{"id":"W3016457421","doi":"10.1038/s41467-020-15796-7","title":"Perturbing proteomes at single residue resolution using base editing","year":2020,"lang":"en","type":"article","venue":"Nature Communications","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; PROTEO","funders":"Japan Society for the Promotion of Science; Canadian Institutes of Health Research; Université Laval","keywords":"CRISPR; Genome editing; Computational biology; Proteome; Guide RNA; Gene; Genome; Context (archaeology); Biology; Cas9; Genetics; RNA editing; RNA","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008989268,0.00009745029,0.0000769787,0.00002260839,0.0002593492,0.0000204505,0.0003690973,0.0001954081,0.0000125884],"category_scores_gemma":[0.0003909815,0.0001045422,0.00005251225,0.0001146039,0.0000461439,0.00000389335,0.0004658332,0.0002978884,0.000005772981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003256631,"about_ca_system_score_gemma":0.00002659103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000677635,"about_ca_topic_score_gemma":0.00005972607,"domain_scores_codex":[0.999385,0.0000543411,0.0001378573,0.0001902562,0.00008518305,0.0001473906],"domain_scores_gemma":[0.9991004,0.00002150659,0.0000505256,0.00067847,0.0000749822,0.00007408673],"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":[0.000008954402,0.00001856119,0.0006948288,0.00001319923,0.00001469383,5.453379e-7,0.0001357621,0.001306854,0.9946498,0.00007573717,0.002784021,0.0002969896],"study_design_scores_gemma":[0.000375902,0.0001127817,0.001212665,0.00004916906,0.00003894245,0.0000210679,0.0002437348,0.03180928,0.8094901,0.00001501922,0.1563157,0.0003156753],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8897026,0.0488217,0.03631335,0.01781988,0.0004102024,0.0008945547,0.00007996056,0.0001935218,0.00576425],"genre_scores_gemma":[0.9731371,0.0001228649,0.02559105,0.0005635038,0.0003510562,0.00001318692,0.0001489567,0.00002112116,0.00005117925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1851598,"threshold_uncertainty_score":0.4263106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03128846891149772,"score_gpt":0.3205597658883621,"score_spread":0.2892712969768644,"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."}}