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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "f3ad096d-492a-4da2-a390-03c7e7453821",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/maris/miniconda3/envs/dnagpt/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"Generating train split: 25600 examples [00:00, 82207.06 examples/s]\n"
]
},
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['seq_id_a', 'seq_id_b', 'seq_type_a', 'seq_type_b', 'seq_a', 'seq_b', 'label'],\n",
" num_rows: 25600\n",
" })\n",
"})"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datasets import load_dataset\n",
"from transformers import AutoTokenizer, DataCollatorWithPadding\n",
"from transformers import Trainer\n",
"import evaluate\n",
"import numpy as np\n",
"from transformers import TrainingArguments\n",
"from transformers import AutoModelForSequenceClassification\n",
"\n",
"raw_datasets = load_dataset('csv', data_files='central_dogma.csv')\n",
"raw_datasets"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8d89ca1d-1968-43d3-8d47-f9f87b02cd02",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'LKIELASSYGFCFGVKRAIKIAENAGDAATIGPLIHNNEEINRLATNFNVKTLHGINELKDEKKAIIRTHGITKSDLAELKKTDIKVIDATCPFVTKPQQICEDMSNAGYDVVIFGDENHPEVKGVKSYASGKVYVVLDESELEGVKFRQKVALVSQTTRKVEKFMQIANYLMLRVKEVRVFNTICNATFENQEAVKNLAKRADVMIVIGGKNSSNTKQLYLISKNFCEDSYLIESEHEVEKSWFEGKNLCGISAGASTPDWIIQKVVDAIEKF*'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from Bio.Seq import Seq\n",
"\n",
"def translate_dna_to_protein_biopython(dna_sequence):\n",
" \"\"\"Translate a DNA sequence into its corresponding protein sequence using Biopython.\"\"\"\n",
" # 确保输入的是大写的DNA序列\n",
" dna_seq = Seq(dna_sequence.upper())\n",
" \n",
" # 使用Biopython内置方法进行翻译\n",
" protein_seq = dna_seq.translate(to_stop=False) # 如果需要在终止密码子处停止，请设置to_stop=True\n",
" \n",
" return str(protein_seq)\n",
"\n",
"def trim_sequence(sequence, front, length):\n",
" \"\"\"Trim the specified number of characters from the start and end of a string.\"\"\"\n",
" # if len(sequence) <= front + back:\n",
" # raise ValueError(\"The sequence is too short to trim the specified number of characters.\")\n",
" # return sequence[front:-back] if back > 0 else sequence[front:]\n",
" return sequence[front:front+length]\n",
"\n",
"translate_dna_to_protein_biopython(\"TTGAAGATTGAGCTTGCTAGCAGCTACGGCTTTTGCTTTGGGGTAAAGCGCGCCATAAAGATAGCCGAAAATGCGGGCGATGCCGCTACTATCGGGCCTCTCATACATAATAACGAAGAGATAAACCGCCTGGCTACGAATTTCAATGTCAAGACCCTCCACGGCATAAATGAGCTAAAGGACGAGAAAAAGGCCATCATACGCACTCACGGTATCACAAAAAGCGATCTGGCCGAGCTTAAAAAGACCGATATCAAAGTCATAGACGCCACTTGCCCGTTCGTGACCAAGCCGCAGCAAATTTGCGAGGATATGAGCAACGCAGGATACGATGTCGTGATATTTGGCGATGAAAATCATCCCGAAGTCAAAGGAGTGAAGTCCTATGCCAGCGGAAAGGTTTATGTCGTGCTCGATGAGAGCGAGCTTGAGGGAGTGAAATTTAGACAAAAGGTAGCACTCGTCAGTCAAACGACGCGCAAAGTCGAAAAATTTATGCAAATAGCGAACTACTTGATGCTACGCGTCAAAGAGGTGCGAGTTTTCAACACTATCTGCAACGCGACCTTCGAGAATCAGGAGGCGGTCAAAAATTTAGCCAAAAGAGCCGATGTGATGATAGTCATCGGTGGTAAAAATAGCTCTAATACAAAGCAGCTTTATCTGATATCTAAAAATTTCTGCGAGGACAGCTACCTGATAGAGAGCGAACACGAAGTCGAGAAAAGCTGGTTTGAAGGCAAGAATTTATGCGGTATAAGTGCGGGAGCGAGCACGCCTGATTGGATCATACAAAAAGTCGTCGACGCGATAGAGAAATTTTAA\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "682a14ef-7492-434e-bed0-af0f5c03db86",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/maris/miniconda3/envs/dnagpt/lib/python3.11/site-packages/Bio/Seq.py:2879: BiopythonWarning: Partial codon, len(sequence) not a multiple of three. Explicitly trim the sequence or add trailing N before translation. This may become an error in future.\n",
" warnings.warn(\n"
]
}
],
"source": [
"#获得完全匹配的正例\n",
"not_match_list = []\n",
"pos_select_list = [] #正例\n",
"neg_select_list = [] #负例\n",
"\n",
"for item in raw_datasets[\"train\"]:\n",
" example_dna = item[\"seq_a\"]\n",
" example_protein = item[\"seq_b\"]\n",
" label = item[\"label\"]\n",
"\n",
" if 0==label: #负例都要\n",
" neg_select_list.append(item)\n",
" \n",
" dna_length = len(example_protein)*3\n",
" trimmed_sequence = trim_sequence(example_dna,100,dna_length)\n",
" protein_trans = translate_dna_to_protein_biopython(trimmed_sequence)\n",
"\n",
" if 1==label:\n",
" if protein_trans[1:-2]!=example_protein[1:-2]: #运行有前后1个字符不一样的\n",
" not_match_list.append(item)\n",
" else:\n",
" pos_select_list.append(item)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9abb1e66-5d3a-4d87-9bf3-577cec8efcf9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"最长的蛋白质序列: MARRPLVMGNWKLNGSKAFTKELITGLKDELNAVSGCDVAIAPPVMYLAEAETALVSSDIALGTQNVDLNKQGAFTGDISTEMLKDFGVKYVIIGHSERRQYHHESDEFIAKKFGVLKDAGLVPVLCIGESEAENEAGKTEEVCARQIDAVMNTLGVEAFNGAVIAYEPIWAIGTGKSATPAQAQAVHAFIRGHIAKQSQAVAERVIIQYGGSVNDANAAELFTQPDIDGALVGGASLKASAFAVIVKAAAKAKN\n"
]
}
],
"source": [
"from Bio.Seq import Seq\n",
"\n",
"def find_longest_orf(dna_sequence_str):\n",
" dna_sequence = Seq(dna_sequence_str)\n",
" # 定义可能的终止密码子\n",
" stop_codons = ['TAA', 'TAG', 'TGA']\n",
" \n",
" # 初始化最长ORF和其长度\n",
" longest_orf = ''\n",
" longest_length = 0\n",
" \n",
" # 遍历正向链的三个阅读框\n",
" for frame in range(3):\n",
" seq = dna_sequence[frame:]\n",
" start = None\n",
" for i in range(0, len(seq) - 2, 3):\n",
" codon = seq[i:i+3]\n",
" if codon == 'ATG' and start is None:\n",
" start = i\n",
" if codon in stop_codons and start is not None:\n",
" orf = seq[start:i+3]\n",
" if len(orf) > longest_length:\n",
" longest_orf = orf\n",
" longest_length = len(orf)\n",
" start = None\n",
" \n",
" # 遍历反向互补链的三个阅读框\n",
" rev_seq = dna_sequence.reverse_complement()\n",
" for frame in range(3):\n",
" seq = rev_seq[frame:]\n",
" start = None\n",
" for i in range(0, len(seq) - 2, 3):\n",
" codon = seq[i:i+3]\n",
" if codon == 'ATG' and start is None:\n",
" start = i\n",
" if codon in stop_codons and start is not None:\n",
" orf = seq[start:i+3]\n",
" if len(orf) > longest_length:\n",
" longest_orf = orf\n",
" longest_length = len(orf)\n",
" start = None\n",
" \n",
" # 翻译最长ORF，去除终止符号\n",
" if longest_orf:\n",
" protein_sequence = longest_orf.translate(to_stop=True)\n",
" return str(protein_sequence)\n",
" else:\n",
" return \"-\"\n",
"\n",
"# 示例DNA序列\n",
"dna_sequence = \"TCAGTTTTTTGCTTTCGCCGCCGCTTTAACAATGACAGCGAACGCTGACGCTTTTAACGATGCACCACCAACTAATGCACCATCAATATCAGGCTGAGTAAACAATTCTGCTGCATTTGCATCATTGACGGAACCGCCATATTGAATAATTACCCGTTCAGCAACGGCTTGGCTTTGTTTTGCAATATGACCTCGAATAAAGGCATGTACTGCTTGTGCTTGAGCTGGAGTCGCCGATTTACCTGTACCGATAGCCCAAATCGGTTCATAAGCGATTACTGCACCGTTAAATGCTTCAACACCTAGTGTATTCATCACCGCATCAATTTGACGTGCACAAACCTCTTCCGTTTTGCCTGCTTCATTTTCAGCTTCGCTTTCACCGATACATAATACAGGAACTAAACCAGCATCTTTTAACACACCAAATTTTTTCGCAATAAATTCATCACTTTCATGATGATATTGACGTCGCTCAGAATGACCGATAATGACATATTTTACACCAAAGTCTTTTAACATTTCTGTTGAAATATCACCGGTAAATGCACCTTGTTTGTTTAAATCAACATTTTGAGTACCTAAAGCAATATCACTGCTGACCAGTGCAGTTTCAGCTTCCGCTAAATACATGACAGGCGGTGCAATTGCCACATCACAGCCTGACACCGCATTAAGTTCATCTTTTAAACCGGTAATAAGTTCTTTTGTAAAGGCTTTACTACCATTTAATTTCCAGTTACCCATGACTAAAGGACGACGAGCCAT\"\n",
"\n",
"# 获取最长的蛋白质序列\n",
"protein_sequence = find_longest_orf(dna_sequence)\n",
"print(\"最长的蛋白质序列:\", protein_sequence)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "f42457cf-f946-46e5-9d31-b3c8947c0182",
"metadata": {},
"outputs": [],
"source": [
"#获得ORF完全匹配的\n",
"not_match_list1 = []\n",
"for item in not_match_list:\n",
" example_dna = item[\"seq_a\"]\n",
" example_protein = item[\"seq_b\"]\n",
" label = item[\"label\"]\n",
" \n",
" protein_trans = find_longest_orf(example_dna)\n",
"\n",
" if 1==label:\n",
" if example_protein.find(protein_trans[1:-2])==-1:#包含即可,前后可以相差几个字母\n",
" not_match_list1.append(item)\n",
" else:\n",
" pos_select_list.append(item)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "4116c8f9-277d-41eb-bd4b-e66e20336ab5",
"metadata": {},
"outputs": [],
"source": [
"#ORF匹配较多的\n",
"from Bio import Align\n",
"\n",
"# 创建PairwiseAligner对象\n",
"aligner = Align.PairwiseAligner()\n",
"\n",
"# 设置比对模式为全局比对\n",
"aligner.mode = \"global\"\n",
"\n",
"for item in not_match_list1:\n",
" example_dna = item[\"seq_a\"]\n",
" example_protein = item[\"seq_b\"]\n",
" label = item[\"label\"]\n",
" \n",
" protein_trans = find_longest_orf(example_dna)\n",
"\n",
" \n",
" if 1==label:\n",
"\n",
" alignments = aligner.align(example_protein, protein_trans)\n",
" score = alignments[0].score\n",
" protein_trans_len = len(protein_trans)\n",
"\n",
" sim_score = score/protein_trans_len\n",
"\n",
" \n",
" if sim_score > 0.8:#匹配较高的\n",
" pos_select_list.append(item)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b7a303cc-1bfc-4fe4-9428-addca7b000ea",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"17067 6309\n"
]
}
],
"source": [
"print(len(neg_select_list),len(pos_select_list))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "37ee3361-2fd6-441b-bb59-f355a95debae",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"target 0 MA--ES-REPR-GAVE----A----EL-DPVEYTLRK------R-----L------P-H-\n",
" 0 |---|--|----||------|----|--|-------|------|-----|------|-|-\n",
"query 0 M-WQE-AR---HGA--WHRRATSGSE-QD-------KKKFIGGRWSHTPLVQKTTVPVHG\n",
"\n",
"target 28 ---RLP--R-RPN-DVYVNMKTDFK-AQL------A----RCQKLLDCG-ARG-------\n",
" 60 ---|-|--|-|---|------|----|--------|----|-----|---|-|-------\n",
"query 45 HGER-PTSRGR--QD------T---TA--PRTPSSAGSVPR-----D--VA-GGPPRRLA\n",
"\n",
"target 62 Q-SACSEIYIHGLG-L----AIN----RA--INIA-LQLQAGS-F---GALQ--VAAN--\n",
" 120 |-|-------|-|--|----|------|---|--|-|-|-----|---|-----|-----\n",
"query 83 QKS-------H-L-WLGYWGA--TLKTR-MWI--AEL-L----RFRITG---SRV---SV\n",
"\n",
"target 101 ------TS-TVELVDELEPE--TDT-RE-PVI-------RN-----RNNS--AIHIRVF-\n",
" 180 ------||-|||-|--|-----|---|--|---------|------|-----|---|---\n",
"query 118 SGSSSSTSSTVE-V--L---AAT--CR-AP--KLPACSCR-AMLMAR---LMA---R--P\n",
"\n",
"target 135 RVAPQ-- 140\n",
" 240 |--|--- 247\n",
"query 158 R--P-WM 162\n",
"\n",
"比对得分: 55.0\n",
"查询序列: MWQEARHGAWHRRATSGSEQDKKKFIGGRWSHTPLVQKTTVPVHGHGERPTSRGRQDTTAPRTPSSAGSVPRDVAGGPPRRLAQKSHLWLGYWGATLKTRMWIAELLRFRITGSRVSVSGSSSSTSSTVEVLAATCRAPKLPACSCRAMLMARLMARPRPWM\n",
"目标序列: MAESREPRGAVEAELDPVEYTLRKRLPHRLPRRPNDVYVNMKTDFKAQLARCQKLLDCGARGQSACSEIYIHGLGLAINRAINIALQLQAGSFGALQVAANTSTVELVDELEPETDTREPVIRNRNNSAIHIRVFRVAPQ\n",
"-----------------------------------------------------------\n",
"\n"
]
}
],
"source": [
"from Bio import Align\n",
"\n",
"# 创建PairwiseAligner对象\n",
"aligner = Align.PairwiseAligner()\n",
"\n",
"# 设置比对模式为全局比对\n",
"aligner.mode = \"global\"\n",
"\n",
"\n",
"# 示例蛋白质序列\n",
"seq1 = \"MAESREPRGAVEAELDPVEYTLRKRLPHRLPRRPNDVYVNMKTDFKAQLARCQKLLDCGARGQSACSEIYIHGLGLAINRAINIALQLQAGSFGALQVAANTSTVELVDELEPETDTREPVIRNRNNSAIHIRVFRVAPQ\"\n",
"seq2 = \"MWQEARHGAWHRRATSGSEQDKKKFIGGRWSHTPLVQKTTVPVHGHGERPTSRGRQDTTAPRTPSSAGSVPRDVAGGPPRRLAQKSHLWLGYWGATLKTRMWIAELLRFRITGSRVSVSGSSSSTSSTVEVLAATCRAPKLPACSCRAMLMARLMARPRPWM\"\n",
"\n",
"\n",
"# 执行比对\n",
"alignments = aligner.align(seq1, seq2)\n",
"\n",
"# 输出比对结果\n",
"for alignment in alignments:\n",
" print(alignment)\n",
" print(f\"比对得分: {alignment.score}\")\n",
" print(f\"查询序列: {alignment.query}\")\n",
" print(f\"目标序列: {alignment.target}\")\n",
" print(\"-----------------------------------------------------------\\n\")\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a3499a28-f01d-4e48-9c49-916763cde800",
"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",
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|