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

• Published in: BMC genomics Vol. 13; no. 1; p. 325
• Main Authors: Ding, Yijun, Chen, Minjun, Liu, Zhichao, Ding, Don, Ye, Yanbin, Zhang, Min, Kelly, Reagan, Guo, Li, Su, Zhenqiang, Harris, Stephen C, Qian, Feng, Ge, Weigong, Fang, Hong, Xu, Xiaowei, Tong, Weida
• Format: Journal Article
• Language: English
• Published: London BioMed Central 20.07.2012; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Animal Genetics and Genomics; biomarkers; Biomarkers - metabolism; Biomedical and Life Sciences; Breast cancer; Case studies; Cluster Analysis; Computational biology; Computer programs; Data processing; Databases, Protein; Disease biomarker; Functional module; Genomics; Humans; Integration; Interactomes; KEGG pathway analysis; Leukemia; Life Sciences; Medical research; Medicine, Experimental; Metabolic Networks and Pathways; Microarrays; Microbial Genetics and Genomics; Network analysis; Network management software; Plant Genetics and Genomics; Protein interaction; Protein Interaction Maps; Protein-protein interaction; Protein-protein interactions; Proteomics; Software; Statistical analysis; Systemic lupus erythematosus; Toxicity; Transcriptomic methods; Type 2 diabetes; User-Computer Interface; Visualization tool; United States; Disease biomarker; Protein-protein interaction; Functional module; Network analysis; Genomics; KEGG pathway analysis; Visualization tool
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-13-325

Background 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. Results 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. Conclusion 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: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm .