Orchestrating high-throughput genomic analysis with Bioconductor

Nature Methods volume 12, pages115–121(2015)Cite this article

Subjects

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

Rent or Buy

All prices are NET prices.

Figure 1: Example uses of the Ranges algebra.
Figure 2: The integrative data container SummarizedExperiment.
Figure 3: Visualization along genomic coordinates with ggbio.

References

  1. 1

    Gentleman, R.C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

    Article  Google Scholar 

  2. 2

    R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014).

  3. 3

    Hahne, F., Huber, W., Gentleman, R. & Falcon, S. Bioconductor Case Studies (Springer, 2008).

    Google Scholar 

  4. 4

    Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

    CAS  Article  Google Scholar 

  5. 5

    The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  6. 6

    Ohnishi, Y. et al. Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages. Nat. Cell Biol. 16, 27–37 (2014).

    CAS  Article  Google Scholar 

  7. 7

    Finak, G. et al. OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis. PLoS Comput. Biol. 10, e1003806 (2014).

    Article  Google Scholar 

  8. 8

    Chelaru, F., Smith, L., Goldstein, N. & Corrada Bravo, H. Epiviz: interactive visual analytics for functional genomics data. Nat. Methods 11, 938–940 (2014).

    CAS  Article  Google Scholar 

  9. 9

    Gentleman, R. Reproducible research: a bioinformatics case study. Stat. Appl. Genet. Mol. Biol. 4, Article 2 (2005).

    Article  Google Scholar 

  10. 10

    Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    CAS  Article  Google Scholar 

  11. 11

    Laufer, C., Fischer, B., Billmann, M., Huber, W. & Boutros, M. Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping. Nat. Methods 10, 427–431 (2013).

    CAS  Article  Google Scholar 

  12. 12

    Waldron, L. et al. Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. J. Natl. Cancer Inst. 106, dju049 (2014).

    PubMed  PubMed Central  Google Scholar 

  13. 13

    Riester, M. et al. Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples. J. Natl. Cancer Inst. 106, dju048 (2014).

    Article  Google Scholar 

  14. 14

    McMurdie, P.J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).

    Article  Google Scholar 

  15. 15

    Goecks, J., Nekrutenko, A., Taylor, J. & The Galaxy Team. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 11, R86 (2010).

    Article  Google Scholar 

  16. 16

    Pérez, F. & Granger, B.E. IPython: a system for interactive scientific computing. Comput. Sci. Eng. 9, 21–29 (2007).

    Article  Google Scholar 

  17. 17

    Anonymous. Credit for code. Nat. Genet. 46, 1 (2014).

  18. 18

    Altschul, S. et al. The anatomy of successful computational biology software. Nat. Biotechnol. 31, 894–897 (2013).

    CAS  Article  Google Scholar 

  19. 19

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  20. 20

    Lawrence, M. & Morgan, M. Scalable genomics with R and Bioconductor. Stat. Sci. 29, 214–226 (2014).

    Article  Google Scholar 

  21. 21

    Brazma, A. et al. Minimum information about a microarray experiment (MIAME) - toward standards for microarray data. Nat. Genet. 29, 365–371 (2001).

    CAS  Article  Google Scholar 

  22. 22

    Cabezas-Wallscheid, N. et al. Identification of regulatory networks in HSCs and their immediate progeny via integrated proteome, transcriptome, and DNA methylome analysis. Cell Stem Cell 15, 507–522 (2014).

    CAS  Article  Google Scholar 

  23. 23

    Anders, S., Reyes, A. & Huber, W. Detecting differential usage of exons from RNA-seq data. Genome Res. 22, 2008–2017 (2012).

    CAS  Article  Google Scholar 

  24. 24

    Obenchain, V. et al. VariantAnnotation: a Bioconductor package for exploration and annotation of genetic variants. Bioinformatics 30, 2076 (2014).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank all contributors to the Bioconductor and R projects. Bioconductor is supported by the National Human Genome Research Institute of the US National Institutes of Health (U41HG004059 to M.M.). Additional support is from the US National Science Foundation (1247813 to M.M.) and the European Commission FP7 project RADIANT (to W.H.). A. Bruce provided graphics support for Figure 2.

Author information

Affiliations

  1. European Molecular Biology Laboratory, Heidelberg, Germany

    Wolfgang Huber, Simon Anders, Andrzej K Oleś & Alejandro Reyes

  2. Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA

    Vincent J Carey

  3. Harvard School of Public Health, Boston, Massachusetts, USA

    Vincent J Carey, Rafael A Irizarry & Michael I Love

  4. Genentech, South San Francisco, California, USA

    Robert Gentleman & Michael Lawrence

  5. Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA

    Marc Carlson, Valerie Obenchain, Hervé Pagès, Paul Shannon, Dan Tenenbaum & Martin Morgan

  6. Department of Medical Genetics, School of Medical Sciences, State University of Campinas, Campinas, Brazil

    Benilton S Carvalho

  7. Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA

    Hector Corrada Bravo

  8. Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA

    Sean Davis

  9. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Laurent Gatto

  10. Institute for Integrative Genome Biology, University of California, Riverside, Riverside, California, USA

    Thomas Girke

  11. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA

    Raphael Gottardo

  12. Novartis Institutes for Biomedical Research, Basel, Switzerland

    Florian Hahne

  13. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA

    Kasper D Hansen

  14. Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA

    Kasper D Hansen

  15. Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Rafael A Irizarry & Michael I Love

  16. Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA

    James MacDonald

  17. Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Gordon K Smyth

  18. Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia

    Gordon K Smyth

  19. School of Urban Public Health at Hunter College, City University of New York, New York, New York, USA

    Levi Waldron

Authors
  1. Wolfgang Huber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vincent J Carey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robert Gentleman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Simon Anders
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marc Carlson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Benilton S Carvalho
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hector Corrada Bravo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sean Davis
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Laurent Gatto
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Thomas Girke
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Raphael Gottardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Florian Hahne
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Kasper D Hansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Rafael A Irizarry
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Michael Lawrence
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Michael I Love
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. James MacDonald
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Valerie Obenchain
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Andrzej K Oleś
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Hervé Pagès
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Alejandro Reyes
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Paul Shannon
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Gordon K Smyth
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Dan Tenenbaum
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Levi Waldron
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Martin Morgan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wolfgang Huber.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Huber, W., Carey, V., Gentleman, R. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods 12, 115–121 (2015). https://doi.org/10.1038/nmeth.3252

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing