Viral population estimation using pyrosequencing

TL;DR

This paper introduces computational methods to analyze pyrosequencing data for estimating the structure of viral populations within hosts, aiding understanding of viral diversity and evolution.

Contribution

It presents a novel statistical and combinatorial approach for reconstructing viral haplotypes and estimating their frequencies from pyrosequencing data.

Findings

Effective population reconstruction demonstrated through simulations

Accurate haplotype frequency estimation using EM algorithm

Validation with real HIV sequence data

Abstract

The diversity of virus populations within single infected hosts presents a major difficulty for the natural immune response as well as for vaccine design and antiviral drug therapy. Recently developed pyrophosphate based sequencing technologies (pyrosequencing) can be used for quantifying this diversity by ultra-deep sequencing of virus samples. We present computational methods for the analysis of such sequence data and apply these techniques to pyrosequencing data obtained from HIV populations within patients harboring drug resistant virus strains. Our main result is the estimation of the population structure of the sample from the pyrosequencing reads. This inference is based on a statistical approach to error correction, followed by a combinatorial algorithm for constructing a minimal set of haplotypes that explain the data. Using this set of explaining haplotypes, we apply a statistical model to infer the frequencies of the haplotypes in the population via an EM algorithm. We demonstrate that pyrosequencing reads allow for effective population reconstruction by extensive simulations and by comparison to 165 sequences obtained directly from clonal sequencing of four independent, diverse HIV populations. Thus, pyrosequencing can be used for cost-effective estimation of the structure of virus populations, promising new insights into viral evolutionary dynamics and disease control strategies.