Search Results - EGA European Genome-Phenome Archive

RNA-seq study of longitudinal blood cell samples from children at risk of type 1 diabetes

The appearance of type 1 diabetes (T1D)-associated autoantibodies is the first and only measurable parameter to predict progression toward T1D in genetically susceptible individuals. However, autoantibodies indicate an active autoimmune reaction, wherein the immune tolerance is already broken. Therefore, there is a clear and urgent need for new biomarkers that predict the onset of the autoimmune reaction preceding auto-antibody positivity or reflect progressive b-cell destruction. Here we report the mRNA sequencing–based analysis of 306 samples including fractionated samples of CD4+ and CD8+ T cells as well as CD4, CD8 cell fractions and unfractionated PBMC samples longitudinally collected from seven children who developed beta-cell autoimmunity (Case subjects) at a young age and matched control subjects.

Study EGAS00001004071

The molecular landscape of glioma in patients with Neurofibromatosis 1.

Neurofibromatosis type 1 (NF1) is a common tumor predisposition syndrome in which glioma is one of the most prominent tumor type. Gliomagenesis in NF1 results in heterogeneous spectrum of tumors, from low grade to high grade glioma, occurring during the entire patient lifespan. In this study, we present the molecular landscape of low- and high-grade glioma in children and adults affected by NF1 (NF1-glioma). 59 tumor samples from 56 NF1-glioma patients with 43 matched normal were profiled with Whole Exome Sequencing, DNA Methylation array (31 tumors) and RNA sequencing (29 tumors).

Study EGAS00001003186

Implementation, Adoption, and Utility of Family History in Diverse Care Settings

The purpose of this study is to address the key question of whether and how family health history (FHH) is adopted as a tool to more efficiently manage patients at risk for breast, colon, ovarian, and hereditary cancer syndromes as well as thrombophilia and coronary heart disease (CHD) and to provide evidence supporting clinical utility -- improved health behaviors in patients and physician screening recommendations. Five health care delivery organizations will participate in this demonstration project: Duke University, the Medical College of Wisconsin, the Air Force, Essentia Health, University of North Texas. Duke will serve as a coordinating center for this project (Pro00043372) as well as a site. Healthcare Effectiveness Data and Information Set (HEDIS) measures as intermediate clinical effectiveness measures for Coronary Heart Disease (CHD) and selected cancers as well as survey/formative data and electronic medical record (EMR) data will be used as outcomes measures. The research model is purposely designed to mimic clinical delivery as an important step toward widespread implementation and sustainability. In addition, a cost-effectiveness analysis comparing usual care to the FHH guided preventive health model will be used. The completion of this project will result in an optimal strategy for integration of FHH data collection and clinical decision support (CDS) tools into an EMR and demonstrate the utility of the FHH intervention among diverse primary care patients, their settings, their providers, and the health systems that deliver their care. Specific Aim 1: To optimize the collection of patient entered FHH in diverse clinical environments for coronary heart disease, thrombosis, and selected cancers Specific Aim 2: To export FHH data to an open clinical decision support (open CDS) platform and return CDS results to providers and patients (and to EMRs where relevant). To explore the integration of genetic risk and FHH data at selected sites. Specific Aim 3: To assess the clinical and personal utility of FHH using a pragmatic observational study design to assess reach, adoption, integrity, exposure, and sustainability, and to capture, analyze, and report effectiveness outcomes at each stakeholder level: patient, provider, and clinic/system. Specific Aim 4: To take a leadership role in the dissemination of guidelines for a FHH intervention across in diverse practice settings. AAA Hypertension Lupus Multiple sclerosis Obesity Osteoporosis Thyroid disease Rheumatoid arthritis Blood clotting (6): Blood Clot (high risk features) Factor V PT mutation AT III deficiency Protein S deficiency Protein C deficiency Brain Disorders (5): Dementia Hemorrhagic stroke Ischemic stroke Macrocephaly Seizure Cancer/Adenomas (32): Bone cancer Brain cancer Breast cancer Colon cancer Adrenal cortex tumor Neuroendocrine tumor Paraganglioma Pheochromoctyoma Pituitary adenoma Medullary thyroid cancer Non-medullary (follicular or papillary) thyroid cancer Don't know type of thyroid cancer Thyroid nodule Other type of endocrine cancer Esophageal cancer Kidney cancer Leukemia Lipoma Liver cancer Muscle cancer Ovarian cancer Pancreatic cancer Prostate cancer Rectal cancer Retinoblastoma Melanoma Non-melanoma skin cancer Do not know type of skin cancer Small bowel cancer Stomach cancer Uterine cancer Other type of cancer Cardiovascular/heart/artery disease (5): Atrial fibrillation Carotid stenosis Heart attack/coronary artery disease Peripheral arterial disease Other heart disease Diabetes (3): Gestational diabetes Diabetes type I Diabetes type 2 Digestive Disorders (5): Colon polyp Crohn's disease Irritable bowel syndrome Ulcerative colitis Other digestive disorder Eye Disorder (3): Blindness Glaucoma Macular degeneration Hereditary Cancer Syndromes (22): Birt-Hogg-Dube syndrome Cowden syndrome Familial adenomatous polyposis Hereditary breast and ovarian cancer syndrome Hereditary diffuse gastric cancer Hereditary leiomyomatosis and renal cell carcinoma syndrome Hereditary melenoma Hereditary papillary renal cancer syndrome Hereditary paraganglioma-pheochromocytoma syndrome Hereditary retinoblastoma Juvenile polyposis Li-Fraumeni syndrome Lynch syndrome Mutyh-associated polyposis Malignant hyperthermia susceptibility Multiple endocrine neoplasia type 1 Multiple endocrine neoplasia type 2 Nevoid basal cell carcinoma syndrome Peutz-Jeghers syndrome Tuberous Sclerosis complex Von-Hippel Lindau syndrome Other hereditary cancer Hereditary Cardiovascular syndromes (10): Long qt Brugada Catecholaminergic polymorphic ventricular tachycardia Hypertrophic cardiomyopathy Dilated cardiomyopathy Left ventricular non-compaction syndrome Arrhythmogenic right ventricular dysplasia Ehlers-Danlos syndrome Marfan syndrome Other hereditary cardiovascular syndrome High cholesterol (2): Hyperlipidemia Familial hypercholesterolemia Kidney Disease (6): Cystic kidney disease Diabetic kidney disease Kidney nephrosis Nephritis Nephrotic syndrome Other kidney disease Liver Disease (6): Alpha 1 antitrypsinase deficiency Auto-immune hepatitis Hereditary hemochromatosis Primary biliary cirrhosis Sclerosing cholangitis Wilson's disease Lung Disease (3): Asthma COPD/chronic bronchitis/emphysema Other lung disease Psychological disorder (14): Addiction ADHD Autism Bipolar Depression Eating disorder Intellectual disability Obsessive compulsive disorder Panic disorder Personality disorder PTSD Schizophrenia Social phobia Sickle cell/thalassemia (3): Sickle cell disease Sickle cell trait Thalassemia

Study phs001641

University of Bergen Rare Monogenic Autoimmune Syndrome (APS-1) Data Access Committee

This is the DAC appointed by the University of Bergen for the datasets related to the study of APS-1 in the Department of Clinical Sciences . These data are from single cell transcriptomic analysis of expanded Tregs from patients with the rare monogenic autoimmune syndrome “APS-1” compared to healthy donors.

Dac EGAC50000000081

FFPE CRC WGS Data

The data consists of 3 BAM files. Two of three BAMs are tumour FFPE samples (1 repaired-FFPE; 1 unrepaired-FFPE); The other BAM file is sequenced from normal colon tissue

Dataset EGAD00001007716

ApoA-1 and Atherosclerosis in Psoriasis

Apolipoprotein A-1 is a protein localized to high-density lipoprotein that functions to remove cholesterol from tissues for transport back to the liver. Psoriasis is a systemic inflammatory disease associated with poor high-density lipoprotein function and accelerated non-calcified burden by coronary computed tomographic angiography. In this study, 310 psoriasis patients from The Psoriasis, Atherosclerosis, and Cardiometabolic Disease Initiative (PACI) were studied at baseline to determine if levels of circulating apolipoprotein A-1 predict early onset coronary artery disease in inflammatory conditions. Of the 310 participants, 124 were followed for four years to determine if apolipoprotein A-1 predicts non-calcified coronary burden over time. The primary outcome of this study was non-calcified coronary burden by coronary computed tomography angiography. To assess non-calcified coronary burden, participants underwent coronary computed tomography angiography using the same scanner (320-detector row Aquilion ONE ViSION, Toshiba, Japan). For ApoA-1 quantification, fasting blood draw was performed the same day as the coronary computed tomography angiography. The 400MHz proton Vantera Clinical Analyzer was used to quantify Plasma apolipoprotein A-1. The study determined that low levels of apolipoprotein A-1 are associated with increased coronary artery burden and that this relationship persists over time. The data are available through dbGaP and the dataset includes all variables reported in the manuscript.

Study phs003231

ICARUS-BREAST01 Dataset

A phase II clinical trial (NCT04965766) of patritumab deruxtecan in 99 breast cancer patients. Whole-exome sequencing (WES) is available for 43 tumor samples and 43 blood samples collected at entry into the trial. RNA-squencing is available for 44 samples comprising 22 samples collected at entry into the trial and 22 samples collected during treatment (cycle 1 day 3, cycle 1 day 19, or cycle 2 day 3) from 22 patients.

Dataset EGAD50000000773

Genotypic data of the individuals in HPP project

Dataset contains imputed SNP genetics data for human phenotype project https://humanphenotypeproject.org/. The samples are genotypes of 8958 individuals that underwent genetic sequencing based on buccal swab.Sequencing is performed at Neogen GeneSeek Laboratories using Illumina Novaseq at low-pass, targeting coverage levels of 0.5Ã— and 1Ã—. Imputation was done using loimpute-v0.1.0 over the aligned reads. Imputation is done for the autosomal chromosomes (1-22) using The 1000 Genomes phase 3 haplotypes (1KGP3) as a reference panel. The samples are provided in merged PLINK 1 binaries (.bed/.bim/.fam)

Dataset EGAD00010002714

Resistance_to_MAPK_inhibitor_induces_internal_duplication_in_BRAF

2 BRAFV600E cell lines that have been made resistance to 1. the BRAF inhibitor PLX4720 and 2. the combination therapy of dabrafenib and trametinib seem to have a internal duplication in the kinase domain. We would like to know if this is caused by a translocation.

Study EGAS00001001304

Pre_clinical_evolution_of_haematological_malignancies

Deep targeted sequencing of 291 peripheral blood samples taken 1-20 years prior diagnosis of a haematological malignancy alongside age- and sex-matched controls. Key objective is to distinguish indolent age-related clonal haematopoiesis from pre-cancer and to derive predictive models for early risk-stratification.

Study EGAS00001002964

Isotype_resolved_sequencing_of_B_cell_receptor__in_health_and_disease

Sequencing of B-cell receptor repertoires in healthy individuals and patients with chronic lymphocytic leukemia. 1) This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/

Study EGAS00001002634

Genetic coupling of enhancer activity and connectivity in gene expression control

In this study, we investigated whether the genetic effects of eQTLs on enhancer activity and enhancer-promoter contacts (connectivity) are inherently coupled or distinct. To address this, we performed Capture Hi-C targeting ~1100 eQTL loci, along with ATAC-seq and RNA-seq, in monocytes from 34 healthy male donors. Using single-modality and multi-modality Bayesian statistical approaches, we demonstrated that eQTL effects are predominantly shared across enhancer activity, connectivity, and gene expression. However, we also identified a locus where an eQTL disrupts a CTCF binding site at a putative insulator, facilitating contacts between the PCK2 gene promoter and downstream enhancers. To validate these findings, we used 4C-seq to confirm the allelic effects of eQTLs on enhancer-promoter connectivity at the PCK2 locus and another eQTL region associated with THBS1 expression, both of which showed consistent eQTL effects across all three modalities. Additionally, we conducted Hi-C on monocytes from two donors to map reference TADs and performed ChIP-seq to confirm the allelic effects of the PCK2 eQTL on CTCF binding. Together, these data provide comprehensive insights into the genetic coupling of enhancer activity, connectivity, and gene expression.

Study EGAS50000000756

Treatment-mediated selection of lethal prostate cancer clones defined by copy number architectures

Despite initial responses to hormone treatment, metastatic prostate cancer invariably evolves to a lethal state. To characterize the intra-patient relationships of metastases that evade treatment, we performed genome- wide copy number profiling and bespoke approaches targeting the androgen receptor (AR) on 142 metastatic regions from 10 organs harvested post-mortem from nine men who died from prostate cancer. We identified diverse and patient-unique alterations clustering around the AR in metastases from every patient with evidence of independent acquisition of related genomic changes within an individual and, in some patients, the co-existence of AR-neutral clones. Using the genomic boundaries of pan-autosome copy number change, we confirmed a common clone of origin across metastases and diagnostic biopsies; and identified in individual patients, clusters of metastases occupied by dominant clones with diverged autosomal copy number alterations. Autosome-defined clusters were characterized by cluster-specific AR gene architectures that in two index cases were topologically more congruent than by chance (p-values 0.03, 3.07x10-8). Integration with anatomical site suggested patterns of spread and points of genomic divergence. Copy number boundaries identified treatment-selected clones with putatively distinct lethal trajectories.

Study EGAS00001006598

SIGi001-A-1 / SAMEA4451096 WGS data

The iPSC line SIGi001-A-1 / SAMEA4451096 has undergone whole genome sequencing. Whole genome sequencing was done using Illumina HiSeq X Five platform with a reference genome of GRCh38. Raw data as FASTQ files and analysed data as CRAM files are available for this sample, in this dataset. The iPSC line SIGi001-A-1 is available for research use at www.EBiSC.org.

Dataset EGAD50000001073

Single-cell colony targeted DNAseq

Targeted DNA sequencing was applied to colonies grown from single-cells of patient A.6. The protocol followed is described in https://doi.org/10.1038/s41467-021-21650-1 and the methods of the manuscript

Dataset EGAD00001010193

Dual targeting of polyamine synthesis and uptake in diffuse intrinsic pontine gliomas

Diffuse intrinsic pontine glioma (DIPG) is an incurable malignant childhood brain tumour, with no active systemic therapies and a 5-year survival of less than 1%. Polyamines are small organic polycations that are essential for DNA replication, translation and cell proliferation. Ornithine decarboxylase 1 (ODC1), the rate limiting enzyme in polyamine synthesis, is irreversibly inhibited by difluoromethylornithine (DFMO). Herein we show that polyamine synthesis is upregulated in DIPG, leading to sensitivity to DFMO. DIPG cells compensate for ODC1 inhibition by upregulation of the polyamine transporter SLC3A2. Treatment with the polyamine transporter inhibitor AMXT 1501 reduced uptake of polyamines in DIPG cells, and co-administration of AMXT 1501 and DFMO led to potent in vitro activity, and significant extension of survival in three aggressive DIPG orthotopic animal models. Collectively, these results demonstrate the potential of dual targeting of polyamine synthesis and uptake as a therapeutic strategy for incurable DIPG.

Study EGAS00001004905

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

National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS)

NIAGADS is an NIH controlled-access data repository that collaborates with researchers around the world to facilitate data access and sharing for genomic and associated omics data related to Alzheimer's Disease (AD), Alzheimer's Disease Related Dementias (ADRD), as well as healthy aging. Serving as the data coordinating center for the Alzheimer's Disease Sequencing Project (ADSP), NIAGADS hosts all ADSP-generated whole-exome, whole-genome and associated phenotypes. Other data types hosted by NIAGADS include genotype SNP arrays, functional omics, fluid biomarkers, aggregated imaging data (such as volumetric data for brain regions of interest), and summary statistics. In adherence with NIH security best practices and genome data sharing policies, NIAGADS supports data access requests through review by an NIH-appointed and registered Data Access Committee. Data stored within NIAGADS cannot be requested via dbGaP. Data requests must go through the NIAGADS Data Sharing Service (DSS) via the following NIAGADS url: https://dss.niagads.org/. Questions related to the request of data should be directed to the NIAGADS admin team at: niagads@pennmedicine.upenn.edu. DSS is a FISMA certified platform built by a team of data scientists at the University of Pennsylvania to support the request and access of AD/ADRD data by qualified researchers internationally. Questions related to the use of data hosted in the DSS should be directed to the Portal admin team at: help@niagads.org.

Study phs004180

Chromatin remodeling enhances MAP3K8 expression in HAM: a key pathogenesis for therapeutic intervention

Human T-cell leukemia virus type 1 (HTLV-1)-associated myelopathy (HAM) is a debilitating neuroinflammatory disease with no available effective treatments. A hallmark of HAM is the transformation of HTLV-1-infected cells into T helper type 1 (Th1)-like cells, characterized by excessive interferon (IFN)-gamma production that drives chronic inflammation. However, the molecular mechanisms fuel this aberrant Th1-like transformation and sustained inflammation remain poorly understood. We hypothesized that HAM-characteristic chromatin remodeling plays a pivotal role in the overexpression of key genes driving inflammatory pathogenesis. Using transcriptomic analysis, chromatin accessibility profiling, and biomarker evaluations across HTLV-1-related diseases, we identify MAP3K8 as a key gene that defines the unique inflammatory profile of HAM. MAP3K8 overexpression promotes Th1-like differentiation and constitutively activates the MEK-ERK signaling pathway. Furthermore, we elucidate the mechanism by which HTLV-1 Tax, Fosl2, and c-Jun collaboratively induce HAM-characteristic chromatin remodeling at the enhancer region of the MAP3K8 locus. Crucially, we demonstrate that mitogen-activated protein kinase kinase (MEK) inhibitors effectively suppress the MAP3K8-MEK signaling cascade and significantly mitigated inflammatory pathogenesis in an ex vivo culture assay. Our findings provide critical insights into the virus-host interactions underpinning HAM and propose the MAP3K8-MEK-ERK axis as a promising therapeutic target for this challenging condition.

Study JGAS000835

Skin Microbiome in Disease States: Atopic Dermatitis and Immunodeficiency

The NIH Intramural Skin Microbiome Consortium (NISMC) is a collaboration of investigators with primary expertise in genomics, bioinformatics, large-scale DNA sequencing, dermatology, immunology, allergy, infectious disease, and clinical microbiology. Atopic dermatitis (AD, "eczema") is a chronic relapsing skin disorder that affects ~15% of U.S. children and is associated with $1 billion of medical costs annually. AD is characterized by dry, itchy skin, infiltrated with immune cells. Colonization by Staphylococcus aureus (S. aureus) is ten-fold more common in AD patients and is associated with disease flares. We hypothesize that, in addition to S. aureus, AD may also be associated with additional novel microbes and/or selective shifts of commensal microbes that are relevant to disease progression. The NISMC seeks to define the microbiota that resides in and on the skin and nares of three patient groups, all of whom have eczematous lesions and are currently seen at the NIH Clinical Center: (1) AD patients; (2) Wiskott-Aldrich syndrome (WAS) patients; and (3) Hyper IgE syndrome (HIES) syndrome patients. Examination of the microbiome of patients with WAS or HIES syndromes, both rare immunodeficiencies, will advance our understanding of how an individual's immune system shapes their cutaneous microbial community. We are performing a prospective longitudinal study that follows these groups of patient thorough the cycles of eczema flares, ascertaining clinical data and samples at each stage.

Study phs000266

Clinical data of liver cancer patients from EuCanImage - Use case 1 Synthetic

This study is part of EuCanImage project. It contains the standardized clinical information following FHIR model. The data was collected together with MRI of patients diagnosed with liver cancer (HCC) and controls (non-HCC). All data is synthetic.

Study EGAS50000001444

Whole genome sequencing data of paediatric KMT2A-rearranged acute lymphoblastic leukemia

Pediatric KMT2A-rearranged leukemia is an aggressive subtype of leukemia commonly seen in infants (less tha 1 year old) and young children. It involves rearrangements of the KMT2A gene (formerly MLL) on chromosome 11q23, resulting in fusion genes that drive uncontrolled cell growth. This subtype is most often found in acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) and is associated with a poor prognosis. In infants, the 5-year event-free and overall survival rates are around 35–40%. Despite intensive chemotherapy, outcomes remain challenging, prompting ongoing research into targeted therapies and novel treatment strategies.

Study EGAS50000001079

16S-based fecal microbiota composition

The dataset reports the 16S rRNA gene sequencing of the fecal microbiota of donors from the Milieu Intérieur Cohort. The Milieu Intérieur cohort includes a total of 1,000 healthy individuals of western European ancestry, recruited in France as part of the Milieu Intérieur project. To assess their fecal microbiota composition, 16S rRNA profiles were generated from stool samples of 863 of the 1,000 donors. Human stool samples were produced at home no more than 24 hours before the scheduled medical visit and collected in a double-lined sealable bag maintaining strict anaerobic conditions. Upon reception at the clinical site, the fresh stool samples were aliquoted and stored immediately at -80°C. DNA was extracted from stool and barcoding PCR was carried out using indexed primers targeting the V3-V5 region of the 16S rRNA gene. Equal volumes of normalized PCR reaction were pooled and thoroughly mixed. The amplicon libraries were sequenced on Illumina MiSeq.

Dataset EGAD00001004979

DNA whole-exome sequencing data from patients with metastatic basal cell carcinoma

Whole-exome sequencing data in fastq format of matched tumour and germline DNA from 8 patients with metastatic basal cell carcinoma. Samples are labeled as Primary, Local or Metastasis: Primary: Sample was obtained from primary tumour. Local: Sample was obtained from local recurrence of primary tumour. Metastasis: Sample was obtained from a metastatic site. Germline: Sample was obtained from normal adjacent tissue. DNA was isolated from FFPE tissue sections of the tumor biopsies using the AllPrep DNA/RNA FFPE Kit (Qiagen) and quality controls conducted using the Qubit fluorometer (Thermo Fisher Scientific). Library preparation was performed using the Agilent SureSelect Human All Exon v7 XTHS2 probes and sequenced on a NovaSeq 6000 S2 2x100bp

Dataset EGAD00001008675

Immunodeficiency_

We are doing single-cell RNAseq from B cells from monozygotic twins discordant for common variable immunodeficiency 1) This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/

Study EGAS00001002667

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