Computational Biology - Ontario Institute for Cancer Research

Research > Programs

Computational Biology

Introduction

What we do

The Computational Biology Program is the driving force behind Ontario research and data studies in cancer. The program’s team uses powerful computers and software tools to collect, manage and study data from many sources. The findings we uncover from the data are shared with other cancer research groups to speed up the discovery of new information about cancer.

Why we do it

Our goal is to learn more about cancer and create better treatments by using computers to help answer questions and solve problems. The scale and range of cancer research projects have grown in size and scope. This has led to the creation of large amounts of data. It takes a lot of effort to organize, study and view this data. For these reasons, computers have become vital for researchers to search and explore data quickly.

How we do it

We use computers, math, and statistics to study large amounts of data and make sense of it. We also share our data with other cancer research groups, teach other researchers to use our software and tools, and promote dialogue with the broader cancer research community. By doing all this, we speed up the process of creating new findings in cancer research and empower the next generation of researchers so they can create better treatments.

Computational Biology Summary

Inquiries

Program Manager
Dr. Robin Haw
robin.haw@oicr.on.ca

Telephone: 647-260-7985

The Computational Biology Program is the scientific engine of research and analytics at OICR. The program’s investigators lead local and international cancer genomics research studies and programs. In many cases, the program’s teams develop new algorithms, software, visualization tools and other necessary components to interrogate and interpret the large and complex datasets. Our resources and expertise are shared with the Ontario and the international cancer research community, with the goal of supporting the acceleration of cancer research.

Our mission is to advance the knowledge and treatment of cancer through computational biology.

Our research objectives are to:

Gain new and deeper understanding of cancer biology through the application of computational and data-intensive techniques;
Train the next generation of computational biologists to work on cancer-related problems;
Foster efficiency, communication and collaboration within and among Computational Biology and Genome Informatics, OICR and the wider community.

Opportunities to Collaborate

Collaborate with us

The Computational Biology Program is open to and encourages research collaborations. Please contact any of the Principal Investigators or review our Collaborative Research Resources Directory for more information.

Programs Expertise and Capabilities

Principal investigators and senior scientists in the Computational Biology Program have a broad set of research interests and expertise, ranging from open research and reproducibility, to algorithm development for long-read sequences, pipelines for sequencing and analysis, biomarker discovery, viral detection, and population-based genomics approaches to cancer, as well as pathway and network analysis. While our research activities and expertise focus on cancer, they also have broader application in genomic research.

Projects

The Computational Biology Program is involved in a wide variety of research projects. We play both leadership, and collaborative, scientific roles in many cancer research projects, with a strong mandate to output to the scientific community open-source, open-access data, tools and resources.

Projects under the Computational Biology Program include:

Canadian Prostate Cancer Genome Network

(CPC-GENE), a project aimed at understanding the prostate cancer genome to better predict treatment failure for intermediate risk prostate cancers.

PRONTO

A pan-Canada research project to rapidly develop novel diagnostic markers for early prostate cancer.

Canadian Data Integration Centre

Which supports large scale genomics projects on population-wide and clinical cohorts, and provides analytical and bioinformatics support through access to the software and analytic systems needed to collect and harmonize diverse health and lifestyle data, analyze it and electronically publish the results. Researchers can request access and services for their project needs.

ICGC-TCGA DREAM Somatic Mutation Calling Challenge

Which provides global coordination of benchmarking algorithms for analyzing cancer genomes.

Ontario Health Study

The Ontario Health Study is a provincial cohort built to understand the development of cancer and chronic diseases in cancer.

The Canadian Partnership for Tomorrow Project

The Canadian Partnership for Tomorrow Project is a national cohort built to understand the development of cancer and chronic diseases in cancer.

Reactome

An open, curated knowledgebase of biological pathways in humans.

WormBase

An online biological database about the biology and genome of the nematode model organism Caenorhabditis elegans.

GMOD

The Generic Model Organism Database project is a collection of open source software tools for managing, visualising, storing, and disseminating genetic and genomic data.

MISO

Is an open-source LIMS for small-to-large scale sequencing centres developed in collaboration with Earlham Institute.

Data, Software & Tools