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Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt - Nature Protocols

  • ️Huber, Wolfgang
  • ️Thu Jul 23 2009
  • Protocol
  • Published: 23 July 2009

Nature Protocols volume 4, pages 1184–1191 (2009)Cite this article

Abstract

Genomic experiments produce multiple views of biological systems, among them are DNA sequence and copy number variation, and mRNA and protein abundance. Understanding these systems needs integrated bioinformatic analysis. Public databases such as Ensembl provide relationships and mappings between the relevant sets of probe and target molecules. However, the relationships can be biologically complex and the content of the databases is dynamic. We demonstrate how to use the computational environment R to integrate and jointly analyze experimental datasets, employing BioMart web services to provide the molecule mappings. We also discuss typical problems that are encountered in making gene-to-transcript–to-protein mappings. The approach provides a flexible, programmable and reproducible basis for state-of-the-art bioinformatic data integration.

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References

  1. R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2008) ISBN 3-900051-07-0.

  2. Gentleman, R.C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5 (10): R80 (2004).

    Article  Google Scholar 

  3. Kasprzyk, A. et al. Ensmart: a generic system for fast and flexible access to biological data. Genome Res. 14 (1): 160–169 (2004).

    Article  CAS  Google Scholar 

  4. Hubbard, T.J. et al. Ensembl 2009. Nucleic Acids Res. 37 (Database issue): D690–D697 (2009).

    Article  CAS  Google Scholar 

  5. Rogers, A. et al. Wormbase 2007. Nucleic Acids Res. 36 (Database issue): D612–D617 (2008).

    CAS  PubMed  Google Scholar 

  6. Matthews, L. et al. Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res. 37 (Database issue): D619–D622 (2009).

    Article  CAS  Google Scholar 

  7. Durinck, S. et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21, 3439–3440 (2005).

    Article  CAS  Google Scholar 

  8. Durinck, S. Integrating biological data resources into R with biomaRt. The Newsletter of the R Project 6/5, 40–45 (2006).

    Google Scholar 

  9. Boutros, M. et al. Analysis of cell-based RNAi screens. Genome Biol. 7, R66 (2006).

    Article  Google Scholar 

  10. Wei, J.S. et al. The MYCN oncogene is a direct target of miR-34a. Oncogene 27 (39): 5204–5213 (2008).

    Article  CAS  Google Scholar 

  11. Hahne, F. et al. Bioconductor Case Studies. Springer Verlag, New York, USA, (2008).

    Book  Google Scholar 

  12. Pruitt, K.D., Tatusova, T. & Maglott, D.R. NCBI reference sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35 (Database issue): D61–D65 (2007).

    Article  CAS  Google Scholar 

  13. Bruford, E.A. et al. The HGNC database in 2008: a resource for the human genome. Nucleic Acids Res. 36 (Database issue): D445–D448 (2008).

    Article  CAS  Google Scholar 

  14. Neve, R.M. et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell 10, 515–527 (2006).

    Article  CAS  Google Scholar 

  15. Parkinson, H. et al. Arrayexpress update – from an archive of functional genomics experiments to the atlas of gene expression. Nucleic Acids Res. 37, D868–D872 (2009).

    Article  CAS  Google Scholar 

  16. Irizarry, R.A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

    Article  Google Scholar 

Download references

Acknowledgements

We thank Arek Kasprzyk and Rhoda Kinsella for insightful discussions.

This work was partially funded by the U24 CA126551 grant.

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Authors and Affiliations

  1. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Steffen Durinck & Paul T Spellman

  2. European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK

    Ewan Birney & Wolfgang Huber

Authors

  1. Steffen Durinck

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  2. Paul T Spellman

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  3. Ewan Birney

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  4. Wolfgang Huber

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Corresponding author

Correspondence to Steffen Durinck.

Supplementary information

Supplementary Data 1

Zip archive containing the raw data of the Neve et al. study on a panel of 51 breast cell lines. It consists of Affymetrix CEL files of gene expression measurements deposited in ArrayExpress as experiment E-TABM-157, and Array CGH and protein quantification data which are available from http://cancer.lbl.gov/breastcancer. (ZIP 168067 kb)

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Durinck, S., Spellman, P., Birney, E. et al. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc 4, 1184–1191 (2009). https://doi.org/10.1038/nprot.2009.97

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  • Published: 23 July 2009

  • Issue Date: August 2009

  • DOI: https://doi.org/10.1038/nprot.2009.97

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