{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:38:07Z","timestamp":1740184687299,"version":"3.37.3"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2017,1,19]],"date-time":"2017-01-19T00:00:00Z","timestamp":1484784000000},"content-version":"vor","delay-in-days":21,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01HL104608\/HL\/NHLBI"],"award-info":[{"award-number":["R01HL104608\/HL\/NHLBI"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Variant calling from next-generation sequencing (NGS) data is susceptible to false positive calls due to sequencing, mapping and other errors. To better distinguish true from false positive calls, we present a method that uses genotype array data from the sequenced samples, rather than public data such as HapMap or dbSNP, to train an accurate classifier using Random Forests. We demonstrate our method on a set of variant calls obtained from 642 African-ancestry genomes from the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA), sequenced to high depth (30X).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We have applied our classifier to compare call sets generated with different calling methods, including both single-sample and multi-sample callers. At a False Positive Rate of 5%, our method determines true positive rates of 97.5%, 95% and 99% on variant calls obtained using Illuminas single-sample caller CASAVA, Real Time Genomics multisample variant caller, and the GATK UnifiedGenotyper, respectively. Since NGS sequencing data may be accompanied by genotype data for the same samples, either collected concurrent to sequencing or from a previous study, our method can be trained on each dataset to provide a more accurate computational validation of site calls compared to generic methods. Moreover, our method allows for adjustment based on allele frequency (e.g. a different set of criteria to determine quality for rare versus common variants) and thereby provides insight into sequencing characteristics that indicate call quality for variants of different frequencies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and Implementation<\/jats:title>\n                  <jats:p>Code is available on Github at: https:\/\/github.com\/suyashss\/variant_validation<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btw786","type":"journal-article","created":{"date-parts":[[2016,12,8]],"date-time":"2016-12-08T12:05:45Z","timestamp":1481198745000},"page":"1147-1153","source":"Crossref","is-referenced-by-count":2,"title":["Using genotype array data to compare multi- and single-sample variant calls and improve variant call sets from deep coverage whole-genome sequencing data"],"prefix":"10.1093","volume":"33","author":[{"given":"Suyash S","family":"Shringarpure","sequence":"first","affiliation":[{"name":"Departments of Genetics and Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rasika A","family":"Mathias","sequence":"additional","affiliation":[{"name":"23 and Me Inc, Mountain View, CA, USA"},{"name":"Department of Medicine, Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryan D","family":"Hernandez","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, MD, USA"},{"name":"Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA"},{"name":"Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timothy D","family":"O\u2019Connor","sequence":"additional","affiliation":[{"name":"Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA"},{"name":"Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA"},{"name":"Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zachary A","family":"Szpiech","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raul","family":"Torres","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco M","family":"De La Vega","sequence":"additional","affiliation":[{"name":"Departments of Genetics and Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos D","family":"Bustamante","sequence":"additional","affiliation":[{"name":"Departments of Genetics and Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kathleen C","family":"Barnes","sequence":"additional","affiliation":[{"name":"Department of Medicine, Johns Hopkins University, Baltimore, MD, USA"},{"name":"Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, MD, USA"},{"name":"Department of Medicine, University of Colorado, Aurora, Colorado, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Margaret A","family":"Taub","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Bloomberg School of Public Health, JHU, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"CAAPA Consortium","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2016,12,29]]},"reference":[{"key":"2023020205013012200_btw786-B1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. 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