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Open Access Research article

Investigating selection on viruses: a statistical alignment approach

Saskia de Groot1*, Thomas Mailund12, Gerton Lunter3 and Jotun Hein1

Author Affiliations

1 Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG, UK

2 BiRC affliation: Bioinformatics Research Center, University of Aarhus, Hoeg-Guldbergsgade 90, 8000 Aarhus, Denmark

3 MRC Functional Genetics Unit, Department of Physiology, Anatomy & Genetics, University of Oxford, 1 South Parks Road, Oxford OX1 3QX, UK

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BMC Bioinformatics 2008, 9:304  doi:10.1186/1471-2105-9-304

Published: 10 July 2008

Abstract

Background

Two problems complicate the study of selection in viral genomes: Firstly, the presence of genes in overlapping reading frames implies that selection in one reading frame can bias our estimates of neutral mutation rates in another reading frame. Secondly, the high mutation rates we are likely to encounter complicate the inference of a reliable alignment of genomes. To address these issues, we develop a model that explicitly models selection in overlapping reading frames. We then integrate this model into a statistical alignment framework, enabling us to estimate selection while explicitly dealing with the uncertainty of individual alignments. We show that in this way we obtain un-biased selection parameters for different genomic regions of interest, and can improve in accuracy compared to using a fixed alignment.

Results

We run a series of simulation studies to gauge how well we do in selection estimation, especially in comparison to the use of a fixed alignment. We show that the standard practice of using a ClustalW alignment can lead to considerable biases and that estimation accuracy increases substantially when explicitly integrating over the uncertainty in inferred alignments. We even manage to compete favourably for general evolutionary distances with an alignment produced by GenAl. We subsequently run our method on HIV2 and Hepatitis B sequences.

Conclusion

We propose that marginalizing over all alignments, as opposed to using a fixed one, should be considered in any parametric inference from divergent sequence data for which the alignments are not known with certainty. Moreover, we discover in HIV2 that double coding regions appear to be under less stringent selection than single coding ones. Additionally, there appears to be evidence for differential selection, where one overlapping reading frame is under positive and the other under negative selection.