Variational inference for rare variant detection in deep, heterogeneous next-generation sequencing data

BMC Bioinformatics

Methodology Article

Fan Zhang¹  Patrick Flaherty² 

[1] Department of Biomedical Engineering, Worcester Polytechnic Institute, 100 Institute Road, 01609, Worcester, USA;Department of Biomedical Engineering, Worcester Polytechnic Institute, 100 Institute Road, 01609, Worcester, USA;Department of Mathematics and Statistics, University of Massachusetts, Amherst, 710 N. Pleasant Street, 01003, Amherst, USA;

关键词: Single nucleotide variant detection;    Next-generation sequencing;    Bayesian statistical method;    Variational inference;   

DOI  :  10.1186/s12859-016-1451-5

 received in 2016-06-17, accepted in 2016-12-22,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundThe detection of rare single nucleotide variants (SNVs) is important for understanding genetic heterogeneity using next-generation sequencing (NGS) data. Various computational algorithms have been proposed to detect variants at the single nucleotide level in mixed samples. Yet, the noise inherent in the biological processes involved in NGS technology necessitates the development of statistically accurate methods to identify true rare variants.ResultsWe propose a Bayesian statistical model and a variational expectation maximization (EM) algorithm to estimate non-reference allele frequency (NRAF) and identify SNVs in heterogeneous cell populations. We demonstrate that our variational EM algorithm has comparable sensitivity and specificity compared with a Markov Chain Monte Carlo (MCMC) sampling inference algorithm, and is more computationally efficient on tests of relatively low coverage (27× and 298×) data. Furthermore, we show that our model with a variational EM inference algorithm has higher specificity than many state-of-the-art algorithms. In an analysis of a directed evolution longitudinal yeast data set, we are able to identify a time-series trend in non-reference allele frequency and detect novel variants that have not yet been reported. Our model also detects the emergence of a beneficial variant earlier than was previously shown, and a pair of concomitant variants.ConclusionsWe developed a variational EM algorithm for a hierarchical Bayesian model to identify rare variants in heterogeneous next-generation sequencing data. Our algorithm is able to identify variants in a broad range of read depths and non-reference allele frequencies with high sensitivity and specificity.

【 授权许可】

CC BY   
© The Author(s) 2017

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