{"id":"W2180318265","doi":"10.1093/bioinformatics/btv487","title":"EPGA2: memory-efficient <i>de novo</i> assembler","year":2015,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Program for New Century Excellent Talents in University; National Natural Science Foundation of China","keywords":"Computer science; Sequence assembly; Pipeline (software); Contig; De Bruijn sequence; De Bruijn graph; Construct (python library); Parallels; Genome; Reference genome; Graph; Theoretical computer science; Parallel computing; Programming language; Biology; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001169041,0.002856119,0.001077855,0.001397064,0.001315804,0.001983461,0.003268044,0.001072915,0.0117864],"category_scores_gemma":[0.004522267,0.001504609,0.001488304,0.00156837,0.0006676195,0.002068807,0.00234728,0.002409786,0.01260914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006709002,"about_ca_system_score_gemma":0.001246666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00325259,"about_ca_topic_score_gemma":0.003582481,"domain_scores_codex":[0.9990602,0.0001597847,0.00008039371,0.0003510025,0.0002387591,0.0001097908],"domain_scores_gemma":[0.998446,0.000448976,0.0001232349,0.0005394071,0.0003445895,0.00009782381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00206538,0.0004012117,0.006914955,0.001413641,0.0004054373,0.0009150981,0.0008105434,0.02917851,0.1383729,0.01155877,0.2620817,0.5458819],"study_design_scores_gemma":[0.0008078488,0.0004694299,0.006603714,0.000178435,0.0002971392,0.0016092,0.0002384918,0.4588234,0.2396953,0.01601322,0.2748409,0.0004228362],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01735146,0.0004858156,0.698118,0.000306398,0.0002966728,0.0003696372,0.003457358,0.2736438,0.005970795],"genre_scores_gemma":[0.06431088,0.000428805,0.8688828,0.0006740133,0.0001196109,0.001003518,0.02190332,0.03224047,0.01043668],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0117864,"threshold_uncertainty_score":0.03942943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02265554522203696,"score_gpt":0.2465160893019883,"score_spread":0.2238605440799513,"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."}}