Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage

Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage

Author Zukurov, Jean P. Autor UNIFESP Google Scholar
do Nascimento-Brito, Sieberth Autor UNIFESP Google Scholar
Volpini, Angela C. Google Scholar
Oliveira, Guilherme C. Google Scholar
Janini, Luiz Mario R. Autor UNIFESP Google Scholar
Antoneli, Fernando Autor UNIFESP Google Scholar
Abstract Background: In this paper we propose a method and discuss its computational implementation as an integrated tool for the analysis of viral genetic diversity on data generated by high-throughput sequencing. The main motivation for this work is to better understand the genetic diversity of viruses with high rates of nucleotide substitution, as HIV-1 and Influenza. Most methods for viral diversity estimation proposed so far are intended to take benefit of the longer reads produced by some next-generation sequencing platforms in order to estimate a population of haplotypes which represent the diversity of the original population. The method proposed here is custom-made to take advantage of the very low error rate and extremely deep coverage per site, which are the main features of some neglected technologies that have not received much attention due to the short length of its reads, which precludes haplotype estimation. This approach allowed us to avoid some hard problems related to haplotype reconstruction (need of long reads, preliminary error filtering and assembly). Results: We propose to measure genetic diversity of a viral population through a family of multinomial probability distributions indexed by the sites of the virus genome, each one representing the distribution of nucleic bases per site. Moreover, the implementation of the method focuses on two main optimization strategies: a read mapping/alignment procedure that aims at the recovery of the maximum possible number of short-reads; the inference of the multinomial parameters in a Bayesian framework with smoothed Dirichlet estimation. The Bayesian approach provides conditional probability distributions for the multinomial parameters allowing one to take into account the prior information of the control experiment and providing a natural way to separate signal from noise, since it automatically furnishes Bayesian confidence intervals and thus avoids the drawbacks of preliminary error filtering. Conclusions: The methods described in this paper have been implemented as an integrated tool called Tanden (Tool for Analysis of Diversity in Viral Populations) and successfully tested on samples obtained from HIV-1 strain NL4-3 (group M, subtype B) cultivations on primary human cell cultures in many distinct viral propagation conditions. Tanden is written in C# (Microsoft), runs on the Windows operating system, and can be downloaded from:
Keywords Viral diversity
Bayesian inference
Dirichlet distribution
xmlui.dri2xhtml.METS-1.0.item-coverage London
Language English
Sponsor Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG)
Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)
Fogarty International Center National Institutes of Health
Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)
Grant number FAPEMIG: 309/2014
FAPEMIG: 312/2012-4
FAPEMIG: 306362/2012-0
CNPq: PQ-306362/2012-0
Fogarty International Center National Institutes of Health: TW007012
FAPESP: 2009/14543-0
Date 2016
Published in Algorithms For Molecular Biology. London, v. 11, p. -, 2016.
ISSN 1748-7188 (Sherpa/Romeo, impact factor)
Publisher Biomed Central Ltd
Extent -
Access rights Open access Open Access
Type Article
Web of Science ID WOS:000371955800001

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