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 hi

SUBMITTER: Xu Y 

PROVIDER: S-EPMC4697941 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature