Upload folder using huggingface_hub

.gitattributes

@@ -57,3 +57,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+MHA/go_info/go-basic.obo filter=lfs diff=lfs merge=lfs -text
+MHA/learning_superset/large_learning_superset.fasta filter=lfs diff=lfs merge=lfs -text
+MHA/learning_superset/large_learning_superset_term.tsv filter=lfs diff=lfs merge=lfs -text
+dataset/go_info/go-basic.obo filter=lfs diff=lfs merge=lfs -text
+dataset/learning_superset/large_learning_superset.fasta filter=lfs diff=lfs merge=lfs -text
+dataset/learning_superset/large_learning_superset_term.tsv filter=lfs diff=lfs merge=lfs -text
+src/mlflow.db filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,32 @@
+# User specified ignores
+uniprot_f_set/
+config.json
+plan.md
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# IDEs
+.vscode/
+.idea/
+
+# Large files
+*.pth
+*.ckpt
+
+# Output directories
+metrics/
+mlruns/
+outputs/
+preds_temp/

README.md

@@ -0,0 +1,2 @@
+# taxonomy_aware_ESM2
+This repository implements a Taxonomy-Aware Protein Function Prediction model. It synergizes the structural language understanding of ESM2 (Evolutionary Scale Modeling) with explicit phylogenetic lineage information.

azure_ml_taxonomy_aware_submit.ipynb

@@ -0,0 +1,252 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# Taxonomy Aware ESM Training Submission\n",
+                "\n",
+                "This notebook submits the training job to Azure ML.\n",
+                "It uses `src/train.py` and the dataset `azureml:MHD:2`."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Connecting to Azure ML...\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Found the config file in: .\\config.json\n",
+                        "Overriding of current TracerProvider is not allowed\n",
+                        "Overriding of current LoggerProvider is not allowed\n",
+                        "Overriding of current MeterProvider is not allowed\n",
+                        "Attempting to instrument while already instrumented\n",
+                        "Attempting to instrument while already instrumented\n",
+                        "Attempting to instrument while already instrumented\n",
+                        "Attempting to instrument while already instrumented\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Connected to workspace: azure_ML\n"
+                    ]
+                }
+            ],
+            "source": [
+                "from azure.ai.ml import MLClient, command, Input\n",
+                "from azure.identity import InteractiveBrowserCredential\n",
+                "from azure.ai.ml.entities import Environment\n",
+                "import logging\n",
+                "import os\n",
+                "\n",
+                "# Decrease logging noise for Azure Identity\n",
+                "logging.getLogger(\"azure.identity\").setLevel(logging.WARNING)\n",
+                "\n",
+                "# 1. Connect to Azure ML\n",
+                "print(\"Connecting to Azure ML...\")\n",
+                "\n",
+                "# STRICT CONFIGURATION\n",
+                "TENANT_ID = \"3569a77b-5841-417b-b138-12b5fe698c9c\"\n",
+                "\n",
+                "# We enforce InteractiveBrowserCredential to ensure the correct tenant login.\n",
+                "try:\n",
+                "    credential = InteractiveBrowserCredential(tenant_id=TENANT_ID)\n",
+                "    # Force a token fetch to trigger the login prompt immediately\n",
+                "    credential.get_token(\"https://management.azure.com/.default\")\n",
+                "    \n",
+                "    ml_client = MLClient.from_config(credential=credential)\n",
+                "    print(f\"Connected to workspace: {ml_client.workspace_name}\")\n",
+                "except Exception as e:\n",
+                "    print(f\"Authentication failed: {e}\")\n",
+                "    raise e"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Using existing environment: cafa6-torch-env:5\n"
+                    ]
+                }
+            ],
+            "source": [
+                "# --- Environment Configuration ---\n",
+                "USE_EXIST_ENV = True  # Set to False to rebuild environment\n",
+                "ENV_NAME = \"cafa6-torch-env\"\n",
+                "ENV_VERSION = \"5\"      # Increment version\n",
+                "ENV_PATH = \"C:/workspace/CAFA_6_hjs/env\"\n",
+                "\n",
+                "if USE_EXIST_ENV:\n",
+                "    print(f\"Using existing environment: {ENV_NAME}:{ENV_VERSION}\")\n",
+                "    env = f\"{ENV_NAME}:{ENV_VERSION}\"\n",
+                "else:\n",
+                "    print(f\"Building new environment: {ENV_NAME}:{ENV_VERSION} from {ENV_PATH}\")\n",
+                "    env = Environment(\n",
+                "        name=ENV_NAME,\n",
+                "        version=ENV_VERSION,\n",
+                "        image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04\",\n",
+                "        conda_file=f\"{ENV_PATH}/conda.yaml\",\n",
+                "        description=\"Environment with PyTorch, ESM, and PEFT for LoRA\"\n",
+                "    )\n",
+                "    ml_client.environments.create_or_update(env)\n",
+                "    print(\"Environment built and registered.\")\n"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Parameter Center\n",
+                "Adjust the training hyperparameters below."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Training Command: python train.py --data_path ${inputs.dataset} --epochs 20 --batch_size 64 --lr 0.0001 --min_lr 1e-06 --num_workers 8 --T_0 10 --T_mult 1 --esm_model_name facebook/esm2_t6_8M_UR50D --gamma_neg 4 --gamma_pos 0 --clip 0.05 --use_lora True --lora_rank 8\n"
+                    ]
+                }
+            ],
+            "source": [
+                "# --- Training Hyperparameters ---\n",
+                "ESM_MODEL = \"facebook/esm2_t6_8M_UR50D\" \n",
+                "LEARNING_RATE = 1e-4\n",
+                "MIN_LR = 1e-6\n",
+                "BATCH_SIZE = 64\n",
+                "EPOCHS = 20\n",
+                "NUM_WORKERS = 8\n",
+                "\n",
+                "# --- Scheduler Settings (CosineAnnealingWarmRestarts) ---\n",
+                "T_0 = 10 \n",
+                "T_MULT = 1\n",
+                "\n",
+                "# --- Asymmetric Loss Parameters ---\n",
+                "GAMMA_NEG = 4\n",
+                "GAMMA_POS = 0\n",
+                "CLIP = 0.05\n",
+                "\n",
+                "# --- LoRA Parameters ---\n",
+                "USE_LORA = True\n",
+                "LORA_RANK = 8\n",
+                "\n",
+                "# --- Command Construction ---\n",
+                "training_command = (\n",
+                "    f\"python train.py \"\n",
+                "    f\"--data_path ${{inputs.dataset}} \"\n",
+                "    f\"--epochs {EPOCHS} \"\n",
+                "    f\"--batch_size {BATCH_SIZE} \"\n",
+                "    f\"--lr {LEARNING_RATE} \"\n",
+                "    f\"--min_lr {MIN_LR} \"\n",
+                "    f\"--num_workers {NUM_WORKERS} \"\n",
+                "    f\"--T_0 {T_0} \"\n",
+                "    f\"--T_mult {T_MULT} \"\n",
+                "    f\"--esm_model_name {ESM_MODEL} \"\n",
+                "    f\"--gamma_neg {GAMMA_NEG} \"\n",
+                "    f\"--gamma_pos {GAMMA_POS} \"\n",
+                "    f\"--clip {CLIP} \"\n",
+                "    f\"--use_lora {USE_LORA} \"\n",
+                "    f\"--lora_rank {LORA_RANK}\"\n",
+                ")\n",
+                "\n",
+                "print(f\"Training Command: {training_command}\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# 2. Configure Job\n",
+                "\n",
+                "job = command(\n",
+                "    code=\"./src\",\n",
+                "    command=training_command,\n",
+                "    inputs={\n",
+                "        \"dataset\": Input(\n",
+                "            type=\"uri_folder\",\n",
+                "            path=\"azureml:cafa6:3\"\n",
+                "        )\n",
+                "    },\n",
+                "    environment=f\"{ENV_NAME}:{ENV_VERSION}\", \n",
+                "    compute=\"FOR-CAFA-6\",\n",
+                "    display_name=\"TaxonomyAwareESM_LoRA\",\n",
+                "    experiment_name=\"taxonomy_aware_esm\"\n",
+                ")\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "\u001b[32mUploading src (9.53 MBs): 100%|##########| 9533825/9533825 [00:02<00:00, 3421392.45it/s] \n",
+                        "\u001b[39m\n",
+                        "\n",
+                        "Use of {} for parameters is deprecated, instead use ${{}}.\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Job submitted. Studio URL: https://ml.azure.com/runs/stoic_bone_p2s7v1g7zg?wsid=/subscriptions/c39e1d7a-fa06-4185-94ce-18b724cac3d8/resourcegroups/js_p_j/workspaces/azure_ML&tid=3569a77b-5841-417b-b138-12b5fe698c9c\n"
+                    ]
+                }
+            ],
+            "source": [
+                "# 3. Submit Job\n",
+                "returned_job = ml_client.create_or_update(job)\n",
+                "print(f\"Job submitted. Studio URL: {returned_job.studio_url}\")"
+            ]
+        }
+    ],
+    "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": "3.11.8"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
+}

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dataset/taxon_embedding/vocab/class_vocab.json

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+{
+  "<UNK>": 0,
+  "Acidimicrobiia": 1,
+  "Acidithiobacillia": 2,
+  "Aconoidasida": 3,
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+  "Wallemiomycetes": 270,
+  "Xanthophyceae": 271,
+  "Zygnematophyceae": 272
+}

dataset/taxon_embedding/vocab/family_vocab.json

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+{
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+  "Zygophyllaceae": 1990,
+  "Zymomonadaceae": 1991
+}

dataset/taxon_embedding/vocab/order_vocab.json

@@ -0,0 +1,784 @@
+{
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+  "Pelliales": 529,
+  "Peltigerales": 530,
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+  "Peptostreptococcales": 532,
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+  "Percopsiformes": 535,
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+  "Petromyzontiformes": 541,
+  "Petrotogales": 542,
+  "Pezizales": 543,
+  "Phaeomoniellales": 544,
+  "Phaffomycetales": 545,
+  "Phasmatodea": 546,
+  "Phlebobranchia": 547,
+  "Phoenicopteriformes": 548,
+  "Pholidota": 549,
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+  "Phyllodocida": 551,
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+  "Pilosa": 558,
+  "Pimascovirales": 559,
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+  "Pirellulales": 562,
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+  "Porphyridiales": 578,
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+  "Procellariiformes": 585,
+  "Prochlorotrichales": 586,
+  "Promethearchaeales": 587,
+  "Propionibacteriales": 588,
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+  "Prymnesiales": 591,
+  "Pseudanabaenales": 592,
+  "Pseudoditrichales": 593,
+  "Pseudomonadales": 594,
+  "Pseudonocardiales": 595,
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+  "Psittaciformes": 597,
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+  "Pucciniales": 600,
+  "Puniceicoccales": 601,
+  "Pyrenomonadales": 602,
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+  "Ranunculales": 605,
+  "Reovirales": 606,
+  "Rhabditida": 607,
+  "Rheiformes": 608,
+  "Rhizophydiales": 609,
+  "Rhizostomeae": 610,
+  "Rhodellales": 611,
+  "Rhodobacterales": 612,
+  "Rhodocyclales": 613,
+  "Rhodospirillales": 614,
+  "Rhodothalassiales": 615,
+  "Rhodothermales": 616,
+  "Rhodymeniales": 617,
+  "Rhynchobdellida": 618,
+  "Rickettsiales": 619,
+  "Rodentia": 620,
+  "Rosales": 621,
+  "Rowavirales": 622,
+  "Rubrobacterales": 623,
+  "Russulales": 624,
+  "Sabellida": 625,
+  "Saccharomycetales": 626,
+  "Saccharomycodales": 627,
+  "Salmoniformes": 628,
+  "Salviniales": 629,
+  "Sanitavirales": 630,
+  "Santalales": 631,
+  "Sapindales": 632,
+  "Saprolegniales": 633,
+  "Saprospirales": 634,
+  "Sarcoptiformes": 635,
+  "Saxifragales": 636,
+  "Scandentia": 637,
+  "Schizaeales": 638,
+  "Schizosaccharomycetales": 639,
+  "Scleractinia": 640,
+  "Scleralcyonacea": 641,
+  "Scolopendromorpha": 642,
+  "Scombriformes": 643,
+  "Scorpiones": 644,
+  "Scutigeromorpha": 645,
+  "Sebacinales": 646,
+  "Selaginellales": 647,
+  "Selenomonadales": 648,
+  "Semaeostomeae": 649,
+  "Semionotiformes": 650,
+  "Sepiida": 651,
+  "Sepolyvirales": 652,
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+  "Siphonaptera": 655,
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+  "Sobelivirales": 657,
+  "Solanales": 658,
+  "Solirubrobacterales": 659,
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+  "Sphaerobacterales": 662,
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+  "Sphaeropleales": 664,
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+  "Sphenisciformes": 666,
+  "Sphenodontia": 667,
+  "Sphingobacteriales": 668,
+  "Sphingomonadales": 669,
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+  "Spirogyrales": 671,
+  "Spirulinales": 672,
+  "Spizellomycetales": 673,
+  "Splachnales": 674,
+  "Spongillida": 675,
+  "Sporadotrichida": 676,
+  "Sporidiobolales": 677,
+  "Squaliformes": 678,
+  "Squamata": 679,
+  "Stellavirales": 680,
+  "Stolidobranchia": 681,
+  "Stomiiformes": 682,
+  "Streptosporangiales": 683,
+  "Strigeidida": 684,
+  "Strigiformes": 685,
+  "Struthioniformes": 686,
+  "Stylommatophora": 687,
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+  "Suessiales": 689,
+  "Sulfolobales": 690,
+  "Suliformes": 691,
+  "Synbranchiformes": 692,
+  "Syndiniales": 693,
+  "Synechococcales": 694,
+  "Synergistales": 695,
+  "Syngnathiformes": 696,
+  "Syntrophales": 697,
+  "Syntrophobacterales": 698,
+  "Takakiales": 699,
+  "Temnopleuroida": 700,
+  "Terebellida": 701,
+  "Terriglobales": 702,
+  "Testudines": 703,
+  "Tetractinellida": 704,
+  "Tetraodontiformes": 705,
+  "Thalassiosirales": 706,
+  "Thelebolales": 707,
+  "Thelephorales": 708,
+  "Thermales": 709,
+  "Thermoanaerobacterales": 710,
+  "Thermococcales": 711,
+  "Thermodesulfobacteriales": 712,
+  "Thermodesulfovibrionales": 713,
+  "Thermofilales": 714,
+  "Thermoleophilales": 715,
+  "Thermomicrobiales": 716,
+  "Thermoplasmatales": 717,
+  "Thermoproteales": 718,
+  "Thermosediminibacterales": 719,
+  "Thermostichales": 720,
+  "Thermosulfidibacterales": 721,
+  "Thermotogales": 722,
+  "Thiotrichales": 723,
+  "Thraustochytrida": 724,
+  "Thysanoptera": 725,
+  "Tinamiformes": 726,
+  "Tissierellales": 727,
+  "Tolivirales": 728,
+  "Tombendovirales": 729,
+  "Torpediniformes": 730,
+  "Trachichthyiformes": 731,
+  "Tremellales": 732,
+  "Trichinellida": 733,
+  "Trichomonadida": 734,
+  "Trichoplacea": 735,
+  "Trichosporonales": 736,
+  "Tricladida": 737,
+  "Trigonopsidales": 738,
+  "Tritrichomonadida": 739,
+  "Trochida": 740,
+  "Trochodendrales": 741,
+  "Trogoniformes": 742,
+  "Trombidiformes": 743,
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+  "Trypanosomatida": 745,
+  "Trypetheliales": 746,
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+  "Tubulavirales": 748,
+  "Tubulidentata": 749,
+  "Tymovirales": 750,
+  "Ulotrichales": 751,
+  "Ulvales": 752,
+  "Umbelopsidales": 753,
+  "Unionida": 754,
+  "Uranoscopiformes": 755,
+  "Ustilaginales": 756,
+  "Valvatida": 757,
+  "Vaucheriales": 758,
+  "Veillonellales": 759,
+  "Velatida": 760,
+  "Venerida": 761,
+  "Venturiales": 762,
+  "Verongiida": 763,
+  "Verrucomicrobiales": 764,
+  "Vibrionales": 765,
+  "Vinavirales": 766,
+  "Vitales": 767,
+  "Wallemiales": 768,
+  "Welwitschiales": 769,
+  "Winmispirales": 770,
+  "Wolframvirales": 771,
+  "Xenopneusta": 772,
+  "Xiphosura": 773,
+  "Xylariales": 774,
+  "Zeiformes": 775,
+  "Zingiberales": 776,
+  "Zoantharia": 777,
+  "Zurhausenvirales": 778,
+  "Zygentoma": 779,
+  "Zygnematales": 780,
+  "Zygophyllales": 781
+}

dataset/taxon_embedding/vocab/phylum_vocab.json

@@ -0,0 +1,122 @@
+{
+  "<UNK>": 0,
+  "Acanthocephala": 1,
+  "Acidobacteriota": 2,
+  "Actinomycetota": 3,
+  "Annelida": 4,
+  "Apicomplexa": 5,
+  "Aquificota": 6,
+  "Armatimonadota": 7,
+  "Arthropoda": 8,
+  "Artverviricota": 9,
+  "Ascomycota": 10,
+  "Bacillariophyta": 11,
+  "Bacillota": 12,
+  "Bacteroidota": 13,
+  "Basidiomycota": 14,
+  "Bdellovibrionota": 15,
+  "Blastocladiomycota": 16,
+  "Brachiopoda": 17,
+  "Bryozoa": 18,
+  "Campylobacterota": 19,
+  "Candidatus Bipolaricaulota": 20,
+  "Candidatus Cloacimonadota": 21,
+  "Candidatus Fraseribacteriota": 22,
+  "Candidatus Handelsmaniibacteriota": 23,
+  "Candidatus Heimdallarchaeota": 24,
+  "Candidatus Odinarchaeota": 25,
+  "Candidatus Terryibacteriota": 26,
+  "Candidatus Thorarchaeota": 27,
+  "Candidatus Yanofskyibacteriota": 28,
+  "Cercozoa": 29,
+  "Chaetognatha": 30,
+  "Chlamydiota": 31,
+  "Chlorobiota": 32,
+  "Chloroflexota": 33,
+  "Chlorophyta": 34,
+  "Chordata": 35,
+  "Chrysiogenota": 36,
+  "Chytridiomycota": 37,
+  "Ciliophora": 38,
+  "Cnidaria": 39,
+  "Commensaviricota": 40,
+  "Coprothermobacterota": 41,
+  "Cossaviricota": 42,
+  "Cressdnaviricota": 43,
+  "Ctenophora": 44,
+  "Cyanobacteriota": 45,
+  "Deferribacterota": 46,
+  "Deinococcota": 47,
+  "Dictyoglomota": 48,
+  "Discosea": 49,
+  "Duplornaviricota": 50,
+  "Echinodermata": 51,
+  "Elusimicrobiota": 52,
+  "Endomyxa": 53,
+  "Entoprocta": 54,
+  "Euglenozoa": 55,
+  "Evosea": 56,
+  "Fibrobacterota": 57,
+  "Foraminifera": 58,
+  "Fornicata": 59,
+  "Fusobacteriota": 60,
+  "Gemmatimonadota": 61,
+  "Haptophyta": 62,
+  "Hemichordata": 63,
+  "Heterolobosea": 64,
+  "Hofneiviricota": 65,
+  "Ignavibacteriota": 66,
+  "Kitrinoviricota": 67,
+  "Lenarviricota": 68,
+  "Methanobacteriota": 69,
+  "Microcaldota": 70,
+  "Microsporidia": 71,
+  "Mollusca": 72,
+  "Mucoromycota": 73,
+  "Mycoplasmatota": 74,
+  "Myxococcota": 75,
+  "Nanobdellota": 76,
+  "Negarnaviricota": 77,
+  "Nematoda": 78,
+  "Nemertea": 79,
+  "Nitrososphaerota": 80,
+  "Nitrospinota": 81,
+  "Nitrospirota": 82,
+  "Nucleocytoviricota": 83,
+  "Onychophora": 84,
+  "Oomycota": 85,
+  "Parabasalia": 86,
+  "Peploviricota": 87,
+  "Perkinsozoa": 88,
+  "Phixviricota": 89,
+  "Pisuviricota": 90,
+  "Placozoa": 91,
+  "Planctomycetota": 92,
+  "Platyhelminthes": 93,
+  "Porifera": 94,
+  "Preplasmiviricota": 95,
+  "Priapulida": 96,
+  "Produgelaviricota": 97,
+  "Promethearchaeota": 98,
+  "Pseudomonadota": 99,
+  "Rhodophyta": 100,
+  "Rhodothermota": 101,
+  "Rotifera": 102,
+  "Saleviricota": 103,
+  "Spirochaetota": 104,
+  "Streptophyta": 105,
+  "Synergistota": 106,
+  "Taleaviricota": 107,
+  "Tardigrada": 108,
+  "Thermodesulfobacteriota": 109,
+  "Thermomicrobiota": 110,
+  "Thermoplasmatota": 111,
+  "Thermoproteota": 112,
+  "Thermosulfidibacterota": 113,
+  "Thermotogota": 114,
+  "Tubulinea": 115,
+  "Uroviricota": 116,
+  "Verrucomicrobiota": 117,
+  "Xenacoelomorpha": 118,
+  "Zoopagomycota": 119
+}

dataset/taxon_embedding/vocab/subspecies_vocab.json

@@ -0,0 +1,2111 @@
+{
+  "<UNK>": 0,
+  "Acanthamoeba castellanii str. Neff": 1,
+  "Acaryochloris marina MBIC11017": 2,
+  "Acetivibrio thermocellus ATCC 27405": 3,
+  "Acetoanaerobium sticklandii DSM 519": 4,
+  "Acetobacterium woodii DSM 1030": 5,
+  "Acetohalobium arabaticum DSM 5501": 6,
+  "Acholeplasma laidlawii PG-8A": 7,
+  "Achromobacter xylosoxidans A8": 8,
+  "Acidaminococcus fermentans DSM 20731": 9,
+  "Acidianus hospitalis W1": 10,
+  "Acidilobus saccharovorans 345-15": 11,
+  "Acidimicrobium ferrooxidans DSM 10331": 12,
+  "Acidiphilium cryptum JF-5": 13,
+  "Acidiphilium multivorum AIU301": 14,
+  "Acidithiobacillus caldus ATCC 51756": 15,
+  "Acidithiobacillus ferrooxidans ATCC 23270": 16,
+  "Acidithiobacillus ferrooxidans ATCC 53993": 17,
+  "Acidobacterium capsulatum ATCC 51196": 18,
+  "Acidothermus cellulolyticus 11B": 19,
+  "Aciduliprofundum boonei T469": 20,
+  "Acinetobacter baumannii 1295743": 21,
+  "Acinetobacter baumannii 532279": 22,
+  "Acinetobacter baumannii AB0057": 23,
+  "Acinetobacter baumannii AB307-0294": 24,
+  "Acinetobacter baumannii ACICU": 25,
+  "Acinetobacter baumannii ATCC 17978": 26,
+  "Acinetobacter baumannii ATCC 19606 = CIP 70.34 = JCM 6841": 27,
+  "Acinetobacter baumannii AYE": 28,
+  "Acinetobacter baumannii IS-123": 29,
+  "Acinetobacter baumannii MDR-ZJ06": 30,
+  "Acinetobacter baumannii SDF": 31,
+  "Acinetobacter baylyi ADP1": 32,
+  "Acinetobacter calcoaceticus subsp. anitratus": 33,
+  "Acinetobacter oleivorans DR1": 34,
+  "Acinetobacter pittii PHEA-2": 35,
+  "Acinetobacter venetianus RAG-1 = CIP 110063": 36,
+  "Actinobacillus pleuropneumoniae serovar 3 str. JL03": 37,
+  "Actinobacillus pleuropneumoniae serovar 5b str. L20": 38,
+  "Actinobacillus pleuropneumoniae serovar 7 str. AP76": 39,
+  "Actinobacillus succinogenes 130Z": 40,
+  "Actinomyces naeslundii str. Howell 279": 41,
+  "Actinomycetospora chiangmaiensis DSM 45062": 42,
+  "Actinoplanes missouriensis 431": 43,
+  "Actinosynnema mirum DSM 43827": 44,
+  "Actinosynnema pretiosum subsp. auranticum": 45,
+  "Advenella mimigardefordensis DPN7": 46,
+  "Aegilops tauschii subsp. strangulata": 47,
+  "Aerococcus viridans ATCC 11563 = CCUG 4311": 48,
+  "Aeromicrobium erythreum (NRRL B-3381)": 49,
+  "Aeromonas hydrophila subsp. hydrophila ATCC 7966": 50,
+  "Aeromonas salmonicida subsp. salmonicida": 51,
+  "Aeromonas salmonicida subsp. salmonicida A449": 52,
+  "Aeromonas veronii B565": 53,
+  "Aeropyrum pernix K1": 54,
+  "Afipia carboxidovorans OM5": 55,
+  "Agathobacter rectalis ATCC 33656": 56,
+  "Aggregatibacter actinomycetemcomitans D11S-1": 57,
+  "Aggregatibacter aphrophilus NJ8700": 58,
+  "Agkistrodon contortrix contortrix": 59,
+  "Agkistrodon piscivorus leucostoma": 60,
+  "Agkistrodon piscivorus piscivorus": 61,
+  "Agrobacterium fabrum str. C58": 62,
+  "Agrobacterium tumefaciens (strain 15955)": 63,
+  "Agrobacterium tumefaciens (strain Ach5)": 64,
+  "Agrobacterium tumefaciens (strain B2A)": 65,
+  "Agrobacterium tumefaciens (strain II CHRYS)": 66,
+  "Agrobacterium tumefaciens (strain RS5)": 67,
+  "Agrobacterium tumefaciens (strain T37)": 68,
+  "Agrobacterium tumefaciens (strain apple 185)": 69,
+  "Akkermansia muciniphila ATCC BAA-835": 70,
+  "Akodon lutescens puer": 71,
+  "Alcanivorax borkumensis SK2": 72,
+  "Alces alces alces": 73,
+  "Alces alces cameloides": 74,
+  "Aliarcobacter butzleri RM4018": 75,
+  "Alicycliphilus denitrificans K601": 76,
+  "Alicyclobacillus acidocaldarius subsp. acidocaldarius": 77,
+  "Alicyclobacillus acidocaldarius subsp. acidocaldarius DSM 446": 78,
+  "Alicyclobacillus acidoterrestris ATCC 49025": 79,
+  "Aliivibrio fischeri ES114": 80,
+  "Aliivibrio fischeri MJ11": 81,
+  "Aliivibrio salmonicida LFI1238": 82,
+  "Alkalihalophilus pseudofirmus OF4": 83,
+  "Alkalilimnicola ehrlichii MLHE-1": 84,
+  "Alkaliphilus metalliredigens QYMF": 85,
+  "Alkaliphilus oremlandii OhILAs": 86,
+  "Allochromatium vinosum DSM 180": 87,
+  "Allomeiothermus silvanus DSM 9946": 88,
+  "Allorhizobium ampelinum S4": 89,
+  "Alteracholeplasma palmae J233": 90,
+  "Alteromonas mediterranea DE": 91,
+  "Amanita muscaria Koide BX008": 92,
+  "Aminobacterium colombiense DSM 12261": 93,
+  "Amphibacillus xylanus NBRC 15112": 94,
+  "Amycolatopsis mediterranei S699": 95,
+  "Amycolatopsis mediterranei U32": 96,
+  "Anaerococcus prevotii DSM 20548": 97,
+  "Anaerolinea thermophila UNI-1": 98,
+  "Anaeromyxobacter dehalogenans 2CP-1": 99,
+  "Anaeromyxobacter dehalogenans 2CP-C": 100,
+  "Anaerostipes caccae L1-92": 101,
+  "Anaplasma marginale str. Florida": 102,
+  "Anaplasma marginale str. Illinois": 103,
+  "Anaplasma marginale str. St. Maries": 104,
+  "Anaplasma marginale str. Virginia": 105,
+  "Anaplasma phagocytophilum str. HZ": 106,
+  "Anas platyrhynchos platyrhynchos": 107,
+  "Ancylobacter novellus DSM 506": 108,
+  "Androctonus mauritanicus mauritanicus": 109,
+  "Anoxybacillus flavithermus WK1": 110,
+  "Anser anser anser": 111,
+  "Anterhynchium flavomarginatum micado": 112,
+  "Aotus azarai infulatus": 113,
+  "Aphanizomenon flos-aquae 2012/KM1/D3": 114,
+  "Apis cerana cerana": 115,
+  "Apis mellifera carnica": 116,
+  "Apis mellifera ligustica": 117,
+  "Apis mellifera scutellata": 118,
+  "Aquifex aeolicus VF5": 119,
+  "Aquipseudomonas alcaligenes NBRC 14159": 120,
+  "Arabidopsis halleri subsp. gemmifera": 121,
+  "Arabidopsis halleri subsp. halleri": 122,
+  "Arabidopsis lyrata subsp. lyrata": 123,
+  "Arabidopsis lyrata subsp. petraea": 124,
+  "Arcanobacterium haemolyticum DSM 20595": 125,
+  "Archaeoglobus fulgidus DSM 4304": 126,
+  "Archaeoglobus profundus DSM 5631": 127,
+  "Arcobacter nitrofigilis DSM 7299": 128,
+  "Aromatoleum aromaticum EbN1": 129,
+  "Arthrobacter globiformis NBRC 12137": 130,
+  "Artibeus glaucus watsoni": 131,
+  "Ascidia sydneiensis samea": 132,
+  "Aspergillus aculeatus ATCC 16872": 133,
+  "Aspergillus brasiliensis CBS 101740": 134,
+  "Aspergillus campestris IBT 28561": 135,
+  "Aspergillus carbonarius ITEM 5010": 136,
+  "Aspergillus clavatus NRRL 1": 137,
+  "Aspergillus eucalypticola CBS 122712": 138,
+  "Aspergillus fischeri NRRL 181": 139,
+  "Aspergillus flavus AF70": 140,
+  "Aspergillus flavus NRRL3357": 141,
+  "Aspergillus fumigatus A1163": 142,
+  "Aspergillus fumigatus Af293": 143,
+  "Aspergillus homomorphus CBS 101889": 144,
+  "Aspergillus luchuensis CBS 106.47": 145,
+  "Aspergillus luchuensis IFO 4308": 146,
+  "Aspergillus neoniger CBS 115656": 147,
+  "Aspergillus nidulans FGSC A4": 148,
+  "Aspergillus niger ATCC 1015": 149,
+  "Aspergillus niger CBS 101883": 150,
+  "Aspergillus niger CBS 513.88": 151,
+  "Aspergillus nomiae NRRL 13137": 152,
+  "Aspergillus novofumigatus IBT 16806": 153,
+  "Aspergillus oryzae 3.042": 154,
+  "Aspergillus oryzae RIB40": 155,
+  "Aspergillus parasiticus SU-1": 156,
+  "Aspergillus ruber CBS 135680": 157,
+  "Aspergillus sclerotiicarbonarius CBS 121057": 158,
+  "Aspergillus terreus NIH2624": 159,
+  "Aspergillus tubingensis CBS 134.48": 160,
+  "Aspergillus vadensis CBS 113365": 161,
+  "Aspergillus violaceofuscus CBS 115571": 162,
+  "Aster yellows witches'-broom phytoplasma AYWB": 163,
+  "Asticcacaulis excentricus CB 48": 164,
+  "Ateles fusciceps robustus": 165,
+  "Athroisma gracile subsp. psyllioides": 166,
+  "Atractaspis microlepidota microlepidota": 167,
+  "Aurantimonas manganoxydans SI85-9A1": 168,
+  "Auricularia subglabra TFB-10046 SS5": 169,
+  "Azorhizobium caulinodans ORS 571": 170,
+  "Azospira oryzae PS": 171,
+  "Azospirillum lipoferum 4B": 172,
+  "Azotobacter chroococcum (strain mcd 1)": 173,
+  "Azotobacter vinelandii DJ": 174,
+  "Bacillus amyloliquefaciens DSM 7 = ATCC 23350": 175,
+  "Bacillus anthracis str. A0248": 176,
+  "Bacillus anthracis str. CDC 684": 177,
+  "Bacillus atrophaeus 1942": 178,
+  "Bacillus cereus 03BB102": 179,
+  "Bacillus cereus AH187": 180,
+  "Bacillus cereus AH820": 181,
+  "Bacillus cereus ATCC 10987": 182,
+  "Bacillus cereus ATCC 14579": 183,
+  "Bacillus cereus B4264": 184,
+  "Bacillus cereus E33L": 185,
+  "Bacillus cereus G9842": 186,
+  "Bacillus cereus H3081.97": 187,
+  "Bacillus cereus HuB5-5": 188,
+  "Bacillus cereus MSX-D12": 189,
+  "Bacillus cereus Q1": 190,
+  "Bacillus cereus VD014": 191,
+  "Bacillus cereus VD045": 192,
+  "Bacillus cereus VD146": 193,
+  "Bacillus cytotoxicus NVH 391-98": 194,
+  "Bacillus licheniformis DSM 13 = ATCC 14580": 195,
+  "Bacillus methanolicus MGA3": 196,
+  "Bacillus mycoides KBAB4": 197,
+  "Bacillus paralicheniformis ATCC 9945a": 198,
+  "Bacillus pumilus ATCC 7061": 199,
+  "Bacillus pumilus SAFR-032": 200,
+  "Bacillus spizizenii TU-B-10": 201,
+  "Bacillus spizizenii str. W23": 202,
+  "Bacillus subtilis BSn5": 203,
+  "Bacillus subtilis subsp. amylosacchariticus": 204,
+  "Bacillus subtilis subsp. natto": 205,
+  "Bacillus subtilis subsp. natto BEST195": 206,
+  "Bacillus subtilis subsp. subtilis str. 168": 207,
+  "Bacillus thuringiensis BMB171": 208,
+  "Bacillus thuringiensis serovar kurstaki str. HD73": 209,
+  "Bacillus thuringiensis str. Al Hakam": 210,
+  "Bacillus velezensis FZB42": 211,
+  "Bacillus velezensis YAU B9601-Y2": 212,
+  "Bacteroides fragilis 638R": 213,
+  "Bacteroides fragilis NCTC 9343": 214,
+  "Bacteroides fragilis YCH46": 215,
+  "Bacteroides helcogenes P 36-108": 216,
+  "Bacteroides ovatus ATCC 8483": 217,
+  "Bacteroides thetaiotaomicron VPI-5482": 218,
+  "Bacteroides uniformis ATCC 8492": 219,
+  "Balaenoptera acutorostrata scammoni": 220,
+  "Bartonella bacilliformis KC583": 221,
+  "Bartonella grahamii as4aup": 222,
+  "Bartonella henselae str. Houston-1": 223,
+  "Bartonella quintana str. Toulouse": 224,
+  "Bartonella schoenbuchensis R1": 225,
+  "Bartonella tribocorum CIP 105476": 226,
+  "Bartonella vinsonii subsp. berkhoffii": 227,
+  "Batrachochytrium dendrobatidis JAM81": 228,
+  "Baumannia cicadellinicola str. Hc (Homalodisca coagulata)": 229,
+  "Bdellovibrio bacteriovorus HD100": 230,
+  "Beauveria bassiana ARSEF 2860": 231,
+  "Beijerinckia indica subsp. indica ATCC 9039": 232,
+  "Beta vulgaris subsp. maritima": 233,
+  "Beta vulgaris subsp. vulgaris": 234,
+  "Beutenbergia cavernae DSM 12333": 235,
+  "Bifidobacterium adolescentis ATCC 15703": 236,
+  "Bifidobacterium animalis subsp. lactis AD011": 237,
+  "Bifidobacterium animalis subsp. lactis BB-12": 238,
+  "Bifidobacterium animalis subsp. lactis Bl-04": 239,
+  "Bifidobacterium animalis subsp. lactis DSM 10140": 240,
+  "Bifidobacterium bifidum JCM 1254": 241,
+  "Bifidobacterium breve ACS-071-V-Sch8b": 242,
+  "Bifidobacterium breve UCC2003": 243,
+  "Bifidobacterium dentium Bd1": 244,
+  "Bifidobacterium longum DJO10A": 245,
+  "Bifidobacterium longum NCC2705": 246,
+  "Bifidobacterium longum subsp. infantis": 247,
+  "Bifidobacterium longum subsp. infantis ATCC 15697 = JCM 1222 = DSM 20088": 248,
+  "Bifidobacterium longum subsp. longum": 249,
+  "Bifidobacterium longum subsp. longum JCM 1217": 250,
+  "Bilophila wadsworthia 3_1_6": 251,
+  "Bipolaris maydis ATCC 48331": 252,
+  "Bipolaris maydis C5": 253,
+  "Bipolaris sorokiniana ND90Pr": 254,
+  "Bipolaris victoriae FI3": 255,
+  "Bipolaris zeicola 26-R-13": 256,
+  "Bison bison bison": 257,
+  "Blastomyces dermatitidis ER-3": 258,
+  "Blastomyces gilchristii SLH14081": 259,
+  "Blattabacterium sp. (Periplaneta americana) str. BPLAN": 260,
+  "Blautia hydrogenotrophica DSM 10507": 261,
+  "Blumeria hordei DH14": 262,
+  "Bordetella avium 197N": 263,
+  "Bordetella bronchiseptica RB50": 264,
+  "Bordetella parapertussis 12822": 265,
+  "Bordetella pertussis 18323": 266,
+  "Bordetella pertussis CS": 267,
+  "Bordetella petrii DSM 12804": 268,
+  "Borrelia duttonii Ly": 269,
+  "Borrelia hermsii DAH": 270,
+  "Borrelia recurrentis A1": 271,
+  "Borrelia turicatae 91E135": 272,
+  "Borreliella afzelii PKo": 273,
+  "Borreliella bavariensis PBi": 274,
+  "Borreliella bissettiae DN127": 275,
+  "Borreliella burgdorferi B31": 276,
+  "Borreliella burgdorferi JD1": 277,
+  "Borreliella burgdorferi N40": 278,
+  "Borreliella burgdorferi ZS7": 279,
+  "Bothrops bilineatus smaragdinus": 280,
+  "Bothrops neuwiedi urutu": 281,
+  "Botryobasidium botryosum FD-172 SS1": 282,
+  "Botrytis cinerea B05.10": 283,
+  "Botrytis cinerea BcDW1": 284,
+  "Botrytis cinerea T4": 285,
+  "Brachybacterium faecium DSM 4810": 286,
+  "Brachyphylla nana pumila": 287,
+  "Brachyspira hyodysenteriae WA1": 288,
+  "Bradyrhizobium diazoefficiens USDA 110": 289,
+  "Brassica rapa subsp. chinensis": 290,
+  "Brassica rapa subsp. pekinensis": 291,
+  "Brassica rapa subsp. rapa": 292,
+  "Brevibacillus brevis NBRC 100599": 293,
+  "Brevundimonas diminuta ATCC 11568": 294,
+  "Brucella abortus 2308": 295,
+  "Brucella abortus S19": 296,
+  "Brucella abortus bv. 1 str. 9-941": 297,
+  "Brucella anthropi ATCC 49188": 298,
+  "Brucella canis ATCC 23365": 299,
+  "Brucella melitensis ATCC 23457": 300,
+  "Brucella melitensis bv. 1 str. 16M": 301,
+  "Brucella ovis ATCC 25840": 302,
+  "Brucella suis 1330": 303,
+  "Brucella suis ATCC 23445": 304,
+  "Bryonia cretica subsp. dioica": 305,
+  "Buchnera aphidicola BCc": 306,
+  "Buchnera aphidicola str. 5A (Acyrthosiphon pisum)": 307,
+  "Buchnera aphidicola str. APS (Acyrthosiphon pisum)": 308,
+  "Buchnera aphidicola str. Bp (Baizongia pistaciae)": 309,
+  "Buchnera aphidicola str. Sg (Schizaphis graminum)": 310,
+  "Buchnera aphidicola str. Tuc7 (Acyrthosiphon pisum)": 311,
+  "Bufo gargarizans andrewsi": 312,
+  "Bungarus flaviceps flaviceps": 313,
+  "Burkholderia ambifaria AMMD": 314,
+  "Burkholderia ambifaria MC40-6": 315,
+  "Burkholderia cenocepacia H111": 316,
+  "Burkholderia cenocepacia HI2424": 317,
+  "Burkholderia cenocepacia J2315": 318,
+  "Burkholderia mallei ATCC 23344": 319,
+  "Burkholderia mallei NCTC 10229": 320,
+  "Burkholderia mallei NCTC 10247": 321,
+  "Burkholderia mallei SAVP1": 322,
+  "Burkholderia multivorans ATCC 17616": 323,
+  "Burkholderia orbicola AU 1054": 324,
+  "Burkholderia orbicola MC0-3": 325,
+  "Burkholderia pseudomallei 1026b": 326,
+  "Burkholderia pseudomallei 1106a": 327,
+  "Burkholderia pseudomallei 1106b": 328,
+  "Burkholderia pseudomallei 1710b": 329,
+  "Burkholderia pseudomallei 668": 330,
+  "Burkholderia pseudomallei K96243": 331,
+  "Burkholderia thailandensis E264": 332,
+  "Burkholderia vietnamiensis G4": 333,
+  "Buthus occitanus mardochei": 334,
+  "Buthus occitanus tunetanus": 335,
+  "Caiman crocodilus apaporiensis": 336,
+  "Caldalkalibacillus mannanilyticus JCM 10596": 337,
+  "Caldalkalibacillus thermarum TA2.A1": 338,
+  "Caldanaerobacter subterraneus subsp. tengcongensis MB4": 339,
+  "Caldanaerobacter subterraneus subsp. yonseiensis": 340,
+  "Caldicellulosiruptor bescii DSM 6725": 341,
+  "Caldicellulosiruptor owensensis OL": 342,
+  "Caldicellulosiruptor saccharolyticus DSM 8903": 343,
+  "Calditerrivibrio nitroreducens DSM 19672": 344,
+  "Caldivirga maquilingensis IC-167": 345,
+  "Callicebus personatus nigrifrons": 346,
+  "Callicebus personatus personatus": 347,
+  "Callipepla squamata castanogastris": 348,
+  "Callipepla squamata pallida": 349,
+  "Campylobacter concisus 13826": 350,
+  "Campylobacter curvus 525.92": 351,
+  "Campylobacter fetus subsp. fetus 82-40": 352,
+  "Campylobacter hominis ATCC BAA-381": 353,
+  "Campylobacter jejuni RM1221": 354,
+  "Campylobacter jejuni subsp. doylei 269.97": 355,
+  "Campylobacter jejuni subsp. jejuni 81-176": 356,
+  "Campylobacter jejuni subsp. jejuni 81116": 357,
+  "Campylobacter jejuni subsp. jejuni NCTC 11168 = ATCC 700819": 358,
+  "Campylobacter lari RM2100": 359,
+  "Candida albicans SC5314": 360,
+  "Candida albicans WO-1": 361,
+  "Candida dubliniensis CD36": 362,
+  "Candida parapsilosis CDC317": 363,
+  "Candida tropicalis MYA-3404": 364,
+  "Candidatus Amoebophilus asiaticus 5a2": 365,
+  "Candidatus Blochmanniella pennsylvanica str. BPEN": 366,
+  "Candidatus Carsonella ruddii PV": 367,
+  "Candidatus Cloacimonas acidaminovorans str. Evry": 368,
+  "Candidatus Desulforudis audaxviator MP104C": 369,
+  "Candidatus Karelsulcia muelleri DMIN": 370,
+  "Candidatus Karelsulcia muelleri GWSS": 371,
+  "Candidatus Karelsulcia muelleri SMDSEM": 372,
+  "Candidatus Korarchaeum cryptofilum OPF8": 373,
+  "Candidatus Liberibacter africanus subsp. capensis": 374,
+  "Candidatus Liberibacter asiaticus str. psy62": 375,
+  "Candidatus Methanomassiliicoccus intestinalis Issoire-Mx1": 376,
+  "Candidatus Methanomethylophilus alvi Mx1201": 377,
+  "Candidatus Moranella endobia PCIT": 378,
+  "Candidatus Pelagibacter ubique HTCC1002": 379,
+  "Candidatus Pelagibacter ubique HTCC1062": 380,
+  "Candidatus Phytoplasma mali AT": 381,
+  "Candidatus Protochlamydia amoebophila UWE25": 382,
+  "Candidatus Riesia pediculicola USDA": 383,
+  "Candidatus Ruthia magnifica str. Cm (Calyptogena magnifica)": 384,
+  "Candidatus Zinderia insecticola CARI": 385,
+  "Canis lupus familiaris": 386,
+  "Canis mesomelas elongae": 387,
+  "Capnocytophaga canimorsus Cc5": 388,
+  "Capnocytophaga gingivalis ATCC 33624": 389,
+  "Capnocytophaga granulosa ATCC 51502": 390,
+  "Capnocytophaga ochracea DSM 7271": 391,
+  "Capra ibex ibex": 392,
+  "Capsaspora owczarzaki ATCC 30864": 393,
+  "Carboxydothermus hydrogenoformans Z-2901": 394,
+  "Cardiobacterium hominis ATCC 15826": 395,
+  "Castellaniella defragrans 65Phen": 396,
+  "Catenibacterium mitsuokai DSM 15897": 397,
+  "Catenulispora acidiphila DSM 44928": 398,
+  "Caulobacter segnis ATCC 21756": 399,
+  "Caulobacter vibrioides CB15": 400,
+  "Caulobacter vibrioides NA1000": 401,
+  "Cecembia lonarensis LW9": 402,
+  "Cellulomonas fimi ATCC 484": 403,
+  "Cellulomonas flavigena DSM 20109": 404,
+  "Cellulophaga algicola DSM 14237": 405,
+  "Cellvibrio japonicus Ueda107": 406,
+  "Cenarchaeum symbiosum A": 407,
+  "Ceratotherium simum cottoni": 408,
+  "Ceratotherium simum simum": 409,
+  "Cereibacter sphaeroides 2.4.1": 410,
+  "Cereibacter sphaeroides ATCC 17025": 411,
+  "Cereibacter sphaeroides ATCC 17029": 412,
+  "Cereibacter sphaeroides KD131": 413,
+  "Cervus canadensis nelsoni": 414,
+  "Cervus canadensis roosevelti": 415,
+  "Cervus elaphus hippelaphus": 416,
+  "Cervus nippon aplodontus": 417,
+  "Cervus nippon hortulorum": 418,
+  "Cervus nippon nippon": 419,
+  "Cervus nippon pseudaxis": 420,
+  "Cervus nippon taiouanus": 421,
+  "Cervus nippon yesoensis": 422,
+  "Chaetomium globosum CBS 148.51": 423,
+  "Chamaesiphon minutus PCC 6605": 424,
+  "Chilabothrus strigilatus fosteri": 425,
+  "Chilabothrus strigilatus mccraniei": 426,
+  "Chilabothrus strigilatus strigilatus": 427,
+  "Chironomus thummi thummi": 428,
+  "Chitinophaga pinensis DSM 2588": 429,
+  "Chlamydia abortus S26/3": 430,
+  "Chlamydia felis Fe/C-56": 431,
+  "Chlamydia muridarum str. Nigg": 432,
+  "Chlamydia psittaci 6BC": 433,
+  "Chlamydia trachomatis A/HAR-13": 434,
+  "Chlamydia trachomatis B/Jali20/OT": 435,
+  "Chlamydia trachomatis B/TZ1A828/OT": 436,
+  "Chlamydia trachomatis D/UW-3/CX": 437,
+  "Chlamydia trachomatis G/9301": 438,
+  "Chlamydia trachomatis L2/434/Bu": 439,
+  "Chlamydia trachomatis L2b/UCH-1/proctitis": 440,
+  "Chlamydia trachomatis Sweden2": 441,
+  "Chlamys nipponensis akazara": 442,
+  "Chlorobaculum parvum NCIB 8327": 443,
+  "Chlorobaculum tepidum TLS": 444,
+  "Chlorobium chlorochromatii CaD3": 445,
+  "Chlorobium limicola DSM 245": 446,
+  "Chlorobium phaeobacteroides BS1": 447,
+  "Chlorobium phaeobacteroides DSM 266": 448,
+  "Chlorobium phaeovibrioides DSM 265": 449,
+  "Chloroflexus aggregans DSM 9485": 450,
+  "Chloroflexus aurantiacus J-10-fl": 451,
+  "Chloroflexus aurantiacus Y-400-fl": 452,
+  "Chlorogloeopsis fritschii PCC 9212": 453,
+  "Chloroherpeton thalassium ATCC 35110": 454,
+  "Choristoneura occidentalis biennis": 455,
+  "Christiangramia forsetii KT0803": 456,
+  "Chromobacterium violaceum ATCC 12472": 457,
+  "Chromohalobacter israelensis DSM 3043": 458,
+  "Chrysemys picta bellii": 459,
+  "Chthonomonas calidirosea T49": 460,
+  "Cistus creticus subsp. creticus": 461,
+  "Citrifermentans bemidjiense Bem": 462,
+  "Citrobacter koseri ATCC BAA-895": 463,
+  "Citrobacter rodentium ICC168": 464,
+  "Cladophialophora bantiana CBS 173.52": 465,
+  "Clavibacter michiganensis subsp. michiganensis": 466,
+  "Clavibacter michiganensis subsp. michiganensis NCPPB 382": 467,
+  "Claviceps purpurea 20.1": 468,
+  "Clavispora lusitaniae ATCC 42720": 469,
+  "Clostridioides difficile 630": 470,
+  "Clostridioides difficile CD196": 471,
+  "Clostridioides difficile CD3": 472,
+  "Clostridioides difficile R20291": 473,
+  "Clostridioides mangenotii LM2": 474,
+  "Clostridium acetobutylicum ATCC 824": 475,
+  "Clostridium beijerinckii NCIMB 8052": 476,
+  "Clostridium botulinum A str. ATCC 19397": 477,
+  "Clostridium botulinum A str. Hall": 478,
+  "Clostridium botulinum A2 str. Kyoto": 479,
+  "Clostridium botulinum A3 str. Loch Maree": 480,
+  "Clostridium botulinum B str. Eklund 17B (NRP)": 481,
+  "Clostridium botulinum B1": 482,
+  "Clostridium botulinum Ba4 str. 657": 483,
+  "Clostridium botulinum E3 str. Alaska E43": 484,
+  "Clostridium botulinum F str. Langeland": 485,
+  "Clostridium cellulovorans 743B": 486,
+  "Clostridium kluyveri DSM 555": 487,
+  "Clostridium kluyveri NBRC 12016": 488,
+  "Clostridium ljungdahlii DSM 13528": 489,
+  "Clostridium novyi NT": 490,
+  "Clostridium perfringens ATCC 13124": 491,
+  "Clostridium perfringens SM101": 492,
+  "Clostridium perfringens str. 13": 493,
+  "Clostridium sporogenes ATCC 15579": 494,
+  "Clostridium sporogenes PA 3679": 495,
+  "Clostridium tetani E88": 496,
+  "Coccidioides immitis RS": 497,
+  "Coccidioides posadasii C735 delta SOWgp": 498,
+  "Coccidioides posadasii str. Silveira": 499,
+  "Colletotrichum gloeosporioides Cg-14": 500,
+  "Colletotrichum graminicola M1.001": 501,
+  "Colletotrichum higginsianum IMI 349063": 502,
+  "Colletotrichum orbiculare MAFF 240422": 503,
+  "Collinsella aerofaciens ATCC 25986": 504,
+  "Colwellia psychrerythraea 34H": 505,
+  "Comamonas testosteroni KF-1": 506,
+  "Conexibacter woesei DSM 14684": 507,
+  "Conus anabathrum floridanus": 508,
+  "Conus buxeus loroisii": 509,
+  "Coprinopsis cinerea okayama7#130": 510,
+  "Coprothermobacter proteolyticus DSM 5265": 511,
+  "Coraliomargarita akajimensis DSM 45221": 512,
+  "Corallus hortulanus enydris": 513,
+  "Cordyceps fumosorosea ARSEF 2679": 514,
+  "Cordyceps militaris CM01": 515,
+  "Corynebacterium aurimucosum ATCC 700975": 516,
+  "Corynebacterium diphtheriae NCTC 13129": 517,
+  "Corynebacterium diphtheriae PW8": 518,
+  "Corynebacterium efficiens YS-314": 519,
+  "Corynebacterium glutamicum ATCC 13032": 520,
+  "Corynebacterium glutamicum K051": 521,
+  "Corynebacterium glutamicum R": 522,
+  "Corynebacterium jeikeium K411": 523,
+  "Corynebacterium kroppenstedtii DSM 44385": 524,
+  "Corynebacterium lipophiloflavum DSM 44291": 525,
+  "Corynebacterium pseudotuberculosis 1002": 526,
+  "Corynebacterium pseudotuberculosis C231": 527,
+  "Corynebacterium pseudotuberculosis FRC41": 528,
+  "Corynebacterium resistens DSM 45100": 529,
+  "Corynebacterium urealyticum DSM 7109": 530,
+  "Coxiella burnetii CbuG_Q212": 531,
+  "Coxiella burnetii CbuK_Q154": 532,
+  "Coxiella burnetii Dugway 5J108-111": 533,
+  "Coxiella burnetii RSA 331": 534,
+  "Coxiella burnetii RSA 493": 535,
+  "Crambe hispanica subsp. abyssinica": 536,
+  "Crassula rupestris subsp. marnieriana": 537,
+  "Cratogeomys goldmani goldmani": 538,
+  "Cratogeomys goldmani subsp. rubellus": 539,
+  "Crocidura gueldenstaedtii": 540,
+  "Crocidura rapax kurodai": 541,
+  "Crocosphaera subtropica ATCC 51142": 542,
+  "Cronobacter sakazakii ATCC BAA-894": 543,
+  "Cronobacter turicensis z3032": 544,
+  "Crotalus cerastes cerastes": 545,
+  "Crotalus durissus cumanensis": 546,
+  "Crotalus durissus durissus": 547,
+  "Crotalus durissus ruruima": 548,
+  "Crotalus durissus terrificus": 549,
+  "Crotalus lepidus lepidus": 550,
+  "Crotalus lutosus abyssus": 551,
+  "Crotalus mitchellii mitchellii": 552,
+  "Crotalus molossus molossus": 553,
+  "Crotalus molossus nigrescens": 554,
+  "Crotalus ruber ruber": 555,
+  "Crotalus scutulatus scutulatus": 556,
+  "Crotalus viridis viridis": 557,
+  "Cryphonectria parasitica EP155": 558,
+  "Cryptococcus deneoformans B-3501A": 559,
+  "Cryptococcus deneoformans JEC21": 560,
+  "Cryptococcus deuterogattii R265": 561,
+  "Cryptococcus gattii WM276": 562,
+  "Cryptococcus neoformans H99": 563,
+  "Cryptosporidium muris RN66": 564,
+  "Cryptosporidium parvum Iowa II": 565,
+  "Cucumis melo subsp. agrestis": 566,
+  "Cucumis melo subsp. melo": 567,
+  "Culex pipiens pipiens": 568,
+  "Cupriavidus metallidurans CH34": 569,
+  "Cupriavidus necator H16": 570,
+  "Cupriavidus necator N-1": 571,
+  "Cupriavidus pinatubonensis JMP134": 572,
+  "Cupriavidus taiwanensis LMG 19424": 573,
+  "Cutibacterium acnes KPA171202": 574,
+  "Cutibacterium acnes SK137": 575,
+  "Cyanidioschyzon merolae strain 10D": 576,
+  "Cyberlindnera jadinii NRRL Y-1542": 577,
+  "Cyclobacterium marinum DSM 745": 578,
+  "Cylindrotheca sp. (STRAIN N1)": 579,
+  "Cynara cardunculus subsp. cardunculus": 580,
+  "Cyphellophora europaea CBS 101466": 581,
+  "Cytophaga hutchinsonii ATCC 33406": 582,
+  "Dactylococcopsis salina PCC 8305": 583,
+  "Damaliscus lunatus korrigum": 584,
+  "Damaliscus pygargus phillipsi": 585,
+  "Daucus carota subsp. sativus": 586,
+  "Debaryomyces hansenii CBS767": 587,
+  "Dechloromonas aromatica RCB": 588,
+  "Deferribacter desulfuricans SSM1": 589,
+  "Dehalobacter restrictus DSM 9455": 590,
+  "Dehalococcoides mccartyi 195": 591,
+  "Dehalococcoides mccartyi BAV1": 592,
+  "Dehalococcoides mccartyi CBDB1": 593,
+  "Dehalococcoides mccartyi VS": 594,
+  "Dehalogenimonas lykanthroporepellens BL-DC-9": 595,
+  "Deinococcus deserti VCD115": 596,
+  "Deinococcus geothermalis DSM 11300": 597,
+  "Deinococcus proteolyticus MRP": 598,
+  "Deinococcus radiodurans R1 = ATCC 13939 = DSM 20539": 599,
+  "Delftia acidovorans SPH-1": 600,
+  "Dendraspis polylepis polylepis": 601,
+  "Dendroaspis jamesoni jamesoni": 602,
+  "Dendroaspis jamesoni kaimosae": 603,
+  "Dendrothele bispora CBS 962.96": 604,
+  "Denitrovibrio acetiphilus DSM 12809": 605,
+  "Desmonostoc sp. PCC 7906": 606,
+  "Desulfarculus baarsii DSM 2075": 607,
+  "Desulfitobacterium hafniense DCB-2": 608,
+  "Desulfitobacterium hafniense Y51": 609,
+  "Desulfomicrobium baculatum DSM 4028": 610,
+  "Desulforamulus reducens MI-1": 611,
+  "Desulforapulum autotrophicum HRM2": 612,
+  "Desulfosudis oleivorans Hxd3": 613,
+  "Desulfotalea psychrophila LSv54": 614,
+  "Desulfovibrio desulfuricans ATCC 27774": 615,
+  "Desulfurispirillum indicum S5": 616,
+  "Desulfurococcus amylolyticus 1221n": 617,
+  "Desulfurococcus mucosus DSM 2162": 618,
+  "Desulfuromonas acetoxidans DSM 684": 619,
+  "Dicerorhinus sumatrensis harrissoni": 620,
+  "Dicerorhinus sumatrensis sumatrensis": 621,
+  "Diceros bicornis minor": 622,
+  "Dichelobacter nodosus VCS1703A": 623,
+  "Dickeya chrysanthemi Ech1591": 624,
+  "Dickeya dadantii 3937": 625,
+  "Dickeya parazeae Ech586": 626,
+  "Dictyoglomus thermophilum H-6-12": 627,
+  "Dictyoglomus turgidum DSM 6724": 628,
+  "Dinoroseobacter shibae DFL 12 = DSM 16493": 629,
+  "Dipodomys merriami margaritae": 630,
+  "Dothistroma septosporum NZE10": 631,
+  "Drepanopeziza brunnea f. sp. 'multigermtubi' MB_m1": 632,
+  "Drosophila pseudoobscura bogotana": 633,
+  "Drosophila pseudoobscura pseudoobscura": 634,
+  "Drymarchon melanurus erebennus": 635,
+  "Dyadobacter fermentans DSM 18053": 636,
+  "Echis carinatus sochureki": 637,
+  "Echis pyramidum leakeyi": 638,
+  "Ectopseudomonas mendocina ymp": 639,
+  "Ectopseudomonas oleovorans CECT 5344": 640,
+  "Edwardsiella ictaluri 93-146": 641,
+  "Edwardsiella tarda FL6-60": 642,
+  "Eggerthella lenta DSM 2243": 643,
+  "Ehrlichia canis str. Jake": 644,
+  "Ehrlichia chaffeensis str. Arkansas": 645,
+  "Ehrlichia ruminantium str. Gardel": 646,
+  "Ehrlichia ruminantium str. Welgevonden": 647,
+  "Ehrlichia sennetsu str. Miyayama": 648,
+  "Elusimicrobium minutum Pei191": 649,
+  "Emiliania huxleyi CCMP1516": 650,
+  "Encephalitozoon cuniculi GB-M1": 651,
+  "Encephalitozoon hellem ATCC 50504": 652,
+  "Encephalitozoon intestinalis ATCC 50506": 653,
+  "Entamoeba dispar SAW760": 654,
+  "Entamoeba histolytica HM-1:IMSS": 655,
+  "Enterobacter cloacae JD6301": 656,
+  "Enterobacter cloacae subsp. cloacae ATCC 13047": 657,
+  "Enterobacter hormaechei subsp. hoffmannii UCI 50": 658,
+  "Enterocloster bolteae ATCC BAA-613": 659,
+  "Enterococcus casseliflavus EC10": 660,
+  "Enterococcus faecalis 62": 661,
+  "Enterococcus faecalis EnGen0062": 662,
+  "Enterococcus faecalis EnGen0310 = MMH594": 663,
+  "Enterococcus faecalis OG1RF": 664,
+  "Enterococcus faecalis TX4000": 665,
+  "Enterococcus faecalis V583": 666,
+  "Enterococcus faecium Aus0004": 667,
+  "Enterococcus faecium DO": 668,
+  "Enterococcus gallinarum EG2": 669,
+  "Enterococcus hirae ATCC 9790": 670,
+  "Enterococcus italicus DSM 15952": 671,
+  "Enterocytozoon bieneusi H348": 672,
+  "Eospalax fontanierii baileyi": 673,
+  "Eospalax fontanierii cansus": 674,
+  "Epichloe festucae E2368": 675,
+  "Epichloe festucae Fl1": 676,
+  "Equus hemionus kulan": 677,
+  "Equus quagga burchellii": 678,
+  "Eremothecium cymbalariae DBVPG#7215": 679,
+  "Eremothecium gossypii ATCC 10895": 680,
+  "Erwinia amylovora ATCC 49946": 681,
+  "Erwinia amylovora CFBP1430": 682,
+  "Erwinia billingiae Eb661": 683,
+  "Erwinia pyrifoliae DSM 12163": 684,
+  "Erwinia pyrifoliae Ep1/96": 685,
+  "Erwinia tasmaniensis Et1/99": 686,
+  "Erysipelothrix rhusiopathiae str. Fujisawa": 687,
+  "Erythrobacter litoralis HTCC2594": 688,
+  "Eryx colubrinus colubrinus": 689,
+  "Eryx colubrinus loveridgei": 690,
+  "Eryx miliaris nogaiorum": 691,
+  "Escherichia albertii TW07627": 692,
+  "Escherichia coli 042": 693,
+  "Escherichia coli 536": 694,
+  "Escherichia coli 55989": 695,
+  "Escherichia coli APEC O1": 696,
+  "Escherichia coli ATCC 25922": 697,
+  "Escherichia coli ATCC 8739": 698,
+  "Escherichia coli B str. REL606": 699,
+  "Escherichia coli BL21(DE3)": 700,
+  "Escherichia coli CFT073": 701,
+  "Escherichia coli DH1": 702,
+  "Escherichia coli ED1a": 703,
+  "Escherichia coli ETEC H10407": 704,
+  "Escherichia coli HS": 705,
+  "Escherichia coli IAI1": 706,
+  "Escherichia coli IAI39": 707,
+  "Escherichia coli IHE3034": 708,
+  "Escherichia coli K-12": 709,
+  "Escherichia coli KO11FL": 710,
+  "Escherichia coli KTE188": 711,
+  "Escherichia coli MS 115-1": 712,
+  "Escherichia coli MS 21-1": 713,
+  "Escherichia coli NC101": 714,
+  "Escherichia coli O104:H4 str. 2009EL-2071": 715,
+  "Escherichia coli O111:H- str. 11128": 716,
+  "Escherichia coli O127:H6 str. E2348/69": 717,
+  "Escherichia coli O139:H28 str. E24377A": 718,
+  "Escherichia coli O145:H28 str. RM12581": 719,
+  "Escherichia coli O157:H7 str. EC4115": 720,
+  "Escherichia coli O157:H7 str. EC869": 721,
+  "Escherichia coli O157:H7 str. TW14359": 722,
+  "Escherichia coli O26:H11 str. 11368": 723,
+  "Escherichia coli O55:H7 str. CB9615": 724,
+  "Escherichia coli O55:H7 str. RM12579": 725,
+  "Escherichia coli O83:H1 str. NRG 857C": 726,
+  "Escherichia coli RS218": 727,
+  "Escherichia coli S88": 728,
+  "Escherichia coli SE11": 729,
+  "Escherichia coli SE15": 730,
+  "Escherichia coli SMS-3-5": 731,
+  "Escherichia coli STEC_O31": 732,
+  "Escherichia coli TW11681": 733,
+  "Escherichia coli UMEA 3162-1": 734,
+  "Escherichia coli UMEA 4076-1": 735,
+  "Escherichia coli UMN026": 736,
+  "Escherichia coli UTI89": 737,
+  "Escherichia coli W": 738,
+  "Escherichia fergusonii ATCC 35469": 739,
+  "Eucalyptus globulus subsp. globulus": 740,
+  "Eulemur fulvus collaris": 741,
+  "Eulemur fulvus fulvus": 742,
+  "Eulemur macaco macaco": 743,
+  "Evansella cellulosilytica DSM 2522": 744,
+  "Ewingella americana ATCC 33852": 745,
+  "Exiguobacterium artemiae 255-15": 746,
+  "Exophiala dermatitidis NIH/UT8656": 747,
+  "Exserohilum turcica Et28A": 748,
+  "Fagopyrum esculentum subsp. ancestrale": 749,
+  "Felis silvestris lybica": 750,
+  "Ferrimonas balearica DSM 9799": 751,
+  "Ferroglobus placidus DSM 10642": 752,
+  "Fervidobacterium nodosum Rt17-B1": 753,
+  "Fervidobacterium pennivorans DSM 9078": 754,
+  "Fibrobacter succinogenes subsp. succinogenes S85": 755,
+  "Filifactor alocis ATCC 35896": 756,
+  "Finegoldia magna ATCC 29328": 757,
+  "Fischerella ambigua UTEX 1903": 758,
+  "Flavobacterium frigoris PS1": 759,
+  "Flavobacterium johnsoniae UW101": 760,
+  "Flavobacterium psychrophilum JIP02/86": 761,
+  "Formosa agariphila KMM 3901": 762,
+  "Francisella cf. novicida Fx1": 763,
+  "Francisella philomiragia subsp. philomiragia ATCC 25017": 764,
+  "Francisella tularensis subsp. holarctica FTNF002-00": 765,
+  "Francisella tularensis subsp. holarctica LVS": 766,
+  "Francisella tularensis subsp. holarctica OSU18": 767,
+  "Francisella tularensis subsp. mediasiatica FSC147": 768,
+  "Francisella tularensis subsp. novicida": 769,
+  "Francisella tularensis subsp. novicida U112": 770,
+  "Francisella tularensis subsp. tularensis FSC198": 771,
+  "Francisella tularensis subsp. tularensis SCHU S4": 772,
+  "Francisella tularensis subsp. tularensis WY96-3418": 773,
+  "Frankia alni ACN14a": 774,
+  "Frateuria aurantia DSM 6220": 775,
+  "Fructilactobacillus sanfranciscensis DSM 20451": 776,
+  "Fusarium fujikuroi IMI 58289": 777,
+  "Fusarium graminearum PH-1": 778,
+  "Fusarium odoratissimum NRRL 54006": 779,
+  "Fusarium oxysporum f. sp. conglutinans Fo5176": 780,
+  "Fusarium oxysporum f. sp. lycopersici 4287": 781,
+  "Fusarium proliferatum ET1": 782,
+  "Fusarium pseudograminearum CS3096": 783,
+  "Fusarium vanettenii 77-13-4": 784,
+  "Fusarium verticillioides 7600": 785,
+  "Fusobacterium nucleatum subsp. nucleatum ATCC 23726": 786,
+  "Fusobacterium nucleatum subsp. nucleatum ATCC 25586": 787,
+  "Gadus morhua callarias": 788,
+  "Galerina marginata CBS 339.88": 789,
+  "Gallibacterium anatis UMN179": 790,
+  "Gallionella capsiferriformans ES-2": 791,
+  "Gelatoporia subvermispora B": 792,
+  "Geminocystis herdmanii PCC 6308": 793,
+  "Gemmatimonas aurantiaca T-27": 794,
+  "Geobacillus kaustophilus HTA426": 795,
+  "Geobacillus stearothermophilus 10": 796,
+  "Geobacillus thermodenitrificans NG80-2": 797,
+  "Geobacter metallireducens GS-15": 798,
+  "Geobacter sulfurreducens KN400": 799,
+  "Geobacter sulfurreducens PCA": 800,
+  "Geodermatophilus obscurus DSM 43160": 801,
+  "Geomys bursarius lutescens": 802,
+  "Geotalea daltonii FRC-32": 803,
+  "Geotalea uraniireducens Rf4": 804,
+  "Giardia duodenalis ATCC 50581": 805,
+  "Giardia lamblia ATCC 50803": 806,
+  "Giardia lamblia P15": 807,
+  "Gillisia limnaea DSM 15749": 808,
+  "Gimesia maris DSM 8797": 809,
+  "Glaciozyma antarctica PI12": 810,
+  "Glaesserella parasuis SH0165": 811,
+  "Glarea lozoyensis ATCC 20868": 812,
+  "Gloeobacter kilaueensis JS1": 813,
+  "Gloeobacter violaceus PCC 7421": 814,
+  "Gloeophyllum trabeum ATCC 11539": 815,
+  "Gloeothece citriformis PCC 7424": 816,
+  "Gloeothece membranacea PCC 6501": 817,
+  "Glossina fuscipes fuscipes": 818,
+  "Glossina morsitans morsitans": 819,
+  "Glossina palpalis palpalis": 820,
+  "Gloydius blomhoffii blomhoffii": 821,
+  "Gloydius brevicaudus siniticus": 822,
+  "Gluconacetobacter diazotrophicus PA1 5": 823,
+  "Gluconobacter oxydans 621H": 824,
+  "Gonapodya prolifera JEL478": 825,
+  "Gordonia bronchialis DSM 43247": 826,
+  "Gordonia otitidis NBRC 100426": 827,
+  "Gorilla gorilla gorilla": 828,
+  "Gottschalkia acidurici 9a": 829,
+  "Granulibacter bethesdensis CGDNIH1": 830,
+  "Granulicella tundricola MP5ACTX9": 831,
+  "Grosmannia clavigera kw1407": 832,
+  "Haematobia irritans exigua": 833,
+  "Haemophilus influenzae 10810": 834,
+  "Haemophilus influenzae 3655": 835,
+  "Haemophilus influenzae 86-028NP": 836,
+  "Haemophilus influenzae PittEE": 837,
+  "Haemophilus influenzae PittGG": 838,
+  "Haemophilus influenzae R2866": 839,
+  "Haemophilus influenzae Rd KW20": 840,
+  "Haemophilus parainfluenzae T3T1": 841,
+  "Hahella chejuensis KCTC 2396": 842,
+  "Halalkalibacter akibai JCM 9157": 843,
+  "Halalkalibacterium halodurans C-125": 844,
+  "Halalkalicoccus jeotgali B3": 845,
+  "Haliangium ochraceum DSM 14365": 846,
+  "Haliotis discus hannai": 847,
+  "Haliscomenobacter hydrossis DSM 1100": 848,
+  "Haloarcula hispanica ATCC 33960": 849,
+  "Haloarcula japonica DSM 6131": 850,
+  "Haloarcula marismortui ATCC 43049": 851,
+  "Halobacillus halophilus DSM 2266": 852,
+  "Halobacteriovorax marinus SJ": 853,
+  "Halobacterium salinarum (strain Mex)": 854,
+  "Halobacterium salinarum (strain Port)": 855,
+  "Halobacterium salinarum (strain Shark)": 856,
+  "Halobacterium salinarum NRC-34001": 857,
+  "Haloferax gibbonsii ATCC 33959": 858,
+  "Haloferax mediterranei ATCC 33500": 859,
+  "Haloferax prahovense DSM 18310": 860,
+  "Haloferax volcanii DS2": 861,
+  "Haloferax volcanii DSM 14919": 862,
+  "Halogeometricum borinquense DSM 11551": 863,
+  "Halomicrobium mukohataei DSM 12286": 864,
+  "Halomonas elongata DSM 2581": 865,
+  "Halomonas halodenitrificans ATCC 12084": 866,
+  "Halopiger xanaduensis SH-6": 867,
+  "Haloplanus natans DSM 17983": 868,
+  "Haloquadratum walsbyi C23": 869,
+  "Haloquadratum walsbyi DSM 16790": 870,
+  "Halorhabdus utahensis DSM 12940": 871,
+  "Halorhodospira halophila SL1": 872,
+  "Halorubrum lacusprofundi ATCC 49239": 873,
+  "Haloterrigena turkmenica DSM 5511": 874,
+  "Halothermothrix orenii H 168": 875,
+  "Halothiobacillus neapolitanus c2": 876,
+  "Hapsidospora chrysogenum ATCC 11550": 877,
+  "Helicobacter acinonychis str. Sheeba": 878,
+  "Helicobacter felis ATCC 49179": 879,
+  "Helicobacter hepaticus ATCC 51449": 880,
+  "Helicobacter mustelae 12198": 881,
+  "Helicobacter pylori 26695": 882,
+  "Helicobacter pylori 52": 883,
+  "Helicobacter pylori B128": 884,
+  "Helicobacter pylori B38": 885,
+  "Helicobacter pylori G27": 886,
+  "Helicobacter pylori HPAG1": 887,
+  "Helicobacter pylori J99": 888,
+  "Helicobacter pylori P12": 889,
+  "Helicobacter pylori Shi470": 890,
+  "Helicobacter pylori UM084": 891,
+  "Heliomicrobium modesticaldum Ice1": 892,
+  "Heloderma horridum horridum": 893,
+  "Heloderma suspectum cinctum": 894,
+  "Heloderma suspectum suspectum": 895,
+  "Herbaspirillum seropedicae SmR1": 896,
+  "Herpetosiphon aurantiacus DSM 785": 897,
+  "Heterobasidion irregulare TC 32-1": 898,
+  "Heterosigma akashiwo CCMP452": 899,
+  "Heterosigma akashiwo NIES-293": 900,
+  "Heterostelium album PN500": 901,
+  "Hipposideros armiger terasensis": 902,
+  "Hirschia baltica ATCC 49814": 903,
+  "Histophilus somni 129PT": 904,
+  "Histophilus somni 2336": 905,
+  "Histoplasma capsulatum G186AR": 906,
+  "Histoplasma capsulatum H143": 907,
+  "Histoplasma capsulatum var. duboisii H88": 908,
+  "Hoeflea phototrophica DFL-43": 909,
+  "Homo sapiens neanderthalensis": 910,
+  "Hordeum murinum subsp. leporinum": 911,
+  "Hordeum vulgare subsp. spontaneum": 912,
+  "Hordeum vulgare subsp. vulgare": 913,
+  "Hyalomma asiaticum asiaticum": 914,
+  "Hyaloperonospora arabidopsidis Emoy2": 915,
+  "Hyaloscypha variabilis F": 916,
+  "Hydrogenobacter thermophilus TK-6": 917,
+  "Hydrogenovibrio crunogenus XCL-2": 918,
+  "Hyperthermus butylicus DSM 5456": 919,
+  "Hypholoma sublateritium FD-334 SS-4": 920,
+  "Hyphomonas neptunium ATCC 15444": 921,
+  "Idiomarina loihiensis L2TR": 922,
+  "Ignavibacterium album JCM 16511": 923,
+  "Ignicoccus hospitalis KIN4/I": 924,
+  "Ignisphaera aggregans DSM 17230": 925,
+  "Ilumatobacter coccineus YM16-304": 926,
+  "Ilyobacter polytropus DSM 2926": 927,
+  "Indibacter alkaliphilus LW1": 928,
+  "Intrasporangium calvum DSM 43043": 929,
+  "Isosphaera pallida ATCC 43644": 930,
+  "Jasminum simplicifolium subsp. suavissimum": 931,
+  "Jonesia denitrificans DSM 20603": 932,
+  "Kangiella koreensis DSM 16069": 933,
+  "Ketogulonicigenium vulgare Y25": 934,
+  "Kineococcus radiotolerans SRS30216 = ATCC BAA-149": 935,
+  "Kitasatospora setae KM-6054": 936,
+  "Klebsiella aerogenes KCTC 2190": 937,
+  "Klebsiella michiganensis KCTC 1686": 938,
+  "Klebsiella pneumoniae subsp. ozaenae": 939,
+  "Klebsiella pneumoniae subsp. pneumoniae HS11286": 940,
+  "Klebsiella pneumoniae subsp. pneumoniae MGH 78578": 941,
+  "Klebsiella pneumoniae subsp. rhinoscleromatis": 942,
+  "Klebsiella variicola 342": 943,
+  "Klebsiella variicola At-22": 944,
+  "Kluyveromyces lactis NRRL Y-1140": 945,
+  "Kluyveromyces marxianus DMKU3-1042": 946,
+  "Kocuria rhizophila DC2201": 947,
+  "Komagataeibacter sucrofermentans DSM 15973": 948,
+  "Komagataella phaffii CBS 7435": 949,
+  "Komagataella phaffii GS115": 950,
+  "Kosmotoga olearia TBF 19.5.1": 951,
+  "Kribbella flavida DSM 17836": 952,
+  "Kyrpidia tusciae DSM 2912": 953,
+  "Kytococcus sedentarius DSM 20547": 954,
+  "Laccaria bicolor S238N-H82": 955,
+  "Lachancea kluyveri NRRL Y-12651": 956,
+  "Lachancea thermotolerans CBS 6340": 957,
+  "Lachesis muta muta": 958,
+  "Lachesis muta rhombeata": 959,
+  "Lachnoclostridium phytofermentans ISDg": 960,
+  "Lachnospira eligens ATCC 27750": 961,
+  "Lacticaseibacillus casei BD-II": 962,
+  "Lacticaseibacillus casei BL23": 963,
+  "Lacticaseibacillus casei str. Zhang": 964,
+  "Lacticaseibacillus paracasei ATCC 334": 965,
+  "Lacticaseibacillus rhamnosus GG": 966,
+  "Lacticaseibacillus rhamnosus LMS2-1": 967,
+  "Lacticaseibacillus rhamnosus Lc 705": 968,
+  "Lactiplantibacillus plantarum JDM1": 969,
+  "Lactiplantibacillus plantarum WCFS1": 970,
+  "Lactobacillus acidophilus NCFM": 971,
+  "Lactobacillus crispatus ST1": 972,
+  "Lactobacillus delbrueckii subsp. bulgaricus": 973,
+  "Lactobacillus delbrueckii subsp. bulgaricus ATCC 11842 = JCM 1002": 974,
+  "Lactobacillus delbrueckii subsp. bulgaricus ATCC BAA-365": 975,
+  "Lactobacillus delbrueckii subsp. lactis": 976,
+  "Lactobacillus gasseri ATCC 33323 = JCM 1131": 977,
+  "Lactobacillus helveticus DPC 4571": 978,
+  "Lactobacillus johnsonii NCC 533": 979,
+  "Lactobacillus kefiranofaciens subsp. kefiranofaciens": 980,
+  "Lactococcus cremoris subsp. cremoris MG1363": 981,
+  "Lactococcus cremoris subsp. cremoris NZ9000": 982,
+  "Lactococcus cremoris subsp. cremoris SK11": 983,
+  "Lactococcus garvieae Lg2": 984,
+  "Lactococcus lactis subsp. hordniae": 985,
+  "Lactococcus lactis subsp. lactis": 986,
+  "Lactococcus lactis subsp. lactis Il1403": 987,
+  "Lactococcus lactis subsp. lactis KF147": 988,
+  "Lancefieldella parvula DSM 20469": 989,
+  "Lancefieldella rimae ATCC 49626": 990,
+  "Laribacter hongkongensis HLHK9": 991,
+  "Latilactobacillus sakei L45": 992,
+  "Latilactobacillus sakei subsp. sakei 23K": 993,
+  "Lavandula pedunculata subsp. lusitanica": 994,
+  "Lawsonia intracellularis PHE/MN1-00": 995,
+  "Leadbetterella byssophila DSM 17132": 996,
+  "Legionella longbeachae NSW150": 997,
+  "Legionella pneumophila 2300/99 Alcoy": 998,
+  "Legionella pneumophila str. Corby": 999,
+  "Legionella pneumophila str. Lens": 1000,
+  "Legionella pneumophila str. Paris": 1001,
+  "Legionella pneumophila subsp. pneumophila str. Philadelphia 1": 1002,
+  "Leifsonia xyli subsp. xyli str. CTCB07": 1003,
+  "Leishmania mexicana MHOM/GT/2001/U1103": 1004,
+  "Leiurus quinquestriatus quinquestriatus": 1005,
+  "Lens culinaris subsp. culinaris": 1006,
+  "Lens culinaris subsp. tomentosus": 1007,
+  "Lentilactobacillus buchneri NRRL B-30929": 1008,
+  "Lentilactobacillus hilgardii DSM 20176 = ATCC 8290": 1009,
+  "Leptospira biflexa serovar Patoc strain 'Patoc 1 (Ames)'": 1010,
+  "Leptospira biflexa serovar Patoc strain 'Patoc 1 (Paris)'": 1011,
+  "Leptospira borgpetersenii serovar Hardjo-bovis str. JB197": 1012,
+  "Leptospira borgpetersenii serovar Hardjo-bovis str. L550": 1013,
+  "Leptospira interrogans serovar Copenhageni str. Fiocruz L1-130": 1014,
+  "Leptospira interrogans serovar Lai str. 56601": 1015,
+  "Leptospirillum ferriphilum ML-04": 1016,
+  "Leptothrix cholodnii SP-6": 1017,
+  "Leptotrichia buccalis C-1013-b": 1018,
+  "Leptotrichia shahii DSM 19757": 1019,
+  "Leptotrichia wadei F0279": 1020,
+  "Leuconostoc citreum KM20": 1021,
+  "Leuconostoc gasicomitatum LMG 18811": 1022,
+  "Leuconostoc kimchii IMSNU 11154": 1023,
+  "Leuconostoc mesenteroides subsp. cremoris": 1024,
+  "Leuconostoc mesenteroides subsp. mesenteroides": 1025,
+  "Leuconostoc mesenteroides subsp. mesenteroides ATCC 8293": 1026,
+  "Levilactobacillus brevis ATCC 367": 1027,
+  "Liasis mackloti savuensis": 1028,
+  "Ligilactobacillus salivarius CECT 5713": 1029,
+  "Ligilactobacillus salivarius UCC118": 1030,
+  "Limnospira platensis NIES-39": 1031,
+  "Limosilactobacillus fermentum CECT 5716": 1032,
+  "Limosilactobacillus fermentum IFO 3956": 1033,
+  "Limosilactobacillus reuteri SD2112": 1034,
+  "Limosilactobacillus reuteri subsp. reuteri": 1035,
+  "Limosilactobacillus reuteri subsp. reuteri JCM 1112": 1036,
+  "Limosilactobacillus reuteri subsp. rodentium": 1037,
+  "Limosilactobacillus reuteri subsp. suis": 1038,
+  "Listeria innocua Clip11262": 1039,
+  "Listeria monocytogenes 08-5578": 1040,
+  "Listeria monocytogenes 08-5923": 1041,
+  "Listeria monocytogenes 10403S": 1042,
+  "Listeria monocytogenes EGD": 1043,
+  "Listeria monocytogenes EGD-e": 1044,
+  "Listeria monocytogenes HCC23": 1045,
+  "Listeria monocytogenes serotype 4b str. CLIP 80459": 1046,
+  "Listeria monocytogenes serotype 4b str. F2365": 1047,
+  "Listeria seeligeri serovar 1/2b str. SLCC3954": 1048,
+  "Listeria welshimeri serovar 6b str. SLCC5334": 1049,
+  "Litchfieldella anticariensis FP35 = DSM 16096": 1050,
+  "Lodderomyces elongisporus NRRL YB-4239": 1051,
+  "Lysinibacillus sphaericus C3-41": 1052,
+  "Macaca fuscata fuscata": 1053,
+  "Macrococcoides caseolyticum JCSC5402": 1054,
+  "Macrophomina phaseolina MS6": 1055,
+  "Macrovipera lebetina obtusa": 1056,
+  "Macrovipera lebetina transmediterranea": 1057,
+  "Magnetococcus marinus MC-1": 1058,
+  "Magnetospirillum gryphiswaldense MSR-1": 1059,
+  "Magnolia fraseri subsp. pyramidata": 1060,
+  "Malacoplasma penetrans HF-2": 1061,
+  "Malassezia globosa CBS 7966": 1062,
+  "Malassezia restricta CBS 7877": 1063,
+  "Malassezia sympodialis ATCC 42132": 1064,
+  "Maricaulis maris MCS10": 1065,
+  "Maridesulfovibrio salexigens DSM 2638": 1066,
+  "Marinitoga hydrogenitolerans DSM 16785": 1067,
+  "Marinitoga piezophila KA3": 1068,
+  "Marinobacter adhaerens HP15": 1069,
+  "Marinobacter nauticus ATCC 49840": 1070,
+  "Marinobacter nauticus VT8": 1071,
+  "Marinomonas mediterranea MMB-1": 1072,
+  "Marinomonas posidonica IVIA-Po-181": 1073,
+  "Marivirga tractuosa DSM 4126": 1074,
+  "Marmota marmota marmota": 1075,
+  "Medicago sativa subsp. falcata": 1076,
+  "Mediterraneibacter gnavus ATCC 29149": 1077,
+  "Mediterraneibacter gnavus CC55_001C": 1078,
+  "Megalodesulfovibrio gigas DSM 1382 = ATCC 19364": 1079,
+  "Meiothermus ruber DSM 1279": 1080,
+  "Melampsora larici-populina 98AG31": 1081,
+  "Melioribacter roseus P3M-2": 1082,
+  "Melissococcus plutonius ATCC 35311": 1083,
+  "Merluccius australis australis": 1084,
+  "Mesobacillus selenatarsenatis SF-1": 1085,
+  "Mesomycoplasma conjunctivae HRC/581": 1086,
+  "Mesomycoplasma hyopneumoniae 232": 1087,
+  "Mesomycoplasma hyopneumoniae 7448": 1088,
+  "Mesomycoplasma hyopneumoniae J": 1089,
+  "Mesomycoplasma hyorhinis HUB-1": 1090,
+  "Mesoplasma florum L1": 1091,
+  "Mesorhizobium japonicum MAFF 303099": 1092,
+  "Metallosphaera cuprina Ar-4": 1093,
+  "Metallosphaera sedula DSM 5348": 1094,
+  "Metamycoplasma arthritidis 158L3-1": 1095,
+  "Metamycoplasma hominis ATCC 23114": 1096,
+  "Metarhizium acridum CQMa 102": 1097,
+  "Metarhizium album ARSEF 1941": 1098,
+  "Metarhizium brunneum ARSEF 3297": 1099,
+  "Metarhizium guizhouense ARSEF 977": 1100,
+  "Metarhizium majus ARSEF 297": 1101,
+  "Metarhizium robertsii ARSEF 23": 1102,
+  "Methanobacterium formicicum DSM 3637": 1103,
+  "Methanobrevibacter ruminantium M1": 1104,
+  "Methanobrevibacter smithii ATCC 35061": 1105,
+  "Methanocaldococcus fervens AG86": 1106,
+  "Methanocaldococcus infernus ME": 1107,
+  "Methanocaldococcus jannaschii DSM 2661": 1108,
+  "Methanocaldococcus vulcanius M7": 1109,
+  "Methanocella arvoryzae MRE50": 1110,
+  "Methanocella paludicola SANAE": 1111,
+  "Methanococcoides burtonii DSM 6242": 1112,
+  "Methanococcus aeolicus Nankai-3": 1113,
+  "Methanococcus maripaludis C5": 1114,
+  "Methanococcus maripaludis C6": 1115,
+  "Methanococcus maripaludis C7": 1116,
+  "Methanococcus maripaludis S2": 1117,
+  "Methanococcus vannielii SB": 1118,
+  "Methanococcus voltae A3": 1119,
+  "Methanocorpusculum labreanum Z": 1120,
+  "Methanoculleus marisnigri JR1": 1121,
+  "Methanohalobium evestigatum Z-7303": 1122,
+  "Methanohalophilus mahii DSM 5219": 1123,
+  "Methanolacinia petrolearia DSM 11571": 1124,
+  "Methanopyrus kandleri AV19": 1125,
+  "Methanoregula boonei 6A8": 1126,
+  "Methanosalsum zhilinae DSM 4017": 1127,
+  "Methanosarcina acetivorans C2A": 1128,
+  "Methanosarcina barkeri str. Fusaro": 1129,
+  "Methanosarcina mazei Go1": 1130,
+  "Methanosarcina thermophila TM-1": 1131,
+  "Methanosphaera stadtmanae DSM 3091": 1132,
+  "Methanosphaerula palustris E1-9c": 1133,
+  "Methanospirillum hungatei JF-1": 1134,
+  "Methanothermobacter marburgensis str. Marburg": 1135,
+  "Methanothermobacter thermautotrophicus str. Delta H": 1136,
+  "Methanothermobacter thermautotrophicus str. Winter": 1137,
+  "Methanothermococcus okinawensis IH1": 1138,
+  "Methanothermus fervidus DSM 2088": 1139,
+  "Methanothrix harundinacea 6Ac": 1140,
+  "Methanothrix soehngenii GP6": 1141,
+  "Methanothrix thermoacetophila PT": 1142,
+  "Methanotorris igneus Kol 5": 1143,
+  "Methylacidiphilum fumariolicum SolV": 1144,
+  "Methylacidiphilum infernorum V4": 1145,
+  "Methylibium petroleiphilum PM1": 1146,
+  "Methylobacillus flagellatus KT": 1147,
+  "Methylobacter tundripaludum SV96": 1148,
+  "Methylobacterium nodulans ORS 2060": 1149,
+  "Methylobacterium radiotolerans JCM 2831": 1150,
+  "Methylocella silvestris BL2": 1151,
+  "Methylococcus capsulatus str. Bath": 1152,
+  "Methylomonas methanica MC09": 1153,
+  "Methylorubrum extorquens AM1": 1154,
+  "Methylorubrum extorquens CM4": 1155,
+  "Methylorubrum extorquens DM4": 1156,
+  "Methylorubrum extorquens PA1": 1157,
+  "Methylorubrum populi BJ001": 1158,
+  "Methylotenera mobilis JLW8": 1159,
+  "Methylotuvimicrobium alcaliphilum 20Z": 1160,
+  "Methyloversatilis universalis FAM5": 1161,
+  "Methylovorus glucosotrophus SIP3-4": 1162,
+  "Meyerozyma guilliermondii ATCC 6260": 1163,
+  "Microbacterium testaceum StLB037": 1164,
+  "Micrococcus luteus NCTC 2665": 1165,
+  "Microcystis aeruginosa NIES-843": 1166,
+  "Microcystis aeruginosa NIES-88": 1167,
+  "Microcystis aeruginosa PCC 7806": 1168,
+  "Micromonas pusilla CCMP1545": 1169,
+  "Micromonospora megalomicea subsp. nigra": 1170,
+  "Microsporum canis CBS 113480": 1171,
+  "Micrurus tener microgalbineus": 1172,
+  "Micrurus tener tener": 1173,
+  "Millerozyma farinosa CBS 7064": 1174,
+  "Moesziomyces antarcticus T-34": 1175,
+  "Moniliophthora perniciosa FA553": 1176,
+  "Moorella thermoacetica ATCC 39073": 1177,
+  "Moorena producens JHB": 1178,
+  "Moraxella catarrhalis BBH18": 1179,
+  "Mus musculus bactrianus": 1180,
+  "Mus musculus castaneus": 1181,
+  "Mus musculus molossinus": 1182,
+  "Musa acuminata subsp. malaccensis": 1183,
+  "Musicola paradisiaca Ech703": 1184,
+  "Mustela putorius furo": 1185,
+  "Mycetohabitans rhizoxinica HKI 454": 1186,
+  "Mycobacterium avium 104": 1187,
+  "Mycobacterium avium subsp. hominissuis TH135": 1188,
+  "Mycobacterium avium subsp. paratuberculosis": 1189,
+  "Mycobacterium avium subsp. paratuberculosis K-10": 1190,
+  "Mycobacterium intracellulare ATCC 13950": 1191,
+  "Mycobacterium intracellulare subsp. intracellulare MTCC 9506": 1192,
+  "Mycobacterium leprae Br4923": 1193,
+  "Mycobacterium leprae TN": 1194,
+  "Mycobacterium marinum M": 1195,
+  "Mycobacterium tuberculosis CDC1551": 1196,
+  "Mycobacterium tuberculosis F11": 1197,
+  "Mycobacterium tuberculosis H37Ra": 1198,
+  "Mycobacterium tuberculosis H37Rv": 1199,
+  "Mycobacterium tuberculosis KZN 1435": 1200,
+  "Mycobacterium tuberculosis str. Erdman = ATCC 35801": 1201,
+  "Mycobacterium tuberculosis variant bovis AF2122/97": 1202,
+  "Mycobacterium tuberculosis variant bovis BCG str. Pasteur 1173P2": 1203,
+  "Mycobacterium tuberculosis variant bovis BCG str. Tokyo 172": 1204,
+  "Mycobacterium ulcerans Agy99": 1205,
+  "Mycobacteroides abscessus ATCC 19977": 1206,
+  "Mycolicibacterium gilvum PYR-GCK": 1207,
+  "Mycolicibacterium gilvum Spyr1": 1208,
+  "Mycolicibacterium hassiacum DSM 44199": 1209,
+  "Mycolicibacterium smegmatis MC2 155": 1210,
+  "Mycolicibacterium smegmatis MKD8": 1211,
+  "Mycolicibacterium thermoresistibile ATCC 19527": 1212,
+  "Mycolicibacterium vanbaalenii PYR-1": 1213,
+  "Mycoplasma capricolum subsp. capricolum ATCC 27343": 1214,
+  "Mycoplasma capricolum subsp. capripneumoniae": 1215,
+  "Mycoplasma mobile 163K": 1216,
+  "Mycoplasma mycoides subsp. mycoides SC": 1217,
+  "Mycoplasmoides gallisepticum str. F": 1218,
+  "Mycoplasmoides gallisepticum str. R": 1219,
+  "Mycoplasmoides genitalium G37": 1220,
+  "Mycoplasmoides pneumoniae FH": 1221,
+  "Mycoplasmoides pneumoniae M129": 1222,
+  "Mycoplasmopsis agalactiae PG2": 1223,
+  "Mycoplasmopsis bovis PG45": 1224,
+  "Mycoplasmopsis fermentans PG18": 1225,
+  "Mycoplasmopsis pulmonis UAB CTIP": 1226,
+  "Mycoplasmopsis synoviae 53": 1227,
+  "Myxococcus fulvus HW-1": 1228,
+  "Myxococcus xanthus DK 1622": 1229,
+  "Naja annulata annulata": 1230,
+  "Naja haje haje": 1231,
+  "Nakamurella multipartita DSM 44233": 1232,
+  "Nakaseomyces glabratus CBS 138": 1233,
+  "Nannizzia gypsea CBS 118893": 1234,
+  "Nanoarchaeum equitans Kin4-M": 1235,
+  "Natranaerobius thermophilus JW/NM-WN-LF": 1236,
+  "Natrialba asiatica DSM 12278": 1237,
+  "Natrialba magadii ATCC 43099": 1238,
+  "Natrinema altunense JCM 12890": 1239,
+  "Natronomonas pharaonis DSM 2160": 1240,
+  "Nautilia profundicola AmH": 1241,
+  "Neisseria elongata subsp. glycolytica": 1242,
+  "Neisseria gonorrhoeae FA 1090": 1243,
+  "Neisseria gonorrhoeae NCCP11945": 1244,
+  "Neisseria lactamica 020-06": 1245,
+  "Neisseria meningitidis 053442": 1246,
+  "Neisseria meningitidis 8013": 1247,
+  "Neisseria meningitidis ATCC 13091": 1248,
+  "Neisseria meningitidis FAM18": 1249,
+  "Neisseria meningitidis H44/76": 1250,
+  "Neisseria meningitidis MC58": 1251,
+  "Neisseria meningitidis Z2491": 1252,
+  "Neisseria mucosa ATCC 25996": 1253,
+  "Neolewinella persica DSM 23188": 1254,
+  "Neorickettsia risticii str. Illinois": 1255,
+  "Neurospora crassa OR74A": 1256,
+  "Niastella koreensis GR20-10": 1257,
+  "Nitratidesulfovibrio oxamicus (strain Monticello)": 1258,
+  "Nitratidesulfovibrio vulgaris DP4": 1259,
+  "Nitratidesulfovibrio vulgaris str. 'Miyazaki F'": 1260,
+  "Nitratidesulfovibrio vulgaris str. Hildenborough": 1261,
+  "Nitrobacter hamburgensis X14": 1262,
+  "Nitrobacter winogradskyi Nb-255": 1263,
+  "Nitrosococcus halophilus Nc 4": 1264,
+  "Nitrosococcus oceani ATCC 19707": 1265,
+  "Nitrosomonas europaea ATCC 19718": 1266,
+  "Nitrosomonas eutropha C91": 1267,
+  "Nitrosopumilus maritimus SCM1": 1268,
+  "Nitrosospira multiformis ATCC 25196": 1269,
+  "Nitrospina gracilis 3/211": 1270,
+  "Nocardia brasiliensis ATCC 700358": 1271,
+  "Nocardia farcinica IFM 10152": 1272,
+  "Nocardia uniformis subsp. tsuyamanensis": 1273,
+  "Nocardiopsis dassonvillei subsp. dassonvillei DSM 43111": 1274,
+  "Nonlabens dokdonensis DSW-6": 1275,
+  "Nosema bombycis CQ1": 1276,
+  "Nostoc punctiforme PCC 73102": 1277,
+  "Notechis scutatus niger": 1278,
+  "Notechis scutatus scutatus": 1279,
+  "Novosphingobium aromaticivorans DSM 12444": 1280,
+  "Nyctalus plancyi velutinus": 1281,
+  "Oceanicola granulosus HTCC2516": 1282,
+  "Oceanobacillus iheyensis HTE831": 1283,
+  "Odobenus rosmarus divergens": 1284,
+  "Odobenus rosmarus rosmarus": 1285,
+  "Odocoileus virginianus virginianus": 1286,
+  "Oenococcus oeni PSU-1": 1287,
+  "Oenothera elata subsp. hookeri": 1288,
+  "Ogataea parapolymorpha DL-1": 1289,
+  "Oidiodendron maius Zn": 1290,
+  "Oleidesulfovibrio alaskensis G20": 1291,
+  "Olsenella uli DSM 7084": 1292,
+  "Onion yellows phytoplasma OY-M": 1293,
+  "Ophrys x arachnitiformis subsp. archipelagi": 1294,
+  "Opitutus terrae PB90-1": 1295,
+  "Orbilia oligospora ATCC 24927": 1296,
+  "Orientia tsutsugamushi str. Boryong": 1297,
+  "Orientia tsutsugamushi str. Ikeda": 1298,
+  "Ostreococcus lucimarinus CCE9901": 1299,
+  "Ovis ammon darwini": 1300,
+  "Ovis aries musimon": 1301,
+  "Oxyuranus scutellatus canni": 1302,
+  "Oxyuranus scutellatus scutellatus": 1303,
+  "Paenarthrobacter aurescens TC1": 1304,
+  "Paenibacillus alvei DSM 29": 1305,
+  "Paenibacillus larvae subsp. larvae B-3650": 1306,
+  "Paenibacillus macquariensis subsp. macquariensis": 1307,
+  "Paenibacillus polymyxa E681": 1308,
+  "Paenibacillus polymyxa SC2": 1309,
+  "Paeonia mascula subsp. russoi": 1310,
+  "Paludibacter propionicigenes WB4": 1311,
+  "Panthera pardus orientalis": 1312,
+  "Panthera pardus saxicolor": 1313,
+  "Panthera tigris altaica": 1314,
+  "Panthera tigris sumatrae": 1315,
+  "Panthera tigris tigris": 1316,
+  "Pantoea ananatis AJ13355": 1317,
+  "Pantoea ananatis LMG 20103": 1318,
+  "Pantoea stewartii subsp. stewartii": 1319,
+  "Pantoea vagans C9-1": 1320,
+  "Parabacteroides distasonis ATCC 8503": 1321,
+  "Paraburkholderia graminis C4D1M": 1322,
+  "Paraburkholderia phymatum STM815": 1323,
+  "Paraburkholderia phytofirmans PsJN": 1324,
+  "Paraburkholderia xenovorans LB400": 1325,
+  "Parachartergus vespiceps testaceus": 1326,
+  "Paracidovorax avenae ATCC 19860": 1327,
+  "Paracidovorax citrulli AAC00-1": 1328,
+  "Paraclostridium sordellii ATCC 9714": 1329,
+  "Paracoccidioides brasiliensis Pb03": 1330,
+  "Paracoccidioides brasiliensis Pb18": 1331,
+  "Paracoccidioides lutzii Pb01": 1332,
+  "Paracoccus denitrificans PD1222": 1333,
+  "Paramagnetospirillum magneticum AMB-1": 1334,
+  "Paramaledivibacter caminithermalis DSM 15212": 1335,
+  "Parastagonospora nodorum SN15": 1336,
+  "Parasynechococcus marenigrum WH 8102": 1337,
+  "Parvibaculum lavamentivorans DS-1": 1338,
+  "Pasteurella multocida subsp. multocida str. Pm70": 1339,
+  "Pectobacterium atrosepticum SCRI1043": 1340,
+  "Pectobacterium carotovorum subsp. carotovorum": 1341,
+  "Pectobacterium carotovorum subsp. carotovorum PC1": 1342,
+  "Pectobacterium parmentieri WPP163": 1343,
+  "Pediculus humanus corporis": 1344,
+  "Pediococcus pentosaceus ATCC 25745": 1345,
+  "Pedobacter heparinus DSM 2366": 1346,
+  "Pedosphaera parvula Ellin514": 1347,
+  "Pelagibacterium halotolerans B2": 1348,
+  "Pelobacter propionicus DSM 2379": 1349,
+  "Pelodictyon luteolum DSM 273": 1350,
+  "Pelodictyon phaeoclathratiforme BU-1": 1351,
+  "Pelodiscus sinensis japonicus": 1352,
+  "Pelophylax porosus brevipodus": 1353,
+  "Pelotomaculum thermopropionicum SI": 1354,
+  "Penicillium camemberti FM 013": 1355,
+  "Penicillium digitatum PHI26": 1356,
+  "Penicillium digitatum Pd1": 1357,
+  "Penicillium oxalicum 114-2": 1358,
+  "Penicillium roqueforti FM164": 1359,
+  "Penicillium rubens Wisconsin 54-1255": 1360,
+  "Perinereis vancaurica tetradentata": 1361,
+  "Perodicticus potto edwarsi": 1362,
+  "Perognathus flavescens apache": 1363,
+  "Peromyscus maniculatus bairdii": 1364,
+  "Persephonella marina EX-H1": 1365,
+  "Pestalotiopsis fici W106-1": 1366,
+  "Petrotoga mobilis SJ95": 1367,
+  "Petunia axillaris subsp. parodii": 1368,
+  "Phaeodactylum tricornutum CCAP 1055/1": 1369,
+  "Phalaenopsis aphrodite subsp. formosana": 1370,
+  "Phanerochaete carnosa HHB-10118-sp": 1371,
+  "Phanerochaete chrysosporium RP-78": 1372,
+  "Phasianus colchicus colchicus": 1373,
+  "Phenylobacterium zucineum HLK1": 1374,
+  "Phlebiopsis gigantea 11061_1 CR5-6": 1375,
+  "Phoca vitulina richardii": 1376,
+  "Phocaeicola plebeius DSM 17135": 1377,
+  "Phocaeicola vulgatus ATCC 8482": 1378,
+  "Phocoenoides dalli dalli": 1379,
+  "Phoradendron leucarpum subsp. tomentosum": 1380,
+  "Photobacterium angustum S14": 1381,
+  "Photobacterium damselae subsp. damselae": 1382,
+  "Photobacterium damselae subsp. piscicida": 1383,
+  "Photobacterium leiognathi subsp. mandapamensis": 1384,
+  "Photobacterium profundum SS9": 1385,
+  "Photorhabdus asymbiotica subsp. asymbiotica ATCC 43949": 1386,
+  "Photorhabdus laumondii subsp. laumondii TTO1": 1387,
+  "Phycomyces blakesleeanus NRRL 1555(-)": 1388,
+  "Phytophthora infestans T30-4": 1389,
+  "Phytophthora nicotianae INRA-310": 1390,
+  "Phytophthora sojae strain P6497": 1391,
+  "Picrophilus oshimae DSM 9789": 1392,
+  "Piloderma croceum F 1598": 1393,
+  "Pinus mugo subsp. uncinata": 1394,
+  "Pirellula staleyi DSM 6068": 1395,
+  "Planctopirus limnophila DSM 3776": 1396,
+  "Planigale maculata maculata": 1397,
+  "Planigale maculata sinualis": 1398,
+  "Planktothrix agardhii NIVA-CYA 126/8": 1399,
+  "Plasmodium berghei ANKA": 1400,
+  "Plasmodium chabaudi chabaudi": 1401,
+  "Plasmodium cynomolgi strain B": 1402,
+  "Plasmodium cynomolgi strain Berok": 1403,
+  "Plasmodium cynomolgi strain Ceylon": 1404,
+  "Plasmodium cynomolgi strain Gombak": 1405,
+  "Plasmodium cynomolgi strain London": 1406,
+  "Plasmodium cynomolgi strain Mulligan/NIH": 1407,
+  "Plasmodium falciparum 7G8": 1408,
+  "Plasmodium falciparum CAMP/Malaysia": 1409,
+  "Plasmodium falciparum CDC/Honduras": 1410,
+  "Plasmodium falciparum FC27/Papua New Guinea": 1411,
+  "Plasmodium falciparum FCBR/Columbia": 1412,
+  "Plasmodium falciparum FCH-5": 1413,
+  "Plasmodium falciparum FCM17/Senegal": 1414,
+  "Plasmodium falciparum Fid3/India": 1415,
+  "Plasmodium falciparum IMR143": 1416,
+  "Plasmodium falciparum K1": 1417,
+  "Plasmodium falciparum Mad20/Papua New Guinea": 1418,
+  "Plasmodium falciparum Mad71/Papua New Guinea": 1419,
+  "Plasmodium falciparum NF7/Ghana": 1420,
+  "Plasmodium falciparum Nig32/Nigeria": 1421,
+  "Plasmodium falciparum Palo Alto/Uganda": 1422,
+  "Plasmodium falciparum RO-33": 1423,
+  "Plasmodium falciparum T4/Thailand": 1424,
+  "Plasmodium falciparum TAK 9": 1425,
+  "Plasmodium falciparum THTN/Thailand": 1426,
+  "Plasmodium falciparum V1": 1427,
+  "Plasmodium knowlesi strain H": 1428,
+  "Plasmodium knowlesi strain Nuri": 1429,
+  "Plasmodium vivax Brazil I": 1430,
+  "Plasmodium vivax Sal-1": 1431,
+  "Plasmodium vivax strain Belem": 1432,
+  "Plasmodium yoelii yoelii": 1433,
+  "Plenodomus lingam JN3": 1434,
+  "Pleurotus ostreatus PC15": 1435,
+  "Pneumocystis carinii B80": 1436,
+  "Pochonia chlamydosporia 123": 1437,
+  "Podospora anserina S mat+": 1438,
+  "Polaromonas naphthalenivorans CJ2": 1439,
+  "Polybia scutellaris rioplatensis": 1440,
+  "Polynucleobacter asymbioticus QLW-P1DMWA-1": 1441,
+  "Polynucleobacter necessarius STIR1": 1442,
+  "Polytolypa hystricis UAMH7299": 1443,
+  "Porphyromonas endodontalis ATCC 35406": 1444,
+  "Porphyromonas gingivalis ATCC 33277": 1445,
+  "Porphyromonas gingivalis W83": 1446,
+  "Postia placenta Mad-698-R": 1447,
+  "Prescottella equi 103S": 1448,
+  "Priestia megaterium DSM 319": 1449,
+  "Priestia megaterium NBRC 15308 = ATCC 14581": 1450,
+  "Priestia megaterium QM B1551": 1451,
+  "Priestia megaterium WSH-002": 1452,
+  "Procavia capensis habessinica": 1453,
+  "Prochlorococcus marinus str. AS9601": 1454,
+  "Prochlorococcus marinus str. DV1": 1455,
+  "Prochlorococcus marinus str. MIT 9211": 1456,
+  "Prochlorococcus marinus str. MIT 9215": 1457,
+  "Prochlorococcus marinus str. MIT 9301": 1458,
+  "Prochlorococcus marinus str. MIT 9303": 1459,
+  "Prochlorococcus marinus str. MIT 9312": 1460,
+  "Prochlorococcus marinus str. MIT 9313": 1461,
+  "Prochlorococcus marinus str. MIT 9515": 1462,
+  "Prochlorococcus marinus str. NATL1A": 1463,
+  "Prochlorococcus marinus str. NATL2A": 1464,
+  "Prochlorococcus marinus subsp. marinus str. CCMP1375": 1465,
+  "Prochlorococcus marinus subsp. pastoris str. CCMP1986": 1466,
+  "Prochlorococcus marinus subsp. pastoris str. PCC 9511": 1467,
+  "Propionibacterium freudenreichii subsp. freudenreichii": 1468,
+  "Propionibacterium freudenreichii subsp. shermanii": 1469,
+  "Propionibacterium freudenreichii subsp. shermanii CIRM-BIA1": 1470,
+  "Propithecus diadema diadema": 1471,
+  "Prosthecochloris aestuarii DSM 271": 1472,
+  "Proteus mirabilis HI4320": 1473,
+  "Providencia stuartii MRSN 2154": 1474,
+  "Pseudanabaena galeata PCC 6901": 1475,
+  "Pseudanabaena tenuis PCC 7409": 1476,
+  "Pseudarthrobacter chlorophenolicus A6": 1477,
+  "Pseudarthrobacter phenanthrenivorans Sphe3": 1478,
+  "Pseudoalteromonas luteoviolacea 2ta16": 1479,
+  "Pseudoalteromonas translucida TAC125": 1480,
+  "Pseudocercospora fijiensis CIRAD86": 1481,
+  "Pseudogymnoascus destructans 20631-21": 1482,
+  "Pseudomonas [fluorescens] SBW25": 1483,
+  "Pseudomonas aeruginosa BWHPSA011": 1484,
+  "Pseudomonas aeruginosa LESB58": 1485,
+  "Pseudomonas aeruginosa PA103": 1486,
+  "Pseudomonas aeruginosa PAK": 1487,
+  "Pseudomonas aeruginosa PAO1": 1488,
+  "Pseudomonas aeruginosa UCBPP-PA14": 1489,
+  "Pseudomonas brassicacearum subsp. brassicacearum NFM421": 1490,
+  "Pseudomonas chlororaphis subsp. chlororaphis": 1491,
+  "Pseudomonas entomophila L48": 1492,
+  "Pseudomonas fluorescens Pf0-1": 1493,
+  "Pseudomonas fluorescens Q2-87": 1494,
+  "Pseudomonas fulva 12-X": 1495,
+  "Pseudomonas knackmussii B13": 1496,
+  "Pseudomonas paraeruginosa PA7": 1497,
+  "Pseudomonas protegens CHA0": 1498,
+  "Pseudomonas protegens Pf-5": 1499,
+  "Pseudomonas putida B6-2": 1500,
+  "Pseudomonas putida DOT-T1E": 1501,
+  "Pseudomonas putida F1": 1502,
+  "Pseudomonas putida GB-1": 1503,
+  "Pseudomonas putida KT2440": 1504,
+  "Pseudomonas putida S16": 1505,
+  "Pseudomonas putida W619": 1506,
+  "Pseudomonas savastanoi pv. phaseolicola 1448A": 1507,
+  "Pseudomonas syringae pv. syringae B728a": 1508,
+  "Pseudomonas syringae pv. syringae FF5": 1509,
+  "Pseudomonas syringae pv. tomato str. DC3000": 1510,
+  "Pseudonaja textilis textilis": 1511,
+  "Pseudonocardia dioxanivorans CB1190": 1512,
+  "Pseudooceanicola batsensis HTCC2597": 1513,
+  "Pseudothermotoga lettingae TMO": 1514,
+  "Psychrobacter arcticus 273-4": 1515,
+  "Psychrobacter cryohalolentis K5": 1516,
+  "Psychrobacter lutiphocae DSM 21542": 1517,
+  "Psychroflexus torquis ATCC 700755": 1518,
+  "Psychromonas ingrahamii 37": 1519,
+  "Pteridium aquilinum subsp. aquilinum": 1520,
+  "Puccinia graminis f. sp. tritici CRL 75-36-700-3": 1521,
+  "Pyrenophora teres f. teres 0-1": 1522,
+  "Pyrenophora tritici-repentis Pt-1C-BFP": 1523,
+  "Pyricularia oryzae 70-15": 1524,
+  "Pyricularia oryzae Y34": 1525,
+  "Pyrobaculum aerophilum str. IM2": 1526,
+  "Pyrobaculum arsenaticum DSM 13514": 1527,
+  "Pyrobaculum calidifontis JCM 11548": 1528,
+  "Pyrobaculum islandicum DSM 4184": 1529,
+  "Pyrobaculum neutrophilum V24Sta": 1530,
+  "Pyrococcus abyssi GE5": 1531,
+  "Pyrococcus furiosus DSM 3638": 1532,
+  "Pyrococcus horikoshii OT3": 1533,
+  "Rahnella aquatilis CIP 78.65 = ATCC 33071": 1534,
+  "Ralstonia pickettii 12D": 1535,
+  "Ralstonia pickettii 12J": 1536,
+  "Ralstonia pseudosolanacearum GMI1000": 1537,
+  "Ralstonia solanacearum UW551": 1538,
+  "Renibacterium salmoninarum ATCC 33209": 1539,
+  "Rhabdophis tigrinus tigrinus": 1540,
+  "Rhinolophus pusillus blythi": 1541,
+  "Rhizobium etli CFN 42": 1542,
+  "Rhizobium etli CIAT 652": 1543,
+  "Rhizobium johnstonii 3841": 1544,
+  "Rhizobium leguminosarum bv. trifolii WSM1325": 1545,
+  "Rhizobium leguminosarum bv. trifolii WSM2304": 1546,
+  "Rhizobium rhizogenes K84": 1547,
+  "Rhizophagus irregularis DAOM 181602=DAOM 197198": 1548,
+  "Rhizopus delemar RA 99-880": 1549,
+  "Rhizorhabdus wittichii DC-6": 1550,
+  "Rhizorhabdus wittichii RW1": 1551,
+  "Rhodobacter capsulatus SB 1003": 1552,
+  "Rhodococcus erythropolis PR4": 1553,
+  "Rhodococcus erythropolis XP": 1554,
+  "Rhodococcus jostii RHA1": 1555,
+  "Rhodococcus opacus B4": 1556,
+  "Rhodoferax ferrireducens T118": 1557,
+  "Rhodomicrobium vannielii ATCC 17100": 1558,
+  "Rhodopirellula baltica SH 1": 1559,
+  "Rhodopseudomonas palustris BisA53": 1560,
+  "Rhodopseudomonas palustris BisB18": 1561,
+  "Rhodopseudomonas palustris BisB5": 1562,
+  "Rhodopseudomonas palustris CGA009": 1563,
+  "Rhodopseudomonas palustris HaA2": 1564,
+  "Rhodopseudomonas palustris TIE-1": 1565,
+  "Rhodospirillum centenum SW": 1566,
+  "Rhodospirillum rubrum ATCC 11170": 1567,
+  "Rhodospirillum rubrum F11": 1568,
+  "Rhodothermus marinus DSM 4252": 1569,
+  "Rhodothermus marinus SG0.5JP17-172": 1570,
+  "Rickettsia africae ESF-5": 1571,
+  "Rickettsia akari str. Hartford": 1572,
+  "Rickettsia bellii OSU 85-389": 1573,
+  "Rickettsia bellii RML369-C": 1574,
+  "Rickettsia canadensis str. McKiel": 1575,
+  "Rickettsia conorii str. Malish 7": 1576,
+  "Rickettsia conorii subsp. caspia A-167": 1577,
+  "Rickettsia felis URRWXCal2": 1578,
+  "Rickettsia japonica YH": 1579,
+  "Rickettsia massiliae MTU5": 1580,
+  "Rickettsia peacockii str. Rustic": 1581,
+  "Rickettsia prowazekii str. Madrid E": 1582,
+  "Rickettsia rickettsii str. 'Sheila Smith'": 1583,
+  "Rickettsia rickettsii str. Iowa": 1584,
+  "Rickettsia sibirica 246": 1585,
+  "Rickettsia sibirica subsp. mongolitimonae": 1586,
+  "Rickettsia slovaca 13-B": 1587,
+  "Rickettsia typhi str. Wilmington": 1588,
+  "Rippkaea orientalis PCC 8801": 1589,
+  "Roseburia hominis A2-183": 1590,
+  "Roseibium aggregatum IAM 12614": 1591,
+  "Roseibium alexandrii DFL-11": 1592,
+  "Roseiflexus castenholzii DSM 13941": 1593,
+  "Roseobacter denitrificans OCh 114": 1594,
+  "Roseobacter litoralis Och 149": 1595,
+  "Roseovarius nubinhibens ISM": 1596,
+  "Rothia mucilaginosa DY-18": 1597,
+  "Rubinisphaera brasiliensis DSM 5305": 1598,
+  "Rubrivivax gelatinosus IL144": 1599,
+  "Rubrobacter xylanophilus DSM 9941": 1600,
+  "Ruegeria lacuscaerulensis ITI-1157": 1601,
+  "Ruegeria pomeroyi DSS-3": 1602,
+  "Ruminiclostridium cellulolyticum H10": 1603,
+  "Ruminococcus albus 7 = DSM 20455": 1604,
+  "Runella zeae DSM 19591": 1605,
+  "Rusa unicolor swinhoei": 1606,
+  "Saccharolobus islandicus HVE10/4": 1607,
+  "Saccharolobus islandicus L.D.8.5": 1608,
+  "Saccharolobus islandicus L.S.2.15": 1609,
+  "Saccharolobus islandicus M.14.25": 1610,
+  "Saccharolobus islandicus M.16.27": 1611,
+  "Saccharolobus islandicus M.16.4": 1612,
+  "Saccharolobus islandicus REY15A": 1613,
+  "Saccharolobus islandicus Y.G.57.14": 1614,
+  "Saccharolobus islandicus Y.N.15.51": 1615,
+  "Saccharolobus shibatae B12": 1616,
+  "Saccharolobus solfataricus 98/2": 1617,
+  "Saccharolobus solfataricus P2": 1618,
+  "Saccharomonospora viridis DSM 43017": 1619,
+  "Saccharomyces cerevisiae AWRI1631": 1620,
+  "Saccharomyces cerevisiae AWRI796": 1621,
+  "Saccharomyces cerevisiae CEN.PK113-7D": 1622,
+  "Saccharomyces cerevisiae EC1118": 1623,
+  "Saccharomyces cerevisiae FostersB": 1624,
+  "Saccharomyces cerevisiae FostersO": 1625,
+  "Saccharomyces cerevisiae JAY291": 1626,
+  "Saccharomyces cerevisiae Kyokai no. 7": 1627,
+  "Saccharomyces cerevisiae Lalvin QA23": 1628,
+  "Saccharomyces cerevisiae RM11-1a": 1629,
+  "Saccharomyces cerevisiae S288C": 1630,
+  "Saccharomyces cerevisiae VL3": 1631,
+  "Saccharomyces cerevisiae Vin13": 1632,
+  "Saccharomyces cerevisiae YJM789": 1633,
+  "Saccharomyces kudriavzevii IFO 1802": 1634,
+  "Saccharomyces pastorianus CBS 1503": 1635,
+  "Saccharomyces pastorianus CBS 1513": 1636,
+  "Saccharomyces uvarum CLIB 533": 1637,
+  "Saccharophagus degradans 2-40": 1638,
+  "Saccharopolyspora erythraea NRRL 2338": 1639,
+  "Saccharothrix espanaensis DSM 44229": 1640,
+  "Saccharothrix mutabilis subsp. capreolus": 1641,
+  "Sagittula stellata E-37": 1642,
+  "Saguinus bicolor bicolor": 1643,
+  "Saguinus midas midas": 1644,
+  "Saimiri boliviensis boliviensis": 1645,
+  "Salinibacter ruber DSM 13855": 1646,
+  "Salinibacter ruber M8": 1647,
+  "Salinispora arenicola CNS-205": 1648,
+  "Salinispora tropica CNB-440": 1649,
+  "Salipiger bermudensis HTCC2601": 1650,
+  "Salmonella bongori NCTC 12419": 1651,
+  "Salmonella enterica subsp. arizonae": 1652,
+  "Salmonella enterica subsp. diarizonae": 1653,
+  "Salmonella enterica subsp. enterica": 1654,
+  "Salmonella enterica subsp. enterica serovar Agona str. SL483": 1655,
+  "Salmonella enterica subsp. enterica serovar Choleraesuis str. SC-B67": 1656,
+  "Salmonella enterica subsp. enterica serovar Dublin str. CT_02021853": 1657,
+  "Salmonella enterica subsp. enterica serovar Enteritidis str. 2009K0958": 1658,
+  "Salmonella enterica subsp. enterica serovar Enteritidis str. P125109": 1659,
+  "Salmonella enterica subsp. enterica serovar Gallinarum str. 287/91": 1660,
+  "Salmonella enterica subsp. enterica serovar Heidelberg str. 579083-10": 1661,
+  "Salmonella enterica subsp. enterica serovar Heidelberg str. SL476": 1662,
+  "Salmonella enterica subsp. enterica serovar Newport str. SL254": 1663,
+  "Salmonella enterica subsp. enterica serovar Paratyphi A str. AKU_12601": 1664,
+  "Salmonella enterica subsp. enterica serovar Paratyphi A str. ATCC 9150": 1665,
+  "Salmonella enterica subsp. enterica serovar Paratyphi B str. SPB7": 1666,
+  "Salmonella enterica subsp. enterica serovar Paratyphi C str. RKS4594": 1667,
+  "Salmonella enterica subsp. enterica serovar Schwarzengrund str. CVM19633": 1668,
+  "Salmonella enterica subsp. enterica serovar Typhimurium str. D23580": 1669,
+  "Salmonella enterica subsp. enterica serovar Typhimurium str. LT2": 1670,
+  "Salmonella enterica subsp. enterica serovar Typhimurium str. SL1344": 1671,
+  "Salmonella enterica subsp. enterica serovar Typhimurium str. ST4/74": 1672,
+  "Salmonella enterica subsp. enterica serovar Typhimurium str. UK-1": 1673,
+  "Salmonella enterica subsp. enterica serovar Virchow str. SL491": 1674,
+  "Salmonella enterica subsp. houtenae": 1675,
+  "Salmonella enterica subsp. londinensis": 1676,
+  "Sanguibacter keddieii DSM 10542": 1677,
+  "Saprolegnia parasitica CBS 223.65": 1678,
+  "Scheffersomyces stipitis CBS 6054": 1679,
+  "Schizophyllum commune H4-8": 1680,
+  "Schizosaccharomyces japonicus yFS275": 1681,
+  "Schizosaccharomyces pombe 972h-": 1682,
+  "Sclerotinia sclerotiorum 1980 UF-70": 1683,
+  "Scolia decorata ventralis": 1684,
+  "Scolopendra viridicornis nigra": 1685,
+  "Sebaldella termitidis ATCC 33386": 1686,
+  "Sediminispirochaeta smaragdinae DSM 11293": 1687,
+  "Segniliparus rotundus DSM 44985": 1688,
+  "Segniliparus rugosus ATCC BAA-974": 1689,
+  "Serpula lacrymans var. lacrymans S7.9": 1690,
+  "Serratia proteamaculans 568": 1691,
+  "Shewanella amazonensis SB2B": 1692,
+  "Shewanella baltica OS155": 1693,
+  "Shewanella baltica OS185": 1694,
+  "Shewanella baltica OS195": 1695,
+  "Shewanella baltica OS223": 1696,
+  "Shewanella denitrificans OS217": 1697,
+  "Shewanella frigidimarina NCIMB 400": 1698,
+  "Shewanella halifaxensis HAW-EB4": 1699,
+  "Shewanella loihica PV-4": 1700,
+  "Shewanella oneidensis MR-1": 1701,
+  "Shewanella pealeana ATCC 700345": 1702,
+  "Shewanella piezotolerans WP3": 1703,
+  "Shewanella putrefaciens CN-32": 1704,
+  "Shewanella sediminis HAW-EB3": 1705,
+  "Shewanella violacea DSS12": 1706,
+  "Shewanella woodyi ATCC 51908": 1707,
+  "Shigella boydii CDC 3083-94": 1708,
+  "Shigella boydii Sb227": 1709,
+  "Shigella dysenteriae Sd197": 1710,
+  "Shigella flexneri 2002017": 1711,
+  "Shigella flexneri 5 str. 8401": 1712,
+  "Shigella flexneri 5a str. M90T": 1713,
+  "Shigella sonnei Ss046": 1714,
+  "Shimwellia blattae DSM 4481 = NBRC 105725": 1715,
+  "Shouchella clausii KSM-K16": 1716,
+  "Silene latifolia subsp. alba": 1717,
+  "Singulisphaera acidiphila DSM 18658": 1718,
+  "Sinorhizobium fredii HH103": 1719,
+  "Sinorhizobium fredii NGR234": 1720,
+  "Sinorhizobium fredii USDA 257": 1721,
+  "Sinorhizobium medicae WSM419": 1722,
+  "Sinorhizobium meliloti 1021": 1723,
+  "Sinorhizobium meliloti 2011": 1724,
+  "Sistrurus catenatus edwardsi": 1725,
+  "Sistrurus miliarius barbouri": 1726,
+  "Sistrurus miliarius streckeri": 1727,
+  "Slackia exigua ATCC 700122": 1728,
+  "Slackia heliotrinireducens DSM 20476": 1729,
+  "Sodalis glossinidius str. 'morsitans'": 1730,
+  "Sodiomyces alkalinus F11": 1731,
+  "Solenopsis globularia littoralis": 1732,
+  "Solenopsis nigella gensterblumi": 1733,
+  "Solibacillus silvestris StLB046": 1734,
+  "Solidesulfovibrio magneticus RS-1": 1735,
+  "Sorangium cellulosum So ce56": 1736,
+  "Sordaria macrospora k-hell": 1737,
+  "Sorex shinto sadonis": 1738,
+  "Sphaerobacter thermophilus DSM 20745": 1739,
+  "Sphaerobolus stellatus SS14": 1740,
+  "Sphex argentatus argentatus": 1741,
+  "Sphingobacterium faecium NBRC 15299": 1742,
+  "Sphingobium herbicidovorans NBRC 16415": 1743,
+  "Sphingobium indicum B90A": 1744,
+  "Sphingobium indicum UT26S": 1745,
+  "Sphingopyxis alaskensis RB2256": 1746,
+  "Sphyrna tiburo tiburo": 1747,
+  "Sphyrna tiburo vespertina": 1748,
+  "Spirosoma linguale DSM 74": 1749,
+  "Sporisorium reilianum SRZ2": 1750,
+  "Sporothrix schenckii ATCC 58251": 1751,
+  "Stachybotrys chartarum IBT 7711": 1752,
+  "Stachybotrys chlorohalonatus IBT 40285": 1753,
+  "Stackebrandtia nassauensis DSM 44728": 1754,
+  "Staphylococcus aureus 04-02981": 1755,
+  "Staphylococcus aureus RF122": 1756,
+  "Staphylococcus aureus subsp. aureus COL": 1757,
+  "Staphylococcus aureus subsp. aureus ED98": 1758,
+  "Staphylococcus aureus subsp. aureus JH1": 1759,
+  "Staphylococcus aureus subsp. aureus JH9": 1760,
+  "Staphylococcus aureus subsp. aureus MRSA252": 1761,
+  "Staphylococcus aureus subsp. aureus MSSA476": 1762,
+  "Staphylococcus aureus subsp. aureus MW2": 1763,
+  "Staphylococcus aureus subsp. aureus Mu3": 1764,
+  "Staphylococcus aureus subsp. aureus Mu50": 1765,
+  "Staphylococcus aureus subsp. aureus N315": 1766,
+  "Staphylococcus aureus subsp. aureus NCTC 8325": 1767,
+  "Staphylococcus aureus subsp. aureus ST398": 1768,
+  "Staphylococcus aureus subsp. aureus TCH60": 1769,
+  "Staphylococcus aureus subsp. aureus TW20": 1770,
+  "Staphylococcus aureus subsp. aureus USA300": 1771,
+  "Staphylococcus aureus subsp. aureus str. JKD6008": 1772,
+  "Staphylococcus aureus subsp. aureus str. Newman": 1773,
+  "Staphylococcus carnosus subsp. carnosus TM300": 1774,
+  "Staphylococcus cohnii subsp. cohnii": 1775,
+  "Staphylococcus epidermidis ATCC 12228": 1776,
+  "Staphylococcus epidermidis RP62A": 1777,
+  "Staphylococcus haemolyticus JCSC1435": 1778,
+  "Staphylococcus lugdunensis HKU09-01": 1779,
+  "Staphylococcus pseudintermedius ED99": 1780,
+  "Staphylococcus pseudintermedius HKU10-03": 1781,
+  "Staphylococcus saprophyticus subsp. saprophyticus ATCC 15305 = NCTC 7292": 1782,
+  "Staphylothermus marinus F1": 1783,
+  "Stenotrophomonas maltophilia K279a": 1784,
+  "Stenotrophomonas maltophilia R551-3": 1785,
+  "Stereum hirsutum FP-91666 SS1": 1786,
+  "Stigmatella aurantiaca DW4/3-1": 1787,
+  "Streptantibioticus cattleyicolor NRRL 8057 = DSM 46488": 1788,
+  "Streptobacillus moniliformis DSM 12112": 1789,
+  "Streptococcus agalactiae 2603V/R": 1790,
+  "Streptococcus agalactiae A909": 1791,
+  "Streptococcus agalactiae ATCC 13813": 1792,
+  "Streptococcus agalactiae COH1": 1793,
+  "Streptococcus agalactiae NEM316": 1794,
+  "Streptococcus dysgalactiae subsp. equisimilis": 1795,
+  "Streptococcus dysgalactiae subsp. equisimilis GGS_124": 1796,
+  "Streptococcus equi subsp. equi 4047": 1797,
+  "Streptococcus equi subsp. zooepidemicus ATCC 35246": 1798,
+  "Streptococcus equi subsp. zooepidemicus H70": 1799,
+  "Streptococcus equi subsp. zooepidemicus MGCS10565": 1800,
+  "Streptococcus gallolyticus UCN34": 1801,
+  "Streptococcus gallolyticus subsp. gallolyticus ATCC 43143": 1802,
+  "Streptococcus gordonii str. Challis": 1803,
+  "Streptococcus intermedius SK54 = ATCC 27335": 1804,
+  "Streptococcus macedonicus ACA-DC 198": 1805,
+  "Streptococcus mitis B6": 1806,
+  "Streptococcus mutans NN2025": 1807,
+  "Streptococcus mutans UA159": 1808,
+  "Streptococcus oralis Uo5": 1809,
+  "Streptococcus parasanguinis ATCC 15912": 1810,
+  "Streptococcus pneumoniae 70585": 1811,
+  "Streptococcus pneumoniae ATCC 700669": 1812,
+  "Streptococcus pneumoniae CGSP14": 1813,
+  "Streptococcus pneumoniae D39": 1814,
+  "Streptococcus pneumoniae G54": 1815,
+  "Streptococcus pneumoniae Hungary19A-6": 1816,
+  "Streptococcus pneumoniae JJA": 1817,
+  "Streptococcus pneumoniae P1031": 1818,
+  "Streptococcus pneumoniae R6": 1819,
+  "Streptococcus pneumoniae TIGR4": 1820,
+  "Streptococcus pneumoniae Taiwan19F-14": 1821,
+  "Streptococcus pyogenes MGAS10270": 1822,
+  "Streptococcus pyogenes MGAS10394": 1823,
+  "Streptococcus pyogenes MGAS10750": 1824,
+  "Streptococcus pyogenes MGAS2096": 1825,
+  "Streptococcus pyogenes MGAS315": 1826,
+  "Streptococcus pyogenes MGAS6180": 1827,
+  "Streptococcus pyogenes MGAS8232": 1828,
+  "Streptococcus pyogenes MGAS9429": 1829,
+  "Streptococcus pyogenes NZ131": 1830,
+  "Streptococcus pyogenes SSI-1": 1831,
+  "Streptococcus pyogenes str. Manfredo": 1832,
+  "Streptococcus salivarius 57.I": 1833,
+  "Streptococcus salivarius CCHSS3": 1834,
+  "Streptococcus salivarius JIM8777": 1835,
+  "Streptococcus sanguinis SK36": 1836,
+  "Streptococcus suis 05ZYH33": 1837,
+  "Streptococcus suis 89/1591": 1838,
+  "Streptococcus suis 98HAH33": 1839,
+  "Streptococcus suis BM407": 1840,
+  "Streptococcus suis GZ1": 1841,
+  "Streptococcus suis P1/7": 1842,
+  "Streptococcus suis SC84": 1843,
+  "Streptococcus thermophilus CNRZ1066": 1844,
+  "Streptococcus thermophilus LMD-9": 1845,
+  "Streptococcus thermophilus LMG 18311": 1846,
+  "Streptococcus uberis 0140J": 1847,
+  "Streptomyces achromogenes subsp. streptozoticus": 1848,
+  "Streptomyces albus G": 1849,
+  "Streptomyces albus subsp. albus DSM 41398": 1850,
+  "Streptomyces ambofaciens ATCC 23877": 1851,
+  "Streptomyces avermitilis MA-4680 = NBRC 14893": 1852,
+  "Streptomyces bingchenggensis BCW-1": 1853,
+  "Streptomyces coelicolor A3(2)": 1854,
+  "Streptomyces collinus Tu 365": 1855,
+  "Streptomyces davaonensis JCM 4913": 1856,
+  "Streptomyces griseus subsp. griseus NBRC 13350": 1857,
+  "Streptomyces hygroscopicus subsp. jinggangensis 5008": 1858,
+  "Streptomyces hygroscopicus subsp. limoneus": 1859,
+  "Streptomyces nodosus subsp. asukaensis": 1860,
+  "Streptomyces peucetius subsp. caesius": 1861,
+  "Streptomyces pratensis ATCC 33331": 1862,
+  "Streptomyces pristinaespiralis ATCC 25486": 1863,
+  "Streptomyces rapamycinicus NRRL 5491": 1864,
+  "Streptomyces rimosus subsp. rimosus ATCC 10970": 1865,
+  "Streptomyces roseochromogenus subsp. oscitans": 1866,
+  "Streptomyces scabiei 87.22": 1867,
+  "Streptomyces somaliensis DSM 40738": 1868,
+  "Streptomyces sviceus ATCC 29083": 1869,
+  "Streptomyces tsukubensis NRRL18488": 1870,
+  "Streptomyces venezuelae ATCC 10712": 1871,
+  "Streptomyces violaceusniger Tu 4113": 1872,
+  "Streptomyces viridochromogenes DSM 40736": 1873,
+  "Streptomyces viridosporus ATCC 14672": 1874,
+  "Streptosporangium roseum DSM 43021": 1875,
+  "Stromatopelma calceatum griseipes": 1876,
+  "Stutzerimonas stutzeri A1501": 1877,
+  "Succinatimonas hippei YIT 12066": 1878,
+  "Sulfobacillus thermosulfidooxidans DSM 9293": 1879,
+  "Sulfolobus acidocaldarius DSM 639": 1880,
+  "Sulfurihydrogenibium azorense Az-Fu1": 1881,
+  "Sulfurimonas autotrophica DSM 16294": 1882,
+  "Sulfurimonas denitrificans DSM 1251": 1883,
+  "Sulfurisphaera tokodaii str. 7": 1884,
+  "Sulfurospirillum multivorans DSM 12446": 1885,
+  "Symbiobacterium thermophilum IAM 14863": 1886,
+  "Synechococcus elongatus PCC 6301": 1887,
+  "Synechococcus elongatus PCC 7942 = FACHB-805": 1888,
+  "Syntrophobacter fumaroxidans MPOB": 1889,
+  "Syntrophomonas palmitatica JCM 14374": 1890,
+  "Syntrophomonas wolfei subsp. wolfei str. Goettingen G311": 1891,
+  "Syntrophotalea carbinolica DSM 2380": 1892,
+  "Syntrophus aciditrophicus SB": 1893,
+  "Tachyglossus aculeatus aculeatus": 1894,
+  "Talaromyces marneffei ATCC 18224": 1895,
+  "Talaromyces stipitatus ATCC 10500": 1896,
+  "Tannerella forsythia 92A2": 1897,
+  "Taylorella asinigenitalis MCE3": 1898,
+  "Taylorella equigenitalis MCE9": 1899,
+  "Teredinibacter turnerae T7901": 1900,
+  "Tetrahymena thermophila SB210": 1901,
+  "Thalictrum flavum subsp. glaucum": 1902,
+  "Thauera butanivorans NBRC 103042": 1903,
+  "Thauera linaloolentis 47Lol = DSM 12138": 1904,
+  "Thermacetogenium phaeum DSM 12270": 1905,
+  "Thermanaerovibrio acidaminovorans DSM 6589": 1906,
+  "Thermoanaerobacter italicus Ab9": 1907,
+  "Thermoanaerobacter pseudethanolicus ATCC 33223": 1908,
+  "Thermoanaerobacterium saccharolyticum JW/SL-YS485": 1909,
+  "Thermoanaerobacterium thermosaccharolyticum DSM 571": 1910,
+  "Thermobaculum terrenum ATCC BAA-798": 1911,
+  "Thermobifida fusca YX": 1912,
+  "Thermobispora bispora DSM 43833": 1913,
+  "Thermochaetoides thermophila DSM 1495": 1914,
+  "Thermoclostridium stercorarium subsp. stercorarium DSM 8532": 1915,
+  "Thermococcus barophilus MP": 1916,
+  "Thermococcus gammatolerans EJ3": 1917,
+  "Thermococcus kodakarensis KOD1": 1918,
+  "Thermococcus litoralis DSM 5473": 1919,
+  "Thermococcus onnurineus NA1": 1920,
+  "Thermococcus sibiricus MM 739": 1921,
+  "Thermocrinis albus DSM 14484": 1922,
+  "Thermodesulfobacterium geofontis OPF15": 1923,
+  "Thermodesulfovibrio yellowstonii DSM 11347": 1924,
+  "Thermofilum pendens Hrk 5": 1925,
+  "Thermomicrobium roseum DSM 5159": 1926,
+  "Thermomonospora curvata DSM 43183": 1927,
+  "Thermoplasma acidophilum DSM 1728": 1928,
+  "Thermoplasma volcanium GSS1": 1929,
+  "Thermoproteus tenax Kra 1": 1930,
+  "Thermoproteus uzoniensis 768-20": 1931,
+  "Thermosediminibacter oceani DSM 16646": 1932,
+  "Thermosipho africanus H17ap60334": 1933,
+  "Thermosipho africanus TCF52B": 1934,
+  "Thermosipho melanesiensis BI429": 1935,
+  "Thermosphaera aggregans DSM 11486": 1936,
+  "Thermosulfidibacter takaii ABI70S6": 1937,
+  "Thermosynechococcus vestitus BP-1": 1938,
+  "Thermothelomyces thermophilus ATCC 42464": 1939,
+  "Thermothielavioides terrestris NRRL 8126": 1940,
+  "Thermotoga maritima MSB8": 1941,
+  "Thermotoga neapolitana DSM 4359": 1942,
+  "Thermotoga petrophila RKU-1": 1943,
+  "Thermovibrio ammonificans HB-1": 1944,
+  "Thermus aquaticus Y51MC23": 1945,
+  "Thermus scotoductus SA-01": 1946,
+  "Thermus thermophilus HB27": 1947,
+  "Thermus thermophilus HB8": 1948,
+  "Thioalkalivibrio nitratireducens DSM 14787": 1949,
+  "Thioalkalivibrio sulfidiphilus HL-EbGr7": 1950,
+  "Thiobacillus denitrificans ATCC 25259": 1951,
+  "Thiomicrospira cyclica ALM1": 1952,
+  "Thiomonas intermedia K12": 1953,
+  "Thiothrix nivea DSM 5205": 1954,
+  "Tolumonas auensis DSM 9187": 1955,
+  "Toxoplasma gondii GT1": 1956,
+  "Toxoplasma gondii ME49": 1957,
+  "Toxoplasma gondii VEG": 1958,
+  "Trachemys scripta elegans": 1959,
+  "Trametes coccinea BRFM310": 1960,
+  "Trametes versicolor FP-101664 SS1": 1961,
+  "Treponema denticola ATCC 35405": 1962,
+  "Treponema pallidum subsp. pallidum SS14": 1963,
+  "Treponema pallidum subsp. pallidum str. Nichols": 1964,
+  "Treponema pallidum subsp. pertenue": 1965,
+  "Treponema succinifaciens DSM 2489": 1966,
+  "Trichlorobacter lovleyi SZ": 1967,
+  "Trichoderma asperellum CBS 433.97": 1968,
+  "Trichoderma atroviride IMI 206040": 1969,
+  "Trichoderma reesei QM6a": 1970,
+  "Trichoderma reesei RUT C-30": 1971,
+  "Trichoderma virens Gv29-8": 1972,
+  "Trichodesmium erythraeum IMS101": 1973,
+  "Trichophyton benhamiae CBS 112371": 1974,
+  "Trichophyton equinum CBS 127.97": 1975,
+  "Trichophyton interdigitale MR816": 1976,
+  "Trichophyton rubrum CBS 118892": 1977,
+  "Trichophyton tonsurans CBS 112818": 1978,
+  "Trichophyton verrucosum HKI 0517": 1979,
+  "Trichormus variabilis ATCC 29413": 1980,
+  "Triticum turgidum subsp. durum": 1981,
+  "Tropheryma whipplei TW08/27": 1982,
+  "Tropheryma whipplei str. Twist": 1983,
+  "Truepera radiovictrix DSM 17093": 1984,
+  "Trypanosoma brucei brucei": 1985,
+  "Trypanosoma brucei brucei TREU927": 1986,
+  "Trypanosoma brucei gambiense": 1987,
+  "Trypanosoma brucei gambiense DAL972": 1988,
+  "Trypanosoma brucei rhodesiense": 1989,
+  "Trypanosoma cruzi strain CL Brener": 1990,
+  "Tsukamurella paurometabola DSM 20162": 1991,
+  "Tuber melanosporum Mel28": 1992,
+  "Tulipa saxatilis subsp. bakeri": 1993,
+  "Uncinocarpus reesii 1704": 1994,
+  "Ureaplasma parvum serovar 3 str. ATCC 27815": 1995,
+  "Ureaplasma parvum serovar 3 str. ATCC 700970": 1996,
+  "Ureaplasma urealyticum serovar 10 str. ATCC 33699": 1997,
+  "Vairimorpha ceranae BRL01": 1998,
+  "Vanderwaltozyma polyspora DSM 70294": 1999,
+  "Varecia variegata variegata": 2000,
+  "Variovorax paradoxus EPS": 2001,
+  "Variovorax paradoxus S110": 2002,
+  "Veillonella parvula DSM 2008": 2003,
+  "Verminephrobacter eiseniae EF01-2": 2004,
+  "Verrucomicrobium spinosum DSM 4136 = JCM 18804": 2005,
+  "Verticillium alfalfae VaMs.102": 2006,
+  "Verticillium dahliae VdLs.17": 2007,
+  "Vespa simillima xanthoptera": 2008,
+  "Vespa velutina flavitarsus": 2009,
+  "Vespa velutina nigrithorax": 2010,
+  "Vibrio alginolyticus NBRC 15630 = ATCC 17749": 2011,
+  "Vibrio anguillarum 775": 2012,
+  "Vibrio atlanticus LGP32": 2013,
+  "Vibrio campbellii ATCC BAA-1116": 2014,
+  "Vibrio campbellii HY01": 2015,
+  "Vibrio cholerae M66-2": 2016,
+  "Vibrio cholerae MJ-1236": 2017,
+  "Vibrio cholerae MO10": 2018,
+  "Vibrio cholerae O1 biovar El Tor str. N16961": 2019,
+  "Vibrio cholerae O395": 2020,
+  "Vibrio coralliilyticus ATCC BAA-450": 2021,
+  "Vibrio harveyi 1DA3": 2022,
+  "Vibrio parahaemolyticus AQ3810": 2023,
+  "Vibrio parahaemolyticus RIMD 2210633": 2024,
+  "Vibrio ruber DSM 16370": 2025,
+  "Vibrio splendidus 12B01": 2026,
+  "Vibrio vulnificus CMCP6": 2027,
+  "Vibrio vulnificus YJ016": 2028,
+  "Vicia sativa subsp. nigra": 2029,
+  "Vigna unguiculata subsp. cylindrica": 2030,
+  "Vigna unguiculata subsp. sesquipedalis": 2031,
+  "Vigna unguiculata subsp. unguiculata": 2032,
+  "Vipera ammodytes ammodytes": 2033,
+  "Vipera ammodytes meridionalis": 2034,
+  "Vipera aspis aspis": 2035,
+  "Vipera aspis zinnikeri": 2036,
+  "Vipera berus berus": 2037,
+  "Vulcanisaeta distributa DSM 14429": 2038,
+  "Vulcanisaeta moutnovskia 768-28": 2039,
+  "Wallemia ichthyophaga EXF-994": 2040,
+  "Weissella koreensis KACC 15510": 2041,
+  "Weissella oryzae SG25": 2042,
+  "Wickerhamomyces anomalus NRRL Y-366": 2043,
+  "Wickerhamomyces anomalus NRRL Y-366-8": 2044,
+  "Wickerhamomyces ciferrii NRRL Y-1031": 2045,
+  "Winmispira thermophila DSM 6192": 2046,
+  "Winmispira thermophila DSM 6578": 2047,
+  "Wolbachia endosymbiont of Culex quinquefasciatus Pel": 2048,
+  "Wolbachia endosymbiont strain TRS of Brugia malayi": 2049,
+  "Wolfiporia cocos MD-104 SS10": 2050,
+  "Wolinella succinogenes DSM 1740": 2051,
+  "Xanthomonas campestris pv. campestris str. 8004": 2052,
+  "Xanthomonas campestris pv. campestris str. ATCC 33913": 2053,
+  "Xanthomonas campestris pv. campestris str. B100": 2054,
+  "Xanthomonas citri pv. citri str. 306": 2055,
+  "Xanthomonas euvesicatoria pv. vesicatoria str. 85-10": 2056,
+  "Xanthomonas euvesicatoria pv. viticola LMG 965": 2057,
+  "Xanthomonas oryzae pv. oryzae KACC 10331": 2058,
+  "Xanthomonas oryzae pv. oryzae MAFF 311018": 2059,
+  "Xanthomonas oryzae pv. oryzae PXO99A": 2060,
+  "Xanthomonas oryzae pv. oryzicola BLS256": 2061,
+  "Xenorhabdus bovienii SS-2004": 2062,
+  "Xenorhabdus nematophila ATCC 19061": 2063,
+  "Xylanibacter ruminicola 23": 2064,
+  "Xylanimonas cellulosilytica DSM 15894": 2065,
+  "Xylella fastidiosa 9a5c": 2066,
+  "Xylella fastidiosa M12": 2067,
+  "Xylella fastidiosa M23": 2068,
+  "Xylella fastidiosa Temecula1": 2069,
+  "Xylocopa appendiculata circumvolans": 2070,
+  "Yarrowia lipolytica CLIB122": 2071,
+  "Yersinia enterocolitica subsp. enterocolitica 8081": 2072,
+  "Yersinia enterocolitica subsp. palearctica Y11": 2073,
+  "Yersinia enterocolitica subsp. palearctica YE-P4": 2074,
+  "Yersinia mollaretii ATCC 43969": 2075,
+  "Yersinia pestis Angola": 2076,
+  "Yersinia pestis Antiqua": 2077,
+  "Yersinia pestis D106004": 2078,
+  "Yersinia pestis D182038": 2079,
+  "Yersinia pestis Nepal516": 2080,
+  "Yersinia pestis Pestoides F": 2081,
+  "Yersinia pestis Z176003": 2082,
+  "Yersinia pseudotuberculosis IP 31758": 2083,
+  "Yersinia pseudotuberculosis IP 32953": 2084,
+  "Yersinia pseudotuberculosis PB1/+": 2085,
+  "Yersinia pseudotuberculosis YPIII": 2086,
+  "Yersinia ruckeri ATCC 29473": 2087,
+  "Zea mays subsp. huehuetenangensis": 2088,
+  "Zea mays subsp. mexicana": 2089,
+  "Zea mays subsp. parviglumis": 2090,
+  "Zunongwangia profunda SM-A87": 2091,
+  "Zygosaccharomyces rouxii CBS 732": 2092,
+  "Zymomonas mobilis subsp. mobilis ATCC 10988": 2093,
+  "Zymomonas mobilis subsp. mobilis NCIMB 11163": 2094,
+  "Zymomonas mobilis subsp. mobilis ZM4 = ATCC 31821": 2095,
+  "Zymomonas mobilis subsp. pomaceae ATCC 29192": 2096,
+  "Zymoseptoria tritici IPO323": 2097,
+  "Zymoseptoria tritici ST99CH_3D7": 2098,
+  "[Acidovorax] ebreus TPSY": 2099,
+  "[Bacillus thuringiensis] serovar konkukian str. 97-27": 2100,
+  "[Bacillus] selenitireducens MLS10": 2101,
+  "[Clostridium] saccharolyticum WM1": 2102,
+  "[Clostridium] scindens ATCC 35704": 2103,
+  "[Clostridium] symbiosum WAL-14163": 2104,
+  "[Enterobacter] lignolyticus SCF1": 2105,
+  "[Eubacterium] cellulosolvens 6": 2106,
+  "[Haemophilus] ducreyi 35000HP": 2107,
+  "[Mannheimia] succiniciproducens MBEL55E": 2108
+}

local_train.py

@@ -0,0 +1,70 @@
+import os
+import sys
+import subprocess
+import argparse
+
+def main():
+    parser = argparse.ArgumentParser(description="Local training launcher for Taxon-aware ESM2")
+    parser.add_argument("--dry_run", action="store_true", help="Run a quick dry run to verify pipeline")
+    
+    args = parser.parse_args()
+
+    # Define paths
+    ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+    SRC_DIR = os.path.join(ROOT_DIR, "src")
+    DATASET_DIR = os.path.join(ROOT_DIR, "dataset")
+
+    # Verify input directories
+    if not os.path.exists(SRC_DIR):
+        print(f"Error: Source directory not found at {SRC_DIR}")
+        return
+    if not os.path.exists(DATASET_DIR):
+        print(f"Error: Dataset directory not found at {DATASET_DIR}")
+        return
+
+    print(f"Root Dir: {ROOT_DIR}")
+    print(f"Src Dir:  {SRC_DIR}")
+    print(f"Data Dir: {DATASET_DIR}")
+
+    # Construct the command
+    # We run from SRC_DIR to match Azure ML behavior and allow relative imports
+    cmd = [
+        sys.executable, "train.py",
+        "--data_path", DATASET_DIR,
+        "--epochs", "5",
+        "--batch_size", "64",
+        "--lr", "1e-4",
+        "--min_lr", "5e-6",
+        "--num_workers", "8",  # 0 for local windows debugging usually safer
+        "--esm_model_name", "facebook/esm2_t6_8M_UR50D",
+        "--use_lora", "True",
+        "--lora_rank", "8",
+        # Asymmetric Loss defaults
+        "--gamma_neg", "4",
+        "--gamma_pos", "0",
+        "--clip", "0.05",
+        
+        # Absolute locations for output
+        "--output_dir", os.path.join(ROOT_DIR, "outputs"),
+        "--mlflow_dir", os.path.join(ROOT_DIR, "mlruns")
+    ]
+    
+    if args.dry_run:
+        cmd.append("--dry_run")
+
+    print(f"Running command: {' '.join(cmd)}")
+    print("-" * 50)
+
+    # Run the training script
+    try:
+        # cwd=SRC_DIR is crucial for relative imports
+        subprocess.run(cmd, cwd=SRC_DIR, check=True)
+    except subprocess.CalledProcessError as e:
+        print(f"Training failed with error code {e.returncode}")
+    except KeyboardInterrupt:
+        print("\nTraining interrupted by user.")
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+
+if __name__ == "__main__":
+    main()

model_analysis.py

@@ -0,0 +1,179 @@
+import torch
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+import os
+import sys
+from transformers import AutoTokenizer
+
+# Add src to sys.path to allow imports from src/model.py and src/dataset.py
+sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
+
+# Import your modules
+from model import TaxonomyAwareESM
+from dataset import ProteinTaxonomyDataset
+
+def analyze_attention(checkpoint_path, data_path, target_ids, device='cuda'):
+    print(f"=== Loading Checkpoint: {checkpoint_path} ===")
+    
+    # 1. Load Model Architecture
+    
+    checkpoint = torch.load(checkpoint_path, map_location=device)
+    state_dict = checkpoint.get('model_state_dict', checkpoint) # Handle full checkpoint vs state dict only
+    
+    # Infer num_classes from the classifier weights in state_dict
+    # Model definition: self.classifier = nn.Linear(..., num_classes)
+    # So key is 'classifier.weight'
+    if 'classifier.weight' in state_dict:
+        num_classes = state_dict['classifier.weight'].shape[0]
+    elif 'classifier.3.weight' in state_dict: # Keeping fallback if user had MLP before
+        num_classes = state_dict['classifier.3.weight'].shape[0]
+    else:
+        # Try to guess or fail
+        print("Warning: Could not infer num_classes from state_dict keys. Keys:", state_dict.keys())
+        raise KeyError("Could not find classifier weights")
+        
+    print(f"Detected Num Classes: {num_classes}")
+    
+    model = TaxonomyAwareESM(
+        num_classes=num_classes, 
+        pretrained_model_name="facebook/esm2_t6_8M_UR50D",
+        freeze_backbone=True
+    )
+    
+    model.load_state_dict(state_dict, strict=False) # STRICT=FALSE because we might miss some metadata
+    model.to(device)
+    model.eval()
+    
+    # 2. Hook Cross-Attention to capture weights
+    attn_weights_storage = {}
+    
+    def get_attn_weights(name):
+        def hook(module, input, output):
+            # output of MultiheadAttention is (attn_output, attn_output_weights)
+            # attn_output_weights shape: [Batch, Target_Len, Source_Len]
+            # Here: [1, Seq_Len, 7] (7 is Taxonomy Ranks)
+            # Note: batch_first=True in model definition
+            attn_weights_storage[name] = output[1].detach().cpu()
+        return hook
+        
+    model.cross_attention.register_forward_hook(get_attn_weights('cross_attn'))
+    
+
+    # 3. Load Samples from Large Learning Superset
+    print("Loading dataset...")
+    # Setup Paths
+    fasta_path = os.path.join(data_path, "learning_superset", "large_learning_superset.fasta")
+    term_path = os.path.join(data_path, "learning_superset", "large_learning_superset_term.tsv")
+    species_vec = os.path.join(data_path, "taxon_embedding", "species_vectors.tsv")
+    go_vocab = os.path.join("src", "go_terms.json") # Relative to root
+    
+    tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D")
+    
+    # Lightweight dataset init 
+    # Note: parsing the huge fasta might take a moment.
+    # To be faster, we could scan for the IDs first, but ProteinTaxonomyDataset loads everything.
+    # Given the constraints, let's load it.
+    dataset = ProteinTaxonomyDataset(
+        fasta_path, term_path, species_vec, go_vocab, 
+        max_len=512, 
+        esm_tokenizer=tokenizer
+    )
+    
+    print(f"Dataset loaded. Total samples: {len(dataset)}")
+    
+    # Filter for target IDs
+    target_indices = []
+    # We need to map ID to index.
+    # Dataset doesn't expose a quick map, so we iterate.
+    # This might be slow for 75MB fasta, but acceptable for a one-off script.
+    
+    print(f"Searching for target IDs: {target_ids}")
+    found_count = 0
+    
+    # Optimally, we access dataset information if available.
+    # Checking dataset implementation... unique_ids list usually exists.
+    if hasattr(dataset, 'protein_ids'):
+        for idx, pid in enumerate(dataset.protein_ids):
+            if pid in target_ids:
+                target_indices.append(idx)
+                found_count += 1
+    else:
+        # Fallback: iterate (slower)
+        for i in range(len(dataset)):
+            if dataset[i]['entry_id'] in target_ids:
+                target_indices.append(i)
+                found_count += 1
+            if found_count >= len(target_ids):
+                break
+                
+    print(f"Found {len(target_indices)} samples matching targets.")
+
+    # Create output directory
+    output_dir = os.path.join("outputs", "attention_map")
+    os.makedirs(output_dir, exist_ok=True)
+
+    for idx in target_indices:
+        sample = dataset[idx]
+        
+        # Prepare batch
+        input_ids = sample['input_ids'].unsqueeze(0).to(device)
+        attention_mask = sample['attention_mask'].unsqueeze(0).to(device)
+        tax_vector = sample['tax_vector'].unsqueeze(0).to(device)
+        prot_id = sample.get('entry_id', 'Unknown')
+        
+        print(f"Analyzing Protein ID: {prot_id}")
+        
+        # 4. Forward Pass
+        # Clear previous hooks just in case
+        attn_weights_storage.clear()
+        
+        with torch.no_grad():
+            _ = model(input_ids, attention_mask, tax_vector)
+            
+        # 5. Visualize
+        if 'cross_attn' not in attn_weights_storage:
+            print(f"Error: Hook did not capture attention weights for {prot_id}.")
+            continue
+
+        # attn_weights shape: [1, Seq_Len, 7]
+        weights = attn_weights_storage['cross_attn'][0] # Remove batch dim -> [Seq_Len, 7]
+        
+        # Remove padding from visualization
+        seq_len = attention_mask.sum().item()
+        weights = weights[:seq_len, :] # [Real_Seq_Len, 7]
+        
+        # Plot
+        plt.figure(figsize=(12, 8))
+        # Transpose for easier reading: Y-axis = Taxonomy Ranks, X-axis = Sequence Position
+        sns.heatmap(weights.T.numpy(), cmap='viridis', 
+                    yticklabels=["Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species"])
+        plt.title(f"Cross-Attention Map - Protein {prot_id}")
+        plt.xlabel("Sequence Position (Residues)")
+        plt.ylabel("Taxonomic Rank")
+        
+        save_path = os.path.join(output_dir, f"{prot_id}.png")
+        plt.savefig(save_path)
+        plt.close() # Close plot to save memory
+        print(f"Analysis saved to {save_path}")
+
+    print("\n[Interpretation Guide]")
+    print("- Uniform Color? -> Model hasn't learned to distinguish ranks yet.")
+    print("- Vertical Stripes? -> Specific residues attend to ALL ranks (Structural importance).")
+    print("- Horizontal Stripes? -> Some ranks are universally more important.")
+    print("- Scattered Hotspots? -> IDEAL. Specific residues attend to specific ranks.")
+
+if __name__ == "__main__":
+    # Target IDs from earlier investigation of large_learning_superset.fasta
+    targets = [
+        "P0DPQ6", "A0A0C5B5G6", "P40205", 
+        "F5H094", "Q6RFH8", "Q0D2H9",
+        "L0R8F8", "P0DMW2", "Q6L8H1", "A0A1B0GTW7"
+    ]
+    
+    analyze_attention(
+        checkpoint_path="outputs/best_model_fmax.pth", 
+        data_path=".", 
+        target_ids=targets,
+        device='cuda' if torch.cuda.is_available() else 'cpu'
+    )

requirements.txt

@@ -0,0 +1,19 @@
+numpy>=1.21.0
+pandas>=1.3.0
+scipy>=1.7.0
+scikit-learn>=1.0.0
+h5py>=3.0.0
+torch>=2.0.0 --index-url https://download.pytorch.org/whl/cu118
+torchvision>=0.15.0 --index-url https://download.pytorch.org/whl/cu118
+tqdm>=4.62.0
+matplotlib>=3.4.0
+seaborn>=0.11.0
+biopython>=1.79
+mlflow>=2.0.0
+peft>=0.4.0
+gputil
+transformers
+goatools
+adjustText
+onnx
+onnxscript

src/CAFA-evaluator-PK/.gitattributes

@@ -0,0 +1 @@
+src/*.ipynb filter=strip-notebook-output

src/CAFA-evaluator-PK/.gitignore

@@ -0,0 +1,12 @@
+src/__pycache__/
+src/cafaeval/__pycache__/
+eval/
+process_df.ipynb
+ia.ipynb
+harmonic_mean.ipynb
+.idea
+/src/cafaeval.egg-info/
+/.venv/
+/dist/
+src/.ipynb_checkpoints
+

src/CAFA-evaluator-PK/CHANGELOG.md

@@ -0,0 +1,43 @@
+# Change Log
+All notable changes to this project will be documented in this file.
+
+## [current] - 
+- We are working to include the calculation of bootstrap confidence intervals.
+
+## [1.2.1] - 2024-03-26
+- Minor bugfix affecting multi-thread calculation. 
+
+## [1.2.0] - 2024-02-01 
+- We included micro-average metrics. Now precision, recall and F-score 
+in addition to previously reported metrics are calculated as micro-averages 
+by averaging the confusion matrices over targets before calculating aggregated metrics
+(precision, recall, ect.).
+- We include the calculation of the average precision score (APS) in the plot notebook.
+
+### Changed
+- plot.ipynb, added calculation of average precision score (APS) in the plot notebook.
+- evaluation.py, micro-average calculation, some refactoring of the core functions.
+- parser.py, minor fixes and improvements.
+
+## [1.1.0] - 2024-01-24
+  
+- We changed the way alternative identifiers in ontology files are considered.
+Now alternative identifiers are recognized in both the ground truth and prediction files
+and mapped to the "canonical" term.
+ 
+### Added
+- CHANGELOG.md, this file!
+ 
+### Changed
+- graph.py, changed the Graph class.
+- parser.py, changed the ground truth and prediction parsers in order 
+to replace alternative identifiers with canonical terms.
+- plot.ipynb, cleaned up.
+
+ 
+## [1.0.0] - 2023-08-04
+ 
+First release after CAFA5 challenge closed. The version used in Kaggle is
+provided under the 'Kaggle' branch. The 'main' branch includes instead a
+packed version of the code. While usage and performance has been improved 
+the calculation is exactly the same.

src/CAFA-evaluator-PK/LICENCE.md

@@ -0,0 +1,674 @@
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+    CAFA-evaluator, CAFA style evaluation of DAG predictions
+    Copyright (C) 2022  Damiano Piovesan
+
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src/CAFA-evaluator-PK/README.md

@@ -0,0 +1,181 @@
+# CAFA-evaluator (CAFA4+ Edition)
+
+This repo was branched from the [CAFA-evaluator](https://github.com/BioComputingUP/CAFA-evaluator) and additional functionality was added.
+Visit the original [CAFA-evaluator Wiki](https://github.com/BioComputingUP/CAFA-evaluator/wiki) for more information about the algorithm.
+
+
+The two main new functionalities are:
+* **Flexible terms-of-interest**
+  A file can be passed in with a list of ontology terms that will be evaluated for all proteins. Only terms included in the file
+  will be evaluated. This can be used to exclude terms that were added to the ontology since predictions were collected or to
+  exclude terms that have been deemed uninformative or for whatever reason should not be included in evaluation.  
+
+  In the figure below, the terms of interest are shown with a red outline.
+  These terms will be evaluated for _all proteins_ for which there are annotations in the ground truth file.
+  
+* **Protein-specific known annotations**
+  To evaluate under the "Partial Knowledge" evaluation setting, any annotations known previous to prediction should be excluded from
+  evaluation. This exclusion is done similarly to the terms-of-interst, but for each individual protein.
+  
+  In the figure below, the annotations are shown for a single protein. Terms with annotations known before the prediction phase are
+  shown in yellow in the Cellular Component and Molecular Function aspects of the Gene Ontology. These terms should be excluded from
+  evaluation. New annotations are shown to the right in blue. Evaluation should happen _only_ for these terms. If the newly annotated
+  terms do not appear in the terms-of-interest file, they will not be evaluated.  
+
+  A known-annotations file can be passed in for any evaluation setting and will not have and effect in No-Knowledge or Limited-Knowledge
+  evaluation. For example in the figure below, if a known-annotations file is passed in with the terms in yellow listed, the Limited-Knowlege
+  evaluation of the Biological Process (BP) aspect will produce the same results as if the file was not passed in at all. This is because
+  there are no known terms in BP for this process and they will not appear in the known annotations file.
+
+ ![Evaluting CAFA Partial Knowledge setting and using terms of interest](EvalutingCAFAPartial.jpg)
+
+
+
+## Citation
+Please cite the original CAFA Evaluator and, once available, the forthcoming CAFA 5 papers if you use this code in published research  
+[CAFA-evaluator: A Python Tool for Benchmarking Ontological Classification Methods](https://doi.org/10.1093/bioadv/vbae043)  
+*D Piovesan, D Zago, P Joshi, MC De Paolis Kaluza, M Mehdiabadi, R Ramola, AM Monzon, W Reade, I Friedberg, P Radivojac, SCE Tosatto*  
+**Bioinformatics Advances (2024)** - DOI: [10.1093/bioadv/vbae043](https://doi.org/10.1093/bioadv/vbae043)
+
+Crowdsourcing the fifth critical assessment of protein function annotation algorithms (CAFA 5) yields improvement in protein function prediction  
+*TBD*  
+**TBD** - DOI: TBD
+
+
+## Usage
+
+The program can be executing the command line interface or as a library.
+Both the command line and the library accept the following positional arguments:
+
+* **Ontology file** in OBO format
+
+* _**NEW**_: **Terms of Interst file** contains term names that appear in the OBO file to be included in evaluation
+  
+* _**NEW**_: **Known annotations file** contains annotations known before the evaluation phase
+
+* **Prediction folder** contain prediction files. Files can be organized into sub-folders, 
+sub-folders are processed recursively and the sub-folder name is used as prefix for the method name
+
+* **Ground truth file** containing targets and associated ontology terms
+
+Example input files are provided inside the `data/example` folder. 
+
+### Command line
+
+When executed from the command line the script logs information about the calculation in the console (standard error) and
+will create a folder named `results` containing the evaluation results. 
+A different folder can be specified using the `-out_dir` option. 
+ 
+The original CAFA-evaluator functionality works as before without additional imput arguments
+
+```bashcon
+python3 /path/to/CAFA-evaluator/src/cafaeval/__main__.py ontology_file prediction_folder ground_truth_file 
+```
+
+_**NEW**_: By default, all terms in the ontology will be considered in the evaluation. To include only specific terms, provide a terms-of-interest file
+```bashcon
+python3 /path/to/CAFA-evaluator/src/cafaeval/__main__.py ontology_file prediction_folder ground_truth_file -toi terms_of_interest_file
+```
+
+_**NEW**_: To evaluate Partial Knowledge annotations, the known annotations file must be passed in with the `-known` option.
+```bashcon
+python3 /path/to/CAFA-evaluator/src/cafaeval/__main__.py ontology_file prediction_folder ground_truth_file -known known_annotations_file
+```
+
+_**NEW**_: You can pass in both terms-of-interest and known annotations:
+```bashcon
+python3 /path/to/CAFA-evaluator/src/cafaeval/__main__.py ontology_file prediction_folder ground_truth_file -toi terms_of_interest_file -known known_annotations_file
+```
+
+
+## Input files
+**Prediction file** - Tab separated file with the target ID, term ID and score columns.
+
+~~~txt
+A0A0A6YY25  GO:0010468  3.6396e-05
+O54963	GO:0010033	0.0617
+O54963	GO:1990841	0.035
+X1WHY6	GO:0010033	0.1352
+X1WHY6	GO:0048731	0.0478
+...
+~~~
+
+**Ground truth file** - Tab separated file with the target ID and term ID. 
+Additional columns are discarded.
+~~~
+A0A0A6YY25	GO:0009892	
+A0A0A6YY25	GO:0010468
+O54963	GO:1990841
+O54963	GO:0010033
+O88898	GO:1990841
+X1WHY6	GO:0048609
+X1WHY6	GO:0048731
+...
+~~~
+
+**Information accretion file (optional)** - If not provided, the weighted and S statistics are not generated.
+Information accretion (IA) can be calculated as described in
+[Wyatt and Radivojac, Bioinformatics, 2013](https://pubmed.ncbi.nlm.nih.gov/23813009/) 
+and implemented in [https://github.com/claradepaolis/InformationAccretion](https://github.com/claradepaolis/InformationAccretion)
+
+```
+GO:0009892	0.3286
+GO:0010468	0.0184
+GO:0008150  0
+GO:1990841  3.1613
+...
+```
+
+_**NEW**_: **Known annotations (optional)** - Tab separated file with target ID and term ID. 
+File containing known annotations to exclude from partial-knowledge evaluation. 
+If not provided, all terms will be used in evaluation
+
+```
+A0A009IHW8	GO:0072523
+A0A009IHW8	GO:0046700
+O54963	GO:0045596
+O54963	GO:2000064
+O54963	GO:0062014
+...
+```
+
+_**NEW**_: **Terms of Interest (optional)** - File with term ID to include in evaluation for all proteins, one ID per line.  
+If not provided, all terms will be used in evaluation. 
+This file is used to specify terms that will be evaluated, usually used to exclude terms in the ontology that have since
+been obsoleted or are not of interest for the evaluation.
+
+```
+GO:0055039
+GO:0072523
+GO:0003882
+GO:0010139
+...
+```
+
+## Output files
+
+Output files are generated in the `results` folder. The same files are gerated by both
+the command line and the `write_results` function.
+
+* `evaluation_all.tsv` corresponds to the first object returned by the `cafa_eval` function.
+* `evaluation_best_< metric >.tsv` corresponds to the second object returned by the `cafa_eval` function. 
+A different file for each metric is created.
+
+**Note**: Weighted scores are generated only if the *Information Accretion* file is provided.
+
+
+## Optional parameters
+
+|  Argument   | Default value | Description                                                                                                                                                                                                                                                               |
+|:-----------:|------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+|  -out_dir   |   'results' | Output directory (tsv files + log). Either relative to current path or absolute                                                                                                                                                                                           |
+|     -ia     |            | Information accretion file                                                                                                                                                                                                                                                |
+|   -known    |            | Known annotations for each protein that should be excluded in partial-knowledge evaluation                                                                                                                                                                               |
+|    -toi     |            | Terms of interest file   (terms considered for all proteins)                                                                                                                                                                                                              |
+| -no_orphans |  False (flag) | Exclude orphans nodes (e.g. roots) from the calculation                                                                                                                                                                                                                   |
+|    -norm    |     'cafa'  | Normalization strategy. `cafa` normalize precision by the number of predicted targets and recall by the number of targets in the ground truth. `pred` normalize by the number of  predicted targets. `gt` normalize by the number of ground truth proteins                |
+|    -prop    |     'max'  | Ancestor propagation strategy. `max` propagate the max score of the traversed subgraph iteratively. `fill` propagate with max until a different score is found                                                                                                            |
+|  -th_step   |      0.01  | Step size of prediction score thresholds to consider in the range [0, 1). A smaller step, means more calculation                                                                                                                                                          |
+| -max_terms  |            | Number of terms for protein and namespace to consider in the evaluation. Parsing stops when the target limit for every namespace is reached. The score is not checked, meaning that terms are not sorted before the check, and the check is performed before propagation. |
+|  -threads   |       4    | Parallel threads. `0` means use all available CPU threads. Do not use multi thread if you are short in memory                                                                                                                                                             |
+

src/CAFA-evaluator-PK/example/.python-version

@@ -0,0 +1 @@
+cafa

src/CAFA-evaluator-PK/example/README.md

@@ -0,0 +1,94 @@
+# Example Evaluation
+
+This directory contains files for an example of partial knowledge evaluation and the use of a terms-of-interest file.
+
+This is a "toy" example using a subgraph of the Biological Process (BP) aspect of the Gene Ontology. It is only for
+illustrative purposess.
+
+![Example of partial knowledge evaluation](sample_eval.jpg)
+
+
+
+To run the example,
+``python3 /path/to/CAFA-evaluator/src/cafaeval/__main__.py go-sample.obo predictions ground_truth_partial.tsv -toi toi.tsv -known known_t0.tsv``
+
+
+## Known annotations
+
+Partial knowledge evaluation is used when there are existing known annotations for a target (protein) in some aspect of the 
+ontology on which evaluation will be done. These known annotations must be excluded from the evaluation since 
+predictions are not needed for known annotations.
+
+To exclude known terms, a list of annotations is passed in with the `-known` input option followed by a path 
+to the file containing the list. In this example, the known annotations are known with a yellow background.
+The proteins target1 and target2 both have known annotations in this aspect which we will exclude from evaluation.
+
+
+Known annotations file (`known_t0.tsv`): list all known annotations for all targets  
+```
+target1 GO:0008150
+target1 GO:0040011
+target1 GO:0032501
+target1 GO:0031987
+target2 GO:0008150
+target2 GO:0040011
+target2 GO:0033058
+```
+
+## Ground truth
+New annotations for target1 and target2 are considered in partial knowledge evaluation since these targets 
+already had knonw annotations before/during the prediction phase.
+
+Ground truth file (`ground_truth_partial.tsv`): list new annotations on which to evaluate
+```
+target1 GO:0033058
+target1 GO:0043056
+target1 GO:0050879
+target1 GO:0071965
+target2 GO:0031987
+target2 GO:0032501
+```
+
+Note: if any of the known annotations are listed in the ground truth file, they will be ignored during 
+evaluation. 
+
+## Terms of Interest
+It is also possible to exclude some terms from evaluation for _all_ targets. The terms-of-interest file
+is an optional file that is used to list all terms on which to evaluate predictions. This list is 
+applied for all targets. The list of terms is passed in with the `-toi` input option. If no file is 
+passed in, all terms in the graph will be used for evaluation
+
+In this example, terms of interest are shown in figure with a red, bold outline. Note that two of the 
+terms in the ontology are excluded (GO:0008150 and GO:0050879) which will not be included in any computations
+for evaluation, even if they appear in the ground truth file.
+
+Terms-of-Interest file (`toi.tsv`):
+```
+GO:0040011
+GO:0032501
+GO:0033058
+GO:0031987
+GO:0071965
+GO:0043056
+GO:0043057
+``` 
+
+
+## Example Prediction
+
+
+
+The example predictions both achieve precision (pr) and recall (rc) of 1.0 (see table below). 
+Looking at the precition files, pred_2 predicts eveything perfect, while pred_1 predicts many terms "incorrectly." 
+However, all of the "errors" in pred_1 are on terms that are excluded either because they were not in the terms of interest 
+or because they are previously known annotations.
+
+
+![Example of predictions](predictions.png)
+
+
+|   filename    |   ns                  |   tau    |   n      |   tp     |   fp     |   fn     |   pr     |   rc     |   cov    |   mi     |   ru     |   f      |   s      |   pr_micro  |   rc_micro  |   f_micro  |   cov_max  |
+|---------------|-----------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|-------------|-------------|------------|------------|
+|   pred_1.tsv  |   biological_process  |   0.010  |   2.000  |   2.500  |   0.000  |   0.000  |   1.000  |   1.000  |   1.000  |   0.000  |   0.000  |   1.000  |   0.000  |   1.000     |   1.000     |   1.000    |   1.000    |
+|   pred_2.tsv  |   biological_process  |   0.010  |   2.000  |   2.500  |   0.000  |   0.000  |   1.000  |   1.000  |   1.000  |   0.000  |   0.000  |   1.000  |   0.000  |   1.000     |   1.000     |   1.000    |   1.000    |
+|               |                       |          |          |          |          |          |          |          |          |          |          |          |          |             |             |            |            |

src/CAFA-evaluator-PK/example/go-sample.obo

@@ -0,0 +1,105 @@
+format-version: 1.2
+default-namespace: gene_ontology
+ontology: go
+
+[Term]
+id: GO:0008150
+name: biological_process
+namespace: biological_process
+alt_id: GO:0000004
+alt_id: GO:0007582
+alt_id: GO:0044699
+def: "A biological process represents a specific objective that the organism is genetically programmed to achieve. Biological processes are often described by their outcome or ending state, e.g., the biological process of cell division results in the creation of two daughter cells (a divided cell) from a single parent cell. A biological process is accomplished by a particular set of molecular functions carried out by specific gene products (or macromolecular complexes), often in a highly regulated manner and in a particular temporal sequence." [GOC:pdt]
+comment: Note that, in addition to forming the root of the biological process ontology, this term is recommended for use for the annotation of gene products whose biological process is unknown. When this term is used for annotation, it indicates that no information was available about the biological process of the gene product annotated as of the date the annotation was made; the evidence code 'no data' (ND), is used to indicate this.
+subset: goslim_candida
+subset: goslim_chembl
+subset: goslim_metagenomics
+subset: goslim_pir 
+subset: goslim_plant
+subset: goslim_pombe
+subset: goslim_yeast
+synonym: "biological process" EXACT []
+synonym: "physiological process" EXACT []
+synonym: "single organism process" RELATED []
+synonym: "single-organism process" RELATED []
+xref: Wikipedia:Biological_process
+
+[Term]
+id: GO:0032501
+name: multicellular organismal process
+namespace: biological_process
+alt_id: GO:0044707
+alt_id: GO:0050874
+def: "Any biological process, occurring at the level of a multicellular organism, pertinent to its function." [GOC:curators, GOC:dph, GOC:isa_complete, GOC:tb]
+subset: gocheck_do_not_annotate
+subset: goslim_pir 
+synonym: "organismal physiological process" EXACT []
+synonym: "single-multicellular organism process" RELATED []
+is_a: GO:0008150 ! biological_process
+
+[Term]
+id: GO:0040011
+name: locomotion
+namespace: biological_process
+def: "Self-propelled movement of a cell or organism from one location to another." [GOC:dgh]
+subset: goslim_chembl
+subset: goslim_pir
+is_a: GO:0008150 ! biological_process
+
+[Term]
+id: GO:0032501
+name: multicellular organismal process
+namespace: biological_process
+alt_id: GO:0044707
+alt_id: GO:0050874
+def: "Any biological process, occurring at the level of a multicellular organism, pertinent to its function." [GOC:curators, GOC:dph, GOC:isa_complete, GOC:tb]
+subset: gocheck_do_not_annotate
+subset: goslim_pir
+synonym: "organismal physiological process" EXACT []
+synonym: "single-multicellular organism process" RELATED []
+is_a: GO:0008150 ! biological_process
+
+[Term]
+id: GO:0050879
+name: multicellular organismal movement
+namespace: biological_process
+def: "Any physiological process involved in changing the position of a multicellular organism or an anatomical part of a multicellular organism." [GOC:dph, GOC:mtg_muscle, GOC:tb]
+subset: goslim_pir
+is_a: GO:0032501 ! multicellular organismal process
+
+[Term]
+id: GO:0031987
+name: locomotion involved in locomotory behavior
+namespace: biological_process
+def: "Self-propelled movement of a cell or organism from one location to another in a behavioral context; the aspect of locomotory behavior having to do with movement." [GOC:mah]
+synonym: "locomotion during locomotory behaviour" EXACT []
+is_a: GO:0040011 ! locomotion
+
+[Term]
+id: GO:0033058
+name: directional locomotion 
+namespace: biological_process
+def: "Self-propelled movement of a cell or organism from one location to another along an axis." [GOC:mtg_MIT_16mar07]
+is_a: GO:0040011 ! locomotion
+
+[Term]
+id: GO:0071965
+name: multicellular organismal locomotion
+namespace: biological_process
+def: "Locomotion in a multicellular organism, i.e. self-propelled movement of a multicellular organism from one location to another." [GOC:mah]
+is_a: GO:0040011 ! locomotion
+is_a: GO:0050879 ! multicellular organismal movement
+
+[Term]
+id: GO:0043056
+name: forward locomotion
+namespace: biological_process
+def: "Anterior movement of an organism, following the direction of the head of the animal." [GOC:go_curators]
+is_a: GO:0033058 ! directional locomotion
+
+[Term]
+id: GO:0043057
+name: backward locomotion
+namespace: biological_process
+def: "Posterior movement of an organism, e.g. following the direction of the tail of an animal." [GOC:go_curators]
+is_a: GO:0033058 ! directional locomotion 

src/CAFA-evaluator-PK/example/ground_truth_limited.tsv

@@ -0,0 +1,4 @@
+target3 GO:0008150
+target3 GO:0040011
+target3 GO:0032501
+target3 GO:0031987

src/CAFA-evaluator-PK/example/ground_truth_partial.tsv

@@ -0,0 +1,6 @@
+target1 GO:0033058      
+target1 GO:0043056
+target1 GO:0050879
+target1 GO:0071965
+target2 GO:0031987
+target2 GO:0032501

src/CAFA-evaluator-PK/example/known_t0.tsv

@@ -0,0 +1,7 @@
+target1	GO:0008150
+target1	GO:0040011
+target1	GO:0032501
+target1	GO:0031987
+target2	GO:0008150
+target2	GO:0040011
+target2	GO:0033058

src/CAFA-evaluator-PK/example/predictions/pred_1.tsv

@@ -0,0 +1,27 @@
+target1	GO:0008150	0.0001
+target1	GO:0040011	0.0001
+target1	GO:0031987	0.0001
+target1	GO:0033058	0.9999
+target1	GO:0043056	0.9999
+target1	GO:0043057	0.0001
+target1	GO:0032501	0.0001
+target1	GO:0050879	0.0001
+target1	GO:0071965	0.9999
+target2 GO:0008150	0.0000
+target2 GO:0040011	0.0001
+target2 GO:0031987	0.9999
+target2 GO:0033058	0.0001
+target2 GO:0043056	0.0001
+target2 GO:0043057	0.0001
+target2 GO:0032501	0.9999
+target2 GO:0050879	0.0001
+target2 GO:0071965	0.0001
+target3 GO:0008150	0.0001
+target3 GO:0040011	0.0001
+target3 GO:0031987	0.9999
+target3 GO:0033058	0.0001
+target3 GO:0043056	0.0001
+target3 GO:0043057	0.0001
+target3 GO:0032501	0.9999
+target3 GO:0050879	0.0001
+target3 GO:0071965	0.0001

src/CAFA-evaluator-PK/example/predictions/pred_2.tsv

@@ -0,0 +1,27 @@
+target1	GO:0008150	0.9999
+target1	GO:0040011	0.9999
+target1	GO:0031987	0.9999
+target1	GO:0033058	0.9999
+target1	GO:0043056	0.9999
+target1	GO:0043057	0.0001
+target1	GO:0032501	0.9999
+target1	GO:0050879	0.9999
+target1	GO:0071965	0.9999
+target2 GO:0008150	0.9999
+target2 GO:0040011	0.9999
+target2 GO:0031987	0.9999
+target2 GO:0033058	0.9999
+target2 GO:0043056	0.0001
+target2 GO:0043057	0.0001
+target2 GO:0032501	0.9999
+target2 GO:0050879	0.0001
+target2 GO:0071965	0.0001
+target3 GO:0008150	0.9999
+target3 GO:0040011	0.9999
+target3 GO:0031987	0.9999
+target3 GO:0033058	0.0001
+target3 GO:0043056	0.0001
+target3 GO:0043057	0.0001
+target3 GO:0032501	0.9999
+target3 GO:0050879	0.0001
+target3 GO:0071965	0.0001

src/CAFA-evaluator-PK/example/toi.tsv

@@ -0,0 +1,7 @@
+GO:0040011
+GO:0032501
+GO:0033058
+GO:0031987
+GO:0071965
+GO:0043056
+GO:0043057

src/CAFA-evaluator-PK/pyproject.toml

@@ -0,0 +1,43 @@
+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "cafaeval"
+version = "1.2.1"
+authors = [
+    {name = 'Damiano Piovesan', email = 'damiano.piovesan@unipd.it'},
+    {name = 'Parnal Joshi', email = 'parnal@iastate.edu'}
+]
+description = 'CAFA evaluator code'
+readme = {file = 'README.md', 'content-type' = 'text/markdown'}
+#url='https://github.com/BioComputingUP/CAFA-evaluator'
+requires-python = '>=3.6'
+keywords = ['CAFA', 'structured data', 'ontology', 'Gene Ontology', 'protein function']
+license = {file = 'LICENCE.md', 'content-type' = 'text/markdown'}
+classifiers=[
+    'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
+    'Programming Language :: Python :: 3',
+    'Operating System :: OS Independent',
+]
+dependencies = [
+    'pandas',
+    'matplotlib',
+    'numpy',
+]
+
+[project.urls]
+Homepage = "https://github.com/BioComputingUP/CAFA-evaluator"
+Repository = "https://github.com/BioComputingUP/CAFA-evaluator"
+Changelog = "https://github.com/BioComputingUP/CAFA-evaluator/blob/main/CHANGELOG.md"
+Wiki = "https://github.com/BioComputingUP/CAFA-evaluator/wiki"
+
+[tool.setuptools.packages.find]
+where = ["src"]
+namespaces = false
+
+[project.scripts]
+cafaeval = "cafaeval.__main__:command_line"
+
+[tool.setuptools.package-data]
+cafaeval = ["data/example/*", "data/example/predictions/*"]

src/CAFA-evaluator-PK/setup.py

@@ -0,0 +1,3 @@
+from setuptools import setup
+
+setup()

src/CAFA-evaluator-PK/src/cafaeval/__main__.py

@@ -0,0 +1,69 @@
+import argparse
+from cafaeval.evaluation import cafa_eval, write_results
+import logging
+
+
+def command_line():
+
+    logging.info("CAFA-evaluator. Calculate precision-recall curves and F-max / S-min")
+
+    parser = argparse.ArgumentParser(description='CAFA-evaluator. Calculate precision-recall curves and F-max / S-min')
+
+    parser.add_argument('obo_file', help='Ontology file, OBO format')
+    parser.add_argument('pred_dir', help='Predictions directory. Sub-folders are iterated recursively')
+    parser.add_argument('gt_file', help='Ground truth file')
+
+    parser.add_argument('-out_dir', default='results',
+                        help='Output directory. By default it creates \"results/\" in the current directory')
+    parser.add_argument('-ia', help='Information Accretion file (columns: <term> <information_accretion>)')
+    parser.add_argument('-no_orphans', action='store_true', default=False,
+                        help='Exclude terms without parents, e.g. the root(s), in the evaluation')
+    parser.add_argument('-norm', choices=['cafa', 'pred', 'gt'], default='cafa',
+                        help='cafa - implements the CAFA normalization strategy. '
+                             'Precision is normalized by the number of predicted targets, '
+                             'all other metrics by the number of ground truth targets. '
+                             'pred - All metrics are normalized by the number of predicted targets. '
+                             'gt - All metrics are normalized by the number of ground truth targets')
+    parser.add_argument('-prop', choices=['max', 'fill'], default='max',
+                        help='Ancestor propagation strategy. max - Propagate the max score of the traversed subgraph '
+                             'iteratively. fill - Propagate with max until a different score is found')
+    parser.add_argument('-known', default=None,
+                        help='Known annotations to exclude from evaluation. Same format at ground truth file')
+    parser.add_argument('-toi', default=None,
+                        help='File with GO IDs for terms of interest on which to evaluate. '
+                             'Usually, this is to exclude terms that were removed from GO '
+                             'since evaluation obo file was created. If None (default), all terms will be'
+                             'included in evaluation, possibly excluding roots if -no_orphans is passed')
+    parser.add_argument('-th_step', type=float, default=0.01,
+                        help='Threshold step size in the range [0, 1). A smaller step, means more calculation')
+    parser.add_argument('-max_terms', type=int, default=None,
+                        help='Number of terms for protein and namespace to consider in the evaluation')
+    parser.add_argument('-threads', type=int, default=4,
+                        help='Parallel threads. 0 means use all available CPU threads. '
+                             'Do not use multithread if you are short in memory')
+    parser.add_argument('-log_level', type=str, choices=['debug', 'info', 'warning', 'error', 'critical'],
+                        default='info', help='Log level')
+
+    args = parser.parse_args()
+
+    # Set the logger
+    logging.basicConfig()
+    root_logger = logging.getLogger()  # root logger
+    root_logger.setLevel(logging.getLevelName(args.log_level.upper()))
+    log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s")
+    root_handler = root_logger.handlers[0]
+    root_handler.setFormatter(log_formatter)
+
+    # Run the evaluation
+    df, dfs_best = cafa_eval(args.obo_file, args.pred_dir, args.gt_file,
+                             ia=args.ia, no_orphans=args.no_orphans, norm=args.norm, prop=args.prop,
+                             exclude=args.known, toi_file=args.toi,
+                             max_terms=args.max_terms, th_step=args.th_step, n_cpu=args.threads)
+
+    # Write the results
+    write_results(df, dfs_best, out_dir=args.out_dir, th_step=args.th_step)
+
+
+if __name__ == "__main__":
+
+    command_line()

src/CAFA-evaluator-PK/src/cafaeval/evaluation.py

@@ -0,0 +1,355 @@
+import os
+import numpy as np
+import pandas as pd
+import multiprocessing as mp
+from cafaeval.parser import obo_parser, gt_parser, pred_parser, gt_exclude_parser, update_toi
+from cafaeval.tests import test_norm_metric, test_intersection
+import logging
+logging.getLogger(__name__).addHandler(logging.NullHandler())
+
+
+# Return a mask for all the predictions (matrix) >= tau
+def solidify_prediction(pred, tau):
+    return pred >= tau
+
+
+# computes the f metric for each precision and recall in the input arrays
+def compute_f(pr, rc):
+    n = 2 * pr * rc
+    d = pr + rc
+    return np.divide(n, d, out=np.zeros_like(n, dtype=float), where=d != 0)
+
+
+def compute_s(ru, mi):
+    return np.sqrt(ru**2 + mi**2)
+    # return np.where(np.isnan(ru), mi, np.sqrt(ru + np.nan_to_num(mi)))
+
+
+def compute_confusion_matrix(tau_arr, g, pred_matrix, toi, n_gt, ic_arr=None):
+    """
+    Perform the evaluation at the matrix level for all tau thresholds
+    The calculation is
+    """
+    # n, tp, fp, fn, pr, rc (fp = misinformation, fn = remaining uncertainty)
+    metrics = np.zeros((len(tau_arr), 6), dtype='float')
+
+    for i, tau in enumerate(tau_arr):
+
+        # Filter predictions based on tau threshold
+        p = solidify_prediction(pred_matrix, tau)
+
+        # Terms subsets
+        intersection = np.logical_and(p, g)  # TP
+        mis = np.logical_and(p, np.logical_not(g))  # FP, predicted but not in the ground truth
+        remaining = np.logical_and(np.logical_not(p), g)  # FN, not predicted but in the ground truth
+
+        # Weighted evaluation
+        if ic_arr is not None:
+            p = p * ic_arr[toi]
+            intersection = intersection * ic_arr[toi]  # TP
+            mis = mis * ic_arr[toi]  # FP, predicted but not in the ground truth
+            remaining = remaining * ic_arr[toi]  # FN, not predicted but in the ground truth
+
+        n_pred = p.sum(axis=1)  # TP + FP (number of terms predicted in each protein)
+        n_intersection = intersection.sum(axis=1)  # TP (number of TP terms per protein)
+        # Number of proteins with at least one term predicted with score >= tau
+        metrics[i, 0] = (p.sum(axis=1) > 0).sum()
+
+        # Sum of confusion matrices
+        metrics[i, 1] = n_intersection.sum()  # TP (total terms)
+        metrics[i, 2] = mis.sum(axis=1).sum()  # FP
+        metrics[i, 3] = remaining.sum(axis=1).sum()  # FN
+
+        # Macro-averaging
+        metrics[i, 4] = np.divide(n_intersection, n_pred, out=np.zeros_like(n_intersection, dtype='float'), where=n_pred > 0).sum()  # Precision
+        metrics[i, 5] = np.divide(n_intersection, n_gt, out=np.zeros_like(n_gt, dtype='float'), where=n_gt > 0).sum()  # Recall
+
+    return metrics
+
+
+def compute_confusion_matrix_exclude(tau_arr, g_perprotein, pred_matrix, toi_perprotein, n_gt, ic_arr=None):
+    """
+    Perform the evaluation at the matrix level for all tau thresholds
+    The calculation is
+
+    Here, g is the full ground truth matrix without filtering terms of interest (toi).
+    Instead,
+    """
+    # n, tp, fp, fn, pr, rc (fp = misinformation, fn = remaining uncertainty)
+    metrics = np.zeros((len(tau_arr), 6), dtype='float')
+
+    for i, tau in enumerate(tau_arr):
+
+        # Filter predictions based on tau threshold
+        p_perprotein = [solidify_prediction(pred_matrix[p_idx, tois], tau) for p_idx, tois in enumerate(toi_perprotein)]
+
+        # Terms subsets
+        intersection = [np.logical_and(p_i, g_i) for p_i, g_i in zip(p_perprotein, g_perprotein)]  # TP
+        mis = [np.logical_and(p_i, np.logical_not(g_i)) for p_i, g_i in zip(p_perprotein, g_perprotein)]  # FP, predicted but not in the ground truth
+        remaining = [np.logical_and(np.logical_not(p_i), g_i) for p_i, g_i in zip(p_perprotein, g_perprotein)]  # FN, not predicted but in the ground truth
+
+        # Weighted evaluation
+        if ic_arr is not None:
+            p_perprotein = [p_i * ic_arr[tois] for p_i, tois in zip(p_perprotein, toi_perprotein)]
+            intersection = [inter * ic_arr[tois] for inter, tois in zip(intersection, toi_perprotein)]  # TP
+            mis = [misinf * ic_arr[tois] for misinf, tois in zip(mis, toi_perprotein)]  # FP, predicted but not in the ground truth
+            remaining = [rem * ic_arr[tois] for rem, tois in zip(remaining, toi_perprotein)]  # FN, not predicted but in the ground truth
+
+        n_pred = np.array([p_i.sum() for p_i in p_perprotein])  # TP + FP
+        n_intersection = np.array([inter.sum() for inter in intersection])  # TP
+        precision = np.divide(n_intersection, n_pred, out=np.zeros_like(n_intersection, dtype='float'), where=n_pred > 0)
+        recall = np.divide(n_intersection, n_gt, out=np.zeros_like(n_gt, dtype='float'), where=n_gt > 0)
+
+        # metrics tests
+        test_norm_metric(precision, name='precision')
+        test_norm_metric(recall, name='recall')
+        test_intersection(n_intersection, n_pred, n_gt)
+
+
+        # Number of proteins with at least one term predicted with score >= tau
+        metrics[i, 0] = (n_pred > 0).sum()
+
+        # Sum of confusion matrices
+        metrics[i, 1] = n_intersection.sum()  # TP
+        metrics[i, 2] = np.sum([m.sum() for m in mis])  # FP
+        metrics[i, 3] = np.sum([r.sum() for r in remaining])  # FN
+
+        # Macro-averaging
+        metrics[i, 4] = precision.sum()  # Precision
+        metrics[i, 5] = recall.sum()  # Recall
+
+    print("metrics calculated")
+    return metrics
+
+
+def compute_metrics(pred, gt_matrix, tau_arr, toi, gt_exclude=None, ic_arr=None, n_cpu=0):
+    """
+    Takes the prediction and the ground truth and for each threshold in tau_arr
+    calculates the confusion matrix and returns the coverage,
+    precision, recall, remaining uncertainty and misinformation.
+    Toi is the list of terms (indexes) to be considered
+    """
+    # Parallelization
+    if n_cpu == 0:
+        n_cpu = mp.cpu_count()
+
+    columns = ["n", "tp", "fp", "fn", "pr", "rc"]
+    # filter out proteins with no annotations in Terms-Of-Interest (toi)
+    proteins_has_gt = gt_matrix[:, toi].sum(1) > 0
+    proteins_with_gt = np.where(proteins_has_gt)[0]
+    gt_with_annots = gt_matrix[proteins_with_gt, :]
+    g = gt_with_annots[:, toi]
+    p = pred[proteins_has_gt, :][:, toi]
+
+    if gt_exclude is not None:
+        # g_exclude = gt_exclude.matrix[proteins_with_gt, :][:, toi]
+        toi_perprotein = [np.setdiff1d(toi, gt_exclude.matrix[p, :].nonzero()[0],
+                                       assume_unique=True) for p in
+                          proteins_with_gt] # only include proteins with annotations
+        gt_perprotein = [gt_with_annots[p_idx, tois] for p_idx, tois in enumerate(toi_perprotein)]
+        # The number of GT annotations per proteins will change to exclude the set from g_exclude
+        # count_g = np.logical_and(np.logical_not(g_exclude), g)  # count terms in g only if they are not in exclude list
+        n_gt = np.array([gpp.sum().item() for gpp in gt_perprotein])  # number of terms annotated in each protein
+        if np.any(n_gt==0):
+            print(f'Proteins with no annotations in TOI {np.count_nonzero(n_gt==0)}')
+        if ic_arr is not None:
+            n_gt = np.array([(gpp * ic_arr[tois]).sum().item() for gpp, tois in zip(gt_perprotein, toi_perprotein)])
+    else:
+        count_g = g
+        # Simple metrics: number of terms annotated in each protein
+        if ic_arr is None:
+            n_gt = count_g.sum(axis=1)
+        # Weighted metrics
+        else:
+            n_gt = (count_g * ic_arr[toi]).sum(axis=1)
+
+    if gt_exclude is None:
+        arg_lists = [[tau_arr, g, p, toi, n_gt, ic_arr] for tau_arr in np.array_split(tau_arr, n_cpu)]
+        with mp.Pool(processes=n_cpu) as pool:
+            metrics = np.concatenate(pool.starmap(compute_confusion_matrix, arg_lists), axis=0)
+    else:
+        arg_lists = [[tau_arr, gt_perprotein, pred[gt_matrix[:,toi].sum(1)>0, :], toi_perprotein, n_gt, ic_arr] for tau_arr in np.array_split(tau_arr, n_cpu)]
+        with mp.Pool(processes=n_cpu) as pool:
+            metrics = np.concatenate(pool.starmap(compute_confusion_matrix_exclude, arg_lists), axis=0)
+
+    print("Jobs on all CPUs completed.")
+    return pd.DataFrame(metrics, columns=columns)
+
+
+def normalize(metrics, ns, tau_arr, ne, normalization):
+
+    # Normalize columns
+    for column in metrics.columns:
+        if column != "n":
+            # By default normalize by gt
+            denominator = ne
+            # Otherwise normalize by pred
+            if normalization == 'pred' or (normalization == 'cafa' and column == "pr"):
+                denominator = metrics["n"]
+            metrics[column] = np.divide(metrics[column], denominator,
+                                        out=np.zeros_like(metrics[column], dtype='float'),
+                                        where=denominator > 0)
+
+    metrics['ns'] = [ns] * len(tau_arr)
+    metrics['tau'] = tau_arr
+    metrics['cov'] = metrics['n'] / ne
+    metrics['mi'] = metrics['fp']
+    metrics['ru'] = metrics['fn']
+
+    metrics['f'] = compute_f(metrics['pr'], metrics['rc'])
+    metrics['s'] = compute_s(metrics['ru'], metrics['mi'])
+
+    # Micro-average, calculation is based on the average of the confusion matrices
+    metrics['pr_micro'] = np.divide(metrics['tp'], metrics['tp'] + metrics['fp'],
+                                    out=np.zeros_like(metrics['tp'], dtype='float'),
+                                    where=(metrics['tp'] + metrics['fp']) > 0)
+    metrics['rc_micro'] = np.divide(metrics['tp'], metrics['tp'] + metrics['fn'],
+                                    out=np.zeros_like(metrics['tp'], dtype='float'),
+                                    where=(metrics['tp'] + metrics['fn']) > 0)
+    metrics['f_micro'] = compute_f(metrics['pr_micro'], metrics['rc_micro'])
+
+    return metrics
+
+
+def evaluate_prediction(prediction, gt, ontologies, tau_arr, gt_exclude=None, normalization='cafa', n_cpu=0):
+
+    dfs = []
+    dfs_w = []
+
+    # Unweighted metrics
+    for ns in prediction:
+        # number of proteins with positive annotations
+        proteins_has_gt = gt[ns].matrix[:, ontologies[ns].toi].sum(1) > 0
+        proteins_with_gt = np.where(proteins_has_gt)[0]
+        num_annot_prots = proteins_has_gt.sum()  # number of proteins with positive annotations in TOIs
+        if gt_exclude is None:
+            exclude = None
+        else:
+            exclude = gt_exclude[ns]
+            toi_perprotein = [
+                np.setdiff1d(ontologies[ns].toi, gt_exclude[ns].matrix[p, :].nonzero()[0],
+                             assume_unique=True) for p in proteins_with_gt]
+            # update the number of proteins with positive annotations, now on protein-specific TOIs
+            num_annot_prots = sum([gt[ns].matrix[p, toi_perprotein[p_idx]].sum()>0 for
+                                   p_idx, p in enumerate(proteins_with_gt)])
+
+        ne = np.full(len(tau_arr), num_annot_prots)
+
+        dfs.append(normalize(compute_metrics(
+            prediction[ns].matrix, gt[ns].matrix, tau_arr, ontologies[ns].toi, exclude, None, n_cpu),
+                             ns, tau_arr, ne, normalization))
+
+        # Weighted metrics
+        if ontologies[ns].ia is not None:
+
+            # number of proteins with positive annotations
+            proteins_has_gt = gt[ns].matrix[:, ontologies[ns].toi_ia].sum(1) > 0
+            num_annot_prots = (proteins_has_gt).sum()
+
+            if gt_exclude is None:
+                exclude = None
+            else:
+                exclude = gt_exclude[ns]
+                toi_perprotein_ia = [
+                    np.setdiff1d(ontologies[ns].toi_ia, gt_exclude[ns].matrix[p, :].nonzero()[0],
+                                 assume_unique=True) for p in proteins_with_gt]
+                # update the number of proteins with positive annotations, now on protein-specific TOIs
+                num_annot_prots = sum([gt[ns].matrix[p, toi_perprotein_ia[p_idx]].sum() > 0 for
+                                       p_idx, p in enumerate(proteins_with_gt)])
+
+            ne = np.full(len(tau_arr), num_annot_prots)
+
+            dfs_w.append(normalize(compute_metrics(
+                prediction[ns].matrix, gt[ns].matrix, tau_arr, ontologies[ns].toi_ia, exclude, ontologies[ns].ia, n_cpu),
+                ns, tau_arr, ne, normalization))
+
+    dfs = pd.concat(dfs)
+
+    # Merge weighted and unweighted dataframes
+    if dfs_w:
+        dfs_w = pd.concat(dfs_w)
+        dfs = pd.merge(dfs, dfs_w, on=['ns', 'tau'], suffixes=('', '_w'))
+
+    return dfs
+
+
+def cafa_eval(obo_file, pred_dir, gt_file, ia=None, no_orphans=False, norm='cafa', prop='max',
+              exclude=None, toi_file=None, max_terms=None, th_step=0.01, n_cpu=1):
+
+    # Tau array, used to compute metrics at different score thresholds
+    tau_arr = np.arange(th_step, 1, th_step)
+
+    # Parse the OBO file and creates a different graphs for each namespace
+    ontologies = obo_parser(obo_file, ("is_a", "part_of"), ia, not no_orphans)
+    if toi_file is not None:
+        ontologies = update_toi(ontologies, toi_file)
+
+    # Parse ground truth file
+    gt = gt_parser(gt_file, ontologies)
+    if exclude is not None:
+        gt_exclude = gt_exclude_parser(exclude, gt, ontologies)
+    else:
+        gt_exclude = None
+
+    # Set prediction files looking recursively in the prediction folder
+    pred_folder = os.path.normpath(pred_dir) + "/"  # add the tailing "/"
+    pred_files = []
+    for root, dirs, files in os.walk(pred_folder):
+        for file in files:
+            pred_files.append(os.path.join(root, file))
+    logging.debug("Prediction paths {}".format(pred_files))
+
+    # Parse prediction files and perform evaluation
+    dfs = []
+    for file_name in pred_files:
+        print(file_name)
+        prediction = pred_parser(file_name, ontologies, gt, prop, max_terms)
+        if not prediction:
+            logging.warning("Prediction: {}, not evaluated".format(file_name))
+        else:
+            df_pred = evaluate_prediction(prediction, gt, ontologies, tau_arr, gt_exclude,
+                                          normalization=norm, n_cpu=n_cpu)
+            df_pred['filename'] = file_name.replace(pred_folder, '').replace('/', '_')
+            dfs.append(df_pred)
+            logging.info("Prediction: {}, evaluated".format(file_name))
+
+    # Concatenate all dataframes and save them
+    df = None
+    dfs_best = {}
+    if dfs:
+        df = pd.concat(dfs)
+
+        # Remove rows with no coverage
+        df = df[df['cov'] > 0].reset_index(drop=True)
+        df.set_index(['filename', 'ns', 'tau'], inplace=True)
+
+        # Calculate the best index for each namespace and each evaluation metric
+        for metric, cols in [('f', ['rc', 'pr']), ('f_w', ['rc_w', 'pr_w']), ('s', ['ru', 'mi']), ('f_micro', ['rc_micro', 'pr_micro']), ('f_micro_w', ['rc_micro_w', 'pr_micro_w'])]:
+            if metric in df.columns:
+                index_best = df.groupby(level=['filename', 'ns'])[metric].idxmax() if metric in ['f', 'f_w', 'f_micro', 'f_micro_w'] else df.groupby(['filename', 'ns'])[metric].idxmin()
+                df_best = df.loc[index_best]
+                if metric[-2:] != '_w':
+                    df_best['cov_max'] = df.reset_index('tau').loc[[ele[:-1] for ele in index_best]].groupby(level=['filename', 'ns'])['cov'].max()
+                else:
+                    df_best['cov_max'] = df.reset_index('tau').loc[[ele[:-1] for ele in index_best]].groupby(level=['filename', 'ns'])['cov_w'].max()
+                dfs_best[metric] = df_best
+    else:
+        logging.info("No predictions evaluated")
+
+    return df, dfs_best
+
+
+def write_results(df, dfs_best, out_dir='results', th_step=0.01):
+
+    # Create output folder here in order to store the log file
+    out_folder = os.path.normpath(out_dir) + "/"
+    if not os.path.isdir(out_folder):
+        os.makedirs(out_folder)
+
+    # Set the number of decimals to write in the output files based on the threshold step size
+    decimals = int(np.ceil(-np.log10(th_step))) + 1
+
+    df.to_csv('{}/evaluation_all.tsv'.format(out_folder), float_format="%.{}f".format(decimals), sep="\t")
+
+    for metric in dfs_best:
+        dfs_best[metric].to_csv('{}/evaluation_best_{}.tsv'.format(out_folder, metric), float_format="%.{}f".format(decimals), sep="\t")

src/CAFA-evaluator-PK/src/cafaeval/graph.py

@@ -0,0 +1,182 @@
+import numpy as np
+import copy
+import logging
+logging.getLogger(__name__).addHandler(logging.NullHandler())
+
+
+class Graph:
+    """
+    Ontology class. One ontology == one namespace
+    DAG is the adjacence matrix (sparse) which represent a Directed Acyclic Graph where
+    DAG(i,j) == 1 means that the go term i is_a (or is part_of) j
+    Parents that are in a different namespace are discarded
+    """
+    def __init__(self, namespace, terms_dict, ia_dict=None, orphans=False):
+
+        """
+        terms_dict = {term: {name: , namespace: , def: , alt_id: , rel:}}
+        """
+        self.namespace = namespace
+        self.dag = []  # [[], ...] terms (rows, axis 0) x parents (columns, axis 1)
+        self.terms_dict = {}  # {term: {index: , name: , namespace: , def: }  used to assign term indexes in the gt
+        self.terms_dict_alt = {}  # {alt_id: set(term, ...) }  alternative ids to canonical ids
+        self.terms_list = []  # [{id: term, name:, namespace: , def:, adj: set(), children: set()}, ...]
+        self.idxs = None  # Number of terms
+        self.order = None
+        self.toi = None
+        self.toi_ia = None
+        self.ia = None
+
+        rel_list = []
+        for self.idxs, (term_id, term) in enumerate(terms_dict.items()):
+            rel_list.extend([[term_id, rel, term['namespace']] for rel in term['rel']])
+            self.terms_list.append({'id': term_id, 'name': term['name'], 'namespace': namespace, 'def': term['def'],
+                                 'adj': set(), 'children': set()})
+            self.terms_dict[term_id] = {'index': self.idxs, 'name': term['name'], 'namespace': namespace, 'def': term['def']}
+            for a_id in term['alt_id']:
+                self.terms_dict_alt.setdefault(a_id, set()).add(term_id)
+
+        self.idxs += 1
+
+        self.dag = np.zeros((self.idxs, self.idxs), dtype='bool')
+
+        # id1 term (row, axis 0), id2 parent (column, axis 1)
+        for id1, id2, ns in rel_list:
+            if self.terms_dict.get(id2):
+                i = self.terms_dict[id1]['index']
+                j = self.terms_dict[id2]['index']
+                self.dag[i, j] = 1
+                # Remove duplicates in adj and children lists
+                # This ensures that a parent-child term does not have multiple edges, which could lead to wrong topological sorting
+                self.terms_list[i]['adj'].add(j)
+                self.terms_list[j]['children'].add(i)
+                logging.debug("i,j {},{} {},{}".format(i, j, id1, id2))
+            else:
+                logging.debug('Skipping branch to external namespace: {}'.format(id2))
+        logging.debug("dag {}".format(self.dag))
+        
+        # Topological sorting
+        self.top_sort()
+        logging.debug("order sorted {}".format(self.order))
+
+        if orphans:
+            self.toi = np.arange(self.dag.shape[0])  # All terms, also those without parents
+        else:
+            self.toi = np.nonzero(self.dag.sum(axis=1) > 0)[0]  # Only terms with parents
+        logging.debug("toi {}".format(self.toi))
+
+        if ia_dict is not None:
+            self.set_ia(ia_dict)
+
+        logging.info("Ontology: {}, total {}, roots {}, leaves {}, alternative_ids {}".format(self.namespace,
+                                                                len(np.where(self.dag.sum(axis=1) != 0)[0]),
+                                                                len(np.where(self.dag.sum(axis=1) == 0)[0]),
+                                                                len(np.where(self.dag.sum(axis=0) == 0)[0]),
+                                                                len(self.terms_dict_alt)))
+
+        return
+
+    def top_sort(self):
+        """
+        Takes a sparse matrix representing a DAG and returns an array with nodes indexes in topological order
+        https://en.wikipedia.org/wiki/Topological_sorting
+        """
+        indexes = []
+        visited = 0
+        (rows, cols) = self.dag.shape
+
+        # create a vector containing the in-degree of each node
+        in_degree = self.dag.sum(axis=0)
+        # logging.debug("degree {}".format(in_degree))
+
+        # find the nodes with in-degree 0 (leaves) and add them to the queue
+        queue = np.nonzero(in_degree == 0)[0].tolist()
+        # logging.debug("queue {}".format(queue))
+
+        # for each element of the queue increment visits, add them to the list of ordered nodes
+        # and decrease the in-degree of the neighbor nodes
+        # and add them to the queue if they reach in-degree == 0
+        while queue:
+            visited += 1
+            idx = queue.pop(0)
+            indexes.append(idx)
+            in_degree[idx] -= 1
+            l = self.terms_list[idx]['adj']
+            if len(l) > 0:
+                for j in l:
+                    in_degree[j] -= 1
+                    if in_degree[j] == 0:
+                        queue.append(j)
+
+        # if visited is equal to the number of nodes in the graph then the sorting is complete
+        # otherwise the graph can't be sorted with topological order
+        if visited == rows:
+            self.order = indexes
+        else:
+            raise Exception("The sparse matrix doesn't represent an acyclic graph")
+
+    def set_ia(self, ia_dict):
+        self.ia = np.zeros(self.idxs, dtype='float')
+        for term_id in self.terms_dict:
+            if ia_dict.get(term_id):
+                self.ia[self.terms_dict[term_id]['index']] = ia_dict.get(term_id)
+            else:
+                logging.debug('Missing IA for term: {}'.format(term_id))
+        # Convert inf to zero
+        np.nan_to_num(self.ia, copy=False, nan=0, posinf=0, neginf=0)
+        self.toi_ia = np.nonzero(self.ia > 0)[0]
+
+
+class Prediction:
+    """
+    The score matrix contains the scores given by the predictor for every node of the ontology
+    """
+    def __init__(self, ids, matrix, namespace=None):
+        self.ids = ids
+        self.matrix = matrix  # scores
+        self.namespace = namespace
+
+    def __str__(self):
+        return "\n".join(["{}\t{}\t{}".format(index, self.matrix[index], self.namespace) for index, _id in enumerate(self.ids)])
+
+
+class GroundTruth:
+    def __init__(self, ids, matrix, namespace=None):
+        self.ids = ids
+        self.matrix = matrix
+        self.namespace = namespace
+
+
+def propagate(matrix, ont, order, mode='max'):
+    """
+    Update inplace the score matrix (proteins x terms) up to the root taking the max between children and parents
+    """
+    if matrix.shape[0] == 0:
+        raise Exception("Empty matrix")
+
+    deepest = np.where(np.sum(matrix[:, order], axis=0) > 0)[0][0]
+    if deepest.size == 0:
+        raise Exception("The matrix is empty")
+
+    # Remove leaves
+    order_ = np.delete(order, [range(0, deepest)])
+
+    for i in order_:
+        # Get direct children
+        children = np.where(ont.dag[:, i] != 0)[0]
+        if children.size > 0:
+            # Add current terms to children
+            cols = np.concatenate((children, [i]))
+            if mode == 'max':
+                matrix[:, i] = matrix[:, cols].max(axis=1)
+            elif mode == 'fill':
+                # Select only rows where the current term is 0
+                rows = np.where(matrix[:, i] == 0)[0]
+                if rows.size:
+                    idx = np.ix_(rows, cols)
+                    matrix[rows, i] = matrix[idx].max(axis=1)
+    return
+
+
+
+