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• 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 h3k36me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007480

• 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 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

• Raw count matrix for 418 baseline samples

  Count matrix from 418 pre-treatment (Ven-Obi or Clb-Obi) CD19+ B cells

  Dataset EGAD00001009501

• SF10679

  SF10679 snATAC Seq. Oligodendroglioma, Anaplastic (WHO gr. 3). Tumor Location: Frontal Age: 43. Sex: Male.

  Dataset EGAD00001006312

• OV2295-052021 dataset

  OV2295-052021 dataset

  Dataset EGAD00001007756

• Reference epigenome KNIH006 WGBS data generated from KEP study

  A KNIH006 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for beta cells

  Dataset EGAD00001002754

• Reference epigenome KNIH007 WGBS data generated from KEP study

  A KNIH007 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for adipocytes

  Dataset EGAD00001002755

• Hi-C of cohesin-mutated and wildtype adult AMLs

  High-throughput chromosome conformation capture (Hi-C) data generated for cohesin-mutated (STAG2 or RAD21) and cohesin-wildtype AMLs.

  Dataset EGAD00001011198

• ONT Minion reads for a patient with ataxia-pancytopenia syndrome.

  ONT Minion reads to provide 30x coverage for a patient with ataxia-pancytopenia syndrome.

  Dataset EGAD00001005022

• 4C-Seq datasets of EndoC-bh1

  2 Circular chromosome conformation capture (4C-Seq) datasets of the human beta cell line EndoC-bh1.

  Dataset EGAD00001005210

• DLP+ Single Cell Genomic Library A98167

  DLP+ Single Cell Genomic Library A98167 for Diffuse large B-cell lymphoma patient sample TFRIPAIR4_DLBCL

  Dataset EGAD00001010249

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

  Kidney-Podocyte01 input ChIP-Seq paired end data

  Dataset EGAD00001007234

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

  Kidney-Podocyte01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007232

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

  Fat-adipocyte03 input ChIP-Seq paired end data

  Dataset EGAD00001007477

• Reference epigenome KNIH001 WGBS data generated from KEP study

  A KNIH001 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for islet cells

  Dataset EGAD00001002749

• CYP2C19 long-read sequencing

  We have assessed the added value of long-read sequencing for PGx focusing on the clinically important and highly polymorphic CYP2C19 gene within 48 samples.

  Dataset EGAD00001009883

• 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 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-beta01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007215

• 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 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007278

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

  IPS-NPC01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007279

• 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-Islet07 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007458

• 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

• DLP+ Single Cell Genomic Library A98203

  DLP+ Single Cell Genomic Library A98203 for Diffuse large B-cell lymphoma patient sample TFRIPAIR8_DLBCL

  Dataset EGAD00001010242

• DLP+ Single Cell Genomic Library A98180

  DLP+ Single Cell Genomic Library A98180 for Diffuse large B-cell lymphoma patient sample TFRI_Pair_13_DLBCL

  Dataset EGAD00001010240

• DLP+ Single Cell Genomic Library A98225

  DLP+ Single Cell Genomic Library A98225 for Diffuse large B-cell lymphoma patient sample TFRIPAIR12_DLBCL_rel

  Dataset EGAD00001010246

• Subfraction targeted high-througput sequencing

  This dataset include the Fastq files from Capture-based targeted high throughput sequencing of bulk, monocytic and progenitor subfractions of PHENOMUT11 sample.

  Dataset EGAD00001009628

• Reference epigenome ADMSC02_smRNA-Seq data generated from KEP study

  A ADMSC02_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003868

• Reference epigenome ADMSC03_smRNA-Seq data generated from KEP study

  A ADMSC03_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003869

• CMF RNA sequencing

  RNA sequencing will be undertaken to reconstruct rearrangements at level of transcription to determine pathogenomic genomic events in chondromyxoid fibroma.

  Dataset EGAD00001000824

• UK10K_RARE_NMWG REL-2013-03-06

  UK10K_RARE_NMWG REL-2013-03-06

  Dataset EGAD00001000804

• Reference epigenome DB31_N_Alpha_smRNA-Seq data generated from KEP study

  A DB31_N_Alpha_smRNA-Seq single end data for alpha cells(PSA-NCAM(-), pancreas)

  Dataset EGAD00001003494

• OT2_Illumina_RNA_T_CELL

  RNAseq on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer primary tumor sample (OT2_cohort)

  Dataset EGAD00001003368

• Reference epigenome DB31_N_Alpha_mRNA-Seq data generated from KEP study

  A DB31_N_Alpha_mRNA-Seq paired end data for alpha cells(PSA-NCAM(-), pancreas)

  Dataset EGAD00001003485

• OT2_Illumina_WXS_MET_CELL

  Whole-exome sequencing on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer metastasis sample (OT2_cohort)

  Dataset EGAD00001003380

• 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

• BC WGS Dataset 1

  Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells

  Dataset EGAD00001001350

• OT2_Illumina_WGS_MET_CELL

  Whole-genome sequencing on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer metastasis sample (OT2_cohort)

  Dataset EGAD00001003373

• Thymic epithelial transplantation for complete DiGeorge syndrome Spatial (2025-07-28)

  Spatial transcriptomics study of thymic transplant biopsies . This dataset contains all the data available for this study on 2025-07-28.

  Dataset EGAD00001015659

• H3Africa EPIGEN Phenotype

  Phenotype data from pregnant mothers unexposed and exposed to the Rwandan genocide from 59 whole blood samples.

  Dataset EGAD00001009739

• Geographic variation of mutagenic exposures in kidney cancer genomes – sequence data (Mutographs)

  Geographic variation of mutagenic exposures in kidney cancer genomes – sequence data (Mutographs)

  Dataset EGAD00001012102

• SF4007

  SF4007 snATAC Seq. Oligodendroglioma (WHO gr. 2) Tumor Location: Left frontotemporal. Age:33. Sex: Female.

  Dataset EGAD00001006317

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

  IPS-Fibroblast01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007264

• 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 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

• The long term effects of chemotherapy on normal blood - WGS dataset

  In developed countries, ~10% of individuals are exposed to systemic chemotherapy for cancer and other diseases. Many chemotherapeutic agents act by increasing DNA damage in cancer cells, hence triggering cell death. However, there is limited understanding of the extent and consequences of collateral DNA damage to normal tissues. To investigate the impact of chemotherapy on mutation burdens and cell population structure of a normal tissue we sequenced blood cell genomes from 23 individuals, aged 3-80 years, treated with a range of chemotherapy regimens. Substantial additional mutation loads with characteristic mutational signatures were imposed by some chemotherapeutic agents, but there were differences in burden between different classes of agent, different agents of the same class and different blood cell types. Chemotherapy also induced premature changes in the cell population structure of normal blood, similar to those of normal ageing. The results constitute an initial survey of the long-term biological consequences of cytotoxic agents to which a substantial fraction of the population is exposed during the course of their disease management, raising mechanistic questions and highlighting opportunities for mitigation of adverse effects.

  Dataset EGAD00001015339

• INCLUDE: The Epidemiology of Transient Leukemia in Newborns with Down Syndrome

  The INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) Project is an NIH-wide collaboration that seeks to improve health and quality-of-life for people with Down syndrome. The INCLUDE Project Data Coordinating Center and partners created the INCLUDE Data Hub, a centralized data resource that allows access to large-scale clinical and multi-omics datasets specific to Down syndrome and supports collaborative, cloud-based analysis to accelerate scientific discoveries related to Down syndrome and its co-occurring conditions.Down syndrome (DS) is associated with a remarkably high risk of childhood acute myeloid leukemia (AML), with an approximately 500-fold increased risk of the rare subtype acute megakaryoblastic leukemia. Though largely treatable, a proportion of myeloid leukemia in DS (ML-DS) patients relapse with dismal survival. ML-DS is preceded by a preleukemia, transient abnormal myelopoiesis (TAM), which presents clinically at birth and is driven by somatic truncating mutations in the erythro-megakaryocytic transcription factor gene GATA1. Up to 15% of DS newborns have TAM, and this can be fatal in up to 20% of cases. A further 10-15% of newborns with DS harbor subclonal GATA1 mutations with clinically silent disease (i.e., Silent TAM). Of the DS newborns with either TAM or Silent TAM (i.e., transient leukemia), up to 15% will acquire additional somatic mutations and progress to ML-DS. We predict that germline variation modifies the risk of developing somatic truncating GATA1 mutations, the first step towards ML-DS, however this has yet to be examined. To address this, the main aim of this X01 study is to perform the first genetic association study of GATA1 mutations in DS, through whole-genome sequencing (WGS) of 470 DS newborns in the Oxford Down Syndrome Cohort Study (ODSCS). Targeted sequencing of GATA1 has already been performed in the 470 DS newborns in the ODSCS, with 130 (28%) confirmed to harbor a somatic truncating GATA1 mutation. We will perform a genome-wide association study (GWAS) of GATA1 mutations to assess genetic variation genome-wide, in particular focusing on variants in candidate genes on the trisomic chromosome 21, RUNX1, ERG, ETS2, and DYRK1A, which have previously been associated with upregulation of GATA1. We will also carry out a chromosome X-wide association study (XWAS) to investigate genetic variation at the GATA1 gene, which resides on chromosome X. WGS data will also enable us to assess the potential role of copy number variation in the risk of transient leukemia in DS. Discovering genetic risk factors for GATA1 mutations in DS may reveal targets for new targeted therapies for TAM, and also inform the etiology of ML-DS as well as AML in the non-DS population. Finally, the ODSCS includes rich phenotypic data for the 470 DS newborns, including clinical diagnosis of TAM and other hematologic complications, complete blood counts, maternal pregnancy complications, and information on congenital heart disease and other birth defects. Thus, whole genome sequencing data in DS newborns from the ODSCS will provide a rich resource for the DS research community.

  Study phs002982

• Exome sequencing in HIV+ Viremic Non-Progressors (VNPs) and HIV+ Progressors

  This dataset contains 184 paired fastq files sequences with Illumina NovaSeq600 from 38 participants. Relevant clinical and demographical data is also included.

  Dataset EGAD50000000111

• WGS of Biobank iPSC lines

  WGS data from healthy reference iPSC lines. The median coverage is 41-50x. >85% have a coverage of >30x. 97% of the variants are known.

  Dataset EGAD00001008769

• Whole exome sequencing of a sinonasal glomangiopericytoma case

  The is dataset includes the whole exome sequencing of the tumor from a sinonasal glomangiopericytoma case together with the matching blood. The whole exome sequencing revealed somatic PIK3CA and CTNNB1 mutations.

  Dataset EGAD00001008291

• Childhood arthritis DNA (2020-01-15)

  The aim of this study is to describe the transcriptome of single arthritic cells. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005785

• Phylogenetic development of childhood tumours

  The aim of this study is to reconstruct the phylogenetic development of childhood tumours

  Dataset EGAD00001005770

• NIDCD Otitis Media Genetic Susceptibility and Middle Ear Microbial Shifts

  The study funded by R01 DC015004 aims to: (1) Identify rare OM susceptibility variants within families and in probands; and (2) Identify differences in the middle ear microbiome due to OM susceptibility variants. This study will also elucidate mechanisms responsible for the formation and growth of cholesteatoma, and bony erosion and complications due to cholesteatoma e.g. intracranial, hearing/vestibular loss, facial nerve palsy. A better understanding of cholesteatoma pathogenesis will potentially reveal novel drug targets, which can lead to new non-surgical therapies for cholesteatomatous OM. Our specific aims for this administrative supplement are: Aim 1 -- Perform RNA-sequencing using cholesteatoma samples collected from Filipino and Colorado patients who will be screened for FUT2, SPINK5 and A2ML1 variants; and Aim 2 -- Using network analysis, identify bone-expressed proteins potentially interacting with FUT2, SPINK5 or A2ML1, then test protein expression in archival human temporal bone in direct contact with cholesteatoma.

  Study phs001941

• Homozygous inactivation of CHEK2 is linked to a familial case of multiple primary lung cancer with accompanying cancers in other organs

  In clinical practice, there are a number of cancer patients with clear family histories, but not belonging to existing familial malignancies. Recent advances in genomic technologies have enhanced the possibility of identifying causative genes in such cases. Two siblings, an elder sister and a younger brother, were found to have multiple primary lung cancers at the age of 60. The former subsequently developed breast cancer, and had a history of myoma uteri. The latter had initially developed prostate cancer at the age of 59, and had a history of colon cancer. SNP genotyping revealed that approximately 10% of the genomes were homozygous in both patients. Exome sequencing revealed non-synonymous mutations in four genes in the runs of homozygosity: CHEK2, FCGRT, INPP5J, and SFI1. Evolutionary conservation of primary protein structures suggested the functional importance of the CHEK2 mutation, R474C. This mutation altered the tertiary structure of CHK2 by disrupting the salt bridge between R474 and E394. No such structural changes were observed with the other three mutated genes. Subsequent cell-based transfection analysis revealed that CHK2 R474C was unstable and scarcely activated. We concluded that CHEK2 is a causative gene in this case of familial cancer. Although homozygous inactivation of CHEK2 in mice led to cancers in multiple organs, accumulation of additional human cases is needed to establish its pathogenic role in humans.

  Study JGAS000057

• EGA synthetic data

  Synthetic Data One of the limitations in genomics research is that human genomics data is not openly available; access must be controlled according to participant consent agreements and data protection regulations such as GDPR. Obtaining authorization to access such data can sometimes take a long time, resulting in delays to important research work. In this context, synthetic genomic and phenotype data can be useful resources for researchers to avoid these delays. Synthetic data are artificially generated datasets, often created with algorithms, which can be used without the need for authorization to test new products and tools, build technical demonstrators, validate data models, and train AI models. The EGA provides access to synthetic cohort datasets augmented with rich synthetic metadata that overcomes these real data usage restrictions. Whilst synthetic datasets are not included in the general EGA mandate and services, we can consider such submissions and evaluate their acceptance on the basis of their unique use cases not already covered by existing synthetic datasets. Access to synthetic data studies is managed by the EGA Helpdesk Data Access Committee. Study ID Title Located in EGAS00001002472 CINECA synthetic cohort EUROPE UK1 referencing fake samples Central EGA EGAS00001005591 Synthetic data - Genome in a Bottle Central EGA EGAS00001005042 Test Study for EGA using data from 1000 Genomes Project - Phase 3 Central EGA EGAS00001005702 Human genomic and phenotypic synthetic data for the study of rare diseases Central EGA EGAS50000000190 EOSC4Cancer Synthetic Colorectal Cancer Genomic data Central EGA EGAS50000000086 Synthetic - FEGA Sweden Heilsa synthetic dataset December 2023 Federated EGA Sweden EGAS50000000678 Synthetic - GDI synthetic data Federated EGA Spain

  Documentation synthetic-data

• Memory-like HCV-specific CD8+ T cells retain a chronic scar after cure of chronic HCV infection

  Chronic hepatitis C virus (HCV) infection is associated with CD8+ T-cell exhaustion characterized by limited effector functions and thus compromised anti-viral activity. Exhausted HCV-specific CD8+ T cells are comprised of memory-like and terminally exhausted CD8+ T-cell subsets. So far, little is not known about the molecular profile and fate of these cells after elimination of chronic antigen stimulation by direct acting antiviral therapy (DAA). Here, we report an antigen-driven molecular core signature underlying exhausted CD8+ T-cell subset heterogeneity in chronic viral infection with a progenitor/progeny relationship of memory-like and terminally exhausted HCV-specific CD8+ T cells via an intermediate stage. Furthermore, transcriptional profiling reveals that the memory-like cells remain after DAA-mediated cure while terminally exhausted HCV-specific CD8+ T-cell subsets are lost. Thus, the memory polarization of the overall HCV-specific CD8+ T-cell response after cure does not result from re-differentiation of exhausted T cells. Consequently, antigen elimination has little impact on the exhausted core signature of memory-like CD8+ T cells that remains clearly different from bona fide T-cell memory. These results identify a molecular signature of T-cell exhaustion that is imprinted like a chronic scar in HCV-specific CD8+ T cells even after HCV cure, highlighting the requirement of re-programming to elicit full effector potential of exhausted T cells.

  Dataset EGAD00001006259

• Resuscitation Outcomes Consortium Trial of Continuous Compressions Versus Standard CPR in Patients With out-of-Hospital Cardiac Arrest (ROC CCC-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions.Objectives: To compare the rate of survival to hospital discharge after continuous chest compressions versus standard American Heart Association recommended cardiopulmonary resuscitation with interrupted chest compressions in patients with out-of-hospital cardiac arrest.Background: Standard cardiopulmonary resuscitation (CPR) consists of manual chest compressions to maintain blood flow and positive-pressure ventilation to maintain oxygenation until spontaneous circulation is restored. Chest compressions during standard CPR are interrupted frequently by ventilations. These interruptions reduce blood flow and potentially reduce the effectiveness of CPR. In animals with cardiac arrest, the interruption of chest compressions has been associated with decreased survival. Observational studies involving humans with out-of-hospital cardiac arrest of presumed cardiac cause have suggested that continuous compressions are associated with higher rates of survival than interrupted compressions.Participants: Of 35,904 patients with out-of-hospital cardiac arrest who were screened, 26,148 were eligible for participation in the trial and were enrolled in the trial during either the run-in phase or the active-enrollment phase. The active-enrollment phase included 23,711 patients, of whom 12,653 were assigned to the intervention group and 11,058 to the control group.Design: There were 114 participating EMS agencies across the eight participating Resuscitation Outcomes Consortium (ROC) sites, which were grouped into 47 clusters. Clusters of agencies were randomly assigned, in a 1:1 ratio, to perform continuous chest compressions or interrupted chest compressions during all the out-of-hospital cardiac arrests to which they responded. Each cluster was required to begin enrolling patients in a run-in phase to demonstrate adherence to the protocol before entering the active-enrollment phase of the trial. Twice per year, each cluster was crossed over to the other resuscitation strategy.Patients assigned to the group that received continuous chest compressions (intervention group) were to receive continuous chest compressions at a rate of 100 compressions per minute, with asynchronous positive-pressure ventilations delivered at a rate of 10 ventilations per minute. Patients assigned to the group that received interrupted chest compressions (control group) were to receive compressions that were interrupted for ventilations at a ratio of 30 compressions to two ventilations; ventilations were to be given with positive pressure during a pause in compressions of less than 5 seconds in duration. The primary outcome was the rate of survival to hospital discharge. Secondary outcomes included neurologic function at discharge, which was measured with the use of the modified Rankin scale, and adverse events. Conclusions: During the active-enrollment phase, 1129 of 12,613 patients in the continuous chest compression group and 1072 of 11,035 patients in the interrupted chest compressions group survived to hospital discharge.In patients with out-of-hospital cardiac arrest, continuous chest compressions during CPR performed by EMS providers did not result in significantly higher rates of survival or favorable neurologic function than did interrupted chest compressions.

  Study phs003901

• Transcriptomic analysis of liver CD8+ T cells

  Sorted single CD8+T cells expressing CD14 from human liver for SMARTSeq2. Livers processed: Kucykowicz et al STAR Prot 2022:pubmed.ncbi.nlm.nih.gov/35516846/ Published: Pallett et al Nature 2022 Tissue CD14+CD8+T-cells are reprogrammed by myeloid cells and modulated by LPS The liver is bathed in bacterial products including LPS transported from the intestinal portal vasculature, but maintains a state of tolerance exploited by persistent pathogens and tumours1,2,3,4. The cellular basis mediating this tolerance, yet allowing a switch to immunity or immunopathology, needs to be better understood for successful immunotherapy of liver diseases. Here we show that a variable proportion of CD8+T-cells compartmentalised in the human liver co-stain for CD14 and other prototypic myeloid membrane proteins; CD14+CD8+T-cells preferentially accumulate within the donor pool in liver allografts, amongst hepatic virus-specific and tumour-infiltrating responses, and in cirrhotic ascites. CD14+CD8+T-cells exhibit increased turnover, activation and constitutive immunomodulatory features with high homeostatic IL-10/IL-2 production directly ex vivo, and an enhanced capacity to rapidly produce antiviral/anti-tumour cytokines upon TCR engagement. This CD14+CD8+T-cell profile can be recapitulated by acquisition of membrane proteins, including the LPS receptor complex, from mononuclear phagocytes, resulting in augmented tumour killing by TCR-redirected T-cells in vitro. CD14+CD8+T-cells express integrins and chemokine receptors favouring interactions with the local stroma, which can promote their induction through CXCL12. LPS can also increase the frequency of CD14+CD8+T-cells in vitro and in vivo, and skew their function towards the production of chemotactic and regenerative cytokines. Thus, bacterial products in the gut-liver axis and tissue stromal factors can tune liver immunity by driving myeloid instruction of CD8+T-cells with immunomodulatory capacity. For the analysis of the single cell transcriptome two intrahepatic perfusate samples were isolated and frozen at -80 'C for storage prior to transportation to Newcastle, UK. Once thawed in pre-warmed HBSS containing 0.001% DNaseI, intrahepatic leukocytes were sorted by FACS to index classical mononuclear phagocytes (MNP) defined as Singlets, CD3-CD56-CD19-CD14hiHLA-DR+ or CD14± CD8+T-cells as follows: Singlets, CD3+CD56-CD19-CD4-CD8+V7.2- and split in to CD14± based on an isotype control. Single cells were sorted into 96-well skirted LoBind plates (Eppendorf) containing 5 µL lysis buffer (TCL buffer [Qiagen], supplemented with 1% b-mercaptoethanol [Sigma Aldrich; Merck]) on a BD Bioscience FACSFusion. The sorted plates were subsequently immediately sealed, centrifuged, and placed on dry ice to flash-freeze the cell lysate and stored at -80 'C. A modified SMART-seq2 protocol57 was performed on the single flow cytometry sorted-cells as previously described58. After cDNA generation, libraries were prepared (384 cells per library) using the Illumina Nextera XT kit (Illumina). Each library was sequenced to achieve a minimum depth of 1-2 million raw reads per cell using an Illumina HiSeq 4000 using v. 4 SBS chemistry to generate 75-bp paired-end reads.

  Study EGAS00001006885

• prcmd-G-1

  Genetic studies of pregnancy-related cardiometabolic disorders in Central Asian, Northern European, and Colombian populations

  Dataset EGAD00010001212

• ctgap-G-1

  Illumina HumanCoreExome-12v1-1_A chip typing in a Greek adolescent population

  Dataset EGAD00010001005

• GSA reference

  reference whole exome sequence serving as a reference of individuals. Includes the raw GSa files and the called variants in vcf format merge for all individuals (S1-S5).

  Dataset EGAD50000000481

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0187_002

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0167_003 for Diffuse large B-cell lymphoma patient sample TFRIPAIR10

  Dataset EGAD50000001372

• scRNA-seq Next GEM Single Cell 3ʹ

  Single-cell RNA sequencing (scRNA-seq) was generated using the 10x Genomics Chromium Next GEM Single Cell 3ʹ platform

  Dataset EGAD50000001185

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0169_001

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0169_001 for Follicular lymphoma patient sample TFRIPAIR4_FL

  Dataset EGAD00001010217