While metabolomics is playing an increasing role in clinical and translational research, the field suffers from 1) lack of computational tools for identifying metabolites in large-scale experiments; 2) difficulty in defining biological factors that affect metabolic phenotypes; 3) transparency and availability of computational methods and tools that facilitate discovery of disease-related metabolites. To this end, the goal of this proposal is to integrate metabolite and gene pathway annotations for integrative pathway analysis, thereby facilitating and improving biological interpretation of disease-related metabolic phenotypes. The rationale for integrating biological pathways of metabolites and genes is that it expands our understanding of the molecular mechanisms that underlie disease-specific metabolite profiles. Such deeper understanding may accelerate finding novel therapeutic targets, and guide identification of clinically useful diagnostic and prognostic biomarkers. Toward these goals, I propose a collaborative approach to 1) construct and depoy RaMP, Relational database of Metabolomics Pathways, that includes gene and metabolite annotations, and 2) develop and implement pathway enrichment methods for improved pathway enrichment analysis of disease-specific metabolites/genes. Importantly RaMP and associated analysis tools will be developed in support of the open science and reproducibility paradigms, where developments (source code, database structure) will be open to the public. It is our hope that RaMP would drive further cross-talks between the fields of genomics and metabolomics, which are critical in our search for improved disease biomarkers and therapeutic options. Completion of this proposal will yield an integrated database of gene and metabolite biological pathways, including tools that perform key aspects of pathway enrichment analysis. To help ensure these tools are adopted by the metabolomics community and yield reproducible results, RaMP will be accessible as 1) a standalone database easily integrable with other tools, and 2) a web application accessing RaMP for complex queries and pathway enrichment analysis. Meeting these goals could help elucidate molecular functions that underlie disease-related metabolites, and could unravel novel gene targets that modulate these molecular functions toward a non-disease state.
The metabolomics field aims to study small molecules, called metabolites, in human biofluids (e.g. blood, urine) and tissues. These metabolites are produced in response to cellular processes involving genes and proteins. In recent years, the field has gained more and more momentum for its utility in uncovering metabolites that could be measured in the clinic, and could help predict diagnosis, prognosis, and treatment outcome. Due to technical and data analysis limitations, metabolomics data is difficult to analyze and interpret. For example, the biological (e.g. genes, proteins) and environmental (e.g. diet, pollution) factors that may influence the levels of these metabolites are generally poorly understood. To address these limitations, the goal of this proposal is to integrate known biological information and processes for genes and metabolites to better understand the production and function of disease-related metabolites. To accomplish this, we propose to develop an integrative and comprehensive database of biological processes with user-friendly access and analysis tools.
| Zhang, Bofei; Hu, Senyang; Baskin, Elizabeth et al. (2018) RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites. Metabolites 8: |
