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Open Access Methodology article

Accurate single nucleotide variant detection in viral populations by combining probabilistic clustering with a statistical test of strand bias

Kerensa McElroy12*, Osvaldo Zagordi3, Rowena Bull2, Fabio Luciani2 and Niko Beerenwinkel45

Author Affiliations

1 Centre for Marine Bioinnovation and School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia

2 Inflammation and Infection Research Group, Evolutionary Dynamics of Infectious Diseases, School of Medical Sciences, Sydney, NSW, Australia

3 Institute for Medical Virology, University of Zurich, Zurich, Switzerland

4 Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland

5 SIB Swiss Institute of Bioinformatics, Basel, Switzerland

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BMC Genomics 2013, 14:501  doi:10.1186/1471-2164-14-501

Published: 24 July 2013

Abstract

Background

Deep sequencing is a powerful tool for assessing viral genetic diversity. Such experiments harness the high coverage afforded by next generation sequencing protocols by treating sequencing reads as a population sample. Distinguishing true single nucleotide variants (SNVs) from sequencing errors remains challenging, however. Current protocols are characterised by high false positive rates, with results requiring time consuming manual checking.

Results

By statistical modelling, we show that if multiple variant sites are considered at once, SNVs can be called reliably from high coverage viral deep sequencing data at frequencies lower than the error rate of the sequencing technology, and that SNV calling accuracy increases as true sequence diversity within a read length increases. We demonstrate these findings on two control data sets, showing that SNV detection is more reliable on a high diversity human immunodeficiency virus sample as compared to a moderate diversity sample of hepatitis C virus. Finally, we show that in situations where probabilistic clustering retains false positive SNVs (for instance due to insufficient sample diversity or systematic errors), applying a strand bias test based on a beta-binomial model of forward read distribution can improve precision, with negligible cost to true positive recall.

Conclusions

By combining probabilistic clustering (implemented in the program ShoRAH) with a statistical test of strand bias, SNVs may be called from deeply sequenced viral populations with high accuracy.