Projects

Nanopore sequencing for cancer pathology

The Cancer Bioinformatics group are interested in the utility of real-time, streaming analysis of nanopore DNA and RNA sequencing data. Their investigations aim to identify repeat expansions, detect copy number alterations, discover low-frequency nucleotide variants, characterise novel isoforms, and analyse variations in polyA tail length.

Developing minimal multi-omic signatures for diagnosis and prognosis

The Cancer Bioinformatics group has created a comprehensive suite of tools designed to identify minimal transcriptomic, proteomic, and metabolomic signatures for predicting disease states. They aim to apply these signatures to predict drug susceptibility, survival time, and other clinical outcomes. To date, they have successfully utilised the approach to discover biomarkers that differentiate various types of blood cancers.

Developing solutions for linking multi-omic data with clinical outcome and epidemiological data

The group are actively developing methodologies to link multi-omic data, such as genomic and transcriptomic information, with clinical and epidemiological outcomes. They are developing these methods in the HIV/HCV genomics domain, and will explore applications of these approaches in the cancer genomics space.

Using AI to learn from health data

The Cancer Bioinformatics group is investigating federated artificial intelligence (AI) methods to facilitate collaborative learning from linked omic and clinical data.

This enables researchers to extract insights from diverse datasets across institutions, improving overall understanding of disease mechanisms and treatment outcomes.