An exploration of alternative visualisations of the basic helix-loop-helix protein interaction network

Network representations that arrange nodes (proteins) according to either continuous or discrete information are investigated, revealing the existence of protein sub-families and the retention of interactions following gene duplication events. Methods of network visualisation in conjunction with a phylogenetic tree are presented, highlighting the evolutionary relationships between proteins, and clarifying the context of network hubs and interaction clusters. Finally, an optimisation technique is used to create a three-dimensional layout of the phylogenetic tree upon which the protein-protein interactions may be projected.We show that by incorporating secondary genomic, functional or phylogenetic information into network visualisation, it is possible to move beyond simple layout algorithms based on network topology towards more biologically meaningful representations. These new visualisations can give structure to complex networks and will greatly help in interpreting their evolutionary origins and functional implications. Three open source software packages (InterView, TVi and OptiMage) implementing our methods are available."Graphics reveal data. Indeed graphics can be more precise and revealing than conventional statistical computations." Edward R. Tufte, The Visual Display of Quantitative Information [1].The effective visual representation of complex data is an integral but perhaps undervalued part of a bioinformatician's job [2]. For an increasing number of researchers, this largely concerns the representation of networks, defined as sets of nodes (also called vertices) with corresponding sets of connections (undirected edges or directed arcs) between nodes. Methodologies that make the depiction of biological networks more accessible to biologists need to be developed in order to make these complex data sets as meaningful, and useful, as possible.Biological networks come in many shapes and sizes. Signalling networks, food webs, metabolic pathways and gene regula