Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets

There is a growing collection of genomics data sets generated for identifying the gene targets under control of transcription regulators (TRs). TR ChIP-seq and RNA expression experiments that perturb TR activity are the most common strategies for mapping TRs to genes at a genomic scale. However, the collection, preprocessing, summarization, and integration of these data sets requires a non-trivial degree of bioinformatics experience. In this study, we set out a framework to accomplish these tasks. We focus on eight TRs in both mouse and human, encompassing nearly 500 experiments, with two main objectives. The first is a detailed examination of the properties of the contributing experiments, to better learn of potential biases and pitfalls when aggregating diverse data sets. The second is to provide summarized, transparent, and convenient TR-target rankings based upon these genomic data sets for community use. Our work thus catalogues the state of the literature for a subset of important mammalian TRs, prioritizes gene targets based upon available empirical evidence, and provides a framework for ready expansion to more TR data sets.