Analysis and interpretation of high-throughput chemical-genetic interaction screens.
2018-08
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Analysis and interpretation of high-throughput chemical-genetic interaction screens.
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2018-08
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Screening chemical compounds against genome-wide mutant arrays identifies genetic perturbations that cause sensitivity or resistance to compounds of interest. The resulting chemical-genetic interaction profiles contain information on the cellular functions perturbed by compounds and can be used to elucidate their modes of action. When performed at high throughput, chemical-genetic interaction screens can be used to functionally profile entire libraries of chemical compounds in an unbiased manner to identify promising compounds with diverse modes of action. My contributions to the field of chemical-genetic interaction screening come primarily in the form of two software pipelines, called BEAN-counter and CG-TARGET, that were developed to interface with the large-scale datasets generated from screens of thousands of compounds performed by collaborators. The former pipeline processes the raw data into chemical-genetic interaction scores and provides tools to remove systematic biases and other unwanted signals from large-scale datasets. The latter provides for interpretation of chemical-genetic interaction profiles via a compendium of reference genetic interaction profiles, with a focus on controlling the false discovery rate and prioritizing the highest-confidence predictions for further study. Enabled by the tools I developed to analyze and interpret these data, our collaboration characterized novel compounds, identified general trends surrounding the interactions between compounds and biological systems, and demonstrated the value of performing chemical-genetic interaction screens to functionally annotate compounds at high throughput.
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University of Minnesota Ph.D. dissertation. August 2018. Major: Biomedical Informatics and Computational Biology. Advisor: Chad Myers. 1 computer file (PDF); x, 174 pages + 8 supplementary data files
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Simpkins, Scott. (2018). Analysis and interpretation of high-throughput chemical-genetic interaction screens.. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/206377.
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