Motivation: Most approaches used to identify cancer driver genes focus, true to their name, on entire genes and assume that a gene, treated as one entity, has a specific role in cancer. This approach may be correct to describe effects of gene loss or changes in gene expression; however, mutations may have different effects, including their relevance to cancer, depending on which region of the gene they affect. Except for rare and well-known exceptions, there are not enough data for reliable statistics for individual positions, but an intermediate level of analysis, between an individual position and the entire gene, may give us better statistics than the former and better resolution than the latter approach.
Results: We have developed e-Driver, a method that exploits the internal distribution of somatic missense mutations between the protein's functional regions (domains or intrinsically disordered regions) to find those that show a bias in their mutation rate as compared with other regions of the same protein, providing evidence of positive selection and suggesting that these proteins may be actual cancer drivers. We have applied e-Driver to a large cancer genome dataset from The Cancer Genome Atlas and compared its performance with that of four other methods, showing that e-Driver identifies novel candidate cancer drivers and, because of its increased resolution, provides deeper insights into the potential mechanism of cancer driver genes identified by other methods.
Availability and implementation: A Perl script with e-Driver and the files to reproduce the results described here can be downloaded from https://github.com/eduardporta/e-Driver.git.
© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: email@example.com.