Centre for Computational Medicine, SickKids Research Institute

License: GNU Library or "Lesser" General Public License version 2.0 (LGPLv2)

Web Page: https://gsoc.ccm.sickkids.ca/

Mailing List: ccm-mentors@cs.toronto.edu

The Centre for Computational Medicine (CCM) is a Core Facility within the SickKids Research Institute, providing computational expertise, including High Performance Computing resources, Bioinformatics Analysis consulting and Software Development. At the CCM, we develop free open source software for clinical genetics, which aims to empower scientists to transform their data into knowledge. Our tools have a user base which includes many clinical and research facilities across Canada, the US and Europe. Our targets are non-technical users, which we help cross the technological barrier. Our software aims to make their lives easier by integrating seamlessly in their work routine and shielding them from technical complexities. From a more technical/development standpoint, we focus on usability and accessibility, as well as on big data visualization and interpretation assistance.

Projects

• Applications for global genomics data sharing The goal of the project is to create a set of tools for global genomics data sharing which can be facilitated by MedSavant, a platform providing a search engine for genomic variants.
• Development of machine learning methods for modeling the evolution of tumor The goal of this project is to implement the machine learning methods in C/C++ to model the clonal evolution of cancer tumors, based on the research prototype written in Python. The main focus will be on writing highly optimized code in C/C++ using graph data structures and algorithms, and Markov chain Monte Carlo methods.
• Pathway View Extension of MedSavant Discovery Application This extension will allow researchers to see which metabolic pathways are most affected by a set of mutations in human genetic sequence.
• Visualizing the evolution of tumors The project is about writing a visualization framework to represent the evolution of tumor in a network representation where nodes and links represents mutations and evolutionary relationships respectively. Currently, the tool outputs the network representation using python's pygraphviz library which it refers as partial order plots. The current network representation is pretty basic and it needs to be improved to convey the information to the medical users in a better way.