Identifying essential proteins from active PPI networks constructed with dynamic gene expression

BMC Genomics

Proceedings

Jianxin Wang¹  Xiaoqing Peng¹  Yi Pan²  Fang-xiang Wu³  Qianghua Xiao⁴ 

[1] School of Information Science and Engineering, Central South University, 410083, Changsha, China;School of Information Science and Engineering, Central South University, 410083, Changsha, China;Department of Computer Science, Georgia State University, 30302-4110, Atlanta, USA;School of Information Science and Engineering, Central South University, 410083, Changsha, China;Division of Biomedical Engineering, University of Saskatchewan, S7N 5A9 SK, Saskatoon, Canada;School of Information Science and Engineering, Central South University, 410083, Changsha, China;School of Mathematics and Physics, University of South China, 421001, HengYang, China;

关键词: Essential proteins;    Protein-protein interaction;    Dynamic gene expression profiles;    Active protein-protein interaction networks;    Centrality measures;   

DOI  :  10.1186/1471-2164-16-S3-S1

来源: Springer

【 摘 要 】

Essential proteins are vitally important for cellular survival and development, and identifying essential proteins is very meaningful research work in the post-genome era. Rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein essentiality at the network level. A series of centrality measures have been proposed to discover essential proteins based on the PPI networks. However, the PPI data obtained from large scale, high-throughput experiments generally contain false positives. It is insufficient to use original PPI data to identify essential proteins. How to improve the accuracy, has become the focus of identifying essential proteins. In this paper, we proposed a framework for identifying essential proteins from active PPI networks constructed with dynamic gene expression. Firstly, we process the dynamic gene expression profiles by using time-dependent model and time-independent model. Secondly, we construct an active PPI network based on co-expressed genes. Lastly, we apply six classical centrality measures in the active PPI network. For the purpose of comparison, other prediction methods are also performed to identify essential proteins based on the active PPI network. The experimental results on yeast network show that identifying essential proteins based on the active PPI network can improve the performance of centrality measures considerably in terms of the number of identified essential proteins and identification accuracy. At the same time, the results also indicate that most of essential proteins are active.

【 授权许可】

Unknown   
© Xiao et al.; licensee BioMed Central Ltd. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.