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• Whole Exome Sequencing of High grade T1 non-muscle invasive bladder cancer

  The dataset consists of a multisample VCF (version 4.1) and the corresponding annotated MAF file, containing the somatic point mutations found from exome sequencing across the high-grade T1 bladder cancer cohort (HGT1, n=61 samples). The VCF file is in accordance with the HTS format specifications (https://samtools.github.io/hts-specs/). The dataset comprises also a CSV file with clinical data.

  Dataset EGAD00001006346

• PIAMA nasal RNAseq dataset

  This study included nasal gene expression data collected from nasal brushes of adolescents in PIAMA birth cohort, which is used in the project Nasal DNA methylation at three CpG sites predicts childhood allergic disease. 186 samples were included in this analysis, and phenotypes (age and sex) were provided together with a gene expression count table. Gene expression was measured by the Illumina HiSeq2500 sequencer.

  Dataset EGAD00001008767

• Targeted DNA sequencing on bulk bone marrow and peripheral blood

  Targeted DNA sequencing was performed on 195 bone marrow samples to identify cases of clonal haematopoiesis, and on 99 paired peripheral blood samples. The SeqCap EZ HyperCap protocol was followed, and targeted capture performed against a panel of 97 genes recurrently mutated in myeloid malignancies and clonal hematopoiesis. One BAM file (mapped to the hg38 reference genome) is provided per sample.

  Dataset EGAD00001011083

• Chromatin accessibility (ATAC-seq) of human acute leukemias and healthy donors

  ATAC-seq of 79 primary samples obtained from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). ATAC-seq of CD34+ HSPCs from 3 healthy donors is also included.

  Dataset EGAD00001011050

• 47 urothelial cancer patients WES and 38 RNAseq

  We performed a global mutational landscape analysis using tumor samples from the 47 urothelial cancer patients included in MATCH-R or MOSCATO studies, with advanced metastatic disease and WES available (RNAseq is included for 38 patient samples).

  Dataset EGAD00001011063

• DNA and RNAseq of serial biopsies from 75 DLBCL patients

  This data set includes serial biopsies from 75 patients with DLBCL. Tumour tissue was preserved either in FFPE or frozen. Each biopsy was sequenced with either whole genome or exome. A custom targeted sequencing data set is available to match most whole genome samples. RNAseq data were available for a subset of biopsies.

  Dataset EGAD00001011816

• Spatially resolved cellular and molecular drivers of cardiac remodelling in healthy and failing human hearts: Adult RNA (2025-07-31)

  Cell Atlas of the diseased human heart. 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/ . This dataset contains all the data available for this study on 2025-07-31.

  Dataset EGAD00001015668

• Submitting array based metadata

  Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

  Documentation submission/metadata/submission/array

• Angelman, Rett, Prader-Willi Syndromes Consortium (ARP) Angelman Syndrome Natural History Protocol

  This study is a longitudinal multidisciplinary investigation on the natural history, morbidity and mortality of Angelman Syndrome (AS). We will collect detailed longitudinal data on a cohort of AS individuals to gain a better understanding of the disease progression, and to follow the natural history of the clinical features of this cohort including assessment of quality of life and longevity. The participants to be recruited for the study will include 1) patients who have a documented molecular diagnosis of AS and 2) patients with a clear clinical diagnosis of Angelman Syndrome as determined by the Principal Investigator (PI) and the Co-investigators in this study but who do not have a known molecular defect. One of the goals of the natural history study will be to characterize the phenotypic differences between patients with Class I deletions and those with Class II deletions, particularly with respect to the issue of autism. A blood sample may be collected on the participants in order to create a DNA repository, and in some cases, to establish cell lines if further material is required for molecular studies. Alternatively, DNA may be obtained by buccal mucosa swabs/brushing. In those AS patients with known deletions involving the 15q11-q13 regions, a blood sample will be collected to perform comparative genome hybridization (CGH) microarray studies to characterize the extent of the deletion. No drugs or treatments will be administered through this protocol. In rare instances, a skin biopsy to establish a fibroblast cell line may be requested (using separate consent).

  Study phs000576

• Family Investigation of Nephropathy and Diabetes (FIND) Study

  The Family Investigation of Nephropathy and Diabetes (FIND) is a multicenter study designed to identify genetic determinants of diabetic kidney disease. Study subjects were recruited from eleven centers and in many ethnic groups throughout the United States. A genome-wide association study (GWAS) was conducted with the Affymetrix 6.0 chip. Subjects (index cases) with diabetes and kidney disease were initially recruited, and their parents and siblings were invited to participate. Genetic material from these participants was used to genotype markers throughout the genome. For association-based testing, a case-control design was implemented with study subjects selected primarily from the index cases of the families. Unrelated controls were selected from families where a case was not already selected. Several study sites also contributed non-FIND subjects, both cases and controls (consent forms for the release of FIND and non-FIND subjects/samples are included in this dbGaP release). Cases were selected if they met study criteria for diabetic nephropathy or met inclusion criteria based on elevated serum creatinine levels and abnormal urine protein excretion. Similarly, controls were long-term diabetics with otherwise normal kidney function. See inclusion/exclusion criteria section for a detailed description for the FIND study as a whole and this GWAS. The goal of the FIND study is to identify genes that influence susceptibility to diabetic kidney disease, leading to a better understanding of how kidney disease develops. In the long run, this may lead to improved treatment and prevention of diabetic kidney disease.

  Study phs000333