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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 (Chu et al., 1998). Large amounts of microarray and RNA-seq transcript expression, measured under a plethora of conditions enable mining for concordantly expressed genes. Source text: Szklarczyk et al., 2016.

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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.
CoGAPS / Coordinated Gene Activity in Pattern Sets
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.
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.
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.
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.
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.
GSCALite / Gene Set Cancer Analysis
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.
Maps the functional networks of long or circular forms of non-coding RNAs (ncRNA). circlncRNAnet allows in-depth analyses of ncRNA biology. The software provides three features: (1) a framework for processing of user-defined next-generation sequencing (NGS)-based expression data, (2) assigning and assessing the regulatory networks of ncRNAs selected by users and (3) a workflow suitable for all types of ncRNAs. It can be used to get multiple lines of functionally relevant information on the circular RNA/ long non-coding RNA (circRNAs/lncRNAs) of users’ interest.
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.
COMAN / Comprehensive Metatranscriptomics Analysis
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.
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.
AMEN / Annotation Mapping Expression and Network
Enables biological and medical researchers with basic bioinformatics training to manage and explore genome annotation, chromosomal mapping, protein -protein interaction (PPI), expression profiling and proteomics data. AMEN provides modules for (i) uploading and pre-processing data from microarray expression profiling experiments, (ii) detecting groups of significantly co-expressed genes, and (iii) searching for enrichment of functional annotations within those groups. AMEN facilitates the design and execution of optimized procedures for processing, analysis and interpretation of multifaceted high-throughput data.
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.
ATGC transcriptomics
Provides non-expert computer users with accessible biological data management and simple data integration, as it can be used without any prior knowledge of programming, database administration. ATGC transcriptomics presents a more comprehensive interface for ontological storage and browsing as a method to explore information and relationships between transcripts and other features of interest, such as the concept of alternative splicing, and relationships between transcripts and genes (or genomic loci) with functional annotation. ATGC offers the opportunity to expand the database schema by adding other modules that store information from different data sources.
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