Predicting variant effects within ciliary proteins

DC6 will be dedicated to predicting variant effects within ciliary proteins. (1) As the group has done for other diseases and protein classes, we will define features that are most predictive of ciliary pathogenesis including many features used by the group previously and details on gene expression and the interactome. These features will then be used to predict variant details using ML as we have done previously. Beyond just whether variants are “pathogenic” or not the focus will be on specific protein (e.g. stability, interactions, molecular mechanism etc.) or phenotypic outcomes, particularly focusing on tissue relevance and overall disease severity as is of particular interest in the ciliopathies. (2) DC6 will also interrogate and attempt to predict sets of candidate modifiers and other potential variant types of interest. We envision that the fellow will also work very closely with experimental partners in the consortium, particularly those generating omics data (e.g. P6-UT and P1-RUMC) and those with a focus on patient variant data (e.g. P5-Imagine and AP4-UEDIN).  It is with these partners that we plan secondments. We also expect numerous collaborations with other partners on particular variants, genes or datasets of interest.

Fellow profile: Master degree (or equivalent) in a natural science (preferably with a Biological component) subject. This project is very suitable for students with programming experience.