Predicting functional effect of human missense mutations

Abstract

Our aim is to prioritize human missense mutations by their probability of being disease causing. Such a computational method could be used to obtain a reduced set of mutations with a relatively large fraction of disease related mutations, thereby aiding in the search for this type of mutation within a large mutation set.Whereas a range of methods is available for this purpose, only few employ the availability of the 1000G data to obtain a set of neutral mutations. The novelty of our approach is the use of separate classifiers that were trained on a subset of mutations from one amino acid to any other amino acid. The combined performance of these classifiers show an improved performance compared to the often used prediction method PolyPhen2.Pattern Recognition and Bioinformatic