Phosphorylation sites often have key regulatory functions and are central to many cellular signaling pathways, so mutations that modify them have the potential to contribute to pathological states such as cancer. Although many classifiers exist for prioritization of coding genomic variants, to our knowledge none of them explicitly account for the alteration or creation of kinase recognition motifs that alter protein structure, function, regulation of activity, and interaction networks through modifying the pattern of phosphorylation. We present a novel computational pipeline that uses a random forest classifier to predict the pathogenicity of a variant, according to its direct or indirect effect on local phosphorylation sites and the predicted functional impact of perturbing a phosphorylation event. We call this classifier PhosphoEffect and find that it compares favorably and with increased accuracy to the existing classifier PolyPhen 2.2.2 when tested on a dataset of known variants enriched for phosphorylation sites and their neighbors
