{"id":"W2137177315","doi":"10.1186/2049-2618-1-23","title":"mPUMA: a computational approach to microbiota analysis by de novo assembly of operational taxonomic units based on protein-coding barcode sequences","year":2013,"lang":"en","type":"article","venue":"Microbiome","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Plant Biotechnology Institute; Saskatchewan Research Council (Canada); University of Saskatchewan; Agriculture and Agri-Food Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Metagenomics; Operational taxonomic unit; Barcode; Computational biology; DNA sequencing; Sequence analysis; Sequence assembly; Microbial ecology; Microbiome; Genetics; Gene; 16S ribosomal RNA; Computer science; Bacteria","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00306193,0.003010667,0.002380694,0.00323504,0.001483632,0.002818036,0.004186504,0.002341666,0.003907753],"category_scores_gemma":[0.01059953,0.001885912,0.003678393,0.001715018,0.001064488,0.001951484,0.002964538,0.003542527,0.0014441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009862196,"about_ca_system_score_gemma":0.002342848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003247397,"about_ca_topic_score_gemma":0.004820062,"domain_scores_codex":[0.9989328,0.0003327708,0.00009283297,0.0003106004,0.0002646482,0.00006630267],"domain_scores_gemma":[0.9966281,0.002415297,0.0002871407,0.0002603711,0.000249759,0.0001593379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001268166,0.0006270108,0.01198456,0.002552892,0.002977844,0.0009888065,0.001102272,0.6241934,0.04668052,0.02746843,0.01577716,0.264379],"study_design_scores_gemma":[0.0000446214,0.00006352324,0.0004013091,0.00004147571,0.00006695713,0.00007256866,0.00003699029,0.9851585,0.003348685,0.007708248,0.003018511,0.0000385617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01622104,0.0003731323,0.9634735,0.0001593859,0.0001118638,0.0002018546,0.001202648,0.0176476,0.0006089826],"genre_scores_gemma":[0.04044434,0.0002588273,0.9542432,0.0001286166,0.00005755428,0.000861894,0.002020277,0.001609439,0.000375822],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004186504,"threshold_uncertainty_score":0.01619327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411459519783428,"score_gpt":0.2413245791831742,"score_spread":0.2272099839853399,"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."}}