Abstract
High dimensional characterization of drug targets, compound effects and disease phenotypes are crucial for increased efficiency of drug discovery. High-throughput gene expression measurements are one of the most frequently used data acquisition methods for such a systems level analysis of biological phenotypes. RNA sequencing allows genome wide quantification of transcript abundances, recently even on the level of single cells. However, the correct, mechanistic interpretation of transcriptomic measurements is complicated by the fact that gene expression changes can be both the cause and the consequence of altered phenotype. Perturbation gene expression profiles, where gene expression is measured after a genetic or chemical perturbation, can help to overcome these problems by directly connecting the causal perturbations to their gene expression consequences. In this Review, we discuss the main large scale perturbation gene expression profile datasets, and their application in the drug discovery process, covering mechanisms of action identification, drug repurposing, pathway activity analysis and quantitative modelling.