Of the tens of thousand of protein-coding genes in the human genome, only a small portion have an experimentally defined function. For the rest, how can we determine what they do? My lab develops computational predictions based on co-expression networks. We are applying our predictions to understand neuropsychiatric disorders.
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 become more so as genomic information becomes more detailed. Jesse Gillis and colleagues are computational biologists who are presently challenging an oft-taken approach to the problem in which the functions of genes are interpreted 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, and other sources. 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 neuropsychiatric gene network data.
NIH grant awarded for interneuron research
April 4, 2019
CSHL postdoc Maggie Crow will use her NIH grant to pursue the quantification and analysis of specific types of neurons in the brain.
Genetic ‘usual suspects’ identified in researchers’ new list
March 4, 2019
An exhaustive ranked list of “usual suspect” genes involved in disease may prove invaluable for future research and drug discovery.
Portrait of a Neuroscience Powerhouse
April 27, 2018
A relatively small neuroscience group at CSHL is having an outsized impact on a dynamic and highly competitive field
New leadership roles in BRAIN Initiative and International Brain Lab reflect CSHL’s excellence in neuroscience
October 24, 2017
The BRAIN Initiative Cell Census Network establishes a Center and a Collaboratory for the Mouse Brain Cell Atlas at Cold Spring Harbor Laboratory
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,
Crow, M. and Paul, A. and Ballouz, S. and Huang, Z. J. and Gillis, J. (2018) Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat Commun, 9(1) pp. 884.
Ballouz, S. and Gillis, J. (2017) Strength of functional signature correlates with effect size in autism. Genome Med, 9(1) pp. 64.Additional materials of the author at
CSHL Institutional Repository