Development of new signal processing algorithms of ion mobility MS data to improve the quantification of phosphoproteomics samples
Supervision
David Bouyssié
IPBS, 1st Supervisor
Veit Schwämmle
SDU, 2nd Supervisor
Objectives
Improve the processing and quantitative analysis of phosphoproteomics data. Localization of labile PTMs such as phosphate groups on the backbone of peptide sequences is a challenging process and can lead to ambiguous assignments, depending on the presence or not of discriminant fragment ions in the MS/MS data. Additionally, isobaric species may not be resolved during the LC separation, generating mixed MS/MS spectra and making impossible the accurate quantification of peptide isoforms bearing the PTM at different positions. The introduction of the ion mobility dimension in new instruments improves separation of such isoforms. The objective is to develop new algorithms taking advantage of this additional dimension for accurate characterization and quantification of phosphopeptide isoforms across different MS runs.
Methodology
The ESR will update the mzDB file format specification to add support for ion mobility, develop new peak-pickers taking advantage of the ion mobility dimension, and develop new algorithms for retention time alignment.
Required Skills
Knowledge of mass spectrometry data analysis
C++ programming language (experimented)
Basics of Python or R
Expected Results
We expect to provide optimized methods and tools for the localization of PTMs and for the quantification of isobaric forms detected in different runs obtained using an ion mobility separation. These will be implemented in the MS-Angel software currently developed in the team, and applied to the analysis of large-scale phosphoproteomic datasets.
Planned Secondments
Host: SDU (V. Schwämmle), Duration: 3 Months; When: Year 1; Goal: Data generation.
Host: FHOOE (V. Dorfer), Duration: 3 Months; When: Year 3; Goal: Benchmark phosphoproteomics analysis.
Enrolment in doctoral programs
Ph.D. in Bioinformatics from University of Toulouse.
References
1. Bouyssié, D., Dubois, M., Nasso, S., Gonzalez de Peredo, A., Burlet-Schiltz, O., Aebersold, R., & Monsarrat, B. (2015). mzDB: a file format using multiple indexing strategies for the efficient analysis of large LC-MS/MS and SWATH-MS data sets. Molecular & cellular proteomics : MCP, 14(3), 771–781. https://doi.org/10.1074/mcp.O114.039115
2. Locard-Paulet, M., Bouyssié, D., Froment, C., Burlet-Schiltz, O., & Jensen, L. J. (2020). Comparing 22 Popular Phosphoproteomics Pipelines for Peptide Identification and Site Localization. Journal of proteome research, 19(3), 1338–1345. https://doi.org/10.1021/acs.jproteome.9b00679
3. Bouyssié, D., Hesse, A. M., Mouton-Barbosa, E., Rompais, M., Macron, C., Carapito, C., Gonzalez de Peredo, A., Couté, Y., Dupierris, V., Burel, A., Menetrey, J. P., Kalaitzakis, A., Poisat, J., Romdhani, A., Burlet-Schiltz, O., Cianférani, S., Garin, J., & Bruley, C. (2020). Proline: an efficient and user-friendly software suite for large-scale proteomics. Bioinformatics (Oxford, England), 36(10), 3148–3155. https://doi.org/10.1093/bioinformatics/btaa118