Bioconductor: open software development for computational biology and bioinformatics

By Robert Gentleman, Vincent J. Carey, Douglas M. Bates, Benjamin M. Bolstad, Marcel Dettling, Sandrine Dudoit, B. Ellis, Laurent Gautier, Yongchao Ge, Jeff Gentry and 15 more. Genome Biology, 2004

12,915 citations recorded by the source corpus.

The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.

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Citations in the corpus

DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment (2015, methodology): “DECIPHER was written in the C and R programming languages, and is available from DECIPHER.cee.wisc.edu or BioConductor [47].”
Network-based global inference of human disease genes (2008, methodology): “See the section of Materials and methods for more details of the data sources and the model.”
SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis (2015, methodology): “org) for the implementation of TF importance metrics, G1DBN [56] for the implementation of expression-based regulatory interaction inference, Bioconductor::Biobase [68] for data management, tightClust [47] and ConsensusClustPlus [46] for the implementation of alternative clustering methods for cell ”
Unifying Optimization Algorithms to Aid Software System Users: optimx for R (2011, background): “Several packages for optimization specialized to problems in genomics are in the Bioconductor collection (Gentleman et al. 2004).”
QuasR: quantification and annotation of short reads in R (2014, unknown): “QuasR supports different experiment types (including RNA-seq, ChIP-seq and Bis-seq) and analysis variants (e.g. paired-end, stranded, spliced and allele-specific), and is integrated in Bioconductor so that its output can be directly processed for statistical analysis and visualization.”
Genevestigator V3: A Reference Expression Database for the Meta-Analysis of Transcriptomes (2008, methodology): “Although gene expression data and statistical tools are now widely available [1–4], the simultaneous analysis of hundreds or thousands of gene expression arrays in their biological context remains a major challenge for biologists.”
The Gene Ontology (GO) project in 2006 (2005, methodology): “Several available tools are now being integrated into caBIG, including GOminer (29,30), caArray, caWorkbench, RProteomics, Bioconductor (31), Reactome (32) and the cancer Pathways Interaction Database.”
Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi (2016, methodology): “The minﬁ package in R/Bioconductor (Gentleman et al., 2004; Huber et al., 2015) is a widely used software package for analyzing data from the Illumina HumanMethyla-tion450 array (Aryee et al., 2014).”
ChIP-Seq: advantages and challenges of a maturing technology (2009, unknown): “Some are available as a plug-in package for the statistical language R, a powerful system for data analysis that is popular among bioinformatician”
RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics (2012, methodology): “Finally, methods for statistical inference of DGE from mapped RNA-Seq reads have been established in the Bioconductor project (9) are being continuously developed by leading biostatisticians, including edgeR (10), DESeq (11) and baySeq (12).”
ArrayExpress update—trends in database growth and links to data analysis tools (2012, methodology): “To simplify the interface between ArrayExpress and analytical platforms, we are now providing links to popular analytical tools such as Bioconductor and GenePattern (11), as well as developing robust internal pipelines for HTS data processing.”
The Gene Expression Omnibus database (2016, methodology): “Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J (2004) Bioconductor: open software development f”