A Perspective on Unintentional Fragments and Their Impact on the Dark Metabolome, Untargeted Profiling, Molecular Networking, Public Data, and Repository Scale Analysis
Status PubMed-not-MEDLINE Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection
Typ dokumentu časopisecké články, přehledy
PubMed
41450660
PubMed Central
PMC12728610
DOI
10.1021/jacsau.5c01063
Knihovny.cz E-zdroje
- Klíčová slova
- analytical artifact, dark metabolome, electrospray ionization, in-source fragmentation, mass spectrometry, metabolomics,
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
In/postsource fragments (ISFs) arise during electrospray ionization or ion transfer in mass spectrometry when molecular bonds break, generating ions that can complicate data interpretation. Although ISFs have been recognized for decades, their contribution to untargeted metabolomicsparticularly in the context of the so-called "dark matter" (unannotated MS or MS/MS spectra) and the "dark metabolome" (unannotated molecules)remains unsettled. This ongoing debate reflects a central tension: while some caution against overinterpreting unidentified signals lacking biological evidence, others argue that dismissing them too quickly risks overlooking genuine molecular discoveries. These discussions also raise a deeper question: what exactly should be considered part of the metabolome? As metabolomics advances toward large-scale data mining and high-throughput computational analysis, resolving these conceptual and methodological ambiguities has become essential. In this perspective, we propose a refined definition of the "dark metabolome" and present a systematic overview of ISFs and related ion forms, including adducts and multimers. We examine their impact on metabolite annotation, experimental design, statistical analysis, computational workflows, and repository-scale data mining. Finally, we provide practical recommendationsincluding a set of dos and do nots for researchers and reviewersand discuss the broader implications of ISFs for how the field explores unknown molecular space. By embracing a more nuanced understanding of ISFs, metabolomics can achieve greater rigor, reduce misinterpretation, and unlock new opportunities for discovery.
Department of Electronic Engineering and IISPV Universitat Rovira i Virgili Tarragona 43007 Spain
Institute for Molecular Systems Biology ETH Zurich Otto Stern Weg 3 Zurich 8093 Switzerland
Zobrazit více v PubMed
Giera M., Aisporna A., Uritboonthai W., Siuzdak G.. The hidden impact of in-source fragmentation in metabolic and chemical mass spectrometry data interpretation. Nat. Metab. 2024;6:1647. doi: 10.1038/s42255-024-01076-x. PubMed DOI PMC
El Abiead Y.. et al. Discovery of metabolites prevails amid in-source fragmentation. Nat. Metab. 2025;7:435–437. doi: 10.1038/s42255-025-01239-4. PubMed DOI
Chi Y., Mitchell J. M., Zheng S., Li S.. Systematic pre-annotation explains the ‘dark matter’ in LC-MS metabolomics. bioRxiv. 2025:2025.02.04.636472. doi: 10.1101/2025.02.04.636472. DOI
Strachan, J. The Dark Metabolome: A Figment of Our Fragmentation? The Analytical Scientist. 2024, https://theanalyticalscientist.com/fields-applications/the-dark-metabolome-a-figment-of-our-fragmentation.
Strachan, J. Dark Metabolome Debate: A Call for Context. The Analytical Scientist. 2025, https://theanalyticalscientist.com/issues/2025/articles/june/dark-metabolome-debate-a-call-for-context/.
Strachan, J. The Dark Metabolome Debate Continues: Siuzdak and Giera Respond. The Analytical Scientist. 2025, https://theanalyticalscientist.com/issues/2025/articles/june/the-dark-metabolome-debate-continues-siuzdak-and-giera-respond/.
Strachan, J. The Dark Metabolome: No Mere Figment? The Analytical Scientist. 2025, https://theanalyticalscientist.com/issues/2025/articles/june/the-dark-metabolome-no-mere-figment/.
Strachan, J. Gary Patti: Metabolomics Is Not in Crisis. The Analytical Scientist. 2025, https://theanalyticalscientist.com/issues/2025/articles/june/gary-patti-metabolomics-is-not-in-crisis/.
Strachan, J. The Past, Present, and Future of the ‘Dark Metabolome’. The Analytical Scientist. 2025, https://theanalyticalscientist.com/issues/2025/articles/september/the-past-present-and-future-of-the-dark-metabolome/?md5=ff77e58d7a81fe20f621192b49e3e7fe&mktId=23257088&utm_medium=email&utm_campaign=eNews&utm_source=TEX-TAS-MASSSPEC-NEWSLETTER-09-25-25&mkt_tok=ODI0LVhPRy0wNTQAAAGdH-O6yLym89xuMmfy946QLUFa5fBXqitTAwn05XvPWwxwwLT3SHRR-WAhq87eQMkv658FmUroTNdOluly0FQr1nuvf8HE2ygZQz2srbPyuvW77xQp.
Strachan, J. Does In-Source Fragmentation Require a Soft Touch? The Analytical Scientist. 2025, https://www.theanalyticalscientist.com/issues/2025/articles/september/does-insource-fragmentation-require-a-soft-touch.
da Silva R. R., Dorrestein P. C., Quinn R. A.. Illuminating the dark matter in metabolomics. Proc. Natl. Acad. Sci. U.S.A. 2015;112:12549–12550. doi: 10.1073/pnas.1516878112. PubMed DOI PMC
Little J. L., Cleven C. D., Brown S. D.. Identification of ‘known unknowns’ utilizing accurate mass data and chemical abstracts service databases. J. Am. Soc. Mass Spectrom. 2011;22:348–359. doi: 10.1007/s13361-010-0034-3. PubMed DOI
Mitchell Crow J.. Canada’s scientists are elucidating the dark metabolome. Nature. 2021;599:S14–S15. doi: 10.1038/d41586-021-03062-9. DOI
Peisl B. Y. L., Schymanski E. L., Wilmes P.. Dark matter in host-microbiome metabolomics: Tackling the unknowns-A review. Anal. Chim. Acta. 2018;1037:13–27. doi: 10.1016/j.aca.2017.12.034. PubMed DOI
Baker E. S., Patti G. J.. Perspectives on data analysis in metabolomics: Points of agreement and disagreement from the 2018 ASMS fall Workshop. J. Am. Soc. Mass Spectrom. 2019;30:2031–2036. doi: 10.1007/s13361-019-02295-3. PubMed DOI PMC
El Abiead Y.. et al. Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets. Anal. Chim. Acta. 2022;1229:340352. doi: 10.1016/j.aca.2022.340352. PubMed DOI
Mahieu N. G., Spalding J. L., Gelman S. J., Patti G. J.. Defining and detecting complex peak relationships in mass spectral data: The mz. Unity algorithm. Anal. Chem. 2016;88:9037–9046. doi: 10.1021/acs.analchem.6b01702. PubMed DOI PMC
Nash W. J., Ngere J. B., Najdekr L., Dunn W. B.. Characterization of electrospray ionization complexity in untargeted metabolomic studies. Anal. Chem. 2024;96:10935–10942. doi: 10.1021/acs.analchem.4c00966. PubMed DOI PMC
Xu Y.-F., Lu W., Rabinowitz J. D.. Avoiding misannotation of in-source fragmentation products as cellular metabolites in liquid chromatography-mass spectrometry-based metabolomics. Anal. Chem. 2015;87:2273–2281. doi: 10.1021/ac504118y. PubMed DOI PMC
Edwards-Hicks J., Mitterer M., Pearce E. L., Buescher J. M.. Metabolic dynamics of in vitro CD8+ T cell activation. Metabolites. 2021;11:12. doi: 10.3390/metabo11010012. PubMed DOI PMC
Atanasov A. G., Zotchev S. B., Dirsch V. M., Orhan I. E., Barreca D., Weckwerth W., Bauer R., Bayer E. A., BanachRollinger M. J. M. C. T.. et al. Natural products in drug discovery: advances and opportunities. Nat. Rev. Drug Discovery. 2021;20:200–216. doi: 10.1038/s41573-020-00114-z. PubMed DOI PMC
Wolfender J.-L., Marti G., Thomas A., Bertrand S.. Current approaches and challenges for the metabolite profiling of complex natural extracts. J. Chromatogr. A. 2015;1382:136–164. doi: 10.1016/j.chroma.2014.10.091. PubMed DOI
Wolfender J.-L., Nuzillard J.-M., van der Hooft J. J. J., Renault J.-H., Bertrand S.. Accelerating metabolite identification in natural product research: Toward an ideal combination of liquid chromatography-high-resolution tandem mass spectrometry and NMR profiling, in silico databases, and chemometrics. Anal. Chem. 2019;91:704–742. doi: 10.1021/acs.analchem.8b05112. PubMed DOI
Allard P.-M., Genta-Jouve G., Wolfender J.-L.. Deep metabolome annotation in natural products research: towards a virtuous cycle in metabolite identification. Curr. Opin. Chem. Biol. 2017;36:40–49. doi: 10.1016/j.cbpa.2016.12.022. PubMed DOI
Pye C. R., Bertin M. J., Lokey R. S., Gerwick W. H., Linington R. G.. Retrospective analysis of natural products provides insights for future discovery trends. Proc. Natl. Acad. Sci. U.S.A. 2017;114:5601–5606. doi: 10.1073/pnas.1614680114. PubMed DOI PMC
Skinnider M. A., Magarvey N. A.. Statistical reanalysis of natural products reveals increasing chemical diversity. Proc. Natl. Acad. Sci. U.S.A. 2017;114:E6271–E6272. doi: 10.1073/pnas.1708560114. PubMed DOI PMC
Palazzolo A. M. E., Simons C. L. W., Burke M. D.. The natural productome. Proc. Natl. Acad. Sci. U.S.A. 2017;114:5564–5566. doi: 10.1073/pnas.1706266114. PubMed DOI PMC
Foster M.. et al. Uncovering PFAS and other xenobiotics in the dark metabolome using ion mobility spectrometry, mass defect analysis, and machine learning. Environ. Sci. Technol. 2022;56:9133–9143. doi: 10.1021/acs.est.2c00201. PubMed DOI PMC
Jones O. A. H.. Illuminating the dark metabolome to advance the molecular characterisation of biological systems. Metabolomics. 2018;14:101. doi: 10.1007/s11306-018-1396-y. PubMed DOI
Carpenter, S. Putting a spotlight on the dark metabolome. Open Access Government. 2020, https://www.openaccessgovernment.org/putting-a-spotlight-on-the-dark-metabolome/82464/.
Janda M.. et al. Determination of abundant metabolite matrix adducts illuminates the dark metabolome of MALDI-mass spectrometry imaging datasets. Anal. Chem. 2021;93:8399–8407. doi: 10.1021/acs.analchem.0c04720. PubMed DOI PMC
Monge M. E., Dodds J. N., Baker E. S., Edison A. S., Fernández F. M.. Challenges in identifying the dark molecules of life. Annu. Rev. Anal. Chem. 2019;12:177–199. doi: 10.1146/annurev-anchem-061318-114959. PubMed DOI PMC
Stein S.. Mass spectral reference libraries: an ever-expanding resource for chemical identification. Anal. Chem. 2012;84:7274–7282. doi: 10.1021/ac301205z. PubMed DOI
Wang X.. et al. MS-RT: A method for evaluating MS/MS clustering performance for metabolomics data. J. Proteome Res. 2025;24:1778–1790. doi: 10.1021/acs.jproteome.4c00881. PubMed DOI PMC
Heuckeroth S.. et al. Reproducible mass spectrometry data processing and compound annotation in MZmine 3. Nat. Protoc. 2024;19:2597. doi: 10.1038/s41596-024-00996-y. PubMed DOI
Gloaguen Y., Kirwan J. A., Beule D.. Deep learning-assisted peak curation for large-scale LC-MS metabolomics. Anal. Chem. 2022;94:4930–4937. doi: 10.1021/acs.analchem.1c02220. PubMed DOI PMC
El Abiead Y.. et al. Power of mzRAPP-based performance assessments in MS1-based nontargeted feature detection. Anal. Chem. 2022;94:8588–8595. doi: 10.1021/acs.analchem.1c05270. PubMed DOI PMC
Lawson T. N.. et al. MsPurity: Automated evaluation of precursor ion purity for mass spectrometry-based fragmentation in metabolomics. Anal. Chem. 2017;89:2432–2439. doi: 10.1021/acs.analchem.6b04358. PubMed DOI
Stancliffe E., Schwaiger-Haber M., Sindelar M., Patti G. J.. DecoID improves identification rates in metabolomics through database-assisted MS/MS deconvolution. Nat. Methods. 2021;18:779–787. doi: 10.1038/s41592-021-01195-3. PubMed DOI PMC
Baran R.. Untargeted metabolomics suffers from incomplete raw data processing. Metabolomics. 2017;13:107. doi: 10.1007/s11306-017-1246-3. DOI
Vanderstraeten S., Searle A.. Biology’s dark matter: From galaxies to microbes. Theor. Cult. Soc. 2025;42:75–94. doi: 10.1177/02632764241304719. DOI
Najjar, D. Most Microbial Species Are ‘Dark Matter’. In Scientific American, 2019. PubMed
Osburn E. D., McBride S. G., Strickland M. S.. Microbial dark matter could add uncertainties to metagenomic trait estimations. Nat. Microbiol. 2024;9:1427–1430. doi: 10.1038/s41564-024-01687-w. PubMed DOI
Bellali S., Lagier J. C., Million M., Anani H., Haddad G., Francis R., Kuete Yimagou E., Khelaifia S., Levasseur A., Raoult D.. et al. Running after ghosts: are dead bacteria the dark matter of the human gut microbiota? Gut Microbes. 2021;13:1897208. doi: 10.1080/19490976.2021.1897208. PubMed DOI PMC
Solden L., Lloyd K., Wrighton K.. The bright side of microbial dark matter: lessons learned from the uncultivated majority. Curr. Opin. Microbiol. 2016;31:217–226. doi: 10.1016/j.mib.2016.04.020. PubMed DOI
Lynch M. D. J., Neufeld J. D.. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 2015;13:217–229. doi: 10.1038/nrmicro3400. PubMed DOI
Zhan A., Xiong W., He S., Macisaac H. J.. Influence of artifact removal on rare species recovery in natural complex communities using high-throughput sequencing. PLoS One. 2014;9:e96928. doi: 10.1371/journal.pone.0096928. PubMed DOI PMC
Huse S. M., Welch D. M., Morrison H. G., Sogin M. L.. Ironing out the wrinkles in the rare biosphere through improved OTU clustering: Ironing out the wrinkles in the rare biosphere. Environ. Microbiol. 2010;12:1889–1898. doi: 10.1111/j.1462-2920.2010.02193.x. PubMed DOI PMC
Samusevich R.. et al. Discovery and characterization of terpene synthases powered by machine learning. bioRxiv. 2024:2024.01.29.577750. doi: 10.1101/2024.01.29.577750. DOI
Lowe, D. Phantom Metabolites? Science. 2024, https://www.science.org/content/blog-post/phantom-metabolites.
Chen L., Pan H., Zhai G., Luo Q., Li Y., Fang C., Shi F.. Widespread occurrence of in-source fragmentation in the analysis of natural compounds by liquid chromatography-electrospray ionization mass spectrometry. Rapid Commun. Mass Spectrom. 2023;37:e9519. doi: 10.1002/rcm.9519. PubMed DOI
Guo J., Shen S., Xing S., Yu H., Huan T.. ISFrag: De Novo recognition of in-source fragments for liquid chromatography-mass spectrometry data. Anal. Chem. 2021;93:10243–10250. doi: 10.1021/acs.analchem.1c01644. PubMed DOI
Houriet J.. et al. Multilaboratory untargeted mass spectrometry metabolomics collaboration to identify bottlenecks and comprehensively annotate A single dataset. Anal. Chem. 2025;97:16110. doi: 10.1021/acs.analchem.4c05577. PubMed DOI PMC
Uritboonthai W., Hoang L., Aisporna A., Giera M., Siuzdak G.. The dark metabolome/lipidome and in-source fragmentation. Anal. Sci. Adv. 2025;6:e70012. doi: 10.1002/ansa.70012. PubMed DOI PMC
Criscuolo A., Zeller M., Fedorova M.. Evaluation of lipid in-source fragmentation on different Orbitrap-based mass spectrometers. J. Am. Soc. Mass Spectrom. 2020;31:463–466. doi: 10.1021/jasms.9b00061. PubMed DOI
Gabelica V., Pauw E. D.. Internal energy and fragmentation of ions produced in electrospray sources. Mass Spectrom. Rev. 2005;24:566–587. doi: 10.1002/mas.20027. PubMed DOI
Abrankó L., García-Reyes J. F., Molina-Díaz A.. In-source fragmentation and accurate mass analysis of multiclass flavonoid conjugates by electrospray ionization time-of-flight mass spectrometry. J. Mass Spectrom. 2011;46:478–488. doi: 10.1002/jms.1914. PubMed DOI
Hoang C.. et al. Tandem mass spectrometry across platforms. Anal. Chem. 2024;96:5478–5488. doi: 10.1021/acs.analchem.3c05576. PubMed DOI PMC
Rappaport S. M.. Genetic factors are not the major causes of chronic diseases. PLoS One. 2016;11:e0154387. doi: 10.1371/journal.pone.0154387. PubMed DOI PMC
Blanco, A. ; Blanco, G. . Amino Acid Metabolism. In Medical Biochemistry; Elsevier, 2017; pp 367–399.
Li G., Li Z., Liu J.. Amino acids regulating skeletal muscle metabolism: mechanisms of action, physical training dosage recommendations and adverse effects. Nutr. Metab. 2024;21:41. doi: 10.1186/s12986-024-00820-0. PubMed DOI PMC
Reddy, P. ; Jialal, I. . Biochemistry, fat soluble vitamins. In StatPearls; StatPearls Publishing: Treasure Island, FL, 2025. PubMed
Saini R. K., Keum Y.-S.. Omega-3 and omega-6 polyunsaturated fatty acids: Dietary sources, metabolism, and significance - A review. Life Sci. 2018;203:255–267. doi: 10.1016/j.lfs.2018.04.049. PubMed DOI
Ansari N. A., Rasheed Z.. Non-enzymatic glycation of proteins: From diabetes to cancer. Biochem. Moscow Suppl. Ser. B. 2009;3:335–342. doi: 10.1134/S1990750809040027. PubMed DOI
Lapolla A., Traldi P., Fedele D.. Importance of measuring products of non-enzymatic glycation of proteins. Clin. Biochem. 2005;38:103–115. doi: 10.1016/j.clinbiochem.2004.09.007. PubMed DOI
Mohanty I.. et al. The underappreciated diversity of bile acid modifications. Cell. 2024;187:1801–1818.e20. doi: 10.1016/j.cell.2024.02.019. PubMed DOI PMC
Hu C.-W.. et al. A Novel Adductomics Workflow Incorporating FeatureHunter Software: Rapid Detection of Nucleic Acid Modifications for Studying the Exposome. Environ. Sci. Technol. 2024;58:75–89. doi: 10.1021/acs.est.3c04674. PubMed DOI PMC
Mohanty I.. et al. The changing metabolic landscape of bile acids - keys to metabolism and immune regulation. Nat. Rev. Gastroenterol. Hepatol. 2024;21:493. doi: 10.1038/s41575-024-00914-3. PubMed DOI PMC
Nie Q.. et al. Gut symbionts alleviate MASH through a secondary bile acid biosynthetic pathway. Cell. 2024;187:2717–2734.e33. doi: 10.1016/j.cell.2024.03.034. PubMed DOI
Mullowney M. W., Fiebig A., Schnizlein M. K., McMillin M., Rose A. R., Koval J., Rubin D., Dalal S., Sogin M. L., Chang E. B.. et al. Microbially catalyzed conjugation of GABA and tyramine to bile acids. J. Bacteriol. 2024;206:e00426-23. doi: 10.1128/jb.00426-23. PubMed DOI PMC
Agongo J.. et al. Discovery and identification of three homocysteine metabolites by chemical derivatization and mass spectrometry fragmentation. Anal. Chem. 2024;96:11639–11643. doi: 10.1021/acs.analchem.4c01706. PubMed DOI PMC
Wang X., Yu N., Jiao Z., Li L., Yu H., Wei S.. Machine learning-enhanced molecular network reveals global exposure to hundreds of unknown PFAS. Sci. Adv. 2024;10:eadn1039. doi: 10.1126/sciadv.adn1039. PubMed DOI PMC
Jansen R. S.. et al. N-lactoyl-amino acids are ubiquitous metabolites that originate from CNDP2-mediated reverse proteolysis of lactate and amino acids. Proc. Natl. Acad. Sci. U.S.A. 2015;112:6601–6606. doi: 10.1073/pnas.1424638112. PubMed DOI PMC
Elloumi A., Mas-Normand L., Bride J., Reversat G., Bultel-Poncé V., Guy A., Oger C., Demion M., Le Guennec J. Y., Durand T.. et al. From MS/MS library implementation to molecular networks: Exploring oxylipin diversity with NEO-MSMS. Sci. Data. 2024;11:193. doi: 10.1038/s41597-024-03034-4. PubMed DOI PMC
Yuan B.. et al. Discovery of N-acyl amino acids and novel related N-, O-acyl lipids by integrating molecular networking and an extended in silico spectral library. Anal. Chem. 2023;95:8443–8451. doi: 10.1021/acs.analchem.2c04822. PubMed DOI
Ferrell M.. et al. A terminal metabolite of niacin promotes vascular inflammation and contributes to cardiovascular disease risk. Nat. Med. 2024;30:424–434. doi: 10.1038/s41591-023-02793-8. PubMed DOI PMC
Elmassry M. M.. et al. A meta-analysis of the gut microbiome in inflammatory bowel disease patients identifies disease-associated small molecules. Cell Host Microbe. 2025;33:218–234.e12. doi: 10.1016/j.chom.2025.01.002. PubMed DOI PMC
Liu C.. et al. Gut commensal Christensenella minuta modulates host metabolism via acylated secondary bile acids. Nat. Microbiol. 2024;9:434–450. doi: 10.1038/s41564-023-01570-0. PubMed DOI
Qiang H.. et al. Language model-guided anticipation and discovery of unknown metabolites. bioRxiv. 2024:2024.11.13.623458. doi: 10.1101/2024.11.13.623458. DOI
Mannochio-Russo H.. et al. The microbiome diversifies N-acyl lipid pools - including short-chain fatty acid-derived compounds. bioRxiv. 2024:2024.10.31.621412. doi: 10.1101/2024.10.31.621412. DOI
Nijdam F. B.. et al. Pharmacometabolomics enables real-world drug metabolism sciences. Metabolites. 2025;15:39. doi: 10.3390/metabo15010039. PubMed DOI PMC
Cho S.. et al. Discovery of unprecedented human stercobilin conjugates. Drug Metab. Dispos. 2024;52:981–987. doi: 10.1124/dmd.124.001725. PubMed DOI
Sheokand P. K., James A. M., Jenkins B., K. Lysyganicz P., Lacabanne D., King M. S., Kunji E. R. S., Siniossoglou S., Koulman A., Murphy M. P.. et al. TRAM-LAG1-CLN8 family proteins are acyltransferases regulating phospholipid composition. Sci. Adv. 2025;11:eadr3723. doi: 10.1126/sciadv.adr3723. PubMed DOI PMC
Swainston N., Smallbone K., Hefzi H., Dobson P. D., Brewer J., Hanscho M., Zielinski D. C., Ang K. S., Gardiner N. J., Gutierrez J. M.. et al. Recon 2.2: from reconstruction to model of human metabolism. Metabolomics. 2016;12:109. doi: 10.1007/s11306-016-1051-4. PubMed DOI PMC
Orth J. D., Conrad T. M., Na J., Lerman J. A., Nam H., Feist A. M., Palsson B. Ø.. A comprehensive genome-scale reconstruction of Escherichia coli metabolism–2011. Mol. Syst. Biol. 2011;7:535. doi: 10.1038/msb.2011.65. PubMed DOI PMC
El Abiead Y., Strobel M., Payne T., Fahy E., O’Donovan C., Subramamiam S., Vizcaíno J. A., Yurekten O., Deleray V., Zuffa S.. et al. Enabling pan-repository reanalysis for big data science of public metabolomics data. Nat. Commun. 2025;16:4838. doi: 10.1038/s41467-025-60067-y. PubMed DOI PMC
Clark T. N.. et al. Interlaboratory comparison of untargeted mass spectrometry data uncovers underlying causes for variability. J. Nat. Prod. 2021;84:824–835. doi: 10.1021/acs.jnatprod.0c01376. PubMed DOI PMC
Gao Y., Luo M., Wang H., Zhou Z., Yin Y., Wang R., Xing B., Yang X., Cai Y., Zhu Z. J.. Charting unknown metabolic reactions by mass spectrometry-resolved stable-isotope tracing metabolomics. Nat. Commun. 2025;16:5059. doi: 10.1038/s41467-025-60258-7. PubMed DOI PMC
Mahieu N. G., Patti G. J.. Systems-level annotation of a metabolomics data set reduces 25 000 features to fewer than 1000 unique metabolites. Anal. Chem. 2017;89:10397–10406. doi: 10.1021/acs.analchem.7b02380. PubMed DOI PMC
Wang L.. et al. Peak Annotation and Verification Engine for untargeted LC-MS metabolomics. Anal. Chem. 2019;91:1838–1846. doi: 10.1021/acs.analchem.8b03132. PubMed DOI PMC
Pandya C., Farelli J. D., Dunaway-Mariano D., Allen K. N.. Enzyme promiscuity: engine of evolutionary innovation. J. Biol. Chem. 2014;289:30229–30236. doi: 10.1074/jbc.R114.572990. PubMed DOI PMC
Khersonsky O., Roodveldt C., Tawfik D. S.. Enzyme promiscuity: evolutionary and mechanistic aspects. Curr. Opin. Chem. Biol. 2006;10:498–508. doi: 10.1016/j.cbpa.2006.08.011. PubMed DOI
Chi Y.. et al. Constructing a consensus serum metabolome. bioRxiv. 2025:2025.05.07.652782. doi: 10.1101/2025.05.07.652782. DOI
Koppel N., Maini Rekdal V., Balskus E. P.. Chemical transformation of xenobiotics by the human gut microbiota. Science. 2017;356:eaag2770. doi: 10.1126/science.aag2770. PubMed DOI PMC
Bishai J. D., Palm N. W.. Small molecule metabolites at the host-Microbiota interface. J. Immunol. 2021;207:1725–1733. doi: 10.4049/jimmunol.2100528. PubMed DOI PMC
Steed A. L.. et al. The microbial metabolite desaminotyrosine protects from influenza through type I interferon. Science. 2017;357:498–502. doi: 10.1126/science.aam5336. PubMed DOI PMC
Hsiao E. Y.. et al. Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell. 2013;155:1451–1463. doi: 10.1016/j.cell.2013.11.024. PubMed DOI PMC
Nakatsuji T., Chen T. H., Narala S., Chun K. A., Two A. M., Yun T., Shafiq F., Kotol P. F., Bouslimani A., Melnik A. V.. et al. Antimicrobials from human skin commensal bacteria protect against Staphylococcus aureus and are deficient in atopic dermatitis. Sci. Transl. Med. 2017;9:eaah4680. doi: 10.1126/scitranslmed.aah4680. PubMed DOI PMC
Claesen J., Spagnolo J. B., Ramos S. F., Kurita K. L., Byrd A. L., Aksenov A. A., Melnik A. V., Wong W. R., Wang S., Hernandez R. D.. et al. A Cutibacterium acnes antibiotic modulates human skin microbiota composition in hair follicles. Sci. Transl. Med. 2020;12:eaay5445. doi: 10.1126/scitranslmed.aay5445. PubMed DOI PMC
Jain A.. et al. Comparison of two arylsulfatases for targeted mass spectrometric analysis of microbiota-derived metabolites. J. Pharm. Biomed. Anal. 2021;195:113818. doi: 10.1016/j.jpba.2020.113818. PubMed DOI
Pascal Andreu V., Roel-Touris J., Dodd D., Fischbach M. A., Medema M. H.. The gutSMASH web server: automated identification of primary metabolic gene clusters from the gut microbiota. Nucleic Acids Res. 2021;49:W263–W270. doi: 10.1093/nar/gkab353. PubMed DOI PMC
Guo C.-J.. et al. Discovery of reactive Microbiota-derived metabolites that inhibit host proteases. Cell. 2017;168:517–526.e18. doi: 10.1016/j.cell.2016.12.021. PubMed DOI PMC
Kim J. Y., Whon T. W., Lim M. Y., Kim Y. B., Kim N., Kwon M. S., Kim J., Lee S. H., Choi H. J., Nam I. H.. et al. The human gut archaeome: identification of diverse haloarchaea in Korean subjects. Microbiome. 2020;8:114. doi: 10.1186/s40168-020-00894-x. PubMed DOI PMC
Chibani C. M.. et al. A catalogue of 1,167 genomes from the human gut archaeome. Nat. Microbiol. 2022;7:48–61. doi: 10.1038/s41564-021-01020-9. PubMed DOI PMC
Gaci N., Borrel G., Tottey W., O’Toole P. W., Brugère J.-F.. Archaea and the human gut: new beginning of an old story. World J. Gastroenterol. 2014;20:16062–16078. doi: 10.3748/wjg.v20.i43.16062. PubMed DOI PMC
Robey M. T., Caesar L. K., Drott M. T., Keller N. P., Kelleher N. L.. An interpreted atlas of biosynthetic gene clusters from 1,000 fungal genomes. Proc. Natl. Acad. Sci. U.S.A. 2021;118:e2020230118. doi: 10.1073/pnas.2020230118. PubMed DOI PMC
Mogilnicka I., Ufnal M.. Gut mycobiota and fungal metabolites in human homeostasis. Curr. Drug Targets. 2018;20:232–240. doi: 10.2174/1389450119666180724125020. PubMed DOI
Llinás-Caballero K., Caraballo L.. Helminths and bacterial Microbiota: The interactions of two of humans’ ‘old friends’. Int. J. Mol. Sci. 2022;23:13358. doi: 10.3390/ijms232113358. PubMed DOI PMC
Walusimbi B., Lawson M. A. E., Nassuuna J., Kateete D. P., Webb E. L., Grencis R. K., Elliott A. M.. The effects of helminth infections on the human gut microbiome: a systematic review and meta-analysis. Front. Microbiomes. 2023;2:1174034. doi: 10.3389/frmbi.2023.1174034. DOI
Dragoš A.. et al. Phages carry interbacterial weapons encoded by biosynthetic gene clusters. Curr. Biol. 2021;31:3479–3489.e5. doi: 10.1016/j.cub.2021.05.046. PubMed DOI
Jamet A., Touchon M., Ribeiro-Gonçalves B., Carriço J. A., Charbit A., Nassif X., Ramirez M., Rocha E. P. C.. A widespread family of polymorphic toxins encoded by temperate phages. BMC Biol. 2017;15:75. doi: 10.1186/s12915-017-0415-1. PubMed DOI PMC
Guengerich F. P.. Cytochrome p450 and chemical toxicology. Chem. Res. Toxicol. 2008;21:70–83. doi: 10.1021/tx700079z. PubMed DOI
Alonso A.. et al. AStream: an R package for annotating LC/MS metabolomic data. Bioinformatics. 2011;27:1339–1340. doi: 10.1093/bioinformatics/btr138. PubMed DOI
Brown M.. et al. Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets. Bioinformatics. 2011;27:1108–1112. doi: 10.1093/bioinformatics/btr079. PubMed DOI PMC
Kuhl C., Tautenhahn R., Böttcher C., Larson T. R., Neumann S.. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 2012;84:283–289. doi: 10.1021/ac202450g. PubMed DOI PMC
Kachman M.. et al. Deep annotation of untargeted LC-MS metabolomics data with Binner. Bioinformatics. 2020;36:1801–1806. doi: 10.1093/bioinformatics/btz798. PubMed DOI PMC
Senan O.. et al. CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network. Bioinformatics. 2019;35:4089–4097. doi: 10.1093/bioinformatics/btz207. PubMed DOI PMC
Kouřil S. ˇ., de Sousa J., Václavík J., Friedecký D., Adam T.. CROP: correlation-based reduction of feature multiplicities in untargeted metabolomic data. Bioinformatics. 2020;36:2941–2942. doi: 10.1093/bioinformatics/btaa012. PubMed DOI
DeFelice B. C.. et al. Mass Spectral Feature List Optimizer (MS-FLO): A tool to minimize false positive peak reports in untargeted liquid chromatography-mass spectroscopy (LC-MS) data processing. Anal. Chem. 2017;89:3250–3255. doi: 10.1021/acs.analchem.6b04372. PubMed DOI PMC
Schmid R., Petras D., Nothias L. F., Wang M., Aron A. T., Jagels A., Tsugawa H., Rainer J., Garcia-Aloy M., Dührkop K.. et al. Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment. Nat. Commun. 2021;12:3832. doi: 10.1038/s41467-021-23953-9. PubMed DOI PMC
Li S., Zheng S.. Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data. Anal. Chem. 2023;95:6212–6217. doi: 10.1021/acs.analchem.2c05810. PubMed DOI PMC
Yu H., Ding J., Shen T., Liu M., Li Y., Fiehn O.. MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers. Nat. Commun. 2025;16:5487. doi: 10.1038/s41467-025-60640-5. PubMed DOI PMC
Gaquerel E., Kuhl C., Neumann S.. Computational annotation of plant metabolomics profiles via a novel network-assisted approach. Metabolomics. 2013;9:904–918. doi: 10.1007/s11306-013-0504-2. DOI
Broeckling C. D., Afsar F. A., Neumann S., Ben-Hur A., Prenni J. E.. RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data. Anal. Chem. 2014;86:6812–6817. doi: 10.1021/ac501530d. PubMed DOI
Domingo-Almenara X.. et al. Autonomous METLIN-guided in-source fragment annotation for untargeted metabolomics. Anal. Chem. 2019;91:3246–3253. doi: 10.1021/acs.analchem.8b03126. PubMed DOI PMC
Su X.. et al. In-source CID ramping and covariant ion analysis of hydrophilic interaction chromatography metabolomics. Anal. Chem. 2020;92:4829–4837. doi: 10.1021/acs.analchem.9b04181. PubMed DOI PMC
Lu W.. et al. Improved annotation of untargeted metabolomics data through buffer modifications that shift adduct mass and intensity. Anal. Chem. 2020;92:11573–11581. doi: 10.1021/acs.analchem.0c00985. PubMed DOI PMC
Aron A. T.. et al. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat. Protoc. 2020;15:1954–1991. doi: 10.1038/s41596-020-0317-5. PubMed DOI
Wang M.. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 2016;34:828–837. doi: 10.1038/nbt.3597. PubMed DOI PMC
Watrous J., Roach P., Alexandrov T., Heath B. S., Yang J. Y., Kersten R. D., van der Voort M., Pogliano K., Gross H., Raaijmakers J. M.. et al. Mass spectral molecular networking of living microbial colonies. Proc. Natl. Acad. Sci. U.S.A. 2012;109:E1743–E1752. doi: 10.1073/pnas.1203689109. PubMed DOI PMC
Treen D. G. C., Wang M., Xing S., Louie K. B., Huan T., Dorrestein P. C., Northen T. R., Bowen B. P.. SIMILE enables alignment of tandem mass spectra with statistical significance. Nat. Commun. 2022;13:2510. doi: 10.1038/s41467-022-30118-9. PubMed DOI PMC
Li Y.. et al. Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification. Nat. Methods. 2021;18:1524–1531. doi: 10.1038/s41592-021-01331-z. PubMed DOI PMC
Aisporna A.. et al. Neutral loss mass spectral data enhances molecular similarity analysis in METLIN. J. Am. Soc. Mass Spectrom. 2022;33:530–534. doi: 10.1021/jasms.1c00343. PubMed DOI PMC
Abramson F. P.. Automated identification of mass spectra by the reverse search. Anal. Chem. 1975;47:45–49. doi: 10.1021/ac60351a028. DOI
Huber F.. et al. Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships. PLoS Comput. Biol. 2021;17:e1008724. doi: 10.1371/journal.pcbi.1008724. PubMed DOI PMC
Huber F., van der Burg S., van der Hooft J. J. J., Ridder L.. MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra. J. Cheminform. 2021;13:84. doi: 10.1186/s13321-021-00558-4. PubMed DOI PMC
Engler Hart C., Kind T., Dorrestein P. C., Healey D., Domingo-Fernández D.. Weighting low-intensity MS/MS ions and m/z frequency for spectral library annotation. J. Am. Soc. Mass Spectrom. 2024;35:266–274. doi: 10.1021/jasms.3c00353. PubMed DOI PMC
Bolyen E.. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 2019;37:852–857. doi: 10.1038/s41587-019-0209-9. PubMed DOI PMC
de Jonge N. F., Hecht H., Strobel M., Wang M., van der Hooft J. J. J., Huber F.. Reproducible MS/MS library cleaning pipeline in matchms. J. Cheminform. 2024;16:88. doi: 10.1186/s13321-024-00878-1. PubMed DOI PMC
Chen L.. et al. Metabolite discovery through global annotation of untargeted metabolomics data. Nat. Methods. 2021;18:1377–1385. doi: 10.1038/s41592-021-01303-3. PubMed DOI PMC
Shen X., Wang R., Xiong X., Yin Y., Cai Y., Ma Z., Liu N., Zhu Z. J.. Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics. Nat. Commun. 2019;10:1516. doi: 10.1038/s41467-019-09550-x. PubMed DOI PMC
Zhou Z., Luo M., Zhang H., Yin Y., Cai Y., Zhu Z. J.. Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking. Nat. Commun. 2022;13:6656. doi: 10.1038/s41467-022-34537-6. PubMed DOI PMC
Wang X.. et al. Enhanced structure-guided molecular networking annotation method for untargeted metabolomics data from Orbitrap Astral mass spectrometer. Anal. Chem. 2025;97:11506–11514. doi: 10.1021/acs.analchem.5c00314. PubMed DOI
Tsugawa H.. et al. A lipidome atlas in MS-DIAL 4. Nat. Biotechnol. 2020;38:1159–1163. doi: 10.1038/s41587-020-0531-2. PubMed DOI
Olivon F.. et al. MetGem software for the generation of molecular networks based on the t-SNE algorithm. Anal. Chem. 2018;90:13900–13908. doi: 10.1021/acs.analchem.8b03099. PubMed DOI
Bazzano C. F.. et al. NP3MS Workflow: An open-source software system to empower natural product-based drug discovery using untargeted metabolomics. Anal. Chem. 2024;96:7460–7469. doi: 10.1021/acs.analchem.3c05829. PubMed DOI PMC
The R Project for Statistical Computing. https://www.R-project.org/ (accessed 10/20/2025).
Research Portal. https://hdl.handle.net/10863/44744 (accessed 10/20/2025).
van Rijswijk M., Beirnaert C., Caron C., Cascante M., Dominguez V., Dunn W. B., Ebbels T. M. D., Giacomoni F., Gonzalez-Beltran A., Hankemeier T.. et al. The future of metabolomics in ELIXIR. F1000Res. 2017;6:1649. doi: 10.12688/f1000research.12342.2. PubMed DOI PMC
Guitton Y.. et al. Create, run, share, publish, and reference your LC-MS, FIA-MS, GC-MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics. Int. J. Biochem. Cell Biol. 2017;93:89–101. doi: 10.1016/j.biocel.2017.07.002. PubMed DOI
Rainer J.. et al. A modular and expandable ecosystem for metabolomics data annotation in R. Metabolites. 2022;12:173. doi: 10.3390/metabo12020173. PubMed DOI PMC
Van Rossum, G. ; Drake, F. L., Jr. . Python 3 Reference Manual; Createspace, 2009.
Liu B., Tang Z., Huan T.. Adduct-induced variability in tandem mass spectrometry. Anal. Chem. 2025;97:17058. doi: 10.1021/acs.analchem.5c02792. PubMed DOI
Reher R., Aron A. T., Fajtová P., Stincone P., Wagner B., Pérez-Lorente A. I., Liu C., Shalom I. Y. B., Bittremieux W., Wang M.. et al. Native metabolomics identifies the rivulariapeptolide family of protease inhibitors. Nat. Commun. 2022;13:4619. doi: 10.1038/s41467-022-32016-6. PubMed DOI PMC
Aron A. T.. et al. Native mass spectrometry-based metabolomics identifies metal-binding compounds. Nat. Chem. 2022;14:100–109. doi: 10.1038/s41557-021-00803-1. PubMed DOI PMC
Leney A. C., Heck A. J. R.. Native Mass Spectrometry: What is in the Name? J. Am. Soc. Mass Spectrom. 2017;28:5–13. doi: 10.1007/s13361-016-1545-3. PubMed DOI PMC
Savitski M. M., Kjeldsen F., Nielsen M. L., Zubarev R. A.. Relative specificities of water and ammonia losses from backbone fragments in collision-activated dissociation. J. Proteome Res. 2007;6:2669–2673. doi: 10.1021/pr070121z. PubMed DOI
NIST23 . NIST23. https://www.nist.gov/programs-projects/nist20-updates-nist-tandem-and-electron-ionization-spectral-libraries (accessed 10/20/2025).
MassBank of North America. https://mona.fiehnlab.ucdavis.edu/ (accessed 10/20/2025).
mzCloud – Advanced Mass Spectral Database. https://www.mzcloud.org/ (accessed 10/20/2025).
Horai H.. et al. MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 2010;45:703–714. doi: 10.1002/jms.1777. PubMed DOI
Zhou Y.. et al. Bottom-up structural analysis of amides by identifying collision-induced dissociation fragment ions: An application in bile acid-amino acid conjugates-targeted sub-metabolome profiling. Anal. Chim. Acta. 2025;1367:344314. doi: 10.1016/j.aca.2025.344314. PubMed DOI
Baygi S. F., Kumar Y., Barupal D. K.. IDSL.CSA: Composite spectra analysis for chemical annotation of untargeted metabolomics datasets. Anal. Chem. 2023;95:9480–9487. doi: 10.1021/acs.analchem.3c00376. PubMed DOI PMC
Xue J.. et al. EISA-EXPOSOME: One highly sensitive and autonomous exposomic platform with enhanced in-source fragmentation/annotation. Anal. Chem. 2023;95:17228–17237. doi: 10.1021/acs.analchem.3c02697. PubMed DOI
Wang X.-C.. et al. AntDAS-DDA: A new platform for data-dependent acquisition mode-based untargeted metabolomic profiling analysis with advantage of recognizing insource fragment ions to improve compound identification. Anal. Chem. 2023;95:638–649. doi: 10.1021/acs.analchem.2c01795. PubMed DOI
Seitzer P. M., Searle B. C.. Incorporating in-source fragment information improves metabolite identification accuracy in untargeted LC-MS data sets. J. Proteome Res. 2019;18:791–796. doi: 10.1021/acs.jproteome.8b00601. PubMed DOI
Xing S.. et al. Structural annotation of full-scan MS data: A unified solution for LC-MS and MS imaging analyses. bioRxiv. 2025:2024.10.14.618269. doi: 10.1101/2024.10.14.618269. DOI
Baquer G., Sementé L., Ràfols P., Martín-Saiz L., Bookmeyer C., Fernández J. A., Correig X., García-Altares M.. rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation. J. Cheminform. 2023;15:80. doi: 10.1186/s13321-023-00756-2. PubMed DOI PMC
Xue J.. et al. Enhanced in-source fragmentation annotation enables novel data independent acquisition and autonomous METLIN molecular identification. Anal. Chem. 2020;92:6051–6059. doi: 10.1021/acs.analchem.0c00409. PubMed DOI PMC
Xing S., Charron-Lamoureux V., El Abiead Y., Dorrestein P. C.. Annotating full-scan MS data using tandem MS libraries. bioRxiv. 2024:2024.10.14.618269. doi: 10.1101/2024.10.14.618269. DOI
Palmer A., Alexandrov T.. et al. FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nat. Methods. 2017;14:57–60. doi: 10.1038/nmeth.4072. PubMed DOI
Barker M., Rayens W.. Partial least squares for discrimination. J. Chemom. 2003;17:166–173. doi: 10.1002/cem.785. DOI
Ho, T. K. Random decision forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition; IEEE Computer Society Press, 2002; Vol. 1, pp 278–282.
Li Q.. et al. GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis. Bioinformatics. 2020;36:257–263. doi: 10.1093/bioinformatics/btz488. PubMed DOI PMC
Xu Y., Goodacre R.. Mind your Ps and Qs – Caveats in metabolomics data analysis. Trends Anal. Chem. 2025;183:118064. doi: 10.1016/j.trac.2024.118064. DOI
Sun J., Xia Y.. Pretreating and normalizing metabolomics data for statistical analysis. Genes Dis. 2024;11:100979. doi: 10.1016/j.gendis.2023.04.018. PubMed DOI PMC
Benjamini Y., Hochberg Y.. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc., B. 1995;57:289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x. DOI
Sands C. J.. et al. Representing the metabolome with high fidelity: Range and response as quality control factors in LC-MS-based global profiling. Anal. Chem. 2021;93:1924–1933. doi: 10.1021/acs.analchem.0c03848. PubMed DOI
Suvitaival T., Rogers S., Kaski S.. Stronger findings from mass spectral data through multi-peak modeling. BMC Bioinf. 2014;15:208. doi: 10.1186/1471-2105-15-208. PubMed DOI PMC
Mahalanobis P. C.. Reprint of Mahalanobis, P.c. 1936 ‘on the generalised distance in statistics.’. Sankhya, Ser. A. 2018;80:1–7. doi: 10.1007/s13171-019-00164-5. DOI
Gregorutti B., Michel B., Saint-Pierre P.. Correlation and variable importance in random forests. Stat. Comput. 2017;27:659–678. doi: 10.1007/s11222-016-9646-1. DOI
Avalon N. E.. et al. Leptochelins A-C, cytotoxic metallophores produced by geographically dispersed Leptothoe strains of marine Cyanobacteria. J. Am. Chem. Soc. 2024;146:18626–18638. doi: 10.1021/jacs.4c05399. PubMed DOI PMC
Houriet J., Vidar W. S., Manwill P. K., Todd D. A., Cech N. B.. How low can you go? Selecting intensity thresholds for untargeted metabolomics data preprocessing. Anal. Chem. 2022;94:17964–17971. doi: 10.1021/acs.analchem.2c04088. PubMed DOI
Woodall D. W.. et al. Melting of Hemoglobin in Native Solutions as measured by IMS-MS. Anal. Chem. 2020;92:3440–3446. doi: 10.1021/acs.analchem.9b05561. PubMed DOI PMC
Frańska M., Stȩżycka O., Jankowski W., Hoffmann M.. Gas-phase internal ribose residue loss from Mg-ATP and Mg-ADP complexes: Experimental and theoretical evidence for phosphate-Mg-adenine interaction. J. Am. Soc. Mass Spectrom. 2022;33:1474–1479. doi: 10.1021/jasms.2c00071. PubMed DOI PMC
Ganapathy V., Thangaraju M., Gopal E., Martin P. M., Itagaki S., Miyauchi S., Prasad P. D.. Sodium-coupled monocarboxylate transporters in normal tissues and in cancer. AAPS J. 2008;10:193–199. doi: 10.1208/s12248-008-9022-y. PubMed DOI PMC
Giné R.. et al. HERMES: a molecular-formula-oriented method to target the metabolome. Nat. Methods. 2021;18:1370–1376. doi: 10.1038/s41592-021-01307-z. PubMed DOI PMC
Yurekten O.. et al. MetaboLights: open data repository for metabolomics. Nucleic Acids Res. 2024;52:D640–D646. doi: 10.1093/nar/gkad1045. PubMed DOI PMC
Sud M.. et al. Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res. 2016;44:D463–D470. doi: 10.1093/nar/gkv1042. PubMed DOI PMC
Chae W., Cho J.-Y., Kang K. B.. Metabolomics Data Curation Center (MDCC) Introducing Korea metabolomics data repository (KMAP): bridging Korean metabolomics data to global data sharing infrastructure. Metabolomics. 2025;21:86. doi: 10.1007/s11306-025-02285-5. PubMed DOI PMC
European Organization for Nuclear Research & OpenAIRE; Zenodo, 2013.
MetaboBank. https://www.ddbj.nig.ac.jp/metabobank/index-e.html (accessed 10/20/2025).
Jasny B. R.. Realities of data sharing using the genome wars as case study - an historical perspective and commentary. EPJ Data Sci. 2013;2:1. doi: 10.1140/epjds13. DOI
Maxson Jones K., Ankeny R. A., Cook-Deegan R.. The Bermuda triangle: The pragmatics, policies, and principles for data sharing in the history of the Human Genome Project. J. Hist. Biol. 2018;51:693–805. doi: 10.1007/s10739-018-9538-7. PubMed DOI PMC
Kaye J., Heeney C., Hawkins N., de Vries J., Boddington P.. Data sharing in genomics–re-shaping scientific practice. Nat. Rev. Genet. 2009;10:331–335. doi: 10.1038/nrg2573. PubMed DOI PMC
Goeddel L. C., Patti G. J.. Maximizing the value of metabolomic data. Bioanalysis. 2012;4:2199–2201. doi: 10.4155/bio.12.210. PubMed DOI
Gentry E. C.. et al. Reverse metabolomics for the discovery of chemical structures from humans. Nature. 2024;626:419–426. doi: 10.1038/s41586-023-06906-8. PubMed DOI PMC
Mannochio-Russo H.. et al. The microbiome diversifies long- to short-chain fatty acid-derived N-acyl lipids. Cell. 2025;188:4154–4169.e19. doi: 10.1016/j.cell.2025.05.015. PubMed DOI PMC
Bushuiev R., Bushuiev A., Samusevich R., Brungs C., Sivic J., Pluskal T.. Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS. Nat. Biotechnol. 2025:1–11. doi: 10.1038/s41587-025-02663-3. PubMed DOI
Witting M.. (Re-)use and (re-)analysis of publicly available metabolomics data. Proteomics. 2023;23:e2300032. doi: 10.1002/pmic.202300032. PubMed DOI
Ding J., Ji J., Rabow Z., Shen T., Folz J., Brydges C. R., Fan S., Lu X., Mehta S., Showalter M. R.. et al. A metabolome atlas of the aging mouse brain. Nat. Commun. 2021;12:6021. doi: 10.1038/s41467-021-26310-y. PubMed DOI PMC
Yu D.. et al. A multi-tissue metabolome atlas of primate pregnancy. Cell. 2024;187:764–781.e14. doi: 10.1016/j.cell.2023.11.043. PubMed DOI
Bae H., Jung S., Le J., Tamburini I., Kim J., Wang E., Song W. S., Jang K. H., Kang T., Lopez M.. et al. An atlas of inter-organ metabolite trafficking in health and atherogenic conditions. Soc. Sci. Res. Netw. 2024:ssrn.4869929. doi: 10.2139/ssrn.4869929. DOI
Zuffa S.. et al. microbeMASST: a taxonomically informed mass spectrometry search tool for microbial metabolomics data. Nat. Microbiol. 2024;9:336–345. doi: 10.1038/s41564-023-01575-9. PubMed DOI PMC
West K. A., Schmid R., Gauglitz J. M., Wang M., Dorrestein P. C.. foodMASST a mass spectrometry search tool for foods and beverages. npj Sci. Food. 2022;6:22. doi: 10.1038/s41538-022-00137-3. PubMed DOI PMC
Zhao H. N.. et al. Empirically establishing drug exposure records directly from untargeted metabolomics data. bioRxiv. 2024:2024.10.07.617109. doi: 10.1101/2024.10.07.617109. PubMed DOI PMC
Zuffa S., Allaband C., Charron-Lamoureux V., Caraballo-Rodriguez A. M., Patan A., Mohanty I., Agongo J., Bostick J. W., Connerly T. J., Thron T.. et al. A multi-organ Murine metabolomics atlas reveals molecular dysregulations in Alzheimer’s Disease. bioRxiv. 2025:2025.04.28.651123. doi: 10.1101/2025.04.28.651123. DOI
Damiani T.. et al. A universal language for finding mass spectrometry data patterns. Nat. Methods. 2025;22:1247–1254. doi: 10.1038/s41592-025-02660-z. PubMed DOI PMC
Mongia M.. et al. Fast mass spectrometry search and clustering of untargeted metabolomics data. Nat. Biotechnol. 2024;42:1672–1677. doi: 10.1038/s41587-023-01985-4. PubMed DOI
Wang M.. et al. Mass spectrometry searches using MASST. Nat. Biotechnol. 2020;38:23–26. doi: 10.1038/s41587-019-0375-9. PubMed DOI PMC
Charron-Lamoureux V., Mannochio-Russo H., Lamichhane S., Xing S., Patan A., Portal Gomes P. W., Rajkumar P., Deleray V., Caraballo-Rodríguez A. M., Chua K. V.. et al. A guide to reverse metabolomics-a framework for big data discovery strategy. Nat. Protoc. 2025;20:2960–2993. doi: 10.1038/s41596-024-01136-2. PubMed DOI PMC
Deutsch E. W.. et al. Universal Spectrum Identifier for mass spectra. Nat. Methods. 2021;18:768–770. doi: 10.1038/s41592-021-01184-6. PubMed DOI PMC
Bittremieux W., Chen C., Dorrestein P. C., Schymanski E. L., Schulze T., Neumann S., Meier R., Rogers S., Wang M.. Universal MS/MS visualization and retrieval with the Metabolomics Spectrum Resolver web service. bioRxiv. 2020:2020.05.09.086066. doi: 10.1101/2020.05.09.086066. DOI
Li Y., Fiehn O.. Flash entropy search to query all mass spectral libraries in real time. Nat. Methods. 2023;20:1475–1478. doi: 10.1038/s41592-023-02012-9. PubMed DOI PMC
McClendon S., Zhadin N., Callender R.. The approach to the Michaelis complex in lactate dehydrogenase: the substrate binding pathway. Biophys. J. 2005;89:2024–2032. doi: 10.1529/biophysj.105.062604. PubMed DOI PMC
Grant L. K., Ftouni S., Nijagal B., De Souza D. P., Tull D., McConville M. J., Rajaratnam S. M. W., Lockley S. W., Anderson C.. Circadian and wake-dependent changes in human plasma polar metabolites during prolonged wakefulness: A preliminary analysis. Sci. Rep. 2019;9:4428. doi: 10.1038/s41598-019-40353-8. PubMed DOI PMC
Brunmair J., Gotsmy M., Niederstaetter L., Neuditschko B., Bileck A., Slany A., Feuerstein M. L., Langbauer C., Janker L., Zanghellini J.. et al. Finger sweat analysis enables short interval metabolic biomonitoring in humans. Nat. Commun. 2021;12:5993. doi: 10.1038/s41467-021-26245-4. PubMed DOI PMC
Li Z.. et al. Excretion profiles and half-lives of ten urinary polycyclic aromatic hydrocarbon metabolites after dietary exposure. Chem. Res. Toxicol. 2012;25:1452–1461. doi: 10.1021/tx300108e. PubMed DOI PMC
Truver M. T.. et al. Urinary pharmacokinetics of immediate and controlled release oxycodone and its phase I and II metabolites using LC-MS-MS. J. Anal. Toxicol. 2023;46:1025–1031. doi: 10.1093/jat/bkab123. PubMed DOI PMC
Yang H.. et al. Determination of ten antipsychotics in blood, hair and nails: Validation of a LC-MS/MS method and forensic application of keratinized matrix analysis. J. Pharm. Biomed. Anal. 2023;234:115557. doi: 10.1016/j.jpba.2023.115557. PubMed DOI
Jiang S.. et al. UPLC-MS/MS method for the simultaneous quantification of caffeine and illicit psychoactive drugs in hair using a single-step high-speed grinding extraction - Insights into a cut-off value for caffeine abuse. J. Pharm. Biomed. Anal. 2022;209:114489. doi: 10.1016/j.jpba.2021.114489. PubMed DOI
Kuwayama K.. et al. Time-course measurements of drug concentrations in hair and toenails after single administrations of pharmaceutical products: Time-course measurements of drug concentrations in hair and toenails. Drug Test. Anal. 2017;9:571–577. doi: 10.1002/dta.1991. PubMed DOI
Palmeri A., Pichini S., Pacifici R., Zuccaro P., Lopez A.. Drugs in nails: physiology, pharmacokinetics and forensic toxicology: Physiology, pharmacokinetics and forensic toxicology. Clin. Pharmacokinet. 2000;38:95–110. doi: 10.2165/00003088-200038020-00001. PubMed DOI
Jarmusch A. K.. et al. Enhanced characterization of drug metabolism and the influence of the intestinal microbiome: A pharmacokinetic, microbiome, and untargeted metabolomics study: Drug metabolism and the intestinal microbiome. Clin. Transl. Sci. 2020;13:972–984. doi: 10.1111/cts.12785. PubMed DOI PMC
Quinn R. A.. et al. Global chemical effects of the microbiome include new bile-acid conjugations. Nature. 2020;579:123–129. doi: 10.1038/s41586-020-2047-9. PubMed DOI PMC
Linington R. G.. An assessment of chemical diversity in microbial natural products. ACS Cent. Sci. 2025;11:1536. doi: 10.1021/acscentsci.5c00804. PubMed DOI PMC
Hoffmann N.. et al. MzTab-M: A data standard for sharing quantitative results in mass spectrometry metabolomics. Anal. Chem. 2019;91:3302–3310. doi: 10.1021/acs.analchem.8b04310. PubMed DOI PMC
Sumner L. W.. et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI): Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI) Metabolomics. 2007;3:211–221. doi: 10.1007/s11306-007-0082-2. PubMed DOI PMC
Schymanski E. L.. et al. Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environ. Sci. Technol. 2014;48:2097–2098. doi: 10.1021/es5002105. PubMed DOI
Journal of Cosmology.com. Journal of Cosmology. https://thejournalofcosmology.com/Abiogenesis113.html (accessed 10/20/2025).
Ball P.. Chemistry: what chemists want to know. Nature. 2006;442:500–502. doi: 10.1038/442500a. PubMed DOI
O’Hagan S., Kell D. B.. Analysing and navigating natural products space for generating small, diverse, but representative chemical libraries. Biotechnol. J. 2018;13:1700503. doi: 10.1002/biot.201700503. PubMed DOI
Liu X.. et al. DrugLLM: Open large language model for few-shot molecule generation. arXiv. 2024:arXiv:2405.06690. doi: 10.48550/arXiv.2405.06690. DOI
Ding Y.. et al. NaFM: Pre-training a foundation model for small-molecule natural products. arXiv. 2025:arXiv:2503.17656. doi: 10.48550/arXiv.2503.17656. DOI
Sakano K., Furui K., Ohue M.. NPGPT: Natural product-like compound generation with GPT-based chemical language models. arXiv. 2024:arXiv:2411.12886. doi: 10.48550/arXiv.2411.12886. DOI
Athersuch T.. Metabolome analyses in exposome studies: Profiling methods for a vast chemical space. Arch. Biochem. Biophys. 2016;589:177–186. doi: 10.1016/j.abb.2015.10.007. PubMed DOI
Uppal K.. et al. Computational metabolomics: A framework for the million metabolome. Chem. Res. Toxicol. 2016;29:1956–1975. doi: 10.1021/acs.chemrestox.6b00179. PubMed DOI PMC
Tierney B. T.. et al. The landscape of genetic content in the gut and oral human microbiome. Cell Host Microbe. 2019;26:283–295.e8. doi: 10.1016/j.chom.2019.07.008. PubMed DOI PMC
Zimmerman S., Tierney B. T., Patel C. J., Kostic A. D.. Quantifying shared and unique gene content across 17 microbial ecosystems. mSystems. 2023;8:e00118-23. doi: 10.1128/msystems.00118-23. PubMed DOI PMC
Pasolli E.. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell. 2019;176:649–662.e20. doi: 10.1016/j.cell.2019.01.001. PubMed DOI PMC
Barupal D. K., Fiehn O.. Generating the Blood Exposome Database using a comprehensive text mining and database fusion approach. Environ. Health Perspect. 2019;127:097008. doi: 10.1289/ehp4713. PubMed DOI PMC
Wishart D. S.. et al. HMDB 5.0: The Human Metabolome Database for 2022. Nucleic Acids Res. 2022;50:D622–D631. doi: 10.1093/nar/gkab1062. PubMed DOI PMC
Petras D.. et al. GNPS Dashboard: collaborative exploration of mass spectrometry data in the web browser. Nat. Methods. 2022;19:134–136. doi: 10.1038/s41592-021-01339-5. PubMed DOI PMC
Bittremieux W., Avalon N. E., Thomas S. P., Kakhkhorov S. A., Aksenov A. A., Gomes P. W. P., Aceves C. M., Caraballo-Rodríguez A. M., Gauglitz J. M., Gerwick W. H.. et al. Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics. Nat. Commun. 2023;14:8488. doi: 10.1038/s41467-023-44035-y. PubMed DOI PMC