A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization

We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape.The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure.By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications.Network graphs provide a valuable conceptual framework for representing and mining high-throughput experimental datasets, as well as for extracting and interpreting their biological information by the means of graph-based analysis approaches [1-8]. In cellular systems, network nodes typically refer to biomolecules, such as genes or proteins, and the edge connections the type of relationships the network is encoding, including physical or functional information. Network visualization aims to organize the complex network structures in a way that provides the user with readily apparent insights into the most interesting biological patterns and relationships within the data, such as components of biological pathways, processes or complexes, which can be further investigated by follow-up computation