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Correlation between gene expression and GO semantic similarity.

José L Sevilla1, Víctor Segura, Adam Podhorski

  • 1Strathmore University, Ole Sangale Road, Madaraka Estate, PO Box 59857, 00200 Nairobi, Kenya. jsevilla@strathmore.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|October 19, 2006
PubMed
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This study validates Gene Ontology (GO) annotation similarity measures against gene expression data. The Resnik measure showed strong correlation, suggesting its utility for bioinformatics tools.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene products with related functions often exhibit similar expression patterns.
  • Gene Ontology (GO) provides a framework for annotating gene functions.
  • The relationship between gene expression similarity and GO annotation similarity requires empirical validation.

Purpose of the Study:

  • To analyze the relationship between gene expression, gene function, and GO annotation.
  • To validate semantic similarity measures for GO annotation.
  • To compare gene expression similarity with GO semantic similarity.

Main Methods:

  • Utilized Pearson correlation coefficient to measure similarity between gene expression profiles.
  • Applied Resnik, Jiang, and Lin semantic similarity measures to GO annotations.

Related Experiment Videos

  • Computed correlation coefficients to compare expression similarity against GO semantic similarity.

    • Main Results:

    • The Resnik similarity measure demonstrated superior performance for GO annotation.
    • A correlation was observed between GO semantic similarity and gene expression across three GO ontologies.
    • This correlation was negligible at low similarity values, becoming nearly linear at higher values.

    Conclusions:

    • The Resnik measure is well-suited for GO annotation analysis.
    • Gene expression similarity and GO semantic similarity are correlated, particularly at higher similarity levels.
    • Findings can enhance clustering algorithms and aid in developing tools for gene product characterization.