Gene Ontology Enricher

Perform GO enrichment analysis to identify overrepresented biological processes, molecular functions, and cellular components in your gene list.

What is Gene Ontology (GO) Enrichment Analysis?

GO enrichment analysis identifies overrepresented Gene Ontology terms in a gene set compared to a reference genome. It helps interpret biological meaning from high-throughput genomic data.

Select from Predefined Gene Lists
0 genes
Enter gene symbols (e.g., TP53, BRCA1) or Ensembl IDs, one per line
Reference gene set for enrichment calculation
Select the species for your gene list
(e.g., 0.05)
Maximum p-value for significant enrichment (adjusted for multiple testing)
Minimum number of genes in input list for a GO term
All Categories
Biological Process
Molecular Function
Cellular Component
Advanced Options
Method for correcting multiple hypothesis testing
Maximum number of genes in input list for a GO term
Analyzing GO enrichment... 0%

This may take a moment. Analyzing genes and calculating enrichment statistics...

Understanding Gene Ontology Enrichment

Gene Ontology (GO) enrichment analysis is a powerful method to interpret large-scale genomic data by identifying overrepresented biological functions in a gene set.

Gene Ontology Categories:

  • Biological Process (BP): Larger biological programs accomplished by multiple molecular activities (e.g., "cell cycle", "DNA repair")
  • Molecular Function (MF): Molecular activities of gene products (e.g., "kinase activity", "DNA binding")
  • Cellular Component (CC): Locations in cells where gene products are active (e.g., "nucleus", "mitochondrion")

Statistical Methods

1

Hypergeometric Test (Fisher's Exact Test): The most common method for GO enrichment analysis

p-value = Σ (C(M, k) × C(N-M, n-k)) / C(N, n) for k = x to min(n, M)

Where: N = total genes in background, M = genes annotated to GO term, n = genes in input list, x = genes in input list annotated to GO term

2

Multiple Testing Correction: Essential due to testing thousands of GO terms simultaneously

  • False Discovery Rate (FDR): Controls the expected proportion of false discoveries
  • Bonferroni Correction: Very conservative, divides threshold by number of tests
  • Benjamini-Hochberg: Less conservative, controls FDR
3

Enrichment Score Calculation:

Enrichment = (x/n) / (M/N)

Values > 1 indicate enrichment; values < 1 indicate depletion

Quick Analysis

Select a common gene set for quick analysis

GO Category Quick Reference

  • Biological Process
    Larger biological programs
  • Molecular Function
    Molecular activities
  • Cellular Component
    Cellular locations
  • Statistical Thresholds
    P-value < 0.05, FDR < 0.05

Recent Analyses

Cancer Genes
45 genes, 12 significant terms
Immune Response
38 genes, 8 significant terms
Metabolic Pathways
52 genes, 15 significant terms