Automated de novo metabolite identification with mass spectrometry and cheminformatics

Leiden Repository

Automated de novo metabolite identification with mass spectrometry and cheminformatics

Title: Automated de novo metabolite identification with mass spectrometry and cheminformatics
Author: Peironcely Miguel, Julio Eduardo
Publisher: Department of Analytical Biosciences, Leiden Academic Centre for Drug Research (LACDR), Faculty of Science, Leiden University
Issue Date: 2014-09-03
Keywords: Metabolomics
Metabolite identification
Structure elucidation
Structure generation
Cheminformatics
Machine learning
Abstract: In this thesis new algorithms and methods that enable the de novo identification of metabolites have been developed. The aim was to find methods to propose candidate structures for unknown metabolites using MSn data as starting point. These methods have been integrated into a semi-automated pipeline to identify new human metabolites. The discovery of new metabolites will improve our capability to understand disease via its metabolic fingerprint, to develop personalized treatments and to discover new drugs. In addition, the cheminformatics methods presented in this thesis increase our understanding on the properties of human metabolites. The research described in this thesis has shown that the success of de novo metabolite identification relies on the synergy between analytical chemistry methods (i.e. LC-MSn) and cheminformatics tools.
Description: Promotor: T. Hankemeier, Co-Promotores: T. Reijmers, L. Coulier
With Summary in Dutch
Faculty: Faculteit der Wiskunde en Natuurwetenschappen
Citation: Peironcely Miguel, J.E., 2014, Doctoral Thesis, Leiden University
ISBN: 9789074538855
Sponsor: Netherlands Organization for Applied Scientific Research (TNO) Netherlands Metabolomics Centre
Handle: http://hdl.handle.net/1887/28508
 

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