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• PRDM9_loss_of_function_follow_up_from_Born_in_Bradford_Autozygosity_sequencing

  In the autozygosity exome sequencing of Born-in-Bradford samples of Pakistani origin thereis a mother who is homozygous for an apparent truncating stop codon in PRDM9, the generesponsible for localising recombination during meiosis. We plan to deep sequence motherand child with X10, and physically phase the mother with PacBio sequencing.We will use this data to identify recombination locations, and test whether these areconsistent with the known fine scale recombination map.

  Study EGAS00001001301

• BCR_repertoire_sequencing

  Isotype-specific B-cell receptor repertoire sequencing in six immune-mediated diseases at diagnosis and during therapy reveals an unexpectedly complex B-cell architecture, which may provide a platform for a better understanding of pathological mechanisms and treatment responses. 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/

  Study EGAS00001003185

• International consensus definition of DNA methylation subgroups in juvenile myelomonocytic leukemia

  Known clinical and genetic markers have limitations in predicting disease course and outcome in juvenile myelomonocytic leukemia (JMML). DNA methylation (DNAme) patterns in JMML have correlated with outcome across multiple studies, suggesting it as a biomarker to improve patient stratification. However, standardized approaches to classify JMML based on DNAme patterns are lacking. We therefore sought to define an international consensus for DNAme subgroups in JMML and develop classification methods for clinical implementation.

  Study EGAS00001004682

• RNA sequencing data of in vitro differentiated megakaryocyte cells transduced with E527K and WT SRC

  For RNA sequencing (RNAseq), CD34+ hematopoietic stem cells (HSC) were isolated from healthy controls before transduction with WT-SRC and E527K-SRC lentiviral vectors in triplicate and differentiation to MK. RNA was extracted from the complete cell population as the aim was to detect differences in genes regulating MK maturation and differentiation. A total of six WT-SRC and six E527K-SRC RNA samples were used for RNAseq to identify differentially expressed genes.

  Study EGAS00001005808

• scRNA-seq of total bone marrow mononuclear cells and CD3+ T cells of multiple myeloma patients and healthy donors

  The study includes 50 scRNA-seq samples of bone marrow aspirates of 11 multiple myeloma patients and 3 healthy donors generated with the 10x Genomics platform (Chromium Single Cell 3’ Solution v2). For the multiple myeloma patients, paired longitudinal samples are available from initial diagnosis and long-term surivor state. For each patient and timepoint, total bone marrow mononuclear cells and CD3+ T cells have been sequenced separately.

  Study EGAS00001006980

• Resequencing candidate genes for male spermatogenic impairment

  This study aims to identify novel candidate variants from human Y-chromosomal genes DAZ, BPY2 and CDY1/2 by resequencing the coding regions of these genes from male patients with spermatogenic impairment. The coding regions of the genes plus a selection of phylogenetically informative Y-chromosomal markers have been amplified by standard PCR, amplicon lengths range from 178 to 486 bp. Amplicons were quantified by gel electrophoresis and pooled in approx. equimolar concentrations per patient. For each of the 480 submitted samples, approx. 1 microgram of amplified DNA pool was provided in a total volume of 120 microlitres. The samples were indexed and libraries prepared for PE250bp Illumina MiSeq runs. 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 EGAD00001006784

• Organ maturation in preparation for birth (Peds RFA) to develop a tissue resource and a single-cell atlas of organ development and maturation for dissemination among the scientific and clinical community: RNA (2025-10-14)

  Knowledge about abnormal organ development is important to understand pathology and to develop novel treatment approaches for individuals with congenital and acquired disease. Most of our current understanding is based on examination of tissues from the embryo and early foetus, collected from women undergoing termination of pregnancy in the first trimester (third) of pregnancy. There is very little known about normal and abnormal organ development from a developmental perspective during the crucial last two-thirds of pregnancy when much remodelling of foetal tissues occurs. This study will generate a single-cell atlas of late-foetal lungs, blood, heart, bone and immune organs. . This dataset contains all the data available for this study on 2025-10-14.

  Dataset EGAD00001015737

• PCA Atlas Visium spatial transcriptomics data (FASTQs, spaceranger BAMs and Visium mapping tables)

  This dataset contains Visium spatial transcriptomics data for the PCA Atlas study EGAS00001008332. It includes: - Raw Visium FASTQ runs (EGAR00004172272–EGAR00004172283) for all libraries. - spaceranger alignment BAM analyses for those libraries where BAM output was generated. - visium.csv: a slide-level mapping table linking Visium slide samples to EGA sample accessions, sequencing runs/experiments and spaceranger BAM analyses. - visium_tma.csv: a TMA mapping table describing how mini-TMA slides relate slide-level Visium libraries to tissue-level samples/cores. Together these objects provide the raw and aligned Visium data and the technical/biological mappings needed to understand and reuse the spatial transcriptomics component of the PCA Atlas. High resolution histology images are available from the following Zenodo repository, along with additional spaceranger outs for each slide: - https://zenodo.org/records/17411292

  Dataset EGAD00001015796

• Surgery in combination with immune checkpoint therapy as an effective treatment for patients with metastatic cancer.

  Abstract Surgical removal of primary tumors was shown to reverse tumor-mediated immune suppression in pre-clinical models with metastatic disease. However, how cytoreductive surgery in the metastatic setting modulates the immune responses in patients, especially in the context of immune checkpoint therapy (ICT)-containing treatments is not understood. Here, we report the first prospective, non-comparative clinical trial (N=104) using three different ICT-containing strategies plus cytoreductive or “debulking” surgery to remove the primary tumor-bearing kidney or a metastatic lesion as a treatment for patients (N=43) with metastatic clear cell renal cell carcinoma (mccRCC). For those patients (N=61) who were not candidates for cytoreductive surgery, a biopsy was obtained instead for correlative biological studies. Our data demonstrated that the combination of ICT with cytoreductive surgery was safe and feasible in patients with mccRCC. The 2-year overall survival was 84% with a median OS of 54.7 months for patients who received ICT containing regimens plus surgery. Immune-monitoring studies with co-detection by indexing (CODEX) identified distinct tumor spatial conformation of cellular subsets as a novel and improved predictor of response to ICT. Importantly, single-cell RNA-sequencing data demonstrated that surgical removal of the tumor increased antigen-presenting dendritic cell population with a concurrent reduction in KDM6B expressing immune-suppressive myeloid cells in the peripheral blood. Together, this study highlighted the feasibility of combining ICT with cytoreductive surgery in a metastatic setting and demonstrated potential enhancement of immune responses following ICT plus cytoreductive surgery in patients with metastatic disease.

  Study EGAS00001005667

• Kids First and INCLUDE: Down Syndrome, Heart Defects, and Acute Lymphoblastic Leukemia

  This project is a collaboration with the trans-NIH INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE (INCLUDE) Project, which seeks to improve health and quality-of-life for individuals with Down syndrome, and NHLBI's TransOmics for Precision Medicine (TOPMed) program, which seeks to apply omics technologies to improve scientific understanding of the fundamental biological processes that underlie heart, lung, blood, and sleep (HLBS) disorders. The data is also available at the INCLUDE Data Hub. Down syndrome (DS), which occurs due to trisomy 21, is one of the strongest risk factors for both congenital heart disease (CHD) and acute leukemia. For instance, children with DS have a 2000-fold increased risk of atrioventricular septal defects (AVSD) and a 20-fold increased risk of acute lymphoblastic leukemia (ALL). An important and innovative aspect of the Kids First program is understanding the overlap between structural birth defects and childhood cancer. Notably, the background of DS predisposes children to both of these phenotypes, however, the genomic architecture of risk remains largely undiscovered. In this joint Kids First/TOPMed/INCLUDE project, we are currently including >2,000 samples for whole-genome sequencing, including: 1) paired germline-tumor samples from children with DS-ALL; and 2) samples from families with DS-CHD. This work will advance our understanding of the developmental pathways that may lead to both structural birth defects and childhood cancer. The objectives of this study are to determine the genetic variants underlying AVSD and ALL risk in children with DS. Therefore, the aims of our study are: 1) compare whole-genome sequencing (WGS) data between children with documented DS-AVSD and children with DS who have structurally normal hearts to identify genetic variants that perturb heart development; and 2) compare WGS data between children with documented DS-ALL and children (from Aim 1) with DS who do not have a known history of ALL. We will also examine associations between germline mutations and somatic genomic features. This study will address the fundamental question of why children with DS have an elevated risk of AVSD and ALL. Insights into the genes that drive DS-AVSD and DS-ALL may have implications for improved genetic counseling, surveillance, clinical management, and treatment strategies for these children. Additionally, our findings may inform targeted therapies or interventions for children without DS who are at risk for structural birth defects and cancer. Additional Pediatric Cardiac Genetics Consortium (PCGC) data from children affected with Down syndrome and congenital heart disease are accessible through two separate dbGaP studies: phs001138 (Kids First) and phs001194 (TOPMed).

  Study phs002330

• Serrated Colorectal Cancer: An Emerging Disease Subtype

  This dataset represents two combined study populations. Serrated Colorectal Cancer: An Emerging Disease Subtype (called the Advanced Colorectal Cancer of Serrated Subtype Study or ACCESS Study) was a grant awarded to investigate a newly-recognized, biologically-distinct subtype of colorectal cancer (CRC) called “serrated CRC.” The objective of this project was to characterize factors related to the genetic predisposition, clinical presentation, and prognosis of serrated CRC. The study recruited incident invasive CRC cases diagnosed between April 2016 and December 2018, aged 20-74 years at diagnosis. Cases were identified through the Surveillance, Epidemiology and End Results (SEER) cancer registry serving 13 counties in western Washington State. Eligibility for all individuals was limited to those who were English-speaking and could consent. Participation included completing a baseline epidemiologic questionnaire shortly after diagnosis, optional donation of a saliva sample for genetic analysis, and optional consent to release of medical records and tissue specimens related to their diagnosis. Tumor specimens were tested for serrated CRC-defining molecular characteristics. Further, we have vital status on all participants and cause of death on those that have died since enrollment. Hormones and Colon Cancer: Epigenetic Subtypes, Risks, and Survival (called the Post-Menopausal Hormones Study or PMH Study) was a grant awarded to investigate the impact of post-menopausal hormone use on colon cancer risk, tumor molecular characteristics, and outcomes. Eligible cases were females, newly diagnosed with invasive colorectal adenocarcinoma between October 1998 and February 2002, aged 50 to 74 years. Cases were residents of 10 out of the 13 counties in western Washington State served by the Surveillance, Epidemiology and End Results (SEER) cancer registry. Eligibility for all individuals was limited to those who were English-speaking with available telephone numbers, in which they could be contacted. Unrelated population-based controls were randomly selected according to age distribution (in 5-year age intervals) of the eligible cases by using lists of licensed drivers from the Washington State Department of Licensing (for individuals aged 50 to 64 years) and rosters from the Health Care Financing Administration (now the Centers for Medicare and Medicaid, for individuals older than 64 years). Participation included completing a baseline epidemiologic questionnaire, optional donation of a saliva sample for genetic analysis, and (for cases only) optional consent to release of medical records and tissue specimens related to their diagnosis. Tumor specimens were tested for epigenetic and other molecular characteristics. The ACCESS study was supported by funding from the National Cancer Institute of the National Institutes of Health (NCI/NIH) (R01CA196337, PI: Newcomb, PA), as was the PMH Study (R01CA076366, PI: Newcomb, PA). Additional support for the PMH Study came from the Seattle site of the Colon Cancer Family Registry (SCCFR) (U01CA167551, PI: Jenkins, M, and U01/U24CA074794, PI: Newcomb, PA). Additional support for case ascertainment was provided by the Cancer Surveillance System of the Fred Hutchinson Cancer Center, which is funded by Contract Number HHSN261201300012I; NCI Control Number: N01 PC-2013-00012; Contract Number HHSN261201800004I; and NCI Control Number: N01 PC-2018-00004 from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute with additional support from the Fred Hutchinson Cancer Center and the State of Washington. This research was also supported by the Genomics and Bioinformatics, Comparative Medicine, Specialized Pathology, Collaborative Data Services, and Experimental Histopathology Shared Resources of the Fred Hutch/University of Washington Cancer Consortium (P30 CA015704).Tumor marker testing was performed using formalin-fixed paraffin-embedded diagnostic tumor tissue specimens, and DNA extracted from those specimens. Testing for microsatellite instability (MSI) was based on either a 10-gene panel (BAT25, BAT26, BAT40, MYCL, D5S346, D17S250, ACTC, D18S55, D10S197, BAT34C4) or a 4-marker immunohistochemistry panel of DNA mismatch repair proteins (MLH1, MSH2, MSH6, PMS2). CpG island methylator phenotype (CIMP) testing was based on a validated quantitative DNA methylation assay using a five-gene panel (CACNA1G, IGF2, NEUROG1, RUNX3, SOCS1) or eight-gene panel (CACNA1G, IGF2, NEUROG1, RUNX3, SOCS1, MLH1, CRABP1, CDKN2A). Somatic p.V600E BRAF mutation status was tested for using a fluorescent allele-specific PCR assay. KRAS mutations in codons 12 and 13 were also assessed through forward and reverse sequencing of amplified tumor DNA. DNA was extracted from blood/saliva samples using conventional methods. The genotyping panel completed was the Build37 OncoArray500K-C, including 1%-6% blinded duplicates to monitor the quality of the genotyping. Quality control procedures were performed to 1) make sure that there were no patterns of missing data by batch, study, or plate, 2) check for gender discrepancies and kinship, 3) complete Principal Component Analysis, and 4) test for Hardy-Weinberg equilibrium (HWE). Samples were excluded based on call rate, heterozygosity, unexpected duplicates, gender discrepancy, and unexpectedly high identity-by-descent or unexpected genotypic concordance (>65%) with another individual. In addition, variants were excluded based on call rate (98%), lack of HWE in controls (P

  Study phs002171

• Array data for oesophageal and related samples – sj_paper_methyl_normal_release

  Array data for oesophageal and related samples – sj_paper_methyl_normal_release

  Dataset EGAD00010001834

• Mutation detection of T follicular helper cell lymphomas

  Whole-exome sequencing data for study of tumor heterogeneity and immune-evasive microenvironment in T follicular helper cell lymphomas

  Study EGAS50000000276

• Roifman_DAC

  This Data Access Committee (DAC) will be set up to review applications, and render decisions for requests submitted to EGA to access data deposited into ega-box-1592 by Dr. Chaim Roifman at the Hospital for Sick Children in Toronto, Canada. Contact: Dr. Chaim Roifman. email:chaim.roifman@sickkids.ca

  Dac EGAC50000000243

• Young-Boost Whole Exome Sequencing (WES)

  Datasets associated with Young Boost Trial for Breast Cancer patients. Whole Exome Sequencing (WES) performed on 109 Samples (75 Tumor and 34 Normal). WES was performed to identify potential predictive biomarkers for treatment response.

  Dataset EGAD50000001171

• Clinical and ctDNA data for IMpassion031

  Clinical and ctDNA data for IMpassion031, including survival, response, and ctDNA data from baseline through post-surgery time points. 222 samples run on Signatera assay. File type is csv.

  Dataset EGAD50000001420

• Dataset for "HPV integration induces gene fusions" (ONT)

  ONT whole-genome sequencing data for "HPV integration induces gene fusions" . We sequenced five HPV-positive head and neck cancer samples using Oxford Nanopore platform. The sequences was submitted in fastq format.

  Dataset EGAD50000001302

• DNAmet

  DNAmet analysis of olfactory mucosa (OM) cells derived from cognitively healthy and individuals with AD exposed to traffic-related ultrafine particles (UFPs) for 72h in submerged cultures. The UFPs used for exposures were: A0 and A20. Exposures were compared to the corresponding blank samples.

  Dataset EGAD50000001531

• WGS dataset for two NUP98-Rearranged Acute Myeloid Leukemia PDX samples

  This dataset includes whole genome sequencing of two patient-derived xenograft (PDX) samples with NUP98-Rearranged Acute Myeloid Leukemia. Bam files are provided for each sample.

  Dataset EGAD50000001563

• Chromatin accessibility profiling of primary human hepatocytes

  The chromatin accessibility profiling of primary human hepatocytes using Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq).

  Study EGAS50000001230

• snRNAseq prostate cancer

  single-nucleus RNA seq characterization of human fresh frozen prostate cancer samples and non-malignant controls for the same patients. Biopsies were sectioned in 100 micron thick sections, adjacent to those used for spatial transcriptomics.

  Dataset EGAD50000001633

• Whole exome sequence analysis in multiple system atrophy

  To identify a susceptible gene for multiple system atrophy, exome sequence analysis was conducted in 14 patients and 7 controls.

  Study JGAS000009

• Bulk RNA sequencing of ALS patients

  We performed RNA sequencing to screen for alternative splicing alterations in patients with ALS to identify those linked to ALS pathology.

  Study JGAS000851

• Multi-omics characterisation of immune cells in Long Covid

  We generated 10X Multiomics (paired RNA+ATAC) for Long Covid patients with up to 14 months follow-up.

  Study EGAS50000000142

• WTCCC2 Pre-eclampsia Study

  A WTCCC2 project genome-wide association study for pre-eclampsia (PA) in 4375 individuals from Colombia, genotyped on the Affymetrix 6.0 array.

  Study EGAS00001003349

• Indonesian Genome Diversity Project 2, genotyping data

  This study reports Illumina Omni 2.5M genotyping chip data for 48 individuals from Aru, Mentawai, New Guinea and Sumba islands.

  Study EGAS00001003670

• scATAC sequence runs of 29 samples of RRMM (multiple myeloma) tumors

  scATAC sequencing was performed of 29 samples of RRMM tumors using 10xGenomics for the preparation and NovaSeq6000 for sequencing.

  Dataset EGAD00001009683

• A95626A

  Whole genome sequencing for single cells for library A95626A 1040 cells; filetype=bam

  Dataset EGAD00001007103

• A96199B

  Whole genome sequencing for single cells for library A96199B 1170 samples; filetype=bam

  Dataset EGAD00001008251

• A96205A

  Whole genome sequencing for single cells for library A96205A 1907 samples; filetype=bam

  Dataset EGAD00001008253

• 10x Genomics Single Cell Gene Expression for SA1051AX1XB01452

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1051 passage 1

  Dataset EGAD00001009124

• 10x Genomics Single Cell Gene Expression for SA1035X8XB03425

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 8

  Dataset EGAD00001009148

• 10x Genomics Single Cell Gene Expression for SA1052DX1XB01519

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1052 passage 1

  Dataset EGAD00001009122

• 10x Genomics Single Cell Gene Expression for SA1050FX1XB01446

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1050 passage 1

  Dataset EGAD00001009125

• 10x Genomics Single Cell Gene Expression for SA1093CX1XB01917

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1093 passage 1

  Dataset EGAD00001009129

• A96146A

  Whole genome sequencing for single cells for library A96146A 1195 samples; filetype=bam

  Dataset EGAD00001008233

• 10x Genomics Single Cell Gene Expression for SA1184SA

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient SA1184

  Dataset EGAD00001009145

• A118869B

  Whole genome sequencing for single cells for library A118869B 1124 cells; filetype=bam

  Dataset EGAD00001007100

• A96225C

  Whole genome sequencing for single cells for library A96225C 1034 cells; filetype=bam

  Dataset EGAD00001007120

• Single Cell Genome Sequence for DLP+ library A95632C

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA609 passage 6 on DLP+ library A95632C

  Dataset EGAD00001009322

• 10x Genomics Single Cell Gene Expression for OV2295

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma cell line OV2295

  Dataset EGAD00001009143

• A98244A

  Whole genome sequencing for single cells for library A98244A 1271 cells; filetype=bam

  Dataset EGAD00001007628

• Single Cell Genome Sequence for DLP+ library A96141A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 8 on DLP+ library A96141A

  Dataset EGAD00001009777

• A96184A

  Whole genome sequencing for single cells for library A96184A 1733 samples; filetype=bam

  Dataset EGAD00001008248

• A96172B

  Whole genome sequencing for single cells for library A96172B 1694 samples; filetype=bam

  Dataset EGAD00001008241

• A96220B

  Whole genome sequencing for single cells for library A96220B 1267 samples; filetype=bam

  Dataset EGAD00001008258

• A96244A

  Whole genome sequencing for single cells for library A96244A 1374 samples; filetype=bam

  Dataset EGAD00001008259

• 10x Genomics Single Cell Gene Expression for SA604X8XB02164

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA604 passage 8

  Dataset EGAD00001009118

• 10x Genomics Single Cell Gene Expression for SA1049AX1XB01417

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1049 passage 1

  Dataset EGAD00001009121

• 10x Genomics Single Cell Gene Expression for SA1096AX1XB02034

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1096 passage 1

  Dataset EGAD00001009132