{"id":"W1966822396","doi":"10.1101/gr.089532.108","title":"ABySS: A parallel assembler for short read sequence data","year":2009,"lang":"en","type":"article","venue":"Genome Research","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3765,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"Washington University School of Medicine in St. Louis; Genome British Columbia; Michael Smith Health Research BC; Genome Canada","keywords":"Contig; Sequence assembly; Biology; Hybrid genome assembly; Massive parallel sequencing; Genome; Reference genome; Human genome; Computational biology; DNA sequencing; Sequence (biology); Genetics; Software; Massively parallel; Computer science; DNA; Parallel computing; Gene; Programming language","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":[0.003542758,0.003741099,0.003040862,0.00303427,0.001802169,0.002564374,0.002815939,0.001351037,0.007933212],"category_scores_gemma":[0.006295764,0.002279494,0.002562538,0.00279074,0.0007119794,0.001164377,0.00192009,0.00359556,0.01408408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007660898,"about_ca_system_score_gemma":0.001920076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002798665,"about_ca_topic_score_gemma":0.003722103,"domain_scores_codex":[0.9967989,0.0007710906,0.000439529,0.000694707,0.001092965,0.0002028381],"domain_scores_gemma":[0.9976992,0.0006794554,0.0003820816,0.0003921685,0.0006478597,0.0001991992],"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":[0.002761313,0.0004167293,0.003646842,0.004335844,0.00147194,0.001373332,0.00141469,0.02178955,0.4451821,0.008970708,0.2116231,0.297014],"study_design_scores_gemma":[0.001020334,0.0007537163,0.005084319,0.0004581262,0.0004209432,0.001306824,0.000166404,0.1963251,0.2786739,0.01407435,0.501232,0.0004839164],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01292658,0.002205068,0.7789263,0.0002523412,0.0006555145,0.001355267,0.02377719,0.1751502,0.004751587],"genre_scores_gemma":[0.01630832,0.001132092,0.9207388,0.0002560045,0.0001057244,0.003432873,0.03953106,0.01387479,0.004620323],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.007933212,"threshold_uncertainty_score":0.02653921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3038703311146536,"score_gpt":0.4524103728411613,"score_spread":0.1485400417265077,"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."}}