|Résumé||Royer L, Reimann M, Andreopoulos B, Schroeder M (2008) Unraveling Protein Networks with Power Graph Analysis. PLoS Comput Biol 4(7): e1000108.
Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power
Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting
the abundance of cliques and bicliques as elementary topological motifs. We demonstrate with five examples the
advantages of Power Graph Analysis. Investigating protein-protein interaction networks, we show how the catalytic
subunits of the casein kinase II complex are distinguishable from the regulatory subunits, how interaction profiles and
sequence phylogeny of SH3 domains correlate, and how false positive interactions among high-throughput interactions are
spotted. Additionally, we demonstrate the generality of Power Graph Analysis by applying it to two other types of networks.
We show how power graphs induce a clustering of both transcription factors and target genes in bipartite transcription
networks, and how the erosion of a phosphatase domain in type 22 non-receptor tyrosine phosphatases is detected. We
apply Power Graph Analysis to high-throughput protein interaction networks and show that up to 85% (56% on average) of
the information is redundant. Experimental networks are more compressible than rewired ones of same degree distribution,
indicating that experimental networks are rich in cliques and bicliques. Power Graphs are a novel representation of
networks, which reduces network complexity by explicitly representing re-occurring network motifs. Power Graphs
compress up to 85% of the edges in protein interaction networks and are applicable to all types of networks such as protein
interactions, regulatory networks, or homology networks.