Assessing the impact of network depth on the analysis of PPI networks: a case study

Authors:
Jaine K. Blayney;Haiying Wang;Huiru Zheng;Francisco Azuaje
Affiliations:
Faculty of Computing and Engineering, University of Ulster, Newtownabbey, Northern Ireland;Faculty of Computing and Engineering, University of Ulster, Newtownabbey, Northern Ireland;Faculty of Computing and Engineering, University of Ulster, Newtownabbey, Northern Ireland;Department of Cardiovascular Diseases, Public Research Centre for Health, Luxembourg
Venue:
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Year:
2009

Citing 4
Cited 0

BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks

Bioinformatics
PIANA: protein interactions and network analysis

Bioinformatics
Computing topological parameters of biological networks

Bioinformatics
Not all scale free networks are Born equal: the role of the seed graph in PPI network emulation

RECOMB'06 Proceedings of the joint 2006 satellite conference on Systems biology and computational proteomics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent years have seen a growing interest in the incorporation of protein-protein interaction (PPI) networks to support functional genomic research. Often a default depth is assumed by network inference software. This case study considers the impact of network depth on the analysis of PPI networks using seven proteins known to be relevant to heart failure as inputs into the analysis. This paper analyses how the characteristics of a PPI network vary according to the level examined, suggesting that the investigation of network topology is an essential first step in PPI analysis. The classification of nodes, in terms of degree and betweenness centrality, within the network is also considered. The effect of network depth is also proved to be significant in the identification of potentially essential proteins with large connectivity and/or high betweenness centrality values.