{"id":"W4385638678","doi":"10.1038/s41551-023-01078-2","title":"Sensing the DNA-mismatch tolerance of catalytically inactive Cas9 via barcoded DNA nanostructures in solid-state nanopores","year":2023,"lang":"en","type":"article","venue":"Nature Biomedical Engineering","topic":"Nanopore and Nanochannel Transport Studies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council; Cambridge Trust; University of Cambridge; European Commission; Research Councils UK; Oxford Nanopore Technologies","keywords":"DNA; Nanopore; Cas9; DNA sequencing; Biology; Nanopore sequencing; Ribonucleoprotein; Computational biology; Nucleic acid; Biophysics; CRISPR; Cell biology; Nanotechnology; Biochemistry; RNA; Materials science; Gene","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":[0.0003010309,0.0003526456,0.000521082,0.0003941889,0.00009801939,0.00001371593,0.0003220226,0.0003840881,0.000006880046],"category_scores_gemma":[0.000131975,0.0002664579,0.0001201885,0.001539465,0.0001563223,0.00009187232,0.0001320615,0.0008666448,0.000006315729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000926478,"about_ca_system_score_gemma":0.0000333367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004525043,"about_ca_topic_score_gemma":0.00006758227,"domain_scores_codex":[0.9978883,0.00001581611,0.0005279415,0.0003023933,0.0005385051,0.0007270248],"domain_scores_gemma":[0.999205,0.0002493822,0.00004690848,0.0002813353,0.00006847247,0.0001488958],"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":[0.00009038211,0.00005712215,0.0003242062,0.001377329,0.000466685,0.00104432,0.006293928,0.07207091,0.9054154,0.000662717,0.002384629,0.009812405],"study_design_scores_gemma":[0.001773322,0.0001225502,0.01780656,0.0009843523,0.00009200699,0.0001129951,0.0005236613,0.2432741,0.7248527,0.001947419,0.007248245,0.001262072],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895182,0.001905785,0.004317789,0.0008686922,0.002002319,0.0004087447,0.00008416861,0.000741446,0.0001528316],"genre_scores_gemma":[0.9990762,0.0001846716,0.0003479011,0.0000615069,0.0001780235,0.00001215116,0.00004179824,0.00006776912,0.00002996532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1805627,"threshold_uncertainty_score":0.9999788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003595586087765367,"score_gpt":0.2142533245644038,"score_spread":0.2106577384766384,"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."}}