Abstract
We apply our recently developed information-theoretic measures for the characterisation and comparison of protein–protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large–scale analysis of protein–protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast–two–hybrid methods are sufficiently consistent to allow for intra–species comparisons (between different experiments) and inter–species comparisons, while data from affinity–purification mass–spectrometry methods show large differences even within intra–species comparisons.
Highlights
Comparative genomics has revolutionised the study of biology by shifting its focus from component characterisation to the study of systemic properties
Others employ alignment strategies where phylogenetic information is derived by the identification of paralogues [14,15]. These studies illustrate the additional information provided by comparative interactomics, beyond comparative genomics, and the benefit of intra-species comparison [16]
We characterise each protein-protein interaction network (PPIN) by its degree distribution p(k) and its normalised degree–degree correlations (DDCs) P(k, k’)
Summary
Comparative genomics has revolutionised the study of biology by shifting its focus from component characterisation to the study of systemic properties. One can compare protein-protein interaction network (PPIN) datasets by counting the fraction of common interactions, referred to as ‘overlap’. Some focus on identifying conserved ‘modules’ or recurrent geometrically defined motifs, envisaged to capture biological and functional properties of the underlying networks [10,11] or common functional cores of ancestral origin [12,13]. Others employ alignment strategies where phylogenetic information is derived by the identification of paralogues [14,15]. These studies illustrate the additional information provided by comparative interactomics, beyond comparative genomics, and the benefit of intra-species comparison [16]
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