…. other than the obligatory roles like mentoring their research?

When I first started my PhD I have just obtained a Masters degree in Bioinformatics from a good-rated university in the UK, having learnt a fair amount of Perl, Java, statistics, R and recapped pretty much all of its biology component. I have three supervisors, two geneticists from the lab I am staying currently and another theoretical physicist/mathematicians. The system seemed simple: if I had problems in biology, I’d go for my main supervisor, and if I had problems in mathematics, I’d go for the mathematicians.

It didn’t work out quite that way in the end, as most of the maths problems I encounter would be learning it, and it’s just too trivial to ask an university academic…. so in my personal opinion the system is a total failure if the students themselves are not trained to at least engage a intellctual conversation with their supervisors.

There is no attending lectures or qualification to take during the Phd in the UK system, by assuming all the starting students are talented and can dash in the project in no time! I was lucky that I did the Masters in the first place, though the statistics was new to me, I adapted pretty quickly. Still, the following 3 scenarios below are quite common:

  1. someone who sequenced lots of genes and performed all their analyses in DNAsp and excel
  2. someone performed all his PhD analysis by refreshing web blast searches all his first two years (it’s really true!) and in Excel
  3. someone who is studying prediction of protein-protein interaction networks, who knew little about background of the biological significance of PINs.

In my personal view the geneticists are the “early bioinformaticians” in the biology field, and you can see people from scenario 1 and 2 come from a biology background, and 3 from a CS background. Which situation seem more dreadful? I say 3. Maybe it’s an excuse for me being biology-background-not-as-good-as-the-rest-in-Maths person, but knowing the biological background and its significance is absolutely crucial in your research. Sure, it’s a bit ridiculous of being a bioinformatics Phd knowing little programming, but your lack of programming skills won’t show on your thesis. For CS people, you might get away with that and joke about “oh I know not much about it but I am good in Maths!”, but one day you are going to fall hard. The ending of scenario 3 was bad: this person didn’t know what’s the difference between the self-interaction proteins and subunits-of-one-protein interacting each other (they are all unique entities in one single database) so he made wrong assumptions about nodes with lots of edges in the first place…

Now the main point to focus: do the supervisors need to discipline their students to learn everything associated with the research or specialise? If you are the molecular geneticist, will your supervisor push you to learn Perl/R in your spare work on top of the experimental work? If you are the CS person, will they draw you into the fascinating sides of biology? (As I read through I might be a bit biased :P) For bioinformaticians, I guess you need to learn all (or not). Will it be too generalised and perhaps in a way waste of too much time ?

Another point that I think is just important: the writing style (hence the image above). You have to write in a way that the biologists understand the maths, and the rest understand its biology. Easy for some, though in the case of myself, I often write quite the opposite way. I think this is one of the main roles of your supervisors: to train your writing, whether they match the standards themselves is another issue.

The paradox is here, the Phd is all about specialising. But! naively bioinformaticians = generalists, how can you specialise on being a generalist?

I’ll use myself as an example, as I can’t see myself specialising on anything yet! I like to know a piece of work roughly 90%. If I see reversible jump MCMC, I’ll first start with Bayesian statistics for dummies then go from there. It takes time, but the satisfaction of reading someone’s elegant statistics while appreciating the significance of why (i.e. why you analyse/write/publish in the first place) is immense. This is often encouraged by my supervisors by listening to how I explain the theories. One of my supervisor would spent ages sitting down with me reading my drafts sentence to sentence. It takes usually hours, and often her comments are more than the draft itself, no nonsense!

However, being a generalist means I might not be able to get any jobs once I finish my PhD, I may, disappointingly, not able to fit any of criteria that any lab wants. I will be in effect too underqualified for post doc jobs. So we will see how this will turn out.


I am Perl

You are Perl. People have a hard time understanding you, but you are always able to help them with almost all of their problems.
Which Programming Language are You?

I wonder why…


This book is about conflicts in a research lab – as myself who has always been in a lab of less than four people at one time, this was a great read. The environment is familiar: confined lab spaces and jokes about repeatedly failed experiments, frustration and jealousy of post docs when one can not reproduce the results. The books captured in essence the relationship and feelings and roles of different people working in a research lab: the fast and furious PI, the greater good PI, post doc who can produce results, post doc who can not produce results, technicans who wished the institute to be burnt down, and the mice! The amazing results published in Nature seem to be published too fast to outcompete rivals, and the aftermath is daunting…

To sum up: be nice to your fellow post docs, and make sure you record every single step in your lab log book and lock it up with highest security!

Hello world

I shall start writing stuff…