Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.25.266072v1?rss=1 Authors: Watson, J., Schwartz, J.-M., Francavilla, C. Abstract: Mass spectrometry-based quantitative phosphoproteomics has become an essential approach in the study of cellular processes such as cell signaling. Commonly used methods to analyze phosphoproteomics datasets depend on generic, gene-centric annotations such as Gene Ontology terms and do not account for the function of a protein in a given phosphorylation state. Thus, analysis of phosphoproteomics data is hampered by a lack of phosphorylated site-specific annotations. Here, we propose a method that combines shotgun phosphoproteomics data, protein-protein interactions and functional annotations from ontologies or pathway databases into a heterogeneous multilayer network. Phosphorylation sites are then associated to potential functions using a random walk on heterogeneous network (RWHN) algorithm. We validated our approach using a dataset modelling the MAPK/ERK pathway and were able to associate differentially regulated sites on the same protein to their previously described functions. Random permutation analysis proved that these associations were not random and were determined by the network topology. We then applied the RWHN algorithm to two previously published datasets; the algorithm was able to reproduce the experimentally validated conclusions from the publications, and associate phosphorylation sites with both new and known functions based on their regulatory patterns. The approach described here provides a robust, phosphorylation site-centric method to analyzing phosphoproteomics data and identifying potential context-specific functions for sites with similar phosphorylation profiles. Copy rights belong to original authors. Visit the link for more info