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Nat Methods. 2015 Feb;12(2):115-21. doi: 10.1038/nmeth.3252.

Orchestrating high-throughput genomic analysis with Bioconductor.

Author information

1
European Molecular Biology Laboratory, Heidelberg, Germany.
2
1] Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. [2] Harvard School of Public Health, Boston, Massachusetts, USA.
3
Genentech, South San Francisco, California, USA.
4
Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
5
Department of Medical Genetics, School of Medical Sciences, State University of Campinas, Campinas, Brazil.
6
Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
7
Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
8
Department of Biochemistry, University of Cambridge, Cambridge, UK.
9
Institute for Integrative Genome Biology, University of California, Riverside, Riverside, California, USA.
10
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
11
Novartis Institutes for Biomedical Research, Basel, Switzerland.
12
1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA. [2] Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.
13
1] Harvard School of Public Health, Boston, Massachusetts, USA. [2] Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
14
Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.
15
1] Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia. [2] Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia.
16
School of Urban Public Health at Hunter College, City University of New York, New York, New York, USA.

Abstract

Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.

PMID:
25633503
PMCID:
PMC4509590
DOI:
10.1038/nmeth.3252
[Indexed for MEDLINE]
Free PMC Article
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