Data science methods for biomedical research

The development of data science methods for biomedical research is our deeply grounded research focus. The technological developments, in particular for high-throughput methods (omics) and imaging, have led to unprecedented changes on how biomedical research is being performed, but has also significantly increased the promises that are associated with the data generated by these technologies for patients, researchers and other stakeholders. Through interfaces between heterogeneous data repositories, it is our goal to build Big (FAIR) Data ecosystems. The bioinformatics workflow framework ( that we co-develop since 2020 (Ewels et al., Nature Biotech, 2020) is our workhorse technology to provide highly reproducible and scalable preprocessing of biomedical high-throughput and imaging data. Nf-core has become and
outstanding international open-source project, counting today more than 6,000 data scientists contributing and using the framework. Following data FAIRification, we are currently working on new methods for reproducible, robust and scalable data propressing and translational AI, alike. Some of this work got recently accepted for publication in Oxford Bioinformatics (Heumos et al., 2023). With this research we intend to increase the knowledge on the technical needs for clinical translational of AI methods.


Prof. Dr. Sven Nahnsen

Head of the research group

Personenprofil: More about the person

Sven Nahnsen studied Biomathematics at the University of Greifswald (Greifswald) and Biotechnology and the Universtiy of Strasbourg (France). He complemented his studied with a research stay at the University of Cambridge (UK) and graduated with a Diplôme d’ingénieure en biotechnology from the Ecole superieure de biotechnology (ESBS) of the Universities of Strasbourg, Basel and Freiburg. In 2010 he gradueated with a PhD in Bioinformatics from the University of Tübingen. Since 2012 he is heading the Quantitative Biology Center (QBiC) and become its scientific director in 2018. Since 2021 he is professor for Biomedical Data Science at the Department of Computer Science at the Science Faculty and also co-opted at the Faculty of Medicine of the University of Tübingen.

Our research team
  • Quantitative Biology Center (QBiC), Eberhard-Karls University of Tübingen, Tübingen, Germany.

  • Biomedical Data Science, Department of Computer Science, Eberhard-Karls University of Tübingen, Tübingen, Germany

  • Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard-Karls University of Tübingen, Tübingen, Germany

Big data
FAIR Data ecosystems.
high-throughput methods
international open-source project

Selected publications

  • Heumos, L., Ehmele, P., Menden, K., Cuellar, L. K., Miller, E., Lemke, S., Gabernet, G. & Nahnsen, S. Mlf-core: A framework for deterministic machine learning. Oxford Bioinformatics (2023). Volume 39, Issue 4, April 2023, btad164,
  • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P.,Nahnsen, S. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. (2020). doi:10.1038/s41587-020-0439-x
  • Rajaraman, S, Canjuga, D, Ghosh, M, Codrea, MC, Sieger, R, Wedekink, F, Tatagiba, M, Koch, M, Lauer, UM, Nahnsen, S, Rammensee, HG, Mühlebach, MD, Stevanovic, S, Tabatabai, G. Measles virus-based treatments trigger a pro-inflammatory cascade and a treatment-specific immunopeptidome in glioblastoma. Mol Ther Oncolytics. Vol. 12, 2019, 147-161.
  • Mohr, C, Friedrich, A, Wojnar, D, Kenar, E, Polatkan, AC, Codrea, MC, Czemmel, S, Kohlbacher, O, Nahnsen S (2018). qPortal: A platform for data-driven biomedical research. PLoS One;13: e0191603.
  • Korkmaz, AG, Popov, T, Peisl, L, Codrea, MC, Nahnsen, S, Steimle, A, Velic, A, Macek, B, von Bergen, M, Bernhardt, J, Frick, JS (2017). Proteome and phosphoproteome analysis of commensally induced dendritic cell maturation states. J Proteomics. 2018;180: 11–24.
  • Röst, HL, Sachsenberg, T, Aiche, S, Bielow, C, Weisser, H, Aicheler, F, Andreotti, S, Ehrlich, HC, Gutenbrunner, P, Kenar, E, Liang, X, Nahnsen, S, Nilse, L, Pfeuffer J, Rosenberger, G, Rurik, M, Schmitt, U, Veit, J, Walzer, M, Wojnar, D, Wolski, WE, Schilling, O, Choudhary, JS, Malmström, L, Aebersold, R, Reinert, K, Kohlbacher, O (2016). OpenMS: a flexible open-source software platform for mass spectrometry data analysis Nat Methods. 13(9):741-8.
  • Löffler, MW, Chandran, PA, Laske, K, Schroeder, C, Bonzheim, I, Walzer, M, Hilke, FJ, Trautwein, N, Kowalewski, DJ, Schuster, H, Günder, M, Carcamo Yañez, VA, Mohr, C, Sturm, M, Nguyen, HP, Riess, O, Bauer, P, Nahnsen, S, Nadalin, S, Zieker, D, Glatzle, J, Thiel, K, Schneiderhan- Marra, N, Clasen, S,
  • Bösmüller, H, Fend, F, Kohlbacher, O, Gouttefangeas, C, Stevanović, S, Königsrainer, A, Rammensee, HG (2016). Personalized peptide vaccine-induced immune response associated with long-term survival of a metastatic cholangiocarcinoma patient. J Hepatol. 0168-8278(16)30323-3.
  • Dammeier, S*, Nahnsen, S*, Veit, J*, Wehner, F, Ueffing, M, Kohlbacher, O (2016). Mass-Spectrometry-Based Proteomics Reveals Organ-Specific Expression Patterns To Be Used as Forensic Evidence. J Proteome Res 15: 182-92
  • Walzer, M, Pernas, LE, Nasso, S, Bittremieux, W, Nahnsen, S, Kelchtermans, P, Pichler, P, van den Toorn, HW, Staes, A, Vandenbussche, J, Mazanek, M, Taus, T, Scheltema, RA, Kelstrup, CD, Gatto, L, van Breukelen, B, Aiche, S, Valkenborg, D, Laukens, K, Lilley, KS, Olsen, JV, Heck, AJ, Mechtler, K, Aebersold, R, Gevaert, K, Vizcaino, JA, Hermjakob, H, Kohlbacher, O, Martens, L (2014). qcML: an exchange format for quality control metrics from mass spectrometry experiments. Mol Cell Proteomics 13: 1905-13
  • Weisser, H, Nahnsen, S, Grossmann, J, Nilse, L, Quandt, A, Brauer, H, Sturm, M, Kenar, E, Kohlbacher, O, Aebersold, R, Malmstrom, L (2013). An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics. J Proteome Res 15 (12), 4686-4695.