A full feature profile for Janet can be found here
1. Having started research in bioinformatics when it was a relatively young field, have you seen the fields of computer science and bioinformatics change significantly since you started your career? If so, in what ways, and what do you think are the most dramatic changes in the last few years?
There have been a number of dramatic changes since I started. The number of computational biologists has grown enormously – mainly driven by emergence of ‘omics technologies. The backgrounds of computational people in structural biology has also changed: in the early days of structural biology were trained as physicists (like me); now multiple disciplines participate in interpreting biological and medical data (CS; mathematicians; statisticians as well as biologists/chemists and physicists). Computer power and graphics capabilities have also improved beyond recognition, and databases become ever more important – for me of course PDB but also UniProt/Ensembl/Expression Atlas: these have all become very professional and
grown enormously in number of entries; many of the large public resources are global endeavours.
Some of the dramatic changes in biology and computational biology as a whole in the last few years include large improvements in biological imaging at many different scales, the increase in single cell data, the increase of the role of machine learning and AI in bioinformatics, the emergence of cloud resources for computing and sharing data, and the increase of clinical data. We've also seen the emergence of very large consortia to tackle huge bioinformatics problems. In addition, there are now MUCH closer links to medicine/health and environmental worlds. Recently we've seen more of a return to 'Theoretical Biology' to model different biological functions (e.g how cells migrate), and there is an increased need to transverse different scales of biology - from molecules to cells, organs, organisms, and ultimately ecosystems.
2. As the director of the EBI, and organizer of past ISCB conferences and a key member of ELIXIR, have you witnessed significant changes in the gender balance of computational biology and bioinformatics since you started research in those fields? If so, how has this balance changed and do you think it still needs to change?
There has been a significant shift in the gender balance of new students entering the field to approach equality. Success rates in ERC grants are now neutral with respect to gender. However, the number of applications from women remains low at the senior levels (reflecting past trends) but is improving for Starting Grants. In terms of senior staff, the balance of genders is still far from equal. The gender distribution in computer science tends to be very shifted towards men, but actually for those interested in Life Sciences, computational biology is a really accessible field, for men and women alike, to enter and be successful.
3. Would you say there are particular assets or challenges to being a woman in bioinformatics?
I strongly believe that science is gender neutral – and the quality of your research does not reflect one’s gender. Personally I think for me being a woman has been a positive attribute. This probably reflects my area of research in structural biology, where there was a history of excellent women scientists – of course starting with Dorothy Hodgkin – who was awarded the Nobel prize. Considering the pros and cons:
Pro: Maybe I got noticed more because I was a women – though I was not aware of this; maybe I was invited to give more lectures for this reason – though again I never felt this especially in the early days, when this was not something considered important (I do not enjoy the feeling now that maybe I was invited for this reason either to speak or represent my science); Maybe I was invited to sit on grant panels and so gained important insights into how the system worked at an early age – again I did not even consider this as a reason (maybe I was naïve).
Cons/challenges: Being a working mother is very hard work – but I was fortunate to be able to work part-time and enjoyed this very much. In the early days I did not travel much – but this allowed me to focus on my own research. Sometimes I feel a bit overawed in large
meetings/panels – but I am sure we all (men and women) feel this. I cannot remember being excluded from discussions/opportunities – but maybe again I was blinkered. 4. Do you have any advice for young women/womxn starting out in academic research careers, especially in computer science, bioinformatics, and STEM?
1. Do what you enjoy doing and are really interested in.
2. Follow your dreams
3. Focus on the here and now – the current problem and do not worry too much about the future (I know this is easy to say in retrospect but I think it is the right strategy – life is challenging and ‘events happen’)
4. Take opportunities when they occur – ie be proactive
5. Find good colleagues; work closely with them and help each other
6. Try to strike a good balance between your own research: collaborations: services to the community; and of course your home life. All are important, but at any one time one aspect of life is often dominant.
7. Follow the data
5. What recent developments in bioinformatics and computer science are you most impressive to you? What future areas and avenues of research are you most excited about?
For me the predictions made by AlphaFold recently (predictions of protein structure from sequence) have been very exciting and inspiring. They show the power of the new deep learning methods – for handling very complex data. The combination of new imaging technologies applied to biological problems combined with theoretical modelling holds much promise for the future understanding of biological processes.
My own research today has 3 strands: the structure, function and evolution of enzymes; the interpretation of genetic variants using protein structures and their links to disease: the molecular basis of ageing and its links to diseases. Each of these gives me immense pleasure and challenges (too many to go into).
I think the area of protein design is going to flourish; I think that the use of molecular technologies in medicine and to study biodiversity will revolutionise both these areas of human endeavour; I think the possibility to really understand the molecular basis of many diseases and therefore stand a chance to provide therapeutic solutions is one area where progress is certain; the idea of harnessing biology to tackle climate change for crop and food production is becoming critical – and of course understanding how the brain works is a challenge requiring the combination of novel experimental and computational approaches.