Nuclear receptors (NRs) are a superfamily of ligand-regulated transcription factors that interact with coregulators and other transcription factors to direct tissue-specific programs of gene expression. Recent years have witnessed a rapid acceleration of the output of high-content data platforms in this field, generating discovery-driven datasets that have collectively described: the organization of the NR superfamily (phylogenomics); the expression patterns of NRs, coregulators and their target genes (transcriptomics); ligand- and tissue-specific functional NR and coregulator sites in DNA (cistromics); the organization of nuclear receptors and coregulators into higher order complexes (proteomics); and their downstream effects on homeostasis and metabolism (metabolomics). Significant bioinformatics challenges lie ahead both in the integration of this information into meaningful models of NR and coregulator biology, as well as in the archiving and communication of datasets to the global nuclear receptor signaling community. While holding great promise for the field, the ascendancy of discovery-driven research in this field brings with it a collective responsibility for researchers, publishers and funding agencies alike to ensure the effective archiving and management of these data. This review will discuss factors lying behind the increasing impact of discovery-driven research, examples of high-content datasets and their bioinformatic analysis, as well as a summary of currently curated web resources in this field. This article is part of a Special Issue entitled: Translating nuclear receptors from health to disease.
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