Bioconductor is an open source, open development software project to
provide tools for the analysis and comprehension of high-throughput
genomic data. It is based primarily on the
R programming language.
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 the
Fred Hutchinson Cancer Research Center, and by
other members coming from US and international institutions.
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
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:
- To provide widespread access to a broad range of powerful statistical
and graphical methods for the analysis of genomic data.
- To facilitate the inclusion of biological metadata in the analysis of
genomic data, e.g. literature data from PubMed, annotation data from
- To provide a common software platform that enables the rapid development
and deployment of extensible, scalable, and interoperable software.
- To further scientific understanding by producing high-quality
documentation and reproducible research.
- To train researchers on computational and
statistical methods for the analysis of genomic data.
Main Project Features
- The R Project for Statistical Computing. Using
R provides a broad range of advantages
to the Bioconductor project, including:
- A high-level interpreted language to easily and quickly prototype
new computational methods.
- A well established system for packaging together software with
- An object-oriented framework for addressing the diversity and
complexity 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 a
strong commitment to good documentation and software design.
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
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
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.
- Open source. The Bioconductor project has a commitment to full
open source discipline, with distribution via a public subversion
(version control) server. All contributions exist under an open
source license such 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 software
- 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 community is 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
Bioconductor compliant packages
or documentation. Additionally Bioconductor provides a mechanism for
linking together different groups with common goals to foster
collaboration on software, often at the level of shared development.