{"id":"W2029985290","doi":"10.1371/journal.pone.0036009","title":"Generation and Analysis of a Mouse Intestinal Metatranscriptome through Illumina Based RNA-Sequencing","year":2012,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; McMaster University; University of Toronto; Hospital for Sick Children","funders":"H2020 European Research Council; National Human Genome Research Institute; Hospital for Sick Children; National Institutes of Health; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics; Ontario Genomics Institute","keywords":"Metagenomics; Biology; Computational biology; Genetics; Genome; Sequence analysis; Deep sequencing; Context (archaeology); Ribosomal RNA; Illumina dye sequencing; Gene; DNA sequencing; RNA; Phylogenetic tree","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":[0.0007355637,0.0006350157,0.0004219289,0.0009577469,0.0003530539,0.0004912138,0.0004076887,0.0003236126,0.001135657],"category_scores_gemma":[0.0004329477,0.0003287397,0.0006420953,0.0005314965,0.0002726405,0.0002257418,0.0003827015,0.0007603934,0.0008908924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002409558,"about_ca_system_score_gemma":0.0004403701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008729467,"about_ca_topic_score_gemma":0.002399977,"domain_scores_codex":[0.9995539,0.00004883213,0.00003384758,0.000125254,0.0001845332,0.00005359545],"domain_scores_gemma":[0.9996116,0.00006827179,0.00008395252,0.00007460016,0.0001011341,0.00006045397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007012724,0.00001232415,0.0004502829,0.00003976783,0.00001169055,0.00002814563,0.00002021483,0.0002989225,0.9958484,0.0001240271,0.00005784463,0.003038139],"study_design_scores_gemma":[0.00002795353,0.000341504,0.01208529,0.00002331982,0.00006214954,0.0001681703,0.00003911224,0.006716454,0.9719096,0.0004432942,0.008152624,0.00003062913],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6301178,0.001013726,0.3325458,0.0002395931,0.0001212139,0.000787137,0.02958805,0.002365298,0.003221356],"genre_scores_gemma":[0.4417031,0.001775158,0.5101966,0.000396112,0.00005890342,0.001444615,0.03717486,0.001065887,0.006184728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001135657,"threshold_uncertainty_score":0.003890097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09206210233177117,"score_gpt":0.2756764685422181,"score_spread":0.1836143662104469,"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."}}