ABSTRACT
Genetic interactions provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. However, they have remained underutilized in this capacity across recent chemical-genetic interaction screening efforts and their ability to interpret chemical-genetic interaction profiles on a large scale has not been tested. We developed a method, which we refer to as CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates the data from large-scale chemical-genetic interaction screens with genetic interaction data to predict the biological processes perturbed by compounds. CG-TARGET compared favorably to a standard enrichment approach across a variety of benchmarks, achieving similar performance on measures of accuracy and substantial improvement in the ability to control the false discovery rate of its predictions. We found that one-third to one-half of gene mutants in the data contribute to the highest-confidence biological process predictions and that these contributions overwhelmingly come from negative chemical-genetic interactions. This method was used to prioritize over 1500 out of over 13,000 compounds for further study in a recently-completed chemical-genetic interaction screen in Saccharomyces cerevisiae, enabling the rapid functional annotation of unknown compounds to biological processes through targeted biological validations. We present here a detailed characterization of the method and further biological validations to demonstrate the utility of genetic interactions in the interpretation of chemical-genetic interaction profiles and the effectiveness of our implementation of this concept.