What Is GO Enrichment Analysis? Gene Ontology in RNA-Seq
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GO analysis (also called Gene Ontology analysis or GO enrichment analysis) is a method for identifying gene functions that are significantly overrepresented in a given gene set compared to the full set of genes. It is widely used in RNA-Seq analysis to interpret the biological roles of differentially expressed genes.
What is Gene Ontology?
Gene Ontology (GO) is a standardized vocabulary for describing gene function. It is organized into three categories:
Biological Process (BP)
Describes the metabolic and signaling pathways a gene product participates in, such as apoptosis and cell cycle regulation.
Cellular Component (CC)
Describes where a gene product is located within the cell, such as the cell membrane or mitochondria.
Molecular Function (MF)
Describes the biochemical activity of a gene product, such as enzymatic activity or ligand binding.
Each concept in Gene Ontology is assigned an identifier called a GO term. GO terms are organized hierarchically: when a gene is annotated with a specific GO term, it is also implicitly annotated with all of its parent terms. Tracing any GO term up through its ancestors ultimately leads to one of the three root categories -- Biological Process, Cellular Component, or Molecular Function.
Relationships between GO terms are also formally defined. The most commonly used relationship types are:
| Relation | Description | Example |
| is a | 'B is a A' means that B is a subtype of A. | mitotic cell cycle is a cell cycle |
| part of | 'B part of A' means that B is a component of A. | inner mitochondrial membrane is part of mitochondrion |
Example of Gene Ontology
What is GO Enrichment Analysis?
GO enrichment analysis detects GO terms that appear significantly more often in a given gene list than would be expected by chance.
For example, suppose an RNA-Seq experiment identifies 357 differentially expressed genes. Among those 357 genes, 13 are annotated with the GO term "GO:0007156". In the background set of 9,975 genes, only 71 carry this annotation. Compared to what you would expect from randomly drawing 357 genes from the background, "GO:0007156" is significantly overrepresented among the differentially expressed genes.
Because this test is performed for every GO term simultaneously, multiple testing correction is applied to determine which terms are truly enriched.
Example of Gene Ontology Analysis Result
GO enrichment results are often visualized as a dot plot. Each circle represents a GO term: its size indicates the number of associated genes, and its color indicates the p-value.
Example of Dot Plot
How to Perform GO Enrichment Analysis
Several R/Bioconductor packages support GO enrichment analysis, including clusterProfiler, topGO, and GOseq. When starting from raw RNA-Seq data (FASTQ files), you first need to quantify gene expression and identify differentially expressed genes before running GO analysis. Our RNA-Seq Data Analysis Software handles the entire workflow -- from raw FASTQ files through expression quantification, differential expression analysis, and GO enrichment analysis.
RNA-Seq Data Analysis Software
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Users can perform gene expression quantification, identification of differentially expressed genes, gene ontology(GO) analysis, pathway analysis, as well as drawing volcano plots, MA plots, and heatmaps.
About the Author
BxINFO LLC
A research support company specializing in bioinformatics.
We provide tools and information to support life science research, with a focus on RNA-Seq analysis.