATAGTCC": 28328, + "GTCAAATTTT": 28329, + "TAAGGGAAA": 28330, + "CCGGCTCC": 28331, + "GTGGAGTCA": 28332, + "TCCCCCGA": 28333, + "TTTTTAGTAAA": 28334, + "TCAAAAAGTGGGCAAA": 28335, + "TCGTCAAACCCATGCTAAATATGTCAAA": 28336, + "GACGCCTT": 28337, + "GCGCAATG": 28338, + "TCACTTATT": 28339, + "TTACGAA": 28340, + "TCCGGCCA": 28341, + "CTGTATTAA": 28342, + "GTTGAAATG": 28343, + "GTTTATTAAA": 28344, + "GCACAACAA": 28345, + "GCATGACAA": 28346, + "CCACTTTTA": 28347, + "TGTGGGGAA": 28348, + "CACAGTCAA": 28349, + "CACACAGAAA": 28350, + "GAAAGGATG": 28351, + "TGTTGCTAA": 28352, + "CAAAGGAGTG": 28353, + "GAGAGGCTT": 28354, + "CAGGCTGTT": 28355, + "TAAAATAATTA": 28356, + "CTCATCTC": 28357, + "TTAATAAAAA": 28358, + "CAAAACCCA": 28359, + "TTATTCATA": 28360, + "CTGAATCAA": 28361, + "TTTTTTĊ": 28362, + "CCAAATGGA": 28363, + "TAATATTATTA": 28364, + "GAGAACAA": 28365, + "GCTGAGGCTT": 28366, + "TATTTTTGTA": 28367, + "GTGTTCGA": 28368, + "TCCTGGCAA": 28369, + "GAGCAGATG": 28370, + "TGAGGCAAA": 28371, + "TGAGGTCAA": 28372, + "CAACAGTAAA": 28373, + "TCATAGATT": 28374, + "TATTTAGTTA": 28375, + "GCCATTCTGACTG": 28376, + "GTGGACATG": 28377, + "GTAGGGAAAA": 28378, + "TTAGGTCCA": 28379, + "CTCGGGAGGCTGAGGCA": 28380, + "TCTCGGCTCACTGCAACCTCTGCCTCC": 28381, + "GCCAAAATTGACAAATGGGA": 28382, + "GAAATATCAATAACCTCAGATATG": 28383, + "CTTTTGTTATTGACCAAA": 28384, + "CAGAGGAATGGATACAGAAAA": 28385, + "CATCTCCTG": 28386, + "GGCGAAAA": 28387, + "GGGATGAAA": 28388, + "TTTTTCCAA": 28389, + "TGATAAAAAA": 28390, + "TGAGTTGTT": 28391, + "TGATCATCA": 28392, + "TCCCGTGA": 28393, + "GTGGCTTAA": 28394, + "GTTGCAGAA": 28395, + "GGACGTGA": 28396, + "GGACAAGGA": 28397, + "GCCGGTGA": 28398, + "TAAAGACACA": 28399, + "CTCGACCA": 28400, + "GTCTATTTG": 28401, + "TGTGGTAAA": 28402, + "TATACAATG": 28403, + "TCTTTAGTA": 28404, + "CATTCGAA": 28405, + "CAGAGCAGG": 28406, + "CCAAGGGCA": 28407, + "GACAGCATG": 28408, + "TCAAAGGAAA": 28409, + "GGGACC": 28410, + "GCAAACCAAA": 28411, + "GAATTCTCAGTAA": 28412, + "GAGAACTA": 28413, + "CTGTTGTTCTG": 28414, + "CTGTGTAAA": 28415, + "CATCACTAA": 28416, + "GAGCAGCTT": 28417, + "GGATTCCTG": 28418, + "GAATGGTGA": 28419, + "TAATAATATT": 28420, + "CAGCCTGCA": 28421, + "TTATGGATT": 28422, + "TAGCAGAGA": 28423, + "GCGATGAA": 28424, + "CCGGCCCC": 28425, + "GCAATTCGA": 28426, + "GCACTGCAGGATT": 28427, + "CTCACCGTTCTTGTGA": 28428, + "GTTCCTCTGCCTTTT": 28429, + "GGTAAATTA": 28430, + "GATAAAAATT": 28431, + "TAACCTGAAA": 28432, + "TTTTTATGA": 28433, + "TTAGTAAA": 28434, + "GAATGACTT": 28435, + "GTGCTGATT": 28436, + "GTTCCAGCA": 28437, + "CCACTTCTG": 28438, + "TCTTTATATT": 28439, + "TGTTGACTG": 28440, + "CATTTCTGTT": 28441, + "GTCATTTTAA": 28442, + "TCTCTAATA": 28443, + "TACATCTAA": 28444, + "GCCACTAAA": 28445, + "CGAGTTGATG": 28446, + "CAAAATCTA": 28447, + "GCTACAATT": 28448, + "GGAAATGA": 28449, + "GGAAATGTA": 28450, + "TCCTTGATG": 28451, + "GGCCCTTA": 28452, + "TATTATAAAA": 28453, + "GTGTTCACA": 28454, + "TCCTGGTCA": 28455, + "CAGCGAGG": 28456, + "GTTTGGAAAA": 28457, + "CATTATATT": 28458, + "TACTTCAA": 28459, + "TAGTTTCAGAA": 28460, + "CAACAGTTTT": 28461, + "CCACACCTT": 28462, + "TCATGACATTCTAA": 28463, + "GCCCGGGCGG": 28464, + "TAAAAAAAATG": 28465, + "TGAGACGGAGTCTCGCTCTG": 28466, + "GTTTTTACCAAAAA": 28467, + "TCCAGGCCCGTCTGAA": 28468, + "CACTCCAAACTCCACTATGGCCAAGACCAAA": 28469, + "TAGTGAATT": 28470, + "GCTTTTTTTTTTTT": 28471, + "TCACAAATG": 28472, + "CTTGTTCAA": 28473, + "GTGCAGGAA": 28474, + "GGACAAGAA": 28475, + "GTAGCCAGG": 28476, + "TAAACATATA": 28477, + "TATATCTC": 28478, + "GAGATGTGA": 28479, + "TGAAGATTA": 28480, + "CAGGCCTGA": 28481, + "CTCAGATCA": 28482, + "CTCCCGTT": 28483, + "TACAGTAAAA": 28484, + "CGACACTT": 28485, + "GGCAGTGAA": 28486, + "TAATAGCAAA": 28487, + "CTCTTGTTTT": 28488, + "GTATTTCAAA": 28489, + "GGAGGTAGA": 28490, + "CAAAAAGTTA": 28491, + "GAAAAATTTG": 28492, + "CATTTTTATT": 28493, + "TCAGGGCTT": 28494, + "GTATGGTA": 28495, + "GGAGATCTG": 28496, + "CCCTCCACA": 28497, + "TCACCCCCA": 28498, + "TAAGAGTCA": 28499, + "TCACAGTTTT": 28500, + "CACACACGG": 28501, + "GTTTCCTTTT": 28502, + "GGATTACAGGCATGAGCCA": 28503, + "CAGCATCAGTCCTT": 28504, + "GCAGGCAGATTCTTTACCA": 28505, + "TCCCAACCGTGAA": 28506, + "GGGACCAGGACGACTGA": 28507, + "CTTTAAGAGGCTCCTCTGTCCTCCA": 28508, + "TCTGCAATAGGTAAGCAGCTTGGTTTGAAGAAA": 28509, + "TGATCACAAGAACGGTGA": 28510, + "GTCATGCTGAAAGACCCAGCCACGTT": 28511, + "TGGGTTTGACGAAATTAGGA": 28512, + "GCGAAATA": 28513, + "TAATGTTTA": 28514, + "TCCTTATA": 28515, + "TCCCTCCTG": 28516, + "CTTCTGCAA": 28517, + "GTTCTAATT": 28518, + "GTTGCTAAA": 28519, + "GGATĊ": 28520, + "TAAAGAAGG": 28521, + "TGTGGATTA": 28522, + "GAAAGAGC": 28523, + "CTGAGGTTA": 28524, + "GCTGTACCA": 28525, + "TTTTAGGTG": 28526, + "GAGGGGGCA": 28527, + "GTTATACC": 28528, + "CTCCTGATT": 28529, + "TCCAGTCTG": 28530, + "GATGTATTA": 28531, + "CCCCCCAAAA": 28532, + "CATAGAATT": 28533, + "GCTACGAA": 28534, + "CGCTCGCC": 28535, + "TTTTTTGCTT": 28536, + "TTTTTTGAAAA": 28537, + "TAAAAATGGA": 28538, + "TGAAAGAGA": 28539, + "CCATTCAAA": 28540, + "CCATTGTCA": 28541, + "GCATTCAA": 28542, + "GCATTTAAAA": 28543, + "GGCTGCGG": 28544, + "GGCTGTTTG": 28545, + "TTTTTTTTTTTTTTA": 28546, + "TGAGGGCAA": 28547, + "TATATATATATAA": 28548, + "GACTGTAGCC": 28549, + "CCTCGGTT": 28550, + "CTCCATCTCC": 28551, + "CTTCACGG": 28552, + "TAACATCTA": 28553, + "CTGTACACA": 28554, + "GCGATGTT": 28555, + "CAATAGATT": 28556, + "CCCAGGCTGGAGTGCAGTG": 28557, + "GAGTCGGACACAA": 28558, + "GTGCAGCTT": 28559, + "TGATTTTAAAA": 28560, + "CCTCCACCTCCCGG": 28561, + "TCTTCACAGAATT": 28562, + "CTATCTAGGTTGGTCA": 28563, + "GAGACGGAGTCTCGCTCTG": 28564, + "GCAGACGCTTTTATCCAAA": 28565, + "TCTTTCACTTTCATCAA": 28566, + "TAGGAACACTTTTACACTGTTGGTG": 28567, + "CATCTCTGA": 28568, + "TAATTTTATG": 28569, + "TGATCTGAA": 28570, + "TTAGGGAAA": 28571, + "GAAGGAATT": 28572, + "TCCAAAAAAA": 28573, + "TCCTCGTG": 28574, + "GGACCCAGG": 28575, + "CACATCTGA": 28576, + "CAGGTGATT": 28577, + "GTGAGGTGA": 28578, + "CTCATATAA": 28579, + "CCTTCAGCC": 28580, + "TATGCTGAA": 28581, + "TATGAAATA": 28582, + "GAGGTGTA": 28583, + "CCAACATTA": 28584, + "TACATATATA": 28585, + "CTTACGAA": 28586, + "TCCACCTCTG": 28587, + "GGGGTGCC": 28588, + "TGGGCTCC": 28589, + "CCCCCGAA": 28590, + "TAATTCAGAA": 28591, + "TTCATACA": 28592, + "GATACAATT": 28593, + "TTTTTTTAAATT": 28594, + "GCAAACTGAA": 28595, + "GCTCACTGA": 28596, + "GAGAATGTG": 28597, + "CTTAAGCAA": 28598, + "GTTTGGCAA": 28599, + "TGATGCTGCCA": 28600, + "CAATGGAGA": 28601, + "CAGTATGATATT": 28602, + "CCATGGTG": 28603, + "GGGCĊ": 28604, + "CTTTACAAAA": 28605, + "CTTCCTAGA": 28606, + "CTTCACTTTT": 28607, + "TCCTCCAAAA": 28608, + "CAAGATCAA": 28609, + "TAAGGCAGA": 28610, + "GTTGACATT": 28611, + "GCCCAGCCTG": 28612, + "CACACATGTG": 28613, + "CTCTTAACCACTGA": 28614, + "GCTATTATAAATAAGGCTG": 28615, + "TACTTTAACGAGA": 28616, + "GCAGATTCAGTCTTCCCAGCCTGGTGCAGG": 28617, + "CCGAGAAA": 28618, + "CCGCTCAA": 28619, + "TTTTTCATT": 28620, + "TGACGCTG": 28621, + "TGAGGATA": 28622, + "GAATAAATG": 28623, + "CAAGTTATT": 28624, + "GTTCTTTTTT": 28625, + "CTCGTATT": 28626, + "GTCCATTAA": 28627, + "TCTTGGCAA": 28628, + "TGTTCGTA": 28629, + "CAAAGACAAA": 28630, + "CATTCCTTA": 28631, + "GCTGTACTG": 28632, + "GTAACAGTA": 28633, + "CTAAGATAA": 28634, + "GCCATATCA": 28635, + "CGATAGAA": 28636, + "TGAAATGTG": 28637, + "GTGTTTCTCA": 28638, + "GTCTTTAAA": 28639, + "TATAAGAATG": 28640, + "TCTAAGTTA": 28641, + "CTCTGTAAA": 28642, + "GTTTGGTCA": 28643, + "TGATGCACA": 28644, + "TGAGAĊ": 28645, + "GAGCGGTT": 28646, + "GACTTCTCA": 28647, + "GATGAGAAA": 28648, + "CCAGGCCCA": 28649, + "TATAAATTTA": 28650, + "TCGACATT": 28651, + "CAAGGCCTG": 28652, + "CAAGGCCCA": 28653, + "TCACCCCTG": 28654, + "TGACACTCA": 28655, + "TGACACTAA": 28656, + "CTCCTTCCTT": 28657, + "CCCCCACCCCA": 28658, + "CAACTCCGTTTATGAAA": 28659, + "CCAAAAAATG": 28660, + "TACTCTAA": 28661, + "TAAGAGATT": 28662, + "TAACCACAA": 28663, + "TTTTTTAAAAA": 28664, + "TCAGTATA": 28665, + "GAATGAGAA": 28666, + "GTGTCAGA": 28667, + "GCACTTGA": 28668, + "GGATAGC": 28669, + "CTATAAATG": 28670, + "CTCTTAATG": 28671, + "GAAAGGTCA": 28672, + "TGAACAGTG": 28673, + "CCTTGAGGA": 28674, + "CATGCAGTA": 28675, + "GCAACCTA": 28676, + "GTAAGAATT": 28677, + "TATGCAAATT": 28678, + "CCAAGGCTA": 28679, + "TCTACTGTT": 28680, + "GCCACGTA": 28681, + "GGGGGTGGGGG": 28682, + "CATACTGTT": 28683, + "GGCACTGAA": 28684, + "GATATTTTCTT": 28685, + "TCTGATATT": 28686, + "TCTGATGGA": 28687, + "TCATTGGTG": 28688, + "GGAAAGGGAA": 28689, + "CGTTCGA": 28690, + "GTGAATGGA": 28691, + "CACCCGCC": 28692, + "TGATTGCCATT": 28693, + "GCTCACCTG": 28694, + "GACCCAAAA": 28695, + "CAGTTTGTT": 28696, + "CCCCACCTG": 28697, + "CCCTGGGAA": 28698, + "CCTCCCTGA": 28699, + "TGAGGGCTT": 28700, + "CAATGGTA": 28701, + "CCTCTTTTTT": 28702, + "TCGGCTCC": 28703, + "TCAGGTTAA": 28704, + "TCTGTGGAGG": 28705, + "GTGCTGGAGAA": 28706, + "TCATACAGG": 28707, + "CCGGCCTG": 28708, + "CTACAGTTA": 28709, + "CTCTCCTTA": 28710, + "GTTTCTCTGTGTA": 28711, + "CACATCTTAAACCAATTTATTTT": 28712, + "TAAAAAAAAAAAAAA": 28713, + "TCCGAAAA": 28714, + "CAATAAGAA": 28715, + "CTTGAATA": 28716, + "CTTTCTATG": 28717, + "GCAGTTATT": 28718, + "GCATTATA": 28719, + "GCACĊ": 28720, + "CCAGTCTGTG": 28721, + "CTATTTTTA": 28722, + "GTCGACTG": 28723, + "TCTTTGAGTT": 28724, + "GTGAGGCCA": 28725, + "TCTCTCCCTCC": 28726, + "GTTTTTTTTTTTTTT": 28727, + "TCTAGGCC": 28728, + "TCTATCTAA": 28729, + "GCCACCATT": 28730, + "CTTTTGAAAAA": 28731, + "GACAGAATG": 28732, + "GGTGCCCA": 28733, + "TAATTGTAAA": 28734, + "GGCAGCCTT": 28735, + "CCAAAGCTT": 28736, + "GCTCACGG": 28737, + "CAGCATATTAAAAA": 28738, + "GTGTTCAAA": 28739, + "CAGTGCCTT": 28740, + "TCCTGCGG": 28741, + "CTCTGTATA": 28742, + "GCATTGGTT": 28743, + "TAATGGAGG": 28744, + "GTTTGAAAATTTATGTGAGGTT": 28745, + "AAATTTATA": 28746, + "TCAAAATGAA": 28747, + "CACTGGGA": 28748, + "CACCATTTG": 28749, + "TAGTTTATA": 28750, + "TTATGGTCA": 28751, + "CTTGGTTTA": 28752, + "TCATATCTT": 28753, + "GCACAGCTA": 28754, + "GTTCTGAAAA": 28755, + "GACTCTGCA": 28756, + "GGCGGAAA": 28757, + "CACCTCAGCCTCCCAAA": 28758, + "CTCAAAATTCTCCAA": 28759, + "GTTCATATTGTTGTT": 28760, + "CCCGAAAA": 28761, + "TAACTGCTG": 28762, + "TTTTGAATT": 28763, + "TCAGTCTA": 28764, + "TGACAACA": 28765, + "GAACAAAAAA": 28766, + "TCCTATATT": 28767, + "GGAGTGGTG": 28768, + "GTAGTTGTT": 28769, + "CACAGGGTT": 28770, + "CAGGTACTT": 28771, + "CTCACAGTA": 28772, + "GCAATCAAA": 28773, + "GTTTTTAGA": 28774, + "CGAGCGG": 28775, + "CAAAATACC": 28776, + "GGTTGAAAA": 28777, + "GGGACAAAA": 28778, + "CGCTCGC": 28779, + "GCAAAGCC": 28780, + "TCATTCATA": 28781, + "CGTTCAGCA": 28782, + "GGCCGGCC": 28783, + "CAGAACCAAA": 28784, + "GAAAAATTAAA": 28785, + "GTTTGCCTG": 28786, + "TGATGGAAAA": 28787, + "CACTGTAGCTG": 28788, + "GAGTTTAGA": 28789, + "CAGCCCATG": 28790, + "GCTTAGTGA": 28791, + "TCTCTGCCTT": 28792, + "TAGGAGGAA": 28793, + "GGAGAĊ": 28794, + "GGAGAGTAA": 28795, + "CTTTACCTG": 28796, + "TCACCCATG": 28797, + "CCACATCAA": 28798, + "GTTCATCAA": 28799, + "TCCATGTAGTT": 28800, + "GCACACGCA": 28801, + "GAACCAGAAA": 28802, + "CACTTAAAAA": 28803, + "TGACTTATT": 28804, + "TTATAAAAA": 28805, + "CTGTCCCCA": 28806, + "TGAATATC": 28807, + "TGAAGGTGA": 28808, + "GGAAGGGAAA": 28809, + "GTGAGACAA": 28810, + "GCTGGACAA": 28811, + "TTTTACAGAA": 28812, + "TATGCATCA": 28813, + "GATTTCAAAA": 28814, + "CTAACAGTG": 28815, + "CTAAGGGAA": 28816, + "GCCATATTA": 28817, + "GGGGTCCA": 28818, + "TGTACTTAA": 28819, + "TCTCATGGG": 28820, + "CGTGCTA": 28821, + "TCCTTCCTGA": 28822, + "CTAAACTAAA": 28823, + "TGCCTACA": 28824, + "TGATTGCCAA": 28825, + "CAGCAGATGCTG": 28826, + "TCTTATCTG": 28827, + "GACCTCTGA": 28828, + "CTCTGCTGTT": 28829, + "TGATGTAGA": 28830, + "TACAAAAAAAA": 28831, + "TACTTTAAA": 28832, + "GATAATATA": 28833, + "GAAGGCGA": 28834, + "GAAGGTAGA": 28835, + "TTTTTGTTTTA": 28836, + "CTGCATCAA": 28837, + "TCCATTTGTT": 28838, + "CAGTCTGGG": 28839, + "CGTCATTA": 28840, + "CAATACTTTT": 28841, + "TGACAGTTTT": 28842, + "TGATACAGA": 28843, + "CTATACATT": 28844, + "CTACATCTG": 28845, + "GGTTTTAAAA": 28846, + "GGCAGGGATG": 28847, + "CTTTGAGAGG": 28848, + "TATCCACTTA": 28849, + "TATCCATTCCTCTGTT": 28850, + "CGCCTATAATCCCAGCA": 28851, + "GTGATGTTTTGGGGCTGTCGCTGGGCAA": 28852, + "TTTTGGCA": 28853, + "GAACAAGAA": 28854, + "CAACCTTTT": 28855, + "CTTTCGTT": 28856, + "GCAGAGGTG": 28857, + "GCCTGGGTT": 28858, + "GTCTAATAA": 28859, + "GTCTTTTAAA": 28860, + "TGTGGCCAA": 28861, + "GAAATATC": 28862, + "TCTTGAGTA": 28863, + "GAGATATAA": 28864, + "CAGAGGGTT": 28865, + "TACATTAGG": 28866, + "TTAACACAA": 28867, + "CGATAATT": 28868, + "CCCACAATT": 28869, + "GACATAATT": 28870, + "TCTCAGTCC": 28871, + "TCTGAGCCACCAGGGAA": 28872, + "GGAAATAGG": 28873, + "CGTTGATG": 28874, + "GTGAAAAATG": 28875, + "CTCTTGGA": 28876, + "GTATTCATA": 28877, + "CTCTGTCTCAAAA": 28878, + "TGAGGGATT": 28879, + "TTATGCTG": 28880, + "GAAGGCTA": 28881, + "CGTCCATG": 28882, + "GTGGGGTG": 28883, + "CCTAACTCA": 28884, + "TATAAATAAA": 28885, + "CAAGGTGAA": 28886, + "GGAGAAGGAGAA": 28887, + "CTCATTTCAA": 28888, + "CCAGACAAA": 28889, + "GTAGATATT": 28890, + "GAGTCGGTGATG": 28891, + "GTGCCCGA": 28892, + "CAACAGGTGCTGGAGAGGATGTGGAGAAA": 28893, + "CTTTTTTTTTTTTTTTTTTTTTTTT": 28894, + "CCCACCAACAGTG": 28895, + "TCATAACAA": 28896, + "TGAGTAGG": 28897, + "GTTGCATA": 28898, + "GCATCACTG": 28899, + "GGAGTTTGA": 28900, + "CCATCACTT": 28901, + "CCATCCCC": 28902, + "GCCCĊ": 28903, + "TATTTAAATG": 28904, + "GAGAGGGTA": 28905, + "TGAATACTG": 28906, + "TAAAACATCA": 28907, + "CATGTCTGA": 28908, + "GTAATACC": 28909, + "TTTTATTTTTTA": 28910, + "CGGCACTT": 28911, + "GTTACTGTT": 28912, + "CTCCTACCA": 28913, + "CTCCCCAGG": 28914, + "TTAAGCTT": 28915, + "GCTAGTAAA": 28916, + "GGCACGCA": 28917, + "TCTCACTAA": 28918, + "CGCAAAAAA": 28919, + "TCTCCTGGAA": 28920, + "GGAAATGGA": 28921, + "TCCTTTCTTA": 28922, + "CTCTTTATG": 28923, + "GCTGACATT": 28924, + "TCTTACTTTT": 28925, + "CGCCTGAA": 28926, + "CACCAGAGA": 28927, + "GTGTGGTTTT": 28928, + "CATCTAAAAA": 28929, + "CATCCCTG": 28930, + "CTTGGGATG": 28931, + "CAAATTTCTG": 28932, + "CTCCATATG": 28933, + "CAAGGCACA": 28934, + "GAACACCTT": 28935, + "GCGACGCC": 28936, + "TATTTATCTT": 28937, + "GTCCAGTCC": 28938, + "GGGCCTTTG": 28939, + "GGGTCTGGG": 28940, + "TCCACCTGCCTCTGCCTCC": 28941, + "TCAGGTGGTCTG": 28942, + "GTCGTGTCTGACTCTTTG": 28943, + "GTAAAAAAATATTTT": 28944, + "CCGGGCGCGGTG": 28945, + "GGTCAGGCTGGTCTTGAA": 28946, + "GAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAA": 28947, + "GGAGGTTTTCACTCAAAA": 28948, + "GAACAGGTT": 28949, + "CTTGTACA": 28950, + "CTTGTGTTA": 28951, + "CCACTTCCA": 28952, + "GCCGCTC": 28953, + "CTCGAATT": 28954, + "TGTGCCCC": 28955, + "CACAGCCTG": 28956, + "CAAACACTA": 28957, + "CAGGGGTCA": 28958, + "GTGAGATAA": 28959, + "CCTTGACAA": 28960, + "GCAATCTCA": 28961, + "GAGGCTGTT": 28962, + "GAGGCTCTTTA": 28963, + "GATTCCATG": 28964, + "CTCCCGTG": 28965, + "GAAAACGTT": 28966, + "GCCATCTTA": 28967, + "TAGGCAGCA": 28968, + "GACATATTTT": 28969, + "CTGAATGTA": 28970, + "TGAAATGGA": 28971, + "CACCCGTT": 28972, + "TATTATCAA": 28973, + "CAGAACAAA": 28974, + "TGAGATCAA": 28975, + "GAGCACTG": 28976, + "CACACACACATA": 28977, + "TACTTTCTT": 28978, + "TTATGGTTA": 28979, + "TAAATTGTA": 28980, + "GAAGGGCTT": 28981, + "GCATGGAAA": 28982, + "CTTGGTGGG": 28983, + "GGAGACGA": 28984, + "CTTCTTCCTT": 28985, + "GGGCGAGG": 28986, + "TCCCATAGA": 28987, + "CTCAAACAAATTTA": 28988, + "TTACATCAA": 28989, + "CTTTCATTTA": 28990, + "GCAAAAGAAACTACCATCAGA": 28991, + "TCATGGCTGCAGTCACCATCTG": 28992, + "TATAAAAATT": 28993, + "TCACAAGAA": 28994, + "TTACTAAA": 28995, + "GTTGAGATT": 28996, + "GTTTGACTT": 28997, + "GCAGGAATT": 28998, + "GGATGAGGA": 28999, + "GCCTGTGTG": 29000, + "TAAATATATG": 29001, + "CACATTTTAA": 29002, + "CATTCACTA": 29003, + "TGAAGTATT": 29004, + "CAGGCGTT": 29005, + "GGAATGTGA": 29006, + "TAAAACCTA": 29007, + "CCTTCTCAA": 29008, + "GAAAACATTA": 29009, + "CCCACCCTA": 29010, + "CTCTTTATA": 29011, + "GTTAAGATG": 29012, + "TCTTAGTA": 29013, + "CGCAGGTT": 29014, + "CAATTGTCA": 29015, + "TCCTGCAAA": 29016, + "GGATTGTTTT": 29017, + "GAGTTGGGG": 29018, + "TTATGGCAA": 29019, + "GCTCCTTTA": 29020, + "TAAATTAAAAAA": 29021, + "GGATGCGG": 29022, + "TAGCTCTCA": 29023, + "GAAGATCTCA": 29024, + "TCAGATCCA": 29025, + "CTTTAGTCA": 29026, + "GGTTATTTG": 29027, + "TCACCCAGA": 29028, + "GCGATGTG": 29029, + "TCATAGCTG": 29030, + "CCAGACCTG": 29031, + "TCCATGAAAA": 29032, + "CACATGCTCCACTATGTTCATA": 29033, + "CAAGATCTCACCTC": 29034, + "CGGTTCCAGAGTAAACGTGCGCA": 29035, + "TTACCTCA": 29036, + "CTGTAATAA": 29037, + "CTTGGATA": 29038, + "CTTTAAAAAAA": 29039, + "GGACGAGG": 29040, + "TAAACAGCC": 29041, + "CTCGATAA": 29042, + "GTCTTTTTTTT": 29043, + "GAGATAGTT": 29044, + "GCTTTATGA": 29045, + "GAGGTAGGA": 29046, + "GATTCATCA": 29047, + "CTAAGCATT": 29048, + "GCCATACTT": 29049, + "TAGAGTTAA": 29050, + "GGTTGGGGA": 29051, + "GGCACCCC": 29052, + "GGAAAGGGA": 29053, + "GGAAATTCA": 29054, + "GCTGATGAA": 29055, + "TCTTATCTA": 29056, + "CTTTGTTTTTT": 29057, + "GTATTCTGA": 29058, + "CGAACAAA": 29059, + "TCCTGCTTA": 29060, + "CTATTGAAA": 29061, + "GCCTGCTA": 29062, + "GCCTGGCAA": 29063, + "TGAGGCCTG": 29064, + "GAATGTCTG": 29065, + "TCATGGATG": 29066, + "GTGTGCACA": 29067, + "CAATGGTAA": 29068, + "GAGTATTTA": 29069, + "CCAGGCCTT": 29070, + "GAGAAAGAAAAA": 29071, + "TATATTTATG": 29072, + "TCGACAAA": 29073, + "GCCAGGAAAA": 29074, + "TCCAAATTTT": 29075, + "GAACATATA": 29076, + "TGATACCAA": 29077, + "TATTTACATG": 29078, + "CAAGAATTTG": 29079, + "GCATGACATAA": 29080, + "CTTGTGATTTTTG": 29081, + "CATCCATGTCCCTACAAAGGACA": 29082, + "CTTTGGATGCAACTCAGCATTCTTTGTCCTCCAAA": 29083, + "TAACCAGAA": 29084, + "CAATGTTAA": 29085, + "CTGCTTCC": 29086, + "GTTCAAATG": 29087, + "GCATGAGTT": 29088, + "GGACCAAAA": 29089, + "CCACGTCA": 29090, + "CCACGATG": 29091, + "CTATTATA": 29092, + "TATTCTGTG": 29093, + "TCTGTCCTG": 29094, + "CTGACATAA": 29095, + "GTGATACAA": 29096, + "GTGAGACTT": 29097, + "CTCACATGA": 29098, + "CCTTTAGTT": 29099, + "GCAAGGGCA": 29100, + "GTCAGGATT": 29101, + "CGGTCAAA": 29102, + "CGGGATA": 29103, + "CGGCCTC": 29104, + "GATTGGCA": 29105, + "GTTATACTG": 29106, + "TACACCCTG": 29107, + "CAAAACCTG": 29108, + "TGCCCTGAA": 29109, + "CACCCGTG": 29110, + "GAGAAGGGAA": 29111, + "TCTTAGTAA": 29112, + "GTATTTCTAGTTCTA": 29113, + "TGATGCTTA": 29114, + "GAGCTCTC": 29115, + "GTCTGCATT": 29116, + "GAATGTCTT": 29117, + "GAATGTCAA": 29118, + "TATTTGGAA": 29119, + "CTTGGCATT": 29120, + "GTGGAGCTT": 29121, + "GTTCCTCAA": 29122, + "CTATAGAAA": 29123, + "TCAGCATTTT": 29124, + "GTCTCACTATGTT": 29125, + "CTGAGGTGGGAGGA": 29126, + "GTGAACAGGCAACCTACAGAATG": 29127, + "TAACTGCA": 29128, + "TCATAATGA": 29129, + "TCAGTAGA": 29130, + "TGATAAAATT": 29131, + "TCCTAACC": 29132, + "CTGTCGG": 29133, + "GCATTATTTT": 29134, + "GCATCCTCA": 29135, + "CCATCTGAA": 29136, + "CCATCACTG": 29137, + "GTAGTTTCA": 29138, + "GTAGAATA": 29139, + "TAAACCTTA": 29140, + "GTCGATTTT": 29141, + "GTCTGTTTA": 29142, + "TGTGCCCTG": 29143, + "TATTTCTTAA": 29144, + "TGTTTCATG": 29145, + "TGAAGTCTT": 29146, + "CAGGCGC": 29147, + "CTGAGGTGA": 29148, + "GCAACATTTT": 29149, + "GTCACCTCA": 29150, + "TATGCTGTT": 29151, + "TCCATCTAA": 29152, + "GAAAACTGAA": 29153, + "GTTTTGATA": 29154, + "CGATACTG": 29155, + "GACATACTT": 29156, + "CATACCATG": 29157, + "GCTACTTAA": 29158, + "TTATTCATTA": 29159, + "CTAAACCAA": 29160, + "GTGAATGGAA": 29161, + "GTTAAGGAA": 29162, + "CAATTCTTTT": 29163, + "TCCTGGTG": 29164, + "CTCTGCGA": 29165, + "CTATTCAA": 29166, + "CAGGATTTG": 29167, + "GAATGTATA": 29168, + "GAATGCTTA": 29169, + "CAATGCTAA": 29170, + "GCTCGAGG": 29171, + "GTGGGCGCCTGTAGTCCCAGCTA": 29172, + "GCAAAATTAA": 29173, + "CTCCATCAA": 29174, + "CGTGAGCCACTG": 29175, + "CTGCTGGATT": 29176, + "CAAGGGGAA": 29177, + "CCAGACATT": 29178, + "CAGTCCAAA": 29179, + "GTTGAGAGAATGCCAA": 29180, + "GGAAAGAATGAATGGGGCCATG": 29181, + "GACGTATT": 29182, + "TGATCCATT": 29183, + "GTATATATG": 29184, + "CAAATTTTTT": 29185, + "TCTGGGCAA": 29186, + "GGAAGCATT": 29187, + "GTGACACAA": 29188, + "GATTCATTA": 29189, + "CCAAGACTG": 29190, + "TCCATAGTG": 29191, + "GAAAAGCC": 29192, + "GTTTTCTAAA": 29193, + "CATACTGAA": 29194, + "TGGATAGG": 29195, + "GTATTTCATT": 29196, + "CCATTTATG": 29197, + "CAGCGTCA": 29198, + "GGATTTATA": 29199, + "TGAGGTAGA": 29200, + "TAATAATAAAAA": 29201, + "GGCCAGAGG": 29202, + "GCAGGCTCA": 29203, + "TAAGACTTA": 29204, + "CAATATCTA": 29205, + "TAAATATGTG": 29206, + "CTATAGATG": 29207, + "CCATATGTG": 29208, + "GGATAGAAA": 29209, + "GGCTCCAGG": 29210, + "GTCACATGGGGTAA": 29211, + "TACTGATAACAAGTTCAAACAGGTGCCATTAA": 29212, + "TAAGTTCTA": 29213, + "TCAGGAGGA": 29214, + "GCACCTCTG": 29215, + "CCAGCCCA": 29216, + "CTCTAGGTT": 29217, + "GTCCAGGTG": 29218, + "CACACTCAA": 29219, + "TGTTTAGGA": 29220, + "GAGAGCTCA": 29221, + "CATGGTAAAA": 29222, + "GTCATAGAA": 29223, + "TCTCTCGG": 29224, + "TCCACCTGA": 29225, + "TAGACATGA": 29226, + "TGGAGAAAAA": 29227, + "GAATTGTCA": 29228, + "CTTAAGTAA": 29229, + "GTATTGATG": 29230, + "GGAGGTGA": 29231, + "CCATTCTTA": 29232, + "CTCTGCTAA": 29233, + "CTATTCTGA": 29234, + "GCCTGGTG": 29235, + "TACAACAAA": 29236, + "GTCTGCATG": 29237, + "CAATGCAGA": 29238, + "CCATGCTA": 29239, + "GATCAGAAA": 29240, + "GACCATCTG": 29241, + "GGGTGGAGG": 29242, + "GCAGCACTCTT": 29243, + "CTTCCTATG": 29244, + "GCAGAGCCA": 29245, + "CAAGATATG": 29246, + "CTCTACATT": 29247, + "CATACATTTT": 29248, + "GGTCTGAAA": 29249, + "TTACAGTA": 29250, + "GAATTAAAAA": 29251, + "TCCCGTCC": 29252, + "GTTTCTTTTA": 29253, + "GCAGTTGA": 29254, + "TATTCCCCA": 29255, + "TATATATTTG": 29256, + "TCTTTCTGTT": 29257, + "GAGAGATTA": 29258, + "TCTGGTTTG": 29259, + "GCTGCGCA": 29260, + "CTCATCTGA": 29261, + "TTTTATATCA": 29262, + "CGGGGTCA": 29263, + "GTTATCATT": 29264, + "CCAATATCC": 29265, + "TACAGCAAAA": 29266, + "TACATTTTTA": 29267, + "GGGGCACC": 29268, + "CCCAGCCCC": 29269, + "CAAAAGGTG": 29270, + "GACATCTCA": 29271, + "TCAAACCCATCCTAATT": 29272, + "GGAAATATCC": 29273, + "TCCTTCAGA": 29274, + "CGTTTCCA": 29275, + "GTGAACATT": 29276, + "CTTTGTCTA": 29277, + "GTATTCTCA": 29278, + "TCTTTTCATT": 29279, + "TATAATGAAA": 29280, + "CGGATGAA": 29281, + "CTGTGCTTTT": 29282, + "GAGGAGGAGGAA": 29283, + "AAATTAAAAAA": 29284, + "CAGTTCCTT": 29285, + "CCTCCCCCC": 29286, + "CCCAAGAA": 29287, + "CCCAACAA": 29288, + "GGCTTTGTG": 29289, + "GTATGGCCATTTT": 29290, + "TCAGACTTA": 29291, + "TCGACGTG": 29292, + "TCGACGAA": 29293, + "CTTATTTATT": 29294, + "TTATATCTG": 29295, + "TGATACACA": 29296, + "TCATATGTG": 29297, + "GTATACTTA": 29298, + "GGTCCTGCC": 29299, + "GAGTTTTAAA": 29300, + "TCTTTGTCAGTGGGCAAACGTA": 29301, + "GCCCAGCGACAGCCCCAAAACATCA": 29302, + "GGCTĊ": 29303, + "GTGGAAAAAA": 29304, + "GTTCAGGTT": 29305, + "GCAGTCAAA": 29306, + "GTACCTTTT": 29307, + "GTACCTCTG": 29308, + "GCCTATC": 29309, + "CTCGAGCC": 29310, + "GAAACACAAA": 29311, + "GCTTAAAAAA": 29312, + "GCTTCCTTA": 29313, + "GTGATAGTT": 29314, + "GTTAGTTAA": 29315, + "CTCCCGAA": 29316, + "GAAAAGACAA": 29317, + "TTTGTATTA": 29318, + "TCTAGTGTT": 29319, + "GCCAGCTTTT": 29320, + "GTCCCGC": 29321, + "CTTTTCATTTT": 29322, + "CGATACTT": 29323, + "GACATACC": 29324, + "GGTTGGCA": 29325, + "CATACAATT": 29326, + "GGCAGCTCC": 29327, + "TTTTTTTCTTA": 29328, + "CTAAATGCA": 29329, + "CACCTCCAA": 29330, + "TGATTTAATT": 29331, + "GTCAAGATG": 29332, + "CAAAAACAAAAAA": 29333, + "GCATTCCCA": 29334, + "GCATTTCACTG": 29335, + "GCCTGTAGG": 29336, + "AAATTTGAA": 29337, + "TGAGATATA": 29338, + "CATTACAA": 29339, + "GGATTGATT": 29340, + "CTTGGGGAA": 29341, + "AAATACAAAAATTA": 29342, + "TCCCATCAA": 29343, + "GTTGATCTTTT": 29344, + "GATCGATT": 29345, + "GCTCTTATG": 29346, + "GGAAAGGAAAGGAAAGGAAA": 29347, + "TGGGGTAGAGGATT": 29348, + "TAATCAATG": 29349, + "TAAGTTTGA": 29350, + "TCACAGATT": 29351, + "TCACAACA": 29352, + "TCATAAGG": 29353, + "TCAGCCCTG": 29354, + "GAACCTC": 29355, + "GAATCTGAA": 29356, + "CAATTTTATT": 29357, + "CAAGTTCTT": 29358, + "CTTTCACC": 29359, + "GTGGCCAGA": 29360, + "GTTGGAATT": 29361, + "GCATTACA": 29362, + "GCCCTCAAA": 29363, + "GCCTCACTG": 29364, + "TAAAGCCTT": 29365, + "TAAAGATAAA": 29366, + "GTCTCGGA": 29367, + "CAGGCATCA": 29368, + "TCAATCAAAA": 29369, + "GCTTTATGAA": 29370, + "GTGACGCA": 29371, + "GTGAGTTTG": 29372, + "CTCAGTGAAA": 29373, + "TTTTAGAGG": 29374, + "CTCCCCTGA": 29375, + "CTAACAGTT": 29376, + "GACAGCCAA": 29377, + "GGTGCCCC": 29378, + "GCTAGCCAA": 29379, + "TCTCAGACA": 29380, + "TCCTTGCCTT": 29381, + "CACCTAGG": 29382, + "CAGCACACA": 29383, + "GTTAAGCAA": 29384, + "TAATGCCTG": 29385, + "CATAATAAA": 29386, + "TCAGGCTGA": 29387, + "GGAGATAAA": 29388, + "GGAGACGGA": 29389, + "GTTTAĊ": 29390, + "TGACAGAATT": 29391, + "CTGGATATA": 29392, + "CCAGATGTT": 29393, + "CTGCAAAAAA": 29394, + "GTCTACAAA": 29395, + "CCTCTTGAGAAA": 29396, + "GTCCACCTG": 29397, + "GATGAAGCTG": 29398, + "GACAGGAAAA": 29399, + "TTTTATGAAAA": 29400, + "TAATCCCAGCACTTTGGGAGGCCGAGGTGGG": 29401, + "TCCTCTTTTATT": 29402, + "GCAGTGAGCCGAGATCGTG": 29403, + "TGACACAGGTCAAACGTTTT": 29404, + "CAGTTTTATT": 29405, + "GGTATTAAA": 29406, + "TCCTGAGGA": 29407, + "CTTTAAAATT": 29408, + "GTTTATATG": 29409, + "GCCGATGA": 29410, + "GCCCCACTG": 29411, + "TGTGGACC": 29412, + "TATTGTAAAA": 29413, + "TATTGGCAA": 29414, + "GAGAGGAAAA": 29415, + "GGAACAGTG": 29416, + "CCTTTATGA": 29417, + "CCTTTCTCTT": 29418, + "GCAAGACTAA": 29419, + "GTAATTTTAA": 29420, + "GATTTACTA": 29421, + "CCAACGTG": 29422, + "CCAAGTGTT": 29423, + "TTTGTTATT": 29424, + "CTTTTCTAAA": 29425, + "CGTTGCTG": 29426, + "GGTACGTT": 29427, + "CTCTTGTCA": 29428, + "CTCTTCCCCA": 29429, + "GTCTTCAGA": 29430, + "CTCAACTA": 29431, + "GTCAAGGAA": 29432, + "CCATTGATT": 29433, + "TAATGTCTA": 29434, + "GAGCATTTA": 29435, + "TCATGTATG": 29436, + "TAAATTCTAA": 29437, + "AAATGTATA": 29438, + "TCAGGCATT": 29439, + "CTTCTTGAGTT": 29440, + "GCCAGGTGTGGTG": 29441, + "TGATACTAA": 29442, + "GTGGACACA": 29443, + "TCTATATCTATA": 29444, + "GTACATATG": 29445, + "GAACCTGAA": 29446, + "GATGAAAGTGAAAGAGGA": 29447, + "CAAGATGAAAATATAA": 29448, + "GTTCACTCAGACTCA": 29449, + "TGAGTCCCTGGCGG": 29450, + "TCATCTTAA": 29451, + "TCACTACA": 29452, + "TGATCCAAA": 29453, + "TGAGCGTG": 29454, + "GAACCGG": 29455, + "CTTCAACA": 29456, + "CCAGCTGTT": 29457, + "CTGATATTTT": 29458, + "GTGAGGGCA": 29459, + "GCTGGGAGG": 29460, + "GTCAGGTCA": 29461, + "TTTTACAGTT": 29462, + "CCTGCGCA": 29463, + "TCCACTAAA": 29464, + "TTAAGATA": 29465, + "CGACATAA": 29466, + "CAAAATACTG": 29467, + "TGTAGGGG": 29468, + "TGCACCCA": 29469, + "GTAAAGATA": 29470, + "GCAAATACA": 29471, + "TCCTTGCAA": 29472, + "CAGAACAGAA": 29473, + "GTTAAGTA": 29474, + "CAGCGGCA": 29475, + "TAATGCTCA": 29476, + "CTATTCACA": 29477, + "GAGCATCTTTT": 29478, + "TATCAGAAA": 29479, + "TACTTCCAAA": 29480, + "GAATGGGGG": 29481, + "GCAGGCAAA": 29482, + "TGTGTTTGTT": 29483, + "GAGTCTTTG": 29484, + "GGGCTCCACGCAAGATCTCA": 29485, + "TCAGCATGACAATGATCCCAAACACACCGCC": 29486, + "CATAAAAATT": 29487, + "TTTTCCATT": 29488, + "TGACTCCTT": 29489, + "CAATTTTAAA": 29490, + "CAAGCATA": 29491, + "CTTTCAAAAA": 29492, + "GCACTTCC": 29493, + "CCACTTTAA": 29494, + "GAGATGATT": 29495, + "GAGATATTA": 29496, + "TAAAACAGTT": 29497, + "CATGTAGTT": 29498, + "GTAAGATAA": 29499, + "TATGTATATG": 29500, + "GATTCAATT": 29501, + "GATTTGTGA": 29502, + "TCTCTCTTTG": 29503, + "TCTCTAGAAA": 29504, + "CTCCTACTG": 29505, + "TCCATCACC": 29506, + "GAAAACACTT": 29507, + "GAAAATATATT": 29508, + "TCTACGTA": 29509, + "GCCATATC": 29510, + "CTTTTCCAAA": 29511, + "CGATACCA": 29512, + "CGATCGG": 29513, + "GGTTTAATT": 29514, + "TTTTTTCTG": 29515, + "CGTTCTTA": 29516, + "CTCTTGGAA": 29517, + "TATTTTCTTA": 29518, + "GCATTCAGA": 29519, + "GCCTTGAAA": 29520, + "GCCTTGGCC": 29521, + "GACTTTGAA": 29522, + "TTATGTTATG": 29523, + "TAAATTTCAA": 29524, + "CCATGGTGA": 29525, + "CCTGAGTTA": 29526, + "GATTATTTTA": 29527, + "TGACACTTA": 29528, + "GTTGGTAGG": 29529, + "GTTTTAATTA": 29530, + "GCTCAAGGGCA": 29531, + "TCTACATTA": 29532, + "GTAATGGGGGTG": 29533, + "GGTTTGTTTT": 29534, + "TCTTTACCTA": 29535, + "CATTCTTTCCTTTACA": 29536, + "TAAAAGTGTTCCTATTTCTCCACATCCTCTCCAGCA": 29537, + "CCGTCTATGGGGTCGCACAGA": 29538, + "TGATAAGG": 29539, + "TGAGCACA": 29540, + "GAAGTGATT": 29541, + "TCCCTCTCA": 29542, + "GTGCAATA": 29543, + "GTGCACAAA": 29544, + "GTATGAATT": 29545, + "TATTCAATA": 29546, + "TCTTGGCTA": 29547, + "CTCATGGAA": 29548, + "CCTTGTAAA": 29549, + "CATGCCCTT": 29550, + "GTAAGACC": 29551, + "CCAATTATT": 29552, + "CTAAGTCC": 29553, + "TCCATCCATTTG": 29554, + "GAAAAGCTA": 29555, + "GTTTTTACTT": 29556, + "CCCAGGATT": 29557, + "TAGACAGTT": 29558, + "GACACTAAA": 29559, + "TTCAGCAAA": 29560, + "GGAAATATA": 29561, + "GGCCTACA": 29562, + "GCTGACGG": 29563, + "GTTAACTAA": 29564, + "GCATTGTTA": 29565, + "TGATGCAGA": 29566, + "TACAATAAA": 29567, + "GAGCAGGTT": 29568, + "GCTCGGA": 29569, + "GCAGGGCAA": 29570, + "CTTGGCAAA": 29571, + "CTTTAGATG": 29572, + "TCGAGCAA": 29573, + "GTTGGCAGA": 29574, + "GCCCAGGAGG": 29575, + "GTTGAATTTT": 29576, + "TATAAAAGACACCTGTCCACA": 29577, + "TCAGTCCTGAA": 29578, + "CAGGCAGGCAGGCAGG": 29579, + "TCAAATACTTATG": 29580, + "GGTAGGAAA": 29581, + "TAAGCGA": 29582, + "TGACTGCA": 29583, + "GAAAAAAAAAAAAA": 29584, + "TCCTATTAA": 29585, + "CCAGTTTA": 29586, + "GTATATTTG": 29587, + "CTATCCCTT": 29588, + "CTCTAATTTT": 29589, + "GTCTGAATG": 29590, + "TGTGCTGTT": 29591, + "TGTGCACTG": 29592, + "TATTGGATT": 29593, + "TCAATACTG": 29594, + "TCTCGGA": 29595, + "CCAACCCTT": 29596, + "TCCACTCAA": 29597, + "GCCACGCTG": 29598, + "CCCATCCTT": 29599, + "TGGGGTCC": 29600, + "TAGATACC": 29601, + "TAATTCTTAA": 29602, + "CGTTCGTT": 29603, + "GGCCCGTG": 29604, + "GTGAATGTA": 29605, + "GTATTCTTTT": 29606, + "GTCTTCTTA": 29607, + "TCTTTTGATTTT": 29608, + "TAATGTCAAA": 29609, + "GGGAAGGGTA": 29610, + "GAGCTCCAA": 29611, + "CTATGCTA": 29612, + "GAGAAATTAA": 29613, + "CTTGGCACA": 29614, + "TTTTTGGTAGAGA": 29615, + "GAAGAATGAA": 29616, + "GTTGTTAAAA": 29617, + "TTTTTAGATT": 29618, + "GAAAAAAATTA": 29619, + "CATATACATATA": 29620, + "GCCTGACCAACATG": 29621, + "GGTGAGATCTT": 29622, + "CTCCTGACCTCAGGTGATCCACCCA": 29623, + "GTAGAATAATAGTGAAGACA": 29624, + "TAACCCAGG": 29625, + "TCAGCCCC": 29626, + "GAAGTTCTT": 29627, + "TCCGTAAA": 29628, + "TCCCTGTA": 29629, + "TCCCGAAA": 29630, + "CTTGCCAAA": 29631, + "CTTCGGTG": 29632, + "GCATCAGTG": 29633, + "CCATTTTAAA": 29634, + "GCCTCTCAA": 29635, + "GCCCGCTT": 29636, + "GCCTACAAA": 29637, + "CTCGCCTG": 29638, + "TGTGCATTTT": 29639, + "TATATAAATT": 29640, + "CAAAGAAATA": 29641, + "CTGAGTCTG": 29642, + "GCTTGACAA": 29643, + "GTGATACTG": 29644, + "GCTGCAGTA": 29645, + "GCAAGGTCA": 29646, + "CGGGCTA": 29647, + "GATTCTCTT": 29648, + "GTTAGGGAA": 29649, + "TACAGTGGG": 29650, + "TCCAGTTTG": 29651, + "GCCATCACAAA": 29652, + "CCCAGCAGTG": 29653, + "TAGACGTA": 29654, + "GATACCTA": 29655, + "TCTCCCCCC": 29656, + "CGTGTCCCCCTG": 29657, + "TGATTTCTTA": 29658, + "GCTCAGTA": 29659, + "TCTTTTTTTTTTTTTTTTTT": 29660, + "CGGAGACA": 29661, + "GAAAAAGAGG": 29662, + "TTTTAATGTT": 29663, + "GCCTTGGAA": 29664, + "GGCTGTGGGTTTG": 29665, + "GAGGAGACA": 29666, + "CAATGGTGTCA": 29667, + "TCAGGTGTT": 29668, + "CTATGGAAA": 29669, + "TAGCTTATT": 29670, + "CTCCAGAAAA": 29671, + "CTCCACGA": 29672, + "TCGATCTT": 29673, + "CTATACTTA": 29674, + "GTGTACATG": 29675, + "GTAGACTCA": 29676, + "TCCCTTGTAAGTT": 29677, + "GGCGGGCGGA": 29678, + "GTTGGAGACCA": 29679, + "TATATATATATATATATATATATATATATATATATATATATATA": 29680, + "GACTCCATCTCAAAAAAAAAAAAAAAA": 29681, + "GCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGGCGG": 29682, + "GAGATTATCAGTGGTCTTGTATG": 29683, + "CCGĊ": 29684, + "TAAGCTGTT": 29685, + "TAATCAGTT": 29686, + "TCACCACA": 29687, + "TCACGATG": 29688, + "TCACTCTGTT": 29689, + "TGATAATAAA": 29690, + "TCCTTTTCTT": 29691, + "CAAGCCAAA": 29692, + "CTTCAAAAAA": 29693, + "CCAGCACA": 29694, + "GCCTATAAA": 29695, + "TAAACATACA": 29696, + "CTCTTTGAAA": 29697, + "TATTCATATT": 29698, + "CAAATAAAAAA": 29699, + "GAGACTAAA": 29700, + "TCTGCTGTG": 29701, + "TCAAGGTGA": 29702, + "GTAAGTC": 29703, + "TTTTATACTT": 29704, + "GGTGGCAAAA": 29705, + "TAATTTCCTG": 29706, + "GCAAATAAAA": 29707, + "TCTGACCTG": 29708, + "TCTGACTCC": 29709, + "TGCCCTTTT": 29710, + "GTTAAGGTT": 29711, + "CCATTCTAA": 29712, + "CGGATGGA": 29713, + "TTTTAAGATT": 29714, + "TAATGGTCA": 29715, + "CTATTTAATT": 29716, + "CACTTCTA": 29717, + "TCATGGAGA": 29718, + "TCATGGCAA": 29719, + "CCCTTCTG": 29720, + "GCTAACTTTT": 29721, + "TACTGTTTA": 29722, + "GCTCGGTT": 29723, + "CATAACGTG": 29724, + "CCATGGATT": 29725, + "CCTCGTCA": 29726, + "GAAGAATTTT": 29727, + "AAATACTTTT": 29728, + "TCAGACGA": 29729, + "GGAGGAGAGA": 29730, + "CCATAGATG": 29731, + "GTATTTTTTA": 29732, + "CACACATCTCA": 29733, + "GCCACTGCTGAGTTTT": 29734, + "GTTCAGTTCAGTT": 29735, + "CTCGGGAGGCTGAGGCAGGAGAATGGCGTGAA": 29736, + "GCCCTTTGTCAGATGA": 29737, + "CTCCTGCCTCAGCCTCCTGAGTAGCTGGGACTACAGG": 29738, + "CAGCTAGAGTCAA": 29739, + "TAAGTCCAA": 29740, + "TGATCTTAA": 29741, + "GAACATTAA": 29742, + "TCCGCGG": 29743, + "GGACTTGAA": 29744, + "TATTGCTCA": 29745, + "CAAAGTAAAA": 29746, + "CAGACCTCTA": 29747, + "GAGGGGTA": 29748, + "CGGGGGCA": 29749, + "CTCCTCCCTG": 29750, + "TCCAGGAGG": 29751, + "GTTTTGTTTTTT": 29752, + "GTCCTCCTG": 29753, + "TCTCCCTAA": 29754, + "TAAAAAGCA": 29755, + "TGAAAGTA": 29756, + "TGAAACATT": 29757, + "CAGCACAGCA": 29758, + "GCCAAGGGG": 29759, + "CGAAGCTT": 29760, + "CGGATTAA": 29761, + "CAAAAATATAAA": 29762, + "CCCTGTGTG": 29763, + "GATGACAGCTTTG": 29764, + "TAAATTCCTG": 29765, + "TCGGGAAA": 29766, + "GAGCCCTG": 29767, + "TCGATTTTA": 29768, + "TTATACAGG": 29769, + "GCAGACTCA": 29770, + "CTGTACAGG": 29771, + "TAAGATAAAA": 29772, + "TCATATCTG": 29773, + "CTGGACATT": 29774, + "TATTTACTAA": 29775, + "TAAATAGAAA": 29776, + "TAACCCCAA": 29777, + "TCCCATGGACAGAGGAGCCTG": 29778, + "CAAAGCATGTA": 29779, + "CTCCTGACCTCAGGTGATCCACCTG": 29780, + "CTTTGCTGAAGTTGCTTA": 29781, + "CCTCCTTCCATCAGCAAGGGCATTGAAGATGAAA": 29782, + "TTTTTCTCA": 29783, + "TGATTACA": 29784, + "CAATCTCTT": 29785, + "CAATTACA": 29786, + "CCACAACAA": 29787, + "CCACGAGG": 29788, + "CCAGCCAAA": 29789, + "CCAGTGCC": 29790, + "CACACAATG": 29791, + "TCTTCGC": 29792, + "TCTTGAGCA": 29793, + "TCTTGAGTG": 29794, + "GCTGGGATTA": 29795, + "TATGTCTCA": 29796, + "CGGGGAGG": 29797, + "TCTCCAGTC": 29798, + "TCCACTTAA": 29799, + "TCTATGATT": 29800, + "CGACAGTT": 29801, + "CAAAATCCA": 29802, + "TGGGCCTT": 29803, + "TAGGTATTA": 29804, + "GACACTTAA": 29805, + "CATACTCAA": 29806, + "TCAAACAGG": 29807, + "TAATTCAGG": 29808, + "CTGAAGGTT": 29809, + "CGTTCATG": 29810, + "TATTTTTAAAAA": 29811, + "CGCATGCA": 29812, + "GTCAAGGTT": 29813, + "CGCCTTCA": 29814, + "TGATGTAAA": 29815, + "CGTACATG": 29816, + "TAGAACTA": 29817, + "CATGATTTA": 29818, + "TACTATACTA": 29819, + "CACTATTTG": 29820, + "TTATACAAAAA": 29821, + "TGACACAAAA": 29822, + "TAAATATCAA": 29823, + "CTTGATATT": 29824, + "GTCTAGTTA": 29825, + "CACATGGACA": 29826, + "GCATAGAAA": 29827, + "TCGTTACTATACTT": 29828, + "TAATCCCAGCACTTTGGGAGGCCGAGGCAGG": 29829, + "CCCACCTATGAGTGAGAACATG": 29830, + "CCGCAAAA": 29831, + "CCTAGGAAA": 29832, + "GAATTTTTTTT": 29833, + "TCCCAAGAA": 29834, + "CAACCCAAA": 29835, + "CTTGTTCTG": 29836, + "GCCGCCTT": 29837, + "CAAACAATG": 29838, + "GCTTCACCA": 29839, + "GCTTCAGCC": 29840, + "GAGGGAGTG": 29841, + "TTAATGGG": 29842, + "CGATATGA": 29843, + "TGGGCCAGG": 29844, + "CATACGAA": 29845, + "TGTACAATG": 29846, + "TTATTCAAAA": 29847, + "TCTCCTAGG": 29848, + "TCTCCCACTT": 29849, + "CGTGCCTT": 29850, + "GGAAAGTAA": 29851, + "CTCTTCCAGAGG": 29852, + "TCTTACAGTT": 29853, + "CTCAACACA": 29854, + "TATAACATT": 29855, + "TACCTCAAA": 29856, + "GCCTTTCAAA": 29857, + "GAGGATTTG": 29858, + "TACAATACAA": 29859, + "GTCTGCAGA": 29860, + "GATCAGAGCA": 29861, + "GAAGATAAAA": 29862, + "GTTTACAAAA": 29863, + "GCTCTGCCTCCCGG": 29864, + "TAACACTGAA": 29865, + "CTGTAGCAA": 29866, + "TTAATTTATT": 29867, + "TCAGCCTGGG": 29868, + "TGTTTCCACCCCCATG": 29869, + "GGGGTGGAAACATCATGCTTTGGG": 29870, + "GGGTATATAACAAAGTATTGAGAAA": 29871, + "CTTCACGGTTGGGATG": 29872, + "CCAAAAAATT": 29873, + "GCGGATTA": 29874, + "TAAGCTCTT": 29875, + "GAAGCCAGG": 29876, + "GTGTCATCA": 29877, + "GTTGTATTA": 29878, + "GCACTGGAA": 29879, + "CCAGAACC": 29880, + "CTACCTTTT": 29881, + "TAAATĊ": 29882, + "GTCGCTTA": 29883, + "TATACCCAA": 29884, + "CAAATATC": 29885, + "CAAACAAAAAAA": 29886, + "TGAAGGTCA": 29887, + "TCTGGAGC": 29888, + "CTGACAGC": 29889, + "CTGAGAGC": 29890, + "GTAAGTATT": 29891, + "GATGCGC": 29892, + "TCTAGTGAA": 29893, + "TCTATCCAA": 29894, + "TCTACCCAA": 29895, + "TAGGTAGAA": 29896, + "GCTATTTTAA": 29897, + "GTAAAGTCA": 29898, + "TCTGAGCC": 29899, + "TCATTTCTTA": 29900, + "TGAAACAGA": 29901, + "AAAAAATATT": 29902, + "CTGTTGGA": 29903, + "CTTAATGTG": 29904, + "CAGAAGTCA": 29905, + "GACCTACA": 29906, + "GTCTTCTCC": 29907, + "CCATTGTTTT": 29908, + "TCTAATGTT": 29909, + "CGTCCACA": 29910, + "CTCCAGCCAA": 29911, + "CTTCAGTCC": 29912, + "TAACACCTG": 29913, + "TCACACCTT": 29914, + "TGATACTTA": 29915, + "CCAGACAGG": 29916, + 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02,"CTGTTGGA":29903,"CTTAATGTG":29904,"CAGAAGTCA":29905,"GACCTACA":29906,"GTCTTCTCC":29907,"CCATTGTTTT":29908,"TCTAATGTT":29909,"CGTCCACA":29910,"CTCCAGCCAA":29911,"CTTCAGTCC":29912,"TAACACCTG":29913,"TCACACCTT":29914,"TGATACTTA":29915,"CCAGACAGG":29916,"GTTCACTTA":29917,"CTATACCCA":29918,"TGACCTCAAGTGA":29919,"GCCCACTCC":29920,"CTAATGTTA":29921,"TAACTCCAGTT":29922,"CACCAAAAGCAATG":29923,"TCTTGGCTCACTGCAACCTCTGCCTCC":29924,"TCTACAATTTTGTTTCTGGTGTCCTT":29925,"CAGTTAAAAA":29926,"TAAGAAAAAAA":29927,"TGATCAGA":29928,"CTTCCACC":29929,"CTTTGACAA":29930,"GTGGCGGA":29931,"GGAGTTTCA":29932,"GTCGACCA":29933,"TATAGACTT":29934,"TCTTGTTTTA":29935,"CATTCGGA":29936,"CATTGCTAA":29937,"TGAAGCCAA":29938,"TGAAGCATT":29939,"TCTGGGATT":29940,"GGAATTATG":29941,"GCTTGCTGA":29942,"GTGAGTTAA":29943,"GCAAGGGGA":29944,"TATGTCTGA":29945,"GATTTACCA":29946,"GTTACAGACAGA":29947,"TTAAGTTTT":29948,"GAAAAGGAAAA":29949,"TCTAGGCTG":29950,"TGGGGATA":29951,"TAGGCCCC":29952,"TAATTCATTA":29953,"TTATTGATA":29954,"TCATTAAAAAA":29955,"CTAAACTTA":29956,"CTCTTTCCTT":29957,"TCTTAGTTATT":29958,"CTCAATGAAA":29959,"TCTAAGATG":29960,"GTTTGTCTG":29961,"TGATGGCCAGTGA":29962,"CCTCATTTA":29963,"CCCCACTG":29964,"GGATTTCGTTGGAAACGG":29965,"GATGAGATG":29966,"CCTGACCTT":29967,"GAAGGCACA":29968,"GTGGGCATT":29969,"CTCTATCTT":29970,"GGACATGTG":29971,"TCCCCCATG":29972,"TCCCCTCCA":29973,"CTTGATGTT":29974,"GTTGATGTG":29975,"GTAGATGTG":29976,"GGCTCAAAA":29977,"GGGTCTGTG":29978,"TCATAGTTTTGATGTCTT":29979,"CCCGCCTT":29980,"TCGGAAAAA":29981,"TAACCACCA":29982,"TGAGTAATT":29983,"TCCTCTTAA":29984,"CAACGGAA":29985,"GTGCCTCTT":29986,"CTACAACAA":29987,"TAAAGGTGA":29988,"CTCCAGGAAA":29989,"GGAAGTGTT":29990,"TAAAATTATG":29991,"GCTGTAGC":29992,"CCTGCCTCTG":29993,"GAAAATAATA":29994,"TGGGCCAA":29995,"TGAAACAAAA":29996,"GGTACTTTT":29997,"GCTGACTA":29998,"TCTTACACA":29999} \ No newline at end of file diff --git a/04-gene-sft/llama-sft-2.json b/04-gene-sft/llama-sft-2.json new file mode 100644 index 0000000000000000000000000000000000000000..15e684c545be8833f04314585c103286d6b2c8e6 --- /dev/null +++ b/04-gene-sft/llama-sft-2.json @@ -0,0 +1,602 @@ +[ + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "TTTTTCCCCTTTGCATACATAATATGACAATGAAATCATATAAAAGGTAAATATGCACTTTGAAGAAAAGCATTGACATGTATCTTTTTTAAAAGTCCATCAATTGTAACGTAAGGTTTTGTTGTTTTGACTTTCATCCTAGGTGAAATTTATCCCAAGATGTACCACATCTGTTTCTTTCTGGTGACATACATGGCACCACTGTGTCTCATGGTGTTGGCTTATCTGCAAATATTTCGCAAACTCTGGTGTCGACAGGTATATAGTTTCAAATATTTTGCGTGCATTATTCCTCCACACATAATTTGTTATTTGTTATTCCTTCCAAATATTTTGTCTGTGCTTTTTTTTTAGGATGCACTTATAAACAAAATTTAAGAATGCATTGAACCAATATAAC", + "output": "Donor Sites", + "model_response": "Non-Splice Sites" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCGGCCCGCCCTGCGCCGGCGGCGGCGGCCCCAGTCTCCGCGCTGCTGAGGCGCGCCCGGCCGCTCCCA", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGGGTTGCCAAGAAGAAAGGAGAAGGAGAACAAAAAGGCAGGAGGACTCCCTTGGCAGAGATCCAGAGCAAAGGCAGCTGTTGCTTCTCATGAACACTCCAAAGTTTATCACAGTCACCTGTTACCCTAGCTGGCACAGGTCTTTGAAAGTGTGCTGGGAGTGTGTTTACATGTTTGTGTGTGCTTGTATGTTAAAGGTGCCCAGGTTAATACCAGGTGTATTAAAACGCCCTTGCCTGGCTAGTCCTTGTAGACCTTCTCTGAATGTTTTGGTGAATAAATCTGTTCTTCAGCAACCCT", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACACCATCCCCAACTCCGCCAAGGCCAGAATAAGATCCCGGATGCCAGGAGAGCCTACAGTTCACGACCGTGGGTGATGCTTTACGTTTAGATTCTCCGCCGGAGTCCCAGAGCTCGGTGTTCAGGATGATGAGAAGGCGTGGAACGAGGACTTCAAATCCCAGAGTGCAGTTCTGCCTGAGGCTTCTGCATGCTGGGAACTGAAGTCCCTGAGAAGAGGAGGTAAGGCGCTCCCGGCATGCAACGCGTAAGACTGCGACGGTACCGGGGCGGCGGGGAAGGACCGAGAGGCGGGAGGAG", + "output": "promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACTCACTTCTTCCTAGAGCATCTCAGGGAAGTGGGGAACTTCATCAAAGGGGGCAGCCTTATGTTAGCAGCTAGAGGATTCCTAGGTAATTCCAGGAGTCAAAAAGAAGACCCTGAGAACTGGGGGAACCACTCATCCCATAACAGTGAAAGAAACACGGAATTCCTTCCTGATTTTCATCCTTGGGAGATGATGGACAATTGTAGCCAGATGGGAAAAGTTTCACTTCTTCCTCAGGGAGTTGTCGGGGGGTGCTTTGTATGACTGGATAGGCGTTAGATCAAGACAGAGAAGTCACTG", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "GAACAAATTTTGATTCATCTGACATTAGTTAGAATTAAATTATTATCTAGAGATTATAAATGTTGCATTAATCCTTCTTTAATTTAATAATTAAGATTTATAAGTATTCACAAATATATCTGGAATAAGTAATTTAAAAAGTCTTAAAACTTTAAGATTTGATATCAGGATATAAACATAATTCTATTTATTTATATTTTCCATTTGATATTTGATCCATCCTACTTTATGACTTTCTTTTCATCAAGAAACTGACCCAAAGACTTGAGGATTACTACTAAGGACTTTTCAGGGAAAATAAAAACGAAATAACCCACACCAAACAAAAAGCGGCGGTGGTACAGCCATAATACTTCCTGTTCCTTGTAGATCAATCTGGGACAAAGGCTGTCTAATATTT", + "output": "Donor Sites", + "model_response": " Non-Splice Sites" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCTAGCCCAAAGCCTGCGCACTGTCCTTGACACTGCTTCTTCCTAGGCCCCTCGCCCAAACTCTGCCTAGTTCGCCTCCTCACTCCCCTCACACTGGGCCCTCCCTTTCCTTCCCCCTCACTGTGCACCTCATGGGACCATGCCAGGGTATTCCCGCAGCCTCCTCGGTGGCCTGCCTCTATCTTCCTCCACACAGCTTCGGGAGGCTCTTTCCAAAGCACACATCTGACCACGTGACTGCCCTGCTCCAGAGCCTTCCGTGACTCCCCATTTCCGAGGGACGAGGTCTGGCCTGCTCCC", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTCTCTTGCCAGTTAGCTTAACAGACACTTTTAGCAGCCTATCATAACTTCGAATTGCCCAACCCTCAACCAATTACAACTAAAGCTTGGGCTAGGACGCTCCAGGTCCGGGCGGGGGCGAATACCGGGTTTAGGAAAAGGGGCGGTAAAGAGCGATGACGGGCGGGTATACTAAACCAATCAGAGAGGAGAGCTGCCGCCCCACTATCCAATGGATAGGTTAGAAGTATGAAAGAGGGTGGGCCTGTTGTCATGGGGGAGGTGGTGGCGCTTGGTGGCCACTGGCGGCCGAGGTAGAGG", + "output": "promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AAGAGACAGAAGCCTCCACATGTGTGCGACCGCAGAGGGGCGCGGGCGAGCGAGGAGACGTCTAGCCATT", + "output": "Non-promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTGGGCCGGGGACCACATCAACATGATGCGAGGGCCCGCCTGAGGGAGAGGCCGAGTCTCCAAAATTACGTTGTGTAGGACGTTAGATTTATACGACTAGTGGATAAGACCCCGCAGGCCGCTACTTAGTTAACGGCTCCTCAGTCCCTGCTTTGTGTGGACCAGAGTCGGTCCACAGTTCGGCGCTGGGAAAGGCTTTAAAAATAGGACCGGGAAGCTGAGAATTAGCGTACATGGCAACACGCTTACACACCCACTCCGGACTCCTCCCTCTCGGTTACATGACCCGCCAGAGCCCTC", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "CGAGAATCCTTTGAACCCTGGAGGTGGAGGTTGCAGTGAGCCGAAATTGCGCTACTGCAATCCAGCCTGGGTGACAGACTCTTGTCTCCACCAAAAAAAAAAAAAAAAAAAAAGAAGGAAGAAAATCACTTTTAGTTTTATCATCCAAAGATGACACCCTTCTTCAATATTTTGAATAATTCCACCCTGTCCTTTTCTAGCTATGGGGCTGTTATCTGACTGGTGGTAGCATTTTATTCCCTGTCACTGTTGTTGGGGTTGTGACAGATATTTCCCTCATCTATTATAAATGTTGTAAACCCACCCCTAATAACTGCATCTTATTTCATGCAGGGGCTACCCCATAACTTACTGACCTTTTCTGTTTTGTCATGAAGTGGGTTGGCCAGGAGCTGTACCC", + "output": "Donor Sites", + "model_response": " Donor Sites" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GATCAAGAAGCGTGCACGTGTCCTCCACGCTCCATACTGGAAGTACATTGAATACATGGCGCCTCTTGGG", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGGTGTCTTTAAAGTACAGGTTAATGGTGAACATTTTCCCCCAGTGGCTTCACCTCATTCCTCCCACTGGCCTTACCCCTTCCTTCCCCAGTGGAAGCATTTTCAAAAGCAAAGATAATTTTCGCTGGTGAACTTCAGAACTTGCCTTCAGGGTTAGCTTCATGTAATTTTACCATTTCCCATCCCCATTCCCCACCACATTATGTCAAGATTCAAGTTATAAATTAACGTTTTACTTAGACTTTGAAAGAGATTTCATGAGTAATTTGAATGAACCTTGCTGAATTAATCTGAATAGCA", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGAATAAAAGTGCAGAGTTCTGATTTTGGAATGTCAGTCTTAGATGGGGCAAAGAGAAATCAAAACTTGA", + "output": "Non-promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCCGCGACCCCGGGAACCGTTATTGGCCGGGTCACAAGCCCGCCCGCCCGCTGCTGGAGCCGCGACCATG", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCGCATGCGACAGCGCCTGCGCAGTCATGTCTTAAGAATCCTTTGCGCTCTCGGTGCCCCCCTCCACGCC", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "GTTCTAGTATCTGCCCTTCTATTACAGCTTTTCACCCTTCCCTACAGGCCGGGGTTTGAAGTCTCACGCCTACATTCACAGTGTCCAGTTTAGCCACCATGTTTTCCTCAACCTCCACACCCTCAAGTTTTACTGCCTTCCAGACAACTATGAGATCATCGATTCCTCATTGGAGGATATCACGGTGTGTGTCTAAGAGGGTTGGTTTGGGGTCCATTCTTTAATGAGCCATCCCAGGGCTCCCAGGGGACTGCCCCACCTGAGGACAGGGAAGAATCTCAGTCCCTTTAGGCATCGAGAGGCTGCTTTCTCTTTTGGGAAGAACTGGGGAAACTGAGTAAGGTCAGAATTCATAATTGCAGCCTGTAGTCTGTAACTTAACTGTCATTTCTACTTGTAA", + "output": "Donor Sites", + "model_response": "Splice Sites" + }, + { + "instruction": "Determine the Transcription Factor Prediction of following dna sequence, The result will be one of the following: Background Sequences, Binding Sites.", + "input": "GTCACCCAGGCTGGAGTGCAATGGCATGATTTCAGCTCACTGCAACCTCTGCCTCCTGGGCTCAAATGATTCTCCTGCCTCAGCCTCCTGAGCAGCTGGGA", + "output": "Background Sequences", + "model_response": " Background Sequences" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCACTCTTCTCGGAATTCCACCTGCCTACACAACAGATTCGTATCGCAAAAACTCTGCAGGTCCGAGTAC", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCTCAGCCGAAGAGAGGCGGGGAAAAGCATCGTAATCAGCTGCGTCGCCTTTTGGTGACGCCAGAGAGTGCGCGTCAGCAGTTTATTAGAGAGCTCTGTAGCCAGCCTCTTCTGCGCACCCACCTGCTGCATCTTAGTTCAGTCGGCTCTTAGAGTAGTAACCGCCAGAAAGGAGTCGGAAGAGGTCTCACGAGGCTGTCATCACCGCCATGCCCAAGAATAAAGGTACTGCTGTAAGCCTCTGGGACTATACCTCGGCTTGCTCTGCCAGTAACCCCGACGCCTGTTCCAGGCCGCAGT", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TATGTCTGTGTGTGTGTGTGTGCGCGCGCGCGCGCGCGTCAGGGGCCAATGCTGGAAAGGAAACAGCGGA", + "output": "promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCCTCTGAGCTCCCGCTCCTGGCTCTGTAGCTGAGAGAAGCCCTGGCAGGTCAGTGGCAGGCACTGTCA", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AAGAGGCCTACGTGCGGGTGGTCACCGAGAAGTCCCCGACCGACTGGTGGGCGGCGAGACGGGCCAGGGT", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TTATCACTTCCCTGAGAAGAAAAACACATTTTCAGTTTTGCCTGCATCATAAGAGTGAGCACTCCATTGC", + "output": "promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTCTCAGAGAATACCTAAGTGGTGAGGTCTGCTTGCTAGCTAGCTAGCTACCTACGTACCTACCTACCTT", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTATTGCTAATCCCCAGGAGCCCCGTTTAAAAAAAAAAAAAAAAAAAGGAAGAAAAAAGGGGAGGTGGGGGGACCGGGCAGCTGTCTCTTTAAATGTGATTTCCTTCTATTGTATTTGAATCGTGATCAGGAAAAAGGAAATGAAACCAGAGACAGAGGGAAGCTGAGCGAAAATAGACCTTCCCGAGAGAGGAGGAAGCCCGGAGAGAGACGCACGGTCCCCTCCCCGCCCCTAGGCCGCCGCCCCCTCTCTGCCCTCGGCGGCGAGCAGCGCGCCGCGACCCGGGCCGAAGGTGCGAG", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ATATCGTCCCACAAAGGAAAAGAGTGGGGCACTGAGGAGCGGTCCTCGCAAAAAAGCGAGGAGCCCGATG", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "CCCCACCCTAAATGAAACCTATCATGTATAACATCACATTACCTGGAACAGGTTTGACTTGATTTAGTAAAACCGAATATACAAAAACGTGTACTAAAAGGTCAAGAGATAGAGAAATAATGTTTCAGTTTGAGAAATAAACATTTTTTTAACTTTAAATATTGACTTTTTTATGAATAAGGAAGCTTTTTATTTTACAGGTCTTTGTGGGAAGAAACAAATCACAAAAGCAATTAAGAGAGCTCAAACAATGGGTAAGAAAGAGCACCTCAACAACTGTATTGAGCTAGCTGAAATTTTGCTCATTATGCTTTGTCAAGAACTTTAATTATCTCTTTACAGGGTTTATGCCAGTTACATACAAGGATCCTGCATATTTCAGGGACCCTAAAGTTTATAA", + "output": "Non-Splice Sites", + "model_response": " Non-Splice Sites" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACGAGTACTCCTCTGAGAGACCTCCTTTGGATTGGTGGATGGTCAAGCCCCTTCTCTTTATGTCGGGGGTAGGCGGACCAATGTAAGCACAGCCAAAAAGTAAGAGCCGGCGGAACCCGCCGCTGGGTATGCTACGCCTTGCGTTCGTTTGGCCCCAAGGAGCAGACTTTTAATGCGTTACTCGTGCGGGAAAAAGGCCGACGGGGCGCGCGCGGCCTGGAGGGGGACTTGGTGATGCCAATTGGAAGCACCACATACCCCACAGATACCGCGCTGTAGGGAGCCTGTGCTGTGCCGCGC", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ATCTGTAAAACTTAGCAGTTGCCTCAGCTGAGTATGTCTTCTAAAGATAATGTCGATTGTGTATGGCTGA", + "output": "promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTTATCGCGAGCTCTTTTAGGATACCACATCAGAAAACACAGATGTTCCTAATCATGCTATGCCCACAGCTGGAAGTGCGCGGAACGCAGGCTACTTGTGATCTCAATTTTCACGTGTCATGAGCACTGTCCCACAGTCCGAGATGCAGCCAGTCATTAAAAAAATTAGTGCCAGACTAAAGCACCAGCTTATGATACAGTAACAGGCAGGAAGACTCACTACAGTCATCACCCCAACGCTAGAGGAATGAGACCCCTAACTTAGAGAGTGATCAAGTCCTGATCCTGTCCCTGGCGACT", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine the Transcription Factor Prediction of following dna sequence, The result will be one of the following: Background Sequences, Binding Sites.", + "input": "TCCCCTCCCAGACTTACTGGGCATGCAGCCGCTGAGGCCATACCCCTGCCTGAATCTGAGATTCTCTTCCTCCCGTGGGGTCCTAGCTCTTCCATCCCACT", + "output": "Binding Sites", + "model_response": " Background Sequences" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTTCATCAAATTCCGTTAAATTATGCTGAACGGGTGATGGCGCAGCTCCGCGGCCGCCAAGCCGAGGCGG", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "GGGTTTCACCCTGTTGGCCAGGCTGGTCTCAAACTCCTGACCTCAGGTGATCTGCCCACCTCAGCCTCCCAAAGTGCTGGGATTACAGGGGTGAGCCACCATGCCCGGCCATCTTTGTTCTCCCTTGAGGGCCCCTTCCTCCTCTTAAGCCCTGATAGCAGAAACCCACCCACCCACACCGTGGCTGTTTGTGTTGACAGGGGCTGACATCGCCAACATCTGCAATGAGGCTGCGCTGCACGCGGCGCGGGAGGGACACACTTCCGTGCACACTCTCAACTTCGAGTACGCCGTGGAGCGCGTCCTCGCAGGTACAGGGGGCGCGCCCTGGGTGAAGGCCCTCCTTAGCAGGGCTTGAACCCCAGAAATACCCAGGCAGGTATTGAGAGGTGAGGTGGAT", + "output": "Non-Splice Sites", + "model_response": " Non-Splice Sites" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TGATAAGCCCGGTGGGCCTCTTTGGCTCTGGCGCGAAACACCCTCGCATCTTCGCGACCATGACGCGCTGCGCTCTAAGAGGTAGTCCCCCCGGGCGTGGTGCGGACCGGATGCTGTAACAAATGAGCCGTCTAGCCCTCGTAGGTCGGCGCCGGCGCTGTGCGTGATCTTTATCAGCACTCCCGAGAGCAGGTGAAAGTTAGTAGGTTACGGCTTTTCTCTTCCACATGAAAACCAAATGCAGGGCGTCTCACACCCAGGTTTACTGATGGATGGCCGCCACAGCTGTAGGTGCTGCTA", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGGAGCTTATTTTTTTTTTTTTTTTTTTTTTTACAAATAGTCTGTGGCAACCCTCATGTGAAGGTTTTAA", + "output": "Non-promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGGCGGAAAGCGCCGAGTTTTACCCAACCAATTAGAGTCGCTCATGCTTTTAAAACCCTTTCCATCAGCA", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "ACAAAATTTCAGGTCAATATCTCTGGTGAACATAGATGCAAAAATCTTCAACAGAATACTAGCAAATCAAATCTAGCAGCACATAAACAGAATTAAAACAAACATTGTATGATCATCTCAATAGATGCAGAAAAATCTCTAGATAAATTCCAACATACCTTCATCAAAAAAACTCTTAACAAACTAGGCATTAAAGGAACATTCCTTGAAGAGCCATCTGTGAAAAACCCACAGCCAACATCTGGGTAAAATCTGGAAGCATTACCCTTGAGGATGGAAACAAGACAAGGATGCCCACTCTCAGCACTCCTATTCATAATAGTATGGGAAATCCTAGCCAGAGCAATCAGACAAGAGAAAGAAATAAAGTGCATCCAAATAGGAAAAGAAAAAGTCAAAT", + "output": "Donor Sites", + "model_response": " Donor Sites" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TTGCCGTGCCCCGGCCCCGCTCCAGTTCCCCAATGGGAGTGGTCTGACCGCGAGAACCAAGACTGAAACT", + "output": "Non-promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGCCTCCCTCCCTCTTTCCCCGCCTTGGCACTCAGTCGCCTCCCAGATGAGCACTCTCTCAGACCGCTGC", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine the Transcription Factor Prediction of following dna sequence, The result will be one of the following: Background Sequences, Binding Sites.", + "input": "ACCCCTGACTGCATCTGGGTAGCTTCGATCCACGCACATGTGGCGGGCACAGTGAGGCTGCCACCTGGTCAGACCTCGGGGCTGACCCTGCCTGACAGCAT", + "output": "Background Sequences", + "model_response": " Background Sequences" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTACACGATCGCGGGAAGATGCGTGTGATCCACATACGATGAGTAGCATCGAACGAAGATGGCGGGGCGA", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine the Transcription Factor Prediction of following dna sequence, The result will be one of the following: Background Sequences, Binding Sites.", + "input": "CGCCGCTGGCTCCGTCTGTTGGGGGGCGAACACGCCGCGGTCCTCGTCGTGGTGAGCGCAGCCACTCAGGCTGGTCCTGGGGGTGGGGCTGTAGGGGAAAG", + "output": "Binding Sites", + "model_response": " Binding Sites" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGCAGCGGCGCGGCCGAGCTCGTGGGAGACGCTATCGCAAGATGGCGTCTGCGAGGCCGCGGGATGCGGA", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GAAGCAGTGTATCACCCAGCCGTAGGGGTGGGGGCTCTCCGTACAGTATGTGCTGCTCATATGGAGCAAT", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGGGTTGCTTCACCCCTGGACCTATAGGGCTCAGGTCGATCCCGCGGAGAAAGCCGTTTATGCCCATTTG", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TAGGTCATTCGACTCACGAAAAGGAACCTAGCAAAGTTCCTCACCAAGAACCCAGACTTCCGTGTGGCTGTCGCATGTGGTTTTGGAATTCCAACGAGTGGTGCCGACCGCAGGATGCTACCCCTCACACCCTGCTGAAACGGCACGCGCGTGCGTTATCCTGCCTTACATTCGCTTCACTGCACCCCTTGGGGGAGGCTTTGTGATGTACTCATAATGTTGTCATGTGGTGCAGAGCTTAAACCCCCCAGTCCAAAATGAATCTCGTGCAGAGCTAGGTGCAGAGCTTCAGGCTGAGGC", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TCTGAGGGAGCGGAGGTCTTCCTGTGCGAATCCTCAACATCCCGGGTGGTTACCATATTTCAGTCTGTGA", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "GCTTCCCTACCTCTCCCTATTTCCCACAAAACCTACATAATAGCTATCAGGATTACCTTGCTAAAGCAGAGGTGTGATCACTGTCATTTCCCACCTTCAGGTAGCTGGAAACCCCACAAAACGAGGATCATGCCCATTTAAATCCCGCAGCATTCTCCCTCTCGTTCTACTGATGCCGACTGGATCATTGTACTTGCCAAGTACAGTTTGTTTTTCTCCCCATTACTTGTGTTTTCTAGTTTCTCCACCAGATGGTGATGACAGTACAACTGGGAGCTTTGTGCCCTGTGTTCCTAGGCCAGTGCCAGGCCCACTTGAGGCGCTCACTAACTATGATATGAACGAATGAATGCTTGAGTGAATTGGTCCCCAGAAGCGGTCAGCCTCTCCGCCAAGGCAG", + "output": "Donor Sites", + "model_response": " Donor Sites" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCGCGGGCTGCTGAAGCCGGCGACAGAAGAACCAGCCGAACTAGGAAGCGCCGCGAAGGGGCGGGGCCTGCCGGGTTGGCCCCGCCCCACTTCCCCGATGGGCTTCATCTTGGCGCCTCCAGGCGGCTCCACAAAGTGAGAAACTCCACGTATTGCGTAGGGCGCGTCTTTACGTGGGCGCCTGCCCCTTTAATTACGCCCCTGTCGGAGGCTGAAACACTTAAGGCTTCCGGGCATGCGCACAGCGTTGTGCAAATGAATGCCTTCCACTGAACCGAGGTAGACGGGGTCGAGTTTCTT", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AACCAAGCCGAGGCTTTCTGAGAAAGTGGGGCCAAACCTGTTGCCGTCTGACTGCCACGTGGCTCACTAT", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTGTCCGCCACGACTATGCTTCCCGGGCGGGGGCGGCGCTCCGGTGGGCGCGGCTGCGGGGGATCTCTAG", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine the Transcription Factor Prediction of following dna sequence, The result will be one of the following: Background Sequences, Binding Sites.", + "input": "GCGTTGGCGCCCCGGGTCAGGCTGCGAGGCCGCAATTATAGTGGGATGGGGCGGCCGCCCACGCGCCGCTTGGCGGTTCTTACGAGAACTTTGTTTGTGAC", + "output": "Binding Sites", + "model_response": " Binding Sites" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AACAAAACACAACAAAACTTTTAATGAAATTAAAATACATGAACGCTGGCAGGAGGGAGACCTGTGAAGTTCCGCGGAAAGCTCGTGGTTCGAGTTAGCATACAGTCTACAAACACCATTATGTGTTCTGAGTCGTTGAGGGTTGGTGAAAGATGGAGGTGCGAACTTTGCCCTTTCCGCGAATAAAAAAGTACACTGGGACTTTCCCTCACTTCCGCATCCCGAAGTTAGAAACAGCGGGACTCAAAACCCCGACCCCAACACCCCCTCCTAACAACACCATTTGGCTTTCACTGGCTT", + "output": "promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGGCGCAGTCCGGTGCGATTGGCCACCCCTGGCAGGAGCCGCACCTCGGACAGCAGCAGCGGCGGCGGCC", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine the Transcription Factor Prediction of following dna sequence, The result will be one of the following: Background Sequences, Binding Sites.", + "input": "GGTGTGATTGTGAAGGGATGCTGGCTCCAGCCTCAGCAGCTCAGTTTGGACTTGGCTTTGTGGCTGGGTGTGCTCTGTGATCAGGGAAGTCTGCAGCCAGG", + "output": "Background Sequences", + "model_response": " Binding Sites" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGCACCACTCGTGTCCTGGACGAAGCGGACTCTCCGCGCCACCGGCTGCTGGTGCTGGTGAAAGACCACGGTGAGCCGGCGCTGACAGCGACGGCCACGGTTCTGGTGTCGCTGGTGGAGAGTGGCCAGGCTCCAAAGGCGTCATCACGGGCGTCGGTGGGCGCCGCGGGCCCAGAGGCGGCGCTGGTGGATGTCAACGTGTACCTGATCATCGCCATCTGCGCGGTATCCAGCCTGCTGGTCCTCACGCTACTGCTGTACACAGCGCTGCGGTGCTCGGCGCCACCCACCGAGGGCGCG", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGCCAGGATGTCGTGGCAGTACCATTTACCCAGGGAATAACGTAACGTTTTACGCATTGACCTGGGCCTGGCCCTACATTTGAAATTATGGGCCCAACCACTTGACCTCTTAAAGAAATAAGACGGCCCGGGGCACCTGTTACCGCACCGTATTTCGGGTTGACAGGAAATGGAATCGAGATAGCCTCGCGCGTTTAGCTGGCCGCCGCCAGCTCAAAACAAGGACTCAATTAGCCACGTCAACACTCATCTAATCCGCCCGTCATAAGGGTGGTGAAGCGGTTGGGAAAGTGTCGGTTT", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine the Transcription Factor Prediction of following dna sequence, The result will be one of the following: Background Sequences, Binding Sites.", + "input": "AGGCGGCGGCTCCGCGGCGCCGGGCAGGGGCCGTCCGCGGGTCCGCGCCCTCGGGCGGCGGCGTCTCCTGGGTCCCGGCAGCCGTCCGCGCGCCCGGGCGC", + "output": "Background Sequences", + "model_response": " Background Sequences" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GAATTGCTTAAACCCGGGAGGTGGAGGTTGCAGTGAGCCAAGATTTAGCCACTGCACTCACTCCAGCCTAGGTGACCAGAGAAAGACTCCATCTCAAAAAAAAAAAATATTTCTGTTTCTCACTCAGCCAGTTTCCATCTCAGCTGGGCCAGCTGCACTCCACCTGGTCCTCAAGCTAATAGATTTCACTTCCCTGAAGGCCCACAAAATCTTTTTTTTCTCTTTTTTTTTTCGTTGACACAGAATCTCACTCTGTCACCCAGGCTGGAGTGAAGTGGTACGATTACGGCTCACTGCAAT", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGTTAATTAGCCCCGCACGGCGAGGGGGGAGGCGCCAGTTTTCTGGGGACACTGGCTGCCACTGTACTCC", + "output": "promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GAGTCTCCTCCTCACCCGCACATCGCGGGGCTAGGATGGGATGTCGTGGCTGCCAAATCCTCCGAGGCAACTCCTGCCTGGGACGGCCTACCACAGGATGGGCCTGAGAATCCGGAGCGCAGGCTCGGGCCGCTAGGACACCTTCCCCCGCCCGCGGACCCAGCCTTCCAGCATCACCGGGGTTCGCGGAGGAGAAAGAGGAAAGGCGTTTGGCCTGCCAAGGCCGCCGTAGCGAGGGGGCGGGGCCCGGAAGAGCAGGATGGCGGCGCGGGACAGTGACAGCGAAGAAGATCTGGTCAG", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "CCCTGGCCCGATCACCGACCACTAACCGCCACCCACTAATGCCCCGGAGACCCCAGGTACGTCCGTCCTTGGGGGAGGGGAGCAGGGAGACTCGTCGGACGCCTTACCCCCCTCACGCCATCCCCGCAGGCTGTGTGTGGCGTTACGTCCGGGCCAGCGCCTCCTACTGCCCCTACCTGCCCCCACTGTGCGACCCCAAGGACGGGCACCTGCTGGTGGACGGGTGCTACGTTAACAACGTCCCAGGTCAGCGAGCCCATCGGGCCGGCTGGGCCTCCGGCGTGTCTGAGCGTGTCTGTGCGTGTTTGTGTCTGTGTGTCCCACCGCGCAGGCTCCCTGTGGCGGTACGTGCGCGCCAGCATGACGCTGTCGGGCTACCTGCCCCCGCTGTGCGACCCCA", + "output": "Donor Sites", + "model_response": "Non-Splice Sites" + }, + { + "instruction": "Determine the Transcription Factor Prediction of following dna sequence, The result will be one of the following: Background Sequences, Binding Sites.", + "input": "GCAGCTGCGGGGCGGCCGGCGGCAGCGGGCGGCCGGCTCACGGCCAGAGCTAGCACAAACGGACTGGCGGCGCGGCGGCGAGGACGTGCAGAAAGCAGGAA", + "output": "Binding Sites", + "model_response": " Binding Sites" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AGGAGAGAAGCGACAGTCGCGGGGCCCGGAAAAGAGAGACTTAGAGCGAAGCCAAAGGCGCCTTTCTTCACAGTTTGGGAACTTACTGGTAGTAAACGCCACTCCATAGAAGCGGTGACCGGTTACCATAGAGACCGCGTCGGGGTGGGCGGAGCGTCGCTTCTGAGCTGTGCCACGGCCACATCAGCTCGGCCAGAAACGCGAACACGTCGGAACAGTGACTTCCGGCGGAAGAAGAAGCCGGTCCGGAGTTCTGGCCGACAGCAGGCGAGGAGTGGGTAGCAGCGCCTATGTGAAGTT", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "CACCAGTTATCTTTCAGCTGGGTCAAAAGGATCTACTCTAATACTCTGGGTAATTCGTAATACAAAGAATTTCACTAGAAAATGTGTAATAATAAAGTGTAATTAATTTATAAACTTATCTAAAATTCCCAAATGGTGAGACACTAAATTTTAAGTTACCTAAAGCCAGAAGCTTATCTTTTTCACACTAGTCATAGGAAATGATTTTAATTTAATGTACTGATGTTAAGTTCAAGTCTAGTATTTTTGTTAAGTTACAATAATTACAATATTTACACTGTCGACCTTAGTCACAGCCCCCAAATGGACATAACAAAGGAAAGATAGTTGAAAACACAGGTGTTTGTTTGTATACGTAAGCAATCCGGTATTTAGGAAGCATACCTACAGCTCCATTTAA", + "output": "Donor Sites", + "model_response": " Donor Sites" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "AAGGCGTAACCCTTGGTCCCGCAGAGTTGCCCCTAAAATACCTGAACCTTGCTCCGAGAGGGAGTCCTCG", + "output": "Non-promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine the Transcription Factor Prediction of following dna sequence, The result will be one of the following: Background Sequences, Binding Sites.", + "input": "CCTGGGCCACGCCTCCCGGCGCACCGACGCGCCTCTCCGGTTACTAAGCGGCCTTGGATACCTGGCCGCGGGATGCTGGGCGGCGTCAGGTGAGCGGTGGT", + "output": "Binding Sites", + "model_response": " Binding Sites" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "TGTAATATATATAGTTCAGAAAACTAAATAAATTGTTTATTATTCTGGATTCAAACTAACCCAGAATTTTTACTTTTTTAAAGATGTTTTACATAGTTTTCTCAACTACCAGCCATAATCTAAAGAAAGGAAATGATTATTTTCCTTTGATATTTGTCTAAAAATAATTATGATTTCTTTTTTTTTCCCTGTAGTCCCAGAGAAACTTGGTCTCTGACAAATATTGCTATTAAAACACATACCTTTTGAAAATGGCAGTGTCAAAAATCATCGTATTATATGTGTATTATTATATTGTTATTGTCATTTTAGTTACTTTTTGTTTTACAATTCTTGCATTGTTTTTCACAGAAACTTCCTCTTAGATACCCAGAACGCCTTCAGGTAAATATTCTATTCT", + "output": "Donor Sites", + "model_response": " Donor Sites" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CTGTCCTAGGCAAGGAGAGTTAGGTTTTTTTTGGTTGTTCGTGCTTCGGACCGATGGCACATGTTACGTG", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "CAACTCACTAATTTTACGCATGAGAAAACCTAAGCCTGGGATTAATCCTAAAATGTATAATCAGTCATGGTAGAGGCAGGCTGGAATCCAGATTCCTTGACTCACTGTGGGTATGTGATGTGTATAGTCCTTTACACTCCCAAGCTGAGTAGCAAAAATATATCTTTTCTCGCTTTTTTTTTTTTTTGAGACGGAGTCTGGCTCTGTCACTCAGGCTGGAGTGCAGTCACACGATCTCGGCTCACTGCAACCTCCACCTCCCAGGTTCAAGCAATTCTCCTGTCTCAGCCTCCCAAGTAGCTGGGATTACAGGTGCTGCCACCACCCCAGCTAAGTTTTGAATTTTTAGTAGAGACGGGGTTTCACCATGTTAGCCAGGGTGGTTTCAAACTCCTGACCT", + "output": "Donor Sites", + "model_response": " Donor Sites" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCCCTGAATTCTCATTGGCTGTCCCACTTTTCCCTCCACCCCCAGCACCGACCGCGCGCGGCTAGCCGCTGGGGAAGCGACTCTGAGTCCCGGGCTCGGAGCGCAGGCTCAGCTCCGCGCTGCGAGCGCTACGGGCGCAGGGGCGGGGAGCCGGCCCGGAGCGCAGTTTCCAGTGGGGCCGGGGTTTCACCCGGGCCCTCTCTGTTTGAACCGAACCCGACAAATGGGCGCATGACGATGGAGAGCAGGGAAATGGACTGCTATCTCCGTCGCCTCAAACAGGAGCTGGTAAGAGCGAAG", + "output": "Non-promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACGCCCGCCCTGGCCGAGCGTAGCTGGCGGACCAGAGCCGGTAGCGAGGTTGGGAGAGACGGAGCGGACC", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GAAGCCCCGGCTCAGGGGGCTGGAAAACCCCAGGCTTTTCTCAGGCAGGTTGTCCCACAGCAGAGACAAC", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GCGAGGAGGCGGGCGAGCCGGGTCCCCGGGAGCGCAGAGGAGGCTCGGAGGGGGGCGGAGAGCGGCGGGG", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TAGTTAACCGTTCCGTGCGGTAACCTTGACGGGTACCGCAGAGAAAGCTTTCCAGAGAGTATTCCACATA", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "TGCAGTTTCTGCTTTAAGATCTCCATTATTGAGTTATCCTCATCTTTGTTTTTTGTTGGCTAATGGACATAAGCTAAAGAGTGCTGTTTGTTATTTATTCTAGGAAGTGCGCTGGAGAAATCCTTAGCTGTCAGCCAAGGTGGAGAGCTCTATGAAGAGTGGCTGGAACTTAGAAACGGTTGCCTCTGCTGTTCAGTGAAGTGAGGAATGTGTTTACTGTGTACATGGTTTACTAGAAATGTTTATTGATTATATTTCCAGCTTTAATTTTCTTGAGTAATTTAACTGAATTTACACAGTTTGCTTCATTGTATTTTCAAACAAATAGAAAATAAACTTATTAGGAAGCATTTTCTTAAAGTGTTTTTGCTGTCTTTTCTATCTGCTCTAATGTTTTGGT", + "output": "Acceptor Sites", + "model_response": " Acceptor Sites" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTCGGTCCTCCAGAGACAGAGGGGCCGGAAGTTCTCTTCACGGAGCCGCGCGGCTGCGGGGGCGCAAATA", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "CTAGGCGTTTGTAGCGTCCCCGTTCCTACTTGACCTGTGTGAGGGGCCTGGTCGTGCTCAGCTTCCTATTCTGCAGGCCTTGGGAGCTGCTGGCTGCTGGATGTAGGGTGTCGGGCAGTGCTGACCGAGGCTTTGACACTAACCTGTTCTTTATCAGCTCGGCTCTTTCCCCTTTGGTGTACTCTTAATGATTTCAAAAAGTAAAGAACTCCGTTGAGATGGCTGTTAACAAAGTGGAAACCGGTCTCTGACACGGCAGAGAGACACTGCTGTTTCTCTTCTCCGGGTCCAGAAAGTGCCTGCTGGCTTGGCCCCTCCTCATTGGCGGGGGAGGTGGGGGGTGCTGGTGCTGACTGTCTGCGTCTTATGTTTCAGGCCCAAGGTCCCGGAGGCTATGGCA", + "output": "Donor Sites", + "model_response": " Donor Sites" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "GAGACAAAATAGTACTTGCTTCAAGTCCTTCCCAGGGTTGTTGTGAGCATTGAATGAGTATTACATGTTAATCACTAAGAAGAATGCTTTTTATGAAATATAAAGTAAGCTATTACAGTATATTGAAATGCTATTGTGAAATCAGATTTCTGTTTTTAGAGAAGTTCTGAAATAATGTTTCTTTTCACTAATTGTCCTAGTTTCCTAATGGAAGTTGGCTTTCTAGAAAGTAACAAATAATTGATACAAGATTATGATGGTGTTAATGAGTTTTTAAAACAGTATTCTAACCAGAATATGAACACTGTTCTAATTTAAGTGACATAATATTGTTCAAAAATGTCTTCGTGTAGATTGTTTTAACAGCTCTTTTGAGCTCTAATTGAAATGCAATAAACTG", + "output": "Donor Sites", + "model_response": " Acceptor Sites" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGGCTTGACTCGGGGGAGCAGCGGGTGGATCCTGTGACGTCAGCGGGTTCGAACCGCCGGAGCTGAGCGA", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ACATCAAGTTTGAATTTAGAGGTAGGGAATGGGTGATTTCAGGTAGGAAATTACAACTTCACTTTGAAGTTTAATTTTGTAATAAACTTCACTTAATTTTGAAGTAAATTTGCAAAGTCAGCAAAATTCTTTCAAAGTCAACCTTGCTCTGGCCTAGTTGCTTATTTACTGCTAAGCTGGATGTTTTCATTAAAAGATTAACTTTGAGACTTCAGCCTCTTCTTTAGCCTGAACTGTGGGTAGTGATGTGGTGTGTTTCCTAACACCTCCGGCAGTGAGCCAGGCTTTGAGTGGCTGCGT", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGAGAGGTGTCGTTAAGAAAGCCAGGACGCACATCACGGCCCCGTCGCTGCACTACTCTCGTCTAGGGGTCAACAGTGGAGTCGAGACTCGAAGCTTCCACGCGGCGGAACAGCGTCCCTCTCAGGCGGCGAACGGGCTAGGGAAGCGCCCGGAGGAGACCTAGCGTGAGAACTACAACTCCCGCGGAGCCCGAGGGCGAGCTGCCTGCGTAACTTCCGCTTCCGCCACCTGCCCCTCTCACCCTCTTCACTCGAACCCTACCCGGGAGCGGTCAGGCGCGTGACCCCGCGTGACCGGGG", + "output": "promoter", + "model_response": " promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCGTAGACGCTCCCAAGTAGAAACCGACATTGGTGCACCTCTTAAATGGAAAGTAGGTTCAACCCCCTACTCCCTGGACGATCGTGGTGTGAAGCTGCGCTGCAGTCACTTGTCCCATTATGAGCCCCTAGACCCAACGGCGAGTTTCTGTCCCCACACTCCCCCCCTGAGATGGCCACCCATCACGCAGGCGCTCATTTGGAGGCAAGTGCGGAGGCGCTGGGTTTCCACACTCGATTGCGCCTACGCGCGTCGCTGGCGGGTCGCGGCTTCCAAGCTCTAAATGGAGAGTTGTCCCTA", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GTAAGTCAATACCTGGGGCTTGCCTGGGCCAGGGAGCCCAGGACTGGGGTGAGGACTCAGGGGAGCAGGGAGACCACGTCCCAAGATGCCTGTAAAACTGAAACCACCTGGCCATTCTCCAGGTTGAGCCAGACCAATTTGATGGCAGATTTAGCAAATAAAAATACAGGACACCCAGTTAAATGTGAATTTCAGATGAACAGCAAATACTTTTTTAGTATTAAAAAAGTTCACATTTAGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGGCAGGCAGATCACCTGAGGTCAG", + "output": "Non-promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ATAAGTCAGAAGCATTACCATAGGTCACTTCTGAGATCTGGGACATCAACTGCCCGGAGAGTGGCTGGGT", + "output": "promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "GGAAGAAGGATTTTCTAACGTGGACTCCTGTTTGCAGGATCGCTTAGCCATATGACACAAGCGCTCTAGGCAGCCGGAACCTGAGCGTAAATGATGGCACGACAGACATTTCCCTCCCTTTAGGATTTACAGCACAGGATGGTTCTAAGACGGAGGGGCAGGGCCCAGGATTATGGGTCAGGATTAAGACGTTAGATCCAAGGGGTGGAGTGCTACAGACCAGTTTCCACAGACGTTAGAAGACCAAAACGTGTATTGCGCTTTCACGTGTCTCGGGTTCGATATCAGGTTGTCCAACCT", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGCACATCCTTGTCCCGGCAGCCGCGCAAGCGCAATCGCCCGGAACATGGCTGCCGCCGGCCGCCAACCG", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine the Transcription Factor Prediction of following dna sequence, The result will be one of the following: Background Sequences, Binding Sites.", + "input": "GCGGGCTTTGCAGGATGGCCATGGTGATGAGCGCGATGTACGAGGAGGGGGGCTTTGCCGGCTGCCGGGCATCTTCAGAGGCCGCCGCAGACCTTGGCGGT", + "output": "Background Sequences", + "model_response": " Binding Sites" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "CTTTCTGGAAAACAAGACACACTGTATCCTGTCCTCATTGGGTTTTTGCTCTGTATTTTGAACCAGGAGGACGTGGGCTGCGAGGAGAAGCTGTACTTTGGCTTGAGTGAGTACAGCAAGTCTCTGCAGTGGGGGATCACGAGCCCACTTCTGAGATGTGACGAGACTTTTGAAAAAATGGTGAACACACTCTTGGAGAGGTAAATAGTAAATAAGGCCTTCTGCTCTTCTCTCTAGCTGTACTTGCAGATCTAAAATAGCCAAAAGCCTTTTGTGTCAGTGCTACGGGGTGTTTGATGGAGCCCTTGCAGATGACGTGCAAAGGCCAAATTAAAACAGCCTTTCCTAAGATGCCCTACTATTGTTTTATGTGATCATCCAAGATCAATAAAGGAAAATA", + "output": "Acceptor Sites", + "model_response": "Splice Sites" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "CTGCGTGATCCTGGTGACCAACTCCTTTCTCTGACAGTCACTTCTTGGATGGAGTTGCTGCCAATGGCTTTGGCCACGGGGACCCAGACAGTCAAGAGGCCTGCTGCAACCCTGAAGAGTCCTCCCACAGTCTGGCTCCAGGACCACAACATTGGCACACTCCTTTCTACCTGGCTTTGTTTTCTGTGTGTGTTTTGTAGGTAGCCTGGTAGACTCGTCTGGTCATATCCTGGTCCCTGGAATCTATGATGAAGTGGTTCCTCTTACAGAAGAGGAAATAAATACATACAAAGCCATCCATCTAGACCTAGAAGAATACCGGAATAGCAGCCGGGTTGAGAAATTTCTGTTCGATACTAAGGTATGGCCACAGACTGATGGATAAGCTGGAAGAGGCATG", + "output": "Non-Splice Sites", + "model_response": " Non-Splice Sites" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "CCAGGCGAGGCCTCGTATCCCCAAGTGGGGCTGAGTCCGCCTCAGACACCCTGGGTGGGCTTCTGTCTCTTCGGGGTCCCCCGGGATAGGACTGTGTTCCTCCCAAATGCTGAGGGCAGGATGCCTGTACCCCCACAGCTGATTAGGTGGATGGGGAGGCCAGGTCAGTCCTGACCACTATGTCCTGTTTCTGTAGCAAGCGGTCTCCTGAGGCTGGCTGTGCTGCCTGAGGACCGGCTGCAGATGAAGTGGAGAGAGTCGGAGGGGAGCGGCCTCGGCTACCTGGTGCAGGTGAAGCCCATGGCAGGTGAGGACCTGCCCCTCCCAGGCCCCCTTCCCCATTAGCACGTGCCGAGGACAGTCAGTGTGGTGGCCGCAGCCACCACTCAGGCCCACAGCA", + "output": "Donor Sites", + "model_response": " Non-Splice Sites" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "CTACCAAAAACAAAACAAAACAAAAAACAAAGCCAGGCATGTGACGTGTGCCTGTAGTTCCAGCTACTTGGAGGCTGAGGCAGGAGGATCACTTGAGGCCAGGAGTTTGAGAGACCCTGTCTCTACAAAAAATTAAAATAAAAACAATAGTAACAGGCACTGAGCCCTGGGCCCTCCCCACTGGCCTTTGCAGTTTGCACTGATGCAGTACTCAAACCTCCTGAAGATCCACTTCACCTTCACCCAATTCCGGACCAGCCCGAGCCAGCAGAGCCTGGTGGATCCCATCGTCCAACTGAAAGGCCTGACGTTCACGGCCACGGGCATCCTGACAGTGGTGTAAGCAACCCCGACCCCAGCCTGGCGATGTGACTGCCACCCCCACTTCCTAACCCTGGGT", + "output": "Donor Sites", + "model_response": " Donor Sites" + }, + { + "instruction": "Determine core promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "ATCGACTGTGCCCCACCAGCCGGAACAGTTAAGCTAACGCCAGAGAAAGGCCGGGCCATGATAGCAGGAA", + "output": "Non-promoter", + "model_response": " Non-promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CACATCTAGAAAGGGGGCATCTTCGTGGACTAACTAGACCACTGGGGCAGTGAGTGAAACTCGGTATCGTCGCGGCGCCCACACTTAAGATGGCACCGGCCTGAGACTCAGCTGTGCGGCCTCTCTACCTCGGTTCCTGGTTAGTTGGCCTCATTGGTGGCGTCGGAGGGAGGAAGGTGGGCCTTCTGTCCCGTTTCCGGACCCGTCTCTATGGTGTAGGAGAAACCCGGCCCCCAGAAGATTGTGGGTGTAGTGGCCACAGCCTTACAGGCAGGCAGGGGTGGTTGGTGTCAACAGGGG", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine the Transcription Factor Prediction of following dna sequence, The result will be one of the following: Background Sequences, Binding Sites.", + "input": "GGAGTGCTTAGGAGCCCAGCGAGGGTGCCTACAAGAGGAGTCAAAGGGCAAAGGGTGAGACCCTTCCACCGGTCCAGCTGGACTCTAGCCTCAGGGACGTC", + "output": "Background Sequences", + "model_response": " Binding Sites" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CCTGAGCGGGGAGAGGAGGCCCGCGCGGAGCCCCCGGCGTCCCCCCACGCCGCCTTCCCTCCCCTTTCTTCTTCCCGCCCCTCGCCGGGGGTCGCTGCCCTGGTGTCTCACTCCCGCGGAGCTGGCGGGGGGCAGGGCGGGGGCGCGGCGTTGTTCGCCTTCCCCGAAGTGTGGCCCCGCCGCCGACCCGTAGCCCCGCGTCCGCGTCCCTTTCGGTCTCCAGGCAACCGTGGCCGGGTCCTCGCGGCTGGAGAACCCAACCGACAGTGGGCGGCAGGACGCACCGCGGACCCCGGAGAG", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "TTGCCCTCCTCCTCCCAGTTTCTCCCTCTTATTCCACACTAGACCAGCTTTGTCCACTCAGCACTCTTCTCTCTCCATCTTTTCACATGGGCCTCTCCCACCATCGGGTCTCAGCCCCATCTTCTCTCTCTCTCTCTCTCTCGGCCTCCCTCATTATTTCTCCCTCTCCCTCCGCCTCTCCATCTCTCCTTCCTCCCCCTCCCTCCCCTTCCCCCCTTTCCTCTCCCTCCCTCTCGGCTCCCGCATTTCCCCTCGGCTGCCGGCGGCTCCGACATCATGCTCCGGCTCCTCCGGCCGCTG", + "output": "promoter", + "model_response": "promoter" + }, + { + "instruction": "Determine the Splice Site Prediction of following dna sequence, The result will be one of the following: Non-Splice Sites, Acceptor Sites, Donor Sites", + "input": "ATAGGGTTTACTGTTTTTTTTTTTTATTTTTATTTCTCCCTTTTTCATCCTTTTAAACAATTCCTGAAACAATTGGTCTCCAGGGTGATGTGTGGATCTGCATGGAGCTCATGGATACATCACTAGATAAATTCTACAAACAAGTTATTGATAAAGGCCAGACAATTCCAGAGGACATCTTAGGGAAAATAGCAGTTTCTGTGAGTACATTTTGATCCCTACTGCAAGGATAATGTGAGAAATAAAGTCCCTATGTGTCTTTGGACCTCAGTATGAAGAAATACTACCTGGGCCACAGCTACTATTCGTACATGTCTGTTGACATTTATTGGTGGATGGATAAATACATGCTTTCCTTTCTGAGCACTGTGTAGAAGATTCTAGCACTGCTTTGGTTTGG", + "output": "Acceptor Sites", + "model_response": "Splice Sites" + }, + { + "instruction": "Determine promoter detection of following dna sequence, The result will be one of the following: Non-promoter, promoter.", + "input": "CGGAACGAGTGGCGGCGGACAGAGGCTTTTGGCTGCGCTGTCTGTGGGTTGTCTGGGCTTGGGGAACTGGGCACGGAGACCGTGGCGCCGGCCGCCGGTCATACAGCTCTGCTTCCAGTCGTCGGGCAAAAAGCTGAGGCGCTTCTCCTTTAATTGCGGTGGGCGGGGCGTGCCGACGCGGCCCGCGTTGCTATTGGTGGCCGTTGGGGGCGCGGAAACTGAACGGGCCGCAACGGCCACCGCAGCTGCCTTCGGCGCCGCCAGCCCCGCCATGGCTGCGCCCTGCGCGGTAAGCCCAGC", + "output": "promoter", + "model_response": "promoter" + } +] \ No newline at end of file diff --git a/04-gene-sft/logs/events.out.tfevents.1736573118.autodl-container-a17a40aec7-38bf4077.2399.0 b/04-gene-sft/logs/events.out.tfevents.1736573118.autodl-container-a17a40aec7-38bf4077.2399.0 new file mode 100644 index 0000000000000000000000000000000000000000..3f34ef42125b08fc59539d5a4585fd2c54b9c140 --- /dev/null +++ b/04-gene-sft/logs/events.out.tfevents.1736573118.autodl-container-a17a40aec7-38bf4077.2399.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0a8048b630821833e47bd0cefe8d11ed959856eb05ba418fd28f21c50cf3e6f4 +size 5293 diff --git a/04-gene-sft/logs/events.out.tfevents.1736573372.autodl-container-a17a40aec7-38bf4077.2399.1 b/04-gene-sft/logs/events.out.tfevents.1736573372.autodl-container-a17a40aec7-38bf4077.2399.1 new file mode 100644 index 0000000000000000000000000000000000000000..5b93febc4cb909327900f2c4645b19c5fbf55451 --- /dev/null +++ b/04-gene-sft/logs/events.out.tfevents.1736573372.autodl-container-a17a40aec7-38bf4077.2399.1 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5a7b73da3f319220824779825e67cac5d0a0e7931998898bff122b86be19444 +size 5293 diff --git a/04-gene-sft/logs/events.out.tfevents.1736573682.autodl-container-a17a40aec7-38bf4077.2399.2 b/04-gene-sft/logs/events.out.tfevents.1736573682.autodl-container-a17a40aec7-38bf4077.2399.2 new file mode 100644 index 0000000000000000000000000000000000000000..6660ecaa7f6c5388c3b690c3588ff17572c418c0 --- /dev/null +++ b/04-gene-sft/logs/events.out.tfevents.1736573682.autodl-container-a17a40aec7-38bf4077.2399.2 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ba1c5ee7c7b0028cfeb4e68beea2697b413a468d189c84fc266cc79e0471e26 +size 15356 diff --git a/04-gene-sft/logs/events.out.tfevents.1736574797.autodl-container-a17a40aec7-38bf4077.2399.3 b/04-gene-sft/logs/events.out.tfevents.1736574797.autodl-container-a17a40aec7-38bf4077.2399.3 new file mode 100644 index 0000000000000000000000000000000000000000..0c63ffd168cf843c9ac33af6de7d69e5a3720092 --- /dev/null +++ b/04-gene-sft/logs/events.out.tfevents.1736574797.autodl-container-a17a40aec7-38bf4077.2399.3 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:80c49dd91e9db9617b3659fadf479c82e4bb5257b4302f20e6ef9e739b195106 +size 38983 diff --git a/04-gene-sft/merge_llama_with_dna_lora.py b/04-gene-sft/merge_llama_with_dna_lora.py index 2bdef8906cbae28f9342c6e17489a1a4ce32dbc3..2994b9f8d0bca0d91050b86f534b140f27e03ae7 100644 --- a/04-gene-sft/merge_llama_with_dna_lora.py +++ b/04-gene-sft/merge_llama_with_dna_lora.py @@ -6,15 +6,16 @@ python merge_llama_with_chinese_lora.py \ --output_type [pth|huggingface] \ --output_dir path/to/output/dir """ - +import subprocess import os - -# 设置环境变量 -os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' - -# 打印环境变量以确认设置成功 -print(os.environ.get('HF_ENDPOINT')) - +# 设置环境变量, autodl一般区域 +result = subprocess.run('bash -c "source /etc/network_turbo && env | grep proxy"', shell=True, capture_output=True, text=True) +output = result.stdout +for line in output.splitlines(): + if '=' in line: + var, value = line.split('=', 1) + os.environ[var] = value + import argparse import json import os diff --git a/04-gene-sft/merged_gene_eng_tokenizer_hf/.ipynb_checkpoints/special_tokens_map-checkpoint.json b/04-gene-sft/merged_gene_eng_tokenizer_hf/.ipynb_checkpoints/special_tokens_map-checkpoint.json new file mode 100644 index 0000000000000000000000000000000000000000..451134b2ddc2e78555d1e857518c54b4bdc2e87d --- /dev/null +++ b/04-gene-sft/merged_gene_eng_tokenizer_hf/.ipynb_checkpoints/special_tokens_map-checkpoint.json @@ -0,0 +1,23 @@ +{ + "bos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "unk_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/04-gene-sft/merged_gene_eng_tokenizer_hf/.ipynb_checkpoints/tokenizer_config-checkpoint.json b/04-gene-sft/merged_gene_eng_tokenizer_hf/.ipynb_checkpoints/tokenizer_config-checkpoint.json new file mode 100644 index 0000000000000000000000000000000000000000..0b7e9f15c526cf6df1201d2ea05ee072743d224e --- /dev/null +++ b/04-gene-sft/merged_gene_eng_tokenizer_hf/.ipynb_checkpoints/tokenizer_config-checkpoint.json @@ -0,0 +1,43 @@ +{ + "add_bos_token": true, + "add_eos_token": false, + "add_prefix_space": true, + "added_tokens_decoder": { + "0": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "1": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "2": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "bos_token": "", + "clean_up_tokenization_spaces": false, + "eos_token": "", + "extra_special_tokens": {}, + "legacy": true, + "model_max_length": 1000000000000000019884624838656, + "pad_token": null, + "sp_model_kwargs": {}, + "spaces_between_special_tokens": false, + "tokenizer_class": "LlamaTokenizer", + "unk_token": "", + "use_default_system_prompt": false +} diff --git a/04-gene-sft/peft/.gitignore b/04-gene-sft/peft/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..da99824aa34e856e9aeb2e74bbfc324e7acc6d36 --- /dev/null +++ b/04-gene-sft/peft/.gitignore @@ -0,0 +1,141 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# VSCode +.vscode + +# IntelliJ +.idea + +# Mac .DS_Store +.DS_Store + +# More test things +wandb \ No newline at end of file diff --git a/04-gene-sft/peft/LICENSE b/04-gene-sft/peft/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/04-gene-sft/peft/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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🤗 PEFT

+

+

State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) methods

+

+ +Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters. Fine-tuning large-scale PLMs is often prohibitively costly. In this regard, PEFT methods only fine-tune a small number of (extra) model parameters, thereby greatly decreasing the computational and storage costs. Recent State-of-the-Art PEFT techniques achieve performance comparable to that of full fine-tuning. + +Seamlessly integrated with 🤗 Accelerate for large scale models leveraging DeepSpeed and Big Model Inference. + +Supported methods: + +1. LoRA: [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685.pdf) +2. Prefix Tuning: [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://aclanthology.org/2021.acl-long.353/), [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://arxiv.org/pdf/2110.07602.pdf) +3. P-Tuning: [GPT Understands, Too](https://arxiv.org/pdf/2103.10385.pdf) +4. Prompt Tuning: [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/pdf/2104.08691.pdf) + +## Getting started + +```python +from transformers import AutoModelForSeq2SeqLM +from peft import get_peft_config, get_peft_model, LoraConfig, TaskType +model_name_or_path = "bigscience/mt0-large" +tokenizer_name_or_path = "bigscience/mt0-large" + +peft_config = LoraConfig( + task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1 +) + +model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path) +model = get_peft_model(model, peft_config) +model.print_trainable_parameters() +# output: trainable params: 2359296 || all params: 1231940608 || trainable%: 0.19151053100118282 +``` + +## Use Cases + +### Get comparable performance to full finetuning by adapting LLMs to downstream tasks using consumer hardware + +GPU memory required for adapting LLMs on the few-shot dataset [`ought/raft/twitter_complaints`](https://huggingface.co/datasets/ought/raft/viewer/twitter_complaints). Here, settings considered +are full finetuning, PEFT-LoRA using plain PyTorch and PEFT-LoRA using DeepSpeed with CPU Offloading. + +Hardware: Single A100 80GB GPU with CPU RAM above 64GB + +| Model | Full Finetuning | PEFT-LoRA PyTorch | PEFT-LoRA DeepSpeed with CPU Offloading | +| --------- | ---- | ---- | ---- | +| bigscience/T0_3B (3B params) | 47.14GB GPU / 2.96GB CPU | 14.4GB GPU / 2.96GB CPU | 9.8GB GPU / 17.8GB CPU | +| bigscience/mt0-xxl (12B params) | OOM GPU | 56GB GPU / 3GB CPU | 22GB GPU / 52GB CPU | +| bigscience/bloomz-7b1 (7B params) | OOM GPU | 32GB GPU / 3.8GB CPU | 18.1GB GPU / 35GB CPU | + +Performance of PEFT-LoRA tuned [`bigscience/T0_3B`](https://huggingface.co/bigscience/T0_3B) on [`ought/raft/twitter_complaints`](https://huggingface.co/datasets/ought/raft/viewer/twitter_complaints) leaderboard. +A point to note is that we didn't try to sequeeze performance by playing around with input instruction templates, LoRA hyperparams and other training related hyperparams. Also, we didn't use the larger 13B [mt0-xxl](https://huggingface.co/bigscience/mt0-xxl) model. +So, we are already seeing comparable performance to SoTA with parameter efficient tuning. Also, the final checkpoint size is just `19MB` in comparison to `11GB` size of the backbone [`bigscience/T0_3B`](https://huggingface.co/bigscience/T0_3B) model. + +| Submission Name | Accuracy | +| --------- | ---- | +| Human baseline (crowdsourced) | 0.897 | +| Flan-T5 | 0.892 | +| lora-t0-3b | 0.863 | + +**Therefore, we can see that performance comparable to SoTA is achievable by PEFT methods with consumer hardware such as 16GB and 24GB GPUs.** + +### Parameter Efficient Tuning of Diffusion Models + +GPU memory required by different settings during training is given below. The final checkpoint size is `8.8 MB`. + +Hardware: Single A100 80GB GPU with CPU RAM above 64GB + +| Model | Full Finetuning | PEFT-LoRA | PEFT-LoRA with Gradient Checkpoitning | +| --------- | ---- | ---- | ---- | +| CompVis/stable-diffusion-v1-4 | 27.5GB GPU / 3.97GB CPU | 15.5GB GPU / 3.84GB CPU | 8.12GB GPU / 3.77GB CPU | + + +**Training** +An example of using LoRA for parameter efficient dreambooth training is given in `~examples/lora_dreambooth/train_dreambooth.py` + +```bash +export MODEL_NAME= "CompVis/stable-diffusion-v1-4" #"stabilityai/stable-diffusion-2-1" +export INSTANCE_DIR="path-to-instance-images" +export CLASS_DIR="path-to-class-images" +export OUTPUT_DIR="path-to-save-model" + +accelerate launch train_dreambooth.py \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --instance_data_dir=$INSTANCE_DIR \ + --class_data_dir=$CLASS_DIR \ + --output_dir=$OUTPUT_DIR \ + --train_text_encoder \ + --with_prior_preservation --prior_loss_weight=1.0 \ + --instance_prompt="a photo of sks dog" \ + --class_prompt="a photo of dog" \ + --resolution=512 \ + --train_batch_size=1 \ + --lr_scheduler="constant" \ + --lr_warmup_steps=0 \ + --num_class_images=200 \ + --use_lora \ + --lora_r 16 \ + --lora_alpha 27 \ + --lora_text_encoder_r 16 \ + --lora_text_encoder_alpha 17 \ + --learning_rate=1e-4 \ + --gradient_accumulation_steps=1 \ + --gradient_checkpointing \ + --max_train_steps=800 +``` + +Try out the 🤗 Gradio Space which should run seamlessly on a T4 instance: +[smangrul/peft-lora-sd-dreambooth](https://huggingface.co/spaces/smangrul/peft-lora-sd-dreambooth). + +![peft lora dreambooth gradio space](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/peft_lora_dreambooth_gradio_space.png) + +### Parameter Efficient Tuning of LLMs for RLHF components such as Ranker and Policy +- Here is an exmaple in [trl](https://github.com/lvwerra/trl) library using PEFT+INT8 for tuning policy model: [gpt2-sentiment_peft.py](https://github.com/lvwerra/trl/blob/main/examples/sentiment/scripts/gpt2-sentiment_peft.py) +- Example using PEFT for both reward model and policy [ToDo] + +### INT8 training of large models in Colab using PEFT LoRA and bits_and_bytes + +- Here is now a demo on how to fine tune [OPT-6.7b](https://huggingface.co/facebook/opt-6.7b) (14GB in fp16) in a Google colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1jCkpikz0J2o20FBQmYmAGdiKmJGOMo-o?usp=sharing) + +- Here is now a demo on how to fine tune [whishper-large](openai/whisper-large-v2) (1.5B params) (14GB in fp16) in a Google colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DOkD_5OUjFa0r5Ik3SgywJLJtEo2qLxO?usp=sharing) and [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1vhF8yueFqha3Y3CpTHN6q9EVcII9EYzs?usp=sharing) + +### Save compute and storage even for medium and small models + +Save storage by avoiding full finetuning of models on each of the downstream tasks/datasets, +With PEFT methods, users only need to store tiny checkpoints in the order of `MBs` all the while retaining +performance comparable to full finetuning. + +An example of using LoRA for the task of adapting `LayoutLMForTokenClassification` on `FUNSD` dataset is given in `~examples/token_classification/PEFT_LoRA_LayoutLMForTokenClassification_on_FUNSD.py`. We can observe that with only `0.62 %` of parameters being trainable, we achieve performance (F1 0.777) comparable to full finetuning (F1 0.786) (without any hyerparam tuning runs for extracting more performance), and the checkpoint of this is only `2.8MB`. Now, if there are `N` such datasets, just have these PEFT models one for each dataset and save a lot of storage without having to worry about the problem of catastrophic forgetting or overfitting of backbone/base model. + +Another example is fine-tuning [`roberta-large`](https://huggingface.co/roberta-large) on [`MRPC` GLUE](https://huggingface.co/datasets/glue/viewer/mrpc) dataset suing differenct PEFT methods. The notebooks are given in `~examples/sequence_classification`. + + +## PEFT + 🤗 Accelerate + +PEFT models work with 🤗 Accelerate out of the box. Use 🤗 Accelerate for Distributed training on various hardware such as GPUs, Apple Silicon devices etc during training. +Use 🤗 Accelerate for inferencing on consumer hardware with small resources. + +### Example of PEFT model training using 🤗 Accelerate's DeepSpeed integration + +DeepSpeed version required `v0.8.0`. An example is provided in `~examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py`. + a. First, run `accelerate config --config_file ds_zero3_cpu.yaml` and answer the questionnaire. + Below are the contents of the config file. + ```yaml + compute_environment: LOCAL_MACHINE + deepspeed_config: + gradient_accumulation_steps: 1 + gradient_clipping: 1.0 + offload_optimizer_device: cpu + offload_param_device: cpu + zero3_init_flag: true + zero3_save_16bit_model: true + zero_stage: 3 + distributed_type: DEEPSPEED + downcast_bf16: 'no' + dynamo_backend: 'NO' + fsdp_config: {} + machine_rank: 0 + main_training_function: main + megatron_lm_config: {} + mixed_precision: 'no' + num_machines: 1 + num_processes: 1 + rdzv_backend: static + same_network: true + use_cpu: false + ``` + b. run the below command to launch the example script + ```bash + accelerate launch --config_file ds_zero3_cpu.yaml examples/peft_lora_seq2seq_accelerate_ds_zero3_offload.py + ``` + + c. output logs: + ```bash + GPU Memory before entering the train : 1916 + GPU Memory consumed at the end of the train (end-begin): 66 + GPU Peak Memory consumed during the train (max-begin): 7488 + GPU Total Peak Memory consumed during the train (max): 9404 + CPU Memory before entering the train : 19411 + CPU Memory consumed at the end of the train (end-begin): 0 + CPU Peak Memory consumed during the train (max-begin): 0 + CPU Total Peak Memory consumed during the train (max): 19411 + epoch=4: train_ppl=tensor(1.0705, device='cuda:0') train_epoch_loss=tensor(0.0681, device='cuda:0') + 100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:27<00:00, 3.92s/it] + GPU Memory before entering the eval : 1982 + GPU Memory consumed at the end of the eval (end-begin): -66 + GPU Peak Memory consumed during the eval (max-begin): 672 + GPU Total Peak Memory consumed during the eval (max): 2654 + CPU Memory before entering the eval : 19411 + CPU Memory consumed at the end of the eval (end-begin): 0 + CPU Peak Memory consumed during the eval (max-begin): 0 + CPU Total Peak Memory consumed during the eval (max): 19411 + accuracy=100.0 + eval_preds[:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint'] + dataset['train'][label_column][:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint'] + ``` + +### Example of PEFT model inference using 🤗 Accelerate's Big Model Inferencing capabilities +An example is provided in `~examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb`. + + +## Models support matrix + +### Causal Language Modeling +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +|--------------| ---- | ---- | ---- | ---- | +| GPT-2 | ✅ | ✅ | ✅ | ✅ | +| Bloom | ✅ | ✅ | ✅ | ✅ | +| OPT | ✅ | ✅ | ✅ | ✅ | +| GPT-Neo | ✅ | ✅ | ✅ | ✅ | +| GPT-J | ✅ | ✅ | ✅ | ✅ | +| GPT-NeoX-20B | ✅ | ✅ | ✅ | ✅ | +| LLaMA | ✅ | ✅ | ✅ | ✅ | +| ChatGLM | ✅ | ✅ | ✅ | ✅ | + +### Conditional Generation +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +| --------- | ---- | ---- | ---- | ---- | +| T5 | ✅ | ✅ | ✅ | ✅ | +| BART | ✅ | ✅ | ✅ | ✅ | + +### Sequence Classification +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +| --------- | ---- | ---- | ---- | ---- | +| BERT | ✅ | ✅ | ✅ | ✅ | +| RoBERTa | ✅ | ✅ | ✅ | ✅ | +| GPT-2 | ✅ | ✅ | ✅ | ✅ | +| Bloom | ✅ | ✅ | ✅ | ✅ | +| OPT | ✅ | ✅ | ✅ | ✅ | +| GPT-Neo | ✅ | ✅ | ✅ | ✅ | +| GPT-J | ✅ | ✅ | ✅ | ✅ | +| Deberta | ✅ | | ✅ | ✅ | +| Deberta-v2 | ✅ | | ✅ | ✅ | + +### Token Classification +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +| --------- | ---- | ---- | ---- | ---- | +| BERT | ✅ | ✅ | | | +| RoBERTa | ✅ | ✅ | | | +| GPT-2 | ✅ | ✅ | | | +| Bloom | ✅ | ✅ | | | +| OPT | ✅ | ✅ | | | +| GPT-Neo | ✅ | ✅ | | | +| GPT-J | ✅ | ✅ | | | +| Deberta | ✅ | | | | +| Deberta-v2 | ✅ | | | | + +### Text-to-Image Generation + +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +| --------- | ---- | ---- | ---- | ---- | +| Stable Diffusion | ✅ | | | | + + +### Image Classification + +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +| --------- | ---- | ---- | ---- | ---- | +| ViT | ✅ | | | | +| Swin | ✅ | | | | + +___Note that we have tested LoRA for [ViT](https://huggingface.co/docs/transformers/model_doc/vit) and [Swin](https://huggingface.co/docs/transformers/model_doc/swin) for fine-tuning on image classification. However, it should be possible to use LoRA for any compatible model [provided](https://huggingface.co/models?pipeline_tag=image-classification&sort=downloads&search=vit) by 🤗 Transformers. Check out the respective +examples to learn more. If you run into problems, please open an issue.___ + +The same principle applies to our [segmentation models](https://huggingface.co/models?pipeline_tag=image-segmentation&sort=downloads) as well. + +### Semantic Segmentation + +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +| --------- | ---- | ---- | ---- | ---- | +| SegFormer | ✅ | | | | + + +## Caveats: + +1. Below is an example of using PyTorch FSDP for training. However, it doesn't lead to +any GPU memory savings. Please refer issue [[FSDP] FSDP with CPU offload consumes 1.65X more GPU memory when training models with most of the params frozen](https://github.com/pytorch/pytorch/issues/91165). + + ```python + from peft.utils.other import fsdp_auto_wrap_policy + + ... + + if os.environ.get("ACCELERATE_USE_FSDP", None) is not None: + accelerator.state.fsdp_plugin.auto_wrap_policy = fsdp_auto_wrap_policy(model) + + model = accelerator.prepare(model) + ``` + + Example of parameter efficient tuning with [`mt0-xxl`](https://huggingface.co/bigscience/mt0-xxl) base model using 🤗 Accelerate is provided in `~examples/conditional_generation/peft_lora_seq2seq_accelerate_fsdp.py`. + a. First, run `accelerate config --config_file fsdp_config.yaml` and answer the questionnaire. + Below are the contents of the config file. + ```yaml + command_file: null + commands: null + compute_environment: LOCAL_MACHINE + deepspeed_config: {} + distributed_type: FSDP + downcast_bf16: 'no' + dynamo_backend: 'NO' + fsdp_config: + fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP + fsdp_backward_prefetch_policy: BACKWARD_PRE + fsdp_offload_params: true + fsdp_sharding_strategy: 1 + fsdp_state_dict_type: FULL_STATE_DICT + fsdp_transformer_layer_cls_to_wrap: T5Block + gpu_ids: null + machine_rank: 0 + main_process_ip: null + main_process_port: null + main_training_function: main + megatron_lm_config: {} + mixed_precision: 'no' + num_machines: 1 + num_processes: 2 + rdzv_backend: static + same_network: true + tpu_name: null + tpu_zone: null + use_cpu: false + ``` + b. run the below command to launch the example script + ```bash + accelerate launch --config_file fsdp_config.yaml examples/peft_lora_seq2seq_accelerate_fsdp.py + ``` + +2. When using `P_TUNING` or `PROMPT_TUNING` with `SEQ_2_SEQ` task, remember to remove the `num_virtual_token` virtual prompt predictions from the left side of the model outputs during evaluations. + +3. For encoder-decoder models, `P_TUNING` or `PROMPT_TUNING` doesn't support `generate` functionality of transformers because `generate` strictly requires `decoder_input_ids` but +`P_TUNING`/`PROMPT_TUNING` appends soft prompt embeddings to `input_embeds` to create +new `input_embeds` to be given to the model. Therefore, `generate` doesn't support this yet. + +4. When using ZeRO3 with zero3_init_flag=True, if you find the gpu memory increase with training steps. we might need to set zero3_init_flag=false in accelerate config.yaml. The related issue is [[BUG] memory leak under zero.Init](https://github.com/microsoft/DeepSpeed/issues/2637) +## Backlog: +1. Explore and possibly integrate `(IA)^3` +2. Add tests +3. Add more use cases and examples + +## Citing 🤗 PEFT + +If you use 🤗 PEFT in your publication, please cite it by using the following BibTeX entry. + +```bibtex +@Misc{peft, + title = {PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods}, + author = {Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, Sayak Paul}, + howpublished = {\url{https://github.com/huggingface/peft}}, + year = {2022} +} +``` diff --git a/04-gene-sft/peft/build/lib/peft/__init__.py b/04-gene-sft/peft/build/lib/peft/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e141347f1e5c8fb40dcc3a4a52fcb1a041b18852 --- /dev/null +++ b/04-gene-sft/peft/build/lib/peft/__init__.py @@ -0,0 +1,53 @@ +# flake8: noqa +# There's no way to ignore "F401 '...' imported but unused" warnings in this +# module, but to preserve other warnings. So, don't check this module at all. + +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +__version__ = "0.3.0.dev0" + +from .mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPING, PEFT_TYPE_TO_CONFIG_MAPPING, get_peft_config, get_peft_model +from .peft_model import ( + PeftModel, + PeftModelForCausalLM, + PeftModelForSeq2SeqLM, + PeftModelForSequenceClassification, + PeftModelForTokenClassification, +) +from .tuners import ( + LoraConfig, + LoraModel, + PrefixEncoder, + PrefixTuningConfig, + PromptEmbedding, + PromptEncoder, + PromptEncoderConfig, + PromptEncoderReparameterizationType, + PromptTuningConfig, + PromptTuningInit, +) +from .utils import ( + TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING, + PeftConfig, + PeftType, + PromptLearningConfig, + TaskType, + bloom_model_postprocess_past_key_value, + get_peft_model_state_dict, + prepare_model_for_int8_training, + set_peft_model_state_dict, + shift_tokens_right, +) diff --git a/04-gene-sft/peft/build/lib/peft/mapping.py b/04-gene-sft/peft/build/lib/peft/mapping.py new file mode 100644 index 0000000000000000000000000000000000000000..dbb9f3609fdf5d171b0a4bb21716f692feefc9d4 --- /dev/null +++ b/04-gene-sft/peft/build/lib/peft/mapping.py @@ -0,0 +1,147 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .peft_model import ( + PeftModel, + PeftModelForCausalLM, + PeftModelForSeq2SeqLM, + PeftModelForSequenceClassification, + PeftModelForTokenClassification, +) +from .tuners import LoraConfig, PrefixTuningConfig, PromptEncoderConfig, PromptTuningConfig +from .utils import PromptLearningConfig + + +MODEL_TYPE_TO_PEFT_MODEL_MAPPING = { + "SEQ_CLS": PeftModelForSequenceClassification, + "SEQ_2_SEQ_LM": PeftModelForSeq2SeqLM, + "CAUSAL_LM": PeftModelForCausalLM, + "TOKEN_CLS": PeftModelForTokenClassification, +} + +PEFT_TYPE_TO_CONFIG_MAPPING = { + "PROMPT_TUNING": PromptTuningConfig, + "PREFIX_TUNING": PrefixTuningConfig, + "P_TUNING": PromptEncoderConfig, + "LORA": LoraConfig, +} + +TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING = { + "t5": ["q", "v"], + "mt5": ["q", "v"], + "bart": ["q_proj", "v_proj"], + "gpt2": ["c_attn"], + "bloom": ["query_key_value"], + "opt": ["q_proj", "v_proj"], + "gptj": ["q_proj", "v_proj"], + "gpt_neox": ["query_key_value"], + "gpt_neo": ["q_proj", "v_proj"], + "bert": ["query", "value"], + "roberta": ["query", "value"], + "xlm-roberta": ["query", "value"], + "electra": ["query", "value"], + "deberta-v2": ["query_proj", "value_proj"], + "deberta": ["in_proj"], + "layoutlm": ["query", "value"], + "llama": ["q_proj", "v_proj"], + "chatglm": ["query_key_value"], +} + + +def get_peft_config(config_dict): + """ + Returns a Peft config object from a dictionary. + + Args: + config_dict (`Dict[str, Any]`): Dictionary containing the configuration parameters. + """ + + return PEFT_TYPE_TO_CONFIG_MAPPING[config_dict["peft_type"]](**config_dict) + + +def _prepare_prompt_learning_config(peft_config, model_config): + if peft_config.num_layers is None: + if "num_hidden_layers" in model_config: + num_layers = model_config["num_hidden_layers"] + elif "num_layers" in model_config: + num_layers = model_config["num_layers"] + elif "n_layer" in model_config: + num_layers = model_config["n_layer"] + else: + raise ValueError("Please specify `num_layers` in `peft_config`") + peft_config.num_layers = num_layers + + if peft_config.token_dim is None: + if "hidden_size" in model_config: + token_dim = model_config["hidden_size"] + elif "n_embd" in model_config: + token_dim = model_config["n_embd"] + elif "d_model" in model_config: + token_dim = model_config["d_model"] + else: + raise ValueError("Please specify `token_dim` in `peft_config`") + peft_config.token_dim = token_dim + + if peft_config.num_attention_heads is None: + if "num_attention_heads" in model_config: + num_attention_heads = model_config["num_attention_heads"] + elif "n_head" in model_config: + num_attention_heads = model_config["n_head"] + elif "num_heads" in model_config: + num_attention_heads = model_config["num_heads"] + elif "encoder_attention_heads" in model_config: + num_attention_heads = model_config["encoder_attention_heads"] + else: + raise ValueError("Please specify `num_attention_heads` in `peft_config`") + peft_config.num_attention_heads = num_attention_heads + + if getattr(peft_config, "encoder_hidden_size", None) is None: + setattr(peft_config, "encoder_hidden_size", token_dim) + + return peft_config + + +def _prepare_lora_config(peft_config, model_config): + if peft_config.target_modules is None: + if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING: + raise ValueError("Please specify `target_modules` in `peft_config`") + peft_config.target_modules = TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING[model_config["model_type"]] + if len(peft_config.target_modules) == 1: + peft_config.fan_in_fan_out = True + peft_config.enable_lora = [True, False, True] + if peft_config.inference_mode: + peft_config.merge_weights = True + return peft_config + + +def get_peft_model(model, peft_config): + """ + Returns a Peft model object from a model and a config. + + Args: + model ([`transformers.PreTrainedModel`]): Model to be wrapped. + peft_config ([`PeftConfig`]): Configuration object containing the parameters of the Peft model. + """ + + model_config = model.config.to_dict() + peft_config.base_model_name_or_path = model.__dict__.get("name_or_path", None) + if peft_config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys(): + peft_config = _prepare_lora_config(peft_config, model_config) + return PeftModel(model, peft_config) + if not isinstance(peft_config, PromptLearningConfig): + peft_config = _prepare_lora_config(peft_config, model_config) + else: + peft_config = _prepare_prompt_learning_config(peft_config, model_config) + return MODEL_TYPE_TO_PEFT_MODEL_MAPPING[peft_config.task_type](model, peft_config) diff --git a/04-gene-sft/peft/build/lib/peft/peft_model.py b/04-gene-sft/peft/build/lib/peft/peft_model.py new file mode 100644 index 0000000000000000000000000000000000000000..f73a66ac0890b78437d66e9c46372887b8e9279e --- /dev/null +++ b/04-gene-sft/peft/build/lib/peft/peft_model.py @@ -0,0 +1,947 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import inspect +import os +import warnings +from contextlib import contextmanager + +import torch +from accelerate import dispatch_model, infer_auto_device_map +from accelerate.hooks import AlignDevicesHook, add_hook_to_module, remove_hook_from_submodules +from accelerate.utils import get_balanced_memory +from huggingface_hub import hf_hub_download +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from transformers import PreTrainedModel +from transformers.modeling_outputs import SequenceClassifierOutput, TokenClassifierOutput +from transformers.utils import PushToHubMixin + +from .tuners import LoraModel, PrefixEncoder, PromptEmbedding, PromptEncoder +from .utils import ( + TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING, + WEIGHTS_NAME, + PeftConfig, + PeftType, + PromptLearningConfig, + TaskType, + _set_trainable, + get_peft_model_state_dict, + set_peft_model_state_dict, + shift_tokens_right, +) + + +class PeftModel(PushToHubMixin, torch.nn.Module): + """ + Parameter-Efficient Fine-Tuning Model. Base model encompassing various Peft methods. + + Args: + model ([`PreTrainedModel`]): The base transformer model used for Peft. + peft_config ([`PeftConfig`]): The configuration of the Peft model. + + + **Attributes**: + - **base_model** ([`PreTrainedModel`]) -- The base transformer model used for Peft. + - **peft_config** ([`PeftConfig`]) -- The configuration of the Peft model. + - **modules_to_save** (`list` of `str`) -- The list of sub-module names to save when + saving the model. + - **prompt_encoder** ([`PromptEncoder`]) -- The prompt encoder used for Peft if + `isinstance(self.peft_config, PromptLearningConfig)`. + - **prompt_tokens** (`torch.Tensor`) -- The virtual prompt tokens used for Peft if + `isinstance(self.peft_config, PromptLearningConfig)`. + - **transformer_backbone_name** (`str`) -- The name of the transformer + backbone in the base model if `isinstance(self.peft_config, PromptLearningConfig)`. + - **word_embeddings** (`torch.nn.Embedding`) -- The word embeddings of the transformer backbone + in the base model if `isinstance(self.peft_config, PromptLearningConfig)`. + """ + + def __init__(self, model, peft_config: PeftConfig): + super().__init__() + self.peft_config = peft_config + self.base_model = model + self.config = self.base_model.config + self.modules_to_save = None + if isinstance(self.peft_config, PromptLearningConfig): + self._setup_prompt_encoder() + else: + self.base_model = LoraModel(peft_config, model) + if getattr(self.peft_config, "modules_to_save", None) is not None: + self.modules_to_save = self.peft_config.modules_to_save + _set_trainable(self) + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + def save_pretrained(self, save_directory, **kwargs): + r""" + Args: + This function saves the adapter model and the adapter configuration files to a directory, so that it can be + re-loaded using the `LoraModel.from_pretrained` class method, and also used by the `LoraModel.push_to_hub` + method. + save_directory (`str`): + Directory where the adapter model and configuration files will be saved (will be created if it does not + exist). + **kwargs: + Additional keyword arguments passed along to the `push_to_hub` method. + """ + if os.path.isfile(save_directory): + raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file") + os.makedirs(save_directory, exist_ok=True) + + # save only the trainable weights + output_state_dict = get_peft_model_state_dict(self, kwargs.get("state_dict", None)) + torch.save(output_state_dict, os.path.join(save_directory, WEIGHTS_NAME)) + + # save the config and change the inference mode to `True` + if self.peft_config.base_model_name_or_path is None: + self.peft_config.base_model_name_or_path = ( + self.base_model.__dict__.get("name_or_path", None) + if isinstance(self.peft_config, PromptLearningConfig) + else self.base_model.model.__dict__.get("name_or_path", None) + ) + inference_mode = self.peft_config.inference_mode + self.peft_config.inference_mode = True + self.peft_config.save_pretrained(save_directory) + self.peft_config.inference_mode = inference_mode + + @classmethod + def from_pretrained(cls, model, model_id, **kwargs): + r""" + Args: + Instantiate a `LoraModel` from a pretrained Lora configuration and weights. + model (`transformers.PreTrainedModel`): + The model to be adapted. The model should be initialized with the `from_pretrained` method. from + `transformers` library. + model_id (`str`): + The name of the Lora configuration to use. Can be either: + - A string, the `model id` of a Lora configuration hosted inside a model repo on + huggingface Hub + - A path to a directory containing a Lora configuration file saved using the + `save_pretrained` method, e.g., ``./my_lora_config_directory/``. + """ + from .mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPING, PEFT_TYPE_TO_CONFIG_MAPPING + + # load the config + config = PEFT_TYPE_TO_CONFIG_MAPPING[PeftConfig.from_pretrained(model_id).peft_type].from_pretrained(model_id) + + if getattr(model, "hf_device_map", None) is not None: + remove_hook_from_submodules(model) + + if config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys(): + model = cls(model, config) + else: + model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](model, config) + + # load weights if any + if os.path.exists(os.path.join(model_id, WEIGHTS_NAME)): + filename = os.path.join(model_id, WEIGHTS_NAME) + else: + try: + filename = hf_hub_download(model_id, WEIGHTS_NAME) + except: # noqa + raise ValueError( + f"Can't find weights for {model_id} in {model_id} or in the Hugging Face Hub. " + f"Please check that the file {WEIGHTS_NAME} is present at {model_id}." + ) + + adapters_weights = torch.load( + filename, map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu") + ) + # load the weights into the model + model = set_peft_model_state_dict(model, adapters_weights) + if getattr(model, "hf_device_map", None) is not None: + device_map = kwargs.get("device_map", "auto") + max_memory = kwargs.get("max_memory", None) + no_split_module_classes = model._no_split_modules + if device_map != "sequential": + max_memory = get_balanced_memory( + model, + max_memory=max_memory, + no_split_module_classes=no_split_module_classes, + low_zero=(device_map == "balanced_low_0"), + ) + if isinstance(device_map, str): + device_map = infer_auto_device_map( + model, max_memory=max_memory, no_split_module_classes=no_split_module_classes + ) + model = dispatch_model(model, device_map=device_map) + hook = AlignDevicesHook(io_same_device=True) + if model.peft_config.peft_type == PeftType.LORA: + add_hook_to_module(model.base_model.model, hook) + else: + remove_hook_from_submodules(model.prompt_encoder) + add_hook_to_module(model.base_model, hook) + return model + + def _setup_prompt_encoder(self): + transformer_backbone = None + for name, module in self.base_model.named_children(): + for param in module.parameters(): + param.requires_grad = False + if isinstance(module, PreTrainedModel): + # Make sure to freeze Tranformers model + if transformer_backbone is None: + transformer_backbone = module + self.transformer_backbone_name = name + + if self.peft_config.num_transformer_submodules is None: + self.peft_config.num_transformer_submodules = ( + 2 if self.peft_config.task_type == TaskType.SEQ_2_SEQ_LM else 1 + ) + + for named_param, value in list(transformer_backbone.named_parameters()): + if value.shape[0] == self.base_model.config.vocab_size: + self.word_embeddings = transformer_backbone.get_submodule(named_param.replace(".weight", "")) + break + + if self.peft_config.peft_type == PeftType.PROMPT_TUNING: + prompt_encoder = PromptEmbedding(self.peft_config, self.word_embeddings) + elif self.peft_config.peft_type == PeftType.P_TUNING: + prompt_encoder = PromptEncoder(self.peft_config) + elif self.peft_config.peft_type == PeftType.PREFIX_TUNING: + prompt_encoder = PrefixEncoder(self.peft_config) + else: + raise ValueError("Not supported") + self.prompt_encoder = prompt_encoder + self.prompt_tokens = torch.arange( + self.peft_config.num_virtual_tokens * self.peft_config.num_transformer_submodules + ).long() + + def get_prompt_embedding_to_save(self): + """ + Returns the prompt embedding to save when saving the model. Only applicable when `peft_config.peft_type != + PeftType.LORA`. + """ + prompt_tokens = self.prompt_tokens.unsqueeze(0).expand(1, -1).to(self.device) + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + prompt_tokens = prompt_tokens[:, : self.peft_config.num_virtual_tokens] + prompt_embeddings = self.prompt_encoder(prompt_tokens) + return prompt_embeddings[0].detach().cpu() + + def get_prompt(self, batch_size): + """ + Returns the virtual prompts to use for Peft. Only applicable when `peft_config.peft_type != PeftType.LORA`. + """ + prompt_tokens = self.prompt_tokens.unsqueeze(0).expand(batch_size, -1).to(self.device) + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + prompt_tokens = prompt_tokens[:, : self.peft_config.num_virtual_tokens] + if self.peft_config.inference_mode: + past_key_values = self.prompt_encoder.embedding.weight.repeat(batch_size, 1, 1) + else: + past_key_values = self.prompt_encoder(prompt_tokens) + past_key_values = past_key_values.view( + batch_size, + self.peft_config.num_virtual_tokens, + self.peft_config.num_layers * 2, + self.peft_config.num_attention_heads, + self.peft_config.token_dim // self.peft_config.num_attention_heads, + ) + if self.peft_config.num_transformer_submodules == 2: + past_key_values = torch.cat([past_key_values, past_key_values], dim=2) + past_key_values = past_key_values.permute([2, 0, 3, 1, 4]).split( + self.peft_config.num_transformer_submodules * 2 + ) + if TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING.get(self.config.model_type, None) is not None: + post_process_fn = TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING[self.config.model_type] + past_key_values = post_process_fn(past_key_values) + return past_key_values + else: + if self.peft_config.inference_mode: + prompts = self.prompt_encoder.embedding.weight.repeat(batch_size, 1, 1) + else: + prompts = self.prompt_encoder(prompt_tokens) + return prompts + + def print_trainable_parameters(self): + """ + Prints the number of trainable parameters in the model. + """ + trainable_params = 0 + all_param = 0 + for _, param in self.named_parameters(): + num_params = param.numel() + # if using DS Zero 3 and the weights are initialized empty + if num_params == 0 and hasattr(param, "ds_numel"): + num_params = param.ds_numel + + all_param += num_params + if param.requires_grad: + trainable_params += num_params + print( + f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}" + ) + + def __getattr__(self, name: str): + """Forward missing attributes to the wrapped module.""" + try: + return super().__getattr__(name) # defer to nn.Module's logic + except AttributeError: + return getattr(self.base_model, name) + + def forward(self, *args, **kwargs): + """ + Forward pass of the model. + """ + return self.get_base_model()(*args, **kwargs) + + @contextmanager + def disable_adapter(self): + """ + Disables the adapter module. + """ + if isinstance(self.peft_config, PromptLearningConfig): + old_forward = self.forward + self.forward = self.base_model.forward + else: + self.base_model.disable_adapter_layers() + yield + if isinstance(self.peft_config, PromptLearningConfig): + self.forward = old_forward + else: + self.base_model.enable_adapter_layers() + + def get_base_model(self): + """ + Returns the base model. + """ + return self.base_model if isinstance(self.peft_config, PromptLearningConfig) else self.base_model.model + + +class PeftModelForSequenceClassification(PeftModel): + """ + Peft model for sequence classification tasks. + + Args: + model ([`PreTrainedModel`]): Base transformer model + peft_config ([`PeftConfig`]): Peft config. + + **Attributes**: + - **config** ([`PretrainedConfig`]) -- The configuration object of the base model. + - **cls_layer_name** (`str`) -- The name of the classification layer. + + Example:: + + >>> from transformers import AutoModelForSequenceClassification >>> from peft import + PeftModelForSequenceClassification, get_peft_config >>> config = { + 'peft_type': 'PREFIX_TUNING', 'task_type': 'SEQ_CLS', 'inference_mode': False, 'num_virtual_tokens': + 20, 'token_dim': 768, 'num_transformer_submodules': 1, 'num_attention_heads': 12, 'num_layers': 12, + 'encoder_hidden_size': 768, 'prefix_projection': False, 'postprocess_past_key_value_function': None + } + >>> peft_config = get_peft_config(config) >>> model = + AutoModelForSequenceClassification.from_pretrained("bert-base-cased") >>> peft_model = + PeftModelForSequenceClassification(model, peft_config) >>> peft_model.print_trainable_parameters() trainable + params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117 + """ + + def __init__(self, model, peft_config: PeftConfig): + super().__init__(model, peft_config) + self.modules_to_save = ["classifier", "score"] + + for name, _ in self.base_model.named_children(): + if any(module_name in name for module_name in self.modules_to_save): + self.cls_layer_name = name + break + + # to make sure classifier layer is trainable + _set_trainable(self) + + def forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if not isinstance(self.peft_config, PromptLearningConfig): + return self.base_model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + labels=labels, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + batch_size = input_ids.shape[0] + if attention_mask is not None: + # concat prompt attention mask + prefix_attention_mask = torch.ones(batch_size, self.peft_config.num_virtual_tokens).to(self.device) + attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1) + if kwargs.get("position_ids", None) is not None: + warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.") + kwargs["position_ids"] = None + kwargs.update( + { + "attention_mask": attention_mask, + "labels": labels, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "return_dict": return_dict, + } + ) + + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + return self._prefix_tuning_forward(input_ids=input_ids, **kwargs) + else: + if kwargs.get("token_type_ids", None) is not None: + kwargs["token_type_ids"] = torch.cat( + ( + torch.zeros(batch_size, self.peft_config.num_virtual_tokens).to(self.device), + kwargs["token_type_ids"], + ), + dim=1, + ).long() + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + prompts = self.get_prompt(batch_size=batch_size) + prompts = prompts.to(inputs_embeds.dtype) + inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1) + return self.base_model(inputs_embeds=inputs_embeds, **kwargs) + + def _prefix_tuning_forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + batch_size = input_ids.shape[0] + past_key_values = self.get_prompt(batch_size) + fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys()) + kwargs.update( + { + "input_ids": input_ids, + "attention_mask": attention_mask, + "inputs_embeds": inputs_embeds, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "return_dict": return_dict, + "past_key_values": past_key_values, + } + ) + if "past_key_values" in fwd_params: + return self.base_model(labels=labels, **kwargs) + else: + transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name) + fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys()) + if "past_key_values" not in fwd_params: + raise ValueError("Model does not support past key values which are required for prefix tuning.") + outputs = transformer_backbone_name(**kwargs) + pooled_output = outputs[1] if len(outputs) > 1 else outputs[0] + if "dropout" in [name for name, _ in list(self.base_model.named_children())]: + pooled_output = self.base_model.dropout(pooled_output) + logits = self.base_model.get_submodule(self.cls_layer_name)(pooled_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.base_model.num_labels == 1: + self.config.problem_type = "regression" + elif self.base_model.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.base_model.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.base_model.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class PeftModelForCausalLM(PeftModel): + """ + Peft model for Causal LM + + Args: + model ([`PreTrainedModel`]): Base transformer model + peft_config ([`PeftConfig`]): Peft config. + + + Example:: + + >>> from transformers import AutoModelForCausalLM >>> from peft import PeftModelForCausalLM, get_peft_config + >>> config = { + 'peft_type': 'PREFIX_TUNING', 'task_type': 'CAUSAL_LM', 'inference_mode': False, 'num_virtual_tokens': + 20, 'token_dim': 1280, 'num_transformer_submodules': 1, 'num_attention_heads': 20, 'num_layers': 36, + 'encoder_hidden_size': 1280, 'prefix_projection': False, 'postprocess_past_key_value_function': None + } + >>> peft_config = get_peft_config(config) >>> model = AutoModelForCausalLM.from_pretrained("gpt2-large") >>> + peft_model = PeftModelForCausalLM(model, peft_config) >>> peft_model.print_trainable_parameters() trainable + params: 1843200 || all params: 775873280 || trainable%: 0.23756456724479544 + """ + + def __init__(self, model, peft_config: PeftConfig): + super().__init__(model, peft_config) + self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation + + def forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + if not isinstance(self.peft_config, PromptLearningConfig): + return self.base_model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + labels=labels, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + batch_size = input_ids.shape[0] + if attention_mask is not None: + # concat prompt attention mask + prefix_attention_mask = torch.ones(batch_size, self.peft_config.num_virtual_tokens).to(self.device) + attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1) + + if kwargs.get("position_ids", None) is not None: + warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.") + kwargs["position_ids"] = None + if kwargs.get("token_type_ids", None) is not None: + warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids") + kwargs["token_type_ids"] = None + kwargs.update( + { + "attention_mask": attention_mask, + "labels": labels, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "return_dict": return_dict, + } + ) + + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + past_key_values = self.get_prompt(batch_size) + return self.base_model(input_ids=input_ids, past_key_values=past_key_values, **kwargs) + else: + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + # concat prompt labels + if labels is not None: + prefix_labels = torch.full((batch_size, self.peft_config.num_virtual_tokens), -100).to(self.device) + kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1) + prompts = self.get_prompt(batch_size=batch_size) + prompts = prompts.to(inputs_embeds.dtype) + inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1) + return self.base_model(inputs_embeds=inputs_embeds, **kwargs) + + def generate(self, **kwargs): + self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation + try: + if not isinstance(self.peft_config, PromptLearningConfig): + outputs = self.base_model.generate(**kwargs) + else: + if "input_ids" not in kwargs: + raise ValueError("input_ids must be provided for Peft model generation") + if kwargs.get("attention_mask", None) is not None: + # concat prompt attention mask + prefix_attention_mask = torch.ones( + kwargs["input_ids"].shape[0], self.peft_config.num_virtual_tokens + ).to(kwargs["input_ids"].device) + kwargs["attention_mask"] = torch.cat((prefix_attention_mask, kwargs["attention_mask"]), dim=1) + + if kwargs.get("position_ids", None) is not None: + warnings.warn( + "Position ids are not supported for parameter efficient tuning. Ignoring position ids." + ) + kwargs["position_ids"] = None + if kwargs.get("token_type_ids", None) is not None: + warnings.warn( + "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids" + ) + kwargs["token_type_ids"] = None + + outputs = self.base_model.generate(**kwargs) + except: + self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation + raise + else: + self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation + return outputs + + def prepare_inputs_for_generation(self, *args, **kwargs): + model_kwargs = self.base_model_prepare_inputs_for_generation(*args, **kwargs) + if isinstance(self.peft_config, PromptLearningConfig): + if model_kwargs["past_key_values"] is None and self.peft_config.peft_type == PeftType.PREFIX_TUNING: + past_key_values = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0]) + model_kwargs["past_key_values"] = past_key_values + else: + if model_kwargs["past_key_values"] is None: + inputs_embeds = self.word_embeddings(model_kwargs["input_ids"]) + prompts = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0]) + prompts = prompts.to(inputs_embeds.dtype) + model_kwargs["inputs_embeds"] = torch.cat((prompts, inputs_embeds), dim=1) + model_kwargs["input_ids"] = None + + return model_kwargs + + +class PeftModelForSeq2SeqLM(PeftModel): + """ + Peft model for Seq2Seq LM + + Args: + model ([`PreTrainedModel`]): Base transformer model + peft_config ([`PeftConfig`]): Peft config. + + + Example:: + + >>> from transformers import AutoModelForSeq2SeqLM >>> from peft import PeftModelForSeq2SeqLM, get_peft_config + >>> config = { + 'peft_type': 'LORA', 'task_type': 'SEQ_2_SEQ_LM', 'inference_mode': False, 'r': 8, 'target_modules': + ['q', 'v'], 'lora_alpha': 32, 'lora_dropout': 0.1, 'merge_weights': False, 'fan_in_fan_out': False, + 'enable_lora': None, 'bias': 'none' + } + >>> peft_config = get_peft_config(config) >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> + peft_model = PeftModelForSeq2SeqLM(model, peft_config) >>> peft_model.print_trainable_parameters() trainable + params: 884736 || all params: 223843584 || trainable%: 0.3952474242013566 + """ + + def __init__(self, model, peft_config: PeftConfig): + super().__init__(model, peft_config) + self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation + self.base_model_prepare_encoder_decoder_kwargs_for_generation = ( + self.base_model._prepare_encoder_decoder_kwargs_for_generation + ) + + def forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + decoder_input_ids=None, + decoder_attention_mask=None, + decoder_inputs_embeds=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + if not isinstance(self.peft_config, PromptLearningConfig): + return self.base_model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + decoder_inputs_embeds=decoder_inputs_embeds, + labels=labels, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + batch_size = input_ids.shape[0] + if decoder_attention_mask is not None: + # concat prompt attention mask + prefix_attention_mask = torch.ones(batch_size, self.peft_config.num_virtual_tokens).to(self.device) + decoder_attention_mask = torch.cat((prefix_attention_mask, decoder_attention_mask), dim=1) + + if kwargs.get("position_ids", None) is not None: + warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.") + kwargs["position_ids"] = None + if kwargs.get("token_type_ids", None) is not None: + warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids") + kwargs["token_type_ids"] = None + kwargs.update( + { + "attention_mask": attention_mask, + "decoder_attention_mask": decoder_attention_mask, + "labels": labels, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "return_dict": return_dict, + } + ) + + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + past_key_values = self.get_prompt(batch_size) + return self.base_model( + input_ids=input_ids, decoder_input_ids=decoder_input_ids, past_key_values=past_key_values, **kwargs + ) + else: + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + if decoder_inputs_embeds is None and decoder_input_ids is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + decoder_inputs_embeds = self.word_embeddings(decoder_input_ids) + + if attention_mask is not None: + # concat prompt attention mask + prefix_attention_mask = torch.ones(batch_size, self.peft_config.num_virtual_tokens).to(self.device) + kwargs["attention_mask"] = torch.cat((prefix_attention_mask, attention_mask), dim=1) + # concat prompt labels + if labels is not None: + if self.peft_config.num_transformer_submodules == 1: + kwargs["labels"] = labels + elif self.peft_config.num_transformer_submodules == 2: + prefix_labels = torch.full((batch_size, self.peft_config.num_virtual_tokens), -100).to(self.device) + kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1) + prompts = self.get_prompt(batch_size=batch_size) + prompts = prompts.to(inputs_embeds.dtype) + inputs_embeds = torch.cat((prompts[:, : self.peft_config.num_virtual_tokens], inputs_embeds), dim=1) + if self.peft_config.num_transformer_submodules == 1: + return self.base_model(inputs_embeds=inputs_embeds, **kwargs) + elif self.peft_config.num_transformer_submodules == 2: + decoder_inputs_embeds = torch.cat( + (prompts[:, self.peft_config.num_virtual_tokens :], decoder_inputs_embeds), dim=1 + ) + return self.base_model( + inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, **kwargs + ) + + def generate(self, **kwargs): + self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation + self.base_model._prepare_encoder_decoder_kwargs_for_generation = ( + self._prepare_encoder_decoder_kwargs_for_generation + ) + try: + if not isinstance(self.peft_config, PromptLearningConfig): + outputs = self.base_model.generate(**kwargs) + else: + if "input_ids" not in kwargs: + raise ValueError("input_ids must be provided for Peft model generation") + if kwargs.get("position_ids", None) is not None: + warnings.warn( + "Position ids are not supported for parameter efficient tuning. Ignoring position ids." + ) + kwargs["position_ids"] = None + if kwargs.get("token_type_ids", None) is not None: + warnings.warn( + "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids" + ) + kwargs["token_type_ids"] = None + + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + outputs = self.base_model.generate(**kwargs) + else: + raise NotImplementedError + except: + self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation + self.base_model._prepare_encoder_decoder_kwargs_for_generation = ( + self.base_model_prepare_encoder_decoder_kwargs_for_generation + ) + raise + else: + self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation + self.base_model._prepare_encoder_decoder_kwargs_for_generation = ( + self.base_model_prepare_encoder_decoder_kwargs_for_generation + ) + return outputs + + def prepare_inputs_for_generation(self, *args, **kwargs): + model_kwargs = self.base_model_prepare_inputs_for_generation(*args, **kwargs) + if model_kwargs["past_key_values"] is None and self.peft_config.peft_type == PeftType.PREFIX_TUNING: + batch_size = model_kwargs["decoder_input_ids"].shape[0] + past_key_values = self.get_prompt(batch_size) + model_kwargs["past_key_values"] = past_key_values + return model_kwargs + + +class PeftModelForTokenClassification(PeftModel): + """ + Peft model for sequence classification tasks. + + Args: + model ([`PreTrainedModel`]): Base transformer model + peft_config ([`PeftConfig`]): Peft config. + + **Attributes**: + - **config** ([`PretrainedConfig`]) -- The configuration object of the base model. + - **cls_layer_name** (`str`) -- The name of the classification layer. + + Example:: + + >>> from transformers import AutoModelForSequenceClassification >>> from peft import + PeftModelForTokenClassification, get_peft_config >>> config = { + 'peft_type': 'PREFIX_TUNING', 'task_type': 'TOKEN_CLS', 'inference_mode': False, 'num_virtual_tokens': + 20, 'token_dim': 768, 'num_transformer_submodules': 1, 'num_attention_heads': 12, 'num_layers': 12, + 'encoder_hidden_size': 768, 'prefix_projection': False, 'postprocess_past_key_value_function': None + } + >>> peft_config = get_peft_config(config) >>> model = + AutoModelForTokenClassification.from_pretrained("bert-base-cased") >>> peft_model = + PeftModelForTokenClassification(model, peft_config) >>> peft_model.print_trainable_parameters() trainable + params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117 + """ + + def __init__(self, model, peft_config: PeftConfig): + super().__init__(model, peft_config) + self.modules_to_save = ["classifier", "score"] + + for name, _ in self.base_model.named_children(): + if any(module_name in name for module_name in self.modules_to_save): + self.cls_layer_name = name + break + + # to make sure classifier layer is trainable + _set_trainable(self) + + def forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if not isinstance(self.peft_config, PromptLearningConfig): + return self.base_model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + labels=labels, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + batch_size = input_ids.shape[0] + if attention_mask is not None: + # concat prompt attention mask + prefix_attention_mask = torch.ones(batch_size, self.peft_config.num_virtual_tokens).to(self.device) + attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1) + if kwargs.get("position_ids", None) is not None: + warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.") + kwargs["position_ids"] = None + kwargs.update( + { + "attention_mask": attention_mask, + "labels": labels, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "return_dict": return_dict, + } + ) + + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + return self._prefix_tuning_forward(input_ids=input_ids, **kwargs) + else: + if kwargs.get("token_type_ids", None) is not None: + kwargs["token_type_ids"] = torch.cat( + ( + torch.zeros(batch_size, self.peft_config.num_virtual_tokens).to(self.device), + kwargs["token_type_ids"], + ), + dim=1, + ).long() + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + prompts = self.get_prompt(batch_size=batch_size) + prompts = prompts.to(inputs_embeds.dtype) + inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1) + return self.base_model(inputs_embeds=inputs_embeds, **kwargs) + + def _prefix_tuning_forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + batch_size = input_ids.shape[0] + past_key_values = self.get_prompt(batch_size) + fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys()) + kwargs.update( + { + "input_ids": input_ids, + "attention_mask": attention_mask, + "inputs_embeds": inputs_embeds, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "return_dict": return_dict, + "past_key_values": past_key_values, + } + ) + if "past_key_values" in fwd_params: + return self.base_model(labels=labels, **kwargs) + else: + transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name) + fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys()) + if "past_key_values" not in fwd_params: + raise ValueError("Model does not support past key values which are required for prefix tuning.") + outputs = transformer_backbone_name(**kwargs) + sequence_output = outputs[0] + if "dropout" in [name for name, _ in list(self.base_model.named_children())]: + sequence_output = self.base_model.dropout(sequence_output) + logits = self.base_model.get_submodule(self.cls_layer_name)(sequence_output) + + loss = None + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/04-gene-sft/peft/build/lib/peft/tuners/__init__.py b/04-gene-sft/peft/build/lib/peft/tuners/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..38b7926b8cbda36799de097d012deabb8e8632fd --- /dev/null +++ b/04-gene-sft/peft/build/lib/peft/tuners/__init__.py @@ -0,0 +1,23 @@ +# flake8: noqa +# There's no way to ignore "F401 '...' imported but unused" warnings in this +# module, but to preserve other warnings. So, don't check this module at all + +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .lora import LoraConfig, LoraModel +from .p_tuning import PromptEncoder, PromptEncoderConfig, PromptEncoderReparameterizationType +from .prefix_tuning import PrefixEncoder, PrefixTuningConfig +from .prompt_tuning import PromptEmbedding, PromptTuningConfig, PromptTuningInit diff --git a/04-gene-sft/peft/build/lib/peft/tuners/lora.py b/04-gene-sft/peft/build/lib/peft/tuners/lora.py new file mode 100644 index 0000000000000000000000000000000000000000..0f65cbf55c0233599c0dd8b155187718fe8091f7 --- /dev/null +++ b/04-gene-sft/peft/build/lib/peft/tuners/lora.py @@ -0,0 +1,616 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import importlib +import math +import re +import warnings +from dataclasses import asdict, dataclass, field +from enum import Enum +from typing import List, Optional, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers.pytorch_utils import Conv1D + +from ..utils import PeftConfig, PeftType, transpose + + +def is_bnb_available(): + return importlib.util.find_spec("bitsandbytes") is not None + + +if is_bnb_available(): + import bitsandbytes as bnb + + +@dataclass +class LoraConfig(PeftConfig): + """ + This is the configuration class to store the configuration of a [`~peft.Lora`]. + + Args: + r (`int`): Lora attention dimension + target_modules (`Union[List[str],str]`): The names of the modules to apply Lora to. + lora_alpha (`float`): The alpha parameter for Lora scaling. + lora_dropout (`float`): The dropout probability for Lora layers. + merge_weights (`bool`): + Whether to merge the weights of the Lora layers with the base transformer model in `eval` mode. + fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out) + enable_lora ( `List[bool]`): Used with `lora.MergedLinear`. + bias (`str`): Bias type for Lora. Can be 'none', 'all' or 'lora_only' + modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable + and saved in the final checkpoint. + """ + + r: int = field(default=8, metadata={"help": "Lora attention dimension"}) + target_modules: Optional[Union[List[str], str]] = field( + default=None, + metadata={ + "help": "List of module names or regex expression of the module names to replace with Lora." + "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' " + }, + ) + lora_alpha: int = field(default=None, metadata={"help": "Lora alpha"}) + lora_dropout: float = field(default=None, metadata={"help": "Lora dropout"}) + merge_weights: bool = field( + default=False, metadata={"help": "Merge weights of the original model and the Lora model"} + ) + fan_in_fan_out: bool = field( + default=False, + metadata={"help": "Set this to True if the layer to replace stores weight like (fan_in, fan_out)"}, + ) + enable_lora: Optional[List[bool]] = field(default=None, metadata={"help": "Used with `lora.MergedLinear`."}) + bias: str = field(default="none", metadata={"help": "Bias type for Lora. Can be 'none', 'all' or 'lora_only'"}) + modules_to_save: Optional[List[str]] = field( + default=None, + metadata={ + "help": "List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. " + "For example, in Sequence Classification or Token Classification tasks, " + "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved." + }, + ) + + def __post_init__(self): + self.peft_type = PeftType.LORA + + +class LoraModel(torch.nn.Module): + """ + Creates Low Rank Adapter (Lora) model from a pretrained transformers model. + + Args: + model ([`transformers.PreTrainedModel`]): The model to be adapted. + config ([`LoraConfig`]): The configuration of the Lora model. + + Returns: + `torch.nn.Module`: The Lora model. + + Example:: + + >>> from transformers import AutoModelForSeq2SeqLM, LoraConfig >>> from peft import LoraModel, LoraConfig >>> + config = LoraConfig( + peft_type="LORA", task_type="SEQ_2_SEQ_LM", r=8, lora_alpha=32, target_modules=["q", "v"], + lora_dropout=0.01, ) + >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> lora_model = LoraModel(config, model) + + **Attributes**: + - **model** ([`transformers.PreTrainedModel`]) -- The model to be adapted. + - **peft_config** ([`LoraConfig`]): The configuration of the Lora model. + """ + + def __init__(self, config, model): + super().__init__() + self.peft_config = config + self.model = model + self._find_and_replace() + mark_only_lora_as_trainable(self.model, self.peft_config.bias) + self.forward = self.model.forward + + def _find_and_replace(self): + loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False) + if loaded_in_8bit and not is_bnb_available(): + raise ImportError( + "To use Lora with 8-bit quantization, please install the `bitsandbytes` package. " + "You can install it with `pip install bitsandbytes`." + ) + is_target_modules_in_base_model = False + is_hf_device_map_available = hasattr(self.model, "hf_device_map") + kwargs = { + "r": self.peft_config.r, + "lora_alpha": self.peft_config.lora_alpha, + "lora_dropout": self.peft_config.lora_dropout, + "fan_in_fan_out": self.peft_config.fan_in_fan_out, + "merge_weights": (self.peft_config.merge_weights or self.peft_config.inference_mode) + and not is_hf_device_map_available, + } + key_list = [key for key, _ in self.model.named_modules()] + for key in key_list: + if isinstance(self.peft_config.target_modules, str): + target_module_found = re.fullmatch(self.peft_config.target_modules, key) + else: + target_module_found = any(key.endswith(target_key) for target_key in self.peft_config.target_modules) + if target_module_found: + if not is_target_modules_in_base_model: + is_target_modules_in_base_model = True + parent, target, target_name = self._get_submodules(key) + bias = target.bias is not None + if loaded_in_8bit and isinstance(target, bnb.nn.Linear8bitLt): + kwargs.update( + { + "has_fp16_weights": target.state.has_fp16_weights, + "memory_efficient_backward": target.state.memory_efficient_backward, + "threshold": target.state.threshold, + "index": target.index, + } + ) + if self.peft_config.enable_lora is None: + new_module = Linear8bitLt(target.in_features, target.out_features, bias=bias, **kwargs) + else: + kwargs.update({"enable_lora": self.peft_config.enable_lora}) + new_module = MergedLinear8bitLt(target.in_features, target.out_features, bias=bias, **kwargs) + elif isinstance(target, torch.nn.Linear) and self.peft_config.enable_lora is None: + new_module = Linear(target.in_features, target.out_features, bias=bias, **kwargs) + elif self.peft_config.enable_lora is not None: + kwargs.update({"enable_lora": self.peft_config.enable_lora}) + if isinstance(target, Conv1D): + in_features, out_features = ( + target.weight.ds_shape if hasattr(target.weight, "ds_shape") else target.weight.shape + ) + else: + in_features, out_features = target.in_features, target.out_features + if kwargs["fan_in_fan_out"]: + warnings.warn( + "fan_in_fan_out is set to True but the target module is not a Conv1D. " + "Setting fan_in_fan_out to False." + ) + kwargs["fan_in_fan_out"] = self.peft_config.fan_in_fan_out = False + new_module = MergedLinear(in_features, out_features, bias=bias, **kwargs) + self._replace_module(parent, target_name, new_module, target) + if not is_target_modules_in_base_model: + raise ValueError( + f"Target modules {self.peft_config.target_modules} not found in the base model. " + f"Please check the target modules and try again." + ) + + def _get_submodules(self, key): + parent = self.model.get_submodule(".".join(key.split(".")[:-1])) + target_name = key.split(".")[-1] + target = self.model.get_submodule(key) + return parent, target, target_name + + def _replace_module(self, parent_module, child_name, new_module, old_module): + setattr(parent_module, child_name, new_module) + new_module.weight = old_module.weight + if old_module.bias is not None: + new_module.bias = old_module.bias + if getattr(old_module, "state", None) is not None: + new_module.state = old_module.state + new_module.to(old_module.weight.device) + + # dispatch to correct device + for name, module in new_module.named_modules(): + if "lora_" in name: + module.to(old_module.weight.device) + + def __getattr__(self, name: str): + """Forward missing attributes to the wrapped module.""" + try: + return super().__getattr__(name) # defer to nn.Module's logic + except AttributeError: + return getattr(self.model, name) + + @property + def modules_to_save(self): + return None + + def get_peft_config_as_dict(self, inference: bool = False): + config = {k: v.value if isinstance(v, Enum) else v for k, v in asdict(self.peft_config).items()} + if inference: + config["inference_mode"] = True + return config + + def _set_adapter_layers(self, enabled=True): + for module in self.model.modules(): + if isinstance(module, LoraLayer): + module.disable_adapters = False if enabled else True + + def enable_adapter_layers(self): + self._set_adapter_layers(enabled=True) + + def disable_adapter_layers(self): + self._set_adapter_layers(enabled=False) + + +# Below code is based on https://github.com/microsoft/LoRA/blob/main/loralib/layers.py +# and modified to work with PyTorch FSDP + + +# ------------------------------------------------------------------------------------------ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information. +# ------------------------------------------------------------------------------------------ + + +# had to adapt it for `lora_only` to work +def mark_only_lora_as_trainable(model: nn.Module, bias: str = "none") -> None: + for n, p in model.named_parameters(): + if "lora_" not in n: + p.requires_grad = False + if bias == "none": + return + elif bias == "all": + for n, p in model.named_parameters(): + if "bias" in n: + p.requires_grad = True + elif bias == "lora_only": + for m in model.modules(): + if isinstance(m, LoraLayer) and hasattr(m, "bias") and m.bias is not None: + m.bias.requires_grad = True + else: + raise NotImplementedError + + +class LoraLayer: + def __init__( + self, + r: int, + lora_alpha: int, + lora_dropout: float, + merge_weights: bool, + ): + self.r = r + self.lora_alpha = lora_alpha + # Optional dropout + if lora_dropout > 0.0: + self.lora_dropout = nn.Dropout(p=lora_dropout) + else: + self.lora_dropout = lambda x: x + # Mark the weight as unmerged + self.merged = False + self.merge_weights = merge_weights + self.disable_adapters = False + + +class Linear(nn.Linear, LoraLayer): + # Lora implemented in a dense layer + def __init__( + self, + in_features: int, + out_features: int, + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0.0, + fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out) + merge_weights: bool = True, + **kwargs, + ): + nn.Linear.__init__(self, in_features, out_features, **kwargs) + LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights) + + self.fan_in_fan_out = fan_in_fan_out + # Actual trainable parameters + if r > 0: + self.lora_A = nn.Linear(in_features, r, bias=False) + self.lora_B = nn.Linear(r, out_features, bias=False) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + self.reset_parameters() + if fan_in_fan_out: + self.weight.data = self.weight.data.T + + def reset_parameters(self): + nn.Linear.reset_parameters(self) + if hasattr(self, "lora_A"): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B.weight) + + def train(self, mode: bool = True): + nn.Linear.train(self, mode) + self.lora_A.train(mode) + self.lora_B.train(mode) + if not mode and self.merge_weights and not self.merged: + # Merge the weights and mark it + if self.r > 0: + self.weight.data += ( + transpose(self.lora_B.weight @ self.lora_A.weight, self.fan_in_fan_out) * self.scaling + ) + self.merged = True + elif self.merge_weights and self.merged: + # Make sure that the weights are not merged + if self.r > 0: + self.weight.data -= ( + transpose(self.lora_B.weight @ self.lora_A.weight, self.fan_in_fan_out) * self.scaling + ) + self.merged = False + + def eval(self): + nn.Linear.eval(self) + self.lora_A.eval() + self.lora_B.eval() + + def forward(self, x: torch.Tensor): + if self.disable_adapters: + if self.r > 0 and self.merged: + self.weight.data -= ( + transpose(self.lora_B.weight @ self.lora_A.weight, self.fan_in_fan_out) * self.scaling + ) + self.merged = False + + return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) + elif self.r > 0 and not self.merged: + result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) + if self.r > 0: + result += self.lora_B(self.lora_A(self.lora_dropout(x))) * self.scaling + return result + else: + return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) + + +class MergedLinear(nn.Linear, LoraLayer): + # Lora implemented in a dense layer + def __init__( + self, + in_features: int, + out_features: int, + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0.0, + enable_lora: List[bool] = [False], + fan_in_fan_out: bool = False, + merge_weights: bool = True, + **kwargs, + ): + nn.Linear.__init__(self, in_features, out_features, **kwargs) + LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights) + if out_features % len(enable_lora) != 0: + raise ValueError("The length of enable_lora must divide out_features") + self.enable_lora = enable_lora + self.fan_in_fan_out = fan_in_fan_out + # Actual trainable parameters + if r > 0 and any(enable_lora): + self.lora_A = nn.Linear(in_features, r * sum(enable_lora), bias=False) + self.lora_B = nn.Conv1d( + r * sum(enable_lora), + out_features // len(enable_lora) * sum(enable_lora), + kernel_size=1, + groups=2, + bias=False, + ) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + # Compute the indices + self.lora_ind = self.weight.new_zeros((out_features,), dtype=torch.bool).view(len(enable_lora), -1) + self.lora_ind[enable_lora, :] = True + self.lora_ind = self.lora_ind.view(-1) + self.reset_parameters() + if fan_in_fan_out: + self.weight.data = self.weight.data.T + + def reset_parameters(self): + nn.Linear.reset_parameters(self) + if hasattr(self, "lora_A"): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B.weight) + + def zero_pad(self, x): + result = x.new_zeros((*x.shape[:-1], self.out_features)) + result = result.view(-1, self.out_features) + result[:, self.lora_ind] = x.reshape(-1, self.out_features // len(self.enable_lora) * sum(self.enable_lora)) + return result.view((*x.shape[:-1], self.out_features)) + + def train(self, mode: bool = True): + nn.Linear.train(self, mode) + self.lora_A.train(mode) + self.lora_B.train(mode) + if not mode and self.merge_weights and not self.merged: + # Merge the weights and mark it + if self.r > 0 and any(self.enable_lora): + delta_w = ( + F.conv1d( + self.lora_A.weight.data.unsqueeze(0), + self.lora_B.weight.data, + groups=sum(self.enable_lora), + ) + .squeeze(0) + .transpose(-2, -1) + ) + self.weight.data += transpose(self.zero_pad(delta_w * self.scaling), not self.fan_in_fan_out) + self.merged = True + elif self.merge_weights and self.merged: + # Make sure that the weights are not merged + if self.r > 0 and any(self.enable_lora): + delta_w = ( + F.conv1d( + self.lora_A.weight.data.unsqueeze(0), + self.lora_B.weight.data, + groups=sum(self.enable_lora), + ) + .squeeze(0) + .transpose(-2, -1) + ) + self.weight.data -= transpose(self.zero_pad(delta_w * self.scaling), not self.fan_in_fan_out) + self.merged = False + + def eval(self): + nn.Linear.eval(self) + self.lora_A.eval() + self.lora_B.eval() + + def forward(self, x: torch.Tensor): + if self.disable_adapters: + if self.r > 0 and self.merged and any(self.enable_lora): + delta_w = ( + F.conv1d( + self.lora_A.weight.data.unsqueeze(0), + self.lora_B.weight.data, + groups=sum(self.enable_lora), + ) + .squeeze(0) + .transpose(-2, -1) + ) + self.weight.data -= transpose(self.zero_pad(delta_w * self.scaling), not self.fan_in_fan_out) + self.merged = False + return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) + elif self.merged: + return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) + else: + result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) + if self.r > 0: + after_A = self.lora_A(self.lora_dropout(x)) + after_B = self.lora_B(after_A.transpose(-2, -1)).transpose(-2, -1) + result += self.zero_pad(after_B) * self.scaling + return result + + +if is_bnb_available(): + + class Linear8bitLt(bnb.nn.Linear8bitLt, LoraLayer): + # Lora implemented in a dense layer + def __init__( + self, + in_features, + out_features, + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0.0, + **kwargs, + ): + bnb.nn.Linear8bitLt.__init__( + self, + in_features, + out_features, + bias=kwargs.get("bias", True), + has_fp16_weights=kwargs.get("has_fp16_weights", True), + memory_efficient_backward=kwargs.get("memory_efficient_backward", False), + threshold=kwargs.get("threshold", 0.0), + index=kwargs.get("index", None), + ) + LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=False) + # Actual trainable parameters + if r > 0: + self.lora_A = nn.Linear(in_features, r, bias=False) + self.lora_B = nn.Linear(r, out_features, bias=False) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + self.reset_parameters() + + def reset_parameters(self): + if hasattr(self, "lora_A"): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B.weight) + + def forward(self, x: torch.Tensor): + result = super().forward(x) + + if self.disable_adapters: + return result + elif self.r > 0: + if not torch.is_autocast_enabled(): + expected_dtype = result.dtype + + if x.dtype != torch.float32: + x = x.float() + output = self.lora_B(self.lora_A(self.lora_dropout(x))).to(expected_dtype) * self.scaling + result += output + else: + output = self.lora_B(self.lora_A(self.lora_dropout(x))) * self.scaling + result += output + return result + + class MergedLinear8bitLt(bnb.nn.Linear8bitLt, LoraLayer): + # Lora implemented in a dense layer + def __init__( + self, + in_features: int, + out_features: int, + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0.0, + enable_lora: List[bool] = [False], + **kwargs, + ): + bnb.nn.Linear8bitLt.__init__( + self, + in_features, + out_features, + bias=kwargs.get("bias", True), + has_fp16_weights=kwargs.get("has_fp16_weights", True), + memory_efficient_backward=kwargs.get("memory_efficient_backward", False), + threshold=kwargs.get("threshold", 0.0), + index=kwargs.get("index", None), + ) + LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=False) + if out_features % len(enable_lora) != 0: + raise ValueError("The length of enable_lora must divide out_features") + self.enable_lora = enable_lora + # Actual trainable parameters + if r > 0 and any(enable_lora): + self.lora_A = nn.Linear(in_features, r * sum(enable_lora), bias=False) + self.lora_B = nn.Conv1d( + r * sum(enable_lora), + out_features // len(enable_lora) * sum(enable_lora), + kernel_size=1, + groups=2, + bias=False, + ) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + # Compute the indices + self.lora_ind = self.weight.new_zeros((out_features,), dtype=torch.bool).view(len(enable_lora), -1) + self.lora_ind[enable_lora, :] = True + self.lora_ind = self.lora_ind.view(-1) + self.reset_parameters() + + def reset_parameters(self): + if hasattr(self, "lora_A"): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B.weight) + + def zero_pad(self, x): + result = x.new_zeros((*x.shape[:-1], self.out_features)) + result = result.view(-1, self.out_features) + result[:, self.lora_ind] = x.reshape( + -1, self.out_features // len(self.enable_lora) * sum(self.enable_lora) + ) + return result.view((*x.shape[:-1], self.out_features)) + + def forward(self, x: torch.Tensor): + result = super().forward(x) + if self.disable_adapters: + return result + elif self.r > 0: + if not torch.is_autocast_enabled(): + expected_dtype = result.dtype + if x.dtype != torch.float32: + x = x.float() + after_A = self.lora_A(self.lora_dropout(x)) + after_B = self.lora_B(after_A.transpose(-2, -1)).transpose(-2, -1) + output = self.zero_pad(after_B).to(expected_dtype) * self.scaling + result += output + else: + after_A = self.lora_A(self.lora_dropout(x)) + after_B = self.lora_B(after_A.transpose(-2, -1)).transpose(-2, -1) + output = self.zero_pad(after_B) * self.scaling + result += output + return result diff --git a/04-gene-sft/peft/build/lib/peft/tuners/p_tuning.py b/04-gene-sft/peft/build/lib/peft/tuners/p_tuning.py new file mode 100644 index 0000000000000000000000000000000000000000..b9c38c40e8a5c0e30adeecaaa0f08604a10779ed --- /dev/null +++ b/04-gene-sft/peft/build/lib/peft/tuners/p_tuning.py @@ -0,0 +1,159 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import enum +import warnings +from dataclasses import dataclass, field +from typing import Union + +import torch + +from ..utils import PeftType, PromptLearningConfig + + +class PromptEncoderReparameterizationType(str, enum.Enum): + MLP = "MLP" + LSTM = "LSTM" + + +@dataclass +class PromptEncoderConfig(PromptLearningConfig): + """ + This is the configuration class to store the configuration of a [`~peft.PromptEncoder`]. + + Args: + encoder_reparameterization_type + (Union[[`PromptEncoderReparameterizationType`], `str`]): The type of reparameterization to use. + encoder_hidden_size (`int`): The hidden size of the prompt encoder. + encoder_num_layers (`int`): The number of layers of the prompt encoder. + encoder_dropout (`float`): The dropout probability of the prompt encoder. + """ + + encoder_reparameterization_type: Union[str, PromptEncoderReparameterizationType] = field( + default=PromptEncoderReparameterizationType.MLP, + metadata={"help": "How to reparameterize the prompt encoder"}, + ) + encoder_hidden_size: int = field( + default=None, + metadata={"help": "The hidden size of the prompt encoder"}, + ) + encoder_num_layers: int = field( + default=2, + metadata={"help": "The number of layers of the prompt encoder"}, + ) + encoder_dropout: float = field( + default=0.0, + metadata={"help": "The dropout of the prompt encoder"}, + ) + + def __post_init__(self): + self.peft_type = PeftType.P_TUNING + + +# Based on https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/modules/common/prompt_encoder.py +# with some refactor +class PromptEncoder(torch.nn.Module): + """ + The prompt encoder network that is used to generate the virtual token embeddings for p-tuning. + + Args: + config ([`PromptEncoderConfig`]): The configuration of the prompt encoder. + + Example:: + + >>> from peft import PromptEncoder, PromptEncoderConfig >>> config = PromptEncoderConfig( + peft_type="P_TUNING", task_type="SEQ_2_SEQ_LM", num_virtual_tokens=20, token_dim=768, + num_transformer_submodules=1, num_attention_heads=12, num_layers=12, + encoder_reparameterization_type="MLP", encoder_hidden_size=768 + ) + >>> prompt_encoder = PromptEncoder(config) + + **Attributes**: + - **embedding** ([`~torch.nn.Embedding`]) -- The embedding layer of the prompt encoder. + - **mlp_head** ([`~torch.nn.Sequential`]) -- The MLP head of the prompt encoder if `inference_mode=False`. + - **lstm_head** ([`~torch.nn.LSTM`]) -- The LSTM head of the prompt encoder if `inference_mode=False` and + `encoder_reparameterization_type="LSTM"`. + - **token_dim** (`int`) -- The hidden embedding dimension of the base transformer model. + - **input_size** (`int`) -- The input size of the prompt encoder. + - **output_size** (`int`) -- The output size of the prompt encoder. + - **hidden_size** (`int`) -- The hidden size of the prompt encoder. + - **total_virtual_tokens** (`int`): The total number of virtual tokens of the + prompt encoder. + - **encoder_type** (Union[[`PromptEncoderReparameterizationType`], `str`]): + The encoder type of the prompt encoder. + + + Input shape: (batch_size, total_virtual_tokens) + + Output shape: (batch_size, total_virtual_tokens, token_dim) + """ + + def __init__(self, config): + super().__init__() + self.token_dim = config.token_dim + self.input_size = self.token_dim + self.output_size = self.token_dim + self.hidden_size = config.encoder_hidden_size + self.total_virtual_tokens = config.num_virtual_tokens * config.num_transformer_submodules + self.encoder_type = config.encoder_reparameterization_type + + # embedding + self.embedding = torch.nn.Embedding(self.total_virtual_tokens, self.token_dim) + if not config.inference_mode: + if self.encoder_type == PromptEncoderReparameterizationType.LSTM: + lstm_dropout = config.encoder_dropout + num_layers = config.encoder_num_layers + # LSTM + self.lstm_head = torch.nn.LSTM( + input_size=self.input_size, + hidden_size=self.hidden_size, + num_layers=num_layers, + dropout=lstm_dropout, + bidirectional=True, + batch_first=True, + ) + + self.mlp_head = torch.nn.Sequential( + torch.nn.Linear(self.hidden_size * 2, self.hidden_size * 2), + torch.nn.ReLU(), + torch.nn.Linear(self.hidden_size * 2, self.output_size), + ) + + elif self.encoder_type == PromptEncoderReparameterizationType.MLP: + warnings.warn( + f"for {self.encoder_type}, the `encoder_num_layers` is ignored. Exactly 2 MLP layers are used." + ) + layers = [ + torch.nn.Linear(self.input_size, self.hidden_size), + torch.nn.ReLU(), + torch.nn.Linear(self.hidden_size, self.hidden_size), + torch.nn.ReLU(), + torch.nn.Linear(self.hidden_size, self.output_size), + ] + self.mlp_head = torch.nn.Sequential(*layers) + + else: + raise ValueError("Prompt encoder type not recognized. Please use one of MLP (recommended) or LSTM.") + + def forward(self, indices): + input_embeds = self.embedding(indices) + if self.encoder_type == PromptEncoderReparameterizationType.LSTM: + output_embeds = self.mlp_head(self.lstm_head(input_embeds)[0]) + elif self.encoder_type == PromptEncoderReparameterizationType.MLP: + output_embeds = self.mlp_head(input_embeds) + else: + raise ValueError("Prompt encoder type not recognized. Please use one of MLP (recommended) or LSTM.") + + return output_embeds diff --git a/04-gene-sft/peft/build/lib/peft/tuners/prefix_tuning.py b/04-gene-sft/peft/build/lib/peft/tuners/prefix_tuning.py new file mode 100644 index 0000000000000000000000000000000000000000..fcb207c6007f07ae006429b6862044427cde1264 --- /dev/null +++ b/04-gene-sft/peft/build/lib/peft/tuners/prefix_tuning.py @@ -0,0 +1,101 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from dataclasses import dataclass, field + +import torch + +from ..utils import PeftType, PromptLearningConfig + + +@dataclass +class PrefixTuningConfig(PromptLearningConfig): + """ + This is the configuration class to store the configuration of a [`~peft.PrefixEncoder`]. + + Args: + encoder_hidden_size (`int`): The hidden size of the prompt encoder. + prefix_projection (`bool`): Whether to project the prefix embeddings. + """ + + encoder_hidden_size: int = field( + default=None, + metadata={"help": "The hidden size of the encoder"}, + ) + prefix_projection: bool = field( + default=False, + metadata={"help": "Whether to project the prefix tokens"}, + ) + + def __post_init__(self): + self.peft_type = PeftType.PREFIX_TUNING + + +# Based on https://github.com/THUDM/P-tuning-v2/blob/main/model/prefix_encoder.py +# with some refactor +class PrefixEncoder(torch.nn.Module): + r""" + The torch.nn model to encode the prefix + + Args: + config ([`PrefixTuningConfig`]): The configuration of the prefix encoder. + + Example:: + + >>> from peft import PrefixEncoder, PrefixTuningConfig >>> config = PrefixTuningConfig( + peft_type="PREFIX_TUNING", task_type="SEQ_2_SEQ_LM", num_virtual_tokens=20, token_dim=768, + num_transformer_submodules=1, num_attention_heads=12, num_layers=12, encoder_hidden_size=768 + ) + >>> prefix_encoder = PrefixEncoder(config) + + + **Attributes**: + - **embedding** (`torch.nn.Embedding`) -- + The embedding layer of the prefix encoder. + - **transform** (`torch.nn.Sequential`) -- The + two-layer MLP to transform the prefix embeddings if `prefix_projection` is `True`. + - **prefix_projection** (`bool`) -- Whether to project the prefix embeddings. + + Input shape: (batch_size, num_virtual_tokens) + + Output shape: (batch_size, num_virtual_tokens, 2*layers*hidden) + """ + + def __init__(self, config): + super().__init__() + self.prefix_projection = config.prefix_projection + token_dim = config.token_dim + num_layers = config.num_layers + encoder_hidden_size = config.encoder_hidden_size + num_virtual_tokens = config.num_virtual_tokens + if self.prefix_projection and not config.inference_mode: + # Use a two-layer MLP to encode the prefix + self.embedding = torch.nn.Embedding(num_virtual_tokens, token_dim) + self.transform = torch.nn.Sequential( + torch.nn.Linear(token_dim, encoder_hidden_size), + torch.nn.Tanh(), + torch.nn.Linear(encoder_hidden_size, num_layers * 2 * token_dim), + ) + else: + self.embedding = torch.nn.Embedding(num_virtual_tokens, num_layers * 2 * token_dim) + + def forward(self, prefix: torch.Tensor): + if self.prefix_projection: + prefix_tokens = self.embedding(prefix) + past_key_values = self.transform(prefix_tokens) + else: + past_key_values = self.embedding(prefix) + return past_key_values diff --git a/04-gene-sft/peft/build/lib/peft/tuners/prompt_tuning.py b/04-gene-sft/peft/build/lib/peft/tuners/prompt_tuning.py new file mode 100644 index 0000000000000000000000000000000000000000..1dead1de7137398681a982d6c1d18ebd84972606 --- /dev/null +++ b/04-gene-sft/peft/build/lib/peft/tuners/prompt_tuning.py @@ -0,0 +1,120 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import enum +import math +from dataclasses import dataclass, field +from typing import Optional, Union + +import torch + +from ..utils import PeftType, PromptLearningConfig + + +class PromptTuningInit(str, enum.Enum): + TEXT = "TEXT" + RANDOM = "RANDOM" + + +@dataclass +class PromptTuningConfig(PromptLearningConfig): + """ + This is the configuration class to store the configuration of a [`~peft.PromptEmbedding`]. + + Args: + prompt_tuning_init (Union[[`PromptTuningInit`], `str`]): The initialization of the prompt embedding. + prompt_tuning_init_text ( Optional[`str`]): The text to initialize the prompt embedding. + Only used if `prompt_tuning_init` is `TEXT` + tokenizer_name_or_path ( Optional[`str`]): The name or path of the tokenizer. + Only used if `prompt_tuning_init` is `TEXT` + """ + + prompt_tuning_init: Union[PromptTuningInit, str] = field( + default=PromptTuningInit.RANDOM, + metadata={"help": "How to initialize the prompt tuning parameters"}, + ) + prompt_tuning_init_text: Optional[str] = field( + default=None, + metadata={ + "help": "The text to use for prompt tuning initialization. Only used if prompt_tuning_init is `TEXT`" + }, + ) + tokenizer_name_or_path: Optional[str] = field( + default=None, + metadata={ + "help": "The tokenizer to use for prompt tuning initialization. Only used if prompt_tuning_init is `TEXT`" + }, + ) + + def __post_init__(self): + self.peft_type = PeftType.PROMPT_TUNING + + +class PromptEmbedding(torch.nn.Module): + """ + The model to encode virtual tokens into prompt embeddings. + + Args: + config ([`PromptTuningConfig`]): The configuration of the prompt embedding. + word_embeddings (`torch.nn.Module`): The word embeddings of the base transformer model. + + **Attributes**: + **embedding** (`torch.nn.Embedding`) -- The embedding layer of the prompt embedding. + + Example:: + + >>> from peft import PromptEmbedding, PromptTuningConfig >>> config = PromptTuningConfig( + peft_type="PROMPT_TUNING", task_type="SEQ_2_SEQ_LM", num_virtual_tokens=20, token_dim=768, + num_transformer_submodules=1, num_attention_heads=12, num_layers=12, prompt_tuning_init="TEXT", + prompt_tuning_init_text="Predict if sentiment of this review is positive, negative or neutral", + tokenizer_name_or_path="t5-base", + ) + >>> # t5_model.shared is the word embeddings of the base model >>> prompt_embedding = PromptEmbedding(config, + t5_model.shared) + + + Input Shape: (batch_size, total_virtual_tokens) + + Output Shape: (batch_size, total_virtual_tokens, token_dim) + """ + + def __init__(self, config, word_embeddings): + super().__init__() + + total_virtual_tokens = config.num_virtual_tokens * config.num_transformer_submodules + self.embedding = torch.nn.Embedding(total_virtual_tokens, config.token_dim) + if config.prompt_tuning_init == PromptTuningInit.TEXT: + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name_or_path) + init_text = config.prompt_tuning_init_text + init_token_ids = tokenizer(init_text)["input_ids"] + # Trim or iterate until num_text_tokens matches total_virtual_tokens + num_text_tokens = len(init_token_ids) + if num_text_tokens > total_virtual_tokens: + init_token_ids = init_token_ids[:total_virtual_tokens] + elif num_text_tokens < total_virtual_tokens: + num_reps = math.ceil(total_virtual_tokens / num_text_tokens) + init_token_ids = init_token_ids * num_reps + init_token_ids = init_token_ids[:total_virtual_tokens] + + word_embedding_weights = word_embeddings(torch.LongTensor(init_token_ids)).detach().clone() + word_embedding_weights = word_embedding_weights.to(torch.float32) + self.embedding.weight = torch.nn.Parameter(word_embedding_weights) + + def forward(self, indices): + # Just get embeddings + prompt_embeddings = self.embedding(indices) + return prompt_embeddings diff --git a/04-gene-sft/peft/build/lib/peft/utils/__init__.py b/04-gene-sft/peft/build/lib/peft/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dd949c038749373cd3be1e0867d1f75578a5ffc5 --- /dev/null +++ b/04-gene-sft/peft/build/lib/peft/utils/__init__.py @@ -0,0 +1,30 @@ +# flake8: noqa +# There's no way to ignore "F401 '...' imported but unused" warnings in this +# module, but to preserve other warnings. So, don't check this module at all + +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .adapters_utils import CONFIG_NAME, WEIGHTS_NAME +from .config import PeftConfig, PeftType, PromptLearningConfig, TaskType +from .other import ( + TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING, + _set_trainable, + bloom_model_postprocess_past_key_value, + prepare_model_for_int8_training, + shift_tokens_right, + transpose, +) +from .save_and_load import get_peft_model_state_dict, set_peft_model_state_dict diff --git a/04-gene-sft/peft/build/lib/peft/utils/adapters_utils.py b/04-gene-sft/peft/build/lib/peft/utils/adapters_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f2f8a9517cc532ff18b85ac985ea3ce5d5cafa06 --- /dev/null +++ b/04-gene-sft/peft/build/lib/peft/utils/adapters_utils.py @@ -0,0 +1,18 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +WEIGHTS_NAME = "adapter_model.bin" +CONFIG_NAME = "adapter_config.json" + +# TODO: add automapping and superclass here? diff --git a/04-gene-sft/peft/build/lib/peft/utils/config.py b/04-gene-sft/peft/build/lib/peft/utils/config.py new file mode 100644 index 0000000000000000000000000000000000000000..3e2cf5bd06e2e9bfb8d279de1b006c1c47c1d098 --- /dev/null +++ b/04-gene-sft/peft/build/lib/peft/utils/config.py @@ -0,0 +1,167 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import enum +import json +import os +from dataclasses import asdict, dataclass, field +from typing import Optional, Union + +from huggingface_hub import hf_hub_download +from transformers.utils import PushToHubMixin + +from .adapters_utils import CONFIG_NAME + + +class PeftType(str, enum.Enum): + PROMPT_TUNING = "PROMPT_TUNING" + P_TUNING = "P_TUNING" + PREFIX_TUNING = "PREFIX_TUNING" + LORA = "LORA" + + +class TaskType(str, enum.Enum): + SEQ_CLS = "SEQ_CLS" + SEQ_2_SEQ_LM = "SEQ_2_SEQ_LM" + CAUSAL_LM = "CAUSAL_LM" + TOKEN_CLS = "TOKEN_CLS" + + +@dataclass +class PeftConfigMixin(PushToHubMixin): + r""" + This is the base configuration class for PEFT adapter models. It contains all the methods that are common to all + PEFT adapter models. This class inherits from `transformers.utils.PushToHubMixin` which contains the methods to + push your model to the Hub. The method `save_pretrained` will save the configuration of your adapter model in a + directory. The method `from_pretrained` will load the configuration of your adapter model from a directory. + + Args: + peft_type (Union[[`~peft.utils.config.PeftType`], `str`]): The type of Peft method to use. + """ + peft_type: Optional[PeftType] = field(default=None, metadata={"help": "The type of PEFT model."}) + + @property + def __dict__(self): + return asdict(self) + + def to_dict(self): + return self.__dict__ + + def save_pretrained(self, save_directory, **kwargs): + r""" + This method saves the configuration of your adapter model in a directory. + + Args: + save_directory (`str`): + The directory where the configuration will be saved. + **kwargs: + Additional keyword arguments passed along to the `transformers.utils.PushToHubMixin.push_to_hub` + method. + """ + if os.path.isfile(save_directory): + raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file") + + os.makedirs(save_directory, exist_ok=True) + + output_dict = self.__dict__ + output_path = os.path.join(save_directory, CONFIG_NAME) + + # save it + with open(output_path, "w") as writer: + writer.write(json.dumps(output_dict, indent=2, sort_keys=True)) + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): + r""" + This method loads the configuration of your adapter model from a directory. + + Args: + pretrained_model_name_or_path (`str`): + The directory or the hub-id where the configuration is saved. + **kwargs: + Additional keyword arguments passed along to the child class initialization. + """ + if os.path.isfile(os.path.join(pretrained_model_name_or_path, CONFIG_NAME)): + config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME) + else: + try: + config_file = hf_hub_download(pretrained_model_name_or_path, CONFIG_NAME) + except Exception: + raise ValueError(f"Can't find config.json at '{pretrained_model_name_or_path}'") + + loaded_attributes = cls.from_json_file(config_file) + + config = cls(**kwargs) + + for key, value in loaded_attributes.items(): + if hasattr(config, key): + setattr(config, key, value) + + return config + + @classmethod + def from_json_file(cls, path_json_file, **kwargs): + r""" + Loads a configuration file from a json file. + + Args: + path_json_file (`str`): + The path to the json file. + """ + with open(path_json_file, "r") as file: + json_object = json.load(file) + + return json_object + + +@dataclass +class PeftConfig(PeftConfigMixin): + """ + This is the base configuration class to store the configuration of a :class:`~peft.PeftModel`. + + Args: + peft_type (Union[[`~peft.utils.config.PeftType`], `str`]): The type of Peft method to use. + task_type (Union[[`~peft.utils.config.TaskType`], `str`]): The type of task to perform. + inference_mode (`bool`, defaults to `False`): Whether to use the Peft model in inference mode. + """ + + base_model_name_or_path: str = field(default=None, metadata={"help": "The name of the base model to use."}) + peft_type: Union[str, PeftType] = field(default=None, metadata={"help": "Peft type"}) + task_type: Union[str, TaskType] = field(default=None, metadata={"help": "Task type"}) + inference_mode: bool = field(default=False, metadata={"help": "Whether to use inference mode"}) + + +@dataclass +class PromptLearningConfig(PeftConfig): + """ + This is the base configuration class to store the configuration of a Union[[`~peft.PrefixTuning`], + [`~peft.PromptEncoder`], [`~peft.PromptTuning`]]. + + Args: + num_virtual_tokens (`int`): The number of virtual tokens to use. + token_dim (`int`): The hidden embedding dimension of the base transformer model. + num_transformer_submodules (`int`): The number of transformer submodules in the base transformer model. + num_attention_heads (`int`): The number of attention heads in the base transformer model. + num_layers (`int`): The number of layers in the base transformer model. + """ + + num_virtual_tokens: int = field(default=None, metadata={"help": "Number of virtual tokens"}) + token_dim: int = field( + default=None, metadata={"help": "The hidden embedding dimension of the base transformer model"} + ) + num_transformer_submodules: Optional[int] = field( + default=None, metadata={"help": "Number of transformer submodules"} + ) + num_attention_heads: Optional[int] = field(default=None, metadata={"help": "Number of attention heads"}) + num_layers: Optional[int] = field(default=None, metadata={"help": "Number of transformer layers"}) diff --git a/04-gene-sft/peft/build/lib/peft/utils/other.py b/04-gene-sft/peft/build/lib/peft/utils/other.py new file mode 100644 index 0000000000000000000000000000000000000000..132b033484ff4aace5843d8d5817fa6fda4ab17d --- /dev/null +++ b/04-gene-sft/peft/build/lib/peft/utils/other.py @@ -0,0 +1,159 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + + +# needed for prefix-tuning of bloom model +def bloom_model_postprocess_past_key_value(past_key_values): + past_key_values = torch.cat(past_key_values) + total_layers, batch_size, num_attention_heads, num_virtual_tokens, head_dim = past_key_values.shape + keys = past_key_values[: total_layers // 2] + keys = keys.transpose(2, 3).reshape( + total_layers // 2, batch_size * num_attention_heads, head_dim, num_virtual_tokens + ) + values = past_key_values[total_layers // 2 :] + values = values.reshape(total_layers // 2, batch_size * num_attention_heads, num_virtual_tokens, head_dim) + + return tuple(zip(keys, values)) + + +def prepare_model_for_int8_training( + model, output_embedding_layer_name="lm_head", use_gradient_checkpointing=True, layer_norm_names=["layer_norm"] +): + r""" + This method wrapps the entire protocol for preparing a model before running a training. This includes: + 1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm + head to fp32 + + Args: + model, (`transformers.PreTrainedModel`): + The loaded model from `transformers` + """ + loaded_in_8bit = getattr(model, "is_loaded_in_8bit", False) + + for name, param in model.named_parameters(): + # freeze base model's layers + param.requires_grad = False + + if loaded_in_8bit: + # cast layer norm in fp32 for stability for 8bit models + if param.ndim == 1 and any(layer_norm_name in name for layer_norm_name in layer_norm_names): + param.data = param.data.to(torch.float32) + + if loaded_in_8bit and use_gradient_checkpointing: + # For backward compatibility + if hasattr(model, "enable_input_require_grads"): + model.enable_input_require_grads() + else: + + def make_inputs_require_grad(module, input, output): + output.requires_grad_(True) + + model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) + + # enable gradient checkpointing for memory efficiency + model.gradient_checkpointing_enable() + + if hasattr(model, output_embedding_layer_name): + output_embedding_layer = getattr(model, output_embedding_layer_name) + input_dtype = output_embedding_layer.weight.dtype + + class CastOutputToFloat(torch.nn.Sequential): + r""" + Manually cast to the expected dtype of the lm_head as sometimes there is a final layer norm that is casted + in fp32 + + """ + + def forward(self, x): + return super().forward(x.to(input_dtype)).to(torch.float32) + + setattr(model, output_embedding_layer_name, CastOutputToFloat(output_embedding_layer)) + + return model + + +TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING = { + "bloom": bloom_model_postprocess_past_key_value, +} + + +# copied from transformers.models.bart.modeling_bart +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): + """ + Shift input ids one token to the right. + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): input ids + pad_token_id (`int`): The id of the `padding` token. + decoder_start_token_id (`int`): The id of the `start` token. + """ + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() + shifted_input_ids[:, 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +def _set_trainable(model): + if model.modules_to_save is not None: + for name, param in model.named_parameters(): + if any(module_name in name for module_name in model.modules_to_save): + param.requires_grad = True + + +def fsdp_auto_wrap_policy(model): + import functools + import os + + from accelerate import FullyShardedDataParallelPlugin + from torch.distributed.fsdp.wrap import _or_policy, lambda_auto_wrap_policy, transformer_auto_wrap_policy + + from ..tuners import PrefixEncoder, PromptEmbedding, PromptEncoder + + def lambda_policy_fn(module): + if ( + len(list(module.named_children())) == 0 + and getattr(module, "weight", None) is not None + and module.weight.requires_grad + ): + return True + return False + + lambda_policy = functools.partial(lambda_auto_wrap_policy, lambda_fn=lambda_policy_fn) + transformer_wrap_policy = functools.partial( + transformer_auto_wrap_policy, + transformer_layer_cls=( + PrefixEncoder, + PromptEncoder, + PromptEmbedding, + FullyShardedDataParallelPlugin.get_module_class_from_name( + model, os.environ.get("FSDP_TRANSFORMER_CLS_TO_WRAP", "") + ), + ), + ) + + auto_wrap_policy = functools.partial(_or_policy, policies=[lambda_policy, transformer_wrap_policy]) + return auto_wrap_policy + + +def transpose(weight, fan_in_fan_out): + return weight.T if fan_in_fan_out else weight diff --git a/04-gene-sft/peft/build/lib/peft/utils/save_and_load.py b/04-gene-sft/peft/build/lib/peft/utils/save_and_load.py new file mode 100644 index 0000000000000000000000000000000000000000..c6596c7ce62afb622530350527838cf2c2d2929a --- /dev/null +++ b/04-gene-sft/peft/build/lib/peft/utils/save_and_load.py @@ -0,0 +1,79 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .config import PeftType + + +def get_peft_model_state_dict(model, state_dict=None): + """ + Get the state dict of the Peft model. + + Args: + model ([`PeftModel`]): The Peft model. When using torch.nn.DistributedDataParallel, DeepSpeed or FSDP, + the model should be the underlying model/unwrapped model (i.e. model.module). + state_dict (`dict`, *optional*, defaults to `None`): + The state dict of the model. If not provided, the state dict of the model + will be used. + """ + if state_dict is None: + state_dict = model.state_dict() + if model.peft_config.peft_type == PeftType.LORA: + # to_return = lora_state_dict(model, bias=model.peft_config.bias) + # adapted from `https://github.com/microsoft/LoRA/blob/main/loralib/utils.py` + # to directly with the state dict which is necessary when using DeepSpeed or FSDP + bias = model.peft_config.bias + if bias == "none": + to_return = {k: state_dict[k] for k in state_dict if "lora_" in k} + elif bias == "all": + to_return = {k: state_dict[k] for k in state_dict if "lora_" in k or "bias" in k} + elif bias == "lora_only": + to_return = {} + for k in state_dict: + if "lora_" in k: + to_return[k] = state_dict[k] + bias_name = k.split("lora_")[0] + "bias" + if bias_name in state_dict: + to_return[bias_name] = state_dict[bias_name] + else: + raise NotImplementedError + else: + to_return = {} + if model.peft_config.inference_mode: + prompt_embeddings = model.prompt_encoder.embedding.weight + else: + prompt_embeddings = model.get_prompt_embedding_to_save() + to_return["prompt_embeddings"] = prompt_embeddings + if model.modules_to_save is not None: + for key, value in state_dict.items(): + if any(module_name in key for module_name in model.modules_to_save): + to_return[key] = value + return to_return + + +def set_peft_model_state_dict(model, peft_model_state_dict): + """ + Set the state dict of the Peft model. + + Args: + model ([`PeftModel`]): The Peft model. + peft_model_state_dict (`dict`): The state dict of the Peft model. + """ + + model.load_state_dict(peft_model_state_dict, strict=False) + if model.peft_config.peft_type != PeftType.LORA: + model.prompt_encoder.embedding.load_state_dict( + {"weight": peft_model_state_dict["prompt_embeddings"]}, strict=True + ) + return model diff --git a/04-gene-sft/peft/examples/causal_language_modeling/accelerate_ds_zero3_cpu_offload_config.yaml b/04-gene-sft/peft/examples/causal_language_modeling/accelerate_ds_zero3_cpu_offload_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a4a0bcfaf09bd632e6ac2152c8a4f30e183cc102 --- /dev/null +++ b/04-gene-sft/peft/examples/causal_language_modeling/accelerate_ds_zero3_cpu_offload_config.yaml @@ -0,0 +1,22 @@ +compute_environment: LOCAL_MACHINE +deepspeed_config: + gradient_accumulation_steps: 1 + gradient_clipping: 1.0 + offload_optimizer_device: none + offload_param_device: none + zero3_init_flag: true + zero3_save_16bit_model: true + zero_stage: 3 +distributed_type: DEEPSPEED +downcast_bf16: 'no' +dynamo_backend: 'NO' +fsdp_config: {} +machine_rank: 0 +main_training_function: main +megatron_lm_config: {} +mixed_precision: 'no' +num_machines: 1 +num_processes: 1 +rdzv_backend: static +same_network: true +use_cpu: false \ No newline at end of file diff --git a/04-gene-sft/peft/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb b/04-gene-sft/peft/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..385f5dcdb2023187fd141565010166ea4dad1a2b --- /dev/null +++ b/04-gene-sft/peft/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb @@ -0,0 +1,465 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "71fbfca2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===================================BUG REPORT===================================\n", + "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n", + "For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n", + "================================================================================\n", + "CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n", + "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n", + "CUDA SETUP: Detected CUDA version 117\n", + "CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n" + ] + } + ], + "source": [ + "from transformers import AutoModelForCausalLM\n", + "from peft import PeftModel, PeftConfig\n", + "import torch\n", + "from datasets import load_dataset\n", + "import os\n", + "from transformers import AutoTokenizer\n", + "from torch.utils.data import DataLoader\n", + "from transformers import default_data_collator, get_linear_schedule_with_warmup\n", + "from tqdm import tqdm\n", + "from datasets import load_dataset\n", + "\n", + "device = \"cuda\"\n", + "model_name_or_path = \"bigscience/bloomz-7b1\"\n", + "tokenizer_name_or_path = \"bigscience/bloomz-7b1\"\n", + "dataset_name = \"twitter_complaints\"\n", + "text_column = \"Tweet text\"\n", + "label_column = \"text_label\"\n", + "max_length = 64\n", + "lr = 1e-3\n", + "num_epochs = 50\n", + "batch_size = 8" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1a3648b", + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset = load_dataset(\"ought/raft\", dataset_name)\n", + "\n", + "classes = [k.replace(\"_\", \" \") for k in dataset[\"train\"].features[\"Label\"].names]\n", + "print(classes)\n", + "dataset = dataset.map(\n", + " lambda x: {\"text_label\": [classes[label] for label in x[\"Label\"]]},\n", + " batched=True,\n", + " num_proc=1,\n", + ")\n", + "print(dataset)\n", + "dataset[\"train\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fe12d4d3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "10cabeec92ab428f9a660ebaecbaf865", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Running tokenizer on dataset: 0%| | 0/1 [00:00 100:\n", + " break\n", + "test_preds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1c4ad9c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "vscode": { + "interpreter": { + "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/04-gene-sft/peft/examples/causal_language_modeling/peft_lora_clm_accelerate_ds_zero3_offload.py b/04-gene-sft/peft/examples/causal_language_modeling/peft_lora_clm_accelerate_ds_zero3_offload.py new file mode 100644 index 0000000000000000000000000000000000000000..daf9d1f731c11fa0c651b9b0da543bff4e103b96 --- /dev/null +++ b/04-gene-sft/peft/examples/causal_language_modeling/peft_lora_clm_accelerate_ds_zero3_offload.py @@ -0,0 +1,362 @@ +import gc +import os +import sys +import threading + +import numpy as np +import psutil +import torch +from accelerate import Accelerator +from datasets import load_dataset +from torch.utils.data import DataLoader +from tqdm import tqdm +from transformers import ( + AutoModelForCausalLM, + AutoTokenizer, + default_data_collator, + get_linear_schedule_with_warmup, + set_seed, +) + +from peft import LoraConfig, TaskType, get_peft_model + + +def levenshtein_distance(str1, str2): + # TC: O(N^2) + # SC: O(N^2) + if str1 == str2: + return 0 + num_rows = len(str1) + 1 + num_cols = len(str2) + 1 + dp_matrix = np.empty((num_rows, num_cols)) + dp_matrix[0, :] = range(num_cols) + dp_matrix[:, 0] = range(num_rows) + + for i in range(1, num_rows): + for j in range(1, num_cols): + if str1[i - 1] == str2[j - 1]: + dp_matrix[i, j] = dp_matrix[i - 1, j - 1] + else: + dp_matrix[i, j] = min(dp_matrix[i - 1, j - 1], dp_matrix[i - 1, j], dp_matrix[i, j - 1]) + 1 + + return dp_matrix[num_rows - 1, num_cols - 1] + + +def get_closest_label(eval_pred, classes): + min_id = sys.maxsize + min_edit_distance = sys.maxsize + for i, class_label in enumerate(classes): + edit_distance = levenshtein_distance(eval_pred.strip(), class_label) + if edit_distance < min_edit_distance: + min_id = i + min_edit_distance = edit_distance + return classes[min_id] + + +# Converting Bytes to Megabytes +def b2mb(x): + return int(x / 2**20) + + +# This context manager is used to track the peak memory usage of the process +class TorchTracemalloc: + def __enter__(self): + gc.collect() + torch.cuda.empty_cache() + torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero + self.begin = torch.cuda.memory_allocated() + self.process = psutil.Process() + + self.cpu_begin = self.cpu_mem_used() + self.peak_monitoring = True + peak_monitor_thread = threading.Thread(target=self.peak_monitor_func) + peak_monitor_thread.daemon = True + peak_monitor_thread.start() + return self + + def cpu_mem_used(self): + """get resident set size memory for the current process""" + return self.process.memory_info().rss + + def peak_monitor_func(self): + self.cpu_peak = -1 + + while True: + self.cpu_peak = max(self.cpu_mem_used(), self.cpu_peak) + + # can't sleep or will not catch the peak right (this comment is here on purpose) + # time.sleep(0.001) # 1msec + + if not self.peak_monitoring: + break + + def __exit__(self, *exc): + self.peak_monitoring = False + + gc.collect() + torch.cuda.empty_cache() + self.end = torch.cuda.memory_allocated() + self.peak = torch.cuda.max_memory_allocated() + self.used = b2mb(self.end - self.begin) + self.peaked = b2mb(self.peak - self.begin) + + self.cpu_end = self.cpu_mem_used() + self.cpu_used = b2mb(self.cpu_end - self.cpu_begin) + self.cpu_peaked = b2mb(self.cpu_peak - self.cpu_begin) + # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}") + + +def main(): + accelerator = Accelerator() + model_name_or_path = "bigscience/bloomz-7b1" + dataset_name = "twitter_complaints" + peft_config = LoraConfig(task_type=TaskType.CAUSAL_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1) + text_column = "Tweet text" + label_column = "text_label" + lr = 3e-3 + num_epochs = 20 + batch_size = 8 + seed = 42 + max_length = 64 + do_test = False + set_seed(seed) + + dataset = load_dataset("ought/raft", dataset_name) + classes = [k.replace("_", " ") for k in dataset["train"].features["Label"].names] + dataset = dataset.map( + lambda x: {"text_label": [classes[label] for label in x["Label"]]}, + batched=True, + num_proc=1, + ) + + tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) + + def preprocess_function(examples): + batch_size = len(examples[text_column]) + inputs = [f"{text_column} : {x} Label : " for x in examples[text_column]] + targets = [str(x) for x in examples[label_column]] + model_inputs = tokenizer(inputs) + labels = tokenizer(targets) + for i in range(batch_size): + sample_input_ids = model_inputs["input_ids"][i] + label_input_ids = labels["input_ids"][i] + [tokenizer.pad_token_id] + model_inputs["input_ids"][i] = sample_input_ids + label_input_ids + labels["input_ids"][i] = [-100] * len(sample_input_ids) + label_input_ids + model_inputs["attention_mask"][i] = [1] * len(model_inputs["input_ids"][i]) + for i in range(batch_size): + sample_input_ids = model_inputs["input_ids"][i] + label_input_ids = labels["input_ids"][i] + model_inputs["input_ids"][i] = [tokenizer.pad_token_id] * ( + max_length - len(sample_input_ids) + ) + sample_input_ids + model_inputs["attention_mask"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[ + "attention_mask" + ][i] + labels["input_ids"][i] = [-100] * (max_length - len(sample_input_ids)) + label_input_ids + model_inputs["input_ids"][i] = torch.tensor(model_inputs["input_ids"][i][:max_length]) + model_inputs["attention_mask"][i] = torch.tensor(model_inputs["attention_mask"][i][:max_length]) + labels["input_ids"][i] = torch.tensor(labels["input_ids"][i][:max_length]) + model_inputs["labels"] = labels["input_ids"] + return model_inputs + + def test_preprocess_function(examples): + batch_size = len(examples[text_column]) + inputs = [f"{text_column} : {x} Label : " for x in examples[text_column]] + model_inputs = tokenizer(inputs) + # print(model_inputs) + for i in range(batch_size): + sample_input_ids = model_inputs["input_ids"][i] + model_inputs["input_ids"][i] = [tokenizer.pad_token_id] * ( + max_length - len(sample_input_ids) + ) + sample_input_ids + model_inputs["attention_mask"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[ + "attention_mask" + ][i] + model_inputs["input_ids"][i] = torch.tensor(model_inputs["input_ids"][i][:max_length]) + model_inputs["attention_mask"][i] = torch.tensor(model_inputs["attention_mask"][i][:max_length]) + return model_inputs + + with accelerator.main_process_first(): + processed_datasets = dataset.map( + preprocess_function, + batched=True, + num_proc=1, + remove_columns=dataset["train"].column_names, + load_from_cache_file=True, + desc="Running tokenizer on dataset", + ) + accelerator.wait_for_everyone() + + train_dataset = processed_datasets["train"] + + with accelerator.main_process_first(): + processed_datasets = dataset.map( + test_preprocess_function, + batched=True, + num_proc=1, + remove_columns=dataset["train"].column_names, + load_from_cache_file=False, + desc="Running tokenizer on dataset", + ) + eval_dataset = processed_datasets["train"] + test_dataset = processed_datasets["test"] + + train_dataloader = DataLoader( + train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True + ) + eval_dataloader = DataLoader( + eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True + ) + test_dataloader = DataLoader( + test_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True + ) + + print(next(iter(train_dataloader))) + + # creating model + model = AutoModelForCausalLM.from_pretrained(model_name_or_path) + model = get_peft_model(model, peft_config) + model.print_trainable_parameters() + + # optimizer + optimizer = torch.optim.AdamW(model.parameters(), lr=lr) + + # lr scheduler + lr_scheduler = get_linear_schedule_with_warmup( + optimizer=optimizer, + num_warmup_steps=0, + num_training_steps=(len(train_dataloader) * num_epochs), + ) + + model, train_dataloader, eval_dataloader, test_dataloader, optimizer, lr_scheduler = accelerator.prepare( + model, train_dataloader, eval_dataloader, test_dataloader, optimizer, lr_scheduler + ) + accelerator.print(model) + + is_ds_zero_3 = False + if getattr(accelerator.state, "deepspeed_plugin", None): + is_ds_zero_3 = accelerator.state.deepspeed_plugin.zero_stage == 3 + + for epoch in range(num_epochs): + with TorchTracemalloc() as tracemalloc: + model.train() + total_loss = 0 + for step, batch in enumerate(tqdm(train_dataloader)): + outputs = model(**batch) + loss = outputs.loss + total_loss += loss.detach().float() + accelerator.backward(loss) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage + accelerator.print("GPU Memory before entering the train : {}".format(b2mb(tracemalloc.begin))) + accelerator.print("GPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used)) + accelerator.print("GPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked)) + accelerator.print( + "GPU Total Peak Memory consumed during the train (max): {}".format( + tracemalloc.peaked + b2mb(tracemalloc.begin) + ) + ) + + accelerator.print("CPU Memory before entering the train : {}".format(b2mb(tracemalloc.cpu_begin))) + accelerator.print("CPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.cpu_used)) + accelerator.print("CPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.cpu_peaked)) + accelerator.print( + "CPU Total Peak Memory consumed during the train (max): {}".format( + tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin) + ) + ) + train_epoch_loss = total_loss / len(eval_dataloader) + train_ppl = torch.exp(train_epoch_loss) + accelerator.print(f"{epoch=}: {train_ppl=} {train_epoch_loss=}") + + model.eval() + eval_preds = [] + with TorchTracemalloc() as tracemalloc: + for _, batch in enumerate(tqdm(eval_dataloader)): + batch = {k: v for k, v in batch.items() if k != "labels"} + with torch.no_grad(): + outputs = accelerator.unwrap_model(model).generate( + **batch, synced_gpus=is_ds_zero_3, max_new_tokens=10 + ) # synced_gpus=True for DS-stage 3 + outputs = accelerator.pad_across_processes(outputs, dim=1, pad_index=tokenizer.pad_token_id) + preds = accelerator.gather(outputs) + preds = preds[:, max_length:].detach().cpu().numpy() + eval_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True)) + + # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage + accelerator.print("GPU Memory before entering the eval : {}".format(b2mb(tracemalloc.begin))) + accelerator.print("GPU Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used)) + accelerator.print("GPU Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked)) + accelerator.print( + "GPU Total Peak Memory consumed during the eval (max): {}".format( + tracemalloc.peaked + b2mb(tracemalloc.begin) + ) + ) + + accelerator.print("CPU Memory before entering the eval : {}".format(b2mb(tracemalloc.cpu_begin))) + accelerator.print("CPU Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.cpu_used)) + accelerator.print("CPU Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.cpu_peaked)) + accelerator.print( + "CPU Total Peak Memory consumed during the eval (max): {}".format( + tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin) + ) + ) + + correct = 0 + total = 0 + assert len(eval_preds) == len( + dataset["train"][label_column] + ), f"{len(eval_preds)} != {len(dataset['train'][label_column])}" + for pred, true in zip(eval_preds, dataset["train"][label_column]): + if pred.strip() == true.strip(): + correct += 1 + total += 1 + accuracy = correct / total * 100 + accelerator.print(f"{accuracy=}") + accelerator.print(f"{eval_preds[:10]=}") + accelerator.print(f"{dataset['train'][label_column][:10]=}") + + if do_test: + model.eval() + test_preds = [] + for _, batch in enumerate(tqdm(test_dataloader)): + batch = {k: v for k, v in batch.items() if k != "labels"} + with torch.no_grad(): + outputs = accelerator.unwrap_model(model).generate( + **batch, synced_gpus=is_ds_zero_3, max_new_tokens=10 + ) # synced_gpus=True for DS-stage 3 + outputs = accelerator.pad_across_processes(outputs, dim=1, pad_index=tokenizer.pad_token_id) + preds = accelerator.gather(outputs) + preds = preds[:, max_length:].detach().cpu().numpy() + test_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True)) + + test_preds_cleaned = [] + for _, pred in enumerate(test_preds): + test_preds_cleaned.append(get_closest_label(pred, classes)) + + test_df = dataset["test"].to_pandas() + assert len(test_preds_cleaned) == len(test_df), f"{len(test_preds_cleaned)} != {len(test_df)}" + test_df[label_column] = test_preds_cleaned + test_df["text_labels_orig"] = test_preds + accelerator.print(test_df[[text_column, label_column]].sample(20)) + + pred_df = test_df[["ID", label_column]] + pred_df.columns = ["ID", "Label"] + + os.makedirs(f"data/{dataset_name}", exist_ok=True) + pred_df.to_csv(f"data/{dataset_name}/predictions.csv", index=False) + + accelerator.wait_for_everyone() + model.push_to_hub( + "smangrul/" + + f"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}".replace("/", "_"), + state_dict=accelerator.get_state_dict(model), + use_auth_token=True, + ) + accelerator.wait_for_everyone() + + +if __name__ == "__main__": + main() diff --git a/04-gene-sft/peft/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb b/04-gene-sft/peft/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cf36d219e2fa5d74e70e7f9b09dc6d216c517dfe --- /dev/null +++ b/04-gene-sft/peft/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb @@ -0,0 +1,1358 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "71fbfca2", + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import AutoModelForCausalLM\n", + "from peft import get_peft_config, get_peft_model, PrefixTuningConfig, TaskType, PeftType\n", + "import torch\n", + "from datasets import load_dataset\n", + "import os\n", + "from transformers import AutoTokenizer\n", + "from torch.utils.data import DataLoader\n", + "from transformers import default_data_collator, get_linear_schedule_with_warmup\n", + "from tqdm import tqdm\n", + "from datasets import load_dataset\n", + "\n", + "device = \"cuda\"\n", + "model_name_or_path = \"bigscience/bloomz-560m\"\n", + "tokenizer_name_or_path = \"bigscience/bloomz-560m\"\n", + "peft_config = PrefixTuningConfig(task_type=TaskType.CAUSAL_LM, num_virtual_tokens=30)\n", + "\n", + "dataset_name = \"twitter_complaints\"\n", + "checkpoint_name = f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}_v1.pt\".replace(\n", + " \"/\", \"_\"\n", + ")\n", + "text_column = \"Tweet text\"\n", + "label_column = \"text_label\"\n", + "max_length = 64\n", + "lr = 3e-2\n", + "num_epochs = 50\n", + "batch_size = 8" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e1a3648b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Found cached dataset raft (/home/sourab/.cache/huggingface/datasets/ought___raft/twitter_complaints/1.1.0/79c4de1312c1e3730043f7db07179c914f48403101f7124e2fe336f6f54d9f84)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "56d9908a2c8944b484348cc46b16a261", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/2 [00:00, base_model_name_or_path='bigscience/bloomz-560m', task_type=, inference_mode=False, num_virtual_tokens=30, token_dim=1024, num_transformer_submodules=1, num_attention_heads=16, num_layers=24, encoder_hidden_size=1024, prefix_projection=False)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.peft_config" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b2f91568", + "metadata": {}, + "outputs": [], + "source": [ + "# model\n", + "# optimizer and lr scheduler\n", + "optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n", + "lr_scheduler = get_linear_schedule_with_warmup(\n", + " optimizer=optimizer,\n", + " num_warmup_steps=0,\n", + " num_training_steps=(len(train_dataloader) * num_epochs),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e4fb69fc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + 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train_epoch_loss=tensor(0.0023, device='cuda:0') eval_ppl=tensor(1.0024, device='cuda:0') eval_epoch_loss=tensor(0.0024, device='cuda:0')\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# training and evaluation\n", + "model = model.to(device)\n", + "\n", + "for epoch in range(num_epochs):\n", + " model.train()\n", + " total_loss = 0\n", + " for step, batch in enumerate(tqdm(train_dataloader)):\n", + " batch = {k: v.to(device) for k, v in batch.items()}\n", + " # print(batch)\n", + " # print(batch[\"input_ids\"].shape)\n", + " outputs = model(**batch)\n", + " loss = outputs.loss\n", + " total_loss += loss.detach().float()\n", + " loss.backward()\n", + " optimizer.step()\n", + " lr_scheduler.step()\n", + " optimizer.zero_grad()\n", + "\n", + " model.eval()\n", + " eval_loss = 0\n", + " eval_preds = []\n", + " for step, batch in enumerate(tqdm(eval_dataloader)):\n", + " batch = {k: v.to(device) for k, v in batch.items()}\n", + " with torch.no_grad():\n", + " outputs = model(**batch)\n", + " loss = outputs.loss\n", + " eval_loss += loss.detach().float()\n", + " eval_preds.extend(\n", + " tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)\n", + " )\n", + "\n", + " eval_epoch_loss = eval_loss / len(eval_dataloader)\n", + " eval_ppl = torch.exp(eval_epoch_loss)\n", + " train_epoch_loss = total_loss / len(train_dataloader)\n", + " train_ppl = torch.exp(train_epoch_loss)\n", + " print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "53752a7b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hey @nytimes your link to cancel my subscription isn't working and nobody is answering the chat. Please don't play that kind of stupid game.\n", + "{'input_ids': tensor([[227985, 5484, 915, 54078, 2566, 7782, 24502, 2632, 8989,\n", + " 427, 36992, 2670, 140711, 21994, 10789, 530, 88399, 632,\n", + " 183542, 368, 44799, 17, 29901, 5926, 7229, 861, 11596,\n", + " 461, 78851, 14775, 17, 77658, 915, 210]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n", + "tensor([[227985, 5484, 915, 54078, 2566, 7782, 24502, 2632, 8989,\n", + " 427, 36992, 2670, 140711, 21994, 10789, 530, 88399, 632,\n", + " 183542, 368, 44799, 17, 29901, 5926, 7229, 861, 11596,\n", + " 461, 78851, 14775, 17, 77658, 915, 210, 16449, 5952,\n", + " 3]], device='cuda:0')\n", + "[\"Tweet text : Hey @nytimes your link to cancel my subscription isn't working and nobody is answering the chat. Please don't play that kind of stupid game. Label : complaint\"]\n" + ] + } + ], + "source": [ + "model.eval()\n", + "i = 16\n", + "inputs = tokenizer(f'{text_column} : {dataset[\"test\"][i][\"Tweet text\"]} Label : ', return_tensors=\"pt\")\n", + "print(dataset[\"test\"][i][\"Tweet text\"])\n", + "print(inputs)\n", + "\n", + "with torch.no_grad():\n", + " inputs = {k: v.to(device) for k, v in inputs.items()}\n", + " outputs = model.generate(\n", + " input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3\n", + " )\n", + " print(outputs)\n", + " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "24041ee1", + "metadata": {}, + "outputs": [], + "source": [ + "# saving model\n", + "peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n", + "model.save_pretrained(peft_model_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "527eeaa4", + "metadata": {}, + "outputs": [], + "source": [ + "ckpt = f\"{peft_model_id}/adapter_model.bin\"\n", + "!du -h $ckpt" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "b19f5a90", + "metadata": {}, + "outputs": [], + "source": [ + "from peft import PeftModel, PeftConfig\n", + "\n", + "peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n", + "\n", + "config = PeftConfig.from_pretrained(peft_model_id)\n", + "model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)\n", + "model = PeftModel.from_pretrained(model, peft_model_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "a11a3768", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "@greateranglia Ok thanks...\n", + "{'input_ids': tensor([[227985, 5484, 915, 2566, 14173, 2960, 29906, 387, 20706,\n", + " 49337, 1369, 77658, 915, 210]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n", + "tensor([[227985, 5484, 915, 2566, 14173, 2960, 29906, 387, 20706,\n", + " 49337, 1369, 77658, 915, 210, 1936, 106863, 3]],\n", + " device='cuda:0')\n", + "['Tweet text : @greateranglia Ok thanks... Label : no complaint']\n" + ] + } + ], + "source": [ + "model.to(device)\n", + "model.eval()\n", + "i = 4\n", + "inputs = tokenizer(f'{text_column} : {dataset[\"test\"][i][\"Tweet text\"]} Label : ', return_tensors=\"pt\")\n", + "print(dataset[\"test\"][i][\"Tweet text\"])\n", + "print(inputs)\n", + "\n", + "with torch.no_grad():\n", + " inputs = {k: v.to(device) for k, v in inputs.items()}\n", + " outputs = model.generate(\n", + " input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3\n", + " )\n", + " print(outputs)\n", + " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f890c951", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "463a41a2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c60c7a9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + }, + "vscode": { + "interpreter": { + "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/04-gene-sft/peft/examples/causal_language_modeling/peft_prompt_tuning_clm.ipynb b/04-gene-sft/peft/examples/causal_language_modeling/peft_prompt_tuning_clm.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e289206110366c96d7479012cc100317638cb346 --- /dev/null +++ b/04-gene-sft/peft/examples/causal_language_modeling/peft_prompt_tuning_clm.ipynb @@ -0,0 +1,1198 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "71fbfca2", + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import AutoModelForCausalLM\n", + "from peft import get_peft_config, get_peft_model, PromptTuningInit, PromptTuningConfig, TaskType, PeftType\n", + "import torch\n", + "from datasets import load_dataset\n", + "import os\n", + "from transformers import AutoTokenizer\n", + "from torch.utils.data import DataLoader\n", + "from transformers import default_data_collator, get_linear_schedule_with_warmup\n", + "from tqdm import tqdm\n", + "from datasets import load_dataset\n", + "\n", + "device = \"cuda\"\n", + "model_name_or_path = \"bigscience/bloomz-560m\"\n", + "tokenizer_name_or_path = \"bigscience/bloomz-560m\"\n", + "peft_config = PromptTuningConfig(\n", + " task_type=TaskType.CAUSAL_LM,\n", + " prompt_tuning_init=PromptTuningInit.TEXT,\n", + " num_virtual_tokens=8,\n", + " prompt_tuning_init_text=\"Classify if the tweet is a complaint or not:\",\n", + " tokenizer_name_or_path=model_name_or_path,\n", + ")\n", + "\n", + "dataset_name = \"twitter_complaints\"\n", + "checkpoint_name = f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}_v1.pt\".replace(\n", + " \"/\", \"_\"\n", + ")\n", + "text_column = \"Tweet text\"\n", + "label_column = \"text_label\"\n", + "max_length = 64\n", + "lr = 3e-2\n", + "num_epochs = 50\n", + "batch_size = 8" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1a3648b", + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset = load_dataset(\"ought/raft\", dataset_name)\n", + "\n", + "classes = [k.replace(\"_\", \" \") for k in dataset[\"train\"].features[\"Label\"].names]\n", + "print(classes)\n", + "dataset = dataset.map(\n", + " lambda x: {\"text_label\": [classes[label] for label in x[\"Label\"]]},\n", + " batched=True,\n", + " num_proc=1,\n", + ")\n", + "print(dataset)\n", + "dataset[\"train\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe12d4d3", + "metadata": {}, + "outputs": [], + "source": [ + "# data preprocessing\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n", + "if tokenizer.pad_token_id is None:\n", + " tokenizer.pad_token_id = tokenizer.eos_token_id\n", + "target_max_length = max([len(tokenizer(class_label)[\"input_ids\"]) for class_label in classes])\n", + "print(target_max_length)\n", + "\n", + "\n", + "def preprocess_function(examples):\n", + " batch_size = len(examples[text_column])\n", + " inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n", + " targets = [str(x) for x in examples[label_column]]\n", + " model_inputs = tokenizer(inputs)\n", + " labels = tokenizer(targets)\n", + " for i in range(batch_size):\n", + " sample_input_ids = model_inputs[\"input_ids\"][i]\n", + " label_input_ids = labels[\"input_ids\"][i] + [tokenizer.pad_token_id]\n", + " # print(i, sample_input_ids, label_input_ids)\n", + " model_inputs[\"input_ids\"][i] = sample_input_ids + label_input_ids\n", + " labels[\"input_ids\"][i] = [-100] * len(sample_input_ids) + label_input_ids\n", + " model_inputs[\"attention_mask\"][i] = [1] * len(model_inputs[\"input_ids\"][i])\n", + " # print(model_inputs)\n", + " for i in range(batch_size):\n", + " sample_input_ids = model_inputs[\"input_ids\"][i]\n", + " label_input_ids = labels[\"input_ids\"][i]\n", + " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n", + " max_length - len(sample_input_ids)\n", + " ) + sample_input_ids\n", + " model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n", + " \"attention_mask\"\n", + " ][i]\n", + " labels[\"input_ids\"][i] = [-100] * (max_length - len(sample_input_ids)) + label_input_ids\n", + " model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n", + " model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n", + " labels[\"input_ids\"][i] = torch.tensor(labels[\"input_ids\"][i][:max_length])\n", + " model_inputs[\"labels\"] = labels[\"input_ids\"]\n", + " return model_inputs\n", + "\n", + "\n", + "processed_datasets = dataset.map(\n", + " preprocess_function,\n", + " batched=True,\n", + " num_proc=1,\n", + " remove_columns=dataset[\"train\"].column_names,\n", + " load_from_cache_file=False,\n", + " desc=\"Running tokenizer on dataset\",\n", + ")\n", + "\n", + "train_dataset = processed_datasets[\"train\"]\n", + "eval_dataset = processed_datasets[\"train\"]\n", + "\n", + "\n", + "train_dataloader = DataLoader(\n", + " train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n", + ")\n", + "eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "641b21fe", + "metadata": {}, + "outputs": [], + "source": [ + "def test_preprocess_function(examples):\n", + " batch_size = len(examples[text_column])\n", + " inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n", + " model_inputs = tokenizer(inputs)\n", + " # print(model_inputs)\n", + " for i in range(batch_size):\n", + " sample_input_ids = model_inputs[\"input_ids\"][i]\n", + " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n", + " max_length - len(sample_input_ids)\n", + " ) + sample_input_ids\n", + " model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n", + " \"attention_mask\"\n", + " ][i]\n", + " model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n", + " model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n", + " return model_inputs\n", + "\n", + "\n", + "test_dataset = dataset[\"test\"].map(\n", + " test_preprocess_function,\n", + " batched=True,\n", + " num_proc=1,\n", + " remove_columns=dataset[\"train\"].column_names,\n", + " load_from_cache_file=False,\n", + " desc=\"Running tokenizer on dataset\",\n", + ")\n", + "\n", + "test_dataloader = DataLoader(test_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)\n", + "next(iter(test_dataloader))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "accc5012", + "metadata": {}, + "outputs": [], + "source": [ + "next(iter(train_dataloader))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "218df807", + "metadata": {}, + "outputs": [], + "source": [ + "len(test_dataloader)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47d1fedf", + "metadata": {}, + "outputs": [], + "source": [ + "next(iter(test_dataloader))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a773e092", + "metadata": {}, + "outputs": [], + "source": [ + "# creating model\n", + "model = AutoModelForCausalLM.from_pretrained(model_name_or_path)\n", + "model = get_peft_model(model, peft_config)\n", + "model.print_trainable_parameters()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b2f91568", + "metadata": {}, + "outputs": [], + "source": [ + "# model\n", + "# optimizer and lr scheduler\n", + "optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n", + "lr_scheduler = get_linear_schedule_with_warmup(\n", + " optimizer=optimizer,\n", + " num_warmup_steps=0,\n", + " num_training_steps=(len(train_dataloader) * num_epochs),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e4fb69fc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + 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train_epoch_loss=tensor(0.1827, device='cuda:0') eval_ppl=tensor(1.1968, device='cuda:0') eval_epoch_loss=tensor(0.1796, device='cuda:0')\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# training and evaluation\n", + "model = model.to(device)\n", + "\n", + "for epoch in range(num_epochs):\n", + " model.train()\n", + " total_loss = 0\n", + " for step, batch in enumerate(tqdm(train_dataloader)):\n", + " batch = {k: v.to(device) for k, v in batch.items()}\n", + " # print(batch)\n", + " # print(batch[\"input_ids\"].shape)\n", + " outputs = model(**batch)\n", + " loss = outputs.loss\n", + " total_loss += loss.detach().float()\n", + " loss.backward()\n", + " optimizer.step()\n", + " lr_scheduler.step()\n", + " optimizer.zero_grad()\n", + "\n", + " model.eval()\n", + " eval_loss = 0\n", + " eval_preds = []\n", + " for step, batch in enumerate(tqdm(eval_dataloader)):\n", + " batch = {k: v.to(device) for k, v in batch.items()}\n", + " with torch.no_grad():\n", + " outputs = model(**batch)\n", + " loss = outputs.loss\n", + " eval_loss += loss.detach().float()\n", + " eval_preds.extend(\n", + " tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)\n", + " )\n", + "\n", + " eval_epoch_loss = eval_loss / len(eval_dataloader)\n", + " eval_ppl = torch.exp(eval_epoch_loss)\n", + " train_epoch_loss = total_loss / len(train_dataloader)\n", + " train_ppl = torch.exp(train_epoch_loss)\n", + " print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "53752a7b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "@TommyHilfiger Dramatic shopping exp. ordered 6 jeans same size (30/32) 2 fits / 2 too large / 2 too slim : same brand > different sizing\n", + "{'input_ids': tensor([[227985, 5484, 915, 2566, 226154, 126015, 5385, 259, 239364,\n", + " 3396, 70823, 5853, 17, 57247, 1231, 191040, 5025, 7869,\n", + " 375, 2324, 149349, 12, 415, 122321, 897, 415, 10136,\n", + " 10021, 897, 415, 10136, 6497, 381, 915, 5025, 51950,\n", + " 66869, 5955, 272, 20311, 77658, 915, 210]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n", + "tensor([[227985, 5484, 915, 2566, 226154, 126015, 5385, 259, 239364,\n", + " 3396, 70823, 5853, 17, 57247, 1231, 191040, 5025, 7869,\n", + " 375, 2324, 149349, 12, 415, 122321, 897, 415, 10136,\n", + " 10021, 897, 415, 10136, 6497, 381, 915, 5025, 51950,\n", + " 66869, 5955, 272, 20311, 77658, 915, 210, 16449, 5952,\n", + " 3]], device='cuda:0')\n", + "['Tweet text : @TommyHilfiger Dramatic shopping exp. ordered 6 jeans same size (30/32) 2 fits / 2 too large / 2 too slim : same brand > different sizing Label : complaint']\n" + ] + } + ], + "source": [ + "model.eval()\n", + "i = 33\n", + "inputs = tokenizer(f'{text_column} : {dataset[\"test\"][i][\"Tweet text\"]} Label : ', return_tensors=\"pt\")\n", + "print(dataset[\"test\"][i][\"Tweet text\"])\n", + "print(inputs)\n", + "\n", + "with torch.no_grad():\n", + " inputs = {k: v.to(device) for k, v in inputs.items()}\n", + " outputs = model.generate(\n", + " input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3\n", + " )\n", + " print(outputs)\n", + " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d8ba1f8c", + "metadata": {}, + "outputs": [], + "source": [ + "# saving model\n", + "peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n", + "model.save_pretrained(peft_model_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4928c7f1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "36K\tbigscience/bloomz-560m_PROMPT_TUNING_CAUSAL_LM/adapter_model.bin\n" + ] + } + ], + "source": [ + "ckpt = f\"{peft_model_id}/adapter_model.bin\"\n", + "!du -h $ckpt" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4d9476e1", + "metadata": {}, + "outputs": [], + "source": [ + "from peft import PeftModel, PeftConfig\n", + "\n", + "peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n", + "\n", + "config = PeftConfig.from_pretrained(peft_model_id)\n", + "model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)\n", + "model = PeftModel.from_pretrained(model, peft_model_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "ebe174a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "@greateranglia Ok thanks...\n", + "{'input_ids': tensor([[227985, 5484, 915, 2566, 14173, 2960, 29906, 387, 20706,\n", + " 49337, 1369, 77658, 915, 210]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n", + "tensor([[227985, 5484, 915, 2566, 14173, 2960, 29906, 387, 20706,\n", + " 49337, 1369, 77658, 915, 210, 1936, 106863, 3]],\n", + " device='cuda:0')\n", + "['Tweet text : @greateranglia Ok thanks... Label : no complaint']\n" + ] + } + ], + "source": [ + "model.to(device)\n", + "model.eval()\n", + "i = 4\n", + "inputs = tokenizer(f'{text_column} : {dataset[\"test\"][i][\"Tweet text\"]} Label : ', return_tensors=\"pt\")\n", + "print(dataset[\"test\"][i][\"Tweet text\"])\n", + "print(inputs)\n", + "\n", + "with torch.no_grad():\n", + " inputs = {k: v.to(device) for k, v in inputs.items()}\n", + " outputs = model.generate(\n", + " input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3\n", + " )\n", + " print(outputs)\n", + " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24041ee1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + }, + "vscode": { + "interpreter": { + "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/04-gene-sft/peft/examples/causal_language_modeling/requirements.txt b/04-gene-sft/peft/examples/causal_language_modeling/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..5bed607cdf8b2ca4c743451ee0528dd3c8ca96f0 --- /dev/null +++ b/04-gene-sft/peft/examples/causal_language_modeling/requirements.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:776260d1a68f10fb7c3b9d23593f8dd22ce9f051a3031148fa0b702bd0d5201f +size 64 diff --git a/04-gene-sft/peft/examples/conditional_generation/accelerate_ds_zero3_cpu_offload_config.yaml b/04-gene-sft/peft/examples/conditional_generation/accelerate_ds_zero3_cpu_offload_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a4a0bcfaf09bd632e6ac2152c8a4f30e183cc102 --- /dev/null +++ b/04-gene-sft/peft/examples/conditional_generation/accelerate_ds_zero3_cpu_offload_config.yaml @@ -0,0 +1,22 @@ +compute_environment: LOCAL_MACHINE +deepspeed_config: + gradient_accumulation_steps: 1 + gradient_clipping: 1.0 + offload_optimizer_device: none + offload_param_device: none + zero3_init_flag: true + zero3_save_16bit_model: true + zero_stage: 3 +distributed_type: DEEPSPEED +downcast_bf16: 'no' +dynamo_backend: 'NO' +fsdp_config: {} +machine_rank: 0 +main_training_function: main +megatron_lm_config: {} +mixed_precision: 'no' +num_machines: 1 +num_processes: 1 +rdzv_backend: static +same_network: true +use_cpu: false \ No newline at end of file diff --git a/04-gene-sft/peft/examples/conditional_generation/peft_lora_seq2seq.ipynb b/04-gene-sft/peft/examples/conditional_generation/peft_lora_seq2seq.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6cbd4f1cb97213f3dd01d326c1e8a36bc0f08ca8 --- /dev/null +++ b/04-gene-sft/peft/examples/conditional_generation/peft_lora_seq2seq.ipynb @@ -0,0 +1,486 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5f93b7d1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===================================BUG REPORT===================================\n", + "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n", + "For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n", + "================================================================================\n", + "CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n", + "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n", + "CUDA SETUP: Detected CUDA version 117\n", + "CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n" + ] + } + ], + "source": [ + "from transformers import AutoModelForSeq2SeqLM\n", + "from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, LoraConfig, TaskType\n", + "import torch\n", + "from datasets import load_dataset\n", + "import os\n", + "\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "from transformers import AutoTokenizer\n", + "from torch.utils.data import DataLoader\n", + "from transformers import default_data_collator, get_linear_schedule_with_warmup\n", + "from tqdm import tqdm\n", + "from datasets import load_dataset\n", + "\n", + "device = \"cuda\"\n", + "model_name_or_path = \"bigscience/mt0-large\"\n", + "tokenizer_name_or_path = \"bigscience/mt0-large\"\n", + "\n", + "checkpoint_name = \"financial_sentiment_analysis_lora_v1.pt\"\n", + "text_column = \"sentence\"\n", + "label_column = \"text_label\"\n", + "max_length = 128\n", + "lr = 1e-3\n", + "num_epochs = 3\n", + "batch_size = 8" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d0850ac", + "metadata": {}, + "outputs": [], + "source": [ + "# creating model\n", + "peft_config = LoraConfig(task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1)\n", + "\n", + "model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n", + "model = get_peft_model(model, peft_config)\n", + "model.print_trainable_parameters()\n", + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4ee2babf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Found cached dataset financial_phrasebank (/home/sourab/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3403bf3d718042018b0531848cc30209", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1 [00:00 100:\n", + " break\n", + "test_preds" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5 (v3.10.5:f377153967, Jun 6 2022, 12:36:10) [Clang 13.0.0 (clang-1300.0.29.30)]" + }, + "vscode": { + "interpreter": { + "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/04-gene-sft/peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py b/04-gene-sft/peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py new file mode 100644 index 0000000000000000000000000000000000000000..0e47f87264a48b4fcdac6ab2a40bd1846a53d3f0 --- /dev/null +++ b/04-gene-sft/peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py @@ -0,0 +1,310 @@ +import gc +import os +import sys +import threading + +import numpy as np +import psutil +import torch +from accelerate import Accelerator +from datasets import load_dataset +from torch.utils.data import DataLoader +from tqdm import tqdm +from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, get_linear_schedule_with_warmup, set_seed + +from peft import LoraConfig, TaskType, get_peft_model + + +def levenshtein_distance(str1, str2): + # TC: O(N^2) + # SC: O(N^2) + if str1 == str2: + return 0 + num_rows = len(str1) + 1 + num_cols = len(str2) + 1 + dp_matrix = np.empty((num_rows, num_cols)) + dp_matrix[0, :] = range(num_cols) + dp_matrix[:, 0] = range(num_rows) + + for i in range(1, num_rows): + for j in range(1, num_cols): + if str1[i - 1] == str2[j - 1]: + dp_matrix[i, j] = dp_matrix[i - 1, j - 1] + else: + dp_matrix[i, j] = min(dp_matrix[i - 1, j - 1], dp_matrix[i - 1, j], dp_matrix[i, j - 1]) + 1 + + return dp_matrix[num_rows - 1, num_cols - 1] + + +def get_closest_label(eval_pred, classes): + min_id = sys.maxsize + min_edit_distance = sys.maxsize + for i, class_label in enumerate(classes): + edit_distance = levenshtein_distance(eval_pred.strip(), class_label) + if edit_distance < min_edit_distance: + min_id = i + min_edit_distance = edit_distance + return classes[min_id] + + +# Converting Bytes to Megabytes +def b2mb(x): + return int(x / 2**20) + + +# This context manager is used to track the peak memory usage of the process +class TorchTracemalloc: + def __enter__(self): + gc.collect() + torch.cuda.empty_cache() + torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero + self.begin = torch.cuda.memory_allocated() + self.process = psutil.Process() + + self.cpu_begin = self.cpu_mem_used() + self.peak_monitoring = True + peak_monitor_thread = threading.Thread(target=self.peak_monitor_func) + peak_monitor_thread.daemon = True + peak_monitor_thread.start() + return self + + def cpu_mem_used(self): + """get resident set size memory for the current process""" + return self.process.memory_info().rss + + def peak_monitor_func(self): + self.cpu_peak = -1 + + while True: + self.cpu_peak = max(self.cpu_mem_used(), self.cpu_peak) + + # can't sleep or will not catch the peak right (this comment is here on purpose) + # time.sleep(0.001) # 1msec + + if not self.peak_monitoring: + break + + def __exit__(self, *exc): + self.peak_monitoring = False + + gc.collect() + torch.cuda.empty_cache() + self.end = torch.cuda.memory_allocated() + self.peak = torch.cuda.max_memory_allocated() + self.used = b2mb(self.end - self.begin) + self.peaked = b2mb(self.peak - self.begin) + + self.cpu_end = self.cpu_mem_used() + self.cpu_used = b2mb(self.cpu_end - self.cpu_begin) + self.cpu_peaked = b2mb(self.cpu_peak - self.cpu_begin) + # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}") + + +def main(): + accelerator = Accelerator() + model_name_or_path = "bigscience/T0_3B" + dataset_name = "twitter_complaints" + peft_config = LoraConfig( + task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1 + ) + text_column = "Tweet text" + label_column = "text_label" + lr = 3e-3 + num_epochs = 5 + batch_size = 8 + seed = 42 + do_test = False + set_seed(seed) + + dataset = load_dataset("ought/raft", dataset_name) + classes = [k.replace("_", " ") for k in dataset["train"].features["Label"].names] + dataset = dataset.map( + lambda x: {"text_label": [classes[label] for label in x["Label"]]}, + batched=True, + num_proc=1, + ) + + tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) + target_max_length = max([len(tokenizer(class_label)["input_ids"]) for class_label in classes]) + + def preprocess_function(examples): + inputs = examples[text_column] + targets = examples[label_column] + model_inputs = tokenizer(inputs, truncation=True) + labels = tokenizer( + targets, max_length=target_max_length, padding="max_length", truncation=True, return_tensors="pt" + ) + labels = labels["input_ids"] + labels[labels == tokenizer.pad_token_id] = -100 + model_inputs["labels"] = labels + return model_inputs + + with accelerator.main_process_first(): + processed_datasets = dataset.map( + preprocess_function, + batched=True, + num_proc=1, + remove_columns=dataset["train"].column_names, + load_from_cache_file=True, + desc="Running tokenizer on dataset", + ) + accelerator.wait_for_everyone() + + train_dataset = processed_datasets["train"] + eval_dataset = processed_datasets["train"] + test_dataset = processed_datasets["test"] + + def collate_fn(examples): + return tokenizer.pad(examples, padding="longest", return_tensors="pt") + + train_dataloader = DataLoader( + train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=batch_size, pin_memory=True + ) + eval_dataloader = DataLoader(eval_dataset, collate_fn=collate_fn, batch_size=batch_size, pin_memory=True) + test_dataloader = DataLoader(test_dataset, collate_fn=collate_fn, batch_size=batch_size, pin_memory=True) + + # creating model + model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path) + model = get_peft_model(model, peft_config) + model.print_trainable_parameters() + + # optimizer + optimizer = torch.optim.AdamW(model.parameters(), lr=lr) + + # lr scheduler + lr_scheduler = get_linear_schedule_with_warmup( + optimizer=optimizer, + num_warmup_steps=0, + num_training_steps=(len(train_dataloader) * num_epochs), + ) + + model, train_dataloader, eval_dataloader, test_dataloader, optimizer, lr_scheduler = accelerator.prepare( + model, train_dataloader, eval_dataloader, test_dataloader, optimizer, lr_scheduler + ) + accelerator.print(model) + + is_ds_zero_3 = False + if getattr(accelerator.state, "deepspeed_plugin", None): + is_ds_zero_3 = accelerator.state.deepspeed_plugin.zero_stage == 3 + + for epoch in range(num_epochs): + with TorchTracemalloc() as tracemalloc: + model.train() + total_loss = 0 + for step, batch in enumerate(tqdm(train_dataloader)): + outputs = model(**batch) + loss = outputs.loss + total_loss += loss.detach().float() + accelerator.backward(loss) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage + accelerator.print("GPU Memory before entering the train : {}".format(b2mb(tracemalloc.begin))) + accelerator.print("GPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used)) + accelerator.print("GPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked)) + accelerator.print( + "GPU Total Peak Memory consumed during the train (max): {}".format( + tracemalloc.peaked + b2mb(tracemalloc.begin) + ) + ) + + accelerator.print("CPU Memory before entering the train : {}".format(b2mb(tracemalloc.cpu_begin))) + accelerator.print("CPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.cpu_used)) + accelerator.print("CPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.cpu_peaked)) + accelerator.print( + "CPU Total Peak Memory consumed during the train (max): {}".format( + tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin) + ) + ) + train_epoch_loss = total_loss / len(train_dataloader) + train_ppl = torch.exp(train_epoch_loss) + accelerator.print(f"{epoch=}: {train_ppl=} {train_epoch_loss=}") + + model.eval() + eval_preds = [] + with TorchTracemalloc() as tracemalloc: + for _, batch in enumerate(tqdm(eval_dataloader)): + batch = {k: v for k, v in batch.items() if k != "labels"} + with torch.no_grad(): + outputs = accelerator.unwrap_model(model).generate( + **batch, synced_gpus=is_ds_zero_3 + ) # synced_gpus=True for DS-stage 3 + outputs = accelerator.pad_across_processes(outputs, dim=1, pad_index=tokenizer.pad_token_id) + preds = accelerator.gather(outputs).detach().cpu().numpy() + eval_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True)) + + # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage + accelerator.print("GPU Memory before entering the eval : {}".format(b2mb(tracemalloc.begin))) + accelerator.print("GPU Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used)) + accelerator.print("GPU Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked)) + accelerator.print( + "GPU Total Peak Memory consumed during the eval (max): {}".format( + tracemalloc.peaked + b2mb(tracemalloc.begin) + ) + ) + + accelerator.print("CPU Memory before entering the eval : {}".format(b2mb(tracemalloc.cpu_begin))) + accelerator.print("CPU Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.cpu_used)) + accelerator.print("CPU Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.cpu_peaked)) + accelerator.print( + "CPU Total Peak Memory consumed during the eval (max): {}".format( + tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin) + ) + ) + + correct = 0 + total = 0 + assert len(eval_preds) == len( + dataset["train"][label_column] + ), f"{len(eval_preds)} != {len(dataset['train'][label_column])}" + for pred, true in zip(eval_preds, dataset["train"][label_column]): + if pred.strip() == true.strip(): + correct += 1 + total += 1 + accuracy = correct / total * 100 + accelerator.print(f"{accuracy=}") + accelerator.print(f"{eval_preds[:10]=}") + accelerator.print(f"{dataset['train'][label_column][:10]=}") + + if do_test: + model.eval() + test_preds = [] + for _, batch in enumerate(tqdm(test_dataloader)): + batch = {k: v for k, v in batch.items() if k != "labels"} + with torch.no_grad(): + outputs = accelerator.unwrap_model(model).generate( + **batch, synced_gpus=is_ds_zero_3 + ) # synced_gpus=True for DS-stage 3 + outputs = accelerator.pad_across_processes(outputs, dim=1, pad_index=tokenizer.pad_token_id) + preds = accelerator.gather(outputs).detach().cpu().numpy() + test_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True)) + + test_preds_cleaned = [] + for _, pred in enumerate(test_preds): + test_preds_cleaned.append(get_closest_label(pred, classes)) + + test_df = dataset["test"].to_pandas() + assert len(test_preds_cleaned) == len(test_df), f"{len(test_preds_cleaned)} != {len(test_df)}" + test_df[label_column] = test_preds_cleaned + test_df["text_labels_orig"] = test_preds + accelerator.print(test_df[[text_column, label_column]].sample(20)) + + pred_df = test_df[["ID", label_column]] + pred_df.columns = ["ID", "Label"] + + os.makedirs(f"data/{dataset_name}", exist_ok=True) + pred_df.to_csv(f"data/{dataset_name}/predictions.csv", index=False) + + accelerator.wait_for_everyone() + model.push_to_hub( + "smangrul/" + + f"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}".replace("/", "_"), + state_dict=accelerator.get_state_dict(model), + use_auth_token=True, + ) + accelerator.wait_for_everyone() + + +if __name__ == "__main__": + main() diff --git a/04-gene-sft/peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_fsdp.py b/04-gene-sft/peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_fsdp.py new file mode 100644 index 0000000000000000000000000000000000000000..c2146a5dce5519715ba6ac21077bd560dac62c73 --- /dev/null +++ b/04-gene-sft/peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_fsdp.py @@ -0,0 +1,137 @@ +import os + +import torch +from accelerate import Accelerator +from datasets import load_dataset +from torch.utils.data import DataLoader +from tqdm import tqdm +from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, default_data_collator, get_linear_schedule_with_warmup + +from peft import LoraConfig, TaskType, get_peft_model +from peft.utils.other import fsdp_auto_wrap_policy + + +def main(): + accelerator = Accelerator() + model_name_or_path = "t5-base" + batch_size = 8 + text_column = "sentence" + label_column = "label" + max_length = 64 + lr = 1e-3 + num_epochs = 1 + base_path = "temp/data/FinancialPhraseBank-v1.0" + + peft_config = LoraConfig( + task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1 + ) + model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path) + model = get_peft_model(model, peft_config) + accelerator.print(model.print_trainable_parameters()) + + dataset = load_dataset( + "json", + data_files={ + "train": os.path.join(base_path, "financial_phrase_bank_train.jsonl"), + "validation": os.path.join(base_path, "financial_phrase_bank_val.jsonl"), + }, + ) + + tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) + + def preprocess_function(examples): + inputs = examples[text_column] + targets = examples[label_column] + model_inputs = tokenizer( + inputs, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt" + ) + labels = tokenizer(targets, max_length=2, padding="max_length", truncation=True, return_tensors="pt") + labels = labels["input_ids"] + labels[labels == tokenizer.pad_token_id] = -100 + model_inputs["labels"] = labels + return model_inputs + + with accelerator.main_process_first(): + processed_datasets = dataset.map( + preprocess_function, + batched=True, + num_proc=1, + remove_columns=dataset["train"].column_names, + load_from_cache_file=False, + desc="Running tokenizer on dataset", + ) + + train_dataset = processed_datasets["train"] + eval_dataset = processed_datasets["validation"] + + train_dataloader = DataLoader( + train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True + ) + eval_dataloader = DataLoader( + eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True + ) + + optimizer = torch.optim.AdamW(model.parameters(), lr=lr) + lr_scheduler = get_linear_schedule_with_warmup( + optimizer=optimizer, + num_warmup_steps=0, + num_training_steps=(len(train_dataloader) * num_epochs), + ) + + if getattr(accelerator.state, "fsdp_plugin", None) is not None: + accelerator.state.fsdp_plugin.auto_wrap_policy = fsdp_auto_wrap_policy(model) + + model, train_dataloader, eval_dataloader, optimizer, lr_scheduler = accelerator.prepare( + model, train_dataloader, eval_dataloader, optimizer, lr_scheduler + ) + accelerator.print(model) + + for epoch in range(num_epochs): + model.train() + total_loss = 0 + for step, batch in enumerate(tqdm(train_dataloader)): + outputs = model(**batch) + loss = outputs.loss + total_loss += loss.detach().float() + loss.backward() + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + + model.eval() + eval_loss = 0 + eval_preds = [] + for step, batch in enumerate(tqdm(eval_dataloader)): + with torch.no_grad(): + outputs = model(**batch) + loss = outputs.loss + eval_loss += loss.detach().float() + preds = accelerator.gather_for_metrics(torch.argmax(outputs.logits, -1)).detach().cpu().numpy() + eval_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True)) + eval_epoch_loss = eval_loss / len(eval_dataloader) + eval_ppl = torch.exp(eval_epoch_loss) + train_epoch_loss = total_loss / len(train_dataloader) + train_ppl = torch.exp(train_epoch_loss) + accelerator.print(f"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}") + + correct = 0 + total = 0 + for pred, true in zip(eval_preds, dataset["validation"][label_column]): + if pred.strip() == true.strip(): + correct += 1 + total += 1 + accuracy = correct / total * 100 + accelerator.print(f"{accuracy=}") + accelerator.print(f"{eval_preds[:10]=}") + accelerator.print(f"{dataset['validation'][label_column][:10]=}") + accelerator.wait_for_everyone() + model.push_to_hub( + "smangrul/" + f"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}".replace("/", "_"), + state_dict=accelerator.get_state_dict(model), + use_auth_token=True, + ) + accelerator.wait_for_everyone() + + +if __name__ == "__main__": + main() diff --git a/04-gene-sft/peft/examples/conditional_generation/peft_prefix_tuning_seq2seq.ipynb b/04-gene-sft/peft/examples/conditional_generation/peft_prefix_tuning_seq2seq.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..aa85f9a743e8aded291f6bf3929d1a9ab595f775 --- /dev/null +++ b/04-gene-sft/peft/examples/conditional_generation/peft_prefix_tuning_seq2seq.ipynb @@ -0,0 +1,516 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5f93b7d1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===================================BUG REPORT===================================\n", + "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n", + "For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n", + "================================================================================\n", + "CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n", + "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n", + "CUDA SETUP: Detected CUDA version 117\n", + "CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n" + ] + } + ], + "source": [ + "from transformers import AutoModelForSeq2SeqLM\n", + "from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, PrefixTuningConfig, TaskType\n", + "import torch\n", + "from datasets import load_dataset\n", + "import os\n", + "\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\"\n", + "from transformers import AutoTokenizer\n", + "from torch.utils.data import DataLoader\n", + "from transformers import default_data_collator, get_linear_schedule_with_warmup\n", + "from tqdm import tqdm\n", + "from datasets import load_dataset\n", + "\n", + "device = \"cuda\"\n", + "model_name_or_path = \"t5-large\"\n", + "tokenizer_name_or_path = \"t5-large\"\n", + "\n", + "checkpoint_name = \"financial_sentiment_analysis_prefix_tuning_v1.pt\"\n", + "text_column = \"sentence\"\n", + "label_column = \"text_label\"\n", + "max_length = 128\n", + "lr = 1e-2\n", + "num_epochs = 5\n", + "batch_size = 8" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d0850ac", + "metadata": {}, + "outputs": [], + "source": [ + "# creating model\n", + "peft_config = PrefixTuningConfig(task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, num_virtual_tokens=20)\n", + "\n", + "model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n", + "model = get_peft_model(model, peft_config)\n", + "model.print_trainable_parameters()\n", + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4ee2babf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Found cached dataset financial_phrasebank (/home/sourab/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ec4be98991b84181bfa75f8846422b8b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1 [00:00\n", + " \n", + " \n", + " [45/45 04:44, Epoch 5/5]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "***** Running Evaluation *****\n", + " Num examples = 500\n", + " Batch size = 128\n", + "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9\n", + "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/config.json\n", + "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/pytorch_model.bin\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/preprocessor_config.json\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", + "***** Running Evaluation *****\n", + " Num examples = 500\n", + " Batch size = 128\n", + "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18\n", + "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/config.json\n", + "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/pytorch_model.bin\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/preprocessor_config.json\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", + "***** Running Evaluation *****\n", + " Num examples = 500\n", + " Batch size = 128\n", + "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27\n", + "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/config.json\n", + "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/pytorch_model.bin\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/preprocessor_config.json\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", + "***** Running Evaluation *****\n", + " Num examples = 500\n", + " Batch size = 128\n", + "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36\n", + "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/config.json\n", + "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/pytorch_model.bin\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/preprocessor_config.json\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", + "***** Running Evaluation *****\n", + " Num examples = 500\n", + " Batch size = 128\n", + "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45\n", + "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/config.json\n", + "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/pytorch_model.bin\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/preprocessor_config.json\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", + "\n", + "\n", + "Training completed. Do not forget to share your model on huggingface.co/models =)\n", + "\n", + "\n", + "Loading best model from vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27 (score: 0.96).\n" + ] + } + ], + "source": [ + "trainer = Trainer(\n", + " model,\n", + " args,\n", + " train_dataset=train_ds,\n", + " eval_dataset=val_ds,\n", + " tokenizer=image_processor,\n", + " compute_metrics=compute_metrics,\n", + " data_collator=collate_fn,\n", + ")\n", + "train_results = trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b2NENHxHCejv" + }, + "source": [ + "In just a few minutes, we have a fine-tuned model with 96% validation accuracy. Also, note that we used a very small subset of the training dataset which is definitely impacting the results. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 198 + }, + "id": "_MAd2906jQKG", + "outputId": "7b825531-4e8d-4666-d6fc-eaa1accf3bb6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "***** Running Evaluation *****\n", + " Num examples = 500\n", + " Batch size = 128\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "

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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'eval_loss': 0.14475855231285095,\n", + " 'eval_accuracy': 0.96,\n", + " 'eval_runtime': 3.5725,\n", + " 'eval_samples_per_second': 139.958,\n", + " 'eval_steps_per_second': 1.12,\n", + " 'epoch': 5.0}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainer.evaluate(val_ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qo_scDEyAQER" + }, + "source": [ + "## Sharing your model and inference \n", + "\n", + "Once the fine-tuning is done, we can share the LoRA parameters with the community like so: " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 172, + "referenced_widgets": [ + "980d2f61332f414d9888b76e78774be4", + "cbc0ba8e49a740fcae7b94fe7edb8107", + "b3f07ef160a7425880ffe362008d4400", + "49ed3330fa9645eda8b5aed0fd7cbafe", + "f96302d0c2d849c5b5a0206b65e461ab", + "fcc7ad16a0b14d96acd9be9e03ac6af9", + "1a08961f063346ccae206a863ab7df6b", + "1150e391e753424da7d65bda10463da4", + "e6e36d744e1244aeb7eb0c4ce392372d", + "b2ad992db5a045668fa55c7393ec7870", + "b5fad0f3f2d543ecaed726e52d2d86bb", + "e83fd078f467406da0baf26e18b39e89", + "9b4b67731a7a4bc59be132b53c24eae8", + "e33243b001274d02a25f5940ba41ecf6", + "06e4c619e366427a8ff4c358196ecd12", + "bacd429b42d843299cb75224db3afb1e", + "c53429e699e64b3d8895a355bbd947a6", + "2bfd04824f2e4fd6844dd38e46dbbbdf", + "40a5a50aeca24f0d8990da97971004d1", + "be71e6438e0e49759d2f72feec520cae", + "1902fbc4c1da4ffe90f0947e58eb5d48", + "12a94351242444d7b0d23b8accc1824a" + ] + }, + "id": "TyQvIcnFzLIV", + "outputId": "7ac2819e-080e-4940-9755-15c32832d9a6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Uploading the following files to sayakpaul/vit-base-patch16-224-in21k-finetuned-lora-food101: adapter_config.json,adapter_model.bin\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "980d2f61332f414d9888b76e78774be4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Upload 1 LFS files: 0%| | 0/1 [00:00" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import Image\n", + "import requests\n", + "\n", + "url = \"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/beignets.jpeg\"\n", + "image = Image.open(requests.get(url, stream=True).raw)\n", + "image" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dFuqZgmCW4cu" + }, + "source": [ + "We first instantiate an `image_processor` from the underlying model repo. " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dN4x_pj-VQx8", + "outputId": "b4fddd49-2f10-48e2-de31-0cefd20d405d" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "loading configuration file preprocessor_config.json from cache at /root/.cache/huggingface/hub/models--sayakpaul--vit-base-patch16-224-in21k-finetuned-lora-food101/snapshots/fa2503cc7d91e0dd69728c1dc66ed80d7bd3289b/preprocessor_config.json\n", + "Image processor ViTImageProcessor {\n", + " \"do_normalize\": true,\n", + " \"do_rescale\": true,\n", + " \"do_resize\": true,\n", + " \"image_mean\": [\n", + " 0.5,\n", + " 0.5,\n", + " 0.5\n", + " ],\n", + " \"image_processor_type\": \"ViTImageProcessor\",\n", + " \"image_std\": [\n", + " 0.5,\n", + " 0.5,\n", + " 0.5\n", + " ],\n", + " \"resample\": 2,\n", + " \"rescale_factor\": 0.00392156862745098,\n", + " \"size\": {\n", + " \"height\": 224,\n", + " \"width\": 224\n", + " }\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "image_processor = AutoImageProcessor.from_pretrained(repo_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Dc0rCwC5XAaL" + }, + "source": [ + "We then prepare the sample for inference." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "57C6tcdnVYu1", + "outputId": "f164dd91-8679-482e-fb10-2f530d4ea9f4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1, 3, 224, 224])\n" + ] + } + ], + "source": [ + "# prepare image for the model\n", + "encoding = image_processor(image.convert(\"RGB\"), return_tensors=\"pt\")\n", + "print(encoding.pixel_values.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Pn4T1GyTXC47" + }, + "source": [ + "And run inference!" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YyQznW5WViMc", + "outputId": "d1b7a77c-68b3-4f6b-a945-4b32e85baabe" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted class: beignets\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "# forward pass\n", + "with torch.no_grad():\n", + " outputs = inference_model(**encoding)\n", + " logits = outputs.logits\n", + "\n", + "predicted_class_idx = logits.argmax(-1).item()\n", + "print(\"Predicted class:\", inference_model.config.id2label[predicted_class_idx])" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "0a_bETbqv4P7", + "Y8dSVHoIv7HC", + "qo_scDEyAQER" + ], + "machine_shape": "hm", + "provenance": [] + }, + "gpuClass": "premium", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + 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"description_width": "" + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/04-gene-sft/peft/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb b/04-gene-sft/peft/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f1452bd5293a76a0810146f76f923ebb8330edca --- /dev/null +++ b/04-gene-sft/peft/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb @@ -0,0 +1,8315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "lw1cWgq-DI5k", + "metadata": { + "id": "lw1cWgq-DI5k" + }, + "source": [ + "# Fine-tune FLAN-T5 using `bitsandbytes`, `peft` & `transformers` 🤗 " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "kBFPA3-aDT7H", + "metadata": { + "id": "kBFPA3-aDT7H" + }, + "source": [ + "In this notebook we will see how to properly use `peft` , `transformers` & `bitsandbytes` to fine-tune `flan-t5-large` in a google colab!\n", + "\n", + "We will finetune the model on [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset, that consists of pairs of text-labels to classify financial-related sentences, if they are either `positive`, `neutral` or `negative`.\n", + "\n", + "Note that you could use the same notebook to fine-tune `flan-t5-xl` as well, but you would need to shard the models first to avoid CPU RAM issues on Google Colab, check [these weights](https://huggingface.co/ybelkada/flan-t5-xl-sharded-bf16)." + ] + }, + { + "cell_type": "markdown", + "id": "ShAuuHCDDkvk", + "metadata": { + "id": "ShAuuHCDDkvk" + }, + "source": [ + "## Install requirements" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "DRQ4ZrJTDkSy", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DRQ4ZrJTDkSy", + "outputId": "31b108ee-a34c-4a1f-a970-6fa1809b64c5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K 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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m65.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" + ] + } + ], + "source": [ + "!pip install -q bitsandbytes datasets accelerate loralib\n", + "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main" + ] + }, + { + "cell_type": "markdown", + "id": "QBdCIrizDxFw", + "metadata": { + "id": "QBdCIrizDxFw" + }, + "source": [ + "## Import model and tokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd3c5acc", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350, + "referenced_widgets": [ + "59782bac41834abbbd6cbd6614c2e57b", + "8252a05cb70b46ec8b0480062ea1cb71", + "b77afc7c1f184de0970feb2df8ac5285", + "23e494a586cf47d1afa5e619abfcdbba", + "bdfb4a04e48246a4b0890f52d6dd424b", + "8669a890db6c456cbc3ada28976be30b", + "9e5afa2048c74754816b34a34171fcb0", + "5bfebc75ec424c6cb41b33d210d28d2b", + "ef2fa44d0105457c9aed3812633dd329", + "5a6b38b8fc1345e8bd4e8f3aaea546c9", + "57d4ca686af34205aca630b1c61d4aea", + "dbdb787728184aa1a6906f96c5e6f929", + 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+ " \n", + " \n", + " \n", + " \n", + "
EpochTraining LossValidation Loss
1No log0.017228

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Saving model checkpoint to temp/checkpoint-100\n", + "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n", + "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n", + " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n", + "Saving model checkpoint to temp/checkpoint-200\n", + "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n", + "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n", + " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n", + "***** Running Evaluation *****\n", + " Num examples = 227\n", + " Batch size = 8\n", + "\n", + "\n", + "Training completed. Do not forget to share your model on huggingface.co/models =)\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "TrainOutput(global_step=255, training_loss=0.2569344015682445, metrics={'train_runtime': 377.3565, 'train_samples_per_second': 5.398, 'train_steps_per_second': 0.676, 'total_flos': 1181084919791616.0, 'train_loss': 0.2569344015682445, 'epoch': 1.0})" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "id": "r98VtofiGXtO", + "metadata": { + "id": "r98VtofiGXtO" + }, + "source": [ + "## Qualitatively test our model" + ] + }, + { + "cell_type": "markdown", + "id": "NIm7z3UNzGPP", + "metadata": { + "id": "NIm7z3UNzGPP" + }, + "source": [ + "Let's have a quick qualitative evaluation of the model, by taking a sample from the dataset that corresponds to a positive label. Run your generation similarly as you were running your model from `transformers`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c95d6173", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c95d6173", + "outputId": "ed03a1dc-597a-4053-99d6-eca2cc6da253" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generate config GenerationConfig {\n", + " \"_from_model_config\": true,\n", + " \"decoder_start_token_id\": 0,\n", + " \"eos_token_id\": 1,\n", + " \"pad_token_id\": 0,\n", + " \"transformers_version\": \"4.27.0.dev0\",\n", + " \"use_cache\": false\n", + "}\n", + "\n", + "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n", + " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n", + "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1374: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input sentence: In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\n", + " output prediction: ['positive']\n" + ] + } + ], + "source": [ + "model.eval()\n", + "input_text = \"In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\"\n", + "inputs = tokenizer(input_text, return_tensors=\"pt\")\n", + "\n", + "outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n", + "\n", + "print(\"input sentence: \", input_text)\n", + "print(\" output prediction: \", tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))" + ] + }, + { + "cell_type": "markdown", + "id": "9QqBlwzoGZ3f", + "metadata": { + "id": "9QqBlwzoGZ3f" + }, + "source": [ + "## Share your adapters on 🤗 Hub" + ] + }, + { + "cell_type": "markdown", + "id": "NT-C8SjcKqUx", + "metadata": { + "id": "NT-C8SjcKqUx" + }, + "source": [ + "Once you have trained your adapter, you can easily share it on the Hub using the method `push_to_hub` . Note that only the adapter weights and config will be pushed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcbfa1f9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 359, + "referenced_widgets": [ + "5bb29f3102954b06bec825f6b3a7aaa7", + "90516032070a40979181d1d27db10c4f", + "4b7dc0fb222b4e2a9bb2ef2501e9fd30", + "06069855ef82484f9985e4619095dbe8", + "1ece69c53e37413caad8db70d9160ad5", + "7ce90db727ea47cc9344176858a2225b", + "64f2b70b63cd4e7eb9e22ac2de5589c9", + "57dea1b3e04142bb91868a474774d86a", + "8ac43334e0ad4a78acda3b876fead058", + "04da98e400514cf2847d172916cd0081", + "4dbe49547fe94010ad5a30818cfc35bc", + "99091ca45c1b4809ba0a1b01af85f528", + "06ec124c3dac4fe6b152fb812d20d86d", + "7561c47a97444666816422a0418e1675", + "45ab5d7049e34dfd8a067643ae887a31", + "434e308cac5847f0bee431c7dbb4c04a", + "dd993a4a7cdf40448098544c95468a10" + ] + }, + "id": "bcbfa1f9", + "outputId": "91ef770e-9fc4-4eb2-b02b-24e635101f97" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Token is valid.\n", + "Your token has been saved in your configured git credential helpers (store).\n", + "Your token has been saved to /root/.cache/huggingface/token\n", + "Login successful\n" + ] + } + ], + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "rFKJ4vHNGkJw", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 152, + "referenced_widgets": [ + "00f7d043cb184d69b828c204dac2c0ab", + "1e409cd3d3a04b558d989d63f0b3b5f7", + "d48cbb38078b456fab1634bec5b0a1ba", + "9030744dbca9427ba8a036a76b5c8bf7", + "53dd4444c0e14e16a912532898b32d92", + "5287ac638c22412ab91c55f3316c9b63", + "a43ddb478f044f17adbcfae841ec2114", + "45141234ce584f208a9d301faadf75d2", + "f62ffbdc24734b999f36058d9edca81f", + "972ddebd536d4685bfc3c7c13e5bd8be", + "64156e2c54b44fb9aec661d9b57da962", + "050de732f51f4af8bb41ab3cad0090a4", + "7960ed3beb2a429ba2aca1c6ed032f64", + "726a2eedc7434210bc5aa4d0a772b313", + "07bf5d621cf944258aaf13954669df56", + "93a0896ca66b4111bc4cabe6e1278440", + "cfc78731f7d543ce8529cc254d92ddf5", + "eacf8e9ed6e847faae2b8ecab283ddc4", + "bef1971d92e6479696e3f9a27a757b8a", + "821f2f296acb40ae9bb40fc3faf4103d", + "11efd993475a4f2aabe7df605bab04dd", + "406e4d8561f64d2a94d93a606d02d7d3" + ] + }, + "id": "rFKJ4vHNGkJw", + "outputId": "07425379-64ad-47e8-ba8f-8d9dc26252b6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Uploading the following files to ybelkada/flan-t5-large-lora: adapter_model.bin,adapter_config.json\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "00f7d043cb184d69b828c204dac2c0ab", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Upload 1 LFS files: 0%| | 0/1 [00:00Pro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. 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recent `peft` library and `bitsandbytes` for loading large models in 8-bit.\n", + "The fine-tuning method will rely on a recent method called \"Low Rank Adapters\" (LoRA), instead of fine-tuning the entire model you just have to fine-tune these adapters and load them properly inside the model. \n", + "After fine-tuning the model you can also share your adapters on the 🤗 Hub and load them very easily. Let's get started!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TfBzP8gWzkpv" + }, + "source": [ + "### Install requirements\n", + "\n", + "First, run the cells below to install the requirements:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "otj46qRbtpnd", + "outputId": "2aa109f6-3f4e-4887-a16e-336f51e7cc9a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 KB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m72.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" + ] + } + ], + "source": [ + "!pip install -q bitsandbytes datasets accelerate loralib\n", + "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FOtwYRI3zzXI" + }, + "source": [ + "### Model loading\n", + "\n", + "Here let's load the `opt-6.7b` model, its weights in half-precision (float16) are about 13GB on the Hub! If we load them in 8-bit we would require around 7GB of memory instead." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 408, + "referenced_widgets": [ + "d4de260ffd8a440eb87eb900fc1bb1d3", + "8602b545a9f8474dbb3cc178ac0b8e60", + "b46919912ee54f6f9f2ce9080be1c61a", + "50374e3ab81c4626a182e61fc03b94ce", + "2144bc2897dc40b29f060e30ace12275", + "949ca70002ca4472bbc21fea4d7ac745", + "49943c9dadca43a584b3f354ba45280c", + "6123e53fb26b41f0af9a3a3348ae1afd", + "285ef943d540400ab827c462945a259c", + "95727290446244ccb9626f4594949675", + "61b54aa6c9e94ee1bc45c15a9e3f7917", + "fc2d5ffe254d425b939252ec46ec27cc", + "f65af2e868244edeb0cc9402534874a8", + "e466054f08004bbcabb24e400cb3c7fc", + "6ea40800dfd849e3b106bae71fc53ae3", + "722a01f42b7d4c38836a4546ecb38108", + "1bd5179cdb474b65aa06eca3520ad37b", + "04d367124a3b419ab1fa1dfd4f9004c3", + "4ea667f48b9f4e1f9da7c5a0d3025b85", + "9b96c63630654773acd38b8b88371f28", + "cb69ae47666a4603a07a8778e2ae7d6e", + "8f5e9f2d11d54fd2a08dcbff9f6da05c", + "c6f712eadc4d49019b2bd355968cc155", + "5fd979c05fd34311af877ce1a988ead8", + "179165ff4c0e4586aaf3a40b8502f428", + "cad1d8326e474a4f9ee13db9005121dd", + "b049ac0b3d2e44ba9b1fc1cb2dd3de62", + "921c268ecd724b6a8869dae8f81d558a", + "4cf85a85c0764f15b5134c81b360e910", + "5936d9eb59e147eea6482006decfe0ee", + "fad61c68edc84c8197afeafded84280d", + "451309d8b3ca4cd9b6f538640477039e", + "c7621e14aa16421d9758321e433b92e4", + "5aa74b9b30614172b07f88873cf89471", + "cf39fb025e3d4635a5695135a56d9f64", + "deaf9732f33446a3be015d2ec16aba76", + "bdfd856dd8ee4ae09205ad9d1b9cc806", + "7a49f7a55b054b6d829f290a1a426a7a", + "5b695486a0ac442b8b6a8ef2ebf4e57a", + "03be7cb91cba4afab795aab7aa242ee1", + "8f7890f54d514f80b1eb17905cf0f964", + "ae660d51267544a89f0ad199cc12b6fa", + "d5bb8d7359274c8e9cc79df563175137", + "98d5a80ec50c46c18f9ee991e2982115", + "e73e5388182040a8937ccf1748171a87", + "f797f7b8b13f4b3cb871522d34498631", + "41f47c864e094cacb1c550d37ddfe80e", + "104d982b444947fe8e4fbb2c2f082616", + "2cf8581583c641fb95d1e16aff7d4cd1", + "11808e9097424dd0b22a5af6c77813f3", + "dc08d237860e4788a8ceeff4518c2612", + "7bbf2c40a4ae46fe9e5885db08975263", + "f00cbc4a89f5492787ec489da65ff70b", + "8a7cd2194113493a841b00d034b5f1ba", + "e9673363e85448d494ac9ac5d7ba0efb", + "a994beafbf3f4c20880a7bbe3898db36", + "7769c261781f4f5483c7e9d58c1a5573", + "4a713a8fe16f4e81aa841c69711fa136", + "08d036904ecf47ed88e129ba6e2b285c", + "9f1182fdddab43b59ee98bc701965a17", + "c2302f535b114ad780bfa440445c2e28", + "8fe032f285ba4858b8efd68119c217b0", + "4ca695fca3d140c6ab4e1e1d34df807b", + "b77ceaf55dc04810963cdd01126478f6", + "0ccdefcb25e14d229b2634ffae4a6d3d", + "11d6b952503c4824b36b66e228f87599", + "1e9391f6c89c4d08859ef3413edb19be", + "039bbda2402f469eb21ba7ec7ea589a6", + "5da6eef8fb0048219159f38a68727b64", + "c006e62ee6d04831b2b89273ef04a8ac", + "af0634c2539a43989902daef47776901", + "62fea00ef0364af287e6097b964d00c4", + "9e5ef73f8b244845a6b9002fa5c35d15", + "5cb09c84e1e144e0a92580fb5e1ce2fe", + "0f561c1660744251a8f710b69d434c87", + "9e5f870ef80242f5af09fad70f84ea62", + "6f173ca73dd545deb22b8cd0470d925f", + "4e6d5943bc374b388b93ed115e44b6a5", + "cbe2a6ea41834e95a27d6f02c3c0eeec", + "7beb5f4efefc4593abac253df74d1405", + "370cefbaeebf41f582b7507ad493055f", + "e3df9dda16e244aa9b61d54f9e21ef40", + "23d2ad64a17041b7a006dad1e041e0a1", + "c1fd6a1234274a44b838a09f3f5380c6", + "81bb51d088374394becd9a45ec3b17d4", + "9e762779e5434bb7afcc295b61c2f4e4", + "f539b7a4665449de9eac209a20629969", + "32d528db79ad4f6f836ab2e0df5ac426", + "1ca7684b79c5438fa06b047bd2b3283f", + "a07688185bff4c4b8cbed3af3b4cf802", + "0272f1d9f93f4dd788363a8409cdfd69", + "27b41d23d2c64127ba3ae8464958f855", + "2b54032c0d8e4a2897aed1ac1c79af14", + "ed8fa1048e814f2fa3666899fc42e55a", + "97daf559100c44ac983562fea93c5fac", + "72e511b775604d899ff5b3fa2ebe9fc4", + "da946f86590447d2ab98b9da468fa66b", + "54fe79d5c7254117a2209927a7248dd4", + "1d122e4eaad54e06961288484f31e18b", + "d46b5725c35142a89617e46c0e8d3679", + "c5493c23fd5542738ffd1ff5f09a6a67", + "a1a80d3460984c2496ada5a634875934", + "598c5584ffba4f26815c4e87bb1595c4", + "fe93f25323604447be0bb1d24a0c2c59", + "00c2e2d3ee8b45818ba84da12c6b11e2", + "6ccea64c2e614a9fbdcc2f716cecaea0", + "63fc9a9eebca4f2db2ed8a385fc5e204", + "9a4860dfeac944db85e6e532599bc1cb", + "3b946e1bbab24629b98307275fbe7cbb", + "d9d36f8ff5f747bf90fbc8a7d35a6664" + ] + }, + "id": "cg3fiQOvmI3Q", + "outputId": "135a7675-6a4d-4786-b5dc-34cb867f40c7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===================================BUG REPORT===================================\n", + "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n", + "================================================================================\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d4de260ffd8a440eb87eb900fc1bb1d3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading (…)lve/main/config.json: 0%| | 0.00/651 [00:00\n", + " \n", + " \n", + " [153/200 26:04 < 08:06, 0.10 it/s, Epoch 0.97/2]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", 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StepTraining Loss
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22.200400
32.302300
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51.878700
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1511.945500

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import transformers\n", + "from datasets import load_dataset\n", + "\n", + "data = load_dataset(\"Abirate/english_quotes\")\n", + "data = data.map(lambda samples: tokenizer(samples[\"quote\"]), batched=True)\n", + "\n", + "trainer = transformers.Trainer(\n", + " model=model,\n", + " train_dataset=data[\"train\"],\n", + " args=transformers.TrainingArguments(\n", + " per_device_train_batch_size=4,\n", + " gradient_accumulation_steps=4,\n", + " warmup_steps=100,\n", + " max_steps=200,\n", + " learning_rate=2e-4,\n", + " fp16=True,\n", + " logging_steps=1,\n", + " output_dir=\"outputs\",\n", + " ),\n", + " data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n", + ")\n", + "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n", + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Duak7T_B3VpJ" + }, + "source": [ + "## Share adapters on the 🤗 Hub" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 331, + "referenced_widgets": [ + "262f01ffc5824b5faa8a61afac12ff67", + "c1af10b599da43a3a848f3ba816d7acc", + "be673691e713472980fa1132465714b4", + "01f8b90f0f184dfdb92c0c3bffb28b0f", + "9ad2bd0e92174d339a3a91a38253a180", + "bff17ef6aabd4aa681bfd5ad64b808d9", + "55633800b60a4336abea6a4adfcfdec1", + "73d5c6b4034d49b392b103d889bfb3b4", + "a55954b8d7bf4057b0d7aa6a1cb9e91a", + "4da42eb3846f423d88e2a6462a0cfce8", + "dab39ef354a84be3b37b6f151f9d9b9d", + "b88b03326f464c96a5656eef774e36d5", + "4eccb670e98043b3b2702821a3060ece", + "a333501a50df4b9fa9546d8d965e0dc3", + "1f173cb95c5c44f4b32f6cfe10ee3b03", + "52c8a7e673f24276a07042388a13b58f", + "a0f323ccfbc14fc4b7a5e7046b221ce3" + ] + }, + "id": "DpYr24pR8T_0", + "outputId": "20186456-1bd4-4655-b2f2-8f24f9f37fcc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Token is valid.\n", + "Your token has been saved to /root/.cache/huggingface/token\n", + "Login successful\n" + ] + } + ], + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 133, + "referenced_widgets": [ + "3dbe077ed0c34e4eb1628418138ccbc6", + "a579cb7804774ca3aa9efd800d1af57e", + "9f44e3175835470aba43e239661037b2", + "549bd12e3af64256a7534903688835a8", + "a18d9beb34b848ff8cc541d2cb290c4c", + "876cb42184f54b749c442163290c2c45", + "43b14e0e1263499dbd592b280cff21b0", + "7164feaf360d4300a5083f95064b144a", + "bd7547126a874a45b608afed7ab2958b", + "26e4b6b94e4540728c59df8e481ce43d", + "9d1b77500f1c45308d4791a9c443e307", + "b2693135b6954d35afb3120f3caf4000", + "aeca2429ee48450a814515cb06acbc3e", + "434c09950be04d728cd7ca8d6c134dc6", + "34f58c132d2e4249b1e62a0b57c85999", + "d148369be26a43949257790cb202728d", + "0ecf58c5cbcd40908595fccddff1c6d4", + "fc2314656a2745eb921f636cc3451381", + "7817f8669b7f449fadf02d7145fa89e2", + "06e012eea9714ed589344a362b7421bb", + "504e9e5ced0348cc87aafae0c1c372eb", + "5b538e8389fb4574a5dfdc554624e3c8" + ] + }, + "id": "VxB6UV5XAvvP", + "outputId": "c3b0133b-f5b1-4283-8367-f06524bea46c" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Uploading the following files to ybelkada/opt-6.7b-lora: adapter_config.json,adapter_model.bin\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3dbe077ed0c34e4eb1628418138ccbc6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Upload 1 LFS files: 0%| | 0/1 [00:00Pro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. 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Copy a token from your Hugging Face\ntokens page and paste it below.
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dataset using Hugging Face 🤗 Transformers and 🤗 PEFT. Using 🤗 PEFT and `bitsandbytes`, you can train the `whisper-large-v2` seamlessly on a colab with T4 GPU (16 GB VRAM). In this notebook, with most parts from [fine_tune_whisper.ipynb](https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/fine_tune_whisper.ipynb#scrollTo=BRdrdFIeU78w) is adapted to train using PEFT LoRA+BNB INT8.\n", + "\n", + "For more details on model, datasets and metrics, refer blog [Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "625e47a0", + "metadata": {}, + "source": [ + "## Inital Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eJrPyQM5Xhv5", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eJrPyQM5Xhv5", + "outputId": "cfd6d8c9-964c-492b-b641-8e80e337f783" + }, + "outputs": [], + "source": [ + "!add-apt-repository -y ppa:jonathonf/ffmpeg-4\n", + "!apt update\n", + "!apt install -y ffmpeg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "r_Ivl7qlX0dz", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "r_Ivl7qlX0dz", + "outputId": "2caa9eed-f01a-4603-a527-fe3b0b58b6e2" + }, + "outputs": [], + "source": [ + "!pip install datasets>=2.6.1\n", + "!pip install git+https://github.com/huggingface/transformers\n", + "!pip install librosa\n", + "!pip install evaluate>=0.30\n", + "!pip install jiwer\n", + "!pip install gradio\n", + "!pip install -q bitsandbytes datasets accelerate loralib\n", + "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main" + ] + }, + { + "cell_type": "markdown", + "id": "8a528c1a", + "metadata": {}, + "source": [ + "Linking the notebook to the Hub is straightforward - it simply requires entering your Hub authentication token when prompted. Find your Hub authentication token [here](https://huggingface.co/settings/tokens):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed0OpduhX2JF", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 303, + "referenced_widgets": [ + "c60690c2aee74763bf23115553f4e640", + "7d3d6c198e794219ab5db59f0228c8ab", + "5d5ea0207c6148769ad9f15b7b3dd92d", + "642e28d258ca4c30a5df94c5cf7e0471", + "170ee581427d4f30925dc393d124c1be", + "d5d5aa24182a4e04b3fdae1ca7fad52a", + "6dba643113a547ac9b6e121d008791d6", + "cd9bda1053a14890ad9091c63c0a0acf", + "4522666ebbcf4647b06ce81a6316fbbb", + "989b3df296504f34a62d31ca0d6d88bb", + "e8a7a34c6fb146f0b38a40f389a617fa", + "960553d142c446cd8852523887a5cc04", + "441820fb176048109e0f8f7e9519d735", + "0bb38c654e18429a8396466ebab84504", + "1acfc4a2809e41dd995817c3526650dd", + "6d5801774beb4b529b227ef2f098614e", + "dfdf23cde48c421caebb573060641d6a" + ] + }, + "id": "ed0OpduhX2JF", + "outputId": "ecc2048a-b46a-4b20-b94a-5912924feb3d" + }, + "outputs": [], + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1da5fff", + "metadata": { + "id": "e1da5fff" + }, + "outputs": [], + "source": [ + "# Select CUDA device index\n", + "import os\n", + "\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n", + "model_name_or_path = \"openai/whisper-large-v2\"\n", + "language = \"Marathi\"\n", + "language_abbr = \"mr\"\n", + "task = \"transcribe\"\n", + "dataset_name = \"mozilla-foundation/common_voice_11_0\"" + ] + }, + { + "cell_type": "markdown", + "id": "805b1c56", + "metadata": {}, + "source": [ + "## Load Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2787582-554f-44ce-9f38-4180a5ed6b44", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "020176f5aa0a4d489d022ef5e41ef3f6", + 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"679fa9d2a703494dbb10fbfd19879f1a", + "6717b182e5674afb90006b50dea98cae", + "0887c7aabbbe4d4e8fdc64d2e2657cc7" + ] + }, + "id": "a2787582-554f-44ce-9f38-4180a5ed6b44", + "outputId": "b1729004-591e-41c2-c206-9d0e572eb8e5" + }, + "outputs": [], + "source": [ + "from datasets import load_dataset, DatasetDict\n", + "\n", + "common_voice = DatasetDict()\n", + "\n", + "common_voice[\"train\"] = load_dataset(dataset_name, language_abbr, split=\"train+validation\", use_auth_token=True)\n", + "common_voice[\"test\"] = load_dataset(dataset_name, language_abbr, split=\"test\", use_auth_token=True)\n", + "\n", + "print(common_voice)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce", + "outputId": "dd81bced-f544-4d55-9669-5babb901f842" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['audio', 'sentence'],\n", + " num_rows: 3927\n", + " })\n", + " test: Dataset({\n", + " features: ['audio', 'sentence'],\n", + " num_rows: 1816\n", + " })\n", + "})\n" + ] + } + ], + "source": [ + "common_voice = common_voice.remove_columns(\n", + " [\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\"]\n", + ")\n", + "\n", + "print(common_voice)" + ] + }, + { + "cell_type": "markdown", + "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605", + "metadata": { + "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605" + }, + "source": [ + "## Prepare Feature Extractor, Tokenizer and Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "4d25d9919acf44a19f1b6f8fd625f808", + "b041efb27b6149fcaf206590f1c1b961", + "4668602d021a400a8b0ec88599abc85c", + "92d9732acb964601b27695041f9fbb72", + "c3ea17f7dd94462986d30b751664d77b", + "1b1dc31d9a2b4357a71119300c6d899a", + "7a24ae9d13fb4e82b1993b05f8d71d11", + "e1141861d9f44d4c95313fd432795b70", + "6ef55b5db76d4c78854fa0c27165e480", + "91b103ac79e641b190416acdeb55902c", + "e9e10f1e53b74509bfc9c0bf11502c5e" + ] + }, + "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5", + "outputId": "7abb2062-e755-4f1a-e88b-9b7bf2986dbf" + }, + "outputs": [], + "source": [ + "from transformers import WhisperFeatureExtractor\n", + "\n", + "feature_extractor = WhisperFeatureExtractor.from_pretrained(model_name_or_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 209, + "referenced_widgets": [ + "f436e6a7d3014e2ca44c94455bfeace8", + "f9d6d41ffeba43cf94ec9d7a96af6617", + "bed65245e7234977874d35bf78694e35", + "c28c2fc81e0e467cbc0ded0205d1ee85", + "ea941a078b984f51b66b5f1e8f3d1d82", + "e3f1244dbe2c48bc8102f958c3df6467", + "786d3d34cfd24f81996797c55b10b443", + "3a4bb5b9cf864265af8f1a0b989dff2c", + "ac2e1977ddaf4b948c9cc24d84c08b83", + "dd339bf6baf6433e92665e30dc30b062", + "6a544d6dd5954beab32ce3089d8d2aac", + "f933fbdcc26d41b0bb294dabd0337834", + "950e74921e6042868ab6b7b9070d6f69", + "82dc91c5b065459d827863699a9710e1", + "5b7b6f08765c4c1989f945414f2c3cf4", + "735c3606df924b9297ddc05fae3e92d5", + "d5d632dd16f147e090c62aad38c45d3c", + "24191f38e3654b86a5da3576615e2229", + "4b1f4abe697948d5a1cca9b45b2a6f87", + "8527f474f1a549abafe9519e2b4bd338", + "1031dc4b5d2d45f3b14ab12dbd9bca56", + "ab40616ed9b74f438e74d403f848bed4", + "e6c2dc814c324a0c8cb744ca16707479", + "a615446a48624d0f9a009c1f6d8b1a54", + "383d6e891c5249b4b2fabb60d4900488", + "3c3214f235a54864848902f7e53662db", + "73f6e6860b64491284c9442f0deae8b6", + "6d9826a5adb847e8b4cc4486a31b52ce", + "e8b3093587e44164b0ac043414cea0fa", + "96266c5722f54eeeb682b2707b8025dd", + "f0be69583cd1410da6dbc18302d4439b", + "c24ddbaafa5f4a63b391d981a4f10354", + "629725dfcc684b1db1a57850c3bc7bc3", + "add0f175631742d49dd4695bc004e81a", + "5871a46d60f14da99e4bb8ee74405319", + "64b308019fff4095ab7f1812aab4676a", + "03f62b3f5aa64b6390b157cb3f9d2b9f", + "59657f26f4af490980b1d9bea1526e5e", + "1571fa5d8f494704949c5809f3409fa7", + "5ef218872f2646249888def0abed7149", + "03fcbc61c50c4ddc9c3684e4c085bffb", + "e4234c7c29744fc4be99b8b2ebedc9d1", + "70aa1fc14b09473e911dad3f9030b15b", + "008bead021ea416eb31dd9143d5ae5b9", + "ebd79e22ad4e4256a6d88883af2c1eef", + "c47ba0b11e074f708338863a35a78f7b", + "72b715be2c774235a21602b18d71e75a", + "ff9a0ed54bab49aca6f27bf1be66958e", + "15b5e415b62146ba96215458cf116431", + "8b0dd001d5b04647b1c480aab83d03a2", + "0af935ad4f694fb48094e6a119cbcf82", + "7cfbb542eba34459ba64b881c1040eee", + "fe48e65b2371445bbb01a8d3e9af1f67", + "b8d3539c8a454217a3b6ffab51259054", + "05e96819517e417aaf05f5f38c0c8b76", + "b600ead93bbf44a3a3fe229589c63a61", + "3d4c614fb775434fb0c5b02d46246ee0", + "344d1cc53e28411cae839a4ebba1bf58", + "a1578c0c777f4780a3fdd1635a0909d9", + "41faf55c8878475f8a986b02ad73e8ce", + "b5c8221a09df4dfdb74017d4af544b95", + "65e4b8dae1e4435cb7737a8d5dd13d91", + "6d932eadfd6a448a9a71d741bb64f428", + "dce0d285c7d947dfba9ee5bc1a6ebece", + "bde010029c374a0eb2bb942f380f0e8b", + "a91f37ce798d411ea2a33cfdb1f01251" + ] + }, + "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6", + "outputId": "4c094075-3f95-42db-9f4b-4db0c2fbeb91" + }, + "outputs": [], + "source": [ + "from transformers import WhisperTokenizer\n", + "\n", + "tokenizer = WhisperTokenizer.from_pretrained(model_name_or_path, language=language, task=task)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6", + "metadata": { + "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6" + }, + "outputs": [], + "source": [ + "from transformers import WhisperProcessor\n", + "\n", + "processor = WhisperProcessor.from_pretrained(model_name_or_path, language=language, task=task)" + ] + }, + { + "cell_type": "markdown", + "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c", + "metadata": { + "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c" + }, + "source": [ + "### Prepare Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + }, + "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255", + "outputId": "1f1fe2d1-3ad2-42d4-e6f0-f0929785ae8e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f7e1ef6a2d14f20194999aad5040c5d4bb3ead1377de3e1bbc6e9dba34d18a8a/common_voice_mr_30585613.mp3', 'array': array([-1.3727526e-15, -1.2400461e-13, -1.5159097e-13, ...,\n", + " 4.7928120e-06, 3.5631349e-06, 1.6352631e-06], dtype=float32), 'sampling_rate': 48000}, 'sentence': 'आईचे आजारपण वाढत चालले, तसतशी मथीही नीट खातपीतनाशी झाली.'}\n" + ] + } + ], + "source": [ + "print(common_voice[\"train\"][0])" + ] + }, + { + "cell_type": "markdown", + "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd", + "metadata": { + "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd" + }, + "source": [ + "Since \n", + "our input audio is sampled at 48kHz, we need to _downsample_ it to \n", + "16kHz prior to passing it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model. \n", + "\n", + "We'll set the audio inputs to the correct sampling rate using dataset's \n", + "[`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)\n", + "method. This operation does not change the audio in-place, \n", + "but rather signals to `datasets` to resample audio samples _on the fly_ the \n", + "first time that they are loaded:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f12e2e57-156f-417b-8cfb-69221cc198e8", + "metadata": { + "id": "f12e2e57-156f-417b-8cfb-69221cc198e8" + }, + "outputs": [], + "source": [ + "from datasets import Audio\n", + "\n", + "common_voice = common_voice.cast_column(\"audio\", Audio(sampling_rate=16000))" + ] + }, + { + "cell_type": "markdown", + "id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707", + "metadata": { + "id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707" + }, + "source": [ + "Re-loading the first audio sample in the Common Voice dataset will resample \n", + "it to the desired sampling rate:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87122d71-289a-466a-afcf-fa354b18946b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "87122d71-289a-466a-afcf-fa354b18946b", + "outputId": "727a709a-2b21-4c54-807f-efd40ea1719c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f7e1ef6a2d14f20194999aad5040c5d4bb3ead1377de3e1bbc6e9dba34d18a8a/common_voice_mr_30585613.mp3', 'array': array([-4.4097186e-14, -9.4153831e-14, 3.4645775e-13, ...,\n", + " -7.6018655e-06, -1.8617659e-06, 4.4520480e-06], dtype=float32), 'sampling_rate': 16000}, 'sentence': 'आईचे आजारपण वाढत चालले, तसतशी मथीही नीट खातपीतनाशी झाली.'}\n" + ] + } + ], + "source": [ + "print(common_voice[\"train\"][0])" + ] + }, + { + "cell_type": "markdown", + "id": "91edc72d-08f8-4f01-899d-74e65ce441fc", + "metadata": { + "id": "91edc72d-08f8-4f01-899d-74e65ce441fc" + }, + "source": [ + "Now we can write a function to prepare our data ready for the model:\n", + "1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.\n", + "2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n", + "3. We encode the transcriptions to label ids through the use of the tokenizer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6525c478-8962-4394-a1c4-103c54cce170", + "metadata": { + "id": "6525c478-8962-4394-a1c4-103c54cce170" + }, + "outputs": [], + "source": [ + "def prepare_dataset(batch):\n", + " # load and resample audio data from 48 to 16kHz\n", + " audio = batch[\"audio\"]\n", + "\n", + " # compute log-Mel input features from input audio array\n", + " batch[\"input_features\"] = feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n", + "\n", + " # encode target text to label ids\n", + " batch[\"labels\"] = tokenizer(batch[\"sentence\"]).input_ids\n", + " return batch" + ] + }, + { + "cell_type": "markdown", + "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13", + "metadata": { + "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13" + }, + "source": [ + "We can apply the data preparation function to all of our training examples using dataset's `.map` method. The argument `num_proc` specifies how many CPU cores to use. Setting `num_proc` > 1 will enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1` and process the dataset sequentially." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 197, + "referenced_widgets": [ + "466eeda389c442e487742faff05eeb81", + "664ff40a4e7346869652ba3be663acf2", + "3d8f10b5726b46de934dd0f7ed7244e4", + "b803893882ac4358a4676964dfb3fb31", + "4ff7e3d07f6a48f8935f0f42b86fc20c", + "3e5db1000a6c4871936614e712e769db", + "21dcf39a421847a78ea685eec0983fc6", + "c4119f7ec7464aab90f17d022e674999", + "965ac01b68064dc5a9fc3bb3f244c804", + "f5d7433d15de45e997d12568cac536fc", + "14e284f308844311a9ad40415091d93f", + "58c3766293e64116a131357f6cc66fe5", + "abc8f69eae7b46b1b430cf3b7a231b05", + "979edd227f0840a4be0233082e452b5a", + "12dedd9f089c42f3a8bba9adb01cf9d3", + "3042d874fcf544fabc4701938bca7cf3", + "baa8f739bb7e402bae7ee479e282e813", + "54509d703b7d4416bdee59157f918396", + "ec6017ce2fb3431ab823bde05f977a61", + "1a540a7cba794122abdb1900061479e2", + "d046a46c70ea46ffbb04a3c9f55637d5", + "99b628071c814d88a3cd5d72e4c95f01", + "e213d1c919314315ada180d49e27dfb6", + "0ea1f163e4174684bd6efc2e2433c1d3", + "09511a81a89d4754897a3507a84405be", + "8338339ab8a242c1a485bce8558f9c39", + "8e84abf61e3e45d58efc7ccd0bfe8d37", + "47a5e0cfb3564c16acaedb88613b9020", + "09e357a7187044b0bd2b841893a2601f", + "65e8c45d05be4d41b136439d9785a09c", + "1442a54fad7d44ddbafdacaf7f95279a", + "4bea6b0455ae4019b33d45491dbf4584", + "9c928a14371a4d9aa521953e287fff54", + "b8acdb71c3564972a6c8b27e964ec061", + "688f552e85714fe6a5d3eda82be0106a", + "a8aac44a077040bd9f4638c0c8f7a877", + "89f8ede64593475da4e056e9907f370f", + "851902cb2b0e494998684409d82dafd1", + "328f4d11886d48f49d4578e8e352097f", + "a25ef41450eb42ff9b8b618b40080ac0", + "04144ed3ff5f423f99179702cee5343f", + "8c41e1ee1a2c49b8b3d3d320fbf26262", + "4edf0948ed1346ceba1c0148e9c6fe1c", + "40f2b2ff75ca4562808fc2c7a870901e" + ] + }, + "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b", + "outputId": "eecac4c8-c5f8-427b-a2ba-0d51fae825ac" + }, + "outputs": [], + "source": [ + "common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4be572c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c4be572c", + "outputId": "0383124a-d4b1-4abe-a8a1-868ce6b3884e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Dataset({\n", + " features: ['input_features', 'labels'],\n", + " num_rows: 3927\n", + "})" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "common_voice[\"train\"]" + ] + }, + { + "cell_type": "markdown", + "id": "263a5a58-0239-4a25-b0df-c625fc9c5810", + "metadata": { + "id": "263a5a58-0239-4a25-b0df-c625fc9c5810" + }, + "source": [ + "## Training and Evaluation" + ] + }, + { + "cell_type": "markdown", + "id": "8d230e6d-624c-400a-bbf5-fa660881df25", + "metadata": { + "id": "8d230e6d-624c-400a-bbf5-fa660881df25" + }, + "source": [ + "### Define a Data Collator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5", + "metadata": { + "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5" + }, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "from dataclasses import dataclass\n", + "from typing import Any, Dict, List, Union\n", + "\n", + "\n", + "@dataclass\n", + "class DataCollatorSpeechSeq2SeqWithPadding:\n", + " processor: Any\n", + "\n", + " def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n", + " # split inputs and labels since they have to be of different lengths and need different padding methods\n", + " # first treat the audio inputs by simply returning torch tensors\n", + " input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n", + " batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n", + "\n", + " # get the tokenized label sequences\n", + " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", + " # pad the labels to max length\n", + " labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n", + "\n", + " # replace padding with -100 to ignore loss correctly\n", + " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n", + "\n", + " # if bos token is appended in previous tokenization step,\n", + " # cut bos token here as it's append later anyways\n", + " if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n", + " labels = labels[:, 1:]\n", + "\n", + " batch[\"labels\"] = labels\n", + "\n", + " return batch" + ] + }, + { + "cell_type": "markdown", + "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86", + "metadata": { + "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86" + }, + "source": [ + "Let's initialise the data collator we've just defined:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc834702-c0d3-4a96-b101-7b87be32bf42", + "metadata": { + "id": "fc834702-c0d3-4a96-b101-7b87be32bf42" + }, + "outputs": [], + "source": [ + "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)" + ] + }, + { + "cell_type": "markdown", + "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698", + "metadata": { + "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698" + }, + "source": [ + "### Evaluation Metrics" + ] + }, + { + "cell_type": "markdown", + "id": "66fee1a7-a44c-461e-b047-c3917221572e", + "metadata": { + "id": "66fee1a7-a44c-461e-b047-c3917221572e" + }, + "source": [ + "We'll use the word error rate (WER) metric, the 'de-facto' metric for assessing \n", + "ASR systems. For more information, refer to the WER [docs](https://huggingface.co/metrics/wer). We'll load the WER metric from 🤗 Evaluate:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b22b4011-f31f-4b57-b684-c52332f92890", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "215c3486e13343e091a26d658e1030d2", + "999382fb9e764a98893bd5269261d70b", + "4e9729771294424f959bbfb6bf8b60f3", + "3d2249516ecd43a08b5fba53fddb32e8", + "681711ebd0c64a63bee6b5337ef401db", + "d37791ea2b6c4152991295ca0edb0fb7", + "730f4d93bbd94452a59de43bf3d0a266", + "048e5c87cea34014a8f8a4538f4126bd", + "fbd27061ff114846aedc99bc2d17f7a7", + "5a306409e9e045b2b936267c520f935c", + "9f0638198f544a3bbb31a3a78b7bd2c2" + ] + }, + "id": "b22b4011-f31f-4b57-b684-c52332f92890", + "outputId": "b0a08086-69b9-4ab4-97ac-dbed295f2e15" + }, + "outputs": [], + "source": [ + "import evaluate\n", + "\n", + "metric = evaluate.load(\"wer\")" + ] + }, + { + "cell_type": "markdown", + "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508", + "metadata": { + "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508" + }, + "source": [ + "We then simply have to define a function that takes our model \n", + "predictions and returns the WER metric. This function, called\n", + "`compute_metrics`, first replaces `-100` with the `pad_token_id`\n", + "in the `label_ids` (undoing the step we applied in the \n", + "data collator to ignore padded tokens correctly in the loss).\n", + "It then decodes the predicted and label ids to strings. Finally,\n", + "it computes the WER between the predictions and reference labels:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52", + "metadata": { + "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52" + }, + "outputs": [], + "source": [ + "def compute_metrics(pred):\n", + " pred_ids = pred.predictions\n", + " label_ids = pred.label_ids\n", + "\n", + " # replace -100 with the pad_token_id\n", + " label_ids[label_ids == -100] = tokenizer.pad_token_id\n", + "\n", + " # we do not want to group tokens when computing the metrics\n", + " pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n", + " label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n", + "\n", + " wer = 100 * metric.compute(predictions=pred_str, references=label_str)\n", + "\n", + " return {\"wer\": wer}" + ] + }, + { + "cell_type": "markdown", + "id": "daf2a825-6d9f-4a23-b145-c37c0039075b", + "metadata": { + "id": "daf2a825-6d9f-4a23-b145-c37c0039075b" + }, + "source": [ + "### Load a Pre-Trained Checkpoint" + ] + }, + { + "cell_type": "markdown", + "id": "437a97fa-4864-476b-8abc-f28b8166cfa5", + "metadata": { + "id": "437a97fa-4864-476b-8abc-f28b8166cfa5" + }, + "source": [ + "Now let's load the pre-trained Whisper `small` checkpoint. Again, this \n", + "is trivial through use of 🤗 Transformers!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 220, + "referenced_widgets": [ + "d8c1a66480204f1095ff5f6a7dd2e477", + "9dc736113ef6477d91aaf71c9969ca74", + "79521628c64b4d6f9f22e73749298693", + "c7268972f75e4824893ebe7d893a18e1", + "a87598d464174703b5f5a5eca23543f3", + "e0529b81739144db8912c2d6789e729a", + "13d0a97497274652b081cbcefb3fd17d", + "08b26adf061b48f59078bf0c0b59e643", + "ef056ad59e314089a012acaa73a54e4f", + "7935e298049f4deeaeb278ba3de92291", + "3afca90970fc4925a05a9a1aa5c8d2f2", + "40aba44ef0a74c0d9a385c803c1365a6", + "ae54388d78dc4b7ebd1b21860421ffe4", + "bb4a47c63d254d4aa3220e85f86d37c4", + "cd585c98560b42c8b4a08df5b853b23c", + "a140cd385e5a4c0a88c5562370982d2e", + "fce64f5690024c698701330f0e5d039a", + "abd0e7c414974e51b280a29bf978f776", + "bf7961a79c2f403a89a4fb6d4b1a02e5", + "1d8636d1d1c3442fbbc2fc83cd03fa44", + "313090dc1f034ab19d5ffc573ea1aa5c", + "966c0400dafe4e3ea2c9baebc2e104fa", + "9087be5d992c4198b3ec2c61f4021164", + "6bc07db3471342a6bbc1b86ca734b3e6", + "4f167e9657274d56b8568d48262d1ee6", + "b7fbaa9d4bcd40b5bcf8d1475658c5b1", + "ba21f5ddf2434cc792b70a70c8c1079e", + "21bc84c704874455a57c47239984d28d", + "5b226e580df94448bed54b400c7a9a25", + "b0237154051343adaa076a9cc6dd711d", + "64756452791144859b9c803886c6dc77", + "f027b4358c2c41a8a93c617641135bcd", + "42cb6114956c4a86980c8dafd5a734ae" + ] + }, + "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f", + "outputId": "163d4b39-7e5d-4126-8d78-846c5d94dca6" + }, + "outputs": [], + "source": [ + "from transformers import WhisperForConditionalGeneration\n", + "\n", + "model = WhisperForConditionalGeneration.from_pretrained(model_name_or_path, load_in_8bit=True, device_map=\"auto\")\n", + "\n", + "# model.hf_device_map - this should be {\" \": 0}" + ] + }, + { + "cell_type": "markdown", + "id": "a15ead5f-2277-4a39-937b-585c2497b2df", + "metadata": { + "id": "a15ead5f-2277-4a39-937b-585c2497b2df" + }, + "source": [ + "Override generation arguments - no tokens are forced as decoder outputs (see [`forced_decoder_ids`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.forced_decoder_ids)), no tokens are suppressed during generation (see [`suppress_tokens`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.suppress_tokens)):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62038ba3-88ed-4fce-84db-338f50dcd04f", + "metadata": { + "id": "62038ba3-88ed-4fce-84db-338f50dcd04f" + }, + "outputs": [], + "source": [ + "model.config.forced_decoder_ids = None\n", + "model.config.suppress_tokens = []" + ] + }, + { + "cell_type": "markdown", + "id": "bR-_yaEOPsfQ", + "metadata": { + "id": "bR-_yaEOPsfQ" + }, + "source": [ + "### Post-processing on the model\n", + "\n", + "Finally, we need to apply some post-processing on the 8-bit model to enable training, let's freeze all our layers, and cast the layer-norm in `float32` for stability. We also cast the output of the last layer in `float32` for the same reasons." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "Cl_ZQualPt9R", + "metadata": { + "id": "Cl_ZQualPt9R" + }, + "outputs": [], + "source": [ + "from peft import prepare_model_for_int8_training\n", + "\n", + "model = prepare_model_for_int8_training(model, output_embedding_layer_name=\"proj_out\")" + ] + }, + { + "cell_type": "markdown", + "id": "Vjl4j4RJPmPR", + "metadata": { + "id": "Vjl4j4RJPmPR" + }, + "source": [ + "### Apply LoRA\n", + "\n", + "Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "DQtpDPRHPyOL", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DQtpDPRHPyOL", + "outputId": "1effcbde-7acc-4f62-f24b-e6236a43f833" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "trainable params: 15728640 || all params: 1559033600 || trainable%: 1.0088711365810203\n" + ] + } + ], + "source": [ + "from peft import LoraConfig, PeftModel, LoraModel, LoraConfig, get_peft_model\n", + "\n", + "config = LoraConfig(r=32, lora_alpha=64, target_modules=[\"q_proj\", \"v_proj\"], lora_dropout=0.05, bias=\"none\")\n", + "\n", + "model = get_peft_model(model, config)\n", + "model.print_trainable_parameters()" + ] + }, + { + "cell_type": "markdown", + "id": "3906d436", + "metadata": {}, + "source": [ + "We are ONLY using **1%** of the total trainable parameters, thereby performing **Parameter-Efficient Fine-Tuning**" + ] + }, + { + "cell_type": "markdown", + "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06", + "metadata": { + "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06" + }, + "source": [ + "### Define the Training Configuration" + ] + }, + { + "cell_type": "markdown", + "id": "c21af1e9-0188-4134-ac82-defc7bdcc436", + "metadata": { + "id": "c21af1e9-0188-4134-ac82-defc7bdcc436" + }, + "source": [ + "In the final step, we define all the parameters related to training. For more detail on the training arguments, refer to the Seq2SeqTrainingArguments [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a", + "metadata": { + "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a" + }, + "outputs": [], + "source": [ + "from transformers import Seq2SeqTrainingArguments\n", + "\n", + "training_args = Seq2SeqTrainingArguments(\n", + " output_dir=\"temp\", # change to a repo name of your choice\n", + " per_device_train_batch_size=8,\n", + " gradient_accumulation_steps=1, # increase by 2x for every 2x decrease in batch size\n", + " learning_rate=1e-3,\n", + " warmup_steps=50,\n", + " num_train_epochs=3,\n", + " evaluation_strategy=\"epoch\",\n", + " fp16=True,\n", + " per_device_eval_batch_size=8,\n", + " generation_max_length=128,\n", + " logging_steps=25,\n", + " remove_unused_columns=False, # required as the PeftModel forward doesn't have the signature of the wrapped model's forward\n", + " label_names=[\"labels\"], # same reason as above\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "b3a944d8-3112-4552-82a0-be25988b3857", + "metadata": { + "id": "b3a944d8-3112-4552-82a0-be25988b3857" + }, + "source": [ + "**Few Important Notes:**\n", + "1. `remove_unused_columns=False` and `label_names=[\"labels\"]` are required as the PeftModel's forward doesn't have the signature of the base model's forward.\n", + "\n", + "2. INT8 training required autocasting. `predict_with_generate` can't be passed to Trainer because it internally calls transformer's `generate` without autocasting leading to errors. \n", + "\n", + "3. Because of point 2, `compute_metrics` shouldn't be passed to `Seq2SeqTrainer` as seen below. (commented out)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d546d7fe-0543-479a-b708-2ebabec19493", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "d546d7fe-0543-479a-b708-2ebabec19493", + "outputId": "e2fabe64-2c50-42ff-a7ca-7773813e9408" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The model is loaded in 8-bit precision. To train this model you need to add additional modules inside the model such as adapters using `peft` library and freeze the model weights. Please check the examples in https://github.com/huggingface/peft for more details.\n", + "Using cuda_amp half precision backend\n" + ] + } + ], + "source": [ + "from transformers import Seq2SeqTrainer, TrainerCallback, TrainingArguments, TrainerState, TrainerControl\n", + "from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR\n", + "\n", + "\n", + "class SavePeftModelCallback(TrainerCallback):\n", + " def on_save(\n", + " self,\n", + " args: TrainingArguments,\n", + " state: TrainerState,\n", + " control: TrainerControl,\n", + " **kwargs,\n", + " ):\n", + " checkpoint_folder = os.path.join(args.output_dir, f\"{PREFIX_CHECKPOINT_DIR}-{state.global_step}\")\n", + "\n", + " peft_model_path = os.path.join(checkpoint_folder, \"adapter_model\")\n", + " kwargs[\"model\"].save_pretrained(peft_model_path)\n", + "\n", + " pytorch_model_path = os.path.join(checkpoint_folder, \"pytorch_model.bin\")\n", + " if os.path.exists(pytorch_model_path):\n", + " os.remove(pytorch_model_path)\n", + " return control\n", + "\n", + "\n", + "trainer = Seq2SeqTrainer(\n", + " args=training_args,\n", + " model=model,\n", + " train_dataset=common_voice[\"train\"],\n", + " eval_dataset=common_voice[\"test\"],\n", + " data_collator=data_collator,\n", + " # compute_metrics=compute_metrics,\n", + " tokenizer=processor.feature_extractor,\n", + " callbacks=[SavePeftModelCallback],\n", + ")\n", + "model.config.use_cache = False # silence the warnings. Please re-enable for inference!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de", + "outputId": "cdea5268-f33a-4d48-ea4a-a9c71576f81d" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:346: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", + " warnings.warn(\n", + "***** Running training *****\n", + " Num examples = 3927\n", + " Num Epochs = 3\n", + " Instantaneous batch size per device = 8\n", + " Total train batch size (w. parallel, distributed & accumulation) = 8\n", + " Gradient Accumulation steps = 1\n", + " Total optimization steps = 1473\n", + " Number of trainable parameters = 15728640\n", + "/usr/local/lib/python3.8/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n", + " warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n", + "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n", + " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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+{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kdOhtpergLCQ" + }, + "outputs": [], + "source": [ + "!git clone https://huggingface.co/spaces/smangrul/peft-lora-sd-dreambooth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_LuGk9mihPx7" + }, + "outputs": [], + "source": [ + "%cd \"peft-lora-sd-dreambooth\"\n", + "!pip install -r requirements.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BYKO8e5ElJOX" + }, + "outputs": [], + "source": [ + "!python colab.py" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "gpuClass": "premium", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/04-gene-sft/peft/examples/lora_dreambooth/lora_dreambooth_inference.ipynb b/04-gene-sft/peft/examples/lora_dreambooth/lora_dreambooth_inference.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a98bbe63bd4fb12e41dbd84a757fbcfd25830c79 --- /dev/null +++ b/04-gene-sft/peft/examples/lora_dreambooth/lora_dreambooth_inference.ipynb @@ -0,0 +1,250 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "acab479f", + "metadata": {}, + "outputs": [], + "source": [ + "import argparse\n", + "import gc\n", + "import hashlib\n", + "import itertools\n", + "import logging\n", + "import math\n", + "import os\n", + "import threading\n", + "import warnings\n", + "from pathlib import Path\n", + "from typing import Optional\n", + "import psutil\n", + "import json\n", + "\n", + "import torch\n", + "import torch.nn.functional as F\n", + "import torch.utils.checkpoint\n", + "from torch.utils.data import Dataset\n", + "\n", + "import datasets\n", + "import diffusers\n", + "import transformers\n", + "from accelerate import Accelerator\n", + "from accelerate.logging import get_logger\n", + "from accelerate.utils import set_seed\n", + "from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel\n", + "from diffusers import DDPMScheduler, PNDMScheduler, StableDiffusionPipeline\n", + "from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker\n", + "from diffusers.optimization import get_scheduler\n", + "from diffusers.utils import check_min_version\n", + "from diffusers.utils.import_utils import is_xformers_available\n", + "from huggingface_hub import HfFolder, Repository, whoami\n", + "from PIL import Image\n", + "from torchvision import transforms\n", + "from tqdm.auto import tqdm\n", + "from transformers import AutoTokenizer, PretrainedConfig, CLIPFeatureExtractor\n", + "from peft import LoraModel, LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict\n", + "\n", + "# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\n", + "check_min_version(\"0.10.0.dev0\")\n", + "\n", + "logger = get_logger(__name__)\n", + "\n", + "\n", + "MODEL_NAME = \"CompVis/stable-diffusion-v1-4\" # \"stabilityai/stable-diffusion-2-1-base\"\n", + "INSTANCE_PROMPT = \"a photo of sks dog\"\n", + "ckpt_dir = \"/home/sourab/temp/sd_dog_dreambooth/\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "328161b7", + "metadata": {}, + "outputs": [], + "source": [ + "pipe = StableDiffusionPipeline.from_pretrained(MODEL_NAME, torch_dtype=torch.float16).to(\"cuda\")\n", + "\n", + "prompt = \"a photo of sks dog\"\n", + "image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2efc13f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'pet_config': {'pet_type': 'LORA', 'task_type': None, 'inference_mode': True, 'r': 16, 'target_modules': ['to_q', 'to_v', 'query', 'value'], 'lora_alpha': 27, 'lora_dropout': 0.0, 'merge_weights': False, 'fan_in_fan_out': False, 'enable_lora': None, 'bias': 'none'}, 'text_encoder_pet_config': {'pet_type': 'LORA', 'task_type': None, 'inference_mode': True, 'r': 16, 'target_modules': ['q_proj', 'v_proj'], 'lora_alpha': 17, 'lora_dropout': 0.0, 'merge_weights': False, 'fan_in_fan_out': False, 'enable_lora': None, 'bias': 'none'}}\n" + ] + } + ], + "source": [ + "def load_and_set_lora_ckpt(pipe, ckpt_dir, instance_prompt, device, dtype):\n", + " with open(f\"{ckpt_dir}{instance_prompt}_lora_config.json\", \"r\") as f:\n", + " lora_config = json.load(f)\n", + " print(lora_config)\n", + "\n", + " checkpoint = f\"{ckpt_dir}{instance_prompt}_lora.pt\"\n", + " lora_checkpoint_sd = torch.load(checkpoint)\n", + " unet_lora_ds = {k: v for k, v in lora_checkpoint_sd.items() if \"text_encoder_\" not in k}\n", + " text_encoder_lora_ds = {\n", + " k.replace(\"text_encoder_\", \"\"): v for k, v in lora_checkpoint_sd.items() if \"text_encoder_\" in k\n", + " }\n", + "\n", + " unet_config = LoraConfig(**lora_config[\"peft_config\"])\n", + " pipe.unet = LoraModel(unet_config, pipe.unet)\n", + " set_peft_model_state_dict(pipe.unet, unet_lora_ds)\n", + "\n", + " if \"text_encoder_peft_config\" in lora_config:\n", + " text_encoder_config = LoraConfig(**lora_config[\"text_encoder_peft_config\"])\n", + " pipe.text_encoder = LoraModel(text_encoder_config, pipe.text_encoder)\n", + " set_peft_model_state_dict(pipe.text_encoder, text_encoder_lora_ds)\n", + "\n", + " if dtype in (torch.float16, torch.bfloat16):\n", + " pipe.unet.half()\n", + " pipe.text_encoder.half()\n", + "\n", + " pipe.to(device)\n", + " return pipe\n", + "\n", + "\n", + "pipe = load_and_set_lora_ckpt(pipe, ckpt_dir, INSTANCE_PROMPT, \"cuda\", torch.float16)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e50da571", + "metadata": {}, + "outputs": [], + "source": [ + "prompt = \"a photo of sks dog\"\n", + "negative_prompt = \"low quality, blurry, unfinished\"\n", + "image = pipe(prompt, num_inference_steps=50, guidance_scale=7, negative_prompt=negative_prompt).images[0]\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "53af98c2", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ea05e638bda4403ca91d699609909f40", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/50 [00:00" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prompt = \"sks dog in a steampunk city, 4K render, trending on artstation, masterpiece\"\n", + "image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5, negative_prompt=negative_prompt).images[0]\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "200d3358", + "metadata": {}, + "outputs": [], + "source": [ + "prompt = \"sks dog with Eiffel Tower in the background\"\n", + "image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5, negative_prompt=negative_prompt).images[0]\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0518421e", + "metadata": {}, + "outputs": [], + "source": [ + "prompt = \"sks dog swimming in th pool with sunglasses\"\n", + "image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5, negative_prompt=negative_prompt).images[0]\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cca5106c", + "metadata": {}, + "outputs": [], + "source": [ + "prompt = \"sks dog weaing red sweater\"\n", + "image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5, negative_prompt=negative_prompt).images[0]\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "121b1da3", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f0ecb40", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5 (v3.10.5:f377153967, Jun 6 2022, 12:36:10) [Clang 13.0.0 (clang-1300.0.29.30)]" + }, + "vscode": { + "interpreter": { + "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/04-gene-sft/peft/examples/lora_dreambooth/requirements.txt b/04-gene-sft/peft/examples/lora_dreambooth/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..4ce5eb236f711ecad3990bfbb7953d88dd4593bd --- /dev/null +++ b/04-gene-sft/peft/examples/lora_dreambooth/requirements.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8fb039d0c27f18819d72a190062bf2b46eab47b5b5e66baae6ba8c921dde2482 +size 99 diff --git a/04-gene-sft/peft/examples/lora_dreambooth/train_dreambooth.py b/04-gene-sft/peft/examples/lora_dreambooth/train_dreambooth.py new file mode 100644 index 0000000000000000000000000000000000000000..c06a175ba855cb702c59d8bfa6e6fa206b97d8ec --- /dev/null +++ b/04-gene-sft/peft/examples/lora_dreambooth/train_dreambooth.py @@ -0,0 +1,1081 @@ +import argparse +import gc +import hashlib +import itertools +import json +import logging +import math +import os +import threading +import warnings +from pathlib import Path +from typing import Optional + +import datasets +import diffusers +import numpy as np +import psutil +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +import transformers +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import set_seed +from diffusers import ( + AutoencoderKL, + DDPMScheduler, + DiffusionPipeline, + DPMSolverMultistepScheduler, + UNet2DConditionModel, +) +from diffusers.optimization import get_scheduler +from diffusers.utils import check_min_version +from diffusers.utils.import_utils import is_xformers_available +from huggingface_hub import HfFolder, Repository, whoami +from PIL import Image +from torch.utils.data import Dataset +from torchvision import transforms +from tqdm.auto import tqdm +from transformers import AutoTokenizer, PretrainedConfig + +from peft import LoraConfig, LoraModel, get_peft_model_state_dict + + +# Will error if the minimal version of diffusers is not installed. Remove at your own risks. +check_min_version("0.10.0.dev0") + +logger = get_logger(__name__) + +UNET_TARGET_MODULES = ["to_q", "to_v", "query", "value"] # , "ff.net.0.proj"] +TEXT_ENCODER_TARGET_MODULES = ["q_proj", "v_proj"] + + +def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str): + text_encoder_config = PretrainedConfig.from_pretrained( + pretrained_model_name_or_path, + subfolder="text_encoder", + revision=revision, + ) + model_class = text_encoder_config.architectures[0] + + if model_class == "CLIPTextModel": + from transformers import CLIPTextModel + + return CLIPTextModel + elif model_class == "RobertaSeriesModelWithTransformation": + from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation + + return RobertaSeriesModelWithTransformation + else: + raise ValueError(f"{model_class} is not supported.") + + +def parse_args(input_args=None): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_model_name_or_path", + type=str, + default=None, + required=True, + help="Path to pretrained model or model identifier from huggingface.co/models.", + ) + parser.add_argument( + "--revision", + type=str, + default=None, + required=False, + help="Revision of pretrained model identifier from huggingface.co/models.", + ) + parser.add_argument( + "--tokenizer_name", + type=str, + default=None, + help="Pretrained tokenizer name or path if not the same as model_name", + ) + parser.add_argument( + "--instance_data_dir", + type=str, + default=None, + required=True, + help="A folder containing the training data of instance images.", + ) + parser.add_argument( + "--class_data_dir", + type=str, + default=None, + required=False, + help="A folder containing the training data of class images.", + ) + parser.add_argument( + "--instance_prompt", + type=str, + default=None, + required=True, + help="The prompt with identifier specifying the instance", + ) + parser.add_argument( + "--class_prompt", + type=str, + default=None, + help="The prompt to specify images in the same class as provided instance images.", + ) + parser.add_argument( + "--with_prior_preservation", + default=False, + action="store_true", + help="Flag to add prior preservation loss.", + ) + parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.") + parser.add_argument( + "--num_class_images", + type=int, + default=100, + help=( + "Minimal class images for prior preservation loss. If there are not enough images already present in" + " class_data_dir, additional images will be sampled with class_prompt." + ), + ) + parser.add_argument( + "--validation_prompt", + type=str, + default=None, + help="A prompt that is used during validation to verify that the model is learning.", + ) + parser.add_argument( + "--num_validation_images", + type=int, + default=4, + help="Number of images that should be generated during validation with `validation_prompt`.", + ) + parser.add_argument( + "--validation_steps", + type=int, + default=100, + help=( + "Run dreambooth validation every X steps. Dreambooth validation consists of running the prompt" + " `args.validation_prompt` multiple times: `args.num_validation_images`." + ), + ) + parser.add_argument( + "--output_dir", + type=str, + default="text-inversion-model", + help="The output directory where the model predictions and checkpoints will be written.", + ) + parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") + parser.add_argument( + "--resolution", + type=int, + default=512, + help=( + "The resolution for input images, all the images in the train/validation dataset will be resized to this" + " resolution" + ), + ) + parser.add_argument( + "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution" + ) + parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder") + + # lora args + parser.add_argument("--use_lora", action="store_true", help="Whether to use Lora for parameter efficient tuning") + parser.add_argument("--lora_r", type=int, default=8, help="Lora rank, only used if use_lora is True") + parser.add_argument("--lora_alpha", type=int, default=32, help="Lora alpha, only used if use_lora is True") + parser.add_argument("--lora_dropout", type=float, default=0.0, help="Lora dropout, only used if use_lora is True") + parser.add_argument( + "--lora_bias", + type=str, + default="none", + help="Bias type for Lora. Can be 'none', 'all' or 'lora_only', only used if use_lora is True", + ) + parser.add_argument( + "--lora_text_encoder_r", + type=int, + default=8, + help="Lora rank for text encoder, only used if `use_lora` and `train_text_encoder` are True", + ) + parser.add_argument( + "--lora_text_encoder_alpha", + type=int, + default=32, + help="Lora alpha for text encoder, only used if `use_lora` and `train_text_encoder` are True", + ) + parser.add_argument( + "--lora_text_encoder_dropout", + type=float, + default=0.0, + help="Lora dropout for text encoder, only used if `use_lora` and `train_text_encoder` are True", + ) + parser.add_argument( + "--lora_text_encoder_bias", + type=str, + default="none", + help="Bias type for Lora. Can be 'none', 'all' or 'lora_only', only used if use_lora and `train_text_encoder` are True", + ) + + parser.add_argument( + "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader." + ) + parser.add_argument( + "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images." + ) + parser.add_argument("--num_train_epochs", type=int, default=1) + parser.add_argument( + "--max_train_steps", + type=int, + default=None, + help="Total number of training steps to perform. If provided, overrides num_train_epochs.", + ) + parser.add_argument( + "--checkpointing_steps", + type=int, + default=500, + help=( + "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final" + " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming" + " training using `--resume_from_checkpoint`." + ), + ) + parser.add_argument( + "--resume_from_checkpoint", + type=str, + default=None, + help=( + "Whether training should be resumed from a previous checkpoint. Use a path saved by" + ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' + ), + ) + parser.add_argument( + "--gradient_accumulation_steps", + type=int, + default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.", + ) + parser.add_argument( + "--gradient_checkpointing", + action="store_true", + help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=5e-6, + help="Initial learning rate (after the potential warmup period) to use.", + ) + parser.add_argument( + "--scale_lr", + action="store_true", + default=False, + help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", + ) + parser.add_argument( + "--lr_scheduler", + type=str, + default="constant", + help=( + 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup"]' + ), + ) + parser.add_argument( + "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." + ) + parser.add_argument( + "--lr_num_cycles", + type=int, + default=1, + help="Number of hard resets of the lr in cosine_with_restarts scheduler.", + ) + parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.") + parser.add_argument( + "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." + ) + parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") + parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") + parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") + parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") + parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") + parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") + parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") + parser.add_argument( + "--hub_model_id", + type=str, + default=None, + help="The name of the repository to keep in sync with the local `output_dir`.", + ) + parser.add_argument( + "--logging_dir", + type=str, + default="logs", + help=( + "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" + " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." + ), + ) + parser.add_argument( + "--allow_tf32", + action="store_true", + help=( + "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" + " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" + ), + ) + parser.add_argument( + "--report_to", + type=str, + default="tensorboard", + help=( + 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' + ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' + ), + ) + parser.add_argument( + "--mixed_precision", + type=str, + default=None, + choices=["no", "fp16", "bf16"], + help=( + "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" + " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" + " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." + ), + ) + parser.add_argument( + "--prior_generation_precision", + type=str, + default=None, + choices=["no", "fp32", "fp16", "bf16"], + help=( + "Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" + " 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32." + ), + ) + parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") + parser.add_argument( + "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." + ) + + if input_args is not None: + args = parser.parse_args(input_args) + else: + args = parser.parse_args() + + env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) + if env_local_rank != -1 and env_local_rank != args.local_rank: + args.local_rank = env_local_rank + + if args.with_prior_preservation: + if args.class_data_dir is None: + raise ValueError("You must specify a data directory for class images.") + if args.class_prompt is None: + raise ValueError("You must specify prompt for class images.") + else: + # logger is not available yet + if args.class_data_dir is not None: + warnings.warn("You need not use --class_data_dir without --with_prior_preservation.") + if args.class_prompt is not None: + warnings.warn("You need not use --class_prompt without --with_prior_preservation.") + + return args + + +# Converting Bytes to Megabytes +def b2mb(x): + return int(x / 2**20) + + +# This context manager is used to track the peak memory usage of the process +class TorchTracemalloc: + def __enter__(self): + gc.collect() + torch.cuda.empty_cache() + torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero + self.begin = torch.cuda.memory_allocated() + self.process = psutil.Process() + + self.cpu_begin = self.cpu_mem_used() + self.peak_monitoring = True + peak_monitor_thread = threading.Thread(target=self.peak_monitor_func) + peak_monitor_thread.daemon = True + peak_monitor_thread.start() + return self + + def cpu_mem_used(self): + """get resident set size memory for the current process""" + return self.process.memory_info().rss + + def peak_monitor_func(self): + self.cpu_peak = -1 + + while True: + self.cpu_peak = max(self.cpu_mem_used(), self.cpu_peak) + + # can't sleep or will not catch the peak right (this comment is here on purpose) + # time.sleep(0.001) # 1msec + + if not self.peak_monitoring: + break + + def __exit__(self, *exc): + self.peak_monitoring = False + + gc.collect() + torch.cuda.empty_cache() + self.end = torch.cuda.memory_allocated() + self.peak = torch.cuda.max_memory_allocated() + self.used = b2mb(self.end - self.begin) + self.peaked = b2mb(self.peak - self.begin) + + self.cpu_end = self.cpu_mem_used() + self.cpu_used = b2mb(self.cpu_end - self.cpu_begin) + self.cpu_peaked = b2mb(self.cpu_peak - self.cpu_begin) + # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}") + + +def print_trainable_parameters(model): + """ + Prints the number of trainable parameters in the model. + """ + trainable_params = 0 + all_param = 0 + for _, param in model.named_parameters(): + all_param += param.numel() + if param.requires_grad: + trainable_params += param.numel() + print( + f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}" + ) + + +class DreamBoothDataset(Dataset): + """ + A dataset to prepare the instance and class images with the prompts for fine-tuning the model. + It pre-processes the images and the tokenizes prompts. + """ + + def __init__( + self, + instance_data_root, + instance_prompt, + tokenizer, + class_data_root=None, + class_prompt=None, + size=512, + center_crop=False, + ): + self.size = size + self.center_crop = center_crop + self.tokenizer = tokenizer + + self.instance_data_root = Path(instance_data_root) + if not self.instance_data_root.exists(): + raise ValueError("Instance images root doesn't exists.") + + self.instance_images_path = list(Path(instance_data_root).iterdir()) + self.num_instance_images = len(self.instance_images_path) + self.instance_prompt = instance_prompt + self._length = self.num_instance_images + + if class_data_root is not None: + self.class_data_root = Path(class_data_root) + self.class_data_root.mkdir(parents=True, exist_ok=True) + self.class_images_path = list(self.class_data_root.iterdir()) + self.num_class_images = len(self.class_images_path) + self._length = max(self.num_class_images, self.num_instance_images) + self.class_prompt = class_prompt + else: + self.class_data_root = None + + self.image_transforms = transforms.Compose( + [ + transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), + transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + + def __len__(self): + return self._length + + def __getitem__(self, index): + example = {} + instance_image = Image.open(self.instance_images_path[index % self.num_instance_images]) + if not instance_image.mode == "RGB": + instance_image = instance_image.convert("RGB") + example["instance_images"] = self.image_transforms(instance_image) + example["instance_prompt_ids"] = self.tokenizer( + self.instance_prompt, + truncation=True, + padding="max_length", + max_length=self.tokenizer.model_max_length, + return_tensors="pt", + ).input_ids + + if self.class_data_root: + class_image = Image.open(self.class_images_path[index % self.num_class_images]) + if not class_image.mode == "RGB": + class_image = class_image.convert("RGB") + example["class_images"] = self.image_transforms(class_image) + example["class_prompt_ids"] = self.tokenizer( + self.class_prompt, + truncation=True, + padding="max_length", + max_length=self.tokenizer.model_max_length, + return_tensors="pt", + ).input_ids + + return example + + +def collate_fn(examples, with_prior_preservation=False): + input_ids = [example["instance_prompt_ids"] for example in examples] + pixel_values = [example["instance_images"] for example in examples] + + # Concat class and instance examples for prior preservation. + # We do this to avoid doing two forward passes. + if with_prior_preservation: + input_ids += [example["class_prompt_ids"] for example in examples] + pixel_values += [example["class_images"] for example in examples] + + pixel_values = torch.stack(pixel_values) + pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() + + input_ids = torch.cat(input_ids, dim=0) + + batch = { + "input_ids": input_ids, + "pixel_values": pixel_values, + } + return batch + + +class PromptDataset(Dataset): + "A simple dataset to prepare the prompts to generate class images on multiple GPUs." + + def __init__(self, prompt, num_samples): + self.prompt = prompt + self.num_samples = num_samples + + def __len__(self): + return self.num_samples + + def __getitem__(self, index): + example = {} + example["prompt"] = self.prompt + example["index"] = index + return example + + +def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None): + if token is None: + token = HfFolder.get_token() + if organization is None: + username = whoami(token)["name"] + return f"{username}/{model_id}" + else: + return f"{organization}/{model_id}" + + +def main(args): + logging_dir = Path(args.output_dir, args.logging_dir) + + accelerator = Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + mixed_precision=args.mixed_precision, + log_with=args.report_to, + logging_dir=logging_dir, + ) + + # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate + # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models. + # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate. + if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1: + raise ValueError( + "Gradient accumulation is not supported when training the text encoder in distributed training. " + "Please set gradient_accumulation_steps to 1. This feature will be supported in the future." + ) + + # Make one log on every process with the configuration for debugging. + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, + ) + logger.info(accelerator.state, main_process_only=False) + if accelerator.is_local_main_process: + datasets.utils.logging.set_verbosity_warning() + transformers.utils.logging.set_verbosity_warning() + diffusers.utils.logging.set_verbosity_info() + else: + datasets.utils.logging.set_verbosity_error() + transformers.utils.logging.set_verbosity_error() + diffusers.utils.logging.set_verbosity_error() + + # If passed along, set the training seed now. + if args.seed is not None: + set_seed(args.seed) + + # Generate class images if prior preservation is enabled. + if args.with_prior_preservation: + class_images_dir = Path(args.class_data_dir) + if not class_images_dir.exists(): + class_images_dir.mkdir(parents=True) + cur_class_images = len(list(class_images_dir.iterdir())) + + if cur_class_images < args.num_class_images: + torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32 + if args.prior_generation_precision == "fp32": + torch_dtype = torch.float32 + elif args.prior_generation_precision == "fp16": + torch_dtype = torch.float16 + elif args.prior_generation_precision == "bf16": + torch_dtype = torch.bfloat16 + pipeline = DiffusionPipeline.from_pretrained( + args.pretrained_model_name_or_path, + torch_dtype=torch_dtype, + safety_checker=None, + revision=args.revision, + ) + pipeline.set_progress_bar_config(disable=True) + + num_new_images = args.num_class_images - cur_class_images + logger.info(f"Number of class images to sample: {num_new_images}.") + + sample_dataset = PromptDataset(args.class_prompt, num_new_images) + sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size) + + sample_dataloader = accelerator.prepare(sample_dataloader) + pipeline.to(accelerator.device) + + for example in tqdm( + sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process + ): + images = pipeline(example["prompt"]).images + + for i, image in enumerate(images): + hash_image = hashlib.sha1(image.tobytes()).hexdigest() + image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" + image.save(image_filename) + + del pipeline + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + # Handle the repository creation + if accelerator.is_main_process: + if args.push_to_hub: + if args.hub_model_id is None: + repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) + else: + repo_name = args.hub_model_id + repo = Repository(args.output_dir, clone_from=repo_name) # noqa: F841 + + with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: + if "step_*" not in gitignore: + gitignore.write("step_*\n") + if "epoch_*" not in gitignore: + gitignore.write("epoch_*\n") + elif args.output_dir is not None: + os.makedirs(args.output_dir, exist_ok=True) + + # Load the tokenizer + if args.tokenizer_name: + tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False) + elif args.pretrained_model_name_or_path: + tokenizer = AutoTokenizer.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="tokenizer", + revision=args.revision, + use_fast=False, + ) + + # import correct text encoder class + text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision) + + # Load scheduler and models + noise_scheduler = DDPMScheduler( + beta_start=0.00085, + beta_end=0.012, + beta_schedule="scaled_linear", + num_train_timesteps=1000, + ) # DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") + text_encoder = text_encoder_cls.from_pretrained( + args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision + ) + vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision) + unet = UNet2DConditionModel.from_pretrained( + args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision + ) + + if args.use_lora: + config = LoraConfig( + r=args.lora_r, + lora_alpha=args.lora_alpha, + target_modules=UNET_TARGET_MODULES, + lora_dropout=args.lora_dropout, + bias=args.lora_bias, + ) + unet = LoraModel(config, unet) + print_trainable_parameters(unet) + print(unet) + + vae.requires_grad_(False) + if not args.train_text_encoder: + text_encoder.requires_grad_(False) + elif args.train_text_encoder and args.use_lora: + config = LoraConfig( + r=args.lora_text_encoder_r, + lora_alpha=args.lora_text_encoder_alpha, + target_modules=TEXT_ENCODER_TARGET_MODULES, + lora_dropout=args.lora_text_encoder_dropout, + bias=args.lora_text_encoder_bias, + ) + text_encoder = LoraModel(config, text_encoder) + print_trainable_parameters(text_encoder) + print(text_encoder) + + if args.enable_xformers_memory_efficient_attention: + if is_xformers_available(): + unet.enable_xformers_memory_efficient_attention() + else: + raise ValueError("xformers is not available. Make sure it is installed correctly") + + if args.gradient_checkpointing: + unet.enable_gradient_checkpointing() + # below fails when using lora so commenting it out + if args.train_text_encoder and not args.use_lora: + text_encoder.gradient_checkpointing_enable() + + # Enable TF32 for faster training on Ampere GPUs, + # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices + if args.allow_tf32: + torch.backends.cuda.matmul.allow_tf32 = True + + if args.scale_lr: + args.learning_rate = ( + args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes + ) + + # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs + if args.use_8bit_adam: + try: + import bitsandbytes as bnb + except ImportError: + raise ImportError( + "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`." + ) + + optimizer_class = bnb.optim.AdamW8bit + else: + optimizer_class = torch.optim.AdamW + + # Optimizer creation + params_to_optimize = ( + itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters() + ) + optimizer = optimizer_class( + params_to_optimize, + lr=args.learning_rate, + betas=(args.adam_beta1, args.adam_beta2), + weight_decay=args.adam_weight_decay, + eps=args.adam_epsilon, + ) + + # Dataset and DataLoaders creation: + train_dataset = DreamBoothDataset( + instance_data_root=args.instance_data_dir, + instance_prompt=args.instance_prompt, + class_data_root=args.class_data_dir if args.with_prior_preservation else None, + class_prompt=args.class_prompt, + tokenizer=tokenizer, + size=args.resolution, + center_crop=args.center_crop, + ) + + train_dataloader = torch.utils.data.DataLoader( + train_dataset, + batch_size=args.train_batch_size, + shuffle=True, + collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation), + num_workers=1, + ) + + # Scheduler and math around the number of training steps. + overrode_max_train_steps = False + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if args.max_train_steps is None: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + overrode_max_train_steps = True + + lr_scheduler = get_scheduler( + args.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps, + num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, + num_cycles=args.lr_num_cycles, + power=args.lr_power, + ) + + # Prepare everything with our `accelerator`. + if args.train_text_encoder: + unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + unet, text_encoder, optimizer, train_dataloader, lr_scheduler + ) + else: + unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + unet, optimizer, train_dataloader, lr_scheduler + ) + + # For mixed precision training we cast the text_encoder and vae weights to half-precision + # as these models are only used for inference, keeping weights in full precision is not required. + weight_dtype = torch.float32 + if accelerator.mixed_precision == "fp16": + weight_dtype = torch.float16 + elif accelerator.mixed_precision == "bf16": + weight_dtype = torch.bfloat16 + + # Move vae and text_encoder to device and cast to weight_dtype + vae.to(accelerator.device, dtype=weight_dtype) + if not args.train_text_encoder: + text_encoder.to(accelerator.device, dtype=weight_dtype) + + # We need to recalculate our total training steps as the size of the training dataloader may have changed. + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if overrode_max_train_steps: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + # Afterwards we recalculate our number of training epochs + args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + + # We need to initialize the trackers we use, and also store our configuration. + # The trackers initializes automatically on the main process. + if accelerator.is_main_process: + accelerator.init_trackers("dreambooth", config=vars(args)) + + # Train! + total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps + + logger.info("***** Running training *****") + logger.info(f" Num examples = {len(train_dataset)}") + logger.info(f" Num batches each epoch = {len(train_dataloader)}") + logger.info(f" Num Epochs = {args.num_train_epochs}") + logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") + logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") + logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") + logger.info(f" Total optimization steps = {args.max_train_steps}") + global_step = 0 + first_epoch = 0 + + # Potentially load in the weights and states from a previous save + if args.resume_from_checkpoint: + if args.resume_from_checkpoint != "latest": + path = os.path.basename(args.resume_from_checkpoint) + else: + # Get the mos recent checkpoint + dirs = os.listdir(args.output_dir) + dirs = [d for d in dirs if d.startswith("checkpoint")] + dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) + path = dirs[-1] + accelerator.print(f"Resuming from checkpoint {path}") + accelerator.load_state(os.path.join(args.output_dir, path)) + global_step = int(path.split("-")[1]) + + resume_global_step = global_step * args.gradient_accumulation_steps + first_epoch = resume_global_step // num_update_steps_per_epoch + resume_step = resume_global_step % num_update_steps_per_epoch + + # Only show the progress bar once on each machine. + progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process) + progress_bar.set_description("Steps") + + for epoch in range(first_epoch, args.num_train_epochs): + unet.train() + if args.train_text_encoder: + text_encoder.train() + with TorchTracemalloc() as tracemalloc: + for step, batch in enumerate(train_dataloader): + # Skip steps until we reach the resumed step + if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step: + if step % args.gradient_accumulation_steps == 0: + progress_bar.update(1) + continue + + with accelerator.accumulate(unet): + # Convert images to latent space + latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample() + latents = latents * 0.18215 + + # Sample noise that we'll add to the latents + noise = torch.randn_like(latents) + bsz = latents.shape[0] + # Sample a random timestep for each image + timesteps = torch.randint( + 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device + ) + timesteps = timesteps.long() + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) + + # Get the text embedding for conditioning + encoder_hidden_states = text_encoder(batch["input_ids"])[0] + + # Predict the noise residual + model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample + + # Get the target for loss depending on the prediction type + if noise_scheduler.config.prediction_type == "epsilon": + target = noise + elif noise_scheduler.config.prediction_type == "v_prediction": + target = noise_scheduler.get_velocity(latents, noise, timesteps) + else: + raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") + + if args.with_prior_preservation: + # Chunk the noise and model_pred into two parts and compute the loss on each part separately. + model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) + target, target_prior = torch.chunk(target, 2, dim=0) + + # Compute instance loss + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") + + # Compute prior loss + prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") + + # Add the prior loss to the instance loss. + loss = loss + args.prior_loss_weight * prior_loss + else: + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") + + accelerator.backward(loss) + if accelerator.sync_gradients: + params_to_clip = ( + itertools.chain(unet.parameters(), text_encoder.parameters()) + if args.train_text_encoder + else unet.parameters() + ) + accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + + # Checks if the accelerator has performed an optimization step behind the scenes + if accelerator.sync_gradients: + progress_bar.update(1) + global_step += 1 + + # if global_step % args.checkpointing_steps == 0: + # if accelerator.is_main_process: + # save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") + # accelerator.save_state(save_path) + # logger.info(f"Saved state to {save_path}") + + logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} + progress_bar.set_postfix(**logs) + accelerator.log(logs, step=global_step) + + if ( + args.validation_prompt is not None + and (step + num_update_steps_per_epoch * epoch) % args.validation_steps == 0 + ): + logger.info( + f"Running validation... \n Generating {args.num_validation_images} images with prompt:" + f" {args.validation_prompt}." + ) + # create pipeline + pipeline = DiffusionPipeline.from_pretrained( + args.pretrained_model_name_or_path, + safety_checker=None, + revision=args.revision, + ) + # set `keep_fp32_wrapper` to True because we do not want to remove + # mixed precision hooks while we are still training + pipeline.unet = accelerator.unwrap_model(unet, keep_fp32_wrapper=True) + pipeline.text_encoder = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True) + pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config) + pipeline = pipeline.to(accelerator.device) + pipeline.set_progress_bar_config(disable=True) + + # run inference + generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) + images = [] + for _ in range(args.num_validation_images): + image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0] + images.append(image) + + for tracker in accelerator.trackers: + if tracker.name == "tensorboard": + np_images = np.stack([np.asarray(img) for img in images]) + tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC") + if tracker.name == "wandb": + import wandb + + tracker.log( + { + "validation": [ + wandb.Image(image, caption=f"{i}: {args.validation_prompt}") + for i, image in enumerate(images) + ] + } + ) + + del pipeline + torch.cuda.empty_cache() + + if global_step >= args.max_train_steps: + break + # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage + accelerator.print("GPU Memory before entering the train : {}".format(b2mb(tracemalloc.begin))) + accelerator.print("GPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used)) + accelerator.print("GPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked)) + accelerator.print( + "GPU Total Peak Memory consumed during the train (max): {}".format( + tracemalloc.peaked + b2mb(tracemalloc.begin) + ) + ) + + accelerator.print("CPU Memory before entering the train : {}".format(b2mb(tracemalloc.cpu_begin))) + accelerator.print("CPU Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.cpu_used)) + accelerator.print("CPU Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.cpu_peaked)) + accelerator.print( + "CPU Total Peak Memory consumed during the train (max): {}".format( + tracemalloc.cpu_peaked + b2mb(tracemalloc.cpu_begin) + ) + ) + + # Create the pipeline using using the trained modules and save it. + accelerator.wait_for_everyone() + if accelerator.is_main_process: + if args.use_lora: + lora_config = {} + state_dict = get_peft_model_state_dict(unet, state_dict=accelerator.get_state_dict(unet)) + lora_config["peft_config"] = unet.get_peft_config_as_dict(inference=True) + if args.train_text_encoder: + text_encoder_state_dict = get_peft_model_state_dict( + text_encoder, state_dict=accelerator.get_state_dict(text_encoder) + ) + text_encoder_state_dict = {f"text_encoder_{k}": v for k, v in text_encoder_state_dict.items()} + state_dict.update(text_encoder_state_dict) + lora_config["text_encoder_peft_config"] = text_encoder.get_peft_config_as_dict(inference=True) + + accelerator.print(state_dict) + accelerator.save(state_dict, os.path.join(args.output_dir, f"{args.instance_prompt}_lora.pt")) + with open(os.path.join(args.output_dir, f"{args.instance_prompt}_lora_config.json"), "w") as f: + json.dump(lora_config, f) + else: + pipeline = DiffusionPipeline.from_pretrained( + args.pretrained_model_name_or_path, + unet=accelerator.unwrap_model(unet), + text_encoder=accelerator.unwrap_model(text_encoder), + revision=args.revision, + ) + pipeline.save_pretrained(args.output_dir) + + accelerator.end_training() + + +if __name__ == "__main__": + args = parse_args() + main(args) diff --git a/04-gene-sft/peft/examples/semantic_segmentation/README.md b/04-gene-sft/peft/examples/semantic_segmentation/README.md new file mode 100644 index 0000000000000000000000000000000000000000..abccbcbf28a41191b010e0681d814195db220529 --- /dev/null +++ b/04-gene-sft/peft/examples/semantic_segmentation/README.md @@ -0,0 +1,7 @@ +# Fine-tuning for semantic segmentation using LoRA and 🤗 PEFT + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/peft/blob/main/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb) + +We provide a notebook (`semantic_segmentation_peft_lora.ipynb`) where we learn how to use [LoRA](https://arxiv.org/abs/2106.09685) from 🤗 PEFT to fine-tune an semantic segmentation by ONLY using **14%%** of the original trainable parameters of the model. + +LoRA adds low-rank "update matrices" to certain blocks in the underlying model (in this case the attention blocks) and ONLY trains those matrices during fine-tuning. During inference, these update matrices are _merged_ with the original model parameters. For more details, check out the [original LoRA paper](https://arxiv.org/abs/2106.09685). diff --git a/04-gene-sft/peft/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb b/04-gene-sft/peft/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..09ccbb8201e7a27e69c586ba2e7c1cc2b290fc35 --- /dev/null +++ b/04-gene-sft/peft/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb @@ -0,0 +1,1598 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "JAeWcsvLF2_6" + }, + "source": [ + "## Introduction\n", + "\n", + "In this notebook, we will learn how to use [LoRA](https://arxiv.org/abs/2106.09685) from 🤗 PEFT to fine-tune a SegFormer model variant for semantic segmentation by ONLY using **14%** of the original trainable parameters of the model. \n", + "\n", + "LoRA adds low-rank \"update matrices\" to certain blocks in the underlying model (in this case the attention blocks) and ONLY trains those matrices during fine-tuning. During inference, these update matrices are _merged_ with the original model parameters. For more details, check out the [original LoRA paper](https://arxiv.org/abs/2106.09685). \n", + "\n", + "Let's get started by installing the dependencies. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lveGHtBcGNyc" + }, + "source": [ + "## Install dependencies\n", + "\n", + "Here we're installing `peft` from source to ensure we have access to all the bleeding edge features of `peft`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lbYTKXv4ZTwg", + "outputId": "5a033ebd-6bbd-4bf4-802c-a1bac6a48a07" + }, + "outputs": [], + "source": [ + "!pip install transformers accelerate evaluate datasets loralib git+https://github.com/huggingface/peft -q" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B0fmCvTsGPah" + }, + "source": [ + "## Authentication\n", + "\n", + "We will share our fine-tuned model at the end of training. So, to do that we just authenticate using our 🤗 token. This token is available from [here](https://huggingface.co/settings/tokens). If you don't have a 🤗 account already, we highly encourage you to do so; it's free!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 331, + "referenced_widgets": [ + "f2a722f371904cce80dc1c087b153ad6", + "6c88a55a635b4c9f946a1aa838d69f20", + "6c6d19cd893e4d82bae9972fa10c6d74", + "fc48ee28c2e44f1daa03149c8004c314", + "cb4053f102fc4207a1c9513f81ad6415", + "0dcc5a2866a349e0843673bef499dc66", + "b7431f99d93b4e9b8c8177ac4a7b4070", + "14ac809ba0bc4cd5bcd51f83105947b0", + "e7393e78f41b496495982490b72ef2a3", + "42a7b8268d8945b5bbe9d3f20bc8840c", + "554b55f29a5e4d5a81608d912d3635e8", + "3f69c7b15e5e48039777e4b6b1a51f53", + "5bcfe3da0ffb41ccbb9404ac35ae8945", + "21bf954f54db41e6ba78f76195721614", + "f83dd354396e4aa3acd214a6fd98efb2", + "24fde588dc1a49a397e379cda320ed71", + "9746875e74b845daab06619393b4d46b" + ] + }, + "id": "OYhwMOj5ZTwm", + "outputId": "ff2d4cc4-4363-4093-8bdc-763761cbe3ef" + }, + "outputs": [], + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B9Cu7j_QGVbH" + }, + "source": [ + "## Load a dataset\n", + "\n", + "We're only loading the first 150 instances from the training set of the [SceneParse150 dataset](https://huggingface.co/datasets/scene_parse_150) to keep this example runtime short. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sGJWDwtHZTwn", + "outputId": "260c874e-1844-42ba-9dc2-0f727e2930cc" + }, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "ds = load_dataset(\"scene_parse_150\", split=\"train[:150]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RpSPx8EHGeLM" + }, + "source": [ + "## Prepare train and test splits" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "ydWKIqCUZTwo" + }, + "outputs": [], + "source": [ + "ds = ds.train_test_split(test_size=0.1)\n", + "train_ds = ds[\"train\"]\n", + "test_ds = ds[\"test\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yHtqAQ2WGhlR" + }, + "source": [ + "## Prepare label mappers\n", + "\n", + "We create two dictionaries:\n", + "\n", + "* `label2id`: maps the semantic classes of the dataset to integer ids.\n", + "* `id2label`: `label2id` reversed. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Hu8Y4dEIZTwq", + "outputId": "eba72235-c1a7-4c95-8c89-5ef5588d6581" + }, + "outputs": [], + "source": [ + "import json\n", + "from huggingface_hub import cached_download, hf_hub_url\n", + "\n", + "repo_id = \"huggingface/label-files\"\n", + "filename = \"ade20k-id2label.json\"\n", + "id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type=\"dataset\")), \"r\"))\n", + "id2label = {int(k): v for k, v in id2label.items()}\n", + "label2id = {v: k for k, v in id2label.items()}\n", + "num_labels = len(id2label)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5V8nhdt0HBsk" + }, + "source": [ + "## Prepare datasets for training and evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fNi_TKYpZTwq", + "outputId": "a28647b4-0deb-49cc-a1b8-c4a2ab99bb4d" + }, + "outputs": [], + "source": [ + "from transformers import AutoImageProcessor\n", + "\n", + "checkpoint = \"nvidia/mit-b0\"\n", + "image_processor = AutoImageProcessor.from_pretrained(checkpoint, reduce_labels=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "JAjiYzklZTwr" + }, + "outputs": [], + "source": [ + "from torchvision.transforms import ColorJitter\n", + "\n", + "jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "_HaS12U0ZTwr" + }, + "outputs": [], + "source": [ + "from PIL import Image\n", + "import numpy as np\n", + "\n", + "\n", + "def handle_grayscale_image(image):\n", + " np_image = np.array(image)\n", + " if np_image.ndim == 2:\n", + " tiled_image = np.tile(np.expand_dims(np_image, -1), 3)\n", + " return Image.fromarray(tiled_image)\n", + " else:\n", + " return Image.fromarray(np_image)\n", + "\n", + "\n", + "def train_transforms(example_batch):\n", + " images = [jitter(handle_grayscale_image(x)) for x in example_batch[\"image\"]]\n", + " labels = [x for x in example_batch[\"annotation\"]]\n", + " inputs = image_processor(images, labels)\n", + " return inputs\n", + "\n", + "\n", + "def val_transforms(example_batch):\n", + " images = [handle_grayscale_image(x) for x in example_batch[\"image\"]]\n", + " labels = [x for x in example_batch[\"annotation\"]]\n", + " inputs = image_processor(images, labels)\n", + " return inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "Qyjsvup2ZTws" + }, + "outputs": [], + "source": [ + "train_ds.set_transform(train_transforms)\n", + "test_ds.set_transform(val_transforms)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lu8RjicxHJiO" + }, + "source": [ + "## Evaluation function\n", + "\n", + "Including a metric during training is often helpful for evaluating your model’s performance. You can quickly load a evaluation method with the [🤗 Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [mean Intersection over Union (IoU)](https://huggingface.co/spaces/evaluate-metric/accuracy) metric (see the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric):" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "TMSnlebfZTwt" + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch import nn\n", + "import evaluate\n", + "\n", + "metric = evaluate.load(\"mean_iou\")\n", + "\n", + "\n", + "def compute_metrics(eval_pred):\n", + " with torch.no_grad():\n", + " logits, labels = eval_pred\n", + " logits_tensor = torch.from_numpy(logits)\n", + " # scale the logits to the size of the label\n", + " logits_tensor = nn.functional.interpolate(\n", + " logits_tensor,\n", + " size=labels.shape[-2:],\n", + " mode=\"bilinear\",\n", + " align_corners=False,\n", + " ).argmax(dim=1)\n", + "\n", + " pred_labels = logits_tensor.detach().cpu().numpy()\n", + " # currently using _compute instead of compute\n", + " # see this issue for more info: https://github.com/huggingface/evaluate/pull/328#issuecomment-1286866576\n", + " metrics = metric._compute(\n", + " predictions=pred_labels,\n", + " references=labels,\n", + " num_labels=len(id2label),\n", + " ignore_index=0,\n", + " reduce_labels=image_processor.reduce_labels,\n", + " )\n", + "\n", + " # add per category metrics as individual key-value pairs\n", + " per_category_accuracy = metrics.pop(\"per_category_accuracy\").tolist()\n", + " per_category_iou = metrics.pop(\"per_category_iou\").tolist()\n", + "\n", + " metrics.update({f\"accuracy_{id2label[i]}\": v for i, v in enumerate(per_category_accuracy)})\n", + " metrics.update({f\"iou_{id2label[i]}\": v for i, v in enumerate(per_category_iou)})\n", + "\n", + " return metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r304cnpxHxp5" + }, + "source": [ + "## Load a base model\n", + "\n", + "For this example, we use the [SegFormer B0 variant](https://huggingface.co/nvidia/mit-b0). " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "Krvppe44a_7y" + }, + "outputs": [], + "source": [ + "def print_trainable_parameters(model):\n", + " \"\"\"\n", + " Prints the number of trainable parameters in the model.\n", + " \"\"\"\n", + " trainable_params = 0\n", + " all_param = 0\n", + " for _, param in model.named_parameters():\n", + " all_param += param.numel()\n", + " if param.requires_grad:\n", + " trainable_params += param.numel()\n", + " print(\n", + " f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param:.2f}\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q_Wwl_ewID9I" + }, + "source": [ + "We pass the `label2id` and `id2label` dictionaries to let the `AutoModelForSemanticSegmentation` class know that we're interested in a custom base model where the decoder head should be randomly initialized w.r.t our custom dataset. Note, however, that the rest of the model parameters are pre-trained and will be fine-tuned in a regular transfer learning setup.\n", + "\n", + "We also notice that the 100% parameters in the `model` are trainable. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kcdLdvIlZTwt", + "outputId": "a6b71dce-905e-4389-dcf6-46b43e769fcc" + }, + "outputs": [], + "source": [ + "from transformers import AutoModelForSemanticSegmentation, TrainingArguments, Trainer\n", + "\n", + "model = AutoModelForSemanticSegmentation.from_pretrained(\n", + " checkpoint, id2label=id2label, label2id=label2id, ignore_mismatched_sizes=True\n", + ")\n", + "print_trainable_parameters(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4yhyYVTCInF0" + }, + "source": [ + "## Wrap `model` as a `PeftModel` for LoRA training\n", + "\n", + "This involves two steps:\n", + "\n", + "* Defining a config with `LoraConfig`\n", + "* Wrapping the original `model` with `get_peft_model()` with the config defined in the step above. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YPg4W5eFB__n", + "outputId": "9995eb44-1c30-43e7-cc4e-691ecb1b1878" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===================================BUG REPORT===================================\n", + "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n", + "================================================================================\n", + "trainable params: 564374 || all params: 3883766 || trainable%: 14.53\n" + ] + } + ], + "source": [ + "from peft import LoraConfig, get_peft_model\n", + "\n", + "config = LoraConfig(\n", + " r=32,\n", + " lora_alpha=32,\n", + " target_modules=[\"query\", \"value\"],\n", + " lora_dropout=0.1,\n", + " bias=\"lora_only\",\n", + " modules_to_save=[\"decode_head\"],\n", + ")\n", + "lora_model = get_peft_model(model, config)\n", + "print_trainable_parameters(lora_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4M3wYekOI95X" + }, + "source": [ + " Let's unpack what's going on here. \n", + "\n", + "In order for LoRA to take effect, we need to specify the target modules to `LoraConfig` so that `PeftModel` knows which modules inside our model needs to be amended with LoRA matrices. In this case, we're only interested in targetting the query and value matrices of the attention blocks of the base model. Since the parameters corresponding to these matrices are \"named\" with `query` and `value` respectively, we specify them accordingly in the `target_modules` argument of `LoraConfig`. \n", + "\n", + "We also specify `modules_to_save`. After we wrap our base model `model` with `PeftModel` along with the `config`, we get a new model where only the LoRA parameters are trainable (so-called \"update matrices\") while the pre-trained parameters are kept frozen. These include the parameters of the randomly initialized classifier parameters too. This is NOT we want when fine-tuning the base model on our custom dataset. To ensure that the classifier parameters are also trained, we specify `modules_to_save`. This also ensures that these modules are serialized alongside the LoRA trainable parameters when using utilities like `save_pretrained()` and `push_to_hub()`. \n", + "\n", + "Regarding the other parameters:\n", + "\n", + "* `r`: The dimension used by the LoRA update matrices.\n", + "* `alpha`: Scaling factor.\n", + "* `bias`: Specifying if the `bias` parameters should be trained. `lora_only` denotes only the LoRA `bias` parameters will be trained. \n", + "\n", + "`r` and `alpha` together control the total number of final trainable parameters when using LoRA giving us the flexbility to balance a trade-off between end performance and compute efficiency.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XTF68xfjJEci" + }, + "source": [ + "We can also how many parameters we're actually training. Since we're interested in performing **parameter-efficient fine-tuning**, we should expect to notice a less number of trainable parameters from the `lora_model` in comparison to the original `model` which is indeed the case here. \n", + "\n", + "For sanity, let's also manually verify the modules that are actually trainable in `lora_model`. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PUe1Gzvd1PEP", + "outputId": "7b8ba17f-01fd-4ab4-d703-9a0c01cff31b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "base_model.model.segformer.encoder.block.0.0.attention.self.query.lora_A.weight torch.Size([32, 32])\n", + "base_model.model.segformer.encoder.block.0.0.attention.self.query.lora_B.weight torch.Size([32, 32])\n", + "base_model.model.segformer.encoder.block.0.0.attention.self.value.lora_A.weight torch.Size([32, 32])\n", + "base_model.model.segformer.encoder.block.0.0.attention.self.value.lora_B.weight torch.Size([32, 32])\n", + "base_model.model.segformer.encoder.block.0.1.attention.self.query.lora_A.weight torch.Size([32, 32])\n", + "base_model.model.segformer.encoder.block.0.1.attention.self.query.lora_B.weight torch.Size([32, 32])\n", + "base_model.model.segformer.encoder.block.0.1.attention.self.value.lora_A.weight torch.Size([32, 32])\n", + "base_model.model.segformer.encoder.block.0.1.attention.self.value.lora_B.weight torch.Size([32, 32])\n", + "base_model.model.segformer.encoder.block.1.0.attention.self.query.lora_A.weight torch.Size([32, 64])\n", + "base_model.model.segformer.encoder.block.1.0.attention.self.query.lora_B.weight torch.Size([64, 32])\n", + "base_model.model.segformer.encoder.block.1.0.attention.self.value.lora_A.weight torch.Size([32, 64])\n", + "base_model.model.segformer.encoder.block.1.0.attention.self.value.lora_B.weight torch.Size([64, 32])\n", + "base_model.model.segformer.encoder.block.1.1.attention.self.query.lora_A.weight torch.Size([32, 64])\n", + "base_model.model.segformer.encoder.block.1.1.attention.self.query.lora_B.weight torch.Size([64, 32])\n", + "base_model.model.segformer.encoder.block.1.1.attention.self.value.lora_A.weight torch.Size([32, 64])\n", + "base_model.model.segformer.encoder.block.1.1.attention.self.value.lora_B.weight torch.Size([64, 32])\n", + "base_model.model.segformer.encoder.block.2.0.attention.self.query.lora_A.weight torch.Size([32, 160])\n", + "base_model.model.segformer.encoder.block.2.0.attention.self.query.lora_B.weight torch.Size([160, 32])\n", + "base_model.model.segformer.encoder.block.2.0.attention.self.value.lora_A.weight torch.Size([32, 160])\n", + "base_model.model.segformer.encoder.block.2.0.attention.self.value.lora_B.weight torch.Size([160, 32])\n", + "base_model.model.segformer.encoder.block.2.1.attention.self.query.lora_A.weight torch.Size([32, 160])\n", + "base_model.model.segformer.encoder.block.2.1.attention.self.query.lora_B.weight torch.Size([160, 32])\n", + "base_model.model.segformer.encoder.block.2.1.attention.self.value.lora_A.weight torch.Size([32, 160])\n", + "base_model.model.segformer.encoder.block.2.1.attention.self.value.lora_B.weight torch.Size([160, 32])\n", + "base_model.model.segformer.encoder.block.3.0.attention.self.query.lora_A.weight torch.Size([32, 256])\n", + "base_model.model.segformer.encoder.block.3.0.attention.self.query.lora_B.weight torch.Size([256, 32])\n", + "base_model.model.segformer.encoder.block.3.0.attention.self.value.lora_A.weight torch.Size([32, 256])\n", + "base_model.model.segformer.encoder.block.3.0.attention.self.value.lora_B.weight torch.Size([256, 32])\n", + "base_model.model.segformer.encoder.block.3.1.attention.self.query.lora_A.weight torch.Size([32, 256])\n", + "base_model.model.segformer.encoder.block.3.1.attention.self.query.lora_B.weight torch.Size([256, 32])\n", + "base_model.model.segformer.encoder.block.3.1.attention.self.value.lora_A.weight torch.Size([32, 256])\n", + "base_model.model.segformer.encoder.block.3.1.attention.self.value.lora_B.weight torch.Size([256, 32])\n", + "base_model.model.decode_head.linear_c.0.proj.weight torch.Size([256, 32])\n", + "base_model.model.decode_head.linear_c.0.proj.bias torch.Size([256])\n", + "base_model.model.decode_head.linear_c.1.proj.weight torch.Size([256, 64])\n", + "base_model.model.decode_head.linear_c.1.proj.bias torch.Size([256])\n", + "base_model.model.decode_head.linear_c.2.proj.weight torch.Size([256, 160])\n", + "base_model.model.decode_head.linear_c.2.proj.bias torch.Size([256])\n", + "base_model.model.decode_head.linear_c.3.proj.weight torch.Size([256, 256])\n", + "base_model.model.decode_head.linear_c.3.proj.bias torch.Size([256])\n", + "base_model.model.decode_head.linear_fuse.weight torch.Size([256, 1024, 1, 1])\n", + "base_model.model.decode_head.batch_norm.weight torch.Size([256])\n", + "base_model.model.decode_head.batch_norm.bias torch.Size([256])\n", + "base_model.model.decode_head.classifier.weight torch.Size([150, 256, 1, 1])\n", + "base_model.model.decode_head.classifier.bias torch.Size([150])\n" + ] + } + ], + "source": [ + "for name, param in lora_model.named_parameters():\n", + " if param.requires_grad:\n", + " print(name, param.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can confirm that only the LoRA parameters appended to the attention blocks and the `decode_head` parameters are trainable." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rX75AyI7JYVC" + }, + "source": [ + "## Train!\n", + "\n", + "This is a two-step process: \n", + "\n", + "1. Define your training hyperparameters in [TrainingArguments](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/trainer#transformers.TrainingArguments). It is important you don’t remove unused columns because this’ll drop the image column. Without the image column, you can’t create `pixel_values`. Set `remove_unused_columns=False` to prevent this behavior! The only other required parameter is output_dir which specifies where to save your model. At the end of each epoch, the `Trainer` will evaluate the IoU metric and save the training checkpoint.\n", + "2. Pass the training arguments to [Trainer](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/trainer#transformers.Trainer) along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.\n", + "3. Call `train()` to finetune your model.\n", + "\n", + "\n", + "**Note** that This example is meant to walk you through the workflow when using PEFT for semantic segmentation. We didn't perform extensive hyperparameter tuning to achieve optimal results. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "K6HVcNkDZTwu", + "outputId": "1b28a072-0e16-4b1a-ec32-d78e93630ef3" + }, + "outputs": [], + "source": [ + "model_name = checkpoint.split(\"/\")[-1]\n", + "\n", + "training_args = TrainingArguments(\n", + " output_dir=f\"{model_name}-scene-parse-150-lora\",\n", + " learning_rate=5e-4,\n", + " num_train_epochs=50,\n", + " per_device_train_batch_size=4,\n", + " per_device_eval_batch_size=2,\n", + " save_total_limit=3,\n", + " evaluation_strategy=\"epoch\",\n", + " save_strategy=\"epoch\",\n", + " logging_steps=5,\n", + " remove_unused_columns=False,\n", + " push_to_hub=True,\n", + " label_names=[\"labels\"],\n", + ")\n", + "\n", + "trainer = Trainer(\n", + " model=lora_model,\n", + " args=training_args,\n", + " train_dataset=train_ds,\n", + " eval_dataset=test_ds,\n", + " compute_metrics=compute_metrics,\n", + ")\n", + "\n", + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dacaBLE6KLdu" + }, + "source": [ + "## Saving the model and inference \n", + "\n", + "Here we use the `save_pretrained()` method of the `lora_model` to save the *LoRA-only parameters* locally. However, you can also use thr `push_to_hub()` method to upload these parameters directly to the Hugging Face Hub (as shown [here](https://colab.research.google.com/github/huggingface/peft/blob/main/examples/image_classification/image_classification_peft_lora.ipynb)). " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "pvkLkrQo-6l6" + }, + "outputs": [], + "source": [ + "model_id = \"segformer-scene-parse-150-lora\"\n", + "lora_model.save_pretrained(model_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ur8n41kBK4uj" + }, + "source": [ + "We can see that the LoRA-only parameters are just **2.2 MB in size**! This greatly improves the portability when using very large models. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "grzLeOT-__ht", + "outputId": "1ce26a27-2f38-43f3-9454-8ba11f2cfc59" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 2.2M\n", + "-rw-r--r-- 1 root root 369 Feb 8 03:09 adapter_config.json\n", + "-rw-r--r-- 1 root root 2.2M Feb 8 03:09 adapter_model.bin\n" + ] + } + ], + "source": [ + "!ls -lh {model_id}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KFYC6Z3FLB5F" + }, + "source": [ + "Let's now prepare our `inference_model` and run an inference. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "T7zeMQTaACur", + "outputId": "762b7fbc-07d4-4572-f107-c836e3e7928a" + }, + "outputs": [], + "source": [ + "from peft import PeftConfig\n", + "\n", + "config = PeftConfig.from_pretrained(model_id)\n", + "model = AutoModelForSemanticSegmentation.from_pretrained(\n", + " checkpoint, id2label=id2label, label2id=label2id, ignore_mismatched_sizes=True\n", + ")\n", + "# Load the Lora model\n", + "inference_model = PeftModel.from_pretrained(model, model_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2L1R0LDWLImd" + }, + "source": [ + "Fetch an image." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 444 + }, + "id": "lwjRvZOmA7Hh", + "outputId": "44ab267d-e2b9-4bda-a52b-91968eabce29" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import requests\n", + "\n", + "url = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/semantic-seg-image.png\"\n", + "image = Image.open(requests.get(url, stream=True).raw)\n", + "image" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kdK_bGhsLKKE" + }, + "source": [ + "Preprocess the image." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G0z-3R-PBKc9", + "outputId": "56c91198-0116-4c2c-fc63-147dc7431b89" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1, 3, 512, 512])\n" + ] + } + ], + "source": [ + "# prepare image for the model\n", + "encoding = image_processor(image.convert(\"RGB\"), return_tensors=\"pt\")\n", + "print(encoding.pixel_values.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hJRijta4LLu9" + }, + "source": [ + "Run an inference. " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "z1p-QDoiBP56" + }, + "outputs": [], + "source": [ + "with torch.no_grad():\n", + " outputs = inference_model(pixel_values=encoding.pixel_values)\n", + " logits = outputs.logits\n", + "\n", + "upsampled_logits = nn.functional.interpolate(\n", + " logits,\n", + " size=image.size[::-1],\n", + " mode=\"bilinear\",\n", + " align_corners=False,\n", + ")\n", + "\n", + "pred_seg = upsampled_logits.argmax(dim=1)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gmYIcfL4LNtj" + }, + "source": [ + "Visualize the results.\n", + "\n", + "We need a color palette to visualize the results. Here, we use [one provided by the TensorFlow Model Garden repository](https://github.com/tensorflow/models/blob/3f1ca33afe3c1631b733ea7e40c294273b9e406d/research/deeplab/utils/get_dataset_colormap.py#L51)." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "jy5c6vmzBqzC" + }, + "outputs": [], + "source": [ + "def ade_palette():\n", + " \"\"\"Creates a label colormap used in ADE20K segmentation benchmark.\n", + " Returns:\n", + " A colormap for visualizing segmentation results.\n", + " \"\"\"\n", + " return np.asarray(\n", + " [\n", + " [0, 0, 0],\n", + " [120, 120, 120],\n", + " [180, 120, 120],\n", + " [6, 230, 230],\n", + " [80, 50, 50],\n", + " [4, 200, 3],\n", + " [120, 120, 80],\n", + " [140, 140, 140],\n", + " [204, 5, 255],\n", + " [230, 230, 230],\n", + " [4, 250, 7],\n", + " [224, 5, 255],\n", + " [235, 255, 7],\n", + " [150, 5, 61],\n", + " [120, 120, 70],\n", + " [8, 255, 51],\n", + " [255, 6, 82],\n", + " [143, 255, 140],\n", + " [204, 255, 4],\n", + " [255, 51, 7],\n", + " [204, 70, 3],\n", + " [0, 102, 200],\n", + " [61, 230, 250],\n", + " [255, 6, 51],\n", + " [11, 102, 255],\n", + " [255, 7, 71],\n", + " [255, 9, 224],\n", + " [9, 7, 230],\n", + " [220, 220, 220],\n", + " [255, 9, 92],\n", + " [112, 9, 255],\n", + " [8, 255, 214],\n", + " [7, 255, 224],\n", + " [255, 184, 6],\n", + " [10, 255, 71],\n", + " [255, 41, 10],\n", + " [7, 255, 255],\n", + " [224, 255, 8],\n", + " [102, 8, 255],\n", + " [255, 61, 6],\n", + " [255, 194, 7],\n", + " [255, 122, 8],\n", + " [0, 255, 20],\n", + " [255, 8, 41],\n", + " [255, 5, 153],\n", + " [6, 51, 255],\n", + " [235, 12, 255],\n", + " [160, 150, 20],\n", + " [0, 163, 255],\n", + " [140, 140, 140],\n", + " [250, 10, 15],\n", + " [20, 255, 0],\n", + " [31, 255, 0],\n", + " [255, 31, 0],\n", + " [255, 224, 0],\n", + " [153, 255, 0],\n", + " [0, 0, 255],\n", + " [255, 71, 0],\n", + " [0, 235, 255],\n", + " [0, 173, 255],\n", + " [31, 0, 255],\n", + " [11, 200, 200],\n", + " [255, 82, 0],\n", + " [0, 255, 245],\n", + " [0, 61, 255],\n", + " [0, 255, 112],\n", + " [0, 255, 133],\n", + " [255, 0, 0],\n", + " [255, 163, 0],\n", + " [255, 102, 0],\n", + " [194, 255, 0],\n", + " [0, 143, 255],\n", + " [51, 255, 0],\n", + " [0, 82, 255],\n", + " [0, 255, 41],\n", + " [0, 255, 173],\n", + " [10, 0, 255],\n", + " [173, 255, 0],\n", + " [0, 255, 153],\n", + " [255, 92, 0],\n", + " [255, 0, 255],\n", + " [255, 0, 245],\n", + " [255, 0, 102],\n", + " [255, 173, 0],\n", + " [255, 0, 20],\n", + " [255, 184, 184],\n", + " [0, 31, 255],\n", + " [0, 255, 61],\n", + " [0, 71, 255],\n", + " [255, 0, 204],\n", + " [0, 255, 194],\n", + " [0, 255, 82],\n", + " [0, 10, 255],\n", + " [0, 112, 255],\n", + " [51, 0, 255],\n", + " [0, 194, 255],\n", + " [0, 122, 255],\n", + " [0, 255, 163],\n", + " [255, 153, 0],\n", + " [0, 255, 10],\n", + " [255, 112, 0],\n", + " [143, 255, 0],\n", + " [82, 0, 255],\n", + " [163, 255, 0],\n", + " [255, 235, 0],\n", + " [8, 184, 170],\n", + " 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "color_seg = np.zeros((pred_seg.shape[0], pred_seg.shape[1], 3), dtype=np.uint8)\n", + "palette = np.array(ade_palette())\n", + "\n", + "for label, color in enumerate(palette):\n", + " color_seg[pred_seg == label, :] = color\n", + "color_seg = color_seg[..., ::-1] # convert to BGR\n", + "\n", + "img = np.array(image) * 0.5 + color_seg * 0.5 # plot the image with the segmentation map\n", + "img = img.astype(np.uint8)\n", + "\n", + "plt.figure(figsize=(15, 10))\n", + "plt.imshow(img)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q1aGuHYFLP7i" + }, + "source": [ + "The results are definitely not as expected and as mentioned above, this example is not meant to provide a state-of-the-art model. It exists to familiarize you with the end-to-end workflow. \n", + "\n", + "On the other hand, if you perform full fine-tuning on the same setup (same model variant, same dataset, same training schedule, etc.), the results would not have been any different. This is a crucial aspect of parameter-efficient fine-tuning -- to be able to match up to the results of the full fine-tuning but with a fraction of total trainable parameters. \n", + "\n", + "Here are some things that you can try to get better results:\n", + "\n", + "* Increase the number of training samples. \n", + "* Try a larger SegFormer model variant (know about the available model variants [here](https://huggingface.co/models?search=segformer)). \n", + "* Try different values for the arguments available in `LoraConfig`. \n", + "* Tune the learning rate and batch size. 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b/04-gene-sft/peft/examples/sequence_classification/LoRA.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..44c61e49077c3ac7a0a76d6dfed70efb173424be --- /dev/null +++ b/04-gene-sft/peft/examples/sequence_classification/LoRA.ipynb @@ -0,0 +1,713 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a9935ae2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===================================BUG REPORT===================================\n", + "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n", + "For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n", + "================================================================================\n", + "CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n", + "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n", + "CUDA SETUP: Detected CUDA version 117\n", + "CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n" + ] + } + ], + "source": [ + "import argparse\n", + "import os\n", + "\n", + "import torch\n", + "from torch.optim import AdamW\n", + "from torch.utils.data import DataLoader\n", + "from peft import (\n", + " get_peft_config,\n", + " get_peft_model,\n", + " get_peft_model_state_dict,\n", + " set_peft_model_state_dict,\n", + " LoraConfig,\n", + " PeftType,\n", + " PrefixTuningConfig,\n", + " PromptEncoderConfig,\n", + ")\n", + "\n", + "import evaluate\n", + "from datasets import load_dataset\n", + "from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e3b13308", + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 32\n", + "model_name_or_path = \"roberta-large\"\n", + "task = \"mrpc\"\n", + "peft_type = PeftType.LORA\n", + "device = \"cuda\"\n", + "num_epochs = 20" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0526f571", + "metadata": {}, + "outputs": [], + "source": [ + "peft_config = LoraConfig(task_type=\"SEQ_CLS\", inference_mode=False, r=8, lora_alpha=16, lora_dropout=0.1)\n", + "lr = 3e-4" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c2697d07", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0f74797387a941cbb0709487b8808eba", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading readme: 0%| | 0.00/27.9k [00:00 use the model max length (it's actually the default)\n", + " outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n", + " return outputs\n", + "\n", + "\n", + "tokenized_datasets = datasets.map(\n", + " tokenize_function,\n", + " batched=True,\n", + " remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n", + ")\n", + "\n", + "# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n", + "# transformers library\n", + "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n", + "\n", + "\n", + "def collate_fn(examples):\n", + " return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n", + "\n", + "\n", + "# Instantiate dataloaders.\n", + "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n", + "eval_dataloader = DataLoader(\n", + " tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ed5ac74", + "metadata": {}, + "outputs": [], + "source": [ + "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n", + "model = get_peft_model(model, peft_config)\n", + "model.print_trainable_parameters()\n", + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0d2d0381", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = AdamW(params=model.parameters(), lr=lr)\n", + "\n", + "# Instantiate scheduler\n", + "lr_scheduler = get_linear_schedule_with_warmup(\n", + " optimizer=optimizer,\n", + " num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n", + " num_training_steps=(len(train_dataloader) * num_epochs),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "fa0e73be", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/115 [00:00 use the model max length (it's actually the default)\n", + " outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n", + " return outputs\n", + "\n", + "\n", + "tokenized_datasets = datasets.map(\n", + " tokenize_function,\n", + " batched=True,\n", + " remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n", + ")\n", + "\n", + "# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n", + "# transformers library\n", + "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n", + "\n", + "\n", + "def collate_fn(examples):\n", + " return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n", + "\n", + "\n", + "# Instantiate dataloaders.\n", + "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n", + "eval_dataloader = DataLoader(\n", + " tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6bc8144", + "metadata": {}, + "outputs": [], + "source": [ + "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n", + "model = get_peft_model(model, peft_config)\n", + "model.print_trainable_parameters()\n", + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "af41c571", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = AdamW(params=model.parameters(), lr=lr)\n", + "\n", + "# Instantiate scheduler\n", + "lr_scheduler = get_linear_schedule_with_warmup(\n", + " optimizer=optimizer,\n", + " num_warmup_steps=0, # 0.06*(len(train_dataloader) * num_epochs),\n", + " num_training_steps=(len(train_dataloader) * num_epochs),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "90993c93", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/115 [00:00 use the model max length (it's actually the default)\n", + " outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n", + " return outputs\n", + "\n", + "\n", + "tokenized_datasets = datasets.map(\n", + " tokenize_function,\n", + " batched=True,\n", + " remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n", + ")\n", + "\n", + "# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n", + "# transformers library\n", + "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n", + "\n", + "\n", + "def collate_fn(examples):\n", + " return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n", + "\n", + "\n", + "# Instantiate dataloaders.\n", + "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n", + "eval_dataloader = DataLoader(\n", + " tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3c15af0", + "metadata": {}, + "outputs": [], + "source": [ + "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n", + "model = get_peft_model(model, peft_config)\n", + "model.print_trainable_parameters()\n", + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6d3c5edb", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = AdamW(params=model.parameters(), lr=lr)\n", + "\n", + "# Instantiate scheduler\n", + "lr_scheduler = get_linear_schedule_with_warmup(\n", + " optimizer=optimizer,\n", + " num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n", + " num_training_steps=(len(train_dataloader) * num_epochs),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4d279225", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/115 [00:00 use the model max length (it's actually the default)\n", + " outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n", + " return outputs\n", + "\n", + "\n", + "tokenized_datasets = datasets.map(\n", + " tokenize_function,\n", + " batched=True,\n", + " remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n", + ")\n", + "\n", + "# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n", + "# transformers library\n", + "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n", + "\n", + "\n", + "def collate_fn(examples):\n", + " return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n", + "\n", + "\n", + "# Instantiate dataloaders.\n", + "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n", + "eval_dataloader = DataLoader(\n", + " tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6bc8144", + "metadata": {}, + "outputs": [], + "source": [ + "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n", + "model = get_peft_model(model, peft_config)\n", + "model.print_trainable_parameters()\n", + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "af41c571", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = AdamW(params=model.parameters(), lr=lr)\n", + "\n", + "# Instantiate scheduler\n", + "lr_scheduler = get_linear_schedule_with_warmup(\n", + " optimizer=optimizer,\n", + " num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n", + " num_training_steps=(len(train_dataloader) * num_epochs),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "90993c93", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/115 [00:00\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ngqdEv0rP01q" + }, + "source": [ + "## Introduction\n", + "\n", + "In this notebook, we are going to fine-tune the LayoutLM model by Microsoft Research on the [FUNSD](https://guillaumejaume.github.io/FUNSD/) dataset, which is a collection of annotated form documents. The goal of our model is to learn the annotations of a number of labels (\"question\", \"answer\", \"header\" and \"other\") on those forms, such that it can be used to annotate unseen forms in the future.\n", + "\n", + "* Original LayoutLM paper: https://arxiv.org/abs/1912.13318\n", + "\n", + "* Original FUNSD paper: https://arxiv.org/abs/1905.13538\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6K4S2s33ebY0" + }, + "source": [ + "## Install libraries\n", + "\n", + "Currently you have to first install the `unilm` package, and then the `transformers` package (which updates the outdated `transformers` package that is included in the `unilm` package). The reason we also install the `unilm` package is because we need its preprocessing files. I've forked it, and removed some statements which introduced some issues." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "5cngOTr6SqEf", + "outputId": "6c7a2f76-682b-4f93-a3db-59ab010e5ffe" + }, + "outputs": [], + "source": [ + "# ! rm -r unilm\n", + "# ! pip install unilm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RGMkEG5aRB0D" + }, + "source": [ + "## Getting the data\n", + "\n", + "Here we download the data of the [FUNSD dataset](https://guillaumejaume.github.io/FUNSD/) from the web. This results in a directory called \"data\" being created, which has 2 subdirectories, one for training and one for testing. Each of those has 2 subdirectories in turn, one containing the images as png files and one containing the annotations in json format." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DTFnEZagQm4v", + "outputId": "97ce03ba-a6bb-4444-8eba-77eceece44e0" + }, + "outputs": [], + "source": [ + "# ! wget https://guillaumejaume.github.io/FUNSD/dataset.zip\n", + "# ! unzip dataset.zip && mv dataset data && rm -rf dataset.zip __MACOSX" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UrNMR64LsJXm" + }, + "source": [ + "Let's take a look at a training example. For this, we are going to use PIL (Python Image Library)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "eG-eGcj3sNPs", + "outputId": "69ead0ea-15d6-4d5e-af61-a99a7533d31b" + }, + "outputs": [], + "source": [ + "from PIL import Image, ImageDraw, ImageFont\n", + "import os\n", + "\n", + "base_path = \"/home/sourab/temp/data/dataset\"\n", + "\n", + "image = Image.open(os.path.join(base_path, \"training_data/images/0000971160.png\"))\n", + "image = image.convert(\"RGB\")\n", + "image" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uAVffmnZyUvw" + }, + "source": [ + "Now let's plot its corresponding annotations. Basically, if you type `data['form']`, you get a list of all general annotations. Each general annotation has a label, a bounding box, and one or more words, which in also have their own bounding box. The bounding boxes are in [xleft, ytop, xright, ybottom] format.\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JPKkuJQ4sdZc", + "outputId": "c95bf306-98bb-4480-cc6b-ebb3aea548b3" + }, + "outputs": [], + "source": [ + "import json\n", + "\n", + "with open(os.path.join(base_path, \"training_data/annotations/0000971160.json\")) as f:\n", + " data = json.load(f)\n", + "\n", + "for annotation in data[\"form\"]:\n", + " print(annotation)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Hs4L3S5a2Gfb" + }, + "source": [ + "The PIL library has a handy ImageDraw module, which -you guessed it- allows to draw things (such as rectangles) on an image:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "gWaHFM_LtKPP", + "outputId": "c498e560-035f-4170-b0b9-85ba3956711c" + }, + "outputs": [], + "source": [ + "draw = ImageDraw.Draw(image, \"RGBA\")\n", + "\n", + "font = ImageFont.load_default()\n", + "\n", + "label2color = {\"question\": \"blue\", \"answer\": \"green\", \"header\": \"orange\", \"other\": \"violet\"}\n", + "\n", + "for annotation in data[\"form\"]:\n", + " label = annotation[\"label\"]\n", + " general_box = annotation[\"box\"]\n", + " draw.rectangle(general_box, outline=label2color[label], width=2)\n", + " draw.text((general_box[0] + 10, general_box[1] - 10), label, fill=label2color[label], font=font)\n", + " words = annotation[\"words\"]\n", + " for word in words:\n", + " box = word[\"box\"]\n", + " draw.rectangle(box, outline=label2color[label], width=1)\n", + "\n", + "image" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uyWQNLSCRJN7" + }, + "source": [ + "## Preprocessing the data\n", + "\n", + "Next, we need to turn the document images into individual tokens and corresponding labels (BIOES format, see further). We do this both for the training and test datasets. Make sure to run this from the `/content` directory:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4DWRyOR9RuY6", + "outputId": "4215a24b-8049-4b1a-a23f-5aaa48e14083" + }, + "outputs": [], + "source": [ + "# ! python unilm/layoutlm/examples/seq_labeling/preprocess.py --data_dir data/dataset/training_data/annotations \\\n", + "# --data_split train \\\n", + "# --output_dir data \\\n", + "# --model_name_or_path microsoft/layoutlm-base-uncased \\\n", + "# --max_len 510\n", + "\n", + "# ! python unilm/layoutlm/examples/seq_labeling/preprocess.py --data_dir data/dataset/testing_data/annotations \\\n", + "# --data_split test \\\n", + "# --output_dir data \\\n", + "# --model_name_or_path microsoft/layoutlm-base-uncased \\\n", + "# --max_len 510" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gc4Cu0ZyO5M_" + }, + "source": [ + "Next, we create a labels.txt file that contains the unique labels of the FUNSD dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "8iGOU0s3UR2u" + }, + "outputs": [], + "source": [ + "# ! cat data/train.txt | cut -d$'\\t' -f 2 | grep -v \"^$\"| sort | uniq > data/labels.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mC9FhkG9U8yg" + }, + "source": [ + "## Define a PyTorch dataset\n", + "\n", + "First, we create a list containing the unique labels based on `data/labels.txt` (run this from the content directory):" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "675rRa0QXnMp" + }, + "outputs": [], + "source": [ + "from torch.nn import CrossEntropyLoss\n", + "\n", + "\n", + "def get_labels(path):\n", + " with open(path, \"r\") as f:\n", + " labels = f.read().splitlines()\n", + " if \"O\" not in labels:\n", + " labels = [\"O\"] + labels\n", + " return labels\n", + "\n", + "\n", + "labels = get_labels(\"data/labels.txt\")\n", + "num_labels = len(labels)\n", + "label_map = {i: label for i, label in enumerate(labels)}\n", + "# Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later\n", + "pad_token_label_id = CrossEntropyLoss().ignore_index" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kZ2LGEsez2u2" + }, + "source": [ + "We can see that the dataset uses the so-called BIOES annotation scheme to annotate the tokens. This means that a given token can be either at the beginning (B), inside (I), outside (O), at the end (E) or start (S) of a given entity. Entities include ANSWER, QUESTION, HEADER and OTHER: " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_-qXLkP9Yq_L", + "outputId": "32ab46a4-4cf0-400c-816b-570f950035ec" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['B-ANSWER', 'B-HEADER', 'B-QUESTION', 'E-ANSWER', 'E-HEADER', 'E-QUESTION', 'I-ANSWER', 'I-HEADER', 'I-QUESTION', 'O', 'S-ANSWER', 'S-HEADER', 'S-QUESTION']\n" + ] + } + ], + "source": [ + "print(labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9_ck0ZFfZInR" + }, + "source": [ + "Next, we can create a PyTorch dataset and corresponding dataloader (both for training and evaluation):" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "import os\n", + "\n", + "import torch\n", + "from torch.utils.data import Dataset\n", + "\n", + "logger = logging.getLogger(__name__)\n", + "\n", + "\n", + "class FunsdDataset(Dataset):\n", + " def __init__(self, args, tokenizer, labels, pad_token_label_id, mode):\n", + " if args.local_rank not in [-1, 0] and mode == \"train\":\n", + " torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache\n", + "\n", + " # Load data features from cache or dataset file\n", + " cached_features_file = os.path.join(\n", + " args.data_dir,\n", + " \"cached_{}_{}_{}\".format(\n", + " mode,\n", + " list(filter(None, args.model_name_or_path.split(\"/\"))).pop(),\n", + " str(args.max_seq_length),\n", + " ),\n", + " )\n", + " if os.path.exists(cached_features_file) and not args.overwrite_cache:\n", + " logger.info(\"Loading features from cached file %s\", cached_features_file)\n", + " features = torch.load(cached_features_file)\n", + " else:\n", + " logger.info(\"Creating features from dataset file at %s\", args.data_dir)\n", + " examples = read_examples_from_file(args.data_dir, mode)\n", + " features = convert_examples_to_features(\n", + " examples,\n", + " labels,\n", + " args.max_seq_length,\n", + " tokenizer,\n", + " cls_token_at_end=bool(args.model_type in [\"xlnet\"]),\n", + " # xlnet has a cls token at the end\n", + " cls_token=tokenizer.cls_token,\n", + " cls_token_segment_id=2 if args.model_type in [\"xlnet\"] else 0,\n", + " sep_token=tokenizer.sep_token,\n", + " sep_token_extra=bool(args.model_type in [\"roberta\"]),\n", + " # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805\n", + " pad_on_left=bool(args.model_type in [\"xlnet\"]),\n", + " # pad on the left for xlnet\n", + " pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],\n", + " pad_token_segment_id=4 if args.model_type in [\"xlnet\"] else 0,\n", + " pad_token_label_id=pad_token_label_id,\n", + " )\n", + " # if args.local_rank in [-1, 0]:\n", + " # logger.info(\"Saving features into cached file %s\", cached_features_file)\n", + " # torch.save(features, cached_features_file)\n", + "\n", + " if args.local_rank == 0 and mode == \"train\":\n", + " torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache\n", + "\n", + " self.features = features\n", + " # Convert to Tensors and build dataset\n", + " self.all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n", + " self.all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)\n", + " self.all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)\n", + " self.all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long)\n", + " self.all_bboxes = torch.tensor([f.boxes for f in features], dtype=torch.long)\n", + "\n", + " def __len__(self):\n", + " return len(self.features)\n", + "\n", + " def __getitem__(self, index):\n", + " return (\n", + " self.all_input_ids[index],\n", + " self.all_input_mask[index],\n", + " self.all_segment_ids[index],\n", + " self.all_label_ids[index],\n", + " self.all_bboxes[index],\n", + " )\n", + "\n", + "\n", + "class InputExample(object):\n", + " \"\"\"A single training/test example for token classification.\"\"\"\n", + "\n", + " def __init__(self, guid, words, labels, boxes, actual_bboxes, file_name, page_size):\n", + " \"\"\"Constructs a InputExample.\n", + "\n", + " Args:\n", + " guid: Unique id for the example.\n", + " words: list. The words of the sequence.\n", + " labels: (Optional) list. The labels for each word of the sequence. This should be\n", + " specified for train and dev examples, but not for test examples.\n", + " \"\"\"\n", + " self.guid = guid\n", + " self.words = words\n", + " self.labels = labels\n", + " self.boxes = boxes\n", + " self.actual_bboxes = actual_bboxes\n", + " self.file_name = file_name\n", + " self.page_size = page_size\n", + "\n", + "\n", + "class InputFeatures(object):\n", + " \"\"\"A single set of features of data.\"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " input_ids,\n", + " input_mask,\n", + " segment_ids,\n", + " label_ids,\n", + " boxes,\n", + " actual_bboxes,\n", + " file_name,\n", + " page_size,\n", + " ):\n", + " assert (\n", + " 0 <= all(boxes) <= 1000\n", + " ), \"Error with input bbox ({}): the coordinate value is not between 0 and 1000\".format(boxes)\n", + " self.input_ids = input_ids\n", + " self.input_mask = input_mask\n", + " self.segment_ids = segment_ids\n", + " self.label_ids = label_ids\n", + " self.boxes = boxes\n", + " self.actual_bboxes = actual_bboxes\n", + " self.file_name = file_name\n", + " self.page_size = page_size\n", + "\n", + "\n", + "def read_examples_from_file(data_dir, mode):\n", + " file_path = os.path.join(data_dir, \"{}.txt\".format(mode))\n", + " box_file_path = os.path.join(data_dir, \"{}_box.txt\".format(mode))\n", + " image_file_path = os.path.join(data_dir, \"{}_image.txt\".format(mode))\n", + " guid_index = 1\n", + " examples = []\n", + " with open(file_path, encoding=\"utf-8\") as f, open(box_file_path, encoding=\"utf-8\") as fb, open(\n", + " image_file_path, encoding=\"utf-8\"\n", + " ) as fi:\n", + " words = []\n", + " boxes = []\n", + " actual_bboxes = []\n", + " file_name = None\n", + " page_size = None\n", + " labels = []\n", + " for line, bline, iline in zip(f, fb, fi):\n", + " if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\n", + " if words:\n", + " examples.append(\n", + " InputExample(\n", + " guid=\"{}-{}\".format(mode, guid_index),\n", + " words=words,\n", + " labels=labels,\n", + " boxes=boxes,\n", + " actual_bboxes=actual_bboxes,\n", + " file_name=file_name,\n", + " page_size=page_size,\n", + " )\n", + " )\n", + " guid_index += 1\n", + " words = []\n", + " boxes = []\n", + " actual_bboxes = []\n", + " file_name = None\n", + " page_size = None\n", + " labels = []\n", + " else:\n", + " splits = line.split(\"\\t\")\n", + " bsplits = bline.split(\"\\t\")\n", + " isplits = iline.split(\"\\t\")\n", + " assert len(splits) == 2\n", + " assert len(bsplits) == 2\n", + " assert len(isplits) == 4\n", + " assert splits[0] == bsplits[0]\n", + " words.append(splits[0])\n", + " if len(splits) > 1:\n", + " labels.append(splits[-1].replace(\"\\n\", \"\"))\n", + " box = bsplits[-1].replace(\"\\n\", \"\")\n", + " box = [int(b) for b in box.split()]\n", + " boxes.append(box)\n", + " actual_bbox = [int(b) for b in isplits[1].split()]\n", + " actual_bboxes.append(actual_bbox)\n", + " page_size = [int(i) for i in isplits[2].split()]\n", + " file_name = isplits[3].strip()\n", + " else:\n", + " # Examples could have no label for mode = \"test\"\n", + " labels.append(\"O\")\n", + " if words:\n", + " examples.append(\n", + " InputExample(\n", + " guid=\"%s-%d\".format(mode, guid_index),\n", + " words=words,\n", + " labels=labels,\n", + " boxes=boxes,\n", + " actual_bboxes=actual_bboxes,\n", + " file_name=file_name,\n", + " page_size=page_size,\n", + " )\n", + " )\n", + " return examples\n", + "\n", + "\n", + "def convert_examples_to_features(\n", + " examples,\n", + " label_list,\n", + " max_seq_length,\n", + " tokenizer,\n", + " cls_token_at_end=False,\n", + " cls_token=\"[CLS]\",\n", + " cls_token_segment_id=1,\n", + " sep_token=\"[SEP]\",\n", + " sep_token_extra=False,\n", + " pad_on_left=False,\n", + " pad_token=0,\n", + " cls_token_box=[0, 0, 0, 0],\n", + " sep_token_box=[1000, 1000, 1000, 1000],\n", + " pad_token_box=[0, 0, 0, 0],\n", + " pad_token_segment_id=0,\n", + " pad_token_label_id=-1,\n", + " sequence_a_segment_id=0,\n", + " mask_padding_with_zero=True,\n", + "):\n", + " \"\"\"Loads a data file into a list of `InputBatch`s\n", + " `cls_token_at_end` define the location of the CLS token:\n", + " - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]\n", + " - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]\n", + " `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)\n", + " \"\"\"\n", + "\n", + " label_map = {label: i for i, label in enumerate(label_list)}\n", + "\n", + " features = []\n", + " for ex_index, example in enumerate(examples):\n", + " file_name = example.file_name\n", + " page_size = example.page_size\n", + " width, height = page_size\n", + " if ex_index % 10000 == 0:\n", + " logger.info(\"Writing example %d of %d\", ex_index, len(examples))\n", + "\n", + " tokens = []\n", + " token_boxes = []\n", + " actual_bboxes = []\n", + " label_ids = []\n", + " for word, label, box, actual_bbox in zip(example.words, example.labels, example.boxes, example.actual_bboxes):\n", + " word_tokens = tokenizer.tokenize(word)\n", + " tokens.extend(word_tokens)\n", + " token_boxes.extend([box] * len(word_tokens))\n", + " actual_bboxes.extend([actual_bbox] * len(word_tokens))\n", + " # Use the real label id for the first token of the word, and padding ids for the remaining tokens\n", + " label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))\n", + "\n", + " # Account for [CLS] and [SEP] with \"- 2\" and with \"- 3\" for RoBERTa.\n", + " special_tokens_count = 3 if sep_token_extra else 2\n", + " if len(tokens) > max_seq_length - special_tokens_count:\n", + " tokens = tokens[: (max_seq_length - special_tokens_count)]\n", + " token_boxes = token_boxes[: (max_seq_length - special_tokens_count)]\n", + " actual_bboxes = actual_bboxes[: (max_seq_length - special_tokens_count)]\n", + " label_ids = label_ids[: (max_seq_length - special_tokens_count)]\n", + "\n", + " # The convention in BERT is:\n", + " # (a) For sequence pairs:\n", + " # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]\n", + " # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1\n", + " # (b) For single sequences:\n", + " # tokens: [CLS] the dog is hairy . [SEP]\n", + " # type_ids: 0 0 0 0 0 0 0\n", + " #\n", + " # Where \"type_ids\" are used to indicate whether this is the first\n", + " # sequence or the second sequence. The embedding vectors for `type=0` and\n", + " # `type=1` were learned during pre-training and are added to the wordpiece\n", + " # embedding vector (and position vector). This is not *strictly* necessary\n", + " # since the [SEP] token unambiguously separates the sequences, but it makes\n", + " # it easier for the model to learn the concept of sequences.\n", + " #\n", + " # For classification tasks, the first vector (corresponding to [CLS]) is\n", + " # used as as the \"sentence vector\". Note that this only makes sense because\n", + " # the entire model is fine-tuned.\n", + " tokens += [sep_token]\n", + " token_boxes += [sep_token_box]\n", + " actual_bboxes += [[0, 0, width, height]]\n", + " label_ids += [pad_token_label_id]\n", + " if sep_token_extra:\n", + " # roberta uses an extra separator b/w pairs of sentences\n", + " tokens += [sep_token]\n", + " token_boxes += [sep_token_box]\n", + " actual_bboxes += [[0, 0, width, height]]\n", + " label_ids += [pad_token_label_id]\n", + " segment_ids = [sequence_a_segment_id] * len(tokens)\n", + "\n", + " if cls_token_at_end:\n", + " tokens += [cls_token]\n", + " token_boxes += [cls_token_box]\n", + " actual_bboxes += [[0, 0, width, height]]\n", + " label_ids += [pad_token_label_id]\n", + " segment_ids += [cls_token_segment_id]\n", + " else:\n", + " tokens = [cls_token] + tokens\n", + " token_boxes = [cls_token_box] + token_boxes\n", + " actual_bboxes = [[0, 0, width, height]] + actual_bboxes\n", + " label_ids = [pad_token_label_id] + label_ids\n", + " segment_ids = [cls_token_segment_id] + segment_ids\n", + "\n", + " input_ids = tokenizer.convert_tokens_to_ids(tokens)\n", + "\n", + " # The mask has 1 for real tokens and 0 for padding tokens. Only real\n", + " # tokens are attended to.\n", + " input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\n", + "\n", + " # Zero-pad up to the sequence length.\n", + " padding_length = max_seq_length - len(input_ids)\n", + " if pad_on_left:\n", + " input_ids = ([pad_token] * padding_length) + input_ids\n", + " input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask\n", + " segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids\n", + " label_ids = ([pad_token_label_id] * padding_length) + label_ids\n", + " token_boxes = ([pad_token_box] * padding_length) + token_boxes\n", + " else:\n", + " input_ids += [pad_token] * padding_length\n", + " input_mask += [0 if mask_padding_with_zero else 1] * padding_length\n", + " segment_ids += [pad_token_segment_id] * padding_length\n", + " label_ids += [pad_token_label_id] * padding_length\n", + " token_boxes += [pad_token_box] * padding_length\n", + "\n", + " assert len(input_ids) == max_seq_length\n", + " assert len(input_mask) == max_seq_length\n", + " assert len(segment_ids) == max_seq_length\n", + " assert len(label_ids) == max_seq_length\n", + " assert len(token_boxes) == max_seq_length\n", + "\n", + " if ex_index < 5:\n", + " logger.info(\"*** Example ***\")\n", + " logger.info(\"guid: %s\", example.guid)\n", + " logger.info(\"tokens: %s\", \" \".join([str(x) for x in tokens]))\n", + " logger.info(\"input_ids: %s\", \" \".join([str(x) for x in input_ids]))\n", + " logger.info(\"input_mask: %s\", \" \".join([str(x) for x in input_mask]))\n", + " logger.info(\"segment_ids: %s\", \" \".join([str(x) for x in segment_ids]))\n", + " logger.info(\"label_ids: %s\", \" \".join([str(x) for x in label_ids]))\n", + " logger.info(\"boxes: %s\", \" \".join([str(x) for x in token_boxes]))\n", + " logger.info(\"actual_bboxes: %s\", \" \".join([str(x) for x in actual_bboxes]))\n", + "\n", + " features.append(\n", + " InputFeatures(\n", + " input_ids=input_ids,\n", + " input_mask=input_mask,\n", + " segment_ids=segment_ids,\n", + " label_ids=label_ids,\n", + " boxes=token_boxes,\n", + " actual_bboxes=actual_bboxes,\n", + " file_name=file_name,\n", + " page_size=page_size,\n", + " )\n", + " )\n", + " return features" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "HUJftzeBWh2S" + }, + "outputs": [], + "source": [ + "from transformers import LayoutLMTokenizer\n", + "\n", + "# from .unilm.layoutlm.data.funsd import FunsdDataset, InputFeatures\n", + "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n", + "\n", + "batch_size = 16\n", + "args = {\n", + " \"local_rank\": -1,\n", + " \"overwrite_cache\": True,\n", + " \"data_dir\": \"/home/sourab/temp/data/\",\n", + " \"model_name_or_path\": \"microsoft/layoutlm-base-uncased\",\n", + " \"max_seq_length\": 512,\n", + " \"model_type\": \"layoutlm\",\n", + "}\n", + "\n", + "\n", + "# class to turn the keys of a dict into attributes (thanks Stackoverflow)\n", + "class AttrDict(dict):\n", + " def __init__(self, *args, **kwargs):\n", + " super(AttrDict, self).__init__(*args, **kwargs)\n", + " self.__dict__ = self\n", + "\n", + "\n", + "args = AttrDict(args)\n", + "\n", + "tokenizer = LayoutLMTokenizer.from_pretrained(\"microsoft/layoutlm-base-uncased\")\n", + "\n", + "# the LayoutLM authors already defined a specific FunsdDataset, so we are going to use this here\n", + "train_dataset = FunsdDataset(args, tokenizer, labels, pad_token_label_id, mode=\"train\")\n", + "train_sampler = RandomSampler(train_dataset)\n", + "train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size)\n", + "\n", + "eval_dataset = FunsdDataset(args, tokenizer, labels, pad_token_label_id, mode=\"test\")\n", + "eval_sampler = SequentialSampler(eval_dataset)\n", + "eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=batch_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "18NMUBzgOdqu", + "outputId": "eef47b70-3a9a-4b19-be6b-95900c58337b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(train_dataloader)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "toFjxtn71B1U", + "outputId": "f4651896-cafc-449a-98b4-c81f41177e6d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(eval_dataloader)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 137 + }, + "id": "RhINSBw9I24G", + "outputId": "28738ce2-617c-47d3-b8c9-f949d3066d60" + }, + "outputs": [], + "source": [ + "batch = next(iter(train_dataloader))\n", + "input_ids = batch[0][0]\n", + "tokenizer.decode(input_ids)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "66cEmLDoUFcm" + }, + "source": [ + "## Define and fine-tune the model\n", + "\n", + "As this is a sequence labeling task, we are going to load `LayoutLMForTokenClassification` (the base sized model) from the hub. We are going to fine-tune it on a downstream task, namely FUNSD." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LoRAConfig(pet_type='LORA', task_type='TOKEN_CLS', inference_mode=False, r=16, target_modules=None, lora_alpha=16, lora_dropout=0.1, merge_weights=False, fan_in_fan_out=False, enable_lora=None, bias='all')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from peft import get_peft_config, PeftModel, get_peft_model, LoraConfig, TaskType\n", + "\n", + "peft_config = LoraConfig(\n", + " task_type=TaskType.TOKEN_CLS, inference_mode=False, r=16, lora_alpha=16, lora_dropout=0.1, bias=\"all\"\n", + ")\n", + "peft_config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "da1094982d044ab28eb0effebbfcbb78", + "513e00b619924f5693259cd919a927ab", + "63e819a04f6e4829838c0e30e65516ed", + "d1c3e1a66db04227a74ef8d6481d6daf", + "c75f0da13a1e4dbe94800711d55390a6", + "31642aacae2a44879960da09f938ecc4", + "138a6b922e454ebbaeb315ecd5f476b8", + "a8126ba98376402888e9ba344cf1c538" + ] + }, + "id": "xIdOsFBiTsuw", + "outputId": "95e8811c-025a-41a0-9d03-4285a17f2a9b" + }, + "outputs": [], + "source": [ + "from transformers import LayoutLMForTokenClassification\n", + "import torch\n", + "from transformers import set_seed\n", + "\n", + "seed = 100\n", + "set_seed(seed)\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "model = LayoutLMForTokenClassification.from_pretrained(\"microsoft/layoutlm-base-uncased\", num_labels=num_labels)\n", + "model = get_peft_model(model, peft_config)\n", + "model.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(model.model.layoutlm.encoder.layer[0].attention.self.query.weight)\n", + "print(model.model.layoutlm.encoder.layer[0].attention.self.query.lora_A.weight)\n", + "print(model.model.classifier.weight)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3weFr_pz1mla" + }, + "source": [ + "Now we can start training:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Yu0qePs2cRKo", + "outputId": "cdbb9a03-eb9b-4740-bbe3-da06b9192bae" + }, + "outputs": [], + "source": [ + "from transformers import AdamW, get_linear_schedule_with_warmup\n", + "from tqdm import tqdm\n", + "\n", + "num_train_epochs = 100\n", + "\n", + "optimizer = torch.optim.AdamW(model.parameters(), lr=3e-3)\n", + "lr_scheduler = get_linear_schedule_with_warmup(\n", + " optimizer=optimizer,\n", + " num_warmup_steps=0.06 * (len(train_dataloader) * num_train_epochs),\n", + " num_training_steps=(len(train_dataloader) * num_train_epochs),\n", + ")\n", + "\n", + "\n", + "global_step = 0\n", + "\n", + "t_total = len(train_dataloader) * num_train_epochs # total number of training steps\n", + "\n", + "# put the model in training mode\n", + "model.train()\n", + "for epoch in range(num_train_epochs):\n", + " for batch in tqdm(train_dataloader, desc=\"Training\"):\n", + " input_ids = batch[0].to(device)\n", + " bbox = batch[4].to(device)\n", + " attention_mask = batch[1].to(device)\n", + " token_type_ids = batch[2].to(device)\n", + " labels = batch[3].to(device)\n", + "\n", + " # forward pass\n", + " outputs = model(\n", + " input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels\n", + " )\n", + " loss = outputs.loss\n", + "\n", + " # print loss every 100 steps\n", + " if global_step % 10 == 0:\n", + " print(f\"Loss after {global_step} steps: {loss.item()}\")\n", + "\n", + " # backward pass to get the gradients\n", + " loss.backward()\n", + "\n", + " # print(\"Gradients on classification head:\")\n", + " # print(model.classifier.weight.grad[6,:].sum())\n", + "\n", + " # update\n", + " optimizer.step()\n", + " lr_scheduler.step()\n", + " optimizer.zero_grad()\n", + " global_step += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "u1rNslap5Y3N", + "outputId": "877183d4-1d29-4d09-bd3a-0e5f88611dc8" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Evaluating: 100%|█████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.73it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'loss': 1.7456395626068115, 'precision': 0.7407071622846781, 'recall': 0.817408704352176, 'f1': 0.7771700356718193}\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from seqeval.metrics import (\n", + " classification_report,\n", + " f1_score,\n", + " precision_score,\n", + " recall_score,\n", + ")\n", + "\n", + "eval_loss = 0.0\n", + "nb_eval_steps = 0\n", + "preds = None\n", + "out_label_ids = None\n", + "\n", + "# put model in evaluation mode\n", + "model.eval()\n", + "for batch in tqdm(eval_dataloader, desc=\"Evaluating\"):\n", + " with torch.no_grad():\n", + " input_ids = batch[0].to(device)\n", + " bbox = batch[4].to(device)\n", + " attention_mask = batch[1].to(device)\n", + " token_type_ids = batch[2].to(device)\n", + " labels = batch[3].to(device)\n", + "\n", + " # forward pass\n", + " outputs = model(\n", + " input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels\n", + " )\n", + " # get the loss and logits\n", + " tmp_eval_loss = outputs.loss\n", + " logits = outputs.logits\n", + "\n", + " eval_loss += tmp_eval_loss.item()\n", + " nb_eval_steps += 1\n", + "\n", + " # compute the predictions\n", + " if preds is None:\n", + " preds = logits.detach().cpu().numpy()\n", + " out_label_ids = labels.detach().cpu().numpy()\n", + " else:\n", + " preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)\n", + " out_label_ids = np.append(out_label_ids, labels.detach().cpu().numpy(), axis=0)\n", + "\n", + "# compute average evaluation loss\n", + "eval_loss = eval_loss / nb_eval_steps\n", + "preds = np.argmax(preds, axis=2)\n", + "\n", + "out_label_list = [[] for _ in range(out_label_ids.shape[0])]\n", + "preds_list = [[] for _ in range(out_label_ids.shape[0])]\n", + "\n", + "for i in range(out_label_ids.shape[0]):\n", + " for j in range(out_label_ids.shape[1]):\n", + " if out_label_ids[i, j] != pad_token_label_id:\n", + " out_label_list[i].append(label_map[out_label_ids[i][j]])\n", + " preds_list[i].append(label_map[preds[i][j]])\n", + "\n", + "results = {\n", + " \"loss\": eval_loss,\n", + " \"precision\": precision_score(out_label_list, preds_list),\n", + " \"recall\": recall_score(out_label_list, preds_list),\n", + " \"f1\": f1_score(out_label_list, preds_list),\n", + "}\n", + "print(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "trainable params: 702733 || all params: 113227789 || trainable%: 0.6206365117665593\n" + ] + } + ], + "source": [ + "model.print_trainable_parameters()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "model.save_pretrained(\"peft_layoutlm\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2,8M\tlayoutlm_funsd.pt\r\n" + ] + } + ], + "source": [ + "!du -h \"peft_layoutlm/adapter_model.bin\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + 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0000000000000000000000000000000000000000..a72149d682c8f540bad7e0b8363f8073b5b01e39 --- /dev/null +++ b/04-gene-sft/peft/pyproject.toml @@ -0,0 +1,36 @@ +[tool.black] +line-length = 119 +target-version = ['py36'] + +[tool.ruff] +ignore = ["C901", "E501", "E741", "W605"] +select = ["C", "E", "F", "I", "W"] +line-length = 119 + +[tool.ruff.isort] +lines-after-imports = 2 +known-first-party = ["peft"] + +[isort] +default_section = "FIRSTPARTY" +known_first_party = "peft" +known_third_party = [ + "numpy", + "torch", + "accelerate", + "transformers", +] +line_length = 119 +lines_after_imports = 2 +multi_line_output = 3 +include_trailing_comma = true +force_grid_wrap = 0 +use_parentheses = true +ensure_newline_before_comments = true + +[tool.pytest] +doctest_optionflags = [ + "NORMALIZE_WHITESPACE", + "ELLIPSIS", + "NUMBER", +] \ No newline at end of file diff --git a/04-gene-sft/peft/setup.py b/04-gene-sft/peft/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..8d15e0187b0f5dd58110df669f147f52428b5815 --- /dev/null +++ b/04-gene-sft/peft/setup.py @@ -0,0 +1,77 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from setuptools import find_packages, setup + +extras = {} +extras["quality"] = ["black ~= 22.0", "ruff>=0.0.241"] +extras["docs_specific"] = ["hf-doc-builder"] +extras["dev"] = extras["quality"] + extras["docs_specific"] + +setup( + name="peft", + version="0.3.0.dev0", + description="Parameter-Efficient Fine-Tuning (PEFT)", + license_files=["LICENSE"], + long_description=open("README.md", "r", encoding="utf-8").read(), + long_description_content_type="text/markdown", + keywords="deep learning", + license="Apache", + author="The HuggingFace team", + author_email="sourab@huggingface.co", + url="https://github.com/huggingface/peft", + package_dir={"": "src"}, + packages=find_packages("src"), + entry_points={}, + python_requires=">=3.7.0", + install_requires=[ + "numpy>=1.17", + "packaging>=20.0", + "psutil", + "pyyaml", + "torch>=1.13.0", + "transformers", + "accelerate", + ], + extras_require=extras, + classifiers=[ + "Development Status :: 5 - Production/Stable", + "Intended Audience :: Developers", + "Intended Audience :: Education", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: Apache Software License", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.7", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + ], +) + +# Release checklist +# 1. Change the version in __init__.py and setup.py. +# 2. Commit these changes with the message: "Release: VERSION" +# 3. Add a tag in git to mark the release: "git tag VERSION -m 'Adds tag VERSION for pypi' " +# Push the tag to git: git push --tags origin main +# 4. Run the following commands in the top-level directory: +# python setup.py bdist_wheel +# python setup.py sdist +# 5. Upload the package to the pypi test server first: +# twine upload dist/* -r pypitest +# twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/ +# 6. Check that you can install it in a virtualenv by running: +# pip install -i https://testpypi.python.org/pypi peft +# 7. Upload the final version to actual pypi: +# twine upload dist/* -r pypi +# 8. Add release notes to the tag in github once everything is looking hunky-dory. +# 9. Update the version in __init__.py, setup.py to the new version "-dev" and push to master diff --git a/04-gene-sft/peft/src/peft.egg-info/PKG-INFO b/04-gene-sft/peft/src/peft.egg-info/PKG-INFO new file mode 100644 index 0000000000000000000000000000000000000000..3614f5fe0261dbd592cc8191fb4556f0291d810a --- /dev/null +++ b/04-gene-sft/peft/src/peft.egg-info/PKG-INFO @@ -0,0 +1,416 @@ +Metadata-Version: 2.2 +Name: peft +Version: 0.3.0.dev0 +Summary: Parameter-Efficient Fine-Tuning (PEFT) +Home-page: https://github.com/huggingface/peft +Author: The HuggingFace team +Author-email: sourab@huggingface.co +License: Apache +Keywords: deep learning +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: License :: OSI Approved :: Apache Software License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Requires-Python: >=3.7.0 +Description-Content-Type: text/markdown +License-File: LICENSE +Requires-Dist: numpy>=1.17 +Requires-Dist: packaging>=20.0 +Requires-Dist: psutil +Requires-Dist: pyyaml +Requires-Dist: torch>=1.13.0 +Requires-Dist: transformers +Requires-Dist: accelerate +Provides-Extra: quality +Requires-Dist: black~=22.0; extra == "quality" +Requires-Dist: ruff>=0.0.241; extra == "quality" +Provides-Extra: docs-specific +Requires-Dist: hf-doc-builder; extra == "docs-specific" +Provides-Extra: dev +Requires-Dist: black~=22.0; extra == "dev" +Requires-Dist: ruff>=0.0.241; extra == "dev" +Requires-Dist: hf-doc-builder; extra == "dev" +Dynamic: author +Dynamic: author-email +Dynamic: classifier +Dynamic: description +Dynamic: description-content-type +Dynamic: home-page +Dynamic: keywords +Dynamic: license +Dynamic: provides-extra +Dynamic: requires-dist +Dynamic: requires-python +Dynamic: summary + + + +

🤗 PEFT

+

+

State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) methods

+

+ +Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters. Fine-tuning large-scale PLMs is often prohibitively costly. In this regard, PEFT methods only fine-tune a small number of (extra) model parameters, thereby greatly decreasing the computational and storage costs. Recent State-of-the-Art PEFT techniques achieve performance comparable to that of full fine-tuning. + +Seamlessly integrated with 🤗 Accelerate for large scale models leveraging DeepSpeed and Big Model Inference. + +Supported methods: + +1. LoRA: [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685.pdf) +2. Prefix Tuning: [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://aclanthology.org/2021.acl-long.353/), [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://arxiv.org/pdf/2110.07602.pdf) +3. P-Tuning: [GPT Understands, Too](https://arxiv.org/pdf/2103.10385.pdf) +4. Prompt Tuning: [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/pdf/2104.08691.pdf) + +## Getting started + +```python +from transformers import AutoModelForSeq2SeqLM +from peft import get_peft_config, get_peft_model, LoraConfig, TaskType +model_name_or_path = "bigscience/mt0-large" +tokenizer_name_or_path = "bigscience/mt0-large" + +peft_config = LoraConfig( + task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1 +) + +model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path) +model = get_peft_model(model, peft_config) +model.print_trainable_parameters() +# output: trainable params: 2359296 || all params: 1231940608 || trainable%: 0.19151053100118282 +``` + +## Use Cases + +### Get comparable performance to full finetuning by adapting LLMs to downstream tasks using consumer hardware + +GPU memory required for adapting LLMs on the few-shot dataset [`ought/raft/twitter_complaints`](https://huggingface.co/datasets/ought/raft/viewer/twitter_complaints). Here, settings considered +are full finetuning, PEFT-LoRA using plain PyTorch and PEFT-LoRA using DeepSpeed with CPU Offloading. + +Hardware: Single A100 80GB GPU with CPU RAM above 64GB + +| Model | Full Finetuning | PEFT-LoRA PyTorch | PEFT-LoRA DeepSpeed with CPU Offloading | +| --------- | ---- | ---- | ---- | +| bigscience/T0_3B (3B params) | 47.14GB GPU / 2.96GB CPU | 14.4GB GPU / 2.96GB CPU | 9.8GB GPU / 17.8GB CPU | +| bigscience/mt0-xxl (12B params) | OOM GPU | 56GB GPU / 3GB CPU | 22GB GPU / 52GB CPU | +| bigscience/bloomz-7b1 (7B params) | OOM GPU | 32GB GPU / 3.8GB CPU | 18.1GB GPU / 35GB CPU | + +Performance of PEFT-LoRA tuned [`bigscience/T0_3B`](https://huggingface.co/bigscience/T0_3B) on [`ought/raft/twitter_complaints`](https://huggingface.co/datasets/ought/raft/viewer/twitter_complaints) leaderboard. +A point to note is that we didn't try to sequeeze performance by playing around with input instruction templates, LoRA hyperparams and other training related hyperparams. Also, we didn't use the larger 13B [mt0-xxl](https://huggingface.co/bigscience/mt0-xxl) model. +So, we are already seeing comparable performance to SoTA with parameter efficient tuning. Also, the final checkpoint size is just `19MB` in comparison to `11GB` size of the backbone [`bigscience/T0_3B`](https://huggingface.co/bigscience/T0_3B) model. + +| Submission Name | Accuracy | +| --------- | ---- | +| Human baseline (crowdsourced) | 0.897 | +| Flan-T5 | 0.892 | +| lora-t0-3b | 0.863 | + +**Therefore, we can see that performance comparable to SoTA is achievable by PEFT methods with consumer hardware such as 16GB and 24GB GPUs.** + +### Parameter Efficient Tuning of Diffusion Models + +GPU memory required by different settings during training is given below. The final checkpoint size is `8.8 MB`. + +Hardware: Single A100 80GB GPU with CPU RAM above 64GB + +| Model | Full Finetuning | PEFT-LoRA | PEFT-LoRA with Gradient Checkpoitning | +| --------- | ---- | ---- | ---- | +| CompVis/stable-diffusion-v1-4 | 27.5GB GPU / 3.97GB CPU | 15.5GB GPU / 3.84GB CPU | 8.12GB GPU / 3.77GB CPU | + + +**Training** +An example of using LoRA for parameter efficient dreambooth training is given in `~examples/lora_dreambooth/train_dreambooth.py` + +```bash +export MODEL_NAME= "CompVis/stable-diffusion-v1-4" #"stabilityai/stable-diffusion-2-1" +export INSTANCE_DIR="path-to-instance-images" +export CLASS_DIR="path-to-class-images" +export OUTPUT_DIR="path-to-save-model" + +accelerate launch train_dreambooth.py \ + --pretrained_model_name_or_path=$MODEL_NAME \ + --instance_data_dir=$INSTANCE_DIR \ + --class_data_dir=$CLASS_DIR \ + --output_dir=$OUTPUT_DIR \ + --train_text_encoder \ + --with_prior_preservation --prior_loss_weight=1.0 \ + --instance_prompt="a photo of sks dog" \ + --class_prompt="a photo of dog" \ + --resolution=512 \ + --train_batch_size=1 \ + --lr_scheduler="constant" \ + --lr_warmup_steps=0 \ + --num_class_images=200 \ + --use_lora \ + --lora_r 16 \ + --lora_alpha 27 \ + --lora_text_encoder_r 16 \ + --lora_text_encoder_alpha 17 \ + --learning_rate=1e-4 \ + --gradient_accumulation_steps=1 \ + --gradient_checkpointing \ + --max_train_steps=800 +``` + +Try out the 🤗 Gradio Space which should run seamlessly on a T4 instance: +[smangrul/peft-lora-sd-dreambooth](https://huggingface.co/spaces/smangrul/peft-lora-sd-dreambooth). + +![peft lora dreambooth gradio space](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/peft_lora_dreambooth_gradio_space.png) + +### Parameter Efficient Tuning of LLMs for RLHF components such as Ranker and Policy +- Here is an exmaple in [trl](https://github.com/lvwerra/trl) library using PEFT+INT8 for tuning policy model: [gpt2-sentiment_peft.py](https://github.com/lvwerra/trl/blob/main/examples/sentiment/scripts/gpt2-sentiment_peft.py) +- Example using PEFT for both reward model and policy [ToDo] + +### INT8 training of large models in Colab using PEFT LoRA and bits_and_bytes + +- Here is now a demo on how to fine tune [OPT-6.7b](https://huggingface.co/facebook/opt-6.7b) (14GB in fp16) in a Google colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1jCkpikz0J2o20FBQmYmAGdiKmJGOMo-o?usp=sharing) + +- Here is now a demo on how to fine tune [whishper-large](openai/whisper-large-v2) (1.5B params) (14GB in fp16) in a Google colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DOkD_5OUjFa0r5Ik3SgywJLJtEo2qLxO?usp=sharing) and [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1vhF8yueFqha3Y3CpTHN6q9EVcII9EYzs?usp=sharing) + +### Save compute and storage even for medium and small models + +Save storage by avoiding full finetuning of models on each of the downstream tasks/datasets, +With PEFT methods, users only need to store tiny checkpoints in the order of `MBs` all the while retaining +performance comparable to full finetuning. + +An example of using LoRA for the task of adapting `LayoutLMForTokenClassification` on `FUNSD` dataset is given in `~examples/token_classification/PEFT_LoRA_LayoutLMForTokenClassification_on_FUNSD.py`. We can observe that with only `0.62 %` of parameters being trainable, we achieve performance (F1 0.777) comparable to full finetuning (F1 0.786) (without any hyerparam tuning runs for extracting more performance), and the checkpoint of this is only `2.8MB`. Now, if there are `N` such datasets, just have these PEFT models one for each dataset and save a lot of storage without having to worry about the problem of catastrophic forgetting or overfitting of backbone/base model. + +Another example is fine-tuning [`roberta-large`](https://huggingface.co/roberta-large) on [`MRPC` GLUE](https://huggingface.co/datasets/glue/viewer/mrpc) dataset suing differenct PEFT methods. The notebooks are given in `~examples/sequence_classification`. + + +## PEFT + 🤗 Accelerate + +PEFT models work with 🤗 Accelerate out of the box. Use 🤗 Accelerate for Distributed training on various hardware such as GPUs, Apple Silicon devices etc during training. +Use 🤗 Accelerate for inferencing on consumer hardware with small resources. + +### Example of PEFT model training using 🤗 Accelerate's DeepSpeed integration + +DeepSpeed version required `v0.8.0`. An example is provided in `~examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py`. + a. First, run `accelerate config --config_file ds_zero3_cpu.yaml` and answer the questionnaire. + Below are the contents of the config file. + ```yaml + compute_environment: LOCAL_MACHINE + deepspeed_config: + gradient_accumulation_steps: 1 + gradient_clipping: 1.0 + offload_optimizer_device: cpu + offload_param_device: cpu + zero3_init_flag: true + zero3_save_16bit_model: true + zero_stage: 3 + distributed_type: DEEPSPEED + downcast_bf16: 'no' + dynamo_backend: 'NO' + fsdp_config: {} + machine_rank: 0 + main_training_function: main + megatron_lm_config: {} + mixed_precision: 'no' + num_machines: 1 + num_processes: 1 + rdzv_backend: static + same_network: true + use_cpu: false + ``` + b. run the below command to launch the example script + ```bash + accelerate launch --config_file ds_zero3_cpu.yaml examples/peft_lora_seq2seq_accelerate_ds_zero3_offload.py + ``` + + c. output logs: + ```bash + GPU Memory before entering the train : 1916 + GPU Memory consumed at the end of the train (end-begin): 66 + GPU Peak Memory consumed during the train (max-begin): 7488 + GPU Total Peak Memory consumed during the train (max): 9404 + CPU Memory before entering the train : 19411 + CPU Memory consumed at the end of the train (end-begin): 0 + CPU Peak Memory consumed during the train (max-begin): 0 + CPU Total Peak Memory consumed during the train (max): 19411 + epoch=4: train_ppl=tensor(1.0705, device='cuda:0') train_epoch_loss=tensor(0.0681, device='cuda:0') + 100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:27<00:00, 3.92s/it] + GPU Memory before entering the eval : 1982 + GPU Memory consumed at the end of the eval (end-begin): -66 + GPU Peak Memory consumed during the eval (max-begin): 672 + GPU Total Peak Memory consumed during the eval (max): 2654 + CPU Memory before entering the eval : 19411 + CPU Memory consumed at the end of the eval (end-begin): 0 + CPU Peak Memory consumed during the eval (max-begin): 0 + CPU Total Peak Memory consumed during the eval (max): 19411 + accuracy=100.0 + eval_preds[:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint'] + dataset['train'][label_column][:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint'] + ``` + +### Example of PEFT model inference using 🤗 Accelerate's Big Model Inferencing capabilities +An example is provided in `~examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb`. + + +## Models support matrix + +### Causal Language Modeling +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +|--------------| ---- | ---- | ---- | ---- | +| GPT-2 | ✅ | ✅ | ✅ | ✅ | +| Bloom | ✅ | ✅ | ✅ | ✅ | +| OPT | ✅ | ✅ | ✅ | ✅ | +| GPT-Neo | ✅ | ✅ | ✅ | ✅ | +| GPT-J | ✅ | ✅ | ✅ | ✅ | +| GPT-NeoX-20B | ✅ | ✅ | ✅ | ✅ | +| LLaMA | ✅ | ✅ | ✅ | ✅ | +| ChatGLM | ✅ | ✅ | ✅ | ✅ | + +### Conditional Generation +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +| --------- | ---- | ---- | ---- | ---- | +| T5 | ✅ | ✅ | ✅ | ✅ | +| BART | ✅ | ✅ | ✅ | ✅ | + +### Sequence Classification +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +| --------- | ---- | ---- | ---- | ---- | +| BERT | ✅ | ✅ | ✅ | ✅ | +| RoBERTa | ✅ | ✅ | ✅ | ✅ | +| GPT-2 | ✅ | ✅ | ✅ | ✅ | +| Bloom | ✅ | ✅ | ✅ | ✅ | +| OPT | ✅ | ✅ | ✅ | ✅ | +| GPT-Neo | ✅ | ✅ | ✅ | ✅ | +| GPT-J | ✅ | ✅ | ✅ | ✅ | +| Deberta | ✅ | | ✅ | ✅ | +| Deberta-v2 | ✅ | | ✅ | ✅ | + +### Token Classification +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +| --------- | ---- | ---- | ---- | ---- | +| BERT | ✅ | ✅ | | | +| RoBERTa | ✅ | ✅ | | | +| GPT-2 | ✅ | ✅ | | | +| Bloom | ✅ | ✅ | | | +| OPT | ✅ | ✅ | | | +| GPT-Neo | ✅ | ✅ | | | +| GPT-J | ✅ | ✅ | | | +| Deberta | ✅ | | | | +| Deberta-v2 | ✅ | | | | + +### Text-to-Image Generation + +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +| --------- | ---- | ---- | ---- | ---- | +| Stable Diffusion | ✅ | | | | + + +### Image Classification + +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +| --------- | ---- | ---- | ---- | ---- | +| ViT | ✅ | | | | +| Swin | ✅ | | | | + +___Note that we have tested LoRA for [ViT](https://huggingface.co/docs/transformers/model_doc/vit) and [Swin](https://huggingface.co/docs/transformers/model_doc/swin) for fine-tuning on image classification. However, it should be possible to use LoRA for any compatible model [provided](https://huggingface.co/models?pipeline_tag=image-classification&sort=downloads&search=vit) by 🤗 Transformers. Check out the respective +examples to learn more. If you run into problems, please open an issue.___ + +The same principle applies to our [segmentation models](https://huggingface.co/models?pipeline_tag=image-segmentation&sort=downloads) as well. + +### Semantic Segmentation + +| Model | LoRA | Prefix Tuning | P-Tuning | Prompt Tuning | +| --------- | ---- | ---- | ---- | ---- | +| SegFormer | ✅ | | | | + + +## Caveats: + +1. Below is an example of using PyTorch FSDP for training. However, it doesn't lead to +any GPU memory savings. Please refer issue [[FSDP] FSDP with CPU offload consumes 1.65X more GPU memory when training models with most of the params frozen](https://github.com/pytorch/pytorch/issues/91165). + + ```python + from peft.utils.other import fsdp_auto_wrap_policy + + ... + + if os.environ.get("ACCELERATE_USE_FSDP", None) is not None: + accelerator.state.fsdp_plugin.auto_wrap_policy = fsdp_auto_wrap_policy(model) + + model = accelerator.prepare(model) + ``` + + Example of parameter efficient tuning with [`mt0-xxl`](https://huggingface.co/bigscience/mt0-xxl) base model using 🤗 Accelerate is provided in `~examples/conditional_generation/peft_lora_seq2seq_accelerate_fsdp.py`. + a. First, run `accelerate config --config_file fsdp_config.yaml` and answer the questionnaire. + Below are the contents of the config file. + ```yaml + command_file: null + commands: null + compute_environment: LOCAL_MACHINE + deepspeed_config: {} + distributed_type: FSDP + downcast_bf16: 'no' + dynamo_backend: 'NO' + fsdp_config: + fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP + fsdp_backward_prefetch_policy: BACKWARD_PRE + fsdp_offload_params: true + fsdp_sharding_strategy: 1 + fsdp_state_dict_type: FULL_STATE_DICT + fsdp_transformer_layer_cls_to_wrap: T5Block + gpu_ids: null + machine_rank: 0 + main_process_ip: null + main_process_port: null + main_training_function: main + megatron_lm_config: {} + mixed_precision: 'no' + num_machines: 1 + num_processes: 2 + rdzv_backend: static + same_network: true + tpu_name: null + tpu_zone: null + use_cpu: false + ``` + b. run the below command to launch the example script + ```bash + accelerate launch --config_file fsdp_config.yaml examples/peft_lora_seq2seq_accelerate_fsdp.py + ``` + +2. When using `P_TUNING` or `PROMPT_TUNING` with `SEQ_2_SEQ` task, remember to remove the `num_virtual_token` virtual prompt predictions from the left side of the model outputs during evaluations. + +3. For encoder-decoder models, `P_TUNING` or `PROMPT_TUNING` doesn't support `generate` functionality of transformers because `generate` strictly requires `decoder_input_ids` but +`P_TUNING`/`PROMPT_TUNING` appends soft prompt embeddings to `input_embeds` to create +new `input_embeds` to be given to the model. Therefore, `generate` doesn't support this yet. + +4. When using ZeRO3 with zero3_init_flag=True, if you find the gpu memory increase with training steps. we might need to set zero3_init_flag=false in accelerate config.yaml. The related issue is [[BUG] memory leak under zero.Init](https://github.com/microsoft/DeepSpeed/issues/2637) +## Backlog: +1. Explore and possibly integrate `(IA)^3` +2. Add tests +3. Add more use cases and examples + +## Citing 🤗 PEFT + +If you use 🤗 PEFT in your publication, please cite it by using the following BibTeX entry. + +```bibtex +@Misc{peft, + title = {PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods}, + author = {Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, Sayak Paul}, + howpublished = {\url{https://github.com/huggingface/peft}}, + year = {2022} +} +``` diff --git a/04-gene-sft/peft/src/peft.egg-info/SOURCES.txt b/04-gene-sft/peft/src/peft.egg-info/SOURCES.txt new file mode 100644 index 0000000000000000000000000000000000000000..9d27cf6e4d92892ebde18610ebc637b94597c931 --- /dev/null +++ b/04-gene-sft/peft/src/peft.egg-info/SOURCES.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:157b9bdbc22f6f301571e78ca2531dcfcd2ebc3e7e7ca584e7aebbe25b41f8e0 +size 597 diff --git a/04-gene-sft/peft/src/peft.egg-info/dependency_links.txt b/04-gene-sft/peft/src/peft.egg-info/dependency_links.txt new file mode 100644 index 0000000000000000000000000000000000000000..fd481aab725cd00018d4e32426f317aa39787c65 --- /dev/null +++ b/04-gene-sft/peft/src/peft.egg-info/dependency_links.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b +size 1 diff --git a/04-gene-sft/peft/src/peft.egg-info/requires.txt b/04-gene-sft/peft/src/peft.egg-info/requires.txt new file mode 100644 index 0000000000000000000000000000000000000000..e2af926bfbb75d12936ef318e8821d9803a9c94b --- /dev/null +++ b/04-gene-sft/peft/src/peft.egg-info/requires.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8924adb91ec34e433fef156078fb6c3054736544d53bc0839078408e460f3bd6 +size 197 diff --git a/04-gene-sft/peft/src/peft.egg-info/top_level.txt b/04-gene-sft/peft/src/peft.egg-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..1f9e4bcb2fc3e39a672a2c40990042da3de1e5d2 --- /dev/null +++ b/04-gene-sft/peft/src/peft.egg-info/top_level.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ce2a8a877ba7ebf80de0dba3cf5af7f96c72f2f1f1ca85f9543b9c732491146 +size 5 diff --git a/04-gene-sft/peft/src/peft/__init__.py b/04-gene-sft/peft/src/peft/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e141347f1e5c8fb40dcc3a4a52fcb1a041b18852 --- /dev/null +++ b/04-gene-sft/peft/src/peft/__init__.py @@ -0,0 +1,53 @@ +# flake8: noqa +# There's no way to ignore "F401 '...' imported but unused" warnings in this +# module, but to preserve other warnings. So, don't check this module at all. + +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +__version__ = "0.3.0.dev0" + +from .mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPING, PEFT_TYPE_TO_CONFIG_MAPPING, get_peft_config, get_peft_model +from .peft_model import ( + PeftModel, + PeftModelForCausalLM, + PeftModelForSeq2SeqLM, + PeftModelForSequenceClassification, + PeftModelForTokenClassification, +) +from .tuners import ( + LoraConfig, + LoraModel, + PrefixEncoder, + PrefixTuningConfig, + PromptEmbedding, + PromptEncoder, + PromptEncoderConfig, + PromptEncoderReparameterizationType, + PromptTuningConfig, + PromptTuningInit, +) +from .utils import ( + TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING, + PeftConfig, + PeftType, + PromptLearningConfig, + TaskType, + bloom_model_postprocess_past_key_value, + get_peft_model_state_dict, + prepare_model_for_int8_training, + set_peft_model_state_dict, + shift_tokens_right, +) diff --git a/04-gene-sft/peft/src/peft/mapping.py b/04-gene-sft/peft/src/peft/mapping.py new file mode 100644 index 0000000000000000000000000000000000000000..dbb9f3609fdf5d171b0a4bb21716f692feefc9d4 --- /dev/null +++ b/04-gene-sft/peft/src/peft/mapping.py @@ -0,0 +1,147 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .peft_model import ( + PeftModel, + PeftModelForCausalLM, + PeftModelForSeq2SeqLM, + PeftModelForSequenceClassification, + PeftModelForTokenClassification, +) +from .tuners import LoraConfig, PrefixTuningConfig, PromptEncoderConfig, PromptTuningConfig +from .utils import PromptLearningConfig + + +MODEL_TYPE_TO_PEFT_MODEL_MAPPING = { + "SEQ_CLS": PeftModelForSequenceClassification, + "SEQ_2_SEQ_LM": PeftModelForSeq2SeqLM, + "CAUSAL_LM": PeftModelForCausalLM, + "TOKEN_CLS": PeftModelForTokenClassification, +} + +PEFT_TYPE_TO_CONFIG_MAPPING = { + "PROMPT_TUNING": PromptTuningConfig, + "PREFIX_TUNING": PrefixTuningConfig, + "P_TUNING": PromptEncoderConfig, + "LORA": LoraConfig, +} + +TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING = { + "t5": ["q", "v"], + "mt5": ["q", "v"], + "bart": ["q_proj", "v_proj"], + "gpt2": ["c_attn"], + "bloom": ["query_key_value"], + "opt": ["q_proj", "v_proj"], + "gptj": ["q_proj", "v_proj"], + "gpt_neox": ["query_key_value"], + "gpt_neo": ["q_proj", "v_proj"], + "bert": ["query", "value"], + "roberta": ["query", "value"], + "xlm-roberta": ["query", "value"], + "electra": ["query", "value"], + "deberta-v2": ["query_proj", "value_proj"], + "deberta": ["in_proj"], + "layoutlm": ["query", "value"], + "llama": ["q_proj", "v_proj"], + "chatglm": ["query_key_value"], +} + + +def get_peft_config(config_dict): + """ + Returns a Peft config object from a dictionary. + + Args: + config_dict (`Dict[str, Any]`): Dictionary containing the configuration parameters. + """ + + return PEFT_TYPE_TO_CONFIG_MAPPING[config_dict["peft_type"]](**config_dict) + + +def _prepare_prompt_learning_config(peft_config, model_config): + if peft_config.num_layers is None: + if "num_hidden_layers" in model_config: + num_layers = model_config["num_hidden_layers"] + elif "num_layers" in model_config: + num_layers = model_config["num_layers"] + elif "n_layer" in model_config: + num_layers = model_config["n_layer"] + else: + raise ValueError("Please specify `num_layers` in `peft_config`") + peft_config.num_layers = num_layers + + if peft_config.token_dim is None: + if "hidden_size" in model_config: + token_dim = model_config["hidden_size"] + elif "n_embd" in model_config: + token_dim = model_config["n_embd"] + elif "d_model" in model_config: + token_dim = model_config["d_model"] + else: + raise ValueError("Please specify `token_dim` in `peft_config`") + peft_config.token_dim = token_dim + + if peft_config.num_attention_heads is None: + if "num_attention_heads" in model_config: + num_attention_heads = model_config["num_attention_heads"] + elif "n_head" in model_config: + num_attention_heads = model_config["n_head"] + elif "num_heads" in model_config: + num_attention_heads = model_config["num_heads"] + elif "encoder_attention_heads" in model_config: + num_attention_heads = model_config["encoder_attention_heads"] + else: + raise ValueError("Please specify `num_attention_heads` in `peft_config`") + peft_config.num_attention_heads = num_attention_heads + + if getattr(peft_config, "encoder_hidden_size", None) is None: + setattr(peft_config, "encoder_hidden_size", token_dim) + + return peft_config + + +def _prepare_lora_config(peft_config, model_config): + if peft_config.target_modules is None: + if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING: + raise ValueError("Please specify `target_modules` in `peft_config`") + peft_config.target_modules = TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING[model_config["model_type"]] + if len(peft_config.target_modules) == 1: + peft_config.fan_in_fan_out = True + peft_config.enable_lora = [True, False, True] + if peft_config.inference_mode: + peft_config.merge_weights = True + return peft_config + + +def get_peft_model(model, peft_config): + """ + Returns a Peft model object from a model and a config. + + Args: + model ([`transformers.PreTrainedModel`]): Model to be wrapped. + peft_config ([`PeftConfig`]): Configuration object containing the parameters of the Peft model. + """ + + model_config = model.config.to_dict() + peft_config.base_model_name_or_path = model.__dict__.get("name_or_path", None) + if peft_config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys(): + peft_config = _prepare_lora_config(peft_config, model_config) + return PeftModel(model, peft_config) + if not isinstance(peft_config, PromptLearningConfig): + peft_config = _prepare_lora_config(peft_config, model_config) + else: + peft_config = _prepare_prompt_learning_config(peft_config, model_config) + return MODEL_TYPE_TO_PEFT_MODEL_MAPPING[peft_config.task_type](model, peft_config) diff --git a/04-gene-sft/peft/src/peft/peft_model.py b/04-gene-sft/peft/src/peft/peft_model.py new file mode 100644 index 0000000000000000000000000000000000000000..f73a66ac0890b78437d66e9c46372887b8e9279e --- /dev/null +++ b/04-gene-sft/peft/src/peft/peft_model.py @@ -0,0 +1,947 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import inspect +import os +import warnings +from contextlib import contextmanager + +import torch +from accelerate import dispatch_model, infer_auto_device_map +from accelerate.hooks import AlignDevicesHook, add_hook_to_module, remove_hook_from_submodules +from accelerate.utils import get_balanced_memory +from huggingface_hub import hf_hub_download +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from transformers import PreTrainedModel +from transformers.modeling_outputs import SequenceClassifierOutput, TokenClassifierOutput +from transformers.utils import PushToHubMixin + +from .tuners import LoraModel, PrefixEncoder, PromptEmbedding, PromptEncoder +from .utils import ( + TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING, + WEIGHTS_NAME, + PeftConfig, + PeftType, + PromptLearningConfig, + TaskType, + _set_trainable, + get_peft_model_state_dict, + set_peft_model_state_dict, + shift_tokens_right, +) + + +class PeftModel(PushToHubMixin, torch.nn.Module): + """ + Parameter-Efficient Fine-Tuning Model. Base model encompassing various Peft methods. + + Args: + model ([`PreTrainedModel`]): The base transformer model used for Peft. + peft_config ([`PeftConfig`]): The configuration of the Peft model. + + + **Attributes**: + - **base_model** ([`PreTrainedModel`]) -- The base transformer model used for Peft. + - **peft_config** ([`PeftConfig`]) -- The configuration of the Peft model. + - **modules_to_save** (`list` of `str`) -- The list of sub-module names to save when + saving the model. + - **prompt_encoder** ([`PromptEncoder`]) -- The prompt encoder used for Peft if + `isinstance(self.peft_config, PromptLearningConfig)`. + - **prompt_tokens** (`torch.Tensor`) -- The virtual prompt tokens used for Peft if + `isinstance(self.peft_config, PromptLearningConfig)`. + - **transformer_backbone_name** (`str`) -- The name of the transformer + backbone in the base model if `isinstance(self.peft_config, PromptLearningConfig)`. + - **word_embeddings** (`torch.nn.Embedding`) -- The word embeddings of the transformer backbone + in the base model if `isinstance(self.peft_config, PromptLearningConfig)`. + """ + + def __init__(self, model, peft_config: PeftConfig): + super().__init__() + self.peft_config = peft_config + self.base_model = model + self.config = self.base_model.config + self.modules_to_save = None + if isinstance(self.peft_config, PromptLearningConfig): + self._setup_prompt_encoder() + else: + self.base_model = LoraModel(peft_config, model) + if getattr(self.peft_config, "modules_to_save", None) is not None: + self.modules_to_save = self.peft_config.modules_to_save + _set_trainable(self) + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + def save_pretrained(self, save_directory, **kwargs): + r""" + Args: + This function saves the adapter model and the adapter configuration files to a directory, so that it can be + re-loaded using the `LoraModel.from_pretrained` class method, and also used by the `LoraModel.push_to_hub` + method. + save_directory (`str`): + Directory where the adapter model and configuration files will be saved (will be created if it does not + exist). + **kwargs: + Additional keyword arguments passed along to the `push_to_hub` method. + """ + if os.path.isfile(save_directory): + raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file") + os.makedirs(save_directory, exist_ok=True) + + # save only the trainable weights + output_state_dict = get_peft_model_state_dict(self, kwargs.get("state_dict", None)) + torch.save(output_state_dict, os.path.join(save_directory, WEIGHTS_NAME)) + + # save the config and change the inference mode to `True` + if self.peft_config.base_model_name_or_path is None: + self.peft_config.base_model_name_or_path = ( + self.base_model.__dict__.get("name_or_path", None) + if isinstance(self.peft_config, PromptLearningConfig) + else self.base_model.model.__dict__.get("name_or_path", None) + ) + inference_mode = self.peft_config.inference_mode + self.peft_config.inference_mode = True + self.peft_config.save_pretrained(save_directory) + self.peft_config.inference_mode = inference_mode + + @classmethod + def from_pretrained(cls, model, model_id, **kwargs): + r""" + Args: + Instantiate a `LoraModel` from a pretrained Lora configuration and weights. + model (`transformers.PreTrainedModel`): + The model to be adapted. The model should be initialized with the `from_pretrained` method. from + `transformers` library. + model_id (`str`): + The name of the Lora configuration to use. Can be either: + - A string, the `model id` of a Lora configuration hosted inside a model repo on + huggingface Hub + - A path to a directory containing a Lora configuration file saved using the + `save_pretrained` method, e.g., ``./my_lora_config_directory/``. + """ + from .mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPING, PEFT_TYPE_TO_CONFIG_MAPPING + + # load the config + config = PEFT_TYPE_TO_CONFIG_MAPPING[PeftConfig.from_pretrained(model_id).peft_type].from_pretrained(model_id) + + if getattr(model, "hf_device_map", None) is not None: + remove_hook_from_submodules(model) + + if config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys(): + model = cls(model, config) + else: + model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](model, config) + + # load weights if any + if os.path.exists(os.path.join(model_id, WEIGHTS_NAME)): + filename = os.path.join(model_id, WEIGHTS_NAME) + else: + try: + filename = hf_hub_download(model_id, WEIGHTS_NAME) + except: # noqa + raise ValueError( + f"Can't find weights for {model_id} in {model_id} or in the Hugging Face Hub. " + f"Please check that the file {WEIGHTS_NAME} is present at {model_id}." + ) + + adapters_weights = torch.load( + filename, map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu") + ) + # load the weights into the model + model = set_peft_model_state_dict(model, adapters_weights) + if getattr(model, "hf_device_map", None) is not None: + device_map = kwargs.get("device_map", "auto") + max_memory = kwargs.get("max_memory", None) + no_split_module_classes = model._no_split_modules + if device_map != "sequential": + max_memory = get_balanced_memory( + model, + max_memory=max_memory, + no_split_module_classes=no_split_module_classes, + low_zero=(device_map == "balanced_low_0"), + ) + if isinstance(device_map, str): + device_map = infer_auto_device_map( + model, max_memory=max_memory, no_split_module_classes=no_split_module_classes + ) + model = dispatch_model(model, device_map=device_map) + hook = AlignDevicesHook(io_same_device=True) + if model.peft_config.peft_type == PeftType.LORA: + add_hook_to_module(model.base_model.model, hook) + else: + remove_hook_from_submodules(model.prompt_encoder) + add_hook_to_module(model.base_model, hook) + return model + + def _setup_prompt_encoder(self): + transformer_backbone = None + for name, module in self.base_model.named_children(): + for param in module.parameters(): + param.requires_grad = False + if isinstance(module, PreTrainedModel): + # Make sure to freeze Tranformers model + if transformer_backbone is None: + transformer_backbone = module + self.transformer_backbone_name = name + + if self.peft_config.num_transformer_submodules is None: + self.peft_config.num_transformer_submodules = ( + 2 if self.peft_config.task_type == TaskType.SEQ_2_SEQ_LM else 1 + ) + + for named_param, value in list(transformer_backbone.named_parameters()): + if value.shape[0] == self.base_model.config.vocab_size: + self.word_embeddings = transformer_backbone.get_submodule(named_param.replace(".weight", "")) + break + + if self.peft_config.peft_type == PeftType.PROMPT_TUNING: + prompt_encoder = PromptEmbedding(self.peft_config, self.word_embeddings) + elif self.peft_config.peft_type == PeftType.P_TUNING: + prompt_encoder = PromptEncoder(self.peft_config) + elif self.peft_config.peft_type == PeftType.PREFIX_TUNING: + prompt_encoder = PrefixEncoder(self.peft_config) + else: + raise ValueError("Not supported") + self.prompt_encoder = prompt_encoder + self.prompt_tokens = torch.arange( + self.peft_config.num_virtual_tokens * self.peft_config.num_transformer_submodules + ).long() + + def get_prompt_embedding_to_save(self): + """ + Returns the prompt embedding to save when saving the model. Only applicable when `peft_config.peft_type != + PeftType.LORA`. + """ + prompt_tokens = self.prompt_tokens.unsqueeze(0).expand(1, -1).to(self.device) + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + prompt_tokens = prompt_tokens[:, : self.peft_config.num_virtual_tokens] + prompt_embeddings = self.prompt_encoder(prompt_tokens) + return prompt_embeddings[0].detach().cpu() + + def get_prompt(self, batch_size): + """ + Returns the virtual prompts to use for Peft. Only applicable when `peft_config.peft_type != PeftType.LORA`. + """ + prompt_tokens = self.prompt_tokens.unsqueeze(0).expand(batch_size, -1).to(self.device) + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + prompt_tokens = prompt_tokens[:, : self.peft_config.num_virtual_tokens] + if self.peft_config.inference_mode: + past_key_values = self.prompt_encoder.embedding.weight.repeat(batch_size, 1, 1) + else: + past_key_values = self.prompt_encoder(prompt_tokens) + past_key_values = past_key_values.view( + batch_size, + self.peft_config.num_virtual_tokens, + self.peft_config.num_layers * 2, + self.peft_config.num_attention_heads, + self.peft_config.token_dim // self.peft_config.num_attention_heads, + ) + if self.peft_config.num_transformer_submodules == 2: + past_key_values = torch.cat([past_key_values, past_key_values], dim=2) + past_key_values = past_key_values.permute([2, 0, 3, 1, 4]).split( + self.peft_config.num_transformer_submodules * 2 + ) + if TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING.get(self.config.model_type, None) is not None: + post_process_fn = TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING[self.config.model_type] + past_key_values = post_process_fn(past_key_values) + return past_key_values + else: + if self.peft_config.inference_mode: + prompts = self.prompt_encoder.embedding.weight.repeat(batch_size, 1, 1) + else: + prompts = self.prompt_encoder(prompt_tokens) + return prompts + + def print_trainable_parameters(self): + """ + Prints the number of trainable parameters in the model. + """ + trainable_params = 0 + all_param = 0 + for _, param in self.named_parameters(): + num_params = param.numel() + # if using DS Zero 3 and the weights are initialized empty + if num_params == 0 and hasattr(param, "ds_numel"): + num_params = param.ds_numel + + all_param += num_params + if param.requires_grad: + trainable_params += num_params + print( + f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}" + ) + + def __getattr__(self, name: str): + """Forward missing attributes to the wrapped module.""" + try: + return super().__getattr__(name) # defer to nn.Module's logic + except AttributeError: + return getattr(self.base_model, name) + + def forward(self, *args, **kwargs): + """ + Forward pass of the model. + """ + return self.get_base_model()(*args, **kwargs) + + @contextmanager + def disable_adapter(self): + """ + Disables the adapter module. + """ + if isinstance(self.peft_config, PromptLearningConfig): + old_forward = self.forward + self.forward = self.base_model.forward + else: + self.base_model.disable_adapter_layers() + yield + if isinstance(self.peft_config, PromptLearningConfig): + self.forward = old_forward + else: + self.base_model.enable_adapter_layers() + + def get_base_model(self): + """ + Returns the base model. + """ + return self.base_model if isinstance(self.peft_config, PromptLearningConfig) else self.base_model.model + + +class PeftModelForSequenceClassification(PeftModel): + """ + Peft model for sequence classification tasks. + + Args: + model ([`PreTrainedModel`]): Base transformer model + peft_config ([`PeftConfig`]): Peft config. + + **Attributes**: + - **config** ([`PretrainedConfig`]) -- The configuration object of the base model. + - **cls_layer_name** (`str`) -- The name of the classification layer. + + Example:: + + >>> from transformers import AutoModelForSequenceClassification >>> from peft import + PeftModelForSequenceClassification, get_peft_config >>> config = { + 'peft_type': 'PREFIX_TUNING', 'task_type': 'SEQ_CLS', 'inference_mode': False, 'num_virtual_tokens': + 20, 'token_dim': 768, 'num_transformer_submodules': 1, 'num_attention_heads': 12, 'num_layers': 12, + 'encoder_hidden_size': 768, 'prefix_projection': False, 'postprocess_past_key_value_function': None + } + >>> peft_config = get_peft_config(config) >>> model = + AutoModelForSequenceClassification.from_pretrained("bert-base-cased") >>> peft_model = + PeftModelForSequenceClassification(model, peft_config) >>> peft_model.print_trainable_parameters() trainable + params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117 + """ + + def __init__(self, model, peft_config: PeftConfig): + super().__init__(model, peft_config) + self.modules_to_save = ["classifier", "score"] + + for name, _ in self.base_model.named_children(): + if any(module_name in name for module_name in self.modules_to_save): + self.cls_layer_name = name + break + + # to make sure classifier layer is trainable + _set_trainable(self) + + def forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if not isinstance(self.peft_config, PromptLearningConfig): + return self.base_model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + labels=labels, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + batch_size = input_ids.shape[0] + if attention_mask is not None: + # concat prompt attention mask + prefix_attention_mask = torch.ones(batch_size, self.peft_config.num_virtual_tokens).to(self.device) + attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1) + if kwargs.get("position_ids", None) is not None: + warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.") + kwargs["position_ids"] = None + kwargs.update( + { + "attention_mask": attention_mask, + "labels": labels, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "return_dict": return_dict, + } + ) + + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + return self._prefix_tuning_forward(input_ids=input_ids, **kwargs) + else: + if kwargs.get("token_type_ids", None) is not None: + kwargs["token_type_ids"] = torch.cat( + ( + torch.zeros(batch_size, self.peft_config.num_virtual_tokens).to(self.device), + kwargs["token_type_ids"], + ), + dim=1, + ).long() + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + prompts = self.get_prompt(batch_size=batch_size) + prompts = prompts.to(inputs_embeds.dtype) + inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1) + return self.base_model(inputs_embeds=inputs_embeds, **kwargs) + + def _prefix_tuning_forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + batch_size = input_ids.shape[0] + past_key_values = self.get_prompt(batch_size) + fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys()) + kwargs.update( + { + "input_ids": input_ids, + "attention_mask": attention_mask, + "inputs_embeds": inputs_embeds, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "return_dict": return_dict, + "past_key_values": past_key_values, + } + ) + if "past_key_values" in fwd_params: + return self.base_model(labels=labels, **kwargs) + else: + transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name) + fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys()) + if "past_key_values" not in fwd_params: + raise ValueError("Model does not support past key values which are required for prefix tuning.") + outputs = transformer_backbone_name(**kwargs) + pooled_output = outputs[1] if len(outputs) > 1 else outputs[0] + if "dropout" in [name for name, _ in list(self.base_model.named_children())]: + pooled_output = self.base_model.dropout(pooled_output) + logits = self.base_model.get_submodule(self.cls_layer_name)(pooled_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.base_model.num_labels == 1: + self.config.problem_type = "regression" + elif self.base_model.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.base_model.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.base_model.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class PeftModelForCausalLM(PeftModel): + """ + Peft model for Causal LM + + Args: + model ([`PreTrainedModel`]): Base transformer model + peft_config ([`PeftConfig`]): Peft config. + + + Example:: + + >>> from transformers import AutoModelForCausalLM >>> from peft import PeftModelForCausalLM, get_peft_config + >>> config = { + 'peft_type': 'PREFIX_TUNING', 'task_type': 'CAUSAL_LM', 'inference_mode': False, 'num_virtual_tokens': + 20, 'token_dim': 1280, 'num_transformer_submodules': 1, 'num_attention_heads': 20, 'num_layers': 36, + 'encoder_hidden_size': 1280, 'prefix_projection': False, 'postprocess_past_key_value_function': None + } + >>> peft_config = get_peft_config(config) >>> model = AutoModelForCausalLM.from_pretrained("gpt2-large") >>> + peft_model = PeftModelForCausalLM(model, peft_config) >>> peft_model.print_trainable_parameters() trainable + params: 1843200 || all params: 775873280 || trainable%: 0.23756456724479544 + """ + + def __init__(self, model, peft_config: PeftConfig): + super().__init__(model, peft_config) + self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation + + def forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + if not isinstance(self.peft_config, PromptLearningConfig): + return self.base_model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + labels=labels, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + batch_size = input_ids.shape[0] + if attention_mask is not None: + # concat prompt attention mask + prefix_attention_mask = torch.ones(batch_size, self.peft_config.num_virtual_tokens).to(self.device) + attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1) + + if kwargs.get("position_ids", None) is not None: + warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.") + kwargs["position_ids"] = None + if kwargs.get("token_type_ids", None) is not None: + warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids") + kwargs["token_type_ids"] = None + kwargs.update( + { + "attention_mask": attention_mask, + "labels": labels, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "return_dict": return_dict, + } + ) + + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + past_key_values = self.get_prompt(batch_size) + return self.base_model(input_ids=input_ids, past_key_values=past_key_values, **kwargs) + else: + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + # concat prompt labels + if labels is not None: + prefix_labels = torch.full((batch_size, self.peft_config.num_virtual_tokens), -100).to(self.device) + kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1) + prompts = self.get_prompt(batch_size=batch_size) + prompts = prompts.to(inputs_embeds.dtype) + inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1) + return self.base_model(inputs_embeds=inputs_embeds, **kwargs) + + def generate(self, **kwargs): + self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation + try: + if not isinstance(self.peft_config, PromptLearningConfig): + outputs = self.base_model.generate(**kwargs) + else: + if "input_ids" not in kwargs: + raise ValueError("input_ids must be provided for Peft model generation") + if kwargs.get("attention_mask", None) is not None: + # concat prompt attention mask + prefix_attention_mask = torch.ones( + kwargs["input_ids"].shape[0], self.peft_config.num_virtual_tokens + ).to(kwargs["input_ids"].device) + kwargs["attention_mask"] = torch.cat((prefix_attention_mask, kwargs["attention_mask"]), dim=1) + + if kwargs.get("position_ids", None) is not None: + warnings.warn( + "Position ids are not supported for parameter efficient tuning. Ignoring position ids." + ) + kwargs["position_ids"] = None + if kwargs.get("token_type_ids", None) is not None: + warnings.warn( + "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids" + ) + kwargs["token_type_ids"] = None + + outputs = self.base_model.generate(**kwargs) + except: + self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation + raise + else: + self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation + return outputs + + def prepare_inputs_for_generation(self, *args, **kwargs): + model_kwargs = self.base_model_prepare_inputs_for_generation(*args, **kwargs) + if isinstance(self.peft_config, PromptLearningConfig): + if model_kwargs["past_key_values"] is None and self.peft_config.peft_type == PeftType.PREFIX_TUNING: + past_key_values = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0]) + model_kwargs["past_key_values"] = past_key_values + else: + if model_kwargs["past_key_values"] is None: + inputs_embeds = self.word_embeddings(model_kwargs["input_ids"]) + prompts = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0]) + prompts = prompts.to(inputs_embeds.dtype) + model_kwargs["inputs_embeds"] = torch.cat((prompts, inputs_embeds), dim=1) + model_kwargs["input_ids"] = None + + return model_kwargs + + +class PeftModelForSeq2SeqLM(PeftModel): + """ + Peft model for Seq2Seq LM + + Args: + model ([`PreTrainedModel`]): Base transformer model + peft_config ([`PeftConfig`]): Peft config. + + + Example:: + + >>> from transformers import AutoModelForSeq2SeqLM >>> from peft import PeftModelForSeq2SeqLM, get_peft_config + >>> config = { + 'peft_type': 'LORA', 'task_type': 'SEQ_2_SEQ_LM', 'inference_mode': False, 'r': 8, 'target_modules': + ['q', 'v'], 'lora_alpha': 32, 'lora_dropout': 0.1, 'merge_weights': False, 'fan_in_fan_out': False, + 'enable_lora': None, 'bias': 'none' + } + >>> peft_config = get_peft_config(config) >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> + peft_model = PeftModelForSeq2SeqLM(model, peft_config) >>> peft_model.print_trainable_parameters() trainable + params: 884736 || all params: 223843584 || trainable%: 0.3952474242013566 + """ + + def __init__(self, model, peft_config: PeftConfig): + super().__init__(model, peft_config) + self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation + self.base_model_prepare_encoder_decoder_kwargs_for_generation = ( + self.base_model._prepare_encoder_decoder_kwargs_for_generation + ) + + def forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + decoder_input_ids=None, + decoder_attention_mask=None, + decoder_inputs_embeds=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + if not isinstance(self.peft_config, PromptLearningConfig): + return self.base_model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + decoder_inputs_embeds=decoder_inputs_embeds, + labels=labels, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + batch_size = input_ids.shape[0] + if decoder_attention_mask is not None: + # concat prompt attention mask + prefix_attention_mask = torch.ones(batch_size, self.peft_config.num_virtual_tokens).to(self.device) + decoder_attention_mask = torch.cat((prefix_attention_mask, decoder_attention_mask), dim=1) + + if kwargs.get("position_ids", None) is not None: + warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.") + kwargs["position_ids"] = None + if kwargs.get("token_type_ids", None) is not None: + warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids") + kwargs["token_type_ids"] = None + kwargs.update( + { + "attention_mask": attention_mask, + "decoder_attention_mask": decoder_attention_mask, + "labels": labels, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "return_dict": return_dict, + } + ) + + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + past_key_values = self.get_prompt(batch_size) + return self.base_model( + input_ids=input_ids, decoder_input_ids=decoder_input_ids, past_key_values=past_key_values, **kwargs + ) + else: + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + if decoder_inputs_embeds is None and decoder_input_ids is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + decoder_inputs_embeds = self.word_embeddings(decoder_input_ids) + + if attention_mask is not None: + # concat prompt attention mask + prefix_attention_mask = torch.ones(batch_size, self.peft_config.num_virtual_tokens).to(self.device) + kwargs["attention_mask"] = torch.cat((prefix_attention_mask, attention_mask), dim=1) + # concat prompt labels + if labels is not None: + if self.peft_config.num_transformer_submodules == 1: + kwargs["labels"] = labels + elif self.peft_config.num_transformer_submodules == 2: + prefix_labels = torch.full((batch_size, self.peft_config.num_virtual_tokens), -100).to(self.device) + kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1) + prompts = self.get_prompt(batch_size=batch_size) + prompts = prompts.to(inputs_embeds.dtype) + inputs_embeds = torch.cat((prompts[:, : self.peft_config.num_virtual_tokens], inputs_embeds), dim=1) + if self.peft_config.num_transformer_submodules == 1: + return self.base_model(inputs_embeds=inputs_embeds, **kwargs) + elif self.peft_config.num_transformer_submodules == 2: + decoder_inputs_embeds = torch.cat( + (prompts[:, self.peft_config.num_virtual_tokens :], decoder_inputs_embeds), dim=1 + ) + return self.base_model( + inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, **kwargs + ) + + def generate(self, **kwargs): + self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation + self.base_model._prepare_encoder_decoder_kwargs_for_generation = ( + self._prepare_encoder_decoder_kwargs_for_generation + ) + try: + if not isinstance(self.peft_config, PromptLearningConfig): + outputs = self.base_model.generate(**kwargs) + else: + if "input_ids" not in kwargs: + raise ValueError("input_ids must be provided for Peft model generation") + if kwargs.get("position_ids", None) is not None: + warnings.warn( + "Position ids are not supported for parameter efficient tuning. Ignoring position ids." + ) + kwargs["position_ids"] = None + if kwargs.get("token_type_ids", None) is not None: + warnings.warn( + "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids" + ) + kwargs["token_type_ids"] = None + + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + outputs = self.base_model.generate(**kwargs) + else: + raise NotImplementedError + except: + self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation + self.base_model._prepare_encoder_decoder_kwargs_for_generation = ( + self.base_model_prepare_encoder_decoder_kwargs_for_generation + ) + raise + else: + self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation + self.base_model._prepare_encoder_decoder_kwargs_for_generation = ( + self.base_model_prepare_encoder_decoder_kwargs_for_generation + ) + return outputs + + def prepare_inputs_for_generation(self, *args, **kwargs): + model_kwargs = self.base_model_prepare_inputs_for_generation(*args, **kwargs) + if model_kwargs["past_key_values"] is None and self.peft_config.peft_type == PeftType.PREFIX_TUNING: + batch_size = model_kwargs["decoder_input_ids"].shape[0] + past_key_values = self.get_prompt(batch_size) + model_kwargs["past_key_values"] = past_key_values + return model_kwargs + + +class PeftModelForTokenClassification(PeftModel): + """ + Peft model for sequence classification tasks. + + Args: + model ([`PreTrainedModel`]): Base transformer model + peft_config ([`PeftConfig`]): Peft config. + + **Attributes**: + - **config** ([`PretrainedConfig`]) -- The configuration object of the base model. + - **cls_layer_name** (`str`) -- The name of the classification layer. + + Example:: + + >>> from transformers import AutoModelForSequenceClassification >>> from peft import + PeftModelForTokenClassification, get_peft_config >>> config = { + 'peft_type': 'PREFIX_TUNING', 'task_type': 'TOKEN_CLS', 'inference_mode': False, 'num_virtual_tokens': + 20, 'token_dim': 768, 'num_transformer_submodules': 1, 'num_attention_heads': 12, 'num_layers': 12, + 'encoder_hidden_size': 768, 'prefix_projection': False, 'postprocess_past_key_value_function': None + } + >>> peft_config = get_peft_config(config) >>> model = + AutoModelForTokenClassification.from_pretrained("bert-base-cased") >>> peft_model = + PeftModelForTokenClassification(model, peft_config) >>> peft_model.print_trainable_parameters() trainable + params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117 + """ + + def __init__(self, model, peft_config: PeftConfig): + super().__init__(model, peft_config) + self.modules_to_save = ["classifier", "score"] + + for name, _ in self.base_model.named_children(): + if any(module_name in name for module_name in self.modules_to_save): + self.cls_layer_name = name + break + + # to make sure classifier layer is trainable + _set_trainable(self) + + def forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if not isinstance(self.peft_config, PromptLearningConfig): + return self.base_model( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + labels=labels, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + batch_size = input_ids.shape[0] + if attention_mask is not None: + # concat prompt attention mask + prefix_attention_mask = torch.ones(batch_size, self.peft_config.num_virtual_tokens).to(self.device) + attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1) + if kwargs.get("position_ids", None) is not None: + warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.") + kwargs["position_ids"] = None + kwargs.update( + { + "attention_mask": attention_mask, + "labels": labels, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "return_dict": return_dict, + } + ) + + if self.peft_config.peft_type == PeftType.PREFIX_TUNING: + return self._prefix_tuning_forward(input_ids=input_ids, **kwargs) + else: + if kwargs.get("token_type_ids", None) is not None: + kwargs["token_type_ids"] = torch.cat( + ( + torch.zeros(batch_size, self.peft_config.num_virtual_tokens).to(self.device), + kwargs["token_type_ids"], + ), + dim=1, + ).long() + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + prompts = self.get_prompt(batch_size=batch_size) + prompts = prompts.to(inputs_embeds.dtype) + inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1) + return self.base_model(inputs_embeds=inputs_embeds, **kwargs) + + def _prefix_tuning_forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + labels=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + batch_size = input_ids.shape[0] + past_key_values = self.get_prompt(batch_size) + fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys()) + kwargs.update( + { + "input_ids": input_ids, + "attention_mask": attention_mask, + "inputs_embeds": inputs_embeds, + "output_attentions": output_attentions, + "output_hidden_states": output_hidden_states, + "return_dict": return_dict, + "past_key_values": past_key_values, + } + ) + if "past_key_values" in fwd_params: + return self.base_model(labels=labels, **kwargs) + else: + transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name) + fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys()) + if "past_key_values" not in fwd_params: + raise ValueError("Model does not support past key values which are required for prefix tuning.") + outputs = transformer_backbone_name(**kwargs) + sequence_output = outputs[0] + if "dropout" in [name for name, _ in list(self.base_model.named_children())]: + sequence_output = self.base_model.dropout(sequence_output) + logits = self.base_model.get_submodule(self.cls_layer_name)(sequence_output) + + loss = None + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/04-gene-sft/peft/src/peft/tuners/__init__.py b/04-gene-sft/peft/src/peft/tuners/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..38b7926b8cbda36799de097d012deabb8e8632fd --- /dev/null +++ b/04-gene-sft/peft/src/peft/tuners/__init__.py @@ -0,0 +1,23 @@ +# flake8: noqa +# There's no way to ignore "F401 '...' imported but unused" warnings in this +# module, but to preserve other warnings. So, don't check this module at all + +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .lora import LoraConfig, LoraModel +from .p_tuning import PromptEncoder, PromptEncoderConfig, PromptEncoderReparameterizationType +from .prefix_tuning import PrefixEncoder, PrefixTuningConfig +from .prompt_tuning import PromptEmbedding, PromptTuningConfig, PromptTuningInit diff --git a/04-gene-sft/peft/src/peft/tuners/lora.py b/04-gene-sft/peft/src/peft/tuners/lora.py new file mode 100644 index 0000000000000000000000000000000000000000..0f65cbf55c0233599c0dd8b155187718fe8091f7 --- /dev/null +++ b/04-gene-sft/peft/src/peft/tuners/lora.py @@ -0,0 +1,616 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import importlib +import math +import re +import warnings +from dataclasses import asdict, dataclass, field +from enum import Enum +from typing import List, Optional, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers.pytorch_utils import Conv1D + +from ..utils import PeftConfig, PeftType, transpose + + +def is_bnb_available(): + return importlib.util.find_spec("bitsandbytes") is not None + + +if is_bnb_available(): + import bitsandbytes as bnb + + +@dataclass +class LoraConfig(PeftConfig): + """ + This is the configuration class to store the configuration of a [`~peft.Lora`]. + + Args: + r (`int`): Lora attention dimension + target_modules (`Union[List[str],str]`): The names of the modules to apply Lora to. + lora_alpha (`float`): The alpha parameter for Lora scaling. + lora_dropout (`float`): The dropout probability for Lora layers. + merge_weights (`bool`): + Whether to merge the weights of the Lora layers with the base transformer model in `eval` mode. + fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out) + enable_lora ( `List[bool]`): Used with `lora.MergedLinear`. + bias (`str`): Bias type for Lora. Can be 'none', 'all' or 'lora_only' + modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable + and saved in the final checkpoint. + """ + + r: int = field(default=8, metadata={"help": "Lora attention dimension"}) + target_modules: Optional[Union[List[str], str]] = field( + default=None, + metadata={ + "help": "List of module names or regex expression of the module names to replace with Lora." + "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' " + }, + ) + lora_alpha: int = field(default=None, metadata={"help": "Lora alpha"}) + lora_dropout: float = field(default=None, metadata={"help": "Lora dropout"}) + merge_weights: bool = field( + default=False, metadata={"help": "Merge weights of the original model and the Lora model"} + ) + fan_in_fan_out: bool = field( + default=False, + metadata={"help": "Set this to True if the layer to replace stores weight like (fan_in, fan_out)"}, + ) + enable_lora: Optional[List[bool]] = field(default=None, metadata={"help": "Used with `lora.MergedLinear`."}) + bias: str = field(default="none", metadata={"help": "Bias type for Lora. Can be 'none', 'all' or 'lora_only'"}) + modules_to_save: Optional[List[str]] = field( + default=None, + metadata={ + "help": "List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. " + "For example, in Sequence Classification or Token Classification tasks, " + "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved." + }, + ) + + def __post_init__(self): + self.peft_type = PeftType.LORA + + +class LoraModel(torch.nn.Module): + """ + Creates Low Rank Adapter (Lora) model from a pretrained transformers model. + + Args: + model ([`transformers.PreTrainedModel`]): The model to be adapted. + config ([`LoraConfig`]): The configuration of the Lora model. + + Returns: + `torch.nn.Module`: The Lora model. + + Example:: + + >>> from transformers import AutoModelForSeq2SeqLM, LoraConfig >>> from peft import LoraModel, LoraConfig >>> + config = LoraConfig( + peft_type="LORA", task_type="SEQ_2_SEQ_LM", r=8, lora_alpha=32, target_modules=["q", "v"], + lora_dropout=0.01, ) + >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> lora_model = LoraModel(config, model) + + **Attributes**: + - **model** ([`transformers.PreTrainedModel`]) -- The model to be adapted. + - **peft_config** ([`LoraConfig`]): The configuration of the Lora model. + """ + + def __init__(self, config, model): + super().__init__() + self.peft_config = config + self.model = model + self._find_and_replace() + mark_only_lora_as_trainable(self.model, self.peft_config.bias) + self.forward = self.model.forward + + def _find_and_replace(self): + loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False) + if loaded_in_8bit and not is_bnb_available(): + raise ImportError( + "To use Lora with 8-bit quantization, please install the `bitsandbytes` package. " + "You can install it with `pip install bitsandbytes`." + ) + is_target_modules_in_base_model = False + is_hf_device_map_available = hasattr(self.model, "hf_device_map") + kwargs = { + "r": self.peft_config.r, + "lora_alpha": self.peft_config.lora_alpha, + "lora_dropout": self.peft_config.lora_dropout, + "fan_in_fan_out": self.peft_config.fan_in_fan_out, + "merge_weights": (self.peft_config.merge_weights or self.peft_config.inference_mode) + and not is_hf_device_map_available, + } + key_list = [key for key, _ in self.model.named_modules()] + for key in key_list: + if isinstance(self.peft_config.target_modules, str): + target_module_found = re.fullmatch(self.peft_config.target_modules, key) + else: + target_module_found = any(key.endswith(target_key) for target_key in self.peft_config.target_modules) + if target_module_found: + if not is_target_modules_in_base_model: + is_target_modules_in_base_model = True + parent, target, target_name = self._get_submodules(key) + bias = target.bias is not None + if loaded_in_8bit and isinstance(target, bnb.nn.Linear8bitLt): + kwargs.update( + { + "has_fp16_weights": target.state.has_fp16_weights, + "memory_efficient_backward": target.state.memory_efficient_backward, + "threshold": target.state.threshold, + "index": target.index, + } + ) + if self.peft_config.enable_lora is None: + new_module = Linear8bitLt(target.in_features, target.out_features, bias=bias, **kwargs) + else: + kwargs.update({"enable_lora": self.peft_config.enable_lora}) + new_module = MergedLinear8bitLt(target.in_features, target.out_features, bias=bias, **kwargs) + elif isinstance(target, torch.nn.Linear) and self.peft_config.enable_lora is None: + new_module = Linear(target.in_features, target.out_features, bias=bias, **kwargs) + elif self.peft_config.enable_lora is not None: + kwargs.update({"enable_lora": self.peft_config.enable_lora}) + if isinstance(target, Conv1D): + in_features, out_features = ( + target.weight.ds_shape if hasattr(target.weight, "ds_shape") else target.weight.shape + ) + else: + in_features, out_features = target.in_features, target.out_features + if kwargs["fan_in_fan_out"]: + warnings.warn( + "fan_in_fan_out is set to True but the target module is not a Conv1D. " + "Setting fan_in_fan_out to False." + ) + kwargs["fan_in_fan_out"] = self.peft_config.fan_in_fan_out = False + new_module = MergedLinear(in_features, out_features, bias=bias, **kwargs) + self._replace_module(parent, target_name, new_module, target) + if not is_target_modules_in_base_model: + raise ValueError( + f"Target modules {self.peft_config.target_modules} not found in the base model. " + f"Please check the target modules and try again." + ) + + def _get_submodules(self, key): + parent = self.model.get_submodule(".".join(key.split(".")[:-1])) + target_name = key.split(".")[-1] + target = self.model.get_submodule(key) + return parent, target, target_name + + def _replace_module(self, parent_module, child_name, new_module, old_module): + setattr(parent_module, child_name, new_module) + new_module.weight = old_module.weight + if old_module.bias is not None: + new_module.bias = old_module.bias + if getattr(old_module, "state", None) is not None: + new_module.state = old_module.state + new_module.to(old_module.weight.device) + + # dispatch to correct device + for name, module in new_module.named_modules(): + if "lora_" in name: + module.to(old_module.weight.device) + + def __getattr__(self, name: str): + """Forward missing attributes to the wrapped module.""" + try: + return super().__getattr__(name) # defer to nn.Module's logic + except AttributeError: + return getattr(self.model, name) + + @property + def modules_to_save(self): + return None + + def get_peft_config_as_dict(self, inference: bool = False): + config = {k: v.value if isinstance(v, Enum) else v for k, v in asdict(self.peft_config).items()} + if inference: + config["inference_mode"] = True + return config + + def _set_adapter_layers(self, enabled=True): + for module in self.model.modules(): + if isinstance(module, LoraLayer): + module.disable_adapters = False if enabled else True + + def enable_adapter_layers(self): + self._set_adapter_layers(enabled=True) + + def disable_adapter_layers(self): + self._set_adapter_layers(enabled=False) + + +# Below code is based on https://github.com/microsoft/LoRA/blob/main/loralib/layers.py +# and modified to work with PyTorch FSDP + + +# ------------------------------------------------------------------------------------------ +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information. +# ------------------------------------------------------------------------------------------ + + +# had to adapt it for `lora_only` to work +def mark_only_lora_as_trainable(model: nn.Module, bias: str = "none") -> None: + for n, p in model.named_parameters(): + if "lora_" not in n: + p.requires_grad = False + if bias == "none": + return + elif bias == "all": + for n, p in model.named_parameters(): + if "bias" in n: + p.requires_grad = True + elif bias == "lora_only": + for m in model.modules(): + if isinstance(m, LoraLayer) and hasattr(m, "bias") and m.bias is not None: + m.bias.requires_grad = True + else: + raise NotImplementedError + + +class LoraLayer: + def __init__( + self, + r: int, + lora_alpha: int, + lora_dropout: float, + merge_weights: bool, + ): + self.r = r + self.lora_alpha = lora_alpha + # Optional dropout + if lora_dropout > 0.0: + self.lora_dropout = nn.Dropout(p=lora_dropout) + else: + self.lora_dropout = lambda x: x + # Mark the weight as unmerged + self.merged = False + self.merge_weights = merge_weights + self.disable_adapters = False + + +class Linear(nn.Linear, LoraLayer): + # Lora implemented in a dense layer + def __init__( + self, + in_features: int, + out_features: int, + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0.0, + fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out) + merge_weights: bool = True, + **kwargs, + ): + nn.Linear.__init__(self, in_features, out_features, **kwargs) + LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights) + + self.fan_in_fan_out = fan_in_fan_out + # Actual trainable parameters + if r > 0: + self.lora_A = nn.Linear(in_features, r, bias=False) + self.lora_B = nn.Linear(r, out_features, bias=False) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + self.reset_parameters() + if fan_in_fan_out: + self.weight.data = self.weight.data.T + + def reset_parameters(self): + nn.Linear.reset_parameters(self) + if hasattr(self, "lora_A"): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B.weight) + + def train(self, mode: bool = True): + nn.Linear.train(self, mode) + self.lora_A.train(mode) + self.lora_B.train(mode) + if not mode and self.merge_weights and not self.merged: + # Merge the weights and mark it + if self.r > 0: + self.weight.data += ( + transpose(self.lora_B.weight @ self.lora_A.weight, self.fan_in_fan_out) * self.scaling + ) + self.merged = True + elif self.merge_weights and self.merged: + # Make sure that the weights are not merged + if self.r > 0: + self.weight.data -= ( + transpose(self.lora_B.weight @ self.lora_A.weight, self.fan_in_fan_out) * self.scaling + ) + self.merged = False + + def eval(self): + nn.Linear.eval(self) + self.lora_A.eval() + self.lora_B.eval() + + def forward(self, x: torch.Tensor): + if self.disable_adapters: + if self.r > 0 and self.merged: + self.weight.data -= ( + transpose(self.lora_B.weight @ self.lora_A.weight, self.fan_in_fan_out) * self.scaling + ) + self.merged = False + + return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) + elif self.r > 0 and not self.merged: + result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) + if self.r > 0: + result += self.lora_B(self.lora_A(self.lora_dropout(x))) * self.scaling + return result + else: + return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) + + +class MergedLinear(nn.Linear, LoraLayer): + # Lora implemented in a dense layer + def __init__( + self, + in_features: int, + out_features: int, + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0.0, + enable_lora: List[bool] = [False], + fan_in_fan_out: bool = False, + merge_weights: bool = True, + **kwargs, + ): + nn.Linear.__init__(self, in_features, out_features, **kwargs) + LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights) + if out_features % len(enable_lora) != 0: + raise ValueError("The length of enable_lora must divide out_features") + self.enable_lora = enable_lora + self.fan_in_fan_out = fan_in_fan_out + # Actual trainable parameters + if r > 0 and any(enable_lora): + self.lora_A = nn.Linear(in_features, r * sum(enable_lora), bias=False) + self.lora_B = nn.Conv1d( + r * sum(enable_lora), + out_features // len(enable_lora) * sum(enable_lora), + kernel_size=1, + groups=2, + bias=False, + ) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + # Compute the indices + self.lora_ind = self.weight.new_zeros((out_features,), dtype=torch.bool).view(len(enable_lora), -1) + self.lora_ind[enable_lora, :] = True + self.lora_ind = self.lora_ind.view(-1) + self.reset_parameters() + if fan_in_fan_out: + self.weight.data = self.weight.data.T + + def reset_parameters(self): + nn.Linear.reset_parameters(self) + if hasattr(self, "lora_A"): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B.weight) + + def zero_pad(self, x): + result = x.new_zeros((*x.shape[:-1], self.out_features)) + result = result.view(-1, self.out_features) + result[:, self.lora_ind] = x.reshape(-1, self.out_features // len(self.enable_lora) * sum(self.enable_lora)) + return result.view((*x.shape[:-1], self.out_features)) + + def train(self, mode: bool = True): + nn.Linear.train(self, mode) + self.lora_A.train(mode) + self.lora_B.train(mode) + if not mode and self.merge_weights and not self.merged: + # Merge the weights and mark it + if self.r > 0 and any(self.enable_lora): + delta_w = ( + F.conv1d( + self.lora_A.weight.data.unsqueeze(0), + self.lora_B.weight.data, + groups=sum(self.enable_lora), + ) + .squeeze(0) + .transpose(-2, -1) + ) + self.weight.data += transpose(self.zero_pad(delta_w * self.scaling), not self.fan_in_fan_out) + self.merged = True + elif self.merge_weights and self.merged: + # Make sure that the weights are not merged + if self.r > 0 and any(self.enable_lora): + delta_w = ( + F.conv1d( + self.lora_A.weight.data.unsqueeze(0), + self.lora_B.weight.data, + groups=sum(self.enable_lora), + ) + .squeeze(0) + .transpose(-2, -1) + ) + self.weight.data -= transpose(self.zero_pad(delta_w * self.scaling), not self.fan_in_fan_out) + self.merged = False + + def eval(self): + nn.Linear.eval(self) + self.lora_A.eval() + self.lora_B.eval() + + def forward(self, x: torch.Tensor): + if self.disable_adapters: + if self.r > 0 and self.merged and any(self.enable_lora): + delta_w = ( + F.conv1d( + self.lora_A.weight.data.unsqueeze(0), + self.lora_B.weight.data, + groups=sum(self.enable_lora), + ) + .squeeze(0) + .transpose(-2, -1) + ) + self.weight.data -= transpose(self.zero_pad(delta_w * self.scaling), not self.fan_in_fan_out) + self.merged = False + return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) + elif self.merged: + return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) + else: + result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) + if self.r > 0: + after_A = self.lora_A(self.lora_dropout(x)) + after_B = self.lora_B(after_A.transpose(-2, -1)).transpose(-2, -1) + result += self.zero_pad(after_B) * self.scaling + return result + + +if is_bnb_available(): + + class Linear8bitLt(bnb.nn.Linear8bitLt, LoraLayer): + # Lora implemented in a dense layer + def __init__( + self, + in_features, + out_features, + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0.0, + **kwargs, + ): + bnb.nn.Linear8bitLt.__init__( + self, + in_features, + out_features, + bias=kwargs.get("bias", True), + has_fp16_weights=kwargs.get("has_fp16_weights", True), + memory_efficient_backward=kwargs.get("memory_efficient_backward", False), + threshold=kwargs.get("threshold", 0.0), + index=kwargs.get("index", None), + ) + LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=False) + # Actual trainable parameters + if r > 0: + self.lora_A = nn.Linear(in_features, r, bias=False) + self.lora_B = nn.Linear(r, out_features, bias=False) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + self.reset_parameters() + + def reset_parameters(self): + if hasattr(self, "lora_A"): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B.weight) + + def forward(self, x: torch.Tensor): + result = super().forward(x) + + if self.disable_adapters: + return result + elif self.r > 0: + if not torch.is_autocast_enabled(): + expected_dtype = result.dtype + + if x.dtype != torch.float32: + x = x.float() + output = self.lora_B(self.lora_A(self.lora_dropout(x))).to(expected_dtype) * self.scaling + result += output + else: + output = self.lora_B(self.lora_A(self.lora_dropout(x))) * self.scaling + result += output + return result + + class MergedLinear8bitLt(bnb.nn.Linear8bitLt, LoraLayer): + # Lora implemented in a dense layer + def __init__( + self, + in_features: int, + out_features: int, + r: int = 0, + lora_alpha: int = 1, + lora_dropout: float = 0.0, + enable_lora: List[bool] = [False], + **kwargs, + ): + bnb.nn.Linear8bitLt.__init__( + self, + in_features, + out_features, + bias=kwargs.get("bias", True), + has_fp16_weights=kwargs.get("has_fp16_weights", True), + memory_efficient_backward=kwargs.get("memory_efficient_backward", False), + threshold=kwargs.get("threshold", 0.0), + index=kwargs.get("index", None), + ) + LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=False) + if out_features % len(enable_lora) != 0: + raise ValueError("The length of enable_lora must divide out_features") + self.enable_lora = enable_lora + # Actual trainable parameters + if r > 0 and any(enable_lora): + self.lora_A = nn.Linear(in_features, r * sum(enable_lora), bias=False) + self.lora_B = nn.Conv1d( + r * sum(enable_lora), + out_features // len(enable_lora) * sum(enable_lora), + kernel_size=1, + groups=2, + bias=False, + ) + self.scaling = self.lora_alpha / self.r + # Freezing the pre-trained weight matrix + self.weight.requires_grad = False + # Compute the indices + self.lora_ind = self.weight.new_zeros((out_features,), dtype=torch.bool).view(len(enable_lora), -1) + self.lora_ind[enable_lora, :] = True + self.lora_ind = self.lora_ind.view(-1) + self.reset_parameters() + + def reset_parameters(self): + if hasattr(self, "lora_A"): + # initialize A the same way as the default for nn.Linear and B to zero + nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5)) + nn.init.zeros_(self.lora_B.weight) + + def zero_pad(self, x): + result = x.new_zeros((*x.shape[:-1], self.out_features)) + result = result.view(-1, self.out_features) + result[:, self.lora_ind] = x.reshape( + -1, self.out_features // len(self.enable_lora) * sum(self.enable_lora) + ) + return result.view((*x.shape[:-1], self.out_features)) + + def forward(self, x: torch.Tensor): + result = super().forward(x) + if self.disable_adapters: + return result + elif self.r > 0: + if not torch.is_autocast_enabled(): + expected_dtype = result.dtype + if x.dtype != torch.float32: + x = x.float() + after_A = self.lora_A(self.lora_dropout(x)) + after_B = self.lora_B(after_A.transpose(-2, -1)).transpose(-2, -1) + output = self.zero_pad(after_B).to(expected_dtype) * self.scaling + result += output + else: + after_A = self.lora_A(self.lora_dropout(x)) + after_B = self.lora_B(after_A.transpose(-2, -1)).transpose(-2, -1) + output = self.zero_pad(after_B) * self.scaling + result += output + return result diff --git a/04-gene-sft/peft/src/peft/tuners/p_tuning.py b/04-gene-sft/peft/src/peft/tuners/p_tuning.py new file mode 100644 index 0000000000000000000000000000000000000000..b9c38c40e8a5c0e30adeecaaa0f08604a10779ed --- /dev/null +++ b/04-gene-sft/peft/src/peft/tuners/p_tuning.py @@ -0,0 +1,159 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import enum +import warnings +from dataclasses import dataclass, field +from typing import Union + +import torch + +from ..utils import PeftType, PromptLearningConfig + + +class PromptEncoderReparameterizationType(str, enum.Enum): + MLP = "MLP" + LSTM = "LSTM" + + +@dataclass +class PromptEncoderConfig(PromptLearningConfig): + """ + This is the configuration class to store the configuration of a [`~peft.PromptEncoder`]. + + Args: + encoder_reparameterization_type + (Union[[`PromptEncoderReparameterizationType`], `str`]): The type of reparameterization to use. + encoder_hidden_size (`int`): The hidden size of the prompt encoder. + encoder_num_layers (`int`): The number of layers of the prompt encoder. + encoder_dropout (`float`): The dropout probability of the prompt encoder. + """ + + encoder_reparameterization_type: Union[str, PromptEncoderReparameterizationType] = field( + default=PromptEncoderReparameterizationType.MLP, + metadata={"help": "How to reparameterize the prompt encoder"}, + ) + encoder_hidden_size: int = field( + default=None, + metadata={"help": "The hidden size of the prompt encoder"}, + ) + encoder_num_layers: int = field( + default=2, + metadata={"help": "The number of layers of the prompt encoder"}, + ) + encoder_dropout: float = field( + default=0.0, + metadata={"help": "The dropout of the prompt encoder"}, + ) + + def __post_init__(self): + self.peft_type = PeftType.P_TUNING + + +# Based on https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/modules/common/prompt_encoder.py +# with some refactor +class PromptEncoder(torch.nn.Module): + """ + The prompt encoder network that is used to generate the virtual token embeddings for p-tuning. + + Args: + config ([`PromptEncoderConfig`]): The configuration of the prompt encoder. + + Example:: + + >>> from peft import PromptEncoder, PromptEncoderConfig >>> config = PromptEncoderConfig( + peft_type="P_TUNING", task_type="SEQ_2_SEQ_LM", num_virtual_tokens=20, token_dim=768, + num_transformer_submodules=1, num_attention_heads=12, num_layers=12, + encoder_reparameterization_type="MLP", encoder_hidden_size=768 + ) + >>> prompt_encoder = PromptEncoder(config) + + **Attributes**: + - **embedding** ([`~torch.nn.Embedding`]) -- The embedding layer of the prompt encoder. + - **mlp_head** ([`~torch.nn.Sequential`]) -- The MLP head of the prompt encoder if `inference_mode=False`. + - **lstm_head** ([`~torch.nn.LSTM`]) -- The LSTM head of the prompt encoder if `inference_mode=False` and + `encoder_reparameterization_type="LSTM"`. + - **token_dim** (`int`) -- The hidden embedding dimension of the base transformer model. + - **input_size** (`int`) -- The input size of the prompt encoder. + - **output_size** (`int`) -- The output size of the prompt encoder. + - **hidden_size** (`int`) -- The hidden size of the prompt encoder. + - **total_virtual_tokens** (`int`): The total number of virtual tokens of the + prompt encoder. + - **encoder_type** (Union[[`PromptEncoderReparameterizationType`], `str`]): + The encoder type of the prompt encoder. + + + Input shape: (batch_size, total_virtual_tokens) + + Output shape: (batch_size, total_virtual_tokens, token_dim) + """ + + def __init__(self, config): + super().__init__() + self.token_dim = config.token_dim + self.input_size = self.token_dim + self.output_size = self.token_dim + self.hidden_size = config.encoder_hidden_size + self.total_virtual_tokens = config.num_virtual_tokens * config.num_transformer_submodules + self.encoder_type = config.encoder_reparameterization_type + + # embedding + self.embedding = torch.nn.Embedding(self.total_virtual_tokens, self.token_dim) + if not config.inference_mode: + if self.encoder_type == PromptEncoderReparameterizationType.LSTM: + lstm_dropout = config.encoder_dropout + num_layers = config.encoder_num_layers + # LSTM + self.lstm_head = torch.nn.LSTM( + input_size=self.input_size, + hidden_size=self.hidden_size, + num_layers=num_layers, + dropout=lstm_dropout, + bidirectional=True, + batch_first=True, + ) + + self.mlp_head = torch.nn.Sequential( + torch.nn.Linear(self.hidden_size * 2, self.hidden_size * 2), + torch.nn.ReLU(), + torch.nn.Linear(self.hidden_size * 2, self.output_size), + ) + + elif self.encoder_type == PromptEncoderReparameterizationType.MLP: + warnings.warn( + f"for {self.encoder_type}, the `encoder_num_layers` is ignored. Exactly 2 MLP layers are used." + ) + layers = [ + torch.nn.Linear(self.input_size, self.hidden_size), + torch.nn.ReLU(), + torch.nn.Linear(self.hidden_size, self.hidden_size), + torch.nn.ReLU(), + torch.nn.Linear(self.hidden_size, self.output_size), + ] + self.mlp_head = torch.nn.Sequential(*layers) + + else: + raise ValueError("Prompt encoder type not recognized. Please use one of MLP (recommended) or LSTM.") + + def forward(self, indices): + input_embeds = self.embedding(indices) + if self.encoder_type == PromptEncoderReparameterizationType.LSTM: + output_embeds = self.mlp_head(self.lstm_head(input_embeds)[0]) + elif self.encoder_type == PromptEncoderReparameterizationType.MLP: + output_embeds = self.mlp_head(input_embeds) + else: + raise ValueError("Prompt encoder type not recognized. Please use one of MLP (recommended) or LSTM.") + + return output_embeds diff --git a/04-gene-sft/peft/src/peft/tuners/prefix_tuning.py b/04-gene-sft/peft/src/peft/tuners/prefix_tuning.py new file mode 100644 index 0000000000000000000000000000000000000000..fcb207c6007f07ae006429b6862044427cde1264 --- /dev/null +++ b/04-gene-sft/peft/src/peft/tuners/prefix_tuning.py @@ -0,0 +1,101 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from dataclasses import dataclass, field + +import torch + +from ..utils import PeftType, PromptLearningConfig + + +@dataclass +class PrefixTuningConfig(PromptLearningConfig): + """ + This is the configuration class to store the configuration of a [`~peft.PrefixEncoder`]. + + Args: + encoder_hidden_size (`int`): The hidden size of the prompt encoder. + prefix_projection (`bool`): Whether to project the prefix embeddings. + """ + + encoder_hidden_size: int = field( + default=None, + metadata={"help": "The hidden size of the encoder"}, + ) + prefix_projection: bool = field( + default=False, + metadata={"help": "Whether to project the prefix tokens"}, + ) + + def __post_init__(self): + self.peft_type = PeftType.PREFIX_TUNING + + +# Based on https://github.com/THUDM/P-tuning-v2/blob/main/model/prefix_encoder.py +# with some refactor +class PrefixEncoder(torch.nn.Module): + r""" + The torch.nn model to encode the prefix + + Args: + config ([`PrefixTuningConfig`]): The configuration of the prefix encoder. + + Example:: + + >>> from peft import PrefixEncoder, PrefixTuningConfig >>> config = PrefixTuningConfig( + peft_type="PREFIX_TUNING", task_type="SEQ_2_SEQ_LM", num_virtual_tokens=20, token_dim=768, + num_transformer_submodules=1, num_attention_heads=12, num_layers=12, encoder_hidden_size=768 + ) + >>> prefix_encoder = PrefixEncoder(config) + + + **Attributes**: + - **embedding** (`torch.nn.Embedding`) -- + The embedding layer of the prefix encoder. + - **transform** (`torch.nn.Sequential`) -- The + two-layer MLP to transform the prefix embeddings if `prefix_projection` is `True`. + - **prefix_projection** (`bool`) -- Whether to project the prefix embeddings. + + Input shape: (batch_size, num_virtual_tokens) + + Output shape: (batch_size, num_virtual_tokens, 2*layers*hidden) + """ + + def __init__(self, config): + super().__init__() + self.prefix_projection = config.prefix_projection + token_dim = config.token_dim + num_layers = config.num_layers + encoder_hidden_size = config.encoder_hidden_size + num_virtual_tokens = config.num_virtual_tokens + if self.prefix_projection and not config.inference_mode: + # Use a two-layer MLP to encode the prefix + self.embedding = torch.nn.Embedding(num_virtual_tokens, token_dim) + self.transform = torch.nn.Sequential( + torch.nn.Linear(token_dim, encoder_hidden_size), + torch.nn.Tanh(), + torch.nn.Linear(encoder_hidden_size, num_layers * 2 * token_dim), + ) + else: + self.embedding = torch.nn.Embedding(num_virtual_tokens, num_layers * 2 * token_dim) + + def forward(self, prefix: torch.Tensor): + if self.prefix_projection: + prefix_tokens = self.embedding(prefix) + past_key_values = self.transform(prefix_tokens) + else: + past_key_values = self.embedding(prefix) + return past_key_values diff --git a/04-gene-sft/peft/src/peft/tuners/prompt_tuning.py b/04-gene-sft/peft/src/peft/tuners/prompt_tuning.py new file mode 100644 index 0000000000000000000000000000000000000000..1dead1de7137398681a982d6c1d18ebd84972606 --- /dev/null +++ b/04-gene-sft/peft/src/peft/tuners/prompt_tuning.py @@ -0,0 +1,120 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import enum +import math +from dataclasses import dataclass, field +from typing import Optional, Union + +import torch + +from ..utils import PeftType, PromptLearningConfig + + +class PromptTuningInit(str, enum.Enum): + TEXT = "TEXT" + RANDOM = "RANDOM" + + +@dataclass +class PromptTuningConfig(PromptLearningConfig): + """ + This is the configuration class to store the configuration of a [`~peft.PromptEmbedding`]. + + Args: + prompt_tuning_init (Union[[`PromptTuningInit`], `str`]): The initialization of the prompt embedding. + prompt_tuning_init_text ( Optional[`str`]): The text to initialize the prompt embedding. + Only used if `prompt_tuning_init` is `TEXT` + tokenizer_name_or_path ( Optional[`str`]): The name or path of the tokenizer. + Only used if `prompt_tuning_init` is `TEXT` + """ + + prompt_tuning_init: Union[PromptTuningInit, str] = field( + default=PromptTuningInit.RANDOM, + metadata={"help": "How to initialize the prompt tuning parameters"}, + ) + prompt_tuning_init_text: Optional[str] = field( + default=None, + metadata={ + "help": "The text to use for prompt tuning initialization. Only used if prompt_tuning_init is `TEXT`" + }, + ) + tokenizer_name_or_path: Optional[str] = field( + default=None, + metadata={ + "help": "The tokenizer to use for prompt tuning initialization. Only used if prompt_tuning_init is `TEXT`" + }, + ) + + def __post_init__(self): + self.peft_type = PeftType.PROMPT_TUNING + + +class PromptEmbedding(torch.nn.Module): + """ + The model to encode virtual tokens into prompt embeddings. + + Args: + config ([`PromptTuningConfig`]): The configuration of the prompt embedding. + word_embeddings (`torch.nn.Module`): The word embeddings of the base transformer model. + + **Attributes**: + **embedding** (`torch.nn.Embedding`) -- The embedding layer of the prompt embedding. + + Example:: + + >>> from peft import PromptEmbedding, PromptTuningConfig >>> config = PromptTuningConfig( + peft_type="PROMPT_TUNING", task_type="SEQ_2_SEQ_LM", num_virtual_tokens=20, token_dim=768, + num_transformer_submodules=1, num_attention_heads=12, num_layers=12, prompt_tuning_init="TEXT", + prompt_tuning_init_text="Predict if sentiment of this review is positive, negative or neutral", + tokenizer_name_or_path="t5-base", + ) + >>> # t5_model.shared is the word embeddings of the base model >>> prompt_embedding = PromptEmbedding(config, + t5_model.shared) + + + Input Shape: (batch_size, total_virtual_tokens) + + Output Shape: (batch_size, total_virtual_tokens, token_dim) + """ + + def __init__(self, config, word_embeddings): + super().__init__() + + total_virtual_tokens = config.num_virtual_tokens * config.num_transformer_submodules + self.embedding = torch.nn.Embedding(total_virtual_tokens, config.token_dim) + if config.prompt_tuning_init == PromptTuningInit.TEXT: + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name_or_path) + init_text = config.prompt_tuning_init_text + init_token_ids = tokenizer(init_text)["input_ids"] + # Trim or iterate until num_text_tokens matches total_virtual_tokens + num_text_tokens = len(init_token_ids) + if num_text_tokens > total_virtual_tokens: + init_token_ids = init_token_ids[:total_virtual_tokens] + elif num_text_tokens < total_virtual_tokens: + num_reps = math.ceil(total_virtual_tokens / num_text_tokens) + init_token_ids = init_token_ids * num_reps + init_token_ids = init_token_ids[:total_virtual_tokens] + + word_embedding_weights = word_embeddings(torch.LongTensor(init_token_ids)).detach().clone() + word_embedding_weights = word_embedding_weights.to(torch.float32) + self.embedding.weight = torch.nn.Parameter(word_embedding_weights) + + def forward(self, indices): + # Just get embeddings + prompt_embeddings = self.embedding(indices) + return prompt_embeddings diff --git a/04-gene-sft/peft/src/peft/utils/__init__.py b/04-gene-sft/peft/src/peft/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dd949c038749373cd3be1e0867d1f75578a5ffc5 --- /dev/null +++ b/04-gene-sft/peft/src/peft/utils/__init__.py @@ -0,0 +1,30 @@ +# flake8: noqa +# There's no way to ignore "F401 '...' imported but unused" warnings in this +# module, but to preserve other warnings. So, don't check this module at all + +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .adapters_utils import CONFIG_NAME, WEIGHTS_NAME +from .config import PeftConfig, PeftType, PromptLearningConfig, TaskType +from .other import ( + TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING, + _set_trainable, + bloom_model_postprocess_past_key_value, + prepare_model_for_int8_training, + shift_tokens_right, + transpose, +) +from .save_and_load import get_peft_model_state_dict, set_peft_model_state_dict diff --git a/04-gene-sft/peft/src/peft/utils/adapters_utils.py b/04-gene-sft/peft/src/peft/utils/adapters_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f2f8a9517cc532ff18b85ac985ea3ce5d5cafa06 --- /dev/null +++ b/04-gene-sft/peft/src/peft/utils/adapters_utils.py @@ -0,0 +1,18 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +WEIGHTS_NAME = "adapter_model.bin" +CONFIG_NAME = "adapter_config.json" + +# TODO: add automapping and superclass here? diff --git a/04-gene-sft/peft/src/peft/utils/config.py b/04-gene-sft/peft/src/peft/utils/config.py new file mode 100644 index 0000000000000000000000000000000000000000..3e2cf5bd06e2e9bfb8d279de1b006c1c47c1d098 --- /dev/null +++ b/04-gene-sft/peft/src/peft/utils/config.py @@ -0,0 +1,167 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import enum +import json +import os +from dataclasses import asdict, dataclass, field +from typing import Optional, Union + +from huggingface_hub import hf_hub_download +from transformers.utils import PushToHubMixin + +from .adapters_utils import CONFIG_NAME + + +class PeftType(str, enum.Enum): + PROMPT_TUNING = "PROMPT_TUNING" + P_TUNING = "P_TUNING" + PREFIX_TUNING = "PREFIX_TUNING" + LORA = "LORA" + + +class TaskType(str, enum.Enum): + SEQ_CLS = "SEQ_CLS" + SEQ_2_SEQ_LM = "SEQ_2_SEQ_LM" + CAUSAL_LM = "CAUSAL_LM" + TOKEN_CLS = "TOKEN_CLS" + + +@dataclass +class PeftConfigMixin(PushToHubMixin): + r""" + This is the base configuration class for PEFT adapter models. It contains all the methods that are common to all + PEFT adapter models. This class inherits from `transformers.utils.PushToHubMixin` which contains the methods to + push your model to the Hub. The method `save_pretrained` will save the configuration of your adapter model in a + directory. The method `from_pretrained` will load the configuration of your adapter model from a directory. + + Args: + peft_type (Union[[`~peft.utils.config.PeftType`], `str`]): The type of Peft method to use. + """ + peft_type: Optional[PeftType] = field(default=None, metadata={"help": "The type of PEFT model."}) + + @property + def __dict__(self): + return asdict(self) + + def to_dict(self): + return self.__dict__ + + def save_pretrained(self, save_directory, **kwargs): + r""" + This method saves the configuration of your adapter model in a directory. + + Args: + save_directory (`str`): + The directory where the configuration will be saved. + **kwargs: + Additional keyword arguments passed along to the `transformers.utils.PushToHubMixin.push_to_hub` + method. + """ + if os.path.isfile(save_directory): + raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file") + + os.makedirs(save_directory, exist_ok=True) + + output_dict = self.__dict__ + output_path = os.path.join(save_directory, CONFIG_NAME) + + # save it + with open(output_path, "w") as writer: + writer.write(json.dumps(output_dict, indent=2, sort_keys=True)) + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): + r""" + This method loads the configuration of your adapter model from a directory. + + Args: + pretrained_model_name_or_path (`str`): + The directory or the hub-id where the configuration is saved. + **kwargs: + Additional keyword arguments passed along to the child class initialization. + """ + if os.path.isfile(os.path.join(pretrained_model_name_or_path, CONFIG_NAME)): + config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME) + else: + try: + config_file = hf_hub_download(pretrained_model_name_or_path, CONFIG_NAME) + except Exception: + raise ValueError(f"Can't find config.json at '{pretrained_model_name_or_path}'") + + loaded_attributes = cls.from_json_file(config_file) + + config = cls(**kwargs) + + for key, value in loaded_attributes.items(): + if hasattr(config, key): + setattr(config, key, value) + + return config + + @classmethod + def from_json_file(cls, path_json_file, **kwargs): + r""" + Loads a configuration file from a json file. + + Args: + path_json_file (`str`): + The path to the json file. + """ + with open(path_json_file, "r") as file: + json_object = json.load(file) + + return json_object + + +@dataclass +class PeftConfig(PeftConfigMixin): + """ + This is the base configuration class to store the configuration of a :class:`~peft.PeftModel`. + + Args: + peft_type (Union[[`~peft.utils.config.PeftType`], `str`]): The type of Peft method to use. + task_type (Union[[`~peft.utils.config.TaskType`], `str`]): The type of task to perform. + inference_mode (`bool`, defaults to `False`): Whether to use the Peft model in inference mode. + """ + + base_model_name_or_path: str = field(default=None, metadata={"help": "The name of the base model to use."}) + peft_type: Union[str, PeftType] = field(default=None, metadata={"help": "Peft type"}) + task_type: Union[str, TaskType] = field(default=None, metadata={"help": "Task type"}) + inference_mode: bool = field(default=False, metadata={"help": "Whether to use inference mode"}) + + +@dataclass +class PromptLearningConfig(PeftConfig): + """ + This is the base configuration class to store the configuration of a Union[[`~peft.PrefixTuning`], + [`~peft.PromptEncoder`], [`~peft.PromptTuning`]]. + + Args: + num_virtual_tokens (`int`): The number of virtual tokens to use. + token_dim (`int`): The hidden embedding dimension of the base transformer model. + num_transformer_submodules (`int`): The number of transformer submodules in the base transformer model. + num_attention_heads (`int`): The number of attention heads in the base transformer model. + num_layers (`int`): The number of layers in the base transformer model. + """ + + num_virtual_tokens: int = field(default=None, metadata={"help": "Number of virtual tokens"}) + token_dim: int = field( + default=None, metadata={"help": "The hidden embedding dimension of the base transformer model"} + ) + num_transformer_submodules: Optional[int] = field( + default=None, metadata={"help": "Number of transformer submodules"} + ) + num_attention_heads: Optional[int] = field(default=None, metadata={"help": "Number of attention heads"}) + num_layers: Optional[int] = field(default=None, metadata={"help": "Number of transformer layers"}) diff --git a/04-gene-sft/peft/src/peft/utils/other.py b/04-gene-sft/peft/src/peft/utils/other.py new file mode 100644 index 0000000000000000000000000000000000000000..132b033484ff4aace5843d8d5817fa6fda4ab17d --- /dev/null +++ b/04-gene-sft/peft/src/peft/utils/other.py @@ -0,0 +1,159 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + + +# needed for prefix-tuning of bloom model +def bloom_model_postprocess_past_key_value(past_key_values): + past_key_values = torch.cat(past_key_values) + total_layers, batch_size, num_attention_heads, num_virtual_tokens, head_dim = past_key_values.shape + keys = past_key_values[: total_layers // 2] + keys = keys.transpose(2, 3).reshape( + total_layers // 2, batch_size * num_attention_heads, head_dim, num_virtual_tokens + ) + values = past_key_values[total_layers // 2 :] + values = values.reshape(total_layers // 2, batch_size * num_attention_heads, num_virtual_tokens, head_dim) + + return tuple(zip(keys, values)) + + +def prepare_model_for_int8_training( + model, output_embedding_layer_name="lm_head", use_gradient_checkpointing=True, layer_norm_names=["layer_norm"] +): + r""" + This method wrapps the entire protocol for preparing a model before running a training. This includes: + 1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm + head to fp32 + + Args: + model, (`transformers.PreTrainedModel`): + The loaded model from `transformers` + """ + loaded_in_8bit = getattr(model, "is_loaded_in_8bit", False) + + for name, param in model.named_parameters(): + # freeze base model's layers + param.requires_grad = False + + if loaded_in_8bit: + # cast layer norm in fp32 for stability for 8bit models + if param.ndim == 1 and any(layer_norm_name in name for layer_norm_name in layer_norm_names): + param.data = param.data.to(torch.float32) + + if loaded_in_8bit and use_gradient_checkpointing: + # For backward compatibility + if hasattr(model, "enable_input_require_grads"): + model.enable_input_require_grads() + else: + + def make_inputs_require_grad(module, input, output): + output.requires_grad_(True) + + model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) + + # enable gradient checkpointing for memory efficiency + model.gradient_checkpointing_enable() + + if hasattr(model, output_embedding_layer_name): + output_embedding_layer = getattr(model, output_embedding_layer_name) + input_dtype = output_embedding_layer.weight.dtype + + class CastOutputToFloat(torch.nn.Sequential): + r""" + Manually cast to the expected dtype of the lm_head as sometimes there is a final layer norm that is casted + in fp32 + + """ + + def forward(self, x): + return super().forward(x.to(input_dtype)).to(torch.float32) + + setattr(model, output_embedding_layer_name, CastOutputToFloat(output_embedding_layer)) + + return model + + +TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING = { + "bloom": bloom_model_postprocess_past_key_value, +} + + +# copied from transformers.models.bart.modeling_bart +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): + """ + Shift input ids one token to the right. + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): input ids + pad_token_id (`int`): The id of the `padding` token. + decoder_start_token_id (`int`): The id of the `start` token. + """ + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() + shifted_input_ids[:, 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +def _set_trainable(model): + if model.modules_to_save is not None: + for name, param in model.named_parameters(): + if any(module_name in name for module_name in model.modules_to_save): + param.requires_grad = True + + +def fsdp_auto_wrap_policy(model): + import functools + import os + + from accelerate import FullyShardedDataParallelPlugin + from torch.distributed.fsdp.wrap import _or_policy, lambda_auto_wrap_policy, transformer_auto_wrap_policy + + from ..tuners import PrefixEncoder, PromptEmbedding, PromptEncoder + + def lambda_policy_fn(module): + if ( + len(list(module.named_children())) == 0 + and getattr(module, "weight", None) is not None + and module.weight.requires_grad + ): + return True + return False + + lambda_policy = functools.partial(lambda_auto_wrap_policy, lambda_fn=lambda_policy_fn) + transformer_wrap_policy = functools.partial( + transformer_auto_wrap_policy, + transformer_layer_cls=( + PrefixEncoder, + PromptEncoder, + PromptEmbedding, + FullyShardedDataParallelPlugin.get_module_class_from_name( + model, os.environ.get("FSDP_TRANSFORMER_CLS_TO_WRAP", "") + ), + ), + ) + + auto_wrap_policy = functools.partial(_or_policy, policies=[lambda_policy, transformer_wrap_policy]) + return auto_wrap_policy + + +def transpose(weight, fan_in_fan_out): + return weight.T if fan_in_fan_out else weight diff --git a/04-gene-sft/peft/src/peft/utils/save_and_load.py b/04-gene-sft/peft/src/peft/utils/save_and_load.py new file mode 100644 index 0000000000000000000000000000000000000000..c6596c7ce62afb622530350527838cf2c2d2929a --- /dev/null +++ b/04-gene-sft/peft/src/peft/utils/save_and_load.py @@ -0,0 +1,79 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .config import PeftType + + +def get_peft_model_state_dict(model, state_dict=None): + """ + Get the state dict of the Peft model. + + Args: + model ([`PeftModel`]): The Peft model. When using torch.nn.DistributedDataParallel, DeepSpeed or FSDP, + the model should be the underlying model/unwrapped model (i.e. model.module). + state_dict (`dict`, *optional*, defaults to `None`): + The state dict of the model. If not provided, the state dict of the model + will be used. + """ + if state_dict is None: + state_dict = model.state_dict() + if model.peft_config.peft_type == PeftType.LORA: + # to_return = lora_state_dict(model, bias=model.peft_config.bias) + # adapted from `https://github.com/microsoft/LoRA/blob/main/loralib/utils.py` + # to directly with the state dict which is necessary when using DeepSpeed or FSDP + bias = model.peft_config.bias + if bias == "none": + to_return = {k: state_dict[k] for k in state_dict if "lora_" in k} + elif bias == "all": + to_return = {k: state_dict[k] for k in state_dict if "lora_" in k or "bias" in k} + elif bias == "lora_only": + to_return = {} + for k in state_dict: + if "lora_" in k: + to_return[k] = state_dict[k] + bias_name = k.split("lora_")[0] + "bias" + if bias_name in state_dict: + to_return[bias_name] = state_dict[bias_name] + else: + raise NotImplementedError + else: + to_return = {} + if model.peft_config.inference_mode: + prompt_embeddings = model.prompt_encoder.embedding.weight + else: + prompt_embeddings = model.get_prompt_embedding_to_save() + to_return["prompt_embeddings"] = prompt_embeddings + if model.modules_to_save is not None: + for key, value in state_dict.items(): + if any(module_name in key for module_name in model.modules_to_save): + to_return[key] = value + return to_return + + +def set_peft_model_state_dict(model, peft_model_state_dict): + """ + Set the state dict of the Peft model. + + Args: + model ([`PeftModel`]): The Peft model. + peft_model_state_dict (`dict`): The state dict of the Peft model. + """ + + model.load_state_dict(peft_model_state_dict, strict=False) + if model.peft_config.peft_type != PeftType.LORA: + model.prompt_encoder.embedding.load_state_dict( + {"weight": peft_model_state_dict["prompt_embeddings"]}, strict=True + ) + return model diff --git a/04-gene-sft/peft/tests/test_config.py b/04-gene-sft/peft/tests/test_config.py new file mode 100644 index 0000000000000000000000000000000000000000..34fdc44a7dfe0a9ea6ac42eb4ffb43b6a3ffe6f5 --- /dev/null +++ b/04-gene-sft/peft/tests/test_config.py @@ -0,0 +1,96 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import tempfile +import unittest + +from peft import LoraConfig, PrefixTuningConfig, PromptEncoderConfig, PromptTuningConfig + + +class PeftConfigTestMixin: + all_config_classes = ( + LoraConfig, + PromptEncoderConfig, + PrefixTuningConfig, + PromptTuningConfig, + ) + + +class PeftConfigTester(unittest.TestCase, PeftConfigTestMixin): + def test_methods(self): + r""" + Test if all configs have the expected methods. Here we test + - to_dict + - save_pretrained + - from_pretrained + - from_json_file + """ + # test if all configs have the expected methods + for config_class in self.all_config_classes: + config = config_class() + self.assertTrue(hasattr(config, "to_dict")) + self.assertTrue(hasattr(config, "save_pretrained")) + self.assertTrue(hasattr(config, "from_pretrained")) + self.assertTrue(hasattr(config, "from_json_file")) + + def test_task_type(self): + for config_class in self.all_config_classes: + # assert this will not fail + _ = config_class(task_type="test") + + def test_save_pretrained(self): + r""" + Test if the config is correctly saved and loaded using + - save_pretrained + """ + for config_class in self.all_config_classes: + config = config_class() + with tempfile.TemporaryDirectory() as tmp_dirname: + config.save_pretrained(tmp_dirname) + + config_from_pretrained = config_class.from_pretrained(tmp_dirname) + self.assertEqual(config.to_dict(), config_from_pretrained.to_dict()) + + def test_from_json_file(self): + for config_class in self.all_config_classes: + config = config_class() + with tempfile.TemporaryDirectory() as tmp_dirname: + config.save_pretrained(tmp_dirname) + + config_from_json = config_class.from_json_file(os.path.join(tmp_dirname, "adapter_config.json")) + self.assertEqual(config.to_dict(), config_from_json) + + def test_to_dict(self): + r""" + Test if the config can be correctly converted to a dict using: + - to_dict + - __dict__ + """ + for config_class in self.all_config_classes: + config = config_class() + self.assertEqual(config.to_dict(), config.__dict__) + self.assertTrue(isinstance(config.to_dict(), dict)) + + def test_set_attributes(self): + # manually set attributes and check if they are correctly written + for config_class in self.all_config_classes: + config = config_class(peft_type="test") + + # save pretrained + with tempfile.TemporaryDirectory() as tmp_dirname: + config.save_pretrained(tmp_dirname) + + config_from_pretrained = config_class.from_pretrained(tmp_dirname) + self.assertEqual(config.to_dict(), config_from_pretrained.to_dict()) diff --git a/04-gene-sft/peft/tests/test_peft_model.py b/04-gene-sft/peft/tests/test_peft_model.py new file mode 100644 index 0000000000000000000000000000000000000000..0e85c7ca13903f3d9a2ca2a5d693f7c63009f617 --- /dev/null +++ b/04-gene-sft/peft/tests/test_peft_model.py @@ -0,0 +1,173 @@ +# coding=utf-8 +# Copyright 2023-present the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import tempfile +import unittest + +import torch +from transformers import AutoModelForCausalLM + +from peft import ( + LoraConfig, + PeftModel, + PrefixTuningConfig, + PromptEncoderConfig, + PromptTuningConfig, + get_peft_model, + get_peft_model_state_dict, + prepare_model_for_training, +) + + +class PeftTestMixin: + checkpoints_to_test = [ + "hf-internal-testing/tiny-random-OPTForCausalLM", + ] + config_classes = ( + LoraConfig, + PrefixTuningConfig, + PromptEncoderConfig, + PromptTuningConfig, + ) + config_kwargs = ( + { + "r": 8, + "lora_alpha": 32, + "target_modules": ["q_proj", "v_proj"], + "lora_dropout": 0.05, + "bias": "none", + "task_type": "CAUSAL_LM", + }, + { + "num_virtual_tokens": 10, + "task_type": "CAUSAL_LM", + }, + { + "num_virtual_tokens": 10, + "encoder_hidden_size": 32, + "task_type": "CAUSAL_LM", + }, + { + "num_virtual_tokens": 10, + "task_type": "CAUSAL_LM", + }, + ) + + +class PeftModelTester(unittest.TestCase, PeftTestMixin): + r""" + Test if the PeftModel behaves as expected. This includes: + - test if the model has the expected methods + """ + + def test_attributes_model(self): + for model_id in self.checkpoints_to_test: + for i, config_cls in enumerate(self.config_classes): + model = AutoModelForCausalLM.from_pretrained(model_id) + config = config_cls( + base_model_name_or_path=model_id, + **self.config_kwargs[i], + ) + model = get_peft_model(model, config) + + self.assertTrue(hasattr(model, "save_pretrained")) + self.assertTrue(hasattr(model, "from_pretrained")) + self.assertTrue(hasattr(model, "push_to_hub")) + + def test_prepare_for_training(self): + r""" + A test that checks if `prepare_for_training` behaves as expected + """ + for model_id in self.checkpoints_to_test: + for i, config_cls in enumerate(self.config_classes): + model = AutoModelForCausalLM.from_pretrained(model_id) + config = config_cls( + base_model_name_or_path=model_id, + **self.config_kwargs[i], + ) + model = get_peft_model(model, config) + + dummy_input = torch.LongTensor([[1, 1, 1]]) + dummy_output = model.get_input_embeddings()(dummy_input) + + self.assertTrue(not dummy_output.requires_grad) + + # load with `prepare_model_for_training` + model = AutoModelForCausalLM.from_pretrained(model_id) + model = prepare_model_for_training(model) + + for param in model.parameters(): + self.assertTrue(not param.requires_grad) + + config = config_cls( + base_model_name_or_path=model_id, + **self.config_kwargs[i], + ) + model = get_peft_model(model, config) + + dummy_input = torch.LongTensor([[1, 1, 1]]) + dummy_output = model.get_input_embeddings()(dummy_input) + + self.assertTrue(dummy_output.requires_grad) + + def test_save_pretrained(self): + r""" + A test to check if `save_pretrained` behaves as expected. This function should only save the state dict of the + adapter model and not the state dict of the base model. Hence inside each saved directory you should have: + + - README.md (that contains an entry `base_model`) + - adapter_config.json + - adapter_model.bin + + """ + for model_id in self.checkpoints_to_test: + for i, config_cls in enumerate(self.config_classes): + model = AutoModelForCausalLM.from_pretrained(model_id) + config = config_cls( + base_model_name_or_path=model_id, + **self.config_kwargs[i], + ) + model = get_peft_model(model, config) + model.to(model.device) + + with tempfile.TemporaryDirectory() as tmp_dirname: + model.save_pretrained(tmp_dirname) + + model_from_pretrained = AutoModelForCausalLM.from_pretrained(model_id) + model_from_pretrained = PeftModel.from_pretrained(model_from_pretrained, tmp_dirname) + model_from_pretrained.to(model.device) + + # check if the state dicts are equal + state_dict = get_peft_model_state_dict(model) + state_dict_from_pretrained = get_peft_model_state_dict(model_from_pretrained) + + # check if same keys + self.assertEqual(state_dict.keys(), state_dict_from_pretrained.keys()) + + # check if tensors equal + for key in state_dict.keys(): + self.assertTrue(torch.allclose(state_dict[key], state_dict_from_pretrained[key])) + + # check if `adapter_model.bin` is present + self.assertTrue(os.path.exists(os.path.join(tmp_dirname, "adapter_model.bin"))) + + # check if `adapter_config.json` is present + self.assertTrue(os.path.exists(os.path.join(tmp_dirname, "adapter_config.json"))) + + # check if `pytorch_model.bin` is not present + self.assertFalse(os.path.exists(os.path.join(tmp_dirname, "pytorch_model.bin"))) + + # check if `config.json` is not present + self.assertFalse(os.path.exists(os.path.join(tmp_dirname, "config.json"))) diff --git a/04-gene-sft/pip_list.txt b/04-gene-sft/pip_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..0f64a502f7e4eb45f58e5b38a9224ef29f88575e --- /dev/null +++ b/04-gene-sft/pip_list.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:828ede2bc2aeec77ad37dd1e5ba9ff2fbbb481dbe3c2729b6cdafc4feb7ac669 +size 7419 diff --git a/04-gene-sft/run_clm_pt_with_peft.py b/04-gene-sft/run_clm_pt_with_peft.py index 18d496816de64ce38d1bc4ed66960cc53980cf94..d1124de99ad47931b0d74016036e28699b352693 100644 --- a/04-gene-sft/run_clm_pt_with_peft.py +++ b/04-gene-sft/run_clm_pt_with_peft.py @@ -21,6 +21,17 @@ https://huggingface.co/models?filter=text-generation """ # You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments. +import subprocess +import os +# 设置环境变量, autodl一般区域 +result = subprocess.run('bash -c "source /etc/network_turbo && env | grep proxy"', shell=True, capture_output=True, text=True) +output = result.stdout +for line in output.splitlines(): + if '=' in line: + var, value = line.split('=', 1) + os.environ[var] = value + + import logging import numpy as np import math diff --git a/04-gene-sft/run_pt.sh b/04-gene-sft/run_pt.sh index 49910149663b2183acdcb38eea0703d567a0f7ba..b12e274a3f3416ed2b0f332bbf2d1ca0d00515fd 100644 --- a/04-gene-sft/run_pt.sh +++ b/04-gene-sft/run_pt.sh @@ -6,17 +6,17 @@ modules_to_save="embed_tokens,lm_head" lora_dropout=0.05 pretrained_model=./llama-7b-hf -dna_eng_tokenizer_path=./merged_dna_eng_tokenizer_hf +dna_eng_tokenizer_path=./merged_gene_eng_tokenizer_hf dataset_dir=./train_data data_cache=temp_data_cache_dir -per_device_train_batch_size=32 -per_device_eval_batch_size=32 +per_device_train_batch_size=4 +per_device_eval_batch_size=4 gradient_accumulation_steps=8 output_dir=dnahlm_llama_7b deepspeed_config_file=ds_zero2_no_offload.json -torchrun --nnodes 1 --nproc_per_node 6 run_clm_pt_with_peft.py \ +torchrun --nnodes 1 --nproc_per_node 10 run_clm_pt_with_peft.py \ --deepspeed ${deepspeed_config_file} \ --model_name_or_path ${pretrained_model} \ --tokenizer_name_or_path ${dna_eng_tokenizer_path} \ @@ -39,8 +39,8 @@ torchrun --nnodes 1 --nproc_per_node 6 run_clm_pt_with_peft.py \ --save_total_limit 3 \ --save_steps 200 \ --gradient_accumulation_steps ${gradient_accumulation_steps} \ - --preprocessing_num_workers 128 \ - --block_size 512 \ + --preprocessing_num_workers 64 \ + --block_size 256 \ --output_dir ${output_dir} \ --overwrite_output_dir \ --ddp_timeout 30000 \ diff --git a/04-gene-sft/run_sft.sh b/04-gene-sft/run_sft.sh index e8b9bbebf109f97ed621611baee90fa0465b6bd1..26b6fd178fc2dfda8cc14cbdbf6d183ab0b0dd84 100644 --- a/04-gene-sft/run_sft.sh +++ b/04-gene-sft/run_sft.sh @@ -8,8 +8,8 @@ lora_dropout=0.05 pretrained_model=dnahlm-merge-hf chinese_tokenizer_path=dnahlm-merge-hf dataset_dir=sft_data -per_device_train_batch_size=32 -per_device_eval_batch_size=32 +per_device_train_batch_size=8 +per_device_eval_batch_size=8 gradient_accumulation_steps=8 output_dir=dnahlm-llama7b-sft #peft_model=peft_model/dir @@ -29,7 +29,7 @@ torchrun --nnodes 1 --nproc_per_node 6 run_clm_sft_with_peft.py \ --do_eval \ --seed $RANDOM \ --fp16 \ - --num_train_epochs 8 \ + --num_train_epochs 3 \ --lr_scheduler_type cosine \ --learning_rate ${lr} \ --warmup_ratio 0.03 \ @@ -43,7 +43,7 @@ torchrun --nnodes 1 --nproc_per_node 6 run_clm_sft_with_peft.py \ --save_steps 200 \ --gradient_accumulation_steps ${gradient_accumulation_steps} \ --preprocessing_num_workers 4 \ - --max_seq_length 512 \ + --max_seq_length 256 \ --output_dir ${output_dir} \ --overwrite_output_dir \ --ddp_timeout 30000 \ diff --git a/04-gene-sft/train_data/dna.txt b/04-gene-sft/train_data/dna.txt new file mode 100644 index 0000000000000000000000000000000000000000..e838a022cba133e363d4db4fca8e6b6af3335596 --- /dev/null +++ b/04-gene-sft/train_data/dna.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9b58840b28b5555041e36999374f9f0f7cffd773bbdfe9d757cb55f644b4d0f5 +size 108067086 diff --git a/04-gene-sft/train_data/eng.txt b/04-gene-sft/train_data/eng.txt new file mode 100644 index 0000000000000000000000000000000000000000..4be43e7e963ca9ac541243a17b633c171d9458f0 --- /dev/null +++ b/04-gene-sft/train_data/eng.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:21efe0f1436f5303ab1f33040aec32abefdedb56927bf701463f91977686a0f4 +size 54020491 diff --git a/04-gene-sft/train_data/protein.txt b/04-gene-sft/train_data/protein.txt new file mode 100644 index 0000000000000000000000000000000000000000..970d1b666220a6290804e84700ca0a319facf000 --- /dev/null +++ b/04-gene-sft/train_data/protein.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:be37bbff762a7929889f62eebe37b000a15898dbf38dab5aae4c4f5caca8393c +size 105969407