REALGAR: a web app of integrated respiratory omics data

Area of Science:

• Genomics and Bioinformatics
• Systems Biology
• Computational Biology

Background:

• Post genome-wide association study (GWAS) era requires integrating multi-omics data for functional interpretation of genetic variants.
• Vast amounts of transcriptomic and epigenomic data exist but are challenging for experimental researchers to utilize.
• Need for accessible resources to bridge omics data and experimental validation for disease-associated genetic loci.

Purpose of the Study:

• To develop an integrated web application, REALGAR, for accessing and visualizing multi-omics data.
• To facilitate gene-centric analysis and hypothesis generation for disease-associated genetic variants.
• To aid experimental researchers in designing validation studies by providing tissue-specific omics insights.

Main Methods:

• Designed the Reducing Associations by Linking Genes and omics Results (REALGAR) web app.
• Integrated data from genome-wide association studies (GWAS), transcriptomic, epigenomic, and expression quantitative trait loci (eQTL) studies.
• Focused on asthma datasets for gene-centric analysis and visualization.

Main Results:

• REALGAR provides multi-layered omics information for gene-centric analysis.
• The web app facilitates hypothesis formulation for airways disease-associated genes.
• Integrated omics results aid in designing experimental validation studies.

Conclusions:

• REALGAR simplifies the interpretation of complex omics data for experimental researchers.
• The tool supports biologically meaningful insights into genetic associations, particularly for asthma.
• REALGAR enhances the utility of public omics datasets for disease research.