PhosR enables processing and functional analysis of phosphoproteomic data
Hani Jieun Kim,
Taiyun Kim,
Nolan J. Hoffman,
Di Xiao,
David E. James,
Sean J. Humphrey,
Pengyi Yang
Affiliations
Hani Jieun Kim
School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia; Computational Systems Biology Group, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
Taiyun Kim
School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia; Computational Systems Biology Group, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
Nolan J. Hoffman
Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; School of Environmental and Life Sciences, The University of Sydney, Sydney, NSW, Australia
Di Xiao
Computational Systems Biology Group, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
David E. James
Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; School of Environmental and Life Sciences, The University of Sydney, Sydney, NSW, Australia; Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
Sean J. Humphrey
Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; School of Environmental and Life Sciences, The University of Sydney, Sydney, NSW, Australia
Pengyi Yang
School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia; Computational Systems Biology Group, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; Corresponding author
Summary: 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.
