The M3 Research Center
Malignome, Metabolome and Microbiome

Zurück zum Hauptmenü
Zum Menü der Einrichtung

574

Adresse: Otfried-Müller-Straße 37
72076 Tübingen

Office

Telefonnummer: +49 7071 29-82518
Michael Lehmann

E-Mail-Adresse: m3-office@med.uni-tuebingen.de

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 (https://nf-co.re) 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.

Portraitfoto

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.

Interview
Data science methods for biomedical research – Interview with Prof. Dr. Sven Nahnsen

In this Interview, Prof. Dr. Sven Nahnsen explains how open-source tools like nf-core and FAIR data principles are shaping the future of biomedical research. Learn how the team bridges data, reproducibility, and translational AI to bring innovation into the clinic.

Beim Abspielen des Videos werden Daten an YouTube übertragen. Weitere Hinweise finden Sie in unserer Datenschutzerklärung.

Teamfoto
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

Information on bioinformatics services and data management

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

Selected publications

  • nf-core/crisprseq: a versatile pipeline for comprehensive analysis of CRISPR gene editing and screening assays

    Mir-Pedrol J, Kuhlburger L, Sanvicente-García M, Yazar M, Ryan CJ, Krakau S, Gabernet G, Güell M, Bonfanti M, Nf-Core Community, Nahnsen S. nf-core/crisprseq: a versatile pipeline for comprehensive analysis of CRISPR gene editing and screening assays. NAR Genom Bioinform. 2026 Jan 15;8(1):lqaf214. doi: 10.1093/nargab/lqaf214. PMID: 41551929; PMCID: PMC12805889. https://doi.org/10.1093/nargab/lqaf214

  • mlf-core: a framework for deterministic machine learning

    Heumos L, Ehmele P, Kuhn Cuellar L, Menden K, Miller E, Lemke S, Gabernet G, Nahnsen S. mlf-core: a framework for deterministic machine learning. Bioinformatics. 2023 Apr 3;39(4):btad164. doi: 10.1093/bioinformatics/btad164. PMID: 37004171; PMCID: PMC10089676. https://doi.org/10.1093/bioinformatics/btad164

  • The nf-core framework for community-curated bioinformatics pipelines

    Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PMID: 32055031. https://doi.org/10.1038/s41587-020-0439-x

  • Measles virus-based treatments trigger a pro-inflammatory cascade and a treatment-specific immunopeptidome in glioblastoma

    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 Distinctive Immunopeptidome in Glioblastoma. Mol Ther Oncolytics. 2018 Dec 31;12:147-161. doi: 10.1016/j.omto.2018.12.010. PMID: 30775418; PMCID: PMC6365369. https://doi.org/10.1016/j.omto.2018.12.010

  • qPortal: A platform for data-driven biomedical research

    Mohr C, Friedrich A, Wojnar D, Kenar E, Polatkan AC, Codrea MC, Czemmel S, Kohlbacher O, Nahnsen S. qPortal: A platform for data-driven biomedical research. PLoS One. 2018 Jan 19;13(1):e0191603. doi: 10.1371/journal.pone.0191603. PMID: 29352322; PMCID: PMC5774839. https://doi.org/10.1371/journal.pone.0191603

  • Proteome and phosphoproteome analysis of commensally induced dendritic cell maturation states

    Korkmaz AG, Popov T, Peisl L, Codrea MC, Nahnsen S, Steimle A, Velic A, Macek B, von Bergen M, Bernhardt J, Frick JS. Proteome and phosphoproteome analysis of commensally induced dendritic cell maturation states. J Proteomics. 2018 May 30;180:11-24. doi: 10.1016/j.jprot.2017.11.008. Epub 2017 Nov 15. PMID: 29155090. https://doi.org/10.1016/j.jprot.2017.11.008

  • Personalized peptide vaccine-induced immune response associated with long-term survival of a metastatic cholangiocarcinoma patient

    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. Personalized peptide vaccine-induced immune response associated with long-term survival of a metastatic cholangiocarcinoma patient. J Hepatol. 2016 Oct;65(4):849-855. doi: 10.1016/j.jhep.2016.06.027. Epub 2016 Jul 7. Erratum in: J Hepatol. 2017 Jan;66(1):252-253. doi: 10.1016/j.jhep.2016.10.021. PMID: 27397612; PMCID: PMC5756536. https://doi.org/10.1016/j.jhep.2016.06.027

  • Personalized peptide vaccine-induced immune response associated with long-term survival of a metastatic cholangiocarcinoma patient

    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. Personalized peptide vaccine-induced immune response associated with long-term survival of a metastatic cholangiocarcinoma patient. J Hepatol. 2016 Oct;65(4):849-855. doi: 10.1016/j.jhep.2016.06.027. Epub 2016 Jul 7. Erratum in: J Hepatol. 2017 Jan;66(1):252-253. doi: 10.1016/j.jhep.2016.10.021. PMID: 27397612; PMCID: PMC5756536. https://doi.org/10.1016/j.jhep.2016.06.027

  • Mass-Spectrometry-Based Proteomics Reveals Organ-Specific Expression Patterns To Be Used as Forensic Evidence

    Dammeier S, Nahnsen S, Veit J, Wehner F, Ueffing M, Kohlbacher O. Mass-Spectrometry-Based Proteomics Reveals Organ-Specific Expression Patterns To Be Used as Forensic Evidence. J Proteome Res. 2016 Jan 4;15(1):182-92. doi: 10.1021/acs.jproteome.5b00704. Epub 2015 Dec 8. PMID: 26593679. https://doi.org/10.1021/acs.jproteome.5b00704

  • qcML: an exchange format for quality control metrics from mass spectrometry experiments

    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, Vizcaíno JA, Hermjakob H, Kohlbacher O, Martens L. qcML: an exchange format for quality control metrics from mass spectrometry experiments. Mol Cell Proteomics. 2014 Aug;13(8):1905-13. doi: 10.1074/mcp.M113.035907. Epub 2014 Apr 23. PMID: 24760958; PMCID: PMC4125725. https://doi.org/10.1074/mcp.m113.035907

  • An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics

    Weisser H, Nahnsen S, Grossmann J, Nilse L, Quandt A, Brauer H, Sturm M, Kenar E, Kohlbacher O, Aebersold R, Malmström L. An automated pipeline for high-throughput label-free quantitative proteomics. J Proteome Res. 2013 Apr 5;12(4):1628-44. doi: 10.1021/pr300992u. Epub 2013 Feb 22. PMID: 23391308. https://doi.org/10.1021/pr300992u

  • OpenMS: a flexible open-source software platform for mass spectrometry data analysis

    Sturm M, Bertsch A, Gröpl C, Hildebrandt A, Hussong R, Lange E, Pfeifer N, Schulz-Trieglaff O, Zerck A, Reinert K, Kohlbacher O. OpenMS - an open-source software framework for mass spectrometry. BMC Bioinformatics. 2008 Mar 26;9:163. doi: 10.1186/1471-2105-9-163. PMID: 18366760; PMCID: PMC2311306. https://doi.org/10.1186/1471-2105-9-163