{"id":"W2031260681","doi":"10.1186/1471-2164-15-567","title":"Enhanced whole genome sequence and annotation of Clostridium stercorarium DSM8532T using RNA-seq transcriptomics and high-throughput proteomics","year":2014,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Bundesministerium für Verkehr, Innovation und Technologie; Genome Canada; Austrian Centre of Industrial Biotechnology; Austrian Federal Ministry of Economy, Family and Youth; McGill University","keywords":"Biology; Proteomics; Computational biology; Transcriptome; DNA microarray; RNA-Seq; Whole genome sequencing; Genome; Genetics; Genome project; Annotation; Proteogenomics; Genomics; Sequence (biology); Gene; Gene expression","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":[0.0004877701,0.001011314,0.0005855053,0.0008478832,0.0005769691,0.0006464956,0.0004109676,0.0005226551,0.001304206],"category_scores_gemma":[0.0005842341,0.0002270758,0.0008647178,0.001583929,0.0001954161,0.0002280009,0.0003778236,0.0007677458,0.00130551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006465403,"about_ca_system_score_gemma":0.00155215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006226575,"about_ca_topic_score_gemma":0.007207727,"domain_scores_codex":[0.9996319,0.00002760707,0.00002671058,0.0001040171,0.0001438025,0.00006591856],"domain_scores_gemma":[0.9996606,0.00004379914,0.00007718687,0.00003574243,0.0001272852,0.00005539698],"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":[0.0004262648,0.00007204821,0.002131104,0.0005255129,0.00002472594,0.0002785767,0.0001194585,0.001475616,0.9819523,0.0001308565,0.001208057,0.01165565],"study_design_scores_gemma":[0.0003640992,0.001218828,0.17161,0.0004298514,0.0004544485,0.002099961,0.0008005677,0.03490084,0.7161931,0.0009257391,0.07080155,0.0002009055],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8750763,0.001482393,0.03575213,0.0004662808,0.0001987076,0.000487941,0.08023162,0.002374969,0.003929627],"genre_scores_gemma":[0.619036,0.001347971,0.1272425,0.0003978749,0.00006001589,0.0006617662,0.2458874,0.001047389,0.004319148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006226575,"threshold_uncertainty_score":0.01238066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02516274625689506,"score_gpt":0.2165714640770642,"score_spread":0.1914087178201691,"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."}}