Computational Genomics Lab

Our lab focuses on demonstrating the genomic mechanisms by which variants contribute to complex human diseases, and working towards early stage diagnosis methods and targeted therapies. To do so, we apply existing computational approaches, and develop our own statistical genetics methods for analysis of large-scale next generation sequencing data. Following computational experiments, we perform functional validation of statistical observations using molecular techniques such as high-throughput genome editing and cell phenotyping. We have a very significant focus on the use of single-cell sequence data and technology, due to the phenomenal resolution it offers in being able to identify differences in the genomics processes between individual cells. The laboratory drives projects in a number of areas of medical genomics research, but we also believe strongly in the value of collaboration between groups with differing expertise.

Key projects

OneK1K

The study is currently scaling up to the multi-year TenK10K project, where we’re aiming to recruit 10,000 individuals, which will generate single-cell data from about 50 million cells. 

OneK1K is a pioneering study demonstrating how genetics contribute to the risk of immune disease at a cellular level. Using the ground-breaking technology, we can now work towards solving one of the missing pieces of the puzzle.

By analysing 1,000 cells from 1,000 people, OneK1K will have an impact on three main areas: autoimmunity, immuno-oncology and haematology disease.

The study aims to identify and prioritise new drug targets for specific cell types in individual patients. This will initially enable patients to better manage their disease, but ultimately to prevent an autoimmune disease from developing altogether.

Brain cancer sequencing

This project is a collaboration between Garvan and the Charlie Teo Foundation, using genetics to better understand and treat brain cancer, an incredibly complex disease. 

Our initiative provides an unprecedented look into brain cancer. The research team uses single-cell RNA sequencing to understand the behaviour of individual cells within high-grade glioma (GBM) brain tumours – an extremely aggressive type of brain cancer that’s also the most common. Researchers analyse the individual cells in a tumour to produce a clear and detailed picture of everything that makes up one cancer. This information is used to develop new ways to better diagnose and treat GBM. 

Our mission is to use advanced single-cell RNA sequencing to improve outcomes for patients with brain cancer.

1.     Identify the cell types in more than 100 tumour tissue samples, to characterise the behaviour of brain cancer.

2.     Use data generated through our research to investigate improved treatment options for patients.