Abstract
BackgroundAdvances in high-throughput technology has led to an increased amount of available data on protein-protein interaction (PPI) data. Detecting and extracting functional modules that are common across multiple networks is an important step towards understanding the role of functional modules and how they have evolved across species. A global protein-protein interaction network alignment algorithm attempts to find such functional orthologs across multiple networks.ResultsIn this article, we propose a scalable global network alignment algorithm based on clustering methods and graph matching techniques in order to detect conserved interactions while simultaneously attempting to maximize the sequence similarity of nodes involved in the alignment. We present an algorithm for multiple alignments, in which several PPI networks are aligned. We empirically evaluated our algorithm on three real biological datasets with 6 different species and found that our approach offers a significant benefit both in terms of quality as well as speed over the current state-of-the-art algorithms.ConclusionComputational experiments on the real datasets demonstrate that our multiple network alignment algorithm is a more efficient and effective algorithm than the state-of-the-art algorithm, IsoRankN. From a qualitative standpoint, our approach also offers a significant advantage over IsoRankN for the multiple network alignment problem.
Highlights
Advances in high-throughput technology has led to an increased amount of available data on protein-protein interaction (PPI) data
A PPI network can be represented as an undirected graph in which each vertex indicates a protein and each edge indicates an interaction between two proteins
We present a simple but scalable approach for global multiple network alignment to exploit the sparsity of the PPI networks
Summary
Advances in high-throughput technology has led to an increased amount of available data on protein-protein interaction (PPI) data. A global protein-protein interaction network alignment algorithm attempts to find such functional orthologs across multiple networks. Given several (related) PPI networks and the protein sequence similarity scores between proteins within said networks, the goal of a network alignment algorithm is to find the best alignment, i.e., a mapping, which best represents functional orthologs among proteins within these networks. This problem is equivalent to identifying most biologically consistent match-sets, which are groups of proteins representing functional orthologs. The graph is usually unweighted an edge can often be associated with a confidence value indicating the probability that this edge is a true positive [6,7]
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