{"id":"W2810425915","doi":"10.1093/bioinformatics/bty544","title":"lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Science Foundation","keywords":"Nanopore sequencing; Computer science; Reference genome; Genomics; DNA sequencing; Throughput; Genome; Data mining; Computational biology; Biology; Wireless; Genetics; Gene; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002941894,0.0001347978,0.0001262126,0.00002505923,0.0001779495,0.00004430534,0.0001792126,0.00007564044,0.000002593764],"category_scores_gemma":[0.0000568009,0.0001211478,0.00002718877,0.00003655224,0.0001578556,0.000003559765,0.0005208442,0.0000338867,0.000009412205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001730114,"about_ca_system_score_gemma":0.00007448524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001206323,"about_ca_topic_score_gemma":0.0000381786,"domain_scores_codex":[0.9992017,0.00001248055,0.0002266265,0.0002060815,0.00009810798,0.0002550105],"domain_scores_gemma":[0.9992358,0.00002076672,0.00006478128,0.0004839409,0.0001383611,0.0000563687],"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":[0.0005480583,0.0001177678,0.01081773,0.000866245,0.001266427,0.000009130438,0.0113648,0.0001439586,0.7271973,0.00106738,0.02059729,0.2260039],"study_design_scores_gemma":[0.003968702,0.003521484,0.02063653,0.0001742322,0.0002940452,0.0002523596,0.01208975,0.09326471,0.7191538,0.0002611553,0.1444071,0.001976148],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9520771,0.000211124,0.04518443,0.0001211391,0.0001762279,0.0004598322,0.0004466558,0.000005503643,0.001317993],"genre_scores_gemma":[0.9627799,0.0002731879,0.03546972,0.0003892908,0.0004358267,0.00001084327,0.0003241774,0.00001738203,0.0002996378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2240278,"threshold_uncertainty_score":0.4940265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0409620581989204,"score_gpt":0.2835077408358809,"score_spread":0.2425456826369605,"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."}}