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522 results for “gene regulatory network”
Network analysis reveals that acute stress exacerbates gene regulatory responses of the gill to seawater in Atlantic salmon
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Recent reconfiguration of an ancient developmental gene regulatory network in Heliocidaris Sea Urchins
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Out from under the wing: reconceptualizing the insect wing gene regulatory network as a versatile, general module for body-wall lobes in arthropods
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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>
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>
Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data
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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>
Novel gene regulatory networks identified in response to nitro-conjugated linoleic acid in human endothelial cells
<p>A distinct transcriptome regulated by NO<sub>2-</sub>CLA was revealed in primary human coronary artery endothelial cells (HCAECs) through RNA sequencing. </p>
Single-cell RNA sequencing of Sox17-expressing lineages reveals distinct gene regulatory networks and dynamic developmental trajectories
<p>Two seurat objects contains single-cell RNA sequencing data that captures <em>Sox17</em>-expressing lineages during embryogenesis.</p> <p>sox17_integrated_Figure2B.rds :</p> <p>This is a seurat object that contains single-cell RNA sequencing data from integration of GFP+ cells produced from <em>Sox17<sup>GFPCre</sup></em> allele marking cells that currently express <em>Sox17</em> or short-term progeny of <em>Sox17­-</em>expressing progenitors and TdTomato+ cells produced from <em>R26<sup>LSL.TdTomato</sup></em> reporter allele in the presence of <em>Sox17<sup>GFPCre</sup></em> marking long-term progeny of <em>Sox17</em>-expressing progenitors. Inferred cell types in this seurat object reflects Figure 2B in the article.</p> <p>sox17_Prox1_endoderm_Figure5A.rds :</p> <p>This is a seurat object that contain single-cell RNA sequencing data from integration of <em>Sox17</em>- and <em>Prox1</em>-expressing endoderm dataset. Prox-1 expressing endoderm data is from the Willnow et al. <em>Nature</em>(2021). Inferred cell types in this seurat object reflects Figure 5A in the article.</p>
Double shrinking (DOSH), a regression-based algorithm for gene regulatory network inference from co-expression data
<p>Data for the preprint "Double shrinking (DOSH), a regression-based algorithm for gene regulatory network inference from co-expression data". The preprint is live on ResearchSquare: <a href="http://t.researchsquare.com/track/click/31114617/doi.org?p=eyJzIjoiWVJQQUYtT09mMXFoWnRoMGk0SlpQZTZqWWpJIiwidiI6MSwicCI6IntcInVcIjozMTExNDYxNyxcInZcIjoxLFwidXJsXCI6XCJodHRwczpcXFwvXFxcL2RvaS5vcmdcXFwvMTAuMjEyMDNcXFwvcnMuMy5ycy0yNzM4NjgzXFxcL3YxXCIsXCJpZFwiOlwiZDMzODNjZGNhNWNiNGE2Yjk5NWRkY2UyNmIyODI5NTlcIixcInVybF9pZHNcIjpbXCIzZGQwZTAxMmExMzk4NDhkNTAzYjI4ZTBiZmU1Y2QxMDcxNzhlZTgwXCJdfSJ9">10.21203/rs.3.rs-2738683/v1</a>.</p>
Dataset associated with Inferring Cell-Type-Specific Causal Gene Regulatory Networks during Human Neurogenesis
<p>Full summary statistics for QTLs generated in study titled "Inferring Cell-Type-Specific Causal Gene Regulatory Networks during Human Neurogenesis"</p> <p>The big "data" folder includes datasets for each model under subfolders Model 1A, Model 1B and Model 2 as following tree</p> <p>data<br> │ ├───Model1A<br> │ │ ├───caQTL<br> │ │ │ ├───neuron<br> │ │ │ └───progenitor<br> │ │ └───eQTL<br> │ │ ├───neuron<br> │ │ └───progenitor<br> │ ├───Model1B<br> │ └───Model2</p> <p> </p> <p> </p>
Data from: The invariant nature of a morphological character and character state: insights from gene regulatory networks
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Data from: An integrative approach for mapping differentially expressed genes and network components using novel parameters to elucidate key regulatory genes in colorectal cancer
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Data from: The impact of gene expression variation on robustness and evolvability of a developmental gene regulatory network
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Temporal flexibility of gene regulatory network underlies a novel wing pattern in flies
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Data from: Regulatory gene networks that shape the development of adaptive phenotypic plasticity in a cichlid fish
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Single cell gene expression data and gene regulatory network
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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.