Dr. Gillis is an Associate Professor of Computational Genetics at Cold Spring Harbor Laboratory. Since starting his own lab at Cold Spring Harbor Laboratory in 2012, his research has aimed to understand the flow of information from the genome to whole organism biology through modeling and analysis of functional genomics data. This research is broadly integrative across modalities, systems, and even species, but also integrative across levels of organization, using molecular processes within cells to understand how and why cells diversify and how that diversity, in turn, affects organism phenotype.
As functional genomics data has continued to increase in abundance and specificity, Dr. Gillis’ lab has benefited from the opportunities to provide organizing frameworks, grounded in both biology and statistical insight. They have been particularly interested in determining base vocabularies to compare quite disparate data with the goal of better exploiting conservation as a central principle to understand function in physiological systems.
A particular focus within his lab is the analysis of gene co-expression, or the shared expression profile of genes across conditions. Genes which express under similar conditions will tend to share functions, and by tailoring data and methods, this can usefully model biological systems from cells to organisms to species. In addition to research on cell identity and co-expression, Dr. Gillis has experience in the development of methods and bioinformatics pipelines for external use, including stand-alone packages and web-based user-friendly analysis tools.
He is strongly dedicated to assessments of robustness and replicability, particularly in novel data. His lab’s research focuses on making not only their own methods available, but also improving the utility of related data from other researchers.
Dr. Gillis’ graduate training was in Computational Neuroscience. He obtained a PhD with Frances Skinner from the University of Toronto. His post-doc, with Paul Pavlidis, at the University of Toronto focused on largescale integration of expression data to improve our understanding of gene function.
