lunes, 17 de noviembre de 2014

Computational Framework for Next-Generation Sequencing of Heterogen... - PubMed - NCBI

Computational Framework for Next-Generation Sequencing of Heterogen... - PubMed - NCBI

 2014 Oct 29. pii: btu726. [Epub ahead of print]

Computational Framework for Next-Generation Sequencing of Heterogeneous Viral Populations using Combinatorial Pooling.



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.


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: The source code and experimental data sets are available at CONTACT:
Published by Oxford University Press 2014. This work is written by US Government employees and are in the public domain in the US.

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