The Signaling Pathways Project, an integrated ‘omics knowledgebase for mammalian cellular signaling pathways

Scott A. Ochsner, David Abraham, Kirt Martin, Wei Ding, Apollo McOwiti, Wasula Kankanamge, Zichen Wang, Kaitlyn Andreano, Ross A. Hamilton, Yue Chen, Angelica Hamilton, Marin L. Gantner, Michael Dehart, Shijing Qu, Susan G. Hilsenbeck, Lauren B. Becnel, Dave Bridges, Avi Ma’ayan, Janice M. Huss, Fabio StossiCharles E. Foulds, Anastasia Kralli, Donald P. McDonnell, Neil J. McKenna

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number252
JournalScientific Data
Volume6
Issue number1
DOIs
StatePublished - Dec 1 2019
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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