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An integrative platform for metabolomics and systems biology

2017 Bioinformatics and Computational Biology Competition
Genome Centre(s):
Génome Québec
Project Leader(s):
Jianguo Xia (McGill University), Guillaume Bourque (McGill University), Pierre-Étienne Jacques (Université de Sherbrooke)
Project Description: 

Although a relative newcomer to the ‘omics family, metabolomics – the comprehensive study of small molecules or metabolites in biofluids such as blood or urine – is being increasingly applied together with other ‘omics technologies to understand complex diseases and biological processes. To deal with the resulting big data challenges, easy-to-use and high-performance bioinformatics tools are urgently required for raw data processing, interpretation, and integration with other ‘omics data.

Drs. Jianguo Xia and Guillaume Bourque of McGill University, together with Dr. Pierre-Étienne Jacques of the Université de Sherbrooke, are building on their past success with existing web-based platforms for human health ‘omics analysis to develop SystemsAnalyst, a new-generation computational platform integrating resources from Compute Canada and public cloud with the latest visual analytics technologies. Their goal is to develop a powerful one-stop shop to enable efficient, transparent, and reproducible analysis of large amount of data to support systems metabolomics and multi-omics data integration.

SystemsAnalyst will directly benefit clinicians and laboratory-based biologists by providing a powerful, user-friendly platform to address current bioinformatics gaps in metabolomics and multi-omics studies. Case studies on malaria and inflammatory bowel disease will facilitate the identification of important biomarkers, key pathways and biological processes that play important roles in these diseases, ultimately assisting in the development of new treatment strategies. The project will train at least 10 highly qualified personnel who are urgently needed in multi-omics data analysis and modeling. SystemsAnalyst can be easily applied to studying other complex diseases as well.