%0 Journal Article
%T A generic algorithm for layout of biological networks
%A Falk Schreiber
%A Tim Dwyer
%A Kim Marriott
%A Michael Wybrow
%J BMC Bioinformatics
%D 2009
%I BioMed Central
%R 10.1186/1471-2105-10-375
%X We present a novel algorithm to visualize different biological networks and network analysis results in meaningful ways depending on network types and analysis outcome. Our method is based on constrained graph layout and we demonstrate how it can handle the drawing conventions used in biological networks.The presented algorithm offers the ability to produce many of the fundamental popular drawing styles while allowing the exibility of constraints to further tailor these layouts.Networks play a central role in biological investigation of organisms. They are used to represent processes in biological systems and to capture interactions and dependencies between biological entities such as genes, transcripts, proteins and metabolites. One large application area for network-centered analysis and visualization is Systems Biology, an increasingly important research field which aims at a comprehensive understanding and remodeling of the processes in living beings [1,2]. Due to the steady growth of knowledge in the life sciences such networks are increasingly large and complex. To tackle this complexity and help in analyzing and interpreting the complicated web of interactions meaningful visualizations of biological networks are crucial.Methods for automatic network visualization have gained increased attention from the research community over recent years and various layout algorithms have been developed, e. g. [3-11]. Often standard layout methods such as force directed [12,13], layered [14,15] and circular [16] approaches are used to draw these networks. However, the direct use of standard layout methods is somewhat unsatisfactory since biological networks often have specialized layout requirements reflecting the drawing conventions historically used in manually laid out diagrams (which have been developed to better emphasize relevant biological relationships and concepts). This has led to the development of network- and application-specific layout algorithms, for example,
%U http://www.biomedcentral.com/1471-2105/10/375