A Bayesian Model for SNP Discovery Based on Next-Generation Sequencing Data.

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.

单核苷酸多态性（SNP）是DNA序列中的单个碱基变化，是最常见的多态性。由于一些SNP对疾病易感性有重大影响，检测SNP在生物医学研究中起着重要作用。为了充分利用新一代测序（NGS）技术并更有效地检测SNP，我们提出了一种贝叶斯方法，该方法计算每个覆盖的基因组位置上隐藏核苷酸变异的后验概率。隐藏核苷酸变异后验概率较高的位置更有可能是一个SNP。我们将所提出的方法应用于检测两种细胞系中的SNP：前列腺癌细胞系PC3和胚胎干细胞系H1。我们的结果与dbSNP数据库之间的比较显示出很高的重叠比例（>95%）。那些仅在我们的模型下被检测到但不在dbSNP中的位置可作为新SNP的候选位点。