Examplepictures of DNA-Structures

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Unraveling protein networks with Power Graph Analysis

Loic Royer, Matthias Reimann, Bill Andreopoulos, Michael Schroeder


PLoS Computational Biology, 2008 (paper)

Contact: {royer,reimann,wiliama,ms}
*AT* biotec.tu-dresden.de
ISMB 2008 highlights talk slides

Explore interactively several biological and non-biological networks with Power Graph Analysis. (Java applets)

To start a Java WebStart enabled version of Cytoscape 2.6 with the Cytoscape CyOog2.6 (2008) pre-installed. Or download CyOog2.8.2 (2012).

This command line tool reads widely used graph file formats (edg, sif) and converts them into power graphs (bbl).

See also: Power Graph Analysis in Wikipedia

Networks play a crucial role in biology and are often used as a way to represent experimental results. Yet, their analysis and representation is still an open problem. Recent experimental and computational progress yields networks of increased size and complexity. There are for example small and large scale interaction networks, regulatory networks, genetic networks, protein-ligand interaction networks, and homology networks analyzed and published regularly. A common way to access the information in a network is though direct visualization, but this fails as it often just results in "fur balls" from which little insight can be gathered. On the other hand, clustering techniques manage to avoid the problems caused by the large number of nodes and even larger number of edges by coarse-graining the networks and thus abstracting details. But these fail too since, in fact, much of the biology lies in the details. Our PLoS Computational Biology paper presents a novel methodology for analyzing and representing networks. Power Graphs are a lossless representation of networks which reduces network complexity by explicitly representing re-occurring network motifs. Moreover, power graphs can be clearly visualized: Power Graphs compress up to 90% of the edges in biological networks and are applicable to all types of networks such as protein interaction, regulatory, or homology networks. We demonstrate the usefulness of Power Graph Analysis on five detailed biological examples ranging from protein-ligand binding to regulatory networks and homology networks.

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