{"id":"W2580375224","doi":"10.1139/gen-2016-0127","title":"Enhancement of single guide RNA transcription for efficient CRISPR/Cas-based genomic engineering","year":2017,"lang":"en","type":"article","venue":"Genome","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"CRISPR; Trans-activating crRNA; Guide RNA; Biology; Subgenomic mRNA; Cas9; RNA; Computational biology; Genome editing; RNA polymerase III; Transcription (linguistics); Genetics; DNA; Genome engineering; genomic DNA; RNA polymerase; Gene","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003777497,0.0006480082,0.0007223086,0.0003765616,0.000196287,0.0005080911,0.0005300326,0.0006130516,0.001090203],"category_scores_gemma":[0.0004587889,0.0002746238,0.0005597995,0.0003146527,0.0003037097,0.0002544025,0.0004687488,0.001104327,0.001101567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004385934,"about_ca_system_score_gemma":0.0004247559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004872602,"about_ca_topic_score_gemma":0.0008717768,"domain_scores_codex":[0.9993688,0.0000809467,0.00006588323,0.0001402206,0.0002630262,0.0000811075],"domain_scores_gemma":[0.9998,0.00004563765,0.00005463602,0.0000330387,0.00003864661,0.00002807857],"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":[0.00002144771,0.00001971483,0.0000528395,0.00007098075,0.000004200518,0.00006080896,0.00001263716,0.0002490529,0.9960318,0.0003086894,0.00008322838,0.003084663],"study_design_scores_gemma":[0.000008226355,0.00007009695,0.0003228296,0.000007215778,0.00001294533,0.0001835343,0.000007292826,0.003340285,0.9909552,0.0001021618,0.004980605,0.000009559571],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5479729,0.004799066,0.4326153,0.000447062,0.0004597929,0.0006598445,0.001262876,0.003160873,0.00862226],"genre_scores_gemma":[0.835728,0.002606596,0.1527981,0.0001808472,0.0000447711,0.0003057878,0.001744427,0.0003237339,0.006267663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001090203,"threshold_uncertainty_score":0.003647089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01206414875692246,"score_gpt":0.2793240240429447,"score_spread":0.2672598752860222,"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."}}