Computing topological parameters of biological networks

By Yassen Assenov, Fidel Ramírez, Sven-Eric Schelhorn, Thomas Lengauer, Mario Albrecht. Bioinform., 2008

1,731 citations recorded by the source corpus.

UNLABELLED Rapidly increasing amounts of molecular interaction data are being produced by various experimental techniques and computational prediction methods. In order to gain insight into the organization and structure of the resultant large complex networks formed by the interacting molecules, we have developed the versatile Cytoscape plugin NetworkAnalyzer. It computes and displays a comprehensive set of topological parameters, which includes the number of nodes, edges, and connected components, the network diameter, radius, density, centralization, heterogeneity, and clustering coefficient, the characteristic path length, and the distributions of node degrees, neighborhood connectivities, average clustering coefficients, and shortest path lengths. NetworkAnalyzer can be applied to both directed and undirected networks and also contains extra functionality to construct the intersection or union of two networks. It is an interactive and highly customizable application that requires no expert knowledge in graph theory from the user. AVAILABILITY NetworkAnalyzer can be downloaded via the Cytoscape web site: http://www.cytoscape.org

Subfields: Geometry and Topology, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/acfd9ea27a4183cc6ae1d74998e2e1e0c9e98093

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Citations in the corpus

A travel guide to Cytoscape plugins (2012, methodology): “The NetworkAnalyzer 23 plugin is installed in Cytoscape by default and calculates network metrics such as the distribution of node degrees (node degree refers to the number of interactions involving a node; it has been shown to correlate with the essential status of genes 24 ).”
Comparative analysis of the microbial communities in agricultural soil amended with enhanced biochars or traditional fertilisers (2014, methodology): “NetworkAnalyzer was used to analyse the resulting network in terms of topological parameters (Assenov et al., 2008).”
cytoHubba: identifying hub objects and sub-networks from complex interactome (2014, background): “For example, NetworkAnalyzer [3] and CentiScaPe [4] computes various topological network parameters for undirected and/ or directed networks.”
Maximum Entropy Reconstructions of Dynamic Signaling Networks from Quantitative Proteomics Data (2009, methodology): “Topological properties such as the mean number of nearest neighbors and the average clustering coefficients were computed using the NetworkAnalyzer[30] plugin in the Cytoscape package.”
CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks (2015, methodology): “Based on this powerful general-purpose platform, plenty of centrality tools have been developed, such as CentiLib (Gräßler et al., 2012) and NetworkAnalyzer (Assenov et al., 2008).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “The RINalyzer program was also linked to the NetworkAnalyzer software (http://med.bioinf.mpi-inf.mpg.de/netanalyzer; Assenov et al., 2008) allowing the comparison of protein structure networks and the extension of their analysis to protein–protein interaction networks (Doncheva et al., 2011, 2012b).”
Topological analysis and interactive visualization of biological networks and protein structures (2012, methodology): “Here we demonstrate how to apply two of our Cytoscape plug-ins, NetworkAnalyzer 38 and RINalyzer 39 , for the standard and advanced analysis of network topologies.”
TCMSP: a database of systems pharmacology for drug discovery from herbal medicines (2014, methodology): “Further topological analysis can be implemented with the NetworkAnalyzer [30] plugin in Cytoscape software [31].”