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CompGO: an R package for comparing and visualizing Gene Ontology enrichment differences between DNA binding

Ashley J Waardenberg1,2, Maya Bassett3, Romaric Bouveret4,5

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BMC Bioinformatics
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Summary

CompGO is a new bioinformatics tool for comparing gene ontology (GO) enrichment between DNA-binding experiments. It identifies differentially enriched GO terms (DiEGOs) and pathways, offering a more robust analysis than simple overlap methods.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Gene Ontology (GO) enrichment analysis is crucial for interpreting systems biology experiments.
  • Comparing GO enrichment across different DNA-binding experiments or classifying experiments using GO structure is underexplored.
  • Existing methods for comparing GO enrichments, such as simple overlap, have limitations.

Purpose of the Study:

  • To develop a bioinformatics tool for identifying differentially enriched gene ontologies (DiEGOs) and pathways between experiments.
  • To provide a method for visualizing differences in GO and pathway enrichment.
  • To address limitations in comparing GO enrichments from DNA-binding studies.

Main Methods:

  • Developed the CompGO R/Bioconductor package.
  • Implemented a novel statistic based on a z-score derivation of log odds ratios for comparative GO analysis.
  • Utilized BED data (genomic coordinates) and gene lists as input for comparative analyses.

Main Results:

  • CompGO identifies Differentially Enriched Gene Ontologies (DiEGOs) and pathways.
  • Demonstrated the limitations of the simple overlap approach for comparing GO enrichments using NKX2-5 transcription factor data.
  • Visualized differences at both GO and pathway levels.

Conclusions:

  • CompGO provides a robust method for comparative GO analysis, suitable for DNA-binding experiments.
  • The tool implements a statistic adapted from epidemiology for enhanced comparative insights.
  • CompGO is freely available as an R/Bioconductor package for easy integration into existing bioinformatics pipelines.