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Building pathway graphs from BioPAX data in R.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Biological pathway data is increasingly available in the BioPAX format, utilizing an RDF data model.
  • The rBiopaxParser R package facilitates the retrieval and conversion of BioPAX data into an R-readable format.
  • rBiopaxParser previously offered a function to construct regulatory networks from pathway information.

Purpose of the Study:

  • To describe an extension of the existing rBiopaxParser function for enhanced pathway visualization.
  • To enable the construction of graphs representing entire biological pathways, encompassing both regulated and non-regulated elements.
  • To provide users with maximum pathway information through comprehensive graph visualization.

Main Methods:

  • Development of an extended function within the rBiopaxParser R package.
  • Implementation of graph-building capabilities to include all pathway components.
  • Integration of the new function into the rBiopaxParser distribution available via Bioconductor.

Main Results:

  • A new function has been successfully developed and integrated into rBiopaxParser.
  • The extended function allows for the generation of graphs representing complete biological pathways.
  • The resulting graphs provide a comprehensive view of pathway elements, maximizing information content.

Conclusions:

  • The enhanced rBiopaxParser function significantly improves biological pathway visualization.
  • Researchers can now generate more informative pathway graphs, aiding in systems biology research.
  • This advancement is readily accessible to the R and bioinformatics community through Bioconductor.