My lab measures the patterns of clonal evolution that define carcinogenesis and develops novel mathematical tools for analysis and prediction. By characterising tumour evolution, we aim to find better ways to determine prognosis and more effective ways to treat cancers.
My research interests lie in the area of translational bioinformatics. Current research projects are focused in high-throughput data analysis, integration with clinical data, databases and software development, particularly for pancreatic cancer and breast cancer.
My research group uses unique proteomics and computational approaches to understand how cell signalling pathways driven by the activity of protein kinases contribute to the development of cancer. Increasing this knowledge will be invaluable in advancing personalised cancer therapies.
We are updating the bioinformatics data management system, expanding the analytical modules and functionalities, developing purpose-built graphical pug-ins and designing the bioinformatics infrastructure to allow the querying and analysis of data returned from projects using BCNTB tissues.
I am a Bioinformatician working on the development of pipelines for NGS data analysis, including mutational calling, Single-Cell RNA-seq, ChIP peak calling and methylation, variant annotation and prioritisation, as well as multi-layer data integration strategy and tools.
My research is focussed on the disturbed epigenomic landscape within pancreatic tumours.
In particular, I investigate the bi-directional epigenetic reprogramming between the tumour microenvironment and pancreatic cancer stem cells that leads to cooperative tumour outgrowth.
I apply mathematical and computational approaches to understanding cancer evolution. A lot of my work is inspired by population genetics and evolutionary biology and I have been developing ways to adapt methods and theories from these fields to the study of cancer as an evolutionary system.