Jesse Gillis

Jesse Gillis

Associate Professor
Cancer Center Member

Ph.D., University of Toronto, 2007

jgillis@cshl.edu | 516-422-4041

Gillis Lab Website   Faculty Profile

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. The Gillis lab focuses on characterizing these shared patterns of gene activity through co-expression networks and showing how they can lead to changes in cell function, particularly in single cell expression data.

A dominant interest within computational biology is the analysis of gene networks to provide insight into diverse levels of functional activity, typically starting with regulatory interactions and moving up to more diffuse associations important for understanding systemic dynamics. But trying to understand how genes interact to produce function is a hugely complicated problem and one that appears likely to become more so as genomic information becomes more detailed. Historically, many attempts to understand gene function through networks have leveraged a biological principle known as “guilt by association.” It suggests that genes with related functions tend to share properties (e.g., physical interactions). In the past decade, this approach has been scaled up for application to large gene networks, becoming a favored way to grapple with the complex interdependencies of gene functions in the face of floods of genomics and proteomics data. Gillis’ work centers on identifying the limits of the approach and making fundamental improvements to its operation, as well as applying those improvements to understanding cell biology.

Chen, X. and Sun, Y. C. and Zhan, H. and Kebschull, J. M. and Fischer, S. and Matho, K. and Huang, Z. J. and Gillis, J. and Zador, A. M. (2019) High-Throughput Mapping of Long-Range Neuronal Projection Using In Situ Sequencing. Cell, 179(3) pp. 772-786.e19.

Ballouz, S. and Dobin, A. and Gillis, J. A. (2019) Is it time to change the reference genome?. Genome Biol, 20(1)

Crow, M. and Lim, N. and Ballouz, S. and Pavlidis, P. and Gillis, J. (2019) Predictability of human differential gene expression. Proc Natl Acad Sci U S A,

Crow, M. and Gillis, J. (2018) Co-expression in Single-Cell Analysis: Saving Grace or Original Sin?. Trends Genet, 34(11) pp. 823-831.

Ballouz, S. and Dobin, A. and Gingeras, T. R. and Gillis, J. (2018) The fractured landscape of RNA-seq alignment: the default in our STARs. Nucleic Acids Res,

Additional materials of the author at
CSHL Institutional Repository