Enrichment Analysis

Enrichment Analysis Online Inquiry

Introduction of Enrichment Analysis

Enrichment analysis. In the process of transcriptomics research, the main purpose of our high-throughput sequencing is generally to find differentially expressed genes and explore the possible functions of these genes. For example, comparing the tissue expression profiles of diseased individuals and normal individuals, it is not difficult to think that the genes that show 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 differentially expressed genes, the pathways involved in these genes 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 clusterProfiler package, g:Profiler, GSEA, Cytoscape, EnrichmentMap, and online analysis tools DAVID and KOBAS.

Top 20 enriched KEGG pathways 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.Fig 1.Top 20 enriched KEGG pathways 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 for different types or different Source data is mapped, 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 very important and common analysis content in bioinformatics analysis. If you have a background in bioinformatics data analysis, you can easily master enrichment analysis skills. But if you do not have the relevant skills, or you need a beautiful result pictures, you can contact us at any time. CD Genomics has a professional biological information analysis team. You only need to provide your demands and the original data, and we will provide you with accurate enrichment analysis results that can be directly used for article publication. Hope we have the honor to cooperate!

Reference

  1. 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.
* For Research Use Only. Not for use in diagnostic procedures.
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