Large-scale sequencing projects are uncovering a growing number of missense mutations in human tumors. Understanding the phenotypic consequences of these alterations represents a formidable challenge. In silico prediction of functionally relevant amino acid motifs disrupted by cancer mutations could provide insight into the potential impact of a mutation, and guide functional tests. We have previously described Wregex, a tool for the identification of potential functional motifs, such as nuclear export signals (NESs), in proteins. Here, we present an improved version that allows motif prediction to be combined with data from large repositories, such as the Catalogue of Somatic Mutations in Cancer (COSMIC), and to be applied to a whole proteome scale. As an example, we have searched the human proteome for candidate NES motifs that could be altered by cancer-related mutations included in the COSMIC database. A subset of the candidate NESs identified was experimentally tested using an in vivo nuclear export assay. A significant proportion of the selected motifs exhibited nuclear export activity, which was abrogated by the COSMIC mutations. In addition, our search identified a cancer mutation that inactivates the NES of the human deubiquitinase USP21, and leads to the aberrant accumulation of this protein in the nucleus.