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1,104 results for “regulatory network”
Processed Data for "Improving Gene Regulatory Network Inference using Dropout Augmentation"
<p>Here are the processed dataset that are used in the manuscript "Improving Gene Regulatory Network Inference using Dropout Augmentation"</p>
ZIRFs: zero-inflated random forests for estimating gene regulatory networks from single cell RNA-seq data (assessment of predictive accuracy and VIM stability)
<p>We developed a zero-inflated random forests (ZIRFs) algorithm to produce a metric of connection strength between regulator genes and target genes. This file contains SCENIC results for the aorta and diaphragm tissue data sets from the Tabula Muris Consortium results. SCENIC is a genetic regulatory network analysis published by Aibar et al. (2017). The purpose of the data sets and R source code are described by README files in each directory.</p>
Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Supplementary Tables
<p>This repository contains the Supplementary Tables for Suriyalaksh et al. Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes.</p> <p>The list of table files can be found in <a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/Supplementary%20table%20guide.pdf">Supplementary Tables guide.pdf</a></p> <p>Tables S1, S2 and S3 corresponding to physical gene-gene interaction data are in a separate repository doi:10.5281/zenodo.4382337</p> <p>Details about some of the Supplementary tables:</p> <p>TableS4_inferred_networks.csv - list of inferred GRNs for specified input combinations (set of input regulators, length of the time sequence, NI tool and prior used).</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS5_consensus_network_member.xlsx">TableS5_consensus_network_member.xlsx</a> - list of groups of topologically similar GRNs (from Table S4)</p> <p>Table S6: edge lists (source,target) for each one of the three consensus networks selected according to the GS validation metrics: middle PFE/AUFE, max AUFE, max PFE.<br> TableS6a_max_AUFE_GRN.txt - max AUFE; largest network - this is the one we used in the main analysis and discussion<br> TableS6b_max_PFE_GRN.xt - max PFE<br> TableS6c_middle_AUFE_PFE_GRN.txt - middle PFE/AUFE</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS7_qRTPCR_ddCt_network_accuracy.csv">TableS7_qRTPCR_ddCt_network_accuracy.csv</a> - gene expression count differences for RNAi knockdown GRN validation experiments. </p> <p>Table S8: Group membership for each one of the nodes in each one of the selected networks according to the SBM that best describes the observed network topology. Each column shows the group membership for each level in a SBM block hierarchy. Our analysis is in the second most coarse-grained level (level 1).</p> <p>TableS8a_max_AUFE_SBM.csv<br> TableS8b_max_PFE_SBM.csv<br> TableS8c_middle_AUFE_PFE_SBM.csv</p> <p><a href="https://zenodo.org/api/files/431155b3-c2b2-4fff-92bb-e93cad8bf5c2/TableS9_glp_gs_datasets.pdf">TableS9_glp_gs_datasets.pdf</a> - list of datasets used for defining functional clusters.</p> <p>TableS14a_glp_l1_vs_fem_l1_lifespan_assay.xlsx - Day13 survival of fem-3(q20)ts vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1</p> <p>TableS14b_glp_l1_vs_glp_l4_lifespan_assay.xlsx - Day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L4 vs day 19 survival of glp-1(e2144)ts;rrf-3(pk1426) RNAi from L1</p> <p>TableS15a_glp1_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of glp-1(e2144)ts;rrf-3(pk1426)</p> <p>TableS15b_fem3_in_vivo_fluorescence_data.xlsx - in vivo fluorescent reporter data of fem-3(q20)ts</p> <p>TableS17_input_regulators_annotated.csv - list input regulators used as input for Network Inference Tools annotated by source type (2nd column): GenAge, known transcription factors (TF) and gene with high variability in the gene expression time series (HV). The third column lists whether that regulator has an orthologue in human (y) according to WormBase (v 278).</p> <p>TableS20_epistasis_lifespan_data.xlsx - Epistasis lifespan data of glp-1(e2144)ts</p> <p>All the image (TIF) files represent representative images in the following genetic backgrounds (below) that have been treated </p> <p>with empty vector (EV) or RNAi against the gene highlighted in the title of the image. See methods section for details. </p> <p><strong>femliu1: </strong></p> <p><em>fem-3(q20)ts.; dhs-3p::dhs-3::gfp</em></p> <p><strong>femsod3:</strong></p> <p><em>fem-3(q20)ts.; sod-3p::gfp</em></p> <p><strong>glp1lgg1:</strong></p> <p><em>glp-1(e2144); lgg-1p:lgg-1:gfp</em></p>
Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression
<p>Supplemental Data 1 is single-cell response to rapamycin count data first sequenced in this work and deposited in GEO with accession GSE242556. It is a 173348 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Gene', 'Replicate', 'Pool', and 'Experiment') are cell-specific metadata.</p> <p>Supplemental Data 2 is bulk response to rapamycin count data first sequenced in this work. It is a 33 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Oligo', 'Time', 'Replicate', and 'Sample_barcode') are sample-specific metadata.</p> <p>Supplemental Data 3 is single-cell count data published as GSE125162 and re-analyzed with the pipeline used for single-cell quantification in this work. It is a 65068 rows × 5850 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 7 columns ('Condition', 'Sample', 'Genotype_Group', 'Genotype_Individual', 'Genotype', 'Replicate', 'Cell_Barcode') are cell-specific metadata.</p> <p>Supplemental Data 4 is the four deep learning models trained in this work. It is a TAR.GZ file containing the final biophysical transcription/decay model, the pre-trained decay model, the velocity prediction model, and the count prediction model. Each model file is an h5 file containing a pytorch model that can be loaded with supirfactor\_dynamical.read().</p> <p>Supplemental Data 5 is the prior knowledge network used to constrain the models for TF interpretability. It is a 1574 rows × 204 columns [Genes x TFs] TSV.GZ file where the first row is a header with TF names, the first column is an index of gene names, and TF-gene interactions are indicated by non-zero values in the matrix. There are 2799 TF-gene interactions.</p> <p><br> Supplemental Table 6 is the oligonucleotide sequences used in this work. It is a TSV file with a header row.</p> <p>Supplemental Table 7 is the yeast strains used in this work. It is a TSV file with a header row.</p> <p>Supplemental Table 8 is gene metadata used in this work (e.g. Ribosomal Protein gene labels, etc). It is a TSV file with a header row.</p> <p>Supplemental Table 9 is FY4/5 growth curve data generated in this work. It is a 20 rows × 7 columns TSV file where the first row is a header with replicate IDs, the first column is an index of times in minutes, and values are cell densities in YPD culture, in units of 10$^6$ cells / mL.</p> <p>Supplemental Data 10 is a TAR.GZ file containing the yeast SacCer3 genome, modified to add UTR sequences, that was used to generate transcripts for kallisto pseudoalignment in this work.</p>
Emergence of cooperative bistability and robustness of gene regulatory networks
<p>Simulation and analysis source codes and obtained data set for "Emergence of cooperative bistability and robustness of gene regulatory network" (<a href="https://doi.org/10.1371/journal. pcbi.1007969">PLoS Comput Biol 16 (2020) e1007969</a> and <a href="https://arxiv.org/abs/1907.12030">arXiv:1907.12030</a>) by Nagata and Kikuchi. </p> <p>Source codes and figures are compiled in Jupyter notebook. Detailed discription of data sets is found in "readme.txt" file.</p> <p> </p>
Predicting placenta transcriptional regulatory interactions based on spatial gene expression data and convolutional neural network
<p><strong>Aims:</strong> The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped.</p> <p><strong>Methods and Results:</strong> In this study, first-trimester chorionic villus and decidua tissues were collected. Next, we present a machine-learning system to infer gene interaction networks of the human placenta from immunofluorescence images of trophoblast specific transcription factors obtained by a high-resolution scanner.</p> <p><strong>Conclusions:</strong> The experimental results show that deep learning models reveal regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.</p>
Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.
<p>This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms </p> <p>We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references). </p> <p>This repository contains 3 different files:</p> <p>TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions</p> <p>TableS2_physical_priors.xlsx - contains three tabs:<br> ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) + ChIP-seq datasets (GSE28350, GSE81521) from (Hochbaum et. al, 2011, Li et. al, 2016).</p> <p>eY1HATAC- contains 3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016).</p> <p>motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1</p> <p>TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources)</p> <p>---</p> <p>Description of methodology to obtain regulatory interactions in TableS2:</p> <p>Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations.</p> <p>For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands.</p> <p>An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs.</p> <p>For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair.</p> <p>For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).</p>
A collection of draft gene regulatory networks and perturbation transcriptomics data
<p>These are collections of previously published gene regulatory networks and perturbation transcriptomics data analyzed in our manuscript "A systematic comparison of computational methods for expression forecasting". For more information and related code, see https://github.com/ekernf01/perturbation_benchmarking . </p>
Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network
<p>In this repository we provide raw and processed datasets for the article: “Genome-wide gene expression noise in <em>Escherichia coli </em>is condition-dependent and determined by propagation of noise through the regulatory network<strong>” </strong>by Arantxa Urchueguía, Luca Galbusera, Dany Chauvin, Gwendoline Bellement, Thomas Julou and Erik van Nimwegen.</p> <p>A preprint is available under the following DOI: <a href="https://doi.org/10.1101/795369">https://doi.org/10.1101/795369</a>. </p> <p>The repository consists of the following datasets: </p> <p><strong>1. preprocessed_datasets.zip(~22GB)</strong></p> <ul> <li>This dataset contains raw data from the flow cytometry experiments (FACS Canto II, BD Bioscience) in all measured conditions in RData format. Raw fcs files were processed with the tools described in the publication ''Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria" published here: <a href="https://doi.org/10.1371/journal.pone.0240233">https://doi.org/10.1371/journal.pone.0240233</a>. The tools themselves are available here: <a href="https://github.com/vanNimwegenLab/E-Flow">https://github.com/vanNimwegenLab/E-Flow</a>. Included in the files are the outputs of these processing tools together with all raw values that came directly from the flow cytometer. The file <em>directory_structure_in_preprocessed </em>contains information about how the files are organized.</li> </ul> <p><strong>2. info_files: </strong>This is a set of csv files containing detailed information about the experiments done to acquire the preprocessed_datasets as well as annotation files that we used to retrieve promoter information. </p> <p><strong>3. processed_datasets:</strong> These files correspond to the processed datasets from the raw Rdata files under 1 above. The processed data provide mean and variance estimates in fluorescence of E.coli promoters across the different growth conditions. Note that we discarded flow cytometry measurements from promoter/growth-condition combinations that contained abnormal fluorescence distributions (due to contamination) as well as measurements from reporters with annotation mismatches. The folder contains the following clean dataset files that were used in the paper:</p> <ul> <li><strong>FULL_dataset_mean_var_wreplicates:</strong> In this dataset we include the processed means and variances (in both logarithmic and linear scale) of all promoters in each condition. Included as well are replicate measurements for some conditions.. We also include the name and Blattner number of the gene immediately downstream of each promoter, the DNA sequence of each promoter, and regulatory information (number of unique inputs for transcription factors sites and their names) which we obtained from RegulonDB v 10.5 (<a href="https://doi.org/10.1093/nar/gky1077">https://doi.org/10.1093/nar/gky1077</a>). </li> <li><strong>dataset_with_noise_estimates: </strong>In this dataset we provide noise estimates for all promoters expressed above an expression threshold (mean GFP fluorescence at least as large as autofluorescence). Note that the noise estimate correspond to the difference between the promoter’s variance in log-expression and the minimal variance as a function of its mean expression (i.e. the so called noise floor was subtracted). Apart from the mean, variance, noise and promoter features (sequence, name of gene downstream, number of unique regulatory inputs and name of the TFs binding), we also include the parameters used for fitting the minimal noise, i.e. noise floor, in each of the conditions. </li> <li><strong>time_course_data_SI</strong>: This dataset contains mean and variance measurements of one of the plates of the library measured at different time points during growth in Minimal media 0.4M NaCl: 0h (just after dilution), 1h, 2h, 3h, 5h, 6.5h, 8.5h, 10h and 11h. </li> <li><strong>growth_curves_SI</strong>: Growth data (OD<sub>600</sub> as a function of time) for a subset of the promoters from the library across different growth conditions.</li> <li><strong>singlecell_areas_SI: </strong>Single-cell areas estimated using agar patches of cells growing in each condition. Each row of the table contains data for a single-cell. </li> <li><strong>synthetic_promoters_dataset: </strong>This dataset contains mean, variance and noise measurements of a set of constitutive promoters from <a href="https://doi.org/10.7554/eLife.05856.001">https://doi.org/10.7554/eLife.05856.001</a> across different conditions.</li> <li><strong>MARA_results:</strong> All transcription factor activities results explaining measured noise levels in each condition. This data has been obtained after performing Motif Activity Response Analysis on the noise levels of all measured promoters in each condition.</li> </ul>
TReNCo: Topologically associating domain (TAD) aware regulatory network construction (extended data)
<p>The enclosed files contain all of the extended data from: TReNCo: Topologically associating domain (TAD) aware regulatory network construction</p>
Results from Interpreting Cis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics
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Data for: Induction of C4 genes during de-etiolation of Gynandropsis gynandra evolved through changes in cis allowing integration into ancestral C3 gene regulatory networks
<p>C4 photosynthesis has evolved repeatedly and in doing so repurposed existing enzymes to drive a carbon pump that limits the oxygenation reaction of RuBisCO. C4 proteins accumulate to levels matching those of the photosynthetic apparatus, and to allow this gene expression must be modified over evolutionary time. To better understand this rewiring of gene expression we undertook RNA-SEQ and <span>DNaseI</span>-SEQ on de-etiolating seedlings of C4 <em>Gynandropsis gynandra</em> which is evolutionarily proximate to C3 <em>A. thaliana</em>. Changes in chloroplast ultrastructure and C4 gene expression in <em>G. gynandra</em> were coordinated and rapid. C3 and C4 photosynthesis genes showed similar induction patterns, but C4 genes from <em>G. gynandra</em> were more strongly induced than orthologs from <em>A. thaliana</em>. The cistrome of <em>G. gynandra</em> was enriched in TGA, TCP and homeodomain binding sites. Furthermore,<em> in vivo</em> binding data in <em>G. gynandra</em> highlighted TGA and homeodomain as well as light responsive elements such as G- and I-box motifs as being associated with the rapid increase in transcripts derived from C4 genes. Although promoters of <em>PPDK</em> and <em>ASP1</em> from <em>G. gynandra</em> contained distinct light responsive elements, promoters from both <em>A. thaliana</em> and <em>G. gynandra</em> allowed high expression. Deletion analysis of the <em>Ppa6</em> gene from <em>G. gynandra</em> showed that regions containing G- and I-boxes were necessary for high expression. The data support a model in which accumulation of transcripts derived from C4 genes in leaves of <em>G. gynandra</em> is enhanced compared with homologs in <em>A. thaliana</em> because a variety of modifications in <em>cis</em> allowed integration into ancestral transcriptional networks.</p>
Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize
<p>Relevant Data and Code for <em>Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize </em>by Taylor Ferebee and Edward Buckler.</p> <p><strong>Input Data</strong></p> <p>The inputs of the models are enclosed in <em>Input_data-2022-001.zip</em></p> <p><strong>Output Data</strong></p> <p>The outputs of the models are enclosed in <em>Output_Results-2022-001.zip</em></p> <p><strong>Relevant Code </strong></p> <p>The code for all analyses is enclosed in <em>Code_Archive.zip </em></p>
Tissue-specific Regulatory Network from Badr's work
<p>Mapping perturbed molecular circuits that underlie complex diseases remains a great challenge. We developed a comprehensive resource of 394 cell type- and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity among transcription factors, enhancers, promoters and genes. Integration with 37 genome-wide association studies (GWASs) showed that disease-associated genetic variants--including variants that do not reach genome-wide significance--often perturb regulatory modules that are highly specific to disease-relevant cell types or tissues. Our resource opens the door to systematic analysis of regulatory programs across hundreds of human cell types and tissues (http://regulatorycircuits.org).</p> <p>hs_blood_network.rds : human blood related gene regulatory networks (R program data file)</p> <p>hs_pantissue_network.rds : human pan-tissue related gene regulatory networks (R program data file)</p> <p>mm_blood_network.rds : mouse blood related gene regulatory networks (R program data file)</p> <p>mm_pantissue_network.rds : mouse pan-tissue related gene regulatory networks (R program data file)</p> <ul> <li>PMID: <strong>26950747</strong></li> <li>PMCID: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/pmc4967716/">PMC4967716</a></li> <li>DOI: <a href="https://doi.org/10.1038/nmeth.3799">10.1038/nmeth.3799</a></li> </ul>
Data for: Irreversibility in bacterial regulatory networks
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Simulations of gene regulatory networks with transcriptional adaptation
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Data for: Induction of C4 genes during de-etiolation of Gynandropsis gynandra evolved through changes in cis allowing integration into ancestral C3 gene regulatory networks
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Supplementary data for manuscript "Genetic risk converges on regulatory networks mediating early type 2 diabetes"
<p>Supplementary data for manuscript "Genetic risk converges on regulatory networks mediating early type 2 diabetes" Nature 624, 621–629 (2023). <a href="https://doi.org/10.1038/s41586-023-06693-2">https://doi.org/10.1038/s41586-023-06693-2</a></p> <p>Brief description of the included files is given below. Please visit the manuscript website for latest updates: <a href="http://theparkerlab.org/manuscripts/2021_islet-rfx6/">http://theparkerlab.org/manuscripts/2021_islet-rfx6/</a></p>
Out from under the wing: reconceptualizing the insect wing gene regulatory network as a versatile, general module for body-wall lobes in arthropods
<p>Body plan evolution often occurs through the differentiation of serially homologous body parts, particularly in the evolution of arthropod body plans. Recently, homeotic transformations resulting from experimental manipulation of gene expression have been interpreted as evidence that portions of dorsal and lateral arthropod body-wall are serially homologous to wings. These results, along with comparative data on the expression and function of genes in the wing regulatory network, provided a new perspective on an old question in insect evolution—how did the insect wing, evolve? A proposed ancestral role for the wing regulatory network in patterning body-wall margins motivated a broader comparison of gene function in wings and body-wall. We investigated the roles of a suite of ten wing- and body-wall related genes in a hemimetabolous insect, Oncopeltus fasciatus. Our results indicate that genes involved in wing development in O. fasciatus play similar roles in the development of adult body-wall flattened cuticular evaginations. We found extensive functional similarity between the development of wings and other bilayered evaginations of the body wall. Overall, our results support the existence of a versatile development module for building bilayered cuticular epithelial structures, which may have played a central role in the evolution of wings.</p>
EnGRaiN : A Supervised Ensemble Learning Method for Recovery of Large-scale Gene Regulatory Networks
<p>EnGRaiN is a supervised machine learning method to construct ensemble networks. To benefit from the typical accuracy advantages of supervised learning methods while taking into account the impossibility of knowing true networks for training, we devised a method that uses small training datasets of true positives and true negatives among gene pairs.</p> <p>The datasets used to evaluate the performance of EnGaiN include (i) simulated datasets generated from Yeast networks and (ii) A. thaliana gene expression datasets.</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.