The mission of the Bioconductor project is to develop, support, and disseminate free open source software that facilitates rigorous and reproducible analysis of data from current and emerging biological assays. We are dedicated to building a diverse, collaborative, and welcoming community of developers and data scientists.
Scientific, Technical and Community Advisory Boards provide project oversight.
The Bioconductor release version is updated twice each year, and is appropriate for most users. There is also a development version, to which new features and packages are added prior to incorporation in the release. A large number of meta-data packages provide pathway, organism, microarray and other annotations.
The Bioconductor project started in 2001 and is overseen by a core team, based primarily at Roswell Park Comprehensive Cancer Center, and by other members coming from US and international institutions. A Community Advisory Board and a Technical Advisory Board of key participants meets monthly to support the Bioconductor mission by coordinating training and outreach activities, developing strategies to ensure long-term technical suitability of core infrastructure, and to identify and enable funding strategies for long-term viability. A Scientific Advisory Board including external experts provides annual guidance and accountability.
Key citations to the project include Huber et al., 2015 Nature Methods 12:115-121 and Gentleman et al., 2004 Genome Biology 5:R80
Most Bioconductor components are distributed as R packages. The functional scope of Bioconductor packages includes the analysis of DNA microarray, sequence, flow, SNP, and other data.
The broad goals of the Bioconductor project are:
Documentation and reproducible research. Each Bioconductor package contains one or more vignettes, documents that provide a textual, task-oriented description of the package’s functionality. Vignettes come in several forms. Many are “HowTo”s that demonstrate how a particular task can be accomplished with that package’s software. Others provide a more thorough overview of the package or discuss general issues related to the package.
Statistical and graphical methods. The Bioconductor project provides access to powerful statistical and graphical methods for the analysis of genomic data. Analysis packages address workflows for analysis of oligonucleotide arrays, sequence analysis, flow cytometry. and other high-throughput genomic data. The R package system itself provides implementations for a broad range of state-of-the-art statistical and graphical techniques, including linear and non-linear modeling, cluster analysis, prediction, resampling, survival analysis, and time-series analysis.
Annotation. The Bioconductor project provides software for associating microarray and other genomic data in real time with biological metadata from web databases such as GenBank, Entrez genes and PubMed (annotate package). Functions are also provided for incorporating the results of statistical analysis in HTML reports with links to annotation web resources. Software tools are available for assembling and processing genomic annotation data, from databases such as GenBank, the Gene Ontology Consortium, Entrez genes, UniGene, the UCSC Human Genome Project (AnnotationDbi package). Annotation data packages are distributed to provide mappings between different probe identifiers (e.g. Affy IDs, Entrez genes, PubMed). Customized annotation libraries can also be assembled.
Bioconductor short courses. The Bioconductor project has developed a program of short courses on software and statistical methods for the analysis of genomic data. Courses have been given for audiences with backgrounds in either biology or statistics. All course materials (lectures and computer labs) are available on this site.
Please refer to the Bioconductor Code of Conduct