Our method presented here looks at selection on genomic segments as opposed to nucleotides, and thus in overlapping coding regions can discern selection for different reading frames. We may therefore attempt to draw conclusions about the nature of not only selection but also the interaction of selection on two different genes. Also, to study the imprint of evolution on viral genomes, it is necessary for the samples to have a reasonably high level of divergence. A benchmark herefore in our experience would be an evolutionary distance a + 2b of at least 0.4. Since more divergent genomes are harder to align, this brings uncertainty about the alignment into the inference. We decide to circumvent this problem by considering the set of all possible alignments -and their corresponding likelihood under our model -, as opposed to a fixed 'optimal' alignment. This method has previously been used for similar purposes, to minimize variability in parameter estimation due to uncertain alignments [10, 12] .