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The Computational Biology group has its biological focus on gene regulation, mRNA metabolism, and Epigenomics. We develop and apply statistical methods for the exploration and integrative analysis of high dimensional biomedical data generated from RNA-sequencing, chromatin immunoprecipitation (ChIP), crosslinking and immunoprecipitation (CLIP), bisulfite sequencing and high-throughput microscopic imaging. Further, they develop probabilistic graphical models and efficient inference algorithms for their learning. The goal is to reconstruct intracellular signaling networks using static or time series gene expression measurements.

Analysis of differentiating hematopoietic progenitor cells by time-lapse microscopy (left), using image features extraction and a probabilistic graphical model (middle), yields an unbiased, automated annotation of cell types (right).

Selected publications

  1. Zacher B, Michel M, Schwalb B, Cramer P, Tresch A, Gagneur J. Accurate promoter and enhancer identification in 127 ENCODE and roadmap epigenomics cell types and tissues by GenoSTAN. PloS one, 2017, 12 (1), e0169249
  2. Stricker G, Engelhardt A, Schulz D, Schmid M, Tresch A, Gagneur J. GenoGAM: genome-wide generalized additive models for ChIP-Seq analysis. Bioinformatics, 2017, 33 (15), 2258-2265
  3. Schwalb B, Margaux M, Zacher B, Fühauf K, Demel K, Tresch A, Gagneur J, Cramer P. TT-seq maps the human transient transcriptome, Science, 2016, 352(6290), 1225-1228.
  4. Mehta A, Cordero J, Dobersch S, Romero‐Olmedo A, Savai R, Bodner J, … , Tresch A, Günther A, Barreto G. Non‐invasive lung cancer diagnosis by detection of GATA6 and NKX2‐1 isoforms in exhaled breath condensate. EMBO molecular medicine, 2016, e201606382
  5. Glas J, Dümcke S, Zacher B, Poron D, Gagneur J, Tresch A. Simultaneous characterization of sense and antisense genomic processes by the double-stranded hidden Markov model. Nucleic acids research, 2015, 44 (5), e44-e44
  6. Niederberger T, Failmezger H, Uskat D, Poron D, Glauche I, Scherf N, Roeder I, Schroeder T, Tresch, A. Factor graph analysis of live cell imaging data reveals mechanisms of cell fate decisions, Bioinformatics, 2015, btv040.
  7. Zacher B, Lidschreiber M, Cramer P, Gagneur J, Tresch A. Annotation of directed genomic states unveils variations in the Pol II transcription cycle, Molecular Systems Biology, 2014, 10(12), 768.
  8. Eser P, Demel V, Maier K, Schwalb B, Pirkl N, Martin D, Cramer P, Tresch A. Periodic mRNA synthesis and degradation co‐operate during cell cycle gene expression. Molecular systems Biology, 2014, 10 (1), 717
  9. 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, 2013, 52(1), 52-62.
  10. 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 reveals dynamics of mRNA synthesis and decay in yeast. Molecular Systems Biology, 2011, 7(1), 458.