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

2017
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.

2015
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:

2012
On April 25, 2013, Genome Canada announced the results of the 2012 Bioinformatics and Computational Biology competition. Seven (7) projects received funding through Ontario Genomics, with a combined total investment of $4.7 million ($2.1 million from Genome Canada):


B/CB Project Descriptions:

BridGE-SGA: A novel computational platform to discover genetic interactions underlying human disease

Project Leaders: Charles Boone, University of Toronto; Chad L. Myers, University of Minnesota
Genome Centre: Ontario Genomics
Total Project Funding: $990,910

The ability to sequence the entire human genome at increasingly lower cost has led to a fundamental change in biomedical research. But there is a gap between the amount of data available and our ability to understand and interpret that data. Addressing this gap is essential to realize the promise of precision medicine.

Dr. Charles Boone and Dr. Brenda Andrews of the Donnelly Centre for Cellular and Biomolecular Research at the University of Toronto, and Dr. Chad Myers of the University of Minnesota, have worked together to discover that a significant part of our inability to interpret genomic data likely stems from the reality that disease generally arises from complex genetic interactions. While all humans essentially have the same set of genes, most have around five million unique genetic variants. The effect of any one variant depends on its interactions with other variants. So we need to understand not just the millions of genetic differences that affect gene function, but also how all those genes interact with each other. Current computational methods and technologies lack the statistical power to do so.

Drs. Boone, Andrews, Myers have developed the first complete genetic interaction map for any organism, and have built a computational method, BridGE, to discover genetic interactions. The team is now working to develop an innovative computational platform for genome sequencing data, BridGE-SGA, to enable the discovery of disease-associated genetic interactions from large-scale human genotype data. Their goal is to discover genetic interactions for a variety of diseases. Identifying and understanding these key genetic interactions will improve our ability to interpret data from whole genome sequencing and identify novel gene targets for drug discovery and development.
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Computational tools for Data-Independent Acquisition (DIA) for quantitative proteomics and metabolomics

Project Leaders: Anne-Claude Gingras, Lunenfeld-Tanenbaum Research Institute; Hannes Röst, Donnelly Centre for Cellular & Biomolecular Research, University of Toronto
Genome Centre: Ontario Genomics
Total Project Funding: $1,000,000

When cells lose control over their own behaviour or communication with other cells, diseases such as diabetes or cancer can arise. Protein and small molecule metabolites are responsible for cells’ behaviour, so identifying and quantifying these molecules is key to understanding how disease happens and how to prevent it.

Mass spectrometry has become the workhorse for proteomics and metabolomics. Drs. Anne-Claude Gingras of the Lunenfeld-Tanenbaum Research Institute and Hannes Röst of the Donnelly Centre for Cellular & Biomolecular Research at the University of Toronto are working with a technology called Data-Independent Acquisition (DIA), in which the mass spectrometer systematically identifies and quantifies the proteins and metabolites present in a sample. DIA has been shown to improve quantitative accuracy, reproducibility and throughput over other methods. Since its introduction, however, this approach has only been applied to small-scale studies and in a relatively small number of laboratories. Limitations to this method are due to the lack of user-friendly software that could enable a scalable analysis of the complex data generated in large-scale biomedical and medical research.

The project builds on the team’s proven strength in DIA data analysis and software development and will result in an integrated set of tools available under an open-source license. To encourage uptake of these tool, documentation, webinars and workshops will be made available to potential users. The results of the project could have long-lasting impact on the health sector in Canada by facilitating research into the root causes of disease and assisting with clinical questions such as patient stratification.
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SYNERGx: a computational framework for drug combination synergy prediction

Project Leader: Benjamin Haibe-Kains, Princess Margaret Cancer Centre
Genome Centre: Ontario Genomics
Total Project Funding: $1,032,702

When just one drug is used to treat cancer, the patient may not respond, or may develop resistance to it. Combination therapy, where two or more drugs are used in treatment, is more likely to be successful. Yet, it is impossible to test all drug combinations in clinical trials due to the high cost of required resources and certain ethical considerations. Computational techniques are therefore required to model the large amount of available data to improve current cancer treatment strategies and propose more efficient combinations of drugs.

Dr. Benjamin Haibe-Kains of the Princess Margaret Cancer Centre is developing SYNERGx, a new computational platform that will integrate multiple pharmacogenomic datasets. These datasets will be used to predict possible combinations of known drugs that can act in synergy, meaning that their combined therapeutic efficacy is greater than the sum of their individual effects.

The platform will implement analytic tools to improve modeling of synergistic drug effects. Users will have access to highly curated drug-combination pharmacogenetics data and an open-source machine-learning pipeline for drug synergy prediction. SYNERGx will also implement a new way to optimize drug-screening studies to identify novel synergistic combinations that can be further validated in preclinical studies and then in clinical trials.

SYNERGx will provide an efficient way to leverage massive investments in pharmacogenomics studies by allowing the integration of otherwise disparate datasets. It represents a major step forward in the design of new therapeutic strategies for cancer.
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Software for Peptide Identification and Quantification from Large Mass Spectrometry Data using Data Independent Acquisition

Project Leaders: Bin Ma, University of Waterloo; Michael Moran, Hospital for Sick Children
Genome Centre: Ontario Genomics
Total Project Funding: $925,987

Precision medicine gives patients the opportunity to tailor medical and treatment decisions at the individual level to maximize outcomes and minimize adverse effects. It can be used to treat a wide variety of diseases, including cancer. Decisions are often based on the presence and quantity of biomarkers such as proteins in the blood or tissue samples.

Advances in mass spectrometry instruments have made it feasible to discover and measure protein biomarkers, but researchers lack the necessary bioinformatics software to analyze the data. Drs. Bin Ma of the University of Waterloo and Michael Moran of the Hospital for Sick Children are developing this software to enable more sensitive and accurate protein identification and quantification from the mass spectrometry data generated using a method called data independent acquisition (DIA). They expect that their software will significantly increase the total number of proteins identified and quantified in comparison to existing DIA analytical software. It will be especially effective with post-translational modifications (PTMs), which are critical biomarkers in a proteins’ function and degradation.

The free availability of the software to academic labs coupled with its superior performance can help health researchers discover and trace disease biomarkers. Within the next decade, the software could become an indispensable tool for many proteomics labs performing DIA analysis throughout the world. The new software may also help commercial partners create value-added new products, services and jobs.

Ultimately, this will lead to improvements in human health and reduction in healthcare costs by enabling early disease detection and diagnosis and by facilitating the selection of optimal treatment for individual patients.
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CReSCENT: CanceR Single Cell ExpressioN Toolkit

Project Leaders: Trevor Pugh, Princess Margaret Cancer Centre; Michael Brudno, The Hospital for Sick Children
Genome Centre: Ontario Genomics
Total Project Funding: $1,000,000

Tumours are complex mixtures of cancer, immune, and normal cells that interact and change during treatment. The interplay of all three types of cells can dictate development of cancer over time, as well as response or resistance to treatments. Recent advances in microfluidic and DNA sequencing technologies have enabled researchers to simultaneously analyze tens of thousands of single cells from complex tissues, including tumours. Interpreting these data is challenging, due to the lack of high-quality reference sets of each cell type in the body and a lack of methods to link these data back to tumour biology.

Drs. Trevor Pugh of the Princess Margaret Cancer Centre and Michael Brudno of The Hospital for Sick Children are developing the CanceR Single Cell ExpressioN Toolkit (CReSCENT), a scalable and standardized set of novel algorithmic methods, tools, and a data portal deployed on cloud computing infrastructure. To allow comparison of cells in cancerous and healthy tissues, the system will aggregate single-cell genomic data generated by cancer researchers and connect them to international reference data generated by experts from around the world as part of the Human Cell Atlas. This data sharing and aggregation system is a key differentiating factor in CReSCENT that will increase researcher productivity by accelerating execution and comparison of computational methods, as well as providing contextual data for understanding how cells behave within tumour tissues.

This platform, which will be useable by any researcher on any computing platform, will assemble a crucial data resource to navigate the upcoming wave of single cell cancer genomics research. CReSCENT will bring together researchers across a broad spectrum of scientific areas and disease types and increase the impact of data generated across research programs. In the long term, this system will pave the way for novel single cell diagnostics and discovery of new drug strategies for improved health care.
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Dockstore 2.0: Enhancing a community platform for sharing cloud-agnostic research tools

Project Leaders: Lincoln Stein, Ontario Institute for Cancer Research; Mark Fiume, DNAstack
Genome Centre: Ontario Genomics
Stream: 1 (health)
Total Project Funding: $875,269

With Genome Canada support, Dr. Lincoln Stein of the Ontario Institute for Cancer Research successfully developed Dockstore, a system that enables complex computational biology algorithms to be run reliably and reproducibly across multiple platforms. It has been adopted as the leading packaging technology by the Global Alliance for Genomics and Health and is now used by numerous third-party bioinformatics groups. Marc Fiume of the Canadian company DNAstack is collaborating with Dr. Stein and his team to maximize the utility of Dockstore.

The aim of these enhancements is to promote greater collaboration and sharing among computational biology software developers. Specifically, the enhancements will make Dockstore easier to use, make its packages more powerful and expressive, increase its interoperability and enable these packages to run more easily on a wide range of systems and hardware architectures. The bioinformatics and computational biology community will benefit from this software, while the research results derived from it that are reproducible, portable and reusable.
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From ePlants to eEcosystems: New Frameworks and Tools for Sharing, Accessing, Exploring and Integrating ‘Omic Data from Plants

Project Leaders: Nicholas Provart, University of Toronto; Jörg Bohlmann, University of British Columbia
Genome Centres: Ontario Genomics, Genome BC
Total Project Funding: $1,000,000

Major advances in plant biology over the past decade are in large part thanks to new technologies for DNA sequencing and phenotyping (i.e. mapping the physical expression of genetic traits). The resulting datasets allow researchers to determine how different plants develop and respond to changes in their environment. Yet, in order to take advantage of the tremendous amount of new data, innovative tools are required to integrate and visualize the number of individual data points in different datasets. The original ePlant system, developed as part of a previous Genome Canada effort, integrates many data types but was not configured for phenotype data. Amongst its many applications, phenotype data provide important information on traits of interest to plant breeders.

Drs. Nicholas Provart of the University of Toronto and Jörg Bohlmann of the University of British Columbia are developing a new module to integrate the wide variety of data available, including ecosystem data, phenotypes and genotypes into ePlant. This will be done for the already existing ePlant species and any new ePlant species to be developed as part of this project. The researchers will also open the ePlant system to the research community to build a larger ePlant ecosystem of information. This online system will act as a resource where plant biologists will be able to share their datasets.

Ultimately, these tools can help to accelerate the task of identifying useful genes to feed, shelter and power a world of nine billion people by the year 2050.
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Extracting Signal from Noise: Big Biodiversity Analysis from High-Throughput Sequence Data

Project Leaders: Sarah Adamowicz, Paul Hebert, University of Guelph
Genome Centre: Ontario Genomics
Total Project Funding: $507,231

Surveying biodiversity is critical for environmental health and for managing natural resources. It helps to assess the impact of resource development, but also to identify pests, invasive species, and pathogens in a rapid and cost-effective manner. It is essential to Canada’s economic growth in the forestry, agriculture, and fishery sectors and to decision-making in public health. Genetic methods of surveying biodiversity, such as high-throughput sequencing, are being broadly adopted, but bioinformatics has not kept pace with the data being generated. In addition, current methods are geared toward bacteria and similar organisms, rather than multi-celled plants and animals that need monitoring as well.

Drs. Sarah Adamowicz and Paul Hebert, along with colleagues from the University of Guelph, are creating new bioinformatics tools that will facilitate the rapid and accurate processing of DNA data resulting from high-throughput sequencing. The tools will enable the simultaneous analysis of bulk samples, which are made up of many different species. It will include a de-noising tool to detect errors; a method to cluster DNA sequences into species-like units to permit biodiversity analysis; and a method for assigning sequencing data to higher taxonomic categories to unlock functional biological information. The team will combine these various tools into a biodiversity informatics pipeline that can be incorporated into existing web-based platforms for uptake by a broad variety of users.

The new biodiversity informatics tools will support large-scale biodiversity research by academics; efficient, accurate, and cost-effective environmental assessments for the mining and pulp-and-paper industries; enhanced capacity and accuracy of regulation; and more rapid and accurate biodiversity data for government and private-sector decision-makers.
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Enhanced and automated visualization of complex data

Project Leader: Paul C. Boutros
Institution: Ontario Institute for Cancer Research
Start Date: October 1, 2016
End Date: September 30, 2016
Total Project Funding: $250,000

Modern genomics research generates massive amounts of data. But these data sets are too big and complex to be useful on their own. Researchers must first analyze and interpret biological data to better understand them and turn them into meaningful information. This information can then be used to help solve real-world problems, such as developing new tools or strategies to better diagnose and treat patients, increasing crop yields or monitoring the environment. Increasingly, the ability of the human end-user to interpret the data is the key factor limiting researchers from delivering these much-needed solutions more quickly.

Dr. Paul C. Boutros of the Ontario Institute for Cancer Research is leading a team developing ways of making “big data” results more easily understood by improving the way it is visualized and interpreted. The team will create interactive visualization tools that will integrate tightly with databases scientists already use routinely. The team will use crowdsourcing to capture the best visualization ideas from a broad community of scientists, graphic designers and citizen-scientists. The project will build on the human brain’s ability to interpret images, to make the conclusions of biological data more readily accessible and accelerate the rate of biological discovery and innovation.
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Consolidated epigenetic landscape for congenital, developmental and childhood disorders

Project Leaders: Michael Brudno, Rosanna Weksberg
Institution: Hospital for Sick Children
Start Date: October 1, 2016
End Date: September 30, 2016
Total Project Funding: $249,900

Epigenetics is the study of both genetic and external factors, such as environmental exposure or lifestyle choices by parents or grandparents, which affect gene expression. Epigenetic disruptions play a key role in disease. Finding epigenetic biomarkers, however, is complicated by the complexity of epigenetic signaling in cells or tissues, as well as the fact that many different genetic disorders, such as pediatric developmental disorders, can show similar clinical symptoms. Despite the wealth of data being generated by new technologies, there is a dearth of diagnostic tools that can consolidate epigenetic data collected by diverse groups using different experimental platforms. These tools are essential to relate molecular patterns to clinical presentation.

Drs. Michael Brudno and Rosanna Weksberg of Toronto’s Hospital for Sick Children are developing a novel web-based resource for analyzing epigenetic datasets together with complete clinical information, focusing on developmental disorders such as intellectual disability and autism. Their system will provide a rich context for exploring epigenetic dysregulation in a growing number of childhood epi-genetic diseases.
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Dockstore: A platform for sharing cloud-agnostic tools with the research community

Project Leaders: Vincent Ferretti, Lincoln Stein
Institution: Ontario Institute for Cancer Research
Start Date: October 1, 2016
End Date: September 30, 2016
Total Project Funding: $250,000

An unintended consequence of the development of genomics has been the proliferation of massive datasets, making analysis increasingly difficult. A further problem is the lack of standardization in how analysis tools are packaged, described and executed across computer environments. Drs. Vincent Ferretti and Lincoln Stein of the Ontario Institute for Cancer Research, in collaboration with Dr. Brian O’Connor of the University of California, Santa Cruz, have developed a web application called the Dockstore, which addresses the challenge of encapsulating and sharing bioinformatics tools so that they can be moved from environment to environment.

Now the researchers are adding key features to the Dockstore to continue to enhance and evolve the platform. They will also integrate bioinformatics tools and workflows from the Global Alliance for Genomics and Health (GA4GH) for redistribution to the larger research community and will work with collaborators to facilitate the registration of their high-quality tools into the Dockstore. Finally, the researchers will work with other projects to enable sharing of tools across genomic repositories. These activities will drive increased usage of the Dockstore, thereby increasing tool sharing among scientists in fields as diverse as agriculture, energy and human health.
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Kamphir: A versatile framework to fit models to phylogenetic tree shapes

Project Leader: Art F.Y. Poon
Institution: Western University
Start Date: October 1, 2016
End Date: September 30, 2016
Total Project Funding: $205,365

Phylodynamics is a new and rapidly growing field that combines epidemiology and computational biology to combat infectious disease outbreaks. The field stems from the concept of phylogeny, in which a tree represents how different populations – of virus infections, for example – are related through a series of common ancestors. The genetic similarities among populations are used to reconstruct these ancestral relationships back in time. This is particularly important for viruses, which evolve so quickly that each infection becomes genetically unique within weeks or months of being transmitted from the previous host. Consequently, the virus phylogeny can be used to estimate how the infections spread through the host population. Phylodynamics has already had an enormous impact on our understanding of outbreaks including HIV, hepatitis C virus, and Ebolavirus. Further progress is stymied, however, by simple models that can’t accommodate large data sets.

Dr. Art F.Y. Poon of Western University, Ontario, is developing a completely new approach to phylodynamics that adapts a method from pattern recognition to enable computers to “see” the shared features of different tree shapes. This approach will have an unprecedented capacity for more realistic models and larger data sets, improving global public health initiatives for infectious disease management and eradication.
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ePlants pipeline and navigator for accessing and integrating multi-level ‘omics data for 15 agronomically important species for hypothesis generation

Project Leader: Nicholas Provart
Institution: University of Toronto
Start Date: October 1, 2016
End Date: September 30, 2016
Total Project Funding: $250,000

In the past five years alone, huge amounts of data have been generated for 15 plant species important for Canada, including poplar, maize, rice, barley, wheat, soybeans and tomatoes. Being able to efficiently use these data will be key to improving and managing these crops to feed, shelter and power a world of 9 billion people by the year 2050.

The ePlant Framework, developed under a previous Genome Canada grant, permits researchers to easily see where and when a gene is “active” and whether there are natural genetic variants that might allow it to do its “job” better; populated only with one species, it now needs data from more species. Lead researcher Dr. Nicholas Provart (University of Toronto) plans to develop an ePlant Pipeline to facilitate the ability to create any ePlant, based on genomic or exome sequence data. The ePlant Navigator will permit cross-cultivar and cross-species comparisons, supporting robust hypothesis generation. Easy access to these data sets will enable researchers to explore genetic diversity, gene expression, and other data for important genes towards crop improvement.
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Rapid, accessible genome assembly using long read sequencing

Project Leader: Jared Simpson
Institutions: University of Toronto, Ontario Institute for Cancer Research
Start Date: October 1, 2016
End Date: September 30, 2016
Total Project Funding: $250,000

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

Project Leaders: Robert Beiko, Andrew G. McArthur
Institutions: Dalhousie University, McMaster University
Lead Genome Centre: Genome Atlantic
Co-Lead Genome Centre: Ontario Genomics
Start Date: October 1, 2016
End Date: September 30, 2016
Total Project Funding: $250,000

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.
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Genomic Epidemiology Application Ontology (GenEpiO)

Project Leaders: William Hsiao, Andrew G. McArthur, Fiona S.L. Brinkman
Institutions: University of British Columbia, McMaster University, Simon Fraser University
Lead Genome Centre: Genome British Columbia
Co-Lead Genome Centre: Ontario Genomics
Start Date: October 1, 2016
End Date: September 30, 2016
Total Project Funding: $250,000

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

Project Leaders: Dr. Michael Brudno, Dr. Gary Bader
Institutions: University of Toronto, The Hospital for Sick Children
Start Date: July 1, 2013
End Date: June 30, 2016
Total Project Funding: $998,546

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

Project Leaders: Dr. Anne-Claude Gingras, Dr. Mike Tyers
Institutions:  Samuel Lunenfeld Research Institute at Mount Sinai Hospital, Institut de Recherche en Immunologie et cancérologie, Université de Montréal
Start Date: July 1, 2013
End Date: June 30, 2015
Total Project Funding: $1,000,000

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

Project Leaders: Jordan Lerner-Ellis, Dr. Matthew Lebo
Institution: Mount Sinai Hospital
Start Date: July 1, 2013
End Date: June 30, 2016
Total Project Funding: $1,000,000

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

Project Leaders: Dr. Nicholas Provart, Dr. Stephen Wright
Institution: University of Toronto
Start Date: July 1, 2013
End Date: June 30, 2016
Total Project Funding: $1,000,000

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.
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Applying genomic signal processing methods to accelerate crop breeding

Project Leaders: Dr. Lewis Lukens, Dr. Cortland Griswold
Institution: University of Guelph
Start Date: July 1, 2013
End Date: June 30, 2015
Total Project Funding: $220,000

Selective breeding improves plant and animal products by identifying desirable traits such as quality, yield, and ability to grow in difficult conditions, ensuring that that there is sufficient production for food, fuel and raw materials. Factors like climate change and population growth are making selective breeding more important than ever. One of the largest challenges facing the plant research community is identifying the suite of genes that make organisms well adapted to their environment and using this information in breeding programs.

Drs. Lewis Lukens, Cortland Griswold and their team are using bioinformatics tools to understand how organisms that adapt well to their environments can be selected to accelerate the development of new plant varieties.
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Leveraging meta-transcriptomics for functional interrogation of microbiomes

Project Leader: Dr. John Parkinson
Institution: The Hospital for Sick Children
Start Date: July 1, 2013
End Date: June 30, 2015
Total Project Funding: $249,951

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.
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Pathway and network visualization for personal genomes

Project Leader: Dr. Lincoln Stein
Institution: Ontario Institute for Cancer Research
Start Date: July 1, 2013
End Date: June 30, 2015
Total Project Funding: $249,999

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.
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