atBioNet

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

 

 

Overview

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)

 

Link for manual:

atBioNet Get Started:
Click here for the pdf manual.
 

Requirements:

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

 

References:

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. Article

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 Home 

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. Article
 

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.