Visual Understanding of Metabolic Pathways Across Organisms Using Layout in Two and a Half Dimensions

Abstract

Summary We propose a method for visualizing a set of related metabolic pathways across organisms using 2 1/2 dimensional graph visualization. Interdependent, twodimensional layouts of each pathway are stacked on top of each other so that biologists get a full picture of subtle and significant differences among the pathways. The (dis)similarities between pathways are expressed by the Hamming distances of the underlying graphs which are used to compute a stacking order for the pathways. Layouts are determined by a global layout of the union of all pathway graphs using a variant of the proven Sugiyama approach for layered graph drawing. Our variant layout approach allows edges to cross if they appear in different graphs.

Weighted graph comparison techniques for brain connectivity analysis

“…However, various comparison techniques are proposed for unweighted graphs in a number of domains for problems related to characterization of metabolic pathways [8,36], business process models [2] and software evolution [11].…”

Algorithms for Various Biological Networks

“…This has led to the development of network-and application-specific layout algorithms, for example, for signal transduction maps [17,18], protein interaction networks [3,6], metabolic pathways [4,10,19] and protein-domain interaction networks [20]. Advanced solutions combine different layout styles (such as linear, circular and branching layouts) for sub-networks or use specific layouts styles for particular network parts such as cycles [7,10,21]. …”

“…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.Since last few years 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][4][5][6][7][8][9][10][11]. Often standard layout methods such as force directed [12,13], layered [14,15] and circular [16] approaches are used to draw these networks.…”

“…This has led to the development of network-and application-specific layout algorithms, for example, for signal transduction maps [17,18], protein interaction networks [3,6], metabolic pathways [4,10,19] and protein-domain interaction networks [20]. Advanced solutions combine different layout styles (such as linear, circular and branching layouts) for sub-networks or use specific layouts styles for particular network parts such as cycles [7,10,21].However, current approaches for the automatic visualization of biological networks have four major drawbacks resulting from the specialized nature of these algorithms: 1. Different kinds of biological networks (e. g. protein interaction or metabolic networks) have different layout conventions and this requires the implementation and sometimes development of specialized layout algorithms for each convention.…”

Drawing the AS Graph in 2.5 Dimensions

“…Using 2.5D graph visualization, we then represent the core hierarchy by stacking the induced 2D layouts of the k-cores for increasing k on top of each other in the third dimension. Visualizations in 2.5D have been proposed frequently for network data, for example to display other graph hierarchies [6,9] or evolving graphs over time [4].…”