{{short description|Software project for the analysis of genomic data}} {{Infobox software | name = Bioconductor | logo = Bioconductor logo.svg | logo size = 260px | caption = Screenshot of Bioconductor | screenshot = | developer = | latest_release_version = 3.23 | latest_release_date = {{Start date and age|2026|04|29|df=yes}} | operating_system = Linux, macOS, Windows | platform = R programming language | genre = Bioinformatics | license = Artistic License 2.0 | website = {{URL|//www.bioconductor.org/}} }} '''Bioconductor''' is a free, open source and open development software project for the analysis and comprehension of genomic data generated by wet lab experiments in molecular biology.
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 semiannual 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 many genome annotation packages available that are mainly, but not solely, oriented towards different types of microarrays.
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 R packages, which are add-on modules for R. Initially most of the Bioconductor software packages focused on the analysis of single channel Affymetrix and 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.
== Goals == 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/Entrez. * Provide a common software platform that enables the rapid development and deployment of plug-able, 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.
== 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. * '''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 Affymetrix and Illumina, 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 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. [http://bioconductor.org/packages/release/bioc/html/ggtree.html ggtree], [https://cran.r-project.org/web/packages/phytools/index.html phytools] packages ..). * '''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 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 plug-ins ** 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 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.<ref name="BioCReleasePage">{{cite web |title=Bioconductor – Release Announcements |url=https://bioconductor.org/about/release-announcements/ |website=bioconductor.org |publisher=Bioconductor |accessdate=28 May 2019}}</ref> 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. {| class="wikitable" |- ! Version ! Release date ! Package count ! R dependency |- | align="center" | 3.23 | align="right" | {{dts|2026-04-29|abbr=on|format=dmy}} | align="right" | 2418 | align="center" | R 4.6 |- | align="center" | 3.22 | align="right" | {{dts|2025-10-30|abbr=on|format=dmy}} | align="right" | 2361 | align="center" | R 4.5 |- | align="center" | 3.20 | align="right" | {{dts|2024-10-30|abbr=on|format=dmy}} | align="right" | 2289 | align="center" | R 4.4 |- | align="center" | 3.18 | align="right" | {{dts|2023-10-25|abbr=on|format=dmy}} | align="right" | 2266 | align="center" | R 4.3 |- | align="center" | 3.16 | align="right" | {{dts|2022-11-02|abbr=on|format=dmy}} | align="right" | 2183 | align="center" | R 4.2 |- | align="center" | 3.14 | align="right" | {{dts|2021-10-27|abbr=on|format=dmy}} | align="right" | 2083 | align="center" | R 4.1 |- | align="center" | 3.11 | align="right" | {{dts|2020-04-28|abbr=on|format=dmy}} | align="right" | 1903 | align="center" | R 4.0 |- | align="center" | 3.10 | align="right" | {{dts|2019-10-30|abbr=on|format=dmy}} | align="right" | 1823 | align="center" | R 3.6 |- | align="center" | 3.8 | align="right" | {{dts|2018-10-31|abbr=on|format=dmy}} | align="right" | 1649 | align="center" | R 3.5 |- | align="center" | 3.6 | align="right" | {{dts|2017-10-31|abbr=on|format=dmy}} | align="right" | 1473 | align="center" | R 3.4 |- | align="center" | 3.4 | align="right" | {{dts|2016-10-18|abbr=on|format=dmy}} | align="right" | 1296 | align="center" | R 3.3 |- | align="center" | 3.2 | align="right" | {{dts|2015-10-14|abbr=on|format=dmy}} | align="right" | 1104 | align="center" | R 3.2 |- | align="center" | 3.0 | align="right" | {{dts|2014-10-14|abbr=on|format=dmy}} | align="right" | 934 | align="center" | R 3.1 |- | align="center" | 2.13 | align="right" | {{dts|2013-10-15|abbr=on|format=dmy}} | align="right" | 749 | align="center" | R 3.0 |- | align="center" | 2.11 | align="right" | {{dts|2012-10-03|abbr=on|format=dmy}} | align="right" | 610 | align="center" | R 2.15 |- | align="center" | 2.9 | align="right" | {{dts|2011-11-01|abbr=on|format=dmy}} | align="right" | 517 | align="center" | R 2.14 |- | align="center" | 2.8 | align="right" | {{dts|2011-04-14|abbr=on|format=dmy}} | align="right" | 466 | align="center" | R 2.13 |- | align="center" | 2.7 | align="right" | {{dts|2010-11-18|abbr=on|format=dmy}} | align="right" | 418 | align="center" | R 2.12 |- | align="center" | 2.6 | align="right" | {{dts|2010-04-23|abbr=on|format=dmy}} | align="right" | 389 | align="center" | R 2.11 |- | align="center" | 2.5 | align="right" | {{dts|2009-10-28|abbr=on|format=dmy}} | align="right" | 352 | align="center" | R 2.10 |- | align="center" | 2.4 | align="right" | {{dts|2009-04-21|abbr=on|format=dmy}} | align="right" | 320 | align="center" | R 2.9 |- | align="center" | 2.3 | align="right" | {{dts|2008-10-22|abbr=on|format=dmy}} | align="right" | 294 | align="center" | R 2.8 |- | align="center" | 2.2 | align="right" | {{dts|2008-05-01|abbr=on|format=dmy}} | align="right" | 260 | align="center" | R 2.7 |- | align="center" | 2.1 | align="right" | {{dts|2007-10-08|abbr=on|format=dmy}} | align="right" | 233 | align="center" | R 2.6 |- | align="center" | 2.0 | align="right" | {{dts|2007-04-26|abbr=on|format=dmy}} | align="right" | 214 | align="center" | R 2.5 |- | align="center" | 1.9 | align="right" | {{dts|2006-10-04|abbr=on|format=dmy}} | align="right" | 188 | align="center" | R 2.4 |- | align="center" | 1.8 | align="right" | {{dts|2006-04-27|abbr=on|format=dmy}} | align="right" | 172 | align="center" | R 2.3 |- | align="center" | 1.7 | align="right" | {{dts|2005-10-14|abbr=on|format=dmy}} | align="right" | 141 | align="center" | R 2.2 |- | align="center" | 1.6 | align="right" | {{dts|2005-05-18|abbr=on|format=dmy}} | align="right" | 123 | align="center" | R 2.1 |- | align="center" | 1.5 | align="right" | {{dts|2004-10-25|abbr=on|format=dmy}} | align="right" | 100 | align="center" | R 2.0 |- | align="center" | 1.4 | align="right" | {{dts|2004-05-17|abbr=on|format=dmy}} | align="right" | 81 | align="center" | R 1.9 |- | align="center" | 1.3 | align="right" | {{dts|2003-10-30|abbr=on|format=dmy}} | align="right" | 49 | align="center" | R 1.8 |- | align="center" | 1.2 | align="right" | {{dts|2003-05-29|abbr=on|format=dmy}} | align="right" | 30 | align="center" | R 1.7 |- | align="center" | 1.1 | align="right" | {{dts|2002-10-19|abbr=on|format=dmy}} | align="right" | 20 | align="center" | R 1.6 |- | align="center" | 1.0 | align="right" | {{dts|2002-05-01|abbr=on|format=dmy}} | align="right" | 15 | align="center" | R 1.5 |}
==Application of Bioconductor in small-RNA seq and microRNA data analysis==
===Introduction===
Small RNA sequencing is a widely used technique to study microRNA(miRNAs), small interfering RNAs (siRNAs), piwi-interacting RNA (piRNAs) that play a crucial role in RNA-mediated gene silencing process or known as RNA silencing /Gene silencing process. RNA silencing process employs different types of substrates which give rise to different types of RNA population, namely microRNAs, siRNAs, etc. In the laboratory, small RNA sequencing typically start by extraction of RNA from cells or tissues, followed by Adapter ligation to the 5' and 3' ends of small RNAs, followed by Reverse transcription and PCR amplification to generate cDNA libraries. Finally, High-throughput sequencing ( most commonly Illumina platforms) is used to produce millions of short reads. These resulting data then undergo computational processing to align reads to reference genomes of particular species or miRNA databases.
===Bioconductor in RNA Biology===
Bioconductor(BioC)<ref>{{cite journal |vauthors=Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, Huber W |title=BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis |journal=Bioinformatics |volume=21 |issue=16 |pages=3439–40 |date=August 2005 |pmid=16082012 |doi=10.1093/bioinformatics/bti525 }}</ref> is a widely used open-source platform for analysing different types of small-RNA sequencing and genomic data. It primarily utilizes the R programming language and offers a wide range of packages for bioinformatics and computational biology. Bioconductor provides a wide range of packages<ref>{{cite web | title=Org.Hs.eg.db | url=https://bioconductor.org/packages/release/data/annotation/html/org.Hs.eg.db.html }}</ref> for handling small-RNA seq data among them few are widely used by researchers. Popular Bioconductor packages like DESeq2,<ref name=Love14>{{cite journal |vauthors=Love MI, Huber W, Anders S |title=Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 |journal=Genome Biol |volume=15 |issue=12 |article-number=550 |date=2014 |pmid=25516281 |pmc=4302049 |doi=10.1186/s13059-014-0550-8 |doi-access=free}}</ref> edgeR,<ref name=Robinson10>{{cite journal |vauthors=Robinson MD, McCarthy DJ, Smyth GK |title=edgeR: a Bioconductor package for differential expression analysis of digital gene expression data |journal=Bioinformatics |volume=26 |issue=1 |pages=139–40 |date=January 2010 |pmid=19910308 |pmc=2796818 |doi=10.1093/bioinformatics/btp616 }}</ref> limma + voom,<ref name=Law16>{{cite journal |vauthors=Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, Ritchie ME |title=RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR |journal=F1000Res |volume=5 |article-number=ISCB Comm J-1408 |date=2016 |pmid=27441086 |pmc=4937821 |doi=10.12688/f1000research.9005.3 |doi-access=free}}</ref><ref>{{cite journal |vauthors=Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK |title=limma powers differential expression analyses for RNA-sequencing and microarray studies |journal=Nucleic Acids Res |volume=43 |issue=7 |pages=e47 |date=April 2015 |pmid=25605792 |pmc=4402510 |doi=10.1093/nar/gkv007 }}</ref> GenomicAlignment,<ref name=Huber15>{{cite journal |vauthors=Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M |title=Orchestrating high-throughput genomic analysis with Bioconductor |journal=Nat Methods |volume=12 |issue=2 |pages=115–21 |date=February 2015 |pmid=25633503 |pmc=4509590 |doi=10.1038/nmeth.3252 }}</ref> GenomicFeatures,<ref name=Huber15 /> Rsubread,<ref>{{cite journal |vauthors=Liao Y, Smyth GK, Shi W |title=The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads |journal=Nucleic Acids Res |volume=47 |issue=8 |pages=e47 |date=May 2019 |pmid=30783653 |pmc=6486549 |doi=10.1093/nar/gkz114 }}</ref> ShortRead,<ref name=Morgan09>{{cite journal |vauthors=Morgan M, Anders S, Lawrence M, Aboyoun P, Pagès H, Gentleman R |title=ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data |journal=Bioinformatics |volume=25 |issue=19 |pages=2607–8 |date=October 2009 |pmid=19654119 |pmc=2752612 |doi=10.1093/bioinformatics/btp450 }}</ref> featureCounts<ref>{{cite journal |vauthors=Liao Y, Smyth GK, Shi W |title=featureCounts: an efficient general purpose program for assigning sequence reads to genomic features |journal=Bioinformatics |volume=30 |issue=7 |pages=923–30 |date=April 2014 |pmid=24227677 |doi=10.1093/bioinformatics/btt656 |arxiv=1305.3347 }}</ref> provide robust analysis of RNA-seq data.<ref>{{cite journal |vauthors=Koch CM, Chiu SF, Akbarpour M, Bharat A, Ridge KM, Bartom ET, Winter DR |title=A Beginner's Guide to Analysis of RNA Sequencing Data |journal=Am J Respir Cell Mol Biol |volume=59 |issue=2 |pages=145–157 |date=August 2018 |pmid=29624415 |pmc=6096346 |doi=10.1165/rcmb.2017-0430TR }}</ref>
====DESeq2====
It uses a negative binomial distribution modeling for differential expression analysis of read count from RNA-seq data.<ref name=Chen16>{{cite journal |vauthors=Chen Y, Lun AT, Smyth GK |title=From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline |journal=F1000Res |volume=5 |pages=1438 |date=2016 |pmid=27508061 |pmc=4934518 |doi=10.12688/f1000research.8987.2 |doi-access=free}}</ref> It is popular for dispersion estimation, normalization, and visualization by PCA plots or MA plots.<ref name=Love14 />
====edgeR====
It also uses a negative binomial distribution modeling for differential expression analysis of read count from RNA seq data. In contrast with DESeq2, it is used when sample number is relatively small.<ref name=Robinson10 /><ref name=Chen16/>
====limma + voom====
It is used to estimate the mean-variance relationship of count data and transforms it to log2-counts per million (CPM). It is used for analysing microarray data and also to calculate CPM value from RNA-seq data.<ref name=Law16/>
====GenomicAlignment====
It is widely popular for reads like BAM and SAM file alignment to assign aligned reads to genes or miRNAs for downstream analysis.<ref name=Huber15 /><ref>{{cite web | title=GenomicAlignments (Development version) | url=https://bioconductor.org/packages/devel/bioc/html/GenomicAlignments.html }}</ref>
====GenomicFeatures====
It is used to build transcript-centric annotation databases like TxDb objects which store information about genes, exons, transcripts from GTF/GFF files.<ref name=Huber15 /><ref>{{cite web | title=GenomicFeatures (Development version) | url=https://bioconductor.org/packages/devel/bioc/html/GenomicFeatures.html#:~:text=Extract%20the%20genomic%20locations%20of,tools%20from%20the%20txdbmaker%20package }}</ref>
====Rsubread====
It is used mostly for summarizing the reads and mapping, where functions like align(), featureCounts() are used to provide an efficient alternative to external aligners like STAR or HISAT2.<ref>{{cite journal |vauthors=Liao Y, Smyth GK, Shi W |title=The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote |journal=Nucleic Acids Res |volume=41 |issue=10 |pages=e108 |date=May 2013 |pmid=23558742 |pmc=3664803 |doi=10.1093/nar/gkt214 }}</ref>
===ShortRead===
It is often used to pre-process the raw FASTQ files to check the quality of raw FASTQ data, which comes from a sequencing platform like Illumina sequencing etc.<ref name=Morgan09 />
===Computational Workflow===
====Data Import and Quality Control====
FASTQ files<ref>{{cite journal |vauthors=Cock PJ, Fields CJ, Goto N, Heuer ML, Rice PM |title=The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants |journal=Nucleic Acids Res |volume=38 |issue=6 |pages=1767–71 |date=April 2010 |pmid=20015970 |pmc=2847217 |doi=10.1093/nar/gkp1137 }}</ref> are typically imported by using different Bioconductor packages like ShortRead<ref name=Morgan09 /> which provides quality assessment reports.
====Adapter Trimming and Filtering====
Different external tools like Cutadapt,<ref>{{cite web | title=Cutadapt — Cutadapt 5.1 documentation | url=https://cutadapt.readthedocs.io/en/stable/ }}</ref> trimmomatic<ref>{{cite journal |vauthors=Bolger AM, Lohse M, Usadel B |title=Trimmomatic: a flexible trimmer for Illumina sequence data |journal=Bioinformatics |volume=30 |issue=15 |pages=2114–20 |date=August 2014 |pmid=24695404 |pmc=4103590 |doi=10.1093/bioinformatics/btu170 }}</ref> is used to remove the adapter sequence from the raw FASTQ files. This helps to improve the Reads quality.
====Read Alignment====
The processed Reads are aligned to the reference genome. This alignment can be done by different aligners like Rsubread, or external tools such as STAR, with results stored in standard formats like SAM (Sequence Alignment Map) or BAM (Binary Alignment Map) files .
====Annotation of microRNAs====
Bioconductor supports to integrate miRBase data where different packages like miRBaseConverter,<ref>{{cite journal |vauthors=Xu T, Su N, Liu L, Zhang J, Wang H, Zhang W, Gui J, Yu K, Li J, Le TD |title=miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase |journal=BMC Bioinformatics |volume=19 |issue=Suppl 19 |article-number=514 |date=December 2018 |pmid=30598108 |pmc=6311916 |doi=10.1186/s12859-018-2531-5 |doi-access=free}}</ref> AnnotationHub,<ref>{{cite web | title=AnnotationHub (Development version) | url=https://bioconductor.org/packages/devel/bioc/html/AnnotationHub.html }}</ref> org.Mm.eg.db<ref>{{cite web | title=Org.Mm.eg.db | url=https://bioconductor.org/packages/release/data/annotation/html/org.Mm.eg.db.html }}</ref> are used for annotate reads to known miRNAs.
====Quantification====
Count reads are mapped to known genes or microRNAs, and summarize counts across samples.
====Differential Expression Analysis====
After mapping and quantifying microRNA expression, different well-established packages like DESeq2, edgeR are used for differential expression analysis.
====Visualization====
To interpret and present miRNA expression results, different visualization packages are used like ggplot2,<ref>{{cite web | title=1 Introduction – ggplot2: Elegant Graphics for Data Analysis (3e) | url=https://ggplot2-book.org/introduction.html#:~:text=ggplot2%20is%20an%20R%20package,graphs%20by%20combining%20independent%20components }}</ref> pheatmap,<ref>{{cite web | title=Pheatmap: Pretty Heatmaps version 1.0.13 from CRAN | url=https://rdrr.io/cran/pheatmap/ }}</ref> ComplexHeatmap which generates Volcano plot (statistics), PCA plot (Principal component analysis), MA plot, pheatmap are used to visualize the differential expression data.
== Resources == {{refbegin}} *{{cite book |last1=Gentleman |first1=R. |author1-link=Robert Gentleman (statistician)|author2=Carey, V. |author3=Huber, W.|author3-link=Wolfgang Huber (scientist) |author4=Irizarry, R. |author4-link= Rafael Irizarry (scientist) |author5=Dudoit, S.|author5-link=Sandrine Dudoit |year=2005 |title=Bioinformatics and Computational Biology Solutions Using R and Bioconductor |publisher=Springer |isbn=978-0-387-25146-2}} *{{cite book |last=Gentleman |first=R. |author-link=Robert Gentleman (statistician)|year=2008 |title=R Programming for Bioinformatics |publisher=Chapman & Hall/CRC |isbn=978-1-4200-6367-7 |url=https://books.google.com/books?id=34Y6WjJy8zEC}} *{{cite book |last=Hahne |first=F. |author2=Huber, W. |author2-link=Wolfgang Huber (scientist) |author3=Gentleman, R.|author3-link=Robert Gentleman (statistician)|author4= Falcon, S. |year=2008 |title=Bioconductor Case Studies |publisher=Springer |isbn=978-0-387-77239-4 |url=https://books.google.com/books?id=F3tAehmRHSwC}} *{{cite journal|last1=Gentleman|first1=Robert C.|author1-link=Robert Gentleman (statistician)|last2=Carey|first2=Vincent J.|last3=Bates|first3=Douglas M.|last4=Bolstad|first4=Ben|last5=Dettling|first5=Marcel|author5-link=Marcel Dettling|last6=Dudoit|first6=Sandrine|author6-link=Sandrine Dudoit|last7=Ellis|first7=Byron|last8=Gautier|first8=Laurent|last9=Ge|first9=Yongchao|last10=Gentry|first10=Jeff|last11=Hornik|first11=Kurt|last12=Hothorn|first12=Torsten|last13=Huber|first13=Wolfgang|author13-link= Wolfgang Huber (scientist) |last14=Iacus|first14=Stefano|last15=Irizarry|first15=Rafael |author15-link= Rafael Irizarry (scientist) |last16=Leisch|first16=Friedrich|last17=Li|first17=Cheng|last18=Maechler|first18=Martin|last19=Rossini|first19=Anthony J. |last20=Sawitzki|first20=Gunther|last21=Smith|first21=Colin |last22=Smyth|first22=Gordon |last23=Tierney|first23=Luke|author23-link= Luke Tierney|last24=Yang|first24=Jean Y. H.|author24-link=Jean Yang|last25=Zhang|first25=Jianhua|doi=10.1186/gb-2004-5-10-r80|issue=10|journal=Genome Biology|page=R80|title=Bioconductor: open software development for computational biology and bioinformatics|volume=5|year=2004|pmc=545600|pmid=15461798 |doi-access=free }} {{refend}}
==See also== {{Portal|Free and open-source software|Biology}} *Computational biology *Bioinformatics *List of open source bioinformatics software *List of sequence alignment software *R (programming language) *DNA microarray *Affymetrix, a microarray technology platform
==References== {{Reflist}}
==External links== * {{Official website|//www.bioconductor.org}} * [http://www.r-project.org The R Project] GNU R is a programming language for statistical computing. * [http://www.bioconductor.org/about/release-announcements/ Bioconductor Releases] * The community of the Debian GNU/Linux distribution strives towards an [http://wiki.debian.org/AliothPkgBioc automated building of BioConductor packages] {{Webarchive|url=https://web.archive.org/web/20070811135011/http://wiki.debian.org/AliothPkgBioc |date=2007-08-11 }} for their distribution. [https://web.archive.org/web/20051124175716/http://bioknoppix.hpcf.upr.edu/ BioKnoppix] and [http://dirk.eddelbuettel.com/quantian.html Quantian] are projects extending Knoppix that have contributed bootable Debian GNU/Linux CDs providing BioConductor installations.
Category:Free bioinformatics software Category:Free R (programming language) software Category:Science software for macOS Category:Science software for Windows Category:Science software for Linux