A Profile-Based Method for Measuring the Impact of Genetic Variation
Nicole E Wheeler, Lars Barquist, Fatemeh Ashari Ghomi, Robert A Kingsley, Paul P Gardner
Advances in our ability to generate genome sequence data have increased the need for fast, effective approaches to assessing the functional significance of genetic variation. Traditionally, this has been done by identifying single nucleotide polymorphisms within populations, and calculating derived statistics to prioritize candidates, such as dN/dS. However, these methods commonly ignore the differential selective pressure acting at different positions within a given protein sequence and the effect of insertions and deletions (indels). We present a profile-based method for predicting whether a protein sequence variant is likely to have functionally diverged from close relatives, which takes into account differences in residue conservation and indel rates within a sequence. We assess the performance of the method, and apply it to the identification of functionally significant genetic variation between bacterial genomes. We demonstrate that this method is a highly sensitive measure of functional potential, which can improve our understanding of the evolution of proteins and organisms. An implementation can be found at https://github.com/UCanCompBio/deltaBS.