Applied Bioinformatics

, Volume 3, Issue 4, pp 261–264 | Cite as

GoSurfer

A Graphical Interactive Tool For Comparative Analysis of Large Gene Sets in Gene Ontology™ Space
  • Sheng Zhong
  • Kai-Florian Storch
  • Ovidiu Lipan
  • Ming-Chih J. Kao
  • Charles J. Weitz
  • Wing H. Wong
Application Note

Abstract

Abstract: The analysis of complex patterns of gene regulation is central to understanding the biology of cells, tissues and organisms. Patterns of gene regulation pertaining to specific biological processes can be revealed by a variety of experimental strategies, particularly microarrays and other highly parallel methods, which generate large datasets linking many genes. Although methods for detecting gene expression have improved substantially in recent years, understanding the physiological implications of complex patterns in gene expression data is a major challenge. This article presents GoSurfer, an easy-to-use graphical exploration tool with built-in statistical features that allow a rapid assessment of the biological functions represented in large gene sets. GoSurfer takes one or two list(s) of gene identifiers (Affymetrix® probe set ID) as input and retrieves all the Gene Ontology™ (GO) terms associated with the input genes. GoSurfer visualises these GO terms in a hierarchical tree format. With GoSurfer, users can perform statistical tests to search for the GO terms that are enriched in the annotations of the input genes. These GO terms can be highlighted on the GO tree. Users can manipulate the GO tree in various ways and interactively query the genes associated with any GO term. The user-generated graphics can be saved as graphics files, and all the GO information related to the input genes can be exported as text files.

Availability: GoSurfer is a Windows®-based program freely available for noncommercial use and can be downloaded at http://www.gosurfer.org. Datasets used to construct the trees shown in the figures in this article are available at http://www.gosurfer.org/download/GoSurfer.zip.

Notes

Acknowledgements

We thank Dr Cheng Li for valuable discussions and suggestions. We thank the Editor, Professor Allen Rodrigo, for valuable suggestions on the manuscript revision. This work was supported by NIH grants CA95616 and HG02341. SZ created the algorithm and the software. WHW supervised the software development. KFS, OL and MCJK did data analysis with the software. CJW and WHW supervised the data analysis. The authors have no conflicts of interest that are directly relevant to the content of this article.

References

  1. 1.
    Ashburner M, Ball CA, Blake JA, et al. Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000; 25: 25–9PubMedCrossRefGoogle Scholar
  2. 2.
    Storch KF, Lipan O, Leykin I, et al. Extensive and divergent circadian gene expression in liver and heart. Nature 2002; 417: 78–83PubMedCrossRefGoogle Scholar
  3. 3.
    Singh D, Febbo PG, Ross K, et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 2002; 1: 203–9PubMedCrossRefGoogle Scholar
  4. 4.
    Dhanasekaran SM, Barrette TR, Ghosh D, et al. Delineation of prognostic bio-markers in prostate cancer. Nature 2001; 412: 822–6PubMedCrossRefGoogle Scholar
  5. 5.
    Varambally S, Dhanasekaran SM, Zhou M, et al. The polycomb group protein EZH2 is involved in progression of prostate cancer. Nature 2002; 419: 624–9PubMedCrossRefGoogle Scholar
  6. 6.
    Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000; 100: 57–70PubMedCrossRefGoogle Scholar
  7. 7.
    Zhong S, Li C, Wong WH. ChipInfo: software for extracting gene annotation and gene ontology information for microarray analysis. Nucleic Acids Res 2003; 31: 3483–6PubMedCrossRefGoogle Scholar

Copyright information

© Adis Data Information BV 2004

Authors and Affiliations

  • Sheng Zhong
    • 1
    • 2
  • Kai-Florian Storch
    • 3
  • Ovidiu Lipan
    • 4
  • Ming-Chih J. Kao
    • 5
  • Charles J. Weitz
    • 3
  • Wing H. Wong
    • 2
    • 6
  1. 1.Department of BiostatisticsHarvard School of Public HealthBostonUSA
  2. 2.Department of StatisticsStanford UniversityStanfordUSA
  3. 3.Department of NeurobiologyHarvard Medical SchoolBostonUSA
  4. 4.Center for Biotechnology and Genomic MedicineMedical College of GeorgiaAugustaUSA
  5. 5.University of Michigan Medical SchoolAnn ArborUSA
  6. 6.Department of Health Research and PolicyStanford School of MedicineStanfordUSA

Personalised recommendations