Enrichr-KG: bridging enrichment analysis across multiple libraries.

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Gene and protein set enrichment analysis is crucial for omics data interpretation.
• Existing tools like Enrichr offer extensive gene set libraries but lack cross-library integration.
• Integrating results across diverse knowledge domains can accelerate hypothesis generation.

Purpose of the Study:

• To introduce Enrichr-KG, a knowledge graph database and web-server for integrative enrichment analysis.
• To enable visualization of enrichment results as interconnected subgraphs.
• To facilitate the discovery of hidden associations between genes and enriched terms.

Main Methods:

• Developed Enrichr-KG by combining selected gene set libraries from Enrichr.
• Implemented a knowledge graph structure to represent enrichment results.
• Enabled visualization of gene-term connections, gene-gene links, and predicted genes.

Main Results:

• Enrichr-KG integrates 26 diverse gene set libraries (transcription, pathways, ontologies, diseases/drugs, cell types).
• Enrichment results are visualized as interactive subgraphs, connecting genes to enriched terms.
• The platform facilitates the illumination of cross-dataset associations and potential gene functions.

Conclusions:

• Enrichr-KG provides a powerful platform for integrative enrichment analysis and visualization.
• The knowledge graph approach enhances hypothesis generation by revealing complex gene-gene and gene-term relationships.
• Enrichr-KG is a valuable resource for researchers analyzing omics data.