Abstract
Mining of integrated public transcriptomic and ChIP-Seq (cistromic) datasets can illuminate functions of mammalian cellular signaling pathways not yet explored in the research literature. Here, we designed a web knowledgebase, the Signaling Pathways Project (SPP), which incorporates community classifications of signaling pathway nodes (receptors, enzymes, transcription factors and co-nodes) and their cognate bioactive small molecules. We then mapped over 10,000 public transcriptomic or cistromic experiments to their pathway node or biosample of study. To enable prediction of pathway node-gene target transcriptional regulatory relationships through SPP, we generated consensus ‘omics signatures, or consensomes, which ranked genes based on measures of their significant differential expression or promoter occupancy across transcriptomic or cistromic experiments mapped to a specific node family. Consensomes were validated using alignment with canonical literature knowledge, gene target-level integration of transcriptomic and cistromic data points, and in bench experiments confirming previously uncharacterized node-gene target regulatory relationships. To expose the SPP knowledgebase to researchers, a web browser interface was designed that accommodates numerous routine data mining strategies. SPP is freely accessible at https://www.signalingpathways.org.
Highlights
Signal transduction pathways describe functional interdependencies between distinct classes of molecules that collectively determine the response of a given cell to its afferent endocrine, paracrine and cytokine signals[1]
Having defined relationships within each major signaling pathway module, we proceeded to develop a dataset biocuration pipeline (Fig. 2) that would classify publically archived transcriptomic and ChIP-Seq datasets according to the signaling pathway node(s) whose transcriptional functions they were designed to interrogate, as well as their biosample of study
To make the results of our biocuration efforts routinely and freely available to the research community, we developed a web user interface (UI) for the Signaling Pathways Project (SPP) knowledgebase that would provide for browsing of datasets, as well as for mining of the underlying data points
Summary
Signal transduction pathways describe functional interdependencies between distinct classes of molecules that collectively determine the response of a given cell to its afferent endocrine, paracrine and cytokine signals[1]. The bulk of readily accessible information on these pathways resides in peer-reviewed research articles and in knowledgebases that curate such information[2]. Many such articles are based in part upon discovery-scale datasets documenting, for example, the effects of genetic or small molecule perturbations on gene expression in transcriptomic (expression array or RNA-Seq) datasets, and DNA promoter occupancy in cistromic (ChIP-Seq) datasets. SPP encompasses datasets involving genetic and small molecule perturbations of a broad range of cellular signaling pathway modules - receptors, enzymes, transcription factors and their co-nodes. We have validated the consensomes using alignment with literature knowledge, integration of transcriptomic and ChIP-Seq evidence, and using bench experimental use cases that corroborate signaling pathway node-target regulatory relationships predicted by the consensomes. We have designed a user interface that makes the entire data matrix available for routine data browsing, mining and hypothesis generation by the mammalian cell signaling research community at https://www.signalingpathways.org
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