June 18 – 21 , 2017, Dagstuhl Seminar 17252

Computational Challenges in RNA-Based Gene Regulation: Protein-RNA Recognition, Regulation and Prediction


Rolf Backofen (Universität Freiburg, DE)
Yael Mandel-Gutfreund (Technion – Haifa, IL)
Uwe Ohler (Max-Delbrück-Centrum – Berlin, DE)
Gabriele Varani (University of Washington – Seattle, US)

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All living organism must be able to differentially regulate the expression of genes encoded in their genome. Genes are first transcribed into RNA, which are either translated to proteins or functionally active as non-coding RNAs. Beside the direct regulation of the transcription of DNA into RNA, an important additional layer is the direct regulation of RNAs by RNA binding proteins (RBPs). This layer of regulation controls cellular decisions as part of gene expression networks composed of both proteins and RNAs. While being a dark matter of the cell for a long time, recent years have shown the development of sophisticated high throughput experimental technologies that greatly increased our understanding of protein-RNA recognition and regulation. Nevertheless, the quantitative molecular understanding of the transcriptome-level processes remains very limited. Especially complexity extbf{(both in the form of data as in the required computational approaches)} limits the exploitation of these advances towards a quantitative understanding of post-transcriptional regulation. The objective of the seminar to discuss urgently needed computational approaches allowing to exploit the wealth of new data. More specifically, the seminar focused on

  • addressing major computational challenges in this field
  • mining the extensive genomic information on RNA and associated proteins
  • investigation of RNA-protein interactions on an atomic level
  • quantitative prediction of cellular regulatory networks and their dynamics.
  Creative Commons BY 3.0 Unported license
  Rolf Backofen, Yael Mandel-Gutfreund, Uwe Ohler, and Gabriele Varani


  • Bioinformatics
  • Data Bases / Information Retrieval
  • Data Structures / Algorithms / Complexity


  • RNA-protein interaction
  • Machine learning
  • Genomic and transcriptomic data mining
  • Gene expression networks
  • RNA structure prediction
  • Quantitative biology

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