High genetic variability in viral populations plays an important role in disease progression, pathogenesis, and drug resistance. The last few years has seen significant progress in the development of methods for reconstruction of viral populations using data from next-generation sequencing technologies. These methods identify the differences between individual haplotypes by mapping the short reads to a reference genome. Much less has been published about resolving the population structure when a reference genome is lacking or is not well-defined, which severely limits the application of these new technologies to resolve virus population structure. We describe a computational framework, called Mutant-Bin, for clustering individual haplotypes in a viral population and determining their prevalence based on a set of deep sequencing reads. The main advantages of our method are that: (i) it enables determination of the population structure and haplotype frequencies when a reference genome is lacking; (ii) the method is unsupervised - the number of haplotypes does not have to be specified in advance; and (iii) it identifies the polymorphic sites that co-occur in a subset of haplotypes and the frequency with which they appear in the viral population. The method was evaluated on simulated reads with sequencing errors and 454 pyrosequencing reads from HIV samples. Our method clustered a high percentage of haplotypes with low false-positive rates, even at low genetic diversity.
All Science Journal Classification (ASJC) codes
- Modeling and Simulation
- Molecular Biology
- Computational Mathematics
- Computational Theory and Mathematics