Re: perl's long term place in bioinformatics?

[bobf]

Before I comment on which tool you should use, what are you trying to do with it?

Bioinformatics is a huge field of study that includes an extremely wide range of topics in both scientific domains and computer science/IT. There are as many definitions for "bioinformatics" as there are bioinformaticians, and based on my experience nearly every one of them has an educational and professional background that, combined with their current work, gives them a unique view into the field.

For example, my educational background is in biochemistry and pharmacogenomics. I started to learn to program using shell and VB scripting while in grad school. It wasn't long before I needed a more powerful tool, so on the recommendation of my colleagues I started to learn Perl. After several years in bioinformatics I transitioned to medical informatics. I have been out of the wet lab for 10 years now. Perl is the only language that I code in and I can count on one hand the number of times I needed¹ to learn another language².

Perl has a great library of modules to support bioinformatics/genomics work, and it can be integrated with other languages such as R and C (both of which are common in bioinformatics). I found it to be very intuitive and quite powerful. If you have a limited programming background I would definitely recommend starting with Perl. The recommendation of other languages depends on what you anticipate doing (e.g., statistical analyses, heavy computation, simulations, web apps, data warehousing, system integration/interoperability, etc).

IMO, you're off to a good start. Good luck, and have fun! :-)

¹Due to the preferences or abilities of collaborators, or to the need to work with existing code written in another language.

²Based on conversations with those formally trained in CS, learning several different types of languages can be quite valuable. I am not discouraging this. The statement was made to lend support to the utility of Perl in this field.

[tritan]

It's funny you should mention the broadness of the bioinformatics fields. That's something I've been having a problem with. It's been difficult to define what it is I'm exactly interested in, when bioinformatics as a term itself is so enigmatic. Biology, computers, maybe statistics? I wish it were a bit more rigid, just for the benefit of being more of a guidepost ; )

You mentioned you went from bioinformatics to medical informatics. Do you see the two fields as distinct, or as a subset of one another?

[bobf]

Yes, it is difficult to draw lines. That is probably as much of a reflection of the underlying science than anything, though. Biomedical research is messy. There is a lot of overlap between fields yet there is tremendous depth as well.

I make a somewhat arbitrary distinction between "bioinformatics" and "medical informatics". I see the former as relating more directly to wet lab support: munging data files from lab equipment and public databases, performing basic statistical analyses, etc. Lower-level stuff. Medical informatics (in my mind) focuses more on translational medicine. It tends to concentrate on higher-level concepts and the relationship of them to medical knowledge. Whereas someone in bioinformatics might work on highly specialized projects that support tightly-scoped research (e.g., specific genes, pathways, regulation, biomolecules), those in medical informatics tend to look at how low-level research from many sources can be integrated and translated to something that can more directly impact the standard of medical care (e.g., systems integration, knowledge management, data mining and inference, all from the level of a gene to a multi-center clinical trial).

Just to muddy the waters even further, I view "clinical informatics" as a field that supports and focuses on things related directly to patient care. That sub-specialty tends to be much more dependent on IT knowledge than biological knowledge (e.g., electronic health records, portals for patient access, billing, etc).

These definitions and distinctions are arbitrary and, in many ways, artificial. Ultimately it is experience that will define your professional career and get you your next job, not the words you choose to put on your resume.

[tritan]

In regards to your last point, I definitely think that is true. I think I'm getting caught up in trying to explain things that I lose sight of that. My reasoning though is that if I cannot explain something, it is probably a reflection of my own lack of understanding. So having troubles putting these things into words is a sign to me that I have yet to fully grasp what it is I am doing. Thanks a lot for your replies. Really helpful stuff : )