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Published January 1, 2026 | Version v1
Journal article Open

OmniPath: integrated knowledgebase for multi-omics analysis

Creators

  • 1. Imperial Coll London, Fac Med, Dept Metab Digest & Reprod, London W12 0NN, England
  • 2. Univ Cambridge, Dept Comp Sci & Technol, Cambridge CB3 0FD, England
  • 3. Fraunhofer Inst Algorithms & Sci Comp, D-53757 Schloss Birlinghoven, Germany
  • 4. European Bioinformat Inst EMBL EBI, European Mol Biol Lab, Hinxton CB10 1SD, England
  • 5. UCL, Dept Med Phys & Biomed Engn, London WC1E 6BT, England
  • 6. Rhein Westfal TH Aachen, Inst Inorgan Chem, D-52074 Aachen, Germany
  • 7. Apple Inc, Inst Computat Biol, Helmholtz Ctr Munich, D-85764 Neuherberg, Germany

Description

Analysis and interpretation of omics data largely benefit from the use of prior knowledge. However, this knowledge is fragmented across resources and often is not directly accessible for analytical methods. We developed OmniPath (https://omnipathdb.org/), a database combining diverse molecular knowledge from 168 resources. It covers causal protein-protein, gene regulatory, microRNA, and enzyme-post-translational modification interactions, cell-cell communication, protein complexes, and information about the function, localization, structure, and many other aspects of biomolecules. It prioritizes literature curated data, and complements it with predictions and large scale databases. To enable interactive browsing of this large corpus of knowledge, we developed OmniPath Explorer, which also includes a large language model agent that has direct access to the database. Python and R/Bioconductor client packages and a Cytoscape plugin create easy access to customized prior knowledge for omics analysis environments, such as scverse. OmniPath can be broadly used for the analysis of bulk, single-cell, and spatial multi-omics data, especially for mechanistic and causal modeling.

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