3/16/2023 0 Comments Cytoscape citationAnalysis of these networks can, for example, identify subnetworks that are enriched in (but do not exclusively consist of) differentially expressed genes, or identify non-differentially expressed nodes that are topologically important in the network, both of which would otherwise not be identified. This approach to constructing a network is useful because it identifies a more fully connected network for analysis than would be the case if one restricted interactions to only those that occur between nodes in the gene list. differentially expressed genes, and then identify the high degree nodes in this network. To construct a network, users frequently query interaction databases to identify the interactors of a list of genes of interest, e.g. All of the applications available to date identify hubs based on node connectivity (degree) in a network of interest. Integrating contextual information, such as gene or protein expression data, with standard network analysis can provide insight into what are the most relevant network features in a particular study or context 9– 11.Ĭytoscape has a number of applications to identify hubs in networks including cytoHubba 12, APID2Net 13, PinnacleZ 14, NetworkAnalyzer 15, 16 and CentiScaPe 17, however, only the latter two are compatible with Cytoscape 3+. the network present in a specific cell type at a particular point in time 7, 8. Biological networks, such as the human interactome, however, are not static entities 6, and the extent to which a node acts as a hub can change depending on the biological context e.g. Hubs have also been found to be preferentially targeted by both bacterial and viral pathogens 4 and may be master regulators of biological processes 5. The deletion of genes encoding hub proteins, for example, has been shown to correlate with lethality in yeast (the centrality-lethality rule) 3. The identification of such highly connected nodes, termed hubs, is often of interest as hubs have been shown to be topologically and functionally important. Biological networks (and many other types of networks) have been shown to have a power law distribution of node connectivity, with most nodes having few connections and a few nodes being highly connected 2. Network analysis has emerged as a powerful approach to elucidate biological and disease processes 1. Availability: CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( ). This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. CHAT was used to identify and compare contextual and degree-based hubs in this network. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. genes or proteins that are differentially expressed) than expected by chance. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. The relative importance of a hub node, however, can change depending on the biological context. Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest.
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