{"id":"W2514454343","doi":"10.1093/bioinformatics/btw569","title":"Nanocall: an open source basecaller for Oxford Nanopore sequencing data","year":2016,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":143,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"Canadian Institutes of Health Research; University of Nottingham; Oxford Nanopore Technologies","keywords":"Minion; Nanopore sequencing; Computer science; DNA sequencer; MIT License; DNA sequencing; Nanopore; Open source; Computational biology; Hybrid genome assembly; DNA; Biology; Reference genome; Genetics; Operating system; Software; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003093994,0.0001600538,0.0001562295,0.00002152816,0.0001507636,0.00007138588,0.001104929,0.0001062496,0.00001880837],"category_scores_gemma":[0.0001146801,0.0001087002,0.00003649181,0.00003346417,0.0000674649,0.0000103252,0.00111609,0.00002396885,0.00001007983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001624069,"about_ca_system_score_gemma":0.00008986909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001079939,"about_ca_topic_score_gemma":0.00006391356,"domain_scores_codex":[0.9990566,0.00001547833,0.0003050167,0.000254474,0.00008699824,0.0002814517],"domain_scores_gemma":[0.9986519,0.00002384764,0.0001236357,0.001013347,0.0000871471,0.000100144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002072875,0.0001223585,0.001931409,0.0001643709,0.0003435162,0.000001243425,0.0008414653,0.00005449914,0.6480904,0.0009973275,0.0641213,0.2831248],"study_design_scores_gemma":[0.001219376,0.0005783745,0.0001981612,0.00002902718,0.00003126401,0.00001337421,0.0004575384,0.005772028,0.02347939,0.0002094091,0.9676245,0.0003875345],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.759587,0.0009156931,0.2233701,0.00122486,0.0007191071,0.002169862,0.001970448,0.0000377674,0.01000513],"genre_scores_gemma":[0.8373792,0.001088322,0.1531577,0.002065531,0.0005377744,0.0001026076,0.0008679064,0.00008650083,0.004714379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9035032,"threshold_uncertainty_score":0.4432665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06328263123334449,"score_gpt":0.2926303886907501,"score_spread":0.2293477574574057,"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."}}