{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001016492,0.0001356905,0.000172871,0.00006459904,0.0000351663,0.000008803276,0.0001368733,0.0001482881,0.000003278581],"category_scores_gemma":[0.00003926549,0.0001248012,0.00007241711,0.00006228667,0.00005848644,4.440166e-7,0.0001347965,0.00006127108,0.000006750207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005419,"about_ca_system_score_gemma":0.0002865108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005317453,"about_ca_topic_score_gemma":0.0001047015,"domain_scores_codex":[0.999316,0.00001656307,0.0001518063,0.0002494484,0.00004192319,0.0002242067],"domain_scores_gemma":[0.9994928,0.00001495006,0.0000816923,0.0003042296,0.0000605978,0.00004569639],"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":[0.00003948772,0.00001343526,0.182146,0.00001291782,0.0000598021,5.708672e-7,0.00004281217,0.0009551363,0.8158137,0.000152411,0.0000260428,0.0007377139],"study_design_scores_gemma":[0.001236711,0.001861035,0.1598314,0.00001855425,0.00004108856,0.00007264173,0.0003454867,0.0005003635,0.8333079,0.0008325744,0.001538797,0.0004134454],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973337,0.000896069,0.0001821941,0.00002419319,0.00005981971,0.0001500958,0.00002003806,0.000003443844,0.00133047],"genre_scores_gemma":[0.9972274,0.0003112594,0.002183015,0.00004176099,0.00005632924,0.000005344087,0.000006627487,0.00002103819,0.000147199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02231456,"threshold_uncertainty_score":0.5089247,"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."}}