Title
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.
Year
DOI
Venue
2008
10.1371/journal.pcbi.1000074
PLOS COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
cost effectiveness,expectation maximization,immune response,genetic variation,error correction,em algorithm,statistical model,evolutionary dynamics,algorithms,drug resistance,drug therapy,sequence alignment
Population,Effective population size,Biology,Genetic variation,Haplotype,Population genetics,Pyrosequencing,Statistical model,Evolutionary dynamics,Bioinformatics,Genetics
Journal
Volume
Issue
ISSN
4
5
1553-734X
Citations 
PageRank 
References 
42
3.69
7
Authors
9
Name
Order
Citations
PageRank
Nicholas Eriksson11029.05
Lior Pachter21026121.08
Yumi Mitsuya3423.69
Soo-Yon Rhee4717.16
Chunlin Wang5423.69
Baback Gharizadeh6423.69
Mostafa Ronaghi7746.18
Robert W Shafer88221.01
Niko Beerenwinkel9696102.47