9931 results for "cancer rna-seq"

in 47.45 milliseconds.

• Reference epigenome Islet-derived-MSC02 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007414

• Reference epigenome Islet-derived-MSC02 input ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 input ChIP-Seq paired end data

  Dataset EGAD00001007415

• Reference epigenome Islet-derived-MSC03 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007416

• Reference epigenome Islet-derived-MSC03 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007417

• 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 Islet-derived-MSC03 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007425

• Reference epigenome Islet-derived-MSC03 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007426

• Reference epigenome Islet-derived-MSC03 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007427

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

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

  Dataset EGAD00001007428

• Reference epigenome Islet-derived-MSC04 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007429

• Reference epigenome Islet-derived-MSC04 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007430

• Reference epigenome Islet-derived-MSC04 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007431

• Reference epigenome Islet-derived-MSC04 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007432

• Reference epigenome Islet-derived-MSC04 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007433

• Reference epigenome Islet-derived-MSC04 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007434

• Reference epigenome Islet-derived-MSC04 input ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 input ChIP-Seq paired end data

  Dataset EGAD00001007435

• Reference epigenome Islet-derived-iPSC01 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007436

• Reference epigenome Islet-derived-iPSC01 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007437

• Reference epigenome Islet-derived-iPSC01 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007438

• Reference epigenome Islet-derived-iPSC01 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007439

• Reference epigenome Islet-derived-iPSC01 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007440

• Reference epigenome Islet-derived-iPSC01 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007441

• Reference epigenome Islet-derived-iPSC01 input ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 input ChIP-Seq paired end data

  Dataset EGAD00001007442

• Reference epigenome Islet-derived-iPSC02 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007443

• Reference epigenome Islet-derived-iPSC02 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007444

• Reference epigenome Islet-derived-iPSC02 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007445

• Reference epigenome Islet-derived-iPSC02 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007446

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

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

  Dataset EGAD00001007447

• Reference epigenome Islet-derived-iPSC02 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007448

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

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

  Dataset EGAD00001007449

• ATAC-seq dataset

  This dataset contains ATAC-seq data performed in MM.1S cell line in ETOH (control) or Dexamethasone condition (Treatment)

  Dataset EGAD00001011135

• RNA-Seq of Whole Blood from Patients with Intracranial Aneurysms

  Rupture of intracranial aneurysms (IAs) causes intracranial hemorrhaging that leads to high rates of neurological deficits and death. Although rupture rates are low, clinicians must decide whether to treat or monitor these potentially dangerous lesions. In the current clinical practice, the most common metric to measure risk of rupture is aneurysm size. However, clinical data show that small aneurysms can also rupture. As a result, alternative clinical stratification scores have been proposed, including the PHASES (Population, Hypertension, Age, Size, Earlier subarachnoid hemorrhage, and Site) score based on patient demographics and IA characteristics to stratify ruptured and unruptured IAs, and the Rupture Resemblance Score (RRS) that stratifies ruptured and unruptured IAs based on hemodynamic and morphological properties. However, all metrics require imaging on digital subtraction angiography (DSA), which is invasive, expensive, requires the use of X-rays, and is associated with transient or permanent neurological and non-neurological complications. We hypothesize that individuals with dangerous IAs have detectable gene expression differences in their blood that could be used as biomarkers to determine rupture risk. We propose to use whole blood transcriptomes to develop a "one-stop" diagnostic test that can detect the presence of IAs and determine the risk of rupture based on circulating RNA expression biomarkers using our prototype AneuScreenTM platform. We aim to develop and validate biomarkers to predict risk, as calculated by the currently-used metric of aneurysm size, the clinical PHASES score, and the RRS. Here, we collected blood samples from consented individuals who were receiving cerebral imaging at the Gates Vascular Institute (Buffalo, NY) for intracranial aneurysm. RNA extracted from blood samples (n=43) was subjected to RNA-sequencing and added to our existing database (n=44). When combined with our previous data, we had transcriptome data from 68 aneurysms (after additional sample filtering). This dataset was stratified into low- and high-risk according to IA size, PHASES score, and RRS score. Differentially expressed genes were identified and used to construct predictive models. Fastq files and basic demographic information for the 43 newly-sequenced intracranial aneurysm samples on this project will be available through dbGaP.

  Study phs003072

• 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

• RNA seq on 19 samples of Radiation-Induced Meningiomas

  Purpose: Majority of pediatric cancers require the irradiation of the central nervous system (CNS), and as more patients survive into adulthood from improved oncological therapy the sequelae of brain radiation are increasing in prevalence. Radiation-induced meningiomas (RIMs), one such secondary effect, demonstrate a clinically more aggressive behaviour than sporadic meningiomas (SMs). We aimed to describe the genomic mutational landscape of RIMs.Methods: We analyzed a principal cohort of 18 RIMs, with 31 RIMs overall, from patients who received childhood radiation therapy and 30 SMs, as a comparator population. We performed a multiplatform integrative genomic analysis; including methylation, whole exome and RNA sequencing. Results: RIMs exhibited a five-fold increase in copy number alterations, commonly the loss of chromosome 1p (17/18 RIMs) and 22q (17/18 RIMs), which was significantly more than observed in sporadic meningiomas. Furthermore, RNA sequencing data revealed an NF2 gene fusion event in 35.3% of RIMs. In all 6 cases, there was a complete NF2 exon spliced into a complete exon of a reciprocal gene, suggesting that the breakpoints of genomic rearrangement are intronic. All tumours with the NF2 fusion also possessed monosomy of chromosome 22q, rendering the cells with homozygous disruption of NF2. Clinically, RIMs with the NF2 fusion exhibited ill-defined borders and a tendency to develop in anatomic frontal location. Also, targeted sequencing panel confirmed that RIMs had fewer nonsynonymous NF2 mutations (6.5% vs. 30% in SM) and absence of mutations in TRAF7, SMO, KLF4, PIK3CA and AKT1, genes traditionally involved in SMs.Conclusion: Our study demonstrates that RIMs have distinct genomic drivers of oncogenesis as compared to SMs, specifically NF2 inactivation through fusion event. Radiation therapy possibly triggers genomic structural rearrangements through error-prone repair of double-stranded DNA breaks.

  Study EGAS00001002318

• RNA sequencing

  For RNA sequencing: All sample numbers starting with 6717 are tumor samples which has been sequenced using transcriptomics (see BAM files). It concerns biopsies of metastatic lesions from patients with BRAFV600 mutated melanoma, obtained before, during and after the study treatment (see samples metadata). RNA sequencing is performed using the Illumina Novaseq 6000 system.

  Dataset EGAD50000000383

• RNA sequencing of myxofibrosarcoma

  RNA sequencing (fastq files) of 29 samples of myxofibrosarcoma

  Dataset EGAD00001004102

• MINCR is a MYC-induced lncRNA able to modulate MYC’s transcriptional network in Burkitt lymphoma cells

  Despite the established role of the transcription factor MYC in cancer, little is known about the impact of a new class of transcriptional regulators, the long non-coding RNAs (lncRNAs), on the way MYC is able to influence cellular transcriptome. To this aim we have intersected RNA-sequencing data from two MYC-inducible cell lines and from a cohort of 91 mature B-cell lymphomas carrying, or not carrying, genetic variants resulting in MYC over-expression. By this approach, we identified 13 lncRNAs differentially expressed in IG-MYC-positive Burkitt lymphoma and regulated in the same direction by MYC in the model cell lines. Among them we focused on a lncRNA that we named MINCR, for MYC-Induced long Non-Coding RNA, showing a strong correlation with MYC expression in MYC-positive lymphomas and also in pancreatic ductal adenocarcinomas. To understand its cellular role we performed RNA interference (RNAi) experiments and found that MINCR knock-down is associated with a reduction in cellular viability, due to an impairment in cell cycle progression. Differential gene expression analysis following RNAi showed a strongly significant enrichment of cell cycle genes among the genes down-regulate following MINCR knock-down. Interestingly these genes are enriched in MYC binding sites in their promoters, suggesting that MINCR acts as a modulator of MYC transcriptional program. Accordingly, following MINCR knock-down, we observed a reduction in the binding of MYC to the promoters of selected cell cycle genes. Finally we provide evidences that down-regulation of AURKA, AURKB and CTD1 may explain the reduction in cellular proliferation observed upon MINCR knock-down. We therefore suggest that MINCR is a newly identified player in the MYC transcriptional network able to control the expression of cell cycle genes.

  Dataset EGAD00001001441

• 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

• HipSci - Embryonic Stem Cells - RNA Sequencing - April 2016

  HipSci - Embryonic Stem Cells - RNA Sequencing - April 2016

  Dataset EGAD00001002000

• HipSci - Congenital Hyperinsulinia - RNA Sequencing - July 2017

  HipSci - Congenital Hyperinsulinia - RNA Sequencing - July 2017

  Dataset EGAD00001003534

• ATAC-seq

  ATAC-seq data for 26 CLL samples (7 controls, 19 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005967

• single-cell ATAC-seq

  Single-cell ATAC-seq data for 5 CLL samples (2 controls, 3 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005965

• ALI culture bronchial cells and alveolar lung surgical resection scRNA-Seq

  ALI culture bronchial cells and alveolar lung surgical resection scRNA-Seq

  Dataset EGAD00001006185

• Bolleboom-Gao peri-tumoral snRNA-seq glioblastoma dataset 2022/A

  Bolleboom-Gao peri-tumoral snRNA-seq glioblastoma dataset 2022/A

  Dataset EGAD00001009964

• COVID-19 severity correlates with airway epithelium–immune cell interactions identified by single-cell analysis

  To investigate the immune response and mechanisms associated with severe COVID-19, we performed single-cell RNA-seq on nasopharyngeal and bronchial samples from 19 clinically well-characterized patients with moderate or critical disease and from 5 healthy controls. We identified airway epithelial cell types and states vulnerable to SARS-CoV-2 infection. In COVID-19 patients, epithelial cells showed an average threefold increase in expression of the SARS-CoV-2 entry receptor ACE2, which correlated with interferon signals by immune cells. Compared with moderate cases, critical cases exhibited stronger interactions between epithelial and immune cells, as indicated by ligand–receptor expression profiles, and activated immune cells , including inflammatory macrophages expressing CCL2, CCL3, CCL20, CXCL1, CXCL3, CXCL10, IL8, IL1B and TNF . The transcriptional differences in critical cases compared with moderate cases likely contribute to clinical observations of heightened inflammatory tissue damage, lung injury and respiratory failure. Our data suggest that pharmacologic inhibition of the CCR1 and/or CCR5 pathways may suppress immune hyperactivation in critical COVID-19.

  Dataset EGAD00001006339

• 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

• Human Embryonic Multi-tissue ChIP-seq (Manchester)

  Histone ChIP-seq of 13 human embryonic tissues from weeks 6-8 of gestation. H3K4me3, H3K27me3 and H3K27ac. Biological replicates (n=2) for 11 tissues. Tissues (n): Brain (2); Retinal Pigmented Epithelium (eye)(2); Palate(2); Tongue (1); Left ventricle (heart)(2); Lung(2); Liver(2); Pancreas (2*); Stomach(1); Upper limb (2); Lower limb (2); Adrenal gland (2); Kidney (2)

  Dataset EGAD00001004335

• 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

• Sequencing_component_for_the_whole_genome_methylation_analysis_in_PBMCs_and_cell_subsets__pilot_study_

  DNA methylation has been shown to play a major role in determining cellular phenotype by regulating gene expression. Moreover, dysregulation of differentially methylated genes has been implicated in disease pathogenesis of various conditions including cancer development as well as autoimmune diseases such as systemic Lupus erythematosus and rheumatoid arthritis. Evidence is rapidly accumulating for a role of DNA methylation in regulating immune responses in health and disease. However, the exact mechanisms remain unknown. The overall aim of the project is to investigate the role of epigenetic mechanisms in regulating immunity and their impact on autoimmune disease pathogenesis.The aim of this pilot study is to perform whole genome methylation analysis in peripheral blood mononuclear cells (PBMCs) and cell subsets (CD4, CD8, CD14, CD19, CD16 and whole PBMCs) obtained from 6 healthy volunteers. Whole genome methylation analysis will be performed using two methodological approaches, the Infinium Methylation Bead Array K450 (Illumina) and MeDIP-seq. mRNA expression arrays will also be performed in order to correlate DNA methylation with gene expression as well as genotyping on the Illumina OmniExpress chip

  Study EGAS00001000490

• RNA sequencing of Resistant BCC samples.

  RNA sequencing of Resistant BCC samples.

  Dataset EGAD00001000881

• Identification of Novel Therapeutic Targets for Calcific Aortic Valve Stenosis Using Integrative Genomics

  In order to identify novel therapeutic targets for calcific aortic valve stenosis, we have generated a large transcriptomic dataset from RNA sequencing of human aortic valve tissues from 500 individuals undergoing aortic valve replacement or heart transplant at the Institut universitaire de cardiologie et de pneumologie de Québec – Université Laval (QUEBEC-CAVS-RNA). This dataset represents a unique resource to understand the molecular mechanisms underlying aortic valve diseases.

  Study phs003541

• Genomic and Transcriptomic sequencing of neuroblastoma cell lines

  Genomic and Transcriptomic sequencing of neuroblastoma HSR and ecDNA cell lines. Data represents five 96-well plates that were processed with G&T sequencing, resulting in genomic and transcriptomic data from the same single cells. Dataset contains 95 bam files containing the DNA sequencing data and 95 bam files containing the RNA sequencing data of CHP212 cells, 380 bam files (190 DNA and 190 RNA) for TR14 cells, 188 bam files (94 DNA and 94 RNA) for Kelly cells and 192 bam files (96 DNA and 96 RNA) for IMR5/75 cells.

  Dataset EGAD50000000729

• 3D-GSC_expression_profiles

  In this study, we used RNA-sequencing gene expression profiling in order to characterize specific phenotypic traits in DIPG-derived glioma stem cell (GSC) models. Twenty-two primary GSC models derived from biopsies collected at diagnosis were cultured in an ECM-mimicking compound before for RNA extraction and subsequent rRNA-depletion and sequencing .

  Study EGAS00001007182

• Bulk RNA-sequencing of AML blasts pre and post culture

  This dataset contains 18 Fastq files from 9 samples (pre-culture (n=3), post culture in standard (n=4) or niche-llike (n=2) conditions) from 4 patients.They correspond to bulk RNA sequencing on Illumina instrument of libraries prepared using SMARTer Universal Low Input RNA Kit.

  Dataset EGAD00001008773

• RNA sequencing of skin tissue and peripheral blood mononuclear cells from patients with atopic dermatitis and healthy controls.

  We performed RNA-sequencing of skin tissue and peripheral blood mononuclear cells obtained from patients with atopic dermatitis along with healthy controls in Japanese population.

  Study JGAS000628

• Small RNA sequencing of human oocytes and early embryos

  This study looks at small RNA expression in human oocytes (n=12), zygotes (n=5), and embryos (n=10). The sample preparation and sequencing were done in two batches.

  Study EGAS50000000157

• Mapping the epigenomic landscape of human monocytes following innate immune activation reveals context-specific mechanisms driving endotoxin tolerance

  Background Monocytes are key mediators of innate immunity to infection, undergoing profound and dynamic changes in epigenetic state and immune function which are broadly protective but may be dysregulated in disease. Here, we aimed to advance understanding of epigenetic regulation following innate immune activation, acutely and in endotoxin tolerant states. Methods We exposed human primary monocytes from healthy donors (n=6) to interferon-γ or differing combinations of endotoxin (lipopolysaccharide), including acute response (2hr) and two models of endotoxin tolerance: repeated stimulations (6+6hr) and prolonged exposure to endotoxin (24hr). Another subset of monocytes was left untreated (naïve). We identified context-specific regulatory elements based on epigenetic signatures for chromatin accessibility (ATAC-seq) and regulatory non-coding RNAs from total RNA sequencing. Results We present an atlas of differential gene expression for endotoxin and interferon response, identifying widespread context specific changes. Across assayed states, only 24-29% of genes showing differential exon usage are also differential at the gene level. Overall, 19.9% (6884 of 34616) of repeatedly observed ATAC peaks were differential in at least one condition, the majority upregulated on stimulation and located in distal regions (64.1% vs 45.9% of non-differential peaks) within which sequences were less conserved than non-differential peaks. We identified enhancer-derived RNA signatures specific to different monocyte states that correlated with chromatin accessibility changes. The endotoxin tolerance models showed distinct chromatin accessibility and transcriptomic signatures, with integrated analysis identifying genes and pathways involved in the inflammatory response, detoxification, metabolism and wound healing. We leveraged eQTL mapping for the same monocyte activation states to link potential enhancers with specific genes, identifying 1,946 unique differential ATAC peaks with 1,340 expression associated genes. We further use this to inform understanding of reported GWAS, for example involving FCHO1 and coronary artery disease. Conclusion This study reports context-specific regulatory elements based on transcriptomic profiling and epigenetic signatures for enhancer-derived RNAs and chromatin accessibility in immune tolerant monocyte states, and demonstrates the informativeness of linking such elements and eQTL to inform future mechanistic studies aimed at defining therapeutic targets of immunosuppression and diseases.

  Study EGAS00001007362

• Sample Multiplexing Oligo Comparison

  In this study, we compared various multiplexing reagents, including MULTI-Seq, Hashtag antibody, and CellPlex, in patient-derived xenografts (PDXs). We found that all multiplexing reagents worked well in cell types robust to ex vivo manipulation but suffered from signal-to-noise issues in more delicate sample types. We also compared the performance of fixed scRNA-Seq kits and CRISPR-based destruction of non-informative genes.

  Study EGAS50000000153

• Osteosarcoma sequencing data

  This study shares DNA and RNA sequencing data from embryonal rhadbomyosarcoma patients

  Study EGAS00001005600

• Pilocytic Astrocytoma ICGC PedBrain RNA sequencing

  Pilocytic Astrocytoma ICGC PedBrain RNA sequencing

  Dataset EGAD00001000617

• RNA sequence of HUB_5 cell culture

  RNA sequence data for conditionally reprogrammed cells from patient HUB_5

  Dataset EGAD00001001923

• HipSci - Alport Syndrome - RNA Sequencing - July 2017

  HipSci - Alport Syndrome - RNA Sequencing - July 2017

  Dataset EGAD00001003532

• HipSci - Battens Disease - RNA Sequencing - July 2017

  HipSci - Battens Disease - RNA Sequencing - July 2017

  Dataset EGAD00001003533

• HipSci - Kabuki Syndrome - RNA Sequencing - July 2017

  HipSci - Kabuki Syndrome - RNA Sequencing - July 2017

  Dataset EGAD00001003535

• HipSci - Hypertrophic Cardiomyopathy - RNA Sequencing - July 2017

  HipSci - Hypertrophic Cardiomyopathy - RNA Sequencing - July 2017

  Dataset EGAD00001003540

• HipSci - Macular Dystrophy - RNA Sequencing - July 2017

  HipSci - Macular Dystrophy - RNA Sequencing - July 2017

  Dataset EGAD00001003541

• HipSci - Retinitis Pigmentosa - RNA Sequencing - July 2017

  HipSci - Retinitis Pigmentosa - RNA Sequencing - July 2017

  Dataset EGAD00001003542

• HipSci - Usher Syndrome - RNA Sequencing - July 2017

  HipSci - Usher Syndrome - RNA Sequencing - July 2017

  Dataset EGAD00001003543

• RNA sequencing of uveal melanoma metastases.

  RNA sequencing of uveal melanoma metastases.

  Dataset EGAD00001007912

• Dataset for "Genomic landscape of oral cancers" (Illumina RNA)

  Dataset for "Genomic landscape of oral cancers" (Illumina RNA)

  Dataset EGAD00001004366

• miRNA seq data of 43 cases out of dataset EGAD00001000650 (+ one read_me file)

  miRNA seq data of 43 cases out of dataset EGAD00001000650 (MMML)

  Dataset EGAD00001001619

• Primary Neuroblastoma Circle-seq Data

  Circle-seq data for 21 primary neuroblastoma samples supporting Koche et al. Extrachromosomal circular DNA drives oncogenic genome remodeling in neuroblastoma (2020).

  Dataset EGAD00001006580

• ATAC-seq

  This dataset contains the raw sequencing data (Runs) for all 10x Genomics single-cell ATAC-seq Experiment.

  Dataset EGAD00001009822

• Dataset to study clonal evolution in iAMP21 patient SJBALL030072using scWGS-seq

  scWGS-seq of flow sorted blast and normal cells from SJBALL030072with 66 high quality cells sequenced (61 blast and 5 normal)

  Dataset EGAD00001010288

• Intellance-2: TruSight Tumor 170 panel based DNA-seq

  Intellance-2: TruSight Tumor 170 panel based DNA-seq

  Dataset EGAD00001007945

• ATAC-seq of cohesin-mutated and -wildtype adult AMLs

  ATAC-seq (Illumina TDE1 Transposase) to profile accessible chromatin regions of cohesin-mutated (STAG2 or RAD21 mutations) and -wildtype adult AMLs.

  Dataset EGAD00001011267

• 1M-scBloodNL

  Here we present the 1M-scBloodNL study, in which we performed single-cell RNA-seq on 120 individuals of the Northern Netherlands population cohort Lifelines. For each individual peripheral blood mononuclear cells (PBMCs) were sequenced in an unstimulated condition, and after 3 and 24 hour in vitro stimulation with C. albicans (CA), M. tuberculosis (MTB) and P. aeruginosa (PA), totalling approximately 1.3 million cells. scRNA-seq was conducted with the 10X Genomics 3'-end v2 (72 libraries) and v3 (33 libraries) technology. In general, each library contains PBMCs from 8 donors and 2 different stimulation-timepoint combinations. Donors were demultiplexed using a combination of SoupOrCell (https://www.nature.com/articles/s41592-020-0820-1) and genotype information to assign the correct donor to a donor-specific cell cluster.

  Dataset EGAD00001007764

• scRNA-seq data of BALF and blood cells from COPD and control

  Chronic obstructive pulmonary disease (COPD) is ranked the third leading cause of death worldwide and hence constitutes a significant burden, both medically and financially. To date, the cellular mechanism underlying COPD is poorly understood and thus no effective therapy is currently available. To broaden the current knowledge of the immune landscape in the alveolar space, we performed scRNA-Seq of CD45-enriched bronchoalveolar lavage fluid (BALF) cells and isolated blood leukocytes obtained from COPD patients and healthy controls using both the Seq-Well and the Chromium (10x Genomics) technology.

  Study EGAS00001004369

• Embryonal Rhabdomyosarcoma sequencing data

  This study shares DNA and RNA sequencing data from embryonal rhadbomyosarcoma patients

  Study EGAS00001005502

• Alveolar Rhabdomyosarcoma sequencing data

  This study shares DNA and RNA sequencing data from alveolar rhadbomyosarcoma patients

  Study EGAS00001005387

• Wilm's tumor sequencing data

  This study shares DNA and RNA sequencing data from Wilm's tumor patients

  Study EGAS00001005690

• Epithelioid sarcoma sequencing data

  This study shares DNA and RNA sequencing data from Epithelioid sarcoma patients

  Study EGAS00001005983

• Rhabdoid tumor sequencing data

  This study contains sequencing DNA and RNA sequencing data for rhabdoid tumors

  Study EGAS00001006351

• MuTHER adipose tissue small RNA expression

  MuTHER adipose tissue small RNA expression

  Dataset EGAD00001000105

• RNA sequencing of colon tumor/normal sample pairs

  RNA sequencing of colon tumor/normal sample pairs

  Dataset EGAD00001000215

• Deep RNA sequencing in CLL

  Deep RNA sequencing in CLL

  Dataset EGAD00001000258

• HipSci - Monogenic Diabetes - RNA Sequencing - April 2015

  HipSci - Monogenic Diabetes - RNA Sequencing - April 2015

  Dataset EGAD00001001953

• HipSci - Monogenic Diabetes - RNA Sequencing - January 2016

  HipSci - Monogenic Diabetes - RNA Sequencing - January 2016

  Dataset EGAD00001001955

• TST170 Pilot RNA VCF

  TST170 Pilot RNA VCF

  Dataset EGAD00001005710

• TST170 Pilot RNA FASTQ

  TST170 Pilot RNA FASTQ

  Dataset EGAD00001005711

• Enhancer Mapping in Chronic Lymphocytic Leukemia

  In this study we profile the epigenomic enhancer landscapes of CLL B cells (CD19+/CD5+) harvested from peripheral blood of patients from our Center. Included are results of ChIPseq profiling using chromatin immunoprecipitation of the enhancer histone mark H3K27ac (acetylated lysine 27 on histone H3), and open chromatin profiles using ATAC-seq (assay for transposase accessible chromatin). These profiles are used to define the global enhancer and super enhancer landscape of CLL B cells, and to derive active transcription factor networks associated with this disease. Also included are H3K27ac ChIP-seq and ATAC-seq datasets for non-CLL B cells obtained from the peripheral blood of normal adult donors.

  Study phs001704

• Single-Cell Multi-Omic Analysis of the Vestibular Schwannoma Ecosystem Uncovers a Nerve Injury-Like State

  Vestibular schwannomas are benign tumors that can lead to significant morbidity, including hearing loss, facial nerve paralysis and obstructive hydrocephalus. The genomic landscape of vestibular schwannomas is relatively bland and the molecular pathophysiology of vestibular schwannomas is poorly understood. Therefore, we performed single cell RNA sequencing on 15 sporadic vestibular schwannomas, with paired single cell ATAC sequencing (n = 6) and whole exome sequencing (n = 12) to perform a detailed characterization of the vestibular schwannoma tumor microenvironment at the transcriptional and epigenetic level. We validated our results by analyzing bulk RNA sequencing data from 22 newly sequenced vestibular schwannomas and 173 previously sequenced bulk RNA sequencing data from 195 vestibular schwannomas. Data available in dbGaP include whole exome sequencing of 12 tumors and matched blood samples. Matching single cell RNA sequencing and single cell ATAC sequencing data is available via NCBI GEO.

  Study phs003318

• Genomic Characterization of Head and Neck Squamous Cell Carcinoma Cell Lines

  Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer by incidence worldwide(1). Various chemical carcinogens (tobacco, alcohol and betel nut), human papillomavirus (HPV) infection, and genetic predisposition contribute to the etiology of HNSCC, and to the complex genetic alterations in tumor subsets that differ in prognosis and response to therapies (2). Recently, a comprehensive landscape of genomic and transcriptomic alterations in HNSCC tumors has emerged from The Cancer Genome Atlas (TCGA) Network (3). TCGA revealed novel and previously recognized gene and chromosomal region copy number alterations (CNAs), mutations, and expression clusters, and defined their frequency, co-occurrence, and relationship to common and rare subtypes of HPV(-) and (+) tumors that vary in prognosis. To identify cell line models for determining the functional role and therapeutic importance of these alterations, we are performing whole exome and RNA sequencing and bioinformatic analysis of an expanded panel of 15 HPV(-) and 11 HPV(+) HNSCC cell lines and primary oral keratinocytes. We find that the recurrent genomic alterations in cell lines are remarkably consistent with those found in more aggressive tumors, from which cell lines have traditionally been most readily adapted to culture (4). Genome-wide correlation of CN (copy number) with expression identified a suite of potential drivers or modifier genes that differ by HPV status, and are of potential biologic and therapeutic relevance. Further, our findings elucidate and validate genomic alterations underpinning numerous discoveries made with these widely-used and recently derived HNSCC lines, and provide a roadmap for their potential use as models for future studies of tumor subtypes with worse prognosis. References Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87-108. Van Waes C, Musbahi O. Genomics and advances towards precision medicine for head and neck squamous cell carcinoma. Laryngoscope Investig Otolaryngol. 2017;2(5):310-9. Cancer Genome Atlas N. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature. 2015;517(7536):576-82. White JS, Weissfeld JL, Ragin CC, Rossie KM, Martin CL, Shuster M, et al. The influence of clinical and demographic risk factors on the establishment of head and neck squamous cell carcinoma cell lines. Oral Oncol. 2007;43(7):701-12.

  Study phs001581

• Dataset for bone_cancer-RNA

  Rare cancer sequencing data of 55 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008848

• Dataset for gynecologic_cancer-RNA

  Rare cancer sequencing data of 48 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008856

• Dataset for melanoma-RNA

  Rare cancer sequencing data of 49 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008858

• Dataset for hematopoietic_malignancy-RNA

  Rare cancer sequencing data of 47 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008860

• HipSci - Bardet-Biedl Syndrome - RNA Sequencing - April 2015

  HipSci - Bardet-Biedl Syndrome - RNA Sequencing - April 2015

  Dataset EGAD00001001952

• HipSci - Bardet-Biedl Syndrome - RNA Sequencing - January 2016

  HipSci - Bardet-Biedl Syndrome - RNA Sequencing - January 2016

  Dataset EGAD00001001954