A universal language for finding mass spectrometry data patterns

Tito Damiani; Alan K. Jarmusch; Allegra T. Aron; Daniel Petras; Vanessa V. Phelan; Haoqi Nina Zhao; Wout Bittremieux; Deepa D. Acharya; Mohammed M.A. Ahmed; Anelize Bauermeister; Matthew J. Bertin; Paul D. Boudreau; Ricardo M. Borges; Benjamin P. Bowen; Christopher J. Brown; Fernanda O. Chagas; Kenneth D. Clevenger; Mario S.P. Correia; William J. Crandall; Max Crüsemann; Eoin Fahy; Oliver Fiehn; Neha Garg; William H. Gerwick; Jeffrey R. Gilbert; Daniel Globisch; Paulo Wender P. Gomes; Steffen Heuckeroth; C. Andrew James; Scott A. Jarmusch; Sarvar A. Kakhkhorov; Kyo Bin Kang; Nikolas Kessler; Roland D. Kersten; Hyunwoo Kim; Riley D. Kirk; Oliver Kohlbacher; Eftychia E. Kontou; Ken Liu; Itzel Lizama-Chamu; Gordon T. Luu; Tal Luzzatto Knaan; Helena Mannochio-Russo; Michael T. Marty; Yuki Matsuzawa; Andrew C. McAvoy; Laura Isobel McCall; Osama G. Mohamed; Omri Nahor; Heiko Neuweger; Timo H.J. Niedermeyer; Kozo Nishida; Trent R. Northen; Kirsten E. Overdahl; Johannes Rainer; Raphael Reher; Elys Rodriguez; Timo T. Sachsenberg; Laura M. Sanchez; Robin Schmid; Cole Stevens; Shankar Subramaniam; Zhenyu Tian; Ashootosh Tripathi; Hiroshi Tsugawa; Justin J.J. van der Hooft; Andrea Vicini; Axel Walter; Tilmann Weber; Quanbo Xiong; Tao Xu; Tomáš Pluskal; Pieter C. Dorrestein; Mingxun Wang

doi:10.1038/s41592-025-02660-z

A universal language for finding mass spectrometry data patterns

Tito Damiani, Alan K. Jarmusch, Allegra T. Aron, Daniel Petras, Vanessa V. Phelan, Haoqi Nina Zhao, Wout Bittremieux, Deepa D. Acharya, Mohammed M.A. Ahmed, Anelize Bauermeister, Matthew J. Bertin, Paul D. Boudreau, Ricardo M. Borges, Benjamin P. Bowen, Christopher J. Brown, Fernanda O. Chagas, Kenneth D. Clevenger, Mario S.P. Correia, William J. Crandall, Max CrüsemannEoin Fahy, Oliver Fiehn, Neha Garg, William H. Gerwick, Jeffrey R. Gilbert, Daniel Globisch, Paulo Wender P. Gomes, Steffen Heuckeroth, C. Andrew James, Scott A. Jarmusch, Sarvar A. Kakhkhorov, Kyo Bin Kang, Nikolas Kessler, Roland D. Kersten, Hyunwoo Kim, Riley D. Kirk, Oliver Kohlbacher, Eftychia E. Kontou, Ken Liu, Itzel Lizama-Chamu, Gordon T. Luu, Tal Luzzatto Knaan, Helena Mannochio-Russo, Michael T. Marty, Yuki Matsuzawa, Andrew C. McAvoy, Laura Isobel McCall, Osama G. Mohamed, Omri Nahor, Heiko Neuweger, Timo H.J. Niedermeyer, Kozo Nishida, Trent R. Northen, Kirsten E. Overdahl, Johannes Rainer, Raphael Reher, Elys Rodriguez, Timo T. Sachsenberg, Laura M. Sanchez, Robin Schmid, Cole Stevens, Shankar Subramaniam, Zhenyu Tian, Ashootosh Tripathi, Hiroshi Tsugawa, Justin J.J. van der Hooft, Andrea Vicini, Axel Walter, Tilmann Weber, Quanbo Xiong, Tao Xu, Tomáš Pluskal, Pieter C. Dorrestein, Mingxun Wang

Department of Marine Biology

Research output: Contribution to journal › Article › peer-review

Abstract

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community.

Original language	English
Article number	113
Number of pages	8
Journal	Nature Methods
Early online date	12 May 2025
DOIs	https://doi.org/10.1038/s41592-025-02660-z
State	Published - Jun 2025

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

ASJC Scopus subject areas

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

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10.1038/s41592-025-02660-z

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Damiani, T., Jarmusch, A. K., Aron, A. T., Petras, D., Phelan, V. V., Zhao, H. N., Bittremieux, W., Acharya, D. D., Ahmed, M. M. A., Bauermeister, A., Bertin, M. J., Boudreau, P. D., Borges, R. M., Bowen, B. P., Brown, C. J., Chagas, F. O., Clevenger, K. D., Correia, M. S. P., Crandall, W. J., ... Wang, M. (2025). A universal language for finding mass spectrometry data patterns. Nature Methods, Article 113. https://doi.org/10.1038/s41592-025-02660-z

@article{d70967eae2a343fa9f0c5f7c6d592660,

title = "A universal language for finding mass spectrometry data patterns",

abstract = "Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community.",

author = "Tito Damiani and Jarmusch, \{Alan K.\} and Aron, \{Allegra T.\} and Daniel Petras and Phelan, \{Vanessa V.\} and Zhao, \{Haoqi Nina\} and Wout Bittremieux and Acharya, \{Deepa D.\} and Ahmed, \{Mohammed M.A.\} and Anelize Bauermeister and Bertin, \{Matthew J.\} and Boudreau, \{Paul D.\} and Borges, \{Ricardo M.\} and Bowen, \{Benjamin P.\} and Brown, \{Christopher J.\} and Chagas, \{Fernanda O.\} and Clevenger, \{Kenneth D.\} and Correia, \{Mario S.P.\} and Crandall, \{William J.\} and Max Cr{\"u}semann and Eoin Fahy and Oliver Fiehn and Neha Garg and Gerwick, \{William H.\} and Gilbert, \{Jeffrey R.\} and Daniel Globisch and Gomes, \{Paulo Wender P.\} and Steffen Heuckeroth and James, \{C. Andrew\} and Jarmusch, \{Scott A.\} and Kakhkhorov, \{Sarvar A.\} and Kang, \{Kyo Bin\} and Nikolas Kessler and Kersten, \{Roland D.\} and Hyunwoo Kim and Kirk, \{Riley D.\} and Oliver Kohlbacher and Kontou, \{Eftychia E.\} and Ken Liu and Itzel Lizama-Chamu and Luu, \{Gordon T.\} and \{Luzzatto Knaan\}, Tal and Helena Mannochio-Russo and Marty, \{Michael T.\} and Yuki Matsuzawa and McAvoy, \{Andrew C.\} and McCall, \{Laura Isobel\} and Mohamed, \{Osama G.\} and Omri Nahor and Heiko Neuweger and Niedermeyer, \{Timo H.J.\} and Kozo Nishida and Northen, \{Trent R.\} and Overdahl, \{Kirsten E.\} and Johannes Rainer and Raphael Reher and Elys Rodriguez and Sachsenberg, \{Timo T.\} and Sanchez, \{Laura M.\} and Robin Schmid and Cole Stevens and Shankar Subramaniam and Zhenyu Tian and Ashootosh Tripathi and Hiroshi Tsugawa and \{van der Hooft\}, \{Justin J.J.\} and Andrea Vicini and Axel Walter and Tilmann Weber and Quanbo Xiong and Tao Xu and Tom{\'a}{\v s} Pluskal and Dorrestein, \{Pieter C.\} and Mingxun Wang",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.",

year = "2025",

month = jun,

doi = "10.1038/s41592-025-02660-z",

language = "English",

journal = "Nature Methods",

issn = "1548-7091",

publisher = "Nature Research",

}

Damiani, T, Jarmusch, AK, Aron, AT, Petras, D, Phelan, VV, Zhao, HN, Bittremieux, W, Acharya, DD, Ahmed, MMA, Bauermeister, A, Bertin, MJ, Boudreau, PD, Borges, RM, Bowen, BP, Brown, CJ, Chagas, FO, Clevenger, KD, Correia, MSP, Crandall, WJ, Crüsemann, M, Fahy, E, Fiehn, O, Garg, N, Gerwick, WH, Gilbert, JR, Globisch, D, Gomes, PWP, Heuckeroth, S, James, CA, Jarmusch, SA, Kakhkhorov, SA, Kang, KB, Kessler, N, Kersten, RD, Kim, H, Kirk, RD, Kohlbacher, O, Kontou, EE, Liu, K, Lizama-Chamu, I, Luu, GT, Luzzatto Knaan, T, Mannochio-Russo, H, Marty, MT, Matsuzawa, Y, McAvoy, AC, McCall, LI, Mohamed, OG, Nahor, O, Neuweger, H, Niedermeyer, THJ, Nishida, K, Northen, TR, Overdahl, KE, Rainer, J, Reher, R, Rodriguez, E, Sachsenberg, TT, Sanchez, LM, Schmid, R, Stevens, C, Subramaniam, S, Tian, Z, Tripathi, A, Tsugawa, H, van der Hooft, JJJ, Vicini, A, Walter, A, Weber, T, Xiong, Q, Xu, T, Pluskal, T, Dorrestein, PC & Wang, M 2025, 'A universal language for finding mass spectrometry data patterns', Nature Methods. https://doi.org/10.1038/s41592-025-02660-z

TY - JOUR

T1 - A universal language for finding mass spectrometry data patterns

AU - Damiani, Tito

AU - Jarmusch, Alan K.

AU - Aron, Allegra T.

AU - Petras, Daniel

AU - Phelan, Vanessa V.

AU - Zhao, Haoqi Nina

AU - Bittremieux, Wout

AU - Acharya, Deepa D.

AU - Ahmed, Mohammed M.A.

AU - Bauermeister, Anelize

AU - Bertin, Matthew J.

AU - Boudreau, Paul D.

AU - Borges, Ricardo M.

AU - Bowen, Benjamin P.

AU - Brown, Christopher J.

AU - Chagas, Fernanda O.

AU - Clevenger, Kenneth D.

AU - Correia, Mario S.P.

AU - Crandall, William J.

AU - Crüsemann, Max

AU - Fahy, Eoin

AU - Fiehn, Oliver

AU - Garg, Neha

AU - Gerwick, William H.

AU - Gilbert, Jeffrey R.

AU - Globisch, Daniel

AU - Gomes, Paulo Wender P.

AU - Heuckeroth, Steffen

AU - James, C. Andrew

AU - Jarmusch, Scott A.

AU - Kakhkhorov, Sarvar A.

AU - Kang, Kyo Bin

AU - Kessler, Nikolas

AU - Kersten, Roland D.

AU - Kim, Hyunwoo

AU - Kirk, Riley D.

AU - Kohlbacher, Oliver

AU - Kontou, Eftychia E.

AU - Liu, Ken

AU - Lizama-Chamu, Itzel

AU - Luu, Gordon T.

AU - Luzzatto Knaan, Tal

AU - Mannochio-Russo, Helena

AU - Marty, Michael T.

AU - Matsuzawa, Yuki

AU - McAvoy, Andrew C.

AU - McCall, Laura Isobel

AU - Mohamed, Osama G.

AU - Nahor, Omri

AU - Neuweger, Heiko

AU - Niedermeyer, Timo H.J.

AU - Nishida, Kozo

AU - Northen, Trent R.

AU - Overdahl, Kirsten E.

AU - Rainer, Johannes

AU - Reher, Raphael

AU - Rodriguez, Elys

AU - Sachsenberg, Timo T.

AU - Sanchez, Laura M.

AU - Schmid, Robin

AU - Stevens, Cole

AU - Subramaniam, Shankar

AU - Tian, Zhenyu

AU - Tripathi, Ashootosh

AU - Tsugawa, Hiroshi

AU - van der Hooft, Justin J.J.

AU - Vicini, Andrea

AU - Walter, Axel

AU - Weber, Tilmann

AU - Xiong, Quanbo

AU - Xu, Tao

AU - Pluskal, Tomáš

AU - Dorrestein, Pieter C.

AU - Wang, Mingxun

PY - 2025/6

Y1 - 2025/6

N2 - Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community.

AB - Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community.

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U2 - 10.1038/s41592-025-02660-z

DO - 10.1038/s41592-025-02660-z

M3 - Article

C2 - 40355727

AN - SCOPUS:105004840486

SN - 1548-7091

JO - Nature Methods

JF - Nature Methods

M1 - 113

ER -

A universal language for finding mass spectrometry data patterns

Abstract

Bibliographical note

ASJC Scopus subject areas

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