I'm currently investigating strains recorded from equine hooves. In this case, I wasn't directly involved with the experiment, but I'm helping out with the analysis. The analysis itself fits a common pattern: lots of trials of a similar type that all need to be processed in the same way.
Biologists often tackle this kind of work using spreadsheet programs. The advantages of a spreadsheet include the fact that your analysis is quite accessible, and it's computed in real-time. The main disadvantage (in this case, but also in many others I've seen) is that there is a lot of book-keeping required to synchronize the analysis that is performed on each dataset. The book-keeping introduces plenty of opportunity for error.
My solution to this problem is to take a "vectorized" approach. A good solution is a single program that can process each dataset in turn, and perform the analysis automatically. Thus, the amount of work required for the analysis is de-coupled from the sample size of the experiment. There is no book-keeping work to keep the analysis from each spreadsheet in-sync. It sounds simple enough, but few biologists either understand or implement this idea. The main (only?) drawback of this approach is its somewhat greater complexity; you need a real program to perform the analysis, rather than using a spreadsheet. That's really not much of a drawback though, once your experiment is sufficiently large.