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Research Projects

Algorithms and workflow for the in-depth characterization of the PTM landscape and PTM crosstalk

University of Southern Denmark (SDU)

Odense

Denmark

3/15

Supervision

Veit Schwämmle
SDU, 1st Supervisor

David Bouyssié
(CNRS-IPBS), 2nd Supervisor

Objectives

i) Identify and combine best-performing deconvolution and proteoform identification algorithms, ii) Develop algorithms to confidently identify and quantify all features in complex spectra containing multiple long peptidoforms or entire proteoforms, and iii) Integrate the new data analysis workflow into a user-friendly and highly portable environment.

Methodology

Development of multi-dimensional (non-)linear models applied to unbalanced data
Containerization and assembly of software tools into data analysis workflow
Derivation and optimization of metrics for automatic quality control and benchmarking
Application of smart techniques to visualize highly intertwined multi-layer data

Required Skills

At least intermediate programming skills
Experience with quantitative omics data analysis (preferably mass spectrometry data)
Basics in working on Linux-type environments

Expected Results

Data analysis pipeline designed to efficiently process large-scale data from middle-down and top-down experiments.
Considerable improvement of data analysis of mass spectra from multiple modified peptides and proteins.
Much deeper view into the biological function of proteins and their regulatory control mechanisms.

Planned Secondments

Host: FHOOE (V. Dorfer), Duration: 2 Months; When: Year 1; Goal: Test open database search on long peptides.

Host: EKUT, (O. Kohlbacher), Duration: 1 Month; When: Year 1, Goal: Deconvolution of MS2 spectra.

Host: NOVO (Mads Grønborg), Duration: 1 Month; When: Year 2, Goal: Needs for PTMs analysis in pharma.

Host: CNRS (O. Schiltz), Duration: 3 Month; When: Year 3, Goal: Implementation of the user-friendly data analysis pipeline.

Enrolment in doctoral programs

Ph.D. in Bioinformatics from University of Southern Denmark.

References

1. Kirsch, R., Jensen, O. N., & Schwämmle, V. (2020). Visualization of the dynamics of histone modifications and their crosstalk using PTM-CrossTalkMapper. Methods, 184, 78–85. https://doi.org/10.1016/j.ymeth.2020.01.012

2. Schwämmle, V., Sidoli, S., Ruminowicz, C., Wu, X., Lee, C.-F., Helin, K., & Jensen, O. N. (2016). Systems Level Analysis of Histone H3 Post-translational Modifications (PTMs) Reveals Features of PTM Crosstalk in Chromatin Regulation. Molecular & Cellular Proteomics, 15(8), 2715–2729. https://doi.org/10.1074/mcp.m115.054460

3. Tvardovskiy, A., Schwämmle, V., Kempf, S. J., Rogowska-Wrzesinska, A., & Jensen, O. N. (2017). Accumulation of histone variant H3.3 with age is associated with profound changes in the histone methylation landscape. Nucleic Acids Research, 45(16), 9272–9289. https://doi.org/10.1093/nar/gkx696