Introduction of Enrichment Analysis
In the process of transcriptomic research, the main purpose of our high-throughput sequencing is to find genes expressed differentially and explore the possible functions of these genes. For example, comparing the tissue expression profiles of diseased and normal individuals, it is reasonable to expect that the genes showing significant differences are related to the occurrence of diseases. These genes may be involved in immune-related biological processes, signal pathways, and gene expression levels. Therefore, after identifying these genes, the pathways involved are often annotated. Through GO or KEGG enrichment analysis, these differentially expressed genes can be mapped to GO or KEGG classification entries, so as to understand the regulatory pathways involved in these genes, and finally correlate with the phenotype.
Enrichment Analysis Method
Common enrichment analysis resources include gene set databases (Gene Ontology and Molecular Signatures Database), biochemical pathway databases (Kyoto Encyclopedia of Genes and Genomes, Reactome, Panther, NetPath, National Cancer Institute Pathway Interaction Database and HumanCyc), and pathway metadata databases (Pathway Common and WikiPathways). Commonly used analysis software or toolkits include R language cluster Profiler package, g:Profiler, GSEA, Cytoscape, EnrichmentMap, and online analysis tools DAVID and KOBAS.
Fig 1. Top 20 enriched KEGG pathway analysis of the host genes of circRNAs. The size of the circle represents the number of genes. Green to red indicates that the corrected p-value is gradually becoming smaller. (Zhao X, et al. 2019)
Advantages of Enrichment Analysis
Compared with analyzing only a single gene, transcript, or protein, pathway enrichment analysis can merge thousands or tens of thousands of genes or genomic regions into smaller pathways or systems to quickly obtain useful information. In addition, it can be used to map different types or Source data, such as mapping RNA, DNA or protein data to the same type of pathway at the same time.
Application Field
Research on the occurrence and development of diseases.
Crop traits research.
Gene function research.
GO enrichment analysis and KEGG analysis are common but important in bioinformatic analysis. With a background in bioinformatic data analysis, enrichment analysis can be easily mastered. However, it can be difficult to perform without previous training. Please contact us for help in the enrichment analysis. Our professional biological information analysis team is ready to help you at any time. Just let us know your request and provide the original data, we will provide you with accurate enrichment analysis results that can be directly used for publication. We look forward to hearing from you.
Reference
- Zhao X, et al. Genome-Wide Identification of Circular RNAs Revealed the Dominant Intergenic Region Circularization Model in Apostichopus japonicus[J]. Front Genet. 2019;10:603. Published 2019 Jul 2. doi:10.3389/fgene.2019.00603.
