As part of my postdoctoral training at the University of Edinburgh, I completed the de novo genome assembly for the common wombat. This assembled genome is now accessible to other researchers through the Ensembl website.

As an orthoptist, I have always been intrigued by the connection between eye conditions like optic neuritis and neurological diseases like multiple sclerosis (MS). During my research career, I have focused on applying computational methods to autoimmune diseases, including MS. The immunological aspects of MS, which affect both the brain and the eye, make it a particularly fascinating area of study for me.

I believe one of the most exciting developments in MS research is the growing recognition of the MS prodrome. This pre-diagnostic phase offers a potential window of opportunity for early intervention, potentially delaying or even preventing the onset of MS.

MS can cause neurological damage before symptoms appear. Our research aims to develop blood tests and utilise electronic medical records to identify early biomarkers, enabling earlier diagnosis, prediction, and potential prevention. We also seek to define vague early symptoms more precisely, which could help diagnose MS earlier and more accurately than current methods.

Our research is crucial because it aims to identify early signs of MS, enabling earlier diagnosis and treatment. By understanding the biology behind these early warnings, we hope to improve disease prediction and potentially develop preventative measures. This could significantly impact the lives of people with MS by delaying or preventing the onset of severe symptoms and improving long-term outcomes.

As a computational biologist, my work primarily involves computer-based data analysis. While this might seem different from traditional lab work, it shares many similarities, such as designing and conducting experiments, albeit virtually. One advantage is the reduced reliance on physical reagents and equipment.

I find the most rewarding aspect of my work is discovering interesting results that can be visualised and shared with others. This provides a clear and accessible way to communicate findings. However, there are challenges, such as when software tools do not function as expected or when access to computational resources is limited due to high costs.