About Bioconductor

The mission of the Bioconductor project is to develop, support, anddisseminate free open source software that facilitates rigorous andreproducible analysis of data from current and emerging biological assays.We are dedicated to building a diverse, collaborative, and welcomingcommunity of developers and data scientists.

Scientific,Technical andCommunityAdvisory Boards provide project oversight.

Release and Core Development

The Bioconductorrelease version is updatedtwice each year, and is appropriate for most users.Each Bioconductor release is designed to work with a specificversion of R, with the relationship summarized inthis table. There is also adevelopment version, to which new features andpackages are added prior to incorporation in the release. A largenumber ofmeta-data packagesprovide pathway, organism, microarray and other annotations.

The Bioconductorproject started in 2001 and is overseen by acoreteam. ACommunity Advisory Boardand aTechnical Advisory Board of key participantsmeets monthly to support the Bioconductor mission by coordinatingtraining and outreach activities, developing strategies to ensure long-termtechnical suitability of core infrastructure, and to identify and enablefunding strategies for long-term viability.AScientific Advisory Board including externalexperts provides annual guidance and accountability.

Key citations to the project include Huber et al., 2015NatureMethods 12:115-121 and Gentleman et al., 2004Genome Biology5:R80

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Bioconductor Packages

Most Bioconductor components are distributed asRpackages.The functional scope ofBioconductor packagesincludes the analysis of DNA microarray, sequence, flow, SNP, and other geneticor genomic data.

Project Goals

The broad goals of the Bioconductor project are:

To provide widespread access to a broad range of powerful statisticaland graphical methods for the analysis of genomic data.
To facilitate the inclusion of biological metadata in the analysis ofgenomic data, e.g. literature data from PubMed, annotation data fromEntrez genes.
To provide a common software platform that enables the rapid developmentand deployment of extensible, scalable, and interoperable software.
To further scientific understanding by producing high-qualitydocumentation and reproducible research.
Totrain researchers on computational andstatistical methods for the analysis of genomic data.

Main Project Features

The R project for Statistical Computing. UsingR provides a broad range of advantagesto the Bioconductor project, including:

A high-level interpreted language to easily and quickly prototypenew computational methods.
A well established system for packaging together software withdocumentation.
An object-oriented framework for addressing the diversity andcomplexity of computational biology and bioinformatics problems.
Access to on-line computational biology and bioinformatics data.
Support for rich statistical simulation and modeling activities.
Cutting edge data and model visualization capabilities.
Active development by a dedicated team of researchers with astrong commitment to good documentation and software design.

Documentation and reproducible research

EachBioconductor package contains one or morevignettes, documents that provide atextual, task-oriented description of the package’s functionality.Vignettes come in several forms. Many are “HowTo”s that demonstratehow a particular task can be accomplished with that package’s software.Others provide a more thorough overview of the package or discuss generalissues related to the package.

Statistical and graphical methods

The Bioconductor project provides access to powerful statistical and graphical methods forthe analysis of genomic data.Analysis packages addressworkflows for analysis ofoligonucleotide arrays, sequence analysis, flow cytometry. and otherhigh-throughput genomic data. TheR package systemitself provides implementations for a broad range ofstate-of-the-art statistical and graphical techniques, includinglinear 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 genomicdata in real time with biological metadata from web databases such as GenBank, Entrez genesand PubMed (annotatepackage). Functions are also provided for incorporating the resultsof statistical analysis in HTML reports with links to annotation webresources. Software tools are available for assembling andprocessing genomic annotation data, from databases such as GenBank,the Gene Ontology Consortium, Entrez genes, UniGene, the UCSC HumanGenome Project(AnnotationDbipackage).Annotation data packagesare distributed to provide mappings between different probeidentifiers (e.g. Affy IDs, Entrez genes, PubMed). Customizedannotation libraries can also be assembled.

Bioconductor short courses

The Bioconductor project has developed aprogram ofshort courses on software andstatistical methods for the analysis of genomic data. Courses have beengiven for audiences with backgrounds in either biology or statistics. Allcourse materials (lectures and computer labs)are available on this site. There is also aBioconductor teachingcommittee to consolidate Bioconductor-focusedtraining materials and establish a community of Bioconductor trainers.

Open source

The Bioconductor project has a commitment to fullopen source discipline, with distribution via a public git(version control) server. Almost all contributions exist under anopen source license. There are many different reasons why opensource software is beneficial to the analysis of microarray data andto computational biology in general. The reasons include:

To provide full access to algorithms and their implementation
To facilitate software improvements through bug fixing and softwareextension
To encourage good scientific computing and statistical practice byproviding appropriate tools and instruction
To provide a workbench of tools that allow researchers to explore andexpand the methods used to analyze biological data
To ensure that the international scientific community is the owner ofthe software tools needed to carry out research
To lead and encourage commercial support and development of those toolsthat are successful
To promote reproducible research by providing open and accessible toolswith which to carry out that research (reproducible research is distinctfrom independent verification)

Open development

Users are encouraged to become developers, either by contributingBioconductor compliant packagesor documentation. Additionally Bioconductor provides a mechanism forlinking together different groups with common goals to fostercollaboration on software, often at the level of shared development.