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Fast mass spectrometry search and clustering of untargeted metabolomics data

Nature Biotechnology (2024)Cite this article

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Abstract

The throughput of mass spectrometers and the amount of publicly available metabolomics data are growing rapidly, but analysis tools such as molecular networking and Mass Spectrometry Search Tool do not scale to searching and clustering billions of mass spectral data in metabolomics repositories. To address this limitation, we designed MASST+ and Networking+, which can process datasets that are up to three orders of magnitude larger than those processed by state-of-the-art tools.

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Fig. 1: Fast scoring with indexing.
Fig. 2: MASST+, Clustering+ and Networking+ enable lanthipeptide discovery.

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Data availability

The datasets analyzed are available at gnps.ucsd.edu. Accession codes related to the lanthipeptides part of the study are MSV000090476, MSV000090473, MSV000090472, MSV000090471, MSV000090457, MSV000089818, MSV000089817, MSV000089816, MSV000089815, MSV000089813, MSV000088816, MSV000088801, MSV000088800, MSV000088764 and MSV000088763. For comparing MASST+ and Networking+ against previous state-of-the-art tools, datasets MSV000078787, clustered GNPS, and unclustered GNPS were used. The accession codes for clustered GNPS and unclustered GNPS are available in Supplementary Data 1.

Code availability

MASST+ and Networking+ are available at https://github.com/mohimanilab/MASSTplus. Other custom software used in this work includes Seq2Ripp (https://github.com/mohimanilab/seq2ripp), PepNovo (https://github.com/jmchilton/pepnovo) and Dereplicator (https://ccms-ucsd.github.io/GNPSDocumentation/dereplicator/).

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Acknowledgements

The work of T.M.Y., M.M., Y.L., B.B. and H.M. was supported by National Institutes of Health New Innovator Award DP2GM137413, US Department of Energy award DE-SC0021340, National Science Foundation award DBI-2117640 and National Institute of General Medicine Sciences of the National Institutes of Health award R43GM150301 (B.B. only). The work of P.C.D. and M.W. was supported by R03OD034493, U24DK133658 and R01GM107550 (P.C.D. only).

Author information

Author notes

  1. These authors contributed equally: Mihir Mongia, Tyler M. Yasaka, Yudong Liu.

Authors and Affiliations

  1. Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Mihir Mongia, Tyler M. Yasaka, Yudong Liu, Mustafa Guler, Liang Lu, Aditya Bhagwat, Bahar Behsaz & Hosein Mohimani

  2. Chemia Biosciences Inc., Pittsburgh, PA, USA

    Bahar Behsaz

  3. Computer Science and Engineering, University of California Riverside, Riverside, CA, USA

    Mingxun Wang

  4. Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA

    Pieter C. Dorrestein

  5. Department of Pharmacology and Pediatrics, University of California San Diego, San Diego, CA, USA

    Pieter C. Dorrestein

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Contributions

M.M., T.M.Y., Y.L., M.G., L.L. and A.B. implemented the algorithms. M.M., T.M.Y. and Y.L. performed the analysis. M.W. designed and implemented the GNPS web service for MASST+. B.B., P.C.D. and H.M. designed and directed the work. M.M. and H.M. wrote the manuscript, and all authors contributed to its revision.

Corresponding author

Correspondence to Hosein Mohimani.

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Competing interests

H.M. and B.B. are cofounders of and have equity interests in Chemia.ai, LLC. P.C.D. is an advisor of and holds equity in Cybele, consulted for MSD Animal Health in 2023 and is a cofounder of, holds equity in and is scientific advisor for Ometa Labs, Arome and Enveda with prior approval by the University of California San Diego. The remaining authors declare no competing interests.

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Nature Biotechnology thanks Marnix Medema and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Figs. 1–13, Tables 1–8 and Data 1.

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List of accession codes 1

Accession codes of data used for experiments.

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Mongia, M., Yasaka, T.M., Liu, Y. et al. Fast mass spectrometry search and clustering of untargeted metabolomics data. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-023-01985-4

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