An integrated metabolomics analysis platform for the determination of metabolic profile differences, multi-sample correlation and metabolite identification will be developed. The analytical workflow enabled by this platform can be utilized in the elucidation of biochemical mechanisms perturbed in disease, which is vital information in the development of therapeutics and diagnostics. The proposed software directly addresses three critical bottlenecks that impede the translation of metabolomics studies to clinical applications: accessibility, multi-sample analysis and identification. This platform will be based on and expand XCMS, the current state-of-the-art metabolic profile analysis software. In addition, an enhanced version of the well established METLIN metabolite database will be incorporated to facilitate metabolite identification. The three aims are: 1.) Develop a web-based metabolic profile analysis platform (XCMS- Online) to provide highly accessible software to researchers and clinicians performing metabolomics studies. 2.) Integrate algorithms that allow for multi-sample metabolic profile correlation and logical relationship analysis within the Online-XCMS platform. 3.) Incorporate an enhanced version of the METLIN metabolite database into the XCMS analysis platform to automate metabolite identification. Overall, the objective is to improve the accessibility and efficiency of metabolomics analysis so that metabolite data can be more readily applied to the determination of disease mechanisms and translated to clinical application.
Metabolites are the downstream, small molecule products of complex biochemical pathways driven by genes and proteins. Because of this, an observed change in one or more metabolites can serve as one of the best indicators for the presence of disease. However, one of the critical bottlenecks in translating metabolites into clinical diagnostic or therapeutic applications is analyzing complex metabolic profiles across many samples and identifying specific metabolites that are indicative of disease. The proposed project directly addresses these bottlenecks with the development of metabolic profile analysis and metabolite identification software.