This article is part of the supplement: ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2011
Scalable global alignment for multiple biological networks
Department of Computer Science and Engineering, Ohio State University, Columbus, OH, USA
BMC Bioinformatics 2012, 13(Suppl 3):S11 doi:10.1186/1471-2105-13-S3-S11Published: 21 March 2012
Advances 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.
In 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.
Computational 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.