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Staphylococcus aureus is an opportunistic human pathogen that can cause from minor skin infections to life-threatening invasive diseases. Especially, its notorious ability to adapt against antibiotics makes this bacterium as a global medical concern. Methicillin Resistant Staphylococcus aureus(MRSA) is a S. aureus which have resistant to methicillin.
StaphNet is a functional gene network model for the MRSA. The network is constructed based on the genome of Staphylococcus aureus subsp. USA300_FPR3757, a representative strain of MRSA. The network consist of seven component networks which are constructed by different genomics data. The integrated StaphNet contains 60,513 links among 2,674 USA300 genes.
In this StaphNet web server, users can download the StaphNet, component networks, and gold-standard.
Furthermore, we provide two network analysis algorithms to enable users with utilizing the StaphNet to generate various biological hypotheses. Pathway-Centric Search algorithm is an efficient tool for searching new candidate genes of the pathway of interest. When a user submits genes for a pathway, they are used as guide genes to predict new candidate gene for the pathway. By propagating the information of guide genes to the neighboring genes, the result suggests promising candidate genes for the pathway as well as their ranks and scores.
To find potential regulators of a context, Context-Centric Search algorithm require Deferentially Expressed genes (DEGs) in the context. When users submit the Differentially Expressed Genes (DEGs) of a certain condition, the algorithm finds hub genes (degree ≥ 20) which neighbors are significantly overlapped with the input DEGs. Thus, the Context-Centric Search algorithm result propose hub regulator genes that are likely to modulate the expression profile of the given context.
Kim CY, Lee M, Lee K, Yoon SS, Lee I, Network-based genetic investigation of virulence-associated phenotypes in methicillin-resistant Staphylococcus aureus, Scientific Reports 2018 July 17; 8:10796
Insuk Lee (insuklee (at) yonsei.ac.kr)
