I am currently a 3rd year phd student doing my research on angiogenesis/viral immunology. But I am becoming very interested in computational biology and would like to make a switch from bench research. How can I make that switch after my phd? Or is it even possible to integrate some computational tools in my current research? (I have no idea). What can i do now to find a post-doc position or research scientist position in computational biology after my phd? Should I take computational biology certificate course after my phd? I am currently learning some programming languages such as python but I am very confused what to do next. Any suggestions would be greatly appreciated.
Giving my very clear and honest opinion, as always, please don't feel offended.
Now is not the right time to change everything! It is not the right moment to change your field completely. Don't panic. You need to focus finishing your phd. What you can do: try to integrate computational analyses into your thesis, after consultation with your supervisor. You need to focus on finishing what you invested so much time in already or make a drastic decision about your topic. The rest of this answer builds on that you want to finish your thesis.
Computational biology is of course very interesting, but the CS skills it requires are something very different from wet-lab biology skills.
So when is the right time to go 'computational'. In my opinion computational biology analyses, statistics, and basic programming should become integral part courses in the curriculum of biology, because biology is becoming more and more a computational field.
However, the CS part of it is something completely different from wet-lab and also programming is only a small part of CS.
You noticed that CS, programming and information processing and biology work well together and are very interesting, so what to do now?
- Integrate small pieces of computational analyses into your thesis if still possible after talking to your supervisor, this will increase your credibility for knowing computation
- If you still need credits for your phd curriculum you can try to take as much computational courses as possible
- everything else can be learned after your thesis is finished, if you want to do studies of CS/CB even starting from scratch. E.g. a BSc in pure CS or software engineering (another 3 years!) might serve you better than some programming courses.
- Take course in programming and get certificates after your thesis is finished, that increases credibility about your programming skills.
- Try to take a computationally oriented post-doc, you need to be aware of the fact that you will have a certain disadvantage over applicants with long-standing CS/CB background when applying for heavily computational positions, so possibly choose positions that require both.
Hope this helps!
Is there a question that you can ask, that is relevant to your thesis, that you could do computationally? You could add a whole chapter to your thesis without doing a single wet experiment, just slot it in while you're letting something incubate/react/run etc. That's enough to give you an idea if that's something you could enjoy, and to give you something to talk about in interviews and things.
A thing to note is that a lot of experimental post-docs involve a lot of bioinformatics these days. If you've got a bit of bioinformatics under your belt, then you're more likely to get the post-docs where you're doing the wet science then analysing the data yourself and you can build from there. I have friends who started out on a wet-lab project and by the end of it were entirely computational.
I would like to add my personal perspective which is not too dissimilar from your experience. Some background:
I am a trained wet-lab biologist and used live-cell imaging, molecular biology and cell culture in the context of neurobiology up to my 1st posdoc. Then, due to some results during my PhD, I decided to make a change to study regulation of gene expression in a cell biology lab. The project was mostly wet-lab, but it already had the promise of large datasets and the opportunity to learn how to analyse that data. For a number of reasons, at some point I was mostly sitting on the computer mapping NGS data and plotting in R. And I liked it a lot. So much so, that my supervisor supported me in taking some courses and leaving the wet-lab for others while I did the analysis. Fast forward a couple of years and now I do bioinformatics.
Bioinformatics or computational biology? Even though there is not established distinction between what constitutes What Is Bioinformatics?, in my mind one is more about data analysis and the other more about developing algorithms. The latter has stronger CS component in my view, and the two often go hand-in-hand of course. So do you want to develop algorithms or are you more interested in teasing out answers from data asap? Because, IMO, the path is different. Also bear in mind that bioinformatics and computational biology are very broad churches. You could be doing analysis NGS data from *-seq, doing some protein network analysis, running simulations of evolution, or even developing algorithms for data extraction from images. Even though Biostar focus more on the gen* side, there are many more things out there.
Now, I will answer your questions assuming that you are more interested in bioinformatics.
How can I make that switch after my phd?
For your post-doc choose a lab with a project that you like (very important), and that has both components (wet+dry), or is looking for someone willing to learn the bioinformatics bit. There are more projects/labs like that than you think, specially if you are willing to work with NGS. It is important that if you are going to learn "on the job", as a lot of us do, there will be people next to you to help you in the beginning. This could be a lab colleague or a collaborator. Failing that, the lab should allow you take the courses needed to learn.
In the meantime, you can start by taking a few courses even during your PhD to learn the basics. Let's say how to work with the command-line or programming in phyton/perl/java and R. Your university might have some courses available that you can take, or you can attend a (free) software-carpentry workshop. Learning how to use the command-line is extremely important and useful regardless of what you do. There are also two books that I earthly recommend (getting one of them his enough though - unless there is grant money available):
- Practical Computing for Biologists, a bit older but still current.
- Bioinformatics Data Skills - O'Reilly Media, as of writing only available as ebook, but well worth it.
Both are written from a very practical perspective and will get you started almost immediately. The 1st also contains chapters on python, and the second spends more time evangelizing on the the power of reproducible research. They sort of complement each other, but the first is better to get things done from page 1 and the second is better if you are serious about bioinformatics in the long run. Both will teach you how to use the command-line and it will save you time as soon as you start!
If you don't use Linux or a MacOS, set-up a computer with Linux (Ubuntu*) dual-boot or a virtualBox. Also pay more attention to statistics. Take a course at Uni if possible, or do one online.
Or is it even possible to integrate some computational tools in my current research? (I have no idea).
Without knowing *exactly* what your project is I can't really answer, but chances are that you can. For e.g. during my PhD, which involved cloning receptors, I had to do homology searches, domain predictions, and analysis of imaging data. I did not knew it at the time, but learning some bioinformatics (and R) would have helped a lot.
What can i do now to find a post-doc position or research scientist position in computational biology after my phd?
To be honest,for you to get a completely dry lab position after your PhD you would have to learn quite a bit in the meantime. That said, if you can learn enough to write a chapter your thesis from analysis that you performed, it will go a long way. Remember that most labs/positions will know that a pos-doc does not know everything, but brings something. So say, you go to a computational lab that works in your current field, but from a different perspective, you will bring them biological know-how, and in turn they will teach you the computational side specially if you already shown an active interest in learning.
Should I take computational biology certificate course after my phd?
When I was planning for the switch I considered taking a step back and doing an MSc in bioinformatics. At the time I asked a few bionformaticians that knew what I was doing and they suggested it was a bit of a waste of time. They believed that an MSc would not teach me enough to be worth the time - besides I am more interested in answering question than developing algorithms/software. In subsequent job applications/interviews a formal education in CB was not never an issue. Showing that I could do the analysis was more relevant. That said, depending on what your goal is, some formal education might be worth it. If I could go back I would have probably taken a class in CS and statistics while doing my PhD.
Given the lack of detail in your post it hard to give more specific advice, but I hope this helps. If there something I learned from working with people that came to science at different stages, and changed fields relatively late in the career, is that it is never too late.
*other flavours are available
[edited for grammar]
I agree with what Michael Dondrup wrote above. First, finish your PhD and don't try to go computational just for the sake of it. If the project/question doesn't need computational answers don't try to go down this road. You'll just lower the quality of your PhD. Keep focused on the question(s) at hand and on finding and applying the most suitable answers. The best way to switch fields is to start a postdoc in the new field. If you have never programmed before, have no notions of statistics then you may have a hard time but you can prepare for it by taking some classes. Another way to prepare may be to take on a small programming project on the side (showing some code is better than a certificate from my point of view) but don't let this eat into your PhD. You might also find a postdoc supervisor willing to train you from the ground up.
first, I totally reinforce the idea of finishing your PhD as priority. then, I add that the computational component is much better if done gradually. don't panic about having to know everything in the shortest time possible, give yourself time to familiarize not only with "how" to do bioinformatics stuff but also "why" you might need to do them and which questions you can answer with it.
furthemore, as mentioned above, completely "dry" labs might be hard to get in but do not underestimate the importance of being able to bridge between wet and dry lab. I talk to many bioinformaticians/former engineers that know next to zero about what the data actually mean in a biological context and, on the other hand, I see tons of biologist that get a headache by spelling the word "algorithm" and think that "time complexity" is equal to "time travel". it appears to me that you have the chance to get into a unique niche that will allow you to learn the skills from both side of the coin and work in both environments by allowing ppl to understand each other. it will take time, effort, energy (and lots of coffee) but if you get your mind into it, as I'm sure you will from we can see from what you wrote, you will be able to make yourself indispensable for the lab(s) that will hire you.
at the end, finish your PhD, start getting your feet wet with bioinformatics and, most importantly, keep learning.
Thank you all for your comments and I apologize for delaying my response. Currently I am working on elucidating mechanisms for corneal angiogenesis following HSV-1 infection using mouse model. To be specific I have done microarray gene analysis and protein suspension arrays using commercially available kits to nail down growth factors that could be responsible. Experiments involving neutralization of some of those growth factors are on-going but this is my dissertation project on a nutshell. I am curious to know if anybody has any suggestions as to how I can incorporate some computational approaches into this project.
I appreciate your comments.