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Group

We study the genomics of DNA damage, repair, and mutagenesis. Using a combination of computational biology, machine learning, in colaboration with experimental molecular biologists, we try to understand the underlying mechanisms despite their complexity.

Complementary to understanding these processes, we aim to bring this knowledge into clinical practise for personalised oncology as quickly as possible. For this aim we are also working closely with clinical/ translational groups.

You

see Jobs for current opening of a postdoc position.

You

see Jobs for current opening of a PhD student position.

You

please contact us, if you are looking for a part time project as medical doctor or student.

Research

We study the genomics of DNA damage, repair, and mutagenesis. Using a combination of computational biology, machine learning, clinical and experimental data from public sources and 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.

routes to mutagenesis

Mutations accumulate in every cell of our body throughout life. There are processes that come from within, such as those happening by chance and through the cell's metabolic processes. In addition they occur induced by the environment, such as through UV radiation in our skin, through smoking, or also inflammatory processes. Most mutations are of little consequence (blue) and just accumulate in the 3 billion letters of our DNA without affecting a gene. But some may hit important genes (yellow) and disrupt their function. This contributes to ageing, and may also lead for cells to become cancerous. They then develop mechanisms to accumulate more mutations and through deactivating protective genes they may develop a hypermutator phenotype (red). Also most cancer treatments, chemo- and radiotherapy are based on the principle that they damage DNA. They are very efficient in curing cancer and it is very hard to find better alternatives. But, also the treatment is causing additional mutations (pink) - not only in the possibly surviving cancer cells, but in healthy cells as well. As a consequence, the treatments are causing long term side effects, such as contributing to ageing, and they may also cause a different cancer later in life.

Through research, cancer treatment has improved significantly over the last decades, mostly thanks to harsh regimens of chemo-, and radiotherapy. We are interested to better understand the principles behind these treatments to find ways of tailoring treatments to the individual and reducing the long-term side affects and risks for treatment related secondary cancers.

oxidative DNA damage

One route to mutagenesis is through oxidative damage to the DNA. This can happen through normal cellular processes that produce oxygen radicals, but also through toxins and ionising radiation, such as radioactivity. It acts through adding an oxygen to mainly Gs in the DNA. This has the risk of causing a mutation from the G to a T, which - dependent on where it happens in the genome - has more or less consequences. To prevent this, all of our cells have a repair system (base excision repair) that functions by cutting out the damaged G with the enzyme OGG1 to leave a gap, an apurinic site (AP-site). This gap is then filled through a longer process by an undamaged G.

We are trying to understand this process and how it happens dependent on the location in the genome, how this differs between tissues, and how oxidative DNA damage in repair leads a cross-talk with other mechanisms that lead to mutations. This is relevant both in response to treatment, and also in the development of many cancer types with mutations that are induced by these processes, such as pediatric brain tumours, and cancer types that are linked to inflammatory processes, such as esophageal adenocarcinoma.

When there is increased DNA damage, DNA damage response pathways are activated, which govern either that the cell safely pulls through or kills itself. DNA damage response is one of the central pathways deregulated in cancer and harbors many possibilities for personallised oncology.

To understand the mechanisms of DNA damage, repair, and mutagenesis, we are collaborating on molecular biological techniques to measure DNA damage and DNA damage response. We are building computational methods and machine learning systems to interrogate the resulting data as well as cancer genome sequencing data to understand the mechanisms that drive these biological processes. In addition, we use these techniques to bring basic science together with clinical application, using mechanisms of mutagenesis and DNA damage response to develop strategies of personalised oncology.

Projects

Collaborators

Joanna Loizou group Institute of Cancer Research, Medical University Vienna:
Genomics of mutageneis.

Evi Soutoglou group, IGBMC Strasbourg:
Precision of CRISPR mediated genome editing and Gene Regulation in the DNA damage response.

Dipanjan Chowdhury group, Dana Farber Cancer Institute/ Harvard Medical School:
Gene Regulation in the DNA damage response.

Simon Boulton group, The Francis Crick Institute:
Gene Regulation in the DNA damage response.

Michael Schröder group, Biotec, TU Dresden:
Machine Learning for outcome prediction in cancer.

Hanno Glimm group, NCT Dresden:
Translational Cancer Genomics.

Contact

If you would like to get in touch, please drop us a line here:

Jobs

We are on the lookout for new computational team members, a postdoctoral fellow in computational biology and a PhD student.

Postdoctoral Fellow Computational Biology

We are looking for a highly motivated computational biologist.
The successful candidate will use machine learning to investigate genome specificity behind mechanisms of DNA damage, repair and mutagenesis. This exciting opportunity is embedded in an interdisciplinary group with the local expertise ranging from biochemistry via functional genomics to machine learning.

We offer:

  • Training and commitment for further career development
  • Friendly and collaborative work environment
  • Possibilities for interdisciplinary collaboration

  • The postdoctoral fellow will use machine learning and deep learning data analysis techniques to investigate the behaviour of mutagenic mechanisms and interrogate the mechanistic basis for their specificity in the genome.
    The successful candidate will be expected to also collaborate closely with other members of the Biomedical Genomics group as well as clinical and/or molecular biological collaborating groups.

    Tasks:
  • Lead specific research project;
  • Perform expert analysis of cancer genomics and functional genomics data;
  • Develop and apply machine learning techniques to these kinds of data;
  • Contribute to collaborative projects within the lab and with collaborating labs;
  • Assist with organisational tasks;
  • Assist with supervision of junior lab members;
  • Participate and contribute to lab meetings;
  • Lead and contribute to the preparation of scientific manuscripts.

  • Requirements:
    The post holder should be eager to perform science in an interdisciplinary, collaborative and happy lab, in addition to the following:
  • University and PhD degree in a relevant subject with an extensive analytical component e.g. bioinformatics, cancer genomics, statistics, molecular biology or mathematics or other areas relevant to computational biology;
  • An understanding of programming in a higher-level language (R/C/C++/Python), particularly with regard to big data, data visualisation and machine learning techniques;
  • Experience in bioinformatics or a related biological field, with applying statistical techniques to biological data;
  • A basic knowledge of genomics and/or general molecular biology;
  • A broad comprehension of high throughput genomic technologies;
  • Demonstrable experience of cancer genomics and functional genomics analysis methodologies data analysis will be an asset;
  • Excellent communication skills within an interdisciplinary research environment;
  • Excellent scientific analysis skills;
  • Excellent oral and writing skills in the English language (German is not required).

  • The postdoctoral fellow will initially be hired for 2 years, starting as soon as possible.
    To apply, please send a detailed CV, bibliography, cover letter and the name of two references by 07.08.2020 as a single pdf to anna.poetsch@tu-dresden.de.
    The official job advertisement can be found here.

    PhD student

    We are also recruiting a PhD student in the DIGS-BB fall selection. The deadline has passed, but please stay tuned for future openings!

    If you wish to join on your own funding, please get in touch.

    Placements and Translational Projects

    If you would like to join the lab as a student for a placement in a Masters course or for your thesis, please get in touch!

    We are also happy to host medical doctors, who may want to work part-time on computational projects in translational oncology.

    Please be aware that we are currently exclusively working computationally. Projects that require bench work will have to be arranged together with our collaborators.

    Funding

    We are receiving funding and support from:

    Mildred Scheel Early Career Center of the TU Dresden

    The MSNZ is funded by the German Cancer Aid

    Biotechnology Center Dresden

    National Center for Tumour diseases Dresden