{"id":"W3199566341","doi":"10.33774/chemrxiv-2021-7pdz9","title":"CRISPR-Click Enables Multi-Gene Editing with Modular Synthetic sgRNAs","year":2021,"lang":"en","type":"preprint","venue":"","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Technische Universität München; Canada Research Chairs; Social Sciences and Humanities Research Council of Canada; Deutsche Forschungsgemeinschaft; University of Alberta","keywords":"Guide RNA; CRISPR; Cas9; Computational biology; Genome editing; Gene; Subgenomic mRNA; Biology; Computer science; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000463652,0.0004855634,0.0004212206,0.0003018657,0.0001551876,0.0004289641,0.0005155519,0.0004498441,0.001155658],"category_scores_gemma":[0.0003374056,0.0002803197,0.0003890197,0.0002148302,0.0003944923,0.0003290423,0.0004362899,0.0008228802,0.0008066603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003691954,"about_ca_system_score_gemma":0.0003095997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002806173,"about_ca_topic_score_gemma":0.000493126,"domain_scores_codex":[0.9996113,0.00004022173,0.00003813903,0.0001164843,0.0001375468,0.0000563373],"domain_scores_gemma":[0.9997559,0.00005802621,0.00007500003,0.00003653787,0.00002219692,0.00005242561],"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":[0.00002588072,0.00001351077,0.0000706666,0.00003059034,0.000005306423,0.00006455804,0.00001158087,0.0002307505,0.997236,0.000409319,0.00008464044,0.00181722],"study_design_scores_gemma":[0.000006634172,0.00007736597,0.0002773149,0.00000194432,0.000006021858,0.0001402899,0.000003100366,0.001454688,0.9954236,0.00005651181,0.002546376,0.000006138814],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7429743,0.001115783,0.2460658,0.0002906653,0.000203457,0.000361823,0.001271671,0.003212968,0.004503621],"genre_scores_gemma":[0.8848264,0.0009638726,0.106072,0.0001482339,0.00004059739,0.0002490796,0.001491775,0.0002307557,0.005977304],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001155658,"threshold_uncertainty_score":0.003866017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01091133970167126,"score_gpt":0.2813405475734463,"score_spread":0.2704292078717751,"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."}}