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1,104 results for “regulatory network”
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>
Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens
Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells.
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>
Interpreting Cis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics
<p>Results and code to replicate analysis in "Interpreting Cis-Regulatory Interactions from<br> Large-Scale Deep Neural Networks for Genomics" by Toneyan and Koo.</p>
Benchmark data for "Model-X knockoffs reveal data-dependent limits on regulatory network identification"
<p>This collection of data was used in our manuscript tentatively entitled "<strong>Model-X knockoffs reveal data-dependent limits on regulatory network identification</strong>". It is entirely from public sources, but to enable easy repetition of our analyses, we collect it all here in the exact format we used. Links to related papers and code can be found at the <a href="https://github.com/ekernf01/knockoffs_paper">knockoffs paper</a> homepage.</p>
Data from: Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants
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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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Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens
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Reconciliation of regulatory data: the regulatory networks of Escherichia coli and Bacillus subtilis
<p>The dataset hereby uploaded, presents state-of-art, reconciled transcriptional regulatory networks of <em>Escherichia coli </em>K12 MG1655<em> </em>and <em>Bacillus subtilis </em>str 168<em>. </em>The networks were reconciled through the retrieval and integration of relevant regulatory data from multiple resources, including databases, such as <em>RegulonDB </em>and <em>DBTBS</em> as well as available literature.</p>
Data from: Molecular evolution of the neural crest regulatory network in ray-finned fish
Gene regulatory networks (GRN) are central to developmental processes. They are composed of transcription factors and signaling molecules orchestrating gene expression modules that tightly regulate the development of organisms. The neural crest (NC) is a multipotent cell population that is considered a key innovation of vertebrates. Its derivatives contribute to shaping the astounding morphological diversity of jaws, teeth, head skeleton or pigmentation. Here, we study the molecular evolution of the NC GRN by analyzing patterns of molecular divergence for a total of 36 genes in 16 species of bony fishes. Analyses of non-synonymous to synonymous substitution rate ratios (dN/dS) support patterns of variable selective pressures among genes deployed at different stages of NC development, consistent with the developmental hourglass model. Model-based clustering techniques of sequence features support the notion of extreme conservation of NC-genes across the entire network. Our data show that most genes are under strong purifying selection that is maintained throughout ray-finned fish evolution. Late NC development genes reveal a pattern of increased constraints in more recent lineages. Additionally, seven of the NC-genes showed signs of relaxation of purifying selection in the famously species-rich lineage of cichlid fishes. This suggests that NC genes might have played a role in the adaptive radiation of cichlids by granting flexibility in the development of NC-derived traits – suggesting an important role for NC network architecture during the diversification in vertebrates.
Data from: Structure-based network analysis of activation mechanisms in the ErbB family of receptor tyrosine kinases: the regulatory spine residues are global mediators of structural stability and allosteric interactions
The ErbB protein tyrosine kinases are among the most important cell signaling families and mutation-induced modulation of their activity is associated with diverse functions in biological networks and human disease. We have combined molecular dynamics simulations of the ErbB kinases with the protein structure network modeling to characterize the reorganization of the residue interaction networks during conformational equilibrium changes in the normal and oncogenic forms. Structural stability and network analyses have identified local communities integrated around high centrality sites that correspond to the regulatory spine residues. This analysis has provided a quantitative insight to the mechanism of mutation-induced "superacceptor" activity in oncogenic EGFR dimers. We have found that kinase activation may be determined by allosteric interactions between modules of structurally stable residues that synchronize the dynamics in the nucleotide binding site and the αC-helix with the collective motions of the integrating αF-helix and the substrate binding site. The results of this study have pointed to a central role of the conserved His-Arg-Asp (HRD) motif in the catalytic loop and the Asp-Phe-Gly (DFG) motif as key mediators of structural stability and allosteric communications in the ErbB kinases. We have determined that residues that are indispensable for kinase regulation and catalysis often corresponded to the high centrality nodes within the protein structure network and could be distinguished by their unique network signatures. The optimal communication pathways are also controlled by these nodes and may ensure efficient allosteric signaling in the functional kinase state. Structure-based network analysis has quantified subtle effects of ATP binding on conformational dynamics and stability of the EGFR structures. Consistent with the NMR studies, we have found that nucleotide-induced modulation of the residue interaction networks is not limited to the ATP site, and may enhance allosteric cooperativity with the substrate binding region by increasing communication capabilities of mediating residues.
Data from: Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational (co)variance
Systems biology is accumulating a wealth of understanding about the structure of genetic regulatory networks, leading to a more complete picture of the complex genotype-phenotype relationship. However, models of multivariate phenotypic evolution based on quantitative genetics have largely not incorporated a network-based view of genetic variation. Here we model a set of two-node, two-phenotype genetic network motifs, covering a full range of regulatory interactions. We find that network interactions result in different patterns of mutational (co)variance at the phenotypic level (the M-matrix), not only across network motifs but also across phenotypic space within single motifs. This effect is due almost entirely to mutational input of additive genetic (co)variance. Variation in M has the effect of stretching and bending phenotypic space with respect to evolvability, analogous to the curvature of space-time under general relativity, and similar mathematical tools may apply in each case. We explored the consequences of curvature in mutational variation by simulating adaptation under divergent selection with gene flow. Both standing genetic variation (the G-matrix) and rate of adaptation are constrained by M, so that G and adaptive trajectories are curved across phenotypic space. Under weak selection the phenotypic mean at migration-selection balance also depends on M.
Single cell gene expression data and gene regulatory network
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Extending the gene regulatory network model for B cell differentiation
<p>Scripts used for modelling the gene regulatory network of B cell differentiation</p>
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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.