Created in 2018 by Xiaotao Shen, Xin Xiong and Ruohong Wang from Dr. Zheng-Jiang Zhu lab, Chinese Academy of Sciences.
Metabolite identification is the long-standing challenge for liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we developed MetDNA (Metabolite identification and Dysregulated Network Analysis) for the large-scale and ambiguous identification of metabolites from LC-MS/MS data sets. Users can simply import MS1 peak table, MS/MS data and sample information to perform metabolite identification and dysregulated metabolic pathway analysis. MetDNA implements a metabolic reaction network (MRN) based recursive algorithm for metabolite identification, which supports data from different LC systems (e.g., HILIC and reverse phase) and MS platforms (e.g., Agilent QTOF, Sciex TripleTOF, Thermo Orbitrap, and others).
Please click Analysis tab in MetDNA web server to start your MetDNA journey!