Bioconductor

Source: Wikipedia, the free encyclopedia.
Bioconductor
Stable release
3.17 / 26 April 2023; 11 months ago (2023-04-26)
R programming language
TypeBioinformatics
LicenseArtistic License 2.0
Websitewww.bioconductor.org

Bioconductor is a

wet lab experiments in molecular biology
.

Bioconductor is based primarily on the

genome annotation packages available that are mainly, but not solely, oriented towards different types of microarrays
.

While computational methods continue to be developed to interpret biological data, the Bioconductor project is an open source software repository that hosts a wide range of statistical tools developed in the R programming environment. Utilizing a rich array of statistical and graphical features in R, many Bioconductor packages have been developed to meet various data analysis needs. The use of these packages provides a basic understanding of the R programming / command language. As a result, R and Bioconductor packages, which have a strong computing background, are used by most biologists who will benefit significantly from their ability to analyze datasets. All these results provide biologists with easy access to the analysis of genomic data without requiring programming expertise.

The project was started in the Fall of 2001 and is overseen by the Bioconductor core team, based primarily at the

Fred Hutchinson Cancer Research Center
, with other members coming from international institutions.

Packages

Most Bioconductor components are distributed as

SNP
data.

Goals

The broad goals of the projects are to:

Main features

  • 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, the Bioconductor project is 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.
  • 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 series
    analysis.
  • 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 and others with the 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.This project also contain several functions for genomic analysis and phylogenetic (e.g ggtree, phytools
    packages ..).
  • 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:
  • developers, either by contributing Bioconductor compliant packages or documentation. Additionally Bioconductor provides a mechanism for linking together different groups with common goals to foster collaboration
    on software, possibly at the level of shared development.

Milestones

Each release of Bioconductor is developed to work best with a chosen version of R.[1] In addition to bugfixes and updates, a new release typically adds packages. The table below maps a Bioconductor release to a R version and shows the number of available Bioconductor software packages for that release.

Version Release date Package count R dependency
3.17 26 Apr 2023 2230 R 4.3
3.16 2 Nov 2022 2183 R 4.2
3.14 27 Oct 2021 2083 R 4.1
3.11 28 Apr 2020 1903 R 4.0
3.10 30 Oct 2019 1823 R 3.6
3.8 31 Oct 2018 1649 R 3.5
3.6 31 Oct 2017 1473 R 3.4
3.4 18 Oct 2016 1296 R 3.3
3.2 14 Oct 2015 1104 R 3.2
3.0 14 Oct 2014 934 R 3.1
2.13 15 Oct 2013 749 R 3.0
2.11 3 Oct 2012 610 R 2.15
2.9 1 Nov 2011 517 R 2.14
2.8 14 Apr 2011 466 R 2.13
2.7 18 Nov 2010 418 R 2.12
2.6 23 Apr 2010 389 R 2.11
2.5 28 Oct 2009 352 R 2.10
2.4 21 Apr 2009 320 R 2.9
2.3 22 Oct 2008 294 R 2.8
2.2 1 May 2008 260 R 2.7
2.1 8 Oct 2007 233 R 2.6
2.0 26 Apr 2007 214 R 2.5
1.9 4 Oct 2006 188 R 2.4
1.8 27 Apr 2006 172 R 2.3
1.7 14 Oct 2005 141 R 2.2
1.6 18 May 2005 123 R 2.1
1.5 25 Oct 2004 100 R 2.0
1.4 17 May 2004 81 R 1.9
1.3 30 Oct 2003 49 R 1.8
1.2 29 May 2003 30 R 1.7
1.1 19 Oct 2002 20 R 1.6
1.0 1 May 2002 15 R 1.5

Resources

  • ISBN 978-0-387-25146-2
    .
  • ISBN 978-1-4200-6367-7
    .
  • Hahne, F.;
    ISBN 978-0-387-77239-4
    .
  • PMID 15461798
    .

See also

References

  1. ^ "Bioconductor – Release Announcements". bioconductor.org. Bioconductor. Retrieved 28 May 2019.

External links

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