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• H3Africa Consortium WGS VCF

  Joint called VCF for whole genome sequence data from 410 samples described in the paper: PMID:33116287. It includes 314 high coverage (average 30X) samples sequenced on the Illumina X-Ten, also available as individual datasets under the H3Africa Chip study (EGAS00001002976) and 112 medium coverage (average 10X) samples from the TrypanGen study (EGAS00001002602) sequenced on Illumina HiSeq 2500. Supplementary table 3 of the paper describes the geographic breakdown of the samples. 16 samples from the Southern African Human Genome project have been removed from this VCF.

  Dataset EGAD00001008577

• Dataset for synovial_sarcoma-RNA

  Rare cancer sequencing data of 23 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008844

• Single cell study of infant leukemias

  We have performed single cell RNA-sequencing for infant and childhood B-cell acute lymphoblastic leukemias as well as infant acute myeloid leukemias at diagnosis. The sequencing was performed with 10X Chromium single cell 3’ and 5’ chemistry.

  Dataset EGAD00001007854

• Sequencing data for oesophageal and related samples - Ng, Contino et al (WGS)

  Data supporting "Interplay of processes shapes structural variations undergoing selection in oesophageal adenocarcinoma" Ng, Contino et al. WGS (BAM files) 383 oesophageal adenocarcinoma samples 383 normal samples

  Dataset EGAD00001007808

• Anthropological dataset 1 for The admixture histories of Cabo Verde

  Datasets used in the article "The genetic and linguistic admixture histories of the islands of Cabo Verde" by Laurent R et al. eLife 2023 (DOI: https://doi.org/10.7554/eLife.79827 - URL: https://elifesciences.org/articles/79827) File name "eGAdeposit_233CaboVerde_SampleInfo_FINAL_01062022.txt" Column 1 corresponds to individual alphanumeric codes as in the "eGAdeposit_233CaboVerde_GenotypeFile_FINAL_01062022.vcf" genotype file Column 2 corresponds to individual's biological sex as per genetic inference Column 3 corresponds to individual's self-reported age in years Column 4 corresponds to individual's self-reported cumulated number of years spent in academic or professional education

  Dataset EGAD00001008976

• Dataset for upper_gastrointestinal_tumor-EXON

  Rare cancer sequencing data of 85 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008902

• Dataset of 4 WGS and 35 WES tumor and control runs linked for the ImmuNEO publication out of EGAS00001004813

  Sequencing data of 39 tumor and control runs (different tumors and blood controls), which were uploaded to EGAS00001004813 and reused in this ImmuNEO publication. The sequencing was always paired.

  Dataset EGAD00001009671

• GELATO clinical trial RNA-Seq data (metastatic lesions)

  RNA-Seq data of 46 matched lobular breast cancer metastatic samples obtained from 21 unique patients from GELATO clinical trial assayed at three timepoints: at baseline (directly after patient randomization), pre-atezolizumab (after induction treatment with carboplatin for two weeks) and on atezolizumab (after two cycles of atezolizumab combined with carboplatin). The included raw transcriptome sequencing data in fastq format was generated using Illumina NovaSeq 6000 from fresh frozen material.

  Dataset EGAD00001009834

• Whole Exome and RNA-Sequencing of Germline, Small Bowel Carcinoid Primary Tumor, and Liver Metastasis Trios

  This dataset includes trios of germline/constitutional, primary tumor (small bowel carcinoid), and metastatic tumor (liver) trios for 5 patients (i.e. 15 samples total). Constitutional, primary tumor, and metastatic tumor samples all underwent whole exome sequencing (WES or WXS). Primary and metastatic tumor samples underwent RNA sequencing (RNA-seq).

  Dataset EGAD00001010036

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0188_002A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0188_002A for Diffuse large B-cell lymphoma patient sample TFRIPAIR11_DLBCL

  Dataset EGAD00001010234

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0197_002A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0197_002A for Diffuse large B-cell lymphoma patient sample TFRIPAIR12_DLBCL_rel

  Dataset EGAD00001010235

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0198_002A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0198_002A for Diffuse large B-cell lymphoma patient sample TFRI_Pair_13_DLBCL

  Dataset EGAD00001010236

• Organoid Derivation Project: TGS (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011089

• Organoid Derivation Pilot: RNAseq (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011091

• Organoid Derivation Project - GRCh38 - TGS (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011094

• HiChIP bulk

  This dataset gather HiChIP data for H3K27ac mar in MM.1S cell line in control and Dex condition, both in two biological replicates

  Dataset EGAD00001011138

• Sequencing data for oesophageal and related samples - Black et al (WES)

  Data supporting: "Understanding the malignant potential of gastric metaplasia of the oesophagus and its relevance to Barrett’s Oesophagus surveillance: individual-level data analysis" Black et al (WES OACs/BOs/normals)

  Dataset EGAD00001011188

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0079_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0079_001 for Triple negative breast cancer sample SA1035X6XB03216

  Dataset EGAD00001011241

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0079_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0079_002 for Triple negative breast cancer patient-derived xenograft SA1035X6XB03211

  Dataset EGAD00001011242

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0145_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0145_001 for Triple negative breast cancer patient-derived xenograft SA1035X7XB03338

  Dataset EGAD00001011244

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0145_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0145_002 for Triple negative breast cancer patient-derived xenograft SA1035X7XB03340

  Dataset EGAD00001011245

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0151_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0151_001 for Triple negative breast cancer patient-derived xenograft SA1035X8XB03425

  Dataset EGAD00001011247

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0175_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0175_002 for Triple negative breast cancer patient-derived xenograft SA1035X8XB03631

  Dataset EGAD00001011249

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0014_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0014_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011251

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0015_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0015_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011252

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0019_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0019_001 for Triple negative breast cancer patient-derived xenograft SA604X7XB02089

  Dataset EGAD00001011253

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0020_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0020_002 for Triple negative breast cancer patient-derived xenograft SA604X8XB02164

  Dataset EGAD00001011254

• 10x Single Cell Gene Expression Library TENX063

  10x Single Cell Gene Expression library TENX063 for Triple negative breast cancer patient-derived xenograft SA609X3XB01584

  Dataset EGAD00001011268

• V(D)J and 5' Gene Expression data of bone marrow cells from patients with aplastic anemia

  Viably frozen blood MNCs from 9 cGVHD patients were sorted with BD Influx Cell sorter, to enrich for CD45+ cells. Gene and V(D)J transcript profiles were studied with 10x Genomics Chromium Single Cell Immune Profiling platform. The Chromium Single Cell 5’RNAseq run and library preparation were done using the Chromium Next GEM Single Cell Immune Profiling version 1.1 chemistry. The raw data was processed using Cell Ranger v3.1 pipelines.

  Dataset EGAD00001012117

• Circulating tumor DNA, pathological and immunologic responses to neoadjuvant nivolumab or nivolumab plus relatlimab and chemoradiotherapy in resectable esophageal/gastroesophageal junction cancer

  The dataset for the study “Circulating tumor DNA, pathological and immunologic responses to neoadjuvant nivolumab or nivolumab plus relatlimab and chemoradiotherapy in resectable esophageal/gastroesophageal junction cancer” includes 173 bam files from hybrid capture targeted error-correction next-generation sequencing (TEC-Seq) from plasma cell-free DNA and matched white blood cell DNA from 32 individuals with esophageal/gastroesophageal junction cancer, who received immunotherapy-containing regimens.

  Dataset EGAD00001011822

• Whole-genome sequencing of rhabdomyosarcoma tumouroids (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumoroids data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015707

• Single-cell RNA-sequencing of rhabdomyosarcoma tumouroids (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumouroids data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015708

• Whole Genome-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015709

• Bulk RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the bulk RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015714

• Whole Genome-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015716

• Bulk RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the bulk RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015718

• Cell-free MeDIP-seq of plasma samples from lymphoma patients and healthy controls

  This dataset contains cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) data generated from plasma samples of lymphoma patients and healthy controls. The study aims to characterize genome-wide methylation patterns of circulating cell-free DNA and identify hypermethylated regions associated with lymphoma. Sequencing libraries were prepared using cfMeDIP with anti-5-methylcytidine antibody enrichment and sequenced on an Illumina platform. The dataset supports differential methylation analyses and the development of classification models for lymphoma detection.

  Dataset EGAD50000002108

• Genome-wide expression profiles to compare array-based, RNAseq and NanoString technologies in patients with resected pancreatic ductal carcinoma.

  Using formalin-fixed paraffin embedded (FFPE) tissues collected prospectively in patients with resected pancreatic ductal carcinoma (PDC) we generated genome-wide expression profiles to compare array-based, RNAseq and NanoString technologies. Samples included in the study were collected 17 patients. After careful selection by the pathologist of 17 malignant cells-enriched, 7 stroma-enriched, and 6 normal (normal pancreas in 4 cases and normal duodenum in 2 cases) regions, sections were macrodissected, and total RNA extracted. Two additional FFPE-frozen matched-paired PDAC from our local Biobank were included. Our study represents the most extensive work comparing three technologies to generate gene expression profiles from FFPE samples, and provides technical guidance for future biomarker studies in samples collected prospectively in the context of clinical trials.

  Study EGAS00001002501

• The KATHERINE study of adjuvant trastuzumab emtansine in HER2-positive breast cancer: analysis of patients with HER2-negative residual invasive disease on re-testing

  Following chemotherapy and human epidermal growth factor 2 (HER2)-targeted neoadjuvant therapy for HER2-positive early breast cancer, residual invasive breast cancer at surgery may be HER2-negative on retesting in some patients. We evaluated outcomes with T-DM1 and trastuzumab in patients randomized in the phase III KATHERINE trial based on HER2-positive central testing of the core biopsy with HER2-negative central testing on their corresponding surgical specimen. In the 70/845 (8.3%) patients with HER2-negative residual disease on retesting at surgery, there were 11 IDFS events in the 42 trastuzumab-treated patients (26.2%) and none in the 28 T-DM1-treated patients, suggesting that T-DM1 should not be withheld in this patient population.

  Study EGAS00001006037

• Facial Skin Biophysical Multi-Parameter and Microbiome-Based Korean Skin Cutoype (KSC) Determination

  In this study, we conducted an integrated analysis of skin measurements, clinical BSTI surveys, and the skin microbiome of 950 Korean subjects to examine the ideal skin microbiome-biophysical association. By utilizing four skin biophysical parameters, we identified four distinct Korean Skin Cutotypes (KSCs) and categorized the subjects into three aging groups based on their age distribution. We established strong connections between 15 core genera and the four KSC types within the three aging groups, revealing three prominent clusters of the facial skin microbiome. Together with skin microbiome variations, skin tone/elasticity distinguishes aging groups while oiliness/hydration distinguishes individual differences within aging groups. Our study provides prospective reality data for customized skin care based on the microbiome environment of each skin type.

  Study EGAS00001007334

• Pathogenic variants damage cell compositions and single cell transcription in cardiomyopathies

  Pathogenic variants in genes that cause dilated (DCM) and arrhythmogenic cardiomyopathies (ACM) convey high risks for the development of heart failure (HF) through unknown mechanisms. Using single nucleus RNA sequencing (snRNAseq), we characterized the transcriptome of 880,000 nuclei from 18 control and 61 failing, non-ischemic human hearts with pathogenic variants in DCM and ACM genes or idiopathic disease. We performed genotype-stratified analyses of the ventricular cell lineages and transcriptional states. The resultant DCM and ACM ventricular cell atlas demonstrated distinct right and left ventricular responses, highlighting genotype-associated pathways, intercellular interactions, and differential gene expression at single cell resolution. Together these data illuminate both shared and distinct cellular and molecular architectures of human HF and suggest novel candidate therapeutic targets.

  Study EGAS00001006374

• A108846B

  Whole genome sequencing for single cells for library A108846B 1636 cells; filetype=bam

  Dataset EGAD00001007092

• A90679

  Whole genome sequencing for single cells for library A90679 1034 samples; filetype=bam

  Dataset EGAD00001008219

• A95635B

  Whole genome sequencing for single cells for library A95635B 593 samples; filetype=bam

  Dataset EGAD00001008223

• A95635D

  Whole genome sequencing for single cells for library A95635D 367 samples; filetype=bam

  Dataset EGAD00001008224

• A95654A

  Whole genome sequencing for single cells for library A95654A 901 samples; filetype=bam

  Dataset EGAD00001008225

• A95662A

  Whole genome sequencing for single cells for library A95662A 637 samples; filetype=bam

  Dataset EGAD00001008226

• A95664B

  Whole genome sequencing for single cells for library A95664B 630 samples; filetype=bam

  Dataset EGAD00001008227

• OV04 chemo prediction

  Predicting resistance to chemotherapy using chromosomal instability signatures Joe Sneath Thompson1,2,*, Laura Madrid2,*, Barbara Hernando1,*, Carolin M. Sauer3, Maria Vias3, Maria Escobar-Rey1,2, Wing-Kit Leung2,3, Diego Garcia-Lopez2, Jamie Huckstep3, Magdalena Sekowska3, Karen Hosking4,5, Mercedes Jimenez-Linan5,6, Marika A. V. Reinius3,5,6, Abhipsa Roy2, Omar Abdulle2, Justina Pangonyte3, Harry Dobson2, Amy Cullen2,3, Dilrini De Silva2, David Gómez-Sánchez1,7, Marina Torres1, Ángel Fernández-Sanromán1, Deborah Sanders3, Filipe Correia Martins3,5,6, Ionut-Gabriel Funingana3,4,5, Giovanni Codacci-Pisanelli3,4,8, Miguel Quintela-Fandino1, Florian Markowetz2,3,4, Jason Yip2, James D. Brenton2,3,4,5,6, Anna M. Piskorz#,2,3, Geoff Macintyre#,1,2 1 Spanish National Cancer Research Centre (CNIO), Madrid, Spain 2 Tailor Bio Ltd, Cambridge, UK 3 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK 4 Department of Oncology, University of Cambridge, Cambridge, UK 5 Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK 6 Cancer Research UK Major Centre - Cambridge, University of Cambridge, Cambridge, UK 7 H12O-CNIO Lung Cancer Clinical Research Unit, Health Research Institute Hospital 12 de Octubre (imas12), Madrid, Spain 8 University of Rome "la Sapienza", Rome, Italy

  Dac EGAC50000000612

• A 3D genome atlas of breast cancer progression (BRCA3D)

  In this study, we aimed to understand how the 3D genome changes during breast cancer development and progression, in situ. Towards this goal, we collected cells from patient biopsies and performed Hi-C on four types of biopsies representing different stages of disease progression: healthy mammary tissue, primary breast tumours, liver metastasis and malignant pleural effusions. We survey the changes in the 3D genomes at the level of structural variation, compartments and TADs, as well as ERα associated distal interactions.

  Study EGAS50000000444