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Research Projects

Improved algorithms and tools for the identification of novel proteoforms using top-down proteomics


Kyowon Jeong
EKUT, 1st Supervisor

Sven Degroeve
VIB, 2nd Supervisor


Implementation of sensitive and specific protein species identification and characterization for targeted and untargeted proteomics study by leveraging recently introduced mass deconvolution algorithms. Application of the developed algorithm to reveal novel proteoforms.


Both algorithmic and machine learning-based approaches will be applied for an efficient and sensitive proteoform identification method. The developed algorithm will be used in a stand-alone tool as well as in an interactive visualization tool. The algorithm also could be embedded in instruments for better data acquisition. The development will be performed as part of OpenMS the major C++ open-source framework for computational proteomics.

Required skills

A strong background in algorithm.
Good software engineering skills. Ideally knowledge of the C++ and C# programming language.
Git-based version control system. If available, please provide a link to e.g. your GitHub account and/or link to past projects in your application.
Ideally, a background in statistics
Ideally, knowledge of (computational) mass spectrometry.
Strong command of English.

Expected Results

Tools implementing millisecond order of runtime per spectrum and increased proteoform identification rates at a given false-discovery rate. Application of these tools to large-scale proteomics data to identify novel proteoforms.

Planned Secondments

Host: SDU (V. Schwämmle), Duration: 1 Month; When: Year 2; Goal: Learn top-down mass spectrometry.

Host: CNRS (O. Schiltz), Duration: 1 Month; When: Year 1; Goal: Tool integration and interoperability.

Enrolment in doctoral programs

Ph.D. from the Faculty of Mathematics and Science at Eberhard Karls Universität Tübingen.


1. Jeong, K., Kim, J., Gaikwad, M., Hidayah, S. N., Heikaus, L., Schlüter, H., & Kohlbacher, O. (2020). FLASHDeconv: Ultrafast, High-Quality Feature Deconvolution for Top-Down Proteomics. Cell Systems, 10(2), 213-218.e6.

2. Chen, B., Brown, K. A., Lin, Z., & Ge, Y. (2017). Top-Down Proteomics: Ready for Prime Time? Analytical Chemistry, 90(1), 110–127.