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Our new Leadership Fellow launches a research group in computational cancer genomics

We are delighted to introduce Dr Marketa Tomkova, who has joined the Oxford Branch of the Ludwig Institute for Cancer Research in the Nuffield Department of Medicine, University of Oxford, as a Leadership Fellow. Marketa joins us from the University of California, Davis in the USA where she was a post-doctoral researcher working jointly in the labs of Dr David Segal and Dr Fereydoun Hormozdiari. Marketa is now leading her first independent research programme, focused on computational cancer genomics.

Marketa’s main research interests lie in the mechanisms of mutational processes and epigenomics and how they together and independently contribute to cancer development. This is complemented by her long-term interest in developing computational and deep learning methods; from both large-scale detection of non-coding regulatory cancer drivers in cancer patient sequencing data to developing models of cells and using them to address research questions on arrythmia and development. Marketa has secured funding from the Wellcome Trust and Cancer Research UK to investigate the role of DNA polymerase errors in mutational processes and continue her development of sequencing-based tools for detection of non-clonal mutations, mismatches and DNA damage in single molecules.

The research programme Marketa has launched at Ludwig Oxford is at the interface of technological development, epigenomics and mutagenesis. We are excited that Marketa’s research group will lead the development of computational methods for data analysis of the novel epitranscriptomics sequencing methods developed by Dr Chunxiao Song’s lab and will collaborate with the groups of Dr Parinaz Mehdipour and Professor Yang Shi to investigate the role of RNA modifications in cancer development and progression. She will also use computational cancer genomics, deep learning, and interdisciplinary approaches to research the contribution of individual DNA polymerases to different mutational processes and develop methods to detect new drivers of cancer.

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