{"id":"W1965372971","doi":"10.1371/journal.pone.0101850","title":"Quickly Finding Orthologs as Reciprocal Best Hits with BLAT, LAST, and UBLAST: How Much Do We Miss?","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":185,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada; Wilfrid Laurier University","keywords":"Genome; Reciprocal; Comparative genomics; Genomics; Computer science; Whole genome sequencing; Biology; Computational biology; Lineage (genetic); Sequence alignment; Genetics; Bioinformatics; Gene; Peptide sequence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02069665,0.003737225,0.00517176,0.005380149,0.002635973,0.006783035,0.005218279,0.003637867,0.00718715],"category_scores_gemma":[0.0723719,0.00248911,0.002693682,0.007171804,0.002096964,0.01422582,0.003684179,0.005635396,0.01543511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001194597,"about_ca_system_score_gemma":0.002124023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0019177,"about_ca_topic_score_gemma":0.003108329,"domain_scores_codex":[0.985415,0.006119266,0.001707254,0.002331431,0.003738437,0.0006887585],"domain_scores_gemma":[0.9665155,0.01379948,0.002959101,0.006886914,0.007694043,0.002144878],"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":[0.004140781,0.001003713,0.04752656,0.006802758,0.001598091,0.001991851,0.004274277,0.0035391,0.1043981,0.008716624,0.1913838,0.6246243],"study_design_scores_gemma":[0.001124011,0.003850508,0.05690254,0.007091515,0.002063938,0.02396911,0.01220883,0.07698307,0.1785357,0.1163037,0.5184866,0.002480415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1910948,0.05221759,0.5824554,0.05922983,0.005895873,0.0008465737,0.01016474,0.08843115,0.009664138],"genre_scores_gemma":[0.1293922,0.01097535,0.8176617,0.008940388,0.0006408862,0.0005457592,0.011075,0.01712749,0.003641286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02069665,"threshold_uncertainty_score":0.1094557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02396825139335588,"score_gpt":0.2132889067304994,"score_spread":0.1893206553371435,"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."}}