CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks

Yu Tang; Min Li; Jianxin Wang; Yi Pan; Fang-Xiang Wu

doi:10.1016/j.biosystems.2014.11.005

CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks

Biosystems. 2015 Jan:127:67-72. doi: 10.1016/j.biosystems.2014.11.005. Epub 2014 Nov 15.

Authors

Yu Tang¹, Min Li², Jianxin Wang³, Yi Pan⁴, Fang-Xiang Wu⁵

Affiliations

¹ School of Information Science and Engineering, Central South University, Changsha 410083, China.
² School of Information Science and Engineering, Central South University, Changsha 410083, China. Electronic address: limin@mail.csu.edu.cn.
³ School of Information Science and Engineering, Central South University, Changsha 410083, China. Electronic address: jxwang@mail.csu.edu.cn.
⁴ School of Information Science and Engineering, Central South University, Changsha 410083, China; Department of Computer Science, Georgia State University, Atlanta, GA 30302-4110, USA.
⁵ School of Information Science and Engineering, Central South University, Changsha 410083, China; Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.

PMID: 25451770
DOI: 10.1016/j.biosystems.2014.11.005

Abstract

Background and scope: 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.

Implementation and performance: (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.

Availability: http://apps.cytoscape.org/apps/cytonca.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology / methods*
Protein Interaction Mapping / methods*
Protein Interaction Maps / genetics*
Software*