Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,104
datasets available to search
ShareScore release 0.9.0
Dataset results
1,104 results for “regulatory networks”
Network analysis reveals that acute stress exacerbates gene regulatory responses of the gill to seawater in Atlantic salmon
<p>The transition from freshwater to seawater represents a physiological challenge for Atlantic salmon smolts preparing for downstream migration. Stressors occurring during downstream migration to the ocean impair the ability of smolts to maintain osmotic/ionic homeostasis in seawater. The molecular mechanisms underlying this interaction are not fully understood, especially at the organ level. We combined RNA-Seq with measures of whole-animal homeostasis to examine gene expression dynamics in the gills of smolts associated with impaired seawater tolerance after an aquaculture-related stressor. Smolts were given a 24 h seawater tolerance test before and after exposure to an acute handling/confinement stress. RNA-Seq followed by differential expression and weighted gene correlation network analysis (WGCNA) was used to quantify the transcriptional response of the gill to handling/confinement stress, seawater and their interaction. Exposure to acute stress was associated with a general stress response and impaired osmotic/ionic homeostasis in seawater. We identified gene networks in the gill exhibiting response to acute stress alone, seawater alone, and others exhibiting combined effects of both stress and seawater. Our findings indicate that acute handling/ confinement stress increases the intensity of seawater-related gene expression and suggest that increased investment in mechanisms related to ion transport may be part of a compensatory response to impaired seawater tolerance in smolts.</p>
Recent reconfiguration of an ancient developmental gene regulatory network in Heliocidaris Sea Urchins
<p>Changes in developmental gene regulatory networks (dGRNs) underlie much of the diversity of life, but the evolutionary mechanisms that operate on interactions with these networks remain poorly understood. Closely related species with extreme phenotypic divergence provide a valuable window into the genetic and molecular basis for changes in dGRNs and their relationship to adaptive changes in organismal traits. Here we analyze genomes, epigenomes, and transcriptomes during early development in two sea urchin species in the genus <em>Heliocidaris </em>that exhibit highly divergent life histories and in an outgroup species. Signatures of positive selection and changes in chromatin status within putative gene regulatory elements are both enriched on the branch leading to the derived life history, and particularly so near core dGRN genes; in contrast, positive selection within protein-coding regions have at most a modest enrichment in branch and function. Single-cell transcriptomes reveal a dramatic delay in cell fate specification in the derived state, which also has far fewer open chromatin regions, especially near dGRN genes with conserved roles in cell fate specification. Experimentally perturbing the function of three key transcription factors reveals profound evolutionary changes in the earliest events that pattern the embryo, disrupting regulatory interactions previously conserved for ~225 million years. Together, these results demonstrate that natural selection can rapidly reshape developmental gene expression on a broad scale when selective regimes abruptly change and that even highly conserved dGRNs and patterning mechanisms in the early embryo remain evolvable under appropriate ecological circumstances.</p>
Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets
<p>The uploaded files are source datasets for the scMTNI algorithm. scMTNI is a multi-task learning framework that integrates the cell lineage structure, scRNA-seq and scATAC-seq measurements to enable joint inference of cell type-specific GRNs. See more details at Zhang, S., Pyne, S., Pietrzak, S. et al. Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. Nat Commun 14, 3064 (2023). <a href="https://doi.org/10.1038/s41467-023-38637-9">https://doi.org/10.1038/s41467-023-38637-9</a></p> <p>The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/scMTNI_sourcedata.tar.gz?versionId=a19f69c1-5bb2-436e-9a30-a7efbf1ab3da">scMTNI_sourcedata.tar.gz</a> contains the following 3 parts:</p> <p>1) The cluster-specific scRNA-seq matrices and the prior networks for all three datasets and scMTNI inferred consensus networks.</p> <p>2) Gold standard human and mouse datasets for evaluation.</p> <p>3) Source data for scMTNI figures 2-8 and supplementary figures. The key for each figure and its corresponding file path is in SourceData_Key_v2.xlsx.</p> <p>The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/Buenrostro_Hematopoiesis.tar.gz">Buenrostro_Hematopoiesis.tar.gz</a> contains the scRNA-seq data for human hematopoietic differentiation downloaded from Data S2 of Buenrostro et al.</p> <p>The source data <a href="../api/files/5c75876b-c08a-4186-a08d-da9db78f64f3/RawMotifFiles.tar.gz">RawMotifFiles.tar.gz</a> contains the motif instance files and promoter files for human and mouse for generating prior networks using scATAC-seq data for scMTNI. Check <a href="https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md">https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md</a> for examples and scripts.</p> <p>The <a href="../api/records/11876980/draft/files/Buenrostro_priorNetwork_bamfiles.tar.gz/content">Buenrostro_priorNetwork_bamfiles.tar.gz</a> contains the raw bam files of scATAC-seq data for human hematopoietic differentiation downloaded from Buenrostro et al.</p>
CD4+ T cells re-wire granuloma cellularity and regulatory networks, promoting immunomodulation following Mtb reinfection
<p>Here we include the necessary .ipynb, .h5ad, and .rds (used for cell-cell interaction analyses) files used in our work: "Immunomodulatory re-wiring of granuloma cellularity and regulatory networks by CD4 T cells following Mtb reinfection"</p>
CRISPRi with barcoded expression reporters dissects regulatory networks in human cells
<p>Genome-wide CRISPR screens have emerged as powerful tools for uncovering the genetic underpinnings of diverse biological processes. Incisive screens often depend on directly measuring molecular phenotypes, such as regulated gene expression changes, provoked by CRISPR-mediated genetic perturbations. Here, we provide quantitative measurements of transcriptional responses in human cells across genome-scale perturbation libraries by coupling CRISPR interference (CRISPRi) with barcoded expression reporter sequencing (CiBER-seq). To enable CiBER-seq in mammalian cells, we optimize the integration of highly complex, barcoded sgRNA libraries into a defined genomic context. CiBER-seq profiling of a nuclear factor kappa B (NF-κB) reporter delineates the canonical signaling cascade linking the transmembrane TNF-alpha receptor to inflammatory gene activation and highlights cell-type-specific factors in this response. Importantly, CiBER-seq relies solely on bulk RNA sequencing to capture the regulatory circuit driving this rapid transcriptional response. Our work demonstrates the accuracy of CiBER-seq and its potential for dissecting genetic networks in mammalian cells with superior time resolution.</p>
Brassinosteroid gene regulatory networks at cellular resolution in the Arabidopsis root
<p>Supplemental datasets for "Brassinosteroid gene regulatory networks at cellular resolution in the Arabidopsis root". </p>
Repositiry for the article: "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain Mbebi & Zoran Nikoloski
<p>This is the repository for the manuscript "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain J. Mbebi & Zoran Nikoloski.</p> <p><strong>Organisation</strong></p> <ol> <li>The folder Codes contains the following R scripts with the K-folds cross-validation option to learn the hyperparameters:</li> </ol> <ul> <li>Mixed_L1L21_GRN.R which computes L1L21-solution</li> <li>Mixed_L1L21G_GRN.R which computes L1L21G-solution</li> <li>Mixed_L2L21_GRN.R which computes L2L21-solution</li> <li>Mixed_L2L21G_GRN.R which computes L2L21G-solution</li> <li>L1L21_Dream5_Scerevisiae_example_run.R is an example run using the L1L21-solution with S. cerevisiae data (Network 4 in DREAM5 challenge) All files needed to successfully run "L1L21_Dream5_Scerevisiae_example_run" are locaded in the folder Codes.</li> </ul> <p>2. The folder Figures contains all figures in the manuscript.</p> <p>3. The folder Inferred-networks contains all network objects for each dataset and each inference methods in the comparative analysis.</p> <p><strong>Dependencies and required packages</strong></p> <p>The following packages are required for the contending approaches in the comparative analysis: "devtools", "foreach", "plyr", "glmnet" and "randomForest".</p> <p><strong>GENIE3</strong></p> <p>The GENIE3 package can be installed from: <a href="http://bioconductor.org/packages/release/bioc/html/GENIE3.html">http://bioconductor.org/packages/release/bioc/html/GENIE3.html</a></p> <p><strong>TIGRESS</strong></p> <p>The TIGRESS repository can be obtained from: <a href="https://github.com/jpvert/tigress">https://github.com/jpvert/tigress</a></p> <p><strong>ENNET</strong></p> <p>The ENNET repository can be obtained from: <a href="https://github.com/slawekj/ennet">https://github.com/slawekj/ennet</a></p> <p><strong>PLSNET</strong></p> <p>The Matlab source code of PLSNET can be obtained from: <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17</a></p> <p><strong>PORTIA</strong></p> <p>The PORTIA repository can be obtained from: <a href="https://github.com/AntoinePassemiers/PORTIA">https://github.com/AntoinePassemiers/PORTIA</a></p> <p><strong>D3GRN</strong></p> <p>The Matlab source code of D3GRN can be obtained from: <a href="https://github.com/chenxofhit/D3GRN">https://github.com/chenxofhit/D3GRN</a></p> <p><strong>Fused-LASSO</strong></p> <p>The fused-LASSO repository can be obtained from: <a href="https://github.com/omranian/inference-of-GRN-using-Fused-LASSO">https://github.com/omranian/inference-of-GRN-using-Fused-LASSO</a></p> <p><strong>ANOVerence</strong></p> <p>Because of some technical issues (e.g code's accessibility: <a href="http://www2.bio.ifi.lmu.de/%CB%9Ckueffner/anova.tar.gz">http://www2.bio.ifi.lmu.de/˜kueffner/anova.tar.gz</a>), we were not able to reproduce ANOVerence results and used the inferred network from DREAM5 challenge instead.</p> <p>4. Although the codes here were tested on Fedora 29 (Workstation Edition) using R (version 4.2.2), they can run under any Linux or Windows OS distributions, as long as all the required packages are compatible with the desired R version.</p>
Network analysis reveals that acute stress exacerbates gene regulatory responses of the gill to seawater in Atlantic salmon
Open the record for dataset details and reuse information.
Recent reconfiguration of an ancient developmental gene regulatory network in Heliocidaris Sea Urchins
Open the record for dataset details and reuse information.
Out from under the wing: reconceptualizing the insect wing gene regulatory network as a versatile, general module for body-wall lobes in arthropods
Open the record for dataset details and reuse information.
Input data files for RSS-NET analysis of IBD GWAS summary statistics and NK cell regulatory network
<p>Details of these data files are provided in https://suwonglab.github.io/rss-net/ibd2015_nkcell.</p> <p>Contact:<code> xiangzhu[at]psu.edu </code></p>
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
<p>This repository contains input files from the synthetic, curated, and processed experimental single-cell gene expression datasets used in BEELINE.</p> <p>New in version 3:<br> 1) Ground-truth networks used for analysis of experimental scRNA-seq datasets for mouse and human datasets<br> 2) Changed license to CC BY-NC 4.0 from GPL v3.0 to account for the non-commercial clause for the network data</p>
Data from: The impact of gene expression variation on robustness and evolvability of a developmental gene regulatory network
Regulatory interactions buffer development against genetic and environmental perturbations, but adaptation requires phenotypes to change. We investigated the relationship between robustness and evolvability within the gene regulatory network underlying development of the larval skeleton in the sea urchin Strongylocentrotus purpuratus. We find extensive variation in gene expression in this network throughout development in a natural population, some of which has a heritable genetic basis. Switch-like regulatory interactions predominate during early development, buffer expression variation, and may promote the accumulation of cryptic genetic variation affecting early stages. Regulatory interactions during later development are typically more sensitive (linear), allowing variation in expression to affect downstream target genes. Variation in skeletal morphology is associated primarily with expression variation of a few, primarily structural, genes at terminal positions within the network. These results indicate that the position and properties of gene interactions within a network can have important evolutionary consequences independent of their immediate regulatory role.
Gene regulatory networks for 38 human tissues
<p>We reconstructed gene regulatory networks for 38 tissues from the Genotype-Tissue Expression project (GTEx), and used these networks to investigate gene expression and regulation across these tissues. In the RData file, we share the following objects:</p> <p>- <strong>edges</strong>: an 19,476,492 by 3 data.frame including three columns: TF (the transcription factor's gene symbol), Gene (Ensembl ID), Prior (whether an edge is canonical (1) or non-canonical (0)).<br> <br> - <strong>exp</strong>: a 30,243 by 9,435 matrix including normalized expression data for each sample.<br> <br> - <strong>expTS</strong>: a 30,243 by 38 matrix including, for each gene and each tissue, information on whether the gene is expressed in a tissue-specific manner in that tissue (1) or not (0).<br> <br> - <strong>genes</strong>: a 30,243 by 4 data.frame that includes annotation information (Symbol) for Ensembl gene IDs (Name). This data.frame also includes information on whether genes are also transcription factors (AlsoTF), with options: no, yes/motif (TF with a known DNA-binding motif) yes/nomotif (TF without a known DNA-binding motif). In addition, the multiplicity of the gene (Multiplicity) is given.<br> <br> - <strong>net</strong>: a 19,476,492 by 38 matrix that includes edge weights for each tissue. Edge order corresponds to edge order in the the object "edges".<br> <br> - <strong>netTS</strong>: a 19,476,492 by 38 matrix that includes information of whether edges are specific to a tissue (1) or not (0).<br> <br> - <strong>samples</strong>: a 9,435 by 2 data.frame that includes sample identifiers (matching the identifiers in "exp") and the tissue to which these samples belong.</p>
Transposable element products, functions, and regulatory networks in Arabidopsis thaliana
<h1>README</h1> <p>This dataset includes the main outputs from the work titled <strong>Transposable element products, functions, and regulatory networks in <em>Arabidopsis thaliana</em>.</strong></p> <h2><strong>Summary</strong></h2> <p>Transposable elements (TEs) are DNA sequences with the ability to propagate themselves within and across genomes. Their mobilization is catalyzed by self-encoded factors, yet these factors have been poorly investigated due to difficulties in defining TE genes in genomes. Here, we leveraged extensive long- and short-read transcriptome data, together with structural predictions, transcription factor binding site identification, and transcriptional network analyses, to construct a comprehensive atlas of TE transcripts and TE-encoded products in the model organism <em>Arabidopsis thaliana</em>. We uncovered hundreds of transcriptionally competent TEs, each potentially encoding multiple proteins either through distinct genes, alternative splicing, or post-translational processing. Structural-based protein analyses revealed dozens of hitherto unidentified domains of unknown function, enabling us to predict proteins with multimerization and DNA binding domains forming macromolecular complexes involved in transposition. Furthermore, we demonstrate that TE expression is highly intertwined with the transcriptional network of cellular genes, and identified transcription factors and cis-regulatory elements associated with their coordinated expression during development or in response to environmental cues. This comprehensive atlas of TE-genes and TE-proteins provides a valuable resource for studying the mechanisms involved in transposition and their consequences for genome and organismal function.</p> <h2><strong>File description</strong></h2> <p>It includes the following data:</p> <ol> <li><code>annots/TE_Functional_Annotation.Borreda2024.gtf</code> - Annotation file including Arabidopsis TEs and TE-genes. TE-genes defined in our work are indicated in the 'Source' column of the gtf. TAIR10-defined TEs for whom we did not annotate new transcripts are also included.</li> <li><code>seqs</code> - This folders includes all the transcript sequences (cDNAs.tsv) and the first and longest ORFs found in each of them (prot.csv), which were used for further analyses. The specific copy, gene, isoform and, in the case of proteins, ORF, is indicated for each sequence.</li> <li><code>structures</code> - The zipped folder <code>full_length_prots_pdbs.zip</code> includes all the 3D structures from full-length TE proteins. Note that identical proteins, which would result in identical structures, have been collapsed to reduce the total dataset size; equivalences can be found in <code>identical_proteins</code>.</li> <li><code>structures/SD_Cluster_Functions.tsv</code> - We clustered all Structural Domains (SD) based on 3D similarity and assigned a function to each cluster based on the database hits. This table indicated, for each of these SDs, to which cluster it belongs, the superfamily, family and element containing it, the number of Conserved Domains included within it and the number of hits with resolved (retrieved from the RCSB-PDB database) or predicted (AlphaFold2) protein structures. The last column includes the putative function assigned to each cluster.</li> <li><code>coexpression</code> - Coexpressed genes were classiffied into modules using WGCNA. In the table <code>Gene_Modules.tsv</code> we include, for each gene and TE-gene (provided it has expression in at least one sample, see methods on the publication for details), the TE family and superfamily when applicable and the module to which it belongs. The modules were named based on the results of the GO enrichment analysis of the genes contained. The results of this GO enrichment are included in <code>GO_Enrichment.tsv</code>, where we include the main funciton of the associated GOs, the number of entries and TE-genes within the module, a list of GO terms enriched in that specific module and finally a list of TE families enriched in each module.</li> <li><code>dapseq</code> - We reanalized the DAP-seq dataset from O'Malley 2016, selecting only TFBS with a binding site within a DAP-seq peak. The list of filtered peaks we found is reported in <code>DAPseq_TFBS_Motifs.tsv</code>. The columns include the coordinates of the TFBS (which have been filtered to fall within a DAP-seq peak and include the TFBS motif), the strand of the motif, the score of the motif reported by FIMO, the motif sequence, the Sequence Read identified for the original DAP-seq data, and the family, name and gene of the TF associated with that specific peak.</li> </ol>
Dataset for paper "GRN-Transformer: Predicting Single Cell Gene Regulatory Network based on Axial Transformer"
<p>Dataset for paper "GRN-Transformer: Predicting Single Cell Gene Regulatory Network based on Axial Transformer"</p>
scMEGA: Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference
<p>The increasing availability of single-cell multi-omics data allow to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) to infer gene regulatory network by combining single cell gene expression and chromatin accessibility profiles. This allows to study complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease development. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction.</p>
Emergence of Supercoiling-Mediated Regulatory Networks through the Evolution of Bacterial Chromosome Organization
<p>Data generated using <a href="https://gitlab.inria.fr/tgrohens/evotsc">EvoTSC</a> and used in the <a href="https://doi.org/10.24072/pci.mcb.100198">Emergence of Supercoiling-Mediated Regulatory Networks through the Evolution of Bacterial Chromosome Organization</a> paper.</p> <p>This data is also used in Chapter 5 of my <a href="https://gitlab.inria.fr/tgrohens/phd">PhD thesis</a>.</p>
Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data
Open the record for dataset details and reuse information.
miRNA gene regulatory networks for 38 human tissues
<p>We reconstructed miRNA regulatory networks for 38 tissues from the Genotype-Tissue Expression project (GTEx) using two different prior networks, one obtained with target predictions from TargetScan and one with target predictions from miRanda.</p> <p>We used these networks to investigate gene expression and regulation by miRNAs across these tissues. In the RData file, we share the following objects:</p> <p>- <strong>exp</strong>: a 16,161 by 9,435 data frame including normalized expression data for each sample.</p> <p>- <strong>expTS</strong>: a 16,161 by 38 matrix including the tissue-specificity scores for each gene in each tissue.</p> <p>- <strong>netT</strong>: a 10,391,523 by 41 data frame that includes the miRNA regulatory networks. The column "miRNA" includes the name of the regulating miRNA, the column "Gene" includes the target gene (HGNC symbol), and the column "Prior" the prior regulatory network based on target predictions from TargetScan, with 1 for edges that are canonical and 0 for edges that are non-canonical. The remaining 38 columns contain the PUMA network edge weights for each of the 38 tissues.</p> <p>- <strong>netT_TS</strong>: a 10,391,523 by 38 matrix that includes the tissue-specificity scores of the miRNA regulatory networks that were modeled on the TargetScan prior. Edges are not labelled, but edge order corresponds to the edges in "netT".</p> <p>- <strong>netM</strong>: a 10,391,523 by 41 data frame that includes the miRNA regulatory networks. The column "miRNA" includes the name of the regulating miRNA, the column "Gene" includes the target gene (HGNC symbol), and the column "Prior" the prior regulatory network based on target predictions from miRanda, with 1 for edges that are canonical and 0 for edges that are non-canonical. The remaining 38 columns contain the PUMA network edge weights for each of the 38 tissues.</p> <p>- <strong>netM_TS</strong>: a 10,391,523 by 38 matrix that includes the tissue-specificity scores of the miRNA regulatory networks that were modeled on the miRanda prior. Edges are not labelled, but edge order corresponds to the edges in "netT".</p> <p>- <strong>samples</strong>: a 9,435 by 2 data frame that includes sample identifiers (matching the identifiers in "exp") and the tissue to which these samples belong.</p> <p>- <strong>mirnames</strong>: a 694 by 3 data frame that contains miRNA names of regulators and their matching target miRNA names. The first column "base_miRNA" contains the "base" miRNA, the name of the miRNA without any extensions. The second column "reg_miRNA" contains the 643 regulator miRNA, which may have -3P/-5P extensions, and which matches the miRNAs that are present as regulators in the networks. The third columns "tar_miRNA" contains the 621 target miRNAs, which may have numbered suffix extensions, and for which we have expression data available.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.