Abstract
The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.
Highlights
Metabolomics is undeniably powerful for uncovering disease biomarkers [1,2,3]
The Kyoto Encyclopedia of Genes and Genomes (KEGG) “Human maps”, that represent manually curated human diseases and molecular interactions from various organisms, are incorporated into RaMP
Human Metabolome DataBase (HMDB) is the largest collection of annotations for small molecules found in humans, and is the more complete resource for metabolite annotations
Summary
Metabolomics data can provide information on biological mechanisms that are disrupted in diseases. Analysis of metabolomics data with other omics data, such as transcriptomics, has uncovered relevant gene-metabolite associations and disease-relevant metabolic functions and pathways [5,6,7,8,9]. Finding genes associated with metabolite levels, or whose products catalyze reactions involving disease-related metabolites, or their associated pathways, can generate hypotheses on how these metabolic phenotypes are regulated. These hypotheses could elucidate functional mechanisms that could be targeted to generate a desired metabolomics phenotype. Understanding the regulation of metabolic phenotypes will expand knowledge of disease biology, and could contribute to finding successful interventions, including accurate predictions of diagnosis, prognosis, and treatment outcomes
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