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ShareScore release 0.9.0
Dataset results
26 results for “Gene interaction network”
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>
Gene interaction networks from donor and failing heart gene expression
<p>These are the gene-gene interaction networks obtained from gene expression microarray data in the paper "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure" by Cordero, Parikh, et al. The dataset contains the following items:</p> <p>* networks.zip: Compressed weighted matrices, in numpy format, for various methods in heart failure and donor cohorts, which are in the file name (ARACNE, JGL [Joint Graphical Lasso], Z-score, Pearson correlation, CLR [Context Likelihood of Relatedness])</p> <p>* wgcna.zip: Compressed weighted matrices, in numpy format, for WGCNA. These are the networks used in the paper.</p> <p>* network_gene_names.txt: Gene names for rows and columns of the gene networks in networks.zip</p> <p>* wgcna</p> <p>Construction of the networks were done as follows for each method:</p> <p>* For Pearson, Z-score, and CLR, we simply computed each relevant statistic in a pairwise manner, no thresholds were applied.</p> <p>* For ARACNE, we performed 500 bootstrapped iterations and searched the MI cutoff automatically using default parameters.</p> <p>* For JGL we performed an Akaike Information Criteria guided model selection by scanning the sparsity and group difference L1 penalty strengths.</p>
Siderophore synthetase-receptor gene coevolution reveals habitat and pathogen-specific bacterial iron interaction networks
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Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein-protein interaction networks
<p>The datasets used to evaluate Enhanced Gaussian noise augmentation-based contrastive learning (EGsCL) against predicting the pro-longevity or anti-longevity effect of model organism gene. This repo also includes the pretrained encoders that obtained the best predictive performance for each organism (see Table 2).</p>
Data from: Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis
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CHD4 Interacts With TBX5 to Maintain the Gene Regulatory Network of Postnatal Atrial Cardiomyocytes [bulk RNA-seq]
GEO Series GSE284094. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
ConnecTF: A platform to integrate validated transcription factor-target interactions to build and refine gene regulatory networks
GEO Series GSE152405. Arabidopsis thaliana. 56 samples. Type: Expression profiling by high throughput sequencing.
CHD4 Interacts With TBX5 to Maintain the Gene Regulatory Network [multiome]
GEO Series GSE284093. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
5C analysis of the Epidermal Differentiation Complex locus reveals distinct chromatin interaction networks between gene-rich and gene-poor TADs in skin epithelial cells
GEO Series GSE102519. Mus musculus. 4 samples. Type: Other.
Global ovary gene interaction network structure informs biological effects of Chemical stressors
GEO Series GSE70936. Pimephales promelas. 1470 samples. Type: Expression profiling by array.
Gene Network Transitions in Embryos Depend Upon Interactions between a Pioneer Transcription Factor and Core Histones [RNA-Seq]
GEO Series GSE134463. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
The Properties of Genome Conformation and Spatial Gene Interaction and Regulation Networks of Normal and Malignant Human Cell Types
GEO Series GSE73924. Homo sapiens. 3 samples. Type: Other.
OCEAN-C: mapping hubs of open chromatin interactions across the genome reveals gene regulatory networks
GEO Series GSE100832. Homo sapiens. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.
Gene Network Transitions in Embryos Depend Upon Interactions between a Pioneer Transcription Factor and Core Histones [ATAC-Seq]
GEO Series GSE134464. Mus musculus. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Characterization of gene regulation and protein interaction networks for Matrin 3 encoding mutations linked to amyotrophic lateral sclerosis and myopathy
GEO Series GSE110492. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
CHD4 Interacts With TBX5 to Maintain the Gene Regulatory Network of Postnatal Atrial Cardiomyocytes
GEO Series GSE296675. Mus musculus. 16 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
CHD4 Interacts With TBX5 to Maintain the Gene Regulatory Network of Postnatal Atrial Cardiomyocytes [ChIP-seq]
GEO Series GSE284095. Mus musculus. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Genome regulation and gene interaction networks inferred from muscle transcriptome underlying feed efficiency in Pigs
GEO Series GSE148889. Sus scrofa. 41 samples. Type: Expression profiling by high throughput sequencing.
Gene Network Transitions in Embryos Depend Upon Interactions between a Pioneer Transcription Factor and Core Histones
GEO Series GSE134465. Mus musculus. 30 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
ScienceDex guides
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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.