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

Detalhes bibliográficos
Autor(a) principal: Zukurov, Jean Paulo Lopes
Data de Publicação: 2016
Outros Autores: Brito, Sieberth do Nascimento, Volpini, Angela Cristina, Oliveira, Guilherme Corrêa de, Janini, Luiz Mario Ramos, Antoneli Junior, Fernando Martins
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
DOI: 10.1186/s13015-016-0064-x. eCollection 2016
Texto Completo: https://arca.fiocruz.br/handle/icict/15354
Resumo: Universidade Federal de São Paulo. Escola Paulista de Medicina. Departamento de Medicina. São Paulo, SP, Brasil.
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spelling Zukurov, Jean Paulo LopesBrito, Sieberth do NascimentoVolpini, Angela CristinaOliveira, Guilherme Corrêa deJanini, Luiz Mario RamosAntoneli Junior, Fernando Martins2016-08-23T18:44:25Z2016-08-23T18:44:25Z2016ZUKUROV, Jean Paulo Lopes et al. Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage. Algorithms Mol Biol., v. 11, p. 2, 20161748-7188https://arca.fiocruz.br/handle/icict/1535410.1186/s13015-016-0064-x. eCollection 2016engBioMed CentralEstimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverageinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleUniversidade Federal de São Paulo. Escola Paulista de Medicina. Departamento de Medicina. São Paulo, SP, Brasil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Departamento de Microbiologia. São Paulo, SP, Brasil/ Universidade Federal Rural do Rio de Janeiro. Departamento de Microbiologia e Imunologia Veterinária. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Centro de Pesquisas René Rachou. Grupo de Genomica e Biologia Computacional. Belo Horizonte, MG, Brasil.Fundação Oswaldo Cruz. Centro de Pesquisas René Rachou. Grupo de Genomica e Biologia Computacional. Belo Horizonte, MG, Brasil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Departamento de Medicina. São Paulo, SP, Brasil/Universidade Federal de São Paulo. Escola Paulista de Medicina. Departamento de Microbiologia. São Paulo, SP, Brasil.Universidade Federal de São Paulo. Escola Paulista de Medicina. Departamento de Informática em Saúde. São Paulo, SP, Brasil/Universidade Federal de São Paulo. Escola Paulista de Medicina. Laboratório de Biocomplexidade e Genômica Evolutiva. São Paulo, SP, BrasilBACKGROUND: 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.Bayesian inferenceDirichlet distributionViral diversity10 Redução das desigualdadesinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-82991https://arca.fiocruz.br/bitstreams/bfd7c8c7-9f3a-44da-afa7-6b0d6a8920eb/download5a560609d32a3863062d77ff32785d58MD51falseAnonymousREADORIGINALve_Zukurov_Jean_Estimation_CPqRR_2016.pdfve_Zukurov_Jean_Estimation_CPqRR_2016.pdfapplication/pdf2592734https://arca.fiocruz.br/bitstreams/11f1f13b-eaa3-46fc-844e-157bd25db68c/download828bb4463862e79c125c937639dc1451MD52trueAnonymousREADTEXTve_Zukurov_Jean_Estimation_CPqRR_2016.pdf.txtve_Zukurov_Jean_Estimation_CPqRR_2016.pdf.txtExtracted texttext/plain73713https://arca.fiocruz.br/bitstreams/ce243bfa-7ad4-40e3-97b8-301c82bc1be4/download36f69eb8b82af605de6e63b1bbfbdc7cMD57falseAnonymousREADTHUMBNAILve_Zukurov_Jean_Estimation_CPqRR_2016.pdf.jpgve_Zukurov_Jean_Estimation_CPqRR_2016.pdf.jpgGenerated Thumbnailimage/jpeg5360https://arca.fiocruz.br/bitstreams/40a4f101-d792-4f6e-8d05-d50df0e11609/download03083704ad87debb7a3ee42ca17d932fMD58falseAnonymousREADicict/153542025-07-29 19:04:12.974open.accessoai:arca.fiocruz.br:icict/15354https://arca.fiocruz.brRepositório InstitucionalPUBhttps://www.arca.fiocruz.br/oai/requestrepositorio.arca@fiocruz.bropendoar:21352025-07-29T22:04:12Repositório Institucional da FIOCRUZ (ARCA) - 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dc.title.none.fl_str_mv Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage
title Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage
spellingShingle Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage
Zukurov, Jean Paulo Lopes
Bayesian inference
Dirichlet distribution
Viral diversity
10 Redução das desigualdades
title_short Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage
title_full Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage
title_fullStr Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage
title_full_unstemmed Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage
title_sort Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage
author Zukurov, Jean Paulo Lopes
author_facet Zukurov, Jean Paulo Lopes
Brito, Sieberth do Nascimento
Volpini, Angela Cristina
Oliveira, Guilherme Corrêa de
Janini, Luiz Mario Ramos
Antoneli Junior, Fernando Martins
author_role author
author2 Brito, Sieberth do Nascimento
Volpini, Angela Cristina
Oliveira, Guilherme Corrêa de
Janini, Luiz Mario Ramos
Antoneli Junior, Fernando Martins
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Zukurov, Jean Paulo Lopes
Brito, Sieberth do Nascimento
Volpini, Angela Cristina
Oliveira, Guilherme Corrêa de
Janini, Luiz Mario Ramos
Antoneli Junior, Fernando Martins
dc.subject.en.none.fl_str_mv Bayesian inference
Dirichlet distribution
Viral diversity
topic Bayesian inference
Dirichlet distribution
Viral diversity
10 Redução das desigualdades
dc.subject.ods.none.fl_str_mv 10 Redução das desigualdades
description Universidade Federal de São Paulo. Escola Paulista de Medicina. Departamento de Medicina. São Paulo, SP, Brasil.
publishDate 2016
dc.date.accessioned.fl_str_mv 2016-08-23T18:44:25Z
dc.date.available.fl_str_mv 2016-08-23T18:44:25Z
dc.date.issued.fl_str_mv 2016
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv ZUKUROV, Jean Paulo Lopes et al. Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage. Algorithms Mol Biol., v. 11, p. 2, 2016
dc.identifier.uri.fl_str_mv https://arca.fiocruz.br/handle/icict/15354
dc.identifier.issn.none.fl_str_mv 1748-7188
dc.identifier.doi.none.fl_str_mv 10.1186/s13015-016-0064-x. eCollection 2016
identifier_str_mv ZUKUROV, Jean Paulo Lopes et al. Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage. Algorithms Mol Biol., v. 11, p. 2, 2016
1748-7188
10.1186/s13015-016-0064-x. eCollection 2016
url https://arca.fiocruz.br/handle/icict/15354
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv BioMed Central
publisher.none.fl_str_mv BioMed Central
dc.source.none.fl_str_mv reponame:Repositório Institucional da FIOCRUZ (ARCA)
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
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instacron_str FIOCRUZ
institution FIOCRUZ
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collection Repositório Institucional da FIOCRUZ (ARCA)
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