Viral Population Estimation Using Pyrosequencing

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 expectation–maximization (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. The genetic diversity of viral populations is important for biomedical problems such as disease progression, vaccine design, and drug resistance, yet it is not generally well understood. In this paper, we use pyrosequencing, a novel DNA sequencing technique, to reconstruct viral populations. Pyrosequencing produces DNA sequences, called reads, in numbers much greater than standard DNA sequencing techniques. However, these reads are substantially shorter and more error-prone than those obtained from standard sequencing techniques. Therefore, pyrosequencing data requires new methods of analysis. Here, we develop mathematical and statistical tools for reconstructing viral populations using pyrosequencing. To this end, we show how to correct errors in the reads and assemble them into the different viral strains present in the population. We apply these methods to HIV-1 populations from drug-resistant patients and show that our techniques produce results quite close to accepted techniques at a lower cost and potentially higher resolution.