By EVOBYTE Your partner in bioinformatics
Introduction
When you finish a differential expression analysis and stare at a long list of genes, it’s natural to ask a simple question: what’s the story here? Pathway analysis turns that list into a narrative. Instead of chasing single genes, you test whether entire biological pathways or functional themes are enriched, so you can explain results in terms of signaling cascades, metabolic routes, or coordinated cellular programs.
In this post, we’ll demystify pathway analysis for bioinformatics work. We’ll define what pathway analysis is, clarify where KEGG, Gene Ontology (GO), and Reactome terms come from, and explain how these resources differ. Then we’ll walk through practical options using Enrichr on the web and R packages like clusterProfiler, ReactomePA, and gprofiler2—sprinkled with short code you can adapt today.
What is pathway analysis in bioinformatics?
Pathway analysis is a set of statistical methods that test whether a predefined group of genes—representing a pathway, process, or function—appears more often than expected by chance within your list of interest. Those predefined groups come from “knowledge bases,” which curate biology into gene sets: think “Interferon signaling,” “TCA cycle,” or “DNA repair.”
Two families of tests dominate everyday workflows. Over-representation analysis (ORA) asks, “are genes from pathway X over-represented among my significant genes?” Gene Set Enrichment Analysis (GSEA) goes further by ranking all genes (for example, by log fold-change) and asking whether members of a pathway tend to appear toward the top or bottom of that ranked list, even if many individual genes aren’t independently significant. ORA is fast and intuitive; GSEA is more sensitive to subtle, coordinated shifts.
Either way, the output isn’t just a p-value buffet. The goal is interpretation: link your omics changes to mechanisms, prioritize follow-up experiments, and communicate results in plain biological terms.
KEGG, GO, and Reactome: where the terms come from and how they differ
Although we often say “pathway analysis” as a single concept, the gene sets you test against come from different sources with different philosophies.
KEGG, short for Kyoto Encyclopedia of Genes and Genomes, is a long-running resource that organizes knowledge into pathway maps spanning metabolism, signaling, disease, drugs, and more. KEGG pathways are hand-drawn, navigable diagrams that connect genes, enzymes, and small molecules—great for getting a systems view and for mapping omics results back onto curated charts. KEGG also provides tools like KEGG Mapper and KO (KEGG Orthology) to link genes to functional orthologs across species.
GO, the Gene Ontology, is not a pathway database in the diagram sense. Instead, it’s a structured vocabulary—an ontology—capturing three aspects of gene function: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). GO terms are arranged in a directed acyclic graph, allowing fine-to-coarse reasoning about what a gene product does, where it resides, and which processes it participates in. GO is maintained by the GO Consortium, founded in 1998 around common annotations for model organisms, and today powers enrichment across thousands of species.
Reactome is a curated, peer-reviewed pathway database with a reaction-centric model. Its core unit is the reaction: molecules, complexes, and modifications are linked step by step into pathways with explicit evidence tracking. The result is a detailed, mechanistic network that’s both human-readable (interactive pathway browser) and machine-accessible (APIs and standard formats). Many users appreciate Reactome’s rigorous curation process and frequent releases, which make it well suited for interpretation of high-throughput data.
So how do they differ in practice? KEGG emphasizes intuitive pathway maps and broad coverage, GO provides a standardized functional vocabulary ideal for enrichment summaries, and Reactome offers granular, evidence-backed reactions and rich analysis services. Most analysts use more than one, because each lens reveals a different facet of the same biology.
From gene lists to meaning: ORA and GSEA, and why your background matters
Before you run any test, set the “universe” or background correctly. Your background should reflect all genes that could have been detected in your experiment, not the entire genome. Using a realistic background guards against false positives, especially when platforms or preprocessing steps exclude large swaths of genes.
With ORA, you typically choose a significance threshold, separate “hits” from “non-hits,” and run a hypergeometric (or Fisher’s exact) test to check over-representation. With GSEA, you feed a ranked list plus a gene set collection into an algorithm that computes an enrichment score by walking down the ranked list, increasing the score when a pathway member is encountered and decreasing otherwise; the score is normalized and assessed by permutation. In both cases, adjust for multiple testing—false discovery rate (FDR) control is the norm—and then prioritize terms by effect size (enrichment score), significance, and interpretability.
One pragmatic tip: combine GO for high-level themes, Reactome for mechanistic steps, and KEGG for easily communicable maps. When results converge across resources, your biological story is usually more persuasive.
Practical pathway analysis: Enrichr online and R packages you’ll actually use
If you want quick insight without setup, Enrichr is a friendly web tool from the Ma’ayan Lab. Paste a gene list, pick libraries spanning pathways and ontologies, and get ranked enrichments with multiple scoring options and interactive visualizations. It’s ideal for rapid hypothesis generation and sharing results. An official R wrapper, enrichR, lets you script the same workflow in your pipelines.
If you live in R, clusterProfiler has become the go-to “Swiss army knife” for enrichment. It unifies ORA and GSEA across GO, KEGG, and beyond, with tidy results and publication-ready plots. It also plays well with Bioconductor annotation resources, so you can keep everything reproducible.
For Reactome-specific analyses, ReactomePA extends clusterProfiler’s idioms to Reactome pathways, providing both ORA and GSEA plus network-style visualizations. And if you want a vendor-neutral web+API option, g:Profiler (with the gprofiler2 R client) offers enrichment plus identifier conversion and orthology mapping, useful when your data comes from non-model species.
Here’s a compact example that mirrors a common RNA-seq scenario. Imagine you’ve compared treated versus control samples and assembled a vector of human Entrez IDs for upregulated genes showing a strong antiviral signature. You can test GO Biological Process enrichment using clusterProfiler in just a few lines.
# ORA with GO using clusterProfiler
library(clusterProfiler)
library(org.Hs.eg.db)
up_genes <- c("3630","7124","3456","3627","673","3569") # toy Entrez IDs
ego <- enrichGO(gene = up_genes,
OrgDb = org.Hs.eg.db,
keyType = "ENTREZID",
ont = "BP",
pAdjustMethod = "BH",
qvalueCutoff = 0.05,
readable = TRUE)
dotplot(ego, showCategory = 15, title = "GO BP enrichment (ORA)")
If your effect sizes are modest and broadly distributed, try GSEA. Rank by a signed statistic (for instance, sign(log2FC) × −log10(p-value)) and feed the full vector into gseGO, gseKEGG, or ReactomePA’s functions.
# GSEA-style analysis: rank all genes, then test Reactome pathways
library(ReactomePA)
# 'ranks' is a named numeric vector: names = Entrez IDs, values = ranked scores
gsea_res <- gsePathway(geneList = ranks, pAdjustMethod = "BH", pvalueCutoff = 0.05, verbose = FALSE)
emapplot(gsea_res, showCategory = 20)
Prefer to start in the browser? Enrichr makes it straightforward. Paste your gene list, choose libraries like “GO_Biological_Process” or “KEGG,” and download tables or figures. If you want the same from code, the enrichR package mirrors those steps:
# Enrichr from R
library(enrichR)
dbs <- c("GO_Biological_Process_2023", "KEGG_2021_Human")
genes <- c("STAT1","IRF7","OAS1","ISG15","MX1","IFIH1")
enr <- enrichr(genes, dbs)
enr$GO_Biological_Process_2023[1:5, c("Term","Adjusted.P.value","Overlap","Odds.Ratio")]
Behind the scenes, these tools depend on the curation choices of KEGG, GO, and Reactome. That’s why you’ll sometimes see subtle differences—for example, Reactome may split a signaling cascade into multiple reaction-specific steps, while KEGG might present a unified map and GO may offer process terms at various granularities. Cross-checking enrichments across all three makes your conclusions more robust. KEGG’s pathway maps offer a “big picture” visualization, GO terms summarize the functional themes, and Reactome can reveal the detailed mechanism that explains the theme.
Finally, keep reproducibility in mind. Record the version of each database and tool, because annotations evolve. Many platforms, including Reactome, publish regular release notes and provide APIs or downloadable archives, which helps you freeze analyses for peer review and future audits.
Summary / Takeaways
Pathway analysis reframes noisy gene lists into mechanisms you can act on. Use ORA for quick, high-contrast hits and GSEA when changes are coordinated but subtle. Lean on multiple knowledge bases—KEGG for navigable maps, GO for standardized functional language, and Reactome for reaction-level detail—to triangulate the biology. In practice, a fast path is to sketch hypotheses with Enrichr, then lock down a reproducible R workflow with clusterProfiler, ReactomePA, or gprofiler2.
If you’re starting a new project this week, try this flow: define a realistic gene background, run both ORA and GSEA, cross-check terms across KEGG, GO, and Reactome, and keep database versions in your notes. What pathway surprised you the most—and what experiment will you run next to test it?