Computational and Single-Cell Epigenomics

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Dr. Michael Scherer

Group Leader Computational and Single-Cell Epigenomics

We work on computational solutions for investigating alterations in the epigenomic pattern related to cancer, with a special focus on changes to the DNA methylation pattern. Additionally, we leverage single-cell DNA methylation technologies to investigate methylation changes in rare cell populations.

Illustration depicting cellular structures and epigenomic patterns. On the left, there are clusters of cells represented with colored circles. Below, lines in blue and purple symbolize molecular data. Logos for RnBeads and additional visual content are present at the bottom, indicating data analysis tools.

People

  • A young man with short dark hair and a friendly smile is looking directly at the camera. He is wearing a light gray T-shirt with a red and black patterned pocket. The background is plain white, emphasizing his expression.

    Dr. Michael Scherer

    Group Leader Computational and Single-Cell Epigenomics

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    Belen Otero Carrasco

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    Maxime de Vrieze

Running Projects

Open Projects

We are continuously looking for new people to join our team. At the moment, we have the following open positions:

Bachelor/Master thesis projects in Computational and Single-Cell Epigenomics

We offer a variety of projects ranging from computational tool development, over data analysis, until projects involving wet-lab work. At the moment, we offer the following projects:

  • Data analysis tasks around various cancer types using of the RnBeads (rnbeads.org/) toolsuite
  • Development of a software tool for the analysis of scTAM-seq data
  • Characterizing epigenome heterogeneity in lung cancer
  • Predicting chromothripsis from DNA methylation data

Being embedded in the Division of Cancer Epigenomics, we offer a welcoming work atmosphere with a mix of computational and experimental students.


What we are looking for: We are looking for students that are pursuing their Bachelor's and Master's degree at Heidelberg University and that want to do their Bachelor/Master thesis or lab rotation in Computational Epigenomics. Previous experience with programming in R and with epigenomics are advantages.

Publications

  • Scherer M*, Singh I*, Braun M*, Szu-Tu C*, et al. Clonal tracing with somatic epimutations reveals dynamics of blood ageing. Nature 2025. https://doi.org/10.1038/s41586-025-09041-8
  • Bianchi, A.*, Scherer, M.*, Zaurin, R., Quililan, K., Velten, L., & Beekman, R. (2022). scTAM-seq enables targeted high-confidence analysis of DNA methylation in single cells. Genome Biology, 23(1), 229. https://doi.org/10.1186/s13059-022-02796-7
  • Scherer, M., Nebel, A., Franke, A., Walter, J., Lengauer, T., Bock, C., Müller, F., & List, M. (2020). Quantitative comparison of within-sample heterogeneity scores for DNA methylation data. Nucleic Acids Research, 48(8), e46–e46. https://doi.org/10.1093/nar/gkaa120
  • Müller, F.*, Scherer, M.*, Assenov, Y., Lutsik, P., Walter, J., Lengauer, T., & Bock, C. (2019). RnBeads 2.0: comprehensive analysis of DNA methylation data. Genome Biology, 20(1), 55. https://doi.org/10.1186/s13059-019-1664-9
  • Scherer, M.,* Nazarov, P. V., Toth, R., Sahay, S., Kaoma, T., Maurer, V., Vedeneev, N., Plass, C., Lengauer, T., Walter, J., & Lutsik, P. (2020). Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz. Nature Protocols, 15(10), 3240–3263. https://doi.org/10.1038/s41596-020-0369-6

 

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A young man with short dark hair and a friendly smile is looking directly at the camera. He is wearing a light gray T-shirt with a red and black patterned pocket. The background is plain white, emphasizing his expression.

Dr. Michael Scherer

Group Leader Computational and Single-Cell Epigenomics
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