Last week, I was at a GRAD school in Brighton. These schools, specifically aimed at PhD students, teach the skills missing from postgraduate study, such as team-working, negotiation, communication, and marketing. This might sound a but woolly, but if you are a PhD student in the UK, I really recommend going to one.
In response to a recent post, I had a few emails and comments asking general questions about a career as a bioinformatician. So to answer these all at once here are my thoughts on what I think the background and future of a bioinformatician is.
In a month, I start the third and final year of my PhD. Usually it’s around this time that students begin thinking about what’s gone before and contemplate the future, and I’m never one to buck a trend.
For instance, if you have a set of data, you have to understand many things. First, you have to know the biological relevance. How was it produced, what does the data mean, and what is the significance? Lab biologists need to know this as well, but a bioinformatician must also know, in addition, how to store the data. What is the best method of representing it, given that the data needs to pulled out and manipulated within computer code. Next the bioinformatician needs to know what’s statistically feasible given what’s available. It’s going to be tricky to get answers from only three replicates of a noisy microarray experiment. Can you use a SVD to filter some of this noise? What about a microarray experiment with 10 different drug treatments. Where do you begin, can you use dimensionality reduction? How about using clustering? How many clusters? Did someone say probabilistic?