Prof. Dr. Achim Tresch

The Computational Biology group has its biological focus on gene regulation, mRNA metabolism, and epigenomics. We perform integrative analyses of high dimensional biomedical omics data generated by, e.g., (single cell) RNA-Seq, chromatin immunoprecipitation (ChIP-Seq), bisulfite sequencing and high-throughput microscopic imaging. Our goal is to identify and reconstruct intracellular signaling networks that are active in development, aging, stress response, and disease. To this end, we develop statistical and machine learning methods and efficient inference algorithms for their training.

Our research: We are interested in all aspects of RNA metabolism, foremost the epigenomic control of RNA synthesis, the dynamics of nuclear RNA export, and RNA degradation. Our primary objective is the precise, genome-wide quantification of these processes. As the technology for RNA quantification is under rapid development, we continuously adapt our statistical repertoire for data analysis. Further, we want to know how DNA and RNA binding proteins contribute to RNA metabolism. We construct models for the joint interpretation of multiple ChIP-Seq experiments, which shed light on the composition of the molecular complexes involved in RNA synthesis and their dynamic change during transcription. Recently, we started to do analysis of microscopic time lapse images, as well as of magnetic resonance tomography images. We use novel neuronal network architectures for their automated segmentation and object recognition.

Our successes: 

• We developed Dynamic Transcriptome Analysis (DTA), a bioinformatics-biochemical technique for the reliable, simultaneous quantification of RNA synthesis and degradation rates on a genome-wide scale. [1, 2]. DTA uses non-perturbing metabolic RNA labeling, followed by RNA-Seq. DTA revealed that most mRNA half-lives in S.cerevisiae scatter around a median half-life of 11 minutes, which is much shorter than previously assumed. We monitored the yeast cell cycle at unprecedented temporal resolution [3]. A novel statistical model of periodic gene expression revealed that changes in mRNA synthesis are generally followed by antagonistic periodic changes in their mRNA degradation rates [4]. We demonstrated that the coupling or these processes achieves a concise regulation of periodic RNA expression. Further, we identified the exonuclease Xrn1 as a ‘coordinator’ of RNA synthesis and decay, which ensures homeostasis of total RNA levels [5].
• We invented and implemented a direction-aware hidden Markov model (bdHMM REF3) for the automated, simultaneous analysis of multiple ChIP-Seq tracks. Using bdHMM, we provided an epigenetic annotation of 127 cell lines of the ENCODE consortium, superseding theirs (USING chromhmm) in terms of quality and spatial precision [6].

Our methods/techniques: 

As a computational Biology group, our main work revolves around the development of statistical and machine learning algorithms and their application to biomedical data. Specifically, we use graphical models for the detection and interpretation of gene regulatory interactions. E.g., we created Nested Effects Models, which are able to infer a hierarchical regulatory cascade from the measurement of genome-wide gene expression after targeted interventions. For the learning of these models, which often contain hundreds of parameters, we improve state-of-the-art optimization and inference methods, such as Markov Chain Monte Carlo, or penalized linear models (and their generalizations).

Another focus of our group is on new sequencing technologies, such as single cell RNA-Seq, single cell ATAC-Seq, and direct RNA sequencing (Oxford Nanopore) and the detection of post-transcriptional RNA modifications.