{"id":"W4385173013","doi":"10.1101/2023.07.21.550110","title":"Sequencing 4.3 million mutations in wheat promoters to understand and modify gene expression","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Institute of Food and Agriculture; International Atomic Energy Agency; U.S. Department of Agriculture; Howard Hughes Medical Institute","keywords":"Genetics; Biology; RefSeq; Gene; Promoter; Functional genomics; Coding region; Genomics; Computational biology; Mutation; Gene prediction; Mutant; Genome; Gene expression","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008548204,0.0006261273,0.0005209238,0.0006688145,0.0004271389,0.0003962574,0.0004613023,0.0006996031,0.001291236],"category_scores_gemma":[0.001823939,0.0003708129,0.0008715242,0.0006801361,0.0002353902,0.0003486628,0.0005988825,0.000818759,0.0006859286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005189184,"about_ca_system_score_gemma":0.0004868344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001793558,"about_ca_topic_score_gemma":0.004855408,"domain_scores_codex":[0.9993186,0.00007615954,0.00008273287,0.0002328329,0.0002325955,0.00005723048],"domain_scores_gemma":[0.9988423,0.0004378318,0.0002027875,0.0002653334,0.0001763312,0.00007541482],"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":[0.0007241232,0.0001375627,0.03510616,0.0007564915,0.0002815207,0.0004359659,0.0002763483,0.008238748,0.9143068,0.0007944035,0.002949045,0.03599285],"study_design_scores_gemma":[0.0003070124,0.0009823395,0.2182437,0.0002703095,0.0009883658,0.002174593,0.0004072835,0.08291147,0.6185051,0.003800986,0.07120752,0.0002013182],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9088044,0.001116274,0.03911619,0.000178027,0.0001601803,0.0001253261,0.04556491,0.002999935,0.001934652],"genre_scores_gemma":[0.8212392,0.0008022331,0.05948427,0.0003755783,0.0000415176,0.0002310004,0.1149104,0.001227788,0.001688025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001793558,"threshold_uncertainty_score":0.004520774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02075341493097459,"score_gpt":0.2648702870970344,"score_spread":0.2441168721660598,"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."}}