GLAlign: Using global graph alignment to improve local graph alignment

Abstract

During the last years, the graph alignment has been used as a possible way to compare biological networks in system biology. The techniques for the alignment of biological networks fall into two categories: global alignment, that aims to identify large common subnetworks optimizing a topological alignment quality, and local alignment that aims to evidence single sub-regions optimizing functional alignment quality. In this work, we presented GLAlign (Global Local Aligner), a novel algorithm that integrates global and local alignment, starting from the possibility that the topological information gathered by results of global alignment can be used to improve the local alignment building. Initially, the algorithm enables the calculation of global alignment, then it uses this one to guide the building of the local alignment. GLAlign is based on two global and local algorithms widely used in literature, MAGNA++ and AlignMCL. We tested GLAlign as proof-of-principle using the Protein Interaction Networks (PINs) of three species: fly, yeast and worm. GLAlign is publicly available for academic use at https://sites.google.com/site/globallocalalignment/.