Abstract
Synthetic genetic arrays have been very effective at measuring genetic interactions in yeast in a high-throughput manner and recently have been expanded to measure quantitative changes in interaction, termed 'differential interactions', across multiple conditions. Here, we present a strategy that leverages statistical information from the experimental design to produce a novel, quantitative differential interaction score, which performs favorably compared to previous differential scores. We also discuss the added utility of differential genetic-similarity in differential network analysis. Our approach is preferred for differential network analysis, and our implementation, written in MATLAB, can be found at http://chianti.ucsd.edu/~gbean/compute_differential_scores.m.
Highlights
Genetic interactions are functional dependencies between genes, which become apparent when the phenotypic effect of one mutation is altered by the presence of a second
Using the data from two differential interaction mapping experiments comparing methyl methanesulfonate (MMS) and standard growth conditions [6,7], we found that the variance of the difference for each double mutant was less than half of the expected differential variance, and even less than the variance of static measurements (Figure 2)
The dS score: a quantitative measure of differential interaction we developed a strategy for scoring differential genetic interactions, which accounts for the dependency structure of the data
Summary
Genetic interactions are functional dependencies between genes, which become apparent when the phenotypic effect of one mutation is altered by the presence of a second. In model organisms such as yeast, genetic interactions can be rapidly assessed through the systematic construction of double mutants and measurement of quantitative phenotypes such as growth rate. We used genetic interaction mapping in a ‘differential mode’ to compare the changes in genetic networks across experimental conditions [6,7,8] To demonstrate this approach, called differential epistasis mapping, we compared the difference between quantitative genetic interaction scores derived from yeast grown on standard versus DNA-damaging media [6]. We found substantial changes in interaction patterns and demonstrated that the difference in scores was more effective than the scores in either static condition for highlighting interactions relevant
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