PhosR enables processing and functional analysis of phosphoproteomic data

Mass spectrometry (MS)-based phosphoproteomics has revolutionized our ability to profile phosphorylation-based signaling in cells and tissues on a global scale. To infer the action of kinases and signaling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalization by using a set of “stably phosphorylated sites” and in functional analysis for inferring active kinases and signaling pathways. In particular, we introduce a “signalome” construction method for identifying a collection of signaling modules to summarize and visualize the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating biological knowledge from MS-based phosphoproteomic data. [Display omitted] •PhosR implements a suite of methods for comprehensive phosphoproteomic data analysis•Stably phosphorylated sites are useful for phospho-data normalization and integration•Signalomes constructed from PhosR enable interpretation of global signal transduction•PhosR reveals unappreciated interactions between the AMPK and insulin signaling Protein phosphorylation regulates all aspects of cell biology. Although phosphoproteomics enables profiling of global phosphorylation, extracting knowledge from such data requires specialized computational methods. Kim et al. present PhosR for comprehensive analysis of phosphoproteomic data. Their analysis of muscle phosphoproteome uncovers unappreciated interactions between AMPK and insulin signaling pathways.