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Using the Bioconductor GeneAnswers Package to Interpret Gene Lists

  • Gang Feng
  • Pamela Shaw
  • Steven T. Rosen
  • Simon M. LinEmail author
  • Warren A. Kibbe
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 802)

Abstract

Use of microarray data to generate expression profiles of genes associated with disease can aid in identification of markers of disease and potential therapeutic targets. Pathway analysis methods further extend expression profiling by creating inferred networks that provide an interpretable structure of the gene list and visualize gene interactions. This chapter describes GeneAnswers, a novel gene-concept network analysis tool available as an open source Bioconductor package. GeneAnswers creates a gene-concept network and also can be used to build protein–protein interaction networks. The package includes an example multiple myeloma cell line dataset and tutorial. Several network analysis methods are included in GeneAnswers, and the tutorial highlights the conditions under which each type of analysis is most beneficial and provides sample code.

Key words

Network Disease ontology Gene ontology Pathway analysis GeneAnswers Bioconductor 

References

  1. 1.
    Jordan B (2002) Historical background and anticipated developments. Ann N Y Acad Sci. 975:24–32.PubMedCrossRefGoogle Scholar
  2. 2.
    Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63.PubMedCrossRefGoogle Scholar
  3. 3.
    Reimers M, Carey VJ (2006) Bioconductor: an open source framework for bioinformatics and computational biology. Methods Enzymol. 411:119–134.PubMedCrossRefGoogle Scholar
  4. 4.
    R Development Core Team (2010) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
  5. 5.
  6. 6.
  7. 7.
    Feng G, Du P, Krett NL et al (2010) A collection of Bioconductor methods to visualize gene-list annotations. BMC Res Notes 3:10.PubMedCrossRefGoogle Scholar
  8. 8.
    Ashburner M, Ball CA, Blake JA et al (2000) Gene Ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 25:25–29.PubMedCrossRefGoogle Scholar
  9. 9.
    Dennis G Jr, Sherman BT, Hosack DA et al (2003) DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4:P3.PubMedCrossRefGoogle Scholar
  10. 10.
    Huang da W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57.CrossRefGoogle Scholar
  11. 11.
    Osborne JD, Flatow J, Holko M et al (2009) Annotating the human genome with Disease Ontology. BMC Genomics 10:S6.PubMedCrossRefGoogle Scholar
  12. 12.
    Osborne JD, Zhu LJ, Lin SM et al (2007) Interpreting microarray results with Gene Ontology and MeSH. Methods Mol Biol. 377:223–242.PubMedCrossRefGoogle Scholar
  13. 13.
    Huang da W, Sherman BT, Stephens R et al (2008) DAVID gene ID conversion tool. Bioinformation 2:428–430.Google Scholar
  14. 14.
  15. 15.
    Du P, Feng G, Flatow J et al (2009) From Disease Ontology to Disease-Ontology Lite: Statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations. Bioinformatics 25:i63-i68.PubMedCrossRefGoogle Scholar
  16. 16.
  17. 17.
  18. 18.
  19. 19.

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Gang Feng
    • 1
  • Pamela Shaw
    • 2
  • Steven T. Rosen
    • 3
  • Simon M. Lin
    • 1
    Email author
  • Warren A. Kibbe
    • 1
  1. 1.Biomedical Informatics Center, Clinical and Translational Sciences InstituteNorthwestern UniversityChicagoUSA
  2. 2.Galter Health Sciences LibraryNorthwestern UniversityChicagoUSA
  3. 3.Robert H. Lurie Comprehensive Cancer CenterNorthwestern UniversityChicagoUSA

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