{"id":"W3048609924","doi":"10.1016/j.tube.2020.101983","title":"Efficient genome editing in pathogenic mycobacteria using Streptococcus thermophilus CRISPR1-Cas9","year":2020,"lang":"en","type":"article","venue":"Tuberculosis","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"European Research Council; Medical Research Council; H2020 European Research Council; Cancer Center Amsterdam; University of Arizona Cancer Center","keywords":"Streptococcus thermophilus; CRISPR; Genome editing; Biology; Cas9; Mycobacterium tuberculosis; Streptococcus pyogenes; Trans-activating crRNA; Computational biology; Mycobacterium; Genome; Gene; Genetics; Microbiology; Tuberculosis; Bacteria; Medicine","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":[0.0003580492,0.0003746906,0.0003736565,0.000233253,0.0002260125,0.0004635845,0.000294746,0.0005435421,0.0008145001],"category_scores_gemma":[0.0003088058,0.0002194071,0.0003213109,0.0001846921,0.0002975443,0.0002097278,0.0004998068,0.0006976816,0.0004613844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002499336,"about_ca_system_score_gemma":0.0002896736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005346964,"about_ca_topic_score_gemma":0.0008259478,"domain_scores_codex":[0.9996303,0.00007002176,0.00005395038,0.00007142277,0.0001238203,0.00005036552],"domain_scores_gemma":[0.9998301,0.00003360996,0.00005895544,0.00003363645,0.00001863175,0.00002499],"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":[0.00004260876,0.00001210328,0.0001301149,0.00005607165,0.000004953799,0.0001075873,0.00002051516,0.0002403783,0.9966917,0.0002624931,0.00006507621,0.002366354],"study_design_scores_gemma":[0.000008391124,0.00007633233,0.0007466955,0.00001036661,0.00001058274,0.0003789715,0.00001545582,0.002585589,0.9913094,0.0001114285,0.004738746,0.000008064602],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8957522,0.003657095,0.09337406,0.0003796173,0.0001910484,0.0001423049,0.0007026793,0.001238561,0.004562447],"genre_scores_gemma":[0.952626,0.001302981,0.04155403,0.00009319294,0.00001205428,0.00005597507,0.0005434816,0.0001187889,0.003693598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008145001,"threshold_uncertainty_score":0.002724767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01374678320569082,"score_gpt":0.2770399841911862,"score_spread":0.2632932009854954,"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."}}