Bioconductor 3.0 released today

Today, the latest version (3.0) of the Bioconductor suite of R libraries was released, featuring nearly one thousand software packages as well as an equal number of annotation and experimental data sources.

Despite being a daily user of Bioconductor packages, I am still amazed by the breadth and quality of tools shared in the Bioconductor community, providing open source solutions for metabolomics, cheminformatics, mass spectrometry, genetics or cell biology and many other research areas.

When I first encountered R and Bioconductor as a PhD student at EMBL, I was looking over the shoulders of Wolfgang Huber, a members of the Bioconductor core and co-authors of its official publication in 2004. Back then, I was very impressed at how quickly my ChIP-on-chip data was transformed into scientific plots at Wolfgang's hands. Little did I know that I would spend many hours using R and Bioconductor packages and even contribute code myself in the years to come !

Bioconductor was initially conceived in 2001 to enable reproducible research, share statistical software for biological data analysis and provide training for its growing user base. It is supported by an international team of software developers and scientists based primarily at the Fred Hutchinson Cancer Research Center and other US and international research institutes.

All packages contributed to Bioconductor are reviewed to ensure adherence to established guidelines, including e.g. the availability of vignettes, unit tests, help pages and examples. In addition, both released packages and those under development are automatically tested in a continuous integration environment. A dedicated support site is available where questions are usually answered by package authors and experienced users within hours.

Today, Bioconductor provides important infrastructure for the research community worldwide, and packages are downloaded tens of thousands (sometimes hundreds of thousands of times) every year.

My previous and current research is heavily indebted to the many Bioconductor contributors, some of which I am fortunate to count among friends and colleagues, and to the Bioconductor core team. 

Many thanks for providing an outstanding open-source infrastructure !

References

• Bioconductor: open software development for computational biology and bioinformatics, Gentleman RC et al, Genome Biol. 2004;5(10):R80. Epub 2004 Sep 15.