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• RNAseq dataset

  RNAseq experiment after DIS3 ASO or Control ASO treatment on human cells under plasmablast differentiation.

  Dataset EGAD50000001243

• Whole Exome Sequencing

  Whole Exome Sequencing Data for 10 patients for treatment with the ICI Nivolumab

  Dataset EGAD00001011117

• ChIP-seq of primary AML patients with t(3;3)/inv(3)

  ChIP-seq was conducted in blasts from patients with t(3;3) AML to assess differences of the GATA2 super-enhancer between the translocated allele and the non-translocated allele. The dataset includes 2x H3K27ac ChIP-seq and 1x MYB ChIP-seq. ChIP samples were processed according to the Illumina TruSeq ChIP Sample Preparation Protocol (Illumina) or Diagenode Library V3 preparation protocol (Diagenode) and either sequenced single-end (1x 50 bp) on the HiSeq 2500 platform (Illumina) or paired-end (2x100 bp) on the Novaseq 6000 platform (Illumina). Briefly, reads were aligned to the human reference genome build hg19 with bowtie for single-end runs and bowtie2 for paired-end runs.

  Dataset EGAD00001006821

• Sequencing data for oesophageal and related samples - OACs release 2 (RNA)

  Dataset EGAD00001003839

• Prostate cancer datasets WES

  WES: 48 samples (5 blood samples from 6 patient data): 22 Tumour cores and 26 normal/benign cores (HiSeq)

  Dataset EGAD00001004467

• Exome reads and RNA-seq

  Exome sequencing for 26 patients with matched blood RNA-seq for 41 patients

  Dataset EGAD00001002722

• RNAseq

  RNAseq was performed on CDX, CDX-derived cell line and LNCaP cell line, with triplicates.

  Dataset EGAD00001006008

• WES of der(1;7)(q10;p10) myeloid neoplasms

  The study examined WES of der(1;7)(q10;p10) myeloid neoplasm cases. BAM files of WES of 26 myeloid neoplasm patients with der(1;7)(q10;p10) were used to identify key driver genes in patients with der(1;7)(q10;p10). This study is one of the largest WES for der(1;7)(q10;p10)(+) myeloid neoplasm cases.

  Study EGAS50000000704

• Long-read and short-read isoform sequencing in breast cancer

  The goal of this project was to perform long-read RNA sequencing (LR-seq, PacBio) in combination with short-read RNA-seq for systematic characterization of the isoform diversity in primary breast tumor samples. We sequenced the full-length transcriptomes of 26 breast tumors and 4 normal breast samples.

  Study EGAS00001004819

• alopecia areata

  exome sequencing files from 25 alopecia areata samples from spain.

  Dataset EGAD00001006370

• RNAseq SCLC Cell lines MYC

  RNAseq data set (Mollaoglu et al., MYC drives progression of small cell lung cancer to a variant neuroendocrine subtype with vulnerability to Aurora kinase inhibition) RNA isolation from primary tumors and healthy lungs was performed using RNeasy Mini Kit (Qiagen) with the standard protocol. RNA was subjected to library construction with the Illumina TruSeq Stranded mRNA Sample Preparation Kit (cat# RS-122-2101, RS-122-2102) according to manufacturer’s protocol. Chemically denatured sequencing libraries (25 pM) are applied to an Illumina HiSeq v4 single read flow cell using an Illumina cBot. Hybridized molecules were clonally amplified and annealed to sequencing primers with reagents from an Illumina HiSeq SR Cluster Kit v4-cBot (GD-401-4001). Following transfer of the flowcell to an Illumina HiSeq 2500 instrument (HCSv2.2.38 and RTA v1.18.61), a 50 cycle single-read sequence run was performed using HiSeq SBS Kit v4 sequencing reagents (FC-401-4002).

  Dataset EGAD00001003099

• Prostate cancer datasets WGS

  Low pass WGS: 48 samples (5 blood samples from 6 patient data): 22 Tumour cores and 26 normal/benign cores (Next Seq )

  Dataset EGAD00001004466

• NHLBI TOPMed: Rare Variants for Hypertension in Taiwan Chinese (THRV)

  The THRV-TOPMed study consists of three cohorts: The SAPPHIRe Family cohort (N=1,271), TSGH (Tri-Service General Hospital, a hospital-based cohort, N=160), and TCVGH (Taichung Veterans General Hospital, another hospital-based cohort, N=922), all based in Taiwan. 1,271 subjects were previously recruited as part of the NHLBI-sponsored SAPPHIRe Network (which is part of the Family Blood Pressure Program, FBPP). The SAPPHIRe families were recruited to have two or more hypertensive sibs, some families also with one normotensive/hypotensive sib. The two Hospital-based cohorts (TSGH and TCVGH) both recruited unrelated subjects with different recruitment criteria (matched with SAPPHIRe subjects for age, sex, and BMI category).

  Study phs001387

• Exome-sequencing of human B cell lymphoma cell lines

  26 cell lines derived from human Diffuse Large B Cell lymphomas (DLBCL) or Burkit Lymphomas (BL) were subjected to whole exome sequencing. Exome capture was carried out using the SeqCap EZ Exome Library 2.0 kit (Roche/Nimblegen) and 100 bp single-read sequencing was performed on a HiSeq2500 (Illumina). 82% of the coding region was covered at least 30x.

  Study EGAS00001001463

• UK Biobank whole cohort directly genotyped and imputed data (~500,000 participants)

  Genotype data is available for all 500,000 participants in the UK Biobank cohort. Genotyping has been performed using the Affymetrix UK BiLEVE Axiom array on an initial 50,000 participants and the remaining 450,000 participants have been genotyped using the Affymetrix UK Biobank Axiom® array. The two arrays are extremely similar (with over 95% common content).Quality control and imputation (to over 90 million SNPs, indels and large structural variants) has been performed by a collaborative group headed by the Wellcome Trust Centre for Human Genetics.The following data are available:* A clean set of QC’ed genotype calls* Confidence values that a genotype call is correct* Intensity data to generate cluster plots* Extensive QC information regarding SNPs and samples including SNP metrics, batch effects, population structure and relatedness* Imputed dataFor further information, please refer to the UK Biobank website

  Study EGAS00001002399

• Beacon v2

  Beacon v2: a tool for data discovery Motivation In the era of data-driven health research and personalised medicine, human genomic data has become extremely valuable. These are also identifiable data, as they carry information pointing to a specific individual as well as their own family; and as such, they must be protected. This makes data discovery particularly challenging: this is where “Beacon” comes in. A “Beacon” is an API aiming to enable the search of genomic variants and associated information without jeopardising the privacy of the dataset. Here, we refer to its current version, namely version 2 (v2). Definition Beacon v2 is a term that can refer to different aspects. The EGA is playing a central role in the following aspects: The Beacon v2 protocol is a Global Alliance for Health and Genomics standard. The Beacon v2 Reference Implementation (B2RI) is an “out-of-the-box” Beacon instance developed with ELIXIR, which facilitates Beacon deployment. The EGA Beacon(s) are Beacons following the v2 standard and using the B2RI, deployed on top of data hosted at the EGA and allowing for their discovery. Resources Depending on whether you are visiting us a stakeholder (you need more general information about Beacon), a deployer /implementer (you want to have your own Beacon instance), or an EGA user (you want to query Beacon and start browsing data), you will be interested in the following resources: Your role Beacon aspect Documentation type Stakeholder Beacon v2 protocol Beacon website Beacon page on the GA4GH website Deployer/Implementer Beacon v2 protocol Read the docs: Beacon v2 standard technical description GitHub repository Beacon v2 standard Beacon v2 Reference Implementation Read the docs: B2RI technical description GitHub repository B2RI Guide to deploy Beacon using B2RI EGA user EGA Beacon(s) API in construction UI in construction

  Documentation about/projects-and-funders/beacon

• Transcriptomic profiling of patient-derived xenografts and organoids in prostate cancer

  Dataset contains RNA-seq of 30 samples (normal prostate tissue, human prostate cancer, PDX and organoids).

  Dataset EGAD00001006404

• The Genetic Landscape of Ocular Adnexa MALT Lymphoma Reveals Frequent Aberrations in NFAT and MEF2B Signaling Pathways

  A comprehensive constellation of somatic non-silent mutations and copy number (CN) variations in ocular adnexa marginal zone lymphoma (OAMZL) is unknown. By utilizing whole-exome sequencing in 69 tumors we define the genetic landscape of OAMZL. Mutations and CN changes in CABIN1 (30%), RHOA (26%), TBL1XR1 (22%), and CREBBP (17%) and inactivation of TNFAIP3 (26%) were among the most common aberrations. Candidate cancer driver genes cluster in the B-cell receptor (BCR), NFkB, NOTCH and NFAT signaling pathways. One of the most commonly altered genes is CABIN1, a calcineurin inhibitor acting as a negative regulator of the NFAT and MEF2B transcriptional activity. CABIN1 deletions enhance BCR-stimulated NFAT and MEF2B transcriptional activity, while CABIN1 mutations enhance only MEF2B transcriptional activity by impairing binding of mSin3a to CABIN1. Our data provide an unbiased identification of genetically altered genes that may play a role in the molecular pathogenesis of OAMZL and serve as therapeutic targets.

  Study EGAS00001006631

• Resuscitation Outcomes Consortium (ROC) Trauma Epidemiologic Registry (Trauma Epistry) (ROC-Trauma Epistry-BioLINCC)

  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.Available Data: This dataset only includes traumatic injury participants. For ROC cardiac arrest Epistry data please see: ROC-Cardiac Epistry 1 and 2 and ROC-Cardiac Epistry 3.Objectives: To build a prospective population-based registry of participants with out-of-hospital traumatic injury responded to by Emergency Medical Services (EMS). Specific aims included: to establish whether the results of Resuscitation Outcomes Consortium (ROC) trials can be generalized to the larger population of people that experience traumatic injury; to more fully establish the burden of traumatic injury; and to examine the relationships between variation in EMS structure and process, regional and periodic factors, and participant outcomes.Background: Traumatic injury is a major public health problem generating substantial morbidity, mortality, and economic burden on society. The majority of seriously injured persons are initially evaluated and cared for by prehospital providers, however the effect of EMS systems, EMS clinical care, and EMS interventions on trauma patient outcomes is largely unknown. EMS factors such as service level, number of responding providers, use of procedures or drugs in the field, training, quality assurance/feedback, and response time intervals also vary significantly by region.The Resuscitation Outcomes Consortium (ROC) was established in 2004 to conduct clinical research in the areas of cardiopulmonary arrest and life-threatening traumatic injury with the overall goal of improving resuscitation outcomes. Previous trauma registries have generally focused primarily on hospitalized patients with limited prehospital information. Registries may also exclude trauma cases at far ends of the spectrum, such as those who die in the field or in a non-trauma center and/or patients that are treated and released. These limitations do not allow for detailed, outcome-based assessments of EMS system factors necessary to define prehospital resuscitation best practices. Therefore there was a need for standardized data collection of out-of-hospital traumatic injuries matched to hospital-based outcomes in diverse geographic locations in order to identify the independent effects of prognostic or treatment factors accounting for variations in survival. Participants: The registry included 13,830 traumatic events from 264 EMS agencies transporting to 287 acute care hospitals from the following regional centers: Birmingham, Alabama; Dallas, Texas; Iowa City, Iowa; Milwaukee, Wisconsin; Pittsburgh, Pennsylvania; Portland, Oregon; San Diego, California; Seattle/King County, Washington; Ottawa, Ontario; Toronto, Ontario; and Vancouver, British Columbia.Design: ROC Epistry collected standardized data regarding episode-specific factors, participant demographics, clinical information, pre-hospital interventions and disposition, hospital information, and participant outcome for all out-of-hospital traumatic injuries in the ROC regions. Cases were identified through review of emergency response system records including dispatch centers, EMS ground agencies, and air medical services. Out-of-hospital data were extracted from existing databases whenever possible and augmented with targeted review of EMS reports. Hospital data were abstracted directly from the hospital file in most cases. Sites submitted data using a web-based interface or batch uploads (Newgard, et al., 2008, PMID: 18482792).

  Study phs003809

• Single-Cell TCR/BCR Sequencing for Korean COVID-19 Vaccinated and Patient Samples

  We conducted longitudinal single-cell TCR/BCR sequencing for COVID-19 vaccinated cases (297 samples from 112 cases) and COVID-19 infected cases classified as Asymptomatic (26 samples from 12 cases), Mild (82 samples from 37 cases), Moderate (123 samples from 50 cases), Severe (216 samples from 58 cases), and Critical (142 samples from 41 cases) according to the NIH guideline.

  Study phs003341

• The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia

  Acute lymphoblastic leukaemia (ALL) is the most common childhood malignancy, and implementation of risk-adapted therapy has been instrumental in the dramatic improvements in clinical outcomes. A key to risk-adapted therapies includes the identification of genomic features of individual tumors, including chromosome number (for hyper- and hypodiploidy) and gene fusions, notably ETV6-RUNX1, TCF3-PBX1 and BCR-ABL1 in B-cell ALL (B-ALL). RNA-sequencing (RNA-seq) of large ALL cohorts has expanded the number of recurrent gene fusions recognized as drivers in ALL, and identification of these new entities will contribute to refining ALL risk stratification. We used RNA-seq on 126 ALL patients from our clinical service to test the utility of including RNA-seq in standard of care diagnostic pipelines to detect gene rearrangements and IKZF1 deletions. RNA-seq identified 86% of rearrangements detected by standard of care diagnostics. KMT2A-rearrangements (MLL), whilst usually identified, were the most commonly missed by RNA-seq as a result of low expression. RNA-seq identified rearrangements that were not detected by standard of care testing in nine patients. These were found in patients who were not classifiable using standard molecular assessment. We developed an approach to detect the commonest IKZF1 deletion from RNA-seq data and validated this using a RQ-PCR assay. We applied an expression classifier to identify Ph-like B-ALL patients. T-ALL proved a rich source of novel gene fusions which have clinical implications or provide insights into disease biology. Our experience shows that RNA-seq can be implemented within an individual clinical service to enhance the current molecular diagnostic risk classification of ALL.

  Study EGAS00001004212

• Mechanisms of Therapy Driven Clonal Evolution in Oncogenic RAS Mutant Relapsed Acute Lymphoblastic Leukemia

  Although multi-agent combination chemotherapy is curative in a significant fraction of childhood acute lymphoblastic leukemia (ALL) patients, 20% of cases relapse and most die due to chemo-refractory disease. Here we used whole-exome and whole-genome sequencing to analyze the mutational landscape and pattern of clonal evolution at relapse in pediatric ALL cases. These analyses showed that ALL relapses originate from a common ancestral precursor clone of the diagnosis and relapsed populations and frequently harbor mutations implicated in chemotherapy resistance. RAS-MAPK pathway activating mutations in NRAS, KRAS and PTPN11 were present in 24/55 (44%) cases in our series. Notably, while some cases showed emergence of RAS mutant clones at relapse, in others, RAS mutant clones present at diagnosis were replaced by RAS wild type populations. Mechanistically, functional dissection of mouse and human wild type Kras and mutant Kras (Kras G12D) isogenic leukemia cells demonstrated induction of methotrexate resistance, but also improved response to vincristine, in mutant Kras- expressing lymphoblasts. These results identify chemotherapy driven selection as a central mechanism of leukemia clonal evolution and pave the road for the development of tailored personalized therapies for the treatment of relapsed ALL.

  Study phs001072

• Genetic Studies of Homologous Recombination Deficiency in Hispanic Gastric Cancer

  Approximately 10% of gastric cancer cases show familial clustering and a hereditary cause is likely, however, only 1-3% cases result from known hereditary syndromes – which are predominantly associated with CDH1 and CTNNA1 genes. Hispanics are > 2 times more likely to be diagnosed and to die from gastric cancer compared to non-Hispanic whites. Not only does gastric cancer precision medicine lag behind other cancers – there is an urgent need to understand its etiology. Germline mutations in homologous recombination genes have been implicated as important risk factors for gastric cancer. The purpose of this study is to continue to generate knowledge to improve gastric cancer outcomes and disparities by 1) characterizing prevalence of germline mutations in homologous recombination genes and identify new genes associated with gastric cancer 2) perform molecular characterization of gastric tumors in Hispanic patients. Participants for germline analysis were enrolled from Chile, Colombia, Mexico, Portugal, Spain, Uruguay.Whole exome sequence capture was performed for all germline and somatic samples using Agilent SureSelect Human All Exon v7 Kit. All sequencing was performed on an Illumina NovaSeq 6000. Germline Analysis: Whole exome sequencing was performed on 536 Hispanic patients (290 males, 246 female)

  Study phs003251

• Widespread DNA hypomethylation and differential gene expression in Turner syndrome

  Adults with 45,X monosomy (Turner syndrome) reflect a surviving minority since more than 99% of fetuses with 45,X monosomy die in utero. In adulthood 45,X monosomy is associated with increased morbidity and mortality, although strikingly heterogeneous with some individuals left untouched while others suffer from cardiovascular disease, autoimmune disease and infertility. The present study investigates the leukocyte DNAmethylation profile by using the 450K-Illumina Infinium assay and the leukocyte RNA-expression profile in 45,X monosomy compared with karyotypically normal female and male controls. We present results illustrating that genome wide X-chromosome RNA-expression profile, autosomal DNA-methylation profile, and the X-chromosome methylation profile clearly distinguish Turner syndrome from controls. Our results reveal genome wide hypomethylation with most differentially methylated positions showing a medium level of methylation. Contrary to previous studies, applying a single loci specific analysis at well-defined DNA loci, our results indicate that the hypomethylation extend to repetitive elements. We describe novel candidate genes that could be involved in comorbidity in TS and explain congenital urinary malformations (PRKX), premature ovarian failure (KDM6A), and aortic aneurysm formation (ZFYVE9 and TIMP1)

  Study EGAS00001002190

• WES dataset of a TIL-ACT metastatic melanoma cohort

  Whole-exome sequencing (WES) data from 13 patients with metastatic melanoma in a TIL-ACT cohort, generated to characterize tumor genomics and neoantigen landscapes relevant to adoptive T cell therapy.

  Dataset EGAD50000001731

• Single-cell RNA sequence analysis of iPS cell-derived cardiomyocytes treated with S-RBD-sfGFP or GFP

  To determine the transcriptional response in human cardiomyocytes treated with SARS-CoV-2 spike receptor-binding domain (S-RBD-sfGFP), human cardiomyocytes differentiated from induced pluripotent stem cells (iPSC-CMs) were replated on day 21 after differentiation and treated with S-RBD-sfGFP or control-GFP for 48 hours on day 26-28. iPSC-CMs were dissociated and the single cell suspension was analyzed by single cell RNA-sequence.

  Study JGAS000620

• RNA-Sequencing and Somatic Mutation Status of Adrenocortical Tumors: Novel Pathogenetic Insights

  To investigate the relationship between transcriptome profile and genetic background in a large series of adrenocortical tumors and to identify new potential pathogenetic mechanisms, we performed transcriptome profiling by high-throughput sequencing. We collected snap-frozen tissue from patients with adrenocortical tumors (n = 59) with known genetic background: 26 adenomas with Cushing syndrome, 17 adenomas with mild autonomous cortisol secretion, 9 endocrine-inactive adenomas, 7 adrenocortical carcinomas. Additionally, we sequenced 4 control samples.

  Study EGAS00001004533

• High-depth whole genome sequencing of paired ductal carcinoma in-situ (DCIS) and germline control samples from 26 individuals.

  Deposited here are whole-genome sequencing data for 26 paired breast cancer DCIS and matched-normal samples taken from the same individual. Average sequence coverage is 118x for DCIS samples and 41x for matched-normals. Matched-normal samples are from blood. Sequencing was performed on an Illumina HiseqX. Due to specific restrictions imposed by the ethical approval at sample collection, the use of the germline data is restricted to filtering of somatic mutation calls only and cannot be used outside this purpose.They are a part of a broader project that uses high-depth WGS to investigate the somatic mutation genomic landscape of DCIS in order to uncover biological insights into breast cancer progression as well as possible methods to stratify DCIS patients for individualised therapy or disease monitoring.

  Dataset EGAD50000002237

• Breast Cancer Single-Cell RNA-Seq Dataset

  Single-cell RNA-Sequencing of 26 primary breast cancers from Wu et al. (2021) study. Data was generated using the Chromium controller (10X Genomics) and sequenced on the NextSeq 500 platform.

  Dataset EGAD00001007495

• Altered chromosomal topology drives oncogenic programs in gastrointestinal stromal tumors

  Metabolic lesions with pleiotropic effects on epigenetic regulation and other cellular processes are widely implicated in cancer, yet their oncogenic mechanisms remain poorly understood. Succinate dehydrogenase (SDH) deficiency causes a subset of gastrointestinal stromal tumors (GISTs) with DNA hyper-methylation. Here we associate this hyper-methylation with changes in chromosome topology that activate oncogenic programs. To investigate epigenetic alterations in this disease, we systematically mapped DNA methylation, CTCF insulators, enhancers and chromosome topology in KIT-mutant, PDGFRA-mutant and SDH-deficient GISTs. Although these respective subtypes share similar enhancer landscapes, we identified hundreds of putative insulators where DNA methylation replaced CTCF binding in SDH-deficient GISTs. We focused on disrupted insulators that partitions super-enhancers from FGF3, FGF4 and the KIT oncogene. Recurrent loss of this insulator alters locus topology in SDH-deficient GISTs, allowing aberrant physical interaction between enhancers and oncogenes. CRISPR-mediated excision of the corresponding CTCF motif in an SDH-intact model disrupted the boundary and up-regulated FGFs and KIT expression. Our findings reveal how a metabolic lesion destabilizes chromatin structure to facilitate the initiation and selection of epigenetic alterations that drive oncogenic programs in the absence of canonical mutations.

  Study phs001906

• WGS datasets of pediatric glioblastoma

  WGS with linked reads of pediatric glioblastoma. For each patient, blood and tumor tissue were sequenced. For two patients, we also provide sequencing data for the blood of their parents.

  Dataset EGAD00001005212

• DIPG WES and RNA-Seq

  We characterised H3K27M-mutant diffuse intrinsic pontine glioma (DIPG, n=21) and RNA-Seq (n=26 DIPG, 12 normal brain)

  Dataset EGAD00001006450

• WES of melanoma tumours prior to combined immune checkpoint blockade treatment.

  Paired melanoma tumor and normal (PBMC) WES data from a cohort of 26 patients subsequently treated with combined immune checkpoint blockade.

  Dataset EGAD00001005941

• Cardiovascular disease biomarkers derived from circulating cell-free DNA methylation

  Acute coronary syndrome (ACS) remains a major cause of worldwide mortality. The syndrome occurs when blood flow to the heart muscle is decreased or blocked, causing muscle tissues to die or malfunction. There are three main types of ACS: Non-ST-elevation myocardial infarction, ST-elevation myocardial infarction, and unstable angina. The treatment depends on the type of ACS, and this is decided by a combination of clinical findings, such as electrocardiogram and plasma biomarkers. Circulating cell-free DNA (ccfDNA) is proposed as an additional marker for ACS since the damaged tissues can release DNA to the bloodstream. We used ccfDNA methylation profiles for differentiating between the ACS types and provided computational tools to repeat similar analysis for other diseases. We leveraged cell type specificity of DNA methylation to deconvolute the ccfDNA cell types of origin and to find methylation-based biomarkers that stratify patients. We identified hundreds of methylation markers associated with ACS types and validated them in an independent cohort. Many such markers were associated with genes involved in cardiovascular conditions and inflammation. ccfDNA methylation showed promise as a non-invasive diagnostic for acute coronary events. These methods are not limited to acute events, and may be used for chronic cardiovascular diseases as well.

  Study EGAS00001007263

• Whole-genome bisulfite sequencing

  Raw reads from whole-genome bisulfite sequencing. Whole-genome bisulfite sequencing library preparations and Illumina sequencing of DNA samples from TET2 mutation carriers (Ly9, Ly11, Ly14, Id1) and their age-matched controls (Ly8, Ly10, Ly13, Id2, Id3) was done as a service at BGI (BGI Tech Solutions Co., Ltd., China). Bisulfite treatment was done with EZ DNA Methylation-Gold Kit (Zymo Research, CA, USA) for 300-400bp size-range fragments with methylated adapters in 5' and 3' ends. Sequencing was done with the HiSeq X-Ten platform using paired-end 150 base-pair read length.

  Dataset EGAD00001004779

• Duplex sequencing of 26 genes

  Buccal samples and paired esophageal epithelium were obtained using the three sizes of swabs and endoscopic biopsy, respectively. Forty samples from 10 subjects were analyzed via duplex sequencing. This dataset contains bam files that were mapped to the GRCh37 reference genome.

  Dataset EGAD50000000998

• RNA-seq samples

  Raw FASTQ files obtained by RNA sequencing of tumor samples from patients (age 12-29) with newly diagnosed, recurrent intermediate or high-grade sarcoma.

  Dataset EGAD00001008393

• Somatic mutations in twin breast cancers (2019-04-03)

  The aim of this study is to investigate the somatic mutations in twins with BRCA1/2 negative breast cancer with no strong family history. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004890

• ICGC PCAWG Dataset: ORCA-IN_PCAWG_WGS_BWA

  ICGC PCAWG Dataset for WGS BAM aligned using BWA MEM. Project: ORCA-IN.

  Dataset EGAD00001002120

• The Federated EGA network

  In the last 10 years, most human omics data has been generated in the context of research consortia while recently there has been an emergence of large cohorts of human data generated by healthcare initiatives. Many countries in Europe now have nascent personalised medicine programmes. Thus, human genomics is shifting from being predominantly research-driven to being funded through healthcare systems. Genetic data generated in a healthcare context is subject to more stringent information governance than research data and nonetheless all data must adhere to national data protections laws. In this context, EGA identified the need for the development of a federated network to enable secure sharing of data whilst enabling genetic data to remain within the jurisdiction in which it was generated. The Federated EGA is designed to support national data management requirements for genomic and clinical data collected from citizens as part of healthcare or biomedical research projects. It includes a secure authorised access mechanism to support research use of these data across Europe and worldwide. We have engaged with over 14 ELIXIR countries to develop the federation model. The EGA federated configuration is composed of Central EGA, Federated EGA nodes and Community EGA nodes: Central EGA offers international submissions and helpdesk support, currently EGA co-managed by EMBL-EBI and CRG, Federated EGA nodes offer EGA services to researchers within their national jurisdiction, Community EGA nodes are individual institutions or initiatives with human genetic data intended to be shared with the research community. Key services available in the Federated EGA structure are: Central EGA Federated EGA node Community EGA nodes Data submission Offers international submissions service Offers submission service in a particular jurisdiction Does not offer an external submissions service Helpdesk support Provides external international helpdesk Provides helpdesk support for submitters in its jurisdiction and for approved users of data managed at its facilities Provides helpdesk support only for approved users of data managed at its facilities Data distribution Manages worldwide distribution for data hosted at Central EGA Manages worldwide distribution for data hosted at Federated EGA node Distribution for data hosted at Community EGA node EMBL-EBI and CRG have prepared a series of documents describing the overall governance and coordination framework. These describe the Federated EGA structure, the rights and responsibilities of the different parties and governing committees, and node operation guidelines. The federation governance proposal is under review by the first countries invited to join as Federated nodes, with further interested countries likely to join in the coming years. The Central EGA team has been working closely with many ELIXIR partners, including ELIXIR Finland, Luxembourg, Germany, Norway, Spain and Sweden.

  Blog the-federated-ega-network

• Childhood Cancer Data Initiative (CCDI): OncoKids - NGS Panel for Pediatric Malignancies

  This study aimed to systematically collect clinical, registry and genomic data on pediatric cancer patients, and to contribute to the CCDI Pediatric Data Ecosystem. Clinical, treatment and outcome data on 1,039 pediatric cancer patients whose molecular profile was characterized on OncoKids gene panel at Children's Hospital Los Angeles was abstracted and harmonized for submission to the NCI’s Cancer Data Service. The OncoKids panel was designed to detect DNA mutations and amplification in almost 200 oncogenes and tumor suppressor genes, a limited number of pharmacogenomic targets and 1,700 disease-associated gene fusions at the RNA level. The panel encompasses the vast majority of FDA’s relevant pediatric molecular target list, as well as additional genes associated with ultra-rare pediatric tumors. The final data elements included DNA/RNA sequence data, OncoKids test results, clinical data on demographics, diagnosis, comorbidity and adverse events and treatment data.

  Study phs002518

• Small‑scale mutations are infrequent as mechanisms of resistance in post‑PARP inhibitor tumour samples in high grade serous ovarian cancer

  Targeted DNA sequencing of high-grade serous ovarian cancer (HGSC) tumour and normal samples from 26 patients. Following target hybrid capture of 63 genes involved in DNA repair and response to treatment with an Agilent SureSelect XT panel, sequencing libraries were generated using the SureSelect XT Low Input Target Enrichment System (Agilent) as per the manufacturer's protocol. Libraries were sequenced on an Illumina NextSeq 500 at the Peter MacCallum Cancer Centre (Melbourne, Australia).

  Study EGAS50000000146

• L1 Retrotransposon sequencing in Cocaine Use Disorder - Study 1

  Cocaine Use Disorder (CUD) is a global public health problem, with no effective pharmacotherapies. While CUD is marked by high heritability, discovery of variants associated with risk for CUD has proven difficult. We used Restriction Enzyme Based Enriched L1 sequencing (REBELseq) to amplify L1 retrotransposons in gDNA isolated from medial prefrontal cortex NeuN+ neuronal nuclei from 25 CUD cases and 26 controls. Amplified libraries were sequenced on the Illumina HiSeq 4000 platform, and sequencing data was analyzed with the REBELseq bioinformatics pipeline.

  Study phs001966

• Chromatin accessibility in human monocyte differentiation

  This dataset consists of ATAC-seq data from human monocytes, monocyte-derived dendritic cells or monocyte-derived macrophages as well as monocyte-derived cells that were subjected to siRNA treatment targeting TET2, IRF4 and EGR2. In total, it includes 26 samples.

  Dataset EGAD00001006601

• Data files for PCGP SJACT RNASEQ

  This dataset contains RNA-Seq files for the SJACT cohort associated with the paper "Genetic landscape of pediatric Adrenocortical Tumor". In this paper, we analyse 37 adrenocortical tumours (ACTs) by whole-genome, whole-exome and/or transcriptome sequencing.

  Dataset EGAD00001002680

• About

  About What's the EGA? The European Genome-phenome Archive (EGA) is a global network for permanent archiving and sharing of personally identifiable genetic, phenotypic, and clinical data generated for the purposes of biomedical research projects or in the context of research-focused healthcare systems. Jointly managed by the European Bioinformatics Institute (EMBL-EBI) in Cambridge (UK) and the Centre for Genomic Regulation (CRG) in Barcelona, we aim to advance biomedical research and promote personalised medicine worldwide by enabling discovery of and access to human genomic and health research data. The EGA contains data collected from individuals whose consent agreements authorise data release for specific research use to bona fide researchers. We ensure strict security measures to control access to the data and maintain patient confidentiality. With expertise in data management and technical infrastructure, we promote FAIR data reuse and enable researchers to share their data securely. By leveraging public funding and our strategic partnerships, the EGA provides a free service for permanent data storage, data discovery, and secure data access. In addition, we foster a federated network to provide transnational access to human research data in compliance with legal frameworks. For additional information about the EGA, please contact: Helen Parkinson and Mallory Freeberg EMBL European Bioinformatics Institute Arcadi Navarro, Roderic Guigó, and Jordi Rambla Center for Genomic Regulation History The European Genome-phenome Archive was launched in 2008 at the European Bioinformatics Institute (EMBL-EBI), an outstation of the European Molecular Biology Laboratory (EMBL), to address an identified need for archiving and sharing the results of genome-wide association studies from the Wellcome Trust Case Control Consortium. With the signing of a memorandum of understanding in 2013 and a formal agreement in 2016, the EGA became a joint project of EMBL-EBI and the Centre for Genomic Regulation (CRG). The two institutes work together to support the EGA services, including supporting submissions, website, strategic leadership, and data infrastructure developments. In 2022, the Federated EGA was officially launched with the signature of the first five countries: Finland, Germany, Norway, Spain, and Sweden. With more than 20 additional nodes worldwide preparing to join, the Federated EGA aims to become the largest human omics data sharing initiative towards understanding human health and disease. EGA overview If you're a researcher you may need to deposit, manage, or access genomic data in a secure and regulated way. The European Genome-phenome Archive (EGA) is a platform that facilitates these processes, ensuring that sensitive data is stored and shared in accordance with legal and ethical regulations. Submission process To start a submission, you need to become an EGA submitter. For that, you’ll need to sign a Data Processing Agreement (DPA) with us, that defines the terms and conditions under which your data will be processed and shared within the EGA system. The access to each study is controlled by its Data Access Committee (DAC). The DAC is responsible for managing data access requests and ensuring that the release of data is in accordance with the General Data Protection Regulation (GDPR). Please, note that once your data is released, all public metadata related to your study and dataset(s) will be searchable on the EGA website. However, the files are only accessible under controlled access, which means that a DAC has to agree to a previous data access request. Request process It is possible to submit an access request to data stored at the EGA. The DAC assigned to the study will assess the request and, if approved, grant access to the data. Requesters must provide sufficient justification for your request and comply with the intended data usage in order to get access to it. Each dataset is covered by a Data Access Agreement (DAA) that defines the terms and conditions of use for the specified dataset/s. The DAA is created and provided by the DAC, and must be signed by the individual requesting access to the given dataset/s. Download process Once the request for access is approved, the data and metadata can be downloaded. The EGA offers various download options to fit the needs: it is possible to preview files without downloading them, download specific files of interest, or even download terabytes of data. Overall, the EGA provides a secure and regulated environment for depositing, managing, and accessing human data. Regardless of the role - submitter, DAC member, or requester - the EGA provides assistance for each while ensuring that sensitive data is managed in an ethical and responsible manner. More information on how EGA handles data is available in the EGA dataflow.

  Documentation about/ega

• All you need to know about the new Submitter Portal

  In September we launched new services for all EGA users, including a new version of the former Submitter Portal. Our main objective with this Portal is to transform the metadata submission in a simplified and user-friendly method. Although it is possible to find documentation about the Submitter Portal on our website, we wanted to highlight the new features in this article. Enjoy! Credentials: who needs to create a new user to access the Submitter Portal and who doesn't? Users with an old submission account (ega-box) can already access the new Submitter Portal. If it is the first time that you want to access, it is time to request a submitter role linked to the EGA User. In the form, it is mandatory to explain which type of data you want to deposit, the size of your submission, add any comment you’d like us to read, and manage the EGA Data Processing Agreement (DPA) document. This document should be checked by the legal department of your institution and signed by a person with authority to sign contracts on behalf of the institution. Note that we provide a DPA template for new Users; it can be found and downloaded in the Submitter Request section. With the DPA signed and the Submitter Request sent, it is time to wait for our Helpdesk team to validate the request. Metadata submission now can be collaborative It is possible to add collaborator(s) to your submissions. Only registered EGA Users will appear on the search bar. When adding a collaborator, it is mandatory to define the permission granted: Read only: collaborators with this permission can check if the information of the submission is correct but cannot modify anything. Read & write: collaborators with this label can register new objects and modify the registered ones. We wanted to save time for submitters that usually rely on the same collaborators. For that, we have implemented the possibility of adding the same collaborators from previous submissions; all you need to do is to search the submission, and the exact set of EGA users will have access to your submission. You can check the collaborators and their permissions at any point of the submission by going to the collaborators section. Even adding more people to complete the submission is allowed by following the same steps: looking for the EGA User and defining the permission granted (please, note that during the submission it is not possible to add collaborators from previous submissions). Use external links to complement the study information When filling the information about the study we recommend users to enrich the submission with complementary fields such as PubMeds IDs, custom tags and external links. External links are used to connect a study to an external repository. We have implemented an external link to Euro-Bioimaging for a European Project (EuCanImage). Now, we are looking forward to keeping increasing the list of external repositories! Choose the expected release date for your metadata to be searchable on our website Once all the metadata objects are registered it is time to finalise the submission so that the Helpdesk team can approve it. In the new version of the Submitter Portal users can choose the expected release date for the metadata to be searchable on the EGA website. Please, note that the selected date will have a minimum of one week margin from the day of finalisation. This time is necessary for your files to be archived and for the submission to be validated by the Helpdesk Team. Are you looking for the Array submission within the Submitter Portal? Our Submitter Portal does not support array submissions. The submission of Array-based metadata must be done using the EGA programmatic submission. You will find our Array-based format template and all the necessary information on our website, and you can find all the steps in our submission quickguide! More information about the submission process at the EGA Our Team has developed an extended documentation related to metadata submission. A good first step is to take a closer look at the Submission FAQ. To have a general overview of all the metadata submission process we recommend taking a glimpse of our take-the-tour or watching the tutorial available on our YouTube Channel. Don't miss our section on EGA Schemas, to learn more about how the EGA is built. It can be useful to fully ensure that your submissions comply with the EGA's standards and contribute to a valuable and accessible genomic resource.

  Blog new-submitter-portal

• RNA sequencing from High-grade B-cell lymphoma, not otherwise specified: an LLMPP study

  RNA-seq for 26 newly added samples in High-grade B-cell lymphoma, not otherwise specified: an LLMPP study, and 32 samples from a previously uploaded dataset.

  Dataset EGAD50000001365

• Neuroblastoma_MP-PCR_MultiplexPCR_TargetedSequencing_BAMs

  This dataset contains raw sequencing data (BAM/BAI files) generated from human neuroblastoma patient samples. The data were produced using targeted high-throughput next-generation sequencing and are intended to support genomic analyses of tumor-associated alterations. All files are provided under controlled access in accordance with EGA data protection requirements.

  Dataset EGAD50000002260

• Mutation analysis of 17 genes in plasma DNA of CRC patients using the AVENIO ctDNA Targeted Kit

  Mutation analysis of 17 genes (ALK, APC, BRAF, BRCA1, BRCA2, DPYD, EGFR, ERBB2, KIT, KRAS, MET, NRAS, PDGFRA, RET, ROS1, TP53, UGT1A1) in plasma DNA of CRC patients using the AVENIO ctDNA Targeted Kit.

  Dataset EGAD00001006103