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.
I have broad research interests and experience in bioinformatics, cancer genomics and data analytics. These research areas mainly involve developing and applying bioinformatics and computational approaches to analyse large-scale cancer datasets to uncover novel diagnostic and prognostic biomarkers. I also lead the Cancer Research UK Barts Centre Bioinformatics Core Facility.
I am providing bioinformatics support for several projects focusing on squamous cell carcinoma. This generally involves developing bioinformatics pipelines for large-scale cancer datasets and utilising computational approaches for analysis, with the overall aim being to uncover novel diagnostic and prognostic biomarkers.
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 role focuses on the design and implementation of a data management system for a pancreatic tissue bank hosted by the Institute.
My interest also lies in the development of various web-based computational analyses and data mining tools for biological research.