A day in the life of a bioinformatician
Casey Bergman has an interesting post, “Top N Reasons To Do A Ph.D. or Post-Doc in Bioinformatics/Computational Biology.” Since I work in an informatics-intensive field (genomics), one might think I would endorse it heartily, but I have some disagreements. So let’s get started with Reason 0 (I do like that the list starts with 0):
0. Computing is the key skill set for 21st century biology: As time progresses, Biology is becoming a more quantitative science. Over the last three centuries, biology has transformed from an observational science into an experimental science into a data science. As the low-hanging fruit gets picked, fundamental discoveries are getting harder to make using observation and experiment alone. In the future, new discoveries will require leveraging big datasets and using advanced analytical methods. Big data and complex models require computational skills. Full stop. There is no way to escape this reality.
Sure, computational skills will be important. The problem is that I’ve been doing this for a while, and there are always new pronouncements about what new skills are the key skills one should have. There was a statistics phase, and before that, a ‘molecular tool kit’ phase. It will become one more thing biologists are expected to have familiarity with, some biologists more than others. If you ask me, where a graduate student might want to be in five to ten years, it would be at the intersection of biology and engineering: the valuable data are always the hard to collect data, and the valuable analyses are the ones that are difficult to conduct (we’ll return to that in a bit).
1. Computational skills are highly transferable: Let’s face it, not everyone doing a Ph.D. or Post-Doc. in Biology is going to go on to a career in academic research.
I agree. Time was, mothers told daughters ‘learn how to type.’ This, to me, is the strongest reason: you have other career options.
I kinda agree with reason #2:
2. Computing will help improve your core scientific skills: Biology is inherently a messy subject. While some Biologists are rigorously trained in how to cope with this messiness through good experimental design and statistical analysis (here’s looking at you my Ecologist sisters and brothers), the sad truth is that many (most?) Biologists have bad habits when it comes to data collection and analysis.
I was originally trained as an evolutionary ecologist, so I’m inclined to agree. That said, this sounds more like a reason to learn some ecology. Reason #3:
3. You should use you Ph.D./Post-Doc to develop new skills: Most Biologists come into their Ph.D. with some experimental training from high school and undergraduate studies…. Good luck finding the time to re-tool as a PI.
Agreed. Reason #4:
4. You will develop a more unique skill set in Biology: As noted above, the vast majority of Biologists have experimental training, but very few have advanced Computational training.
I agree about the skill set, but I think the window on this is closing rapidly–shorter than a six year Ph.D. time horizon. Add to this, the movement of computational scientists into biology, and I think things will become tight, sooner rather than later.
I want to lump reasons #5 (“You will publish more papers”), #7 (“You will have more flexibility in working practices”), and #8 (“Computational research is cost-effective”) together, since they suffer from the same problem that I alluded in reason #0: the difficult data and the difficult analyses are always valued much more highly than easy-to-collect data and straightforward analyses. If you want to make your mark, find a field where either the data are hard to generate (e.g., molecular evolution in the early to late-1990s) or the analyses are really difficult to conduct (e.g., large data genomic datasets circa 2010–it’s a fast moving field). Right now is a good time to be a computational biologist, in part, because it’s still not so easy to do. I think that will change very quickly (i.e., less than ‘one dissertation time unit’).
Reason #6 speaks to a cultural problem in biology: students are too often not encouraged to develop their own projects. Hopefully, this will continue in computational biology, but, if history is any guide, funding pressures might give students far less freedom than they currently have. I hope I’m wrong…
Reason #9–”A successful scientist ends up in an office”–well, it depends… I know a lot of senior computational types, and I’m not sure how much actual research they’re doing (as opposed to supervision and management). That said, I think computational biologists (along with field biologists) probably will be relatively better off than many other biologists in this regard at least in the near future.
So I think now is definitely a great time to be in the bioinformatics bidness, but, as I’ve argued before, the next frontier in biology will be automation*–and that’s an engineering problem. On the other hand, if you like doing bioinformatics, follow your heart, since it’s all an humongous unknowable crap shoot anyway.
*The rise of genomics is as much a triumph of engineering as it is computation.