{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001169272,0.0002059983,0.0001911238,0.00004497639,0.00005159384,0.00002686429,0.0001682034,0.0001227372,0.00007885591],"category_scores_gemma":[0.00006146458,0.0002152904,0.0001163316,0.0001839182,0.00002538805,0.000001863281,0.000150643,0.0001188571,0.00001899325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002926131,"about_ca_system_score_gemma":0.00003062027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002420347,"about_ca_topic_score_gemma":0.000009880332,"domain_scores_codex":[0.998775,0.0000424137,0.0002768053,0.0004297496,0.0001244312,0.0003516086],"domain_scores_gemma":[0.9995168,0.00000761501,0.00004907255,0.0002541992,0.0000342779,0.0001379626],"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":[0.00001778651,0.00002606772,0.002795064,0.00003542157,0.00002983683,0.00001183756,0.0002383899,0.03628234,0.9597568,0.000003521619,0.00002834962,0.0007745755],"study_design_scores_gemma":[0.002826184,0.000600391,0.1397136,0.0001217793,0.0002004305,0.00007017946,0.001107643,0.1201012,0.7256137,0.00002081522,0.007791175,0.001832912],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953387,0.001020241,0.00271896,0.0002043453,0.0001562405,0.0002418214,0.00003441075,0.0000258755,0.0002594154],"genre_scores_gemma":[0.9982019,0.000047823,0.0006433079,0.0003034693,0.0006903173,0.00001968064,0.00004548782,0.00004115981,0.000006825869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2341431,"threshold_uncertainty_score":0.8779286,"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."}}