A fast approach to global alignment of protein-protein interaction networks

Abstract

BackgroundGlobal network alignment has been proposed as an effective tool for computing functional orthology. Commonly used global alignment techniques such as IsoRank rely on a two-step process: the first step is an iterative diffusion-based approach for assigning similarity scores to all possible node pairs (matchings); the second step applies a maximum-weight bipartite matching algorithm to this similarity score matrix to identify orthologous node pairs. While demonstrably successful in identifying orthologies beyond those based on sequences, this two-step process is computationally expensive. Recent work on computation of node-pair similarity matrices has demonstrated that the computational cost of the first step can be significantly reduced. The use of these accelerated methods renders the bipartite matching step as the dominant computational cost. This motivates a critical assessment of the tradeoffs of computational cost and solution quality (matching quality, topological matches, and biological significance) associated with the bipartite matching step. In this paper we utilize the state-of-the-art core diffusion-based step in IsoRank for similarity matrix computation, and couple it with two heuristic bipartite matching algorithms – a matrix-based greedy approach, and a tunable, adaptive, auction-based matching algorithm developed by us. We then compare our implementations against the performance and quality characteristics of the solution produced by the reference IsoRank binary, which also implements an optimal matching algorithm.ResultsUsing heuristic matching algorithms in the IsoRank pipeline exhibits dramatic speedup improvements; typically ×30 times faster for the total alignment process in most cases of interest. More surprisingly, these improvements in compute times are typically accompanied by better or comparable topological and biological quality for the network alignments generated. These measures are quantified by the number of conserved edges in the alignment graph, the percentage of enriched components, and the total number of covered Gene Ontology (GO) terms.ConclusionsWe have demonstrated significant reductions in global network alignment computation times by coupling heuristic bipartite matching methods with the similarity scoring step of the IsoRank procedure. Our heuristic matching techniques maintain comparable – if not better – quality in resulting alignments. A consequence of our work is that network-alignment based orthologies can be computed within minutes (as compared to hours) on typical protein interaction networks, enabling a more comprehensive tuning of alignment parameters for refined orthologies.

A review of protein–protein interaction network alignment: From pathway comparison to global alignment

“…It uses iterative spectral clustering algorithm to replace the second phase of IsoRank and generates the final result. Later, Kollias et al [33] , [34] used network similarity decomposition approach and half-approximation algorithm in the two phases of IsoRank, respectively, to speed up IsoRank significantly.…”

Global Alignment of Protein-Protein Interaction Networks: A Survey

“…Significant improvements to IsoRank have been presented [1], [45]. The approach called Network Similarity Decomposition (NSD) [1] makes a significant improvement to the speed of calculating the node similarity matrix R ij .…”

“…Another enhancement that improves the second phase of IsoRank is presented in [45]. The same two-phase IsoRank methodology, implemented with different approximate matching algorithms, makes IsoRank work up to about 30 times faster than the original algorithm discussed above.…”

Implementação e Avaliação do Algoritmo de Leilão nas Arquiteturas Xeon Phi

“…Existem diversos problemas que podem ser modelados através de grafos bipartidos, onde um emparelhamentoótimo deve ser encontrado. O problema de emparelhamento de grafos bipartidosé explorado em diversasáreas tais como a bioinformática, para verificar semelhanças entre proteínas [Kollias et al 2013] e Visão Computacional, onde o objetivó e o emparelhamento de duas imagens, avaliando as semelhanças entre seus pontos [Shokoufandeh and Dickinson 1999]. Por ser capaz de produzir a soluçãoótima e sua natureza distributiva simplificar a adoção de implementações paralelas, o algoritmo de leilão [Bertsekas 1979] tem sido amplamente usado para solucionar esse problema.…”