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Ontological analysis of gene expression data: current tools, limitations, and open problems.

Purvesh Khatri1, Sorin Drăghici

  • 1Department of Computer Science, Wayne State University, Detroit, MI 48202, USA. sod@cs.wayne.edu

Bioinformatics (Oxford, England)
|July 5, 2005
PubMed
Summary

This study compares 14 ontological analysis tools for interpreting gene expression data from microarray experiments. It highlights the limitations of current tools and suggests future research directions for improved biological interpretation.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Microarray experiments generate lists of differentially expressed genes, requiring biological interpretation.
  • Ontological analysis is the standard for secondary analysis of high-throughput experiments.
  • Numerous tools exist for ontological analysis, but a comprehensive comparison is lacking.

Purpose of the Study:

  • To provide a detailed comparison of 14 ontological analysis tools.
  • To assist researchers in selecting appropriate tools for their analyses.
  • To identify intrinsic drawbacks and conceptual limitations of current ontological analysis approaches.

Main Methods:

  • Comparison of 14 ontological analysis tools.
  • Evaluation criteria included scope, visualization, statistical models, multiple comparison correction, reference microarrays, installation, and annotation data.

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  • Analysis of conceptual limitations inherent to the ontological analysis approach.

    • Main Results:

    • A detailed comparison of 14 ontological analysis tools based on predefined criteria.
    • Identification of common intrinsic drawbacks across all evaluated tools.
    • Highlighting conceptual limitations in the current state-of-the-art of ontological analysis.

    Conclusions:

    • The comparison aids researchers in tool selection for microarray data interpretation.
    • Current ontological analysis approaches have inherent limitations.
    • Challenges are proposed for the development of next-generation secondary data analysis tools.