PROPER: global protein interaction network alignment through percolation matching

Kazemi, Ehsan; Hassani, Hamed; Grossglauser, Matthias; Modarres, Hassan Pezeshgi

doi:10.1186/s12859-016-1395-9

Cited by 35 publications

(35 citation statements)

References 70 publications

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“…The results of SAlign and its variant, , are compared with prominent existing aligners on BioGRID (three network pairs) and HINT (five network pairs) datasets. Existing prominent techniques include HubAlign [ 1 ], ModuleAlign [ 7 ], NETAL [ 8 ], PROPER [ 9 ], IBNAL [ 10 ] and Magna++ [ 15 ]. The performance of IsoRank [ 16 ], PISwap [ 17 ], GHOST [ 18 ], PINALOG [ 19 ], L-GRALL [ 20 ], Great [ 21 ] and SPINAL [ 22 ] have been shown to be lower than most of the above mentioned algorithms, so we did not include these algorithms in our analysis.…”

Section: Resultsmentioning

confidence: 99%

“…A single node of network A can align to a single node of network B. The primary goal of such global aligners is to match the maximum number of functionally similar nodes [ 1 , 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…IBNAL develop a clique based index to measure the topology of the proteins [ 10 ]. NETAL and PROPER use local topological measures to calculate the topology [ 8 , 9 ]. Similarly, previous studies use different types of heuristics to align the network.…”

Section: Introductionmentioning

confidence: 99%

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SAlign–a structure aware method for global PPI network alignment

2020

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Background High throughput experiments have generated a significantly large amount of protein interaction data, which is being used to study protein networks. Studying complete protein networks can reveal more insight about healthy/disease states than studying proteins in isolation. Similarly, a comparative study of protein–protein interaction (PPI) networks of different species reveals important insights which may help in disease analysis and drug design. The study of PPI network alignment can also helps in understanding the different biological systems of different species. It can also be used in transfer of knowledge across different species. Different aligners have been introduced in the last decade but developing an accurate and scalable global alignment algorithm that can ensures the biological significance alignment is still challenging. Results This paper presents a novel global pairwise network alignment algorithm, SAlign, which uses topological and biological information in the alignment process. The proposed algorithm incorporates sequence and structural information for computing biological scores, whereas previous algorithms only use sequence information. The alignment based on the proposed technique shows that the combined effect of structure and sequence results in significantly better pairwise alignments. We have compared SAlign with state-of-art algorithms on the basis of semantic similarity of alignment and the number of aligned nodes on multiple PPI network pairs. The results of SAlign on the network pairs which have high percentage of proteins with available structure are 3–63% semantically better than all existing techniques. Furthermore, it also aligns 5–14% more nodes of these network pairs as compared to existing aligners. The results of SAlign on other PPI network pairs are comparable or better than all existing techniques. We also introduce $$\hbox {SAlign}^{\mathrm{mc}}$$ SAlign mc , a Monte Carlo based alignment algorithm, that produces multiple network alignments with similar semantic similarity. This helps the user to pick biologically meaningful alignments. Conclusion The proposed algorithm has the ability to find the alignments that are more biologically significant/relevant as compared to the alignments of existing aligners. Furthermore, the proposed method is able to generate alternate alignments that help in studying different genes/proteins of the specie.

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Section: Resultsmentioning

confidence: 99%

“…A single node of network A can align to a single node of network B. The primary goal of such global aligners is to match the maximum number of functionally similar nodes [ 1 , 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

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SAlign–a structure aware method for global PPI network alignment

2020

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“…ModuleAlign [16] computes an alignment score by combining the homology and topology scores, and then iteratively selects the highest-scoring protein pairs by an optimal bipartite matching. PROPER [17] employs the percolation graph matching to align input networks using the network structures and the seeds generated by sequence similarities. Fuse [18] is a multiple global network alignment algorithm that computes protein similarity scores using the non-negative matrix tri-factorization method to predict associations between proteins whose homology and functional similarity are supported by all networks.…”

Section: Introductionmentioning

confidence: 99%

LePrimAlign: local entropy-based alignment of PPI networks to predict conserved modules

Maskey

Cho

2019

BMC Genomics

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BackgroundCross-species analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved interaction patterns. Identifying such conserved substructures between PPI networks of different species increases our understanding of the principles deriving evolution of cellular organizations and their functions in a system level. In recent years, network alignment techniques have been applied to genome-scale PPI networks to predict evolutionary conserved modules. Although a wide variety of network alignment algorithms have been introduced, developing a scalable local network alignment algorithm with high accuracy is still challenging.ResultsWe present a novel pairwise local network alignment algorithm, called LePrimAlign, to predict conserved modules between PPI networks of three different species. The proposed algorithm exploits the results of a pairwise global alignment algorithm with many-to-many node mapping. It also applies the concept of graph entropy to detect initial cluster pairs from two networks. Finally, the initial clusters are expanded to increase the local alignment score that is formulated by a combination of intra-network and inter-network scores. The performance comparison with state-of-the-art approaches demonstrates that the proposed algorithm outperforms in terms of accuracy of identified protein complexes and quality of alignments.ConclusionThe proposed method produces local network alignment of higher accuracy in predicting conserved modules even with large biological networks at a reduced computational cost.

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“…as measured by network propagation [14]). The comparison of PPI networks across species is well studied and commonly referred to as the network alignment problem [36,50,53,30,39,37,1,65,28,59,41,25]. The goal of network alignment is to establish a mapping between nodes in different networks.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Species Functional Embedding Integrating Sequence and Network Structure

Leiserson

Fan

Cannistra

et al. 2017

Preprint

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Abstract.A key challenge to transferring knowledge between species is that different species have fundamentally different genetic architectures. Initial computational approaches to transfer knowledge across species have relied on measures of heredity such as genetic homology, but these approaches suffer from limitations. First, only a small subset of genes have homologs, limiting the amount of knowledge that can be transferred, and second, genes change or repurpose functions, complicating the transfer of knowledge. Many approaches address this problem by expanding the notion of homology by leveraging high-throughput genomic and proteomic measurements, such as through network alignment.In this work, we take a new approach to transferring knowledge across species by expanding the notion of homology through explicit measures of functional similarity between proteins in different species. Specifically, our kernel-based method, Handl (Homology Assessment across Networks using Diffusion and Landmarks), integrates sequence and network structure to create a functional embedding in which proteins from different species are embedded in the same vector space. We show that inner products in this space and the vectors themselves capture functional similarity across species, and are useful for a variety of functional tasks. We perform the first whole-genome method for predicting phenologs, generating many that were previously identified, but also predicting new phenologs supported from the biological literature. We also demonstrate the Handl embedding captures pairwise gene function, in that gene pairs with synthetic lethal interactions are significantly separated in Handl space, and the direction of separation is conserved across species. Software for the Handl algorithm is available at http://bit.ly/lrgr-handl.

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PROPER: global protein interaction network alignment through percolation matching

Cited by 35 publications

References 70 publications

SAlign–a structure aware method for global PPI network alignment

SAlign–a structure aware method for global PPI network alignment

LePrimAlign: local entropy-based alignment of PPI networks to predict conserved modules

A Multi-Species Functional Embedding Integrating Sequence and Network Structure

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