PROPER: global protein interaction network alignment through percolation matchingReportar como inadecuado




PROPER: global protein interaction network alignment through percolation matching - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

BMC Bioinformatics

, 17:527

Networks analysis

Abstract

BackgroundThe alignment of protein-protein interaction PPI networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PPI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging.

ResultsIn this paper, we introduce a new global network alignment algorithm for PPI networks called PROPER. Compared to other global network alignment methods, our algorithm shows higher accuracy and speed over real PPI datasets and synthetic networks. We show that the PROPER algorithm can detect large portions of conserved biological pathways between species. Also, using a simple parsimonious evolutionary model, we explain why PROPER performs well based on several different comparison criteria.

ConclusionsWe highlight that PROPER has high potential in further applications such as detecting biological pathways, finding protein complexes and PPI prediction. The PROPER algorithm is available at http:-proper.epfl.ch.

KeywordsGlobal network alignment Protein-protein interaction Percolation graph matching Biological network AbbreviationsBLASTBasic local-alignment search tool

PGMPercolation graph matching

PPIProtein-protein interaction

Electronic supplementary materialThe online version of this article doi:10.1186-s12859-016-1395-9 contains supplementary material, which is available to authorized users.

Download fulltext PDF



Autor: Ehsan Kazemi - Hamed Hassani - Matthias Grossglauser - Hassan Pezeshgi Modarres

Fuente: https://link.springer.com/







Documentos relacionados