User-Friendly Approaches to Integrating Metabolomics Data with Other Omics
Ewy Mathe, Department of Biomedical Informatics, The Ohio State University
Translational, clinical, and basic science researchers are generating omics data in the same samples to explore the molecular underpinnings of complex diseases. Of the omics, metabolomics, which aims to measure small molecules such as carbohydrates, hormones, amino acids, nucleotides, xenobiotics, and lipids, is increasingly applied. Analysis of these integrated datasets and functional interpretation of disease-associated metabolites is difficult, and is often hampered by the lack of user-friendly computational tools. To facilitate such analyses, we have developed user-friendly approaches that 1) numerically integrate gene expression and metabolite measurements to identify phenotype- (e.g. cancer) specific gene-metabolite relationships (https://github.com/Mathelab/IntLIM), and 2) perform pathway over-representation analysis using a comprehensive database of biological pathways (https://github.com/Mathelab/RaMP-DB). Application of these approaches facilitates integration of metabolomics and other omics data, with the goal of expanding our understanding of the molecular mechanisms that underlie cancer and other diseases.
Note: Seminars are free and open to the public. Reception to follow.