Centiscape available for Cytoscape 3.0!

[giovanni scardoni]

The CentiScaPe team at CBMC, University of Verona, Italy, is pleased
to announce the release of the CentiScaPe 2.0 app for Cytoscape 3.0!
(see more at http://www.cbmc.it/~scardonig/).
You can install the app through the Cytoscape application’s Plugin Manager
or from cytoscape.org.

Previous versions of CentiScaPe reached more than 7.000 downloads and was cited in

more than 60 articles in international journals.

About CentiScaPe

Centiscape computes specific centrality parameters describing the network topology.
The centrality parameters aid the users to find the most significant nodes in a complex network.
The plugin computation generates both numerical and graphical output making easy to find the key
nodes also in large networks. Network topological quantification can be combined with other 

numerical node attributes to provide biologically meaningful node identification and functional 

classification. CentiScaPe computes:

Network parameters:

Diameter
Average Distance

Node parameters and their min, max and average values:

Eccentricity
Closeness
Betweenness
Stress
Centroid
Radiality
Degree

Potential application

Complex biological networks, such as intracellular signaling networks, are modeled by the evolution 

to accomplish a variety of different regulatory functions. This is achieved by controlling the overall 

topology of the network which, then, affects its dynamic behavior. Biological networks are hierarchichal, 

scale-free, modular structure in which few nodes, the hubs, play a particularly relevant topological role 

and this may reflect a critical role at biological level. However, also nodes with no or lower hub role 

may have critical regulatory role in certain biological phenomena. This could reflect node-specific 

topological properties. Thus, it is of interest to categorize every nodes in a network by means of topological

parameters allowing scoring of the nodes according to their individual topological relevance. Computation 

of centrality indexes may accomplish this goal. The centrality indexes are topological parameters allowing 

a node-by-node quantification of the reciprocal relationship between the nodes. This provides a classification 

of the nodes according to their capability to influence the function of other nodes in the network. Combination

of this analysis with experimental data (node attributes) may help to identify critical nodes and regulatory

circuits in a context-specific manner.

thank you for using CentiScaPe!

--
Giovanni Scardoni
The Center for BioMedical Computing (CBMC)
www.cbmc.it
University of Verona
Strada le Grazie, 15 -37134
Verona -Italy