Analysis of proteomic datasets is difficult. By only considering the top proteins from a list it is often hard to explain how proteins function together in the cellular context. Therefore, using a network context based on experimentally validated interactions between proteins has become a well-established approach to analyze proteomic results. These networks contain edges representing interactions and nodes visualizing the proteins of interest. Searching for a functional module of proteins in the context of subnetworks is an elegant method for finding proteomic subsets contributing to pathway signaling.

We present a new approach, which not only integrates proteomic data with a network context but also applies functional interaction scores, which are based on gene ontology (GO) semantic similarity of the connected proteins. The method is using a previously developed algorithm called heinz (heavy induced subgraphs) to search for an exact optimal solution and find the maximum-scoring subnetwork. The obtained results can be transferred and visualized in the network visualization software Cytoscape for further analysis.

Based on two examples from H9N2 virus-infected gastric cells [1] and T-cells [2] we will demonstrate each of the steps needed for performing the analysis. The topics in the left panel are logically following the stages of analysis, so the user can either go through the whole process or click on a specific topic of interest. Examples are given for each case to avoid confusion.