Ph.D., University of Toronto, 2007
Gene networks; gene function prediction; guilt by association; neuropsychiatric; hub genes; multifunctionality; computational genomics
There has been a growing appreciation in recent years that gene function is frequently context dependent, with a large part of that context provided by the activities of other genes. But trying to understand how genes interact to produce function is a hugely complicated problem and one that appears likely to worsen with more detailed genomic information.
Computational biology has taken up this challenge by interpreting the functions of genes in the context of networks derived from gene association data. Such networks consist of millions of interactions across thousands of genes, derived from protein binding assays, RNA coexpression analysis, etc. Historically, many attempts to understand gene function through networks leverage a biological principle known as “guilt by association” (GBA).
GBA states that genes with related functions tend to share properties (e.g., physical interactions). In the past decade, GBA has been scaled up for application to large gene networks, becoming a favoured way to grapple with the complex interdependencies of gene functions in the face of floods of genomics and proteomics data. My work centers on identifying the limits of the GBA approach and making fundamental improvements to its operation, as well as applying those improvements to neuropsychiatric gene network data.
Gillis ,J., Pavlidis, P. 2012 “Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks. PLoS Comput Biol 8: e1002444.
Gillis, J., and Pavlidis, P. 2011. The role of indirect connections in gene networks in predicting function Bioinformatics 27: 1860–1866.
Gillis, J., and Pavlidis, P. 2011. The Impact of Multifunctional Genes on "Guilt by Association" Analysis PLoS ONE 6 (2): e17258.
Gillis, J., Mistry, and M., Pavlidis, P. 2010. Gene function analysis in complex data sets using ErmineJ Nature Protocols 5: 1148–1159.