Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Zhiwei Zhou; Mingdu Luo; Haosong Zhang; Yandong Yin; Yuping Cai; Zheng-Jiang Zhu

doi:10.1038/s41467-022-34537-6

Nature Communications (Nov 2022)

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Zhiwei Zhou,
Mingdu Luo,
Haosong Zhang,
Yandong Yin,
Yuping Cai,
Zheng-Jiang Zhu

Affiliations

Zhiwei Zhou: Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
Mingdu Luo: Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
Haosong Zhang: Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
Yandong Yin: Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
Yuping Cai: Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
Zheng-Jiang Zhu: Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences

DOI: https://doi.org/10.1038/s41467-022-34537-6
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 15

Abstract

Read online

Unknown metabolite annotation is a grand challenge in untargeted metabolomics. Here, the authors develop knowledge-guided multi-layer networking (KGMN) to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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