Lipid Bioinformatics: Computational strategies for analyses of shotgun lipidomic data

Josch K. Pauling

Research output: ThesisPh.D. thesis

Abstract

Lipidomics is a rapidly emerging eld under the omics umbrella with the dedicated goal to unravel cellular lipids, their structure, compositions, functions and regulation to link them to metabolism. Advances in lipid-centric analytical chemistry and mass spectrometry methods such as shotgun lipidomics, enabled high-throughput generation of high-resolution mass spectrometry data from complex lipid experiments. However, bioinformatics solutions for data analysis have not been well established and is trailing other omics elds. In addition, integration of heterogeneous data sets is a central paradigm of the omics approach and with the aid of bioinformatics data analysis is transformed from lipid-centric to integrative methodology. This thesis presents bioinformatics strategies on both. First, a publicly available analysis platform ALEX123 was developed that provides an automatic solution for the analysis of shotgun lipidomics data from raw mass spectra to a processed integrated dataset. It is capable of revealing molecular lipid species compositions on the basis of distinctive fragmentation patterns. Second, a highly integrative screening method for yet uncharacterized proteins is presented predicting lipid altering function with lipid metabolic pathway-specicity through the application of de novo network enrichment, a well established integrative computational method. Analysis of experimental validation data is fully based on ALEX123.
Original languageEnglish
Awarding Institution
  • University of Southern Denmark
Supervisors/Advisors
  • Ejsing, Christer Stenby, Principal supervisor
Place of PublicationOdense
Publisher
Publication statusPublished - 14. Apr 2016

Note re. dissertation

Print copy of the full thesis is restricted to reference use in the library.

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