AlignNemo: A Local Network Alignment Method to Integrate Homology and Topology

Ciriello, Giovanni; Mina, Marco; Guzzi, Pietro Hiram; Cannataro, Mario; Guerra, Concettina

doi:10.1371/journal.pone.0038107

Cited by 102 publications

(56 citation statements)

References 27 publications

(31 reference statements)

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“…In the original paper of AlignNemo [18], a reference complex is considered to be recovered if at least two of its proteins overlap with a detected cluster, which may introduce the evaluation bias. Imagine that if one cluster contains 10 proteins, with every two belonging to a different reference complex.…”

Section: Methodsmentioning

confidence: 99%

Joint clustering of protein interaction networks through Markov random walk

Wang

Qian

2014

BMC Systems Biology

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Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization problem is formulated by searching for low conductance sets defined on the derived transition matrix of the random walk, which fuses both topology and homology information. The optimization problem of joint clustering is solved by a derived spectral clustering algorithm. Network clustering using several state-of-the-art algorithms has been implemented to both PPI networks within the same species (two yeast PPI networks and two human PPI networks) and those from different species (a yeast PPI network and a human PPI network). Experimental results demonstrate that ASModel outperforms the existing single network clustering algorithms as well as another recent joint clustering algorithm in terms of complex prediction and Gene Ontology (GO) enrichment analysis.

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Section: Methodsmentioning

confidence: 99%

Joint clustering of protein interaction networks through Markov random walk

Wang

Qian

2014

BMC Systems Biology

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“…We tested four previous network alignment algorithms: NetworkBlast [14], MI-GRAAL [17], AlignNemo [18], and PINALOG [19]. However, PINALOG did not work in our experiments because its tool used CFinder to detect modules, which resulted in a memory error with our large data sets.…”

Section: Efficiency Comparison Of Network Alignment Algorithmsmentioning

confidence: 99%

“…MI-GRAAL [17] uses multiple sources of protein similarity information, without any parameters, to form a global network alignment. AlignNemo [18] provides a general framework for local network alignment to predict protein complexes. PINALOG [19] decomposes a PPI network into a set of modules by a graph clustering algorithm, CFinder [20], and weights them by a scoring scheme which integrates sequence similarity and semantic similarity presented in [21].…”

Section: Introductionmentioning

confidence: 99%

Alignment of PPI Networks Using Semantic Similarity for Conserved Protein Complex Prediction

Shui

Cho

2016

IEEE Trans.on Nanobioscience

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Network alignment is a computational technique to identify topological similarity of graph data by mapping link patterns. In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionarily conserved substructures at the system level. In particular, local network alignment of PPI networks searches for conserved functional components between species and predicts unknown protein complexes and signaling pathways. In this article, we present a novel approach of local network alignment by semantic mapping. While most previous methods find protein matches between species by sequence homology, our approach uses semantic similarity. Given Gene Ontology (GO) and its annotation data, we estimate functional closeness between two proteins by measuring their semantic similarity. We adopted a new semantic similarity measure, simVICD, which has the best performance for PPI validation and functional match. We tested alignment between the PPI networks of well-studied yeast protein complexes and the genome-wide PPI network of human in order to predict human protein complexes. The experimental results demonstrate that our approach has higher accuracy in protein complex prediction than graph clustering algorithms, and higher efficiency than previous network alignment algorithms.

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“…On the other hand, due to the huge extension of PINs, such tools have to face both performance and graphical challenges. The availability of semantic annotations of nodes and edges poses further issues in PINs visualization [3]. …”

Section: Introductionmentioning

confidence: 99%

Visualization of protein interaction networks: problems and solutions

2013

Self Cite

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BackgroundVisualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI) are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN) and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins) and edges (interactions), the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology) that enriches the PINs with semantic information, but complicates their visualization.MethodsIn these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i) technology, i.e. availability/license of the software and supported OS (Operating System) platforms; (ii) interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii) visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv) analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the possibility to interact with external databases.ResultsCurrently, many tools are available and it is not easy for the users choosing one of them. Some tools offer sophisticated 2D and 3D network visualization making available many layout algorithms, others tools are more data-oriented and support integration of interaction data coming from different sources and data annotation. Finally, some specialistic tools are dedicated to the analysis of pathways and cellular processes and are oriented toward systems biology studies, where the dynamic aspects of the processes being studied are central.ConclusionA current trend is the deployment of open, extensible visualization tools (e.g. Cytoscape), that may be incrementally enriched by the interactomics community with novel and more powerful functions for PIN analysis, through the development of plug-ins. On the other hand, another emerging trend regards the efficient and parallel implementation of the visualization engine that may provide high interactivity and near real-time response time, as in NAViGaTOR. From a technological point...

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AlignNemo: A Local Network Alignment Method to Integrate Homology and Topology

Cited by 102 publications

References 27 publications

Joint clustering of protein interaction networks through Markov random walk

Joint clustering of protein interaction networks through Markov random walk

Alignment of PPI Networks Using Semantic Similarity for Conserved Protein Complex Prediction

Visualization of protein interaction networks: problems and solutions

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