{"id":"W4394745991","doi":"10.1093/bioinformatics/btae182","title":"Pacybara: accurate long-read sequencing for barcoded mutagenized allelic libraries","year":2024,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"National Institute of General Medical Sciences; National Human Genome Research Institute; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Barcode; Identification (biology); Biology; Genetics; Computational biology; Identifier; DNA sequencing; Computer science; Gene; Programming language","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":[0.006496811,0.003165222,0.002458179,0.002682098,0.001871985,0.004318334,0.007091471,0.00351533,0.02336395],"category_scores_gemma":[0.02241281,0.003198003,0.001758175,0.002280251,0.001490727,0.003671309,0.003194432,0.005701291,0.04093759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001241047,"about_ca_system_score_gemma":0.002772025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0028756,"about_ca_topic_score_gemma":0.004192852,"domain_scores_codex":[0.9935608,0.001253905,0.0005351379,0.001946048,0.002240018,0.0004642119],"domain_scores_gemma":[0.9916653,0.003168603,0.001453889,0.001610312,0.001528436,0.0005733715],"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":[0.003836024,0.0004713985,0.0057534,0.005357483,0.0007926131,0.001790431,0.001675487,0.01130641,0.3586726,0.02242354,0.2828945,0.3050261],"study_design_scores_gemma":[0.0004188009,0.0006605615,0.003955364,0.0007929997,0.0002540991,0.001877755,0.0001700541,0.05726762,0.5455604,0.01100718,0.3773758,0.0006593858],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009344763,0.001995338,0.6646931,0.000794073,0.0005123548,0.00117693,0.01853825,0.2959207,0.007024468],"genre_scores_gemma":[0.03233517,0.001362644,0.8421425,0.00122189,0.0001859048,0.002274032,0.05054474,0.06005224,0.009880886],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02336395,"threshold_uncertainty_score":0.07816017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02109216297062837,"score_gpt":0.2625775321882256,"score_spread":0.2414853692175972,"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."}}