Module extraction software tools | Protein interaction data analysis
A central goal of systems biology is to elucidate the structural and functional architecture of the cell. To this end, large and complex networks of molecular interactions are being rapidly generated for humans and model organisms. A recent focus of bioinformatics research has been to integrate these networks with each other and with diverse molecular profiles to identify sets of molecules and interactions that participate in a common biological function — that is, ‘modules’.
An approach for the statistical analysis of network dynamics combining well-known global topological measures, local motifs and newly derived statistics. SANDY also examines the sub-networks locally by calculating the occurrence of network motifs, which are compact, specific patterns of inter-connection between transcription factors and targets.
A computational framework that identifies high-probability signaling and regulatory paths that connect input data sets. The input includes two weighted lists of condition-related proteins or genes, such as a set of disease-associated genes and a set of differentially expressed disease genes, and a molecular interaction network (i.e., interactome). The output is a sparse, high-probability interactome sub-network connecting the two sets that is biased toward signaling pathways. This sub-network exposes additional proteins and interactions that are potentially involved in the studied condition and their likely modes of action.
A factor graph framework for pathway inference on high-throughput genomic data. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway's activities (e.g. internal gene states, interactions or high-level 'outputs') are altered in the patient using probabilistic inference.
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.
A method for identifying mutually exclusive driver networks in cancer. MEMo identifies networks defined by three properties: first, member genes are recurrently altered via somatic mutation or copy number changes; second, member genes are likely to participate in the same biological pathway or process, as determined from prior pathway and network knowledge; and third, genomic events within the network exhibit a statistically significant level of mutual exclusivity.
An algorithm for the simultaneous biclustering of heterogeneous multiple-species data collections and apply the algorithm to a group of bacteria containing Bacillus subtilis, Bacillus anthracis, and Listeria monocytogenes. The algorithm reveals evolutionary insights into the surprisingly high degree of conservation of regulatory modules across these three species and allows data and insights from well-studied organisms to complement the analysis of related but less well studied organisms.
Identifies conditionally co-regulated modules of genes (biclusters). cMonkey integrates various orthogonal pieces of information which support evidence of gene co-regulation, and optimizes biclusters to be supported simultaneously by one or more of these prior constraints.
Identifies subnetworks which can discriminate given conditions according to PPI network and gene expression data. PinnacleZ is a tool for classifying gene expression profiles by integrating gene expression data and protein networks.
Evaluates the significant modular correlations of various functional categories with gene expression profiles. DynaMod is a web-based application that identifies significant functional modules reflecting the dynamic changes of correlated activities and differential expressions in gene expression profiles under different conditions. User can conveniently interpret the dynamic modular activities of various functional categories and their networks depending on phenotype changes.
A plugin designed to integrate physical and genetic interactions into hierarchical module maps. PanGIA identifies "modules" as sets of proteins whose physical and genetic interaction data matches that of known protein complexes. Higher-order functional cooperativity and redundancy is identified by enrichment for genetic interactions across modules.
Enables a unified, high-level representation of heterogeneous biological information and provide a means for the analysis of the biological system under study in light of very large functional genomics databases. The framework provides the statistical robustness and computational efficiency that is required for large-scale studies and is readily extendable to future experimental techniques. Using samba, large repositories of functional genomics data can be used with maximum effect to enable the characterization of complex organisms and heterogeneous biological processes. The Samba algorithm is available either as a standalone executable or as part of the Expander software suite.
A desktop application for functional analyses of large-scale genomics datasets within the context of molecular networks. The central analysis components, NetWalk and FunWalk, are novel random walk-based network analysis methods that provide unique analysis capabilities to assess the entire data distributions together with network connectivity to prioritize molecular and functional networks, respectively, most highlighted in the supplied data.
Computes a subnetwork of gene and protein interactions that connect genomic perturbations to gene expression changes characteristic of cancer subtypes. The pathways derived in this way predict interlinking genes that may correspond to essential components of cancer signaling and may provide a mechanistic explanation of tumor behavior and suggest subtype-specific drug targets. In addition, the algorithm can extend to generate connecting subnetworks for generic gene sets, and runs a null-model permutation test to determine if these sets are significantly close in pathway space.
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.
Allows users to detect dysregulated modules in cancer. CONTOUR is a standalone software that is able to perform three main features: (i) creating conditional Protein Protein Interactions (PPI) networks; (ii) determining complexes from PPI networks; (iii) permitting to compare different complexes issues from two conditional PPI networks; and (IV) detecting changes between them. The software aims to facilitate the identification of dysfunctional/disrupted complexes in cancer.
Provides a class of extrema-weighted feature extraction models. XWF is a method that finds features of intraoperative blood pressure trajectories that are predictive of postoperative mortality. This application was developed to be suitable for functional predictors on various domains. This model constitutes a modifiable framework for interpretable functional feature extraction.
Predicts disease modules for hundreds of diseases simultaneously based on a protein interaction network and disease phenotypic similarity. GLADIATOR is a global approach for module detection that provides predicted modules and interrelation between disease modules, in the form of shared submodules, providing insights into the etiology and phenotypic mechanisms of diseases.
Performs analysis of signaling pathways. MITHrIL is a knowledge base-driven pathway analysis methodology that augments pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The software enables evaluation of pathway deregulation in cancer. It can contribute to an earlier diagnosis, an early drug resistance assessment, as well as to prognosis in terms of predicting future disease development.
Aims to fill gap by analyzing the network properties of a comprehensive corpus of 70 complex diseases. DIAMOnD allows to uncover the disease module associated with a particular phenotype. This algorithm is based on a systematic analysis of the network properties of known disease proteins along diseases.
Identifies multiple mutated modules displaying specific mutation patterns between and within modules. BeWith is a general framework that reveals complex relations between mutual exclusivity, functional interactions, and co-occurrence. This application can be used to uncover relationships between genes, gene groups, and pathways that were not accessible by previous methods. Its formulation is very general and is appropriate to interrogate other aspects of the mutational landscape by exploring different combinations of within-between definitions and constraints with simple modifications.