{"id":"W4303423564","doi":"10.1186/s12859-022-04972-9","title":"Comparative transcriptomics analysis pipeline for the meta-analysis of phylogenetically divergent datasets (CoRMAP)","year":2022,"lang":"en","type":"review","venue":"BMC Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Compute Canada; Morris Animal Foundation","keywords":"Computational biology; Biology; RNA-Seq; Pipeline (software); De novo transcriptome assembly; DNA microarray; Metadata; Transcriptome; Gene; Gene expression; Computer science; Genetics; World Wide Web","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":[0.01459747,0.004503473,0.003039138,0.006665707,0.002128304,0.003942389,0.004536833,0.00187176,0.01351058],"category_scores_gemma":[0.01518771,0.002201571,0.006029911,0.006078692,0.001031886,0.002720331,0.005154845,0.005346942,0.007924533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001480008,"about_ca_system_score_gemma":0.005767283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002680253,"about_ca_topic_score_gemma":0.003571491,"domain_scores_codex":[0.9953116,0.00120796,0.0004955666,0.001757682,0.0008817432,0.0003454962],"domain_scores_gemma":[0.9949226,0.002319311,0.0006841345,0.0009962929,0.0007335052,0.0003442333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.006798426,0.0007616341,0.02241853,0.02916558,0.01217017,0.003036505,0.005529774,0.03016078,0.2363727,0.04109595,0.347127,0.2653628],"study_design_scores_gemma":[0.001871846,0.001124579,0.04594859,0.002642345,0.004161322,0.002579501,0.001209126,0.1514858,0.1060392,0.1043286,0.5774694,0.001139848],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008149323,0.001776802,0.6244845,0.0006890183,0.0004136144,0.001177565,0.1608792,0.1992503,0.003179567],"genre_scores_gemma":[0.02963243,0.0008723791,0.7428586,0.000733799,0.0001118156,0.006058739,0.1973622,0.0213546,0.001015481],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01459747,"threshold_uncertainty_score":0.07719976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2331256121006938,"score_gpt":0.3716177139744434,"score_spread":0.1384921018737496,"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."}}