SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data

We develop a statistical tool SNVer for calling common and rare variants in analysis of pooled or individual next-generation sequencing (NGS) data. We formulate variant calling as a hypothesis testing problem and employ a binomial–binomial model to test the significance of observed allele frequency...

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Main Authors: Wei, Zhi, Wang, Wei, Hu, Pingzhao, Lyon, Gholson J., Hakonarson, Hakon
Format: Online
Language:English
Published: Oxford University Press 2011
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3201884/
id pubmed-3201884
recordtype oai_dc
spelling pubmed-32018842011-10-26 SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data Wei, Zhi Wang, Wei Hu, Pingzhao Lyon, Gholson J. Hakonarson, Hakon Methods Online We develop a statistical tool SNVer for calling common and rare variants in analysis of pooled or individual next-generation sequencing (NGS) data. We formulate variant calling as a hypothesis testing problem and employ a binomial–binomial model to test the significance of observed allele frequency against sequencing error. SNVer reports one single overall P-value for evaluating the significance of a candidate locus being a variant based on which multiplicity control can be obtained. This is particularly desirable because tens of thousands loci are simultaneously examined in typical NGS experiments. Each user can choose the false-positive error rate threshold he or she considers appropriate, instead of just the dichotomous decisions of whether to ‘accept or reject the candidates’ provided by most existing methods. We use both simulated data and real data to demonstrate the superior performance of our program in comparison with existing methods. SNVer runs very fast and can complete testing 300 K loci within an hour. This excellent scalability makes it feasible for analysis of whole-exome sequencing data, or even whole-genome sequencing data using high performance computing cluster. SNVer is freely available at http://snver.sourceforge.net/. Oxford University Press 2011-10 2011-08-03 /pmc/articles/PMC3201884/ /pubmed/21813454 http://dx.doi.org/10.1093/nar/gkr599 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Wei, Zhi
Wang, Wei
Hu, Pingzhao
Lyon, Gholson J.
Hakonarson, Hakon
spellingShingle Wei, Zhi
Wang, Wei
Hu, Pingzhao
Lyon, Gholson J.
Hakonarson, Hakon
SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data
author_facet Wei, Zhi
Wang, Wei
Hu, Pingzhao
Lyon, Gholson J.
Hakonarson, Hakon
author_sort Wei, Zhi
title SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data
title_short SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data
title_full SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data
title_fullStr SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data
title_full_unstemmed SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data
title_sort snver: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data
description We develop a statistical tool SNVer for calling common and rare variants in analysis of pooled or individual next-generation sequencing (NGS) data. We formulate variant calling as a hypothesis testing problem and employ a binomial–binomial model to test the significance of observed allele frequency against sequencing error. SNVer reports one single overall P-value for evaluating the significance of a candidate locus being a variant based on which multiplicity control can be obtained. This is particularly desirable because tens of thousands loci are simultaneously examined in typical NGS experiments. Each user can choose the false-positive error rate threshold he or she considers appropriate, instead of just the dichotomous decisions of whether to ‘accept or reject the candidates’ provided by most existing methods. We use both simulated data and real data to demonstrate the superior performance of our program in comparison with existing methods. SNVer runs very fast and can complete testing 300 K loci within an hour. This excellent scalability makes it feasible for analysis of whole-exome sequencing data, or even whole-genome sequencing data using high performance computing cluster. SNVer is freely available at http://snver.sourceforge.net/.
publisher Oxford University Press
publishDate 2011
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3201884/
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