BEAMS: backbone extraction and merge strategy for the global many-to-many alignment of multiple PPI networks

Alkan, Ferhat; Erten, Cesim

doi:10.1093/bioinformatics/btt713

Cited by 70 publications

(77 citation statements)

References 25 publications

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“…Most of the early works on PPI-network alignment, such as PathBLAST [24], NetworkBLAST [10], NetAlign [25], MaWISh [26] and Grælin [27], study the LNA problem. More recent methods, such as IsoRank [28, 29], the GRAAL family [16, 23, 30–32], MAGNA and its successor MAGNA++ [33, 34], SPINAL [35], PINALOG [36], Netcoffee [37] and BEAMS [38], are examples of GNA algorithms.…”

Section: Introductionmentioning

confidence: 99%

PROPER: global protein interaction network alignment through percolation matching

et al. 2016

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

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Section: Introductionmentioning

confidence: 99%

PROPER: global protein interaction network alignment through percolation matching

et al. 2016

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“…A few of these methods have been expanded to the multiple network problems (Flannick et al , 2009; Liao et al , 2009). These methods are usually computationally intensive and tend to yield an alignment that contains only a relatively small proportion of the network, although this has been alleviated to some extent in more recent methods (Alkan and Erten, 2014; Hu et al , 2014; Patro and Kingsford, 2012). …”

Section: Introductionmentioning

confidence: 99%

Alignment-free protein interaction network comparison

et al. 2014

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Motivation: Biological network comparison software largely relies on the concept of alignment where close matches between the nodes of two or more networks are sought. These node matches are based on sequence similarity and/or interaction patterns. However, because of the incomplete and error-prone datasets currently available, such methods have had limited success. Moreover, the results of network alignment are in general not amenable for distance-based evolutionary analysis of sets of networks. In this article, we describe Netdis, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction.Results: We first demonstrate that Netdis is able to correctly separate different random graph model types independent of network size and density. The biological applicability of the method is then shown by its ability to build the correct phylogenetic tree of species based solely on the topology of current protein interaction networks. Our results provide new evidence that the topology of protein interaction networks contains information about evolutionary processes, despite the lack of conservation of individual interactions. As Netdis is applicable to all networks because of its speed and simplicity, we apply it to a large collection of biological and non-biological networks where it clusters diverse networks by type.Availability and implementation: The source code of the program is freely available at http://www.stats.ox.ac.uk/research/proteins/resources.Contact: w.ali@stats.ox.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

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“…For this reason, the majority of recent research has focused on global network alignment (see section 1.2). There are also methods that allow alignments between more than two networks (Liao et al, 2009;Hu et al, 2014;Gligorijević et al, 2015;Vijayan and Milenkovic, 2016;Alkan and Erten, 2014).…”

Section: Contextmentioning

confidence: 99%

SANA: simulated annealing far outperforms many other search algorithms for biological network alignment

Mamano

Hayes

2017

Bioinformatics

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Every alignment algorithm consists of two orthogonal components: an objective function M measuring the quality an alignment, and a search algorithm that explores the space of alignments looking for ones scoring well according to M . We introduce a new search algorithm called SANA (Simulated Annealing Network Aligner) and apply it to protein-protein interaction networks using S 3 as the the topological measure. Compared against 12 recent algorithms, SANA produces 5-10 times as many correct node mappings as the others when the correct answer is known. We expose an anti-correlation in many existing aligners between their ability to produce good topological vs. functional similarity scores, whereas SANA usually outscores other methods in both measures. If given the perfect objective function encoding the identity mapping, SANA quickly converges to the perfect solution while many other algorithms falter. We observe that when aligning networks with a known mapping and optimizing only S 3 , SANA creates alignments that are not perfect and yet whose S 3 scores match that of the perfect alignment. We call this phenomenon saturation of the topological score. Saturation implies that a measure's correlation with alignment correctness falters before the perfect alignment is reached. This, combined with SANA's ability to produce the perfect alignment if given the perfect objective function, suggests that better objective functions may lead to dramatically better alignments. We conclude that future work should focus on finding better objective functions, and offer SANA as the search algorithm of choice.

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BEAMS: backbone extraction and merge strategy for the global many-to-many alignment of multiple PPI networks

Abstract: Supplementary material including code implementations in LEDA C++, experimental data and the results are available at http://webprs.khas.edu.tr/~cesim/BEAMS.tar.gz.

Cited by 70 publications

References 25 publications

PROPER: global protein interaction network alignment through percolation matching

PROPER: global protein interaction network alignment through percolation matching

Alignment-free protein interaction network comparison

SANA: simulated annealing far outperforms many other search algorithms for biological network alignment

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