title: 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 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.
  
  
  CONCLUSIONS
  We 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.
authors.affiliation: Department of Biomedical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, 01609, USA.
authors.forename: Fan
authors.initials: F
authors.lastname: Zhang
authors.affiliation: Department of Biomedical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, 01609, USA. flaherty@math.umass.edu.|Department of Mathematics and Statistics, University of Massachusetts, Amherst, 710 N. Pleasant Street, Amherst, 01003, USA. flaherty@math.umass.edu.
authors.forename: Patrick
authors.identifier: http://orcid.org/0000-0001-6097-6572
authors.initials: P
authors.lastname: Flaherty
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D005787:Gene Frequency; D059014:High-Throughput Nucleotide Sequencing; D008390:Markov Chains; D008962:Models, Theoretical; D009010:Monte Carlo Method; D017422:Sequence Analysis, DNA
keywords: Bayesian statistical method; Next-generation sequencing; Single nucleotide variant detection; Variational inference