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A fast and scalable local network alignment tool for the identification of functionally conserved modules in multiple networks. To evaluate the performance and the statistical significance, LocalAli were tested on 26 real datasets and 1040 randomly generated datasets. The results suggest that LocalAli outperforms all existing algorithms in terms of coverage, consistency and scalability, meanwhile retains a high precision in the identification of functionally coherent subnetworks.
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Allows simultaneous visualization and comparison of multiple networks. In addition to computing generic graph properties for individual networks, CompNet allows multigraph comparisons and similarity based grouping of networks. It also allows visual identification and selection of sub-graphs/communities of interest, enabling a general user to work with and compare between sufficiently complex and large networks. CompNet is a valuable tool for biologists and other researchers working in the field of visual data mining.
tYNA / TopNet-like Yale Network Analyzer
A web system for managing, comparing and mining multiple networks, both directed and undirected. tYNA provides five main types of functionality: (1) network management: storing, retrieving and categorizing networks. A comprehensive set of widely used network datasets is preloaded, put into standard form, and categorized with a set of tags; (2) network visualization: displaying networks in an interactive graphical interface; (3) network comparison and manipulation: various kinds of filtering and multiple network operations; (4) network analysis: computing various statistics for the whole network and subsets, and finding motifs and defective cliques; (5) network mining: predicting one network based on the information in another.
MIM notation / Molecular Interaction Maps notation
Provides a list of convention for annotating and organize relationships in bioregulatory systems. MIM notation describes the relationships between multiple entities by using interactions glyphs, a controlled vocabulary and typographical convention. The system is able to display complex set of regulatory network interconnections or to capture different cell types and cell states. It also allows the specification of known molecular data as well as the addition of contingencies.
The identification of protein complexes is a fundamental challenge in interpreting protein-protein interaction data. Cross-species analysis allows coping with the high levels of noise that are typical to these data. The NetworkBLAST web-server provides a platform for identifying protein complexes in protein-protein interaction networks. It can analyze a single network or two networks from different species. In the latter case, NetworkBLAST outputs a set of putative complexes that are evolutionarily conserved across the two networks.
A protein-protein interaction networks (PPINs) alignment method which combines information from protein sequence, function and network topology. Alignment of human and yeast PPINs reveals several conserved subnetworks between them that participate in similar biological processes, notably the proteasome and transcription related processes. PINALOG has been tested for its power in protein complex prediction as well as function prediction. Comparison with PSI-BLAST in predicting protein function in the twilight zone also shows that PINALOG is valuable in predicting protein function.
LNA_GNA / Local Network Alignment_Global Network Alignment
Evaluates local network alignment (LNA) against global network alignment (GNA). LNA_GNA method provides guidelines for researchers to properly demonstrate the superiority of a newly proposed network alignment (NA) method. LNA aims to find small highly conserved network regions and produces many-to-many mapping between nodes of the compared networks, while GNA aims to find large conserved subgraphs and produces one-to-one node mapping. Given the different outputs of LNA and GNA, when a new NA method is proposed, it is compared against existing methods from the same category.
Allows users to study local similarity analysis (LSA). fastLSA is an algorithm using a novel asymptotic upper bound algorithm for calculating the LSA p-value. It replaces a computationally intensive permutation test to compute significance of LSA statistics with a dramatic increase in the size of datasets that can be analyzed on a single desktop machine. It uses real-world and simulated time series datasets from different fields of inquiry, for visualizing the resulting clusters of local similarity.
An algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution.
GASOLINE / Greedy and Stochastic Algorithm for Optimal Local alignment of Interaction Networks
An algorithm for multiple local network alignment based on statistical iterative sampling in connection to a greedy strategy. GASOLINE can compute and visualize local alignments in a user-friendly way, without requiring postprocessing operations. Alignments can also be analyzed, by annotating proteins with corresponding GO categories, provided as input by the user. GASOLINE overcomes the limits of current approaches by producing biologically significant alignments within a feasible running time, even for very large input instances. The method has been extensively tested on a database of real and synthetic biological networks. A comprehensive comparison with state-of-the art algorithms clearly shows that GASOLINE yields the best results in terms of both reliability of alignments and running time on real biological networks and results comparable in terms of quality of alignments on synthetic networks.
MIMO / Molecular Interaction Maps Overlap
Offers a flexible and efficient graph-matching tool for comparing complex biological pathways. The main features of MIMO are: (i) easy-to-use: MIMO takes as input biological networks encoded with the Systems Biology Markup Language (SBML) standard. The SBML standard is widely adopted for biological network modeling and is flexible enough to allow the encoding of quite complex molecular interactions. Most importantly, the choice to adopt a standard format as input avoids the pre-processing phase needed to convert molecular interaction maps in a non-standard format removing all the consequent burden; (ii) flexibility: MIMO implements a flexible procedure for sub-graph matching, which naturally allows the introduction of gaps and mismatches and permits (if required) supervised queries incorporating a priori biological information; (iii) computational efficiency: while the subgraph matching problem is computationally intractable, MIMO implementation is fast enough to allow multiple queries on graph databases.
CUFID-align / Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins
Compares protein-protein interaction (PPI) networks and predicts the correspondence between proteins belonging to conserved functional modules. CUFID-align is a network alignment algorithm that, given a pair of PPI networks, constructs an integrated network and a Markov random walk model on the resulting network. The software can align proteins with identical functional annotations at a relatively low computational. It provides a means of computationally annotating the functions of proteins through comparative analysis of PPI networks.
MaWISh / Maximum Weight Induced Subgraph
A comprehensive alignment tool for PPI networks, which is inspired by duplication/divergence models that focus on understanding the evolution of protein interactions. MaWISh is based on a mathematical model that extends the concepts of match, mismatch, and gap in sequence alignment to that of match, mismatch, and duplication in network alignment and evaluates the similarity between graph structures through a scoring function that accounts for evolutionary events. By relying on evolutionary models, it facilitates interpretation of resulting alignments in terms of not only conservation but also divergence of modularity in PPI networks. Furthermore, as in the case of sequence alignment, MaWISh allows flexibility in adjusting parameters to quantify underlying evolutionary relationships.
Identifies conserved subnetworks in different species using a local alignment algorithm. AlignMCL is based on the idea of first merging two protein interaction networks (PINs) in a single alignment graph, and then mine it to identify potentially conserved subnetworks. AlignMCL is implemented in two components. The first (pyAligner) processes input PINs and orthologies to create the alignment graph. The second (MCL) mines the alignment graph produced by pyAligner to identify conserved modules.
CNetA / Conditional Random Fields based Network Alignment Method
A network alignment method based on the conditional random field model. The method is compared with other four methods on three real protein-protein interaction (PPI) network pairs by using four structural and five biological criteria. Compared with structure-dominated methods, larger biological conserved subnetworks are found, while compared with the node-dominated methods, larger connected subnetworks are found. CNetA preferably balances the biological and topological similarities.
HANDL / Homology Assessment across Networks using Diffusion and Landmarks
Furnishes a method for incorporating proteins from different species into a shared vector space. HANDL is a standalone software based on a diffusion kernel algorithm. It aims to facilitate the detection of functional similarity across species and provides an alternative to standard sequence homology. The application can be used in conjunction with other kernels to link proteins or for evaluating network properties.
A network alignment tool that allows the identification of conserved protein complexes and pathways across organisms, providing valuable hints as to how those interaction networks evolved. NetAligner includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which increases its performance with respect to existing tools.
A network alignment and search tool for comparing protein interaction networks across species to identify protein pathways and complexes that have been conserved by evolution. The basic method searches for high-scoring alignments between pairs of protein interaction paths, for which proteins of the first path are paired with putative orthologs occurring in the same order in the second path. This technique discriminates between true- and false-positive interactions and allows for functional annotation of protein interaction pathways based on similarity to the network of another, well-characterized species.
A conceptual framework and computational system that allows the retrieval of metabolic pathway information and the processing of alignments to reveal the similarities between metabolic pathways. PathAligner extracts metabolic information from biological databases via the Internet and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites etc. It provides an easy-to-use interface to retrieve, display and manipulate metabolic information. PathAligner also provides an alignment method to compare the similarity between metabolic pathways.
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