Achim Tresch

Institute of Medical Statistics and Computational Biology

Research Areas


RNA Metabolism, Statistical and Machine Learning

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. spatial transcriptomics, (single cell) RNA-Seq, DNA binding assays (ChIP-Seq, Cut&Run), bisulfite sequencing.

Research Focus

  • RNA metabolism - RNA and protein homeostasis are essential components of cellular integrity that deteriorate with age. Using RNA labelling techniques, we measure RNA synthesis-, export- and degradation rates at a genome-wide scale.
  • Spatio-temporal and single cell analyses - Single cell technologies quantify cellular heterogeneity within tissues. To discover cell type-specific regulatory processes, we develop statistical methods for sparse, high dimensional data and the integration of different modalities such as transcriptomics & spatial metabolomics.

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 Goals

We want to do magic with math.
We want to infer mechanistic relations in dynamic systems with many variables that are measured with a high degree of uncertainty and a lot of missing data.

  • Characterization of tumor microenvironment by combining spatial transcriptomics and mass spectrometry imaging. We treat multi-omics data integration as a missing data problem. Building on our SPACO algorithm, we construct a novel integration framework that circumvents the need to align spatial data from different modelities.
  • Improving modularity clustering. We have developed a mathematical formalism that allows us to describe Louvain and Leiden clustering very simply. This opens the door to new clustering algorithms that outperform the state-of-the-art methods.
  • Quantification of RNA export in human cells under oxidative stress. The mechanisms by which some mRNA are exported efficiently from the nucleus, while others are retained in the nucleus are not well understood. Under stress conditions, the export efficiencies can change dramatically. We want to apply our RNA labeling techniques to shed light on the mechanisms and the RNA binding proteins involved.


What we did so far:

  • We developed SPACO, a method for dimension reduction, pattern detection and denoising of spatial transcriptomics data [1] and used it to study changes in the transcriptome and ultimately the metabolism of the aging liver [2]. SPACO is an efficient, multivariate spectral filter method with no free parameters to be tuned. It provides a calibrated test for spatial dependence in spatial data.
  • We developed the bioinformatics methods for various RNA labeling techniques such as DTA [3], TT-Seq [4], Half-pipe[5]. This allowed us to measure parameters of RNA metabolism such as synthesis rate, RNA Polymerase II speed, nuclear RNA export and cytosolic RNA degradation on a genome-wide scale.
  • We invented and implemented STAN, a direction-aware hidden Markov model for the automated, simultaneous analysis of multiple ChIP-Seq tracks. Using STAN, we provided an epigenetic annotation of 127 cell lines of the ENCODE consortium. Another hidden Markov model (RTIGER) developed by us serves the purpose of genotyping of recombinant genomes from shallow DNA sequencing data.

Key Publications

  1. Dimension reduction by spatial components analysis improves pattern detection in multivariate spatial data. N Kleinenkuhnen, D Koehler, T Baar, C Nikopoulou, V Kondylis, ... bioRxiv, 2023.10. 12.562016

  2.  Spatial and single-cell profiling of the metabolome, transcriptome and epigenome of the aging mouse liver C Nikopoulou, N Kleinenkuhnen, S Parekh, T Sandoval, C Ziegenhain, ... Nature aging, 1-16

  3.  Dynamic transcriptome analysis measures rates of mRNA synthesis and decay in yeast C Miller, B Schwalb, K Maier, D Schulz, S Dümcke, B Zacher, A Mayer, ... Molecular systems biology 7 (1), 458

  4.  TT-seq maps the human transient transcriptome B Schwalb, M Michel, B Zacher, K Frühauf, C Demel, A Tresch, J Gagneur, ... Science 352 (6290), 1225-1228

  5. Nuclear export is a limiting factor in eukaryotic mRNA metabolism JM Muller, K Moos, T Baar, K Zumer, A Tresch bioRxiv, 2023.05. 04.539375

Research Areas