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PCSF / Prize-Collecting Steiner Forest
Performs analysis of high-throughput data using the interaction networks as a template, and interprets the biological landscape of interactome with respect to the data. PCSF allows user to i) upload the interactome and patient data, ii) compute the PCSF subnetwork solution, iii) perform functional analysis on resulting subnetwork, and iv) interactively visualize the final subnetwork with functional enrichment information. It is a general method that can also be applied to interpret multi-omics data for functional analysis.
iNP / iterative Network Partition
Recursively partitions pre-partitioned network modules. iNP is a modularity-based algorithm that provides a general framework in which any modularity-based methods can be implemented in the network partition step. The software also implements MSG, a greedy-based method, that is implemented to control the iteration step by partitioning the random graphs for evaluating the statistical significance of the modularity value of partitioning a pre-partitioned module. It can be used to analyze biological networks.
Signet / Selection Inference in Gene NETworks
Allows to study the human adaptation from a gene network perspective. Signet is a package that identifies potential undiscovered targets of selection, like pleiotrophin or neuroligins. This method has thus the potential to detect new genetic bases of adaptation in humans, as well as in other species for which gene interactions databases exist or could be inferred. It can be used in other fields, such as for the study of differential gene expression, genome wide association study (GWAS) or any kind of analysis for which a score can be obtained for any given gene.
samplingPaperSoftware
Assess when a given subnetwork differs significantly from randomly generated subnetworks. samplingPaperSoftware is an alternative null model that is based on an ensemble of seed lists generated from the minimum seed list. This method also highlights the need to focus more attention on generative models of biological networks in order to generate parametric models of these systems. It finally compares a statistic of interest against that obtained for an ensemble of subnetworks generated from the same underlying network using a set of seed lists which are randomly chosen under certain constraints.
BioGranat-IG / BioGranat Individuals-Grouping
A BioGranat plugin for the analysis of exome-sequencing data with the aim of identifying groups of genes in biological networks collectively responsible for causing a disease through genetic heterogeneity. BioGranat-IG is used for finding the smallest subnetwork that represents all or most individuals. This plugin uses the additional structure found in biological networks to analyses sequence data for multiple individuals and suggest possible sources of genetic heterogeneity.
LANDD / Liquid Association for Network Dynamics Detection
Finds subnetworks that show substantial dynamic correlations. LANDD focuses on subnetworks that tend to comprise functionally related genes. It uses collective behaviour of genes in a subnetwork, which is a much more reliable indicator of underlying biological conditions compared to using single genes as indicators. This tool finds that signal transduction pathways tend to show extensive dynamic relations with other functional groups.
LncSubpathway
Detects transcriptional subpathway dysregulation that related with dysregulated long non-coding RNAs (lncRNAs). LncSubpathway is a computational method that integrates transcriptional expression, pathway topologies, and lncRNA-mRNA association network. The software simultaneously considers the degree of dysregulation of protein coding genes (PCGs) and edges within a pathway and changes in lncRNA expression and in correlations between lncRNAs and PCGs. It was used to analyze the colorectal cancer and breast cancer datasets.
MinePath
A pathway analysis methodology. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation subpaths. MinePath assess the significance of the pathways as a whole, ranking them by their p-values. It offers dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination.
PhenomeExpress
Allows detection of sub-networks relevant to the disease of interest. PhenomeExpress, utilises both the transciptomics data being analysed and the prior knowledge of all cross-species phenotype to gene associations, including those directly related to the disease understudy. PhenomeExpress can extract sub-networks relevant to the core disease processes, that include proteins associated with the known disease phenotypes and are supported by the literature. This tool can be used in conjunction with examining the most highly ranked genes by fold change, other sub-network detection methods, as well as using predefined pathways and GO enrichment analysis.
EAModules
Provides an active module identification algorithm based on a memetic method. EAModules offers a direct strategy on a set of nodes to get connected subnetworks, thus avoid complicated graph divide operations. This algorithm is a simple genetic algorithm (GA) with basic binary encoding scheme without connectedness guarantee to search highly scored module in molecular networks. This method can be used in general heuristic optimization algorithms like simulated annealing and genetic algorithm.
ChainRank
Finds relevant subnetworks by identifying and scoring chains of interactions that link specific network components. Scores can be generated from integrating multiple general and context specific measures (e.g. experimental molecular data from expression to proteomics and metabolomics, literature evidence, network topology). ChainRank can be used to contextualise networks, identify signaling and regulatory path amongst targeted genes or to analyse synthetic lethality in the context of anticancer therapy.
MSIGNET / Metropolis sampling based SIGnificant NETwork
New
Identifies a global optimal significant network by integrating gene expression data and protein-protein interaction (PPI) network. MSIGNET is a method developed to identify significant network with genes significantly over or lower expressed in a certain condition. The method was applied to real data, including one Parkinson patient data set and another two ovarian cancer patient data sets. It can be applied to any network without size limitation.
GenRev
Obsolete
A network-based software package developed to explore the functional relevance of genes generated as an intermediate result from numerous high-throughput technologies. GenRev searches for optimal intermediate nodes (genes) for the connection of input nodes via several algorithms, including the Klein-Ravi algorithm, the limited kWalks algorithm and a heuristic local search algorithm. Gene ranking and graph clustering analyses are integrated into the package. GenRev has the following features. (1) It provides users with great flexibility to define their own networks. (2) Users are allowed to define each gene's importance in a subnetwork search by setting its score. (3) It is standalone and platform independent. (4) It provides an optimization in subnetwork search, which dramatically reduces the running time. GenRev is particularly designed for general use so that users have the flexibility to choose a reference network and define the score of genes.
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