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Kinga Zielinska

DPhil student

Most diseases, including cancer and the pre-cancerous condition Barrett’s Oesophagus, are driven by mutations. Numerous genomic projects have shown that for a single disease, there are unique patterns and frequencies of mutations found not only at the level of a patient, but also differences between tissues and even single cells. Information about the types of mutations can be of significant importance for the prediction of disease progression and the choice of the treatment.

The way to detect somatic mutations is to sequence and compare healthy and diseased samples from the same patient. The difficulty lies in filtering out technical errors, especially when dealing with small samples. The emerging field of single-cell RNA sequencing is particularly prone to errors, due to obtaining information from individual cells rather than averaging across the whole bulk. This, however, provides a much better resolution of single cells functioning in the context of their microenvironments, and therefore requires specialised methods of analysis. As the tools currently available for mutation analysis from single-cell RNA sequencing are ineffective, there is a high demand for new methods to be developed.

In my work, I combine Machine Learning, statistical approaches and existing bioinformatics tools in order to develop algorithms to detect bona-fide mutations from single-cell RNA sequencing data. In the future I am also planning to expand the software to single-cell “TAPS” sequencing, a new DNA sequencing technique currently under development at the Ludwig Institute. Once this is achieved, it will be possible to gain a much deeper understanding of the complex processes occurring inside healthy and diseased cells and discover novel disease-specific patterns.

Background

I obtained my BSc in Natural Sciences at the University of Exeter, focusing on Mathematical Modelling and Computer Science in biological problems. I completed an internship at Lufthansa Systems where I developed my Software Engineering and Data Analysis skills, I also had a chance to spend some time in a wet lab during the iGEM (The International Genetically Engineered Machines) competition in Synthetic Biology, for which our team received the “Best Environmental Project” award. However, I have always been very passionate about developing tools for analysis of medical data and, therefore, after gathering experience in a wide range of fields, joining Benjamin Schuster-Boeckler’s group for my DPhil appeared to be a suitable choice.

 

Apart from work, I am very interested in learning about all other areas of science and travelling. I am also a competitive long-distance triathlete.