Biomedical Genomics

We study how the genome encodes different layers of information, specifically its own stability, repair, and mutagenesis . Using a combination of computational biology, machine learning, deep learning models for sequence data, clinical and experimental data from collaborating labs, we try to understand the underlying mechanisms despite their complexity, and look for routes to bring this knowledge into clinical use.

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Biomedical Genomics Group

This is our interdisciplinary and diverse team ranging in backgrounds from molecular biology to multimodal machine learning.

Research

We study how genomes encode their own stability. Using a combination of computational biology, machine learning, clinical and experimental data from collaborating labs, we try to understand underlying mechanisms of genome regulation, DNA repair and mutagenesis in the complex context of the underlying DNA sequence. A disturbed balance of genome function and stability can alter somatic genome evolution, which contributes to ageing and cancer development.

Group Leader, acting Chair for Genomics

Dr. Anna Poetsch

Our research

The Biomedical Genomics group employs computational techniques, machine learning, and deep learning approaches to assess the layers of information genomes encode. In this context we are particularly interested how the genome functions in the context of balancing function with DNA damage and mutagenesis.
The group established a novel technique for genome-wide measurements of oxidative DNA damage and contributed to the understanding of genome editing precision and the genome wide distribution of mutations.
For this we use computational biology with different types of functional genomics data (AP-Seq, HiC, ChIP-Seq,...) and cancer genomics data and different machine learning and deep learning techniques, particularly the DNA language models we develop, GROVER and EAGLE-MUT.