Computational and Single-Cell Epigenomics

The research group "Computational and Single-Cell Epigenomics" is embedded in the Division of Cancer Epigenomics at the German Cancer Research Center (DKFZ). 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.

• Members of the research group

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. 

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

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.

• Scherer M, Singh I, Braun M, Szu-Tu C, Kardorff M, Rühle J, et al. Somatic epimutations enable single - cell lineage tracing in native hematopoiesis across the murine and human lifespan. bioRxiv. 2024;1–36. https://doi.org/10.1101/2024.04.01.587514
• 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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