ArticleNature communications2022
Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 165 papers.
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Who cites it
165 citing papers in PubMed.
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- Trends in Computational Metabolomics: A Perspective on Five Years of Software Development, Challenges, and Opportunities (2021-2025).Analytical chemistry · 2026Review
- Evaluation of the Health Status of Largemouth Bass (Animals : an open access journal from MDPI · 2026Article
- The molecular ICU: a primer on omics, informatics and the future of precision critical care.Critical care (London, England) · 2026Review
- Non-Volatile Taste Profile Dynamics Across Developmental Stages ofFoods (Basel, Switzerland) · 2026Article
- Multi-Omics Reveal Extracellular Electron Transfer Mechanism Under Deep-Sea High Salinity.Environmental microbiology · 2026Article
105 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
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Abstract
Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.
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