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Related Experiment Videos

Gene annotation and network inference by phylogenetic profiling.

Jie Wu1, Zhenjun Hu, Charles DeLisi

  • 1Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA. jiewu@bu.edu

BMC Bioinformatics
|March 1, 2006
PubMed
Summary
This summary is machine-generated.

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Correlation Enrichment (CE) significantly improves gene function annotation accuracy, outperforming Standard Guilt by Association (SGA) especially at high coverage. This computational tool enhances the identification of functional gene modules.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Phylogenetic analysis is crucial for gene annotation and identifying functionally related gene modules.
  • Accurate gene function assignment relies on effective correlation measures and decision rules for phylogenetic profiles.
  • Existing methods show performance degradation with increasing gene coverage.

Purpose of the Study:

  • Introduce and evaluate a novel decision rule, Correlation Enrichment (CE), for gene functional categorization.
  • Compare the performance of CE against Standard Guilt by Association (SGA).
  • Assess the utility of CE for annotating unannotated genes and discovering functional modules.

Main Methods:

  • Developed and applied the Correlation Enrichment (CE) decision rule.

Related Experiment Videos

  • Utilized phylogenetic profiles for gene function prediction.
  • Compared CE performance with Standard Guilt by Association (SGA) across varying gene coverage levels.

    • Main Results:

    • CE demonstrated superior performance over SGA, particularly at high gene coverage, achieving approximately 6-fold higher precision.
    • CE accurately assigned 2918 unannotated orthologs to KEGG pathways with 49% average precision (7-fold higher than SGA).
    • Identified dozens of conserved functional gene cliques, including previously unannotated genes, with high precision.

    Conclusions:

    • CE is a robust computational tool for annotating numerous unknown genes and identifying evolutionary and functional modules.
    • The CE method significantly outperforms existing high-throughput gene annotation tools.
    • CE offers substantial improvements in gene function prediction accuracy and module discovery.