Computational and Single-Cell Epigenomics

Dr. Michael Scherer

Team 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.

Running Projects

This diagram illustrates a workflow involving lung cancer research, highlighting whole genome sequencing (WGS) and RNA sequencing (RNA-seq). It shows genetic elements, including a gene and an enhancer, and mentions "pyjacker," a tool for analyzing data related to gene expression. A table indicates gene significance (FDR).

We investigate whether enhancer hijacking is an understudied and overlooked mechanism of oncogene activation in lung cancer.

The image illustrates a process for profiling acute myeloid leukemia (AML) samples at diagnosis, leading to the identification of leukemic blasts. It depicts colored shapes representing cells and a directional arrow indicating the flow from profiling to the identification stage.

Using a combination of bulk assays and single-cell profiling, we are investigating whether epigenetic heterogeneity is associated with leukemogenesis and therapy resistance

We aim to investigate differences in AML biology between biological sexes to define putative biomarkers in a sex-aware manner.

Using DNA methylation profiling of cfDNA, we aim to detect prostate cancer at a very early stage.

RnBeads is an R package for comprehensive analysis of DNA methylation data obtained with any experimental protocol that provides single-CpG resolution. Supported assays include Infinium and EPIC microarrays and bisulfite sequencing protocols, and also MeDIP-seq and MBD-seq once the data have been preprocessed with DNA methylation level inference software. RnBeads implements an analysis workflow that is significantly more comprehensive than those of existing tools. It documents its results in a highly annotated and readable hypertext report, and it scales to the large sample sizes that are becoming the norm for DNA methylation analysis in human cohorts.