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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

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DSigDB: drug signatures database for gene set analysis.

Minjae Yoo¹, Jimin Shin¹, Jihye Kim¹

¹Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA and Department of Computer Science and Engineering, Korea University, Seoul 136-713, South Korea.

Bioinformatics (Oxford, England)

|May 21, 2015

Summary

We created the Drug Signatures Database (DSigDB), a resource linking compounds to target genes for gene set enrichment analysis (GSEA). This database aids in drug repurposing and translational research by connecting gene expression data with drug information.

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Area of Science:

Bioinformatics
Computational Biology
Pharmacogenomics

Background:

Gene set enrichment analysis (GSEA) is crucial for interpreting high-throughput gene expression data.
Identifying relationships between compounds and their target genes is essential for drug discovery.
Existing resources may lack comprehensive links between drugs and gene expression profiles.

Purpose of the Study:

To develop a comprehensive database, Drug Signatures Database (DSigDB), that maps compounds to their target genes.
To facilitate the use of this resource in gene set enrichment analysis (GSEA).
To support drug repurposing and translational research by integrating compound-gene relationships.

Main Methods:

Compiled a database of gene sets linking compounds to target genes.
Developed an online platform for searching, viewing, and downloading data.
Ensured seamless integration with GSEA software.

Main Results:

DSigDB contains 22,527 gene sets, 17,389 unique compounds, and 19,531 genes.
An accessible online resource is now available for researchers.
The database enables linking gene expression data with specific drugs and compounds.

Conclusions:

DSigDB provides a valuable resource for GSEA, drug repurposing, and translational research.
The integrated platform simplifies the exploration of compound-gene interactions.
This resource has the potential to accelerate the discovery of new therapeutic applications for existing compounds.