Network querying software tools | Protein interaction data analysis
Network querying algorithms provide computational means to identify conserved network modules in large-scale biological networks that are similar to known functional modules, such as pathways or molecular complexes. Two main challenges for network querying algorithms are the high computational complexity of detecting potential isomorphism between the query and the target graphs and ensuring the biological significance of the query results.
An interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS, and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than one hundred human tissues and cell types. GIANT is available through a dynamic, interactive web interface. Researchers can query by individual genes or by gene sets of interest to analyze tissue-specific gene function and interactions. In addition to the interface, all of the networks are provided for download, and the full list of input datasets and their sources is available through the webserver.
Allows to find all the occurrences of a query graph in a network and check for its significance as a motif with respect to seven different random models. NetMatch offers the ability to compute a p-value against null models from seven distinct randomizing methods. It suggests to share the network properties of N in terms of degree distribution, cluster coefficient and assortativity.
Allows network querying that does not rely on knowledge of the query topology. TORQUE uses as input a set of proteins from a source species in the protein protein interaction (PPI) network of a target species. It was applied to query protein complexes within three large eukaryotic PPI networks: yeast, fly, and human. This tool combines a dynamic programming exact algorithm, an integer linear programming formulation, and a fast heuristic.
A versatile web server for interactive network analysis and visualization. StemCellNet rapidly generates focused networks based on a large collection of physical and regulatory interactions identified in human and murine stem cells. The StemCellNet web-interface has various easy-to-use tools for selection and prioritization of network components, as well as for integration of expression data provided by the user. As a unique feature, the networks generated can be screened against a compendium of stemness-associated genes. StemCellNet can also indicate novel candidate genes by evaluating their connectivity patterns. Finally, an optional dataset of generic interactions, which provides large coverage of the human and mouse proteome, extends the versatility of StemCellNet to other biomedical research areas in which stem cells play important roles, such as in degenerative diseases or cancer.
Provides several tools for bioinformatics analysis. Corbi is an R package that contains functions for network querying and alignment, subnetwork extraction, search and biomarker identification. The software includes the network querying method named CNetQ, CNetA, which extended CNetQ to the pairwise network alignment problem with one-to-one mapping and MarkRank, an enhanced biomarker ranking method.
Permits pathway alignments. MetaPathwayHunter is based on an approximate pattern matching algorithm for labeled graphs. It was used to build a study on the similarities and variations in the metabolic networks of two organisms, E. coli and S. cerevisiae. This tool can be employed to data-mine the pathway database with a meta-pathway query. It provides a visualization interface to display alignment between two homologous pathways.
A tool for querying a biological graph database to retrieve matches between subgraphs of molecular interactions and biological networks. SAGA implements an efficient approximate subgraph matching algorithm that can be used for a variety of biological graph matching problems such as the pathway matching SAGA uses to compare pathways in KEGG and Reactome.
Allows network querying. RESQUE takes a reduction-based approach, where the target network is iteratively reduced based on the so-called node correspondence scores that are computed using a semi-Markov random walk (SMRW) model. It is able to produce good results in network querying techniques, in terms of computational efficiency and querying accuracy. This tool can deal with node insertions and deletions at arbitrary locations.
Represents a method for network querying problem. CNetQ allows users to find the conserved functionally similar modules and pathways in both undirected and directed biomolecular networks. This method permits users to: (1) realize unlimited insertions/deletions, (2) do efficiently and accurately query networks with cycles, (3) address the directed network querying problem and the undirected network querying problem, and more.
Answers similarity queries in biological network databases. RINQ is a reference-based indexing method whose main advantage is its independence of the pairwise network alignment algorithm the database employs.
Allows querying linear pathways within a given network. QPath searches for matching pathways composed of distinct proteins that are similar to the query proteins in their sequence and interaction patterns. It can estimate the weight of each of terms in the overall score, to maximize the fraction of the functionally significant matching pathways identified. This tool can be applied to query any gene or protein network.
Molecular interactions need to be taken into account to adequately model the complex behavior of biological systems. NatalieQ is a web server for aligning two protein-interaction networks in order to highlight conserved subnetworks. It is an interface to the more general network alignment method Natalie.
Identifies conserved subnetwork regions in the target network that are similar to a given query network. SEQUOIA is a network querying algorithm that compares two networks and estimates the node correspondence scores by using the context-sensitive random walk model. The software can enhance the biological significance of the network querying results by estimating the node correspondence based on the context-sensitive random walk (CSRW) model and minimizing the conductance of matching network modules.
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.
Permits tree queries. QNet can handle queries of up to nine proteins in seconds in a network with about 5000 vertices and 15,000 interactions. It is able to perform a large-scale cross-species comparison of protein complexes, by querying known yeast complexes in the fly protein interaction network. This tool clarifies some algorithmic questions regarding efficient querying of biological networks.
Detects conserved interaction patterns in biological networks. MULE uses a graph simplification technique based on ortholog contraction, which is ideally suited to biological networks, to render this problem computationally tractable and scalable to large numbers of networks. It can be used as a pruning heuristic to simplify the harder graph mining task or a closely related, but computationally simpler task, which also provides significant biological insights by identifying conserved interaction patterns among protein families.
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.
Finds approximate occurrences of a query network in biological networks. APPAGATO handles node, edge and node label mismatches. It applies to large networks and provides high performance as well as statistically significant accurate results allowed to its randomic and parallel nature. The tool can compute efficiently functional and topological node similarity together with fast searching of a large number of query matching within the target graph.
Allows users to align and query biological networks. AbiNet matches connected subgraphs to proceed. It constructs a one-to-one correspondence between pairs of nodes in the two networks. This tool can perform network querying in two different modes, depending on which of the two input networks is chosen to play the role of the Master. It is an asymmetric approach that permits users to conduct kinds of network analysis difficult to carry out with other methods.