Bernoulli mixture models in application to the evaluation of algorithms estimating functionality of missense mutations

Background Whole genome and whole exome sequencing projects yield thousands of missense mutations with unknown functionality. Direct estimation of the sensitivity and specificity of bioinformatic algorithms predicting the impact of missense mutations on protein function requires a ‘gold standard’ or set of mutations with known functionality. In the absence of a gold standard, additional statistical methods are needed to estimate the accuracy of these algorithms. It has been shown informative predictions depend on the algorithm and sequence alignment employed and often algorithms disagree as to which mutations are predicted deleterious or neutral [1].