{"id":"W4401715907","doi":"10.1093/nar/gkae682","title":"A CRISPR-dCas13 RNA-editing tool to study alternative splicing","year":2024,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Laboratoire d'Excellence EpiGenMed; Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas; Institut National Du Cancer; National Institute for Health and Care Research; Ligue Contre le Cancer; Ministerio de Ciencia e Innovación; Centre National de la Recherche Scientifique; Université Paris-Saclay; Queen's University; Queen's University Belfast; State Key Laboratory of Molecular Biology; New York State Department of Health; Sleep Research Society Foundation","keywords":"Biology; RNA splicing; Alternative splicing; Computational biology; CRISPR; Genome editing; Gene; RNA; Intron; Genetics; Context (archaeology); Gene isoform","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001071023,0.0001608481,0.0001352631,0.0002108961,0.000167585,0.0001831805,0.0003635956,0.00008652616,0.0001082198],"category_scores_gemma":[0.0003174758,0.0001532185,0.00007071198,0.000431676,0.00004184228,0.00000625002,0.0004972849,0.0003738911,0.0002057285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004998672,"about_ca_system_score_gemma":0.00005833776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009140844,"about_ca_topic_score_gemma":0.00002450911,"domain_scores_codex":[0.9980375,0.0001004272,0.0002122638,0.0005923525,0.000508405,0.0005490149],"domain_scores_gemma":[0.9991987,0.00004731097,0.00001035498,0.0004310595,0.0001623833,0.0001502008],"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":[0.00005388032,0.0001109126,0.001190675,0.00006951779,0.0001629316,0.0001487157,0.002343854,0.0007287666,0.9066185,0.0001753822,0.00736656,0.0810303],"study_design_scores_gemma":[0.0007829551,0.002226048,0.007476998,0.0001686778,0.00003057456,0.00004869411,0.005408969,0.006881491,0.8823019,0.0002324343,0.09383617,0.0006050639],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828618,0.0006319014,0.01225024,0.0003358191,0.0003366881,0.0005389859,0.00001076965,0.00005124269,0.002982597],"genre_scores_gemma":[0.9961988,0.00004590932,0.001132309,0.0000680641,0.001196035,0.00009568324,0.000009042395,0.00005846114,0.001195752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08646961,"threshold_uncertainty_score":0.6248067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03600648179812218,"score_gpt":0.4160998458692274,"score_spread":0.3800933640711052,"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."}}