Multi-omics data analysis in the MaxQuant and Perseus environments
Development of tools and algorithms for the analysis of proteomics data in conjunction with any other omics type and their Integration into the Perseus software. These will include lipidomics, metabolomics, genomics data, as well as the integration of top-down with bottom-up proteomics data. The student will be involved in the design and implementation of new MaxQuant technology modules for metabolomics and lipidomics. Furthermore, the student contributes to the development of the MaxQB database to multi-omics integration infrastructure.
Algorithm development in the C# based MaxQuant and Perseus environments. Database programming in the MaxQB project. Team-based programming in the Cox lab continuous integration environment.
Excellent programming skills
Good command of English
Background knowledge in biology or mass spectrometry is a plus
Several Perseus plugins applying a multitude of data integration methods. MaxQB database supporting schemas for top-down and bottom-up proteomics integration. Integration of the metabolomics and lipidomics modules of MaxQuant.
Host: TAU (J. Hamari), Duration: 2 Months; When: Year 1; Goal: Gamification strategies.
Host: THERMO (B. Delanghe), Duration: 2 Months; When: Year 2; Goal: Algorithm development.
Host: DDS (P. Garcia), Duration: 1 Months; When: Year 3; Goal: Visualization strategies.
Enrolment in doctoral programs
Ph.D. from the Faculty of Medicine at TUM.
1. Tiwary, S., Levy, R., Gutenbrunner, P., Salinas Soto, F., Palaniappan, K. K., Deming, L., Berndl, M., Brant, A., Cimermancic, P., and Cox, J. (2019) High quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis. Nat. Methods, doi:10.1038/s41592-019-0427-6.
Prianichnikov, N., Koch, H., Koch, S., Lubeck, M., Heilig, R., Brehmer, S., Fischer, R. and Cox, J. (2020) MaxQuant software for ion mobility enhanced shotgun proteomics. Mol. Cell. Proteomics 19, 1058-69, doi:10.1074/mcp.TIR119.001720
2. Sinitcyn, P., Rudolph, J.D. and Cox, J. (2018). Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data. Annual Review of Biomedical Data Science 1, 207-234.
3. Yu, S.H., Kyriakidou, P. and Cox, J. (2020) Isobaric matching between runs and novel PSM-level normalization in MaxQuant strongly improve reporter ion-based quantification. J. Proteome Res. 19, 3945-54, doi:10.1021/acs.jproteome.0c00209.
4. Yu, S.H., Ferretti, D., Schessner, J.P., Rudolph, J.D., Borner, G.H.H and Cox, J. (2020) Expanding the Perseus Software for Omics Data Analysis With Custom Plugins. Curr. Protoc. Bioinformatics 71, e105, doi:10.1002/cpbi.105.
5. Rudolph, J.D. and Cox, J. (2019) A network module for the Perseus software for computational proteomics facilitates proteome interaction graph analysis. J. Proteome Res. 18, 2052–2064, doi: 10.1021/acs.jproteome.8b00927.
6. Sinitcyn, P., Tiwary, S., Rudolph, J.D., Gutenbrunner, P., Wichmann, C., Yilmaz, S., Hamzeiy, H. and Cox, J. (2018). MaxQuant goes Linux. Nature Methods 15, 401.