Nothing makes me happier when rigorous statistical analyses are applied to genomics and proteomics data sets. That is why I had to do a serious double-take when I was informed about R for Proteomics.
If you aren't aware, R is an open source statistics platform that is supported by a large community of programmers and scientists. It is used in everything from population dynamics to microarray studies. The real flexibility in R is gained by the fact that small programs can be relatively easily integrated into R as it has no problem with programs written within its own sub-language or C or C++. The real power comes from the fact that the community actually develops new programs for it and supports them.
R for Proteomics is a smaller sub-branch that is beginning to gain some steam. A new review by Laurent Gatto and Andy Christoforou is currently in press at Elsevier that highlights some of these programs and their capabilities. And there are many, and all of them adhere strongly to the core philosophy of R, that robust statistics come first.
For more information, follow this link.
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