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Computational tools for Data-Independent Acquisition (DIA) for quantitative proteomics and metabolomics

2017 Bioinformatics and Computational Biology Competition
Genome Centre(s):
Ontario Genomics
Project Leader(s):
Anne-Claude Gingras (Lunenfeld-Tanenbaum Research Institute), Hannes Röst (Donnelly Centre for Cellular & Biomolecular Research, University of Toronto)
Project Description: 

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.