Research ResourceNuclear Receptors

Discovering relationships between nuclear receptor signaling pathways, genes, and tissues in Transcriptomine

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Sci. Signal.  25 Apr 2017:
Vol. 10, Issue 476, eaah6275
DOI: 10.1126/scisignal.aah6275

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Data mining to understand nuclear receptor signaling

Transcriptomic data are potentially useful for generating mechanistic hypotheses beyond the original experiment in which they were generated and for independently validating unrelated studies. However, data are often generated and presented in disparate contexts and formats, making it difficult to draw connections between different researchers’ findings. Becnel et al. provide an updated version of Transcriptomine, a data-mining web tool that focuses on nuclear receptor pathway data sets. This tool has been redesigned to be easily used by bench scientists to access and complement data from the published scientific literature. The resource curates more than 500 data sets to allow users to cross-reference information about how different genetic or pharmacological manipulations affect gene expression in different organs or physiological systems and to visualize pathway-gene-tissue relationships. The approach used by the authors can be expanded to other pathways and types of ‘omics data sets.


We previously developed a web tool, Transcriptomine, to explore expression profiling data sets involving small-molecule or genetic manipulations of nuclear receptor signaling pathways. We describe advances in biocuration, query interface design, and data visualization that enhance the discovery of uncharacterized biology in these pathways using this tool. Transcriptomine currently contains about 45 million data points encompassing more than 2000 experiments in a reference library of nearly 550 data sets retrieved from public archives and systematically curated. To make the underlying data points more accessible to bench biologists, we classified experimental small molecules and gene manipulations into signaling pathways and experimental tissues and cell lines into physiological systems and organs. Incorporation of these mappings into Transcriptomine enables the user to readily evaluate tissue-specific regulation of gene expression by nuclear receptor signaling pathways. Data points from animal and cell model experiments and from clinical data sets elucidate the roles of nuclear receptor pathways in gene expression events accompanying various normal and pathological cellular processes. In addition, data sets targeting non-nuclear receptor signaling pathways highlight transcriptional cross-talk between nuclear receptors and other signaling pathways. We demonstrate with specific examples how data points that exist in isolation in individual data sets validate each other when connected and made accessible to the user in a single interface. In summary, Transcriptomine allows bench biologists to routinely develop research hypotheses, validate experimental data, or model relationships between signaling pathways, genes, and tissues.

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