SNP calling using genotype model selection on high-throughput sequencing data

Handle URI:
http://hdl.handle.net/10754/562059
Title:
SNP calling using genotype model selection on high-throughput sequencing data
Authors:
You, Na; Murillo, Gabriel; Su, Xiaoquan; Zeng, Xiaowei; Xu, Jian; Ning, Kang; Zhang, ShouDong; Zhu, Jian-Kang; Cui, Xinping
Abstract:
Motivation: A review of the available single nucleotide polymorphism (SNP) calling procedures for Illumina high-throughput sequencing (HTS) platform data reveals that most rely mainly on base-calling and mapping qualities as sources of error when calling SNPs. Thus, errors not involved in base-calling or alignment, such as those in genomic sample preparation, are not accounted for.Results: A novel method of consensus and SNP calling, Genotype Model Selection (GeMS), is given which accounts for the errors that occur during the preparation of the genomic sample. Simulations and real data analyses indicate that GeMS has the best performance balance of sensitivity and positive predictive value among the tested SNP callers. © The Author 2012. Published by Oxford University Press. All rights reserved.
KAUST Department:
Center for Desert Agriculture; Biological and Environmental Sciences and Engineering (BESE) Division
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
Issue Date:
16-Jan-2012
DOI:
10.1093/bioinformatics/bts001
PubMed ID:
22253293
PubMed Central ID:
PMC3338331
Type:
Article
ISSN:
13674803
Sponsors:
National Science Foundation (DBI0646024 to X.C. and N.Y.); National Institutes of Health (R01GM070795 to J.Z. and S.Z.); National Natural Science Foundation of China (30870572 to X.S., X.Z., J.X. and K.N.).
Additional Links:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3338331
Appears in Collections:
Articles; Center for Desert Agriculture; Biological and Environmental Sciences and Engineering (BESE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorYou, Naen
dc.contributor.authorMurillo, Gabrielen
dc.contributor.authorSu, Xiaoquanen
dc.contributor.authorZeng, Xiaoweien
dc.contributor.authorXu, Jianen
dc.contributor.authorNing, Kangen
dc.contributor.authorZhang, ShouDongen
dc.contributor.authorZhu, Jian-Kangen
dc.contributor.authorCui, Xinpingen
dc.date.accessioned2015-08-03T09:43:48Zen
dc.date.available2015-08-03T09:43:48Zen
dc.date.issued2012-01-16en
dc.identifier.issn13674803en
dc.identifier.pmid22253293en
dc.identifier.doi10.1093/bioinformatics/bts001en
dc.identifier.urihttp://hdl.handle.net/10754/562059en
dc.description.abstractMotivation: A review of the available single nucleotide polymorphism (SNP) calling procedures for Illumina high-throughput sequencing (HTS) platform data reveals that most rely mainly on base-calling and mapping qualities as sources of error when calling SNPs. Thus, errors not involved in base-calling or alignment, such as those in genomic sample preparation, are not accounted for.Results: A novel method of consensus and SNP calling, Genotype Model Selection (GeMS), is given which accounts for the errors that occur during the preparation of the genomic sample. Simulations and real data analyses indicate that GeMS has the best performance balance of sensitivity and positive predictive value among the tested SNP callers. © The Author 2012. Published by Oxford University Press. All rights reserved.en
dc.description.sponsorshipNational Science Foundation (DBI0646024 to X.C. and N.Y.); National Institutes of Health (R01GM070795 to J.Z. and S.Z.); National Natural Science Foundation of China (30870572 to X.S., X.Z., J.X. and K.N.).en
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3338331en
dc.titleSNP calling using genotype model selection on high-throughput sequencing dataen
dc.typeArticleen
dc.contributor.departmentCenter for Desert Agricultureen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.identifier.journalBioinformaticsen
dc.identifier.pmcidPMC3338331en
dc.contributor.institutionDepartment of Statistical Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, Chinaen
dc.contributor.institutionDepartment of Statistics, University of California, Riverside CA 92521, United Statesen
dc.contributor.institutionQingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, Chinaen
dc.contributor.institutionDepartment of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN 47907, United Statesen
dc.contributor.institutionCenter for Plant Cell Biology, Institute for Integrative Genome Biology, University of California, Riverside, CA 92521, United Statesen
kaust.authorZhang, ShouDongen
kaust.authorZhu, Jian-Kangen

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