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Published on: December 10, 2012
A Bayesian Model for SNP Discovery Based on Next-Generation Sequencing Data
Yanxun Xu1, Xiaofeng Zheng2, Yuan Yuan3
1Department of Statistics, Rice University Houston, TX.
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.
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