Single-sample SNP Detection By Empirical Bayesian Method Using Next Generation Sequencing Data

You Na; Xu Qiuya; Kou Qiang; Ding Weijie; Wang Xueqin

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Single-sample SNP Detection By Empirical Bayesian Method Using Next Generation Sequencing Data

You Na *,Xu Qiuya,Kou Qiang,Ding Weijie,Wang Xueqin *

School of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, China 510275

*Correspondence author

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Funding: NSFC（No.11001280）, RFDP(20110171110037) and National Program on Key Basic Research Project（No.2012CB517900）, RFDP （No.20120171120006）, Na You: NSFC （No.11301554）, China (34000-3211702). Xueqin Wang: NCET（No.12-0559）

Opened online:12 November 2013

Accepted by: none

Citation: You Na,Xu Qiuya,Kou Qiang.Single-sample SNP Detection By Empirical Bayesian Method Using Next Generation Sequencing Data[OL]. [12 November 2013] http://en.paper.edu.cn/en_releasepaper/content/4565919

The rapid development of next generation sequencing technology is changing the way of biological research in many aspects, which has been the most popular platform to identify the genome structural variations. In this paper, dealing with the single-sample next generation sequencing data, we propose an empirical Bayesian algorithm to measures the sequencing error rate in the genome scale by combing information across different positions. According to the posterior probability, the minor allele is recognized as a sequencing error or heterozygous allele, consequently for genotyping and SNP calling. The ambiguous positions with moderate posterior probabilities are left ungenotyped to control the sensitivity and specificity. The performances of our proposed method are investigated by simulations and a real dataset.

Keywords:Statistical inference, Next generation sequencing, Single-sample, SNP detection, Empirical Bayesian method.

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