How to Identify Relevant Pathways from GO and KEGG Enrichment Analysis? What Approach and Databases Are Recommended?
The identification of pathways relevant to research objectives using GO and KEGG enrichment analysis can be achieved through the following approach:
Clarifying Research Background and Objectives
Before conducting enrichment analysis, it is crucial to define the research objectives and hypotheses. Understanding the biological processes, diseases, or conditions of interest helps pinpoint the most relevant pathways.
Performing GO and KEGG Enrichment Analysis
Utilize appropriate bioinformatics tools for the dataset to conduct GO and KEGG enrichment analysis. These analyses help identify biological processes, cellular components, and molecular functions (GO) that are significantly enriched in the set of upregulated or downregulated genes under experimental conditions, as well as associated metabolic and signaling pathways (KEGG).
Assessing Statistical Significance
Enrichment analysis results typically include a p-value or an adjusted p-value (such as FDR, False Discovery Rate), which quantifies the significance of each pathway relative to random expectation. Pathways that exhibit statistical significance should be prioritized for further investigation.
To validate the biological relevance of selected pathways, reviewing relevant literature can provide insights into their known functions within the study’s biological context. This validation step also helps uncover potential new research directions.
Recommended resources include:
Gene Ontology (GO): http://geneontology.org/
KEGG PATHWAY Database: https://www.genome.jp/kegg/
DAVID: https://david.ncifcrf.gov/
STRING: https://string-db.org/
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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