SNP calling using genotype model selection on high-throughput sequencing data
KAUST DepartmentCenter for Desert Agriculture
Biological and Environmental Sciences and Engineering (BESE) Division
MetadataShow full item record
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.
SponsorsNational 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.).
PublisherOxford University Press (OUP)
PubMed Central IDPMC3338331
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