{"id":"W4243279847","doi":"10.1101/2021.06.29.450255","title":"SLOW5: a new file format enables massive acceleration of nanopore sequencing data analysis","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"National Cancer Institute; Kinghorn Foundation; National Computational Infrastructure","keywords":"Nanopore sequencing; Nanopore; Computer science; Profiling (computer programming); Genomics; File format; DNA sequencing; Raw data; Genome; Nanotechnology; Database; Operating system; Biology; DNA; Materials science; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.004061507,0.001538181,0.0007320247,0.002434239,0.0008466867,0.00286909,0.003602774,0.001404236,0.02300122],"category_scores_gemma":[0.01410407,0.000842369,0.001253383,0.002072409,0.0006662472,0.003709427,0.002684072,0.001695743,0.009396584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00127354,"about_ca_system_score_gemma":0.001652842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002870629,"about_ca_topic_score_gemma":0.002486547,"domain_scores_codex":[0.9981522,0.0002798417,0.0003379047,0.0003368699,0.0006942871,0.0001987813],"domain_scores_gemma":[0.9899889,0.003816028,0.000661432,0.002536324,0.002451848,0.0005453812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006984861,0.0004737827,0.01275029,0.001608034,0.000419034,0.0009334541,0.0007529127,0.01365641,0.08466913,0.02637369,0.5753322,0.2760462],"study_design_scores_gemma":[0.001131203,0.0006244645,0.009252825,0.0004277961,0.0001662893,0.0006852713,0.0003652813,0.1287276,0.3279334,0.02447794,0.5056485,0.0005594338],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04374509,0.0006566594,0.5801905,0.001446303,0.001660765,0.0006952918,0.1014713,0.2613003,0.008833897],"genre_scores_gemma":[0.1563429,0.0006401234,0.5784247,0.001162179,0.0006583945,0.002564127,0.212163,0.03482168,0.01322274],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02300122,"threshold_uncertainty_score":0.07694674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02745737500640632,"score_gpt":0.2329117720118442,"score_spread":0.2054543970054379,"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."}}