Abstract
BackgroundComplexity is a key problem when visualizing biological networks; as the number of entities increases, most graphical views become incomprehensible. Our goal is to enable many thousands of entities to be visualized meaningfully and with high performance.ResultsWe present a new visualization tool, Arena3D, which introduces a new concept of staggered layers in 3D space. Related data – such as proteins, chemicals, or pathways – can be grouped onto separate layers and arranged via layout algorithms, such as Fruchterman-Reingold, distance geometry, and a novel hierarchical layout. Data on a layer can be clustered via k-means, affinity propagation, Markov clustering, neighbor joining, tree clustering, or UPGMA ('unweighted pair-group method with arithmetic mean'). A simple input format defines the name and URL for each node, and defines connections or similarity scores between pairs of nodes. The use of Arena3D is illustrated with datasets related to Huntington's disease.ConclusionArena3D is a user friendly visualization tool that is able to visualize biological or any other network in 3D space. It is free for academic use and runs on any platform. It can be downloaded or lunched directly from . Java3D library and Java 1.5 need to be pre-installed for the software to run.
Highlights
Complexity is a key problem when visualizing biological networks; as the number of entities increases, most graphical views become incomprehensible
We enabled users to select nodes graphically by making the scene canvas selectable so that some mouse events can pick the geometry of the objects
In addition we developed a third novel layout algorithm that distributes the nodes in a hierarchy depending on the connections
Summary
Complexity is a key problem when visualizing biological networks; as the number of entities increases, most graphical views become incomprehensible. One of the main approaches to this challenge is to apply data analysis and clustering tools, and to visualize the results, usually in two-dimensional (2D) graphs. Ondex [6], MAPMAN [7], Pajek [8] and Shark [9], BioLayout Express3D [10]. Several of these tools provide easy connection to, or import from, commonly used external data analysis and clustering tools, such as Mega [11,12] or Hierarchical Clustering Explorer [13]. We believe that tight integration of analysis and visualization is a key feature for such tools, since it allows users to experiment, to immediately see the results of analysis, and to quickly decide which analyses are most appropriate
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