{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004243001,0.0003364701,0.0003900018,0.00007059969,0.0006055529,0.0001194239,0.0007162827,0.0003039723,0.0001735133],"category_scores_gemma":[0.0002420286,0.0003343195,0.000153509,0.00004967174,0.0002842235,0.00001450182,0.0004991833,0.0001655517,0.00004276612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005977607,"about_ca_system_score_gemma":0.00009840434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009219702,"about_ca_topic_score_gemma":0.0001468816,"domain_scores_codex":[0.9979442,0.0000759296,0.0004415195,0.000726188,0.00009949251,0.0007127019],"domain_scores_gemma":[0.9984095,0.00001849658,0.0002612859,0.001001364,0.0001335596,0.0001757784],"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":[0.00002120433,0.00002177091,0.002774077,0.00003390233,0.000094915,0.00001478033,0.00003656321,0.006962084,0.9893144,0.00005282154,0.000124498,0.0005490127],"study_design_scores_gemma":[0.002003279,0.0009948808,0.05256023,0.00007246196,0.0002626221,0.0002535011,0.000606278,0.001897062,0.789265,0.0008826297,0.1490771,0.002124959],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985496,0.002498912,0.008964784,0.0001737208,0.0007446893,0.0002260737,0.0001368518,0.00004703095,0.001711939],"genre_scores_gemma":[0.9879766,0.0003113794,0.008672374,0.0001876423,0.002102233,0.00001398221,0.0002242535,0.00005397905,0.000457544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2000493,"threshold_uncertainty_score":0.9999109,"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."}}