{"id":"W3143983175","doi":"10.1101/2021.04.07.438882","title":"Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Human Genome Research Institute; Canadian Institutes of Health Research; Defense Advanced Research Projects Agency; Sidney Kimmel Foundation; New York Genome Center; National Institute of General Medical Sciences; National Institute of Mental Health; Cancer Research Institute; American Heart Association; National Cancer Institute; National Institutes of Health","keywords":"Biology; Gene; Genetics; CRISPR; Genome; Genome-wide association study; Transcriptome; Genomics; Computational biology; Expression quantitative trait loci; Quantitative trait locus; CRISPR interference; Genome editing; Gene expression; Single-nucleotide polymorphism; Genotype","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002185258,0.0003115515,0.0004428624,0.00009044535,0.0000738905,0.00008447083,0.0001898613,0.0003643313,0.000003917869],"category_scores_gemma":[0.00004950367,0.0003414048,0.0001142486,0.0001381383,0.0001869561,0.00001739489,0.0003571394,0.000173107,6.769434e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003306406,"about_ca_system_score_gemma":0.0005665259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000155975,"about_ca_topic_score_gemma":0.000005445917,"domain_scores_codex":[0.9983763,0.00008155133,0.0004475905,0.0006848997,0.000161903,0.0002477633],"domain_scores_gemma":[0.9984174,0.00001154638,0.0004379715,0.0006778875,0.0003594401,0.00009580658],"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":[0.00001740444,0.0001077902,0.00197764,0.000535195,0.0001751597,0.000007358957,0.00001544015,0.0001895509,0.996925,0.00003891968,0.000006672243,0.000003902608],"study_design_scores_gemma":[0.0003869728,0.00008253006,0.005576486,0.0001891225,0.0002644963,8.037321e-8,0.0000430314,0.0002257566,0.9928129,0.000001255579,0.00007259649,0.000344724],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9736245,0.01888746,0.006645884,0.00001145222,0.0001143754,0.0002322792,0.0004638837,0.00001253604,0.000007639855],"genre_scores_gemma":[0.9852197,0.001479173,0.01304552,0.00003075552,0.0001395947,0.00001886735,0.000003310988,0.00005975346,0.000003323213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01740829,"threshold_uncertainty_score":0.9999038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01941145188984574,"score_gpt":0.2134167412073678,"score_spread":0.1940052893175221,"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."}}