11976 results for "cancer rna-seq"

in 39.97 milliseconds.

• Reference epigenome Kidney-Podocyte01 h3k36me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007230

• Reference epigenome Kidney-Podocyte04 h3k27ac ChIP-Seq data generated from KEP study

  Kidney-Podocyte04 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007242

• Reference epigenome Kidney-Podocyte01 h3k9me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007233

• Reference epigenome Kidney-Podocyte03 input ChIP-Seq data generated from KEP study

  Kidney-Podocyte03 input ChIP-Seq paired end data

  Dataset EGAD00001007241

• Reference epigenome Kidney-Podocyte04 h3k4me1 ChIP-Seq data generated from KEP study

  Kidney-Podocyte04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007245

• Reference epigenome Kidney-mesangial01 h3k27ac ChIP-Seq data generated from KEP study

  Kidney-mesangial01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007249

• Reference epigenome Kidney-Podocyte04 h3k4me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007246

• Reference epigenome Kidney-Podocyte04 h3k9me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007247

• Reference epigenome Kidney-mesangial01 h3k4me3 ChIP-Seq data generated from KEP study

  Kidney-mesangial01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007253

• Reference epigenome Kidney-mesangial01 input ChIP-Seq data generated from KEP study

  Kidney-mesangial01 input ChIP-Seq paired end data

  Dataset EGAD00001007255

• Reference epigenome Kidney-mesangial02 h3k4me1 ChIP-Seq data generated from KEP study

  Kidney-mesangial02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007259

• Reference epigenome Kidney-mesangial02 h3k4me3 ChIP-Seq data generated from KEP study

  Kidney-mesangial02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007260

• Reference epigenome Kidney-mesangial02 h3k9me3 ChIP-Seq data generated from KEP study

  Kidney-mesangial02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007261

• Reference epigenome IPS-NPC01 h3k27ac ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007277

• Reference epigenome IPS-NPC01 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007280

• Reference epigenome IPS-NPC01 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007282

• Reference epigenome IPS-ENeuron01 h3k27ac ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007291

• Reference epigenome IPS-ENeuron02 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007303

• Reference epigenome Kidney-mesangial01 h3k4me1 ChIP-Seq data generated from KEP study

  Kidney-mesangial01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007252

• Reference epigenome Islet-derived-iPSC02 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007447

• ICGC MMML-seq Data Freeze July 2013 whole genome sequencing

  ICGC MMML-seq Data Freeze July 2013 whole genome sequencing

  Dataset EGAD00001000645

• ICGC MMML-seq Data Freeze July 2013 miRNA sequencing

  ICGC MMML-seq Data Freeze July 2013 miRNA sequencing

  Dataset EGAD00001000650

• Reference epigenome Islet-derived-MSC03 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007424

• Reference epigenome Pancreas-Islet06 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007454

• Reference epigenome Pancreas-Islet06 h3k27ac ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007450

• Reference epigenome Pancreas-Islet06 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007451

• Reference epigenome Pancreas-Islet06 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007455

• Reference epigenome Pancreas-Islet06 input ChIP-Seq data generated from KEP study

  Pancreas-Islet06 input ChIP-Seq paired end data

  Dataset EGAD00001007456

• Reference epigenome Pancreas-Islet07 h3k4me1 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007460

• Reference epigenome Pancreas-Islet08 h3k27ac ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007464

• Reference epigenome Pancreas-Islet08 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007468

• Reference epigenome Fat-adipocyte03 h3k4me1 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007474

• Reference epigenome Fat-adipocyte03 h3k4me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007475

• Reference epigenome Fat-adipocyte04 h3k27me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007479

• Reference epigenome Fat-adipocyte04 h3k36me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007480

• Reference epigenome Fat-adipocyte04 h3k4me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007482

• Reference epigenome Fat-adipocyte05 h3k9me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007490

• Reference epigenome Kidney-Podocyte01 h3k4me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007232

• Reference epigenome Kidney-Podocyte01 input ChIP-Seq data generated from KEP study

  Kidney-Podocyte01 input ChIP-Seq paired end data

  Dataset EGAD00001007234

• Reference epigenome ADMSC01_smRNA-Seq data generated from KEP study

  A ADMSC01_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003867

• Reference epigenome Fat-adipocyte03 input ChIP-Seq data generated from KEP study

  Fat-adipocyte03 input ChIP-Seq paired end data

  Dataset EGAD00001007477

• Reference epigenome Pancreas-beta01 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-beta01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007215

• Reference epigenome Pancreas-Islet02 h3k27ac ChIP-Seq data generated from KEP study

  Pancreas-Islet02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007193

• Reference epigenome Pancreas-Islet03 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007205

• Reference epigenome Pancreas-Islet04 h3k27ac ChIP-Seq data generated from KEP study

  Pancreas-Islet04 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007207

• Reference epigenome Pancreas-Islet04 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet04 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007208

• Reference epigenome Pancreas-Islet04 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007209

• Reference epigenome Pancreas-Islet04 input ChIP-Seq data generated from KEP study

  Pancreas-Islet04 input ChIP-Seq paired end data

  Dataset EGAD00001007213

• Reference epigenome Pancreas-beta01 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-beta01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007219

• Reference epigenome Fat-Preadipocyte01 h3k27me3 ChIP-Seq data generated from KEP study

  Fat-Preadipocyte01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007222

• Reference epigenome Kidney-Podocyte03 h3k27me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte03 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007236

• Reference epigenome Kidney-Podocyte03 h3k4me1 ChIP-Seq data generated from KEP study

  Kidney-Podocyte03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007238

• Reference epigenome Kidney-Podocyte03 h3k4me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007239

• Reference epigenome Kidney-Podocyte03 h3k9me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007240

• Reference epigenome Kidney-mesangial01 h3k9me3 ChIP-Seq data generated from KEP study

  Kidney-mesangial01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007254

• Reference epigenome IPS-Fibroblast02 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007274

• Reference epigenome IPS-NPC01 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007279

• Reference epigenome IPS-NPC01 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007278

• Reference epigenome Pancreas-Islet07 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007458

• Reference epigenome Pancreas-Islet07 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007459

• Reference epigenome Pancreas-Islet08 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007465

• Reference epigenome Pancreas-Islet08 input ChIP-Seq data generated from KEP study

  Pancreas-Islet08 input ChIP-Seq paired end data

  Dataset EGAD00001007470

• Reference epigenome Fat-adipocyte03 h3k36me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007473

• BLUEPRINT Bisulfite-seq and Whole Genome Sequencing of mantle cell lymphoma

  BLUEPRINT Bisulfite-seq and Whole Genome Sequencing of mantle cell lymphoma

  Dataset EGAD00001001892

• Paired-end ATAC-seq analysis of the 3D spatially mapped GBM samples.

  Dataset contains 70 paired-end ATAC-seq samples from 8 patients.

  Dataset EGAD00001010311

• Reference epigenome Pancreas-Islet03 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007204

• Reference epigenome Fat-Preadipocyte01 h3k4me1 ChIP-Seq data generated from KEP study

  Fat-Preadipocyte01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007224

• Reference epigenome Kidney-Podocyte04 h3k36me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007244

• Reference epigenome IPS-Fibroblast01 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007264

• Reference epigenome IPS-Fibroblast01 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007265

• Reference epigenome IPS-Fibroblast01 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-Fibroblast01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007266

• Reference epigenome IPS-Fibroblast01 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007267

• Reference epigenome IPS-Fibroblast01 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007268

• Reference epigenome IPS-Fibroblast01 input ChIP-Seq data generated from KEP study

  IPS-Fibroblast01 input ChIP-Seq paired end data

  Dataset EGAD00001007269

• Reference epigenome IPS-Fibroblast02 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007271

• Reference epigenome IPS-Fibroblast02 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007272

• Reference epigenome IPS-ENeuron01 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007292

• Reference epigenome IPS-ENeuron01 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007293

• Reference epigenome IPS-ENeuron01 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007294

• Reference epigenome IPS-ENeuron01 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007295

• Reference epigenome IPS-ENeuron01 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007296

• Reference epigenome IPS-ENeuron01 input ChIP-Seq data generated from KEP study

  IPS-ENeuron01 input ChIP-Seq paired end data

  Dataset EGAD00001007297

• Reference epigenome IPS-ENeuron02 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007299

• Reference epigenome IPS-ENeuron02 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007300

• Reference epigenome IPS-ENeuron02 input ChIP-Seq data generated from KEP study

  IPS-ENeuron02 input ChIP-Seq paired end data

  Dataset EGAD00001007304

• Gene Expression and Regulatory Networks in Human Leukocytes - Immunological Variation Consortium

  Profiling of gene expression with microarrays holds great potential for human health, for illuminating disease pathways or providing biomarkers to monitor disease or its resolution. Using high-throughput approaches for genotyping, immunophenotyping and gene expression analysis, the project will examine the basis for the control of gene expression in human immune cells, and how it is influenced by natural genetic variation or aging. In practice, the project will combine several cross-informative approaches: 1) Building on the established sample/data pipelines and robust protocols of the Immunological Genome (ImmGen) project, and on established cohorts of ethnically diverse healthy volunteers at hand, microarray techniques will be used to generate whole-genome expression profiles from purified naive CD4+ lymphocytes and monocytes from 600 healthy volunteers. A dense genetic map will be established for all donors. The results will elucidate how variation in the human genome affects the expression of immune genes, of key importance in understanding gene variants that bring susceptibility to immune or inflammatory disease. Computational analysis of these rich data will allow the reconstruction of regulatory connections between genes, helping to establish general modules and those specific of a given immune cell type. These data will be complemented by an orthogonal data group, generated from a restricted subset of 10 individuals, in which we will profile a larger set of 28 carefully delineated cell populations that exist in human blood. This work will also benefit from powerful interspecies comparison with similar experiments being performed in mice by ImmGen. 2) In addition, RNA from the same set of 28 defined cell populations will be probed with microarrays that explore other aspects of the transcriptome: i) microRNAs and other non-coding RNAs; ii) exon or splice junction arrays that will establish a map of differential splicing in human blood leukocyte. 3) The composition and reactivity of blood cells from the same donors will be established at the time of sample collection using high-throughput flow cytometry, correlating immune phenotypes with gene expression and genetic variation. 4) Genetic variability conditions the baseline levels of gene expression, but also the responsiveness to activating challenges. With samples from the same donors, NanoString technology for transcript counting will be used to analyze the transcriptional response of defined gene sets, representing response signatures of T or dendritic cells, for a fine-grained dissection of responses to different triggers (different bacterial ligands for dendritic cells, different cytokine environment for T cells). In keeping with the resource aspect of this project, all data and interpretations will be made publicly available rapidly upon curation, allowing public querying and browsing of the data, by using and evolving the existing web architectures of the ImmGen project, of the Broad Institute, and of international repositories. RELEVANCE: Exploration with DNA chips of the genome's expression microarrays holds great potential for human health, to better understand disease and to serve as diagnostic tools. Using a combination of high-throughput genomic techniques and computational biology, we will perform a broad exploration of gene expression in human blood cells across groups of African-American, Asian and European ancestry, asking how these profiles are affected by genetic variation or by age. These results will provide an invaluable reference benchmark for the interpretation of genetic and immunological studies. Additionally, over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, in a second study we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution. 15% of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (ATAC-QTLs). ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression, and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression. In another study we analyzed RNA-seq profiles from primary CD4+ T cells of two healthy donors at six time points from 0 to 72 hours after stimulation with bead-bound antibodies against CD3 and CD28.

  Study phs000815

• A Single Cell Atlas of Gene Regulatory Elements in the Human Heart

  Background: Cis-regulatory elements such as enhancers and promoters are crucial for directing gene expression in the human heart. Dysregulation of these elements can result in many cardiovascular diseases that are major leading causes of morbidity and mortality worldwide. In addition, genetic variants associated with cardiovascular disease risk are enriched within cis-regulatory elements. However, the location and activity of these cis-regulatory elements in individual cardiac cell types remains to be fully defined. Methods: We performed single nucleus ATAC-seq and single nucleus RNA-seq to define a comprehensive catalogue of candidate cis-regulatory elements (cCREs) and gene expression patterns for the distinct cell types comprising each chamber of four non-failing human hearts. We used this catalogue to computationally deconvolute dynamic enhancers in failing hearts and to assign cardiovascular disease risk variants to cCREs in individual cardiac cell types. Finally, we applied reporter assays, genome editing, and electrophysiogical measurements in in vitro differentiated human cardiomyocytes to validate the molecular mechanisms of cardiovascular disease risk variants. Results: We defined >287,000 candidate cis-regulatory elements (cCREs) in human hearts at single-cell resolution, which notably revealed gene regulatory programs controlling specific cell types in a cardiac region/structure-dependent manner and during heart failure. We further report enrichment of cardiovascular disease risk variants in cCREs of distinct cardiac cell types, including a strong enrichment of atrial fibrillation variants in cardiomyocyte cCREs, and reveal 38 candidate causal atrial fibrillation variants localized to cardiomyocyte cCREs. Two such risk variants residing within a cardiomyocyte-specific cCRE at the KCNH2/HERG locus resulted in reduced enhancer activity compared to the non-risk allele. Finally, we found that deletion of the cCRE containing these variants decreased KCNH2 expression and prolonged action potential repolarization in an enhancer dosage-dependent manner. Conclusion: This comprehensive atlas of human cardiac cCREs provides the foundation for not only illuminating cell type-specific gene regulatory programs controlling human hearts during health and disease, but also interpreting genetic risk loci for a wide spectrum of cardiovascular diseases.

  Study phs002204

• Circulating RNA profiles of healthy and preeclamptic pregnancies

  This prospective study investigated whether circulating RNA (C-RNA; cell free RNA present in the bloodstream) measurements could non-invasively monitor pregnancy status, with emphasis on detecting molecular markers associated with preterm preeclampsia. Whole blood samples were collected from pregnant women at the time of diagnosis with early-onset preeclampsia with severe features. Blood samples were also obtained normotypical control pregnancies and from patients diagnosed with late-onset preeclampsia for comparison. For all samples, C-RNA was extracted from 4 mL blood and whole transcriptome enrichment performed on cDNA libraries prior to sequencing to a target depth of 50M reads on an Illumina HiSeq 2000 platform. Throughout the course of this work, whole transcriptome RNA sequencing data was obtained from 289 C-RNA samples collected from 178 subjects. The complete dataset consists of samples from the following cohorts: Illumina Preeclampsia Cohort (iPEC): 40 pregnancies at the time of early-onset preeclampsia diagnosis and 73 gestational age-matched controls PEARL preeclampsia cohort: 12 early-onset and 12 late-onset preeclampsia patients PEARL healthy control cohort: 152 longitundinal samples collected from 41 healthy pregnancies

  Study phs002017

• MTLE small RNA-sequencing and RNA-sequencing Data Access Committee

  Dac EGAC00001001427

• SEGA RNA-sequencing and small RNA-sequencing Data Access Commitee

  Dac EGAC00001001467

• Dataset for RNA sequencing glioblastoma data

  This dataset contains RNA sequencing data of 36 glioblastoma samples. Sequencing was performed on Illumina NovaSeq 6000 using TruSeq Stranded RNA Kit. The sequencing was always paired.

  Dataset EGAD50000000688

• The Environmental Determinants of Diabetes in the Young Study (TEDDY)

  Study Overview The Environmental Determinants of Diabetes in the Young (TEDDY) Study is a longitudinal study that investigates genetic and genetic-environmental interactions, including gestational events, childhood infections, dietary exposures, and other environmental factors after birth, in relation to the development of islet autoimmunity and type 1 diabetes (T1D). A consortium of six clinical centers assembled to participate in the development and implementation of the study to identify environmental triggers for the development of islet autoimmunity and T1D in genetically susceptible individuals. Beginning in 2004, the TEDDY study screened over 400,000 newborns for high-risk HLA-DR, DQ genotypes from both the general population and families already affected by T1D. The TEDDY study enrolled around 8,676 participants across six clinical centers worldwide (Finland, Germany, Sweden and three in the United States) in the 15-year prospective follow-up. Participants are followed every three months for islet autoantibody (IA) measurements with blood sampling until four years of age and then at least every six months until the age of 15. After the age of four, autoantibody positive participants continue to be followed at three month intervals and autoantibody negative participants are followed at six-month intervals. In addition to the analysis of autoantibodies, additional data and sample collection are performed at each visit. Parents collect monthly stool samples in early childhood. The parents also fill out questionnaires at regular intervals in connection with study visits and record information about diet and health status in the child's TEDDY Book between visits. Continued long-term follow-up of the currently active TEDDY participants will provide important scientific information on early childhood diet, reported and measured infections, vaccinations, and psychosocial stressors that may contribute to the development of type 1 diabetes and islet autoimmunity. Additional information on the TEDDY study is available in the following articles: Rewers et al., 2008, PMID: 19120261 and Hagopian et al., 2006, PMID: 17130573. Details of the TEDDY protocol can be found in Hagopian et al., 2011, PMID: 21564455. TEDDY data currently available in dbGaP include: gene expression, SNPs, exome, microbiome (gut, nasal, and plasma), RNA sequencing, and whole genome sequencing. For more information on TEDDY Study version history please refer to TEDDY Study dbGaP README File. ImmunoChip SNP DNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for SNP genotyping. Genotyping was performed by the Center for Public Health Genomics at the University of Virginia using the Illumina ImmunoChip SNP array, which contains around 196,000 SNPs from 186 regions associated with 12 autoimmune diseases (Hadley et al., 2015, PMID: 26010309). Data cleaning and validation included the removal of subjects with a low call rate (< 5% SNPs missing) and differences in reported sex and prior genotyping at the TEDDY HLA laboratory. Additionally, SNPs with a low call rate or Hardy-Weinberg equilibrium P value < 10-6, except for chromosome 6 due to HLA eligibility requirements, were removed from the final dataset (Törn et al., 2015, PMID: 25422107).TEDDY-T1DExome ArrayDNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for genotyping. Genotyping was performed by the University of Virginia using the Illumina TEDDY-T1DExome array. The TEDDY-T1DExome array is a custom chip that contains 550,601 markers from the Infinium CoreExome-24 v1.1 BeadChip and an additional 90,214 tagSNPs specifically selected by the TEDDY investigators based on their associations with nutrients, vitamins, type 2 diabetes, autoimmune diseases, body-mass index, or other exposures and phenotypes measured by TEDDY study.The Illumina GenTrain2 algorithm was used for genotype calling. Sample quality control metrics included sample call rate, heterozygosity rate and concordance of gender between the information reported and genotyped. Gene Expression The TEDDY study collected peripheral blood for the extraction of total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA (200 ng) was further used for cRNA amplification and labeling with biotin using Target Amp cDNA synthesis kit (Epicenter catalog no. TAB1R6924). Labeled cRNA was hybridized to the Illumina HumanHT-12 Expression BeadChips based on the manufacturer's instructions. The HumanHT-12 Expression BeadChip provides coverage for more than 47,000 transcripts and known splice variants across the human transcriptome. Microbiome The TEDDY microbiome study aimed to characterize the longitudinal development of the microbiome, including bacteria, viruses and other microorganisms in the gut, plasma, and nasal cavity of prediabetic and diabetic subjects compared to autoantibody negative non-diabetic subjects. Stool samples used were collected monthly from 3 to 48 months, after which stool samples were collected every 3 months. Nasal swab samples were collected every 3 months starting at 9 months of age until 48 months, after which nasal swabs were collected every 6 months. Plasma samples were collected every 3 months starting at 3 months of age until 48 months, after which plasma samples were collected every 6 months. If the subject was autoantibody positive at 48 months then they remained on the 3 month collection interval for nasal swab and plasma samples. Samples underwent 16s rRNA gene sequencing, DNA and viral RNA metagenomics shotgun sequencing, and sequencing of the internal transcribed spacer (ITS) regions. Additional information on the TEDDY microbiome data is available in the following articles: Vatanen et al., 2018, PMID: 30356183, Stewart et al., 2018, PMID: 30356187, and Vehik et al., 2020, PMID: 31792456. RNA Sequencing The TEDDY study aimed to characterize the transcriptome in subjects with islet autoimmunity and type 1 diabetes compared to matched control subjects. Peripheral blood was collected to extract total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA was then sent to the Broad Institute for the generation of the TEDDY RNA sequencing (RNA-Seq) data. The RNA samples were prepped using Superscript III reverse transcriptase and Illumina's TruSeq Stranded mRNA Sample Prep Kit. The TruSeq libraries were run on the Illumina HiSeq2500 platform. Whole Genome Sequencing The TEDDY study aimed to conduct deep whole genome sequencing and examine the genomic variations in subjects with islet autoimmunity and type 1 diabetes compared to matched autoantibody negative and non-diabetic children. DNA from whole blood was obtained from TEDDY children for whole genome sequencing. The WGS data were generated on the Illumina HiSeq X Ten system.

  Study phs001442

• Characterization of Macrophage-Tropic HIV Infection of Central Nervous System Cells and the Influence of Inflammation

  In this study, we utilize macrophage-tropic and T cell-tropic HIV-1 Env proteins to establish accurate infection profiles for multiple CNS cells under basal and interferon alpha (IFN-α) or lipopolysaccharide (LPS)-induced inflammatory states. We found that macrophage-tropic viruses confer entry advantages in primary myeloid cells, including monocyte-derived macrophages, microglia, and induced pluripotent stem cell (iPSC)-derived microglia. Following 24-h IFN-α or LPS treatments, total RNA was harvested from 100,000 to 200,000 cells using an RNeasy RNA isolation kit (Qiagen, 74004). RNA was quantified using a NanoDrop spectrophotometer and TapeStation electrophoresis prior to library preparation. cDNA libraries were prepared using the KAPA RNA HyperPrep kit (Roche, 08098115702). Paired-end RNA sequencing was performed on a NovaSeq S4 platform at a read length of 100 bp, resulting in approximately 60 million reads per sample.

  Study phs003306

• HCA_Reproductive_Tract_Adult_RNA

  HCA Reproductive Tract Adult RNA

  Study EGAS00001006636

• Single Cell Dissection of the Tumour Microenvironment Reveals Dynamic Interplay Shaping the Tumour Immunity Continuum in Ovarian Cancer

  Diverse tumour immune phenotypes with distinct T cell infiltration patterns result in different responses to cancer immunotherapies. However, the key determinants and biology underpinning these phenotypes remain elusive. Here, we provide a high-resolution dissection of the tumour microenvironment representative of different tumour immune phenotypes. By performing single-cell RNA sequencing (scRNAseq) of the tumour, immune, and stromal compartments from 15 ovarian cancer patients, we characterized the diverse cellular and functional phenotypes, as well as dynamic interplay within and between these components. Infiltrated and excluded tumours markedly differed from desert tumours in several tumour cell intrinsic features, including high interferon response, antigen presentation and oxidative phosphorylation. Moreover, these tumours were characterized by tumour associated macrophage (TAM)-like myeloid subsets, while desert tumours were enriched in myeloid derived suppressive cell (MDSC)-like myeloid subsets. Infiltrated and excluded tumours differed in their T cell composition and fibroblast subsets. While dysfunctional CD8+ GZMB T cells and IL1-activated fibroblasts (IL1 CAFs) were associated with infiltrated tumours, pre-dysfunctional CD8+ GZMK T cells and TGFB-activated fibroblasts (TGFB CAFs) were enriched in excluded tumours. These findings validated in bulk transcriptomic profiles of 1071 ovarian tumours and using in situ hybridization. Furthermore, our study revealed chemokine receptor-ligand interactions within and across compartments as potential mechanisms mediating immune cell infiltration, exemplified by the tumour cell-T cell crosstalk via a CXC16-CXCR6 axis, stromal-immune cell crosstalk via CXCL12/14-CXCR4 signalling, and TAM-like macrophage-T cell crosstalk through CXCR3-CXCL9/10/11 signalling. Our in-depth characterization of the tumour environment and intercellular interactions provides novel insights into potential molecular mechanisms that shape the distinct biology of tumour immune phenotypes. Our findings may inform novel therapeutic strategies for potentially shifting tumours along the immunity continuum to improve clinical benefit from cancer immunotherapies.

  Study EGAS00001004935

• Single Cell ATAC-Seq of MELAS

  This dataset includes combined single Assay for Transposase-Accessible Chromatin, using sequencing (ATAC-seq), and mitochondrial DNA sequencing of peripheral blood mononuclear cells from three individuals with m.3243A>G associated MELAS (Mitochondrial Encephalopathy, Lactic Acidosis, and Stroke-like episodes).

  Study phs002217

• scRNA-seq of Patient-derived tumor fragments (PDTFs)

  scRNA-seq of Patient-derived tumor fragments (PDTFs) which were treated with either Isotype (DP47), Isotype-IL2v (DP47-IL2v), anti-PD1 (PembroPGLALA) or PD1-IL2v (PembroPGLALA-IL2v) for 48 hours. Dataset content: 10 samples from 3 donors File type: paired-end fastq files Protocol: Chromium Single Cell 3' Reagent Kits User Guide (Dual Index) with Feature Barcoding technology for Cell Surface Protein (CG000317) Technology: Illumina sequencing Experimentation: scRNA-seq using the 10X technology

  Dataset EGAD50000000584

• Chromatin segmentation of myometrium and UL subclasses

  Histone modification ChIP-seq for chromatin state annotations of myometrium and UL subclasses. Altogether 5 analyses and 210 runs. Includes fastq files; both ChIP and Input for each sample. Some Inputs are used for multiple ChIP experiments. ChIP-seq alignment files for trimmed, mapping q20 and nonredundant reads; both ChIP and Input for each sample. Software: Trim Galore v0.6.7; Bowtie 2 v2.5.0; samtools v1.6

  Dataset EGAD50000001443

• COVID-19 Postmortem Lung snRNA-seq

  A deeper understanding of the pathological mechanisms of SARS-CoV-2 infection is required to combat COVID-19. Through this dataset, we analyze postmortem lung cells from patients that are infected/uninfected with SARS-CoV-2 with snRNA-seq.

  Study EGAS00001004689

• H3K27ac ChIP-seq in primary prostate tumours

  H3K27ac ChIP-seq and input genome sequencing was performed in 19 primary prostate tumours classified as intermediate risk. Sequencing of ChIP DNA was performed on an Illumina HiSeq 2000 as either single end 50 bp reads (for 7 samples) or paired end 100 bp reads (for 12 samples). Input DNA from all samples was sequenced using single-end 50 bp reads. The files provided are in fastq format.

  Dataset EGAD00001003461