Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks

Abstract

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. The relative importance of a hub node, however, can change depending on the biological context. 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. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. 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. 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. 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. Availability: CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat).

Insights from the protein-protein interaction network analysis of Mycobacterium tuberculosis toxinantitoxin systems

“…Identification of hub proteins can be carried out by in silico tools such as Hubba, cytohubba, and CHAT etc. [ 26 , 27 ]. In the present study, cytohubba was used to explore the hub proteins of Mtb TA systems PPI network.…”

Identification of the iduronate-2-sulfatase proteome in wild-type mouse brain

“…Moreover, 31 hubs were predicted, which were mainly involved in redox protection (e.g., peroxiredoxins (Prxs) family) and protein transport (e.g., multifunctional Ywha genes and different molecular chaperons) (Table 3). These hubs might represent targets for functional analysis, since are topologically and functionally important for biological networks and have been considered as a powerful approach to study biological process in pathological conditions [26, 47].…”

“…We predicted contextualized hubs from wild-type complexes that could be functionally relevant in different biological processes [26] such as dynein and peroxiredoxin-1 (Prdx1 ) for HMC ( Table 4). Cytoplasmic dynein (DnyC1h1) is a microtubule motor protein involved in a wide spectrum of functions including axogenesis, cell migration, macromolecular and vesicular transport [69].…”

“…We applied Contextual Hub Analysis Tool (CHAT) to identify hub genes based on their (weighted) connectivity scores. Genes with statistically detectable connectivity (adjusted p < 0.05) were defined as hubs [26].…”

Using the Contextual Hub Analysis Tool (CHAT) in Cytoscape to Identify Contextually Relevant Network Hubs

“…Networks have been observed to have a power-law distribution whereby many nodes have few connections and few nodes have many connections (Barabási & Oltvai, 2004). We have recently released a new Cytoscape 3 application called the Contextual Hub Analysis Tool (CHAT; Muetze et al, 2016), which identifies hub nodes that interact with more "contextual" nodes (e.g., differentially expressed genes or proteins) than statistically expected. To construct a network, researchers frequently query interaction databases to identify the interactors of a list of genes of interest, e.g., differentially expressed genes, and then identify the high degree nodes in this network.…”