19. A STRATEGY FOR ANALYZING HIGH-THROUGHPUT QUANTITATIVE GENETIC INTERACTION DATA IN MULTI-CONDITION EXPERIMENTS

Department: Bioengineering
Research Institute Affiliation: Graduate Program in Bioinformatics
Faculty Advisor(s): Trey Ideker

Primary Student
Name: Gordon J Bean
Email: gbean@ucsd.edu
Phone: 858-822-4665
Grad Year: 2013

Abstract
The technology and methodology to conduct high-throughput screens for quantitative epistatic genetic interactions in yeast have existed for over a decade. Recently this methodology has been expanded to quantitatively measure the changes that occur to genetic interactions across different growth conditions, termed differential genetic interactions. We present a strategy and an accompanying toolkit for accurately measuring differential genetic interactions in SGA/EMAP type experiments. Key components of our methodology include an improved data pre-processing pipeline and an intuitive statistical genetic interaction score motivated by experimental design and previous, single-condition models. We demonstrate that the data processing and statistical scoring of the data result in meaningful improvements over existing options. Our toolkit, written in MATLABŪ, also includes useful tools previously provided for single-condition studies that have been adapted to the newer, differential experimental design.

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