Gene co-expression detection software tools | Transcription data analysis
Ever since the publication of the first gene expression arrays, the correlated expression of genes involved in a related molecular process has been used to predict functional relations between gene pairs. Large amounts of microarray and RNA-seq transcript expression, measured under a plethora of conditions enable mining for concordantly expressed genes.
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. It allows users to construct networks from gene lists for custom organisms and network data. The prediction performed provides a method for leveraging functionally informative associations to explore bacterial gene function.
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.
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.
Allows data mining and integration of microarray expression in Arabidopsis. CORNET permits users to construct molecular networks to address different biological questions. It allows the integration of co-expression and protein-protein interaction (PPI) networks, and a comprehensive visualization of the networks. This tool enables the utilization of different microarray platforms.
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.
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.
Provides numerous additional ways to visualize and explore the dataset initially described in "A Transcriptomic Atlas of Mouse Neocortical Layers". CORTx is a web interface that finds correlated genes. Users have to enter a single mouse gene symbol or gene ID used by Ensembl to sort other genes by expression correlation across all samples from all three sets of dissections. It can also browse functional annotations by average significance of enrichment in a layer.
An R/C++ package to identify patterns and biological process activity in transcriptomic data. CoGAPS provides an integrated package for isolating gene expression driven by a biological process, enhancing inference of biological processes from transcriptomic data. It improves on other enrichment measurement methods by combining a Markov chain Monte Carlo (MCMC) matrix factorization algorithm (GAPS) with a threshold-independent statistic inferring activity on gene sets. coGAPS infers biological activity by identifying overlapping, coregulated sets of genes and applying Z-score based statistics. It can be used to isolate transcription factor (TF) or BP activity in datasets of thousands of genes and tens to thousands of samples. The software is provided as open source C++ code built on top of JAGS software with an R interface.
Allows to identify genes that are coexpressed with a given set of genes of interest. geneRecommender takes a query list of genes, finds experiments in which those genes appear to be coregulated, and then uses only those experiments to generate a ranked list of coexpressed genes. The software was tested using a query of five C. elegans genes involved in the retinoblastoma (Rb) complex.
Allows automatic extraction of co-expressed gene clusters from gene expression data. Clust assists users in production of co-expressed clusters of genes that satisfy the biological expectations of a co-expressed gene cluster. This tool utilizes a number of base clustering methods (e.g. k-means clustering, hierarchical clustering, and self-organizing maps) to produce initial sets of clusters.
Optimizes weighted gene co-expression network analysis (WGCNA) gene co-expression module discovery. CEMiTool permits users to specify network parameters to improve the finding. Thank to discovered modules, this tool is able to perform enrichment analysis. It can return the occurrence of edges between different analyses.
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.
Permits the identification of expression conservation. COMODO is a method for cross-species co-clustering. It relies on the use of large-scale co-expression compendia for each of the species to be compared. This tool allows identification in each of the species the modules that best reflect the processes that are conserved in the core. It can be used in combination with a many-to-many homology map. This permits to study functional relationships between linker genes that mutually exhibit complex homology relations.
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.
Allows users to see expression profiles, to examine expression specificity, co-expression networks across different species. CoNekT is an open-source software which can provide an intuitive interface for combining large-scale expression data with functional and genomic information. This tool can also extract tissue-specific gene allowing comparison of tissue-specific transcriptomes between species.
Provides a genome-scale functional gene network for T. aestivum and a companion web server which provides network information and generates network-based functional hypotheses. WheatNet was constructed by integrating 20 distinct genomics datasets, including 156,000 wheat-specific coexpression links mined from 1,929 DNA microarray datasets. A unique feature of WheatNet is that each network node represents either a single gene or a group of genes. The WheatNet network has 20,230 nodes and 567,000 edges, integrating 20 sources of functional evidence linking pairs of genes. Furthermore, the WheatNet web server provides two options for predicting and prioritizing genes for wheat traits: (i) direct neighbors in the gene network and (ii) context-associated hubs (CAHs).
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.
Analyzes and visualizes the expression, variation and correlation of a gene set in cancers with flexible manner. GSCALite is an interactive web-based application that offers analyses including gene differential expression, overall survival, single nucleotide variation, copy number variation, methylation, pathway activity, miRNA regulation, normal tissue expression and drug sensitivity. It also provides genotype-tissue expression (GTEx) normal tissue module for gene set tissue specificity 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.
Allows users to analyze cross-platform and cross-species microarray data. iArray employs a meta-analysis approach to derive expression patterns from individual microarray dataset and to discover patterns frequently occurring across multiple datasets. It can be used to identify conserved expression patterns across different species. Furthermore, this tool includes a data preprocessing module, a co-expression analysis module, a differential expression analysis module, a functional and transcriptional annotation module and a graphical visualization module.