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THALLINGER LAB

© 2010 - 2024 Thallinger Lab · Institute of Biomedical Informatics · Graz University of Technology · Stremayrgasse 16/I, 8010 Graz, Austria Tel +43-316-873-5343 · Fax +43-316-873-105343 · URL http://genome.tugraz.at ·   · Welcome to Graz · Map

FWF

HIGH-THROUGHPUT IDENTIFICATION OF LIPID MOLECULAR SPECIES IN LC-MS/MS DATA (STAND ALONE PROJECT: 2013-2016)

Gerhard G. Thallinger

LC-MS/MS data from complex lipid samples carries the potential to elucidate many structural features of lipids. It provides information about the fatty acids and in many cases about their regio-isomeric position. However, the MS/MS spectra of lipids can vary tremendously, because the fragmentation process depends on parameters like the used mass spectrometer, fragmentation collision energy, charge state, and adduct ions. Due to this diversity, a generally applicable bioinformatics tool for the automated analysis of lipidomics LC-MS/MS experiments is still missing. The few existing tools do either not exploit the advantages of liquid chromatography or are applicable to specific lipid classes and/or an experimental setup only. Furthermore, end users cannot customize the software for their specific fragmentation spectra. This project's global aim is to develop a versatile and generally applicable method for high throughput determination of lipid structural fatty acid composition from LC-MS/MS data, easily adaptable to different mass spectrometers and experimental setups. The general applicability will be facilitated by a newly developed language for the description of MS/MS fragmentation spectra. Based on this language, a novel algorithm will identify the lipid and its deducible compositional features. Furthermore, we want to supply a graphical user interface for the definition of rules describing the spectra, and supply pre-defined rule-sets for the most common mass spectrometers. LC MS/MS data analysis delivers in a single experiment the most sensitive information about the lipidome of a complex biological sample. A successful implementation of a generally applicable tool for the automated analysis of such data will accelerate lipid analysis enormously. This will give a more complete picture on the lipidome and its changes which is ideally suited for top down approaches and investigation of biological questions.