De novo network enrichment software tools | Protein interaction data analysis
De novo network enrichment approaches have become increasingly popular. Although existing approaches differ in many relevant aspects (optimization criteria, algorithmic implementation, scoring function, etc.), they all aim for extracting connected subnetworks from a larger interaction network. These are significantly enriched with active, i.e. deregulated, biological entities (genes, proteins, metabolites).
Finds high weight subnetworks in a vertex-weighted network. HotNet can recognize significantly mutated groups of interacting genes from large cancer sequencing studies. It is based on an “insulated” heat diffusion process that simultaneously analyzes a gene’s mutations and its local topology. This tool can deal with scores on individual genes/proteins as well as the topology of interactions between genes/ proteins.
The first online platform for de novo pathway enrichment analysis directly in the browser. Given a biological interaction network (e.g. protein-protein interactions) and a series of molecular profiles derived from one or multiple OMICS studies (gene expression, for instance), KeyPathwayMiner extracts connected sub-networks containing a high number of active or differentially regulated genes (proteins, metabolites) in the molecular profiles.
Finds clusters where member nodes show significant changes in expression levels. jActiveModules is a plugin that searches a molecular interaction network to find expression activated subnetworks. Such subnetworks are connected regions of a network that show significant changes in expression over particular subsets of conditions. The method combines a rigorous statistical measure for scoring subnetworks with a search algorithm for finding subnetworks with high score.
A comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules. The BioNet package provides an extensive framework for integrated network analysis in R. This includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes as well as methods for network search and visualization.
Identifies dysregulated pathways. ESEA is an edge-centric method that investigates the changes of inherent biological relationships embedded in pathways in the context of gene expression data. This method consists of: (i) converting pathways into graphs and constructing the background set of edges based on the converted graphs; (ii) estimating differential correlation scores of edges in the context of gene expression data; and (iii) calculating the edge enrichment score for each pathway in the pathway database.
A method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. DEGAS was applied to seven human diseases and obtained statistically significant results that appear to home in on compact pathways enriched with hallmarks of the diseases. In Parkinson's disease, we provide novel evidence for involvement of mRNA splicing, cell proliferation, and the 14-3-3 complex in the disease progression. DEGAS is available as part of the MATISSE software package.
A fast and flexible delimitation of the most interesting areas in a microarray experiment. GiGA identifies all relevant physiological processes, puts them into context, summarizes them in an intuitive format, and associates them with the underlying evidence. It can be applied to experiments with very small numbers of replicates (a single time point in the diauxic shift test case) and can be used with any available functional annotation, including protein interaction networks, co-expression data or literature mining results, as well as in areas beyond microarray analysis. GiGA can be used as a stand-alone tool, but we expect that it will be most useful when integrated into existing microarray analysis software, and for that reason the GiGA algorithm is freely available without restrictions.
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