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“The authors demonstrated an example of a metabolite differentially expressed within two cohorts of mice and utilized MASST to reveal that this metabolite is also differentially expressed in healthy and non-alcoholic fatty liver disease in humans – even without knowing the molecule's chemical structure.”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [155]
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“The Mass Spectrometry Search Tool (MASST)155 can be utilized to add context to molecules detected in a bioactivity experiment.”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [155]
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“The MASST tool finds all occurrences of a query MS/MS spectrum across all public metabolomics experiments.”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [155]
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“The enzymes responsible for these conjugation reactions are unknown.”
From Bile Acids Are Substrates for Amine N-Acyl Transferase Activity by Bile Salt Hydrolase (2022) · cites this paper as [17]
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“Recent studies demonstrated that bacteria, in addition to the host, have bile acid amine N-acyltransferase activity, with dozens of bacterially conjugated amino acid amidates identified1–4,6,17.”
From Bile Acids Are Substrates for Amine N-Acyl Transferase Activity by Bile Salt Hydrolase (2022) · cites this paper as [17]
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“All MCBAs were purified according to literature precedent and characterization data are consistent with reported data31,33,42,77,78.”
From Bile salt hydrolases shape the bile acid landscape and restrict Clostridioides difficile growth in the murine gut (2023) · cites this paper as [42]
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Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2
Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0209-9 · Cited by 23,742
Self-citationSumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., Fan, T. W.-M., Fiehn, O., Goodacre, R., Griffin, J. L., et al. (2007) · Metabolomics · doi:10.1007/s11306-007-0082-2 · Cited by 5,368
Wang, M., Carver, J. J., Phelan, V. V., Sanchez, L. M., Garg, N., Peng, Y., Nguyen, D. D., Watrous, J., Kapono, C. A., Luzzatto-Knaan, T., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3597 · Cited by 4,629
Self-citationMetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis
Chong, J., Soufan, O., Li, C., Caraus, I., Li, S., Bourque, G., Wishart, D. S., & Xia, J. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky310 · Cited by 3,637
HMDB 4.0: the human metabolome database for 2018
Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vázquez-Fresno, R., Sajed, T., Johnson, D., Li, C., Karu, N., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1089 · Cited by 3,438
MassBank: a public repository for sharing mass spectral data for life sciences
Horai, H., Arita, M., Kanaya, S., Nihei, Y., Ikeda, T., Suwa, K., Ojima, Y., Tanaka, K., Tanaka, S., Aoshima, K., et al. (2010) · Journal of Mass Spectrometry · doi:10.1002/jms.1777 · Cited by 2,529
ProteoWizard: open source software for rapid proteomics tools development
Kessner, D., Chambers, M., Burke, R., Agus, D., & Mallick, P. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn323 · Cited by 2,024
XCMS Online: A Web-Based Platform to Process Untargeted Metabolomic Data
Tautenhahn, R., Patti, G. J., Rinehart, D., & Siuzdak, G. E. (2012) · Analytical Chemistry · doi:10.1021/ac300698c · Cited by 1,323
Sud, M., Fahy, E., Cotter, D., Azam, K., Vadivelu, I., Burant, C., Edison, A. S., Fiehn, O., Higashi, R. M., Nair, K. S., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1042 · Cited by 1,029
Mass spectral molecular networking of living microbial colonies
Watrous, J. D., Roach, P. J., Alexandrov, T., Heath, B. S., Yang, J. Y., Kersten, R. D., van der Voort, M., Pogliano, K., Gross, H., Raaijmakers, J. M., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1203689109 · Cited by 974
Self-citationUberon, an integrative multi-species anatomy ontology
Mungall, C. J., Torniai, C., Gkoutos, G. V., Lewis, S. E., & Haendel, M. A. (2012) · Genome biology · doi:10.1186/gb-2012-13-1-r5 · Cited by 732
Haug, K., Salek, R. M., Conesa, P., Hastings, J., de Matos, P., Rijnbeek, M. L., Mahendraker, T., Williams, M., Neumann, S., Rocca‐Serra, P., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1004 · Cited by 647
Qiita: rapid, web-enabled microbiome meta-analysis
Gonzalez, A., Navas-Molina, J. A., Kościółek, T., McDonald, D. T., Vázquez‐Baeza, Y., Ackermann, G. L., DeReus, J., Janssen, S., Swafford, A. D.-E., Orchanian, S. B., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0141-9 · Cited by 606
Self-citationIdentifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics
Lai, Z., Tsugawa, H., Wohlgemuth, G., Mehta, S. S., Mueller, M. R., Zheng, Y., Ogiwara, A., Meissen, J. K., Showalter, M. R., Takeuchi, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4512 · Cited by 575
Human Disease Ontology 2018 update: classification, content and workflow expansion
Schriml, L. M., Mitraka, E., Munro, J. B., Tauber, B., Schor, M., Nickle, L., Felix, V., Jeng, L. J. B., Bearer, C. F., Lichenstein, R., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1032 · Cited by 471
Sawada, Y., Nakabayashi, R., Yamada, Y., Suzuki, M., Sato, M., Sakata, A., Akiyama, K., Sakurai, T., Matsuda, F., Aoki, T., et al. (2012) · Phytochemistry · doi:10.1016/j.phytochem.2012.07.007 · Cited by 372
Recent trends in studies on botanical fungicides in agriculture
Yoon, M.-Y., Cha, B., & Kim, J.-C. (2013) · The Plant Pathology Journal · doi:10.5423/ppj.rw.05.2012.0072 · Cited by 291
Global chemical analysis of biology by mass spectrometry
Aksenov, A. A., Silva, R., Knight, R. D., Lopes, N. P., & Dorrestein, P. C. (2017) · Nature Reviews Chemistry · doi:10.1038/s41570-017-0054 · Cited by 228
Self-citationDiscovering and linking public omics data sets using the Omics Discovery Index
Pérez‐Riverol, Y., Bai, M., Leprevost, F. D. V., Squizzato, S., Park, Y. M., Haug, K., Carroll, A., Spalding, D., Paschall, J. E., Wang, M., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3790 · Cited by 207
Self-citationComputing Fragmentation Trees from Tandem Mass Spectrometry Data
Rasche, F., Svatoš, A., Maddula, R. K., Böttcher, C., & Böcker, S. (2010) · Analytical Chemistry · doi:10.1021/ac101825k · Cited by 159
ReDU: a framework to find and reanalyze public mass spectrometry data
Jarmusch, A. K., Wang, M., Aceves, C. M., Advani, R. S., Aguirre, S., Aksenov, A. A., Aleti, G., Aron, A. T., Bauermeister, A., Bolleddu, S., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0916-7 · Cited by 152
Self-citationData standards can boost metabolomics research, and if there is a will, there is a way
Rocca‐Serra, P., Salek, R. M., Arita, M., Correa, E. S., Dayalan, S., Gonzalez-Beltran, A. N., Ebbels, T. M. D., Goodacre, R., Hastings, J., Haug, K., et al. (2015) · Metabolomics · doi:10.1007/s11306-015-0879-3 · Cited by 150
LIQUID: an-open source software for identifying lipids in LC-MS/MS-based lipidomics data
Kyle, J. E., Crowell, K. L., Casey, C., Fujimoto, G. M., Kim, S., Dautel, S. E., Smith, R. D., Payne, S., & Metz, T. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx046 · Cited by 113
The Critical Assessment of Small Molecule Identification (CASMI): Challenges and Solutions
Schymanski, E., & Neumann, S. (2013) · Metabolites · doi:10.3390/metabo3030517 · Cited by 45
N’Nang, E. O., Bernadat, G., Mouray, E., Kumulungui, B., Grellier, P., Poupon, E., Champy, P., & Beniddir, M. A. (2018) · Organic Letters · doi:10.1021/acs.orglett.8b02961 · Cited by 34
MGFp: An Open Mascot Generic Format Parser Library Implementation
Kirchner, M., Steen, J. A. J., Hamprecht, F. A., & Steen, H. (2010) · Journal of Proteome Research · doi:10.1021/pr100118f · Cited by 10
Research works printed but not linked: 2
- Altschul, S. F. et al. J. Mol. Biol. 215, 403–410 (1990). · DOI · Paper page
- Jarmusch, A.K. et al. Preprint at bioRxiv https://doi.org/10.1101/750471 (2019). · DOI
Cited by
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Self-citationBile acids and the gut microbiota: metabolic interactions and impacts on disease
Collins, S. L., Stine, J. G., Bisanz, J. E., Okafor, C. D., & Patterson, A. D. (2023) · Nature Reviews Microbiology · doi:10.1038/s41579-022-00805-x · Cited by 1,214
Reproducible molecular networking of untargeted mass spectrometry data using GNPS
Aron, A. T., Gentry, E. C., McPhail, K. L., Nothias-Scaglia, L.-F., Nothias-Esposito, M., Bouslimani, A., Petras, D., Gauglitz, J. M., Sikora, N. C., Vargas, F., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0317-5 · Cited by 766
Self-citationGlobal chemical effects of the microbiome include new bile-acid conjugations
Quinn, R. A., Melnik, A. V., Vrbanac, A., Fu, T., Patras, K. A., Christy, M. P., Bodai, Z., Belda-Ferre, P., Tripathi, A., Chung, L. K., et al. (2020) · Nature · doi:10.1038/s41586-020-2047-9 · Cited by 579
Self-citationMass spectrometry-based metabolomics in microbiome investigations
Bauermeister, A., Mannochio-Russo, H., Costa-Lotufo, L. V., Jarmusch, A. K., & Dorrestein, P. C. (2021) · Nature Reviews Microbiology · doi:10.1038/s41579-021-00621-9 · Cited by 554
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Metadata
- Authors
- Wang, M., Jarmusch, A. K., Vargas, F., Aksenov, A. A., Gauglitz, J. M., Weldon, K., Petras, D., da Silva, R., Quinn, R., Melnik, A. V., van der Hooft, J. J. J., Caraballo-Rodríguez, A. M., Nothias, L. F., Aceves, C. M., Panitchpakdi, M., Brown, E., Di Ottavio, F., Sikora, N., Elijah, E. O., Labarta-Bajo, L., Gentry, E. C., Shalapour, S., Kyle, K. E., Puckett, S. P., Watrous, J. D., Carpenter, C. S., Bouslimani, A., Ernst, M., Swafford, A. D., Zúñiga, E. I., Balunas, M. J., Klassen, J. L., Loomba, R., Knight, R., Bandeira, N., & Dorrestein, P. C.
- Affiliations
- University of California San Diego (US)
- University of Connecticut (US)
- Corresponding author
- Dorrestein, Pieter C.
- ORCID
- Wang, Mingxun
- Jarmusch, Alan K.
- Vargas, Fernando
- Aksenov, Alexander A.
- Gauglitz, Julia M.
- Weldon, Kelly
- Petras, Daniel
- Quinn, Robert
- Melnik, Alexey V.
- van der Hooft, Justin J. J.
- Caraballo-Rodríguez, Andrés Mauricio
- Aceves, Christine M.
- Panitchpakdi, Morgan
- Brown, Elizabeth
- Sikora, Nicole
- Labarta-Bajo, Lara
- Gentry, Emily C.
- Shalapour, Shabnam
- Kyle, Kathleen E.
- Puckett, Sara P.
- Watrous, Jeramie D.
- Carpenter, Carolina S.
- Bouslimani, Amina
- Ernst, Madeleine
- Swafford, Austin D.
- Zúñiga, Elina I.
- Balunas, Marcy J.
- Klassen, Jonathan L.
- Loomba, Rohit
- Knight, Rob
- Bandeira, Nuno
- Dorrestein, Pieter C.
- Venue
- Nature Biotechnology
- Published
- 2020-01
- Volume
- 38
- Issue
- 1
- Pages
- 23-26
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC7236533
- OpenAlex
- W2997345427
- Cited by
- 281
- References
- 26
- Citations by year
- 2026: 40 · 2025: 45 · 2024: 57 · 2023: 43 · 2022: 27 · 2021: 39 · 2020: 29
- FWCI
- 11.19
- Type
- journal article
- Language
- en
- Open access
- green
- research.wur.nl
- Licence
- public-domain
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion, public-domain)
- PubMed Central (acceptedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Netherlands eScience Center ASDI eScience grant
- Gordon and Betty Moore Foundation (GBMF7622)
- Alfred P. Sloan Foundation (N/A)
- U.S. Department of Health & Human Services | National Institutes of Health (CA211211, P41 GM103484)
- United States Department of Defense | United States Navy | Office of Naval Research (N00014-15-1-2809)
- United States Department of Justice | National Institute of Justice (2015-DN-BX-K047)
- National Science Foundation (IOS-1656475)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- spectral library search, metabolite annotation, unknown metabolites, cold pressor test, multi-omics, metabolic networks, medicinal plants, traditional East Asian medicine
- MeSH terms
- Algorithms, Mass Spectrometry, Reproducibility of Results, Knowledge Bases, Search Engine
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