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Dragon and Dragon View: Methods for the Annotation, Analysis, and Visualization of Large-Scale Gene Expression Data

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The Analysis of Gene Expression Data

Part of the book series: Statistics for Biology and Health ((SBH))

Abstract

Database Referencing of Array Genes ONline (DRAGON) is a database system that consists of information derived from publicly available databases, including UniGene, Swiss Prot, Pfam, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Through a Web-accessible interface, DRAGON rapidly supplies information pertaining to a range of biological characteristics of all the genes in any large-scale gene expression dataset. The subsequent inclusion of this information during data analysis allows for deeper insight into gene expression patterns. A related set of visualization tools called DRAGON View has been developed to allow for the analysis of large-scale gene expression datasets in relation to biological characteristics of gene sets.

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Authors
  1. Christopher M. L. S. Bouton
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  2. George Henry
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  3. Carlo Colantuoni
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  4. Jonathan Pevsner
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Editor information

Editors and Affiliations

  1. Departments of Oncology, Biostatistics,and Pathology, Johns Hopkins University, Baltimore, MD, 21205-2013, USA

    Giovanni Parmigiani

  2. Departments of Oncology and Biostatistics, Johns Hopkins University, Baltimore, MD, 21205-2013, USA

    Elizabeth S. Garrett

  3. Departments of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205-2013, USA

    Rafael A. Irizarry

  4. Departments of Biostatistics and Epidemiology, Johns Hopkins University, Baltimore, MD, 21205-2013, USA

    Scott L. Zeger

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© 2003 Springer-Verlag New York, Inc.

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Bouton, C.M.L.S., Henry, G., Colantuoni, C., Pevsner, J. (2003). Dragon and Dragon View: Methods for the Annotation, Analysis, and Visualization of Large-Scale Gene Expression Data. In: Parmigiani, G., Garrett, E.S., Irizarry, R.A., Zeger, S.L. (eds) The Analysis of Gene Expression Data. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/0-387-21679-0_8

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  • DOI: https://doi.org/10.1007/0-387-21679-0_8

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-95577-3

  • Online ISBN: 978-0-387-21679-9

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