Abstract
Next-generation sequencing allows for cost-effective probing of virus populations at an unprecedented level of detail. The massively parallel sequencing approach can detect low-frequency mutations and it provides a snapshot of the entire virus population. However, analyzing ultra-deep sequencing data obtained from diverse virus populations is challenging because of PCR and sequencing errors and short read lengths, such that the experiment provides only indirect evidence of the underlying viral population structure. Recent computational and statistical advances allow for accommodating some of the confounding factors, including methods for read error correction, haplotype reconstruction, and haplotype frequency estimation. With these methods ultra-deep sequencing can be more reliably used to analyze, in a quantitative manner, the genetic diversity of virus populations.
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