skills.rest
ya-way
ya-wayCOMMUNITY
Explore all skills by this author →
GitHub

bio-pathway-go-enrichment

Identify enriched GO terms in gene lists using clusterProfiler's enrichGO.

Updated Mar 30, 2026
Search Tags:#ontology#go#bioinformatics#enrichment#gene-list#clusterprofiler#go-terms
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-pathway-go-enrichment
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-pathway-go-enrichment
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/bio-pathway-analysis-go-enrichment
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-pathway-go-enrichment

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

GO enrichment analysis identifies biological functions that are over-represented in a gene list, helping researchers derive biological meaning from differential expression or other gene signatures.

Core Features & Use Cases

  • Supports all three GO ontologies (BP, MF, CC) and multiple ID types (ENTREZID, SYMBOL, ENSEMBL).
  • Uses clusterProfiler's enrichGO to perform over-representation analysis and returns enriched terms with p-values, gene counts, and background information.
  • Practical workflows include interpreting DE results, comparing up- and down-regulated gene sets, and generating concise GO reports for publication or downstream analysis.

Quick Start

Provide a gene list and organism information, then run the GO enrichment analysis to obtain enriched GO terms.

Frequently Asked Questions about bio-pathway-go-enrichment

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform GO enrichment analysis on a gene list?▼

GO enrichment analysis identifies over-represented biological functions in a gene list using clusterProfiler's enrichGO. It applies over-representation analysis across biological processes, molecular functions, and cellular components with configurable p-value thresholds.

What gene identifiers can I use for GO term enrichment?▼

GO term enrichment supports multiple ID types including ENTREZID, SYMBOL, and ENSEMBL. You must provide a gene list with valid identifiers and organism information so the appropriate OrgDb mapping can be applied.

Can I use a custom background for over-representation analysis?▼

Yes, you can provide an optional universe gene list for background correction during over-representation analysis. Without a custom universe, the analysis uses the default background provided by the organism's OrgDb.

Do I need R and clusterProfiler to run GO enrichment?▼

Yes, GO enrichment requires R, clusterProfiler, and the appropriate OrgDb package installed. You must also provide a gene list with valid identifiers to execute the enrichGO over-representation analysis.

What is the difference between BP, MF, and CC in GO enrichment?▼

BP, MF, and CC represent the three GO ontologies: biological processes, molecular functions, and cellular components. GO enrichment analyzes all three categories to identify over-represented biological functions within your gene list.

Related Skills

Explore More Data & Analytics Skills→
tony-zhelonkintony-zhelonkin
1

annotate-te-rnaseq-data

Annotate Ensembl IDs to gene symbols and parse TE identifiers from featureCounts outputs.

Community
Advanced
StarlitnightlyStarlitnightly
1.2k

data-viz-plots

Generate publication-quality plots with matplotlib and seaborn.

Community
Advanced
ya-wayya-way

bio-pathway-kegg-pathways

Perform KEGG pathway and module enrichment analysis on gene lists.

Community
Advanced
jaechang-hitsjaechang-hits
298

quickgo-database

Query the EBI QuickGO REST API for Gene Ontology terms and protein annotations.

Community
Intermediate