Node Handprinting: A Scalable and Accurate Algorithm for Aligning Multiple Biological Networks

Abstract

Due to recent advancements in high-throughput sequencing technologies, progressively more protein-protein interactions have been identified for a growing number of species. Subsequently, the protein-protein interaction networks for these species have been further refined. The increase in the quality and availability of these networks has in turn brought a demand for efficient methods to analyze such networks. The pairwise alignment of these networks has been moderately investigated, with numerous algorithms available, but there is very little progress in the field of multiple network alignment. Multiple alignment of networks from different organisms is ideal at finding abnormally conserved or disparate subnetworks. We present a fast and accurate algorithmic approach, Node Handprinting (NH), based on our previous work with Node Fingerprinting, which enables quick and accurate alignment of multiple networks. We also propose two new metrics for the analysis of multiple alignments, as the current metrics are not as sophisticated as their pairwise alignment counterparts. To assess the performance of NH, we use previously aligned datasets as well as protein interaction networks generated from the public database BioGRID. Our results indicate that NH compares favorably with current methodologies and is the only algorithm capable of performing the more complex alignments.

A novel network aligner for the analysis of multiple protein-protein interaction networks

“…MAPPIN can align two or more networks as well and combines the GO annotation information of proteins with topology and sequence similarity to calculate the similarity of nodes. Node Handprinting (NH) [35]is a global MNA, which solves the weighted bipartite graph matching problem by using the progressive alignment strategy to obtain the final optimal alignment. MultiMAGNA++ [42] is a global MNA designed to maximize the optimization objective function using genetic algorithms and is an extension of MAGNA and MAGNA++ [43], which are pairwise network alignment algorithms.…”

MPGM: Scalable and Accurate Multiple Network Alignment

“…To generate multiple alignments, GEDEVO-M minimizes a generalized graph edit distance measure. NH [Radu and Charleston, 2015] is a many-to-many global MNA heuristic algorithm that uses only network structure. Alkan and Erten [Alkan and Erten, 2014] designed a many-to-many global heuristic method based on a backbone extraction and merge strategy (BEAMS).…”