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

Skums P1, Artyomenko A2, Glebova O2, Ramachandran S2, Mandoiu I2, Campo DS2, Dimitrova Z2, Zelikovsky A2, Khudyakov Y1.

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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: The source code and experimental data sets are available at http://alan.cs.gsu.edu/NGS/?q=content/pooling CONTACT: kki8@cdc.gov.

Published by Oxford University Press 2014. This work is written by US Government employees and are in the public domain in the US.