FORG3D: Force-directed 3D graph editor for visualization of integrated genome scale data

To help researchers to visualize and interpret integrated genomics data, we present a novel visualization method and bioinformatics software tool called FORG3D that is based on real-time three-dimensional force-directed graphs. FORG3D can be used to visualize integrated networks of genome scale data such as interactions between genes or gene products, signaling transduction, metabolic pathways, functional interactions and evolutionary relationships. Furthermore, we demonstrate its utility by exploring gene network relationships using integrated data sets from a Caenorhabditis elegans Parkinson's disease model.We have created an open source software tool called FORG3D that can be used for visualizing and exploring integrated genome scale data.To understand the biological phenomena behind systems biology data, researchers often need to combine different kinds of experimental results, creating complex data sets of integrated information. Systems biology efforts are directed towards acquisition of high-throughput-omics data and then analysis and modeling on a whole organism scale. Modeling provides the ability to infer functions and make predictions based on network perturbations [1-6]. Among the most commonly modeled biological data are protein-protein interactions. Proteins do not act alone, but in concert with other proteins and mapping their interactions can provide insight into the molecular pathways in which they participate [7]. Protein-protein interaction maps also indicate a high level of molecular connectivity between different biological pathways thus highlighting the inter-related functions of many biological processes [8]. When approaches to perturb network interactions are utilized, such as genetic interactions using RNAinterference, null-mutant alleles, or the two in combination, even greater knowledge on the identity of key network sites can be obtained [9-11]. Integration of protein-protein interaction data with transcriptomics data has also been succes