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MF1: Bioinformatics

Bernhard Renard
Thilo Muth


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.

We develop novel bioinformatics algorithms and statistical procedures, but also provide a bioinformatics core service for data analysis. Both areas of our work are closely interlinked. By analyzing data we often identify novel research questions and tools that we developed in bioinformatics research are rapidly transferred to application as part of the core service.

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.


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 head of unit 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 in several projects available from


Postbox: 650261
D-13302 Berlin


PD Dr. Bernhard Renard
Phone: +49 30 18754-2561
Contact: PD Dr. Bernhard Renard

Here, we list selected publications of our unit. A complete listing is available from

Date: 15.06.2017


  • Lindner MS, Strauch B, Schulze JM, Tausch S, Dabrowski PW, Nitsche A, Renard BY (2017): HiLive – Real-Time Mapping of Illumina Reads while Sequencing.
    Bioinformatics 33 (6): 917-919. Epub 2016 Oct 29. doi: 10.1093/bioinformatics/btw659. more

  • Trappe K, Marschall T, Renard BY (2017): Detecting horizontal gene transfer by mapping sequencing reads across species boundaries.
    Bioinformatics 32 (17): i595–i604. Epub Sep 1. doi: 10.1093/bioinformatics/btw423.

  • Piro VC, Lindner MS, Renard BY (2016): DUDes: a top-down taxonomic profiler for metagenomics.
    Bioinformatics 32 (15): 2272-2280. Epub Mar 24. doi: 10.1093/bioinformatics/btw150. more

  • 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

  • Lindner MS, Renard BY (2013): Metagenomic abundance estimation and diagnostic testing on species level.
    Nucleic Acids Res. 41 (1): e10. Epub 2012 Aug 31. doi:10.1093/nar/gks803. more