An empirical Bayes method for genotyping and SNP detection using multi-sample next-generation sequencing data | Zendy

Gongyi Huang | Zendy; Shaoli Wang | Zendy; Xueqin Wang | Zendy; Na You | Zendy

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An empirical Bayes method for genotyping and SNP detection using multi-sample next-generation sequencing data

Author(s) -

Gongyi Huang,

Shaoli Wang,

Xueqin Wang,

Na You

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw409

Subject(s) - bayes' theorem , computer science , genotyping , data mining , sample size determination , word error rate , statistical power , snp genotyping , statistics , bayesian probability , genotype , artificial intelligence , mathematics , biology , genetics , gene

The development of next generation sequencing technology provides an efficient and powerful approach to rare variant detection. To identify genetic variations, the essential question is how to quantity the sequencing error rate in the data. Because of the advantage of easy implementation and the ability to integrate data from different sources, the empirical Bayes method is popularly employed to estimate the sequencing error rate for SNP detection.

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