{"id":"W2582660596","doi":"10.1101/095463","title":"Umap and Bismap: quantifying genome and methylome mappability","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Princess Margaret Cancer Centre; University of Toronto","funders":"University of Toronto; Princess Margaret Cancer Foundation; Natural Sciences and Engineering Research Council of Canada; University Health Network","keywords":"Genome; Bisulfite sequencing; Computational biology; Genetics; Biology; DNA sequencing; Genomics; Whole genome sequencing; Bisulfite; Hybrid genome assembly; Human genome; Reference genome; Cancer genome sequencing; DNA methylation; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002779652,0.001273377,0.0007653605,0.005435366,0.0005631703,0.001553754,0.00114287,0.001134576,0.004399881],"category_scores_gemma":[0.01186854,0.0006612196,0.0008953763,0.00350078,0.0005915212,0.001697667,0.002263552,0.001067263,0.001242434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006241495,"about_ca_system_score_gemma":0.0004912316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001781768,"about_ca_topic_score_gemma":0.002178051,"domain_scores_codex":[0.9977302,0.0004559861,0.0001173103,0.000610623,0.0009658105,0.0001201753],"domain_scores_gemma":[0.9951403,0.002584881,0.0008356469,0.000550888,0.0006781243,0.0002101943],"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":[0.002100701,0.0003028715,0.1001766,0.002242043,0.001266981,0.000398924,0.001007579,0.06457777,0.2513723,0.01278247,0.01511546,0.5486563],"study_design_scores_gemma":[0.00008237264,0.0004240878,0.09136438,0.0001667637,0.0002696807,0.0007971512,0.0003133422,0.5300201,0.332167,0.01930282,0.02470724,0.0003850574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2148138,0.002059297,0.727766,0.0002841958,0.0001417607,0.0003857655,0.01867861,0.03099734,0.004873299],"genre_scores_gemma":[0.3606661,0.0005734594,0.6247321,0.0002160776,0.000054066,0.000951147,0.00886821,0.001440013,0.002498804],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005435366,"threshold_uncertainty_score":0.01471901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02074881616749766,"score_gpt":0.2488106229403132,"score_spread":0.2280618067728155,"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."}}