Topological Analysis: Softwares


 
 
Perhaps the most useful software for topological analysis of a biological network could be Cytoscape.
It is an open source software platform for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
Cytoscape core distribution provides a basic set of features for data integration, analysis, and visualization. Additional analysis and features are available as Apps (formerly called Plugins). Apps are available for network and molecular profiling analyses, new layouts, additional file format support, scripting, and connection with databases. They may be developed by anyone using the Cytoscape open API based on Java technology and App community development is encouraged. Most of the Apps are freely available from Cytoscape App Store.
We have divided topological analysis of biological networks into four major classes:
Calculation of primary network parameters Here are some examples of primary network parameters: degree, connected components, network diameter, network centralization, characteristic path length, average number of neighbors, network heterogeneity, and isolated node parameters.
It is possible to do primary topological analysis using the NetworkAnalyzer tool provided with Cytoscape software. For every node in a network, NetworkAnalyzer could compute its degree (in- and out-degrees for directed networks), its clustering coefficient, the number of self-loops, and a variety of other parameters. NetworkAnalyzer also computes edge betweenness for each edge in the network. If the respective options are enabled, NetworkAnalyzer stores the computed values as attributes of the corresponding nodes and edges.
There is also a library called "libStructural" which is a C/C++ portable software library for analyzing the structural properties of stoichiometric networks. The library supports the analysis of both flux balance and moiety conservation. The library will accept models in the form of either standard SBML or raw stoichiometry matrices.
One could also write its own codes in different programing languages and use various algorithms for calculation of primary features.
Centrality analysis Today, there are lots of centrality indices proposed for finding essential nodes in biological networks, most of them borrowed from the social network field. However, centrality analysis includes lots of challenges and concepts such as essentiality, lethality, etc.
For a good collection of these concepts and available centrality finding algorithms in biological networks, see the following reference: CentiServer
Motif discovery there are different algorithms for network motif finding. Here we provided some of them:
mfinder
FANMOD
Kavosh
G-tries
Mavisto
NeMoFinder
Grochow-Kellis
MODA
N. Alon
Clustering analysis Various algorithms have been implemented to extract clusters (i.e., groups of densely connected nodes) from biological networks:
Blatt, M., Wiseman, S., Domany, E. Superparamagnetic clustering of data, Phys. Rev. Lett. 76, 3251–3254 (1996)
Bader, G.D., Hogue, C.W.V., An automated method for finding molecular complexes in large protein interaction networks, BMC Bioinformatics 4, 2 (2003)
Gagneur, J., Jackson, D.B., Casari, G. Hierarchical analysis of dependency in metabolic networks, Bioinformatics 19, 1027–1034 (2003)
Spirin, V., Mirny, L.A., Protein complexes and functional modules in molecular Networks, Proc. Natl. Acad. Sci. USA 100, 12123–12128 (2003)
King, A.D., Przulj, N., Jurisica, I., Protein complex prediction via cost-based clustering, Bioinformatics 20, 3013–3020 (2004)
Among those, MCL algorithm has been shown to obtain good performances for biological networks (Van Dongen, S., Graph Clustering by Flow Simulation. PhD Thesis, Centers for Mathematics and Computer Science (CWI), University of Utrecht, 2000)
Recent studies on Topological Softwares for GEM models:
Kiwi: a tool for integration and visualization of network topology and gene-set analysis (Link)
KeyPathwayMiner 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with Cytoscape (Link)