BMC Genomics Research article Effect of dataset selection on the topological interpretation of protein interaction networks Luke Hakes, David L Robertson and Stephen G Oliver* BioMed Central Open Access Address: Faculty of Life Sciences, The University of Manchester, Manchester, UK Email: Luke Hakes - luke.hakes@student.manchester.ac.uk; David L Robertson - robertson@bioinf.man.ac.uk; Stephen G Oliver* - steve.oliver@manchester.ac.uk * Corresponding author Published: 20 September 2005 BMC Genomics 2005, 6:131 doi:10.1186/1471-2164-6-131 This article is available from: http://www.biomedcentral.com/1471-2164/6/131 Received: 12 July 2005 Accepted: 20 September 2005 © 2005 Hakes et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Studies of the yeast protein interaction network have revealed distinct correlations between the connectivity of individual proteins within the network and the average connectivity of their neighbours. Although a number of biological mechanisms have been proposed to account for these findings, the significance and influence of the specific datasets included in these studies has not been appreciated adequately. Results: We show how the use of different interaction data sets, such as those resulting from high- throughput or small-scale studies, and different modelling methodologies for the derivation pair- wise protein interactions, can dramatically change the topology of these networks. Furthermore, we show that some of the previously reported features identified in these networks may simply be the result of experimental or methodological errors and biases. Conclusion: When performing network-based studies, it is essential to define what is meant by the term "interaction" and this must be taken into account when interpreting the topologies of the networks generated. Consideration must be given to the type of data included and appropriate controls that take into account the idiosyncrasies of the data must be selected Background In recent years, there has been an unprecedented growth in both the volume and the type of experimental data available to researchers interested in elucidating the bio- logical networks that underpin the functions of living cells. To date, the majority of available eukaryotic data comes from the yeast Saccharomyces cerevisiae, where a variety of different networks have been subject to investi- gation, including gene regulatory [1], metabolic [2-4] and protein interaction networks [5]. As the majority of cellu- lar processes are mediated by protein-protein interactions, much attention has been focused on their study in the hope that their investigation on a "global" scale will help us to understand how a dynamically interconnected sys- tem manages to perform multiple functionally related tasks while maintaining stability against deleterious perturbations. The recent deluge of protein interaction data generated from large-scale high-throughput systematic screens [6-9] has presented us with an opportunity to create networks consisting of thousands of interacting proteins. Analysis of the resulting networks has shown that, in common with other naturally occurring and artificial networks, protein-interaction networks display a scale-free topology [10,11] and exhibit "small-world" properties [12]. The Page 1 of 8 (page number not for citation purposes)