Tang Yu, Li Min, Wang Jianxin, Pan Yi, Wu Fang-Xiang
School of Information Science and Engineering, Central South University, Changsha 410083, China.
School of Information Science and Engineering, Central South University, Changsha 410083, China.
Biosystems. 2015 Jan;127:67-72. doi: 10.1016/j.biosystems.2014.11.005. Epub 2014 Nov 15.
Nowadays, centrality analysis has become a principal method for identifying essential proteins in biological networks. Here we present CytoNCA, a Cytoscape plugin integrating calculation, evaluation and visualization analysis for multiple centrality measures.
(i) CytoNCA supports eight different centrality measures and each can be applied to both weighted and unweighted biological networks. (ii) It allows users to upload biological information of both nodes and edges in the network, to integrate biological data with topological data to detect specific nodes. (iii) CytoNCA offers multiple potent visualization analysis modules, which generate various forms of output such as graph, table, and chart, and analyze associations among all measures. (iv) It can be utilized to quantitatively assess the calculation results, and evaluate the accuracy by statistical measures. (v) Besides current eight centrality measures, the biological characters from other sources could also be analyzed and assessed by CytoNCA. This makes CytoNCA an excellent tool for calculating centrality, evaluating and visualizing biological networks.
如今,中心性分析已成为识别生物网络中关键蛋白质的主要方法。在此,我们介绍CytoNCA,这是一款Cytoscape插件,集成了多种中心性度量的计算、评估和可视化分析功能。
(i)CytoNCA支持八种不同的中心性度量,每种度量均可应用于加权和非加权生物网络。(ii)它允许用户上传网络中节点和边的生物学信息,将生物学数据与拓扑数据整合以检测特定节点。(iii)CytoNCA提供多个强大的可视化分析模块,可生成各种形式的输出,如图形、表格和图表,并分析所有度量之间的关联。(iv)它可用于定量评估计算结果,并通过统计方法评估准确性。(v)除了当前的八种中心性度量外,CytoNCA还可分析和评估来自其他来源的生物学特征。这使得CytoNCA成为计算中心性、评估和可视化生物网络的出色工具。