Computational Biology, Bioinformatics and RNA Biology

Speeding up scientific research by combining high-throughput assays and computational evaluation.

RNA has central functions in all living organisms. We are interested in its regulatory functions and resulting regulatory networks, which we study in wet & dry lab. Our mission is to develop and improve high-throughput methods, to design specialised bioinformatics tools for their analysis and to finally turn all this into knowledge about RNA-based regulation.

The Computational Biology group works interdisciplinary and combines expertises in RNA biochemistry, the analysis of NGS data and the development of algorithms for the analysis of RNA structure. Furthermore, we are interested in the interactive visualization of networks, we run the IBVT Galaxy Server and offer bioinformatics consultation.

Most of our projects are based on our general interest in the cellular functions of RNA.

Our Topics of Interest

RNA is not only a passive messenger of information as mRNA, but also possesses regulatory and catalytic functions. A self-contained, non-coding RNA (ncRNA) can regulate other RNAs (mRNAs & other ncRNAs) via complementary base pairing, which hampers translation or triggers degradation of the target. Commonly, ncRNAs have several targets and mRNAs are targeted by several ncRNAs, such that complex regulatory networks exist. These networks are our subjects of interest and we study them experimentally and computationally. The overall aim is to comprehensively describe these networks, to analyse their dynamics (evolutionary, conditionally, temporally) and to predict advantegeous changes or even completely synthetic regulatory circuits.

Direct Duplex Detection methods provide detailed information about RNA:RNA interactions at nucleotide resolution. The analysis of the resulting sequencing data goes beyond standard RNA-seq analysis and needs specialized tools and statistics for significance analysis.

Graphs are common objects in Bioinformatics, e.g. assembly graphs, phylogenetic trees and interaction networks, thus we developed a general tool for the interactive visualisation of such data for the Galaxy system. Details are described in Schäfer, R. and Voß, B, 2016, Bioinformatics, 32, pp. 3525–3527.

RNA functions at the structural level, similar to proteins, thus structure analysis helps to understand the function of an RNA molecule. Noteworthy, the major loss of free energy takes place by the formation of hydrogen bonds between complementary base pairs and, especially, the stacking of these base pairs. As a consequence the secondary structure dominates the shape of an RNA and is thus the subject of interest in structural studies. We work on methods for the analysis of the structure space of RNAs, which delivers information about alternative structures, e.g. as in Riboswitches, folding kinetics and evolutionary conserved structures. For example, we have developed the tool RNAHeliCes (see Software section) which is described in detail in Huang,J. and Voß,B. (2014) Analysing RNA-kinetics based on folding space abstraction. BMC Bioinformatics, 15, 60 and Huang,J., Backofen,R. and Voß,B. (2012) Abstract folding space analysis based on helices. RNA, 18, 2135–2147.

Galaxy system is a web server that enables users without a background in computer science to use state-of-the-art bioinformatics algorithms and to distribute the computation of large high-performance-computing clusters. Furthermore, it has an integrated workflow system, for easy automation of recurring analysis tasks, and an efficient data handling framework that also allows to share data among colleagues. We installed a Galaxy server locally and it is used for data analysis in scientific projects as well as in teaching. 

From within the network of the University of Stuttgart you can reach the IBVT Galaxy Server at https://benz.ibvt.uni-stuttgart.de. There you will also find the information how to get access.

Software

A tool for the interactive visualisation of Graphs within Galaxy. 

Repository on GitLab

Analysis of the folding space of RNA is mainly hampered by its size, because it grows exponentially with sequence length. Structure abstraction is a method to dampen the exponential explosion and to focus on structures that are different in shape rather than single basepairs. RNAHeliCes focusses on helical regions within structures that are represented by the type of structural element they enclose and the position at which they occur in the sequence.

Repositiory on GitLab

Differential RNA-seq is a widely used method for the prediction of Transcriptional Start Sites (TSSs) in bacteria. With RNAseg we make use of this information, but additionally use the overall coverage of reads over the genome to predict Transcriptional Units (TUs) genome-wide.

Repository on GitLab

Publications of the Group

  1. O. S. Alkhnbashi, T. Meier, A. Mitrofanov, R. Backofen, and B. Voß, “CRISPR-Cas Bioinformatics,” Methods, 2019.
  2. B. Schönberger, C. Schaal, R. Schäfer, and B. Voß, “RNA interactomics: recent advances and remaining challenges,” F1000Research, vol. 7, p. 1824, 2018.
  3. S. C. Lott et al., “GLASSgo - Automated and reliable detection of sRNA homologs from a single input sequence,” Frontiers in Genetics, vol. 9, 2018.
  4. R. A. Schäfer and B. Voß, “VisualGraphX: interactive graph visualization within Galaxy,” Bioinformatics, p. btw414, 2016.
  5. D. Stazic and B. Voß, “The complexity of bacterial transcriptomes,” Journal of Biotechnology, vol. 232, pp. 69--78, 2016.

Further Info

Björn Voß
Jun.-Prof. Dr. rer. nat.

Björn Voß

Professor

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