{"id":"W2763878102","doi":"10.1186/s13059-017-1325-9","title":"Perfectly matched 20-nucleotide guide RNA sequences enable robust genome editing using high-fidelity SpCas9 nucleases","year":2017,"lang":"en","type":"article","venue":"Genome biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Genetics","funders":"National Transgenic Science and Technology Program; National Key Research and Development Program of China; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Genome editing; Biology; High fidelity; Guide RNA; Computational biology; Genome; Fidelity; Genetics; Transcription activator-like effector nuclease; Subgenomic mRNA; Gene; Computer science; Telecommunications","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":[0.0004971471,0.0003960123,0.0003678433,0.0002033116,0.0001591723,0.0005601774,0.0004134479,0.000454778,0.0008475326],"category_scores_gemma":[0.0005820475,0.000261606,0.000277686,0.0001721558,0.0003625947,0.0003655766,0.000475044,0.0007915067,0.0004005494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003250634,"about_ca_system_score_gemma":0.0002849164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003571715,"about_ca_topic_score_gemma":0.0007276138,"domain_scores_codex":[0.9994732,0.00009338243,0.00005053821,0.00008993143,0.0002270073,0.00006603228],"domain_scores_gemma":[0.9996082,0.0001058875,0.0001076951,0.00007571243,0.00004580534,0.00005667052],"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":[0.00001792856,0.000009583579,0.00006757848,0.00001714275,0.000002989977,0.00003555778,0.000007151027,0.0002601139,0.9977963,0.0002601478,0.00002371248,0.001501702],"study_design_scores_gemma":[0.000004181862,0.00005670844,0.0004040902,0.000002464724,0.000004939049,0.0001673802,0.00000619813,0.001383275,0.9960581,0.000111503,0.001796302,0.000004937815],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9145047,0.0008183892,0.08046576,0.00015427,0.000047095,0.00007649166,0.0003634958,0.0005578178,0.003012034],"genre_scores_gemma":[0.9401563,0.0005753725,0.05627268,0.00006332995,0.00001323592,0.00003103637,0.0006709072,0.00013457,0.00208259],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008475326,"threshold_uncertainty_score":0.002835214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02337835001626479,"score_gpt":0.3169397544569291,"score_spread":0.2935614044406643,"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."}}