Next Generation Machine Learning for the Accurate Detection of DNA Variations from High-Throughput Sequencing Data

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Deoxyribonucleic acid (DNA) is the hereditary material in humans and almost all other organisms. The emergence of "next generation sequencing" (NGS) technology?has created unprecedented opportunities to study DNA in large scales. While extremely powerful, NGS produces massive quantities of data (~250GB/cancer patient). These large datasets contain errors that distort and obscure the true DNA changes (referred to as variations). Significant leap in the development of algorithms for the detection of DNA variations from different tissue types and technologies is needed, because: (a) the hand-crafted and parameterized algorithms developed so far still produce thousands of errors and miss true DNA variations; (b) while the recent single cell DNA sequencing (SCS) technologies are now able to characterize the DNA of each cell, algorithms for detection of DNA variations in SCS data lag far behind the data generation throughout; and (c) a common approach to archive tissue material is formalin fixation, which introduces false DNA variations and poses a challenge to the identification of true DNA variations in the stored tissue. Our long-term objective is to develop computational methods to identify DNA variations that cause biological abnormalities. Within this cycle of the Discovery program, we will develop novel machine learning frameworks (based on deep learning and ensemble learning) that detect DNA variations (regardless of whether they cause abnormalities) from various sources of tissue sequenced by NGS technologies. Model datasets will be used to validate these novel algorithms and enrich their approaches. Successful execution of this basic research program will lead to a novel class of algorithms and software that will (a) maximize the benefit of significant resources committed to DNA sequencing saving millions of dollars in follow up experiments to validate the DNA variations identified from noisy NGS data; (b) enable SCS to more effectively characterize cells which will have a broad impact on many diverse fields including microbiology, neurobiology, development, immunology and cancer; and (c) open the door for the effective assessment of DNA sequence in formalin-fixed tissue, providing an explosion in data to screen and comprehensively evaluate disease markers. To the best of our knowledge, the proposed program is unique and novel in Canada and will train 2 PhD and 1 MSc student, as well as 7 undergraduate students with highly demanded skills in academia and industry. A detailed training plan that allows individuals to reach their full potential is integrated within the development of research objectives to ensure a high quality, interactive, flourishing, and an equitable research environment for HQP as per UBC Equity and Diversity Strategic Plan and Policy #2.