Regulatory models for RNA processing

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Over the past 5 years, there has been exponential growth in the number of high-throughput biology datasets, making it possible to imagine an era of truly quantitative biology and data-driven medicine. However, more data doesn't necessarily lead to better understanding or deep insights, and this is especially true for biological data. Compared to the exponential growth of data profiling genomes, biomolecules and cellular processes, our gain in knowledge and applications to medicine are very limited. Recently, artificial intelligence techniques, especially techniques for machine learning (ML) and “deep learning”, have proven to be effective in taking advantage of big datasets. Deep learning has revolutionized application areas such as computer vision and natural language processing. As a research lab working on genome biology and ML, our goal is to develop techniques to make full use of large biological datasets and advance genomic research. My group has made significant progress in the past few years, resulting in two highly influential publications (Xiong et al, Science 2015, Alipanahi et al, Nature Biotechnology 2015) and a startup spin-out, Deep Genomics. ******The human body consists of trillions of cells whose processes govern normal function and also disease states. Yet, all the cells contain the same genetic information, the genome, which is made up of DNA molecules. Differences between cells are mainly governed by the ‘regulatory' instructions in the genome, which instruct when, where, and how much to produce each kind of cellular molecule, whether DNA, RNA or protein. My long-term research objectives are to introduce principled models and ML algorithms that can disentangle the determinants of regulation, and to infer regulatory models that advance scientific understanding and enable biomedical technologies. In this proposal, we will focus on three distinct, but related, biochemical processes: protein binding to DNA and RNA molecules, alternative polyadenylation and RNA editing, and build accurate regulatory models using deep learning techniques. These processes are all important players of the regulatory network in a cell, and disruption of them contributes to a wide range of human diseases, such as neurological disorders and cancer. I believe we can succeed in this challenging project, based on the diverse expertise cultivated in my group on both genome biology and ML, the experience and lessons learned from our previous work on regulatory models, and promising preliminary results. I expect this work to lead to major advances in genome biology and enable a range of downstream applications, such as in genetic testing and therapy discovery. It will also help to train world-class leaders in the field as well as scientists and engineers, which will benefit the community and Canada.