1. Research Background:
Our group does research in Computational Biology, with a focus on
- Gene regulation
- RNA metabolism, in particular RNA decay and RNA export
- Statistical methods for single cell technologies
- Single molecule RNA sequencing
- Image analysis using convolutional neural networks
We have developed a metabolic RNA labeling method in yeast, which was the first non-perturbing method for the genome-wide quantification of mRNA synthesis- and degradation rates . We complemented this technique with dynamic transcriptome analysis (DTA), a statistical estimation procedure for the robust extraction of dynamic parameters from this data . DTA revealed that RNA degradation feeds back onto RNA synthesis, and vice versa, leading to a buffering of RNA levels . Further, we used metabolic RNA labeling for the mapping of nascent RNAs at single nucleotide resolution .
2. Research questions addressed by the group:
- Which distinct mRNA export mechanisms exist, what is their kinetic, and which export factors are involved in the respective processes?
- Can RNA export be the rate-limiting step in mRNA metabolism, i.e., is there mRNA retention? If RNA retention exists, what are the biochemical features that mark mRNA for nuclear retention?
With increasing age, transcription fidelity deteriorates, and malformed mRNA-protein complexes (mRNPs) need to be recognized and degraded. It is known that the nuclear pore complex plays a role in this process by retaining malformed mRNPs. How is this achieved?
Single cell / single molecule RNA-Seq technologies:
- How can we model gene expression profiles from undersampled, zero-inflated single cell RNA-Sequencing data? Based on this, we want to develop a nonparametric test for differential expression?
- How can we integrate time course data to reconstruct trajectories in gene expression space?
- Can we use Nanopore sequencing for the recognition/quantification of post-transcriptionally modified RNAs?
We want to apply these methods for the investigation of podocyte aging and their degeneration in pathological processes such as diabetes.
3. Possible projects:
- Development of a bioinformatics-biochemical assay to quantify RNA export, and nuclear/cytosolic degradation (based on the recently published SLAM-Seq method).
- Investigation of RNA regulatory processes on the single cell level, using epigenetic information (chromatin states, topologically associated domains).
- Development of a statistical method that relates genomic alterations to high dimensional phenotypes (such as RNA expression profiles) as a whole, thereby alleviating multiple testing issues.
- Mapping of podocyte degeneration in kidney disease, using cell type specific in vivo RNA labeling and Nanopore sequencing.
4. Applied Methods and model organisms:
Statistical models, machine learning methods (Bayesian Networks, Hidden Markov Models, Support Vector Machines, Neural Networks, Random Forest, Generalized Linear Models)
We mainly work with data from human cells, but also with other model organisms like mouse, rat, yeast, drosophila.
5. Desirable skills and qualifications:
- A background (Master) in Statistics, Mathematics, Physics, or Quantitative Biology.
- Basic knowledge in Molecular Biology.
- Programming skills, preferably in R, Python.
- Strong social skills and an interest in interdisciplinary research.
- Miller C, Schwalb B, Maier K, Schulz D, Dümcke S, Zacher B, Mayer A, Sydow J, Marcinowski L, Dölken L, Martin D, Tresch A, Cramer P. Dynamic transcriptome analysis measures rates of mRNA synthesis and decay in yeast. Molecular Systems Biology, 7(1), 458 (2011).
- Sun M, Schwalb B, Schulz D, Pirkl N, Etzold S, Larivière L, Maier K, Seizl M, Tresch A, Cramer P. Comparative Dynamic Transcriptome Analysis (cDTA) reveals mutual feedback between mRNA synthesis and degradation. Genome Research, 22(7), 1350-1359 (2012).
- Sun M, Schwalb B, Pirkl N, Maier K, Failmezger H, Tresch A, Cramer P. Global analysis of mRNA degradation reveals Xrn1-dependent buffering of transcript levels. Molecular Cell, 52(1), 52-62 (2013).
- Schwalb B, Michel M, Zacher B, Frühauf K, Demel C, Tresch A, Gagneur J, Cramer P. TT-Seq captures the human transient transcriptome. Science, 352(6290), 1225-1228 (2016).