PTMsToPathways: An R Package to Turn Post-Translational Modification Data into Networks to Outline Cell Signaling Pathways

Abstract Protein post-translational modifications (PTMs) in the cytoplasm and nucleus serve as direct molecular readouts of cell signaling pathways that govern cell differentiation, cancer, and other diseases. However, interpreting raw PTM data into pathways requires complex computational steps that may not be accessible to all researchers. We present the R package PTMsToPathways (P2P), which implements previously validated methods to integrate PTM proteomic data with protein−protein interactions (PPIs) and cell signaling pathways to increase access to this analysis pipeline. The strategy clusters PTMs to reveal patterns in their expression under different experimental conditions. These clustering patterns are then used to filter protein−protein interactions from external PPI databases and pathway−pathway interactions from BioPlanet’s curated sets of proteins. The resulting networks are navigable data structures that reveal data-supported interactions among both individual proteins and groups of proteins representing well-defined cell signaling pathways. These networks can be interrogated by investigators to facilitate tailored, hypothesis-driven investigation and exploratory data analysis. P2P also enables the creation of consistent, visually interpretable graphs in Cytoscape, an open-source network visualization application. To promote usability, the package has been submitted to Bioconductor. Detailed documentation and tutorial vignettes reproducing examples from this paper are available on the package Web site (http://ptmstopathways.umaal.org/), and package source code is available at https://github.com/UM-Applied-Algorithms-Lab/PTMsToPathways.