Bachelor and Master projects

Students interested in doing their bachelor or master project within the research group Analytical Biochemistry are encouraged to read the Annual Reports to learn more about the research topics of the group. For more information, please contact Prof. Peter Horvatovich.

We offer the following Master projects:

PASTAQ (Pipelines And Systems for Threshold Avoiding Quantification) for LC-MS/MS pre-processing

PASTAQ (Pipelines And Systems for Threshold Avoiding Quantification) is a c++ library with Python providing accurate quantification of LC-MS/MS data with rich annotation such as features, MS/MS event and proteomics identifications. We are looking for c++ enthusiasts for further development of PASTAQ functionality such as improving accuracy of pseudo spectra extraction, implementation of ion mobility dimension and GPU visualization and processing. Further details are available here.

Untargeted metabolomics of human plasma with LC-EC-MS

Metabolism is a complex and dynamic process involving numerous metabolic pathways and enzymes, converting a wide range of nutrients, xenobiotics, and drugs, into energy, cellular building blocks, or waste. Quantitative analysis of metabolites is commonly performed by liquid chromatography (LC) coupled to mass spectrometry (MS). Identification of these compounds is not straightforward due to the limited compound-specific information obtained even with high resolution MS and MS/MS data. In this project we will evaluate the merits of a novel method, combining electrochemistry (EC) with LC-MS for more specific analysis of (drug) metabolites in plasma samples.

We will use plain plasma and plasma spiked with a variety of drug compounds to generate an untargeted metabolomics dataset on high resolution mass spectrometry instrumentation. Next, the sample will be analyzed with the electrochemistry cell coupled on-line with LC-MS and data sets at various cell potentials will be recorded. The combined data sets will be analyzed with available metabolomics software packages to determine the metabolite composition of the plasma samples. The spiked drug compounds and known metabolites in plasma (e.g. amino acids) will be used as references to track the overall performance of the method. Further optimization of EC parameters may be required to generate improved data sets.

Click here for more information.

Computational mass spectrometry bioinformatics projects

Large amount of data is generated by modern mass spectrometry platforms providing deep protein, glycoprotein and metabolome profiles of biological samples used for fundamental or clinical research. We propose several bioinformatics projects in the domain of computational mass spectrometry requiring different level of programming expertise in programming, statistics and knowing data structures of LC-MS/MS proteomics and metabolomics data. For further information see details here.