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Pairwise alignment of protein interaction networks (2006)

by M Koyuturk
Venue:J. Comput. Biol
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Global alignment of protein–protein interaction networks by graph matching methods

by Mikhail Zaslavskiy, Francis Bach, et al. - BIOINFORMATICS , 2009
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Abstract - Cited by 41 (1 self) - Add to MetaCart
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...graphs of interactions. These methods include PathBLAST [13], [14] and NetworkBLAST [18], which adapt the ideas of the BLAST algorithm to the search for local alignments between graphs, the method of =-=[15]-=-, inspired by biological models of deletion and duplication, Graemlin [10], which uses networks of modules to infer the alignment, or the Bayesian approach of [4]. Less attention has been paid to the ...

Integrative network alignment reveals large regions of global network similarity in yeast and human

by Oleksii Kuchaiev - Bioinformatics , 2011
"... Motivation: High-throughput methods for detecting molecular interactions have produced large sets of biological network data with much more yet to come. Analogous to sequence alignment, efficient and reliable network alignment methods are expected to improve our understanding of biological systems. ..."
Abstract - Cited by 40 (0 self) - Add to MetaCart
Motivation: High-throughput methods for detecting molecular interactions have produced large sets of biological network data with much more yet to come. Analogous to sequence alignment, efficient and reliable network alignment methods are expected to improve our understanding of biological systems. Unlike sequence alignment, network alignment is computationally intractable. Hence, devising efficient network alignment heuristics is currently a foremost challenge in computational biology. Results: We introduce a novel network alignment algorithm, called Matching-based Integrative GRAph ALigner (MI-GRAAL), which can integrate any number and type of similarity measures between network nodes (e.g., proteins), including, but not limited to, any topological network similarity measure, sequence similarity, functional similarity, and structural similarity. Hence, we resolve the ties in
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...he duplication/divergence models that focus on understanding the evolution of protein interactions; it constructs a weighted global alignment graph and tries to find a maximum induced subgraph in it (=-=Koyuturk et al., 2006-=-). Graemlin algorithm scores a possibly conserved module between different networks by computing the log-ratio of the probability that the module is subject to evolutionary constraints and the probabi...

Identification of functional modules from conserved ancestral protein–protein interactions

by Janusz Dutkowski, Jerzy Tiuryn - BIOINFORMATICS , 2007
"... Motivation: The increasing availability of large-scale protein–protein interaction (PPI) data has fuelled the efforts to elucidate the building blocks and organization of cellular machinery. Previous studies have shown cross-species comparison to be an effective approach in uncovering functional mod ..."
Abstract - Cited by 38 (1 self) - Add to MetaCart
Motivation: The increasing availability of large-scale protein–protein interaction (PPI) data has fuelled the efforts to elucidate the building blocks and organization of cellular machinery. Previous studies have shown cross-species comparison to be an effective approach in uncovering functional modules in protein networks. This has in turn driven the research for new network alignment methods with a more solid grounding in network evolution models and better scalability, to allow multiple network comparison. Results: We develop a new framework for protein network alignment, based on reconstruction of an ancestral PPI network. The reconstruction algorithm is built upon a proposed model of protein network evolution, which takes into account phylogenetic history of the proteins and the evolution of their interactions. The application of our methodology to the PPI networks of yeast, worm and fly reveals that the most probable conserved ancestral interactions are often related to known protein complexes. By projecting the conserved ancestral interactions back onto the input networks we are able to identify the corresponding conserved protein modules in the considered species. In contrast to most of the previous methods, our algorithm is able to compare many networks simultaneously. The performed experiments demonstrate the ability of our method to uncover many functional modules with high specificity. Availability: Information for obtaining software and supplementary results are available at

Assessing significance of connectivity and conservation in protein interaction networks

by Mehmet Koyutürk, Ananth Grama, Wojciech Szpankowski - Journal of Computational Biology , 2006
"... Computational and comparative analysis of protein-protein interaction (PPI) networks enable understanding of the modular organization of the cell through identification of functional modules and protein complexes. These analysis techniques generally rely on topological features such as connectedness ..."
Abstract - Cited by 25 (5 self) - Add to MetaCart
Computational and comparative analysis of protein-protein interaction (PPI) networks enable understanding of the modular organization of the cell through identification of functional modules and protein complexes. These analysis techniques generally rely on topological features such as connectedness, based on the premise that functionally related proteins are likely to interact densely and that these interactions follow similar evolutionary trajectories. Significant recent work in our lab, and in other labs has focused on efficient algorithms for identification of modules and their conservation. Application of these methods to a variety of networks has yielded novel biological insights. In spite of algorithmic advances, development of a comprehensive infrastructure for interaction databases is in relative infancy compared to corresponding sequence analysis tools such as BLAST and CLUSTAL. One critical component of this infrastructure is a measure of the statistical significance of a match or a dense subcomponent. Corresponding sequence-based measures such as E-values are key components of sequence matching tools. In the absence of an analytical measure, conventional methods rely on computer simulations based on ad-hoc models for quantifying significance. This paper presents the first such effort, to the best of our knowledge, aimed at analytically quantifying statistical significance
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...pecies. Based on this observation, PPI network alignment methods superpose PPI networks that belong to different species and search for connected, dense, or heavy subgraphs in these superposed graphs =-=[14, 18, 20, 19, 29, 30]-=-. There are two critical aspects of identifying meaningful structures in data – the algorithm for the identification and a method for scoring an identified pattern. In this context, the score of a pat...

LOCAL OPTIMIZATION FOR GLOBAL ALIGNMENT OF PROTEIN INTERACTION NETWORKS

by Leonid Chindelevitch, Chung-shou Liao, Bonnie Berger
"... We propose a novel algorithm, PISwap, for computing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other NP-hard problems, such as the Traveling Salesman Problem. Our algorithm be ..."
Abstract - Cited by 17 (2 self) - Add to MetaCart
We propose a novel algorithm, PISwap, for computing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other NP-hard problems, such as the Traveling Salesman Problem. Our algorithm begins with a sequence-based network alignment and then iteratively adjusts the alignment by incorporating network structure information. It has a worst-case pseudo-polynomial running-time bound and is very efficient in practice. It is shown to produce improved alignments in several well-studied cases. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignments. Finally, this algorithm can yield interesting insights into the evolutionary history of the compared species.

Domain-oriented edge-based alignment of protein interaction networks

by Xin Guo, Alexander J. Hartemink - VOL. 25 ISMB 2009, PAGES I240–I246 , 2009
"... ..."
Abstract - Cited by 11 (0 self) - Add to MetaCart
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A comparison of algorithms for the pairwise alignment of biological networks

by Connor Clark, Jugal Kalita - Bioinformatics , 2014
"... Motivation: As biological inquiry produces ever more network data, such as protein-protein interaction networks, gene regulatory networks, and metabolic networks, many algorithms have been proposed for the purpose of pairwise network alignment – finding a mapping from the nodes of one network to the ..."
Abstract - Cited by 11 (0 self) - Add to MetaCart
Motivation: As biological inquiry produces ever more network data, such as protein-protein interaction networks, gene regulatory networks, and metabolic networks, many algorithms have been proposed for the purpose of pairwise network alignment – finding a mapping from the nodes of one network to the nodes of another in such a way that the mapped nodes can be considered to correspond with respect to both their place in the network topology and their biological attributes. This technique is helpful in identifying previously undiscovered homologies between proteins of different species and revealing functionally similar subnetworks. In the past few years, a wealth of different aligners have been published, but few of them have been compared to one another, and no comprehensive review of these algorithms has yet appeared. Results: We present the problem of biological network alignment, provide a guide to existing alignment algorithms, and comprehensively benchmark existing algorithms on both synthetic and real-world biological data, finding dramatic differences between existing algorithms in the quality of the alignments they produce. Additionally, we find that many of these tools are inconvenient to use in practice, and there remains a need for easy-to-use, cross-platform tools for performing network alignment. Contact:

Finding Approximate and Constrained Motifs in Graphs

by Riccardo Dondi, Guillaume Fertin, Stéphane Vialette
"... Abstract. One of the emerging topics in the analysis of biological networks is the inference of motifs inside a network. In the context of metabolic network analysis, a recent approach introduced in [14], represents the network as a vertex-colored graph, while a motif M is represented as a multiset ..."
Abstract - Cited by 9 (0 self) - Add to MetaCart
Abstract. One of the emerging topics in the analysis of biological networks is the inference of motifs inside a network. In the context of metabolic network analysis, a recent approach introduced in [14], represents the network as a vertex-colored graph, while a motif M is represented as a multiset of colors. An occurrence of a motif M in a vertexcolored graph G is a connected induced subgraph of G whose vertex set is colored exactly as M. We investigate three different variants of the initial problem. The first two variants, Min-Add and Min-Substitute, deal with approximate occurrences of a motif in the graph, while the third variant, Constrained Graph Motif (or CGM for short), constrains the motif to contain a given set of vertices. We investigate the classical and parameterized complexity of the three problems. We show that Min-Add and Min-Substitute are NP-hard, even when M is a set, and the graph is a tree of degree bounded by 4 in which each color appears at most twice. Moreover, we show that Min-Substitute is in FPT when parameterized by the size of M. Finally, we consider the parameterized complexity of the CGM problem, and we give a fixed-parameter algorithm for graphs of bounded treewidth, while we show that the problem is W [2]-hard, even if the input graph has diameter 2. 1

Aligning Biomolecular Networks Using Modular Graph Kernels

by Fadi Towfic, M. Heather, West Greenlee, Vasant Honavar
"... Abstract. Comparative analysis of biomolecular networks constructed using measurements from different conditions, tissues, and organisms offer a powerful approach to understanding the structure, function, dynamics, and evolution of complex biological systems. We explore a class of algorithms for ali ..."
Abstract - Cited by 7 (1 self) - Add to MetaCart
Abstract. Comparative analysis of biomolecular networks constructed using measurements from different conditions, tissues, and organisms offer a powerful approach to understanding the structure, function, dynamics, and evolution of complex biological systems. We explore a class of algorithms for aligning large biomolecular networks by breaking down such networks into subgraphs and computing the alignment of the networks based on the alignment of their subgraphs. The resulting subnetworks are compared using graph kernels as scoring functions. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit. Our experiments using Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository of protein-protein interaction data demonstrate that the performance of the proposed algorithms (as measured by % GO term enrichment of subnetworks identified by the alignment) is competitive with some of the state-of-the-art algorithms for pair-wise alignment of large protein-protein interaction networks. Our results also show that the inter-species similarity scores computed based on graph kernels can be used to cluster the species into a species tree that is consistent with the known phylogenetic relationships among the species. 1
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...n nodes in the two networks to be aligned and to design heuristics that strike a balance between the speed, accuracy and robustness of the alignment of large biological networks. For instance, MaWISh =-=[29]-=- is a pairwise network alignment algorithm with a runtime complexity of O(mn) (where m and n are the number of vertices in the two networks being compared) that relies on a scoring function that takes...

3Message-Passing Algorithms for Sparse Network Alignment

by David F. Gleich, Ying Wang Google
"... Network alignment generalizes and unifies several approaches for forming a matching or alignment be-tween the vertices of two graphs. We study a mathematical programming framework for network alignment problem and a sparse variation of it where only a small number of matches between the vertices of ..."
Abstract - Cited by 5 (1 self) - Add to MetaCart
Network alignment generalizes and unifies several approaches for forming a matching or alignment be-tween the vertices of two graphs. We study a mathematical programming framework for network alignment problem and a sparse variation of it where only a small number of matches between the vertices of the two graphs are possible. We propose a new message passing algorithm that allows us to compute, very efficiently, approximate solutions to the sparse network alignment problems with graph sizes as large as hundreds of thousands of vertices. We also provide extensive simulations comparing our algorithms with two of the best solvers for network alignment problems on two synthetic matching problems, two bioinformatics prob-lems, and three large ontology alignment problems including a multilingual problem with a known labeled alignment.
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