BOL

A web applet for browsing protein-protein interactions was implemented. It enables the display of interaction relationships, based upon neighboring distance and biological function.

Availability: The Java applet is available at http://www.charite.de/bioinformatics

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

My vote goes to R (http://cran.at.r-project.org) environment is an easy to use and write programs and custom functions. The R programming syntax is extremely easy to learn, even for users with no previous programming experience.

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.