{"id":"W4408908307","doi":"10.1002/cpz1.70124","title":"Streaming Long‐Read Sequence Alignments for HLA Predictions Using HLAminer","year":2025,"lang":"en","type":"article","venue":"Current Protocols","topic":"T-cell and B-cell Immunology","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Genome British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Nanopore sequencing; Human leukocyte antigen; Protocol (science); Software; DNA sequencing; MIT License; Shotgun sequencing; Source code; Hybrid genome assembly; Computational biology; Data mining; Biology; Genetics; Gene; Programming language","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":[0.0001834302,0.0002515544,0.0003135245,0.0001672071,0.0004178111,0.00003459281,0.0003470441,0.0002526408,0.000216662],"category_scores_gemma":[0.00008022527,0.0002273911,0.0001335488,0.0002034214,0.0002480554,0.000139377,0.0001591511,0.0002968123,0.00009542775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001289417,"about_ca_system_score_gemma":0.0001894383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002548425,"about_ca_topic_score_gemma":0.000007468176,"domain_scores_codex":[0.9984176,0.0001252427,0.0004331034,0.000460157,0.00003634626,0.0005275859],"domain_scores_gemma":[0.9991602,0.0001227199,0.0001634452,0.0004112907,0.0001205727,0.00002171794],"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":[0.0001334974,0.0004879968,0.005558147,0.0002202986,0.0001820699,0.000001467706,0.00008348704,0.00002330963,0.9592183,0.001775049,0.003694318,0.0286221],"study_design_scores_gemma":[0.004735964,0.0003416753,0.0009493422,0.001091231,0.000165515,0.00003469066,0.0001057017,0.0002235077,0.3988604,0.001080056,0.5919763,0.0004355568],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4034759,0.007574955,0.164133,0.000727991,0.0129603,0.4019072,0.001193113,0.001302623,0.006724894],"genre_scores_gemma":[0.6885758,0.00005560554,0.0009325163,0.0001813825,0.0002292914,0.2820881,0.0007628069,0.00006700765,0.02710744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.588282,"threshold_uncertainty_score":0.927274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07443846455481846,"score_gpt":0.3725897048677749,"score_spread":0.2981512403129564,"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."}}