{"id":"W3111279221","doi":"10.1016/j.xpro.2020.100229","title":"Quantification of mRNA ribosomal engagement in human neurons using parallel translating ribosome affinity purification (TRAP) and RNA sequencing","year":2020,"lang":"en","type":"article","venue":"STAR Protocols","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Hospital for Sick Children","funders":"Canadian Institutes of Health Research; Canada First Research Excellence Fund; Ontario Institute for Regenerative Medicine; Ontario Brain Institute","keywords":"Ribosome; Ribosomal RNA; Translation (biology); RNA; Ribosome profiling; 5.8S ribosomal RNA; Messenger RNA; Biology; Computational biology; Ribosomal binding site; Cell biology; Transcriptome; Ribosomal protein; Molecular biology; Gene; Gene expression; 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":[0.0008930384,0.0006073797,0.0005730272,0.001013728,0.000559227,0.0007535891,0.0004771645,0.000470591,0.001288278],"category_scores_gemma":[0.0007314538,0.0004131848,0.0004938521,0.0009071408,0.0004011501,0.0002065534,0.0004693761,0.0007770009,0.001348571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004533307,"about_ca_system_score_gemma":0.0005543972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001301229,"about_ca_topic_score_gemma":0.003407833,"domain_scores_codex":[0.9991061,0.0001135223,0.00007301004,0.0003285785,0.0003145171,0.00006424289],"domain_scores_gemma":[0.9996461,0.0001019763,0.00005238627,0.00007165408,0.0001036593,0.00002413168],"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":[0.0001104907,0.00002316466,0.0008662061,0.00008435235,0.00002401794,0.00004770778,0.00007555565,0.0002796139,0.9867727,0.000392793,0.0002158811,0.0111076],"study_design_scores_gemma":[0.00001267342,0.0001156196,0.00801894,0.00002603509,0.0000605137,0.0002541936,0.0000462485,0.003648568,0.9804835,0.0005278726,0.00678667,0.00001920485],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3952691,0.005185186,0.5761162,0.0001987942,0.000141668,0.0005791456,0.01072787,0.0031069,0.008675172],"genre_scores_gemma":[0.3054106,0.005823477,0.6610571,0.000426169,0.00006017981,0.001930038,0.01493152,0.001084425,0.009276529],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001301229,"threshold_uncertainty_score":0.004722893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1230271971681739,"score_gpt":0.3651408620188922,"score_spread":0.2421136648507183,"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."}}