Automatic Identification of Potential Cellular MRS Metabolites

Motivation: Manual identification of potential metabolites from untargeted MRS-based metabolomics studies is often tedious, labor-intensive, and prone to error. Goal(s): To develop an automated and customizable program to systematically identify metabolites from spectral regions of interest (ROIs) based on databases, such as Human Metabolome Database (HMDB) for specific medical conditions. Approach: We integrated experimental data — including ROIs, statistical significance, group trends/comparisons, and tissue- and disease-specific information — with automated HMDB searching, to output relevant and significant potential metabolites. Results: Given spectral ROIs, and relevant significance and trend data, our program is capable of identifying possible disease- and tissue-specific metabolites. Impact: Our program automates the manual database-searching process required for metabolite identification in MRS-based metabolomics research, enabling fast, robust, and reliable identification and categorization of metabolites based on user-customizable factors such as significance, trend, and tissue- and disease-specificity.