{"id":"W4360612265","doi":"10.1038/s41587-023-01714-x","title":"Global detection of human variants and isoforms by deep proteome sequencing","year":2023,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":202,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; National Institutes of Health; Max-Planck-Gesellschaft; National Human Genome Research Institute; University of Toronto","keywords":"Protein isoform; Biology; Proteome; Exon; Computational biology; Human proteome project; Shotgun proteomics; Proteomics; Alternative splicing; Gene isoform; Deep sequencing; Genetics; Protein sequencing; Peptide sequence; Gene; Genome","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":[0.0006988895,0.0005619957,0.0007058047,0.001098043,0.0004249221,0.0007991327,0.0004108203,0.0005120792,0.001332904],"category_scores_gemma":[0.0008843805,0.0002442757,0.0005953821,0.001675021,0.0002527372,0.000399591,0.0009283327,0.0007404061,0.0009257204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002665813,"about_ca_system_score_gemma":0.0002507372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005923395,"about_ca_topic_score_gemma":0.00124866,"domain_scores_codex":[0.9993399,0.0000571173,0.00004929223,0.0003145346,0.0001664695,0.00007273229],"domain_scores_gemma":[0.9994606,0.0001108146,0.0001163355,0.0001471023,0.0001056365,0.0000594957],"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":[0.0009063995,0.0000796864,0.03711693,0.0005721564,0.0003243325,0.0004353878,0.0001855279,0.00153645,0.9035442,0.0008819248,0.007567533,0.04684941],"study_design_scores_gemma":[0.0001562749,0.0005999111,0.3768564,0.0001552586,0.0006430883,0.00552171,0.0003177053,0.02194757,0.4724544,0.008318523,0.1128643,0.0001649896],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8277761,0.002970859,0.06389897,0.0002510374,0.0000739845,0.0001303596,0.1004378,0.0009414564,0.003519385],"genre_scores_gemma":[0.6249194,0.001980631,0.1124172,0.0005736766,0.00006144427,0.0003872074,0.2562193,0.000532398,0.00290877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001332904,"threshold_uncertainty_score":0.004459023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006294549324722416,"score_gpt":0.2721235155636439,"score_spread":0.2658289662389215,"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."}}