Year: 2012
Type: dissertation
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\nof the major bottlenecks in metabolomics research. Hence, the annotation and\nthe 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\nall existing metabolites. Multistage mass spectrometry (MSn) is a powerful\nanalytical technique that helps identifying all these metabolites. This technique provides detailed structural information of the unknown metabolite by fragmenting the metabolite and its\nfragments recursively. However, only computational tools can provide a fast\nand straightforward analysis of the large amount of complex data that is generated by using\nMSn 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
Author: Miguel Rojas‐Chertó
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2577531463