The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
METLIN: A Technology Platform for Identifying Knowns and Unknowns
Open accessAbstract
METLIN originated as a database to characterize known metabolites and has since expanded into a technology platform for the identification of known and unknown metabolites and other chemical entities. Through this effort it has become a comprehensive resource containing over 1 million molecules including lipids, amino acids, carbohydrates, toxins, small peptides, and natural products, among other classes. METLIN's high-resolution tandem mass spectrometry (MS/MS) database, which plays a key role in the identification process, has data generated from both reference standards and their labeled stable isotope analogues, facilitated by METLIN-guided analysis of isotope-labeled microorganisms. The MS/MS data, coupled with the fragment similarity search function, expand the tool's capabilities into the identification of unknowns. Fragment similarity search is performed independent of the precursor mass, relying solely on the fragment ions to identify similar structures within the database. Stable isotope data also facilitate characterization by coupling the similarity search output with the isotopic m/ z shifts. Examples of both are demonstrated here with the characterization of four previously unknown metabolites. METLIN also now features in silico MS/MS data, which has been made possible through the creation of algorithms trained on METLIN's MS/MS data from both standards and their isotope analogues. With these informatic and experimental data features, METLIN is being designed to address the characterization of known and unknown molecules.
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Cites
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Self-citationMETLIN—a metabolite mass spectral database
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Gut microbiota-derived metabolites as key actors in inflammatory bowel disease
Lavelle, A., & Sokol, H. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-019-0258-z · Cited by 2,015
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Self-citationSystematic classification of unknown metabolites using high-resolution fragmentation mass spectra
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Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics
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Metadata
- Authors
- Guijas, C., Montenegro-Burke, J. R., Domingo-Almenara, X., Palermo, A., Warth, B., Hermann, G., Koellensperger, G., Huan, T., Uritboonthai, W., Aisporna, A. E., Wolan, D. W., Spilker, M. E., Benton, H. P., & Siuzdak, G.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-01-30
- Volume
- 90
- Issue
- 5
- Pages
- 3156-3164
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2786394271
- Cited by
- 927
- References
- 45
- FWCI
- 36.18
- Type
- journal article
- Language
- en
- Open access
- green
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- Guijas, C., Montenegro-Burke, J. R., Domingo-Almenara, X., Palermo, A., Warth, B., Hermann, G., Koellensperger, G., Huan, T., Uritboonthai, W., Aisporna, A. E., Wolan, D. W., Spilker, M. E., Benton, H. P., & Siuzdak, G. (2018). METLIN: A Technology Platform for Identifying Knowns and Unknowns. Analytical Chemistry. https://doi.org/10.1021/acs.analchem.7b04424
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
