Doctoral candidate in phage-microbe interaction proteomics
Within the scope of the H2020 grant Epic-XS (https://epic-xs.eu/), which aims at developing and advancing proteomics exploration within life sciences, we are looking to fill a position for a doctoral candidate in phage-microbe interaction proteomics. The ideal candidate should have a biological or chemical background and a high interest in mass-spectrometry based proteomics and its application in life sciences. More information.
Projects for internship, Bachelor's thesis and Masters' thesis
We have projects for internship, Bachelor's thesis and Masters' thesis. Therefore, we are always looking for motivated students to join our team to work on proteomics, metabolomics and bioinformatics topics. To find more information, please send your CV to
karin.kleigrewe [at] tum.de (For metabolomics projects)
tina.Ludwig [at] tum.de (For proteomics projects)
chen.meng [at] tum.de (For bioinformatics projects)
Bioinformatics Project: Evaluation of substrate based kinase activity inference algorithms
(master thesis or internship)
Protein kinase is the key enzyme catalyzing the process known as protein phosphorylation, where phosphate groups are attached to specific substrate proteins, mostly on the Serine (S), threonine (T) and tyrosine (Y). Phosphorylation is a crucial mechanism regulating signaling transduction in cells. As the upstream controller of phosphorylation, measuring kinase activity has received increasing attention of researchers.
Nowadays, mass spectrometry can be used to measures protein abundance, the abundance of phosphorylation peptides, but can hardly measure the activity of kinases directly. As a compromise, several algorithms have been proposed to estimate the kinase activity based on the substrate of kinases. However, there is still no systematic evaluation of how accurate these estimations are. In this project, we will evaluate different substrate-based kinase activity estimation algorithms.
link.springer.com/protocol/10.1007/978-1-4939-7493-1_6 (there are many methods mentioned in this article)
Bioinformatics project: Visualization of multi-omics statistical analysis (master thesis or internship)
Integrative analysis of multiple omics data sets is crucial to reveal the biology that is less prominent in each individual omics data. In the past few years, I have developed several algorithms for the purpose of exploratory analysis, clustering analysis and enrichment analysis, etc. However, better visualization of results, particular in an interactive manner, is critical but still missing in order to derive testable hypotheses from the integrative analysis. This project requires developing a few data visualization modules using shiny (see related works) and apply them in the data analysis.