name = Bioconductor
caption = Screenshot of Bioconductor
latest_release_version = 2.2
May 1, 2008
Linux, Mac OS X, Windows
R programming language
genre = Analysis of genomic information
license = Artistic License 2.0
website = [http://www.bioconductor.org/ www.bioconductor.org]
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 biannual 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 a large number of
genome annotationpackages available that are mainly, but not solely, oriented towards different types of microarrays.
The project was started in the Fall of
2001and is overseen by the Bioconductor core team, based primarily at the Fred Hutchinson Cancer Research Centerwith other members coming from various US and international institutions.
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
Affymetrixand 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.
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.
*Provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software.
*Further scientific understanding by producing high-quality documentation and reproducible research.
*Train researchers on computational and statistical methods for the analysis of genomic data.
* The R Project for Statistical Computing. R and the R package system provides a broad range of advantages to the Bioconductor project including:
** It contains a high-level
interpreted languagein which one can easily and quickly prototype new computational methods.
** It includes a well established system for packaging together software components and documentation.
** It can address the diversity and complexity of
computational biologyand bioinformaticsproblems in a common object-oriented framework.
** It provides to on-line
computational biologyand bioinformaticsdata sources.
** It supports a rich set of statistical simulation and modeling activities.
** It contains cutting edge data and model visualization capabilities.
** It has been the basis for pathbreaking research in parallel statistical computing.
** It is under very active development by a dedicated team of researchers with a strong commitment to good documentation and
* Documentation and reproducible research. Each Bioconductor package contains at least one vignette, which is a document that provides a textual, task-oriented description of the package's functionality. These vignettes come in several forms. Many are simple "
How-to"s that are designed to demonstrate how a particular task can be accomplished with that package's software. Others provide a more thorough overview of the package or might even discuss general issues related to the package. In the future, we are looking towards providing vignettes that are not specifically tied to a package, but rather are demonstrating more complex concepts. As with all aspects of the Bioconductor project, users are encouraged to participate in this effort.
* Statistical and graphical methods. The Bioconductor project aims to provide access to a wide range of powerful statistical and graphical methods for the analysis of genomic data. Analysis packages are available for: pre-processing
Affymetrixand cDNA array data; identifying differentially expressed genes; graph theoretical analyses; plotting genomic data. In addition, 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 seriesanalysis.
* Genome Annotation. The Bioconductor project provides software for associating microarray and other genomic data in real time to biological metadata from web databases such as
GenBank, LocusLink and PubMed(annotate package). Functions are also provided for incorporating the results of statistical analysis in HTML reports with links to annotation WWW resources. Software tools are available for assembling and processing genomic annotation data, from databases such as GenBank, the Gene Ontology Consortium, LocusLink, UniGene, the UCSC Human Genome Project (AnnotationDbi package). Data packages are distributed to provide mappings between different probe identifiers (e.g. Affy IDs, LocusLink, PubMed). Customized annotation libraries can also be assembled.
Open source. The Bioconductor project has a commitment to full open source discipline, with distribution via a SourceForge.net-like platform. All contributions are expected to exist under an open source licensesuch as Artistic 2.0, GPL2, or BSD. There are many different reasons why open--source software is beneficial to the analysis of microarray data and to computational biology in general. The reasons include:
** To provide full access to algorithms and their implementation
** To facilitate software improvements through bug fixing and
** To encourage good scientific computing and statistical practice by providing appropriate tools and instruction
** To provide a workbench of tools that allow researchers to explore and expand the methods used to analyze biological data
** To ensure that the international
scientific communityis the owner of the software tools needed to carry out research
** To lead and encourage commercial support and development of those tools that are successful
** To promote reproducible research by providing open and accessible tools with which to carry out that research (reproducible research is distinct from independent verification)
* Open development. Users are encouraged to become developers, either by contributing Bioconductor compliant packages or documentation. Additionally Bioconductor provides a mechanism for linking together different groups with common goals to foster
collaborationon software, possibly at the level of shared development.
*cite book |last=Gentleman |first=R. |coauthors=Carey, V.; Huber, W,; Irizarry, R.; Dudoit, S.|year=2005 |title=Bioinformatics and Computational Biology Solutions Using R and Bioconductor |publisher=Springer
*cite book |last=Gentleman |first=R. |year=2008 |title=R Programming for Bioinformatics |publisher=Chapman & Hall/CRC
*cite book |last=Hahne |first=F. |coauthors=Huber, W.; Gentleman, R.; Falcon, S.|year=2008 |title=Bioconductor Case Studies |publisher=Springer
Affymetrix, a microarray technology platform
List of sequence alignment software
* [http://www.bioconductor.org Official Website]
* Genome Biology 2004 article: [http://genomebiology.com/content/pdf/gb-2004-5-10-r80.pdf Bioconductor: open software development for computational biology and bioinformatics]
* [http://www.r-project.org The R Project]
GNUR is a programming language for statistical computing.
* The community of the Debian GNU/Linux distribution strives towards an [http://wiki.debian.org/AliothPkgBioc automated building of BioConductor packages] for their distribution. [http://bioknoppix.hpcf.upr.edu/ BioKnoppix] and [http://dirk.eddelbuettel.com/quantian.html Quantian] are projects extending
Knoppixthat have contributed bootable Debian GNU/Linux CDs providing BioConductor installations.
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