{"id":"W4392968913","doi":"10.1093/nar/gkae174","title":"Expanding the flexibility of base editing for high-throughput genetic screens in bacteria","year":2024,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"H2020 European Research Council; National Institutes of Health; National Institute of General Medical Sciences; Bayerisches Staatsministerium für Wissenschaft und Kunst","keywords":"Biology; CRISPR; Cas9; Genome editing; Computational biology; Genetic screen; Bacterial genome size; Synthetic biology; Genetics; Flexibility (engineering); Genome; Gene; Phenotype","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":[0.001966106,0.0007476976,0.0008354074,0.0007218548,0.0003999959,0.001102883,0.0007106186,0.0007795362,0.001147955],"category_scores_gemma":[0.002464096,0.0005107348,0.0006789141,0.0003821968,0.0007559969,0.0006861957,0.001068373,0.001469615,0.000522666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004272197,"about_ca_system_score_gemma":0.0004661295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002631234,"about_ca_topic_score_gemma":0.0008548949,"domain_scores_codex":[0.9983342,0.0004155574,0.0001804074,0.000268735,0.0006934274,0.0001076095],"domain_scores_gemma":[0.9979273,0.0009527188,0.0003548925,0.0005102903,0.0001278952,0.0001269461],"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":[0.00006526323,0.00004174951,0.0002626767,0.00009980414,0.00001699427,0.00006853612,0.00002099337,0.002463123,0.9870907,0.0009014692,0.00007222356,0.008896487],"study_design_scores_gemma":[0.00002245884,0.0002893074,0.001040517,0.00003262692,0.00004032684,0.0005464631,0.00001668008,0.01312548,0.978113,0.001220693,0.005508595,0.00004386447],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3392822,0.002091553,0.649363,0.0005619899,0.00009772777,0.0004974811,0.0006541187,0.003061092,0.004390894],"genre_scores_gemma":[0.6160317,0.002457505,0.377998,0.0002543531,0.00002916295,0.0003590172,0.0005529328,0.0004218058,0.001895571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001966106,"threshold_uncertainty_score":0.01039785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04469916373789339,"score_gpt":0.4002500311103482,"score_spread":0.3555508673724548,"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."}}