Bioinformatics and Computational Biology (B/CB) Competition

Genome Canada’s Bioinformatics and Computational Biology (B/CB) Competition supports research projects that address current challenges in bioinformatics and computational biology. Launched in 2012, in partnership with the Canadian Institutes of Health Research (CIHR), this open competition was designed to create an environment that supports the creation and evolution of new tools and methodologies required by the research community to analyze and integrate the influx of large amounts of complex data produced by modern genomics technologies for application across industries.

Funded Ontario B/CB Projects

On February 4, 2019, The Honourable Kirsty Duncan, Minister of Science and Sport, announced the funding recipients from Genome Canada’s 2017 Bioinformatics and Computational Biology Competition (B/CB). Eight (8) of these projects are based in Ontario – driving $3.6 million of federal funding into the province and an additional $3.7 million in investments by industry, the Ontario government and other funding partners, for a total of $7.3 million. This investment will support the development of next generation tools and methodologies to deal with the influx of large amounts of data produced by modern genomics technologies and will provide broad access to these tools to the research community.

On September 13, 2016, Parliamentary Secretary for International Development, Karina Gould, on behalf of the Honourable Kirsty Duncan, Minister of Science, announced the funding recipients from Genome Canada’s 2015 Bioinformatics and Computational Biology competition. Eight (8) projects received funding through Ontario Genomics– with two projects co-led with British Columbia and Atlantic – representing a combined total investment of $1.96 million:

Pathway and network visualization for personal genomes


Cancer is a disease caused by the accumulation of multiple genetic mutations. Highly specific drugs that target mutated proteins in cancer cells are currently being used to treat the disease. However, since cancer patients have different mutation profiles, a drug that is effective in one may not have the same result in another. Personalized medicine based on genomic data would allow doctors to determine the best targeted therapy for each patient.

Dr. Lincoln Stein and his team aim to develop software that will improve the treatment of cancer patients by enabling physicians to study and visualize the genomic aberrations of individual patients. It will help identify genes related to cancers and other disease.

Leveraging meta-transcriptomics for functional interrogation of microbiomes


Bacteria do not live in isolation but tend to form parts of microbial communities (called “microbiomes”), displaying complex inter-dependencies between themselves and their environments. The composition of these communities is increasingly viewed as having a significant impact on human health and disease.

To understand more about how bacteria function within their communities, whole-microbiome gene expression profiling has emerged as a powerful tool to study their influence on their environment. However, few methods and tools to fully understand the data resulting from this profiling have been developed.

Dr. John Parkinson and team aim to bridge this gap by developing new software that enable the identification of genes and pathways that have critical roles within the microbiome. Such genes and pathways represent potential targets for new treatments that help maintain healthy microbiomes and reduce the risk of diseases such as Type 1 diabetes, irritable bowel disease and rheumatoid arthritis.

Large data sets and novel tools for plant biology for use in international consolidation-tier data repositories and portals


New technologies allow plant biologists to identify important DNA sequences in an organism’s genome. Among other things, these advances have helped gain insight into the expression level of genes in many different parts of plants under different conditions, the interactions between the proteins present in organism and the 3D structures of these proteins. However, researchers still find it difficult to draw meaningful conclusions from the huge amounts of data that confront them.

The research team led by Drs. Nicholas Provart and Stephen Wright aims to develop data visualization tools and applications to accelerate advances in plant biology. Their contribution to making vast amounts of data easier to interpret will increase our understanding of plant biology, which is important for feeding, housing, clothing and providing energy to the world’s growing population.

Development of a unified Canadian clinical genomic database as a community resource for standardizing and sharing genetic interpretations


Canadian scientists have made exciting discoveries about the complex relationship between genetic mutations and disease. However, much of this information is spread across dozens of databases in widely differing formats. In order to use this information to improve patient outcomes, researchers and clinicians need a more widely-accessible resource designed for sharing and collaboration.

Drs. Jordan Lerner-Ellis, Matthew Lebo and their team aim to address this issue by creating a shared, open-source genetic database that will amalgamate the work of participating clinical and research laboratories across Canada and internationally. This resource will provide sophisticated new tools for the diagnosis and management of common and rare diseases, while improving the effectiveness of healthcare delivery.

ProHits Next Generation: A flexible system for tracking, analyzing and reporting functional proteomics data


Human cells are built from tens of thousands of different proteins that perform most of the activities necessary for life. To gain insight into the cause of a disease and to develop new approaches to treat disease, it is necessary to understand how proteins interact with and modify each other.  Mass spectrometry is now being used to identify proteins and their modifications and interactions.

Drs. Anne-Claude Gingras, Mike Tyers and team aim to develop innovative ways to analyze the data generated by mass spectrometry and to increase the amount of information about protein interactions and modifications. Their research will improve the analysis of protein interactions and increase understanding of the effects of disease states and drug treatments.

MedSavant: An integrative framework for clinical and research analysis of human genomes


Physicians will soon be able to use patients’ whole genome sequence to search for information about the person’s risk of developing a disease, thereby improving clinical decision-making. This promises significant medical and economic benefits, including early detection and treatment of high-risk patients and eliminating multiple genetic tests.

Integrating whole genome sequencing into clinical practice requires software that will allow clinicians to identify relevant genetic variants in patients. Drs. Michael Brudno, Gary Bader and team aim to improve health care by developing broadly shared software that will prioritize the genetic variants in patients who may require medical attention.

Genomic Epidemiology Application Ontology (GenEpiO)


Infectious disease outbreaks have significant impacts on human health, agri-food production, animal health and the economy. Ineffective public health responses can result in outbreaks that spread diseases like the Zika virus and food-borne illnesses, with enormous impacts on health and high economic costs. DNA sequencing provides the complete “fingerprint” of a microbe, enabling an unprecedented tracing of how infectious diseases spread. When outbreaks become global, however (think SARS, or microbes resistant to antimicrobials) data needs to be shared across public health organizations securely and efficiently. Unfortunately, data is often held in institution-specific formats, making it difficult, time consuming and costly to do so.

Drs. William Hsiao (UBC), Andrew G. McArthur (McMaster University) and Fiona Brinkman (Simon Fraser University) will improve data integration and sharing of infectious disease and antimicrobial resistance information across public health agencies, with the Genomic Epidemiology Application Ontology (GenOpiO). The platform will enable public health workers to share outbreak-related information faster and to perform more powerful analyses, helping to reduce the negative health and economic impact of disease outbreaks.

Rapid prediction of antimicrobial resistance from metagenomic samples: Data, models, and methods


Antimicrobials (antibiotics) have been central to combating infectious disease for nearly a century. However, their effectiveness is slipping due to the increase in antimicrobial resistance (AMR). There is an increasingly urgent need to know more about AMR to better understand its consequences and monitor its presence in the environment, agri-foods industry, individual patients, and on a population level.  Being able to analyze the genomes of resistant microorganisms is essential, but slow and costly to do one at a time. Metagenomics allows genetic profiling of microbes as a community, but datasets are huge and contain much irrelevant data. Currently, there is no software designed to specifically predict AMR profiles directly from metagenomic data, which would enable more rapid AMR profiling and aid prioritization of candidate genes for further research.

Drs. Robert Beiko of Dalhousie University, Andrew G. McArthur of McMaster University, and Fiona Brinkman of Simon Fraser University are leading a project to develop new software and database tools that will provide a near-instantaneous picture of AMR organisms in a sample, aiding AMR research and responding to AMR threats impacting both agri-food production and public health.

Rapid, accessible genome assembly using long read sequencing


DNA sequencing technology has progressed from sequencing single reference genomes at great cost and time, to the current era of inexpensive, high-throughput short read sequencing. The emerging “third generation” of DNA sequencing technology offers the prospect of putting long read genome sequencing in the hands of more researchers and enabling new applications, through portable instruments that will decentralize sequencing technology.

Dr. Jared Simpson of the University of Toronto is developing robust and efficient genome assembly software that is easy to use, to match the capabilities of these emerging sequencing instruments. The software will target biologists and other end users of sequencing who don’t have substantial bioinformatics expertise.