{"id":"W2345920351","doi":"10.1038/nmeth.3865","title":"A hybrid approach for de novo human genome sequence assembly and phasing","year":2016,"lang":"en","type":"article","venue":"Nature Methods","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":248,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute of Genetics; National Institute of General Medical Sciences; National Human Genome Research Institute; University of California, San Francisco; National Institute of Standards and Technology","keywords":"Genome; Human genome; Sequence assembly; Computational biology; Biology; Genomics; Reference genome; Genetics; Whole genome sequencing; Hybrid genome assembly; ENCODE; Sequence (biology); DNA sequencing; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006211263,0.0001291844,0.0001423704,0.00002484754,0.0001218462,0.0000168898,0.0001286575,0.0001712787,0.000001124297],"category_scores_gemma":[0.0001696786,0.00009265071,0.00006228083,0.00002507774,0.00007164272,6.954656e-7,0.00008975055,0.0000814043,1.342985e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001522689,"about_ca_system_score_gemma":0.00003235659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001932741,"about_ca_topic_score_gemma":9.184327e-7,"domain_scores_codex":[0.9991793,0.00009492365,0.0001098012,0.0003373008,0.00004328088,0.0002354115],"domain_scores_gemma":[0.9995698,0.00005093287,0.00004940895,0.0002058517,0.00006548047,0.0000585407],"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":[0.00002156166,0.00001293361,0.0002958247,0.00001754087,0.0000494803,6.543364e-7,0.00002502468,0.000002084057,0.978627,0.0002752508,0.00006134435,0.02061128],"study_design_scores_gemma":[0.0008287533,0.0002272453,0.003776173,0.000008095611,0.00004454821,0.00009217775,0.0000232493,0.00004458549,0.9071733,0.002651047,0.08485045,0.0002803521],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6712417,0.00470706,0.322932,0.0001096735,0.00009275171,0.0001817528,0.00004709423,0.000004213481,0.0006837414],"genre_scores_gemma":[0.6205899,0.0001257429,0.37854,0.0002023016,0.0002077431,0.00002848157,0.00001245173,0.00001496004,0.00027842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0847891,"threshold_uncertainty_score":0.3778186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03487652413705169,"score_gpt":0.3797559351166531,"score_spread":0.3448794109796015,"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."}}