NOA: a novel Network Ontology Analysis method

NOA: a novel Network Ontology Analysis method
Jiguang Wang1, Qiang Huang1, Zhi-Ping Liu2, Yong Wang1, Ling-Yun Wu1, Luonan Chen2,3,* and Xiang-Sun Zhang1,*
+ Author Affiliations

1Key Laboratory of Management, Decision and Information Systems, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, 2Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200233, China and 3Collaborative Research Center for Innovative Mathematical Modelling, Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan
*To whom correspondence should be addressed. Tel: +86-21-6436-5937; Fax: +86-21-5497-2551; Email: lnchen@sibs.ac.cn
Correspondence may also be addressed to Xiang-Sun Zhang. Tel: +86-10-6256-1440; Fax: +86-10-6256-1963; Email: zxs@amt.ac.cn
Received December 13, 2010.
Revision received April 3, 2011.
Accepted April 5, 2011.
Abstract

Gene ontology analysis has become a popular and important tool in bioinformatics study, and current ontology analyses are mainly conducted in individual gene or a gene list. However, recent molecular network analysis reveals that the same list of genes with different interactions may perform different functions. Therefore, it is necessary to consider molecular interactions to correctly and specifically annotate biological networks. Here, we propose a novel Network Ontology Analysis (NOA) method to perform gene ontology enrichment analysis on biological networks. Specifically, NOA first defines link ontology that assigns functions to interactions based on the known annotations of joint genes via optimizing two novel indexes ‘Coverage’ and ‘Diversity’. Then, NOA generates two alternative reference sets to statistically rank the enriched functional terms for a given biological network. We compare NOA with traditional enrichment analysis methods in several biological networks, and find that: (i) NOA can capture the change of functions not only in dynamic transcription regulatory networks but also in rewiring protein interaction networks while the traditional methods cannot and (ii) NOA can find more relevant and specific functions than traditional methods in different types of static networks. Furthermore, a freely accessible web server for NOA has been developed at http://www.aporc.org/noa/.