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Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap

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Reimand, Jüri, Isserlin, Ruth, Voisin, Veronique, Kucera, Mike, Tannus-Lopes, Christian, Rostamianfar, Asha, Wadi, Lina, Meyer, Mona +4 more·2019·Nature Protocols
DOI 10.1038/s41596-018-0103-9PMC PMC6607905

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Abstract

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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  • Whole transcriptome analysis via RNA-seq followed by biological pathway enrichment analysis has been widely used for prediction of a specific autophagy status and/or step.

    “In the last few years, whole transcriptome analysis via RNA-seq technology followed by biological pathway enrichment analysis [12] has been widely used to this end [13, 14].”

    From A gene toolbox for monitoring autophagy transcription (2021) · cites this paper as [12]

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Authors
Reimand, J., Isserlin, R., Voisin, V., Kucera, M., Tannus-Lopes, C., Rostamianfar, A., Wadi, L., Meyer, M., Wong, J., Xu, C., Merico, D., & Bader, G. D.
Affiliations
  • Ontario Institute for Cancer Research (CA)
  • University of Toronto (CA)
  • Hospital for Sick Children (CA)
Corresponding author
Bader, Gary D.
ORCID
Venue
Nature Protocols
Publisher
Springer Science and Business Media LLC
Published
2019-01-21
Volume
14
Issue
2
Pages
482-517
ISSN
  • 1750-2799 (ISSN-L)
  • 1754-2189
DOI
10.1038/s41596-018-0103-9
PMC
PMC6607905
OpenAlex
W2910111050
Cited by
1,970
References
20
Citations by year
2026: 218 · 2025: 298 · 2024: 345 · 2023: 281 · 2022: 320 · 2021: 306 · 2020: 155 · 2019: 42 · 2018: 1 · 2017: 1
FWCI
69.88
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Protocols (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Ontario Institute for Cancer Research
  • Government of Ontario
  • National Institutes of Health (GM 103504)
  • National Human Genome Research Institute (R01HG009979, U41HG006623)
  • National Cancer Institute (R33CA183654, R01CA130826, R01CA121941)
  • National Institute of General Medical Sciences (R01GM070743, P41GM103504)
  • National Institute of Allergy and Infectious Diseases (R01AI073724)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
Keywords
pathway enrichment, gene set enrichment analysis, omics data interpretation, gene list, differentially expressed genes, cancer mutations, pathway visualization, g:Profiler, Cytoscape, RNA-seq, genome sequencing
MeSH terms
Humans, Immunity, Innate, Neoplasm Proteins, Neoplasms, Software, Genome, Human, Gene Expression Regulation, Neoplastic, Computational Biology, Gene Expression Profiling, Protein Interaction Mapping, Databases, Genetic, Gene Regulatory Networks, Datasets as Topic

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