{"id":"W7108681698","doi":"10.5376/cmb.2025.15.0015","title":"High-Performance Computing Pipelines for NGS Variant Calling","year":2025,"lang":"","type":"article","venue":"Computational Molecular Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workflow; Orchestration; Benchmark (surveying); Process (computing); Task (project management); Mutation; Pipeline transport; SPARK (programming language); Middleware (distributed applications)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001694423,0.001257491,0.0007059089,0.001657911,0.001284081,0.001627158,0.001932685,0.0007172508,0.006882542],"category_scores_gemma":[0.004435833,0.0007009559,0.001198224,0.002722066,0.0006325036,0.001763084,0.001456516,0.001694103,0.004120862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001440622,"about_ca_system_score_gemma":0.003251782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009404257,"about_ca_topic_score_gemma":0.008034267,"domain_scores_codex":[0.9983955,0.0002198692,0.0001292868,0.0003427802,0.0007387407,0.000173818],"domain_scores_gemma":[0.9980388,0.000469382,0.0001165404,0.0005237694,0.0006861687,0.0001652675],"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":[0.001487702,0.0003327318,0.01338421,0.001324967,0.0003785524,0.0008057075,0.0009637098,0.1312717,0.1114645,0.05132965,0.06574589,0.6215107],"study_design_scores_gemma":[0.0001743963,0.0002611465,0.006764059,0.0001292331,0.0001305103,0.0004070371,0.0001991258,0.7364123,0.0947431,0.04135541,0.1192435,0.0001802993],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02528367,0.001259129,0.9219577,0.000538216,0.0002658994,0.0004664558,0.002449693,0.03789606,0.009883231],"genre_scores_gemma":[0.1721135,0.001193338,0.8095409,0.000291133,0.0001005629,0.0006151627,0.009498535,0.002276775,0.004370074],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009404257,"threshold_uncertainty_score":0.02302444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008896724754100378,"score_gpt":0.2745310821449927,"score_spread":0.2656343573908923,"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."}}