Updated project metadata. Protein arginine (R) methylation is a post-translational modification that has been shown to play a role in various biological processes, such as RNA splicing, DNA repair, immune response, signal transduction and tumor development. Here, we present a dataset of high-quality methylations obtained from several different heavy methyl SILAC (hmSILAC) experiments analyzed with a machine learning model that was trained to recognize hmSILAC doublets and show that this model allows for improved high-confidence identification of methyl-peptides. The results of our analysis of the interactions between R-methylated proteins further support the idea that this modification plays a role in modulating protein:protein interactions and suggest a potential new role of R methylation in immunity and macrophage metabolism. Moreover, we intersect the methyl-site dataset with a phosphosite dataset to investigate the cross-talk between R methylation and phosphorylation. Finally, we explore the application of hmSILAC to identify unconventional methylated residues on both histone and non-histone proteins.