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• National Heart Lung and Blood Institute (NHLBI) GO-ESP: Heart Cohorts Component of the Exome Sequencing Project (JHS)

  The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. HeartGO is a consortium of six well-phenotyped NHLBI cohorts: Atherosclerosis Risk in Communities (ARIC) study, the Coronary Artery Risk Development in Young Adults (CARDIA) study, the Cardiovascular Health Study, the Framingham Heart Study, the Jackson Heart Study, and the Multi-Ethnic Study of Atherosclerosis. Together, these cohorts have provided DNA and phenotype datasets from a diverse cohort of individuals of African-American, Caucasian, Asian, and Hispanic ancestry to be made available for use by qualified investigators in dbGaP. HeartGO investigators will conduct genotype-phenotype analyses for phenotypes related not only to heart disease but with other variables that will be contributed to dbGaP. The HeartGO dataset provides investigators with genotype-phenotype analytic opportunities for traits not only related to heart disease but also associated with ancillary variables that will be contributed to dbGaP, including disease endpoints, risk factors, biomarkers, and subclinical disease measures. The phenotypes planned for investigation as part of the GO-ESP HeartGO project include early-onset myocardial infarction (EOMI), low density lipoprotein (LDL) cholesterol, body mass index/type 2 diabetes (BMI/T2D), blood pressure and ischemic stroke. Results of the proposed analyses as well as relevant replication/follow-up analyses will be reported in peer-reviewed journals. This study phs000402 contains the Jackson Heart Study (JHS) subset of GO-ESP/Heart-GO. Additional GO-ESP data is also available via dbGaP.

  Study phs000402

• National Heart Lung and Blood Institute (NHLBI) GO-ESP: Heart Cohorts Component of the Exome Sequencing Project (CHS)

  The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. HeartGO is a consortium of six well-phenotyped NHLBI cohorts: Atherosclerosis Risk in Communities (ARIC) study, the Coronary Artery Risk Development in Young Adults (CARDIA) study, the Cardiovascular Health Study, the Framingham Heart Study, the Jackson Heart Study, and the Multi-Ethnic Study of Atherosclerosis. Together, these cohorts have provided DNA and phenotype datasets from a diverse cohort of individuals of African-American, Caucasian, Asian, and Hispanic ancestry to be made available for use by qualified investigators in dbGaP. HeartGO investigators will conduct genotype-phenotype analyses for phenotypes related not only to heart disease but with other variables that will be contributed to dbGaP. The HeartGO dataset provides investigators with genotype-phenotype analytic opportunities for traits not only related to heart disease but also associated with ancillary variables that will be contributed to dbGaP, including disease endpoints, risk factors, biomarkers, and subclinical disease measures. The phenotypes planned for investigation as part of the GO-ESP HeartGO project include early-onset myocardial infarction (EOMI), low density lipoprotein (LDL) cholesterol, body mass index/type 2 diabetes (BMI/T2D), blood pressure and ischemic stroke. Results of the proposed analyses as well as relevant replication/follow-up analyses will be reported in peer-reviewed journals. This study phs000400 contains the Cardiovascular Health Study (CHS) subset of GO-ESP/Heart-GO. Additional GO-ESP data is also available via dbGaP.

  Study phs000400

• Genetic Epidemiology of COPD (COPDGene) Funded by the National Heart, Lung, and Blood Institute

  Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States and the only leading cause of death that is steadily increasing in frequency. This project will establish a racially diverse cohort that is sufficiently large and appropriately designed for genome-wide association analysis of COPD. A total of 10,000 subjects will be recruited, including control smokers, definite COPD cases (GOLD Stage 2 to 4), and subjects not included in either group (GOLD 1 or GOLD-Unclassified). This cohort will be used for cross-sectional analysis, although long-term longitudinal follow-up will be a future goal. The primary focus of the study will be genome-wide association analysis to identify the genetic risk factors that determine susceptibility for COPD and COPD-related phenotypes. Detailed phenotyping of both cases and controls, including chest CT scan assessment of emphysema and airway disease, will allow identification of genetic determinants for the heterogeneous components of the COPD syndrome. The hypotheses to be studied are: 1) Precise phenotypic characterization of COPD subjects using computed tomography, as well as clinical and physiological measures, will provide data that will enable the broad COPD syndrome to be decomposed into clinically significant subtypes. 2) Genome-wide association studies will identify genetic determinants for COPD susceptibility that will provide insight into clinically relevant COPD subtypes. 3) Distinct genetic determinants influence the development of emphysema and airway disease. The initial phase of genome-wide association analysis included 500 COPD cases and 500 control subjects (all non-Hispanic White) genotyped with the Illumina Omni-1 chip. The second phase genotyped the entire study cohort using the Illumina Omni-Express chip. Unique aspects of the study include: 1) Inclusion of large numbers of African American subjects (approximately 1/3 of the cohort); 2) Obtaining chest CT scans (including inspiratory and expiratory images); and 3) Inclusion of the full range of disease severity. The COPDGene_v6 Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs000179 COPDGene_v6 Cohort. phs000296 ESP LungGO COPDGene phs000765 COPDGene_Geno

  Study phs000179

• Changes in Oral and Gut Microbiota and Incidence and Severity of Patient-Reported Symptoms in Pre- and Post-Kidney Transplant Patients

  This novel prospective cohort explores the relationships between oral and fecal microbiome features (alpha and beta diversity, differential relative abundances of bacterial taxa, and functional genes) and the incidence and severity of psychoneurological symptoms (e.g., pain, fatigue, depression, anxiety, and sleep disturbance) before and after patients receive a kidney transplant from a live donor. Thirty-five subjects provided fecal and saliva specimens for shotgun metagenomic sequencing of gut and oral microbiomes at 3 timepoints: 1) within 4 weeks prior to the transplant surgery (at end-stage kidney disease), 2) 1-3 weeks after the transplant surgery, and 3) at 3 months after the transplant (after restoration of kidney function). Subjects were enrolled between October, 2018, to November, 2021, and all study visits were completed by March, 2022. Data collected includes: Demographic (age, biological sex at birth, race/ethnicity, marital status); clinical data (body mass index, dialysis status, type of dialysis, high vs. low risk immune-suppression protocol, antibiotics during study period); laboratory data (serum creatinine; estimated glomerular filtration rate; serum blood urea nitrogen; immunosuppression trough levels), and outcome data (symptoms such as pain interference, pain intensity, fatigue, anxiety, depression, sleep disturbance, acute rejection, delayed graft failure, graft loss, post-transplant infections, and health-related quality of life). Incidence and severity of symptoms, including pain interference, pain intensity, fatigue, sleep dysfunction, and anxiety/depression-like symptoms, were measured using the Patient Reported Outcome Measurement Systems (PROMIS) 57 v2.1. Health related quality of life was assessed using the Kidney Disease Quality of Life Short Form (KDQOL-SF v1.3). The objective of this longitudinal study is to characterize changes in microbial features at baseline, before restoration of kidney function, and over time, after restoration of kidney function via kidney transplantation, and to identify relationships between microbiome features and transplant outcomes (rejection, infection, delayed graft function, graft failure), symptom burden, and health-related quality of life. Shotgun metagenomic sequencing of the oral and gut microbiomes will allow a higher-resolution examination of the gut microbiome compared to 16S rRNA gene amplicon sequencing, and will provide direct evidence of metabolic capabilities of the microbiota. This approach will allow us to explore the dynamic nature of symptom burden, and the effects of demographic and clinical variables on the oral and gut microbiomes before and after restoration of kidney function.

  Study phs002199

• Acute Respiratory Distress Network (ARDSNet) Studies 06 and 08 Prospective, Randomized, Multicenter Trial of Aerosolized Albuterol Versus Placebo for the Treatment of Acute Lung Injury (ALTA) (ARDSNet-ALTA-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. Biospecimens Access to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from ARDSNet-ALTA include Plasma, DNA, Urine, and BAL. Please note that use of biospecimens in genetic research is subject to a tiered consent. Objectives This clinical trial was designed to test the hypothesis that an aerosolized beta-2-agonist, albuterol, would improve clinical outcomes in participants with acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). Background In participants with acute lung injury and acute respiratory distress syndrome (ALI/ARDS), inflammation of the pulmonary circulation increases vascular permeability. Fluid leaks from blood vessels into the pulmonary interstitium and alveoli. Recovery from this form of acute respiratory failure requires that the pulmonary edema resolve. The resolution of alveolar edema is driven by active transport of sodium and chloride ions from the luminal space across both type I and type II alveolar epithelial cells, creating an osmotic gradient for the reabsorption of water. In the ex vivo human lung, the rate of alveolar fluid clearance can be doubled by treatment with a cyclic AMP beta-2 adrenergic receptor agonist. On the basis of the preclinical observations that treatment with beta-2-agonists could reduce pulmonary edema and accelerate the rate of alveolar fluid clearance, the ALTA study was designed and undertaken with the hypothesis that treatment of participants with ALI/ARDS with beta-agonist therapy would accelerate the resolution of alveolar edema and improve clinical outcomes. Participants There were 282 participants. Design Study staff conducted a multicenter, randomized, placebo-controlled clinical trial in which participants were randomized to receive aerosolized albuterol (5 mg) or saline placebo every 4 hours for up to 10 days. The primary outcome variable for the trial was ventilator-free days. Conclusions Ventilator-free days were not significantly different between the albuterol and placebo groups. The results suggest that aerosolized albuterol does not improve clinical outcomes in participants with ALI. Routine use of beta-2 agonist therapy in mechanically ventilated participants with ALI cannot be recommended (Matthay, et al., 2011, PMID: 21562125).

  Study phs003743

• dbGaP Collection: Compilation of Individual-Level Genomic Data for General Research Use

  This dbGaP Collection contains all authorized individual-level genomic datasets currently in dbGaP that are approved for General Research Use (GRU) and have no further limitations beyond those outlined in the model Data Use Certification Agreement. Access to this study will include any additional authorized individual-level GRU datasets that become available. Renewal of this study is required annually. Data Included in this Study Individual-level genomic data designated for general research use with no further use limitations or restrictions, that is, Data use does not require approval by an Institutional Review Board for secondary analyses Data have no publication embargo Data have no other use limitations (e.g., requirement for collaboration or publication; restricted for use by academic or not-for-profit organizations; restricted to health, medical, and/or biomedical research) In response to requests from the scientific community, the NIH implemented a change in the procedures for accessing individual-level GRU genomic data. Under the modified procedures, interested investigators can request GRU individual-level genomic datasets with a single application. However, please note that these data have not been harmonized. Additionally, to help expedite the processing of requests for individual-level GRU genomic datasets, the requests will be reviewed by a single, central Data Access Committee. The process for requesting these datasets is identical to the request process for non-GRU individual-level, controlled-access data, such as the expectation that investigators will abide by the NIH Policy for Sharing of Data Obtained in NIH Supported or Conducted Genome-Wide Association Studies and Data Use Certification Agreement. Effective April 24, 2018, the Data Use Limitations (DUL) for the Genes and Blood Clotting Study (GABC) (phs000304) was updated to Health/Medical/Biomedical, Not-for-profit Use Only (HMB-NPU). Additionally, the submitting institutions have requested the removal of the National Heart, Lung, and Blood Institute (NHLBI) "Grand Opportunity" Exome Sequencing Project (GO-ESP) Lung Cohorts Exome Sequencing Project: Genetic modifiers of Pseudomonas aeruginosa (Pa) lung infection acquisition in cystic fibrosis (phs000254) from the dbGaP Compilation of Individual-Level Genomic Data for General Research Use (dbGaP Compilation) (phs000688). As such, data from studies phs000304 and phs000254 are no longer available as part of the dbGaP Compilation (phs000688). Investigators must submit separate Data Access Requests (DARs) for the two studies and be approved by the NHLBI Data Access Committee.

  Study phs000688

• Study of Clinical Efficacy of Antimicrobial Therapy Strategy Using Pragmatic Design in Idiopathic Pulmonary Fibrosis (CleanUP IPF-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. Objectives To investigate whether the addition of antimicrobial treatments improves outcomes compared to usual care alone among patients with idiopathic pulmonary fibrosis. Background Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease. Lung dysbiosis, characterized by increased bacterial load and/or loss of diversity, has been reported in patients with IPF and may contribute to hospitalization and death. Clinical trials investigating other chronic disorders suggest that antimicrobial therapy favorably alters the lung microbial community. The CleanUP IPF study was initiated to determine if an antimicrobial treatment reduces respiratory hospitalization or death among patients with IPF. Participants A total of 513 participants were enrolled. 254 participants were randomized to receive antimicrobials, of those 128 were randomized to receive co-trimoxazole and 126 were randomized to receive doxycycline. 259 participants were randomized to receive usual care alone. Design CleanUP IPF was a randomized, unblinded, multicenter study. Patients were randomized to receive antimicrobials in addition to usual care or usual care alone. Antimicrobials included co-trimoxazole (trimethoprim 160 mg/sulfamethoxazole 800 mg twice daily plus folic acid 5 mg daily) or doxycycline (100 mg once daily if body weight Data collected included diffusion capacity of lungs for carbon monoxide (DLCO), forced vital capacity (FVC), and occurrence of severe adverse events. Several questionnaires were administered to assess quality of life, including the impacts of shortness of breath, fatigue, and chronic cough on participants. The primary end point was time to first nonelective respiratory hospitalization or all-cause mortality. The study was terminated early due to futility. Conclusions Among adults with idiopathic pulmonary fibrosis, the addition of co-trimoxazole or doxycycline to usual care, compared with usual care alone, did not significantly improve time to nonelective respiratory hospitalization or death. Martinez FJ, Yow E, Flaherty KR, et al. Effect of Antimicrobial Therapy on Respiratory Hospitalization or Death in Adults With Idiopathic Pulmonary Fibrosis: The CleanUP-IPF Randomized Clinical Trial. JAMA. 2021;325(18):1841-1851. doi:10.1001/jama.2021.4956

  Study phs004314

• Clinical outcomes in ctDNA-positive urothelial carcinoma patients treated with adjuvant immunotherapy

  Minimally invasive approaches to detect residual disease after surgery are urgently needed to select patients at highest risk for metastatic relapse for additional therapies. Circulating tumour DNA (ctDNA) holds promise as a biomarker for molecular residual disease (MRD) and relapse,1-3 but its clinical value has yet to be demonstrated in a randomised clinical trial. We evaluated outcomes in post-surgical ctDNA-positive (+) patients in a randomised phase III trial of adjuvant atezolizumab versus observation. IMvigor010 enrolled 809 patients with muscle-invasive urothelial carcinoma and did not meet its primary endpoint of disease-free survival (DFS) in the intent-to-treat population. Within the study, an exploratory planned analysis of prospectively collected plasma was performed, which tested the utility of ctDNA to identify patients who may benefit from adjuvant atezolizumab treatment. ctDNA was measured at the start of therapy (cycle 1 day 1; C1D1) and at week 6 (cycle 3 day 1; C3D1), and 581 patients were evaluable for ctDNA. The prevalence of ctDNA positivity at C1D1 was 37% (n=214), and ctDNA positivity identified patients with poor prognosis (observation arm DFS HR= 6.19 (4.29, 8.91), p<0.0001). Here we show that ctDNA(+) patients had improved DFS and overall survival (OS) with atezolizumab versus observation (DFS HR= 0.56 (0.41-0.77); p=0.0003 and OS HR= 0.58 (0.4-0.86); p=0.0063). No difference in DFS or OS between arms was noted for ctDNA-negative patients. The rate of ctDNA clearance was higher with atezolizumab (18%) versus observation (4%) (p=0.0041). Transcriptomic analysis revealed that tumours from ctDNA(+) patients had higher expression of cell cycle and keratin genes. Within the ctDNA(+) patient population in the atezolizumab arm, non-relapsing patients were further enriched in prominent immune response signatures including PD-L1, IFNG, CXCL9, and high tumour mutational burden, whereas relapse was associated with angiogenesis and fibroblast-transforming growth factor-beta signatures (F-TBRS). TCGA molecular subset analysis revealed increased efficacy of atezolizumab in patients with basal-squamous tumours, consistent with underlying tumour-immune contexture. Together these findings suggest that adjuvant atezolizumab may be associated with improved outcomes compared with observation in this high-risk ctDNA(+) population. These findings, if validated in other settings, would shift approaches to post-operative cancer care.

  Study EGAS00001004997

• Effect of the Placental Transcriptome on Stunting in a Longitudinal African Cohort

  Mammals with placentas and flowering plants evolved genomic imprinting in nutritive tissues, the placenta and endosperm, respectively. In these tissues, the genomes inherited from the mother and father are in conflict over resource allocation to offspring. In imprinted genes, transcription is repressed on the paternal or the maternal allele, resulting in allele specific expression (ASE). We investigated the variation in ASE of imprinted genes in term human placentas in order to detect loss of imprinting (LOI), which leads to partial expression of the repressed allele. We collected the placental tissue from birth to women in our multigenerational, prospective cohort study in Mali, West Africa. We followed the women from their own infancies, early childhood to adulthood (age 18+ years). Contingent on prior informed consent, we collected placental tissue and umbilical cord tissue. We conducted RNA-Seq of the fetal compartment of the placentas and targeted DNA-Seq of the umbilical cord tissue, which is fetal, and of saliva samples from parents. Our sequencing effort produced two datasets. The first dataset is the whole transcriptome RNA-Seq dataset of 111 biopsies (59 placentas) and genotyping of 96 genes. Birth length, weight, and placental weight for the children associated with this dataset are listed in the phenotype file (DOI: 10.1093/molbev/msz226). The second dataset includes targeted RNA-Seq of 262 samples (251 placentas) and targeted DNA-Seq for 766 genes. The genes were selected because they met any one of the following criteria: (1) imprinted and reported in some studies, (2) highly expressed in placenta, or (3) relevant to diseases of interest in this cohort (https://doi.org/10.1093/g3journal/jkab176). This study provides the first systematic analysis of variation in LOI across genes and individuals in a human population, and should shed light on the hypothesis that the modulation of imprinting is an epigenetic mechanism and plays a role in the regulation of offspring growth.

  Study phs001782

• Whole Exome Sequencing of Calcitonin Producing Pancreatic Neuroendocrine Neoplasms (CT-pNENs) Indicates a Unique Molecular Signature

  Calcitonin-producing pancreatic neuroendocrine neoplasms (CT-pNENs), which can mimic clinical and laboratory features of medullary thyroid carcinoma, are an extremely rare clinical entity with approximately 60 cases reported worldwide. The molecular profile of CT-pNENs is not yet known. Whole-exome sequencing (WES) was performed on tumor and corresponding serum samples of five patients with increased calcitonin serum levels and histologically proven calcitonin-positive CT-pNENs. cBioPortal analysis and gene enrichment analysis with DAVID were performed to validate candidate genes. Immunohistochemistry was used to detect protein expression of MUC4 and MUC16 in CT-pNEN specimens. Common somatic mutations of pNENs like MEN1, ATRX and PIK3CA were identified in cases of CT-pNENs. New somatic SNVs in ATP4A, HES4, and CAV3 have not yet been described in CT-pNENs. Pathogenic germline mutations in FGFR4 and DPYD were found in three of five cases. Mutations of CALCA (calcitonin) and the corresponding receptor CALCAR were found in all five tumor samples, but none of them resulted in clinical or protein sequelae. All five tumor cases showed single nucleotide variations (SNVs) in MUC4, and four cases showed SNVs in MUC16, both of which were membrane-bound mucins. Immunohistochemistry showed protein expression of MUC4 and MUC16 in one case each, and the liver metastasis of a third case was double positive for MUC4 and MUC16. The homologous recombination deficiency (HRD) score of all tumors was low.CT-pNENs have a unique molecular signature compared to other pNEN subtypes, specifically involving the FGFR4, DPYD, MUC4, MUC16 and the KRT family genes. However, these WES data from only five cases must be interpreted with caution because causal interpretation of the mutational landscape in CT-pNENs is difficult. Further research is needed to explain differences in pathogenesis compared with other pNENs. In particular, multi-omics data such as RNASeq, methylation, and whole genome sequencing could be informative.

  Study phs003060

• Metadata Public API

  Metadata Rest Endpoints Common Aspects Functionality Identifiers Requests and Responses Request Response Querying Querying by Object Cross-Querying by Object Common Aspects Functionality The Metadata REST API allows EGA users to request metadata from the EGA. Using this api you will be able to obtain the publicly available information from EGA study, sample, experiment, run, analysis, policy, dac and dataset. The API also allows for objects to be cross-referenced in order to obtain for example all the datasets linked to a dac. Identifiers EGA objects can be identified by their unique accession. These are ID's displayed everywhere, shared among all EGA locations and specific for each data type (More information on the list below) EGA Accession ID EGA Object description EGAS EGA Study Accession ID EGAC EGA DAC Accession ID EGAP EGA Policy Accession ID EGAN EGA Sample Accession ID EGAR EGA Run Accession ID EGAX EGA Experiment ID EGAZ EGA Analysis Accession ID EGAD EGA Dataset Accession ID EGAB EGA Submission ID EGAF EGA File Unique Accession ID Server Request and Response Request All the endpoints implement these two HTTP methods, GET and HEAD. Moreover, there is an authentication layer that allows to disclose the private information about each object if the user runs the endpoint with their token and has permissions to the related dataset. HTTP Method Description GET Returns the objects and their public information (including private information if the user has permissions) HEAD Returns the count of objects to be returned Response API responses are diverse but they always return a status call. Below you have a summary on what these codes mean and some tips that can help you troubleshooting them: HTTP Status Code Description Resolution 200 OK No error handling necessary 206 Partial Content Expected status code when paginating the results. No error handling necessary 400 BAD REQUEST Request incorrectly formulated 401 UNAUTHORISED/FORBIDDEN You do not have permissions over the object you are trying to access. / The authorised user is not permitted to make the given request. 404 NOT FOUND The object/resource you are trying to access does not exist. 500 INTERNAL SERVER ERROR This is a server-side error, try later. If the issue persists, contact the Heldpesk. Querying Querying by Object In the below documentation, worked examples can be found on how to use the API, to query about each unique EGA object. https://metadata.ega-archive.org/analyses/{id} https://metadata.ega-archive.org/dacs/{id} https://metadata.ega-archive.org/datasets/{id} https://metadata.ega-archive.org/runs/{id} https://metadata.ega-archive.org/samples/{id} https://metadata.ega-archive.org/studies/{id} Worked example for the EGA data access committee(DAC) EGAC00001000514 https://metadata.ega-archive.org/dacs/EGAC00001000514 Points to notice Please, be advised that all EGA objects can be queried by using the same endpoint. For example: https://metadata.ega-archive.org/datasets Returns all the datasets registered at the EGA. https://metadata.ega-archive.org/datasets?limit=5&offset=15 Returns 5 results skipping the initial 15 results. Cross-Querying by Object The EGA REST API Metadata also allows for the crossquerying of public metadata. For example, should I want to query the datasets included in the ICGC DAC: https://metadata.ega-archive.org/dacs/EGAC00001000010/datasets Below, you can find worked examples on how to perform the call to the API. https://metadata.ega-archive.org/datasets/EGAD00001000645/analyses https://metadata.ega-archive.org/datasetsEGAD00001000645/files https://metadata.ega-archive.org/datasets/EGAD00001000620/runs https://metadata.ega-archive.org/datasets/EGAD00001000620/samples https://metadata.ega-archive.org/samples/EGAN00001092114/datasets https://metadata.ega-archive.org/files/EGAF00000056146/dacs https://metadata.ega-archive.org/files/EGAF00000056146/datasets https://metadata.ega-archive.org/files/EGAF00000056146/studies There are more endpoints included in this category. Please, find all the possibilities (green tick) in the table below. Table The table displays in a ordered fashion the objects that can be crossreferenced using the metadata API. BY Analysis Dac Dataset Experiment Policy Run Sample Study File Analysis ✔ ✔ Dac ✔ ✔ Dataset ✔ ✔ ✔ ✔ Experiment ✔ ✔ ✔ ✔ Policy ✔ Run ✔ ✔ ✔ Sample ✔ ✔* ✔ Study ✔ ✔ ✔ ✔ ✔ ✔* File ✔ *: These cross queries do not include data from Array-File submissions. Check out the SPEC

  Documentation discovery/metadata/public-metadata-api

• RNA-Seq in Patients with Primordial Dwarfism

  Transcriptome studies in patients with rare genetic diseases can potentially aid in the interpretation of likely causal genetic variation through identification of altered transcript abundance and/or structure. RNA-Seq is the most sensitive assay for both investigating transcript structure and abundance The primary aim of this pilot project is to investigate to what degree integrating exome-Seq and RNA-Seq data on the same individual can accelerate the identification of causal alleles for rare genetic diseases. There are two main strands to this: (i) identifying which variants discovered in exome-seq appear to be having a functional impact on transcripts, and (ii) identifying transcript outliers, especially among known causal genes, that may not necessarily have a causal variant identified from exome sequencing. The latter may identify the presence of causal variants that lie far from coding regions (e.g. the formation of cryptic splice sites deep within introns, or loss of long range regulatory elements), which can be confirmed with further targeted genetic assays. Just over 50% of all disease-causing variants recorded in the Human Gene Mutation Database (HGMD) affect transcript structure and abundance (e.g. nonsense SNVs, essential splice site SNVs, frameshifting indels, CNVs). This pilot project will study RNA from lymphoblastoid cell-lines from 12 patients with primordial dwarfism syndromes, for 10 of these samples we have previously generate exome data as part of our collaboration with the group of Prof Andrew Jackson. The two remaining samples are positive controls where the causal mutation is known, and is known to affect transcript structure and/or abundance. Primordial dwarfism is a prime candidate for these RNA-seq studies because all known causal mutations to date have key roles in DNA replication and thus, unsurprisingly, the products of the causal genes are typically ubiquitously expressed. Each RNA will be sequenced, with two technical replicates (independent RT-PCR and libraries) per sample, and each replicate run in 1/2 of a HiSeq lane using 100bp paired reads. Samples preparation was as follows :The cells were grown to confluency, then pellets frozen at -80. RNA samples were prepared using the Qiagen RNeasy kit, then nanodropped and analyzed using the bioanalyzer to determine concentration and purity. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001000640

• Exceptional Outcomes in a Phase Ib Study Combining PARP and MEK Inhibition, With or Without Anti-PD-L1, for BRCA-Wildtype Platinum-Sensitive Recurrent Ovarian Cancer

  To evaluate regimens combining PARP and MEK inhibition, with or without PD-L1 inhibition, for BRCA-wildtype platinum-sensitive recurrent ovarian cancer (PSROC).

  Study EGAS00001007496

• MDACC Lymphoma & Myeloma WGS of Tumor and Non-tumor Cells in Multiple Myeloma

  FastQ files from paired tumor and non-tumor WGS data from patients diagnosed with newly diagnosed (n = 6), refractory/relapsed multiple myeloma (n = 3) or plasma cell leukemia (n = 1). These samples presented >95% tumor PCs in bone marrow aspirates. Plasma cells and T cells (non-tumor cells) were isolated from bone marrow aspirates using a magnetic approach.

  Dac EGAC50000000282

• Mutational Profile in Newly Diagnosed Diffuse Large B-Cell Lymphoma: Insights from the GAINED Study

  The GAINED study (NCT01659099) is a randomized phase 3 trial comparing obinutuzumab (G) to rituximab (R) in combination with ACVBP or CHOP14 induction, followed by PET-guided consolidation. This post-hoc analysis focuses on primary mediastinal B-cell lymphoma (PMBL) patients, confirmed through expert pathological review and characterized using gene-expression profiling (GEP) and Next-Generation Sequencing (NGS).

  Dac EGAC50000000540

• DAC for Cornell-NCI DLBCL NGS Genomic Project

  Genome-wide sequencing experiments were conducted on 103 DLBCL biopsy samples, of which the datasets (called Cornell-NCI DLBCL cohort) are available for authorized scientists. The Center of Technology Licensing at Cornell University is in charge of this data access policy and approval. Particularly, Cornell University will email a DAA to each requestor to have their institution sign before access is granted

  Dac EGAC50000000704

• BCL11B Enhancer Hijacking by t(14;16)(q32;q24) Translocation Defines a Novel High-Risk Subtype of T-ALL

  We performed integrated analysis of pediatric and adult T-ALL and mixed phenotype acute leukemias (MPALs) by whole-genome and whole-transcriptome sequencing.

  Study EGAS50000001254

• Whole-genome sequencing data of metastatic salivary gland cancer

  This dataset contains whole-genome sequencing data of three patients with metastatic salivary gland cancer who underwent autopsy to harvest tumor biopsies from spatially separated metastatic sites and biopsies from healthy skin as a control. Two patients had the subtype adenoid cystic carcinoma (patients 1 and 4) and one patient had the subtype myoepithelial carcinoma.

  Dataset EGAD50000002056

• DCIS_FF_RNAseq

  This RNAseq dataset contains full transcriptome data on n=77 pure DCIS samples. The RNA is derived from fresh-frozen DCIS samples, obtained from two hospital based cohorts (NKI cohort and Oslo cohort). The libraries were sequenced with 54 bp paired end reads using a Novaseq instrument (Illumina). We share fastq data files of paired end sequencing (two files per sample).

  Dataset EGAD50000002123

• Baseline epigenetic clock measures in the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA)

  Epigenetic clock measures for a subset of 1,870 participants within the NICOLA cohort. The following epigenetic clocks were generated: (±PC)PhenoAge, (±PC)GrimAge, (±PC)Horvath1, (±PC)Hannum, PCDNAmTL and DunedinPACE. +PC represents the use of the principal component adjusted version of the clock.

  Dataset EGAD50000002063

• Whole exome sequencing data of matched pairs of primary tumour and normal frozen tissue of seven osteosarcoma patients

  This dataset contains whole exome sequencing data of matched pairs of primary tumour and normal frozen tissue of seven osteosarcoma patients. Whole exome sequencing with a minimum coverage of 100x was performed using the Illumina Novaseq6000 platform and the Agilent SureSelect Human All Exon V7 kit.

  Dataset EGAD50000002264

• Single-cell whole-genome sequencing dataset of sorted CD3+, CD33+, and CD34+ cells from aplastic anemia

  This dataset contains raw single-cell whole-genome sequencing data generated from a single patient with aplastic anemia using the BioSkryb platform. Single cells were isolated based on surface marker expression (CD3+, CD33+, and CD34+) and subjected to whole-genome amplification followed by sequencing on an Illumina platform.

  Dataset EGAD50000002195

• Dataset for single cell transcriptome sequencing

  This datasets contains single cell transcriptome sequencing of 10 control samples. Sequencing has been performed on Illumina NovaSeq 6000 using 10xGenomics Chromium Next GEM Single Cell 5 Reagent Kits. Sequencing was always paired. This dataset additionally contains a .QS file from Seurat Analysis performed in R, generated as described in the related publication.

  Dataset EGAD50000001549

• WGS of Newly Diagnosed and Refractory/Relapsed Multiple Myeloma

  This dataset includes paired tumor and non-tumor WGS data from patients diagnosed with newly diagnosed (n = 6), refractory/relapsed multiple myeloma (n = 3) or plasma cell leukemia (n = 1). These samples presented >95% tumor PCs in bone marrow aspirates. Plasma cells and T cells (non-tumor cells) were isolated from bone marrow aspirates using a magnetic approach.

  Dataset EGAD50000001177

• TOTHER3 dataset

  This dataset contains the FASTQ and BAM files for TOTHER3 study. Targeted DNA-Seq experiment were realized with 49 samples. Since this data was obtained with a panel of genes protected by Intellectual Property (IP), these files only contain information to validate the results published and, in consequence, sensible data had to be specifically removed.

  Dataset EGAD50000000562

• Total RNA sequencing of fibroblasts from an control individual

  Total RNA sequencing of fibroblasts from an individual with 30-55x GCC repeats in the FMR1 5'UTR. Libraries were prepared with total RNA with Ribo-Zero ribosomal RNA depletion (illumina), and sequenced paired-end, 75bp on a NextSeq 550 system. This dataset contains three RNA-sequencing replicates from the same cell line.

  Dataset EGAD50000000922

• Highly complex single-cell mixture of 5 individuals of high cell number

  Mixture of 5 unrelated individuals sequenced by 10x as a scATAC-seq. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile using the De-goulash pipeline. The separation file is submitted as the processed file. The bam are submitted as unprocessed files.

  Dataset EGAD50000000480

• scRNA/TCR-seq of CD8+ T cells from a melanoma patient

  Specific in vitro stimulation of patient-derived PBMC with the neoantigens SYTL4-S363F and KIF2C-P13L was followed by enriching for CD137+ activated T cells. Restimulated T cells as well as an unstimulated patient PBMC sample from the same time point were used for scRNA-Seq/scTCR-Seq.

  Dataset EGAD50000000854

• Single-cell RNA-sequencing of peripheral blood mononuclear cells from patients with achalasia

  The data published here contains single-cell RNA-sequencing (scRNAseq) data as obtained using the 3' scRNAseq using Chromium Single Cell 3’ Reagent from 10X Genomics on peripheral blood mononuclear cells (PBMC) from patients with achalasia. Sequencing was performed in a paired-ended fashion on the NovaSeq6000.

  Dataset EGAD50000000250

• Results of RNA sequencing from all patients/samples

  Results of sequencing the targeted RNA-seq panel of breast cancer patients and controls. This dataset comprises of FASTQ files of 295 samples (both breast cancer patients and controls) and the results of mapping the FASTQ files to the reference human genome (hg38) available in a form of raw gene expression matrix.

  Dataset EGAD50000000017

• Methylation Array data for: Three-dimensional patient-derived models of glioblastoma retain intra-tumoral heterogeneity

  3 sets of matched patient-derived-organoids (PDOs),  -neurospheres (PDNs), and parent glioblastoma tissue (9 samples total) underwent methylation array analysis. 200ng of each sample DNA extracted via a commercial kit (Qiagen) and loaded onto the Illumina EPIC v2.0 array. Data is provided as raw red and green .idat files.

  Dataset EGAD00010002745

• SNP Array data for: Three-dimensional patient-derived models of glioblastoma retain intra-tumoral heterogeneity

  4 sets of matched patient-derived-organoids (PDOs),  -neurospheres (PDNs), and parent glioblastoma tissue (12 samples total) underwent SNP array analysis. 200ng of each sample DNA extracted via a commercial kit (Qiagen) and loaded onto the Global Screening Array v2.0 array. Data is provided as raw red and green .idat files.

  Dataset EGAD00010002744

• Immunotesting cohort with RNA-seq data of melanoma samples

  This dataset contains RNA-seq raw data in fastq format from 9 melanoma samples. The samples are formalin-fixed paraffin-embedded (FFPE) treated. For target enrichment SureSelect XT Human All Exon V6 was used. The libraries were sequenced in paired-end mode (2 x 50 nt) on a NovaSeq6000 S2 flow cell.

  Dataset EGAD00001006783

• Paired-end RNA-seq analysis of the TERT promoter mutant GBM cell lines

  This dataset contains 9 RNA-seq BAM files. RNA was derived from TERT promoter mutant GBM cell lines and sequenced on an Illumina HiSeq4000 sequencer with paired-end reads and an average read length of 50 base pairs. Reads were aligned with TopHat (v2.0.14) using a GENCODE V19 transcriptome-guided alignment.

  Dataset EGAD00001005435

• RNA sequencing data of pediatric ETV6::RUNX1 acute lymphoblastic leukemia (set 4)

  The dataset includes RNA sequencing FASTQ files from a diagnostic tumor sample of one ETV6::RUNX1 ALL patient (ALLK-132). Novogene's internal method was used in directional library preparation including rRNA removal. The diagnostic tumor sample was sequenced using Illumina Novaseq X Plus, and the target sequencing depth was 21 Gb.

  Dataset EGAD50000002590

• Isotype-resolved sequencing of B cell receptor in measles virus infection (2017-09-13)

  Isotype-resolved sequencing of B cell receptor in measles virus infection 1) This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/ This dataset contains all the data available for this study on 2017-09-13.

  Dataset EGAD00001003749

• WXS files for paper titled 'Genomic Landscape of Pediatric Myelodysplastic Syndromes'

  Paired whole exome sequencing for 32 primary MDS, 14 MDS/MPN, and 8 AML-MRC cases (total = 54). Normal comparator genomic DNA was extracted from lymphocytes purified by flow cytometry. Bulk myeloid cells were used as a source of tumor gDNA. Files uploaded are mapped BAM files.

  Dataset EGAD00001003781

• Whole exome profiling of spatial biopsies of high grade serous epithelial ovarian cancer patients

  This data set is comprised of data from seven distinct high grade serous epithelial ovarian cancer (HGS-EOC) partients, from whom multiple biopsies were taken at the time of surgery, from the ovary and from different locations in the peritoneal cavity. This data set contains 28 samples, sequenced with a whole exome sequencing approach.

  Dataset EGAD00001004154

• NGS-ProToCol prostate cancer RNA-seq data.

  NGS-ProToCol RNA-seq dataset contains 41x normal adjacent prostate and 51x prostate cancer samples taken from fresh frozen radical prostatectomies, sequenced using random-hexamer priming. RNA-seq was performed on the Illumina HiSeq 2500 platform, 2 x 126 bp stranded paired-end reads at a depth of 70 mln reads.

  Dataset EGAD00001004215

• Primary breast cancers and paired brain metastases

  Targeted next-generation-sequencing of 494 cancer-associated genes was done in a series of 14 frozen pairs of matched primary breast cancers and brain metastases (28 samples). DNA libraries of all coding exons were prepared using the Haloplex Target Enrichment System. Sequencing was done using the 2*150bp paired-end technology on the Illumina NextSeq500 platform.

  Dataset EGAD00001004309

• ENGAGE - Amendment "500 genes exon sequencing"

  The ENGAGE project is a FP7 funded EU project aiming to combine genetic and phenotype information from European population based cohorts. In this sub-project we aim to do whole exome sequencing of individuals selected from Health 2000 and FINRISK cohorts. Individuals have been selected based on their metabolic trait phenotypes

  Dataset EGAD00001000403

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB10_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB10_C

  Dataset EGAD00001001694

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB10_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB10_F

  Dataset EGAD00001001695

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB10_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB10_M

  Dataset EGAD00001001696

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB15_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB15_C

  Dataset EGAD00001001697

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB15_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB15_F

  Dataset EGAD00001001698

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB15_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB15_M

  Dataset EGAD00001001699

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB1_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB1_C

  Dataset EGAD00001001700

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB1_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB1_F

  Dataset EGAD00001001701

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB1_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB1_M

  Dataset EGAD00001001702