title: 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.
authors.affiliation: University of California, Genome Center, Davis, CA, USA.
authors.forename: Kwanjeera
authors.initials: K
authors.lastname: Wanichthanarak
authors.affiliation: University of California, Genome Center, Davis, CA, USA.
authors.forename: Johannes F
authors.initials: JF
authors.lastname: Fahrmann
authors.affiliation: CDS Creative Data Solutions, Ballwin, MO, USA.
authors.forename: Dmitry
authors.initials: D
authors.lastname: Grapov
keywords: bioinformatics; data analysis; data integration; genomics; metabolomics; networks; omics; proteomics