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Recent advances in inferring viral diversity from high-throughput sequencing data.

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      Abstract

      Rapidly evolving RNA viruses prevail within a host as a collection of closely related variants, referred to as viral quasispecies. Advances in high-throughput sequencing (HTS) technologies have facilitated the assessment of the genetic diversity of such virus populations at an unprecedented level of detail. However, analysis of HTS data from virus populations is challenging due to short, error-prone reads. In order to account for uncertainties originating from these limitations, several computational and statistical methods have been developed for studying the genetic heterogeneity of virus population. Here, we review methods for the analysis of HTS reads, including approaches to local diversity estimation and global haplotype reconstruction. Challenges posed by aligning reads, as well as the impact of reference biases on diversity estimates are also discussed. In addition, we address some of the experimental approaches designed to improve the biological signal-to-noise ratio. In the future, computational methods for the analysis of heterogeneous virus populations are likely to continue being complemented by technological developments.

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      Affiliations
      [1 ] Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland; SIB, Basel, Switzerland.
      [2 ] Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland; SIB, Basel, Switzerland. Electronic address: niko.beerenwinkel@bsse.ethz.ch.
      Journal
      Virus Res.
      Virus research
      Elsevier BV
      1872-7492
      0168-1702
      Sep 28 2016
      27693290 S0168-1702(16)30413-0 10.1016/j.virusres.2016.09.016

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