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

ABSTRACT:

Background

The 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.

Results

We 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 Ch

SUBMITTER: Zhang F 

PROVIDER: S-EPMC5244592 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature