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 chances to the DNA methylation pattern. Additionally, we leverage single-cell DNA methylation technologies to investigate methylation changes in rare cell populations.

People

  • Employee image

    Dr. Michael Scherer

    Group Leader Computational and Single-Cell Epigenomics

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  • Employee image

    Belen Otero Carrasco

  • Employee image

    Sergio Manzano Sanchez

    Technician

  • Employee image

    Maxime de Vrieze

Projects

We are using/developing computational tools for dissecting epigenetic dysregulation in cancer. Additionally, we use single-cell epigenomics techniques (e.g, scTAM-seq) to zoom-in on epigenetic heterogeneity across single cells in cancer samples. 

 

Positions

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
  • Generation of a targeted panel of around 1,000 CpGs to be investigated with scTAM-seq for dissecting epigenetic heterogeneity in Acute Myleoid Leukemia
  • Data analysis of scTAM-seq data and single-cell RNA-seq 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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