Analyzing causal relationships in proteomic profiles using CausalPath

Augustin Luna, Metin Can Siper, Anil Korkut, Funda Durupinar, Ugur Dogrusoz, Joseph E. Aslan, Chris Sander, Emek Demir, Ozgun Babur

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


CausalPath ( evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021).

Original languageEnglish (US)
Article number100955
JournalSTAR Protocols
Issue number4
StatePublished - Dec 17 2021


  • Bioinformatics
  • Proteomics
  • Systems biology

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Neuroscience


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