Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. We explain the procedures of pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome-sequencing experiments. The protocol comprises three major steps: definition of a gene list from omics data, determination of statistically enriched pathways, and visualization and interpretation of the results. We describe how to use this protocol with published examples of differentially expressed genes and mutated cancer genes; however, the principles can be applied to diverse types of omics data. The protocol describes innovative visualization techniques, provides comprehensive background and troubleshooting guidelines, and uses freely available and frequently updated software, including g:Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape and EnrichmentMap. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training.

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Data availability

The protocol uses publicly available software packages (GSEA v.3.0 or higher, g:Profiler, Enrichment Map v.3.0 or higher, Cytoscape v.3.6.0 or higher) and custom R scripts that apply publicly available R packages (edgeR, Roast, Limma, Camera). Custom scripts are available in the Supplementary Protocols and at our GitHub web sites (https://github.com/BaderLab/Cytoscape_workflows/tree/master/EnrichmentMapPipeline and https://baderlab.github.io/Cytoscape_workflows/EnrichmentMapPipeline/index.html).

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Key references using this protocol

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Pajtler, K. W. et al. Cancer Cell 27, P728–P743 (2015): https://doi.org/10.1016/j.ccell.2015.04.002

Cavalli, F. M. G. et al. Cancer Cell 31, P737–P754 (2017): https://doi.org/10.1016/j.ccell.2017.05.005


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The authors are grateful to J. Mesirov for comments on the manuscript. This project was supported by an Investigator Award to J.R. from the Ontario Institute for Cancer Research through funding from the Government of Ontario and by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant to J.R. (RGPIN-2016-06485). This work was supported by US National Institutes of Health grants P41 GM103504, R01 GM070743, U41 HG006623 and R01 CA121941 to G.D.B.

Author information

Author notes

  1. These authors contributed equally: Jüri Reimand, Ruth Isserlin.


  1. Computational Biology Program, Ontario Institute for Cancer Research, Toronto, ON, Canada

    • Jüri Reimand
    • , Lina Wadi
    •  & Mona Meyer
  2. Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

    • Jüri Reimand
  3. The Donnelly Centre, University of Toronto, Toronto, ON, Canada

    • Ruth Isserlin
    • , Veronique Voisin
    • , Mike Kucera
    • , Christian Tannus-Lopes
    • , Asha Rostamianfar
    • , Jeff Wong
    • , Changjiang Xu
    •  & Gary D. Bader
  4. Deep Genomics Inc., Toronto, ON, Canada

    • Daniele Merico
  5. The Centre for Applied Genomics (TCAG), The Hospital for Sick Children, Toronto, ON, Canada

    • Daniele Merico
  6. Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada

    • Gary D. Bader
  7. Department of Computer Science, University of Toronto, Toronto, ON, Canada

    • Gary D. Bader


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J.R., R.I., V.V., A.R., D.M. and G.D.B. wrote the manuscript. R.I. created the step-by-step protocols, figures, R scripts and R notebooks, except for g:Profiler (J.R.). M.K. and C.T.-L. developed EnrichmentMap 3.0 and AutoAnnotate Cytoscape applications. L.W., M.M., J.W., C.X. and V.V. tested the protocol. All authors read and approved the final manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Gary D. Bader.

Supplementary information

  1. Supplementary Tables and Methods

    Supplementary Tables 1–13 and Supplementary Protocols 1–4

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