Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems

Area of Science:

• Genomics
• Systems Biology
• Computational Biology

Background:

• Complex diseases involve subtle biological perturbations detectable by omics platforms.
• Integrating diverse molecular and statistical data for mechanistic hypothesis generation is challenging.
• A public tool is needed to consolidate multi-omics data for identifying disease targets.

Purpose of the Study:

• To develop a public computational tool for integrating multi-omics data.
• To identify perturbed biological processes and key regulators in complex diseases.
• To facilitate the translation of omics data into testable mechanistic hypotheses.

Main Methods:

• Developed Mergeomics, a modular computational pipeline for multi-omics data integration.
• Leveraged multi-omics association data to identify perturbed biological processes.
• Overlayed disease-associated processes onto molecular interaction networks to pinpoint key regulators.
• Accepted and integrated datasets across platforms, data types, and species.

Main Results:

• Mergeomics successfully integrates diverse omics datasets, including genome-wide, epigenome-wide, and transcriptome-wide data.
• The pipeline identified key regulators and provided statistical and contextual evidence for prioritizing wet lab investigations.
• Demonstrated versatility in human and mouse studies of total cholesterol and fasting glucose.

Conclusions:

• Mergeomics is a flexible and robust pipeline for multidimensional data integration in complex trait studies.
• It outperforms existing tools and is applicable to various datasets, species, and omics data types.
• The software is freely available as a Bioconductor R package.