Determining modular organization of protein interaction networks by maximizing modularity density

Zhang, Shihua; Ning, Xue-Mei; Ding, Chris; Xia, Yu

doi:10.1186/1752-0509-4-s2-s10

Cited by 39 publications

(23 citation statements)

References 29 publications

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“…Other works [18] use SA to highlight the biological modules of the protein interaction networks by maximizing some quantitative measures such the modularity density, that is a measure for describing the modular organization of a network.…”

Section: Methodsmentioning

confidence: 99%

Visualization of protein interaction networks: problems and solutions

2013

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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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Section: Methodsmentioning

confidence: 99%

Visualization of protein interaction networks: problems and solutions

2013

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“…Complex networks from various fields clearly display community structure, meaning that the networks consist of communities or modules, i.e., groups of vertices that are densely interconnected while only sparsely connected with the rest of the network [1]. Detecting such community structures in the networks is of considerable importance for understanding the organization structures and functions of the networks [1][2][3]. In recent years, various methods for community detection in complex networks have been proposed and analyzed [4][5][6][7][8][9][10][11][12] (see refs.…”

Section: Introductionmentioning

confidence: 99%

Analysis of community-detection methods based on Potts spin model in complex networks

et al. 2015

Can. J. Phys.

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Detection of community structures in complex networks is a common challenge in the study of complex networks. Recently, various methods have been proposed to discover community structures at different scales. Here, the multiscale methods based on Potts spin model for community detection are described and compared in the analysis of community structures of several networks. We give a critical analysis of the multiscale methods, showing a kind of limitation that the methods may suffer from when the community size difference is very broad, the breakup of (large) communities will appear before the merger of (small) communities disappears. In particular, we give the explicit expressions for the critical points of the merger and breakup of communities and derive the sufficient conditions (in the form of upper limits) that indicate when the Potts model methods suffer from the limitation. We apply the theoretical results to model networks and show that the method using the configuration null model (i.e., a random graph model as comparison that has the same degree distribution as the network under study) may not recover the full structure of the model network, whereas the method using the Erdös-Rényi null model will do so. PACS Nos.: 89.75.Hc, 05.10.-a, 89.75.Fb.Résumé : La détection de structures de communautés des réseaux complexes est un défi fréquent dans l'étude de ces réseaux complexes. Récemment, différentes méthodes ont été proposées pour la structure de communautés des réseaux à différentes échelles. Nous décrivons ici les méthodes multi-échelles basées sur le modèle de spin de Potts pour détecter les communautés et les comparons dans l'analyse des structures de communautés de plusieurs réseaux. Nous offrons une analyse critique des méthodes multi-échelles, montrant une sorte de limitation dont peuvent souffrir ces méthodes lorsque la différence en grosseur des communautés devient grande -l'éclatement des grandes communautés apparaitra avant que la réunion des petites communautés disparaisse -. Plus précisément, nous donnons des expressions explicites pour les points de réunion et d'éclatement des communautés et nous dérivons les conditions suffisantes (sous la forme de limites supérieures) qui indiquent quand les méthodes basées sur le modèle de Potts souffrent de cette limitation. Nous appliquons ces résultats théoriques pour modéliser des réseaux et montrons que la méthode utilisant le modèle nul de configuration (i.e. un modèle à graphes aléatoires comme comparaison qui a le même distribution de degrés que le réseau étudié) peut ne pas pouvoir recouvrer la pleine structure du réseau modèle, alors que la méthode utilisant le modèle nul de Erdös-Rényi le peut. [Traduit par le Rédaction]

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“…Many complex networks, including social, ecological, biological and technological networks, display community structures, meaning the existence of groups of vertices within which connections are dense while between which they are sparser [1]. Detecting such communities in the networks can provide a useful coarse-grained representation of various complex networks, and will help in understanding the structure and function of the networks, such as the functional modules in the protein-protein interaction networks and the cycles and pathways in metabolic networks [1,2]. In past decade, many community detection methods have been proposed based on various approaches, such as similarity measures [3], random walk dynamics [4,5], label propagation [6][7][8], statistical models [9,10] and so on (see refs [1,11,12] for reviews).…”

Section: Introductionmentioning

confidence: 99%

Local modularity for community detection in complex networks

Zhang

et al. 2016

Physica A: Statistical Mechanics and its Applications

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Determining modular organization of protein interaction networks by maximizing modularity density

Cited by 39 publications

References 29 publications

Visualization of protein interaction networks: problems and solutions

Visualization of protein interaction networks: problems and solutions

Analysis of community-detection methods based on Potts spin model in complex networks

Local modularity for community detection in complex networks

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