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• BLUEPRINT release August 2014, RNA-Seq for hematopoietic multipotent progenitor cell

  RNA-Seq data for 3 hematopoietic multipotent progenitor cell sample(s). 9 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000919

• BLUEPRINT release August 2014, Bisulfite-Seq for alternatively activated macrophage

  Bisulfite-Seq data for 1 alternatively activated macrophage sample(s). 10 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001000920

• BLUEPRINT release August 2016, DNase-Hypersensitivity for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell, on genome GRCh38

  DNase-Hypersensitivity data for 3 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 3 run(s), 3 experiment(s), 3 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_dnaseseq_analysis_20160816

  Dataset EGAD00001002468

• BLUEPRINT release August 2014, Bisulfite-Seq for germinal center B cell

  Bisulfite-Seq data for 1 germinal center B cell sample(s). 8 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001000943

• BLUEPRINT release January 2015, RNA-Seq for granulocyte monocyte progenitor cell

  RNA-Seq data for 3 granulocyte monocyte progenitor cell sample(s). 3 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001146

• BLUEPRINT release August 2015, DNase-Hypersensitivity for CD14-positive, CD16-negative classical monocyte, on genome GRCh38

  DNase-Hypersensitivity data for 4 CD14-positive, CD16-negative classical monocyte sample(s). 4 run(s), 4 experiment(s), 4 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_dnaseseq_analysis_20150820

  Dataset EGAD00001001476

• BLUEPRINT release August 2015, DNase-Hypersensitivity for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell, on genome GRCh38

  DNase-Hypersensitivity data for 1 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 1 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_dnaseseq_analysis_20150820

  Dataset EGAD00001001524

• Genetics_of_gene_expression_in_human_macrophage_response_to_Salmonella

  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/

  Study EGAS00001002236

• Whole_Exome_PC9_and_A375

  Drug resistant population of PC9(human non-small cell lung cancer) or A375 (human melanoma) cell lines were used for this study. By exome sequencing, we will analyse mutations of cells in drug tolerent state and after drug holiday.

  Study EGAS00001002493

• Whole-transcriptome sequencing of hepatocellular carcinoma biopsies (TACE study)

  We performed a transcriptomic analysis of hepatocellular carcinomas (HCCs) and paired non-tumor biopsies to identify predictive biomarkers for response to transarterial chemoembolization (TACE). We identified several transcripts which are predictive of response to TACE.

  Study EGAS00001005558

• eccDNA in maternal plasma

  We developed a new bioinformatics method for detecting the eccDNA in plasma. We revealed that the biological properties between eccDNA and linear DNA are different. eccDNA could be potentially provided as a new class of circulating biomarker.

  Study EGAS00001003827

• Comprehensive de novo variant discovery with HiFi long-read sequencing

  We performed whole-genome sequencing of 8 trios with intellectual disability using Pacbio HiFi long-reads. We then called variants with PBSV and Deepvariant. We filtered for high-quality de novo variants and validated them.

  Study EGAS00001006479

• HCA_Thymus_Paediatric_ThyDesign_RNA_Managed_Access

  T cells are central to adaptive immunity and thus crucial for understanding and treating human disease. While extensively studied in model organisms, translating this knowledge to humans can sometimes be limited by their rapid cross-species evolution and diverse

  Study EGAS00001007687

• Sequencing data for oesophageal / related samples - Kazachenka et al (DNA)

  Data supporting: "The transcriptional landscape of endogenous retroelements delineates esophageal adenocarcinoma subtypes" WGS for 452 samples

  Dataset EGAD00001011095

• CRUK Accelerator: oesophageal adenocarcinoma whole exome and RNA-seq raw sequencing data

  Whole exome and RNASeq raw sequencing data for a cohort of 7 male patients with oesophageal adenocarcinoma. Median age at diagnosis was 68. Tumour tissue and PBMCs were used for whole exome sequencing and RNA sequencing. This data was generated as part of a study funded by a Cancer Research UK Centres Network Accelerator Award Grant (A21998).

  Dataset EGAD00001008489

• RNA sequencing of high-risk paediatric cancers for identifying T-cell infiltration signatures

  Transcriptome profiling of 121 high-risk paediatric cancer samples for identifying T-cell infiltration signatures using poly-A capture by Truseq and sequenced on NextSeq 500

  Dataset EGAD00001010920

• RNA-seq dataset of Oncogenic and immunological targets for matched therapy of pediatric blood cancer patients: Dutch iTHER study experience

  RNA-seq dataset of Oncogenic and immunological targets for matched therapy of pediatric blood cancer patients: Dutch iTHER study experience

  Dataset EGAD00001015639

• ATAC-Seq of GM adipose tissue samples

  This dataset contains 28 fastq files (11 files for subcutaneous tissue and 17 files for visceral tissue) from nine individuals. All samples were initially sequenced by SEx50 mode (16 files) and some of them were also sequenced by PEx100 mode (12 files). Sequencing was done on the Illumina HiSeq2000 platform.

  Dataset EGAD00001010254

• A95654A

  Whole genome sequencing for single cells for library A95654A 901 samples; filetype=bam

  Dataset EGAD00001008225

• A95635D

  Whole genome sequencing for single cells for library A95635D 367 samples; filetype=bam

  Dataset EGAD00001008224

• Dataset for transcriptome and whole genome bisulfite sequencing of glioblastoma(GBM) tumor cells

  This dataset contains transcriptome and whole genome bisulfite sequencing for 40 samples with glioblastoma(GBM). The sequencing was performed on Illumina NextSeq 2000 and Illumina NextSeq 550, . The sequencing was always paired.

  Dataset EGAD00001015614

• AWI-GEN 2 Phenotype Dataset

  Core phentoype data for AWI-Gen Phase 2 Microbiome Project for 1824 samples.

  Dataset EGAD00001015440

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

  Cell Atlas of the diseased human heart. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2025-07-31.

  Dataset EGAD00001015668

• BLUEPRINT release August 2014, RNA-Seq for hematopoietic stem cell

  RNA-Seq data for 3 hematopoietic stem cell sample(s). 8 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000939

• BLUEPRINT release January 2015, RNA-Seq for hematopoietic stem cell

  RNA-Seq data for 6 hematopoietic stem cell sample(s). 13 run(s), 6 experiment(s), 6 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001156

• BLUEPRINT release January 2015, RNA-Seq for common lymphoid progenitor

  RNA-Seq data for 5 common lymphoid progenitor sample(s). 20 run(s), 5 experiment(s), 5 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001184

• BLUEPRINT release August 2015, Bisulfite-Seq for erythroblast, on genome GRCh38

  Bisulfite-Seq data for 2 erythroblast sample(s). 35 run(s), 2 experiment(s), 2 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001484

• BLUEPRINT release August 2015, Bisulfite-Seq for Acute Myeloid Leukemia - CTR, on genome GRCh38

  Bisulfite-Seq data for 1 Acute Myeloid Leukemia - CTR sample(s). 18 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001530

• BLUEPRINT release August 2016, Bisulfite-Seq for Acute Lymphocytic Leukemia, on genome GRCh38

  Bisulfite-Seq data for 7 Acute Lymphocytic Leukemia sample(s). 132 run(s), 9 experiment(s), 14 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002313

• BLUEPRINT release August 2016, Bisulfite-Seq for Chronic Lymphocytic Leukemia, on genome GRCh38

  Bisulfite-Seq data for 6 Chronic Lymphocytic Leukemia sample(s). 84 run(s), 6 experiment(s), 12 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002396

• BLUEPRINT release August 2016, Bisulfite-Seq for Multiple Myeloma, on genome GRCh38

  Bisulfite-Seq data for 5 Multiple Myeloma sample(s). 63 run(s), 7 experiment(s), 10 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002521

• Genotype data

  This dataset contains the imputed genotypes for 197 individuals. All individuals were genotyped with the Illumina HumanCoreExome-24 array. The individuals were phased with SHAPEIT and imputed to the 1000 Genomes Project Phase III using IMPUTE2. This dataset was generated as part of the following study: Panousis et al (2019). Combined genetic and transcriptome analysis of patients with SLE: Distinct, targetable signatures for susceptibility and severity

  Dataset EGAD00001005038

• Cell type-specific transcriptomics of esophageal adenocarcinoma

  3' mRNA-Seq obtained from distinct isolated cell types (epithelia cells,immune cells, fibroblasts) of endoscopically obtained esophageal adenocarcinoma tissue as well as normal esophageal mucosa. Libraries for RNA-sequencing were prepared using the QuantSeq 3' mRNA-Seq Library Prep Kit FWD for Illumina according to the low input protocol. Libraries were sequenced on a HiSeq 4000 (Illumina) by 1x 50 bases.

  Dataset EGAD00001005508

• scRNA-seq raw data

  This dataset contains scRNA-seq fastq files (trimmed for quality and adapters using Trim Galore) for 3739 intestinal plasma cells of known or unknown antigen specificities from in total 12 individuals (4 untreated coeliac disease patients, 3 treated coeliac disease patients, 5 controls). The data was generated using the Smart-seq2 protocol and sequenced on the Illumina NextSeq500 platform with 75 bp paired-end reads.

  Dataset EGAD00001006436

• Lung Tissue Research Consortium (LTRC-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 LTRC include DNA, Plasma, Serum, Tissue - FFPE Cassettes, Tissue – RNALater Frozen, and Tissue - Snap Frozen. Please note that use of biospecimens in genetic research is subject to a tiered consent. Objectives: The LTRC was a biobank resource established by the NHLBI to collect and distribute lung tissue, blood samples, clinical data, and radiographic studies from participants with chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), other related idiopathic interstitial pneumonias (IIP) and interstitial pneumonias associated with connective tissue diseases who undergo medically-indicated lung resection. All tissue and blood specimens and clinical data were banked centrally and stored for distribution to external investigators who have approved study proposals to investigate the pathogenesis or management of lung diseases. The ultimate goal of this program was to enable research that illuminates the pathobiology of lung diseases and leads to novel interventional treatments for these conditions. Background: Chronic lung diseases are a main cause of death and disability in the United States. COPD affects over 14 million individuals in the United States and represents the third leading cause of mortality. Cigarette smoking is a major risk factor. However, only one of six individuals who smoke develops COPD. This could imply either an individual susceptibility or an additional immunologic or infectious injury to lung cells. Current treatments offer symptomatic relief, but do not prevent disease progression. Better understanding of disease pathogenesis, including the potential roles of lung parenchymal cell apoptosis, immunologic injury, and inflammation may lead to therapies that improve survival and quality of life. Interstitial pneumonias, including IPF and those associated with connective tissue disease, are less common than COPD, but for many of these diseases there are poor outcomes. For example, IPF has a 50% survival rate 2-3 years following diagnosis, and currently no treatment exists which prolongs survival. The prevalence of IPF is approximately 28 cases per 100,000. The underlying histology of IPF is usual interstitial pneumonia (UIP), which can also occur in connective tissue diseases. The incidences of other interstitial pneumonias such as non-specific interstitial pneumonia (NSIP) or acute interstitial pneumonia (AIP) are less frequent but also occur as an expression of interstitial lung disease in the connective tissue diseases. Moreover, there is significant crossover of these three interstitial pneumonias so that cases of IPF/UIP may also reveal fibrotic NSIP and be complicated by episodes of AIP. This implies common injuries but dissimilar histological responses. All of these processes are characterized by epithelial injury, uncontrolled fibroproliferation and the deposition of collagen, irrespective of the histology. It is clear that a better understanding of the genesis of the interstitial pneumonias is required before effective interventions can be developed. Participants: A total of 4,486 participants were enrolled, and lung tissue was obtained from 3,333 of these participants.Design: Written informed consent of each participant was required before any LTRC procedure was performed. Phenotypic data were then obtained that included recording of relevant medical information, a limited exposure history, radiological evaluation, and pulmonary physiological and lung function testing. Questionnaires were administered to determine the extent of symptoms, associated medical illnesses, smoking, environmental and occupational exposures, and quality of life. Laboratory testing included pulmonary function testing, a six-minute walk test, and chest x-ray CT. Blood specimens were collected both for defining the clinical phenotype of donors and to obtain serum, plasma, and DNA for later investigative purposes. At the time of surgery, lung tissues were collected and processed for long-term storage. The LTRC collected only the 'non-tumorous' portions of lung tissue from surgical procedures performed for primary or metastatic lung tumors and received those specimens only after the local pathologist had procured all tissue required for clinical care. Samples of appropriate size were cut and placed in formalin, RNAlater, glutaraldehyde, or liquid nitrogen within 30 minutes of excision (approximately 5% of cases exceeded this target time). Blood and tissue specimens were subsequently shipped to a central Tissue Repository for further processing and long-term storage. A Radiology Center provided quality control and quality assessment of CT data. A Data Coordinating Center managed study operations and maintained a repository of study data. Conclusions: LTRC established a biospecimen collection that is unique in its size, diseases included, standardization of methods, and extent of phenotypic data, serving as a valuable resource to facilitate research on the pathobiology of lung diseases.

  Study phs003913

• NHGRI GREGoR Consortium: Genomics Research to Elucidate the Genetics of Rare Disease

  The NHGRI GREGoR (Genomics Research to Elucidate the Genetics of Rare Disease) Consortium was established in June 2021 with the goal of developing novel tools and approaches to advance the discovery of the genetic basis of rare conditions. Numerous types of molecular data are generated in GREGoR and available on the AnVIL cloud platform via dbGaP application, including short- and long-read genome and exome sequencing, transcriptomics, metabolomics, methylomics, and proteomics. De-identified clinical and demographic data is obtained, with a focus on standardized ontologies.Visit the GREGoR Consortium data webpage for summary information about the GREGoR Dataset, including numbers of participants and data types, methods documentation, and Release Notes. The Consortium comprises five Research Centers (RCs - see below), a Data Coordinating Center (DCC), and various partner members and external collaborators.Baylor College of Medicine Research Center (BCM-GREGoR) The Baylor College of Medicine GREGoR program, which is part of the GREGoR consortium, enrolls individuals, families, and cohorts with suspected rare disease across a range of syndromic and non-syndromic phenotypes. Subjects are enrolled from national and international collaborating physician referrals. Subjects provide written informed consent for future re-contact. Data generated and shared include family structure, detailed phenotypes, exome or short-read genome data, and in some cases long-read genome or RNA-sequencing, and these are shared upon completion of standard quality control checks and annotation. Broad Institute (Broad) The Broad Center for Mendelian Genomics, part of the GREGoR consortium uses next-generation sequencing (exome, genome, transcriptome, and long read sequencing), computational approaches, and functional studies to discover the variants and genes that underlie Mendelian conditions with a particularly focus on neuromuscular, neurodevelopmental, and syndromic phenotypes. Samples come from collaborators and direct enrollment through the Rare Genomes Project and we are committed to rapid data sharing without an embargo period. University of California, Irvine (UCI-GREGoR) To accelerate the pace of Mendelian disease gene discovery and clinical implementation, we propose a Mendelian Genomics Research Center, part of the GREGoR Consortium, leveraging the broad pediatric and adult clinical and research expertise at Children's National Hospital and University of California, Irvine. Our goal is to develop best practices to increase the diagnostic yield of rare diseases, engage the community to reduce health disparities for complex diagnoses, while creating a dataset accessible to all. Our Center will unite world class experts combining basic and translational research with innovative approaches to phenotyping, variant identification and functional investigation of both coding and non-coding sequence changes with the goals of discovering novel Mendelian gene variations and identifying variants not detected on current sequencing pipelines, disambiguating uncertain variants into disease-causing versus benign categorizations, and sharing information by working collaboratively with the GREGoR community.GREGoR Stanford Site (GSS) The goal of the GREGoR Stanford Site (GSS) is to provide a platform for functional genomics research and validation to improve diagnosis in Mendelian disease. Participants included individuals with undiagnosed suspected Mendelian disease who had non-diagnostic exome sequencing and their immediate family members. Participants and their family members provided written, informed consent and biological samples from which DNA, RNA, plasma, fibroblasts, PBMCs and other cell types were generated and stored. Samples from research participants and their immediate family members may have undergone short and long-read genome sequencing, transcriptome sequencing, metabolomics and/or lipidomics profiling, methyl-capture-sequencing and ATAC-sequencing. De-identified clinical data extracted from participant medical records are linked to the samples. University of Washington Center for Rare Disease Research (UW-CRDR) The goals of the University of Washington Center for Rare Disease Research are to: (1) maximize novel gene discovery for Mendelian conditions by recruitment, short- and long-read whole genome sequencing, transcriptome sequencing and analysis of families with rare conditions for which the gene is either unknown or the gene is known but no pathogenic variant can be identified via clinical testing; (2) develop new strategies for gene discovery for Mendelian conditions caused by variants that are difficult to detect using conventional testing strategies, variants of unknown function effect (e.g., regulatory, structural variants) or have unusual modes of inheritance; and (3) implement high-throughput screening and targeted follow-up functional studies to prioritize and validate candidate non-coding variants. De-identified data and phenotypic information are shared via MyGene2, ClinVar, and AnVIL.

  Study phs003047

• Data Access Commitee for the project : Methylome profiling of human mesenchymal chondrosarcoma

  Dac EGAC00001003217

• Quantitative analysis of a novel DNA hypermethylation panel for lung cancer diagnosis

  Study EGAS00001007008

• Data Access Commitee for single-cell analysis of multiple myeloma and precursors

  Dac EGAC00001002920

• Data Access Committee for the Metastatic Breast Cancer Whole-exome sequencing study

  Dac EGAC00001000434

• DAC for patient-derived cell line samples

  Dac EGAC00001000594

• DAC for Direct Detection of Early-stage Cancers Using Circulating Tumor DNA

  Dac EGAC00001000701

• Data Access Committee for desmoplastic small round cell tumor (DSRCT) RNAseq data.

  Dac EGAC00001000851

• DAC for Oncogenic fate conversion by PRDM16s causes acute myeloid leukemia

  Dac EGAC00001001023

• EMBL genome biology research group for structural variation (Strand-seq application)

  Dac EGAC00001001091

• DAC for Integrated Genomic, Epigenetic, and Expression Analyses of Ovarian Cancer Cell Lines

  Dac EGAC00001001101

• Stanford Data Access Committee for Multi-Region WES of Metastatic Colorectal Cancer

  Dac EGAC00001001164

• DAC for the study of tumor-derived somatic mutation detection in cfDNA

  Dac EGAC00001001569

• Nicola Murray Centre for Ovarian Cancer Research Data Access Committee

  Dac EGAC00001001588

• Cancer Clinical Research Trust DAC for WES of HER2+ metastatic cancer samples

  Dac EGAC00001001632

• Data Access Commitee of the Princess Maxima Center for Pediatric Oncology

  Dac EGAC00001001948

• Data access committee for Gut microbiome of BA patients and normal controls

  Dac EGAC00001002146

• "DAC for RNAseq from regions of insitu and invasive human mammary ductal disease"

  Dac EGAC00001002538

• Chromatin Profiles in PRAD - Englander Institute for Precision Medicine - Weill Cornell Medicine

  Dac EGAC00001002559

• Single cell multi-omics group for somatic structural variation in human blood lineages

  Dac EGAC00001002852

• EGAD00010000831

  BLUEPRINT EpiMatch: harnessing epigenetics for haematopoietic stem cell transplantation

  Dataset EGAD00010000831

• irish_fineSTRUCTURE

  SNP data for 991 Irish individuals

  Dataset EGAD00010001479

• DNA Methylation data for EGAS00001002592

  Islet_HumanMethylation450K_ThurnerEtAl

  Dataset EGAD00010001695

• DAC for Tuebingen-Stuttgart cohort

  Dac EGAC00001000317

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Jackson Heart Study (JHS)

  Study phs002907

• A multi-center genome-wide association study for nasopharyngeal carcinoma

  Study EGAS00001006062

• DAC for Clonal driver neoantigen loss under EGFR TKI and immune selection pressures

  Dac EGAC00001003522

• Validation of cfDNA fragmentome analyses for early detection of liver cancer

  Study EGAS00001008111

• Germline Sequencing for Aggressive Prostate Carcinoma

  This study aims to elucidate the genetic determinants of susceptibility to aggressive prostate carcinoma. To this end, germ line whole exome sequence has been generated from patients with aggressive prostate cancer. Whole genome sequence for a representative subset of tumors from patients whose cancer was resistant to primary therapy is included as well. This deep genomic interrogation will allow the identification of rare, functionally disruptive variants that may play a role in disease progession and response to treatment.

  Study phs000661

• Extracellular microRNA Biomarkers for Diagnostic and Prognostic Assessment of Preeclampsia at Triage

  This study is comprised of samples from 123 donors (71 cases and 52 controls) undergoing evaluation for preeclampsia. All samples were collected between 20 and 40 weeks of gestation. Small RNA sequencing was done on serum RNA obtained from donors. We were able to identify 3 bivariate extracellular miRNA biomarkers that can enable early diagnosis of preeclampsia. The raw fastq small-RNA sequencing fastq files will be submitted to dbGaP.

  Study phs003169

• The MALT1 Locus and Peanut Avoidance in the Risk for Peanut Allergy

  The LEAP study was a randomized controlled clinical trial in children at increased risk for peanut allergy, in which intervention using dietary introduction of peanut protein beginning in the first 4 to 11 months of life, continued until age 5 years, significantly decreased the frequency of peanut allergy later in childhood and modulated the immune response to peanuts. All subject phenotype data will be hosted on ITN Trial Share under the LEAP study.

  Study phs001851

• NHLBI TOPMed: Trans-Omics for Precision Medicine (TOPMed) Whole Genome Sequencing Project: Cardiovascular Health Study

  Participants from the Cardiovascular Health Study (CHS), a large population-based longitudinal cohort study (phs000287), have been included in the TOPMed project. Whole genome sequencing will be performed to contribute to multiple analyses, including cardiovascular disease risk factors, subclinical disease measures, the occurrence of myocardial infarction (MI) and stroke, and analyses of venous thromboembolism (VTE).Comprehensive phenotypic and pedigree data for study participants are available through dbGaP phs000287.

  Study phs001368

• Identification of Novel Therapeutic Targets for Calcific Aortic Valve Stenosis Using Integrative Genomics

  In order to identify novel therapeutic targets for calcific aortic valve stenosis, we have generated a large transcriptomic dataset from RNA sequencing of human aortic valve tissues from 500 individuals undergoing aortic valve replacement or heart transplant at the Institut universitaire de cardiologie et de pneumologie de Québec – Université Laval (QUEBEC-CAVS-RNA). This dataset represents a unique resource to understand the molecular mechanisms underlying aortic valve diseases.

  Study phs003541

• DNA methylation database for gynecological cancer detection, classification and assay development

  DNA methylation changes are associated with cancer the best predictor of the presence of cancer. In order to detect and classify gynecological cancer, we performed MeD-seq to generate DNA methylation profiles of vulvar, cervical, endometrial, fallopian tube and ovarian cancers and their associated healthy tissues. DNA methylation profiles were used to find general cancer, regional cancer and specific cancer biomarkers that can be used for cancer detection, classification and treatment monitoring.

  Study EGAS50000000417

• WES Breast Patient-derived Tumor Organoid

  This dataset includes 18 Whole Exome Sequencing (WES) samples from 5 subjects. WES is performed on a matched pair of case/control samples, e.g., tumor/control or organoid/control. For each patient, the same control sample is used for the analysis of tumor and organoid samples. The sample name structure identifies the type of sample: <subjectId>_[TON][12]_[Case|Ctrl]_EX2; where T, O, and N refer to Tumor tissue, Organoid, and control sample, respectively. The number 1 or 2 refers to the specific tissue, and Case and Ctrl indicate a tumor tissue (or organoid) and a control sample, respectively. For example, ICSBCS007_T1_Case_EX2 is the first tumor tissue of subject ICSBCS007; ICSBCS007_O1_Case_EX2 is the tumor organoid derived from that tumor sample, and ICSBCS007_N1_Ctrl_EX2 is the matching control sample. More information can be found in the sample information table.

  Dataset EGAD50000000961

• Oesophageal adenocarcinoma scRNAseq from LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19)

  This dataset contains single-cell RNA-seq generated from the LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19) and additional donors with Barrett's oesophagus using the 5' Single Cell Gene Expression assay from 10x Genomics. Samples were generated from oesophageal tumours, Barrett's oesophagus, and normal oesophagus and gastric tissues, with the aim of generating a reference atlas for cell types found in normal and disease-associated tissue states in the upper GI tract. This reference atlas was used as the basis for a deconvolution workflow to estimate the cell composition of bulk transcriptomes from these tissues, and to assess cell type-specific expression patterns of potential predictive biomarkers for immunochemotherapy regimens in this setting.

  Dataset EGAD00001009401

• Transcriptome-Wide Association Study (TWAS) to Identify Susceptibility Genes for Colorectal Cancer

  This study included genotype data from 420 study participants of East Asian descent, along with RNA-seq (n=300) and small RNA seq data (n=400) generated from their colorectal tissue samples. We used the transcriptome data and genotype data from these individuals to build a prediction model for each coding gene and long non-coding RNA, and perform a Transcriptome-Wide Association Study (TWAS) of colorectal cancer in East Asian populations.

  Study phs002813

• Indonesian Microbiome Ecology and Evolution v1 (raw data)

  This dataset comprises 124 human faecal samples collected across Indonesia between 2015 and 2021, together with 6 controls. It was collected to study patterns of human microbiome variation across Indonesia’s diverse geography and lifestyles, and to generate the Indonesian Microbiome Ecology and Evolution (IndoMEE) metagenome-assembled genome (MAG) reference database. The dataset consists of 250 bp paired-end metagenomic reads from 124 faecal samples acquired from 116 individuals, at ca. 10.6 Gb / sample, together with 2 positive controls (ZymoBIOMICS Microbial Community DNA Standard by Zymo Research Cat# D6305), and 4 negative controls (for sampling: Blank, for extraction: Buffer EB by Qiagen Cat# 19086, for library prep: Nuclease Free Water by Ambion-Invitrogen Cat# AM9937). Library preparation was carried out using Illumina's TruSeq DNA Nano DNA kit, and sequencing was performed on the Illumina NovaSeq 6000 platform.

  Dataset EGAD50000001399

• NCI's Collection of Studies for General Cancer Research

  This collection contains all of NCIs authorized individual-level genomic datasets currently in dbGaP that are approved for Cancer Research (DS-CA) use only and limitations outlined in the model Data Use Certification Agreement. Access to this study will include any additional authorized individual-level DS-CA datasets that become available. Renewal of this study is required annually. To request access to this study collection, select phs003967.v1.p1 in the dbGaP Authorized Access System.

  Study phs003967

• An atlas of transcribed enhancers across helper T cell diversity for decoding human diseases

  At 5' ends of RNAs, there is a unique "cap signature" derived from a 7-methylguanosine cap. By leveraging this signature, we developed ReapTEC (read-level pre-filtering and transcribed enhancer call), a method for simultaneously profiling gene expression and enhancer activities using 5'-end single-cell RNA sequencing (5' scRNA-seq). We applied ReapTEC and single-nucleus ATAC-seq to a total of ~1 million single human CD4+ T cells.

  Study JGAS000689

• A prospective trial comparing adaptive long-read sequencing and short-read genome sequencing for genetic diagnosis of cerebellar ataxia

  This study evaluated the effectiveness of long- and short-read sequencing as diagnostic tools for CA. We recruited 110 individuals (48 females, 62 males) with a clinical diagnosis of CA. Short-read genome sequencing (SR-GS) was performed to identify pathogenic RE and also non-RE variants in 356 genes associated with CA. Long-read adaptive sequencing (LR-AS) was performed to identify pathogenic RE in 67 genes.

  Study EGAS50000000573

• Benchmark Dataset for Somatic Mutation Calling in Cell-Free DNA

  This study provides a comprehensive benchmarking resource for somatic variant detection in cell-free DNA (cfDNA) from cancer patients. Longitudinal plasma samples from colorectal and breast cancer cohorts were selected to create patient-matched dilution series spanning ultra-low to high circulating-tumour-DNA (ctDNA) fractions, while preserving each individual’s germline and clonal haematopoiesis background. Deep whole-genome sequencing (150×) and ultra-deep whole-exome sequencing (2,000×) generated a reference call set of ~37,000 single-nucleotide variants and ~58,000 insertions/deletions. These data enabled systematic evaluation of nine somatic variant callers across variable ctDNA levels and sequencing depths, and were further used to explore machine-learning–guided parameter tuning. The resulting dataset offers an openly accessible framework for developers and clinicians to assess and optimize somatic variant calling in liquid biopsy applications.

  Dataset EGAD50000001870

• Cell-free DNA methylome and fragmentome analysis for disease relapse monitoring in patients with Ewing Sarcoma

  We conducted whole-genome and methylome sequencing of cfDNA from 87 serial samples of 23 patients with EwS and 3 patients with CIC-rearranged sarcoma (CIC). With EwingSign, a new machine learning model, we identified EwS or CIC in a test set for 11 out of 16 patients at diagnosis and 15 out of 18 clinically confirmed relapse events. 0 out of 24 non-cancer controls were detected positive with EwingSign.

  Study EGAS50000001415

• Identification of responsible genes and development of standardized medicine for familial breast cancer by genetic analysis with NGS technology

  Aims: To clarify risk of germline variants of known causal genes for Hereditary Breast Ovarian Cancer Syndrome in Japan by combinatorial analysis of somatic and germline variants in the breast cancer tissue. Methods: Target caputure analysis by using Illumina HiSeq 2000; the panel genes are 119 genes including 30 HBOC known causal genes. Participants/Materials: 108 whole blood and 124 primary tumor samples from familial breast cancer patients.

  Study JGAS000224

• An efficient procedure for the recovery of DNA from formalin-fixed paraffin-embedded tissue sections

  We investigated the effects of different incubation temperatures and duration, and formalin scavenger (Tris) concentrations on the recovery of DNA from FFPE tissue sections. We optimized the conditions for preserving the integrity and maximizing the yield of DNA from FFPE tissue sections. Furthermore, DNA obtained using the optimized procedure was compared with that obtained using a commonly used extraction procedure with regard to their performance in whole-genome and targeted gene panel sequencing.

  Study JGAS000520

• Development of blood-based biomarkers for precision medicine in castration-resistant prostate cancer

  Cell-free DNA (cfDNA) testing is emerging as a tool for cancer precision medicine. However, little is known about the clinical significance of low-frequency variants detected in circulating cell-free tumor DNA (ctDNA) from castration-resistant prostate cancer (CRPC) patients. We conducted targeted sequencing of cfDNA and paired leukocyte DNA from CRPC patients using molecular barcodes, and ctDNA variants with a variant allele frequency ??? 0.1% were detected using an in-house pipeline.

  Study JGAS000330

• NGS data of human NES cells and tumors

  Raw RNA-seq data for WT and 2 Gorlin NES cells, tumors derived from MYCN mis-expressed WT NES cells, tumors derived from Gorlin NES cells, and tumors derived from Gorlin NES cells transduced with mutant DDX3X (R351W and R534S) and CRISPR/Cas9 targeting GSE1 (each sample has 3 replicates/tumors except Gorlin NES cell tumors have 4). Raw whole exome sequencing data for WT and Gorlin 1 NES cells, tumors derived from MYCN mis-expressed WT NES cells, and tumors derived from Gorlin NES cells (each sample has 3 replicates/tumors except Gorlin NES cell tumors have 4). Raw data for amplicon sequencing of GSE1 and KDM3B at regions targeted by CRISPR/Cas9 in Gorlin NES cells.

  Dataset EGAD00001004990

• Evaluation of protocols for rRNA depletion-based RNA sequencing of nanogram inputs of mammalian total RNA

  Next generation RNA-Sequencing (RNA-seq) is a flexible approach that can be applied to e.g. global quantification of transcript expression, the characterization of RNA structure such as splicing patterns and profiling of expressed mutations. Many RNA-seq protocols require up to microgram levels of total RNA input amounts to generate high quality data, and thus remain impractical for the limited starting material amounts typically obtained from rare cell populations, such as those from early developmental stages or from laser micro-dissected clinical samples. Here, we present an assessment of the contemporary ribosomal RNA depletion-based protocols, and identify those that are suitable for inputs as low as 1-10 ng of intact total RNA and 100-500 ng of partially degraded RNA from formalin-fixed paraffin-embedded tissues.

  Dataset EGAD00001005316

• Identification of genes involved in congenital disorders of glycosylation and 3-methylglutaconic aciduria (2018-03-14)

  Identification of genes involved in congenital disorders of glycosylation and 3-methylglutaconic aciduria. There are more than 100 genes known for congenital disorders of glycosylation and new disorders are discovered each year. WE included patients with a so far unsolved glycosylation disease. The diagnostic group 3-methyglutaconic aciduria is a heterogenous group of disorders mostly caused by abnormal phospholipid synthesis or in association with mitochondrial dysfunction. We included patients with a so far unsolved disease and 3-methylglutaconic aciduria. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2018-03-14.

  Dataset EGAD00001004038

• Anaplastic Oligodendroglioma AO Exome-seq data

  Anaplastic oligodendrogliomas (AOs) are rare primary brain tumors which are generally incurable, with heterogeneous prognosis and few treatment targets identified. Most oligodendrogliomas have chromosome 1p/19q co-deletion and IDH mutation. We analyzed 51 AOs by whole-exome sequencing, identifying previously reported frequent somatic mutations in CIC and FUBP1. We also identified recurrent mutations in TCF12 and in an additional series of 83 AO. Overall 7.5% of AO are mutated for TCF12, which encodes an oligodendrocyte-related transcription factor. 80% of TCF12 mutations identified were in either the bHLH domain, which is important for TCF12 function as a transcription factor, or were frame shift mutations leading to TCF12 truncated for this domain. We show that these mutations compromise TCF12 transcriptional activity and are associated with a more aggressive tumor type. Our analysis provides further insights into the unique and shared pathways driving AO.

  Dataset EGAD00001001452

• Genome-wide genotyping data and exome sequencing of 100 European-descent and 100 African-descent Belgians used in the EGAS00001001895 study

  We recruited 100 healthy, male donors of self-reported European descent (EUB) and 100 of self-reported African descent (AFB) (Ghent, Belgium). For each participant, peripheral blood mononuclear cells (PBMCs) were isolated from whole blood on Ficoll-Paque density gradients. Monocytes were then positively selected with magnetic CD14 microbeads and exposed for 6 hours to different ligands activating TLR4 (LPS), TLR1/2 (Pam3CSK4), TLR7/8 (R848) and to a human seasonal influenza A virus (IAV). High-quality RNA was obtained from unstimulated and stimulated monocytes for 970 of the 1000 samples (200 x 5 conditions), and was sequenced on an Illumina HiSeq2000. On average, 34 million 101-bp single-end reads were obtained per sample.

  Dataset EGAD00001002714

• Variant calls for "Quality of Whole Genome Sequencing from Blood versus Saliva Derived DNA in Cardiac Patients"

  Paired blood and saliva samples from five unrelated individuals were directly compared for quality of whole genome sequencing. Two (Sample Pairs 1 and 2) were female probands diagnosed with tetralogy of Fallot, a type of congenital heart disease, and three (Sample Pairs 3, 4 and 5) were male probands diagnosed with hypertrophic cardiomyopathy. WGS was performed using Illumina HiSeq X to a target average coverage depth of 30x and a read length of 150 bp. The resulting reads were not filtered for minimum quality in order to avoid losing possible contaminant reads. Sequencing read alignment was done using Isaac Aligner to human genome build hg19. Short variant i.e. single-nucleotide variant (SNV) and small insertion-deletion (indel) calling was performed using Isaac Variant Caller with default parameters.

  Dataset EGAD00001005772

• WGBS data (CancerLocator study) of cell-free DNA derived from human blood

  The blood samples of eight lung cancer patients and one benign lung tumor patient are collected for this dataset. Blood samples were centrifuged first at 1,600 × g for 10 minutes, and then the plasma was transferred into new micro tubes and centrifuged at 16,000 × g for another 10 minutes. The plasma was collected and stored at -80⁰C. CfDNA was extracted from 5 ml plasma using the Qiagen QIAamp Circulating Nucleic Acids Kit and quantified by Qubit 3.0 Fluoromter (Thermo Fisher Scientific). Bisulfite conversion of cfDNA was performed by using EZ-DNA-Methylation-GOLD kit (Zymo Research). After that, Accel-NGS Methy-Seq DNA library kit (Swift Bioscience) was used to prepare the sequencing libraries. The DNA libraries were then sequenced with 150bp paired-end reads.

  Dataset EGAD00001003168

• Sequencing of an organoid biobank for childhood soft tissue sarcoma.

  Rhabdomyosarcomas are diverse tumors of mesenchymal origin and the most common childhood soft tissue sarcoma. Patients receive intense treatment with a nevertheless poor prognosis for high-risk patients. New therapy would benefit from additional preclinical models and their rapid establishment. Tumor organoid models (tumoroids) have so far only been established from epithelial cancers. Here, we describe generation and characterization of a collection of pediatric RMS tumoroids comprising all major subtypes.

  Study EGAS00001005912

• Genetic scoring guide for personalized risk assessment in pediatric B-cell precursor Acute Lymphoblastic Leukemia

  In this study a next-generation sequencing based method was applied to comprehensively screen for recurrent, disease-relevant copy number aberrations in a cohort of Hungarian patients. Diagnostic bone marrow samples from 260 children with B-cell acute lymphoblastic leukemia and were investigated by digital multiplex ligation-dependent probe amplification using the disease-specific D007 probemix. Whole chromosome gains and losses, as well as subchromosomal copy number aberrations were simultaneously profiled.

  Study EGAS00001007239

• Bulk-tissue RNA-sequencing paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen tissues from 4 post-mortem neuropathologically-confirmed control individuals ( anterior prefrontal cortex &amp; cerebellar cortex – 4 individuals, putamen- 3 individuals)

  Profiling of paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen samples by bulk-tissue RNA-sequencing. Samples were derived from 4 post-mortem neuropathologically-confirmed control individuals ( anterior prefrontal cortex – 4 individuals, cerebellar cortex – 4 individuals, putamen- 3 individuals). Paired-end FASTQ files for each of the human samples are provided. Fastp (v 0.20.0), a fast all-in-one FASTQ pre-processor, was used for adapter trimming, read filtering and base correction. Fastp default settings were used for quality filtering and base correction. Further details on parameters used are available here: https://github.com/RHReynolds/RNAseqProcessing .

  Dataset EGAD00001009264

• Deep targeted DNA sequencing dataset for the study "Molecular characteristics in Burkitt lymphoma over age groups"

  We performed deep targeted DNA sequencing with a panel of 134 selected cancer-related genes previously identified to be recurrently mutated in BL/B-AL. A Nextera rapid capture custom kit was designed using the Illumina DesignStudio. For every gene, all regions in which previous mutations had been described were covered. For 40 of these genes, the entire coding sequence was covered. Targeted deep sequencing was performed on a MiSeq-Sequencer using MiSeq Reagent Kit v2 (300 cycle) with 24 samples per run. There are 396 samples in this targetedDNAseq-dataset with 298 tumors (288 patients and 10 cell lines) and 98 normals. There are 132 female, 262 male, and 2 unknown gender samples in this dataset.

  Dataset EGAD00001007708

• A developmental cell atlas of the human thyroid gland

  Thyroid hormones are essential for health, but human thyroid development and cellular heterogeneity are poorly understood. We generated a high-resolution, spatiotemporal transcriptional atlas of human fetal thyroid, and examined its perturbations in trisomy 21 (T21), where aberrant thyroid development is associated with congenital hypothyroidism, and in papillary thyroid carcinoma (PTC). Normal tissue revealed two functional follicular cell states (fTFC1, fTFC2) with divergent PAX8 expression levels, persisting into adulthood. Additionally, the rare C-cell population was comprehensively profiled at single-cell resolution for the first time. T21 fetal thyroid was hypoplastic, with disrupted follicular morphology and altered expression of genes mediating cellular topology and extracellular matrix interactions. Finally, the transcriptional signals of both fetal follicular cell states were found to be perturbed in PTC. Overall, we defined a reference atlas for normal human fetal thyrocyte heterogeneity and provided novel insights into thyroid disorders.

  Dataset EGAD00001015783

• Fastq files for the single cell RNAseq data of Follicular lymphoma study

  Fastq files for the single cell RNAseq data of Follicular lymphoma study. This dataset includes the paired single cell RNA sequencing data for 23 samples.

  Dataset EGAD00001008595

• Influence of culture media on airway differentiation at the Air-Liquid Interface

  Single-cell dataset of human airways after in vitro regeneration at the Air-Liquid Interface. Samples from 2 donors were used for the establishment of Human Bronchial Epithelial Cell cultures (HBECs) and were analyzed during proliferation, at the onset of differentiation (ALI0) and after full differentiation (ALI28), in 4 distinct culture media. Single-cell RNAseq was performed with 10x Genomics 3’ v2 RNA-seq using hashtag antibodies for multiplexing, and sequenced on a NextSeq 500. Samples from 3 additional donors were used for the establishment of Human Nasal Epithelial Cell cultures (HNECs) and were analyzed during proliferation in three distinct culture media. Single-cell RNAseq was performed with 10x Genomics 3’ v3 RNA-seq without multiplexing, and sequenced on a NextSeq 2000.

  Dataset EGAD00001011362

• Prevention and Early Treatment of Acute Lung Injury (PETAL) Crystalloid Liberal or Vasopressors Early Resuscitation in Sepsis (CLOVERS) (PETAL CLOVERS-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. Access to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from PETAL CLOVERS include plasma, and whole blood. Please note that use of biospecimens in genetic research is subject to a tiered consent. Available Data: The data available for request now include Long Term Outcome data.Objectives: To compare the effects of a restrictive fluid strategy (with early use of vasopressors) to a liberal fluid strategy in participants with sepsis-induced hypotension.Background: Intravenous fluid resuscitation is a common therapy used in the initial treatment of participants with septic shock and sepsis-induced hypotension. The goal of initial fluid therapy is to increase depleted or functionally reduced intravascular volume that occurs in sepsis due to a vasodilated vascular network. However, intravenous fluid resuscitation can create dilutional coagulopathy, fluid overload, and pathogenic edema in the lungs and other organs. Vasopressor agents are also commonly used to treat hypoperfusion by inducing constriction of arterioles and venules and increasing cardiac contractility. Vasopressor therapy also comes with risks that include vasoconstriction resulting in tissue ischemia, increased cardiac workload, and arrhythmias. Clinicians have used these strategies, typically in combination, to provide supportive care for participants with sepsis-induced hypoperfusion. However, at the time of the CLOVERS study, there was limited data to guide specific use of these therapies, including fluid volumes, in the early care of participants with sepsis-induced hypotension. The CLOVERS study hypothesized that a restrictive fluid strategy used during the first 24-hours of resuscitation for sepsis-induced hypotension would lead to lower mortality before discharge home by day 90 than a liberal fluid strategy.Participants: A total of 1,563 participants, from 60 medical centers, of the planned 2,230 participants were enrolled, with 782 assigned to the restrictive fluid group and 781 to the liberal fluid group. Enrollment in the trial was ended after the second interim analysis due to a lack of significant difference observed between the two 24-hour strategies.Design: This study was a multi-center, prospective, phase 3 randomized non-blinded interventional trial of fluid treatment strategies in the first 24 hours for participants with sepsis-induced hypotension. Participants were randomly assigned in a 1:1 ratio to either a restrictive fluid strategy (with early vasopressor use) or a liberal fluid strategy. In each group, the assigned protocol was followed for a period of 24 hours. The restrictive fluid protocol prioritized vasopressors as the primary treatment for sepsis-induced hypotension, with 'rescue fluids' being permitted for prespecified indications that suggested severe intravascular volume depletion. The liberal fluid protocol consisted of a recommended initial 2000-ml intravenous infusion of isotonic crystalloid, followed by fluid boluses administered on the basis of clinical triggers (e.g., tachycardia) with 'rescue vasopressors' permitted for prespecified indications. A protocol amendment implemented in October, 2019, allowed for limiting the initial infusion to 1,000 ml if the participant's blood pressure and heart rate had stabilized and the clinical assessment was that the participant was unlikely to benefit from additional intravenous fluid administration. The clinical team could override the protocol-specified care instructions at any time if it was judged to be in the best interest of the participant.The primary outcome was death from any cause before discharge home by day 90. Secondary outcomes included 28-day measures of the number of days free from ventilator use, days free from renal-replacement therapy, days free from vasopressor use, days out of the ICU, and days out of the hospital. Conclusions: Among participants with sepsis-induced hypotension, the restrictive fluid strategy that was used in this trial did not result in significantly lower (or higher) mortality, or other measures of recovery such as length of hospital stay, before discharge home by day 90 than the liberal fluid strategy.

  Study phs004080

• How to upload GPG files

  Uploading files Users that holds an ega-box-XXX account can upload files using either INBOX or FTP. Users who have a Submitter role associated with their email will only be able to upload files using INBOX. Before uploading your files please make sure that any files that will be uploaded to EGA do not use special characters in their naming convention such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &. This can cause issues with the archiving process, leading to problems for end users. The EGA is a shared, public service with limited storage. In order to manage the available resources, we enforce a limit of 10Tb per submission account at any one time. Please do not exceed this limit. INBOX FTP The FTP is only compatible with files encrypted using the EGACryptor tool Before uploading Once your submission files have been prepared using the EGAryptor, the resulting encrypted files and associated md5sum files can be uploaded to your submission account using Aspera or FTP. The EGA is a shared, public service with limited resources. In order to manage the available resources, EGA submission boxes should not exceed 8Tb in size, and cannot exceed 12Tb. If you are approaching this limit please contact contact EGA Helpdesk so that we can advise on how to register the associated metadata and trigger the archiving of files, so that you can continue with your submission. If we note that your submission account increases above 10Tb on a consistent base your password will be changed until metadata is associated. Aspera Download Aspera Using Aspera FTP FTP windows FTP Linux / Unix FTP client (Filezilla) FTP and TLS Troubleshooting Troubleshooting Aspera Download Aspera is a commercial file transfer protocol that may provide faster transfer speeds than ftp especially over longer distances. The Aspera ascp command line client. Please select Aspera Connect. The ascp command line client is distributed as part of the aspera connect highperformance transfer browser plugin and is free to use, without registration. The minimum required version of the IBM Aspera Cli is V4. Further instructions. Using the Aspera ascp command line program The location of the ascp program in the filesystem: Mac: on the desktop go cd /Applications/Aspera\ Connect.app/Contents/Resources/ there you'll see the command line utilities where you're going to use ascp. Windows: the downloaded files are a bit hidden. For instance, in Windows 7 the ascp.exe is located in the users home directory in: AppData\Local\Programs\Aspera\Aspera Connect\bin\ascp.exe Linux: should be in your user's home directory, cd /home/username/.aspera/connect/bin/ there you'll see the command line utilities where you're going to use ascp. Your command should look similar to this: ascp -P33001 -O33001 -QT -l300M -L- /path/file ega-box-N@fasp.ega.ebi.ac.uk:/path If you wish to upload several files without being requested the password, please use the below command : ASPERA_SCP_PASS=ega-box-password ascp -P33001 -O33001 -QT -l300M /path/file ega-box-N@fasp.ega.ebi.ac.uk:/path/ Explanation of parameters l300M option sets the upload speed limit to 30MB/s. You may wish to lower this value to increase the reliability of the transfer. L option is for printing logs out while transferring files to upload can be a file mask (e.g. '/homes/submitter/*.srf) or a list of files. ega-box-N is your submission account login. Add k2 switch for transfer restarts Check the command line transfer usage for more configuration details. Using FTP to upload your prepared files Use your preferred ftp client. For example, lftp is a popular choice for Linux and Mac users. Use binary mode for file transfers. Use ftp.ega.ebi.ac.uk as the target host. Login with your ega-box username and password. Upload files to your private ega-box upload area. Depending on your network setting you might wish to start FTP in passive or active mode. Using default FTP command line client in Windows Start the command line interpreter: press WinR, type cmd, hit enter Enter ftp ftp.ega.ebi.ac.uk Enter your submission username Enter your submission password Type binary to enter binary mode for transfer To see a list of available ftp commands type help. Type ls command to check the content of your submission account. Type prompt to switch off confirmation for each file uploaded. Use mput command to upload files: mput *.bam* Use bye command to exit the ftp client. Use exit command to exit the command line interpreter. Using default FTP command line client in Linux / Unix Open a terminal and type ftp ftp.ega.ebi.ac.uk Enter your submission username Enter your submission password Type binary to enter binary mode for transfer To see a list of available ftp commands type help. Type ls command to check the content of your submission account. Type prompt to switch off confirmation for each file uploaded. Use mput command to upload files: mput *.bam* Use bye command to exit the ftp client. Using FTP client FileZilla We recommend the use of FileZilla, a free FTP client . FileZilla is open source software distributed free of charge under the terms of the GNU General Public License. Use the following connection details (File - Site Manager) and add yoursubmission account username and password: Using FTP client FileZilla Select the files you wish to upload and then select upload: Using FTP client Filezilla Using LFTP with TLS We recommend the following to force the use of a secure connection. lftp > set ftp:ssl-force yes We also recommend setting the following for not encrypting the bulk data itself for performance reasons (theauthentication will still be encrypted): lftp > ftp:ssl-protect-data no In order to verify the certificate,the recommended way would be to use the CA certificates from your machine. To do that use this command in lftp adjusting the path to your ca-certificates location. lftp > ssl:ca-file "/etc/ssl/certs/ca-certificates.crt" If that is not possible or certificates are old and you can't update them, you can download the certificates needed from Quo Vadis Digital Repository The two certificates to download in PEM format are: QuoVadis Root CA2 G3 QuoVadis EV SSL ICA G3 Then you can concatenate them in a file one after the other and save it as lftp-certificates.pem Once this is done you have to point the ssl:ca-file variable to the path lftp > set ssl:ca-file "/path/to/lftp-certificates.pem" Also note that you can save this configuration at ~/.lftp/rc Another option is to download the certificates and add them to the ca-certificates of your machine. For example: In RHEL7 and cenots and others box the process to add the certificates globally is: Download the two certificates (in PEM or DER format, doesn't matter) and save them to "/etc/pki/ca-trust/source/anchors/" Run "update-ca-trust extract" Another less secure option is to turn off certificate verification with the following command: lftp > set ssl:verify-certificate false Troubleshooting If you are having problems with Aspera connection timeouts, it can be down to either one of the following. Transfers cannot start the connection fails instantly. Ensure that TCP traffic on port 33001 is allowed (open) for outbound connections through your computer's firewall and network's firewall. The connection is made, transfers are started, but 0 bytes (0%) are uploaded for each file. Ensure that UDP traffic on port 33001 is allowed (open) for outbound connections through your computer's firewall and network's firewal

  Documentation submission/data/uploading-files/ftp

• Berlin Institute for Medical Systems Biology - ccfDNA Methylation Data Access Commitee

  Dac EGAC00001003283

• The data access committee for Detecting Liver Cancer Using Cell-Free DNA Fragmentomes

  Dac EGAC00001003279

• DAC for the study of response to EGFR Blockade in Colorectal Cancer

  Dac EGAC00001000360