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• Prognostic markers of DNA methylation and NGS sequencing in progressive glioblastoma from the EORTC-26101 trial

  These dataset contains NGS data from 380 samples from the EORTC-26101 clinical trial for patients with progressive glioblastoma.

  Study EGAS00001007421

• WGS and WES data for manuscript titled: ctDNA as a biomarker of progression in oesophageal adenocarcinoma

  WGS and WES data for manuscript titled: ctDNA as a biomarker of progression in oesophageal adenocarcinoma

  Dataset EGAD00001008554

• Tumour sample for patient SA1058

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1058

  Dataset EGAD00001009673

• Normal sample for patient SA1058

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1058

  Dataset EGAD00001009672

• Tumour sample for patient SA998

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA998

  Dataset EGAD00001009674

• WGS files for Klco-NUP98 data

  WGS files for paper titled "Fusion oncoproteins and cooperating mutations define disease phenotypes in NUP98-rearranged leukemia" PMID: 39974131, PMCID: PMC11838931, DOI: 10.1101/2025.01.21.25320683

  Dataset EGAD00001015443

• WXS files for Klco-NUP98 data

  WXS files for paper titled "Fusion oncoproteins and cooperating mutations define disease phenotypes in NUP98-rearranged leukemia" PMID: 39974131, PMCID: PMC11838931, DOI: 10.1101/2025.01.21.25320683

  Dataset EGAD00001015444

• RNASeq files for Klco-NUP98 data

  RNASeq files for paper titled "Fusion oncoproteins and cooperating mutations define disease phenotypes in NUP98-rearranged leukemia" PMID: 39974131, PMCID: PMC11838931, DOI: 10.1101/2025.01.21.25320683

  Dataset EGAD00001015445

• scRNASeq files for Klco-NUP98 data

  scRNASeq files for paper titled "Fusion oncoproteins and cooperating mutations define disease phenotypes in NUP98-rearranged leukemia" PMID: 39974131, PMCID: PMC11838931, DOI: 10.1101/2025.01.21.25320683

  Dataset EGAD00001015479

• DNA WGS Short Read Sequence (Illumina NovaSeq) for manuscript titled: "Performance of Somatic Structural Variant Calling in Lung Cancer using Oxford Nanopore Sequencing Technology"

  DNA WGS Short Read Sequence (Illumina NovaSeq) for manuscript titled: "Performance of Somatic Structural Variant Calling in Lung Cancer using Oxford Nanopore Sequencing Technology"

  Dataset EGAD00001015399

• Dataset for EGAS00001007937

  WGS, RNAseq, Methylation, variant calling analysis for a Novel paediatric case of a spinal high-grade astrocytoma with piloid features in a patient with Noonan Syndrome

  Dataset EGAD00001015412

• Whole Exome Sequencing of healthy Spanish individuals - VCF file

  The need for a detailed catalogue of local variability for the study of rare diseases within the context of the Medical Genome Project motivated the whole exome sequencing of 267 unrelated individuals, representative of the healthy Spanish population.

  Dataset EGAD00001003101

• V4 Colorectal panel test (2018-03-07)

  Targeted gene screen of cell line tumours for testing the new V4 Colorectal gene panel. . This dataset contains all the data available for this study on 2018-03-07.

  Dataset EGAD00001004000

• BLUEPRINT release January 2015, DNase-Hypersensitivity for Acute myeloid leukemia

  DNase-Hypersensitivity data for 1 Acute myeloid leukemia sample(s). 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_dnaseseq_analysis_20140811

  Dataset EGAD00001001190

• EUROBATS RNAseq BAM files for LCLs

  These analysis are the BAM files for the LCLs samples of the EUROBATS samples.

  Dataset EGAD00001001086

• EUROBATS RNAseq BAM files for the Skin samples

  RNAseq BAM files for the Skin samples of the EUROBATS project.

  Dataset EGAD00001001087

• EUROBATS RNAseq BAM files for the Blood samples

  RNAseq BAM files for the blood samples of the EUROBATS project

  Dataset EGAD00001001088

• EUROBATS RNAseq BAM files for the Fat samples

  RNAseq BAM files for the Fat samples of the EUROBATS project

  Dataset EGAD00001001089

• WGBS data for ependymomas and normal controls (fetal and adult)

  Whole genome bisulfite sequencing data for 6 ependymomas plus 3 fetal controls (f1, f2, f4) and 3 adult controls (a2, a3, a4). See Mack, Witt et al. Nature 506(7489):445-50, 2014 (PMID: 24553142).

  Dataset EGAD00001000966

• Cancer-Normal chronic myeloid leukaemia sequencing data for "A comparative analysis of algorithms for somatic SNV detection in cancer", Bioinformatics 29 (2013) 2223

  This dataset contains the fastq sequencing data collected from bone marrow DNA of a chronic myeloid leukaemia patient at time of diagnosis.

  Dataset EGAD00001000967

• APCDR Uganda GWAS - UG2G dataset: Whole genome low depth sequence data for 2000 Ugandans (BAMs).

  Low depth (4x) Illumina HiSeq raw sequence data for 2000 Ugandans from various ethno-linguistic group from rural South-West Uganda (related individuals included).

  Dataset EGAD00001001639

• Exome sequencing of Non-syndromic Congenital Heart Defects (2019-04-24)

  Exome sequencing of ~1,800 patients with non-syndromic Congenical Heart Defects (CHD) to identify genes enriched for damaging rare variants that increase risk of CHD . This dataset contains all the data available for this study on 2019-04-24.

  Dataset EGAD00001004972

• DATA FILES FOR PCGP SJCBF EXCAP

  Additional files for "The Genomic Landscape of Core-Binding Factor Acute Myeloid Leukemias" (EGAS00001000349). This dataset includes the processed Excap data referenced in this paper.

  Dataset EGAD00001002667

• WGS data (19 tumor/control pairs) for EGAS00001000383

  Part of WGS data for Prostate (ICGC)

  Dataset EGAD00001002669

• Chromatin Profiling in Twins (2019-08-21)

  50 lymphoblastoid cell lines of adult female Twins which are part of the MuTHER study, subjected to Chromatin Immunoprecipitation using an antibody for H3K4me1. H3K4me1 peaks mark distal regulatory elements. . This dataset contains all the data available for this study on 2019-08-21.

  Dataset EGAD00001005274

• WGS files for Millighan BiTE WGS

  WGS files for Mullighan BiTE WGS paper titled "Tumor intrinsic and extrinsic mechanisms of response and resistance to blinatumomab in relapsed/refractory acute lymphoblastic leukemia"

  Dataset EGAD00001005729

• WXS files for Mullighan BiTE WXS

  WXS files for Mullighan BiTE WXS paper titled "Tumor intrinsic and extrinsic mechanisms of response and resistance to blinatumomab in relapsed/refractory acute lymphoblastic leukemia"

  Dataset EGAD00001005730

• RNAseq files for Mullighan BiTE RNASEQ1

  RNAseq files for Mullighan BiTE RNASEQ1 paper titled "Tumor intrinsic and extrinsic mechanisms of response and resistance to blinatumomab in relapsed/refractory acute lymphoblastic leukemia"

  Dataset EGAD00001005731

• lowinput RNASEQ files for Mullighan BiTE RNASEQ2

  lowinput RNASEQ files for Mullighan BiTE RNASEQ2 paper titled "Tumor intrinsic and extrinsic mechanisms of response and resistance to blinatumomab in relapsed/refractory acute lymphoblastic leukemia"

  Dataset EGAD00001005732

• Analysis File for 87 Argentinean individuals

  VCF for 87 Argentinean samples. Only SNPs (no indels) that passed the Affymetrix QC. Data from Luisi et al. 2020. Plos One. Fine-Scale Genomic Analyses Of Admixed Individuals Reveal Unrecognized Genetic Ancestry Components In Argentina. Reference Allele column does NOT contain reference allele from genome assembly.

  Dataset EGAD00001006227

• WGBS data for EGAS00001004660

  WGBS data for EGAS00001004660, "Aggressive PDACs show hypomethylation of repetitive elements and the execution of an intrinsic IFN program linked to a ductal cell-of-origin"

  Dataset EGAD00001006538

• RNA data for EGAS00001004660

  RNA data for EGAS00001004660, "Aggressive PDACs show hypomethylation of repetitive elements and the execution of an intrinsic IFN program linked to a ductal cell-of-origin"

  Dataset EGAD00001006539

• ChIPseq data for Glioblastoma (EGAS00001003953, H016)

  ChIPseq data for human glioblastoma patients, EGAS00001003953. Mix of input, H3K27ac, H3K27me1, H3K27me3, H3K36me3, H3K4me1, H3K4me3, H3K9me3 and BRD, 20 human samples, 2 cell lines (LN229, ZH487).

  Dataset EGAD00001006548

• RNASeq files for Mullighan TXVI dataset

  RNASeq files for paper titled "Prognostic and therapeutic significance of leukemia subtypes and minimal residual disease measurements in pediatric acute lymphoblastic leukemia treated with contemporary risk-directed trial: a cohort study"

  Dataset EGAD00001006609

• High-throughput sequencing data for study of molecular drivers of resistance to castration in localised prostate cancer

  Aligned whole-genome sequencing and RNA-seq of localised prostate cancer for study 'Loss of SNAI2 in prostate cancer correlates with clinical response to androgen deprivation therapy'.

  Dataset EGAD00001006640

• CBD-RAW-REPERTOIRE-T: T cell bulk repertoire sequence files

  fastq and filtered fasta files for T-cell receptor sequencing. For further information regarding this dataset, please contact Rachael Bashford-Rogers at contact@combat.ox.ac.uk.

  Dataset EGAD00001007961

• COVID-19 scRNA-seq, TCR-seq and BCR-seq

  COVID-19 scRNA-seq, TCR-seq and BCR-seq for 291 samples collected from 109 patients. Among 291 samples, 249 of them have two libraries (sequencing runs) for each assay, while 42 have only one library.

  Dataset EGAD00001007995

• APCDR Uganda GWAS: Genome-wide sequence variation and susceptibility loci for cardiometabolic traits in a sub-Saharan African population (UGWAS component)

  Genomic studies in African populations provide unique opportunities to understand disease aetiology, human genetic diversity and population history in a regional and a global context. To leverage the relative benefits of different strategies, we undertook a combined approach of genotyping and whole-genome sequencing (WGS) in a population-based study of 6,400 individuals from a geographically defined rural community in South-West Uganda. We present data from 4,778 individuals with genotypes for ~2.2 million SNPs from the Uganda GWAS resource (UGWAS), and sequence data on up to 1,978 individuals spanning 41.5M SNPs and 4.5M indels (UG2G); 343 individuals overlap between the two datasets. We highlight the value of the largest sequence panel from Africa to date as a global resource for variant discovery, imputation and understanding the mutational spectrum and its clinical relevance in African populations. Alongside phenotype data, we provide a rich new genomic resource for researchers in Africa and globally.

  Study EGAS00001001558

• Sequencing of an organoid biobank for childhood kidney cancers that captures disease and tissue heterogeneity.

  Kidney tumors are among the most common solid tumors in children, comprising several distinct subtypes differing in many aspects, including cell-of-origin, genetics, and pathology. Pre-clinical cell models capturing the disease heterogeneity are currently lacking. Here, we describe the first pediatric cancer organoid biobank. It contains tumor and matching normal organoids from over 50 children with different subtypes of kidney cancer, including Wilms tumors, malignant rhabdoid tumors, renal cell carcinomas, and congenital mesoblastic nephromas. The malignant rhabdoid tumor organoids represent the first organoid model for tumors of non-epithelial origin. The tumor organoids retain key properties of native tumors, useful for revealing patient specific drug vulnerabilities. We further demonstrate that organoid cultures derived from Wilms tumors consist of multiple different cell types, including epithelial, stromal and blastemal-like. Our organoid biobank captures the cellular heterogeneity of pediatric kidney tumors, providing a representative collection of well-characterized models for basic cancer research, drug-screening, and personalized medicine.

  Study EGAS00001003853

• Reference exome data for Australian Aboriginal populations to support health-based research

  Whole exome sequencing (WES) is a popular and successful technology which is widely used in both research and clinical settings. However, there is a paucity of reference data for Aboriginal Australians to underpin the translation of health-based genomic research. Here we provide a catalogue of variants called after sequencing the exomes of 50 Aboriginal individuals from the Northern Territory (NT) of Australia and compare these to 72 previously published exomes from a Western Australian (WA) population of Martu origin. Sequence data for both NT and WA samples were processed using an ‘intersect-then-combine’ (ITC) approach, using GATK and SAMtools to call variants. A total of 289,829 variants were identified in at least one individual in the NT cohort and 248,374 variants in at least one individual in the WA cohort. Of these, 166,719 variants were present in both cohorts, whilst 123,110 variants were private to the NT cohort and 81,655 were private to the WA cohort. Our data set provides a useful reference point for genomic studies on Aboriginal Australians.

  Study EGAS00001003745

• The ITCC-P4 PDX platform of pediatric cancers for preclinical testing

  Cancer is the leading cause of disease-related deaths among children in high-income countries. Tumor heterogeneity and lack of mechanism-of-action-based therapeutic options are key challenges to overcome in order to improve pediatric cancer patients’ survival. Here, we report the EU-IMI-2 funded public-private partnership “ITCC-Pediatric Preclinical Proof-of-Concept Platform” (ITCC-P4), which has built a large repertoire of patient-derived xenograft (PDX) models, representing all major solid pediatric cancer types, for in vivo drug testing. Three-hundred-fifty-three PDX models from diagnostic and relapsed pediatric cancers have been established and molecularly characterized, together with matched germline/tumor samples. As proof-of-concept, we present in vivo drug screening data in neuroblastoma and rhabdomyosarcoma models. PDX data, accessible at http://r2platform.com/itcc-p4, allow the selection of models based on oncogenic drivers and/or potential biomarkers for preclinical testing. Operated by a non-profit entity (www.itccp4.com), this sustainable platform aids academic and industrial researchers in developing and prioritizing innovative therapies for pediatric cancer.

  Study EGAS00001008330

• NIDA Genetic Epidemiology of Opioid Dependence in Bulgaria (GEODB)

  The study was conducted in Bulgaria as a collaboration between the Molecular Medicine Center (Medical University, Sofia), the Bulgarian Addictions Institute (Sofia), the Initiative for Health Foundation (Sofia) and Washington University School of Medicine (Saint Louis, MO, USA). This is a case-population control study. Participants (N=2,499 interviews; N=1,995 with GWAS data) in our study are primarily active heroin users (78%) aged 18 and over, with a median 8.9 years of daily heroin use (minimum of 12 months). Virtually all participants (99.1%) qualified for a lifetime DSM-IV diagnosis of dependence, symptom endorsement probabilities ranging from 77% to 99.3%; and 61% endorsing all symptoms. 85% were heroin injectors and 97% reported using (or having used) heroin daily. Anonymized, population-representative control samples (no phenotypic data, N = 1,062 with GWAS data) were drawn from the MMC repository. Unlike other studies, where samples are clinically ascertained (e.g., from ORT programs), our participants are primarily untreated. PTSD, major depression, suicidality, mania, alcohol, nicotine and substance dependence (separating heroin and opioids) were assessed using the MAGIC (Todd et al., 2003, PMID: 14627881), a broad semi-structured psychiatric assessment tool with good reliability and prospective stability. The onset of (any) illicit drug use in this sample is not particularly remarkable (age~16), but the age of onset of heroin use is much lower than in the USA: 25% by age 16; 50% by age 19, with no differences between ethnicities or gender. This is consistent with findings from other studies in Bulgaria. In contrast, in 2008, SAMHSA estimated an average age of 23.4 for new heroin initiates in the USA. Further, progression from first use to regular use appears to be more rapid, with the majority of participants reporting onset of dependence problems within one year of initiation. Another unique aspect of this study population are the comparatively low levels of polysubstance use (outside of nicotine and alcohol). This early-use, fast-transition and monosubstance heroin pattern stands in sharp contrast to what is reported in many studies in the Western World. In our study, 47% of Ethnic Bulgarians and 66% of Roma used only opioids, even years after the onset of drug use. 15.4% of Ethnic Bulgarians and 66.2% of Roma reported heroin as the very first illicit drug used. Lifetime, 12.5% of Ethnic Bulgarians and 52.8% of Roma report never having used another illicit drug other than heroin more than five times. While both Roma and Ethnic Bulgarians certainly experiment with other drugs, abuse/dependence rates for these drugs are low in Ethnic Bulgarians (from 8.4% for inhalants to 28% for stimulants) and in the Roma population (from 1.3% for hallucinogens to 11.2% for stimulants).

  Study phs001804

• Pathogenesis and Immunity in Endemic Burkitt Lymphoma

  This genomic landscape of Burkitt lymphoma represents a multimodal sequencing of tumors and control tissues and individuals to better understand the etiology, and molecular pathogenesis of Burkitt lymphoma including the roles of the associated Plasmodium falciparum malaria and EBV infections. Comprehensive sequencing set includes genomic, transcriptomic, and epigenomic datasets in concert with variable clinical phenotypes and outcome information such as anatomical presentation site, in-hospital survival rates, and EBV genome type. This deposit includes additional small RNA-seq data from endemic BL (eBL) tumors and RNA-sequencing data from sorted NK cell subsets from the peripheral blood of eBL patients. The additional small RNA-seq data are from tumor specimens from 17 eBL cases, of the previously deposited polyA RNA-seq data from 28 eBL cases (phs1282.V1). The additional RNAseq data from Fluorescence-activated cell sorting (FACS) of NK cell subsets, isolated from the peripheral blood of 7 eBL cases. The additional RNAseq data are from eBL tumor samples of 5 eBL cases, BL cell lines established from these tumor samples and also NSG mouse tumors created by implanting the established BL cell lines into NSG mice. The dataset also includes RNAseq data of the NSG mouse tumors after treatment with Rituximab (RIT) and Phosphate buffered saline (PBS). The fifth batch of data includes mRNA-seq from different passages of a single eBL cell line that have variable EBV phenotypes (low passage with lytic EBV and high passage with latent EBV). This batch also includes lytic and latent lymphoblastoid cell line (LCL) clones from a single blood donor generated through infection with the strain of EBV isolated from the aforementioned eBL cell line. For initial transcriptome analysis see: Kaymaz et al. "Comprehensive Transcriptome and Mutational Profiling of Endemic Burkitt Lymphoma Reveals EBV Type-specific Difference." Molecular Cancer Research, 2017, PMID: 28465297. For initial microRNA analysis see: Oduor et al. "Human and Epstein-Barr Virus miRNA Profiling as Predictive Biomarkers for Endemic Burkitt Lymphoma." Frontiers in Microbiology 8, 2017, PMID: 28400759. For initial NK subsets RNA expression analysis see: Forconi et al. "A new hope for CD56negCD16pos NK cells as unconventional cytotoxic mediators: an adaptation to chronic diseases." Frontiers in Cellular and Infection Microbiology, 2020, PMID: 32373555.For initial eBL cell line and mouse avatar RNA expression analysis see: Saikumar et al. "Endemic Burkitt lymphoma avatar mouse models for exploring inter-patient tumor variation and testing targeted therapies." Life Science Alliance, 2023, PMID: 36878637. For general overview of cohort study see: Buckle et al. "Factors influencing survival among Kenyan children diagnosed with endemic Burkitt lymphoma between 2003 and 2011: A historical cohort study." Int J Cancer. 15:1231, 2016, PMID: 27136063.

  Study phs001282

• Sickle Cell Disease Natural History Data Resource (SCD NHDR)

  Data Access NOTE Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. BackgroundThe NHLBI initiated the Sickle Cell Disease Natural History Data Resource (SCD NHDR) to leverage existing SCD studies to build a data resource to provide phenotypic data on contemporaneous control cohorts for gene therapy trials and other studies. The potential uses of the NHDR are broad, including providing matched controls for clinical trials, observing a cohort of untreated patients as part of a natural history study, or conducting comparative effectiveness research on a subpopulation with selected co-morbidities or drug treatment profiles. The SCD NHDR includes two multi-center SCD longitudinal cohort studies with extensive annual clinical data and patient surveys, the Sickle Cell Disease Implementation Consortium (SCDIC-I and SCDIC-II) and the Globin Research Network for Data and Discovery (GRNDaD). The SCDIC-I, initiated in 2017 and concluded in June 2022, enrolled over 2400 patients with SCD from 8 US treatment centers. The SCDIC-II reconsented 1220 of the SCDIC-I patients as well as enrolling an additional 450 new patients from the same 8 centers between September 2022 and October 2023, for a total of 1670 in SCDIC-II. Only two of the many GRNDaD centers are submitting data to the SCD NHDR for deposit into BDC (approximately 122 patients). Data submissions from the SCD NHDR to BDC will be annually starting in early 2024. Description of the Study Data All data obtained for the SCD NHDR are considered common data elements (CDEs) important for studies of SCD. These CDEs were identified from several workgroups of SCD experts convened under the NHLBI's Cure-SC Initiative (https://curesickle.org/cde-catalog). The following types of data elements were abstracted from the medical record – variables that describe the SCD diagnosis, SCD co-morbidities and other clinical complications, elements from the physical examination, medications and transfusions, insurance type, laboratory measurements, healthcare utilization, history of SCD-related procedures, and measurements from cardiac procedures. The following types of data were self-reported on the patient survey – demographics, diagnosis information, pain experience, social and mental health information, other patient reported outcome domains (e.g., sleep, fatigue), and alcohol and smoking history. Standard PRO measures were used from ASCQ-Me, PROMIS and Neuro-QoL. Patient surveys were completed approximately annually. Under SCDIC-I, two medical record abstractions and labs were reported at enrollment and approximately 3 years later. Under SCDIC-II and GRNDaD, all data are collected annually. Patients may refuse to complete the survey.

  Study phs003529

• Providing safe access to sensitive human data across borders: Federated EGA becomes a reality

  Enabling discovery and access to sensitive data across national boundaries is vital for improving human health. It enables more powerful and efficient research by increasing the volume and diversity of data available for analysis. It allows us to better understand the causes of diseases – cancer, rare diseases, infectious diseases like COVID-19 – and develop new medicines and treatments. Sensitive human omics data are typically generated by research initiatives and shared using specialist repositories which provide services for data submission, discovery, and access. The EGA is one such repository. Established in 2008 at EMBL’s European Bioinformatics Institute (EMBL-EBI) in the UK, since 2012 the EGA (“Central EGA”) has been jointly managed by EMBL-EBI and the Centre for Genomic Regulation (CRG) in Spain. Many countries have emerging personalised medicine programmes which generate data from national initiatives. These programmes are driving a transition in human genomics from being research-driven to receiving funding through healthcare. Data generated in a clinical context are subject to stricter governance than research data and must follow national data protection legislation. To solve these challenges, the Federated EGA provides a network of connected resources to enable transnational discovery of and access to human omics data for research, while also respecting jurisdictional data protection regulations. In this way, the Federated EGA infrastructure supports the goals of European initiatives such as the 1+ Million Genomes initiative (1+MG), the European Health Data Space, and a number of EU-funded 1+MG implementation projects including Beyond 1 Million Genomes. The Federated EGA is made up of “Nodes” – typically nationally funded and operated – which store and manage data locally while allowing global discovery within the Federated EGA network. Since 2016, multiple parallel efforts, supported by ELIXIR and other transnational and national initiatives, have created technical and legal frameworks for establishing the Federated EGA. In 2022, the first five Nodes – Finland, Germany, Norway, Spain, and Sweden – officially joined the Federated EGA by signing Federated EGA Collaboration Agreements with Central EGA. The Finnish FEGA Node is operated by CSC - IT Center for Science and provides data management services according to national laws and the requirements of the EU General Data Protection Regulation (GDPR). These services provide tools and support for the whole life-cycle of sensitive research data from collating to analysis, publication, and authorised re-use. The development of the services has been a joint effort with the other Nordic nodes within NeIC's Tryggve and Heilsa projects, and funded by Finnish Ministry of Education and Culture and projects coordinated by ELIXIR Finland. Read more about Finnish FEGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. In Norway, a key component of the infrastructure is the services of The Sensitive Data Service (TSD) offered by USIT at University of Oslo. The Federated EGA Norway Node is developed by ELIXIR Norway and operated by the University of Oslo as the responsible legal entity. Core software modules are developed jointly with the other Nordic nodes in the NeIC Tryggve and Heilsa projects. Read more about FEGA Norway signing. The Spanish FEGA (es-FEGA) is a national service for storing sensitive biomedical data in Spain. Supported by the Spanish Institute of Bioinformatics (INB) in collaboration with Central EGA, sensitive research datasets are primarily hosted at the Barcelona Supercomputing Centre facilities. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. By providing a solution for secure and efficient management of human omics data, the Federated EGA aims to foster data reuse, enable reproducibility, accelerate biomedical research, and improve human health. Find out more Interested in setting up your own Federated EGA Node? Check out the FEGA Onboarding Knowledge Base for more information. The ELIXIR Federated Human Data Community is a great entry point for anyone interested in learning more about the Federated EGA. You can: Join the ELIXIR Federated Human Data Community mailing list (select “Human Data”) Attend the ELIXIR Federated Human Data Community calls

  Blog safe-access-to-sensitive-human-data-federated-ega

• From research to data sharing: exploring EGA user's experiences

  The Cell Plasticity and Regeneration Group at the Bellvitge Biomedical Research Institute-IDIBELL focuses on the process of recruitment of macrophages that takes place in the small intestine during injury and healing. They recently published a paper titled “Mucosal Macrophages Govern Intestinal Regeneration in Response to Injury" in Gastroenterology Journal. As part of the research, some experiments were conducted using human intestinal organoid lines. These cells were processed for RNA sequencing, and the sequencing data were deposited at the EGA to be made available to the scientific community. When dealing with human genomic information, repositories must ensure the availability of the datasets while ensuring data protection. In this context, the European Genome-phenome Archive stands as a service for secure archiving and sharing of genetic, phenotypic and clinical data resulting from biomedical research. Following the recent publication of their paper, we took the opportunity to talk to Ilias Moraitis, first author, and Jordi Guiu, group leader, to find out about their experience with data sharing. Could you explain the focus of your research? In the lab, we study intestinal regeneration and how immune cells participate in this process. We use several techniques: engineered mouse models, image tracing, as well as mouse and human cells intestinal organoids. What challenges do you face regarding data management? We didn’t have a lot of problems. Always the informatic part, the data analysis, can give problems. But everything was smooth and working. We think it’s very important to deposit the data in repositories. For the mouse data it is very straightforward, but for us it was the first time depositing human data, which is sensitive because it comes from patients, and this is legally regulated. That’s why we thought about the EGA, and it was our first time. Why do you think it is important to submit data to repositories such as EGA? Because we think data is important for the science. Nowadays, we are sequencing a lot everywhere worldwide and having these resources shared with the scientific community it’s not only good for science, but it is also saving money and reducing costs. And there is so much data in there that can be used for other projects and for other questions. And it saves time! How was the process of submitting the data to the EGA? The communication worked very well but it was slower than we would like. But I think this is something we learn on the way. People usually wait until the last minute to do this before publication but if you know it in advance, you can start the process earlier. If you had to repeat the process, would you do anything differently? It’s like everything, isn’t it? For the first time you don’t know, you aren’t sure, but for the second time you know the steps, you know what to do and it’s faster. Of course, we would to do it earlier. Also, there were some things related to the control access that we didn’t know how to manage, but it was something more internal to us. Who is going to receive the communications and when, is our ethical committee going to evaluate this? All these technicalities that we didn’t know before starting this process, so we had to think about all these things. Next time it will be faster on our side. Do you have any suggestions for us to improve the submission process? Maybe we’ll need to ask our bioinformatician who did it! He was in charge of this part. Besides that, we think if the process were faster, it would be better for everyone. Also, in science a lot of times you have to do a lot of things at the very last minute, because of the nature of the experiments, or the need to accomplish for submitting a paper. Being faster in the process would be a plus. On your side, we went through the process and were able to find some information, but there were aspects we weren't aware of and weren't sure how to handle. I'm not sure if other institutes have different protocols for managing data requests or how they handle these internally. We believe the main issue lies in how the forms are filled out. Do you have any recommendations for other submitters? The main advice is to do this with enough time. Submit the data when you have the sequences, and don’t wait until the last minute. And then it can be under embargo, so you don’t need to make it public at that moment. As soon as you sequence, upload it and then it will be there for whenever you need to publish. How do you think we could encourage other researchers to submit their data? There are different layers here. One is that it is mandatory. You have to do this. The other is that we belong to a research community, to the same community and sharing this is making our research community stronger and more efficient. And then, also because this gives you visibility. The data is there, other people can analyse your data, and this also will bring citations to your papers. It has many advantages.

  Blog from-research-to-data-sharing

• Human Companion Data for 'A Transcriptomic Atlas of Mouse Cerebellar Cortex Reveals Novel Cell Types'

  In this study, we used single-nucleus RNA-seq to comprehensively define the cell specialization of the mouse cerebellar cortex and vermis. We focused in particular on two interneuron populations—the unipolar brush cells (UBCs) and the molecular layer interneurons (MLIs), identifying continuous variation within the former population, and a new discrete axis of variation in the latter. Human postmortem tissue was used to demonstrate that these specializations are evolutionarily conserved in primates. Together, these findings provide the first comprehensive cellular atlas of the cerebellar cortex, and outline a methodological and conceptual framework for the integration of molecular, morphological, and physiological ontologies for defining brain cell types.

  Study phs002414

• Transcriptomic Profiling of Peripheral Immune Cells in a Trial of Ruxolitinib with Nivolumab for Anti-PD1 Non-Responsive cHL

  Treatment with checkpoint inhibitors is effective in multiple cancer types. However, even in cancers with high response rates such as Hodgkin lymphoma, some patients fail to respond or relapse following an initial response to checkpoint inhibitors. In this study, we examined patients enrolled in a clinical trial of ruxolitinib with nivolumab in relapsed or refractory Hodgkin lymphoma (NCT03681561). Patients relapsed or refractory to anti-PD1 therapy received ruxolitinib for 8 days followed by nivolumab every 4 weeks and continued ruxolitinib twice daily. For this transcriptomic study, peripheral blood mononuclear cells (PBMCs) were collected at baseline and 8 days of ruxolitinib therapy and analyzed by single-cell and bulk RNA-sequencing.

  Study phs003601

• University of Zurich (UZH) - Snedeker lab

  Authorised Personnel: The individuals at the User Institution to whom University of Zurich (UZH) grants access to the Data. This includes the User, the individuals listed in Appendix II and any other individuals for whom the User Institution subsequently requests access to the Data. Details of the initial Authorised Personnel are set out in Appendix II. Data: The managed access datasets to which the User Institution has requested access. Data Producers: UZH and the collaborators listed in Appendix I responsible for the development, organisation, and oversight of these Data. External Collaborator: A collaborator of the User, working for an institution other than the User Institution. Project: The project for which the User Institution has requested access to these Data. A description of the Project is set out in Appendix II. Publications: Includes, without limitation, articles published in print journals, electronic journals, reviews, books, posters and other written and verbal presentations of research. Research Participant: An individual whose data form part of these Data. Research Purposes: Shall mean research that is seeking to advance the understanding of genetics and genomics, including the treatment of disorders, and work on statistical methods that may be applied to such research. User: The principal investigator for the Project. User Institution(s): The Institution that has requested access to the Data. UZH: University of Zurich, Raemistrasse 71, 8001 Zurich, Switzerland

  Dac EGAC50000000817

• Ultra-deep Error-corrected Sequencing of Peripheral Blood for Clonal Hematopoiesis in Patients Undergoing AAA Surgery

  Patients undergoing elective abdominal aortic aneurysm (AAA) surgery were enrolled to investigate the prevalence and characteristics of clonal hematopoiesis. Peripheral blood was collected at the time of surgical admission using PAXgene DNA tubes. Genomic DNA was extracted and subjected to ultra-deep error-corrected targeted sequencing using a custom gene panel covering recurrent clonal hematopoiesis-associated genes. Sequencing libraries were prepared following established protocols and sequenced on an Illumina NovaSeq X system. The resulting dataset includes paired-end FASTQ files for each participant, enabling high-sensitivity detection of somatic variants down to low variant allele frequencies. This study provides a comprehensive sequencing resource for evaluating clonal hematopoiesis in AAA patients.

  Study JGAS000864