A Web Tool for Consensus Gene Regulatory Network Construction.
Ontology highlight
ABSTRACT:
SUBMITTER: Sarkar C
PROVIDER: S-EPMC8652126 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundReverse engineering approaches to infer gene regulatory networks using computational methods are of great importance to annotate gene functionality and identify hub genes. Although various statistical algorithms have been proposed, development of computational tools to integrate results from different methods and user-friendly online tools is still lagging.ResultsWe developed a web server that efficiently constructs gene networks from expression data. It allows the user to use ten different network construction methods (such as partial correlation-, likelihood-, Bayesian- and mutual information-based methods) and integrates the resulting networks from multiple methods. Hub gene information, if available, can be incorporated to enhance performance.ConclusionsGeNeCK is an efficient and easy-to-use web application for gene regulatory network construction. It can be accessed at http://lce.biohpc.swmed.edu/geneck .
| S-EPMC6323745 | biostudies-literature
Project description:Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named "SPREd" is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g., correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step towards incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction.
| S-EPMC10680606 | biostudies-literature
Project description:Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies.
| S-EPMC4782181 | biostudies-literature
Project description:Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data. Regulatory interaction between TFs and TGs is very complex, specifically multiple-to-multiple relations. Experimental data from TF Chromatin Immunoprecipitation sequencing is useful but produces one-to-multiple relations between TF and TGs. On the other hand, co-expression networks of genes can be useful for constructing condition transcriptional networks, but there are many false positive relations in co-expression networks. In this paper, we propose a novel method to construct a condition-specific and combinatorial transcriptional network, applying kernel canonical correlation analysis (kernel CCA) to identify multiple-to-multiple TF-TG relations in certain biological condition. Kernel CCA is a well-established statistical method for computing the correlation of a group of features vs. another group of features. We, therefore, employed kernel CCA to embed TFs and TGs into a new space where the correlation of TFs and TGs are reflected. To demonstrate the usefulness of our network construction method, we used the blood transcriptome data for the investigation on the response to high fat diet in a human and an arabidopsis data set for the investigation on the response to cold/heat stress. Our method detected not only important regulatory interactions reported in previous studies but also novel TF-TG relations where a module of TF is regulating a module of TGs upon specific stress.
| S-EPMC8172963 | biostudies-literature
Project description:Pseudomonas syringae, a Gram-negative plant pathogen, infects more than 50 crops with its type III secretion system (T3SS) and causes severe economic losses around the world. Although the mechanisms of virulence-associated regulators of P. syringae T3SS have been studied for decades, the crosstalk and network underlying these regulators are still elusive. Previously, we have individually studied a group of T3SS regulators, including AefR, HrpS, and RhpRS. In the present study, we found 4 new T3SS regulator genes (envZ, ompR, tsiS and phoQ) via transposon-mediated mutagenesis. Two-component systems EnvZ and TsiS natively regulate T3SS. In order to uncover the crosstalk between 16 virulence-associated regulators, (including AefR, AlgU, CvsR, GacA, HrpL, HrpR, HrpS, MgrA, OmpR, PhoP, PilR, PsrA, RhpR, RpoN, TsiR and Vfr) in P. syringae, we mapped an intricate network named PSVnet (Pseudomonas syringae Virulence Regulatory Network) by combining differentially expression genes in RNA-seq and binding loci in ChIP-seq of all regulators.
2021-03-29 | GSE138204 | GEO
Project description:BackgroundGene Regulatory Networks (GRN) produce powerful insights into transcriptional regulation in cells. The power of GRNs has been underutilized in malaria research. The Arboreto library was incorporated into a user-friendly web-based application for malaria researchers ( http://malboost.bi.up.ac.za ). This application will assist researchers with gaining an in depth understanding of transcriptomic datasets.MethodsThe web application for MALBoost was built in Python-Flask with Redis and Celery workers for queue submission handling, which execute the Arboreto suite algorithms. A submission of 5-50 regulators and total expression set of 5200 genes is permitted. The program runs in a point-and-click web user interface built using Bootstrap4 templates. Post-analysis submission, users are redirected to a status page with run time estimates and ultimately a download button upon completion. Result updates or failure updates will be emailed to the users.ResultsA web-based application with an easy-to-use interface is presented with a use case validation of AP2-G and AP2-I. The validation set incorporates cross-referencing with ChIP-seq and transcriptome datasets. For AP2-G, 5 ChIP-seq targets were significantly enriched with seven more targets presenting with strong evidence of validated targets.ConclusionThe MALBoost application provides the first tool for easy interfacing and efficiently allows gene regulatory network construction for Plasmodium. Additionally, access is provided to a pre-compiled network for use as reference framework. Validation for sexually committed ring-stage parasite targets of AP2-G, suggests the algorithm was effective in resolving "traditionally" low-level signatures even in bulk RNA datasets.
| S-EPMC8278594 | biostudies-literature
Project description:BackgroundDNA microarray technology provides us with a first step toward the goal of uncovering gene functions on a genomic scale. In recent years, vast amounts of gene expression data have been collected, much of which are available in public databases, such as the Gene Expression Omnibus (GEO). To date, most researchers have been manually retrieving data from databases through web browsers using accession numbers (IDs) or keywords, but gene-expression patterns are not considered when retrieving such data. The Connectivity Map was recently introduced to compare gene expression data by introducing gene-expression signatures (represented by a set of genes with up- or down-regulated labels according to their biological states) and is available as a web tool for detecting similar gene-expression signatures from a limited data set (approximately 7,000 expression profiles representing 1,309 compounds). In order to support researchers to utilize the public gene expression data more effectively, we developed a web tool for finding similar gene expression data and generating its co-expression networks from a publicly available database.ResultsGEM-TREND, a web tool for searching gene expression data, allows users to search data from GEO using gene-expression signatures or gene expression ratio data as a query and retrieve gene expression data by comparing gene-expression pattern between the query and GEO gene expression data. The comparison methods are based on the nonparametric, rank-based pattern matching approach of Lamb et al. (Science 2006) with the additional calculation of statistical significance. The web tool was tested using gene expression ratio data randomly extracted from the GEO and with in-house microarray data, respectively. The results validated the ability of GEM-TREND to retrieve gene expression entries biologically related to a query from GEO. For further analysis, a network visualization interface is also provided, whereby genes and gene annotations are dynamically linked to external data repositories.ConclusionGEM-TREND was developed to retrieve gene expression data by comparing query gene-expression pattern with those of GEO gene expression data. It could be a very useful resource for finding similar gene expression profiles and constructing its gene co-expression networks from a publicly available database. GEM-TREND was designed to be user-friendly and is expected to support knowledge discovery. GEM-TREND is freely available at http://cgs.pharm.kyoto-u.ac.jp/services/network.
| S-EPMC2748096 | biostudies-literature
Project description:Accessions of a plant species can show considerable genetic differences that are analyzed effectively by using recombinant inbred line (RIL) populations. Here we describe the results of genome-wide expression variation analysis in an RIL population of Arabidopsis thaliana. For many genes, variation in expression could be explained by expression quantitative trait loci (eQTLs). The nature and consequences of this variation are discussed based on additional genetic parameters, such as heritability and transgression and by examining the genomic position of eQTLs versus gene position, polymorphism frequency, and gene ontology. Furthermore, we developed an approach for genetic regulatory network construction by combining eQTL mapping and regulator candidate gene selection. The power of our method was shown in a case study of genes associated with flowering time, a well studied regulatory network in Arabidopsis. Results that revealed clusters of coregulated genes and their most likely regulators were in agreement with published data, and unknown relationships could be predicted.
| S-EPMC1785256 | biostudies-literature
Project description:Thyroid-carcinoma (THCA) is the most common malignancy with an increasing incidence. Recent evidence has emphasized the role of microRNA (miRNA) in THCA. However, knowledge concerning the roles of miRNAs in THCA is still limited. We therefore use a miRNA-target gene differential regulatory network (MGDRN) to identify key miRNAs and characterize their synergistic regulation in THCA. Both miRNA-target gene interactions from multiple databases and negative expression correlations between miRNA-target genes were used to characterize the interactions. Then, two regulatory networks involving normal and tumor conditions were constructed, respectively. The MGDRN was finally constructed using different interactions between the above two regulatory networks. By analyzing topological features of the MGDRN, four miRNAs (hsa-mir-152-3p, hsa-mir-148a, hsa-mir-130b and hsa-mir-15b) are identified as key miRNAs in THCA. Over-expression of mir-152-3p inhibited proliferation and colony formation of TPC-1 cells. Furthermore, mir-152-3p negatively regulated ERBB3 by binding to the 3'-UTR of ERBB3, and down-regulation of ERBB3 by small interfering (si)RNAs inhibited proliferation and colony formation of TPC-1 cells, indicating that mir-152-3p acted as an anti-tumor miRNA by negatively regulating ERBB3. Finally, two synergistically dysregulated modules were identified which may contribute to the initiation and progression of THCA. Overall, the results provided a better understanding of the molecular basis of THCA, and suggested novel treatment strategies for this cancer.
| S-EPMC5453480 | biostudies-literature
Project description:Pseudomonas syringae Virulence Regulatory Network construction
| PRJNA575032 | ENA