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. It gained widespread exposure in a 2004 Genome Biology paper.
Bioconductor Packages
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.
Project Goals
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 Entrez genes.
- 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 documentation.
- 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.
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