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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

• Whole Exome Sequencing analysis of three tumor biopsies from a LUAD patient

  Whole Exome Sequencing (WES) analysis was performed on three distinct tumor biopsies collected at different time points, along with their matched germline non-tumor sample, from the same LUAD patient. DNA was extracted using the DNeasy Blood and Tissue Kit (Qiagen, Germantown, MD) according to the manufacturer’s instructions and quantified using the Qubit Fluorometer assay (Life Technologies, Carlsbad, CA). To minimize FFPE-related sequencing artifacts (e.g., C:G > T:A transitions), the extracted DNA was treated with the DNA repair enzyme Uracil-DNA-Glycosylase (UDG) following the manufacturer’s protocol (New England Biolabs, Ipswich, MA). Whole-exome capture was performed with the Twist Human Core Exome + RefSeq + Mito-Panel kit (Twist Bioscience), in accordance with the manufacturer’s guidelines. Sequencing generated paired-end 100-bp reads on the Illumina NovaSeq 6000 platform. The resulting reads were aligned to the reference human genome (GRCh38) using the Burrows-Wheeler Aligner (BWA, v0.7.12).

  Dataset EGAD50000001198

• Single-nuclei RNA sequencing of pheochromocytoma and paraganglioma arising from germline SDHB mutations

  10x genomics single-nuclei RNA sequencing of 9 SDHB-deficient non-metastatic and metastatic pheochromocytoma and paraganglioma. snRNA-seq was performed using the ‘Frankenstein’ protocol (dx.doi.org/10.17504/protocols.io.bqxymxpw). Briefly, nuclei were extracted from frozen tissues and subjected to fluorescence-activated nuclei sorting (FANS) using 4′,6-diamidino-2-phenylindole (DAPI). Sorting was performed using a BD FACSaria 2 instrument, sorting between 3000 and 10,000 nuclei per sample, capturing both diploid and tetraploid populations. FAN-sorted nuclei were immediately processed using either the 10x Chromium Single Cell 5’ (PN-1000006, 4 samples) or 3’ (PN-1000075, 4 samples) Library & Gel Bead Kit (10x Genomics, USA). Once processed, snRNA-seq libraries were sequenced on the Illumina Nova-Seq 6000 (Illumina, USA) using 150bp paired-end sequencing. This dataset contains raw sequencing reads in FASTQ format.

  Dataset EGAD50000000505

• The Transcriptional Landscape of SHH Medulloblastoma

  To further understand the biology of Sonic hedgehog medulloblastoma and its molecular subtypes, we studied 250 human Shh-MB using strand-specific RNA sequencing. We identified novel alterations within the cAMP dependent pathway and found that 18% of tumors have genetic events that directly target the abundance and/or stability of MYCN. We also discovered an extensive network of fusions in focally amplified regions, and several loss-of-function fusions in tumor suppressor genes PTCH, SUFU and NCOR1. Molecular convergence on a core of specific genes by nucleotide variants, copy number aberrations, and gene fusions highlights key roles of specific pathways in the pathogenesis of Sonic hedgehog medulloblastoma.

  Dataset EGAD00001006305

• Plasma cfDNA dsDNA and ssDNA shallow whole genome and exome sequencing data

  This dataset includes fastq files from sWGS and exome sequencing data derived from dsDNA and ssDNA libraries of plasma cfDNA samples extracted by a column- or bead-based DNA extraction method

  Dataset EGAD00001007019

• RNA-seq of high-grade serous ovarian cancer in long-term survivors (MOCOG study)

  RNA-seq dataset of high-grade serous ovarian cancer (HGSC) tumours from long-term survivors performed as part of the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) study. The dataset includes fastq files from 56 HGSC tumours (53 primary tumours and 3 recurrent tumours) from 53 long-term survivor patients. Libraries were generated using the Illumina Stranded mRNA Prep and 150 bp paired-end sequencing was performed to a minimum of 100 million reads on Illumina NovaSeq 6000 instruments.

  Dataset EGAD00001008537

• Organoid Derivation Project - RNAseq (2024-10-14)

  Sequencing of tissue samples and their derived organoids. This dataset contains a subset of colorectal and colorectal liver metastasis samples.

  Dataset EGAD00001015425

• Screening for tryptophan conversion in human stool samples

  The goal of this study was to compare the tryptophan metabolism of different human fecal microbiota. To this end we enriched human stool samples in culture medium and measured growth and metabolite concentrations after 48h of culture. We quantified the consumption of tryptophan and the production of typical tryptophan derivaties.

  Study EGAS50000000548

• Research for biological features by gene expression analysis and biomarkers at diagnosis/therapeutics in lung cancer

  WGS, WES, ATAC-seq and methylation analysis with DNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients. RNA-seq analysis with RNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients.

  Study JGAS000610

• Research for biological features by gene expression analysis and biomarkers at diagnosis/therapeutics in lung cancer

  WGS, WES, ATAC-seq and methylation analysis with DNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients. RNA-seq analysis with RNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients.

  Study JGAS000609

• Research for biological features by gene expression analysis and biomarkers at diagnosis/therapeutics in lung cancer

  WGS, WES, ATAC-seq and methylation analysis with DNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients. RNA-seq analysis with RNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients.

  Study JGAS000557

• EM-seq datatset of primary human thymocyte subsets

  FASTQ-files of EM-seq experiments of 6 primary human thymocyte subsets, isolated by flow-cytometry (FACS) from neonatal thymus biopsies. All samples were sorted to be negative for the expression of the lineage surface markers CD11c, CD19, CD56, CD141 and CD303 and additionally had the following surface phenotypes: DN2 (CD4-CD8-CD7+CD1a-CD161-CD38+CD3-TCRab-); DN3 (CD4-CD8-CD7+CD1a+); ISP (CD4+CD8-CD3-TCRab-CD1a+); DPE (CD4+CD8+TCRab-CD69-); DPL (CD4+CD8+TCRab+CD69+); SP4 (CD4+CD8-). Samples were sorted from 1-2 pediatric patients undergoing heart surgery.

  Dataset EGAD50000001600

• Oxford Nanopore long-read sequencing data of high-grade serous ovarian cancer

  This dataset comprises whole-genome Oxford Nanopore long-read sequencing data from six high-grade serous ovarian cancer (HGSOC) patients, including cryopreserved, pre-treatment tumor tissues and matched peripheral blood samples (12 samples in total). High-molecular-weight DNA was prepared using the SQK-LSK114 Ligation Sequencing Kit and sequenced on FLO-PRO114M flow cells (R10.4.1). Basecalling was conducted using the super-accurate (SUP) model, enabling simultaneous detection of 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). The dataset includes BAM files containing base modification information for downstream genomic and epigenomic analyses.

  Dataset EGAD50000001509

• Visium CytAssist Spatial Gene Expression analysis for glioblastoma

  The glioblastoma spatial transcriptomics dataset was generated from 4 FFPE fixed adult glioblastoma samples, using the 10x Genomics Visium platform, producing raw FASTQ files, spatial feature matrices (.h5, .mtx, .tsv), and paired histology images (.tif). Spot-level transcript counts were aligned to tissue architecture and processed in Seurat, with results stored as .rds objects. Data visualization was performed using SpatialFeaturePlot, and quantitative analyses were supported by custom Matlab scripts. The dataset provides both raw and processed files, offering reproducible spatially resolved transcriptomic profiles of glioblastoma tissue sections

  Dataset EGAD50000001767

• Transcriptome data of WS patients and controls

  Fibroblasts of 4 WS patients and 4 sex matched controls were transformed into iPSCs, which were then differentiated to Neuroepithelial Stem Cells (NESCs), and further differentiated into neurons. RNA sequencing was performed at the NESC stage and after 4 and 8 weeks of neural development for 3 replicates per individual using the Illumina TruSeq stranded library polyA method on the NovaSeq6000 platform at NGI Stockholm. The data was preprocessed using the nf-core/rnaseq (https://nf-co.re/rnaseq) that includes quality control, data cleaning, and read alignment to GRCh38 (STAR, v2.6.1d)

  Dataset EGAD50000001728

• Sequencing data for oesophageal and related samples - BOs release 4 (RNA)

  Dataset EGAD00001004023

• RNA sequencing data of pediatric B-other acute lymphoblastic leukemia (set 1)

  The dataset includes RNA sequencing FASTQ files from diagnostic tumor samples of four B-other ALL patients (ALLT-314, ALLT-319, ALLT-327, ALLT-334), and from relapse sample of one B-other ALL patient (ALLT-316) . NEBNext Ultra Directional RNA Library Prep Kit (New England Biolabs) was used in library preparation, and Globin-Zero Gold rRNA Removal Kit (Illumina) was utilized for rRNA removal. The diagnostic and relapse tumor samples were sequenced using Illumina Novaseq 6000, and the target sequencing depth was 21 Gb per sample.

  Dataset EGAD50000002598

• Establishing multiple omics baselines for three Southeast Asian populations in the Singapore Integrative Omics Study

  In this study, we established population reference measurements across multiple omic technologies and investigated the inter-population variations in each omic and phenotypic measurements in three major populations in Singapore. The measurements include genome-wide SNPs, gene probesets expression, lipid species and circulating miRNAs.

  Study EGAS00001002527

• Angiopredict: predicting response for bevacizumab treatment

  In ANGIOPREDICT, academic cancer biologists and industry-based biotechnology researchers worked with clinicians to identify biomarkers to predict whether individual metastatic colorectal cancer patients will respond positively to bevacizumab containing therapy. In this retrospective study, copy number profiles were determined using whole genome shallow sequencing, and progression free survival and treatment data was collected.

  Study EGAS00001002724

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - mobile_elements

  Samples from Edwards et al 2015 - doi:10.1186/s12864-015-1685-z

  Dataset EGAD00001001048

• ICGC Prostate Cancer Whole Genome Sequencing

  Whole Genome Sequencing Illumina HiSeq data from 20 men with prostate cancer. 20 samples were taken from primary tissue obtained at prostatectomy (target sequencing depth 50X) with matched blood control (target sequencing depth 30X). These were submitted for use in the ICGC Pan-Cancer Analysis of Whole Genomes project. Same raw data submitted in EGAD00001001116. As of September 2020, some of the studies using these data include: Wedge et al, Nature Genetics 2018 (PMID: 29662167) Pan-Cancer Analysis of Whole Genomes, Nature 2020 (PMID: 32025007)

  Dataset EGAD00001000892

• Finding structural variation from the patient-derived xenografts of pediatric T-cell leukemia at the single-cell level

  In this study, we aimed to identify somatic structural variation of T-cell acute lymphoblastic leukemias (T-ALLs_ from patient-derived xenografts (PDX) at the single-cell level. For this purpose, we performed strand-specific single-cell sequencing of PDX-derived T-ALL relapse samples from two juvenile patients (P1, P33). To validate structural variation detected via scTRIP, we profiled whole exome sequencing (WES) data from P33 (samples taken during initial disease, remission, relapse), and mate-pair sequencing data from P1 (relapse).

  Dataset EGAD00001004499

• Sequence Data for DEEP Release August 2016

  DEEP (German Epigenome Project) sequence data of following samples (Sequencing Types: Chip-Seq, WGBS-Seq, RNA-Seq, sncRNA-Seq, NOMe-Se, DNase-Seq): 41_Hf01_LiHe_Ct, 41_Hf02_LiHe_Ct, 41_Hf03_LiHe_Ct, 01_HepG2_LiHG_Ct1, 01_HepG2_LiHG_Ct2, 01_HepaRG_LiHR_D31, 01_HepaRG_LiHR_D32, 01_HepaRG_LiHR_D33, 43_Hm01_BlMo_Ct, 43_Hm03_BlMo_Ct, 43_Hm05_BlMo_Ct, 43_Hm03_BlMa_Ct, 43_Hm05_BlMa_Ct, 43_Hm03_BlMa_TO, 43_Hm05_BlMa_TO, 43_Hm03_BlMa_TE, 43_Hm05_BlMa_TE, 51_Hf01_BlCM_Ct, 51_Hf03_BlCM_Ct, 51_Hf04_BlCM_Ct, 51_Hf02_BlCM_Ct, 51_Hf05_BlCM_Ct, 51_Hf06_BlCM_Ct, 51_Hf06_BlCM_T1, 51_Hf06_BlCM_T2, 51_Hf03_BlEM_Ct, 51_Hf04_BlEM_Ct, 51_Hf02_BlEM_Ct, 51_Hf05_BlEM_Ct, 51_Hf06_BlEM_Ct, 51_Hf06_BlEM_T1, 51_Hf06_BlEM_T2, 51_Hf03_BlTN_Ct, 51_Hf04_BlTN_Ct, 51_Hf02_BlTN_Ct, 51_Hf05_BlTN_Ct, 51_Hf06_BlTN_Ct, 51_Hf06_BlTN_T1, 51_Hf06_BlTN_T2, 51_Hf07_BmTM4_Ct, 51_Hf08_BlTM4_Ct, 51_Hf08_BmTM4_SP1, 51_Hf08_BmTM4_SP2, 51_Hf05_BlTA_Ct, 44_Mm01_WEAd_C2, 44_Mm03_WEAd_C2, 44_Mm02_WEAd_C2, 44_Mm07_WEAd_C2, 44_Mm04_WEAd_C1, 44_Mm05_WEAd_C1

  Dataset EGAD00001002527

• Low input LC (WGS) (2019-04-01)

  R&D project to develop low input library construction methods. . This dataset contains all the data available for this study on 2019-04-01.

  Dataset EGAD00001004878

• Evolution of the cancer epigenome in myeloproliferative neoplasms. (2019-04-01)

  Evolution of the cancer epigenome in myeloproliferative neoplasms. . This dataset contains all the data available for this study on 2019-04-01.

  Dataset EGAD00001004879

• Sequencing data for oesophageal and related samples - Mourikis et al (WGS)

  Data supporting: "Patient-specific detection of cancer genes reveals recurrently perturbed processes in esophageal adenocarcinoma." Mourikis et al. WGS (BAM files) 521 samples

  Dataset EGAD00001004775

• Deep exome sequencing

  Bam files from deep exome sequencing of blood DNA samples from five TET2 mutation carriers (Ly1, Ly2, Ly9, Ly11, Ly14) and three wild-type family members (Ly8, Ly10, Ly13) extracted at multiple time points. Library preparations were performed with SeqCap EZ Exome v3 (Roche, Switzerland) using six different index primers per sample for which paired-end Illumina sequencing was done with 75bp read length and HiSeq4000 sequencer. After alignment (bwa version 0.7.12), base recalibration (GATK 3.5), realignment around indels (GATK 3.5) and duplicate removal (MarkDuplicates; Picard Tools version 1.79), data from libraries with six different indexes were merged.

  Dataset EGAD00001004780

• small RNA-sequencing and RNA-sequencing of human brain tissue with temporal lobe epilepsy

  This data set contains the raw .fastq files from two RNA-sequencing experiments and two small RNA-sequencing experiments. Both control brain tissue and tissue from sufferers of mesial temporal lobe epilepsy were sequenced. Two different brain regions were sequenced; the cortex and the hippocampus. For more details please see: Mills, James D., et al. "Coding and non-coding transcriptome of mesial temporal lobe epilepsy: Critical role of small non-coding RNAs." Neurobiology of disease 134 (2020): 104612.

  Dataset EGAD00001005735

• Sequencing data for oesophageal and related samples - Nowicki-Osuch, Zhuang et al (scRNA)

  Data supporting: “Deep molecular phenotyping reveals the identity of Barrett’s esophagus and its malignant transition.” Nowicki-Osuch, Zhuang et al. scRNAseq (BAM files) 38 Barrett's and normal samples

  Dataset EGAD00001005438

• Sequencing data for oesophageal and related samples - Nowicki-Osuch, Zhuang et al (WGS)

  Data supporting: “Deep molecular phenotyping reveals the identity of Barrett’s esophagus and its malignant transition.” Nowicki-Osuch, Zhuang et al. WGS (BAM files) 5 Barrett's samples 5 normal oesophageal samples 5 normal gastric cardia samples 5 normal duodenal samples

  Dataset EGAD00001006874

• Sequencing data for oesophageal and related samples - Nowicki-Osuch, Zhuang et al (bulk RNA)

  Data supporting: “Deep molecular phenotyping reveals the identity of Barrett’s esophagus and its malignant transition.” Nowicki-Osuch, Zhuang et al. RNAseq (BAM files) 12 Barrett's samples 12 normal oesophageal samples 11 normal gastric cardia samples

  Dataset EGAD00001006882

• Circulating cell-free DNA analyses for Patient Monitoring in Small Cell Lung Cancer

  This study consists of over 200 data files from cfDNA and germline DNA from 69 patients and 32 healthy normal volunteers discussed in this publication.

  Dataset EGAD00001007070

• Dataset for identification of the dismal subtype of B-ALL with dysregulation of CDX2 and UBTF

  WGS (tumor and germline samples) was performed to identify structural variants in the UBTF/CDX2 subgroup. RNA-seq was performed to detect gene fusion in the UBTF/CDX2 subgroup. HiChIP was performed to investigate 3D chromatin architecture and enhancer landscapes of representative patient samples and cell lines harboring Type I and II FLT3-PAN3 deletions and amplifications.

  Dataset EGAD00001008416

• WGS data subfolder HFG3FCCXY from multifocal ileal NETs study

  This dataset includes linked-read whole-genome sequencing data (subfolder HFG3FCCXY) for multifocal ileal tumor samples from one patient. Samples were sequenced using the 10x Genomics linked-read whole-genome sequencing (WGS) approach.

  Dataset EGAD00001008496

• Vumc_organoids

  Organoid cultures derived from normal colon and/or colorectal adenomas and/or colorectal carcinomas. RNA and DNA was isolated from these cultures for genome wide profiling.

  Dataset EGAD00001008479

• WGS data subfolder HF3FKCCXY from multifocal ileal NETs study

  This dataset includes linked-read whole-genome sequencing data (subfolder HF3FKCCXY) for multifocal ileal tumor samples from one patient. Samples were sequenced using the 10x Genomics linked-read whole-genome sequencing (WGS) approach.

  Dataset EGAD00001008492

• cfMeDIP data for 67 VPC samples

  We analyzed the cell free DNA methylomes using 67 plasma samples from patients with mCRPC prostate cancer in the VPC project. Methylation was profiled using the methylated DNA immunoprecipitation coupled to next generation sequencing (MeDIP) technology.

  Dataset EGAD00001008711

• sWGS of OV04 PDX samples for ACN rascal study

  This dataset contains 148 .bam files of shallow WGS data (~0.1X) from OV04 PDX samples. Sequencing reads were aligned to the 1000 Genomes Project GRCh37-derived reference genome using the BWA aligner (v.0.07.17; CRUK-CI alignment pipeline).

  Dataset EGAD00001008119

• Dataset RNA-Seq of tumors for ImmuNEO already used in study EGAS00001004813

  Sequencing data of 20 tumor runs (different tumors), which were uploaded to EGAS00001004813 and used in the ImmuNeo publication. The sequencing was always paired and run on Illumina HiSeq sequencers.

  Dataset EGAD00001009670

• CASCADE low-pass whole genome sequencing data

  Low-pass whole genome sequencing data (median coverage: 0.37X, range: 0.07X-5.8X) from diagnostic formalin-fixed paraffin-embedded tissue and fresh frozen postmortem tissues from ten organs and postmortem blood from patients who participated in the CASCADE rapid autopsy program and died of metastatic castration resistant prostate cancer. For samples CA27_11, CA34_10, CA35_5, CA35_6, CA36_3, CA36_11, CA63_13, CA63_34, CA76_4, CA76_11, CA79_4, CA83_14 and CA83_26, we subsampled (using `samtools view -s 0.01` after mapping) from respective high coverage data from "CASCADE tumour high-coverage whole genome sequencing data" dataset.

  Dataset EGAD00001009494

• Solve-RD_GENTURIS_cohort-1_DF1+2_V1

  Solve-RD data submitted to the ERN-GENTURIS cohort for re-analysis (Data freeze 1+2) v1

  Dataset EGAD00001009767

• WGS data for ATLAS paper (123 patients)

  WGS data of multi-region samples from PLANET 123 Patient cohort

  Dataset EGAD00001009858

• Whole genome, RNA-seq and single-cell Multiome profile of multiple myeloma

  We analyzed multiple myeloma samples from two patients included in the observational prospective cohort MYRACLE before talquetamab treatment and after relapse. Five other myelomas from the same cohort were included for comparison. Normal plasma cells were also retrieved. All samples were analyzed by whole genome sequencing and single-nucleus Multiome, except one that could only be analyzed by bulk RNA sequencing.

  Dataset EGAD00001010057

• Single-cell expression of Hodgkin and Reed-Sternberg (HRS) cell

  Hybrid capture sequencing was performed to 3 purified Hodgkin and Reed-Sternberg (HRS) cell samples. In brief, Probes for 177 genes were designed and synthesized by Twist Bioscience. Hybridization capture of DNA libraries was performed using Twist Hybridization and Wash Kit (Twist Bioscience). The captured library was measured using Agilent Bioanalyzer High Sensitivity chip and Qubit dsDNA HS Assay Kit and run on Illumina Nextseq550. The BAM files were generated from the raw sequencing data using Cell Ranger (v6.0.2) mkfastq and count commands

  Dataset EGAD00001010892

• Bulk RNA sequencing of SARC PDOs and UroCa PDOs.

  This dataset contains raw FASTQ files from bulk RNA sequencing of SarBC-01 organoids at different passages (Passage 6, Pssage 19, Passage 59), UroBC-01 organoids (Passage 70), UroBC-16 organoids (Passage 19) and UroBC-22 organoids (Passage 8). RNA was isolated using the Quick-DNA/RNA Miniprep kit (Zymo Research, Irvine, CA, USA, D7001) and subjected to bulk RNA sequencing. TruSeq Stranded mRNA kit (Illumina, 20020594) was used for the library preparation according to manufacturer’s guidelines. Sequencing was performed on Illumina NovaSeq 6000 using paired-end 100-bp reads.

  Dataset EGAD00001011156

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

  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 (WGS OACs/BOs/normals)

  Dataset EGAD00001011187

• Raw molecular data for "Subclonal immune evasion in non-small cell lung cancer"

  Whole exome, RNA sequencing and TCR sequencing of organoid samples derived from TRACERx patients. The dataset also includes whole exome sequencing from the tumour samples from which the organoids were generated, as well as whole exome sequencing from PDX derived from said tumours.

  Dataset EGAD00001015537

• Single-cell ATAC-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 single-cell ATAC sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015715

• Single-cell 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 single-cell RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015710

• Single-cell ATAC-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 single-cell ATAC sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015711

• Single-cell 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 single-cell RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015717