Although we cannot know in advance how big a threat they will pose, novel human viruses must be anticipated in public health planning and surveillance programmes for emerging infectious diseases (King et al. 2006; Jones et al. 2008 ). However, current approaches to virus discovery are largely passive, usually relying on investigation of reports of human disease with unfamiliar clinical symptoms and uncertain aetiology. Recently, there have been calls for more active discovery programmes for viruses and other pathogens involving 'systematic sampling and phylogeographic analysis of related pathogens in diverse animal species' ( Wolfe et al. 2007) . We consider that such calls are supported by the results reported here. Virus discovery curve M. E. J. Woolhouse et al. 2115  Investigating selection on viruses: a statistical alignment approach 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. In the past few years we have witnessed an explosion in the viral genomic data available. GenBank alone holds over 80,000 close to complete viral genomes, and numbers are rising fast. For example, since the submission of the first SARS genome in May 2003, over 140 more have been published. With this genomic data at hand we hope to finally be able to tackle our understanding of viruses. Mechanisms of selection, that is to say the rate f at which a mutation resulting in a change in amino acid is accepted, and evolution on viruses are still strongly debated, and a methodology which is trimmed towards answering these questions is required. A step towards this is our attempt to develop a method which can deal with the vast amount of viral data, as well as the complexity of viral genomes and their high divergence and subsequent unreliability of alignment.