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RefMet: a reference nomenclature for metabolomics

Nature Methods volume 17, pages 1173–1174 (2020)Cite this article

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To the Editor — The past decade has seen an explosive growth in metabolomics, with advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR) enabling the detection of hundreds or even thousands of metabolite species in a single experiment. The wide range of available analytical methods coupled with the even wider range of metabolite databases (vendor-supplied proprietary databases, public-domain databases and private in-house databases) has unfortunately led to a pervasive problem wherein the same metabolite species may be reported by many different names. This nomenclature issue represents a significant barrier for comparative analysis of metabolomics data across studies generated by different institutions and/or platforms1. To this end, a repository of over 280,000 named analytes from over 1,400 MS and NMR studies in the National Metabolomics Data Repository (NMDR) on the Metabolomics Workbench2 has been leveraged to generate a highly curated analytical-chemistry-centric database of common names for metabolite structures and isobaric species. This Reference Set of Metabolite Names (RefMet) has been linked to a metabolite classification system, with numerous positive outcomes including data-sharing potential, facilitation of meta-analysis across studies, and integrated statistical analysis.

RefMet is composed of four groups of annotations (Supplementary Table 1):

  1. 1.

    Annotations with complete structural characterization of regiochemistry, stereochemistry and double bond geometry obtained from targeted assays. These annotations have associated InChIKey, SMILES and molfiles and can be linked to exact structures in a metabolite database. Examples are prostaglandin E2 (PGE2), 12(S)-hydroxyeicosatetraenoic acid (12(S)-HETE) and α-muricholic acid.

  2. 2.

    Annotations with characterization of the regiochemistry (structural ‘skeleton’ of the molecule) but with some or all chiral centers undefined and/or double bond geometry unknown. Examples are 12-HETE, 3-hydroxytetradecanoic acid, muricholic acid and 6,9-hexadecadienoic acid. These annotations may also have associated InChIKey, SMILES and molfiles and metabolite database entries, albeit with undefined chirality and/or double bond geometry.

  3. 3.

    Annotations with information on structural features, but where complete regiochemistry is unknown. This is commonly encountered in experiments using tandem MS where molecular product ion and neutral-loss data is diagnostic for head groups, acyl chains and other fragments. Examples are phosphatidylcholine (PC) 16:0_18:1, ceramide (Cer) 18:1;O2/24:1 and hydroxytetradecanoic acid. These annotations are not linked to molecular database entries and do not have InChIKey, SMILES and molfiles because the regiochemistry is unknown.

  4. 4.

    Annotations with information on metabolite class and sum-composition information. This level of annotation is typically encountered in untargeted MS experiments where the determinants are precursor ion m/z values and retention time information, and where tandem MS is not informative enough to characterize substructures. Examples are PC 34:1, triacylglycerol (TG) 54:3 and Cer 42:2;O2.

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Fig. 1: Overview of the central role of RefMet in the Metabolomics Workbench infrastructure.

References

  1. Pham, D. N. K., Roy, M., Kreider-Mueller, A., Golen, J. A. & Manke, D. R. Metabolites 9, 28 (2019).

    Article  CAS  Google Scholar 

  2. Sud, M. et al. Nucleic Acids Res. 44, D463–D470 (2016). D1.

    Article  CAS  Google Scholar 

  3. Fahy, E., Cotter, D., Sud, M. & Subramaniam, S. Biochim. Biophys. Acta 1811, 637–647 (2011).

    Article  CAS  Google Scholar 

  4. Liebisch, G. et al. J. Lipid Res. https://doi.org/10.1194/jlr.S120001025 (2020).

  5. Fahy, E. et al. J. Lipid Res. 46, 839–861 (2005).

    Article  CAS  Google Scholar 

  6. Fahy, E. et al. J. Lipid Res. 50, S9–S14 (2009). (Suppl.).

    Article  Google Scholar 

  7. Djoumbou Feunang, Y. et al. J. Cheminform. 8, 61 (2016).

    Article  Google Scholar 

  8. Sumner, L. W. et al. Metabolomics 3, 211–221 (2007).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work is supported by NIH grants U2C-DK119886 and OT2-OD030544.

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Authors and Affiliations

  1. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

    Eoin Fahy & Shankar Subramaniam

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  1. Eoin Fahy
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  2. Shankar Subramaniam
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Contributions

RefMet was conceived by E.F. along with discussions with S.S. E.F. carried out the development and the curation efforts. The manuscript was drafted by E.F. and revised by S.S. and E.F. S.S. is the principal investigator for the project.

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Correspondence to Eoin Fahy.

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The authors declare no competing interests.

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Editorial note: This article has been peer reviewed.

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Supplementary Information

Supplementary Fig. 1 and Tables 1–3

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Fahy, E., Subramaniam, S. RefMet: a reference nomenclature for metabolomics. Nat Methods 17, 1173–1174 (2020). https://doi.org/10.1038/s41592-020-01009-y

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