Using machine learning to understand mutations

A single harmful base substitution in a DNA sequence can, on occasion, cause a devastating fatal disease. Determining why this happens for some mutations, but not for others, is critical to developing effective treatments and poses a central challenge to modern medicine. Mitochondrial DNA (mtDNA) is especially vulnerable, mutating about 100 times faster than the nuclear genome. Uncovering how DNA’s electronic fingerprint influences its mutation spectrum is of critical importance to genetics and evolutionary biology. At the same time, machine learning, with the added capabilities of deep architectures that saw tremendous advances recently, provides a framework that allows recognizing patterns in data, even when these patterns are governed by very complex interlinked properties. We are investigating the capability of machine learning, with a focus on deep learning architectures, for detecting and predicting potential mutation locations in mtDNA. We demonstrate that these models can learn to discriminate between locations on the DNA where mutations can occur versus stable locations, to the extent that these situations are effectively represented in the available data.