Fast Metabolite Identification in Nuclear Magnetic Resonance Metabolomic Studies: Statistical Peak Sorting and Peak Overlap Detection for More Reliable Database Queries

Abstract

A lot of time is spent by researchers in the identification of metabolites in NMR-based metabolomic studies. The usual metabolite identification starts employing public or commercial databases to match chemical shifts thought to belong to a given compound. Statistical total correlation spectroscopy (STOCSY), in use for more than a decade, speeds the process by finding statistical correlations among peaks, being able to create a better peak list as input for the database query. However, the (normally not automated) analysis becomes challenging due to the intrinsic issue of peak overlap, where correlations of more than one compound appear in the STOCSY trace. Here we present a fully automated methodology that analyzes all STOCSY traces at once (every peak is chosen as driver peak) and overcomes the peak overlap obstacle. Peak overlap detection by clustering analysis and sorting of traces (POD-CAST) first creates an overlap matrix from the STOCSY traces, then clusters the overlap traces based on their similarity and finally calculates a cumulative overlap index (COI) to account for both strong and intermediate correlations. This information is gathered in one plot to help the user identify the groups of peaks that would belong to a single molecule and perform a more reliable database query. The simultaneous examination of all traces reduces the time of analysis, compared to viewing STOCSY traces by pairs or small groups, and condenses the redundant information in the 2D STOCSY matrix into bands containing similar traces. The COI helps in the detection of overlapping peaks, which can be added to the peak list from another cross-correlated band. POD-CAST overcomes the generally overlooked and underestimated presence of overlapping peaks and it detects them to include them in the search of all compounds contributing to the peak overlap, enabling the user to accelerate the metabolite identification process with more successful database queries and searching all tentative compounds in the sample set.