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Gene Essentiality and
Genetic Interaction Lab

A systems biology lab at the interface of computational and experimental biology

Traver Hart, PhD

We seek to decipher the complex web of relationships that governs how eukaryotic cells process information, respond to their environments, and transform into cancer. As part of The University of Texas MD Anderson Cancer Center, we also search for emergent vulnerabilities of tumor cells, and we collaborate extensively with preclinical and clinical researchers to translate these findings into new therapeutic opportunities. As part of this work, we develop computational and experimental tools for CRISPR-mediated genetic engineering in mammalian cells.

Update March 2023: We are now part of the NCI CTD^2 Network!
Watch this space for more exciting updates.


Functional Genomics and Genetic Interactions

New York
Cells from different tissues have different genetic dependencies, but all share a common set of "core fitness genes" that are required for proliferation. We use this difference between core and context essentials to guide our estimates of screen quality and gene classification, and to identify tumor-specific pathways and targets

New CRISPR technologies enable precise multiplex genetic perturbations, allowing us to go beyond simple single-gene knockouts. We can now begin to explore genetic interaction networks in mammalian cells, as has been done in yeast, and decipher cellular organization as well as discover synthetic lethals.

New York
Genes with correlated knock-out fitness profiles across a diverse set of screens operate in the same biological process or biochemical pathway. But there are many ways to construct these functional interaction networks. Which is best?

See the paper (Gheorghe & Hart) here

New York
Correlation networks integrate functional interactions across all cell states represented in the underlying data. But sometimes those interactions vary across cell states. This variation can give insight into cellular organization and information flow.

We use approaches like these to ask fundamental biological questions about the hierarchical organization of the cell, and to guide the development of new technologies to address the blind spots in our data.

See the paper (Kim et al.) here

New York
New York
New York
One such blind spot is functional buffering by paralogs, where a single gene knockout shows no phenotype in a CRISPR/Cas9 screen because a closely related gene shoulders the burden.

We used the enCas12a system to perform targeted double knockouts of selected paralogs. By measuring genetic interaction between the gene pairs, we were able to identify dozens of synthetic lethals. For example, in the Cop9 signalosome complex, most subunits are essential but the COPS7A/B genes encode proteins that can substitute for each other.

See the paper (Dede, McLaughlin, et al.) here


Interactive exploration of CRISPR screens

Explore the loss-of-function fitness profile of your favorite genes across hundreds of cell lines screened with CRISPR knockout libraries.

Click Here to go to PICKLES

See the paper (Novak et al.) here



Open-source Python tools for screen analysis


Download our BAGEL2 and DrugZ software for analyzing fitness screens and drug-gene interaction screens. All software is free for any use, with attribution.

Click Here to visit our Github repository


A photo from 2020. Updates coming soon...ish.