Literature DB >> 29381867

METLIN: A Technology Platform for Identifying Knowns and Unknowns.

Carlos Guijas, J Rafael Montenegro-Burke, Xavier Domingo-Almenara, Amelia Palermo, Benedikt Warth, Gerrit Hermann1, Gunda Koellensperger, Tao Huan, Winnie Uritboonthai, Aries E Aisporna, Dennis W Wolan, Mary E Spilker, H Paul Benton, Gary Siuzdak.   

Abstract

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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Year:  2018        PMID: 29381867      PMCID: PMC5933435          DOI: 10.1021/acs.analchem.7b04424

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  41 in total

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Journal:  J Cheminform       Date:  2017-03-27       Impact factor: 5.514

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  208 in total

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3.  Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics.

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Review 6.  Gut microbiota-derived metabolites as key actors in inflammatory bowel disease.

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7.  RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies.

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8.  Single-Cell Metabolomics by Mass Spectrometry Imaging.

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9.  Microbial Metabolomics: From Methods to Translational Applications.

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10.  Cognitive analysis of metabolomics data for systems biology.

Authors:  Erica L-W Majumder; Elizabeth M Billings; H Paul Benton; Richard L Martin; Amelia Palermo; Carlos Guijas; Markus M Rinschen; Xavier Domingo-Almenara; J Rafael Montenegro-Burke; Bradley A Tagtow; Robert S Plumb; Gary Siuzdak
Journal:  Nat Protoc       Date:  2021-01-22       Impact factor: 13.491

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