Abstract
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].
Highlights
Whole genome and whole exome sequencing projects yield thousands of missense mutations with unknown functionality
We have developed two statistical models called Bernoulli mixture (BM) and augmented Bernoulli mixture (ABM) based on the capture-recapture technique that employs these disjoint categories
There are 2” disjoint categories employed by the ABM model, which includes 2n + 3 parameters, and the BM model is a special case of the ABM model that includes 2n + 1 parameters
Summary
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]
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