• 34MetExplore - Metabolic network content1
  • 35MetExplore - Metabolomic data mapping2
  • 36MetExplore - Pathway based analysis of metabolomics data3
  • 37MetExplore - Network based analysis of metabolomics data4

MetExplore - Metabolomic data mapping


How to map metabolomics data in MetExplore using identifiers (KEGG, or model identifiers) ?

At the end of the course, you will be able to :

  • Find metabolite identifiers
  • Map metabolites in MetExplore

No particular knowledge required.


Mapping metabolites in metabolic networks consist in establishing a link between a measured molecule and its counterpart in the network. The challenge is that network and metabolomics databases are not necesseraly using the same conventions to identify metabolites. There is thus a preliminary step consisting in finding right identifiers for each metabolite of a metabolic fingerprint. Then data can be uploaded in MetExplore to see where they stand in the network.


The slideshow will present the challenge of identifiers through examples. It will indroduce the concept of mapping.


This course allowed to discover the issue of metabolomics data mapping and in particular the lack of shared identifiers and models. It is now possible to map data in networks. The next step is the use of this mapping in order to identify metabolic pathways of interest.

Heller,S.R., McNaught,A., Pletnev,I., Stein,S. and Tchekhovskoi,D. (2015). InChI, the IUPAC International Chemical Identifier. J. Cheminform., 7, 23. https://doi.org/10.1186/s13321-015-0068-4
Merlet,B., Paulhe,N., Vinson,F., Frainay,C., Chazalviel,M., Poupin,N., Gloaguen,Y., Giacomoni,F. and Jourdan,F. (2016). A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks. Front. Mol. Biosci., 3, 2. https://doi.org/10.3389/fmolb.2016.00002
Wohlgemuth,G., Haldiya,P.K., Willighagen,E., Kind,T. and Fiehn,O. (2010). The Chemical Translation Service--a web-based tool to improve standardization of metabolomic reports. Bioinformatics, 26, 2647–2648. https://doi.org/10.1093/bioinformatics/btq476