Since we wish to analyse selection happening over a reasonable evolutionary distance, our aim is to be able to draw conclusions without relying on a prior alignment. Instead of estimating evolutionary parameters using observed substitution counts froma fixed alignment, we will therefore use an alignment model to generate expected substitution counts and from these use a maximum likeli-hood method to estimate all evolutionary parameters. In this manner we may sum over the uncertainty of the alignment -an uncertainty that will be high for distantly related viruses. Since our alignment model includes a substitution model, we iteratively switch between both it and our ML-procedure. Figure 6 depicts the basic outline of our program.