Abstract
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.
Highlights
Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use
The clustering algorithm Structural Clustering Algorithm for Networks (SCAN) is used to identify functional modules based on the network structural similarity, and these modules are ranked according to their significance, i.e., the number of seed nodes, total number of nodes, or modularity score
We present three case studies below to demonstrate the utility of atBioNet in clinical applications: the differentiation of acute myeloid leukemia from acute lymphoblastic leukemia [39], diagnosis of systemic lupus erythematosus [40], and prognosis of breast cancer [41]
Summary
Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. 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. Network-based biomarkers have successfully been used for classification of metastatic versus non-metastatic tumors, and demonstrated higher reproducibility compared to individual marker genes identified by conventional approaches [4]. The common way to interpret and contextualize these biomarkers is with enrichment analysis using Gene Ontology [5], Kyoto Encyclopedia of Genes and Genomes (KEGG) [6] and other similar approaches. This type of analysis emphasizes the functional relationship of markers. The omics data can be interrogated based on their inherent connection and association in a network form
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