{"id":"W4413814807","doi":"10.1093/bioinformatics/btaf474","title":"Autocycler: long-read consensus assembly for bacterial genomes","year":2025,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Health and Medical Research Council; Institute of Infection and Immunity; Centers for Disease Control and Prevention; State Government of Victoria","keywords":"Computer science; Contig; Bacterial genome size; Sequence assembly; Nanopore sequencing; Scalability; De Bruijn graph; Genome; Automation; De Bruijn sequence; Software; Data mining; Levenshtein distance; Graph; Computational biology; Artificial intelligence; Theoretical computer science; Programming language; Biology; Database; Genetics; Engineering; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005210673,0.002762377,0.001822187,0.00198059,0.001452704,0.001786168,0.003037996,0.001569098,0.01205082],"category_scores_gemma":[0.01192723,0.00172499,0.001967309,0.001717133,0.0009186692,0.001857222,0.002060075,0.003492442,0.01762111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008459715,"about_ca_system_score_gemma":0.001876941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001970854,"about_ca_topic_score_gemma":0.001965764,"domain_scores_codex":[0.9959522,0.001281024,0.0003827167,0.001195192,0.0009821228,0.0002066853],"domain_scores_gemma":[0.995765,0.001760474,0.0005881642,0.0007164695,0.0009045421,0.0002653995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002835561,0.0003031864,0.005425185,0.008341976,0.0007708228,0.0008357669,0.001671821,0.04781623,0.3153249,0.01713662,0.2434359,0.356102],"study_design_scores_gemma":[0.0007178618,0.0009965217,0.004437896,0.001113303,0.0002468771,0.001429185,0.0002308438,0.3476034,0.357565,0.02691922,0.2580607,0.0006790732],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01414302,0.002233523,0.7465469,0.0003685378,0.0003341976,0.0006710404,0.01170618,0.2203419,0.003654683],"genre_scores_gemma":[0.04834621,0.0009425368,0.8888788,0.0003812946,0.0001171416,0.001429819,0.0278672,0.0288538,0.003183098],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01205082,"threshold_uncertainty_score":0.04031396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01358981607725253,"score_gpt":0.2576264745324185,"score_spread":0.244036658455166,"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."}}