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. 2008 Aug 15;80(16):6382-9.
doi: 10.1021/ac800795f. Epub 2008 Jul 16.

XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization

H P Benton  1 , D M Wong, S A Trauger, G Siuzdak
Affiliations

XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization

H P Benton et al. Anal Chem. .

Abstract

Mass spectrometry based metabolomics represents a new area for bioinformatics technology development. While the computational tools currently available such as XCMS statistically assess and rank LC-MS features, they do not provide information about their structural identity. XCMS(2) is an open source software package which has been developed to automatically search tandem mass spectrometry (MS/MS) data against high quality experimental MS/MS data from known metabolites contained in a reference library (METLIN). Scoring of hits is based on a "shared peak count" method that identifies masses of fragment ions shared between the analytical and reference MS/MS spectra. Another functional component of XCMS(2) is the capability of providing structural information for unknown metabolites, which are not in the METLIN database. This "similarity search" algorithm has been developed to detect possible structural motifs in the unknown metabolite which may produce characteristic fragment ions and neutral losses to related reference compounds contained in METLIN, even if the precursor masses are not the same.

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Figures

Figure 1
Figure 1
The general workflow of XCMS and XCMS2 employing the “sniper” approach: a single feature is found with statistical confidence and selected for MS/MS. The data from the MS/MS is then put through XCMS2 and the feature is structurally identified using the METLIN database.
Figure 2
Figure 2
The comparison of the different matches at the reference collision energy. As the voltage increases, more low mass fragment ions are generated. Using a collision energy that is close to the experimentally used value gives a high matching score.
Figure 3
Figure 3
The difference between the collision energies is seen again. However, the figure also shows how high collision energy spectra can match lower collision energy spectra due to signal-to-noise levels of low abundance peaks.
Figure 4
Figure 4
A METLIN search using accurate mass only can yield multiple hits. However, searching with MS/MS data, glycerophosphocholine can be unambiguously identified.
Figure 5
Figure 5
Similarity search. The first spectrum is the MS/MS data collected from an experiment of 1-palitylophosphocholine. The spectra below show similarity to the unknown.
See this image and copyright information in PMC

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