Gene Ontology & Pathway Analysis | ORA, GSEA, Enrichr, STRING Demo | UBC Bioinformatics Workshop
This session covers one of the most essential steps in any omics workflow: translating a list of differentially expressed genes into biological meaning using Gene Ontology and pathway enrichment analysis.
We introduce the conceptual foundations of GO, walk through the two main enrichment methods (Over-Representation Analysis and Gene Set Enrichment Analysis) and discuss how to choose between them based on your data and your question. The session includes live demonstrations using free, browser-based tools: Enrichr, GSEA, STRING, Reactome, and MetaboAnalyst. No installation
is required to follow along.
By the end of this session, you will be able to run an enrichment analysis from start to finish, interpret the output critically, and understand the assumptions behind the method.
This is the final session (session 14) of the 2025–2026 term of the UBC Bioinformatics and Statistics Workshop Series, a free training program for researchers working with omics data at any level.
Code and workshop materials are available on GitHub:
https://github.com/UBCbioinformatics/...
Tools used in this session:
Enrichr — https://maayanlab.cloud/Enrichr
GSEA (Broad Institute) — https://www.gsea-msigdb.org
STRING — https://string-db.org
Reactome — https://reactome.org
MetaboAnalyst — https://www.metaboanalyst.ca
clusterProfiler (R/Bioconductor) — https://bioconductor.org/packages/clu...
Key references:
Ashburner et al., Nature Genetics, 2000
Subramanian et al., PNAS, 2005
Reimand et al., Nature Protocols, 2019
Khatri et al., PLoS Computational Biology, 2012
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