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Interpreting Microarray Results With Gene Ontology and MeSH

  • John D. Osborne
  • Lihua (Julie) Zhu
  • Simon M. Lin
  • Warren A. Kibbe
Part of the Methods in Molecular Biology™ book series (MIMB, volume 377)

Abstract

Methods are described to take a list of genes generated from a microarray experiment and interpret these results using various tools and ontologies. A workflow is described that details how to convert gene identifiers with SOURCE and MatchMiner and then use these converted gene lists to search the gene ontology (GO) and the medical subject headings (MeSH) ontology. Examples of searching GO with DAVID, EASE, and GOMiner are provided along with an interpretation of results. The mining of MeSH using high-density array pattern interpreter with a set of gene identifiers is also described.

Key Words

Microarray GO MeSH protocol DAVID HAPI SOURCE MatchMiner Interpret 

References

  1. 1.
    Bard, J. B. and Rhee, S. Y. (2004) Ontologies in biology: design, applications and future challenges. Nat. Rev. Genet. 5, 213–222.PubMedCrossRefGoogle Scholar
  2. 2.
    Gene Ontology Consortium (2006). The Gene Ontology project in 2006. Nucleic Acids Res. 34 (database issue), D322–D326.CrossRefGoogle Scholar
  3. 3.
    Lowe, H. J. and Barnett, G. O. (1994) Understanding and using the medical subject headings (MeSH) vocabulary to perform literature searches. JAMA 271, 1103–1108.PubMedCrossRefGoogle Scholar
  4. 4.
    Diehn, M., Sherlock, G., Binkley, G., et al. (2003) SOURCE: a unified genomic resource of functional annotations, ontologies, and gene expression data. Nucleic Acids Res. 31, 219–223.PubMedCrossRefGoogle Scholar
  5. 5.
    Bussey, K. J., Kane, D., Sunshine, M., et al. (2003) MatchMiner: a tool for batch navigation among gene and gene product identifiers. Genome Biol. 4, R27.PubMedCrossRefGoogle Scholar
  6. 6.
    Ashburner, M., Ball, C. A., Blake, J. A., et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29.PubMedCrossRefGoogle Scholar
  7. 7.
    Blake, J. (2004) Bio-ontologies-fast and furious. Nat. Biotechnol. 22, 773–774.PubMedCrossRefGoogle Scholar
  8. 8.
    Masys, D. R., Welsh, J. B., Fink, J. L., et al. (2001) Use of keyword hierarchies to interpret gene expression patterns. Bioinformatics 17, 319–326.PubMedCrossRefGoogle Scholar
  9. 9.
    Khatri, P., Draghici, S., Ostermeier, C., and Krawetz, S. (2002) Profiling gene expression using onto-express. Genomics 79, 266–270.PubMedCrossRefGoogle Scholar
  10. 10.
    Al-Shahrour, F., Diaz-Uriarte, R., and Dopazo, J. (2004) FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 20, 578–580.PubMedCrossRefGoogle Scholar
  11. 11.
    Beissbarth, T. and Speed, T. P. (2004) GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 20, 1464–1465.PubMedCrossRefGoogle Scholar
  12. 12.
    Ben-Shaul, Y., Bergman, H., and Soreq, H. (2005) Identifying subtle interrelated changes in functional gene categories using continuous measures of gene expression. Bioinformatics 21, 1129–1137.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press Inc., Totowa, NJ 2007

Authors and Affiliations

  • John D. Osborne
    • 1
  • Lihua (Julie) Zhu
    • 1
  • Simon M. Lin
    • 1
  • Warren A. Kibbe
    • 1
  1. 1.Robert H. Lurie Comprehensive Cancer CenterNorthwestern UniversityChicago

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