Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 408441
Title Elemental Composition determination based on MS
Author(s) Rojas-Cherto, M.; Kasper, P.T.; Willighagen, E.L.; Vreeken, R.; Hankemeijer, Th.; Reijmers, T.H.
Source Bioinformatics 27 (2011)17. - ISSN 1367-4803 - p. 2376 - 2383.
Department(s) Biometris (WU MAT)
PRI Biometris
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) mass-spectrometry data - molecular formulas - chemical markup - spectral data - identification - prediction - accuracy - database - system
Abstract Motivation: Identification of metabolites is essential for its use as biomarkers, for research in systems biology, and for drug discovery. The first step before a structure can be elucidated is to determine its elemental composition. High resolution mass spectrometry, which provides the exact mass, together with common constraint-rules, for rejecting false proposed elemental compositions, can not always provide one unique elemental composition solution. Results: The Multi-stage Elemental Formula (MEF) tool is presented in this paper to enable the correct assignment of elemental composition to compounds, their fragment ions, and neutral losses that originate from the molecular ion by using multi-stage mass spectrometry (MSn). The method provided by MEF reduces the list of predicted elemental compositions for each ion by analyzing the elemental compositions of its parent (precursor ion) and descendants (fragments). MSn data of several metabolites were processed using the MEF tool to assign the correct elemental composition and validate the efficacy of the method. Especially the link between the mass accuracy needed to generate one unique elemental composition and the topology of the MSn tree (the width and the depth of the tree) was addressed. This method makes an important step towards semi-automatic de novo identification of metabolites using MSn data.
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