Keywords: Data Processing, Data Analysis, Metabolomics, metabolomic imaging, nuclear magnetic resonance, spectroscopy, biomarkers, data processing, metabolism, 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.
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