A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY (https://github.com/HuanLab/BUDDY).
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The four reference MS/MS libraries used for evaluation can be freely downloaded at https://mona.fiehnlab.ucdavis.edu/downloads. The MS/MS spectra of five newly discovered compounds can be accessed on GNPS (CCMSLIB00005467952 and CCMSLIB00005716808) and https://www.nature.com/articles/s41592-021-01303-3#MOESM10. ARUS MS/MS libraries can be downloaded at https://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:arus, and corresponding annotation results can be accessed on Zenodo (https://doi.org/10.5281/zenodo.7495955). All LC–MS/MS datasets are available from the MassIVE repository (American Gut Project, MSV000081981; tomato, MSV000081463; Chagas disease, MSV000086988; NIST human fecal material standards, MSV000086988 and MSV000086989). Evaluation results are provided in Supplementary Tables.
BUDDY is written in the C# language on the Universal Windows Platform (UWP). It currently works in the Windows OS (Windows 10 or higher). The standalone software can be freely downloaded from GitHub (https://github.com/HuanLab/BUDDY) and Zenodo (https://doi.org/10.5281/zenodo.7735295). Source codes are also available on GitHub (https://github.com/HuanLab/BUDDY) under the MIT License.
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This study was funded by the University of British Columbia Start-up Grant (grant no. F18-03001), Canada Foundation for Innovation (grant no. CFI 38159) and Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants (grant nos. RGPIN-2020-04895 and RGPIN-2022-05316). We thank A. Hui for proofreading this paper.
The authors declare no competing interests.
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Extended Data Fig. 1 BUDDY graphical user interface.
BUDDY offers an intuitive graphical user interface directly downloadable from GitHub. BUDDY can simultaneously process data files from different sources. For identified metabolites, MS/MS matching information and detailed chemical descriptions are provided to help check the identification quality and further investigate biological insights. If experiment-specific global peak annotation is performed, feature connections are listed with MS/MS similarity, formula difference, and connection description information.
Extended Data Fig. 2 Generation of MS/MS-explainable candidate space.
Both even-electron and odd-electron species are considered in bottom-up MS/MS interrogation.
Extended Data Fig. 3 Structural complexity of tested compounds in reference MS/MS libraries.
The tested compounds cover a broad range of molecular complexity and NP-likeness scores compared to the entire chemical space of PubChem.
Extended Data Fig. 4 Potential factors impacting the formula annotation performance on tested metabolomics datasets.
a, The relative mass deviations of experimental precursor masses from theoretical masses for all identified metabolites. b, The number of reserved fragments (maximum: 50) after low-abundance noise ions and MS/MS isotopic peaks are removed in MS/MS preprocessing. c, The number of valid fragments that are subformula-explainable using the ground-truth molecular formulae.
Extended Data Fig. 5 Validation of FDR estimation on reference MS/MS libraries.
We used four reference MS/MS libraries to evaluate FDR estimation in BUDDY. Q-Q plots of estimated FDR and exact FDR are shown. In both positive and negative ion modes, estimated FDR shows high Pearson’s correlation coefficients (r > 0.98) with exact FDR.
Extended Data Fig. 6 Method evaluation on novel compounds discovered in references.
Bottom-up MS/MS interrogation was evaluated using the MS/MS spectra of five novel compounds, three of which were confirmed in NMR experiments. The correct formulae were ranked first in all cases, with the estimated FDR ranging from 0.6 to 16.3%.
Extended Data Fig. 7 Molecular formula discovery in NIST human fecal material standards.
a, Venn diagram of annotated molecular formulae with estimated FDR < 5%. b, Scatter and density plots of m/z, retention time, DBE value, and hydrogen carbon ratio (H / C) of high-confidence annotated molecular formulae. c, Manual inspection of N-valeryl histamine. The experimental MS/MS of N-valeryl histamine shows a reverse dot product score of 0.99 against the reference MS/MS of histamine. Two neutral losses representing the acyl chain were found. Valeric acid is commonly produced in the gut microbiota (for example, by Clostridia species), and histamine is a well-known diet-derived metabolite.
Supplementary Figs. 1–11 and Notes 1–18.
Supplementary Tables 1–22. Captions are provided within each tab.
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Xing, S., Shen, S., Xu, B. et al. BUDDY: molecular formula discovery via bottom-up MS/MS interrogation. Nat Methods (2023). https://doi.org/10.1038/s41592-023-01850-x