Up till now, other methods dealing with related issues have made use of the concept of K a /K s ratio, which however creates problems when applied to overlapping reading frames [4, 7, [14] [15] [16] . For this reason McCauley et al. [13] decided on a different HMM based approach and estimated selection as acting on a single nucleotide basis, but at the cost of not being able to pry apart selection acting on different reading frames. Most importantly however, all of the above methods use a fixed alignment and are thus prone to a great variability in their estimated parameters, dependent on the alignment. Our method manages to circumvent this problem by using a statistical approach, and thus we account for the uncertainty inherent in the alignment by considering all, rather than picking a single "best" alignment. The improvement we observe by doing this, makes us suggest that our approach of marginalizing over alignments may benefit other sequence-based inferential methods, such as for example methods for identifying conserved binding motifs.