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SPP1935 -- Deciphering the mRNP code :
RNA-bound Determinants of Post-transcriptional Gene Regulation

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laboratoriesDr. Söding

Johannes  Söding Center
Max Planck Institute for Biophysical Chemistry Quantitative and computational biology lab

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Am Fassberg 11 37077 Göttingen, Germany

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+495512012890


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High-resolution mapping and quantitative modeling of cooperative RNA binding in mRNPs

Colaboration with Prof. Dr. Patrick Cramer



Lab InfoPROJECT INFORMATION :

We have previously developed and optimized methods to map mRNP components and other RNA-binding factors onto the transcriptome in vivo (Schulz et al., Cell 2013; Baejen et al., Mol. Cell 2014). These methods enabled the first global mapping of proteins recognizing pre-mRNA and shortlived RNAs, and demonstrated that RNA recognition cannot be described by standard methods that are used to describe DNA recognition such as position weight matrices (PWMs). Here we propose an integrated experimental-theoretical approach to contribute to two major goals of the new Schwerpunktprogramm (SPP). First, we will systematically identify cellular binding sites for mRNA-binding proteins with the use of PAR-CLIP (photoactivateble ribonucleoside-enhanced crosslinking and immunoprecipitation), to study formation and function of mRNPs in the budding yeast S. cerevisiae. Second, we will develop new techniques enabling a quantitative description of RNA recognition by cooperative RNA binding of multiple factors that will allow us to predict RNA sequence-binding preferences. The aim of this work is to understand mRNP formation in vivo and to understand the biogenesis and fate of different RNA classes based on their association with cellular factors. The derived technology and bioinformatic tools will be provided to SPP teams.

Focus of the group
* Transcriptional regulation
* Protein bioinformatics
* Metagenomics
* Systems medicine




Lab techsKEY TECHNOLOGIES :

- Computational biology / bioinformatics
- Bayesian statistical modeling
- Analysis of high-throughput biological data
- Development of efficient algorithms and software




PublicationsPUBLICATIONS :

Siebert M. and Söding, J. (2016) Markov models consistently outperform PWMs at predicting regulatory motifs in nucleotide sequences. Nucleic Acids Res., in press (doi: 10.1093/nar/gkw521).

Siebert, M. and Söding, J. (2014) Universality of core promoter motifs? Nature (Brief Commun. Arising), 511, E11-E12.

Baejen, C.; Torkler, P.; Gressel, S.; Essig, K.; Söding, J.; Cramer, P.: Transcriptome maps of mRNP biogenesis factors define pre-mRNA recognition. Mol. Cell 55 (5), S. 745-757 (2014).

Hartmann, H., Guthörlein, E.; Siebert, M., Luehr, S.; Söding, J.: P-value-based regulatory motif discovery using positional weight matrices. Genome Res. 23: 181-194 (2013).

Remmert, M., Biegert, A., Hauser, A., and Söding, J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nature Methods, 9(2):173–5 (2012).