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

Na You, Gabriel Murillo, Xiaoquan Su, Xiaowei Zeng, Jian Xu, Kang Ning, ShouDong Zhang, Jian-Kang Zhu, Xinping Cui

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)643-650
Number of pages8
JournalBioinformatics
Volume28
Issue number5
DOIs
StatePublished - Jan 16 2012

ASJC Scopus subject areas

  • Biochemistry
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Molecular Biology
  • Statistics and Probability
  • Computer Science Applications

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