Gviz is a really great package for visualising genomics data in R. Recently I have been looking at stranded RNA-seq data, which provides the ability to differentiate sense and antisense expression from a genomic locus thanks to the way in which you generate the libraries (I won’t go into all … Continue reading →

I previously wrote about accessing step data in R using the fitbit API and at the time said I could think of little to do with the data that was not available via the fitbit website. Since then I’ve rejoined the local squash league and thought that I might be able to learn something useful … Continue reading →

I caught a pretty amazing episode of Horizon (the BBC’s in depth science program in the UK) a while back called “The future of medicine is apps“. The programme explored the health benefits of giving people data about their body, health and lifestyle. The more extreme examples … Continue reading →

I use a lot of protein-protein interaction (PPI) data as biological networks represent the systems within which our genes and proteins of interest function. There are many sources of PPI data, including BioGRID and IntAct etc. A recent effort has emerged that attempts to pull all of these … Continue reading →

There was quite a lot of buzz around when the guys from Rstudio launched Shiny, a new web framework for R that promises to “make it super simple for R users like you to turn analyses into interactive web applications … Continue reading … Continue reading →