paxtoolsr

PaxtoolsR is an R package that exposes the BioPAX pathway toolkit and the Pathway Commons web service to the R statistical environment. Maintained by the BioPAX organization and principally developed by Augustin Luna, it is distributed through Bioconductor rather than CRAN, and was described in a 2015/2016 Bioinformatics application note. The package is a thin R binding over the Java library Paxtools, wrapping its algorithms via rJava; consequently a working Java installation is a hard prerequisite. Its purpose is to let R users manipulate biological pathway data encoded in the BioPAX Level 3 standard (BioPAX, an OWL/RDF-based language) and to integrate that data with the wider R analysis ecosystem.

Functionally, paxtoolsr covers five areas: merging multiple BioPAX OWL files into one model, extracting sub-networks, validating BioPAX file integrity, converting between formats, and querying Pathway Commons. Because BioPAX's rich, class-heavy object model is awkward for direct graph analysis, the package's most-used capability is conversion to the Simple Interaction Format (SIF) — a three-column PARTICIPANTA / INTERACTIONTYPE / PARTICIPANT_B representation of binary interactions — plus an extended SIFNX variant that attaches node and edge attributes as data.table columns. This flattened form is what makes the data tractable as a graph: SIF edge lists load directly into igraph for layout, visualization, and standard network-analysis routines. The package also converts BioPAX to GSEA gene-set (.gmt) files for enrichment analysis and to SBGN-ML for standardized pathway diagrams, and offers a traverse() function for pulling specific properties out of BioPAX records by URI.

Pathway Commons integration is the other half of the package. Functions such as graphPc issue neighborhood and paths-between graph queries against the Pathway Commons server, which aggregates content from Reactome, KGML (KEGG, via KEGG), BioGRID, IntAct, HumanCyc, PhosphoSitePlus and other sources, letting users fetch remote sub-networks and merge them with local models.

Its strengths are convenient, reproducible R access to a large curated corpus of human pathway data and a clean bridge from a complex semantic model into analyzable graphs. Limitations follow from its design: the rJava/Java dependency complicates installation and can be memory-hungry on large models; SIF conversion is lossy, discarding much of BioPAX's mechanistic detail (state changes, stoichiometry, cellular location); and the package is narrowly scoped to the BioPAX/Pathway Commons world rather than being a general graph-format converter.