Genomic, Proteomic, and Metabolomic Data Integration Strategies

Abstract

Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these challenges. This review focuses on select methods and tools for the integration of metabolomic with genomic and proteomic data using a variety of approaches including biochemical pathway-, ontology-, network-, and empirical-correlation-based methods.

Keywords: bioinformatics; data analysis; data integration; genomics; metabolomics; networks; omics; proteomics.

Figure 1

Example of a modern metabolomic data analysis workflow integrating three discreet mass spectral analysis platforms. Data from three independent analytical platforms were merged and evaluated using statistical and machine-learning methods to identify significant metabolomic differences and top 10% discriminants between experimental treatments. Partial correlation networks, biochemical enrichment analysis, hierarchical clustering, and biochemical network integration were used to visualize and integrate the high-dimensional omic data within a biological context.