Predicting sequence-specific mutation rates in DNA

Mutations play a critical role in molecular evolution and the development of many diseases, such as cancer and neurodegenerative diseases. A growing body of evidence shows that rates of mutation in DNA are not only highly variable, but also influenced by the specific sites and nearby sequences. We have found that the physics of electron hole localization, most pronounced at or near guanine sites, plays a significant role in influencing sequence-specific mutation rates [M.Y. Suárez-Villagrán, R. B. R. Azevedo, & J. H Miller, Jr., Genome Biology & Evolution, 10, 1039 (2018)]. Most recently we have been applying the predictive capability of Deep Neural Network architectures, among other machine learning approaches, to predict and validate genetic mutation rates in human mitochondrial DNA. Given a segment of a sequence from the neighborhood of a specific base pair, we are able to predict mutation rates with much greater accuracy than that of a random predictor. We are currently testing the limits of automatic predictors for similar tasks, with an aim towards better understanding of both evolution and the emergence of somatic disease states, such as cancer.