{"id":"W4416038602","doi":"10.1101/2025.11.06.687066","title":"A <i>De Novo</i> Algorithm for Allele Reconstruction from Oxford Nanopore Amplicon Reads, with Application to <i>CYP2D6</i>","year":2025,"lang":"","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Pharmacogenetics and Drug Metabolism","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Royal Columbian Hospital; University of British Columbia Hospital; University of British Columbia; Spinal Cord Injury BC","funders":"","keywords":"Nanopore sequencing; Amplicon; Allele; Sequence (biology); Genotyping; Indel; Gene; Genomics; Reference genome","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002406193,0.00150107,0.0009487804,0.001410266,0.0008001842,0.00203726,0.002252253,0.001672915,0.005358399],"category_scores_gemma":[0.006999187,0.00114585,0.001515193,0.0009618716,0.0008509435,0.0007654451,0.00184922,0.002999365,0.003900071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008478275,"about_ca_system_score_gemma":0.001536166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003652435,"about_ca_topic_score_gemma":0.005714486,"domain_scores_codex":[0.9990115,0.0001800412,0.00010776,0.0003286157,0.0002899299,0.00008199637],"domain_scores_gemma":[0.9973833,0.00132153,0.0003891145,0.0003279973,0.0004650919,0.0001129302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001468875,0.0003522542,0.01171883,0.001219847,0.000775766,0.001092264,0.0006580586,0.1731682,0.1699153,0.01314624,0.02590498,0.6005794],"study_design_scores_gemma":[0.00009378595,0.0001020731,0.001851669,0.00004289385,0.0000470923,0.0003182807,0.00008886914,0.9316713,0.05119363,0.006452472,0.008063488,0.00007437087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006777605,0.00009539918,0.9787775,0.0001000789,0.0000444941,0.0001034134,0.0004049563,0.01330766,0.0003888423],"genre_scores_gemma":[0.03146322,0.00004059288,0.9653548,0.0001119342,0.00001813631,0.0002010031,0.001045292,0.0009925743,0.000772476],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005358399,"threshold_uncertainty_score":0.01792562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02589207618818109,"score_gpt":0.3116615685358418,"score_spread":0.2857694923476607,"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."}}