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.

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A randomization algorithm that for the first time provides a practical network motif discovery method for large multi-layer networks, for example those that include transcription factors, microRNAs, and non-regulatory protein coding genes. WaRSwap is applicable to systems with tens of thousands of genes, while accounting for critical aspects of biological networks, including self-loops, large hubs, and target rearrangements.

A software tool that uses the g-trie data structure to count occurrences of subgraphs on larger graphs, determining if they are network motifs. To compile you need a C++ compiler (use GCC if possible) and the make utility (just do 'make' after uncompressing the source). This preliminary release was made for Linux systems.

Identifies network motif modules in integrated gene regulatory networks in plants and worms. NetworkMotifModules provides several features: detection of three-node motifs in real network or two-node motifs, motif enrichment, motif clustering and generation of random networks.

Allows estimation of subgraph concentrations and detection of network motifs at a runtime that is asymptotically independent of the network size. mfinder is based on random sampling of subgraphs. Network motifs are detected with a surprisingly small number of samples in a wide variety of networks. This method can be applied to estimate the concentrations of larger subgraphs in larger networks than was previously possible with exhaustive enumeration algorithms.

Consists in a Cytoscape plugin for detecting functional modules in integrated networks composed of multiple interaction types. CyClus3D utilizes network motifs to query a 3D spectral clustering algorithm. It can notice modules composed of multiple interaction types that reflect regulatory, signaling or compensatory pathway mechanisms in addition to the stable protein complexes found by traditional clustering algorithms.

Provides options for network design. A user defines the number of network components, order and sign of regulatory links between the components. NetExplore allows plotting and visualization of the solution's phase spaces and bifurcation diagrams. It includes three parts (modules): NetExplore Solver, NetExplore Browser and NetExplore Bifurcator. Each module is available online and as a standalone program for download.

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.

Allows evaluation of the degree of sampling uniformity and independence for network motif discovery algorithms. IndeCut permits users to understand the cause of performance variations among different graph sampling methods. It can determine the uniformity and the independence of a network motif discovery algorithm’s sampling regime by employing the cut norm. This tool can be used to confound laboratory validation of motifs.

Allows users to find matches for so-called composite motifs. ISMA is an index-based subgraph matching algorithm dedicated to the search of all occurrences of a given query subgraph in graphs with annotated edges. This algorithm can be used for the discovery and analysis of small and large network motifs in ever growing biological networks.

A network motif detection package. NetMODE also (a) includes a method for generating comparison graphs uniformly at random, (b) can interface with external packages (e.g. R), and (c) can utilize multi-core architectures. NetMODE is available from netmode.sf.net.

Assists users in clustering topological network motifs in integrated large-scale networks. NMCToolbox is a standalone software that uses a clustering algorithm tested on an integrated yeast interaction network. This application proposes several functions such as edge and network motif clustering, figuring network motif enrichment and creation of random networks.

An algorithm for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms. Kavosh is based on counting all k-size sub-graphs of a given graph (directed or undirected). Besides, Kavosh can be employed for finding motifs of size greater than eight, while most of the other algorithms have restriction on motifs with size greater than eight.

Generates network structures with high occurrences. MBN is built upon controlling the occurrences of the basic building blocks of network connectivity and allows user to interpret model parameters. This algorithm offers users to choose the order of edge addition according to certain rules that facilitates the formation of certain structures.

Enables generation of hypotheses on the evolution of metabolic pathways. MOTUS also analyzes some global features of the whole network. It uses the reaction graph as model for the metabolic network. This tool infers coloured motifs in coloured graphs. It can be useful to perform motif search or motif inference. It was developed for structural analysis of metabolic networks but can be used for any vertex-labelled graph.

A tool for the exploration of motifs in network. MAVisto provides a flexible motif search algorithm and different views for the analysis and visualisation of network motifs. It is written in Java and is based on Gravisto, an editor for graphs and a toolkit for implementing graph algorithms. It is available free of charge and can be used with Java Web Start.

A free software tool to detect network motifs os size 3, 4 and 5. It is implemented in Java. acc-MOTIF is based in a recent combinatorial approach to accelerate motif detection.

Assists users in partitioning a network into a set of graphs. NeMoFinder is a graph mining algorithm that permits to discover meso-scale repeated and unique network motifs in a large, genome-scale protein-protein interaction (PPI) network for biological applications. This algorithm utilizes repeated trees method. It is able to utilize the symmetry property of unlabelled trees to further reduce the number of candidate trees enumerated.

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.

Investigates motifs of network structure, nodes and links of any type of network. Pymfinder can study results of long-standing research involving the study of ecological networks. It enables users to define species’ roles, showing them to be evolutionary conserved across communities. This tool identifies all the different n-node patterns of interaction found within a given network. It is useful in a large variety of systems.

Permits users to compute the node overlap and segregation measures and the associated metrics. NOS is an R package that enables assessment of structural patterns ranging from complete node segregation to nesting in a variety of network types. In addition, it provides a measure of network modularity. This method is also available as a web application.

Assists in discovering of network motifs. This method uses a new symmetry breaking technique, an improved isomorphism test, and hashing based on degree sequences. It is based on a novel symmetry-breaking technique, which eliminates repeated isomorphism testing. This model provides several applications: (1) finding larger motifs, (2) querying a particular subgraph, (3) exploring motif clustering, (4) time and space, (5) parallelization.

Finds network motifs of a specified size. MDET employs pattern growth approach and a dynamic expansion tree (DET) which regulates motif discovery mechanism. Its root consists of a size-3 tree and the tree is expanded iteratively by addition of graph elements in each successive level. It can be used to delete costly isomorphism tests. This tool can serve to retrieve large motifs up to size-15 within few hours.

Offers a library of tools dedicated to work in conjunction with the NemoProfile algorithm: a specific network motif data intending to ease instance collection. Nemolib is an extensible application which aims to assist users in facilitating the identification of network motifs.

Serves for network motif discovery. FSM is a scalable algorithm that consists of three different steps: subgraph enumeration, subgraph network motif classification and significance test. This algorithm aims to accelerate network motif discovery by lessening the number of times to perform subgraph isomorphism. It exploits multiple heuristic optimizations and subgraph classification to process.

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.

Identify diﬀerential sub-networks as differential communities in a de-noised version of the different topological (DT) graph. DCD can (i) iteratively re-order the nodes of the DT graph to determine approximate block diagonals present in the DT adjacency matrix, and (ii) re-order DT adjacency matrix traversed along the diagonal to remove all the edges associated with a node.

Provides a method based on fractal theory to count motifs in biological networks. FraMo consists of three phases: (i) a complex network is converted to a multifractal network, (ii), using maximum likelihood estimation, distribution parameters are estimated for the multifractal network, and (iii) the approximate number of network motifs is calculated. It can be used for counting motifs with no restrictions to size.

Indicates path motifs in integrated networks and sets them out in a modular structure. Pathicular is a Cytoscape plug-in that can be used to discover paths of a given type in the source network with the integrated network loaded beforehand. This software gives a p-value to enable verification of the over-representation of the path of interest.

Obsolete

An algorithm that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently. While most of the algorithms rely on induced subgraphs as motifs of the networks, MODA is able to extract both induced and non-induced subgraphs simultaneously.

Obsolete

Provides a fast motif detection algorithm which has a Quaternary Tree data structure in the heart. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network.

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