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Network motif identification software tools | Protein interaction data analysis

Network motif detection is the search for statistically overrepresented subgraphs present in a larger target network. They are thought to represent key structure and control mechanisms. Although the problem is exponential in nature, several algorithms and tools have been developed for efficiently detecting network motifs

Source text:
(Tran et al., 2015) Current innovations and future challenges of network motif detection. Brief Bioinform.

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FANMOD
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A tool for fast network motif detection. FANMOD relies on recently developed algorithms to improve the efficiency of network motif detection by some orders of magnitude over existing tools. This facilitates the detection of larger motifs in bigger networks than previously possible. Additional benefits of FANMOD are the ability to analyze colored networks, a graphical user interface and the ability to export results to a variety of machine- and human-readable file formats including comma-separated values and HTML.
ISMAGS / Index-based Subgraph Matching Algorithm with General Symmetries
Enumerates all instances of a motif in a graph, making optimal use of the motif’s symmetries to make the search more efficient. The ISMAGS algorithm is able to enumerate all instances of a motif within a graph. It makes optimal use of a motif’s symmetries to eliminate as much of the search space as quickly as possible. The visual interface makes the motif search easier, and it allows the user to efficiently use the results and gain insight in the connections between different motifs in a straightforward way.
bmotif
Aims to count motifs and species positions within motifs, in bipartite networks. bmotif allows users to analyze a wide range of bipartite ecological networks permitting to characterize complex networks without discarding important meso-scale structural detail. Moreover, this tool includes two main functions: (1) mcount, for calculating how frequently different motifs occur in a network, and (2) positions, for computing the frequency with which species (nodes) occur in different positions within motifs to quantify a species’ structural role.
POMOC (Partially Overlapping MOtif Counting) / Partially Overlapping MOtif Counting
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Counts novel motifs by employing capacity levels for all interactions. POMOC is based on a method that exposes topological differences of biological networks under different genetic backgrounds and experimental conditions. This algorithm proceeds by computing the number of partially overlapping instances of a given motif in a given network and can extend to large-scale biological networks in practical time.
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