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This review examines R software packages for integrating pathway data in bioinformatics. These tools help contextualize high-throughput data, improving algorithm performance and stability for network reconstruction.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • High-throughput biological data analysis presents challenges in contextualizing results.
  • Prior biological knowledge enhances the performance and stability of bioinformatics algorithms, particularly in network reconstruction.

Purpose of the Study:

  • To review and categorize R software packages for integrating pathway data.
  • To assess different strategies for pathway data integration and visualization within the R framework.

Main Methods:

  • Systematic review of R packages for pathway data integration.
  • Stratification of packages based on data import, data availability, external dependencies, analysis integration, and visualization capabilities.
  • Identification of 12 core pathway integration packages, 5 visualization-specific packages, and 6 connector packages.

Main Results:

  • Identified 12 R packages for pathway data integration, offering diverse features.
  • Highlighted 5 R packages specialized for visualization and 6 connector packages for external tool access.
  • Categorized packages based on import strategies, data scope, dependencies, and analytical integration.

Conclusions:

  • R provides a rich ecosystem of tools for integrating and visualizing pathway data in bioinformatics.
  • The reviewed packages offer various approaches to leverage prior biological knowledge for enhanced data analysis.
  • Selection of appropriate R packages depends on specific analysis needs, data types, and desired visualization outputs.