Even well-known and respected leaders in proteomics agree that it lies 10 years behind genomics. There are several valid reasons for this, including the chemistry of proteins, the technical complexity of the instrumentation (in particular mass-spectrometry - MS) and the vast possibilities in the study of proteins. An often overseen albeit essential component of this failure is arguably the software that is promoted inside the proteomics community. Computational proteomics researchers who value quality software, comprehensive data analysis and reproducible research ought to illustrate how more flexible and advanced tools can effectively be used and demonstrate their advantages. Here, we illustrate some examples of proteomics data analysis in R, in particular low level raw MS data manipulation, labelled and label-free quantitation and peptide identification, taken from the RforProteomics package.

Poster presented at the Bioconductor conference, Seattle, 2013.