Computational framework for next-generation sequencing of heterogeneous viral populations using combinatorial pooling.
Bioinformatics
; 31(5): 682-90, 2015 Mar 01.
Article
em En
| MEDLINE
| ID: mdl-25359889
ABSTRACT
MOTIVATION Next-generation sequencing (NGS) allows for analyzing a large number of viral sequences from infected patients, providing an opportunity to implement large-scale molecular surveillance of viral diseases. However, despite improvements in technology, traditional protocols for NGS of large numbers of samples are still highly cost and labor intensive. One of the possible cost-effective alternatives is combinatorial pooling. Although a number of pooling strategies for consensus sequencing of DNA samples and detection of SNPs have been proposed, these strategies cannot be applied to sequencing of highly heterogeneous viral populations. RESULTS:
We developed a cost-effective and reliable protocol for sequencing of viral samples, that combines NGS using barcoding and combinatorial pooling and a computational framework including algorithms for optimal virus-specific pools design and deconvolution of individual samples from sequenced pools. Evaluation of the framework on experimental and simulated data for hepatitis C virus showed that it substantially reduces the sequencing costs and allows deconvolution of viral populations with a high accuracy. AVAILABILITY AND IMPLEMENTATION The source code and experimental data sets are available at http//alan.cs.gsu.edu/NGS/?q=content/pooling.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Vírus
/
Algoritmos
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DNA Viral
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Genoma Viral
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Análise de Sequência de DNA
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Biologia Computacional
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Sequenciamento de Nucleotídeos em Larga Escala
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
Estados Unidos