Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics

Zijuan Lai, Hiroshi Tsugawa, Gert Wohlgemuth, Sajjan Mehta, Matthew Mueller, Yuxuan Zheng, Atsushi Ogiwara, John Meissen, Megan Showalter, Kohei Takeuchi, Tobias Kind, Peter Beal, Masanori Arita, Oliver Fiehn

Research output: Contribution to journalArticle

70 Scopus citations

Abstract

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives.

Original languageEnglish (US)
Pages (from-to)53-56
Number of pages4
JournalNature Methods
Volume15
Issue number1
DOIs
StatePublished - Jan 3 2018

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

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  • Cite this

    Lai, Z., Tsugawa, H., Wohlgemuth, G., Mehta, S., Mueller, M., Zheng, Y., Ogiwara, A., Meissen, J., Showalter, M., Takeuchi, K., Kind, T., Beal, P., Arita, M., & Fiehn, O. (2018). Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics. Nature Methods, 15(1), 53-56. https://doi.org/10.1038/nmeth.4512