Dresden International Graduate School for Biomedicine and Bioengineering

We study the genomics of DNA damage, repair, and mutagenesis. Using a combination of computational biology, machine learning, 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.

Previously, I developed novel techniques to measure oxidative DNA damage genome wide and established the associated data analysis strategies. We found an unappreciated mechanism that leads to lower damage rates in coding sequence, which is reflected in the related mutation profiles of cancer genomes. We also found that the genome context specificity of DNA repair processes impacts on other cellular processes and genome editing.

Now we are building on this work with three major focus areas:

1. The genomics of DNA damage, repair, and DNA damage response
   This area combines several projects that we pursue together with collaborators on mechanistic questions in the DNA damage response. Combining biochemical data with functional genomics approaches, these highly interdisciplinary projects keep us up to speed with the cutting edge mechanistic questions that currently occupy the field.
2. Learning genome specificity of mutagenesis
   We are using neural networks trained on thousands of whole genome sequencing datasets to model the specificity of mutagenic mechanisms. To investigate the mechanisms and their interactions, we simulate their genome specificity under conditions of different individuals.
   From this investigation we hope to gain a context-specific mechanistic understanding of the genome specificity of mutagenic mechanisms and how individual conditions can influence the location of mutations. Predicting where mutations happen may help to understand one of the roots of long term side effects from cancer treatment and even open possibilities to reduce these without compromising on the desired treatment outcomes.
   Working closely with clinicians, we hope that this combination of looking at mutagenesis from a mechanistic perspective, a clinical perspective, and with cutting edge computational models, will allow us to substantially advance strategies of personalised oncology.
3. Understanding and learning precision of genome editing
   We previously found that precision of CRISPR mediated genome editing is dependent to a large extent on the guide RNA sequence. This is however not the whole story and different cell types and states also play a role, because of the different regulation of their DNA repair programs. This has a substantial impact on how predictable, safe, and precise genome editing is performed.
   We are therefore following up our work on the precision of genome editing by using machine learning on functional genomics data to understand these processes even better and to optimize the design of genome editing approaches with the aim to increase specificity, predictability, and safety.