Automated Metabolite Formula Ranking Using Formula Subset Analysis for LC-MS/MS-Based Metabolomics

Introduction Metabolite identification remains a bottleneck in untargeted liquid chromatography–tandem mass spectrometry (LC-MS) metabolomics studies, especially when the underlying metabolite is not found in the tandem mass spectrometry (MS/MS) databases. Objective A new approach, formula subset analysis (FSA), was developed to effectively rank the chemical formula candidates for an MS/MS spectrum. Methods This approach first computes mother-daughter relationships (MDRs) among possible formulas of fragments and the precursor under a given mass tolerance and then determines the characteristic fragments (CFs) that only present one MDR with the precursor and other fragments. Subsequently, the precursor formula candidates are ranked by the scores derived from the number of MDRs. Results A numerical study using seven large datasets totaling 30357 MS/MS spectra from 6612 metabolites consisting of C, H, O, N, S, and P showed that FSA ranked the correct chemical formula as the top-1 candidate for a metabolite in 84.50% of the cases and in the top-5 candidates in 97.20% of the cases. The average processing time for each spectrum was 0.033 seconds. Moreover, FSA does not require training data, not rely on MS/MS databases, can be applied to a wide mass range, and can be quickly expanded with more chemical elements and formulas to identify different chemical species. Conclusions FSA has not utilized structural information yet and therefore its accuracy may not be competitive with some of the state-of-the-art identification tools. However, its advantages in speed, expandability, and applicability, make it suitable for prescreening candidates in untargeted LC-MS metabolomics studies.