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 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 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.
Processes uploaded raw reads automatically to ultimately achieve functional assignments. COMAN is a web-based application for functional characterisation and comprehensive analysis of high-throughput metatranscriptomic data. It serves as a platform to translate the non-interpretable raw sequencing reads to data tables and high-standard figures that can be easily handled and further analysed. This web app can be run by experimentalists without programming experience and without the hassle of changing tools or working environments for answering their biologically relevant questions.
Analyzes correlations derived from expression microarray data. STARNET provides precomputed networks of closely co-expressed genes from curated microarray experiments. The application screens genes from a correlation network against a database of known interactions followed by an enrichment test. It aims to facilitate the investigation of putative gene regulatory networks of more than 10 species including human, zebrafish, and rice.
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.
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.