atBioNet– an integrated network analysis tool for genomics and biomarker discovery

BMC Genomics

Software

Weida Tong¹  Stephen C Harris¹  Weigong Ge¹  Minjun Chen¹  Xiaowei Xu²  Min Zhang³  Yijun Ding⁴  Zhichao Liu⁴  Yanbin Ye⁴  Hong Fang⁴  Feng Qian⁴  Reagan Kelly⁴  Don Ding⁴  Zhenqiang Su⁴  Li Guo⁵ 

[1]Divisions of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, 72079, Jefferson, AR, USA

[2]Divisions of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, 72079, Jefferson, AR, USA

[3]Department of Information Science, University of Arkansas at Little Rock, 2801 S. University Ave., 72204-1099, Little Rock, AR, USA

[4]Divisions of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, 72079, Jefferson, AR, USA

[5]Department of Lymphoma and Myeloma, University of Texas M D Anderson Cancer Center, 77054, Houston, TX, USA

[6]ICF International at FDA's National Center for Toxicological Research, 3900 NCTR Rd, 72079, Jefferson, AR, USA

[7]State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, 100190, Beijing, P. R. China

关键词: Protein-protein interaction;    Network analysis;    Functional module;    Disease biomarker;    KEGG pathway analysis;    Visualization tool;    Genomics;   

DOI  :  10.1186/1471-2164-13-325

 received in 2012-02-08, accepted in 2012-07-09,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundLarge amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity.ResultsatBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis.ConclusionatBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

【 授权许可】

Unknown   
© Ding et al.; licensee BioMed Central Ltd. 2012. 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/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.