(External) Can incorporating proteomics data concerning post-translational modifications improve the identification of pathogenic missense variants in autism and other disorders?

Advisor: Brett Trost, The Hospital for Sick Children

Suggested co-advisors: Rozita Dara, Angela Canovas, Mazyar Fallah, Zeny Feng, Brandon Lillie

While the control of biological processes is often most closely associated with the regulation of transcription, other regulatory mechanisms are important as well. One such mechanism is the post-translational modifications of proteins, such as phosphorylation. In this project, the student will address the hypothesis that by developing a machine learning model that incorporates information about the effect of missense variants on post-translational modifications, we can more accurately predict the pathogenicity of missense variants in autism and other disorders.

Two-semester project preferred. The student is required to be on-site (Toronto) for the duration of the project.

Knowledge/Skills

Programming in Python and/or R; experience working in a Linux environment; experience using high-performance computing resources; experience in genetics/genomics would be an asset.