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

Yijun Ding1, Minjun Chen2, Zhichao Liu1, Don Ding1, Yanbin Ye1, Min Zhang23, Reagan Kelly1, Li Guo4, Zhenqiang Su1, Stephen C Harris2, Feng Qian1, Weigong Ge2, Hong Fang1*, Xiaowei Xu25* and Weida Tong2*

Author Affiliations

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

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

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

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

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

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BMC Genomics 2012, 13:325  doi:10.1186/1471-2164-13-325

Published: 20 July 2012



Large 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.


atBioNet 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.


atBioNet 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: webcite.

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