{"id":"W4311585301","doi":"10.1093/gigascience/giac099","title":"annotate_my_genomes: an easy-to-use pipeline to improve genome annotation and uncover neglected genes by hybrid RNA sequencing","year":2022,"lang":"en","type":"article","venue":"GigaScience","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Research Institute in Oncology and Hematology; CancerCare Manitoba","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Canadian Institutes of Health Research","keywords":"Genome; Annotation; Gene Annotation; Computational biology; Gene; Gene prediction; Genome project; Pipeline (software); Biology; Transcriptome; DNA sequencing; Identification (biology); Exon; Reference genome; Computer science; Genetics; Gene expression","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":[0.0002153805,0.0001884385,0.0001503912,0.00008242571,0.000456277,0.00009255508,0.0003283505,0.00002584935,0.00001044093],"category_scores_gemma":[0.00007378511,0.000197409,0.00003376302,0.0003128396,0.00007279021,0.000006582298,0.0005141862,0.00007104198,0.000004689954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000652632,"about_ca_system_score_gemma":0.0001235712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001685564,"about_ca_topic_score_gemma":0.00003486325,"domain_scores_codex":[0.9983761,0.0000669287,0.000215099,0.0007285363,0.000220929,0.0003923751],"domain_scores_gemma":[0.9991974,0.00001269867,0.00007027763,0.0003634962,0.0001257195,0.0002304011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004122581,0.00002052847,0.0004809579,0.00000384077,0.00001025586,0.000003229988,0.0003827155,0.003899316,0.9895406,0.000005314087,0.0005557073,0.005056243],"study_design_scores_gemma":[0.0006630108,0.002784522,0.0181138,0.000004341847,0.00003286414,0.00006682605,0.0009962843,0.00156416,0.7421954,0.0001571613,0.232445,0.0009766399],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946011,0.001015366,0.003098864,0.00023331,0.0002090364,0.000361568,0.000406869,0.000009622347,0.0000642577],"genre_scores_gemma":[0.9933923,0.0001473747,0.002739975,0.00273706,0.00009372218,0.00008347094,0.0001030107,0.00002344839,0.0006795925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2473453,"threshold_uncertainty_score":0.8050106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01219868653517652,"score_gpt":0.2297925908934458,"score_spread":0.2175939043582693,"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."}}