Variant Identification & Consequence

Variant consequence prediction remains a challenge even for variants affecting protein-coding genes, and is essentially terra incognita for non-coding regions. Our work hence is relevant to both researchers in cancer genomics as well as anyone working on a disease whose risk is related to genetic variation.

Research Statement

  1. Develop a new population-based approach for structural variant detection
  2. Develop techniques to identify differences among genomes using de novo assembly
  3. Develop a method to predict the impact of genetic variants based on molecular interaction networks
  4. Develop a new version of the snpEff variant annotation tool that predicts the consequences of variants in non-coding regions
  • Management Team

  • Development Team

    • Jean Monlong, Research Team
  • Collaborators

    • Jared Simpson, Collaborator
    • Gary Bader, Collaborator

Latest Publications & Presentations


PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data

Presenter: Toby Hocking

Date: July 2015

Meeting: International Conference on machine learning; Lille, France


Population-based Detection of Structural Variants in Normal and Aberrant Genomes

Presenter: Jean Monlong

Date: June 2015

Meeting: European Society Human Genetics conference; Glasgow, Scotland


Wagih O, Reimand J, Bader GD, (2015)

MIMP: predicting the impact of mutations on kinase-substrate phosphorylation.

Nature methods, 2015;12(6):531-3