Single-sample SNP detection by empirical Bayes method using next-generation sequencing data

The rapid development of next generation sequencing technology is changing the way of biological research in many aspects, which has become the most popular platform for the genomic structural variation detection. In this paper, we focus on the single-sample next generation sequencing data analysis, and propose a hierarchical structure to model the dispersion of minor allele frequency in the genome scale. The empirical Bayes method is employed to estimate the hyper-parameters, and the minor allele is identified as a sequencing error or heterozygous allele according to the posterior probabilities. We suggest to leave the ambiguous positions with moderate posterior probabilities ungenotyped for better genotype-call error control. The performances of our proposed method are investigated by simulations and a real dataset.