In bioinformatics, genome-wide experiments look for important biological differences between two groups at a large number of locations in the genome. Often, the final analysis focuses on a p-value based ranking of locations which might then be investigated further in follow up experiments. However, this strategy may result in small effect sizes, with low p-values, being ranked more favourably than larger more scientifically important effects. Bayesian ranking techniques may offer a solution to this problem provided a good prior distribution for the collective distribution of effect sizes is available.