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Predicts the function of genes and gene sets. GeneMANIA is used for probing of gene function and revealing pairwise connections linking genes in yeast, fly, worm, human and other species. The GeneMANIA app extends the capabilities of the GeneMANIA prediction server by allowing users to quickly construct networks from gene lists for custom organisms and network data. The prediction performed by GeneMANIA provides a method for leveraging functionally informative associations to explore bacterial gene function.
SEEK / Search-Based Exploration of Expression Compendium
A computational gene co-expression search engine. SEEK provides biologists with a way to navigate the massive human expression compendium that now contains thousands of expression datasets. SEEK returns a robust ranking of co-expressed genes in the biological area of interest defined by the user's query genes. In the meantime, it also prioritizes thousands of expression datasets according to the user's query of interest. The unique strengths of SEEK include its support for multi-gene query and cross-platform analysis, as well as its rich visualization features.
WGCNA / WeiGhted Correlation Network Analysis
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A comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. WGCNA includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings.
FNTM / Functional Networks of Tissues in Mouse
Provides biomedical researchers with tissue-specific predictions of functional relationships between proteins in the most widely used model organism for human disease, the laboratory mouse. Users can explore FNTM-predicted functional relationships for their tissues and genes of interest or examine gene function and interaction predictions across multiple tissues, all through an interactive, multi-tissue network browser. FNTM makes predictions based on integration of a variety of functional genomic data, including over 13 000 gene expression experiments, and prior knowledge of gene function.
PlaNet / Plant Network
Allows users to visualize and analyze plant co-function networks. PlaNet integrates genomics, transcriptomics, phenomics, and ontology analyses across seven plant species important both for research and human circumstances. For comparative analyses, it implements NetworkComparer, a pipeline that compares and displays commonalities and differences between the co-expressed node vicinity networks (NVNs) simultaneously across selected species. This platform includes several databases such as famNet database, ensembleNet database.
A module of CATdb to mine co-expression units and decipher Arabidopsis gene functions. GEM2Net explores 387 stress conditions organized into 18 biotic and abiotic stress categories. For each one, a model-based clustering is applied on expression differences to identify clusters of co-expressed genes. To characterize functions associated with these clusters, various resources are analyzed and integrated: Gene Ontology, subcellular localization of proteins, Hormone Families, Transcription Factor Families and a refined stress-related gene list associated to publications. Exploiting protein-protein interactions and transcription factors-targets interactions enables to display gene networks.
WeGET / Weighted Gene Expression Tool and database
A computational tool to find mammalian genes that strongly co-express with a human query gene set of interest. Currently, WeGET uses over one thousand human and murine microarray data sets in its analysis. Importantly, data sets are weighted by their relevance to the query genes. WeGET performs a computational analysis to find genes that co-express with a set of query genes inside a large compendium of human and murine microarray experiments. The central idea used by WeGET is that when the query genes are involved in a common biological system (e.g. pathway, process or function), other (possibly unknown) genes that strongly coexpress with this set of genes might also be relevant. WeGET weights the datasets by their relevance to the query gene set and ranks all other genes by their degree of weighted co-expression. Finally, the human and murine ranks are integrated using a robust method based on rank-order statistics.
A powerful computational approach for selecting candidate genes for screening insertion mutants. We combined ranking of Arabidopsis thaliana regulatory genes according to their expression in response to multiple abiotic stresses (Multiple Stress [MST] score), with stress-responsive RNA co-expression network analysis to select candidate multiple stress regulatory (MSTR) genes. This systems biology-based screening approach combining gene ranking and network analysis could be generally applicable to enhancing identification of genes regulating additional processes in plants and other organisms provided that suitable transcriptome data are available.
GSNCA / Gene Sets Net Correlations Analysis
Provides a multivariate differential coexpression test that accounts for the complete correlation structure between genes. GSNCA characterizes differences in coexpression networks, without requiring the network inference step. GSNCA should be a valuable addition to gene set analysis approaches because (i) it identifies differentially coexpressed pathways that are overlooked otherwise, (ii) eigenvectors are computed efficiently and (iii) it provides information about the importance of genes in pathways that may result in new biological hypotheses.
Identifies coexpressed genes or gene coexpression neighborhoods associated with cis-regulatory motifs or Gene Ontology (GO) categories. ATCOECIS allows (1) to analyze the properties and the functional predictive power of coexpression networks in Arabidopsis, (2) to extend coexpression frameworks with information about cis-regulatory elements to functionally annotate genes, (3) to apply GO and motif enrichment analysis to dissect cell cycle regulatory control using publicly available transcriptome data, and (4) to study the organization of cis-regulatory elements in Arabidopsis promoters.
CoExpNetViz / comparative Co-Expression Network construction and Visualization
A computational tool that uses a set of query or "bait" genes as an input (chosen by the user) and a minimum of one pre-processed gene expression dataset. The CoExpNetViz algorithm proceeds in three main steps; (i) for every bait gene submitted, co-expression values are calculated using mutual information and Pearson correlation coefficients, (ii) non-bait (or target) genes are grouped based on cross-species orthology, and (iii) output files are generated and results can be visualized as network graphs in Cytoscape.
RECoN / Rice Environment Coexpression Network
Allows users to obtain tightly coexpressed groups of genes that revealed the modular organization of genes. RECoN is able to explore coexpression clusters within their stress transcriptome and systematically guides follow-up experimental studies for constructing the underlying gene network. It can assist to highlight pathways, processes, regulatory genes, and potential transcriptional regulatory mechanisms critical for drought response in rice.
Predicts key enzyme-coding genes in cancer metabolism by integrating a cancer gene co-expression network with the metabolic network. Met-express identifies the enzyme-coding genes that are co-expressed with significantly more metabolite-sharing enzyme-coding genes in a cancer-specific gene co-expression module. The software is not restricted to the integration of the gene co-expression network and the metabolic networks, or application to cancer metabolism. It allows users to explore other types of diseases, such as neurodegenerative diseases.
ComPlEx / Comparative analysis of Plant co-Expression networks
A comprehensive analysis of gene regulation evolution in plants and built a web tool for comparative analysis of plant co-expression networks. ComPlEx visualizes conserved link in co-expression networks across pairs of species. Gene lists for the comparison can be provided directly or by searching for gene IDs, Gene Ontology annotations or other keywords in the database. ComPlEx allows dynamic manipulation of the networks including relocating nodes, removing nodes (for example unconnected genes) and adding co-expressed genes at any context likelihood of relatedness (CLR) threshold. ComPlEx can be particularly useful for identifying the ortholog with the most conserved regulation among several sequence-similar alternatives and can thus be of practical importance in e.g. finding candidate genes for perturbation experiments.
Provides a sequence-independent comparative framework for two or more genomic datasets, where the variables and operations represent biological reality. The approximately common HO GSVD subspace represents the cell-cycle mRNA expression oscillations, which are similar among the datasets. Applications of HO GSVD in biotechnology include comparison of multiple genomic datasets, each corresponding to (i) the same experiment repeated multiple times using different experimental protocols; (ii) one of multiple types of genomic information, such as DNA copy number, DNA methylation and mRNA expression, collected from the same set of samples; (iii) one of multiple chromosomes of the same organism, to illustrate their relation; and (iv) one of multiple interacting organisms, e.g., in an ecosystem, to illuminate the exchange of biological information in these interactions.
Allows researchers to identify groups of genes that are differentially co-expressed. CoXpress uses a re-sampling method to calculate a p-value for each group, and provides several methods for the visualisation of differentially co-expressed genes. CoXpress uses hierarchical cluster analysis to explore the relationship between genes, cutting the tree to form groups of genes that are co-expressed. This is an intuitive approach that many biologists are familiar with. CoXpress then uses a resampling approach to find those groups that are co-expressed in one set of experiments.
DECODE / Differential Co-expression and Differential Expression
An analytical approach to integrate differential co-expression (DC) and differential expression (DE) analyses of gene expression data. DECODE allows one to study the combined features of DC and DE of each transcript between two conditions. By incorporating information of the dependency between DC and DE variables, two optimal thresholds for defining substantial change in expression and co-expression are systematically defined for each gene based on chi-square maximization. By using these thresholds, genes can be categorized into four groups with either high or low DC and DE characteristics. By complementing the recent DC and the traditional DE analyses, DECODE is a valuable methodology for investigating biological functions of genes exhibiting disease-associated DE and DC combined characteristics, which may not be easily revealed through DC or DE approach alone.
G-NEST / Gene NEighborhood Scoring Tool
Scores the evolutionary conservation of gene neighborhoods using syntenic blocks. G-NEST combines genomic location, gene expression, and evolutionary sequence conservation data to score putative gene neighborhoods across all possible window sizes in terms of gene number or base pair length. This algorithm utilizes quantitative gene expression data, such as that derived from microarray or RNA-sequencing technologies. It also enables the identification of neighborhoods containing paralogous, divergent, or unannotated genes.
iSyTE / integrated Systems Tool for Eye gene discovery
Permits prioritization of candidate genes associated with human congenital cataract. iSyTE was created using an in silico subtraction approach by which lens microarray data sets are compared to a developmentally matched microarray data set representing the whole embryonic body from which the ocular tissue was removed by microdissection. The tool was used in case of human congenital cataract in which a translocation breakpoint ostensibly responsible for the proband’s phenotype was located within a relatively gene-poor genomic interval.
Investigates gene co-expression based on microarray data. RapaNet calculates the evolutionary distance using a neighbor-joining method. It furnishes hierarchical clustering diagrams, scatterplots of the log ratio intensities between two genes of interest, pathway maps, and cis-element lists of promoter regions. This tool represents relationships between sequences, gene IDs, or Gene Ontology (GO) terms by correlational networks, phylogenetic trees, and/or hierarchical clustering diagrams.
A user-friendly, online, coexpression analysis tool for Arabidopsis (Arabidopsis thaliana) microarray expression data that computes patterns of correlated expression between user-entered query genes and the rest of the genes in the genome. CressExpress Performs linear regression using expression values harvested from publicly-available microarray data. When you enter a list of query probe set ids (or genes), the tool performs a linear regression comparing your query's expression values to expression values for all probe sets on a particular array platform.
GeneCAT / Gene Co-expression Analysis Toolbox
Introduces several novel microarray data analyzing tools. GeneCAT provides the user with both standard co-expression tools, such as gene clustering and expression profiling, and also includes tools that use multiple bait genes and makes functional inferences across different organisms by combining BLAST and co-expression. GeneCAT is pre-loaded with datasets for two plant model organisms, Arabidopsis and Barley, and dataset from other species can readily be added.
Permits the identification of coregulated, co-localized and co-expressed genes. CluGene integrates gene expression analysis profiling with automated search and identification of co-expressed and colocalized genes while searching for transcriptional regulatory modules. The software combines a number of pre- and post-processing functionalities together with statistical tools that significantly facilitate and expedite the analysis of gene network co-regulation on a global scale.
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Provides an easy access to Neighbor Counting Ensemble (NCE) predictions with the aim to facilitate hypothesis generation. EnsembleNet provides three approaches to retrieve functional information of genes: (i) by using a query gene to recover other genes with similar function (gene-centric search), (ii) by using a Gene Ontology (GO) term to retrieve genes assigned to the term (gene ontology-centric search), and (iii) by using a group of query genes to retrieve genes with similar function (gene set-centric search).
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