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454 results for “Tissue-specific”
Aligned bam files for "Phylogenetic modeling of enhancer shifts in mole-rats reveals regulatory changes associated with tissue-specific traits"
<p>Aligned bam files used for analysis in "Phylogenetic modeling of enhancer shifts in mole-rats reveals regulatory changes associated with tissue-specific traits".</p> <p>This is an accompanying dataset to Datasets and code for "Phylogenetic modeling of enhancer shifts in mole-rats reveals regulatory changes associated with tissue-specific traits" (https://zenodo.org/record/7442105).</p>
Supplementary datasets for manuscript titled: Seasonal tissue-specific gene expression in wild crown-of-thorns starfish reveals reproductive and stress-related transcriptional systems
<p>Supplementary datasets for manuscript titled: Seasonal tissue-specific gene expression in wild crown-of-thorns starfish reveals reproductive and stress-related transcriptional systems</p>
Quantifying the Tissue-Specific Regulatory Information within Enhancer DNA Sequences
<p>Tables of mouse embryonic differential enhancers and promoters.</p> <p>The tables list the genomic coordinates of differential regulatory elements, the tissues in which they are active (labels column), a differential score, and the DNA sequence of the element. Tissues are numbered from 0 to 7, which correspond to heart, kidney, liver, limb, lung, forebrain, midbrain, and hindbrain, respectively. The tool <a href="https://github.com/pbenner/gonetics/tree/master/tools/segmentationDifferential">segmentationDifferential</a> from the <em>gonetics library</em> was used to compute differential elements. The length of all regions was set to 1000 base pairs around the center. Specifics of the experimental data can be found in the referenced publication.</p>
Molecular adaptations in response to exercise training are associated with tissue-specific transcriptomic and epigenomic signatures
<p>Processed data associated with the manuscript DOI: <a href="https://doi.org/10.1016/j.xgen.2023.100421" target="_blank" rel="noopener">10.1016/j.xgen.2023.100421 </a></p> <p>Analysis code on GitHub: <a href="../doi/10.5281/zenodo.8253917" target="_blank" rel="noopener">10.5281/zenodo.8253917</a></p> <p> </p>
Data from: Tissue-specific O-GlcNAcylation profiling identifies substrates in translational machinery in the Drosophila mushroom body contributing to olfactory learning
<p><em>O-</em>GlcNAcylation is a dynamic post-translational modification that diversifies the proteome. Its dysregulation is associated with neurological disorders that impair cognitive function, and yet identification of phenotype-relevant candidate substrates in a brain-region-specific manner remains unfeasible. By combining an <em>O-</em>GlcNAc binding activity derived from<em> Clostridium perfringens</em> OGA (<em>Cp</em>OGA) with TurboID proximity labeling in <em>Drosophila</em>, we developed an <em>O-</em>GlcNAcylation profiling tool that translates <em>O-</em>GlcNAc modification into biotin conjugation for tissue-specific candidate substrates enrichment. We mapped the <em>O-</em>GlcNAc interactome in major brain regions of <em>Drosophila</em> and found that components of the translational machinery, particularly ribosomal subunits, were abundantly <em>O-</em>GlcNAcylated in the mushroom body of <em>Drosophila</em> brain. Hypo-<em>O-</em>GlcNAcylation induced by ectopic expression of active <em>Cp</em>OGA in the mushroom body decreased local translational activity, leading to olfactory learning deficits that could be rescued by dMyc overexpression-induced increase of protein synthesis. Our study provides a useful tool for future dissection of tissue-specific functions of <em>O-</em>GlcNAcylation in <em>Drosophila</em> and suggests a possibility that <em>O-</em>GlcNAcylation impacts cognitive function via regulating regional translational activity in the brain.</p>
Extended data: Tissue-specific multi-omics analysis of atrial fibrillation
<p>Summary statistics and result repository for the publication Tissue-specific multi-omics analysis of atrial fibrillation:</p> <p>Assum, I., Krause, J., Scheinhardt, M.O. <em>et al.</em> Tissue-specific multi-omics analysis of atrial fibrillation. <em>Nat Commun </em><strong>13, </strong>441 (2022). https://doi.org/10.1038/s41467-022-27953-1</p> <p>For the related source code, see https://doi.org/https://doi.org/10.5281/zenodo.5094276 or https://github.com/heiniglab/symatrial.</p> <p>Ines Assum<sup>1,2,†</sup>, Julia Krause<sup>3,4,†</sup>, Markus O. Scheinhardt<sup>5</sup>, Christian Müller<sup>3,4</sup>, Elke Hammer<sup>6,7</sup>, Christin S. Börschel<sup>4,8</sup>, Uwe Vöker<sup>6,7</sup>, Lenard Conradi<sup>9</sup>, Bastiaan Geelhoed<sup>4,8,10</sup>, Tanja Zeller<sup>3,4,</sup>*, Renate B. Schnabel<sup>4,8,</sup>*, Matthias Heinig<sup>1,2,11,</sup>*</p> <p><sup>† </sup>,* These authors contributed equally.</p> <p><sup> 1</sup> Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany.<br> <sup> 2</sup> Department of Informatics, Technical University Munich, München, Germany.<br> <sup> 3</sup> University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, Hamburg, Germany.<br> <sup> 4</sup> Partner site Hamburg/Kiel/Lübeck, DZHK (German Center for Cardiovascular Research), Hamburg, Germany.<br> <sup> 5</sup> Institute of Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany.<br> <sup> 6</sup> Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.<br> <sup> 7</sup> Partner site Greifswald, DZHK (German Center for Cardiovascular Research), Greifswald, Germany.<br> <sup> 8</sup> Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.<br> <sup> 9</sup> Department of Cardiovascular Surgery, University Heart and Vascular Center Hamburg, Hamburg, Germany.<br> <sup>10 </sup>Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.<br> <sup>11</sup>Partner site Munich, DZHK (German Center for Cardiovascular Research), Munich, Germany.</p> <p> </p> <p>ABSTRACT:</p> <p>Genome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants. Their underlying molecular mechanisms, especially consequences for mRNA and protein expression remain largely elusive. Thus, refined multi-omics approaches are needed for deciphering the underlying molecular networks. Here, we integrate genomics, transcriptomics, and proteomics of human atrial tissue in a cross-sectional study to identify widespread effects of genetic variants on both transcript (cis-eQTL) and protein (cis-pQTL) abundance. We further establish a novel targeted transQTL approach based on polygenic risk scores to determine candidates for AF core genes. Using this approach, we identify two trans-eQTLs and five trans-pQTLs for AF GWAS hits, and elucidate the role of the transcription factor NKX2-5 as a link between the GWAS SNP rs9481842 and AF. Altogether, we present an integrative multi-omics method to uncover trans-acting networks in small datasets and provide a rich resource of atrial tissue-specific regulatory variants for transcript and protein levels for cardiovascular disease gene prioritization.</p> <p>This version contains a reference file identifying effect alleles for all QTL results and adds additional genotype and allele frequency information for all QTL SNPs. </p> <p>TABLE OF CONTENTS:</p> <ul> <li>Reference for effect alleles<br> <em>map_AFHRI_B_effect_alleles.txt</em></li> <li>Reference for genotype and allele frequencies (derived using PLINK) <ul> <li><em>genotype_allele_frequencies_eQTL_SNPs.txt</em></li> <li><em>genotype_allele_frequencies_pQTL_SNPs.txt</em></li> <li><em>genotype_allele_frequencies_resQTL_SNPs.txt</em></li> </ul> </li> <li>Single-omic <em>cis</em>-QTL results <ul> <li><em>cis</em>-eQTLs (all pairs, incl. LD clump info)<br> <em>eQTL_right_atrial_appendage_allpairs_clump.txt</em></li> <li><em>cis</em>-pQTLs (all pairs, incl. LD clump info)<br> <em>pQTL_right_atrial_appendage_allpairs_clump.txt</em></li> <li><em>cis</em>-res eQTLs (all pairs, incl. LD clump info)<br> <em>res_eQTL_right_atrial_appendage_allpairs_clump.txt</em></li> <li><em>cis</em>-res pQTLs (all pairs, incl. LD clump info)<br> <em>res_pQTL_right_atrial_appendage_allpairs_clump.txt</em></li> <li><em>cis</em>-ratioQTLs (all pairs, incl. LD clump info)<br> <em>ratioQTL_right_atrial_appendage_allpairs_clump.txt</em></li> </ul> </li> <li>Functional <em>cis</em>-QTL categories and eQTL/pQTL overlap: <ul> <li>All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with a significant eQTL and pQTL (FDR<0.05)<br> <em>Fig2a_source_data_Shared_eQTL_pQTL_clump.txt</em></li> <li>All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with a significant eQTL but no pQTL (FDR<0.05)<br> <em>Fig2b_source_data_Independent_eQTL_clump.txt</em></li> <li>All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with no eQTL but a significant pQTL (FDR<0.05)<br> <em>Fig2c_source_data_Independent_pQTL_clump.txt</em><span> </span></li> </ul> </li> <li>QTS rankings and enrichment results <ul> <li>eQTS rankings and enrichments<br> <em>TableS6_source_data_eQTS_ranking.txt<br> TableS7_source_data_eQTS_GSEA_results.txt</em></li> <li>pQTS rankings and enrichments<br> <em>TableS8_source_data_pQTS_ranking.txt<br> TableS9_source_data_pQTS_GSEA_results.txt</em></li> </ul> </li> <li><em>Trans</em>-QTLs<br> all tested pairs including <em>trans</em>-pQTLs for <em>trans</em>-eQTLs and <em>trans</em>-eQTLs for <em>trans</em>-pQTLs<br> <em>Table2_source_data_Trans-QTL_results.txt</em></li> </ul> <p> </p>
A benchmark of gene expression tissue-specificity metrics
<p>Supplementary figures and data to the paper "A benchmark of gene expression tissue-specificity metrics"</p> <p><em>Briefings in Bioinformatics</em>, Volume 18, Issue 2, March 2017, Pages 205–214, <a href="https://doi.org/10.1093/bib/bbw008">https://doi.org/10.1093/bib/bbw008</a></p> <p>Previously published at FigShare, republishing because of access problems for some researchers.</p>
Tissue-specific features of the T cell repertoire following allogeneic hematopoietic cell transplantation in human and mouse
<p>T cells are the central drivers of many inflammatory diseases, but the repertoire of tissue-resident T cells at sites of pathology in human organs remains poorly understood. We examined the site-specificity of T cell receptor (TCR) repertoires across tissues (5-18 tissues per patient) in prospectively collected autopsies of patients with and without graft-versus-host disease (GVHD), a potentially lethal tissue-targeting complication of allogeneic hematopoietic cell transplantation, as well as in mouse models of GVHD. Anatomic similarity between tissues was a key determinant of TCR repertoire composition within patients, independent of disease or transplant status. The T cells recovered from peripheral blood and spleen in patients and mice captured a limited portion of the TCR repertoire detected in tissues. Whereas few T cell clones were shared across patients, motif-based clustering revealed shared repertoire signatures across patients in a tissue-specific fashion. T cells at disease sites had a tissue-resident phenotype and were of donor origin based on single-cell chimerism analysis. These data demonstrate the complex composition of T cell populations that persist in human tissues at the end-stage of an inflammatory disorder following lymphocyte-directed therapy. These findings also underscore the importance of studying T cells in tissues rather than blood for tissue-based pathologies and suggest the tissue-specific nature of both the endogenous and post-transplant T cell landscape.</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>
The cacao gene atlas: A transcriptome developmental atlas reveals highly tissue-specific and dynamically-regulated gene networks in Theobroma cacao L
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Tissue-specific features of the T cell repertoire following allogeneic hematopoietic cell transplantation in human and mouse
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Data from: Tissue-specific O-GlcNAcylation profiling identifies substrates in translational machinery in the Drosophila mushroom body contributing to olfactory learning
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A putative telomerase activator has tissue-specific effects on telomere length in a developing songbird
<p>There is good evidence that telomeres predict variation in health and longevity, yet it is unclear whether these patterns are causally derived from telomeres <i>per se</i>, in part because relatively little research directly manipulates telomere length during early life, when telomere shortening is most dynamic. Here, we test how the telomerase activator TA-65 (i.e., cycloastrogenol) affects telomere length in five tissues during the peak of growth in the wild tree swallow (<i>Tachycineta bicolor</i>). Following 8 days of oral TA-65 administration, chicks experienced telomere lengthening in the blood and accelerated feather growth, but no changes to mass over time. TA-65 did not affect telomere length in the brain or spleen and led to shorter telomeres in the liver and adrenals. This whole-organism experimental manipulation of telomere dynamics therefore reveals limitations to telomere protection and biological senescence. In doing so, this work advances our understanding of early-life telomere dynamics and their potential role in generating future variation in health and lifespan.</p>
Data from: Sex- and tissue-specific differences in telomere length in a reptile
The usage of telomere length (TL) in blood as a proxy for the TL of other tissues relies on the assumption that telomere dynamics across all tissues are similar. However, telomere attrition can be caused by reactive oxygen species (ROS) which may vary with metabolic rate, which itself varies across organs depending upon the life history strategy of an organism. Thus we chose to measure the telomeres of various cell types in juvenile painted dragon lizards, Ctenophorus pictus, given their unusual life history strategy. Individuals typically only experience a single mating season. We measured the TL of male and female dragons using qPCR and observed that TL varied with tissue type and sex. Telomeres of blood cells were longer than those of liver, heart, brain, and spleen, and females had longer telomeres than males. Brain telomeres in males were approximately half the length of those in females. Telomeric attrition in the male brain may be due to the need for rapid learning of reproductive tactics (territory patrol and defence, mate-finding). Significant correlations between the TL of tissue types suggest that blood TL may be a useful proxy for the TL of other tissues. Our comparison of organ-specific telomere dynamics, the first in a reptile, suggests that the usage of blood TL as a proxy requires careful consideration of the life history strategy of the organism.
Additional data for "Tissue-specific impacts of aging and genetics on gene expression patterns in humans"
<p>Additional files for paper "Tissue-specific impacts of aging and genetics on gene expression patterns in humans" by Yamamoto and Chung et al. 2021.</p> <p>Contains file of results from joint age and genetics model.</p>
Code and data for "Discovery and validation of tissue-specific DNA methylation as noninvasive diagnostic markers for colorectal cancer".
<p>Code and data for "<strong>Discovery and validation of tissue-specific DNA methylation as noninvasive diagnostic markers for colorectal cancer</strong>".</p> <ul> <li> <p>The publicly available datasets supporting the conclusions of this article are available in the Gene Expression Omnibus repository (<a href="https://www.ncbi.nlm.nih.gov/geo/">https://www.ncbi.nlm.nih.gov/geo/</a>) and UCSC Xena Browser (TCGA, <a href="https://xena.ucsc.edu/">https://xena.ucsc.edu/</a>).</p> </li> </ul>
Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks
<p>Data and Inferred Networks accompanying the manuscript entitled - “Aggregation of recount3 RNA-seq data improves the inference of consensus and context-specific gene co-expression networks” </p> <p>Authors: Prashanthi Ravichandran, Princy Parsana, Rebecca Keener, Kaspar Hansen, Alexis Battle </p> <p>Affiliations: Johns Hopkins University School of Medicine, Johns Hopkins University Department of Computer Science, Johns Hopkins University Bloomberg School of Public Health</p> <p>Description: </p> <p>This folder includes data produced in the analysis contained in the manuscript and inferred consensus and context-specific networks from graphical lasso and WGCNA with varying numbers of edges. Contents include:</p> <ul> <li> <p>all_metadata.rds: File including meta-data columns of study accession ID, sample ID, assigned tissue category, cancer status and disease status obtained through manual curation for the 95,484 RNA-seq samples used in the study. </p> </li> <li> <p>all_counts.rds: log2 transformed RPKM normalized read counts for 5999 genes and 95,484 RNA-seq samples which was utilized for dimensionality reduction and data exploration </p> </li> <li> <p>precision_matrices.zip: Zipped folder including networks inferred by graphical lasso for different experiments presented in the paper using weighted covariance aggregation following PC correction.</p> </li> <ul> <li> <p>The networks can be found as follows. First, select the folder corresponding to the network of interest - for example, Blood, this will then include two or more folders which indicate the data aggregation utilized, select the folder corresponding appropriate level of data aggregation - either all samples/ GTEx for blood-specific networks, this includes precision matrices inferred across a range of penalization parameters. To view the precision matrix inferred for a particular value of the penalization parameter X, select the file labeled lambda_X.rds</p> </li> <li> <p>For select networks, we have included the computed centrality measures which can be accessed at centrality_X.rds for a particular value of the penalization parameter X. </p> </li> <li> <p>We have also included .rds files that list the hub genes from the consensus networks inferred from non-cancerous samples at “normal_hubs.rds”, and the consensus networks inferred from cancerous samples at “cancer_hubs.rds”</p> </li> <li> <p>The file “context_specific_selected_networks.csv” includes the networks that were selected for downstream biological interpretation based on the scale-free criterion which is also summarized in the Supplementary Tables. </p> </li> </ul> <li> <p>WGCNA.zip: A zipped folder containing gene modules inferred from WGCNA for sequentially aggregated GTEx, SRA, and blood studies. Select the data aggregated, and the number of studies based on folder names. For example, blood networks inferred from 20 studies can be accessed at blood/consensus/net_20. The individual networks correspond to distinct cut heights, and include information on the cut height used, the genes that the network was inferred over merged module labels, and merged module colors. </p> </li> </ul>
Genetic modifiers of somatic expansion and clinical phenotypes in Huntington’s disease reveal shared and tissue-specific effects
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Tissue-specific transcriptomics uncovers novel craniofacial genes underlying jaw divergence in specialist pupfishes
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Data from: Sex- and tissue-specific differences in telomere length in a reptile
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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.