This white paper demonstrates how to integrate and analyze ‘omics data to obtain more meaningful biological insights and help identify probable biomarkers.
A major challenge in the integration of ‘omics data (transcriptomics, proteomics and metabolomics) is how best to compare and correlate these large datasets to provide the most meaningful biological context. This white paper uses IPA-Metabolomics® (a component of IPA®) to interpret transcriptomic and metabolomic data from a diabetic mouse study. IPA provides biological context to the metabolomics data, links metabolic changes to disease-relevant pathways and phenotypes and assists in the selection of a subset of metabolomic markers for further study. In the study, the structures of 82 metabolites were identified that discriminated between the urines of diabetic db/db and control db/+ mice. Of these, 70 mapped to biological functions or pathways in IPA. Expected diabetes-related changes in lipid, gluconeogenesis, mitochondrial dysfunction and oxidative stress, and protein and amino acid metabolism were characterized by the metabolomic markers. Metabolites were also linked to changes in leptin, branched chain amino acid degradation, and vitamin metabolism.
IPA Analysis of Metabolomics Data Including Cross-Platform Integration with Transcriptomics Data from a Diabetic Mouse Model