{"id":"W3041018134","doi":"10.3389/fgene.2020.00606","title":"Methodologies for Transcript Profiling Using Long-Read Technologies","year":2020,"lang":"en","type":"review","venue":"Frontiers in Genetics","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":120,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill Genome Centre; McGill University","funders":"Canada Foundation for Innovation; Compute Canada; Genome Canada","keywords":"Computational biology; Nanopore sequencing; Transcriptome; RNA-Seq; Biology; DNA sequencing; De novo transcriptome assembly; RNA; Genomics; Deep sequencing; Profiling (computer programming); Gene; Genome; Computer science; Genetics; Gene expression","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":[0.004245665,0.001728849,0.00187911,0.003400122,0.001037774,0.002492242,0.002486876,0.001882009,0.008122495],"category_scores_gemma":[0.004053799,0.001261633,0.002001022,0.003197253,0.001313571,0.002043018,0.001936537,0.004628514,0.01181864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008853594,"about_ca_system_score_gemma":0.001217666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004490806,"about_ca_topic_score_gemma":0.0008930192,"domain_scores_codex":[0.9935573,0.001571138,0.0004737313,0.001509742,0.002620451,0.0002676512],"domain_scores_gemma":[0.9969473,0.001011835,0.0003237693,0.0006845272,0.0009028704,0.0001297113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001474853,0.0001216246,0.0005800296,0.00241744,0.0001323278,0.0003011875,0.0002793081,0.001961877,0.8682942,0.01103817,0.006066013,0.1086604],"study_design_scores_gemma":[0.0000702867,0.0005484092,0.002733528,0.000549294,0.0001690947,0.001184389,0.0001969802,0.01372711,0.6762954,0.01876649,0.2854927,0.0002663106],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.002374289,0.004895326,0.983767,0.0003645296,0.0004030869,0.000705056,0.001608144,0.002483515,0.003399041],"genre_scores_gemma":[0.01803368,0.008936035,0.9562483,0.0008432516,0.0002796383,0.003221298,0.0050662,0.0009312846,0.006440238],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.008122495,"threshold_uncertainty_score":0.02717251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1491405313929264,"score_gpt":0.3972978139506833,"score_spread":0.2481572825577569,"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."}}