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JRG 4: Bioinformatics

Project Leader:
Bernhard Renard


Dr. Robert Rentzsch
Dr. Carlus Deneke
Dipl. Biomath. Franziska Zickmann
Dipl. Phys. Martin Lindner
Dipl. Biomath. Martina Fischer
Mathias Kuhring, M.Sc.
Kathrin Trappe, M.Sc.
Vitor Piro, M.Sc.
Harald Detering, B.Sc.
Benjamin Strauch, B.Sc.
Jonathan Baumann
Christian Knauth
Yoonjeong Cha


Our work focuses on developing fast and robust bioinformatics procedures for high-throughput experiments, the main aim being to clarify issues related to diagnosing and characterizing pathogens. The scope of our work ranges from formulating the respective question in mathematical terms to developing or adapting algorithms, evaluating biomedical data, and, finally, to implementing and publishing open-source software.

Technological advances in high-throughput processes are allowing previously unimagined insights into biomedical contexts. Today, billions of genetic bases can be sequenced and millions of mass positions can be analyzed for protein identification in a single experiment. The ongoing, rapid growth of technical possibilities can thus generate datasets for single experiments that fill entire computer hard drives. The risk is, however, that the process of data analysis will be unable to keep up with the pace of data acquisition.

The main challenge with high-throughput experiments, therefore, is not so much the actual collection of information as data analysis and the automated extraction of relevant information. In view of the huge amount of data and their structures, we require special algorithms if we are to obtain prompt and reliable results.

Our work focuses on next-generation sequencing data for analyzing DNA and RNA sequences, as well as on mass spectrometry measurements for protein identification and structure predictions. In this context, we are especially interested in the integration of complementary data sources. It is also essential for our bioinformatics procedures and data analysis to consider the statistical effects of high-dimensional data and to determine accurate error rates to avoid potentially misleading interpretations. Another key aspect for us is to ensure the robustness of the algorithms and the integration of experiments.

We develop and adapt machine learning algorithms and analysis methods for these amounts of data and prepare them for practical application in collaboration with experimental groups.

figure of a peptide identification and a genome assemblyExample of peptide identification (top) and genome assembly (bottom). The peptide identification is based on the analysis of a mass spectrum. The peptide fragment ions that make it possible to derive individual amino acids are highlighted in colour. The genome assembly is the result of linking information from millions of next-generation sequencing reads.


Vacancies are published at Robert Koch Institute vacancies.

We are always looking for dedicated and highly motivated students for projects in the context of internships, or theses, and as research assistants. Please contact the group leader for these and other questions.


An important objective of our group is to integrate the methods we have developed into freely available software. We have been and are involved e.g. in the following projects:


Postbox: 650261
D-13302 Berlin


PD Dr. Bernhard Renard
+49 30 18754-2561
PD Dr. Bernhard Renard

Date: 27.08.2014


  • Calvignac-Spencer S, Schulze JM, Zickmann F, Renard BY (2014): Clock rooting further demonstrates that Guinea 2014 EBOV is a member of the Zaïre lineage.
    PLOS Currents Outbreaks 2014: Jun 16. Edition 1. doi: 10.1371/currents.outbreaks.c0e035c86d721668a6ad7353f7f6fe86. more

  • Penzlin A, Lindner MS, Doellinger J, Dabrowski PW, Nitsche A, Renard BY (2014): Pipasic: similarity and expression correction for strain-level identification and quantification in metaproteomics.
    Bioinformatics 30 (12): i149–i156. Epub Jun 15. doi: 10.1093/bioinformatics/btu267. more

  • Fischer M, Zilkenat S, Gerlach RG, Wagner S, Renard BY (2014): Pre- and postprocessing workflow for affinity purification mass spectrometry data.
    J. Proteome Res. 13 (5): 2239-2249. Epub Mar 18. doi: 10.1021/pr401249b. more

  • Zickmann F, Lindner MS, Renard BY (2014): GIIRA – RNA-Seq driven gene finding incorporating ambiguous reads.
    Bioinformatics 30 (5): 606-613. Epub 2013 Oct 11. doi: 10.1093/bioinformatics/btt577. more

  • Giese SH, Zickmann F, Renard BY (2014): Specificity control for read alignments using an artificial reference genome-guided false discovery rate.
    Bioinformatics 30 (1): 9-16. Epub 2013 May 17. doi: 10.1093/bioinformatics/btt255. more

  • Kuhring M, Renard BY (2012): iPiG: Integrating Peptide Spectrum Matches into Genome Browser Visualizations.
    PLoS ONE 7 (12): e50246. Epub Dec 4. doi: 10.1371/journal.pone.0050246. more

  • Renard BY, Xu B, Kirchner M, Zickmann F et al. (2012): Overcoming species boundaries in peptide identification with Bayesian Information Criterion-driven Error-tolerant Peptide Search (BICEPS).
    Mol. Cell. Proteomics 11 (7): M111.014167. Epub Apr 6. DOI: 10.1074/mcp.M111. 014167–1. more

  • Lindner MS, Renard BY (2012): Metagenomic abundance estimation and diagnostic testing on species level.
    Nucleic Acids Res.: Epub Aug 31. doi:10.1093/nar/gks803. more

  • Renard BY, Kirchner M, Koethe U, Pappin DJ, Hamprecht FA, Steen H, Steen JJ (2010): Computational Protein Profile Similarity Screening for Quantitative Mass Spectrometry Experiments.
    Bioinformatics 26 (1): 77-83. 10.1093/bioinformatics/btp607. more

  • Renard BY, Kirchner M, Monigatti F, Ivanov AR, Rappsilber J, Winter D, Steen JAJ, Hamprecht FA, Steen H (2009): When Less Can Yield More - Computational Preprocessing of MS/MS Spectra for Peptide Identification.
    PROTEOMICS 9 (21): 4978-4984. 10.1002/pmic.200900326. more