{"id":"W2963198614","doi":"10.1186/s12864-019-5965-x","title":"Impact of sequencing depth and technology on de novo RNA-Seq assembly","year":2019,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Ministry of Advanced Education; Alberta Innovates; Alberta Innovates - Technology Futures; Western Canada Research Grid; Compute Canada","keywords":"Biology; Deep sequencing; Sequence assembly; Computational biology; DNA sequencing; RNA-Seq; Genetics; Exon; Single cell sequencing; Sequence (biology); Reference genome; RNA; Genome; Sequence analysis; Illumina dye sequencing; Hybrid genome assembly; Gene; Transcriptome; Exome sequencing; Gene expression; Mutation","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":[0.01538818,0.001661963,0.0010374,0.0006994825,0.000929084,0.002973368,0.001285173,0.001486337,0.002612284],"category_scores_gemma":[0.03068295,0.001357423,0.001346555,0.0009331253,0.001047183,0.003108024,0.002201972,0.003266319,0.001522926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001347112,"about_ca_system_score_gemma":0.001186087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0018065,"about_ca_topic_score_gemma":0.003420706,"domain_scores_codex":[0.9906535,0.002816126,0.001048136,0.001804122,0.003227235,0.0004509702],"domain_scores_gemma":[0.9718878,0.02093451,0.001285198,0.001898624,0.003414744,0.0005791524],"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":[0.001267016,0.0002036973,0.01113983,0.002741019,0.0004276317,0.0003822584,0.001070057,0.02644457,0.8676925,0.002470055,0.002547434,0.08361398],"study_design_scores_gemma":[0.00008499893,0.0009504239,0.02699514,0.0006514304,0.0004549626,0.0007564065,0.0005173356,0.08602922,0.845392,0.004225192,0.03368497,0.0002580086],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5461001,0.01647444,0.406507,0.004008945,0.001339443,0.001014192,0.003952845,0.006721163,0.01388191],"genre_scores_gemma":[0.5729468,0.006631805,0.4006233,0.003668797,0.000225558,0.0009021581,0.007251381,0.003003869,0.004746352],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01538818,"threshold_uncertainty_score":0.0813815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01613238436085938,"score_gpt":0.2584564172235159,"score_spread":0.2423240328626566,"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."}}