Muetze Tanja, Goenawan Ivan H, Wiencko Heather L, Bernal-Llinares Manuel, Bryan Kenneth, Lynn David J
EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia.
Animal and Bioscience Research Department, Animal and Grassland Research and Innovation Centre, Teagasc, Meath, Ireland.
F1000Res. 2016 Jul 19;5:1745. doi: 10.12688/f1000research.9118.2. eCollection 2016.
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 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.
CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat).
生物网络中高度连接的节点(枢纽节点)对网络结构具有重要的拓扑意义,并且已被证明与一系列感兴趣的表型优先相关。然而,枢纽节点的相对重要性可能会根据生物学背景而改变。在这里,我们报告了一个Cytoscape应用程序,即上下文枢纽分析工具(CHAT),它使用户能够轻松地从感兴趣的基因或蛋白质列表构建和可视化相互作用网络,整合上下文信息,如基因表达或质谱数据,并识别与上下文节点(例如差异表达的基因或蛋白质)连接程度高于随机预期的枢纽节点。在一个案例研究中,我们使用CHAT构建了登革热(一种病毒感染)中差异表达基因的网络。CHAT用于识别和比较该网络中基于上下文和基于度数的枢纽节点。前20个基于度数的枢纽节点在与细胞周期和癌症相关的通路中富集,这可能是由于参与这些过程的蛋白质通常倾向于高度连接。相比之下,前20个上下文枢纽节点在病毒感染中常见的通路中富集,包括与病毒感染免疫反应相关的通路。该分析表明,此类枢纽节点在生物学上比基于度数的枢纽节点更具相关性,并且仅基于连接性识别枢纽节点的分析可能偏向于一般高度连接而非在感兴趣的特定背景下高度连接的节点。
CHAT适用于Cytoscape 3.0+,可通过Cytoscape应用商店(http://apps.cytoscape.org/apps/chat)安装。