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Development and Validation of a Web-Based Platform for Environmental Omics and Toxicology

2017 Bioinformatics and Computational Biology Competition
Agriculture and Agri Food
Fisheries and Aquaculture
Genome Centre(s):
Génome Québec
Project Leader(s):
Jianguo Xia (McGill University), Niladri Basu (McGill University)
Project Description: 

Environmental risk assessment is rapidly moving to ‘omics tools and systems biology approaches to evaluate the impact of stressors on animal growth, reproduction and survival. Despite great interest among stakeholder groups, it is clear that those in the environmental life sciences cannot adequately deal with the type and amounts of data that are rapidly emerging. There are no accepted and standardized bioinformatics tools to organize, analyze, visualize and interpret ‘omics data from key study species.

Over the past decade, Dr. Jianguo Xia of McGill University has developed a series of web/cloud-based tools for comprehensive ‘omics data analysis, visualization and interpretation. These powerful and user-friendly tools have proven highly popular within human health community with thousands of users around the world. A similar omics tool suite is urgently needed to support the growing numbers of omics studies in environmental life sciences, including in sectors such as mining, agriculture, forestry, aquaculture and water quality management.

Now Drs. Xia and her colleague Dr. Basu are developing eco.OmicsAnalyst, an intuitive, cloud-based tool to support such data analysis and visualization, beginning with 12 key ecological indicator species covering fish, birds, mammals and invertebrates. To refine and validate the tools, they will embark on a series of case studies within a particularly important class of stressors, chemical pollution. Their work will be driven by key end-users from across government and academia. A technical guidance document will be produced to facilitate end-user uptake.

Due to the research team’s close relationships with stakeholders, there is a high likelihood of uptake of the new tool and of overcoming existing barriers for handling ‘omics data across the environmental life sciences. Among the benefits will be improved data quality, more efficient decision making and improved resource utilization.