{"id":"W4411705236","doi":"10.1093/bioinformatics/btaf375","title":"Benchmarking peak calling methods for CUT&amp;RUN","year":2025,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"Canadian Institutes of Health Research","keywords":"Benchmarking; Computer science; Chromatin; Chromatin immunoprecipitation; Histone; Micrococcal nuclease; Identification (biology); Bottleneck; Data mining; Benchmark (surveying); Computational biology; Biology; Nucleosome; DNA; Genetics; Gene; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01683603,0.003470371,0.001661201,0.004973318,0.002125754,0.004388703,0.005092367,0.001946547,0.006356681],"category_scores_gemma":[0.03516074,0.001215103,0.002856035,0.004481063,0.001328836,0.002419493,0.003261288,0.002881933,0.007655026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001443895,"about_ca_system_score_gemma":0.002613206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004508198,"about_ca_topic_score_gemma":0.004960482,"domain_scores_codex":[0.9884706,0.001997374,0.001250271,0.003646471,0.004027021,0.0006084121],"domain_scores_gemma":[0.9785687,0.01030157,0.001187998,0.003688572,0.005530621,0.0007225224],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01098707,0.00127396,0.0589071,0.008629835,0.004489609,0.001063757,0.002549342,0.08181668,0.2268129,0.01031899,0.1431962,0.4499545],"study_design_scores_gemma":[0.0005696389,0.001108357,0.03997406,0.0004837809,0.000711523,0.001613317,0.0007016003,0.3727888,0.449272,0.013272,0.1186629,0.0008420251],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1564894,0.004932469,0.593053,0.0009939163,0.0009857481,0.001055121,0.04660409,0.1882159,0.00767023],"genre_scores_gemma":[0.1256147,0.001174887,0.6948403,0.0007641117,0.000128575,0.001780046,0.1420579,0.03043724,0.003202402],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.983164,"threshold_uncertainty_score":0.08903849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0150960926637293,"score_gpt":0.319743551025407,"score_spread":0.3046474583616777,"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."}}