We have previously developed state-of-the-art techniques for identifying chromosomal rearrangements or structural variants (SVs) using DNA short read sequencing data (e.g. Cameron DL et al. Genome Biol. 2021 12;22(1):202).
This project will involve developing new methods for long reads technologies, such as Oxford Nanopore. Students will benchmark existing methods to identify new directions and approaches that can improve on existing methods. This will involve a range of computer science algorithms.
We develop and apply new bioinformatics methods to drive discoveries in cancer and translate these into improved outcomes for patients.
Our approach is to use mathematics, statistics and computing to make sense of omics data, especially related to cancer evolution spanning initiation, progression and outcome. The main focus of our computational biology research is rare cancers, melanoma and prostate cancer, but our methodological research is also relevant to cancer in general and other diseases.
We have a strong track record in developing methods for analysing genome sequencing data, especially associated with chromosomal rearrangements. A major focus of our research is now the development of new computational and machine learning tools to analyse large, complex multiomics datasets.