Deep Learning for Better Variant Calling for Cancer Diagnosis and Treatment

High-throughput techniques have revolutionized the study of genomics and molecular biology in recent years. These methods provide a large quantity of sequence data, and have applications in different areas of bioinformatics. One can sequence parts or whole of an organism's DNA to determine genetic information about an individual or a population, measure expression levels of different genes under different conditions, and determine binding affinity of proteins to DNA segments revealing details regarding gene regulation, at a higher resolution than before. However, different high-throughput methods that target even a single application have different underlying error models. Robust analytic pipelines are necessary to extract necessary information from the raw data. In this paper, we discuss future research directions for developing such analytics using techniques from Machine Learning and Deep Neural Networks. We focus on two applications that will affect the diagnosis and treatment of cancer.