{"id":"W2112491476","doi":"10.1093/bioinformatics/btp367","title":"<i>De novo</i> transcriptome assembly with ABySS","year":2009,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":410,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"Genome British Columbia; Michael Smith Health Research BC; National Cancer Institute; Genome Canada","keywords":"Contig; Sequence assembly; Transcriptome; Genome; Computational biology; Java; Biology; De novo transcriptome assembly; Software; Computer science; Hybrid genome assembly; Source code; Shotgun sequencing; Reference genome; Perl; Genetics; Gene; Programming language; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008221749,0.0001009066,0.00007508678,0.00003286066,0.00005228736,0.00004040577,0.000137057,0.00008603499,0.000009808737],"category_scores_gemma":[0.000008327382,0.00007901855,0.00003876605,0.00009264829,0.0000224046,0.000007514822,0.000007712173,0.00005189536,0.00001496428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001231678,"about_ca_system_score_gemma":0.00006871361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.826631e-7,"about_ca_topic_score_gemma":0.000001048587,"domain_scores_codex":[0.9994358,0.000009957727,0.0001592548,0.00009631336,0.0001195682,0.0001790934],"domain_scores_gemma":[0.9995604,0.000001801207,0.00006314139,0.0002550034,0.00004053687,0.00007910762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001323271,0.00007364222,0.0003245116,0.00002620607,0.00001900716,0.000001388251,0.0003624748,0.0001012088,0.955516,0.0006376433,0.01457695,0.02822868],"study_design_scores_gemma":[0.001642544,0.001037232,0.007781,0.00004461695,0.00003056975,0.00009293351,0.0004431247,0.001440564,0.4272533,0.0001087238,0.5596684,0.0004569664],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7296356,0.0004617217,0.1758008,0.001721551,0.0002105132,0.0004196342,0.00002332595,0.00009854194,0.09162831],"genre_scores_gemma":[0.9883066,0.00008871114,0.008279017,0.002241423,0.00008810328,0.000008521934,0.00005852093,0.000008194496,0.0009208595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5450915,"threshold_uncertainty_score":0.3222283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009809137250629119,"score_gpt":0.2413464444821044,"score_spread":0.2315373072314753,"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."}}