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

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).

[1]  Karin Breuer,et al.  InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation , 2012, Nucleic Acids Res..

[2]  Fausto Spoto,et al.  Biological network analysis with CentiScaPe: centralities and experimental dataset integration , 2014, F1000Research.

[3]  Nadezhda T. Doncheva,et al.  Topological analysis and interactive visualization of biological networks and protein structures , 2012, Nature Protocols.

[4]  William Stafford Noble,et al.  How does multiple testing correction work? , 2009, Nature Biotechnology.

[5]  Jianfeng Dai,et al.  ISG15 facilitates cellular antiviral response to dengue and west nile virus infection in vitro , 2011, Virology Journal.

[6]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[7]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[8]  Charlotte M. Deane,et al.  Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks , 2009, PLoS Comput. Biol..

[9]  Carlos Prieto,et al.  APID2NET: unified interactome graphic analyzer , 2007, Bioinform..

[10]  Niall J. Lennon,et al.  The Early Whole-Blood Transcriptional Signature of Dengue Virus and Features Associated with Progression to Dengue Shock Syndrome in Vietnamese Children and Young Adults , 2010, Journal of Virology.

[11]  Simon Kasif,et al.  Biological context networks: a mosaic view of the interactome , 2006, Molecular systems biology.

[12]  T. Ideker,et al.  Network-based classification of breast cancer metastasis , 2007, Molecular systems biology.

[13]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[14]  J. Schwartz,et al.  PhenomeExpress: A refined network analysis of expression datasets by inclusion of known disease phenotypes , 2015, Scientific Reports.

[15]  Mark Gerstein,et al.  Target hub proteins serve as master regulators of development in yeast. , 2006, Genes & development.

[16]  I. López-Martínez,et al.  A strong interferon response correlates with a milder dengue clinical condition. , 2014, Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology.

[17]  G. Dimopoulos,et al.  An evolutionary conserved function of the JAK-STAT pathway in anti-dengue defense , 2009, Proceedings of the National Academy of Sciences.

[18]  Fidel Ramírez,et al.  Computing topological parameters of biological networks , 2008, Bioinform..

[19]  Gary D Bader,et al.  PSICQUIC and PSISCORE: accessing and scoring molecular interactions , 2011, Nature Methods.

[20]  Kong-Peng Lam,et al.  RIG-I, MDA5 and TLR3 Synergistically Play an Important Role in Restriction of Dengue Virus Infection , 2011, PLoS neglected tropical diseases.

[21]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[22]  Xujing Wang,et al.  Identification of highly synchronized subnetworks from gene expression data , 2013, BMC Bioinformatics.

[23]  Matthew D. Dyer,et al.  The Landscape of Human Proteins Interacting with Viruses and Other Pathogens , 2008, PLoS pathogens.

[24]  Chung-Yen Lin,et al.  cytoHubba: identifying hub objects and sub-networks from complex interactome , 2014, BMC Systems Biology.

[25]  Ziv Bar-Joseph,et al.  ModuleBlast: identifying activated sub-networks within and across species , 2014, Nucleic acids research.

[26]  A. García-Sastre,et al.  STAT2 signaling and dengue virus infection , 2014, JAK-STAT.

[27]  Mona Singh,et al.  Toward the dynamic interactome: it's about time , 2010, Briefings Bioinform..