{"id":"W2496955077","doi":"10.1101/gr.201160.115","title":"Characterizing polymorphic inversions in human genomes by single-cell sequencing","year":2016,"lang":"en","type":"article","venue":"Genome Research","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"National Institute of General Medical Sciences; European Research Council; National Institutes of Health; University of British Columbia; Canadian Institutes of Health Research; Terry Fox Foundation; Canadian Cancer Society","keywords":"Biology; Structural variation; Computational biology; Genome; Genetics; Human genome; Population; 1000 Genomes Project; Reference genome; Genomics; DNA sequencing; Single cell sequencing; Phenotype; Genotype; Exome sequencing; Gene; Single-nucleotide polymorphism","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":[0.0005922012,0.0002419166,0.0004061002,0.001026882,0.0003294899,0.0005405871,0.0003377392,0.000438145,0.0006269941],"category_scores_gemma":[0.001451759,0.0002302025,0.0003871656,0.001191299,0.0002635148,0.0002307161,0.0005030859,0.0004633845,0.0002684747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002168515,"about_ca_system_score_gemma":0.000298295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0014819,"about_ca_topic_score_gemma":0.004839803,"domain_scores_codex":[0.99962,0.00007400061,0.00002635121,0.0001556105,0.00009052282,0.00003352934],"domain_scores_gemma":[0.9994779,0.0002065688,0.0001007771,0.0001235903,0.00006194995,0.00002923904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001521904,0.00003280703,0.02323255,0.0001922871,0.0001500405,0.0004039152,0.0004651492,0.004412217,0.9215987,0.002427465,0.0004659921,0.04646659],"study_design_scores_gemma":[0.00008011917,0.0004794112,0.3619425,0.0001128297,0.0004964265,0.003807474,0.0008016569,0.05962933,0.5162086,0.01572692,0.04057832,0.0001362834],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8238126,0.001197176,0.1660148,0.0001339449,0.00004131634,0.0001296642,0.005955789,0.000632817,0.002081885],"genre_scores_gemma":[0.827661,0.001153447,0.1620318,0.0002132758,0.00002505062,0.0001818141,0.007495234,0.0002380096,0.001000229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0014819,"threshold_uncertainty_score":0.003131926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07230078745041116,"score_gpt":0.2962530975054994,"score_spread":0.2239523100550882,"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."}}