Applied Bioinformatics

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


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. WongEmail author
Application Note


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 Datasets used to construct the trees shown in the figures in this article are available at


Prostate Cancer Gene Ontology Directed Acyclic Graph Normal Prostate Mitotic Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.


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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
    Email author
  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

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