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.
Dr. Philip Awadalla
Director and Senior Principal Investigator, Computational Biology
Mr. Lars Jorgensen
Director Genome Sequence Informatics
Dr. Quaid Morris
Dr. Jüri Reimand
Dr. Jared Simpson
Dr. Lincoln Stein
Head Adaptive Oncology and Senior Principal Investigator
Dr. Christina Yung
Principal Research Scientist
Program 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.
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; and
- ICGC-TCGA DREAM Somatic Mutation Calling Challenge, which provides global coordination of benchmarking algorithms for analyzing cancer genomes.
- The Ontario Health Study and The Canadian Partnership for Tomorrow Project are provincial and national longitudinal cohorts built to understand the development of cancer and chronic diseases in cancer.
As well, we also maintain active research efforts in many seminal, open-source, open-access community resources including:
- 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; and
- MISO is an open-source LIMS for small-to-large scale sequencing centres developed in collaboration with Earlham Institute.
Data, Software and Tools
- Nanopolish: a nanopore consensus algorithm using a signal-level hidden Markov model;
- SGA: a String Graph Assembler for de novo genome assembly. SGA is very memory efficient, which is achieved by using a compressed representation of DNA sequence reads;
- PCAWG data: over 2,400 consistently analyzed genomes corresponding to over 1,100 unique ICGC donors;
- CaPSID: A bioinformatics platform for computational pathogen sequence identification in human genomes and transcriptomes;
- ObiBa: Open source software for BioBanks;
- ISOWN: Identification of Somatic mutations Without Normal tissues software is a supervised machine learning algorithm for predicting somatic mutations from tumour-only samples;
- ActiveDriver: a method for identifying post-translational modification sites (i.e., active sites) in proteins that are significantly mutated in cancer genomes.
Opportunities to collaborate
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.
Program in the news
Find out more about what’s happening in Computational Biology and Genome Informatics programs at OICR News.
Senior Program Manager
Dr. Michelle Brazas