CEEPC/IPM/CMSC - Abstrakt prezentace

(Česká konference hmotnostní spektrometrie 2019 - PL-01)
Interpretation of mass spectrometry in the metabolomics: GNPS and Sirius programs

Aleš Svatoš 1 *, Riya Menezes 1

  1. MPI for Chemical Ecology, Hans-Knoell-Str. 8, 07745 Jena, Germany


In the lecture I compare two ways of in silico interpretation of CID spectra obtained from extracts from different Arabidopsis thaliana mutants measured on a UPLC-MS/MS system using a DDA acquisition on a Q-Exactive HF-X spectrometer. For intensity normalization, retention time correction, and peak-picking/integration, XCMS was used in the R-environment. The data was interpreted in GNPS [1] (https://gnps.ucsd.edu) created by Pietro Dorrestein. GNPS is a living system that is dependent on supporting contributors/curators of the spectrum. GNPS aggregates the data available in all open mass spectra databases. In contrast to the previous approaches, the CID spectra are clustered upon similarity and thus new metabolites homologues of known substances are considered as well. Spectra of known and new metabolites are part of the same structural cluster, thereby facilitating the manual interpretation of new compounds.
Sirius [2] and CSI: FingerID [3] created in Jena are going even further. Their aim is ad hoc interpretation of MS and MS/MS data leading to the design of possible chemical structures. Information on the exact mass and intensities of isotopic peaks obtained from LC-MS spectra are used to design a possible molecular composition. This is refined in the second step, when CID spectra are converted to oriented graphs, where the nodes constitute m/z values, and the edges are neutral/radical losses. The individual graphs are evaluated according to the number of allowed/forbidden losses and the best used for selecting the correct molecular composition. Data Structure PubChem is used to interpret data. Structures are converted to vectors that contain information about functional groups and structural motifs. Similar vectors are also created from oriented graphs and both vectors are compared.
In the paper I will evaluate the success of both methods on the same samples.

* Korespondující autor: svatos@ice.mpg.de


  1. Wang M. et al.: Nat. Biotech. 34(8), 828-837 (2016).
  2. Rasche F. et al:. Anal. Chem. 83(4), 1243–1251 (2011).
  3. Duhrkop K. et al.: Proc. Natl. Acad. Sci. U. S. A. 112(41), 12580-12585 (2015).

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