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Dr Katherine James combines her love of biology and computing to interpret complex genetic data.
I'm the Bioinformatics Manager, which is a new role at the Museum. I'm here to support our research groups in making sense of the vast amounts of data that are produced by modern genetic sequencing techniques.
Sequencing involves decoding an organism's DNA, enabling us to read the instructions that make the organism function. DNA is a very long molecule, so the process is very challenging.
Recently sequencing technologies have become quicker and efficient, producing a lot more data. So my role is to both support the management of all this data and help to analyse it.
It's quite a diverse role and I work on multiple projects from the Life and Earth Sciences departments, in addition to my own research.
A broad definition would be using computers to analyse genome-scale data. A genome is the complete set of genetic data an organism possesses. However, the data are not restricted to DNA. We can produce genome-wide data for RNA, proteins and other cellular components - what we term 'omics' data. A large part of bioinformatics is integration of these data types.
Having the genome-scale data for a species helps us answer all sorts of questions, from understanding the relationships between species and their places on the evolutionary tree to identifying the specific functions of a gene that an organism might have within a cellular system.
Here at the Museum, we have our own molecular laboratories with the capacity to sequence genomes. We are mostly interested in trying to characterise genes and genomes of species that haven't been sequenced before.
I originally studied molecular biology, and I always loved biology, but I found lab work was not really for me. Maybe I was put off because my undergraduate project didn't work - the design was unviable. I spent months repeating the same initial step and it was very monotonous.
I went to work in the local council where I did a lot of computer training. I really liked computers - I think if computing had been taught when I was younger I would have been a computing geek.
Then a master's degree course in bioinformatics started at Newcastle University and I thought, 'I'll take a year out and study two of the things I love: biology and computing'.
I was halfway through my master's project when my supervisor asked, 'Do you want to do a PhD?' I said yes and that was it. I was at Newcastle for 12 years until I moved here.
What I enjoy most is when I run an analysis and the results make sense. That isn't always the case when we are working with genome-scale data - it can have very high levels of background noise that obscure what you are interested in. Getting results, especially when it's taken days to run, is very satisfying.
I like solving problems, and bioinformatics is very much about solving problems and mysteries. I love the logic of computer science and I'm very data driven. If someone has a dataset I just want to see what I can find out from it.
In some ways it's very similar to lab work, in that you have to be organised and you set up multiple procedures to run at the same time - almost like sets of experiments. But in other ways it's completely different. When you are ready you can press play and walk away, and in a few days you have your results. Lab experiments need a lot more attention.
For me, computing science is a logical way to work. When I learned to programme it was almost like learning a different language, but it made sense to me. I wished I learned to programme when I was a child like children do now, because it was just something that I took to naturally.
The scope here for me to develop new analysis methods is huge. Partly because the technology is improving all the time, but also because this is just such a diverse place to work.
A lot of classical bioinformatics is based on the same few model organisms which have well-characterised references. I've worked with human data, yeast data and lots of bacteria. Analyses in those species are relatively standard now. But here we are working with diverse non-model species which have no references, and looking at some really interesting and novel questions. It's very challenging and exciting.
In the long term, I hope to produce some of my own bespoke techniques and algorithms, depending on what the projects require, and sharing these approaches.
That really depends on what people are interested in, and that's why I like what I do. I can work on six different projects at once and they are all different types of data. Different questions, with different species - the diversity is incredible. I'll be helping teams with everything from day to day advice to actually doing the bioinformatics analysis of their project.
I'm currently helping analyse the genome data from the king scallop, one of the 25 UK species being sequenced as part of the Sanger 25 Genomes Project . This will help us to understand why this species can harbour toxin-producing bacteria with no ill effects. It's proving a challenge as bivalves have very complex genomes.
I'm very interested in the developments that are happening with ancient DNA, which is low-quality DNA isolated from old specimens. The technology is improving and researchers are getting increasingly better data, which can give us fascinating information.
My research focusses on applying machine learning and data integration to new sequencing data types. In particular, newer data types are providing insights into the dynamics of transcription in the cell.
Learn computer programming at school. I never had this chance and learned much later in life, but it is so useful - not just for a career in bioinformatics but for other industries, and for life in general.
I go to the theatre a lot, which is very dangerous to my bank balance now I'm in London!