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This article is part of the supplement: Selected articles from the 7th International Symposium on Bioinformatics Research and Applications (ISBRA'11)

Open Access Proceedings

A methodology for detecting the orthology signal in a PPI network at a functional complex level

Pavol Jancura1*, Eleftheria Mavridou2, Enrique Carrillo-de Santa Pau3 and Elena Marchiori1

Author Affiliations

1 Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, 6500 GL, The Netherlands

2 Department of Medical Microbiology, Radboud University Medical Center, Nijmegen, 6500 HB, The Netherlands

3 Department of Molecular Biology, Nijmegen Centre for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, 6500 HB, The Netherlands

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BMC Bioinformatics 2012, 13(Suppl 10):S18  doi:10.1186/1471-2105-13-S10-S18

Published: 25 June 2012

Abstract

Background

Stable evolutionary signal has been observed in a yeast protein-protein interaction (PPI) network. These finding suggests more connected regions of a PPI network to be potential mediators of evolutionary information. Because more connected regions of PPI networks contain functional complexes, we are motivated to exploit the orthology relation for identifying complexes that can be clearly attributed to such evolutionary signal.

Results

We proposed a computational methodology for detecting the orthology signal present in a PPI network at a functional complex level. Specifically, we examined highly functionally coherent putative protein complexes as detected by a clustering technique in the complete yeast PPI network, in the yeast sub-network which spans only ortholog proteins as determined by a given second organism, and in yeast sub-networks induced by a set of proteins randomly selected. We proposed a filtering technique for extracting orthology-driven clusters with unique functionalities, that is, neither enriched by clusters identified using the complete yeast PPI network nor identified using random sampling. Moreover, we extracted functional categories that can be clearly attributed to the presence of evolutionary signal as described by these clusters.

Conclusions

Application of the proposed methodology to the yeast PPI network indicated that evolutionary information at a functional complex level can be retrieved from the structure of the network. In particular, we detected protein complexes whose functionality could be uniquely attributed to the evolutionary signal. Moreover, we identified functions that are over-represented in these complexes due the evolutionary signal.