Research Statement
- Develop a new population-based approach for structural variant detection
- Develop techniques to identify differences among genomes using de novo assembly
- Develop a method to predict the impact of genetic variants based on molecular interaction networks
- Develop a new version of the snpEff variant annotation tool that predicts the consequences of variants in non-coding regions
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Management Team
- Guillaume Bourque, Principal Investigator
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Development Team
- Jean Monlong, Research Team
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Collaborators
- Jared Simpson, Collaborator
- Gary Bader, Collaborator
Latest Publications & Presentations
Presentation
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data
Date: July 2015
Meeting: International Conference on machine learning; Lille, France
Presentation
Population-based Detection of Structural Variants in Normal and Aberrant Genomes
Date: June 2015
Meeting: European Society Human Genetics conference; Glasgow, Scotland
Publication
MIMP: predicting the impact of mutations on kinase-substrate phosphorylation.
Nature methods, 2015;12(6):531-3
