Filtering and Interpreting Large-Scale Experimental Protein–Protein Interaction Data

Author(s):  
Gabriel Musso ◽  
Andrew Emili ◽  
Zhaolei Zhang
2005 ◽  
Vol 50 (20) ◽  
pp. 2267-2272 ◽  
Author(s):  
Jingchun Sun ◽  
Jinlin Xu ◽  
Yixue Li ◽  
Tieliu Shi

2007 ◽  
Vol 4 (1) ◽  
pp. 40-50 ◽  
Author(s):  
Gautam Chaurasia ◽  
Yasir Iqbal ◽  
Christian Hänig ◽  
Hanspeter Herzel ◽  
Erich E. Wanker ◽  
...  

Summary Protein-protein interactions constitute the backbone of many molecular processes. This has motivated the recent construction of several large-scale human protein-protein interaction maps [1-10]. Although these maps clearly offer a wealth of information, their use is challenging: complexity, rapid growth, and fragmentation of interaction data hamper their usability. To overcome these hurdles, we have developed a publicly accessible database termed UniHI (Unified Human Interactome) for integration of human protein-protein interaction data. This database is designed to provide biomedical researchers a common platform for exploring previously disconnected human interaction maps. UniHI offers researchers flexible integrated tools for accessing comprehensive information about the human interactome. Several features included in the UniHI allow users to perform various types of network-oriented and functional analysis. At present, UniHI contains over 160,000 distinct interactions between 17,000 unique proteins from ten major interaction maps derived by both computational and experimental approaches [1-10]. Here we describe the details of the implementation and maintenance of UniHI and discuss the challenges that have to be addressed for a successful integration of interaction data.


2017 ◽  
Vol 17 (1) ◽  
pp. 722-726 ◽  
Author(s):  
Devin K. Schweppe ◽  
Edward L. Huttlin ◽  
J. Wade Harper ◽  
Steven P. Gygi

2020 ◽  
Author(s):  
Diogo Borges Lima ◽  
Ying Zhu ◽  
Fan Liu

ABSTRACTSoftware tools that allow visualization and analysis of protein interaction networks are essential for studies in systems biology. One of the most popular network visualization tools in biology is Cytoscape, which offers a large selection of plugins for interpretation of protein interaction data. Chemical cross-linking coupled to mass spectrometry (XL-MS) is an increasingly important source for such interaction data, but there are currently no Cytoscape tools to analyze XL-MS results. In light of the suitability of Cytoscape platform but also to expand its toolbox, here we introduce XlinkCyNET, an open-source Cytoscape Java plugin for exploring large-scale XL-MS-based protein interaction networks. XlinkCyNET offers rapid and easy visualization of intra and intermolecular cross-links and the locations of protein domains in a rectangular bar style, allowing subdomain-level interrogation of the interaction network. XlinkCyNET is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/xlinkcynet and at https://www.theliulab.com/software/xlinkcynet.


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