{"id":"W1751510803","doi":"10.1038/nmeth.3580","title":"Cas9 gRNA engineering for genome editing, activation and repression","year":2015,"lang":"en","type":"article","venue":"Nature Methods","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":356,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Canadian Institutes of Health Research; National Human Genome Research Institute; University of California, San Diego; National Cancer Institute; National Institutes of Health; Broad Institute; Hansjörg Wyss Institute for Biologically Inspired Engineering, Harvard University; U.S. Department of Energy","keywords":"Guide RNA; Cas9; CRISPR; Genome editing; Nuclease; Computational biology; RNA editing; Biology; Psychological repression; RNA; Genome engineering; Genome; Genetics; Cell biology; Gene; Gene expression","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0006676405,0.0008283886,0.0007387185,0.0005710424,0.0007371971,0.001255812,0.00147258,0.0009387952,0.004376777],"category_scores_gemma":[0.000615968,0.0006581873,0.000705802,0.0004242771,0.00068577,0.0006531847,0.000960702,0.002180416,0.002837326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001067555,"about_ca_system_score_gemma":0.0006035963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009202803,"about_ca_topic_score_gemma":0.002751941,"domain_scores_codex":[0.9989408,0.0001042461,0.0001070348,0.0002985274,0.0004016699,0.0001476896],"domain_scores_gemma":[0.9997092,0.00006760164,0.00004483605,0.00009783694,0.00003802495,0.0000425141],"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":[0.00007481658,0.00003017304,0.00007897715,0.0001032059,0.00001601439,0.00006670024,0.00002487161,0.0001859678,0.9882235,0.002567309,0.0006689922,0.007959491],"study_design_scores_gemma":[0.000006798598,0.00001932016,0.0001089401,0.000004086969,0.000008277243,0.0001280113,0.000005269562,0.000777828,0.9915125,0.000235635,0.007183831,0.000009496144],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2591656,0.004352112,0.6884773,0.001321073,0.001143173,0.0009613478,0.004666669,0.01154875,0.02836411],"genre_scores_gemma":[0.6919497,0.002801713,0.2567949,0.0004232476,0.00007344679,0.0004763051,0.004305507,0.001628959,0.0415463],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004376777,"threshold_uncertainty_score":0.01464176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01595869249530691,"score_gpt":0.4075014387992125,"score_spread":0.3915427463039056,"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."}}