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• 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

• ICU transcriptomics: Assessing the role of the host immune response in patients with ventilator-associated pneumonia

  This is a study of 100 patients with the aim to provide transcriptomic sequencing data analysis of cells isolated from bronchioalveolar lavage and blood samples from critically ill patients with pneumonia on an intensive care unit in order to investigate the host contribution to disease. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001015407

• 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

• Tandem DNA Repeats Activate hTERT Gene Transcription

  Multiple independent sequence variants of the hTERT locus have been associated with telomere length and cancer risks in genome-wide association studies. Here, we identified an intronic variable number tandem repeat, VNTR2-1, as an enhancer-like element, which activated hTERT transcription in a cell and chromatin-dependent manner. VNTR2-1, consisting of 42-bp repeats with an array of E-boxes, cooperated with the proximal promoter in hTERT regulation by bHLH transcription factors and maintained hTERT expression during embryonic stem cell differentiation. Genomic deletion of VNTR2-1 in MelJuSo melanoma cells markedly reduced hTERT transcription, leading to telomere shortening, cellular senescence, and impairment of xenograft tumor growth. Interestingly, VNTR2-1 lengths varied widely in human populations.  To assess the genetic polymorphism of VNTR2-1 in human populations, we analyzed genomic DNA samples of Caucasian Americans and African Americans enrolled in the Georgia Centenarian Study.  VNTR2-1 in these genomic DNA samples were PCR amplified and subjected to sequencing analysis usinb the PacBio® SMRT method. The result showed that all VNTR2-1 alleles in Caucasian Americans contained 76 or more 42-bp repeats, whereas 32% of VNTR2-1 alleles in African Americans had fewer than 76 repeats.  

  Study phs002428

• The coding and non-coding transcriptional landscape of subependymal giant cell astrocytomas

  Tuberous sclerosis complex (TSC) is an autosomal dominantly inherited neurocutaneous disorder caused by inactivating mutations in TSC1 or TSC2, key regulators of the mechanistic target of rapamycin complex 1 (mTORC1) pathway. In the central nervous system TSC is characterized by cortical tubers, subependymal nodules and subependymal giant cell astrocytomas (SEGAs). SEGAs may lead to the impaired circulation of cerebrospinal fluid resulting in hydrocephalus and raised intracranial pressure in patients with TSC. Currently, surgical resection and mTORC1 inhibitors are the recommended treatment options for patients with SEGA. Here, we performed RNA-Seq and small RNA-Seq on SEGAs (n=19) and periventricular controls (n=8) to gain a better understanding of the underlying molecular basis of SEGAs, so that novel treatment targets could be identified.

  Study EGAS00001003787

• Profiling_heterogeneity_in_Human_derived_IPSC_neurons

  The impact of genetic variants on molecular pathways that give rise to neurodegenerative diseases such as Alzheimer’s and Parkinson’s is best elucidated in the appropriate cell types and molecular contexts. Existing studies have focused on bulk profiling of mixed cell types, but have ignored assaying genetic effects across development and cell differentiation. At the core of this proposal is the idea to use single-cell assays to study genetic effects during differentiation of dopaminergic and cortical neurons to identify the sequence of molecular events from variants to healthy and diseased cell states in a cell-specific manner. 1) This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002885

• Profiling of childhood neuroblastoma and the immune microenvironment by single-cell sequencing of RNA and TCR

  Neuroblastoma tumor cells and their tumor microenvironment are studied by single-cell RNA sequencing of primary neuroblastoma tumors and neuroblastoma-infiltrated tissues/metastases. Both diagnostic samples, taken pre-treatment and samples taken after induction chemotherapy at surgical resection and biopsies from relapse samples are included.  For a number of patients single-cell RNA sequencing of blood samples (controls) are included and additionally we performed single-cell T cell receptor sequencing (TCR) on the tumor and blood samples. With this study, we will focus on identifying differences in tumor cells and their (immune) microenvironment 1) before and after treatment or at relapse and 2) between primary tumors and infiltrated tissues/metastases. Moreover, with TCR sequencing we aim to unveil immune cell dynamics in patients with neuroblastoma.

  Study EGAS00001006823

• Phase Ib of olaparib and capivasertib

  Background: Combining poly(ADP-ribose) polymerase (PARP) with phosphatidylinositol-3-kinase (PI3K) pathway inhibitors is supported by strong preclinical rationale. We sought to assess safety and a recommended phase 2 dose (RP2D) for the PARPi olaparib combined with the AKT inhibitor, capivasertib, and evaluate molecular markers of response and resistance. Methods: As part of a larger phase 1b trial, we performed a safety lead in of olaparib and capivasertib followed by expansion (n=24) in endometrial, triple negative breast, ovarian, fallopian tube, or peritoneal cancer. Olaparib 300mg orally twice daily and capivasertib orally twice daily on a 4 day on/3 day off schedule were evaluated. Two dose levels (DL) were planned: capivasertib 400mg (DL1); capivasertib 320mg (DL-1). Patients underwent biopsies at baseline and after 28 days.

  Study EGAS00001004930

• Targeted Sequencing of 52 Genes for Severe COVID-19

  This dataset comprises targeted sequencing data of 52 genes previously implicated in severe COVID-19 outcomes. The study includes samples from 764 individuals with severe COVID-19 and 3,939 population-based controls from the GCAT cohort (Spain). Molecular Inversion Probes (MIPs) were utilized for cost-effective and precise sequencing of the selected genes. The targeted genes include: Inflammasome/IL-1/TNF Pathway: NLRP3, CASP1, CASP8, IL1B, TNF, RIPK1, RIPK3, MYD88, TNFRSF13B SARS-CoV-2 Entry/Replication: ACE2, TMPRSS2, FURIN, SLC6A20, DDX1, DDX58, TLR4, FYCO1, CTSB, CTSL, ADAM17 Complement System: MBL2, CFH, CFI, CFB, ADAM10, CD46 Interferon Signaling: TLR3, IFIH1, IFITM3, TBK1, TLR7, IL10RB, IFNAR1, IFNAR2, SIGLEC1, MYD88, IFNGR1 Chemokine Receptor Signaling: CCR1, CCR3, CCR2, CCR9, IL8, CXCL3, CXCL10, CXCR6, XCR1, CCL2, CCL20 Immunodeficiency Genes: CASP8, CD46, CFB, CFH, CFI, IFNAR1, IFNAR2, IFNGR1, IFIH1, MYD88, NLRP3, RIPK1, TBK1, TLR3, TLR7

  Dataset EGAD50000001378

• Whole genome and transcriptome sequencing of cancer of unknown primary tumours

  Whole genome and transcriptome sequencing of cancer of unknown primary tumours was used to determine yield of clinical biomarkers for a molecular guided trial or for resolving cancer type of origin. This study includes profiling of germline DNA and tumour DNA and RNA by whole genome and transcriptome sequencing. All samples are in BAM format aligned with GRCh38 reference genome. This dataset includes: 1. Whole genome sequencing (WGS) of 78 cancer of unknown primary tumour samples and 73 matched germline DNA. 2. Whole transcriptome sequencing (WTS) of 69 cancer of unknown primary tumour samples (matched to WGS cases) 3. Whole genome sequencing of 22 cell-free DNA samples from cancer of unknown primary patients and matched Germaine DNA(8 samples matched to tumour WGS)

  Dataset EGAD50000000656