%0 Journal Article
%T SNAVI: Desktop application for analysis and visualization of large-scale signaling networks
%A Avi Ma'ayan
%A Sherry L Jenkins
%A Ryan L Webb
%A Seth I Berger
%A Sudarshan P Purushothaman
%A Noura S Abul-Husn
%A Jeremy M Posner
%A Tony Flores
%A Ravi Iyengar
%J BMC Systems Biology
%D 2009
%I BioMed Central
%R 10.1186/1752-0509-3-10
%X SNAVI is Windows-based desktop application that implements standard network analysis methods to compute the clustering, connectivity distribution, and detection of network motifs, as well as provides means to visualize networks and network motifs. SNAVI is capable of generating linked web pages from network datasets loaded in text format. SNAVI can also create networks from lists of gene or protein names.SNAVI is a useful tool for analyzing, visualizing and sharing cell signaling data. SNAVI is open source free software. The installation may be downloaded from: http://snavi.googlecode.com webcite. The source code can be accessed from: http://snavi.googlecode.com/svn/trunk webciteInteractions between signaling pathways in mammalian cells indicate that a large-scale complex network of interactions is involved in determining and controlling cellular phenotype [1-3]. To visualize and analyze these complex networks, the biochemical networks may be abstracted to directed graphs [4]. To understand the topology of such networks, graph-theory methodologies can be applied to analyze networks' global and local structural properties [5]. Additionally, the value of assembled network datasets is enhanced with network visualization software and web-based information systems. These systems provide summary information, order, and logic for interpretation of sparse experimental results [6,7]. Visualization tools and web-based navigation systems provide an integrative resource that aids in understanding the system under investigation and may lead to the development of new hypotheses.Graph-theory methods have been used in other scientific fields to analyze complex systems abstracted to networks. For example, Watts and Strogatz [8] defined a measure called the "clustering coefficient" (CC) for characterizing the level of clustered interactions within networks by measuring the abundance of triangles in networks (three interactions among three components). For instance, if a node has four
%U http://www.biomedcentral.com/1752-0509/3/10