Published online 8 September 2004
Nucleic Acids Research, Vol. 32 No. 16 © Oxford University Press 2004; all rights reserved
The interactome as a tree—an attempt to visualize the protein–protein interaction network in yeast
1 Bioinformatics Laboratory, Institute of Biophysics and 2 Bioinformatics Research Group, Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, Peoples Republic of China
* To whom correspondence should be addressed. Tel: +86 10 6256 5533 5716; Fax: +86 10 6256 7724; Email: crs{at}sun5.ibp.ac.cn
Correspondence may also be addressed to Dongbo Bu. Tel: +86 10 6256 5533 5716; Fax: +86 10 6256 7724; Email: bdb{at}ncic.ac.cn
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors
Received February 25, 2004; Revised May 28, 2004; Accepted August 20, 2004
The refinement and high-throughput of protein interaction detection methods offer us a protein–protein interaction network in yeast. The challenge coming along with the network is to find better ways to make it accessible for biological investigation. Visualization would be helpful for extraction of meaningful biological information from the network. However, traditional ways of visualizing the network are unsuitable because of the large number of proteins. Here, we provide a simple but information-rich approach for visualization which integrates topological and biological information. In our method, the topological information such as quasi-cliques or spoke-like modules of the network is extracted into a clustering tree, where biological information spanning from protein functional annotation to expression profile correlations can be annotated onto the representation of it. We have developed a software named PINC based on our approach. Compared with previous clustering methods, our clustering method ADJW performs well both in retaining a meaningful image of the protein interaction network as well as in enriching the image with biological information, therefore is more suitable in visualization of the network.
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