88 results for "华为昇腾+市场份额+2023"

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• Lifelines NEXT fecal metagenomics, main batch, release 2023

  This dataset contains fecal metagenomic sequencing of Lifelines NEXT cohort samples (main batch sequenced until first data freeze, end of 2023).

  Dataset EGAD50000001187

• Transcriptome Sequencing PPGL (2)

  RNA-Seq data (n=81) library preparation was performed as previously reported by Calsina et al 2023 using QuantSeq 3' mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen, 015). The prepared libraries were subsequently sequenced on NovaSeq™ system (Illumina). To combine the new CNIO samples (n=81) with the CNIO+TCGA dataset generated in Calsina et al., 2023, RNA data were processed using ComBat-seq as described in that same article.

  Study EGAS50000000013

• DAC-2023-07-05-Ritz (DAC-007)

  Full description of the dataset and all necessary information on metadata may be found under (https://zenodo.org/records/11237107): DAC-2023-07-05-Ritz (DAC-007), raw data in EGA, metadata in Zenodo (https://zenodo.org/records/11237107)

  Dataset EGAD50000001147

• 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 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

• 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

• DAC-AML-Smart-2023

  Dac EGAC00001003232

• ESPAC-DAC-2023-submission-together-with-H015

  Dac EGAC00001003251

• Platelet DNA study Murphy et al 2023 DAC

  Dac EGAC00001003034

• 2023-OE0246-S034-glioma_drugscreen-box-1214-DAC

  Dac EGAC00001003375

• Thirant et al Nature Communications 2023 Data Access Commitee

  Dac EGAC00001003192

• CCA Enhancer project Oct 2023

  Dac EGAC00001003452

• Organoid Derivation Project - GRCh38 - 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 EGAD00001011092

• REL-2017-07-2023

  HipSci - Usher Syndrome - Expression Array - July 2017

  Dataset EGAD00010001374

• Candidate diagnostic variants reported into DECIPHER, Wright NEJM 2023

  Candidate diagnostic variants reported into DECIPHER by 4 April 2022, annotated with clinical and automated pathogenicity assertions (see DOI: 10.1056/NEJMoa2209046). Genomic Diagnosis of Rare Pediatric Disease in the United Kingdom and Ireland, Wright et al, NEJM 2023.

  Dataset EGAD00001010137

• Knoll et al. 2023, Identification of drug candidates targeting monocyte reprogramming in people living with HIV

  Dac EGAC50000000044

• European Genome-phenome Archive 15th Anniversary Celebration

  2023 marks the 15th Anniversary of the EGA, jointly managed by the European Bioinformatics Institute (EMBL-EBI) and the Centre for Genomic Regulation (CRG). To mark the occasion, a simultaneous celebration was held in both institutions on the 13th of December 2023. The teams gathered online to play a quiz game and celebrate all the achievements and milestones with two wonderful anniversary cakes. In 2008, the European Genome-phenome Archive was created at the EBI-EMBL in Cambridge to guarantee that human genome and phenome data were available to the international scientific community while data privacy was preserved. The six-person staff at the beginning of the project is now far behind us, with a team that reaches the number of 35 members. Since 2013, the European Bioinformatics Institute and the Centre for Genomic Regulation share responsibility for The European Genome-phenome Archive (EGA). At that time, the EGA had data for about 0,5 petabytes. Currently, the Archive contains more than 12 petabytes. 2023 comes to an end with several good news. The EGA has been renewed as an ELIXIR Core Data Resource. This was announced during the GA4GH 11th Plenary held in San Francisco last September when ELIXIR-Beacon was also confirmed to be a GA4GH Driver Project. Thus, the Beacon API has maintained this title since 2018. What’s more, the first Federated EGA dataset is now live on our website. This year we also launched new services for EGA users in September. By the numbers, in 2023 the EGA counts 2.5 PB archived, 371 studies published, 208 new submitters and 19 active projects in which the team is participating, among others. We look forward to continuing to support your research in 2024!

  Blog 15-anniversary

• Organoid Derivation Project - GRCh38 - WGS (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 EGAD00001011093

• California Teachers Study (CTS): Whole Genome Sequences From Under-Represented Populations

  The California Teachers Study (CTS) is a prospective observational cohort of N=133,477 adult women who have been followed since 1995. Participants completed up to six surveys and have been linked with cancer, hospitalization, and mortality data. In 2013-2016, over 14,000 CTS participants donated blood samples for future research. For this project, DNA was extracted in 2023 from 1694 buffy coats and 56 blood clots from N=1750 CTS participants whose self-reported race/ethnicity was Asian (398), Asian or Pacific Islander (18), Black or African American (189), Native American (128), Native Hawaiian or Other Pacific Islander (66), White Hispanic (719), or more than one race (232). Whole genome sequencing (WGS) was performed at CIDR in 2023-2024. WGS data and accompanying phenotype data are available through dbGaP. In addition, all CTS data are available to the research community via the CTS Researcher Platform (www.calteachersstudy.org), a secure cloud-based environment that includes data, software, documentation, and collaboration workspaces.

  Study phs002918

• SNPArray_TW

  The compressed file contains plink format file for the Affymetrix Human Origins SNP array data of 55 individuals generated and analyzed in Liu et al 2023 study of Taiwanese groups.

  Dataset EGAD00010002424

• Breast cancer topographs

  Whole genome sequencing to identify subclonal variants for subsequent mapping back to fixed tissue specimens. . This dataset contains all the data available for this study on 2023-03-09.

  Dataset EGAD00001010124

• Redefined indel taxonomy reveals insights into mutational signatures

  Deposited here are whole-genome sequencing bam files from the experimental isogenic cell lines used in the study "Redefined indel taxonomy reveals insights into mutational signatures, Koh, Gene (2023)".

  Dataset EGAD50000000209

• WGS data from 20 cholangiocarcinoma cases

  WGS data from biliary tract cancer samples (Holzapfel, et al., J Gastrointest Oncol,14(1):379-389, 2023). The dataset includes tumour and normal BAM files sequenced on Illumina HiSeq/NovaSeq and aligned against GRCh38 (n=20 tumour/normal pairs).

  Dataset EGAD50000002307

• Genome-Wide Analysis of Aberrant Position and Sequence of Plasma DNA Fragment Ends in Patients With Cancer

  This study investigated fragmentation characteristics in plasma cell-free DNA and evaluated the relevance of such analysis for cancer diagnostics. Specifically, we performed whole genome sequencing (WGS) of healthy individuals and cancer patients, and evaluated genome-wide coverage and sequences surrounding fragment ends. This was a retrospective study of archived plasma samples. The WGS data are available through this submission. Detailed study design and findings were published in Budhraja et al. Science Translational Medicine 2023.

  Study phs003170

• Ballett

  TSO500 study: The project, called "BALLETT" (Belgian Approach of Local Laboratory Extensive Tumor Testing), has a double goal: (1) show the relevance of broad molecular profiling to improve oncological patients care, (2) demonstrate that broad molecular testing can be performed in a decentralized setting by local diagnostics laboratories in a fully standardized and uniform way while complying with the highest quality standards. NCT05058937 The publication number is: Your manuscript ID is bcr-2023-256124. Title: CUP and BRAF V600E meeting the BEACON combination.

  Study EGAS50000000478

• RNAseq of 704 patients with soft tissue tumors

  The dataset contains transcriptome sequencing (RNA-seq) data of 704 soft tissue tumors (STT) from 704 patients. The sequencing data comes in FASTQ format and contains 1408 files. A total of 56 different STT types were included in the present study and each tumor type was represented by >2 samples. The vast majority of the patients had been diagnosed and treated at the sarcoma centers in Lund (Sweden) or Stockholm (Sweden) during the period 1988-2020, but also a few samples from other centers, obtained through previous collaborative projects, were included. RNA was extracted from fresh frozen tumor material and samples were primary lesions unless otherwise indicated. The sequencing was performed using 2x150 bp paired-end chemistry on a series of Illumina instruments at various facilities during the period 2013-2023.Samples were obtained after informed consent from the patients and the study was approved by the regional ethics committee (Etikprövningsmyndigheten, Uppsala, dnr 2023-01550-01, dnr 2025-02997-02).

  Dataset EGAD50000002120

• Bulk RNAseq of Neuroblastoma patient's tumors

  This dataset contains 9 bulk RNAseq of neuroblastoma patient's tumors used to compare with derived PDXs and/or single-cell data in the Thirant C et al, Nature Communications, 2023. They were intially produced for the Berlanga P., Cancer Discovery, 2022.

  Dataset EGAD00001010287

• AfricanNeo WGS aDNA Dataset

  This dataset includes WGS data of 12 ancient individuals (97–688 years BP) from Zambia and South Africa, presented in Fortes-Lima et al. Nature 2023. Further details like C14-dates and archaeological descriptions were reported elsewhere (Meyer et al. Azania 2021; Steyn et al. African Archaeological Review 2022).

  Dataset EGAD00001011320

• Clinical and Genetic Evaluation of Individuals with Undiagnosed Disorders through the Undiagnosed Diseases Network (UDN)

  The National Human Genome Research Institute and the NIH Clinical Center established the intramural Undiagnosed Diseases Program (UDP) in 2008 to make progress in uncovering, understanding, and treating rare disorders. Based on the success of the UDP, the NIH Common Fund established the Undiagnosed Diseases Network (UDN) in FY 2013 (Phase I, 9/16/2015-8/31/2018) to achieve this type of cross-disciplinary approach to disease diagnosis in academic medical centers around the United States. The NIH Common Fund expanded the network in 2018 (Phase II, 9/1/2018-6/20/2023) to increase the availability of diagnostic services, foster opportunities for collaboration between laboratory and clinical investigators, provide resulting data and protocols to the broader community, and assess development of a sustainable national resource after Common Fund support ends. The UDN transitioned from Common Fund in 2023. The National Institute of Neurological Disorders and Stroke (NINDS) will oversee the Network in Phase III, which launched in July 2023, with help from 17 different NIH Institutes and Centers along with the NIH Office of the Director. With help from patients, family members, patient advocacy groups, a Data Management and Coordinating Center (DMCC), several Clinical Sites, and other stakeholders, the NIH envisions the UDN evolving into a larger, diverse, and self-sustained network that fosters scientific discovery and provides expert diagnostic services for undiagnosed patients across the nation. The overarching goals of the UDN in Phase III include: Continuing the proven success of the UDN in improving the clinical evaluation of difficult-to-diagnose patients using a collaborative team approach and investigating and validating promising new diagnostic technologies. Facilitating research into the etiology of undiagnosed diseases by collecting and sharing standardized, high-quality clinical and laboratory data including genotyping, phenotyping, and documentation of environmental exposures. Promoting a broader integrated and collaborative community across multiple Clinical Sites and among laboratory and clinical investigators prepared to investigate the genetic, pathophysiologic, cell biologic, and molecular mechanisms underpinning these difficult-to-diagnose conditions.

  Study phs001232

• BAM files from capture-based targeted sequencing of 4 agressive B-cell lymphoma tumour samples (DLBCL-panel)

  This dataset contains BAM files from capture-based targeted sequencing of 4 agressive B-cell lymphoma tumour samples for cases 2, 3, 4 and 13. The sequencing panel used is a DLBCL-oriented panel containing 136 genes, described in Mozas P. et al., Hematol Oncol 2023.

  Dataset EGAD50000000802

• 340 human whole genome sequences from Angola and Mozambique

  CRAM files of 340 human genomes from Angola and Mozambique. Paired-end reads were generated in an Illumina-X Ten and were mapped against the human reference genome build hg19/GRCh37. More details about the sequencing and the samples in Tallman et al. 2023. Nature Communications.

  Dataset EGAD00001011992

• Development of Novel Synovial Sarcoma Organoids Models for Drug Discovery

  Although synovial sarcoma has been studied using established cell lines and mouse models, effective treatments remain limited. We treated five patients with synovial sarcoma between 2022 and 2023 and established organoid cultures from resected tumor specimens using a modified air-liquid interface method. After serial passaging, organoids were successfully xenografted into NOD-scid IL2Rgnull (NSG) mice in three cases. The established organoids retained SS18-SSX fusion transcripts and demonstrated histological and genetic similarities to the original tumors. These organoid models provide a reliable platform for studying synovial sarcoma biology and hold promise for the development of novel therapeutic strategies.

  Study JGAS000806

• Organoid Derivation Project: WGS (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 EGAD00001011090

• Healthy Never Smokers

  Bronchial brushing dataset from healthy never-smokers after exposure to diesel exhaust. Include 18 samples from 9 research participants who underwent bronchoscopy after controlled exposure to diesel exhaust. Main study design described in detail in Ryu et al 2022 AJRCCM (PMID: 35202552). This dataset was used in Hill et al Nature 2023.

  Dataset EGAD00001010024

• Linguistic utterance counts 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_225CaboVerde_FreeSpeech_Utterance_counts_FINAL_01062022.txt" Column 1 corresponds to individual alphanumeric codes as in the "eGAdeposit_233CaboVerde_GenotypeFile_FINAL_01062022.vcf" genotype file. Note that only 225 unrelated Cabo Verdean-born individuals are considered here, out of the 233 individuals in the genotype file. See Material and Methods in Romain Laurent et al. 2022 - doi pending Each 4831 other column correspond to the respective individual's utterance count in the free speech transcribed in ALUPEC and provided as column header. See See Material and Methods in Romain Laurent et al. 2022 - doi pendingColumn 1 corresponds to individual alphanumeric codes as in the "eGAdeposit_233CaboVerde_GenotypeFile_FINAL_01062022.vcf" genotype file. Note that only 225 unrelated Cabo Verdean-born individuals are considered here, out of the 233 individuals in the genotype file. See Material and Methods in Romain Laurent et al. 2022 - doi pending Each 4831 other column correspond to the respective individual's utterance count in the free speech transcribed in ALUPEC and provided as column header. See Material and Methods in Laurent R et al. eLife 2023

  Dataset EGAD00001008978

• Single-cell characterization of human GBM reveals regional differences in tumor-infiltrating leukocyte activation

  Raw sequencing files from scRNA-seq dataset used in Schmassmann et al. 2023. Single-cell characterization of human GBM reveals regional differences in tumor-infiltrating leukocyte activation. Elife 12 (https://elifesciences.org/articles/92678) Dataset content: 14 samples from 5 donors File type: paired-end fastq files Technology: Illumina sequencing Experimentation used: scRNA-seq using the 10X technology

  Dataset EGAD50000000434

• The chemotherapeutic CX-5461 is extremely mutagenic and may increase cancer risk

  Deposited here are whole-genome sequencing bam or fastq files from the experimental isogenic cell lines used in the study "The chemotherapeutic CX-5461 is extremely mutagenic and may increase cancer risk, Koh, Gene (2023)". The bam files were aligned to GRCh38/hg38 using BWA-MEM. The corresponding variant call data have been deposited on Mendeley Data, V2, doi: 10.17632/d58cv549v6.2

  Dataset EGAD50000000036

• Single-nucleus mRNA-Sequencing of prenatal and postnatal samples from the brain and its border regions

  Single-nucleus mRNA Sequencing of prenatal and postnatal samples from the brain and its border regions. Most samples were multiplexed with several samples run in one 10X reaction. A separate immune cell dataset was combined with published data from Braun et al 2023 and Yang et al 2021 integrated using harmony is included.

  Dataset EGAD50000000044

• Diagnostic yield and clinical utility of whole-exome sequencing in pediatric patients with rare and undiagnosed diseases in the Czech Republic

  In our single-center study, we have launched a pilot program for pediatric patients with undiagnosed diseases in the second-largest university hospital in the Czech Republic. WES was implemented as a first-line test after inclusion in the study as part of the diagnostic workflow. This study was prospectively conducted at the Department of Pediatrics at University Hospital Brno between 2020 and 2023.

  Dataset EGAD50000000640

• Somatic mutation and clonal evolution in premalignant lung disease

  In this study we aim to characterise the landscape of mutation and clonal selection in normal lung and premalignant lung disease. The study combines targeted sequencing and whole-genome sequencing of microbiopsies of lung and bronchial epithelium. The range of patients studied will include healthy individuals, both smokers and non-smokers, and patients with premalignant lung disease. . This dataset contains all the data available for this study on 2023-03-09.

  Dataset EGAD00001010122

• snRNA-seq on patient tumours

  snRNA-seq performed on patient tumours (n = 6 patients, one biopsy sample each sequenced) using the 10x technology. Single nuclei were acquired from six frozen mCRPC biopsies (4 lymph node, 2 liver metastases) obtained from consenting patients treated at the Royal Marsden Hospital between 2018 and 2023 under an institutional review board approved research protocol (Research Ethics Committee approval number: 04/Q0801/60). All six patients had previously received androgen-receptor signalling inhibitor(s) and five of six patients had previously received taxane(s).

  Dataset EGAD50000000874

• 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

• MutWP5: CRUK Mutographs of Cancer: Breast: Reduction Mammoplasty (WG)

  Sequencing of LCM-derived microbiopsies from 10 women who underwent reduction mammoplasty. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this portion of the study will be compared to women who had breast cancer and those who are BRCA 1/2 carriers. This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010109

• Evolutionary landscape of clonal hematopoiesis in 3359 individuals from the general population

  This project contains longitudinal data for processed somatic variant calls and data on core phenotypic variables for 3359 community-based individuals ≥60 years, a sub-cohort of the population-based Lifelines cohort (167,729 participants). Cases with peripheral blood count abnormalities and population-based controls were included. Next-generation sequencing data were generated with a median time period of 43 months between baseline and follow-up visit. For 327 individuals a third visit sample was included. Error-corrected sequencing was performed using single-molecule tagged molecular inversion probes targeting 27 myeloid and lymphoid driver genes. The threshold for variant calling was set at ≥1% VAF and ≥10 consensus variant reads. Details on sample selection, sequencing technique, variant calling procedures and data curation are described (van Zeventer et al. 2023).

  Study EGAS00001007087

• RNA-seq analysis of CCO+/CCO- hepatocytes in normal human liver

  RNAseq data from Passman et al 2023. Clonal CCO-deficient hepatocyte patches and nearby CCO-proficient hepatocytes were identified in morphologically normal human livers and sampled at varying distances along the PT-CV axis. Samples are characterised according to their location within the liver lobule, with "PT" denoting samples abutting the portal triad, "CV" denoting samples abutting the central hepatic vein, and "Mid" sampled acquired midway between these structures. Analysis was performed on an Illumina Nextseq using a high output kit and 100 single-end cycles.

  Dataset EGAD00001010033

• MutWP5: CRUK Mutographs of Cancer: Breast: Reduction Mammoplasty (Exome)

  Sequencing of LCM-derived microbiopsies from 10 women who underwent reduction mammoplasty. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequently be sent for whole-genome sequencing. Results from this portion of the study will be compared to women who are BRCA1/2 germline carriers and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010110

• MutWP5: CRUK Mutographs of Cancer: BRCA Carriers (WG)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 20 women who underwent risk-reducing reduction mastecomies due to germline BRCA1/2. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequenctly be sent for whole-genome sequencing. Results from this poriton of the study will be compared to women who had cosmetic breast reduction surgeries and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010112

• TK-EPN862 - Patient-derived xenograft model of Posterior Fossa A Ependymoma - RNAseq

  In Vivo Loss of Tumorigenicity in a Patient-Derived Orthotopic Xenograft Mouse Model of Ependymoma. Whitehouse et al. 2023 Frontiers in Oncology. We describe the establishment of a patient-derived orthotopic xenograft (PDOX) model of posterior fossa A (PFA) EPN, derived from a metastatic cranial lesion. Patient and PDOX tumors were analyzed using RNA sequencing. RNAseq data (paired end) provided here correspond to Primary tumour, two metastatic lesions (one spinal, one cranial), and a patient-derived xenograft derived from the patient.

  Dataset EGAD00001009870

• MutWP5: CRUK Mutographs of Cancer: Breast: Reduction Mammoplasty (WG)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 10 women who underwent reduction mammoplasty. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequenctly be sent for whole-genome sequencing. Results from this poriton of the study will be compared to women who are BRCA1/2 germline carriers and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010113

• MutWP5: CRUK Mutographs of Cancer: BRCA Carriers (Exome)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 20 women who underwent risk-reducing reduction mastecomies due to germline BRCA1/2. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequenctly be sent for whole-genome sequencing. Results from this poriton of the study will be compared to women who had cosmetic breast reduction surgeries and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010115

• MutWP5: CRUK Mutographs of Cancer: Breast: Reduction Mammoplasty (Exome)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 10 women who underwent reduction mammoplasty. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequenctly be sent for whole-genome sequencing. Results from this poriton of the study will be compared to women who are BRCA1/2 germline carriers and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010116

• Is the Gut Important in Multiple Joint Osteoarthritis? A Multimodal Investigation in Humans and Pet Dogs

  The Gut Health in Multiple Joint Osteoarthritis (MJOA) Study leverages data from parallel community-based cohorts in humans and in pet dogs to elucidate the role of altered microbiota in MJOA. One hundred Johnston County Health Study human participants were 35 to 70 years of age at enrollment (2022-2023), self-identified as Hispanic, White, or Black, and lived in Johnston County, North Carolina. Demographic, clinical information, multiple joint radiographs, and stool samples for microbiome profiling by 16S rRNA gene sequencing were obtained from all participants. Similar data were collected from an independent group of pet dogs (N=115) from the local community, at the North Carolina State University (NCSU) College of Veterinary Medicine. The central hypothesis of the study is that intestinal permeability, with or without dysbiosis, is a major driver in the development and worsening of MJOA.

  Study phs003980

• Analysis of a cohort of familial ademomatous polyposis patients bearing APC gene mutation

  large number of colorectal adenomas with high risk of evolving into colorectal tumors. Mutations of the Adenomatous polyposis coli (APC) gene is often at the origin of this disease, as well as of a high percentage of spontaneous colorectal tumors. APC is therefore considered a tumor suppressor gene. While the role of APC in intestinal epithelium homeostasis is well characterized, its importance in immune responses remains ill defined. We investigated an array of immune cell features in FAP subjects carrying APC mutations. A group of 12 FAP subjects and age and sex-matched healthy controls were studied. In particular, the expression of 579 immune response genes was analyzed in whole blood cells using the Nanostring nCounter Human Immunology kit v2. (Cuche C. et al. Frontiers in Immunology, 2023, in press)

  Study EGAS00001007237

• Projects

  Projects Jointly managed by the European Bioinformatics Institute (EMBL-EBI) in Cambridge (UK) and the Centre for Genomic Regulation (CRG) in Barcelona, the EGA provides an invaluable service to the worldwide biomedical research community. The teams leading the EGA are involved in several international partnerships and consortia in numerous scientific fields, where they contribute to ambitious projects. In addition to the project listed below, The EGA is in a long-standing partnership with the Global Alliance for Genomics and Health (GA4GH), as described on the dedicated page. On-going projects Project Duration Domain Funder Tags EASIGEN-DS | The EASIGEN-DS project aims to conduct a design study to establish a new European Research Infrastructure on Advanced Genomics Technologies, EASIGEN. To develop an excellent scientific, technological and operational design, we will conduct landscape studies, stakeholder consultations, and community surveying. 2025-2028 Genomic and health data Horizon Europe DATA MANAGEMENT DOCUMENTATION INFRASTRUCTURE Go-IMPaCT | Go-IMPaCT will contribute sequenced genomes and provide infrastructure as part of IMPaCT-Cohort, one of the three fundamental pillars of the Precision Medicine Infrastructure associated with Science and Technology (IMPaCT) program in Spain. Along with the Genome of Europe (GoE) project, around 18.000 people will have their genomes sequenced, also contributing to Spain's commitments in 1+MG. Go-IMPaCT will fund the development of an EGA node to manage and share this genomic and phenoclinic data, laying the foundations for regional and ethnic genomic variability in Spain to be available for research purposes. The IMPaCT cohort is created with the spirit of being an open research tool, compatible with the rest of the health research ecosystem, and other international initiatives. 2025-2027 Large-scale genomics and health data; personalised medicine Instituto de Salud Carlos III ACCESS DISCOVERY INFRASTRUCTURE METADATA STANDARDS FAIR-FEGA | This project seeks to accelerate data depositions into FEGA, significantly increasing the data flow in and from FEGA nodes. It will build capacity within the FEGA nodes and increase awareness in a wide range of stakeholders, thus altogether achieving the ultimate goal of enhancing data reuse. The project will be carried out by a strategic consortium comprising seven ELIXIR nodes and two ELIXIR communities. 2025-2026 Not applicable ELIXIR ACCESS DISCOVERY DOCUMENTATION INFRASTRUCTURE METADATA STANDARDS FEGA-Connect | A consortium of six ELIXIR nodes plus the Polish FEGA node (in-kind contribution) joining forces to build a solid base to develop solutions for effective multi-omic sensitive data integration between FEGA nodes and other infrastructures and specialised Data repositories. We aim to promote a more coherent data deposition, discoverability and retrieval of multi-omics datasets, providing FAIRer data and consequently accelerating research. 2025-2026 Multi-omics data ELIXIR ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE METADATA STANDARDS IMPaCT-Data 2 | IMPaCT-Data 2 will develop a digital platform for the integration and modelling of biomedical data associated with IMPaCT (Precision Medicine Infrastructure associated with Science and Technology) projects in Spain. It will deploy a sustainable infrastructure that facilitates the integration, standardisation, interoperability and analysis of clinical, genomic, molecular and medical imaging data. This platform will be aligned with European projects such as Genome of Europe (GoE), the first project to make use of the European Genomic Data Infrastructure (GDI), and EUCAIM. IMPaCT-Data 2 will benefit from advanced Artificial Intelligence and High Computing Capacity Systems capabilities, offering robust and accessible tools for researchers from the National Health System in Spain. 2025-2026 Large-scale genomics and health data; personalised medicine Instituto de Salud Carlos III ACCESS DISCOVERY INFRASTRUCTURE METADATA STANDARDS ERDERA | The European Rare Disease Research Alliance (ERDERA) takes over EJPRD to deliver concrete health benefits to rare disease patients in the next decade by advancing prevention, diagnosis and treatment research. To leave no one behind, over 170 organisations championed by the European Union and member states are working hand in hand to make Europe a world leader in rare diseases research and innovation. 2024-2034 Rare diseases Horizon Europe; "La Caixa" Foundation cofunds CRG's contribution ACCESS DATA ANALYSIS DISCOVERY INFRASTRUCTURE GoE | The Genome of Europe initiative aims to build a European network of national genomic reference cohorts of at least 500.000 citizens. These reference cohorts will be selected to be representative of the European population. 2024-2028 Large-scale genomic and health data Horizon Europe ACCESS DISCOVERY INFRASTRUCTURE METADATA STANDARDS HEREDITARY | HEREDITARY aims to transform the way we approach disease detection, prepare treatment response, and explore medical knowledge by building a robust, interoperable, trustworthy, and secure framework that integrates multimodal health data (including genetic data) while ensuring compliance with cross-national privacy-preserving policies. 2024-2027 Neurodegenerative disorders, gut-brain interplay Horizon Europe DATA MANAGEMENT DATA ANALYSIS EOSC-ENTRUST | The mission of EOSC-ENTRUST is to create a European network of trusted research environments for sensitive data and to drive European interoperability by joint development of a common blueprint for federated data access and analysis. 2024-2026 Trusted Research Environment Horizon Europe INFRASTRUCTURE EBV-MS | "Targeting Epstein-Barr Virus Infection for Treatment and Prevention of Multiple Sclerosis". The ambitious goals of the project are to answer the questions why only a few EBV infected persons develop MS, and define the underlying mechanism of this process, as well as clarify if targeting the EBV infection can prevent MS or improve the disease course. 2023-2028 Viral-host genetics; immune response; disease modelling; Disease prevention Horizon Europe DATA MANAGEMENT DATA ANALYSIS WISDOM | WELL-BEING IMPROVEMENT THROUGH THE INTEGRATION OF HEALTHCARE AND RESEARCH DATA AND MODELS WITHOUT BORDER FOR CHRONIC IMMUNE-MEDIATED DISEASES aims to deploy novel approaches for data processing, harmonisation, management, and secure data sharing and federated access for diseases like multiple sclerosis. Using an end-user guided approach, it will facilitate responsible and critical assessment of the use of AI in healthcare. 2023-2028 Chronic immune-mediated diseases Horizon Europe DATA MANAGEMENT INFRASTRUCTURE EUCAIM | EUropean Federation for CAncer IMages is a project that will build a highly secure, federated and large-scale European cancer imaging platform, with capabilities that will greatly enhance the potential of Artificial Intelligence in oncology. 2023-2027 Cancer Digital Europe Programme (DIGITAL) DISCOVERY CONTAGIO | CONTAGIO (COhorts Network To be Activated Globally In Outbreaks) aims to create coordination mechanisms to rapidly react to infectious disease (re-)emergence in low- and middle-income countries (LMICs). 2023-2026 Infectious Diseases European Commission - Horizon Europe ACCESS DATA MANAGEMENT DISCOVERY Youth-GEMs | Youth-GEMS (Gene Environment Interactions in Mental Health TrajectorieS of Youth) will conduct research into the genetic and environmental factors of mental health in young European people. 2022-2027 Mental health European Commission - Horizon Europe DATA MANAGEMENT DISCOVERY GDI | The European Genomics Data Infrastructure project is enabling access to genomic and related phenotypic and clinical data across Europe. It is doing this by establishing a federated, sustainable and secure infrastructure to access the data. 2022-2026 Genomic and health data European Commission - Horizon Europe; "La Caixa" Foundation cofunds CRG's contribution DISCOVERY DOCUMENTATION INFRASTRUCTURE EOSC4Cancer | EOSC4Cancer builds on existing projects, research outcomes and established community solutions to create the federated FAIR data, analysis and services infrastructure needed for European Cancer research programmes. 2022-2025 Cancer European Commission - Horizon Europe DISCOVERY IMPaCT-T2D | The IMPaCT-T2D project aims at studying the complete genomes of a large cohort of patients with Type 2 Diabetes mellitus (T2D), using modern sequencing technologies and artificial intelligence (AI) in order to improve the stratification and pharmacological treatment in the context of precision medicine. 2022-2025 Cardiovascular and Complex Diseases Spanish Ministry of Science and Innovation; Instituto de Salud Carlos III ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE EuCanImage | A European Cancer Image Platform Linked to Biological and Health Data for Next-Generation Artificial Intelligence and Precision Medicine in Oncology. 2020-2025 AI Solutions in Oncology European Commission - H2020 Programme; "La Caixa" Foundation cofunds CRG's contribution DATA MANAGEMENT METADATA STANDARDS GenoMed4ALL | A consortium built to empower personalised medicine in the field of haematological diseases through the use of AI and the pooling of genomic and clinical data. 2020-2025 Hematological diseases European Commission - H2020 Programme DISCOVERY METADATA STANDARDS Completed projects Project Duration Domain Funder Tags BY-COVID | The BeYond-COVID project aims to make COVID-19 data accessible to scientists in laboratories but also to anyone who can use it, such as medical staff in hospitals or government officials. Going beyond SARS-CoV-2 data, the project will provide a framework for making data from other infectious diseases open and accessible to everyone. 2021-2024 Infectious diseases European Commission - H2020 Programme ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE IMPaCT-Data | IMPaCT-Data aims to create the infrastructure for secondary use of data from Spanish healthcare systems - electronic health records, medical imaging and genomic repositories - and contribute with the knowledge and methodology produced to the healthcare system. 2021-2024 Large-scale genomics and health dataSpanish Ministry of Science and Innovation; Instituto de Salud Carlos III ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE LaMarató | It is a project aimed at creating and developing a catalan interhospitalary network to interrogate genetic variants from thousands of genetic tests carried out in patients with rare diseases from the main catalan hospitals. 2021-2024 Genomic and health data Fundació La Marató de TV3 (catalan foundation) DISCOVERY HealthyCloud | This consortium will contribute a Strategic Agenda towards the European Health Research and Innovation Cloud. The project will work in collaboration with a broad range of stakeholders to ensure that all voices are included and that the results are technically and ethically sound. 2021-2023 Not Applicable European Commission - H2020 Programme DOCUMENTATION B1MG | Beyond 1 Million Genomes aims to create a network of genetic and clinical data across Europe. The project provides coordination and support to the 1+ Million Genomes Initiative (1+MG). This initiative is a commitment of 24 EU countries, the UK and Norway to give cross-border access to one million sequenced genomes by 2022. 2020-2023 Not applicable European Commission - Horizon Europe DATA MANAGEMENT INFRASTRUCTURE METADATA STANDARDS ELIXIR-CONVERGE | An alliance with the goal of Connecting and aligning ELIXIR Nodes to deliver sustainable FAIR life-science data management services. 2020-2023 Data Management and Infectious Diseases European Commission - H2020 Programme DATA MANAGEMENT INFRASTRUCTURE METADATA STANDARDS IHCC | The International HundredK+ Cohorts Consortium aims to create a global platform for translational research – informing the biological and genetic basis for disease and improving clinical care and population health. 2020-2022 Translational research NIH; The Wellcome Trust; CZI INFRASTRUCTURE METADATA STANDARDS PPCG | The Pan Prostate Cancer Group aims to harmonise and interrogate Whole Genome DNA Sequence data generated around the world from over 2000 men with prostate cancer, with associated transcriptome and methylome data to include men from different clinical categories, and ethnicities. This project is about providing breakthrough advances through analysis of a very large series of Whole Genome DNA data from prostate cancer contributed by many of the leading scientists and clinicians working in prostate cancer genomics. 2019-2024 Cancer Cancer Research UK DATA MANAGEMENT CINECA | Consortium providing a Federated solution enabling population-scale genomic and biomolecular data accessible across international borders accelerating research and improving the health of individuals resident across continents. 2019-2023 Large-scale Genomics and Health Data European Commission - H2020 Programme ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE EASI-Genomics | A project designed to provide easy access to cutting-edge DNA sequencing technologies to researchers from academia and industry, within a framework that ensures compliance with ethical and legal requirements, as well as FAIR and secure data management. 2019-2023 Next Generation Sequencing European Commission - H2020 Programme ACCESS EJP-RD | An European consortium built to create a comprehensive, sustainable ecosystem allowing a virtuous circle between research, care, and medical innovation. 2019-2023 Rare diseases European Commission - H2020 Programme ACCESS DATA MANAGEMENT DOCUMENTATION METADATA STANDARDS EOSC-Life | EOSC-Life brings together the 13 Life Science research infrastructures (LS RIs) to create an open, digital and collaborative space for biological and medical research. The project will publish 'FAIR' data and a catalogue of services provided by participating RIs for the management, storage and reuse of data in the European Open Science Cloud (EOSC). 2019-2023 Not applicable European Commission - H2020 Programme DOCUMENTATION EUCANCan | A federated network aiming at implementing a cultural, technological and legal integrated framework across Europe and Canada, to enable and facilitate the efficient sharing of cancer genomic data. 2019-2023 Cancer European Commission - H2020 Programme DATA MANAGEMENT METADATA STANDARDS The Federated EGA framework: supporting sensitive data management across the ELIXIR Nodes | This project is a direct continuation of the FHD IS with the goal to position the FEGA framework as the core infrastructure driver to support human data sharing for research. 2019-2023 Human genomic data ELIXIR INFRASTRUCTURE UK Biobank | UK Biobank is a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants. This project is to archive whole genome sequencing and other genetic data for UK Biobank participants. 2019-2023 Large-scale Genomics and Health Data The Wellcome Trust; UKRI; Amgen; AstraZeneca; GSK; Johnson & Johnson DATA MANAGEMENT INFRASTRUCTURE VEIS | The core mission of VEIS is to create an open ecosystem of technologies that will address and adapt to the requirements of the systems used to analyse and interpret -omics and clinical data in research and application environments in biomedicine. The aim of the project is to leverage the value of the EGA for both industry and society. 2019-2022 Oncology and Rare diseases Generalitat de Catalunya and European Regional Development Fund (ERDF) ACCESS DISCOVERY ELIXIR BEACON IS | This study follows on from a number of earlier activities that have established the ELIXIR Beacon Project. The main aim is to extend the Beacon protocol, developed at EGA, to become the reference ELIXIR Data Discovery product 2019-2021 Not applicable ELIXIR DISCOVERY ELIXIR FHD IS | This project coordinates the delivery of FAIR compliant metadata standards, interfaces, and reference implementation to support the federated ELIXIR network of human data resources. 2019-2021 Human genomic data ELIXIR INFRASTRUCTURE ELIXIR Rare Disease | The Rare Disease Community extends and generalises the system of access authorisation and high volume secure data transfer developed within the EGA. The goal of the Community is to create a federated infrastructure that will enable researchers to discover, access and analyse different rare disease repositories across Europe. It is doing this in partnership with other European infrastructure projects, namely RD-CONNECT, BBMRI-ERIC and E-Rare.2019-2021 Rare diseases ELIXIR INFRASTRUCTURE Solve-RD | Solve-RD - solving the unsolved rare diseases - is a research project funded by the European Commission. It echoes the ambitious goals set out by the International Rare Diseases Research Consortium (IRDiRC) to deliver diagnostic tests for most rare diseases by 2020. The current diagnostic and subsequent therapeutic management of rare diseases is still highly unsatisfactory for a large proportion of rare disease patients - the unsolved RD cases. For these unsolved rare diseases, we are unable to explain the etiology responsible for the disease phenotype, predict the individual disease risk and/or rate of disease progression, and/or quantitate the risk of relatives to develop the same disorder. 2018-2024 Rare diseases European Commission - H2020 Programme ACCESS DATA MANAGEMENT METADATA STANDARDS EuCanShare | An EU-Canada joint infrastructure for next-generation multi-Study Heart research. 2018-2022 Cardiovascular Diseases European Commission - H2020 Programme ACCESS METADATA STANDARDS

  Documentation about/projects-and-funders/projects

• Genotype data for The admixture histories of Cabo Verde - genotype data

  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) As per Materials and Methods herein, the genotype data corresponds to 2,118,722 autosomal SNPs genotyped from the IlluminaOmni 2.5 Million BeadChip for 233 Cabo Verdean volunteer participants, family unrelated at the 2nd degree based on population genetics analyses (see Material and Methods). SNP rsID, Chromosome position and genetic position in (bp) are in Build GRCh38. Cabo Verdean individuals are designated with an alphanumeric unique code

  Dataset EGAD00001008979

• TK-EP862 - Patient-derived xenograft model of Posterior Fossa A Ependymoma - WSG data

  In Vivo Loss of Tumorigenicity in a Patient-Derived Orthotopic Xenograft Mouse Model of Ependymoma. Whitehouse et al. 2023 Frontiers in Oncology. We describe the establishment of a patient-derived orthotopic xenograft (PDOX) model of posterior fossa A (PFA) EPN, derived from a metastatic cranial lesion. Patient and PDOX tumors were analyzed using RNA sequencing. WSG data (paired end) provided here correspond to germline DNA, Surgical sample 4 (described in the above manuscript as a cranial metastasis of PFA ependymoma), and a patient-derived xenograft derived from the patient.

  Dataset EGAD00001010008

• Renal habitat WXS

  This project is correlating the molecular profiling of renal tumours with multiparametric and 13C-MRI including by 13C-MRSI. . This dataset contains all the data available for this study on 2023-03-09.

  Dataset EGAD00001010125

• DAC-2023-07-05-Ritz (DAC-007) - Diagnosis of tuberculosis infection in children with a novel skin test and the traditional tuberculin skin test: an observational study

  Diagnosis of tuberculosis infection in children with a novel skin test and the traditional tuberculin skin test: an observational study This is only the location for the raw data and any check if the dataset may be relevant please refer to the MF-DAC Zenodo community (https://zenodo.org/records/11237107).

  Study EGAS50000000780

• MutWP5: CRUK Mutographs of Cancer: Lung: PD43291_Novaseq (WG)

  Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutaiton burden in different portions of the lung. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this poriton of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010111

• Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA

  These data relate to Abbosh et al 2023 'Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA'. Some patients were included in this work that were excluded from the other TRACERx 421 papers (Frankell et al and Al-bakir et al) often due to exclusion criteria in the TRACERx clinical trial. Reasons include partial contamination with C>A artefact mutational signature (CRUK0480 ,0490) which were included for ctDNA detection analyses using high confidence clonal mutations but not clonality analyses, incomplete resection at primary surgery (CRUK0291, 0234, 0230, 0387, 0622) and a concurrent oesophageal second primary cancer (CRUK0498). The primary tissue exome sequencing for these patients that were used in this study are included in this repository. Deep targeted sequencing of (median 200) tumour specific mutations in plasma for 1069 samples from 198 TRACERx patients are also included. Additional details related to these patients and samples can be found in the supplementary or data repositories from Frankell et al and Abbosh et al.

  Study EGAS00001006923

• Multi-Omic Profiling of Glioma Patient Tumors and Patient-Derived Model Systems

  We have created a resource of transcriptomic (RNAseq), genomic (whole-exome seq), and lipidomic (untargeted shotgun) profiling data from over 175 molecularly diverse glioma tumors and derivative models in orthotopic mouse xenografts and gliomasphere cultures from over 110 unique patient tumor lines. Provided in this dataset are matched bulk RNA and whole-exome paired-end sequencing data from resected gliomas and derived model systems performed on Illumina HiSeq and NovaSeq sequencing instruments. This comprehensive dataset powers multiple studies aiming to derive molecular signatures of intertumoral heterogeneity and perturbation responses in glioma. Through integrative multi-omic analyses in Minami, et al., 2023, PMID: 37236196, we identified that CDKN2A deletion remodels the GBM lipidome and consequently primes CDKN2A-deleted tumors for ferroptosis. Lipidomics data related to this study are publicly available here. In another study, we used a combination of molecular profiling and functional profiling of apoptotic potential to stratify patients into groups with differential vulnerability to the current standard-of-care in gliomas, and in combination with drugs targeting intrinsic apoptosis, largely informed but not limited to TP53 mutation status.

  Study phs003286

• Whole genome sequencing data of pediatric B-other subtype acute lymphoblastic leukemia

  For a long time, approximately 70% of childhood B-ALL cases could be classified routinely, and the remaining 30% were classified into the “B-other” group lacking the known genetic features. However, more recently, new genomic tools have uncovered new subtypes among the B-other groups. Approximately 10% of B-ALL cases lack the recurrent genetic abnormalities, and are referred as "B-other". They are typically more heterogeneous than other subtypes of B-ALL, and their prognoses are more variable. In a more recent update of the B-ALL classification, 27 subtypes were identified (Arber et al., 2022). The identification of some of these subtypes is dependent on NGS methods, such as RNA sequencing, which are not routinely used in all clinical laboratories. For example, ETV6::RUNX1-like and DUX4-rearranged subtypes have been identified using RNA sequencing. For this study, subtype predictions for some of the cases is acquired from ALLIUM (Krali et al., NPJ Precis Oncol., 2023) and ALLCatchR (Beder et al., Blood, 2022) based on the RNA-seq profiles.

  Study EGAS50000001497

• RNA sequencing of CCO- and CCO+ human hepatocytes

  RNAseq data from Passman et al 2023. Clonal CCO-deficient hepatocyte patches and nearby CCO-proficient hepatocytes were identified in morphologically normal human livers and sampled at varying distances along the PT-CV axis. Samples are named according to their location within the liver lobule, with "PT" denoting samples abutting the portal triad, "CV" denoting samples abutting the central hepatic vein, and "Mid" sampled acquired midway between these structures. Normal human liver is thought to be generally quiescent, however clonal hepatocyte expansions have been observed but neither their cellular source nor their expansion dynamics have been determined. Knowing the hepatocyte cell of origin, and their subsequent dynamics and trajectory within the human liver will provide an important basis to understand disease-associated dysregulation. Here we use in vivo lineage tracing and a combination of methylation sequence analysis to demonstrate normal human hepatocyte ancestry. We exploit next generation mitochondrial sequencing to determine hepatocyte clonal expansion dynamics across spatially-distinct areas of laser-capture microdissected clones, in tandem with computational modelling in morphologically-normal human liver.

  Study EGAS00001006984

• Anthropological dataset 2 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_GEOcoordFULL_FINAL_01062022.txt" Column 1 corresponds to individual alphanumeric codes as in the "eGAdeposit_233CaboVerde_GenotypeFile_FINAL_01062022.vcf" genotype file Column 2-3 corresponds to X-Y GPS coordinates of individual's interview location in Cabo Verde Column 4-5 corresponds to X-Y GPS coordinates of individual's self-reported residence location at the time of the interview Column 6-7 corresponds to X-Y GPS coordinates of individual's self-reported birth-place location Column 8-9 corresponds to X-Y GPS coordinates of individual's self-reported paternal birth-place location Column 10-11 corresponds to X-Y GPS coordinates of individual's self-reported maternal birth-place location

  Dataset EGAD00001008977

• Chronic Renal Insufficiency Cohort (CRIC) - GWAS

  The Chronic Renal Insufficiency Cohort (CRIC study) was established in 2001 by the National Institute of Diabetes, Digestive, and Kidney Diseases (NIDDK) to improve the understanding of the relationship between chronic kidney disease and cardiovascular disease. The goals of the CRIC Study are to examine risk factors for progression of chronic kidney disease and cardiovascular disease among patients with chronic kidney disease and to develop predictive models to identify high-risk subgroups, informing future treatment trials and increasing application of available preventive therapies. Data available at dbGaP include genotype data for 3527 participants released in version 1. Plasma metabolomics and plasma proteomics data will be available at dbGaP in "Chronic Renal Insufficiency Cohort (CRIC) Study Metabolomics and Proteomics". Additional phenotypic, clinical, and outcomes data are available at the NIDDK Central Repository and can be linked to data available at dbGaP. More than 330 manuscripts have been published, reporting on the effects of a broad range of risk factors (from molecular biomarkers of disease pathways to clinical, demographic, and behavioral characteristics) on the progression of CKD and other health consequences. The full CRIC bibliography can be found at http://cristudy.org/Chronic-Kidney-Disease/Chronic-Renal-Insufficiency-Cohort-Study/bibliography-2024-2023.

  Study phs000524

• Synthetic - FEGA Sweden Heilsa synthetic dataset December 2023

  Synthetic - This submission contains a subset of a synthetic dataset derived from the project Heilsa Tryggvedottir - a Nordic collaboration on sharing sensitive human data. Heilsa Tryggvedottir is funded by the Nordic e-Infrastructure Collaboration (NeIC), the ELIXIR nodes of Finland, Norway, and Sweden, Computerome in Denmark, and the Estonian Scientific Computing Infrastructure (ETAIS). In the synthetic data creation process, it was attempted to strike a fine balance between the usability of the datasets (e.g. technical FEGA development, testing, user training, and basic bioinformatics) and compliance with GDPR. File names and file content (e.g. headers in fastq) are anonymized. Moreover, the X, Y, and mitochondrial sequences have been discarded from the original data since these data can be used for maternal, paternal, or ethnic origin tracing. The dataset does not follow natural haplotype distribution (inherent to imputation panels). The only inputs derived from real sequence data are variant distribution density per chromosome and learning sequencing error models. The synthetic dataset consists of two fastq files, a cram file, a vcf file, and two index files.

  Dataset EGAD50000000119

• Genomics of acral lentiginous melanoma

  Melanoma is the most aggressive type of skin cancer, causing about 75% of dermatological cancer deaths. Acral lentiginous melanoma (ALM) is the most common subtype of melanoma in admixed Latin American populations, but very few tumour genomes and exomes, all from European-descent individuals, have been analysed across several studies. Because of this, the genomic landscape of ALM is mostly unknown. Our aim in this project is to define this landscape and identify driver somatic alterations by whole-exome sequencing a collection of ALM germline/tumour paired FFPE samples from the National Cancer Institute of Mexico. . This dataset contains all the data available for this study on 2023-03-09.

  Dataset EGAD00001010126

• Melanoma PEACE Dataset

  Dataset for the initial melanoma PEACE paper in Cancer Discovery, March 2023.

  Dataset EGAD00001010877

• Fecal microbiome of T2D patients undergoing semaglutide or empagliflozin treatment

  The gut microbiome has been shown to be affected by the use of many human-targeted medications, and the interaction can be bidirectional. This has been clearly demonstrated for type 2 diabetes medications that have been in clinical use for several decades. However, the bidirectional effects of novel type 2 diabetes drugs semaglutide, empagliflozin, and the gut microbiome have yet to be clearly described. We investigated the effect of semaglutide and empagliflozin initiation on the gut microbiome of type 2 diabetes patients. In addition, we analyzed whether the pre-treatment gut microbiome can predict the treatment efficacy. Twenty subjects (10 women and 10 men) were enrolled in the study between November 2019 and January 2023 from the University of Tartu Hospital, Tartu, Estonia. All participants had been diagnosed with type 2 diabetes. The study group taking semaglutide included 6 women and 4 men, and the group prescribed empagliflozin included 4 women and 6 men. Gut microbiome fecal samples donated at four timepoints (Baseline, Month 1, Month 3; Month 12) were studied using 16S ribosomal RNA gene sequencing and analysis.

  Dataset EGAD50000000756

• Somatic mutation and clonal evolution normal breast tissue WGS

  Cancer is a genetic disease caused by an accumulation of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from breast tissue to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. We will sample from a wide age range of individuals (<20 to >70 years old) to determine whether these processes differ in pre- and post-menopausal women. We will also be comparing the tissue from healthy individuals (samples from breast reduction surgery) to those at elevated risk of breast cancer (mastectomy from BRCA1/2 patients) and those who have breast cancer (adjacent normal, distal normal, and tumour tissue from mastectomy). This will allow us to determine how these processes are different between these groups of individuals, and gain insight into the earliest stages of tumour development. . This dataset contains all the data available for this study on 2023-03-09.

  Dataset EGAD00001010123

• MutWP5: CRUK Mutographs of Cancer: Cancer Mastectomy (Exome)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 40 women who underwent mastecomies due to breast cancer. LCM and sequencing will be conducted on both normal, unaffected breast, and, where possible, tumour tissue. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue, and compare findings between the normal and associated cancer tissues. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this poriton of the study will be compared to women who had cosmetic breast reduction surgeries and those who are BRCA carriers. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010117

• Duplexseq of the interstrand crosslinks_WGS

  One of the most dangerous forms of DNA damage are interstrand crosslinks (ICLs), which covalently crosslink the two strands of the DNA double helix. The repair of these lesions is crucial for cellular survival due to their ability to block transcription and DNA replication. Initially, the major pathway that has been described in ICL repair involves a network of 22 genes that are mutated in a severe human genetic disease known as Fanconi Anemia (FA). Using synthetic lethality screens in the near-haploid human HAP1 cell line, we recently identified two potentially novel regulators of ICL repair, C1orf112 and THAP12. Loss of C1orf112 and THAP12 causes hypersensitivity to ICL-inducing DNA damaging agents, such as Mitomycin C (MMC). Additionally, C1orf112-depleted cells show elevated levels of micronuclei and accumulation of DNA damage in S-phase. To better understand how C1orf112 and THAP12 mediate the repair of ICLs, we want to perform mutational signature analysis, using the BotSeq method. Therefore, WT, C1orf112 and THAP12 knockout cells were cultured in vehicle or MMC treated conditions for 10 days and the genomic DNA was isolated. FANCA and FANCD2 knockout cells are taken along as controls in this experimental setting. . This dataset contains all the data available for this study on 2023-04-20.

  Dataset EGAD00001010298

• AC-ICAM: An Atlas and Compass of Immune-CAncer-Microbiome Interactions in Colon Cancer

  Fresh frozen tumor samples and matched normal colon specimens from chemotherapy naïve, consecutive patients with a histological diagnosis of colon adenocarcinoma at Leiden University Medical Center (LUMC) were subjected to multi-omics profiling at Sidra Integrated Genomics Services, part of Sidra Medicine, Doha, Qatar. This resulted in a high-quality dataset of samples collected between 2001 and 2015. Multi-step QC was performed, including assessment of the quality of the nucleic acid extraction, sequencing, and genetic concordance between DNA/RNA sequences from the same patient. After QC, this resulted in RNA sequencing on 348 tumor samples, whole exome sequencing (WES) on 281 tumor-normal sample pairs (n=562 samples), TCR sequencing of the TCR β locus (TRB) for 114 tumor and 10 matched normal samples, and 16S rDNA sequencing on 246 tumor-normal sample pairs (n=492 samples) and 42 tumor only samples, and 167 WGS non human-aligned reads of the tumor samples. Extensive clinicopathological and survival data of the cohort are available. The median follow-up time for this cohort is 4.6 years, and the distribution of gender and pathological stage are within expected ranges for this patient population with a mean age of 68. We refer to this resource as the Sidra-LUMC (SILU) colon adenocarcinoma (COAD) Atlas and Compass of Immune-Colon cancer-Microbiome interactions (AC-ICAM)."Reprinted from Nature Medicine,Roelands, J., Kuppen, P.J.K., Ahmed, E.I. et al. An integrated tumor, immune and microbiome atlas of colon cancer. Nat Med (2023). https://doi.org/10.1038/s41591-023-02324-5, with permission from Nature."

  Study phs002978

• EGA synthetic data

  Synthetic Data One of the limitations in genomics research is that human genomics data is not openly available; access must be controlled according to participant consent agreements and data protection regulations such as GDPR. Obtaining authorization to access such data can sometimes take a long time, resulting in delays to important research work. In this context, synthetic genomic and phenotype data can be useful resources for researchers to avoid these delays. Synthetic data are artificially generated datasets, often created with algorithms, which can be used without the need for authorization to test new products and tools, build technical demonstrators, validate data models, and train AI models. The EGA provides access to synthetic cohort datasets augmented with rich synthetic metadata that overcomes these real data usage restrictions. Whilst synthetic datasets are not included in the general EGA mandate and services, we can consider such submissions and evaluate their acceptance on the basis of their unique use cases not already covered by existing synthetic datasets. Access to synthetic data studies is managed by the EGA Helpdesk Data Access Committee. Study ID Title Located in EGAS00001002472 CINECA synthetic cohort EUROPE UK1 referencing fake samples Central EGA EGAS00001005591 Synthetic data - Genome in a Bottle Central EGA EGAS00001005042 Test Study for EGA using data from 1000 Genomes Project - Phase 3 Central EGA EGAS00001005702 Human genomic and phenotypic synthetic data for the study of rare diseases Central EGA EGAS50000000190 EOSC4Cancer Synthetic Colorectal Cancer Genomic data Central EGA EGAS50000000086 Synthetic - FEGA Sweden Heilsa synthetic dataset December 2023 Federated EGA Sweden EGAS50000000678 Synthetic - GDI synthetic data Federated EGA Spain

  Documentation synthetic-data

• Light at Night and Prostate Cancer in the Health Professionals Follow-Up Study

  The Health Professionals Follow-Up Study (HPFS) is a prospective cohort study of men designed to evaluate hypotheses about men's health related to diet and lifestyle to the incidence of serious illnesses, such as cancer, heart disease, and other vascular diseases. It consisted of 51,529 U.S. male health professionals who were 40-75 years old at baseline in 1986 and completed a mailed six-page baseline questionnaire. Information on age, height, weight, ancestry, medications, disease history, physical activity, lifestyle factors, and diet were gathered, with follow-up questionnaires sent biennially to update information and record health outcomes with every four years to update their diet. Light at Night and Prostate Cancer project uses HPFS participants to conduct research assessing the impact of light at night on prostate cancer incidence, with follow-up from baseline in 1986 to 2016. Average annual outdoor light at night (exposure) was derived from satellite imagery data from the US Defense Meteorological Satellite Program's Operational Linescan System (maintained by the National Oceanic and Atmospheric Administration's Earth Observation Group). Prostate cancer cases (outcome) were self-reported by study participants, and then confirmed by the HPFS team through medical record and pathology report review. The data for this project are arranged in a long format, meaning that there are multiple rows for each subject ID. The various rows correspond to different questionnaire periods. Because the exposure in this dataset, light at night, and several important covariates are time-varying variables, the format of this dataset captures the variation in exposure status by questionnaire period. The outcome variables are incidence of prostate cancer, including incidence of prostate cancer clinical subtypes, such as advanced, aggressive, high- and low-grade prostate cancer. For the published study, please see Chowdhury-Paulino et al.'s 2023 publication in Cancer Epidemiology Biomarkers and Prevention entitled, “Association between Outdoor Light at Night and Prostate Cancer in the Health Professionals Follow-up Study.” (PMID: 37462694).

  Study phs003538

• Perceptions, Prevalence, and Patterns of Cannabis Use among Cancer Patients in NCI-Designated Cancer Centers

  This study aimed to better understand the evolving landscape of cannabis use among patients undergoing cancer treatment across diverse geographic regions. It sought to characterize patterns of use, patient perceptions, and contextual influences such as legal status and provider engagement that affect cannabis use. Given the shifting legal environment, this study addresses a critical gap in the literature by providing data on how cancer patients integrate cannabis into their care. The available delivery methods of cannabis have also undergone dramatic changes and now include edibles, oils, tinctures, topicals, and inhaled forms. State-based policy changes are rapidly changing. Yet research on cannabis use by cancer patients across a variety of geographic settings remains limited. The extent of cannabis use, the perceived and real benefits and risks of use, potential interactions with cancer treatment and other medications, and impact on comorbid conditions are uncertain. Clinicians should be aware of the extent of use in order to assess potential drug-drug interactions, side effects, and contraindications; hence, an understanding of how cancer patients and clinicians engage in discussions about cannabis use is essential. Twelve NCI-designated cancer centers were awarded supplemental funding to conduct surveys assessing cannabis use among recently diagnosed cancer patients. Selected sites were in 8 states with varied legal status for medical and recreational cannabis use at the time of the survey. The 12 cancer centers independently received approval from their institutional review boards (IRB) and collected data from September 2021 to August 2023. Eligible participants were cancer patients or survivors who resided in their respective cancer center's catchment area. Cancer centers were responsible for sampling patients within their catchment area with the goal of 1,000 completed surveys. Ten out of 12 cancer centers drew probability samples of patients from patient lists defining their catchment area with some stratifying the sample in various ways including by cancer type, by sex, by race/ethnicity or by a combination of demographic variables.

  Study phs004046

• A prospective study identifies MisMatch Repair genes as candidate predisposing genes for uveal melanoma.

  Objective: The aim of this study is to identify new predisposing genes for uveal melanoma (UM), the most common intraocular malignancy in adults. Design: Prospective cohort study from Institut Curie. Participants: 381 unselected patients diagnosed with UM between July 2021 and February 2023 at Institut Curie. Methods: All patients received genetic counseling and consented to extended genetic testing. A panel of 122 genes was analyzed by targeted sequencing on germline DNA from these patients. Main outcomes measures: UM was diagnosed by ophthalmologists at Institut Curie, and a senior pathologist confirmed the diagnosis when tumor or biopsy was available. Classification of the germline genetic variants was performed according to the ACMG guidelines. Results: We identified at least one pathogenic variant (PV) in 79 patients. Among those variants, 20 were found in clinically relevant genes for the patient’s follow-up and genetic counseling. We found a significant enrichment of germline pathogenic variants in Mismatch Repair (MMR) MLH1, MSH2 and MSH6 genes in this UM cohort, suggesting that MMR germline alterations could predispose to UM. From this cohort, one tumor was available from a patient carrying a MLH1 germline mutation. The tumor was characterized by a monosomy 3, in line with a loss of heterozygosity (LOH) of MLH1, located on chromosome 3. Loss of expression of MLH1 and PMS2 was observed by immunohistochemistry, and MMR mutational signatures SBS6, ID1 and ID2 were identified from the whole genome sequencing of the tumor. These data suggest that MLH1 is implicated in the oncogenesis of this UM. Conclusions: This study is the largest prospective constitutional study performed on UM patients, and the first demonstrating that MMR germline alterations are significantly enriched among these patients. Further analyses will be needed to estimate the risk of developing UM for patients with Lynch syndrome

  Study EGAS50000000914

• CDK4/6 inhibition in advanced chordoma: final results of the NCT PMO-1601

  Background. Chordoma is a rare bone tumor with no approved systemic therapy. Inactivation of the tumor suppressor p16 (encoded by CDKN2A) leads to aberrant cyclin D-CDK4/6-RB pathway activity, which can be inhibited by palbociclib in chordoma cell lines. Patients and Methods. We conducted a phase II single-arm, open-labeled trial on palbociclib in adult patients with advanced chordomas with p16 loss (by immunohistochemistry) or CDKN2A loss (by genomic analysis) and CDK4/6 and RB1 presence (by immunohistochemistry or RNA-Sequencing). Patients received 125mg palbociclib once daily for 21 days in a 28-day cycle. The study used a Simon optimal 2-stage design. The primary endpoint was disease control rate after six cycles. Response was evaluated according to Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Secondary endpoints included progression-free survival, overall survival, and biomarker analysis. The study was considered positive if 25% of patients reached the primary endpoint. Results. Between December 2017 and April 2023, 28 patients (median age 60 years, range 31-84) were enrolled. After a median follow-up of 28 months, the disease control rate was 39%, with 11 out of 28 patients achieving stable disease. No objective responses were observed. The median progression-free survival was 5.6 months, and the median overall survival was 24.6 months. Treatment was well tolerated without new safety signals. CDKN2A deletions were the most common genomic alterations underlying p16 loss and were accompanied by MTAP co-deletion. There was no correlation between responder phenotypes by immunohistochemistry and outcome. RNA-based analysis showed heterogeneous pathways deregulation at study entry suggesting underlying primary resistance mechanisms Conclusion. This trial met its primary endpoint with a 39% disease control rate, though antitumor activity was modest. Molecularly tailored combination therapies should be considered to improve efficacy. (ClinicalTrials.gov number, NCT03110744).

  Study EGAS00001007985

• Synthetic - FEGA Sweden Heilsa synthetic dataset December 2023

  Synthetic - This submission contains a subset of a synthetic dataset derived from the project Heilsa Tryggvedottir - a Nordic collaboration on sharing sensitive human data. Heilsa Tryggvedottir is funded by the Nordic e-Infrastructure Collaboration (NeIC), the ELIXIR nodes of Finland, Norway, and Sweden, Computerome in Denmark, and the Estonian Scientific Computing Infrastructure (ETAIS). In the synthetic data creation process, it was attempted to strike a fine balance between the usability of the datasets (e.g. technical FEGA development, testing, user training, and basic bioinformatics) and compliance with GDPR. File names and file content (e.g. headers in fastq) are anonymized. Moreover, the X, Y, and mitochondrial sequences have been discarded from the original data since these data can be used for maternal, paternal, or ethnic origin tracing. The dataset does not follow natural haplotype distribution (inherent to imputation panels). The only inputs derived from real sequence data are variant distribution density per chromosome and learning sequencing error models. The synthetic dataset consists of two fastq files, a cram file, a vcf file, and two index files.

  Study EGAS50000000086

• MutWP5: CRUK Mutographs of Cancer: Cancer Mastectomy (WG)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 40 women who underwent mastecomies due to breast cancer. LCM and sequencing will be conducted on both normal, unaffected breast, and, where possible, tumour tissue. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue, and compare findings between the normal and associated cancer tissues. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this poriton of the study will be compared to women who had cosmetic breast reduction surgeries and those who are BRCA carriers. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010114

• Multiomic Landscape of Multiple Myeloma Precursor and Relapsed Disease

  Multiple myeloma (MM) is a treatable, though incurable, plasma cell malignancy. The molecular mechanisms driving disease onset and the emergence of drug resistance remain elusive. To better characterize the mutational, transcriptional, and epigenetic alterations that accompany MM disease evolution, we accessed molecular data from MM patients treated at Moffitt Cancer Center, with bone marrow biopsies collected between 2011 and 2023.We performed scMultiome (single-cell, paired RNA/ATAC-Seq, 10x Genomics) on 18 CD138+-selected bone marrow aspirate samples, including: 2 healthy donors, 3 patients with monoclonal gammopathy of undetermined significance (MGUS), 4 with smoldering myeloma (SMM), 3 with newly diagnosed MM (NDMM), 1 with early relapse MM (ERMM, 1 to 3 lines of therapy), and 5 with late relapse MM (LRMM, 4 or more lines of therapy). Our dataset includes a sequential sample: a patient with a premalignant condition ("scMultiome_SMM_4") that progressed to active myeloma ("scMultiome_NDMM_3"). These data allow for the assessment of transcriptional and epigenetic dysregulation at the cellular level. Our findings illustrate the dynamic epigenetic and transcriptional landscapes that accompany MM disease progression.We also conducted CUT&Tag analysis on 4 NDMM and 4 LRMM samples to map histone modifications, specifically H3K27ac, to investigate their role in transcriptional reprogramming observed in advanced stages of MM. This analysis revealed sample heterogeneity in terms of super-enhancer-regulated genes associated with active transcription.This cohort also includes samples from the clinical trial NCT04151667, “Daratumumab-Based Response-Adaptive Therapy for Older Adults With Newly Diagnosed Multiple Myeloma.” We performed scRNAseq (single-cell RNA sequencing, 10x Genomics) on 30 CD138+-selected bone marrow aspirate samples and 31 pre-sort samples featuring bone marrow mononuclear cells of various cell types from 5 healthy donors and 11 patients with newly diagnosed MM (NDMM). Our dataset includes sequential samples for all 11 NDMM patients, both prior to induction therapy (denoted by ‘a') with Daratumumab and Dexamethasone, and at cycle 2 day 22 (C2D22, i.e., 60 days after induction therapy start, denoted by ‘b'). For three NDMM patients, we also have samples at relapse (denoted by ‘c'). These data allow for the assessment of transcriptional dysregulation at the single-cell level in both the tumor and immune-tumor microenvironment, associated with Daratumumab treatment in NDMM patients.In addition to the molecular data, demographic, and phenotypic information for all samples is provided.

  Study phs003892

• 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

• Programmatic submission based on XML

  Programmatic submissions (XML based) For further information please check our Submission FAQs, submission quickguide as well as submission terms! Introduction Besides the Submitter Portal tool, EGA supports programmatic sequence and clinical data metadata submissions. If you are not sure what this means, you may want to explore our brief metadata introduction. Programmatic submissions are recommended for array-based submission. Moreove, it may be of help if your submission is recurrent or it is difficult to manage manually due to its sheer size. Otherwise, we highly recommend using the Submitter Portal to perform submissions. In this page we will guide you through the required steps to programmatically submit data to the EGA. Programmatic submissions require your metadata to be structured for an easy and straightforward validation and archival. It basically consists in formatting your metadata as Extensible markup language (XML) files and submitting them to the EGA using the WEBIN Before submitting metadata to the EGA, it is important to ensure that the information in your XML files is compliant with our standards. You can see further details on how these standards are maintained at EGA at our EGA Schemas documentation page. Using WEBIN, you can validate your XML files against EGA's schemas to ensure that your metadata is compliant before submission. WEBIN services WEBIN production service WEBIN test service We advise you to submit your metadata to the test service when submitting to the production service for the first time. The test service is identical to the production service except that all submissions will be discarded in the following 24 hours. This allows you to learn about the submission process without having to worry about data being submitted. Authentication Authentication is required each time a submission is made. The submission service uses HTTPS protocol for metadata encryption and identification to provide a secure submission environment. Data file upload Both Runs and Analyses reference files (e.g. FASTQ need to be uploaded to the EGA before these metadata objects are submitted. In other words, if you submit a Run that references a file that we cannot find associated with your account, the metadata submission will fail. See further details on how to upload your files in our File Upload documentation. Metadata model of the EGA Our metadata model is formed by multiple metadata objects. Check further details in our documentation at our EGA Schema documentation page. Working with EGA XMLs files Now that the basic concepts of the EGA metadata have been described, you can start preparing your programmatic submission through XML. Here you will find the guidance on how to prepare the XML files. Programmatic Submission Tutorial Video Take a look at the Programmatic Submission Tutorial Video, which explains the workflow of a programmatic submission and goes over an example metadata submission. Programmatic Submission Tutorial Video. When building your XML files, we recommend using text editors (e.g.Sublime Text or VisualStudio) that allow you to visualise the structure of the XML with ease. Furthermore, these editors constantly check the consistency of the XML structure. Alternatively, and if the submission consists of a big number of objects (specially analyses), you may find the tool star2xml handy. This tool allows for a direct conversion between metadata in a tabular format (e.g. a spreadsheet) into XMLs. Identifying objects: Aliases and center names Every EGA object must be uniquely identified within the submission account using their alias attribute. The aliases can be used in submissions to make references between EGA objects. Let us dig into EGA's use of aliases and center names: alias: every object should have a name that is unique within your submission account. Once submitted successfully, every alias will be assigned a unique and permanent accession (EGA ID). refname: when an object references another by its alias, the alias of the referenced object goes into the "refname" attribute of the referencing object. For example, if a sample has the alias "sample1", and an experiment uses this sample, then the experiment's "EXPERIMENT/SAMPLE/refname" attribute should be "sample1". center_name: The "center_name" attribute is required within the submission XML and, if not provided when the object is submitted, it will be automatically filled using your default EGA account center_name. This element is the "controlled vocabulary acronym or abbreviation that is provided to the account holder when the account is first generated". If the submitter is brokering a submission for another institute, the submitter should use their special broker account name in broker_name while the data centre acronym remains in center_name. Log-in details should have been provided when you requested a submission account. Please contact our Helpdesk team if you have any questions. run_center: Many submitting centers contract out the actual sample sequencing to another center. In these cases, the sequencing center should be acknowledged in the run_center attribute. Again, this is controlled vocabulary and the acronym should be sought from EGA helpdesk before submitting. Please contact our Helpdesk team if you have any questions. Prepare your XMLs The goal of this section is to provide sufficient information to be able to create the metadata XML documents required for programmatic submissions. Please note, the EGA utilises the XML schemas maintained at the European Nucleotide Archive (ENA). It is important due to the fact that by using a similar system, some pieces of documentation from the ENA's programmatic submission can also help you with your programmatic submission to the EGA. For example, you can submit programmatically without using a Submission XML by following the steps at Submission actions without submission XML. A submission does not have to contain all different types of XMLs. For example, it is possible to submit only a few samples; or a study that is later to be referenced. You can submit each object one by one, or submit all in a batch: you choose what method of submission works best for you. We do recommend, nevertheless, that you submit the objects to be referenced (e.g. samples or studies) first, and the objects that reference these (e.g. experiments or datasets) afterwards. You can see a graphical view of these objects and their relationships at our EGA Schemas page. Independently of the submission scenario, you will always require a Dataset XML. The entity of a dataset is what is used to control access to the given data, in the form of runs or analyses. In other words, when a requester is granted access, it is through the dataset and the objects (e.g. runs or analyses) that the dataset contains, granting access to them in one go. Given the nature of the EGA, a dataset XML will always be required for the data access. First, we will differentiate between submissions of "raw" and "processed" data: Runs and Analyses, respectively. Run data submissions Raw data derives from instruments "as is". For example, a plain sequence file (e.g. FASTQ or unaligned BAM files) would be considered raw data. A typical raw (unaligned) sequence read submission consists of 8 XMLs: Submission Study Sample Experiment Run DAC Policy Dataset When technical reads (e.g. barcodes, adaptors or linkers) are included in the submitted raw sequences, a spot descriptor must be submitted to describe the position of the technical reads so that they can be removed. The following data files can be submitted without providing spot descriptor information in the experiment/run XML: BAM files (single reads) SFF files (single reads without barcodes) FastQ files (single reads without any technical reads) Complete Genomics files Analysis data submissions Processed data is, in some way, refined raw data. This includes raw data that has been processed by some form of analysis method (e.g. alignment, noise reduction, etc.). For example, an aligned sequence (e.g. BAM file), that was created using raw FASTQ files, would be a processed file. This category includes most types of data: sequence alignment files (e.g. BAM or CRAM), clinical data (e.g. phenopackets), sequence variation files (e.g. VCF), sequence annotation, etc. A typical EGA analysis data submission consists of 7 EGA XML: Submission Study Sample Analysis DAC Policy Dataset We accept three different types of analysis data submissions: BAM files (for multiple read alignments) VCF files (for sequence variations) Phenotype files (in any format) In anycase, keep in mind that samples must be created in order to be referenced in the analyses. In other words, the provenance of the information within the BAM, VCF and phenotype files Example XMLs Below you can find a non-extensive list of example XMLs with descriptive fields (i.e. explaining what to provide in each field). Furthermore, you can also find real examples (i.e. the true value of the provided fields) in our GitHub repository. Submission XML The submission XML is used to validate, submit or update any number of other objects. The submission XML refers to other XMLs. New submissions use the ADD action to submit new objects. Object updates are done using the MODIFY action and objects can be validated using the VERIFY action. Descriptive submission XML example True values submission XML example Study XML The study XML is used to describe the study containing a title, a study type and abstract as it would appear in a publication. Descriptive study XML example True values study XML example Please use the following notation within the property "STUDY_LINKS" when including PubMed citations in the Study XML: <STUDY_LINKS> <STUDY_LINK> <XREF_LINK> <DB>PUBMED</DB> <ID>18987735</ID> </XREF_LINK> </STUDY_LINK> </STUDY_LINKS> Sample XML The sample XML is used to describe the samples used to obtain the data, whether they were sequenced, measured in any other way, or have an associated phenotype. The mandatory fields include information about the taxonomy of the sample, sex, subject ID and phenotype. For example, the mandatory attribute fields for each sample would look like these, within the array of "SAMPLE_ATTRIBUTES": <SAMPLE_ATTRIBUTES> <SAMPLE_ATTRIBUTE> <TAG>subject_id</TAG> <VALUE>free text!</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>sex</TAG> <VALUE>female/male/unknown</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>phenotype</TAG> <VALUE>Free text, EFO terms (e.g. EFO:0000574) are recommended</VALUE> </SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTES> Sample is one of the most important objects to be described biologically, it is highly recommended that “TAG-VALUE” pairs are generated as SAMPLE_ATTRIBUTES to describe the sample in as much detail as possible. For example, were we to give the population ancestry of the sample, we could add a new attribute to the array, in which, for example, we would indicate that the sample derives from an individual of "Mende in Sierra Leone" (MSL), with an african ancestry: <SAMPLE_ATTRIBUTE> <TAG>Population</TAG> <VALUE>MSL</VALUE> </SAMPLE_ATTRIBUTE> Given that VALUE and TAG are free text, the combinations are limitless in order to give you full flexibility on the information you want to provide. We recommend you use the Experimental Factor Ontology (EFO) to describe the phenotypes of your samples. You can provide more than one phenotype by adding more items to the array of SAMPLE_ATTRIBUTES. Phenotypes considered essential for understanding the data submission should be provided. Each phenotype described should be listed as a separate sample attribute <SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTE>. There is no limit to the number of phenotypes that can be submitted. If a suitable EFO accession cannot be found for your phenotype attribute, please consider using another controlled ontology database (e.g. HPO, MONDO, etc.) before using free text. Descriptive sample XML example True values sample XML example Experiment XML The experiment XML is used to describe the experimental setup, including instrument platform and model details, library preparation details, and any additional information required to correctly interpret the submitted data. Where any of these values differ between runs, a new experiment object must exist, since runs are grouped by experiments. Each experiment references a study and a sample by alias, or if previously-submitted, by accession. Pooled data must be demultiplexed by barcode for submission. Descriptive experiment ( Illumina paired read ) XML example True values experiment ( Illumina paired read ) XML example Run XML The run XML is used to associate data files with experiments and typically comprises a single data file (e.g. a FASTQ file). Please note that pooled samples should be de-multiplexed prior submission and submitted as different runs. Descriptive run XML example True values run XML example Analysis XML Given that an analysis can be used to submit any type of processed data to the EGA, we will list below an example of each of the three most common types of analysis XMLs submitted to the EGA: sequence alignments (e.g. BAM files); sequence variation (e.g. VCF files); and clinical metadata or phenotypes (e.g. phenopackets). Regardless of the type of processed data submitted in the analysis, the analysis must be associated with a Study and can reference multiple types of other objects, from samples to experiments, if they are available at the EGA. Just like with Runs, whenever a file is submitted to the EGA through an analysis object, the file MD5 checksums must be present, in order for the EGA to validate file integrity upon transfer. This also includes index files when applicable (e.g. .bai.md5 files). Ideally, any analysis that uses a reference sequence for some kind of alignment (e.g. BAM, CRAM or VCF files), would contain metadata about the alignment, such as INSDC reference assemblies and sequences, by either using accessions (e.g. CM000663.1) or common labels (e.g. GRCh37). Read alignment (BAM) Analysis XML The Analysis can be used to submit BAM alignments to EGA. Only one BAM file can be submitted in each analysis and the samples used within the BAM read groups must be associated with Samples. Descriptive bam alignments XML example True values bam alignments XML example Sequence variation (VCF) Analysis XML The Analysis can be used to submit VCF files to EGA. Only one VCF file can be submitted in each analysis and the samples used within the VCF files must be associated with Samples. Download analysis XML (VCF) Phenotype files The Analysis XML can be used to submit phenotype files to the EGA. Only one phenotype file can be submitted in each analysis and the samples used within the phenotype files must be associated with EGA Samples. Download analysis XML (Phenotype) DAC XML The DAC XML describes the Data Access Committee (DAC) affiliated to the data submission. The DAC may consist of a group or a single individual and is responsible for the data access decisions based on the application procedure described in the POLICY.XML. As with any other object, if it was already submitted to the EGA, there is no need to submit it again: you can reference an existing object within the EGA. Hence, A DAC XML does not need to be provided if your submission is affiliated to an existing EGA DAC.. Further information on DACs can be found here, and you can always contact our Helpdesk team if you have further inquiries. Descriptive dac XML example True values dac XML example Policy XML The Policy XML describes the Data Access Agreement (DAA) to be affiliated to the named Data Access Committee. Descriptive policy XML example True values study XML example Dataset XML The dataset XML describes the data files, defined by the Run.XML and Analysis.XML, that make up the dataset and links the collection of data files to a specified Policy. The dataset xml is commonly the last metadata object to be submitted, since it references multiple other entities. Please consider the number of datasets that your submission consists of. For example, a case-control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Descriptive dataset XML example True values dataset XML example Validating and submitting your EGA Validating EGA's XMLs through Webin After you have ensured that the XMLs are properly formatted and contain all the required information. You can proceed to validate and submit your data. Use the curl command to validate your XML file: Once you have prepared your XML file and asserted you have access to Webin, you can validate your XML file programmatically against EGA's schemas using the curl command. There are multiple ways in which you can validate your XMLs. This variety has to do with the fact that: (1) there are 2 instances of Webin (test and production); and (2) that validation is a default step during submission. In other words, any time that you submit your data through Webin, it will be validated automatically before being accepted. This allows for 4 possible routes of validation, all having the same validation result: validating or submitting to either the production service or the test service of Webin. For example, directly validating a "study" object XML in the testing service (wwwdev…) would look like the following: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" In this command, you would need to replace <USERNAME> and <PASSWORD> with your EGA account username and password, respectively. You would also replace <INPUT_FILE> with the path to your XML file. A mock example would look like the following: curl -u ega-test-data@ebi.ac.uk:egarocks -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" The validation attempt can have different results depending on the given arguments: If your XML file is valid according to EGA's schemas, you will see a message indicating that your XML file is compliant. For example, see below for our mock example, where the "success" was "true" (i.e. no validation errors found). Nevertheless, notice how the "<STUDY accession=" is empty: it is because we were simply validating, so the study did not get an accession or ID. <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:19:28.850+01:00" submissionFile="submission-EBI-TEST_1681222768850.xml" success="true"> <STUDY accession="" alias="Mock example" status="PRIVATE"/> <SUBMISSION accession="" alias="SUBMISSION-11-04-2023-15:19:28:840"/> <MESSAGES> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If there are any errors or warnings, the tool will display them, allowing you to correct them before submitting your data to EGA. For example, in the following response, it is said that the object we were trying to submit was already existing, and therefore the "success" was "false". <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:12:35.609+01:00" submissionFile="submission-EBI-TEST_1681222355609.xml" success="false"> <STUDY alias="Example!_Human Microbiome Project SP56J" status="PRIVATE" holdUntilDate="2023-03-11Z"/> <SUBMISSION alias="SUBMISSION-11-04-2023-15:12:35:576"/> <MESSAGES> <ERROR>In study, alias: "Example!_Human Microbiome Project SP56J". The object being added already exists in the submission account with accession: "ERP127584".</ERROR> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If the curl command retrieves no response at all, please double check if your username and password are correctly provided. Also notice the "ACTION=..." argument passed to the Curl command. This specifies the action to take during the call to Webin, so we do not need a "Submission" XML just for a validation attempt. See more at submission actions without submission XML. Furthermore, validation of multiple files or objects (e.g. sample, experiment, study…) can be done in a single command by adding more arguments (i.e. '-F'). For example: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" -F "SAMPLE=@sample.xml" -F "DATASET=@dataset.xml" As mentioned above, beside "validate" action in the test environment, you can also validate your metadata by three other methods: "Validate" in the production server. From our example above, you simply need to take the "dev" away from the URL. curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the development server. From our example above, you would simply need to replace the action: from "validate" to "add". Whatever is submitted to this service will be discarded in 24h, so whether something gets submitted or not would not matter in the long run. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the productionserver. A combination of the previous two methods, which would render this attempt into a submission. This path is just to be taken when you are sure your metadata is compliant and what you want to submit. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" What happens after the submission of a dataset XML? Once you have completed the registration of your dataset/s please contact our Helpdesk Team to provide a release date for your study. Please note that all datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitter or DAC contact contact the EGA Helpdesk for the study to be released. We strongly advise you not to delete your data until EGA Helpdesk confirms that your data has been successfully archived.

  Documentation submission/metadata/submission/programmatic-submission-xml

• Drug development for pediatric cancers: the importance of patient' s genomic data re-use

  Cancer in children is uncommon and the overall prognosis for most pediatric cancers is good. However, while the combined survival rates have improved over the last decades, certain childhood malignancies, such as high-grade gliomas or metastatic sarcomas, still remain incurable in most patients. New strategies for targeting those devastating diseases are imperative, and patient genomic data can become a key asset in the process [1]. The Pediatric Cancer Genome Project’s datasets At EGA, we store over 500 datasets with sequencing data from pediatric cancer patients. A remarkable case is the datasets belonging to the Pediatric Cancer Genome Project (PCGP) from St. Jude Children’s Research Hospital–Washington University (Table 1 - EGAS list). PCGP is an ambitious effort to identify the mutations that drive childhood cancer and find new cures. Those datasets include 600 patients with complete tumor and normal genomes from 15 different tumor types. PCGP datasets have been an important resource for various studies that pooled genomic data from different public and in-house datasets to perform extensive genomic characterizations and publish comprehensive pan-cancer pediatric studies [2],[3]. Other interesting pediatric cancer datasets are from the Pediatric Brain Tumor Consortium, Sickkids or ICGC PedBrain project. How data reuse impacts drug development for pediatric cancers Recently, in October 2024, Nature Communications published a work led by the University of Michigan where authors used data from EGA (PCGP) check the table below as part of their strategy to pursue the identification of new tumor vulnerabilities susceptible to becoming novel therapeutic targets in diffuse midline glioma. Diffuse midline gliomas (DMG) are treatment-resistant and uniformly fatal pediatric brain tumors. The prognosis of this brainstem tumor is dismal with a median overall survival of 9–12 months from diagnosis. In this study, the authors first developed a lab model of the disease and reanalyzed data from the EGA to identify genes potentially linked to tumor severity. This approach revealed involvement of specific metabolic pathways, suggesting new possibilities for therapeutic intervention. In mice, the study shows that the use of statins can improve survival. For a brief overview of the underlying science, diffuse midline gliomas present intratumor heterogeneity with subpopulations of less-differentiated oligodendrocyte precursors and more differentiated astrocytes. Authors established in vitro models to recapitulate both phenotypes and identify metabolic programs in both subpopulations. To determine the clinical relevance, the authors re-used gene expression data from 76 DMG patients identifying a gene signature predicting decreased overall survival. After extensive metabolic characterization of subpopulations, authors defined strategies to target specific metabolic vulnerabilities. Pre-clinical experiments in mice using OXPHOS inhibitors and statins showed a reduction in tumor burden and increased overall survival [4]. In this scenario, the re-use of high-quality patient’s sequencing data appeared as an advantageous strategy to support the clinical relevance of in-vitro and pre-clinical findings. On the other hand, in rare conditions such as childhood cancer, the availability of public datasets and the possibility of pooling data from different sources can be the only way to obtain impactful results that lead to improvements for patients. References Davidoff, A. M. Pediatric Oncology. Seminars in pediatric surgery 19, 225–233 (2010). Gröbner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018). Venu Thatikonda et al. Comprehensive analysis of mutational signatures reveals distinct patterns and molecular processes across 27 pediatric cancers. Nature Cancer 4, 276–289 (2023). Mbah, N. E. et al. Therapeutic targeting of differentiation-state dependent metabolic vulnerabilities in diffuse midline glioma. Nature Communications 15, (2024). Datasets and Studies ID Title Access Policy Year EGAD00001000134 Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAS0000100192 Somatic Histone H3 Mutations in Diffuse Intrinsic Pontine Gliomas and Non-Brainstem Paediatric Glioblastomas St. Jude Children's Research Hospital - Washington University Pediatric Cancer Genome Project 2017 EGAS00001000575 Whole genome sequencing and whole exome sequencing of DIPG tumors and matched normal tissue The hospital for sick children ("SickKids") 2016 EGAD00001000792 Exome sequencing reads of paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAD00001002006 Whole genome sequencing of paediatric glioblastoma in the ICGC PedBrain project ICGC PedBrain project 2016

  Blog drug-development-for-pediatric-cancers

• All you need to know about our new DAC Portal v2

  An updated version to boost the management of DACs (Data Access Committees), policies, and data access requests. In September of 2023, we launched new services for all EGA users, including a DAC Portal. Since then, our team has been working to refine it and improve the user experience. The version 2 of our DAC Portal is now ready, with a range of new features aimed at streamlining your workflow and boosting productivity. If you are interested in learning more about the latest version of our DAC Portal, then you are in the right place! In this post, you will find information about the implemented features and more. The full documentation is available on our website. Moreover, we highly recommend exploring the DAC Portal through the Take the Tour. This said, let's get cracking! Customise your data access requests table This table offers the possibility to tailor information about the data access requests by making visible or hiding different columns regarding the Dataset Persistent Identifier, the Organisation or the requestor's name s, among others. You can choose which columns to see, depending on your needs and interests. Additionally, we have implemented two new features columns with two new functionalities, DAC Comment and Expiration Date. You can find more details in the following sections! In this table, all data access requests are displayed by default. With the latest version of the Portal, you can now apply filters, combine them, and save them for future use. Leave internal comments on pending requests and visible to all the DAC Members We understand that many Data Access Committees (DACs) cannot instantly approve or deny data access requests. Often, there's an intermediary phase where discussions and the signing of legal documents are necessary. To facilitate this process, we've introduced the DAC comment feature! When you navigate to the DAC column, you'll find two tabs: "User" and "DAC". Under "User", you'll see the message provided by the requester at the time of requesting access to the data. As a DAC member/admin, you can then add an internal comment in the "DAC" tab. These comments are visible only to DAC members/admins, ensuring confidentiality. Requesters won't have access to DAC comments. By adding a DAC comment to a pending request, we'll know that you've begun reviewing it. This allows you to filter pending requests without DAC comments to focus on genuinely new requests, and it prevents you from receiving email notifications about ongoing data requests. Set expiration dates for permissions and revoke them automatically The latest update to the DAC Portal introduces a new feature allowing you to set expiration dates for new access requests. For instance, if you anticipate approving a data access request but only wish to grant access for a limited time, you can now specify an expiration date for the granted permissions. Once this date is reached, the permissions will automatically be revoked. By default, permissions are granted indefinitely without an expiration date. However, if you ever need to revoke permissions, you can easily do so from the History page. Audit your metadata objects Managing and auditing your metadata objects will be easier from now on as the new DAC Portal enhances and facilitates your role as a Data Controller. This is possible thanks to the tables available in each of the primary tabs visible on the homepage: DACs, Policies and Datasets. Starting with DACs, this section allows to check the policies linked to a specific DAC. In Policies, you can obtain a list of the datasets connected to a particular policy. In the Dataset tab, it is possible to group datasets by either DAC or Policy, as well as the release status. Now you have the option of deprecating metadata objects that you don’t want to use anymore, or that were created by mistake. You will find the deprecate button on the EDIT page of the DAC, as well as for registered policies. You can find more information here. What's neat about this feature is that once a DAC or policy is deprecated, it becomes hidden within the DAC Portal. This ensures that you only see information that is consistently up to date! Define your preferences regarding email notifications For pending requests, you will be able to decide how often you want to get the notifications: daily, weekly or fortnightly. Remember, you will not receive email notifications for pending requests with a DAC comment! Furthermore, you have the flexibility to decide which notifications you wish to receive, such as invitations to join a new DAC or notifications regarding DAC acceptance by the Helpdesk, among other options. Check the User preference to see the complete list of available notification settings! What’s next: incoming DAC Portal API In addition to the new DAC Portal, we are excited to announce the release of the DAC API! This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can consult the DAC API specification. This post was written by Aina Jené and Ana T. Alonso.

  Blog new-dac-portal-v2

• Federated EGA

  Federated EGA Federated EGA Index Overview Map of FEGA status across the world Current status of the FEGA Network Documentation Overview Federated EGA is a global network of repositories enabling secure discovery and access to sensitive human data. Our vision is to accelerate scientific discovery and healthcare breakthroughs by creating the go-to worldwide sensitive human data resource. Our values are: Privacy & Trust: Ensuring privacy of data subjects, security of services and acting with integrity to build community trust.FAIRness: Driving reuse of data through standardisation of metadata and clear access policies.Diversity: Expanding our network and representing global diversity in the data we host to bring benefits worldwide. Federated EGA - European by name, global by nature Federated EGA collaborates with European and global initiatives (GA4GH, ELIXIR, 1+ Million Genomes Framework, GDI), demonstrating that a network for transnational discovery of and access to human data is possible. By providing a solution to emerging challenges around secure and efficient management of human omics and associated data, the FEGA Network fosters data reuse, enables reproducibility, and accelerates biomedical research. For general enquiries or to request more information about Federated EGA, please reach out to us here. Read more about the Federated EGA in our commentary in Nature Genetics! Learn more about setting up a Federated EGA Node in our onboarding documentation. Map of FEGA status across the world Current status of the FEGA Network Node Description Joined CEGA Central EGA is managed by the Centre for Genomic Regulation (CRG) and EMBL-EBI. The EGA has been a trusted repository for sensitive data since 2008. CEGA coordinates the FEGA network, hosts central services and the EGA catalogue. 2022 Finland FEGA Finland is hosted by CSC - IT Center for Science Ltd., which is the national ELIXIR Node in Finland. CSC is a state-owned company specialising in providing high-quality IT infrastructure and services for Finnish higher education and research. 2022 Germany The German Human Genome-Phenome archive (GHGA) is part of the German National Research Data Infrastructure (NFDI). It is coordinated by the German Cancer Research Center (DKFZ) and includes over 20 academic partners. 2022 Norway FEGA Norway is a service by ELIXIR Norway, the Norwegian node of the European organisation ELIXIR. University of Oslo as a partner institution in ELIXIR Norway is hosting the service relying on the TSD infrastructure for sensitive data. ELIXIR Norway is a consortium of 5 Norwegian universities and is coordinated by University of Bergen. 2022 Spain FEGA Spain is co-hosted by the Barcelona Supercomputer Center (BSC) and the Centre for Genomic Regulation (CRG), both part of ELIXIR Spain and the Spanish National Infrastructure for Personalised Medicine associated with Science and Technology (IMPaCT). 2022 Sweden FEGA Sweden is hosted by the National Bioinformatics Infrastructure Sweden (NBIS), which legally is part of Uppsala University. NBIS forms the bioinformatics platform at SciLifeLab and constitutes the Swedish node of the European organisation ELIXIR. 2022 Poland FEGA Poland is hosted by Biobank Lodz, which is part of University of Lodz. Biobank Lodz is an element of the infrastructure of the Regional Digital Medicine Centers established by the Medical Research Agency. 2022 Portugal FEGA Portugal is managed by BioData.pt, the distributed infrastructure for Life and Health data for Portugal. This entity is a non-profit association of 15 life sciences R&I organisations spread across the country, and the home of ELIXIR Node in Portugal. 2023 Canada Canadian Genome-phenome Archive is part of the Pan Canadian Genome Library, with the mandate to allow the secure and responsible sharing and analysis of Canadian genomic information. 2024 Switzerland Swiss FEGA is hosted by SIB Swiss Institute of Bioinformatics, which is the national ELIXIR Node in Switzerland, on behalf of the Swiss FEGA Partnership which includes 4 other key partners (ETH Zurich, Health 2030 Genome Center, Swiss Data Science Center and Switch). 2025 Documentation Title Version Description Mission, vision, values Federated EGA Mission, vision, values 1.0 The official Mission-Vision-Values statement for FEGA, produced and formally approved by the FEGA Strategic Committee in September 2025. Structure and Organisation Federated EGA Structure and Organisation 2.0 The structure of the FEGA network and service expectations, responsibilities and commitments. FEGA is organised into three tiers: Central EGA, FEGA Nodes and FEGA Affiliates. Strategic Committee Federated EGA Strategic Committee 1.1 Terms of reference for the FEGA Strategic Committee describe the purpose and objectives of the committee, which is to provide direction and strategic planning for the FEGA network. The committee receives input from, and provides feedback for the EGA Strategic Committee. Operations Committee Federated EGA Operations Committee 1.1 Terms of reference for the FEGA Operations Committee describe the purpose and objectives of the committee, which is to review operational performance and coordinate technical roadmaps of FEGA Nodes and Central EGA. The committee receives advice from, and provides operational reporting to the FEGA Strategic Committee. Guidelines Node Operations guidelines 2.1 An overview of the operational areas which require resources in order to create a FEGA Node. The document is based on more than 15 years' experience of EMBL-EBI and CRG operating the EGA and initial expericences of the inaugural FEGA Nodes. The operational areas of responsability are in line with the FEGA Maturity Model, which guides establishment and operation of FEGA Nodes. Collaboration Federated EGA Node Collaboration Agreement 1.3 Nodes are welcome to make a copy of this current version of the CA to start its review with their legal teams and understand the responsibilities of joining FEGA. Nevertheless, this version (the one with a watermark) shall not be signed: the official version needs to be obtained from FEGA prior signing through its official channels.

  Documentation about/projects-and-funders/federated-ega

• FEGA and European GDI: working together to improve human health

  The Federated EGA is a global resource for discovery of and access to sensitive human omics and associated data consented for secondary use, through a network of human data repositories to accelerate biomedical research and improve human health. The Federated EGA network was launched in September 2022 with five inaugural nodes, and since 2023 seven operational nodes can share data across national borders in adherence to European and national laws. A few weeks after FEGA's official launch, in November 2022, the European Genomic Data Infrastructure (GDI) project was kicked-off . This European Commission co-funded project, coordinated by ELIXIR, is aimed to deliver federated, sustainable and secure data infrastructure to access genomic and related phenotypic and clinical data across Europe. This project supports the aim of the 1+MG initiative (25 EU countries, Norway and UK) to enable personalised medicine and health through a shared framework and infrastructure for securely accessing and integrating high quality genomic data and other health data across borders.1+MG will be an integral component of the European Health Data Space (EHDS) for secondary use (Healthdata@EU) as an authorised participant. How are Federated EGA and GDI similar? Given their shared visions, the Federated EGA and European GDI networks have a lot in common. To begin, they share the same overall goal of establishing networks of “nodes” that host sensitive human data within a jurisdiction and connecting these nodes in a global network to support data discovery and promote human genome data access for research and healthcare. FEGA and GDI are both initially focused on enabling access to human genomic data, while FEGA ambition is to expand the scope to clinical research data and other omics data in the future. FEGA and GDI are both built on open and interoperable software solutions, a subset of which are based on the LocalEGA components. FEGA and GDI implementation solutions are based on international community standards, for example those developed by the Global Alliance for Genomics and Health, which contributes to making them interoperable. Both FEGA and GDI allow for the nodes to make use of any solution that is fully compatible. As illustrated in Figure 1, a final key point is the significant overlap of institutions involved in envisioning and operating FEGA and GDI nodes, with the clear wish to keep them interoperable in the future. What distinguishes Federated EGA from GDI? Despite being largely similar, there are some differences between the FEGA and GDI networks, which we aim to clarify in this post. The first difference is the governance model that the nodes will operate to comply with national laws and GDPR. In the FEGA network, nodes have taken inspiration from the EGA data access model, where the infrastructure is a data processor of the hosted data and data controllership remains with the originating Data Access Committees (i.e. Data controllers) for each dataset. On the other hand, in the GDI network the controllership of datasets will be transferred to a 1+MG European Digital Infrastructure Consortium (EDIC) legal entity created by the Member States (MS), who will make data access decisions, with data holders having veto powers for their datasets. Importantly, FEGA nodes have the flexibility to choose another model to fit with their data protection framework, including becoming data controllers for their datasets. The second difference is the inclusion criteria for data. While FEGA nodes are designed to accept almost any type of omics data in need of control access (e.g. genomics, transcriptomics, genotyping, single cells sequencing, patient-tracked metagenomics), GDI nodes are initially, but not only, focused on accepting whole genome and exome sequencing data and affiliated data from sources such as (i) the Genome of Europe use case of the 1+MG initiative which specifically aims to fulfil the mission of building “a European network of national genomic reference cohorts of at least 500,000 citizens”) (ii) data collection of other types of genomic data identified by countries through the 1+MG dashboard and (iii) genomic data coming from data holders which would need to fulfil EHDS requirements. The third difference is the maturity of the software stacks. The FEGA network provides a set of software for data and metadata submission, storage, permissions management, and file distribution (the LocalEGA package). GDI is building a complete set of open source reference for the five functionalities covering the full data life cycle (a few more compared to the LocalEGA) including federated processing (analytics, AI/ML), which is still under active development. Notably, one of the GDI functionalities - storage and interfaces - can be satisfied by using the LocalEGA storage solution, highlighting the ability of FEGA and GDI to be interoperable. In both FEGA and GDI, nodes are allowed to use alternative solutions as long as they are fully interoperable with the networks. Figure 1 provides a simple overview of these described commonalities and differences. Figure1: schematic overview of the main commonalities and difference among a FEGA and a GDI node Can the same institute run a FEGA and a GDI node at the same time? The answer is Yes! We believe this is entirely possible and encouraged. As long as 1+MG requirements are continuously fulfilled in the implementation. The same trained personnel could operate the infrastructure and leverage the same national funding to run a FEGA and a GDI node. Several European nodes, especially GDI vanguard nodes like Norway, Sweden, Finland and Spain are following this model. The idea is that a lot of the work can be reused, given the overlapping scope and the interoperable technology. Thus, the hosted datasets can be discovered via the two catalogues, perhaps accessible under the same or different governance models. From a FEGA node perspective, the GDI datasets could simply be a subset of the datasets hosted in the FEGA node, which have their specific governance as any other. What's next? So, the amazing teams of people building the Federated EGA network and the European GDI network are working together to create infrastructures able to provide secure access to human genomic and associated data around Europe. Because we all know how much human genomics can improve healthcare and precision medicine. And we all want to collaborate to make it happen. This article has been reviewed collectively by members of the EGA and the GDI coordination team.

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