Towards automated identification of metabolites using mass spectral trees

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Towards automated identification of metabolites using mass spectral trees

Title: Towards automated identification of metabolites using mass spectral trees
Author: Rojas-Chertó, Miquel
Publisher: Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University
Issue Date: 2014-06-19
Keywords: Metabolomics
Metabolites
Multistage mass spectrometry
Databases
Abstract: The detailed description of the chemical compounds present in organisms, organs/tissues, biofluids and cells is the key to understand the complexity of biological systems. The small molecules (metabolites) are known to be very diverse in structure and function. However, the identification of the chemical structure of metabolites is one of the major bottlenecks in metabolomics research. Hence, the annotation and the structure elucidation of the metabolites are essential to understand the biological system under study. Actually, no single analytical platform exists that can measure and identify all existing metabolites. Multistage mass spectrometry (MSn) is a powerful analytical technique that helps identifying all these metabolites. This technique provides detailed structural information of the unknown metabolite by fragmenting the metabolite and its fragments recursively. However, only computational tools can provide a fast and straightforward analysis of the large amount of complex data that is generated by using MSn spectrometry. The aim of this thesis was to develop a novel semi-automatic approach for the identification of metabolites using MS n data. Furthermore, these tools were to be integrated into a pipeline to assign identities to unknown metabolites present in databases but especially to unknown metabolites not present in a database
Description: Promotor: Thomas Hankemeier, Co-promotor: Theo Reijmers
With summary in Dutch
Faculty: Faculteit der Wiskunde en Natuurwetenschappen
Citation: Rojas-Chertó, M., 2014, Doctoral thesis, Leiden University
ISBN: 9789074538848
Handle: http://hdl.handle.net/1887/26892
 

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