Kay Nieselt

Integrative Transcriptomics

University of Tübingen
IMPRS Faculty 

Vita

Kay Nieselt
  • PhD in Mathematics, Max-Planck Institute for Biophysical Chemistry (Prof. Manfred Eigen), Göttingen, German and University of Bielefeld (Prof. Dress), Germany (1988-1992)
  • Postdoctoral training at the MPI Göttingen (1992-1995)
  • Feodor-Lynen fellow at the University of Auckland, New Zealand (1995-1996)
  • Postdoctoral training at the Zoological Institute, University of Munich (1997)
  • Group leader Bioinformatics and Data management, Molecular Cell biology, Max Planck Institute for Biophysical Chemistry, Göttingen (1999-2001)
  • Head of the Integrative Transcriptomics Group, University of Tübingen, since 2002
  • Habilitation (2014)
  • Full professor at the University of Tübingen since 2021

Research Interest

The main focus of the group of Kay Nieselt is on the development of algorithms for analyzing and visualizing large-scale multi-omics data mainly derived from microorganisms. High-throughput sequencing technologies and specific DNA enrichment make it possible to decode the genetic information embedded in human bones and other tissues, such as the DNA of ancient bacterial pathogens. To meet the challenges in analysing such DNA data, our group has developed EAGER and MUSIAL. With these pipelines we have successfully contributed to gaining insight into the evolution of historical diseases such as leprosy or pest.
Over the years, our group has worked out the SuperGenome concept, a general global coordinate system for multiply aligned genomes, which has been used to great advantage in a large variety of projects (GenomeRing, PanGee and Pantetris).
The understanding and analysis of the basic principles of gene expression and moreover gene regulation is still one of the open and unsolved problems in biology. We develop and apply algorithms and tools for the analysis and visualization of large-scale expression data, mainly applied to microorganisms. Besides quantifying gene expression, the identification and/or prediction of transcriptional features is another focus of our research. With TSSpredator we have developed a software for the automated detection and classification of TSS from 5’ enriched RNA-seq data. We have just released and published a highly user-friendly implementation of it, called TSSpredator-web. Moreover, TSSCaptur allows the characterization of TSS signals identified from any 5’ enriched RNA-seq data that cannot be allocated to known labelled genes. 
Visualization plays a major role in bioinformatics. Our group has developed and continues to develop visual analytics tools which address as well as integrate various omics modalities. All our visualisation tools are accessible via our Tuebingen Visualisation server (TueVis, https://tuevis.cs.uni-tuebingen.de), which as of today (February 2026) encompasses 20 tools. Lately we have started to work on computational methods in the context of phage-bacteria systems as well as single-cell bacterial data.

We have been involved in a number of research projects such as the Cluster of Excellence “Machine Learning in the Sciences”, the Cluster of Excellence “Controlling Microbes to Fight Infections, and the transregio TRR261 “Cellular Mechanisms of Antibiotic Action and Production”. Furthermore, we are active members in a consortium that studies the genomic landscape of Treponema pallidum as well as a consortium for the development of a vaccine against Treponema pallidum.

 

Available PhD Projects

  • Currently not recruiting doctoral researchers

Selected Reading

 

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