Bioconductor is a free, open source and open development software project for the analysis and comprehension of genomic data generated by wet lab experiments in molecular biology. Bioconductor is based primarily on the statistical R programming language, but does contain contributions in other programming languages. It has two releases each year that follow the semiannual releases of R. At any one time there is a release version, which corresponds to the released version of R, and a development version, which corresponds to the development version of R. Most users will find the release version appropriate for their needs. In addition there are many genome annotation packages available that are mainly, but not solely, oriented towards different types of microarrays. While computational methods continue to be developed to interpret biological data, the Bioconductor project is an open source software repository that hosts a wide range of statistical tools developed in the R programming environment. Utilizing a rich array of statistical and graphical features in R, many Bioconductor packages have been developed to meet various data analysis needs. The use of these packages provides a basic understanding of the R programming / command language. As a result, R and Bioconductor packages, which have a strong computing background, are used by most biologists who will benefit significantly from their ability to analyze datasets. All these results provide biologists with easy access to the analysis of genomic data without requiring programming expertise. The project was started in the Fall of 2001 and is overseen by the Bioconductor core team, based primarily at the Fred Hutchinson Cancer Research Center, with other members coming from international institutions. Packages Most Bioconductor components are distributed as R packages, which are add-on modules for R. Initially most of the Bioconductor software packages focused on the analysis of single channel Affymetrix and two or more channel cDNA/Oligo microarrays. As the project has matured, the functional scope of the software packages broadened to include the analysis of all types of genomic data, such as SAGE, sequence, or SNP data. Goals The broad goals of the projects are to: Provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data. Facilitate the inclusion of biological metadata in the analysis of genomic data, e.g. literature data from PubMed, annotation data from LocusLink/Entrez. Provide a common software platform that enables the rapid development and deployment of