{"id":"W3034853486","doi":"10.1016/j.csbj.2020.06.014","title":"Handling multi-mapped reads in RNA-seq","year":2020,"lang":"en","type":"review","venue":"Computational and Structural Biotechnology Journal","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Gene; Gene duplication; Biology; Genetics; Genome; Computational biology; Sequence (biology); Locus (genetics); RNA; Transposition (logic); Computer science","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":[0.004773706,0.002046458,0.002287482,0.001830982,0.000603424,0.002278757,0.003264083,0.001524824,0.003281351],"category_scores_gemma":[0.004407336,0.001005051,0.002074749,0.002516025,0.0007232378,0.001586056,0.001783417,0.002550718,0.003423295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007530854,"about_ca_system_score_gemma":0.001296277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001881231,"about_ca_topic_score_gemma":0.003212784,"domain_scores_codex":[0.9977896,0.0006740846,0.00015423,0.0005014072,0.0007951685,0.00008539731],"domain_scores_gemma":[0.9972667,0.001721097,0.0001835356,0.0001877644,0.000578405,0.00006263176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002941376,0.0001009721,0.002643302,0.02688528,0.001498935,0.0005406431,0.0004553211,0.03530721,0.09343596,0.01869138,0.03343803,0.7867088],"study_design_scores_gemma":[0.0001389252,0.000361786,0.01067151,0.004200042,0.0009731535,0.002361405,0.0006573464,0.1568684,0.1492154,0.05500178,0.6189423,0.0006079797],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.006566872,0.1655715,0.8044788,0.001441425,0.002160513,0.0005274295,0.005205079,0.006702289,0.007346192],"genre_scores_gemma":[0.03195929,0.1398563,0.8042099,0.00180854,0.0007347985,0.001326557,0.01313041,0.001533836,0.005440272],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004773706,"threshold_uncertainty_score":0.02524608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02831655207752264,"score_gpt":0.3006646961148464,"score_spread":0.2723481440373237,"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."}}