Science & Research


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 atBioNet - an Integrated Network Analysis Tool for
Knowledge Expansion, Network Modeling and Visualization
in Genomics and Proteomics



atBioNet is a free, user friendly, web-based network analysis tool for analyzing, visualizing, and interpreting genomics or proteomics data. The user supplies atBioNet with a list of proteins or genes, and atBioNet then creates an interactive graphical network model that can identify key functional modules. Pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG) is directly integrated within atBioNet for enrichment analysis and assessment of the biological meaning of modules.

atBioNet database, network modeling and function model identification

atBioNet integrates seven publicly available protein-protein interaction (PPI) databases for its knowledge base1. Interactions from this integrated PPI network are used to expand a user’s protein/gene list into a relevant network. Then, functional modules are detected by applying a fast network clustering algorithm2. The top six functional modules can be visualized either separately or in the context of the whole network3. Detailed statistics about the network are available to the user. Two example cases built using publicly available gene signatures, including acute lymphoblastic leukemia4 and systemic lupus erythematosus5, are provided to assists users in familiarizing themselves with the tool.

Link for software:

FDA version:
Launch atBioNet (FDA)

non-FDA version:
Launch atBioNet (non-FDA)

Citation - Please cite the following for publications that incorporate analysis using atBioNet:
Yijun Ding, Minjun Chen, Zhichao Liu, Don Ding, Yanbin Ye, Min Zhang, Reagan Kelly, Li Guo, Zhenqiang Su, Stephen C Harris, Feng Qian, Weigong Ge, Hong Fang, Xiaowei Xu, Weida Tong, atBioNet– an Integrated Network Analysis Tool for Genomics and Biomarker Discovery. BMC Genomics, 2012, 13:325 icon

Link for manual:

atBioNet Get Started:
Click here for the pdf manual.


Operating system(s): Platform independent; tested on Windows XP/Vista/7, Linux/Ubuntu/Redhat, and Mac (with an Intel core2 duo or better)
Programming language: Java
Other requirements: Java 1.6 or higher, 2 GB RAM
License: None required


1. Martha V-S, Liu Z, Guo L, Su Z, Ye Y, Fang H, Ding D, Tong W, Xu X: Constructing a robust protein-protein interaction network by integrating multiple public databases. BMC Bioinformatics 2011, 12(Suppl 10):S7. Articledisclaimer icon

2. Xu X, Yuruk N, Feng Z, Schweiger T: SCAN: a structural clustering algorithm for networks. In: In Proceedings of the 13th ACM SIGKDD international conference on Knowledge Discovery and Data Mining. San Jose, California, USA; 2007: 824-833.

3. A Adar E: GUESS: a language and interface for graph exploration. In: CHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems. New York, NY, USA: ACM; 2006: 791--800. GUESS Homedisclaimer icon 

4. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA et al: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286(5439):531-537.

5. Arasappan D, Tong W, Mummaneni P, Fang H, Amur S: Meta-analysis of microarray data using a pathway-based approach identifies a 37-gene expression signature for systemic lupus erythematosus in human peripheral blood mononuclear cells. BMC Med 2011, 9:65. Articledisclaimer icon

Contact Information:

For questions or suggestions, please contact Hong Fang, Ph.D., FDA/NCTR at 870-543-7538 or by email.

To report technical problems or get assistance, please contact NCTR Bioinformatic Support.

Contact FDA

NCTR Bioinformatics Support

Food and Drug Administration

3900 NCTR Road

Jefferson, AR 72079

Page Last Updated: 02/08/2017
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