Metabolite discovery through global annotation of untargeted metabolomics data
Metabolite discovery through global annotation of untargeted metabolomics data
About this item
Full title
Author / Creator
Chen, Li , Lu, Wenyun , Wang, Lin , Xing, Xi , Chen, Ziyang , Teng, Xin , Zeng, Xianfeng , Muscarella, Antonio D. , Shen, Yihui , Cowan, Alexis , McReynolds, Melanie R. , Kennedy, Brandon J. , Lato, Ashley M. , Campagna, Shawn R. , Singh, Mona , Rabinowitz, Joshua D. and CABBI, Urbana, IL (United States)
Publisher
New York: Nature Publishing Group US
Journal title
Language
English
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Publication information
Publisher
New York: Nature Publishing Group US
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More information
Scope and Contents
Contents
Liquid chromatography–high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion...
Alternative Titles
Full title
Metabolite discovery through global annotation of untargeted metabolomics data
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8733904
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8733904
Other Identifiers
ISSN
1548-7091,1548-7105
E-ISSN
1548-7105
DOI
10.1038/s41592-021-01303-3
