MAGMa is an online application for the automatic chemical annotation of accurate multistage MSn spectral data.
- MSn data can be uploaded as a hierarchical tree of fragment peaks, either based on m/z values or elemental formulas, or as an mzXML file of the raw data.
- Candidate molecules are automatically retrieved from PubChem, from a subset of PubChem compounds present in Kegg, or from the Human Metabolome Database.
- Candidate molecules can be predicted based on in silico reaction rules describing microbiotic and human biotransformations
- For each candidate molecule, substructures are generated and matched with the observed fragment peaks.
- The web browser enables efficient mining of the automatically annotated data.
- Open Source, source code available at https://github.com/NLeSC/MAGMa
The online webservice is currently not available
- The MAGMa algorithm for substructure based annotation of multistage MSn spectra is described in Ridder
et al. 2012.
- Ridder et al.
2013: MAGMa is used for the automatic annotation of a complete metabolite
profile of green tea.
- Ridder et al.
2014a: The new metabolite prediction module in MAGMa is used to annotate urinary metabolites
of the compounds in green tea. The generated library of 27245 potential green tea derived
metabolite structures can be downloaded
for reuse in other studies:
please refer to the Ridder
- Prize-winning poster presented at the Analytical
Tools for Cutting-edge Metabolomics meeting in London, 30 April 2014
- MAGMa was selected as "best
automated method" in the international CASMI
contest 2013. The MAGMa entry is described by Ridder
et al. (2014b).
- The MAGMa solutions were also submitted to the international CASMI
For more information or feedback, contact: Lars Ridder