cytoHubba: Identifying hub objects and sub-networks from complex interactome

Chia Hao Chin, Shu Hwa Chen, Hsin Hung Wu, Chin Wen Ho, Ming Tat Ko, Chung Yen Lin

研究成果: 雜誌貢獻期刊論文同行評審

1570 引文 斯高帕斯(Scopus)

摘要

Background: Network is a useful way for presenting many types of biological data including protein-protein interactions, gene regulations, cellular pathways, and signal transductions. We can measure nodes by their network features to infer their importance in the network, and it can help us identify central elements of biological networks. Results: We introduce a novel Cytoscape plugin cytoHubba for ranking nodes in a network by their network features. CytoHubba provides 11 topological analysis methods including Degree, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality and six centralities (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress) based on shortest paths. Among the eleven methods, the new proposed method, MCC, has a better performance on the precision of predicting essential proteins from the yeast PPI network. Conclusions:CytoHubba provide a user-friendly interface to explore important nodes in biological networks. It computes all eleven methods in one stop shopping way. Besides, researchers are able to combine cytoHubba with and other plugins into a novel analysis scheme. The network and sub-networks caught by this topological analysis strategy will lead to new insights on essential regulatory networks and protein drug targets for experimental biologists. According to cytoscape plugin download statistics, the accumulated number of cytoHubba is around 6,700 times since 2010.

原文???core.languages.en_GB???
文章編號S11
期刊BMC Systems Biology
8
發行號4
DOIs
出版狀態已出版 - 8 12月 2014

指紋

深入研究「cytoHubba: Identifying hub objects and sub-networks from complex interactome」主題。共同形成了獨特的指紋。

引用此