Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Computational and stastistical methods help us understand evolution as well as genetic disease. Looking at our genomes opens up clinical possibilities, for example in cancer, allowing more genes to be looked at - more quickly and more cheaply, wich can impact prognosis and treatment selection.

Q: Can you tell us a bit about the basic research question that you are interested in?

GL: My group is involved in method development. Broadly, if I have to summarise it in one sentence: we are developing computational and statistical methods to analyse DNA sequence data, with the aim of improving our understanding of evolution and genetic disease.

Q: What is the relationship between disease and evolution?

A: That is a very good question because evolution is very fundamental and doesn't immediately have an impact on disease. However, if you look at a genome from an evolutionary perspective and you compare our genome with that of other animals, you see that certain bits evolve very quickly and certain bits do not change very much. Those bits that do not change very much are very likely to have a biological function. So when interpreting the mutations you find in a patient, it is very helpful to know which bits are likely to have an impact on our biology and which don't. In that way they help us interpret the mutations that humans carry.

Q: What kind of methods do you use to answer these questions?

GL: Very broadly we are using two categories of methods. One is statistical methods and the reason for these is that the data volumes are so big: we see so much signal, and of course, there is noise in this signal. Statistical methods are really crucial to identify which things we see are real - and which things are just there because you have so much data that everything will happen once, so to speak.

The second category of methods is computational, again because the data volumes are so huge we need to be very careful about the kinds of processes we let loose on the data, because if you don't think about it, we will be waiting forever and there are certain situations where that is just not acceptable.

Q: What are the most important lines of research that have emerged in the last 5-10 years?

GL: From my perspective the field of genetics has really undergone an evolution in the last 15 years. And just to give an example, it is only about 15 years ago that the human genome was sequenced for the first time, that we had a first look at the DNA that constitutes our biology. That was the end point of an effort that took 10 years and about 3 billion dollars, so there was a huge effort, it was a real milestone. Only 15 years later, currently I could have my genome sequenced for $1000 in 24 hours; that is like 3000 times faster and 3 million times cheaper and that is really tremendous. It opens up a lot of clinical possibilities as well. I think is for me the most interesting aspect of the field: it is really technology driven.

Q: Why does this line of research matter and why should we fund it?

GL: These possibilities of looking at our genome open up clinical possibilities, particularly for genetic disease, and one very good example of a genetic disease is cancer. Cancer is really a disease of our genomes: certain cells in our body that do not have the right, have changes in the genome and this effectively causes cancer. This technology allows us for the first time to actually have a detailed look at the genome of that cancer and try to figure out what is driving its transformation, and also to try to use the technology to develop early diagnostics which might help. And finally we hope that our understanding of the disease will also lead to new ways of treating this cancer. So that is a good example of why there is a huge amount of interest of using this technology to help drive the research in cancer forward.

Q: How does your work fit in translational medicine within the department?

GL: There are certain aspects of my work which are quite long term and will take a certain amount of time - years, for it to have an impact on clinical practise. But a number of the computational tools we have developed are already being used in clinical practise. One example is a collaboration with Professor Nazneen Rahman at the Institute of Cancer Research: in collaboration with her we have developed a computational pipeline that allows her to screen cancer patients for cancer predisposing mutations. There was a test already in existence but our new pipeline allows her to look at many more genes, much more quickly and also much more cheaply so it is better on all those three counts. Not only does it make the whole process smoother and cheaper, but it actually has an impact on the prognosis of certain patients and in their treatment, so that is really exciting and a good example of a real impact on clinical practise.

Gerton Lunter

Computational genomics

Professor Gerton Lunter is interested in investigating the processes of evolution and biology using computational methods.
His focus is on sequencing data; Professor Lunter develops methods to investigate evolutionary questions in population genetics.

Translational Medicine

From Bench to Bedside

Ultimately, medical research must translate into improved treatments for patients. At the Nuffield Department of Medicine, our researchers collaborate to develop better health care, improved quality of life, and enhanced preventative measures for all patients. Our findings in the laboratory are translated into changes in clinical practice, from bench to bedside.