Network Analysis

The kinome annotation in KinBase provides a starting point for investigating protein phosphorylation networks in mammals. Given the experimentally validated kinase-specific phosphorylation sites, the intracellular phosphorylation networks between kinases and substrates could be reconstructed. In addition to the kinase-substrate phosphorylations, this update has integrated the information of metabolic pathways and protein-protein interactions (PPIs) to implement the network analysis for a group of interested genes/proteins. In this work, a public network visualization software, Cytoscape, is utilized to design a user interface for exploring the protein kinase-substrate phosphorylation networks, as well as the associated metabolic pathways and PPIs. The information of metabolic pathways associated with human, mouse and rat refers to the annotations in KEGG. For the information of experimentally verified physical interactions, over ten PPI databases have been integrated. In addition to physical interactions, the STRING database also consists of predicted functional associations (co-regulation in curated pathway, co-occurrence in literature abstracts, mRNA co-expression and genomic context) with confidence scores between proteins. In order to make the construction of phosphorylation networks feasible, a graph theory has been adopted to formalize the networks between kinases and substrates, based on a KEGG pathway map. RegPhos can let users input a group of proteins/genes and the system efficiently returns the protein phosphorylation networks associated with three network models, such as network with protein-protein interactions, network with subcellular localization, and network with metabolic pathway and protein-protein interactions. Furthermore, in order to provide a cancer analysis for kinases and phosphoproteins, a total of 30 experiment series involved in 39 cancer types from Affymetrix Human Genome U133 Plus 2.0 Array (GPL570), in which consisting of 54675 probe set for over 47000 transcripts, are integrated in this work.

Step 1. Input a group of genes:

Step 2. Select the organism:

Step 3. Choose the type of network analysis: