FCCC LOGO Faculty Publications
Liu H , Beck TN , Golemis EA , Serebriiskii IG
Integrating in silico resources to map a signaling network
Methods Mol Biol. 2014 ;1101 :197-245
PMID: 24233784    PMCID: PMC3831179   
Back to previous list
The abundance of publicly available life science databases offers a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and we discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol for building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature.
Liu, Hanqing Beck, Tim N Golemis, Erica A Serebriiskii, Ilya G P30 CA006927/CA/NCI NIH HHS/United States P50 CA083638/CA/NCI NIH HHS/United States R01 CA063366/CA/NCI NIH HHS/United States U54 CA149147/CA/NCI NIH HHS/United States United States Methods Mol Biol. 2014;1101:197-245. doi: 10.1007/978-1-62703-721-1_11.