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Precision Medicine in Cellular Epigenomics

Status: Active
Competition: 2017 Bioinformatics and Computational Biology Competition
Sector: Health
Genome Centre(s): Génome Québec
Project Leader(s): Celia Greenwood (Lady Davis Institute for Medical Research), Karim Oualkacha (Université du Québec à Montréal)

Project Description: 

DNA methylation refers to the attachment of a methyl molecule to our DNA. This chemical binding makes the DNA inaccessible, and provides a mechanism to influence which parts of the DNA are active or inactive. Since DNA methylation can vary across cell types, with disease, with exposures to contaminants, and with other factors such as genetic factors and age, many clinicians and researchers are excited to identify which parts of the genome are associated with certain diseases or exposures. This could be used to understand mechanisms of disease and to provide potential targets of intervention. for these reasons, there is increasing interest to study DNA methylation in scleroderma.

It has recently become technically and financially feasible to perform single nucleotide-resolution measurement of DNA methylation on a large scale across the genome, using a method called bisulfite sequencing. However, the data are noisy and many measures can be missing. Appropriate methods of analysis for this voluminous and highly correlated data are currently unsatisfactory.

We have assembled a team of experts including leading methodological statisticians, scientific researchers in several domains such as scleroderma, as well as asthma and behavioural development, and experienced bioinformaticians. Together we will develop an algorithm and software package to analyze large scale, high dimensional DNA methylation data so that we can profit from the potential hidden in the messy data. We have already a prototype method in place that shows excellent performance.

Strong support for the ideas in our study have been obtained from patient-led associations including Scleroderma Quebec, and also from two Montreal companies, one that develops machine learning methods and the other that provides a high-performance computational platform. Leading epigenetic researchers and consortia have also expressed their interest in our research. Understanding of epigenetic contributions to disease will be revolutionized through this project.