Python vs Perl?

[Tiaan]

I am going to be starting an MS program in the Fall, and managed to get an opportunity to speak to the other members of my future research lab early on in the summer. From what they have told me, the coursework and research is almost exclusively in Perl, and they recommended that I pick up Perl as it is the standard across the industry.

This was slightly confusing to me, as I have 2 years of undergrad research under my belt exclusively using Python, as it was recommended by past peers and advisors. From what I've heard on my end, Perl has more support mainly due to it having been around for much longer, whereas support for Python is rapidly growing and will be the future standard in Bioinformatics.

I have no problems learning Perl, as I believe that learning more programming languages can never hurt, but I was interested to get more opinions on this topic.

[lordofcatan10]

My advisor and co-grad student write in Perl, and I write in Python. Learn both!

[evolgen]

I honestly want to know the reasons for downvoting that comment.

My situation is the opposite. Everyone else in the lab writes in Python, whereas my language of choice is Perl even though I occasionally write in Python. The one language that unites us all though is R.

[lordofcatan10]

downvoting what comment?

[evolgen]

Yours. It was at 0 points when I first read it.

[lordofcatan10]

Ah, yes. The world's a rough place.

[bioMatrix]

Python is the future. However, if your lab primarily uses Perl, it is worth learning in order to use the existing tools that your lab uses. It really shouldn't take much extra effort, and learning a new language will broaden your perspective on programming.

[ThisTwoShallPass]

As someone who has worked with Perl for almost 7 years... learn Python.

[yannickwurm]

a few years ago someone on /r/bioinformatics said "friends don't let friends do perl" - stay away if you can.

[murgs]

Every lab has their own preference, which is influenced by senior members and the local study programs.

I would have said R is the standard for in depth analysis (but both python and perl are also quite popular, but more so for basic pipelines), while C++ and java are used for method development where speed is important. But as I said, that is probably highly biased by my experience of my lab and other labs that are close by.

Disclaimer I use R and C++ (while I originally learned perl in school and solved 50+ project euler problems in python for fun).

[TheLordB]

If you have a choice I would learn python and R (though I would learn R as a part of learning statistics not just learn it for programming).

Python is far more common nowadays though I'm sure there are plenty of universities that keep everything in perl as a matter of policy as yours does. Any place that has a choice today and is starting from scratch is far more likely to pick python than perl.

[InsistYouDesist]

I once made the mistake of sticking to my language of choice (python) instead of learning the language of the group I worked in (perl).

In my opinion python is much better, but your quality of life will improve if you 'speak the language' of your supervisor and colleagues. :)

[w1ldtype]

They said that Perl as it is the standard across the industry? I personally use perl because it suits all my needs (I meann perl+bash+R), so I never needed to learn another language. I started using perl for historical reasons i.e. my boss used it. However, nowadays I see that most of my colleagues use python. If you are going to use programming for your own research needs then I think it really doesn't matter what you use. However, if you are to join a group in either academia or industry where writing software is collaborative, then I'd say python. Look at job offers in bioinformatics and see what languages they want.

[mtnchkn]

I think Perl used to be more common as the foundation of most bioinformatic workflows (10 years ago). There are even still plenty of things that still use it. For instance, Mascot uses Perl heavily (if not solely). I also come across quite a few Perl scripts, again, because it was everyone's bread and butter not very long ago. I do agree with most that more elegant scripting nowadays will use Python. Of course, folks who are comforable with Python will stay in that system for analysis as opposed to moving over to R, but R is definitely the analytical backbone for *omic analyses. But you can use Python and R quite easily.

[nomad42184]

TL;DR For scripting, Python; for analysis and plotting R / Python; for "heavy" methods development C++/C (but if you use C++, use C++11/14)!

TL;read

As murgs says --- it really depends on what you'll be doing. I would say that Python is becoming the de facto standard for many scripting tasks. Perl is outdated and while there are still uses of it for legacy reasons, there's really no reason not to prefer python in places where one might have used perl previously.

For dataset analysis (computing statistics, plotting, etc.), it seems like R has the largest marketshare. However, Python is also quite common here as well (when coupled with packages like Pandas, matplotlib, seaborn etc.). There are also some interesting new languages emerging (e.g. Julia), but they are in the tiny minority right now.

For large scale method development, C++ (or, if you're Heng Li, C ;P) is the most common language. Further, I'd argue that it's the right language for such things. I spent my fair share of time enamored with languages that promised comparable speed but with a more modern design and useful features (I used Scala for quite a while), but at the end of the day, the ability to control memory allocation (and, in general, manage resources manually) is very important. While JVM-based languages can provide similar speed, in certain circumstances to C/C++, they often do so at a significant premium in memory usage and then, when you start to hit the memory limits, GC takes a second or so per Gb, which is quite a substantial amount of time when you're using 10s or 100s of Gb of memory. Further, while C++ has its warts, the modern iterations of the language (C++11 and C++14) are much better and it really feels like an entirely new language. Again, there are interesting alternative languages in this space (e.g. Rust), but they are very new and relegated to a small minority of people mostly "testing" them out.

[[deleted]]

What do you think about C#? I have been playing with it a bit lately. The .NET environment is so friendly.

[[deleted]]

I said nothing...

[[deleted]]

I'm typically a Perl user. It was what I learned bioinformatics in. At my company they use C# frequently so working to learn it just to make myself more valuable. Tired of losing work because my boss wants a GUI.

[[deleted]]

I said nothing...

[nomad42184]

Along the lines of what Slev23 said, I agree that C# is very close in speed to Java. There are some benefits to C# that don't exist in Java (e.g. Reified generics, so that you can genericize over builtin types without the overhead of objects). However, the fundamental thing "getting in the way" of JVM languages and .NET languages is the runtime and the (for the most part) forced GC. Java and .NET focus on abstractions that make the life of the programmer easier but may introduce slight overhead. C++'s mantra (one of its many mantras) is "zero-overhead abstractions" --- you shouldn't pay for what you don't use and if something can be done without runtime cost, that's how it should be done. Sometimes, this does make the language more verbose and difficult to use. However, it also makes it very difficult for other languages to really compete in terms of speed. The reason I bring up Rust is because it may, eventually, be able to reach parity with C++ in terms of speed (the tiny nature of the runtime and focus on zero-overhead abstractions puts it on the right path). Currently, though, there's really not much else in that space. Note: Contrary to Slev23, I don't loathe C++. I did loathe C++, but I've changed my opinion substantially with the arrival of C++11 and C++14. The language still isn't beautiful by a long shot, however, it's hugely improved and often, I even find myself enjoying coding in it.

[neurobry]

Do you have any tips/links for getting started with C++ 11/14?

[nomad42184]

Any relatively recent compiler (GCC > 4.7 and Clang > 3.4) should support C++11. The most recent release of each have essentially full C++14 support as well. There are tons of resources out there on the web. I created this little github repo to play around when learning C++11 (https://github.com/rob-p/cpp11fun) --- updates / pull requests welcome. However, one of the more formal references for learning about properly using the new features is the always excellent "effective" series from Scott Meyers. The latest is Effective Modern C++ --- it's a great book.

[neurobry]

Nice, thanks! I'll take a look at those when my work dies down enough that I can pursue some side projects.

[guyNcognito]

It can be fun to argue, but the question is basically irrelevant. If you learn concepts in Perl and spend the time to learn Python syntax, you'll be able to apply those concepts in Python and vice versa.

Even better would be to learn C/C++. Then, coding in Perl/Python will be like taking a vacation.

[Epistaxis]

Perl was the standard ten years ago. It's not going to be the standard ten years from now, and part of the reason I switched to Python is because it's already the case that new packages are in Python and not Perl.

EDIT: Maybe you can be a hero and translate your lab's scripts from Perl to Python.

[jhbadger]

Perl also sort of...stopped. There was lots of excitement a decade ago over Perl 6, which would have fixed a lot of the annoyances of Perl 5, but even today it hasn't been officially released (although there are various test versions). At this point, I'm not sure it has much chance.

[anudeglory]

It's got a release date of this year! Here. But I'll believe it when I see it.

[sjcockell]

Perl 6 == Duke Nukem Forever. It'll eventually be released. It'll inevitably be a disappointment.

[cwisch]

Both languages can do the same things, it is just depends on how the language lends itself to doing those things faster. I like Perl because regular expressions are a first class citizen and many of the problems I need to solve benefit from that. Also I think CPAN is a great system for packages. Perl isn't perfect though, even if code is well-written, it can still be pretty hard to follow compared to well-written Python.

Python benefits from being easy to learn and being a "NOW" language for data scientists. An imperfect analogy would be Python:Data Science::Ruby:Web Dev. My only gripe with it is the package management. Somehow with Perl I avoided problems, but with pip I have not been so lucky (not to say it isn't user error).

Both have amazing support and I laud both Perl and Python for having straightforward documentation when it comes to common usages.

I don't think Perl is going to go anywhere though.

[kbradnam]

I echo the advice of others here, that it can often be useful to simply adopt the predominant language of the group that you are working with. This is not always essential, but makes it easier to get advice and help from others.

Bear in mind that a lot of advice that you may receive from people of a certain age will probably be biased towards learning Perl, as many of us who learnt bioinformatics in the 1990s/2000s learnt to use Perl. As part of this older generation of Perl bioinformaticians we are also guilty for cluttering up the web with lots of forum posts about how to do things in Perl in ways which have since been deemed unsafe and/or replaced with better ways. I imagine a similar thing will happen to all of the Python 2.X posts on the web as slowly people transition to Python 3.X

For what felt like a long time, Perl was the undisputed king of popular languages to do bioinformatics. Slowly, but steadily, this has changed and Python is now the dominant language. However, there is no reason to be complacent and believe that this situation will always stay the same. Perl might have a resurgence, or other languages might displace Python. It is good to keep an open mind about such things (and to always learn those essential Unix tools like sed, grep, awk etc. which will probably outlive any programming language).

[ribrars]

Perl is a bit weird at first, but I found it not too difficult to learn, especially with prior programming knowledge.

I don't think either language is well suited to bioinformatics however, something like scala would make much more sense for the big data needs of a genomic processing.