{"id":"W2601359818","doi":"10.1101/120402","title":"Fast functional annotation of metagenomic shotgun data by DNA alignment to a microbial gene catalog","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; National Institutes of Health; York University; National Institute of Allergy and Infectious Diseases; NYU Langone Medical Center; U.S. Department of Veterans Affairs","keywords":"Metagenomics; Shotgun sequencing; Shotgun; Computational biology; Human Microbiome Project; Annotation; Biology; DNA sequencing; Genome; Sequence assembly; Gene prediction; Workflow; Gene; Genetics; Computer science; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002094122,0.001417459,0.001090114,0.005696139,0.0008458497,0.001402723,0.00114227,0.0006562496,0.006256936],"category_scores_gemma":[0.004452485,0.0006106718,0.001244248,0.004087484,0.0003324851,0.001095326,0.001355092,0.001180996,0.004514404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006904583,"about_ca_system_score_gemma":0.001157914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001788992,"about_ca_topic_score_gemma":0.002067993,"domain_scores_codex":[0.9988022,0.0001750685,0.0001589517,0.0003440921,0.0004246951,0.0000949836],"domain_scores_gemma":[0.9985002,0.0003960331,0.0002114161,0.0003483215,0.0004486226,0.00009534889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001684827,0.000363313,0.01086856,0.001954268,0.0003112762,0.0006524172,0.0004519295,0.004882823,0.7171887,0.006345516,0.02885602,0.2264403],"study_design_scores_gemma":[0.0002308406,0.00038628,0.03246545,0.0003042255,0.0001958067,0.0008887055,0.0003257404,0.1446115,0.6997408,0.01254112,0.1080766,0.0002330501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07498962,0.0007269154,0.7905315,0.000340032,0.0002206364,0.0004272918,0.06914259,0.06060173,0.003019738],"genre_scores_gemma":[0.09749368,0.000472911,0.7871556,0.0001356147,0.00007829733,0.0008884988,0.1074309,0.003682743,0.002661676],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006256936,"threshold_uncertainty_score":0.02093154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02254080740284516,"score_gpt":0.2304095008575142,"score_spread":0.2078686934546691,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}