Principle of GO Functional Annotation and Enrichment Analysis

Gene Ontology (GO) is a critical tool in biological research for describing the functions of genes and gene products. With the advancement of high-throughput sequencing technologies, researchers are faced with vast amounts of genetic data. GO functional annotation and enrichment analysis have become essential methods for revealing gene functions, elucidating biological processes, and predicting gene regulatory networks.

 

GO is a database that provides standardized terms for genes and gene products, encompassing three main categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). BP describes the biological processes that gene products participate in, such as signal transduction and cell division; MF defines the molecular activities of gene products, such as enzyme activity and receptor binding; CC describes the locations of gene products within the cell, such as the nucleus and mitochondria. GO functional annotation involves categorizing genes or gene products based on these standardized terms.

 

Principle of GO Functional Annotation

The core of GO functional annotation lies in assigning the most appropriate GO terms to target genes using bioinformatics algorithms based on existing databases (e.g., UniProt, NCBI). This generally involves two methods: sequence similarity-based approaches and domain-based approaches.

 

1. Sequence Similarity-Based Approach

This method involves predicting the function of a target gene by comparing its sequence similarity to annotated genes using tools like BLAST. For instance, if a target gene is highly similar in sequence to a gene with known function, it can be inferred that they may share similar biological functions.

 

2. Domain-Based Approach

The function of gene products is often closely related to their structural domains. By identifying functional domains in gene products (using databases such as Pfam and SMART), GO functional annotation can be assigned to the target gene.

 

Principle of GO Enrichment Analysis

GO enrichment analysis refers to the process of identifying significantly enriched GO terms within a set of differentially expressed genes. The primary goal is to use statistical methods to reveal the biases of a specific gene set toward certain biological processes or molecular functions. Common methods for GO enrichment analysis include hypergeometric tests, Fisher's exact test, and multiple hypothesis correction methods (e.g., Bonferroni correction, Benjamini-Hochberg correction).

 

1. Hypergeometric Test

This is one of the most commonly used methods. It compares the number of genes associated with a specific GO term in the entire gene set to that in the differentially expressed gene set, determining the enrichment of that GO term among the differentially expressed genes.

 

2. Multiple Testing Correction

Since enrichment analysis involves numerous hypothesis tests, directly using raw p-values may lead to false positives. Common correction methods include Bonferroni correction and Benjamini-Hochberg correction, which aim to control the family-wise error rate (FWER) or false discovery rate (FDR), thereby improving the reliability of the results.

 

GO functional annotation and enrichment analysis have a wide range of applications in biological research. They not only help researchers understand gene functions but also explore gene regulatory networks, revealing relationships between genes in specific biological processes. For example, in cancer research, researchers can identify signal pathways or biological processes related to tumorigenesis through GO enrichment analysis, providing a theoretical basis for subsequent experimental validation.