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.
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.
Ballouz, S. and Weber, M. and Pavlidis, P. and Gillis, J. (2017) EGAD: ultra-fast functional analysis of gene networks. Bioinformatics, 33(4) pp. 612-614.
Ballouz, S. and Pavlidis, P. and Gillis, J. (2016) Using predictive specificity to determine when gene set analysis is biologically meaningful. Nucleic Acids Res,
Crow, M. and Paul, A. and Ballouz, S. and Huang, Z. J. and Gillis, J. (2016) Exploiting single-cell expression to characterize co-expression replicability. Genome Biol, 17(1) pp. 101.Additional materials of the author at
CSHL Institutional Repository