{"id":"W3176575851","doi":"10.21203/rs.3.rs-668517/v1","title":"SLOW5: a new file format enables massive acceleration of nanopore sequencing data analysis","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"National Cancer Institute; Kinghorn Foundation; National Computational Infrastructure","keywords":"Nanopore sequencing; Computer science; Acceleration; File format; Nanopore; Computational biology; Database; DNA sequencing; Biology; Nanotechnology; Materials science; Physics; DNA","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":[0.003174301,0.002807075,0.001414542,0.002733008,0.001309137,0.004082432,0.004194216,0.001950093,0.07471593],"category_scores_gemma":[0.01333779,0.001736576,0.001769376,0.003118656,0.0006279214,0.003666775,0.003388429,0.00301164,0.03263649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001151748,"about_ca_system_score_gemma":0.002031816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004225145,"about_ca_topic_score_gemma":0.004905939,"domain_scores_codex":[0.9981686,0.0002605563,0.0002796663,0.0003876381,0.0006630995,0.0002404842],"domain_scores_gemma":[0.9936055,0.002567182,0.0003052745,0.002019461,0.001069368,0.000433264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00349959,0.0002510319,0.002947408,0.001659061,0.0004959041,0.0005284876,0.0005690499,0.005263978,0.0424883,0.01273797,0.7621512,0.1674079],"study_design_scores_gemma":[0.001434982,0.0003255089,0.005587343,0.0003938429,0.0002482252,0.000765682,0.0002955649,0.1098636,0.1817131,0.05323726,0.6455532,0.0005817887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.006638845,0.0002974579,0.3703062,0.0005153295,0.000977464,0.0003195608,0.1511312,0.4635946,0.006219331],"genre_scores_gemma":[0.05786638,0.0005997155,0.4440004,0.0008751902,0.0005703989,0.002404866,0.356084,0.1205147,0.01708435],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.07471593,"threshold_uncertainty_score":0.2499497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1310062950004847,"score_gpt":0.3854169085548017,"score_spread":0.2544106135543169,"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."}}