[JohnK]

One more vote for Python. I spent my grad career developing in Perl and C++. For some reason, I always wrote Perl code as "one-off" scripts. Moreover, the syntax and code structure made it difficult for me to make sense of even my most well-documented Perl scripts.

On the other hand, from the moment I began using Python, I started developing more in the style I do when writing C++ tools: that is, with an emphasis on code "reusability" and clarity. For some reason, Python just "feels" good and lends itself to clean, simple and reusable code. The best part is that its approach to object orientation is rather well done and allows one to think the same way as when writing Java or C++ code.

Therefore, I think learning Python first with a concerted effort towards learning objects, iterators, inheritance and developing modules, would be best. It will make a transition to Java or C++ much easier.

Aw man! I love perl, but scripting languages are all the same to me. Just remember to hit '#' constantly.

[JohnK]

Hello everybody,

My rather limited bioinfromatics skills come from having done some microarray data analysis in R (following a template code) and some minor coursework. So, I consider myself quite a newbie to the subject. I will quite soon be shaking hands with some sequencing data (from a Helicos machine) and need to prepare myself for this.

Being of a younger generation, I would say I can handle computers pretty well. So far I have, as recommended in this nice thread, started to take a look at the Unix and Perl for Biologist tutorial and installed Ubuntu in Virtual PC on my Windows computer.

What I'd like to ask you, SEQanswers community, is whether you can suggest me anything helpful. Am I starting out in the right way? I will get some bioinformatics help along the way, though I am unsure to what extent. Also, I see this as a part of my future career, so I am not just doing this for one particular project.

Edit: I have just realized that the Helicos software package uses Python.

First off- I don't think I'm crossing the line when I say most people in the field are using perl. You probably want to know unix/linux shell programming- it saves invaluable time and suffering, especially with the ridiculous number of formats that are thrown around out there, and can do simple tasks much faster than wasting a time writing a script or program. Java is nice, but for what? It's an OOP language like C++ and how many programs are you going to write in oop for analyses purposes? Leave Java to the application development kinds of things, and leave scripting things to languages like python and perl. If you're a JAVA 'guru', then use Java, but learning perl is easy and you can save a heck of a lot of time once you get good with it. Don't bother with all that abstraction and encapsulation crap. I personally don't know 'R' very well, but I can tell you that every statistician I've met is using it, and they're all Russian. Russians are good at math...

[ymc]

Does Python has a free bioinformatics library like BioPerl? I find that BioPython is quite lacking for now. I am wondering if there are better alternatives.

[quinlana]

Whats a good book/website/tutorial to start learning Python?

I learned the basics from python.org and used O'Reilly's "Python Cookbook" to get a feel for the subtleties and advanced usage. In my case, however, I was mainly just needed to know the syntax and basic data structures as I already knew how to program.

There's a new Python book for Bioinformatics from O'Reilly. No idea what the quality is like.

http://oreilly.com/catalog/9780596154516/

[thinkRNA]

Whats a good book/website/tutorial to start learning Python?

[quinlana]

One more vote for Python. I spent my grad career developing in Perl and C++. For some reason, I always wrote Perl code as "one-off" scripts. Moreover, the syntax and code structure made it difficult for me to make sense of even my most well-documented Perl scripts.

On the other hand, from the moment I began using Python, I started developing more in the style I do when writing C++ tools: that is, with an emphasis on code "reusability" and clarity. For some reason, Python just "feels" good and lends itself to clean, simple and reusable code. The best part is that its approach to object orientation is rather well done and allows one to think the same way as when writing Java or C++ code.

Therefore, I think learning Python first with a concerted effort towards learning objects, iterators, inheritance and developing modules, would be best. It will make a transition to Java or C++ much easier.

[dnusol]

My opinion from a biologist point of view...
I've tried two times to learn Java and finally gave up, maybe because my brain is not made up for understanding so much of classes attributes etc. I taught myself Perl and R (with help from userlists, of course!) with some good results (I would say). My next steps: continue improving Perl and start Python.

HTH

[lh3]

Another vote for python, especially when you are dealing with data in various custom formats. Java is more appropriate when you develop algorithms or when modules are available for tedious format parsing. Between perl and python, it seems that most people who know both perl and python like python better, although I know perl only.

[Broadie]

I was hoping for a uniform answer. This leaves me with some thinking to do.

Thanks for the input.

I suggest you make an evidence-based decision. Go to monster.com, careerbuilder.com, and job sections for various organizations/companies seeking bioinformatics people, do a search for "bioinformatics", find the job listings that sound like what you want to do, and see what languages they are looking for. Based on what I've seen, they look for Java, Python, and Perl and a few others.

[mudshark]

just to add my 2 cents:

a) threading in JAVA is easy! if you plan to work with huge datasets on multicore processors this is an important issue. i managed to reduce computation times in orders of magnitude by exporting my data let's say from R and do multicore processing in java.

b) programming is very easy when you have powerful IDE's that allow for easy variable/method renaming and other fancy stuff. I use intellij IDEA and coding is a charm. if you plan to do a lot of coding this might be a relevant issue as well.

[Calico]

I was hoping for a uniform answer. This leaves me with some thinking to do.

Thanks for the input.

[Broadie]

I taught myself Perl as well, its very easy and I'd say a good introductory language.

[Bukowski]

Hello everybody,

My rather limited bioinfromatics skills come from having done some microarray data analysis in R (following a template code) and some minor coursework. So, I consider myself quite a newbie to the subject. I will quite soon be shaking hands with some sequencing data (from a Helicos machine) and need to prepare myself for this.

Being of a younger generation, I would say I can handle computers pretty well. So far I have, as recommended in this nice thread, started to take a look at the Unix and Perl for Biologist tutorial and installed Ubuntu in Virtual PC on my Windows computer.

What I'd like to ask you, SEQanswers community, is whether you can suggest me anything helpful. Am I starting out in the right way? I will get some bioinformatics help along the way, though I am unsure to what extent. Also, I see this as a part of my future career, so I am not just doing this for one particular project.

Edit: I have just realized that the Helicos software package uses Python.

If you're looking to leverage the Bio* libraries (BioPerl, BioJava, BioPython, BioRuby etc.) just be aware they are not all equally mature, nor equally good as each other for certain tasks.

Depending on what your likely use case is, I would check to see what is mature and developed in your area. I've run into issues a couple of times recently with BioRuby that have been easily solved by switching to BioPython.

As an ex-Perlist, I can really only suggest that for a nice balance of language design and tooling maturity - Python is a good way to go. I'd still love to get some competency in Java however, but if you want things to work, and work fast, let's face it you will get much more traction in Python first.

[thinkRNA]

Learning PERL is very easy:I have taught college student in 7 days using exercises from this awesome bible of mine: http://oreilly.com/catalog/9780596001322
if you have never programmed before, after you can write PERL scripts (~3 months regular practise), you can start python as the transition is easy -> it will also give you a lot of confidence which is really what biologists' biggest problem is.

People, what do you all think about C++ versus Java, which is more sought after in industry?

[sklages]

I'd go for perl :-)