Phosphorylation is one of the most essential post-translational modifications of proteins, regulates a variety of cellular signaling pathways, and at least partially determines the biological diversity.Recent progresses in phosphoproteomics have identified more than 100,000 phosphorylation sites.However, how to extract useful information from flood of data is still a great challenge.During the past seven years, we have taken great efforts in computational analysis of the phosphoproteomic data.We developed a GPS (Group-based Prediction System, http://gps.biocuckoo.org, MCP, 2008, 7:1598-608) algorithm, which can predict kinase-specific phosphorylation sites for 408 human protein kinases (PKs) in hierarchy.Together with this sequence-based algorithm, we further adopted protein-protein interaction information as a major contextual filter to reduce false-positive hits.With this strategy, we developed iGPS (http://igps.biocuckoo.org) to predict 188,288 site-specific kinase-substrate relations (ssKSRs) between 9,247 targets and 1,079 PKs for 44,290 phosphorylation sites from the phosphoproteomic data, whereas the protein phosphorylation networks (PPNs) were modeled in five eukaryotic organisms.Based on the results, we observed that the eukaryotic phospho-regulation is poorly conserved at the site and substrate levels, but preferentially conserved at the pathway levels, e.g., ribosome organization.By analyzing DNA damage response (DDR)-associated PPN, our results suggested that repair processes but not apoptosis are activated immediately after DNA damage.Furthermore, an apoptosis/anti-apoptosis balance in the human liver PPN is demonstrated.We also predicted Polo-like kinases (Plks) phospho-binding proteins from the phosphoproteomic data by developing the GPS-Polo (http:// polo.biocuckoo.org/).Taken together, our efforts provide a powerful toolbox for analyzing the phosphoproteomic data .