Using Big Data to Understand the Cause of Cancer

The sequencing of the human genome encompassed one of the largest biological data generation efforts of the previous millennium. Today, we generate thousands of human genome equivalents of biological data every day, including genomic and genetic data that captures a multi-dimensional persepctive of cancer. Despite many years of interrogation we are still at odds in explaining the exact molecular cause of most tumors presented in the clinic, an important step in establishing prognosis, guiding therapy, and inventing new treatmentds. Our lab focuses on identifying causal mutations and epigenetic events that cause cancer by impacting gene expression. Our approach differs from the traditional brute-force high-throughput methods in that it is founded on allele-specific genomics. cause breast cancer by impacting gene expression, and integrative transcriptomic analysis of schizophrenia.

Big Data

Mammalian Epigenetics

The genetic code, or sequence of nucleotides, is fundamentally important to how the genome is interpreted, but there are additional ornaments on the nucleotides that are also important. This level of regulation that is above ("epi") the genetics, is not well understood, but is critical for a cell to function. We focus on genomic imprinting, a form of epigenetic regulation that silences genes depending on whether they were inherited from the mother or father. Among vertebrates, the phenomenon seems to be restricted to mammals and about 100 genes are imprinted in humans. Why and how would such a mechanism evolve? To answer this question we need to know which genes are imprinted, where they are imprinted (tissue and species), and what their functions are. Comparisons between just two species have already been quite illuminating, and we are interested in extending these efforts to other mammals to complete the story.


Technology/Methods Development

Since we like to think big and the tools to address our interests are not always in place, the core of our lab is developing new molecular and computational tools that complement current technologies. There is only a handful of model organisms and these have long dictated the types of questions researchers ask. Considering there are hundreds of millions of species, what if you had an experimental system that identified the best model organism based on the question YOU want to ask? We have many projects that combine CRISPR-Cas9 engineering, microfluidics, next-gen sequencers, and computational biology, to approach biology research from a new direction. Do you have an idea that fits this general profile? We would love to hear from you!


Our research is supported by:

Queen's University
Cancer Research Society