{"id":"W2126277473","doi":"10.1186/1756-0500-6-503","title":"Impact of RNA-seq attributes on false positive rates in differential expression analysis of de novo assembled transcriptomes","year":2013,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Espace pour la vie; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Canada","keywords":"Transcriptome; False positive paradox; RNA-Seq; Pipeline (software); False discovery rate; Biology; Gene; Computational biology; Differential (mechanical device); Deep sequencing; De novo transcriptome assembly; Gene expression; True positive rate; Expression (computer science); Gene expression profiling; Genetics; Data mining; Computer science; Genome; Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05861542,0.001778877,0.001669678,0.001508126,0.001357566,0.003045062,0.001477377,0.00214251,0.001406932],"category_scores_gemma":[0.1080102,0.000877401,0.002248253,0.001721934,0.002161524,0.001710741,0.001860179,0.002739103,0.000554315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001467813,"about_ca_system_score_gemma":0.0007557631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001137596,"about_ca_topic_score_gemma":0.001529606,"domain_scores_codex":[0.9393075,0.03151182,0.00589861,0.01121015,0.01092686,0.00114511],"domain_scores_gemma":[0.6703447,0.2976967,0.01124129,0.01056118,0.009300127,0.0008560385],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.009359797,0.001056595,0.1980484,0.005564759,0.003657201,0.002832432,0.002212014,0.0829947,0.5165571,0.004576014,0.003416746,0.1697243],"study_design_scores_gemma":[0.0001438217,0.001638978,0.09854596,0.0007068788,0.002037584,0.003135126,0.0003781062,0.1108161,0.7661662,0.007680982,0.008377863,0.0003724512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.603036,0.009417021,0.3743771,0.00159299,0.001147941,0.0005982432,0.002696056,0.003338351,0.003796129],"genre_scores_gemma":[0.8257605,0.001228722,0.1650772,0.001211702,0.0001399552,0.0005543573,0.00362776,0.001308683,0.001091229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9413846,"threshold_uncertainty_score":0.3099917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07979966480563888,"score_gpt":0.3989278487622511,"score_spread":0.3191281839566122,"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."}}