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dc.contributor.authorYou, Na
dc.contributor.authorMurillo, Gabriel
dc.contributor.authorSu, Xiaoquan
dc.contributor.authorZeng, Xiaowei
dc.contributor.authorXu, Jian
dc.contributor.authorNing, Kang
dc.contributor.authorZhang, ShouDong
dc.contributor.authorZhu, Jian-Kang
dc.contributor.authorCui, Xinping
dc.date.accessioned2015-08-03T09:43:48Z
dc.date.available2015-08-03T09:43:48Z
dc.date.issued2012-01-16
dc.identifier.citationYou, N., Murillo, G., Su, X., Zeng, X., Xu, J., Ning, K., … Cui, X. (2012). SNP calling using genotype model selection on high-throughput sequencing data. Bioinformatics, 28(5), 643–650. doi:10.1093/bioinformatics/bts001
dc.identifier.issn13674803
dc.identifier.pmid22253293
dc.identifier.doi10.1093/bioinformatics/bts001
dc.identifier.urihttp://hdl.handle.net/10754/562059
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.
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.).
dc.publisherOxford University Press (OUP)
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3338331
dc.titleSNP calling using genotype model selection on high-throughput sequencing data
dc.typeArticle
dc.contributor.departmentAcademic Affairs
dc.contributor.departmentBiological and Environmental Science and Engineering (BESE) Division
dc.contributor.departmentCenter for Desert Agriculture
dc.contributor.departmentOffice of the VP
dc.identifier.journalBioinformatics
dc.identifier.pmcidPMC3338331
dc.contributor.institutionDepartment of Statistical Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, China
dc.contributor.institutionDepartment of Statistics, University of California, Riverside CA 92521, United States
dc.contributor.institutionQingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China
dc.contributor.institutionDepartment of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN 47907, United States
dc.contributor.institutionCenter for Plant Cell Biology, Institute for Integrative Genome Biology, University of California, Riverside, CA 92521, United States
kaust.personZhang, ShouDong
kaust.personZhu, Jian-Kang
dc.date.published-online2012-01-16
dc.date.published-print2012-03-01


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