Effect of dataset selection on the topological interpretation of protein interaction networks

Luke Hakes, David L. Robertson, Stephen G. Oliver

    Research output: Contribution to journalArticlepeer-review


    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. Conclusions: 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 © 2005 Hakes et al., licensee BioMed Central Ltd.
    Original languageEnglish
    Article number131
    JournalBMC Genomics
    Publication statusPublished - 20 Sept 2005

    Research Beacons, Institutes and Platforms

    • Manchester Institute of Biotechnology


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