That we at least need to know how to program is clear, but how is this with wetlab techniques? Does a bioinformatician need to be able to do a Gel electrophoresis, PCR or a western blot? If so, which techniques should be considered essential?
I was a wet lab biologist before I was a bioinformatician. Really you need some appreciation of the wetlab techniques being used, whether you need to be able to perform them (after all you don't expect the biologists to come in and 'perform' bioinformatics) is another matter.
Part of this comes from a grounding in molecular biology. I think you should have an appreciation of gel electrophoresis, western blots, transformations, ligations, how proteins are purified, how DNA is isolated, restriction digests etc. You should be able to read a paper in the biology realm and understand it.
I think in terms of being a bioinformatician you should also have an appreciation and understanding of the techniques generating the data you analyse, whether this is the sequencing technology and sample prep for next-generation sequencing platforms, an appreciation of how CHiP pulldowns are done, something about the hybridisation steps in a microarray experiment. Otherwise you might not notice things in the data that are due to the technology rather than the experiment.
However nothing will make biologists happier to work with you if you can really grasp their scientific question. You can learn all the techniques in the world, but if you can understand how they are being slotted together by the wet-lab people to answer their research questions, then that is where your input from a bioinformatics perspective can be most useful.
I agree to what Daniel said. I have also come to realize that one very important thing is that you have a very practical understanding about what can go wrong in a wetlab.
Here are some basic examples:
- Did you ever try to open a freezer that contains samples. If you don't recognize this scenario you should at least be aware of it. Here you go. You open the freezer. At least two racks of mostly unlabeled tubes fall out since the freezer is overfull. (They are unlabeled since the PhD student that put them in didn't have time to label them and thought just labeling the tube in the front left would be enough). So what happens next mostly is that you look around to see whether there is anyone in sight, and if not you fill the racks again and put them back in random order. Do you start to understand why sometimes the samples just seem to be the wrong samples? If you don't believe this is a serious problem... In early days a colleague of mine resequenced a commercial (!) clone library used to spot microarrays. 30% of the tubes turned out to contain something completely different (so not just some sequencing error).
- Did you ever try to pipet 5 different solutions in 4 rows of 12 tubes? Try to do it mentally or by putting cards on stacks, and briefly shaking the remaining cards before doing the next. How often did the stack end up to contain 4 or 6 cards?
- Did you ever try to keep on pipetting for a few hours? How often did you simply drop a tip from a pipet in a solution (affecting the volume when you tried to get it out again) or how often did you simply drop a tube?
I could go on and on. So it is not just the techniques, you need some basic idea of the kind of errors people can make, serious errors in part, and how likely they are.
In general, I'd say that it helps to understand the principles behind wet-lab techniques that you are closely supporting, but not necessarily be able to do them yourself.
In some cases this would help you to handle data correctly or to feed back useful information to the bench e.g. advising on experimental design and management of batch effects in microarray experiments. In other cases it helps to know the simple logistics of a technique at the bench e.g. in designing a LIMS that doesn't interfere with the researcher during time-critical operations.
Give than new techniques arise and old ones fall out of use, there can be no definitive list; you just need to keep up with the technology. I used to pour my own sequencing gels, a skill that is worth nothing these days. However, knowing how sequencing works is still valuable.
I am in a computational biology PhD program. Most (~90%) of the students have entirely dry lab research projects, so one of our core courses is Laboratory Methods for Computational Biologists. For what it's worth, here are a few of the segments (each lasting 2-3 class sessions) that they thought were necessary:
- Protein purification
- Gel electrophoresis
- Microscopy and imaging
- Flow cytometry
- Mass spectrometry
- Metagenomic RNA profiling
- X-ray crystallography
- NMR - more theoretical than hands-on
It was a really valuable course.
Hi! Andra, as you know Bioinformaticians comes from varied fields which includes Computer Science, Chemistry, Artifical intelligence, Biophysics, Core biology etc. so it is not must to have knowledge of wet lab techniques, although knowledge of wet lab techniques can make life of a bioinformatician little simple ;)
Now coming to your second question, the wet lab techniques which could come in handy depends upon the area in which one want's to work eg. Genomics or proteomics or HTS etc, it really depends upon the area in which an individual wants to work.
According to me no such THUMB RULE exists, which says, "knowledge of such and such wet lab techniques will be handy or ESSENTIAL".
I can't add much to what Daniel, Chris and Keith have written. All very good answers. I will add that it has helped me to have a molecular biology and cell biology background. I'm the only one in my group who has cultured cells and handled animals, which gives me an ability to ask certain questions or propose certain experiments others may not envision.
I think it is also helpful to know how to plan and execute molecular biology and genomics experiments. Most of the techniques mentioned here pertain to a single gene or low-throughput approach to investigation. That is and will remain valid. Molecular biology has expanded in scale. Thus, it also is beneficial for the bioinformatics scientists to know how the large-scale expts are planned and performed.
I am always energized by a visit to the lab, the place where I can see data generation in action.
I agree with most of what's already been written.
Personally, the reason I started doing wet-lab work is to gain credibility. I'm currently finishing up a Ph.D. in the comp/sys bio. When I started, I had prior experience working on computational aspects of biological problems, but had no wet experience to speak of.
I had no intentions of doing wet-lab work either, but I quickly realized that even at the most prestigious institutions, the "computation-only" people are somewhat relegated. Of course, there are good scientists everywhere who realize the value in what we do, but the majority of people in the life sciences see our work as somewhat of a sideshow, and some go to the extent of resentment because, well, computational biologists don't have to take time-points at 3 a.m., they can work just as easily from home when they need to, they sit relatively comfortably at a desk rather and don't run around with cancer-causing agents, etc etc.
Also, most computational work is at the analysis end, and hence computational biologists are usually the ones who check whether the experiment worked, the hypothesis is correct, etc. Being in this position is exciting because you get to see the actual results and come up with conclusions, but the flip-side is that you have to be the one to tell the army of pipettors that they're wrong...
Once you start doing wet-lab work, and get good at it, you'll most likely notice a world of difference in how your colleagues treat your ideas! So learning wet-lab skills is very useful for that aspect alone!
As far as what skills you should pick up, it's hard to say. I have found that really the ONLY way to pick up a wet-lab technique or a new programming language for that matter, is to do a small project that requires the skill. Also, the best part is that wet-lab work is EASY--ignore what your colleagues tell you. It's hard work, sure, but if you can cook something simple according to a recipe, then you can be good at wet-lab work...it's actually easier because the recipes are much more precise.
Last, from personal experience, I can tell you that wet-lab work is grueling, but it can be quite satisfying. Say, you found a faint hint of a very interesting effect in a huge mess of data that can be a big, big deal -- don't YOU want to be the one to perform the follow-up experiment and test it for real?
The wet lab experience that you will benefit from the most is that which is most related to your dry lab experiments. You will have a much better understanding of and ability to correctly interpret data if you know exactly how it was generated. If you are analyzing data from cell lines being treated by antineoplastic agents, learn how to tissue culture, performs some drug response assays, and watch some cancer cells being killed by exposure to a drug. If you are analyzing RNA-seq data, learn how to isolate RNA, make an Illumina library and watch a sequencing run in action, etc.
Hi Andra, I used to work in wet-lab before concentrating on bioinformatics. Lot of things has already mentioned in previous comments. What I'd like to stress on is that its better to have a in-depth knowledge on both theoretical and practical aspects of the technology from which the data has been generated, its limitations and your objective. This skill will certainly add-up more confidence on your work. As far as the techniques are concerned, a Bioinformatician should at least have basic molecular biology skills but it depends on the work he/she is involved in.