{"id":"W2561707810","doi":"10.1186/s12859-016-1431-9","title":"Fast-GBS: a new pipeline for the efficient and highly accurate calling of SNPs from genotyping-by-sequencing data","year":2017,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":142,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Canadian Field Crop Research Alliance; Université Laval","keywords":"Genotyping; Indel; Genome; Biology; Reference genome; DNA sequencing; Computational biology; Whole genome sequencing; Genetics; Single-nucleotide polymorphism; Deep sequencing; SNP genotyping; Genotype; Gene","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":[0.0001584308,0.0001025679,0.0001138046,0.00001284527,0.0002802495,0.00009093047,0.0005776738,0.0001008677,0.000005591174],"category_scores_gemma":[0.0002118151,0.00007604875,0.00003535114,0.00001600131,0.00008017817,0.00001121595,0.0004129917,0.0000472532,0.000001833097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004895231,"about_ca_system_score_gemma":0.00009433851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001854588,"about_ca_topic_score_gemma":0.00009454849,"domain_scores_codex":[0.9993882,0.000008295021,0.0002399044,0.0001358831,0.00009966926,0.0001280457],"domain_scores_gemma":[0.9987426,0.00003744014,0.000290871,0.0008094623,0.00006364179,0.00005592231],"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":[0.001804956,0.0001525404,0.03063492,0.001900383,0.001285286,0.000002074385,0.01283042,0.2283082,0.2560925,0.001754368,0.178599,0.2866352],"study_design_scores_gemma":[0.001437063,0.00006548187,0.003783171,0.0000460082,0.0001511457,0.000003844425,0.0009753547,0.9277169,0.02297986,0.0001290169,0.04245175,0.0002603817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1590214,0.0008213016,0.8383414,0.0001664016,0.0002093713,0.0002777449,0.001005593,0.000005765092,0.0001509578],"genre_scores_gemma":[0.8738264,0.000187144,0.1239575,0.0002338238,0.0002438449,0.00000217976,0.001199046,0.00001165739,0.0003384216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7148049,"threshold_uncertainty_score":0.3101178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06061099289774968,"score_gpt":0.2842035901364003,"score_spread":0.2235925972386506,"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."}}