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Zukurov JP, do Nascimento-Brito S, Volpini AC, et al. Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage. Algorithms Mol Biol. 2016;11:2doi: 10.1186/s13015-016-0064-x.
Zukurov, J. P., do Nascimento-Brito, S., Volpini, A. C., Oliveira, G. C., Janini, L. M., & Antoneli, F. (2016). Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage. Algorithms for molecular biology : AMB, 112. https://doi.org/10.1186/s13015-016-0064-x
Zukurov, Jean P, et al. "Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage." Algorithms for molecular biology : AMB vol. 11 (2016): 2. doi: https://doi.org/10.1186/s13015-016-0064-x
Zukurov JP, do Nascimento-Brito S, Volpini AC, Oliveira GC, Janini LM, Antoneli F. Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage. Algorithms Mol Biol. 2016 Mar 11;11:2. doi: 10.1186/s13015-016-0064-x. eCollection 2016. PMID: 26973707; PMCID: PMC4788855.
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Algorithms Mol Biol. 2016 Mar 11;11:2. doi: 10.1186/s13015-016-0064-x. eCollection 2016.

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

Algorithms for molecular biology : AMB

Jean P Zukurov, Sieberth do Nascimento-Brito, Angela C Volpini, Guilherme C Oliveira, Luiz Mario R Janini, Fernando Antoneli

Affiliations

  1. Departmento de Medicina, Escola Paulista de Medicina (EPM), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.
  2. Departamento de Microbiologia, Imunologia e Parasitologia, Escola Paulista de Medicina (EPM), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil ; Departamento de Microbiologia e Imunologia Veterinária, Universidade Federal Rural do Rio de Janeiro (UFRRJ), Rio de Janeiro, Brazil.
  3. Genomics and Computational Biology Group, Centro de Pesquisas René Rachou (CPqRR), Fundação Oswaldo Cruz (FIOCRUZ), Belo Horizonte, Brazil.
  4. Departmento de Medicina, Escola Paulista de Medicina (EPM), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil ; Departamento de Microbiologia, Imunologia e Parasitologia, Escola Paulista de Medicina (EPM), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.
  5. Departmento de Informática em Saúde, Escola Paulista de Medicina (EPM), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil ; Laboratório de de Biocomplexidade e Genômica Evolutiva, Escola Paulista de Medicina (EPM), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.

PMID: 26973707 PMCID: PMC4788855 DOI: 10.1186/s13015-016-0064-x

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: http://tanden.url.ph/.

Keywords: Bayesian inference; Dirichlet distribution; Viral diversity

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