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in 104.90 milliseconds.

• Single-nucleus brain transcriptomics reveals microglia dysfunction in Multiple System Atrophy

  Multiple System Atrophy (MSA) is a rare, age-related neurodegenerative disease that shares clinical and pathological features with Parkinson’s disease (PD) but presents a more devastating disease course. To elucidate the distinct cellular pathophysiology underlying multiple system atrophy (MSA) or Parkinson's disease (PD), we performed single-nucleus RNA sequencing on postmortem striatal brain tissue from 7 MSA and 12 PD patients, and 10 non-neurological cases. Using 10x Genomics Chromium Next GEM Single Cell 3' Reagent Kits v3.1, we isolated 130k nuclei covering all major cell types of the striatum. We performed state-of-the-art bioinformatic analyses on the data followed by orthogonal analyses to validate our findings. We identified a disease-specific activation of microglia cells distinguishing the two diseases and possibly explaining some of the detrimental effects observed in both.

  Study EGAS50000001406

• Translational analyses from a phase II study of pembrolizumab and epigenetic modification with azacitidine in platinum-resistant epithelial ovarian cancer

  Epigenetic modulators may sensitize platinum-resistant ovarian cancer (PROC) to immune checkpoint inhibition by reprogramming the tumor microenvironment. We report clinical and translational findings from a phase II non-randomized study of pembrolizumab and oral azacytidine in 34 women with PROC (NCT02900560). The effect of combined epigenetic and immunotherapy was evaluated by transcriptomic analyses of serially biopsied tumors. Differential gene expression analyses revealed an upregulation of inflammatory and cytolytic genes and co-inhibitory checkpoints 6 weeks on-therapy. Upregulation of interferon signaling, antigen presentation and immune cell adhesion and migration gene sets was prominent on-therapy, together with an increased density of CD8+ T -cells. Patients with a CA-125 and/or clinical response showed an enrichment of adaptive and conserved immune response gene sets on-therapy.

  Study EGAS50000001165

• Raw human sequencing data for “Characterization of intestinal immune responses in generalized human and murine lipodystrophy”

  This dataset contains raw FASTQ files generated from peripheral blood and intestinal biopsy samples collected for the study “Characterization of intestinal immune responses in generalized human and murine lipodystrophy.” Peripheral blood sequencing was performed on samples from three healthy donors as controls and from a patient with acquired generalized lipodystrophy and Crohn’s disease (AGLCD) at two distinct timepoints. Intestinal biopsy–derived immune cells were sequenced from three individuals with Crohn’s disease as disease controls, as well as from the AGLCD patient (single timepoint). The data include paired-end FASTQ files derived from single-cell RNA sequencing and T-cell receptor repertoire profiling, enabling analyses of immune composition, transcriptional states, and clonal T-cell expansion. All data are controlled-access human genomic datasets and require appropriate authorization for secondary use.

  Study EGAS50000001517

• Reconstructing the dispersals and adaptive history of Bantu-speaking populations in Africa and North America

  Bantu languages are spoken by ~310 million Africans, yet the genetic history of Bantu-speaking populations remains largely unexplored. We generated genomic data for 1,318 individuals from 35 populations in western central Africa, where Bantu languages originated. We found that early Bantu speakers first moved southward, through the equatorial rainforest, before spreading toward eastern and southern Africa. We also found that genetic adaptation of Bantu speakers was facilitated by admixture with autochthonous populations, particularly for the HLA and LCT loci. Finally, we identified a major contribution of western central African Bantu speakers to the ancestry of African Americans, whose genomes present no strong signals of natural selection. Together, these results highlight the contribution of Bantu-speaking peoples to the complex genetic history of Africans and African Americans.

  Study EGAS00001002078

• BCR-ABL is enriched in S- and G2-cell cycle phases

  To address the unusually long duration of imatinib therapy for chronic myelogenous leukemia, we sought to understand the expression of BCR-ABL gene expression with different phases of the cell cycle. A precedent for dynamic fusion oncoprotein expression already exists for the pediatric solid tumor, rhabdomyosarcoma. Quantitative immunocytochemistry was conducted to compare the expression of BCR-ABL with markers of each cell cycle phase: CDK6 for G1, CDK2 for S, phospho-CDC2 for G2, and phospho-HH3 for M phase. BCR-ABL expression was most strongly correlated with the G2 and S cell cycle phases. Low BCR-ABL expression only coincided with high cell cycle marker expression for the G1. BCR-ABL was rarely if at all expressed in M. Our results suggest a possible explanation for the prolonged nature of imatinib therapy, which may be only effective on S- and G2-phase actively replicating leukemia cells.

  Study EGAS00001006769

• BLUEPRINT ChIP-seq data for cells in the haematopoietic lineages, from adult and cord blood samples.

  ChIP-seq data for cells in the haematopoietic lineages, from adult and cord blood samples.

  Study EGAS00001000326

• Investigate the evolutionary trajectories during invasiveness acquisition in early lung adenocarcinoma

  The evolutionary trajectories of early lung adenocarcinoma (LUAD) have not been fully elucidated. We hypothesize that genomic analysis between pre-invasive and invasive components will facilitate the description of LUAD evolutionary patterns. We micro-dissect malignant pulmonary nodules (MPNs) into paired pre-invasive and invasive components for panel-genomic sequencing and recognize three evolutionary trajectories. Evolutionary mode 1 (EM1) demonstrates none of the common driver events between paired components, but another two modes, EM2A and EM2B, exhibit critical private alterations restricted to pre-invasive and invasive components, respectively. When ancestral clones harbor EGFR mutations, truncal mutation abundance significantly decrease after the acquisition of invasiveness, which may be associated with the intratumoral accumulation of infiltrated B cells. Harboring EGFR mutations is critical to the selective pressure and further impacts the prognosis. Our findings extend the understanding of evolutionary trajectories during invasiveness acquisition in early LUAD.

  Study EGAS00001004754

• Erythroid/megakaryocytic differentiation confers BCL-XL dependency and venetoclax resistance in acute myeloid leukemia

  Myeloid neoplasms with erythroid or megakaryocytic differentiation include pure erythroid leukemia (PEL), myelodysplastic syndrome (MDS) with erythroid features, and acute megakaryoblastic leukemia (FAB M7) and are characterized by poor prognosis and limited treatment options. Here, we investigate the drug sensitivity landscape of these rare malignancies. We show that acute myeloid leukemia (AML) cells with erythroid or megakaryocytic differentiation depend on the anti-apoptotic protein BCL-XL, rather than BCL-2, using combined ex vivo drug sensitivity testing, genetic perturbation, and transcriptomic profiling. Single-cell and bulk transcriptomics of patient samples with erythroid and megakaryoblastic leukemias identified high BCL2L1 expression compared to other subtypes of AML and other hematological malignancies, where BCL2 and MCL1 were more prominent. Our results suggest targeting BCL-XL as a potential therapy option in erythroid/megakaryoblastic leukemias and highlight an AML subgroup with potentially reduced sensitivity to venetoclax-based treatments.

  Study EGAS00001006819

• Characterization of Cell Free Plasma Methyl-DNA From Xenografted Tumors to Guide the Selection of Diagnostic Markers for Early-Stage Cancers

  Circulating cell-free methyl-DNA (mcfDNA) contains promising cancer markers but its low abundance and possibly diverse origin pose challenges toward the accurate diagnosis of early stage cancers. By whole-genome bisulfite sequencing (WGBS) of cell-free DNA (cfDNA) from about 0.5 mL plasma of mice xenografted with human tumors, we obtained and aligned the reads to the human genome, filtered out the mouse and carrier bacterial sequences, and confirmed the tumor origin of methyl-cfDNA (mctDNA) by methylation-sensitive restriction enzyme digestion prior to species-specific PCR. We estimated that human tumor-specific reads (ctDNA) or mctDNA comprised about 0.29 or 0.01%, respectively of the xenograft mouse cfDNA, and about 0.029 or 0.001% of the cfDNA of human early stage cancer patients. Similar WGBS of early stage (0-II, node- and metastasis-free) breast, lung or colorectal cancer samples identified hundreds of specific DMRs (differentially methylated regions) compared to healthy controls. Their association with tumourigenesis was supported by stage-dependent methylation, tumor suppressor or oncogene clusters, and genes also identified in the xenograft samples. Using 20 three-cancer-common and 17 colorectal cancer-specific DMRs in combination (top 0.0018% of the WGBS methylation clusters) was sufficient to distinguish the stage I colorectal cancers from breast and lung cancers and healthy controls. Our data thus confirmed the tumor origin of mctDNA by sequence specificity, and provide a selection threshold for authentic tumor mctDNA markers toward precise diagnosis of early stage cancers solely by top DMRs in combination.

  Dataset EGAD00001008696

• Single cell characterization of T-cell lymphoma: RNA (2025-07-31)

  Lymphomas are types of blood cancer derived from lymphocytes, and can be classified into two main categories, Hodgkin's lymphomas (HL) and non-Hodgkin lymphomas (NHL). T-cell lymphoma is a type of NHL that develops from T lymphocytes and accounts for 10-15% of total NHL cases. As a rare disease, T-cell lymphoma is functionally, pathologically, and clinically complex and heterogeneous. The 2016 revision of the WHO classification has listed 29 subtypes of mature T/NK-cell neoplasms, among which over 25 are T lymphocyte in origin. These subtypes of T-cell lymphomas have different biological behaviours and clinical prognosis. Clinically, T-cell lymphoma ranges from indolent (slow-growing) to aggressive (fast-growing and spreading) disease. According to the guideline booklet provided by the Lymphoma Research Foundation (https://lymphoma.org/aboutlymphoma/nhl/), indolent T-cell lymphoma subtypes include adult T-cell leukaemia/lymphoma (ATLL), cutaneous T-cell lymphoma (CTCL), and mycosis fungoides (MF), while the aggressive subtypes include anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), peripheral T-cell lymphoma (PTCL), and T lymphoblastic lymphoma (TLBL). PTCL and ALCL represent the most common subtypes of aggressive T-cell lymphoma whereas TLBL is a rare and aggressive subtype mostly diagnosed in children. In this study, we will use single cell RNA sequencing and single cell multiomic methods three aggressive T-cell lymphoma subtypes (ALCL, PTCL and TLBL) and try to gain a deeper insight into the genotypic and phenotypic features of these T-cell lymphomas. . This dataset contains all the data available for this study on 2025-07-31.

  Dataset EGAD00001015669

• RNAseq of whole blood in 359 idiopathic pulmonary arterial hypertension patients reveals biological heterogeneity

  Patients with idiopathic, heritable, or drug-induced pulmonary arterial hypertension (referred to throughout as PAH) were recruited from expert centers across the UK as part of the PAH Cohort study (www.ipahcohort.com). In each case, diagnosis was confirmed by right heart catheterization following established international guidelines, which remained unchanged for the duration of this study. Healthy volunteers were recruited at the same centers and samples processed using the same standard operating procedure at all sites. All individuals gave written, informed consent with local ethical committee approval. Whole blood (3 ml) was collected in Tempus Blood RNA Tubes, and RNAseq was performed using established Illumina methodologies (see online supplement for further details).

  Dataset EGAD00001007981

• Genomic analysis for intrahepatic cholangiocarcinoma

  Intrahepatic cholangiocarcinomas (iCCs) are characterized by their rarity, difficulty in diagnosis, and overall poor prognosis. We performed comprehensive genomic characterization of treatment-naive iCC. This study reports a large-scale genomic analysis of iCC. The findings could assist in patient stratification with iCCs and in developing rational therapeutic strategies.

  Dataset EGAD00001008557

• A Phase II Neoadjuvant Study of Palbociclib in Combination with Letrozole and Trastuzumab as Neoadjuvant Treatment of Stage II-III ER+ HER2+ Breast Cancer (PALTAN)

  Women with early stage ER+/HER2+ breast cancer (BC) are less likely to achieve pathological complete response (pCR) after chemotherapy with standard of care dual HER2 blockade than patients with ER-/HER2+ BC. Additionally, the toxicity burden of standard systemic therapy is high. Thus, this single arm phase 2 clinical trial investigated a novel de-escalation regimen with trastuzumab, letrozole, and palbociclib in those with early stage ER+/HER2+ BC. The aim was to determine if this treatment regimen will achieve similar pCR rates as conventional treatment and spare women the toxicity burden of regular treatments. The primary endpoint was pCR after 16 weeks of treatment with trastuzumab, letrozole, and palbociclib. Patients with newly diagnosed ER+ HER2+ invasive BC with clinical stages II and III were eligible for this protocol. Research biopsies were performed during the treatment course for whole exome and RNA sequencing, PAM50 molecular subtyping, and Ki67 assessment using immunohistochemistry for complete cell cycle arrest (CCCA: Ki67 ≤2.7%). Results showed a pCR of 7.7% and a rate of residual cancer burden at 0 or I of 38.5%. CCCA was observed in 85% at Cycle 1 day 15 post palbociclib, trastuzumab, and letrozole treatment (C1D15), compared to 27% at surgery after palbociclib was discontinued. PAM50 subtyping identified 31.2% HER2-E, 43.8% Luminal B, 25% Luminal A. 161 genes were differentially expressed comparing C1D15 to baseline. Whole exome and RNA sequencing data will be available through dbGaP.

  Study phs003147

• CINECA_synthetic_cohort_EUROPE_UK1 referencing fake samples

  Please note: This synthetic data set (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY). Please ensure this preamble is included with this dataset and that the CINECA project (funding: EC H2020 grant 825775) is acknowledged. For any questions please contact isuru@ebi.ac.uk or cthomas@ebi.ac.uk This dataset (CINECA_synthetic_cohort_EUROPE_UK1) consists of 2521 samples which have genetic data based on 1000 Genomes data (https://www.nature.com/articles/nature15393), and synthetic subject attributes and phenotypic data derived from UKBiobank (https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001779). These data were initially derived using the TOFU tool (https://github.com/spiros/tofu), which generates randomly generated values based on the UKBiobank data dictionary. Categorical values were randomly generated based on the data dictionary, continuous variables generated based on the distribution of values reported by the UK Biobank showcase, and date / time values were random. Additionally we split the phenotypes and attributes into 4 main classes - general, cancer, diabetes mellitus, and cardiac. We assigned the general attributes to all the samples, and the cardiac / diabetes mellitus / cancer attributes to a proportion of the total samples. Once the initial set of phenotypes and attributes were generated, the data data was checked for consistency and where possible dependent attributes were calculated from the independent variables generated by TOFU. For example, BMI was calculated from height and weight data, and age at death generated by date of death and date of birth. These data were then loaded to the development instance of Biosamples (https://www.ebi.ac.uk/biosamples/) which accessioned each of the samples. The genetic data are derived from the 1000 Genomes Phase 3 release (https://www.internationalgenome.org/category/phase-3/). The genotype data consists of a single joint call vcf files with call genotypes for all 2504 samples, plus bed, bim, fam, and nosex files generated via plink for these samples and genotypes. The genotype data has had a variety of errors introduced to mimic real data and as a test for quality control pipelines. These include gender mismatches, ethnic background mislabelling and low call rates for a randomly chosen subset of sample data as well as deviations from Hardy Weinberg equilibrium and low call rates for a random selection of variants. Additionally 40 samples have raw genetic data available in the form of both bam and cram files, including unmapped data. The gender of the samples in the 1000 genomes data has been matched to the synthetic phenotypic data generated for these samples. The genetic data was then linked to the synthetic data in BioSamples, and submitted to EGA.

  Dataset EGAD00001006673

• Genetic Discovery and Application in a Clinical Setting: Continuing a Partnership (eMERGE Phase II)

  The electronic Medical Records and Genomics (eMERGE) Network is a consortium of ten participating sites funded by the NHGRI to investigate the use of electronic medical record systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 19,000 individuals using EMR-derived phenotypes and DNA from linked Biorepositories. The eMERGE Network brings together researchers with a wide range of expertise in genomics, statistics, ethics, informatics, and clinical medicine from leading medical research institutions across the country. Each center participating in the consortium is uniquely situated to provide critical resources to this highly collaborative and productive network. Each site combines a biobank or study cohort with extensive genomic data and access to clinical data derived from electronic medical records. Sites are geographically dispersed and have diverse patient populations, including two sites focusing specifically on pediatrics. The eMERGE Network is comprised of 9 sites and one coordinating center. Each site maintains its own biorepository where DNA specimens are linked to phenotypic data contained within EMRs. Children's Hospital of Pennsylvania, PI: Hakon Hakonarson, MD, PhD Cincinnati Children's Medical Center with Boston Children's Hospital, PI: John Harley, MD, PhD - CCHMC and Ingrid Holm, MD, MPH - BCH Geisinger Health System, PI: David Carey, PhD and Marc Williams, MD Group Health Cooperative with University of Washington, PI: Eric Larson, MD, MPH - GHC and Gail Jarvik, MD, PhD - UW Marshfield Clinic with Essentia Institute of Rural Health, PI: Catherine McCarty, PhD, MPH - Essentia and Murray Brilliant, PhD - Marshfield Mayo Clinic, PI: Christopher Chute, MD, DrPH and Iftikhar Kullo, MD Icahn School of Medicine at Mount Sinai, PI: Erwin Bottinger, MD Northwestern University, PI: Rex Chisholm, PhD and Maureen Smith, MS Vanderbilt University, PI: Dan Roden, MD eMERGE Coordinating Center - Vanderbilt University, PI: Paul Harris, PhD Using electronic phenotyping methods, the consortium used DNA samples from all participating sites to explore the genetic determinants of red cell indices, white blood count (WBC) differential, diabetic retinopathy, height, serum lipid levels, specifically total cholesterol, HDL (high density lipoprotein), LDL (low density lipoprotein), and triglycerides, and autoimmune hypothyroidism as well as using the phenome-wide association study (PheWAS) paradigm to replicate and discover relationships between targeted genotypes with multiple phenotypes.

  Study phs000948

• 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

• Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study - Maternal Glycemia and Birthweight GEI Study

  Low and high birth weight are not only major causes of neonatal morbidity and mortality, but epidemiological data have established an association between birth weight and later life risk of adult metabolic diseases. Fetal growth is determined by complex interactions between fetal genes and the maternal uterine environment. Subtle or overt variation in maternal glucose tolerance, which is, in part, genetically determined, is related to fetal size at birth. Moreover, new emerging data suggest that genetic variation in the fetus can impact maternal metabolism (e.g., blood pressure and glucose tolerance). Given the above, we are addressing the hypothesis that, during pregnancy, gene-environment interactions in the context of the maternal-fetal unit impact fetal size at birth and maternal metabolism. Genes that control fetal growth or maternal metabolism during pregnancy are largely unknown, so the first step to address our hypothesis will be to identify genetic variation that impacts fetal growth and maternal metabolism and to determine the interaction of that variation with the intrauterine and fetal environment. To accomplish this, we are performing genome wide association (GWA) mapping on a subset of ~37,000 DNA samples that were collected from mothers and their offspring as part of the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study. HAPO is a multicenter, international study in which high quality phenotypic data related to fetal growth and maternal glucose metabolism has been collected from 25,000 pregnant women of varied racial and socio-demographic backgrounds using standardized protocols that were uniform across centers. For these studies, we are genotyping 1,500 infants and their mothers of European descent, 1,250 Afro-Caribbean infants and mothers, 800 Hispanic (Mexican-American) infants and mothers, and 1200 Thai infants and mothers. Genotyping is being performed using the Illumina Human610 Quad (European ancestry participants), Human1M Duo (Afro-Caribbean and Hispanic participants), and Omni1-Quad_v1-0_B (Thai participants). The specific aims for the project are as follows: (1) To apply analytic approaches for conducting GWA mapping studies on quantitative phenotypes related to offspring size at birth (birth weight, ponderal index, head circumference and adiposity) allowing for other known influences such as gestational age, parity, and maternal weight gain. (2) To apply the above approaches to identify genetic variation that impacts maternal glucose tolerance at ~28 weeks of gestation (fasting glucose, glucose during an oral glucose tolerance test, and insulin sensitivity expressed as quantitative traits) allowing for other known influences such as maternal weight gain, parity and age. (3) To examine the interaction between maternal genes, the intrauterine environment, and fetal genes to identify interactions that modulate genetic regulation of size at birth and fetal genetic variation that impacts on maternal glucose tolerance. GWA mapping will provide initial evidence for association of specific SNPs with the quantitative traits outlined above. As low and high birth weight are not only major causes of neonatal morbidity and mortality but have also been associated with increased risk of metabolic diseases in adults, identification of genes that regulate fetal growth and maternal metabolism will provide novel information about the pathways that regulate these processes as well as important insight into susceptibility genes for chronic diseases like type 2 diabetes. The Version 1 (v1) dbGaP release will include data only from the Hispanic study participants. The Version 2 (v2) dbGaP release will include data from the Hispanic and European ancestry study participants. The Version 3 (v3) dbGaP release will include data from the Afro-Caribbean, Hispanic and European ancestry participants. The Version 4 (v4) dbGaP release will include data from all participants (i.e., Afro-Caribbean, Hispanic, European ancestry, and Thai participants). This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to maternal metabolism and birthweight through large-scale genome-wide association studies of infants and their mothers at multiple international sites. Genotyping was performed at the Broad Institute of MIT and Harvard, and at CIDR of Johns Hopkins University, GENEVA genotyping centers. Data cleaning and harmonization was performed at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000096

• PyEGA3 download client

  PyEGA3 Checks prior to download - Is the dataset available for download? Most datasets are available using the PyEGA3 Download client, except for specific legacy datasets. For these ones, refer to Live Outbox distribution, an alternative way to download these datasets.If the dataset you've been granted access to begins with the ID EGAD5XXXXXXX, please refer to the Live Outbox distribution. If the dataset you have been granted access to contains encrypted data (filename ending in .gpg), contact EGA Helpdesk for detailed information on how to download these files.General Information The PyEGA3 download client is available at its GitHub repository. In its README you will find a step by step guide on how to use it to download files you have been granted access to. PyEGA3 implements the GA4GH-compliant HTSGET protocol supporting requests over genomic ranges. This enables the download of specific regions of interest rather than the entire file. HTSGET is not possible for all the files held by EGA: if the dataset does not contain index files (e.g. filename ending in *.crai), then genomic range requests cannot be performed. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Check out our video tutorial! Frequently Asked Questions General questions How do I download the datasets to which I have been granted access? After setting up your EGA download account, you can proceed to download using the EGA download client – PyEGA3. The pyEGA3 download client is a python-based tool for viewing and downloading files from authorised EGA datasets. This download client is continuously being developed for more user-friendly download experiences. Can I download the dataset’s metadata via the download client? No, the download client cannot be used to download metadata. Registered EGA users can download metadata of an authorised dataset by logging into the EGA webpage and navigating to the dataset of choice. Approximately two thirds down the page you will find the option to download the metadata as a zip file. How do I download large datasets as quickly as possible? The main EGA download client is pyEGA3 download client, a python-based tool for viewing and downloading files from authorised EGA datasets.We could exceptionally consider the option of an Aspera download for users experiencing very slow download rates or facing particular issues.Contact the EGA Helpdesk for further information. Pre-requirement and Installation What are the major requirements for the PyEGA3 download client installation and usage? The PyEGA3 client is compatible with any OS with Python 3.6+ installed. The client requires a connection to the internet, sufficient space on the destination drive, and the EGA download account credentials. Where can I find the PyEGA3 download client installation and download instructions? We strongly recommend checking our video tutorial demonstrating the usage of PyEGA3 from installation to file download. More detailed information is available at PyEGA3 GitHub. What ports are required for the PyEGA3 download client to function? PyEGA3 makes HTTPS calls to the EGA Data API (https://ega.ebi.ac.uk:8443). This port 8443 must be reachable from the location where PyEGA3 is executed to avoid timeouts. PyEGA3 download client How to update the PyEGA3 download client to be on the latest version? Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 --upgrade I do not have an active EGA account. Can I test the download with the PyEGA3 download client? An EGA download test account (ega-test-data@ebi.ac.uk) has been created for troubleshooting and training purposes. The test account does not require an EGA username and password because it contains publicly accessible files from the 1000 Genomes Project. More information about the use of this account and log in details are available at pyEGA3. I lost my connection while the download was in progress. Does this mean that I have to download the file again from the start? The PyEGA3 download client supports automatic resumption of downloads. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the resume feature, downloads will automatically resume if you encounter any errors or if the connection is interrupted. How can I improve the overall download speed of the PyEGA3 download client? Download speeds can be optimised using the --connections parameter which will parallelise download at the file level. If the --connections parameter is provided, all files >100Mb will be downloaded using the specified number of parallel connections. It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Why is my file taking a long time to be saved? Please note that when a file is being saved, it goes through two processes. First, the downloaded file "chunks" are pieced back together to reconstruct the original file. Secondly, PyEGA3 calculates the checksum of the file to confirm that the file was downloaded successfully. Larger files will take more time to reconstruct and validate the checksum. Why is the HTSGET protocol not working with my dataset of interest? PyEGA3 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file, saving both time and storage space. Please note that in order for the genomic range requests to work for a BAM, CRAM or VCF file there must be an associated BAI, CRAI or TBI file, respectively. If the dataset does not contain these index files then genomic range requests cannot be performed. After download Do I need to decrypt data files downloaded through the pyEGA3 download client? Files are transferred over secure HTTPS connections and received unencrypted, thus there is no need for decryption after data download. What is the purpose of an MD5 file? After the download completes, file integrity is verified using checksums. The PyEGA3 download client automatically verifies each file’s unencrypted md5 after download to ensure that the file was downloaded correctly from the EGA. Errors Why do I get a “400 Client Error” while trying to access the download client? Ensure that your credentials are formatted correctly. Please contact the EGA Helpdesk if you are unsure about your account’s username (i.e. your email address). Why do I get a “500 Server Error”? It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Otherwise, 500 server errors are internal server errors from EGA’s end. Retry after some time and, if the 500 error persists: check our homepage or Twitter for any outage information or report it to EGA Helpdesk. Why do I get a “slice error”? This type of error occurs when one slice download was prematurely interrupted. Please re-start the same download. The download client will pick up any partial download and download any missing slices. Why do I get the error “Dataset' EGADXXXXXXXXXXX' is not in the list of your authorised datasets”? Please note that the data access was given to the email (username) provided to the Data Access Committee (DAC) in your official request. Therefore, you shall check that the account that you are using is the same one that you sent to the DAC. Why do I get “ERROR:root:Failed to obtain IP address”? This might mean that the connection ports are blocked. Check that the port 8443 is reachable.In case the port is reachable and the error persists, please try to modify the file pyega3.py on line 44 in the python module installation: Replace endpoint = 'https://ipinfo.io/json' with endpoint = 'https://api.myip.com' Why do I get the error “Invalid username, password or secret key - please check and retry”? Make sure that you are using the correct credentials (username and password) and there is no typo. Contact EGA Helpdesk if the problem persists.

  Documentation access/download/files/pyega3

• RFMix

  This dataset contains the per-chromosome RFMix input and output files for the local ancestry inference of 59 Aboriginal Australian genomes as reported in Malaspinas et al., 2016. Local ancestry was inferred assuming four mixing ancestral populations represented by: Europeans (27 individuals), Asians (29 individuals), Papuans (13 individuals) and Native Australians (7 individuals from the WCD region).

  Dataset EGAD00010001575

• Hepatitis B virus integrations promote local and distant oncogenic driver alterations in hepatocarcinogenesis

  This study contain the WGS and WEX aligned bam files and RNA-seq fastq files for human liver tumors.

  Dataset EGAD00001006426

• SARS-CoV2 mRNA-vaccination-induced Immunological Memory in Human Non-Lymphoid and Lymphoid Tissues

  This dataset includes combined single-cell RNA-Seq and T cell receptor profiling data for SARS-CoV-2 spike protein-reactive CD4+ T cells and NK cells from blood, liver, lungs, and bone marrow of human donors.

  Dataset EGAD50000000062

• Age-dependent association of clonal hematopoiesis with COVID-19 mortality in patients over 60 years

  Targeted sequencing with the Myeloid Solutions™ Panel (MYS) of SOPHiA Genetics for COVID-19 patientds (n=241 deceased, n=239 survivors).

  Dataset EGAD50000000001

• PML complete dataset

  We generated single-cell transcriptomics, single-nuclei chromatin accessibility, CITE-seq and Multiomics of patients with PML. Pools are genetically multiplexed across donors, genotypic variation is included in this dataset to enable demultiplexing.

  Dataset EGAD50000000197

• Oxidative DNA damage and ubiquitin proteasome system dysfunction underpins neurodegeneration in young adults with a DNA repair disorder

  Deposited here are WGS data generated from differentiated neural stem cells (NSCs) of NXPG-32 patient (XPG patient with neurodegeneration) and a matched healthy control CTRL-33.

  Dataset EGAD50000000232

• Oxidative DNA damage and ubiquitin proteasome system dysfunction underpins neurodegeneration in young adults with a DNA repair disorder

  Deposited here are time series bulk RNA-seq data generated from XP patients and controls as well as rescued patient hiPSC cells during cortical neuronal differentiation.

  Dataset EGAD50000000230

• Oxidative DNA damage and ubiquitin proteasome system dysfunction underpins neurodegeneration in young adults with a DNA repair disorder

  Deposited here are time series bulk RNA-seq data generated from isogenic wild-type (WT), XPA and XPG gene knockouts hiPSC cells during cortical neuronal differentiation.

  Dataset EGAD50000000228

• Clonally heritable gene expression imparts a layer of diversity within cell types

  Human datasets associated with paper 'Clonally heritable gene expression imparts a layer of diversity within cell types' published in Cell Systems

  Dataset EGAD50000000231

• scRNA-seq of bronchoalveolar lavage (BAL) samples from patients with severe COVID-19 to assess dexamethasone response in the lungs

  Single-cell RNA-sequencing of bronchoalveolar lavage (BAL) samples from patients with severe COVID-19 with or without Dexamethasone treatment and for responders and non-responders was performed using 10x Genomics technology.

  Dataset EGAD50000000292

• RNAseq of preneoplasia lung adenocarcinoma

  RNA sequencing of 168 pulmonary samples including lung preneoplasia atypical adenomatous hyperplasia (AAH, N=38), adenocarcinoma in situ (AIS, N=22), minimally invasive adenocarcinoma (MIA, N=19) and invasive lung adenocarcinoma (ADC, N=38) and adjacent lung tissues (Normal, N=62).

  Dataset EGAD50000000395

• Whole genome sequencing of preneoplasia lung adenocarcinoma

  Whole genome sequencing of 42 pulmonary samples including lung preneoplasia atypical adenomatous hyperplasia (AAH, N=5), adenocarcinoma in situ (AIS, N=7), minimally invasive adenocarcinoma (MIA, N=6) and invasive lung adenocarcinoma (ADC, N=8) and adjacent lung tissues (Normal, N=16).

  Dataset EGAD50000000396

• Exome sequencing analysis of BCP-LBL patients samples

  In order to perform comprehensive SNV and CNA analyses of a cohort of BCP-LBL patients, we performed whole exome sequencing of 41 tissue samples from BCP-LBL patients. Because the material was available as FFPE, the target coverage of the samples was >200x.

  Dataset EGAD50000000418

• Genome Wide Association Studies in Alopecia Areata

  1054 unrelated alopecia areata patients were genotyped with the Illumina HumanHap550v3.0 genotyping chip. Allele frequencies were compared to publically available data from three population cohorts of controls.

  Study phs000586

• Antibody Repertoires in CVID

  The data consist of the DNA sequences of antibody gene rearrangements from peripheral blood human B cells of subjects with Common Variable Immune Deficiency (CVID) and healthy controls subjects.

  Study phs000934

• The Transcriptomic Landscape of Oncogenic PI3K Reveals Key Functions in Splicing and Gene Expression Regulation

  This study includes RNA-seq of paired tumor biopsies from PIK3CA-mutated breast cancer patients that underwent treatment with PI3Kα inhibitors.

  Study phs002840

• Clones derived from early passage tumoroids of colorectal cancer

  We obtained different clones from early passage CRC tumoroids to study mutational signatures specific for truncal or private somatic alterations. These whole genome sequences are part of a larger study on the heterogeneity and evolution of DNA mutation rates in microsatellite-stable colorectal cancer.

  Dataset EGAD50000000152

• IMSGC and MultipleMS project MS patients from Spain

  Genotype and phenotype data on 151 MS patients from Spain. All individuals were whole-genome genotyped on the Illumina Global Screening Array and files are provided in plink2 format (pgen / pvar / psam files). Provided phenotypes are sex, year of birth, age and Age Related Multiple Sclerosis Severity (ARMSS) score.

  Dataset EGAD00010002492

• IMSGC and MultipleMS project MS patients from Greece

  Genotype and phenotype data on 140 MS patients from Greece. All individuals were whole-genome genotyped on the Illumina Global Screening Array and files are provided in plink2 format (pgen / pvar / psam files). Provided phenotypes are sex, year of birth, age and Age Related Multiple Sclerosis Severity (ARMSS) score.

  Dataset EGAD00010002493

• IMSGC and MultipleMS project MS patients from Norway

  Genotype and phenotype data on 575 MS patients from Netherlands. All individuals were whole-genome genotyped on the Illumina Global Screening Array and files are provided in plink2 format (pgen / pvar / psam files). Provided phenotypes are sex, year of birth, age and Age Related Multiple Sclerosis Severity (ARMSS) score.

  Dataset EGAD00010002485

• IMSGC and MultipleMS project MS patients from Austria

  Genotype and phenotype data on 246 MS patients from Austria. All individuals were whole-genome genotyped on the Illumina Global Screening Array and files are provided in plink2 format (pgen / pvar / psam files). Provided phenotypes are sex, year of birth, age and Age Related Multiple Sclerosis Severity (ARMSS) score.

  Dataset EGAD00010002487

• IMSGC and MultipleMS project MS patients from Netherlands

  Genotype and phenotype data on 209 MS patients from Netherlands. All individuals were whole-genome genotyped on the Illumina Global Screening Array and files are provided in plink2 format (pgen / pvar / psam files). Provided phenotypes are sex, year of birth, age and Age Related Multiple Sclerosis Severity (ARMSS) score.

  Dataset EGAD00010002488

• IMSGC and MultipleMS project MS patients from UK

  Genotype and phenotype data on 943 MS patients from UK. All individuals were whole-genome genotyped on the Illumina Global Screening Array and files are provided in plink2 format (pgen / pvar / psam files). Provided phenotypes are sex, year of birth, age and Age Related Multiple Sclerosis Severity (ARMSS) score.

  Dataset EGAD00010002491

• BELLINI clinical trial bulk WES data: cohorts A & B

  WES data of 30 breast cancer primary tumors obtained from 30 unique patients from BELLINI clinical trial (cohorts A & B) and 30 matched blood samples. The data includes pretreatment samples. The included raw WES sequencing data in fastq format was generated using Illumina NovaSeq 6000.

  Dataset EGAD50000000809

• CLL PacBio LRTS IsoSeq

  This dataset contains long read transcriptome sequencing of 19 chronic lymphocytic leukemia (CLL) patients with or without mutation in SF3B1, as well as 6 B cell samples from healthy individuals. The BAM files are unaligned CCS reads.

  Dataset EGAD50000000076

• CLL RNA-seq

  This dataset contains Illumina stranded RNA-seq from of 19 chronic lymphocytic leukemia (CLL) patients with or without mutation in SF3B1, as well as 6 B cell samples from healthy individuals. RNA was extracted from CLL cells.

  Dataset EGAD50000000078

• Metabolic and molecular consequences of the TBC1D4 p.Arg684Ter variant in human skeletal muscle

  Skeletal muscle of Inuit homozygous carriers of the common Greenlandic TBC1D4 p.Arg684Ter variant is severely insulin resistant but have normal metabolic responses during exercise,

  Study EGAS50000000040

• Shallow Whole Genome Sequencing (sWGS) from REPEAT trial

  Samples associated to: "A phase Ib/II study of regorafenib and paclitaxel in patients with beyond first-line advanced esophagogastric carcinoma (REPEAT)". sWGS from 97 Esophagogastric Carcinoma samples. sWGS was done to carry out Copy Number Analysis. Dataset includes: .fastq, .bam and .bai files.

  Dataset EGAD50000001161

• Human inflammatory cardiomyopathies following SARS-CoV2 infection and COVID-19 vaccination dataset

  This dataset consists of transcriptome data of 25 human samples with myocarditis, thereof: 8 without COVID-19, 10 post COVID-19, 4 after COVID-19 vaccination and 3 with MIS-C (Multisystem Inflammatory Syndrome in Children, PIMS).

  Dataset EGAD50000001130

• High-grade serous ovarian carcinoma tumour whole genome sequencing variants

  Tumour somatic variants (in VCF format) called from paired tumour-germline whole genome sequencing (using IDT library preparation system followed by sequencing on Illumina NovaSeq platform) of three high-grade serous ovarian tumour samples. The dataset comprises three VCF variant files.

  Dataset EGAD50000001131

• APS-1 Global Gene Expression

  Single cell sequencing of expanded regulatory T cells (Tregs) in 9 APS-1 patients and 9 age and gender matched controls. Gene expression (GEX) libraries were generated by using the Library Construction Kit from 10x genomics.

  Dataset EGAD50000000260

• PRPH2-Related Retinal Dystrophies: Mutational Spectrum in 103 Families from a Spanish Cohort

  Various NGS experiments from patients affected by genomic variants at gene PRPH2, one of the most frequently inherited retinal dystrophy (IRD)-causing genes.

  Study EGAS50000000847