Automatic Identification of Potential Cellular Metabolites for Untargeted NMR Metabolomics.

ABSTRACT: An organism's metabolic profile provides vital information pertaining to its physiology or pathology. To monitor these biochemical changes, Nuclear Magnetic Resonance (NMR) spectroscopy has found success in non-invasively observing metabolite changes within intact samples in an untargeted manner. However, biological samples are chemically complex, comprised of many different constituents (amino acids, carbohydrates, and lipids) at varying concentrations depending on physiological and pathological conditions. Due to the narrow spectral window of proton NMR, compound resonance frequencies can often overlap, making the identification and monitoring of metabolites difficult and time consuming, particularly when dealing with large numbers of samples. Here, we introduce a Python program (ROIAL-N

SUBMITTER: Chen J 

PROVIDER: S-EPMC12445015 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature