Fast and Accurate Alignment of Multiple Protein Networks

Kalaev, Maxim; Bafna, Vineet; Sharan, Roded

doi:10.1089/cmb.2009.0136

Cited by 65 publications

(28 citation statements)

References 21 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In case studies a) and b) we compared our method against three different global and local multiple network alignment algorithms: SMETANA [23], IsoRankN [22] and NetworkBLAST-M [19]. To our knowledge, the first two methods are the best global many-to-many aligners of two or more species, while NetworkBlast-M represents the state-of-the-art for the local alignment problem.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

GASOLINE: a Greedy And Stochastic algorithm for Optimal Local multiple alignment of Interaction NEtworks

et al. 2014

View full text Add to dashboard Cite

The analysis of structure and dynamics of biological networks plays a central role in understanding the intrinsic complexity of biological systems. Biological networks have been considered a suitable formalism to extend evolutionary and comparative biology. In this paper we present GASOLINE, an algorithm for multiple local network alignment based on statistical iterative sampling in connection to a greedy strategy. GASOLINE overcomes the limits of current approaches by producing biologically significant alignments within a feasible running time, even for very large input instances. The method has been extensively tested on a database of real and synthetic biological networks. A comprehensive comparison with state-of-the art algorithms clearly shows that GASOLINE yields the best results in terms of both reliability of alignments and running time on real biological networks and results comparable in terms of quality of alignments on synthetic networks. GASOLINE has been developed in Java, and is available, along with all the computed alignments, at the following URL: http://ferrolab.dmi.unict.it/gasoline/gasoline.html.

show abstract

Section: Resultsmentioning

confidence: 99%

“…We compared our system with a selected list of state-of-the-art methods such as SMETANA [23], IsoRank-N [22] and NetworkBlast-M [19]. The experimental results show that GASOLINE outperforms the compared systems yielding the best results in terms of both quality (i.e.…”

Section: Introductionmentioning

confidence: 99%

GASOLINE: a Greedy And Stochastic algorithm for Optimal Local multiple alignment of Interaction NEtworks

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Moreover, we did not use the newer implementations of NetworkBLAST and IsoRank (i.e. NetworkBLAST-M [51] and IsoRankN [52]), since they are intended for multiple network alignments, rather than pairwise comparisons.…”

Section: Methodsmentioning

confidence: 99%

A Novel Framework for the Comparative Analysis of Biological Networks

Pache

Aloy

2012

PLoS ONE

View full text Add to dashboard Cite

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes.

show abstract

“…The PathBlast family tools [9]–[12] are representative of heuristic approaches. NetworkBLAST-M [13], an improved version of NetworkBLAST [11], attempts to align two or more networks by greedily searching conserved regions. Graemlin 2.0 [14] considers phylogenetic relationships to infer a network, then optimizes the learned objective function.…”

Section: Introductionmentioning

confidence: 99%

“…Global algorithms compare entire node-and-edge structures among networks [7], [10], [14]–[16], [18], [20], whereas local algorithms identify local regions in networks that exhibit similar node and edge structures [9], [13]. HGA explores global algorithms to align a pair of networks.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Hybrid Algorithm for Global Network Alignment

Xie

Xiang

Jin

et al. 2016

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

It is challenging to obtain reliable and optimal mapping between networks for alignment algorithms when both nodal and topological structures are taken into consideration due to the underlying NP-hard problem. Here, we introduce an adaptive hybrid algorithm that combines the classical Hungarian algorithm and the Greedy algorithm (HGA) for the global alignment of biomolecular networks. With this hybrid algorithm, every pair of nodes with one in each network is first aligned based on node information (e.g., their sequence attributes) and then followed by an adaptive and convergent iteration procedure for aligning the topological connections in the networks. For four well-studied protein interaction networks, i.e., C.elegans, yeast, D.melanogaster and human, applications of HGA lead to improved alignments in acceptable running time. The mapping between yeast and human PINs obtained by the new algorithm has the largest value of common Gene Ontology (GO) terms compared to those obtained by other existing algorithms, while it still has lower Mean normalized entropy (MNE) and good performances on several other measures. Overall, the adaptive HGA is effective and capable of providing good mappings between aligned networks in which the biological properties of both the nodes and the connections are important.

show abstract

Fast and Accurate Alignment of Multiple Protein Networks

Cited by 65 publications

References 21 publications

GASOLINE: a Greedy And Stochastic algorithm for Optimal Local multiple alignment of Interaction NEtworks

GASOLINE: a Greedy And Stochastic algorithm for Optimal Local multiple alignment of Interaction NEtworks

A Novel Framework for the Comparative Analysis of Biological Networks

An Adaptive Hybrid Algorithm for Global Network Alignment

Contact Info

Product

Resources

About