title: A Bayesian Model for SNP Discovery Based on Next-Generation Sequencing Data.
abstract: A single-nucleotide polymorphism (SNP) is a single base change in the DNA sequence and is the most common polymorphism. Since some SNPs have a major influence on disease susceptibility, detecting SNPs plays an important role in biomedical research. To take fully advantage of the next-generation sequencing (NGS) technology and detect SNP more effectively, we propose a Bayesian approach that computes a posterior probability of hidden nucleotide variations at each covered genomic position. The position with higher posterior probability of hidden nucleotide variation has a higher chance to be a SNP. We apply the proposed method to detect SNPs in two cell lines: the prostate cancer cell line PC3 and the embryonic stem cell line H1. A comparison between our results with dbSNP database shows a high ratio of overlap (>95%). The positions that are called only under our model but not in dbSNP may serve as candidates for new SNPs.
authors.affiliation: Department of Statistics, Rice University Houston, TX.
authors.forename: Yanxun
authors.initials: Y
authors.lastname: Xu
authors.affiliation: Department of Bioinformatics and Computational Biology, The University of Texas, MD Anderson Cancer Center Houston, TX.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Zheng
authors.affiliation: Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine Houston, TX.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Yuan
authors.affiliation: Department of Biochemistry and Molecular Biology, The University of Texas, MD Anderson Cancer Center Houston, TX.
authors.forename: Marcos R
authors.initials: MR
authors.lastname: Estecio
authors.affiliation: Fels Institute for Cancer Research and Molecular Biology, Temple University Philadelphia, PA.
authors.forename: Jean-Pierre
authors.initials: JP
authors.lastname: Issa
authors.affiliation: CCRI, NorthShore University HealthSystem Chicago, IL.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Ji
authors.affiliation: Department of Bioinformatics and Computational Biology, The University of Texas, MD Anderson Cancer Center Houston, TX.
authors.forename: Shoudan
authors.initials: S
authors.lastname: Liang