{"id":"W2952228520","doi":"10.1093/bioinformatics/btz130","title":"LIONS: analysis suite for detecting and quantifying transposable element initiated transcription from RNA-seq","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Chromosomal and Genetic Variations","field":"Agricultural and Biological Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Terry Fox Research Institute; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"RNA-Seq; Transposable element; Suite; Container (type theory); Computer science; Source code; Computational biology; Transcription (linguistics); Transcriptome; Biology; Genetics; Genome; Gene; Gene expression; Programming language","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":[0.0001400412,0.00009196548,0.0001541315,0.00002572189,0.0001871196,0.00008464976,0.00007545124,0.00006329852,0.0002915788],"category_scores_gemma":[0.0000156449,0.00004132445,0.0001053761,0.0003778097,0.00001143964,0.000169571,0.0000104959,0.00004334897,0.00001721124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001222404,"about_ca_system_score_gemma":0.000005095741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001889674,"about_ca_topic_score_gemma":0.001226139,"domain_scores_codex":[0.999302,0.00001443883,0.0002963734,0.0001120532,0.0001072694,0.0001678807],"domain_scores_gemma":[0.9996119,0.0001589858,0.00008888308,0.00004259267,0.000045739,0.00005194116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001098058,0.000158072,0.08202656,0.0001676212,0.0007557977,5.841299e-7,0.005774734,0.0008928393,0.7372777,0.0008989375,0.00004812967,0.1718892],"study_design_scores_gemma":[0.001999366,0.0009690113,0.3226784,0.0001268825,0.001448889,0.000003412608,0.007942821,0.6146985,0.03533062,0.001985437,0.01186642,0.0009501672],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972969,0.00007814736,0.001387385,0.0002343479,0.00007580994,0.0003530392,0.0003152268,0.00004217355,0.0002169788],"genre_scores_gemma":[0.9942523,0.00005065676,0.00502906,0.0001079207,0.00004413327,0.00001733104,0.000465965,6.720679e-7,0.00003192871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7019472,"threshold_uncertainty_score":0.3192583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06000712540145275,"score_gpt":0.2551174693935599,"score_spread":0.1951103439921071,"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."}}