Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking
Chinese Academy of Sciences · Shanghai Institute of Organic Chemistry · +2 more institutions
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,…
Citation impact
- FWCI
- 19.84
- Percentile
- 100%
- References
- 78
Authors
6- ZZZhiwei ZhouCorresponding
Chinese Academy of Sciences, Shanghai Institute of Organic Chemistry
- MLMingdu Luo
Chinese Academy of Sciences, Shanghai Institute of Organic Chemistry, University of Chinese Academy of Sciences
- HZHaosong Zhang
Chinese Academy of Sciences, Shanghai Institute of Organic Chemistry, University of Chinese Academy of Sciences
- YYYandong Yin
Chinese Academy of Sciences, Shanghai Institute of Organic Chemistry
- YCYuping Cai
Chinese Academy of Sciences, Shanghai Institute of Organic Chemistry
Topics & keywords
- Metabolomics
- Metabolome
- Metabolite
- Annotation
- In silico
- Metabolic network
- Computational biology
- Computer science