A Bioinformatician from Poland.
I recommend USA. Study bioinformatics in U.S. universities and colleges may differ from those in your home country in several ways. For one thing, competative research lab, small class sizes are very common. There may be as few as 10 to 20 students in a class, giving you the personal attention you need in order to succeed. While in class, students are encouraged and expected to contribute to the discussion. Studying in the U.S. gives you the opportunity to gain a mentor in your given career field, an invaluable resource.
R is an exceptionally powerful language in manipulation and transformation of data, statistical analysis and graphics. It doesn have support for the wide array of statistical functionality. So in my opinion R is great if all you're doing is statistics. It's got a nice interactive interface and visualization tools.
R discussion http://bioinformaticsonline.com/discussion/view/119/which-are-the-best-statistical-programming-languages-to-study-for-a-bioinformatician
You can also found several other material at http://bioinformaticsonline.com/groups/profile/93/r-and-bioconductor
R has also quickly found a following because statisticians, engineers and scientists without computer programming skills find it easy to use. R is really important to the point that it’s hard to overvalue it and also allows statisticians to do very intricate and complicated analyses without knowing the blood and guts of computing systems. Therefore, my vote goes to R.
I agree with Jitendra, choosing programming platform is more personal and also depend on how long you are working with your favourite language. But for beginners, I would recommend python because of easier syntax and simple oops approach. Now python can be integrate with any language like Cython (with C),PyPy, PyR, etc and python now has very enrich library stores with which you can do anything. for NGS , python is highly recommended as many new good tools made recently in python. R is good for downstream analysis like doing statistical analysis, create fancy graphs(ggplot2), work with rnaseq data (Deseq,degseq,etc), pathway analysis, etc, however R itself just a statistical package not generally into programming lang. Ruby is also getting popular these days because of simple programming style. But someone want to master in programming should go for C/C++,java,C# or python.
I would recommend python and lower level language C because of easier syntax and simple oops approach. Moreover, I do a lot of boinformatics programming, but I don’t use any of the “Bio*” projects, as they never seem to have much that is of use to me, and what little is useful to me is easier for me to code myself than to install any Bio* project and deal with its idiosyncracies.
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