{"id":"W3082024443","doi":"10.1101/2020.09.01.278374","title":"Application of an Optimized Annotation Pipeline to the <i>Cryptococcus Deuterogattii</i> Genome Reveals Dynamic Primary Metabolic Gene Clusters and Genomic Impact of RNAi Loss","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Fungal Infections and Studies","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; National Institutes of Health; Universidade Federal do Rio Grande do Sul; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canadian Institute for Advanced Research","keywords":"Cryptococcus neoformans; Biology; Genome; Gene; Genetics; Computational biology; Cryptococcus; RNA interference; RNA-Seq; Genome project; Intron; Genomics; Transcriptome; RNA; Gene expression","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007500984,0.001055713,0.0006151341,0.000430855,0.0004813135,0.000812261,0.0004609076,0.0004067099,0.0006944941],"category_scores_gemma":[0.001115758,0.0003806948,0.0007142778,0.0004498246,0.0002426144,0.0003113268,0.0004603259,0.0008875004,0.0006641618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006050475,"about_ca_system_score_gemma":0.0009662919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003258664,"about_ca_topic_score_gemma":0.004480319,"domain_scores_codex":[0.9995182,0.00005353637,0.0000433528,0.0002013191,0.0001238831,0.00005972528],"domain_scores_gemma":[0.9994907,0.0001376545,0.00008513467,0.00007322925,0.0001671664,0.00004602444],"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":[0.0002866255,0.00004269166,0.002394055,0.0001199452,0.00003089303,0.0001029925,0.00007321092,0.002606375,0.985204,0.00009415316,0.0004335895,0.008611452],"study_design_scores_gemma":[0.0000268441,0.0001764013,0.0129274,0.00001408888,0.00005497965,0.0001217803,0.00007186342,0.04992837,0.9318951,0.0001973939,0.004547271,0.00003841817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8031247,0.0003569008,0.1746457,0.0002448215,0.000106429,0.0003498976,0.009497382,0.01047312,0.001201093],"genre_scores_gemma":[0.615186,0.0003520283,0.3566868,0.0002162287,0.00002161729,0.000387178,0.02164172,0.00263081,0.002877687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003258664,"threshold_uncertainty_score":0.006479442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137212291241887,"score_gpt":0.2574070086139026,"score_spread":0.2460348857014838,"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."}}