{"id":"W4210419044","doi":"10.3389/fgene.2021.818697","title":"Identification of Alternative Polyadenylation in Cyanidioschyzon merolae Through Long-Read Sequencing of mRNA","year":2022,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"National Institute of Environmental Health Sciences; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Polyadenylation; Biology; RNA splicing; Intron; Alternative splicing; Genetics; Post-transcriptional modification; Organism; Gene; Model organism; Computational biology; RNA; Cell biology; Messenger RNA","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003864624,0.0000831526,0.0001593784,0.0001435133,0.00003576687,0.000005824287,0.0002358776,0.00005771453,0.000008312119],"category_scores_gemma":[0.00006864066,0.00009839776,0.00004448309,0.0002532545,0.00006103831,0.000006600954,0.0001303133,0.0001060349,2.2911e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001042411,"about_ca_system_score_gemma":0.00009934171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001889547,"about_ca_topic_score_gemma":0.00007292975,"domain_scores_codex":[0.9987953,0.0001460454,0.000394741,0.0002225202,0.0002598681,0.0001814903],"domain_scores_gemma":[0.9994758,0.000007778089,0.0001891671,0.0002427894,0.00006091064,0.00002355081],"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":[0.00009490332,0.00004675174,0.1213872,0.00002709464,0.00002425841,0.000004240079,0.0004081205,0.03244646,0.8431823,0.00002704785,0.0001726893,0.002179018],"study_design_scores_gemma":[0.000776274,0.0002954101,0.04076792,0.00002017503,0.000009136935,0.000003750793,0.001339647,0.02090141,0.9350229,0.0004733887,0.0002453571,0.000144628],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9747983,0.00229785,0.02226573,0.00003350426,0.0002375137,0.0002133603,0.00002588786,0.000001934133,0.0001258676],"genre_scores_gemma":[0.9960516,0.0005734501,0.002990173,0.00001547211,0.00003591803,0.00002345194,0.00005822302,0.00001370141,0.0002379761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09184064,"threshold_uncertainty_score":0.4012544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01577090265525568,"score_gpt":0.2782985803470953,"score_spread":0.2625276776918396,"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."}}