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• Genome-wide cell-free DNA termini in patients with cancer

  The structure, fragmentation patterns and terminal sequences of cell-free DNA (cfDNA) are altered by nucleases and biological mechanisms in the blood of cancer patients. The cfDNA fragment-end composition recovered from low coverage WGS (<1 fold coverage) using a bespoke software (FrEIA) is aberrant in the plasma from cancer patient (n = 418, 655 samples) compared to controls (n = 117). As a standalone test FrEIA allows detection down to ~0.2% tumor fraction in vitro and in silico at 95% specificity, leading to a sensitivity of ~71% for detecting lung cancer (14/22 stage I-II, 27/38 stage III, 92/127 stage IV) and ~68% for detecting esophageal adenocarcinoma (26/44 stage II, 46/62 stage III). Additional cfDNA biological patterns can be combined with FrEIA increasing the diagnostic potential of low coverage WGS at minimal cost (mean AUROC = 0.96). Integrating multiple cfDNA biological signal augments the diagnostic performance of liquid biopsy.

  Study EGAS00001006142

• Sequencing data from MethylScan assay on plasma

  Machine learning models in biomedical research are often hindered by demographic imbalances in clinical datasets, leading to biased predictions that disadvantage minority populations. Existing bias correction methods face limitations in handling the heterogeneity of biomedical data and the complexity of demographic influences. Here, we present DeBias, a computational framework for mitigating demographic biases in high-dimensional biomedical datasets. DeBias eliminates bias-associated subspaces within the entire feature space using control samples and enables global bias correction across all features, effectively preserving disease-specific signals while minimizing demographic distortions. To demonstrate its effectiveness, we applied DeBias to cfDNA methylation data for cancer detection. It achieved a >6-fold reduction in the number of features containing biases and outperformed existing methods in improving the cancer detection performance for minority populations. Our method represented a promising step toward developing machine learning models that are more accurate, reliable, and equitable for minority populations.

  Study EGAS00001008127

• Submitting array based metadata

  Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

  Documentation submission/metadata/submission/array

• Molecular characterization of invasive lobular carcinoma

  Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer accounting for 10-15% of cases. ILC differs from invasive ductal carcinoma (IDC)with respect to epidemiology, histology, and clinical presentation. Moreover, ILC is lesssensitive to chemotherapy, more frequently bilateral, and more prone to form gastrointestinal, peritoneal, and ovarian metastases than IDCs. In contrast to IDC, the prognostic value ofhistological grade (HG) in ILC is controversial. One of the three major components of histological grading (tubule formation) is missing in ILC which hinders the process of gradingin this histological subtype and results in the classification of approximately two thirds of ILC as HG 2.Over the last decade, a number of gene expression signatures have shed light onto breast cancer classification, allowing breast cancer care to become more personalized. Withrespect to the management of estrogen receptor (ER)-positive breast cancer, several gene expression signatures provide prognostic and/or predictive information beyond what is possible with current classical clinico-pathological parameters alone. Nevertheless, most studies using gene expression signature have not considered different histologic subtypesseparately. Recently, a comprehensive research program has elucidated some of the biological underpinnings of invasive lobular carcinoma. Genetic material extracted from 200 ILC tumor samples were studied using gene expression profiling and identified ILCmolecular subtypes. These proliferation-driven gene signatures of ILC appear to have prognostic significance. In particular, the Genomic Grade (GG) gene signature improved upon HG in ILC and added prognostic value to classic clinico-pathologic factors. In addition this study demonstrated that most ILC are molecularly characterized as luminal-A (~75%)followed by luminal-B (~20%) and HER2-positve tumors (~5%). Moreover, we investigated the prognostic value of known gene signatures/ gene modules in the same cohort of ILC. As a second step within the scope of this project, we aim to investigate the interactionsbetween somatic ILC tumor mutations to observed transcriptome findings. To this end, we aim to perform somatic mutation analysis for the ILC tumors for which Affymetrix gene expression profiling is available. To this end, we will use a gene screen assay, which specifically interrogates the mutational status of a few hundreds of cancer genes. We believe that this pioneering effort will be fundamental for a tailored treatment of ILC withimprovement in patients' outcome.

  Dataset EGAD00001000288

• Biallelic loss of function PAX4 variants are a Novel Cause of Transient Neonatal Diabetes

  The identification of genes in which loss of function (LoF) variants cause neonatal diabetes mellitus (NDM) can provide unique insights into the development and function of human pancreatic beta cells. We describe the identification of biallelic PAX4 LoF variants in 2 unrelated individuals with transient NDM: one due to a homozygous p.(Arg126*) PAX4 stop gain variant and a second due to a homozygous c.-352_104del 2.65kb deletion affecting the first 4 exons and promoter of the PAX4 Matched Annotation from NCBI and EMBL-EBI select transcript. We confirmed reduced transcript expression due to nonsense mediated decay for the e p.(Arg126*) variant in CRISPR-edited iPSC-derived pancreatic endoderm cells. Analysis of PAX4 binding through integration of CUT&RUN and RNA-sequencing in PAX4 depleted endocrine cells identified genes directly regulated by PAX4 involved in both pancreatic islet development and glucose-sensitive insulin secretion. The remitting and relapsing diabetes in both probands demonstrates that in contrast to mice PAX4 is not essential for the development of pancreatic beta-cells and supports differences in the requirements of functional beta-cell mass across the life-course.

  Study EGAS50000000857

• A Single-Cell and Spatial Atlas of Early Human Olfactory Development

  The human nasal region is a complex anatomical structure comprising neural crest- and placode-derived lineages, yet its developmental dynamics in humans remain poorly understood due to limited access to fetal tissue and challenges in resolving its spatial and cellular complexity. We built a single-cell and spatial transcriptomic atlas of the human fetal nasal region from 10 fetuses spanning post-conceptional weeks 7 to 12. Using snRNA-seq, we identify 34 cell types in the fetal nasal region and reconstruct lineage trajectories in the olfactory epithelium (OE). Spatial analyses uncover regionalized OE organization, dorsoventral gradients of olfactory receptors (ORs) and high-resolution OR expression. Combining snRNAseq and MERFISH, we reveal early onset of OR gene expression, with most olfactory sensory neurons (OSNs) following the one neuron-one receptor rule already during the first trimester. This work provides the first integrated molecular and spatial framework of early human nasal region and it provides an unprecedented molecular blueprint of the human olfactory system development. In addition, this study offers a foundational resource for embryologists and developmental biologists investigating sensory neurogenesis, epithelial patterning, and congenital diseases.

  Study EGAS50000001203

• The mission of the BIOS Consortium is to create a large-scale data infrastructure and to bring together BBMRI researchers focusing on integrative omics studies in Dutch Biobanks.

  The advent of the genome-wide association study (GWAS) led to the successful identification of thousands of variants that are robustly associated with complex disease phenotypes. Dutch biobanks played a substantial role in these discoveries. For most of these variants, however, the mechanisms through which they contribute to these phenotypes remain unknown. The BIOS Consortium applies a functional genomics approach that integrates genome-wide genetic data with data on the epigenome and transcriptome to elucidate these mechanisms. Over 4000 samples from BBMRI-NL biobanks with in-depth information on disease phenotypes and GWAS data are being enriched with RNA-sequencing and genome-wide DNA methylation data. The same is true for samples with whole-genome sequencing data from GoNL. This unique data infrastructure provides a powerful platform to evaluate key questions in integrative omics from establishing comprehensive eQTL and meQTL catalogues to linking molecular pathways across omics levels to phenotypic outcomes.

  Study EGAS00001001077

• The Egyptian Collaborative Cardiac Genomics (ECCO-GEN) Project: Defining a Healthy Volunteer Cohort

  The integration of comprehensive genomic and phenotypic data from diverse ethnic populations offers unprecedented opportunities towards advancements in precision medicine and novel diagnostic technologies. Current reference genomic databases are not representative of the global human population, making variant interpretation challenging, especially in underrepresented populations such as the North African population. To address this, the Egyptian Collaborative Cardiac Genomics (ECCO-GEN) Project launched a study comprising 1000 individuals free of cardiovascular disease (CVD). Here, we present the first 391 Egyptian healthy volunteers (EHVols) recruited to establish a pilot phenotyped control cohort. All individuals underwent detailed clinical investigation, including cardiac MRI, and were sequenced using a targeted panel of 174 genes with reported roles in inherited cardiac conditions (ICC). We identified 1,262 variants in 27 cardiomyopathy genes of which 15.1% were not captured in current global and regional genetic reference databases (here: gnomAD and Great Middle Eastern (GME) Variome). The ECCO-GEN project aims at defining the genetic landscape of an understudied population and providing individual-level genetic and phenotypic data to support future studies in CVD and population genetics.

  Study EGAS00001004434

• MGP Panel: a comprehensive targeted genomics panel for molecular profiling of multiple myeloma patients

  We designed a comprehensive multiple myeloma (MM) targeted sequencing panel to identify common genomic abnormalities in a single assay and validated it against known standards. The panel comprised 228 genes/exons for mutations, 6 regions for translocations, and 56 regions for copy number abnormalities (CNAs). Toward panel validation, targeted sequencing was conducted on 233 patient samples and further validated using clinical fluorescence in situ hybridization (FISH) (translocations), multiplex ligation probe analysis (MLPA) (CNAs), whole genome sequencing (WGS) (CNAs, mutations, translocations) or droplet digital PCR (ddPCR) of known standards (mutations). Canonical IgH translocations were detected in 43.2% of patients by sequencing, and aligned with FISH except for one patient. CNAs determined by sequencing and MLPA for 22 regions were comparable in 103 samples and concordance between platforms was R2=0.969. VAFs for 74 mutations were compared between sequencing and ddPCR with concordance of R2=0.9849. In summary, we have developed a targeted sequencing panel that is as robust or superior to FISH and WGS. This molecular panel is cost effective, comprehensive, clinically actionable and can be routinely deployed to assist risk stratification at diagnosis or post-treatment to guide sequencing of therapies.

  Study EGAS00001006222

• Submitters and requesters Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue Here, we show the amount and global distribution of submitters and requesters Requester accounts created by country The below figure represents the number of requester accounts by country. In most of the cases cases, country was inferred from the requester email domain. Those requesters whose email domain could not be assigned to a country such as @gmail.com, are not included. Note that a requester is a user that has had a granted permission at some point in time. Requester Accounts Created by Country Download as PNG Download as JPG Download as WEBP Download as CSV map pie mapChart('requester-country', 'https://stats.ega-archive.org/requester/country') pieChart('requester-country-pie-chart', 'https://stats.ega-archive.org/requester/country','Requester accounts created by country') Requester accounts created by year The below figure represents the number of requester accounts by year. Note that a requester is a user that has had a granted permission at some point in time. Requester Accounts Created by Year Download as PNG Download as JPG Download as WEBP Download as CSV barChart('requester-year', 'https://stats.ega-archive.org/requester/year', ['Requester Accounts', 'Years'], true) Industry requester accounts The below figure represents the top 15 industry requesting companies. Industry Requester Accounts Download as PNG Download as JPG Download as WEBP Download as CSV pieChart('industry-requester-accounts', 'https://stats.ega-archive.org/industry/requester/accounts', 'Industry Requester Accounts') Submitter accounts created by country The below figure represents the number of submitter accounts by country. Location was infered from the requester email domain. Submitter Accounts Created by Country Download as PNG Download as JPG Download as WEBP Download as CSV map pie mapChart('submitter-accounts', 'https://stats.ega-archive.org/submitter/accounts/country') pieChart('submitter-accounts-pie-chart', 'https://stats.ega-archive.org/submitter/accounts/country','Submitter accounts created by country') Submitter accounts created by year The below figure represents the number of submitter accounts by year. Submitter Accounts Created by Year Download as PNG Download as JPG Download as WEBP Download as CSV barChart('submitter-users-year', 'https://stats.ega-archive.org/submitter/users/year', ['Submitter Accounts', 'Years'], true) DAC Members by Country The below figure represents the number of DAC members by country. DAC Members by Country Download as PNG Download as JPG Download as WEBP Download as CSV map pie mapChart('dac-members-by-country', 'https://stats.ega-archive.org/dac_member/country') pieChart('dac-members-by-country-pie-chart', 'https://stats.ega-archive.org/dac_member/country','DAC members by country')

  Documentation about/statistics/community

• Treatment of Pulmonary Hypertension and Sickle Cell Disease with Sildenafil Therapy (Walk-PHaSST)

  Pulmonary arterial hypertension (PAH) is a progressive condition characterized by narrowing or stiffening pulmonary arterioles resulting in increased pulmonary blood pressure and reduced delivery of oxygenated blood to the body. It is a common complication of sickle cell disease and initially presents with the symptom of shortness of breath (dyspnea) on exertion. As the condition worsens, other symptoms such as dizziness, lower extremity edema, and chest pain can develop. The drug, sildenafil, works by relaxing blood vessels in the lungs which reduces pulmonary blood pressure and allows more oxygenated blood to circulate. Increased levels of oxygenated blood allows individuals with PAH to tolerate more activity, but guidelines for using sildenafil in patients with PAH and sickle cell disease were unavailable at the time of the Walk-PHaSST trial.Participants were screened for the existence of pulmonary hypertension with a six minute walk test and a Doppler echocardiogram that assessed TRV, diastolic function, and valvular and systolic function. Subjects with TRV ≥ 2.7 m/s received further clinical evaluation for possible causes of pulmonary hypertension. Other screening data included medical history, a physical exam, and standard laboratory testing. For individuals with moderate to severe pulmonary hypertension (TRV ≥ 3.0), a cardiac catheterization was done at the baseline and week 16 data collection periods.Subjects eligible for the main intervention trial based on screening results were randomized in a 1:1 double blind fashion to receive sildenafil or placebo for 16 weeks. Subjects received 20 mg of oral sildenafil or matching placebo 3 times daily for 6 weeks, followed by 40 mg 3 times daily for 4 weeks, followed by 80 mg 3 times daily for 6 weeks, as tolerated. Participants could also receive other therapies as needed to manage sickle cell and related complications. The primary outcome measure of the trial was change in the six minute walk test, a standard indicator of a person's heart and lung function and exercise capacity, from baseline to week 16. After completing the study treatment (or placebo), participants could choose to be part of the open-label follow-up phase of the study and continue to be assessed for up to one year.The study was intended to screen about 1000 subjects and randomize 132 subjects, however it was terminated early due to the unforeseen increase in adverse events in participants treated with sildenafil as compared to placebo. When the study was stopped, 33 participants had completed the trial. Subjects continued to be monitored, but were instructed to taper sildenafil treatment over three to seven days.There was no evidence that treatment with sildenafil impacted the six minute walk distance from baseline to week 16. In addition, treatment with sildenafil appeared to increase rates of hospitalization due to sickle cell disease pain.Due to in part to the early termination of the trial, the majority of subject data was collected from the screening phase of the study (n=720), as opposed to the main intervention trial (n=74).Instructions for requesting individual-level data are available on BioData Catalyst at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BioData Catalyst. For questions about availability, you may contact the BioData Catalyst team at https://biodatacatalyst.nhlbi.nih.gov/contact.

  Study phs002383

• Rapid Acceleration of Diagnostics - Underserved Populations (RADx-UP): COVID-19 Testing and Prevention in Correctional Settings

  This study will work with diverse correctional facilities across MN, RI, WA, and FL to characterize the incidence of COVID-19 disease progression and related outcomes. The goal is to identify barriers and facilitators to develop strategies to improve testing in these under-served populations by: Increasing the reach, access, uptake, and impact of COVID-19 testing among incarcerated people and staff in prison and jails. Individuals in congregate settings have been disproportionately affected by COVID-19, with correctional settings accounting for 39 of the 50 largest cluster outbreaks in the country to date. Schema of Correctional Facilities: Correctional facilities are amplifiers of infectious diseases for structural reasons: 1) Dormitory-style living and overcrowded conditions make social distancing nearly impossible 2) They have a focus primarily on security not health 3) Their medical departments are under-resourced and often disconnected from local public health agencies. Correctional facilities must be the focus of urgent intervention to understand the incidence of COVID-19, how it spreads in correctional facilities, and durable long-term strategies to reduce transmission, including testing and vaccines. However, our work from the COVID Prison Project shows that correctional systems have implemented a range of testing strategies, without coordination, such that synthesizing the information on COVID-19 incidence, outcomes and optimal testing strategies (including repeat testing and contact tracing) has been challenging. Incarcerated people decline testing due to concerns about confidentiality, implications for release, and fear of placement in solitary confinement. Through interviews we will identify ethical concerns and potential solutions for COVID-19 testing and vaccine strategies in correctional facilities using a community-engaged strategy. The project's work from the COVID Prison Project shows that correctional systems have implemented a range of testing strategies, without coordination, such that synthesizing the information on COVID-19 incidence, outcomes and optimal testing strategies (including repeat testing and contact tracing) has been challenging. Incarcerated people decline testing due to concerns about confidentiality, implications for release, and fear of placement in solitary confinement. Through interviews we will Identify ethical concerns and potential solutions for COVID-19 testing and vaccine strategies in correctional facilities using a community-engaged strategy. Goals: 1. Identify ethical concerns and potential solutions for COVID-19 testing and vaccine strategies in correctional facilities using a community-engaged strategy. 2. Characterize incidence of COVID-19, disease progression, and related-outcomes and effectiveness of testing and contact tracing in correctional facilities. Participant Selection: a. Potential subjects must be currently incarcerated at key facilities in Washington, Rhode Island, Minnesota, and Florida. These facilities are Everglades Correctional Institution and Homestead Correctional Institution in Florida, Shakopee and Stillwater correctional facilities in Minnesota, the Rhode Island Department of Corrections, and Yakima County Jail in Washington. b. Participating correctional facilities in Florida, Minnesota, Rhode Island, and Washington will provide de-identified data on approximately 16,859 incarcerated adults and correctional staff. Data sources include deidentified data from medical records, administrative data, and policy documents pertaining to COVID-19. c. 3 series of focus groups (6, 12, 18 months) x 3 distinct groups (incarcerated people, medical providers, and correctional leaders) x 4 sites=36 total focus groups over the study period. d. Will utilize 74,812 COVID-19 tests to test and re-test a total of 16,859 incarcerated people and correctional staff for active infection over a six-month period in the FL, MN, and RI state prison systems and the Yakima County Jail in WA.

  Study phs003361

• Resuscitation Outcomes Consortium (ROC) Cardiac Epidemiologic Registry (Cardiac Epistry) Version 3 (ROC-Cardiac Epistry 3-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Available Data The ROC Cardiac Epistry version 3 include all cardiac arrest cases entered into the ROC database from April 1, 2011 to June 30, 2015. This is separate from versions 1 and 2 because of the major changes in how and which data were collected. Version 3 represents the following major changes to the structure and data collection: separate ED Arrival and Hospital Admits forms, mandatory ED and Hospital elements and a revamped CPR Process form. For ROC traumatic injury Epistry data please see: ROC-Trauma Epistry Objectives To build a prospective population-based registry of participants with out-of-hospital cardiac arrest responded to by Emergency Medical Services (EMS). Specific aims included: to establish whether the results of Resuscitation Outcomes Consortium (ROC) trials can be generalized to the larger population of participants that experience cardiac arrest; to more fully establish the burden of cardiac arrest; and to examine the relationships between variation in EMS structure and process, regional and periodic factors, and participant outcomes. Background Cardiac arrest is a common, serious, debilitating and costly public health problem. Although there has been a steady decline in morbidity and mortality from most cardiovascular diseases, high mortality rates for out-of-hospital cardiac arrests continue to pose a challenge for healthcare providers and a significant public health burden. The Resuscitation Outcomes Consortium (ROC) was established in 2004 to conduct clinical research in the areas of cardiopulmonary arrest and life-threatening traumatic injury with the overall goal of improving resuscitation outcomes. Participant and care characteristics can predict favorable outcomes in cardiac arrests, but there is still a wide variation in outcomes that is not well understood. EMS factors such as service level, number of responding providers, use of procedures or drugs in the field, training, quality assurance/feedback, and response time intervals also vary significantly by region. Variations in geographic, socioeconomic and periodic factors may also be associated with differences in outcomes. Prior to ROC Cardiac Epistry, there were no North American population-based registries for out-of-hospital cardiac arrests. Therefore there was a need for standardized data collection of out-of-hospital cardiac arrests in diverse geographic locations in order to identify the independent effects of prognostic or treatment factors accounting for variations in survival. Participants There were 120,306 participants. Design ROC Epistry collected standardized data regarding episode-specific factors, participant demographics, clinical information, pre-hospital interventions and disposition, hospital information, and participant outcome for all out-of-hospital cardiac arrests in the ROC regions. Each ROC site had to ensure capture of all eligible cases within the EMS service areas. Out-of-hospital data were extracted from existing databases whenever possible and augmented with targeted review of EMS reports. Hospital data were abstracted directly from the hospital file in most cases. Alternative methods included linkage to death registries and obituaries if the death occurred within 30 days. Sites submitted data using a web-based interface or batch uploads. Participants were not contacted directly.

  Study phs003726

• EGAD00000000054

  NCI-H209 is an immortal cell line derived from a bone marrow metastasis of a patient with small cell lung cancer, taken before chemotherapy. The specimen showed histologically typical small cells with classic neuroendocrine features. NCI-BL209 is an EBV-transformed B-cell line derived from the same patient as the small cell lung cancer cell line, NCI-H209

  Dataset EGAD00000000054

• ICGC Benchmarking Exercise

  The main objective of this benchmark is the comparison of the full sequencing pipeline of different ICGC partners, including procedures, methods and performance of library preparation and whole-genome deep-sequencing. A secondary objective will be a follow-up comparison of data analysis pipelines for identification of germline and somatic variants subsequent to the results of the ICGC Somatic Variant Calling Pipeline Benchmark.

  Dataset EGAD00001000625

• Whole genome bisulfite sequencing of prostate cancer samples upon oral pimonidazole administration

  Whole genome bisulfite sequencing was performed using laser-capture of microdissected specimen sections comparing PIMO positive and negative carcinoma areas. A hypoxia related methylation molecular signature was generated.

  Study EGAS50000000069

• AmsterdamUMC Data Access Committee for the study entitled "Carboxylesterase 1 mediates a distinctive metabolic profile of dendritic cells to attain an inflammatory phenotype"

  The current data access committee reviews and assesses data requests of researchers that seek to gain access to data presented in the manuscript entitled "Carboxylesterase 1 mediates a distinctive metabolic profile of dendritic cells to attain an inflammatory phenotype" as performed at the AmsterdamUMC, Amsterdam, Netherlands.

  Dac EGAC50000000178

• Heterogeneity and evolution of DNA mutation rates in microsatellite-stable colorectal cancer

  A mutation accumulation experiment in colorectal cancer (CRC) derived tumoroids. A sequential single-cell cloning approach was adopted to measure the mutation rate in eight tumoroids obtained from five patients. WGS was also performed on their matched normal tissue and on standard tumoroids cultures without any cloning step.

  Dataset EGAD50000000411

• NHLBI TOPMed: Boston-Brazil Sickle Cell Disease (SCD) Cohort

  This study involves sequencing of patients with a diagnosis of sickle cell disease from Brazil. No exclusionary criteria were employed and any eligible patients that consented to this study were recruited.

  Study phs001599

• BipEx-Landen: Bipolar Exome Sequencing

  This research project was a collaboration between the Karolinska Institute and the Stanley Center at the Broad Institute. In this project we sequenced and analyzed the whole exomes of 5,876 Bipolar case/control samples from collaborators in Sweden. Genomic DNA from each samples was sequenced to a mean depth of 20x.

  Dataset EGAD50000000307

• Dac for "Combined single-cell transcriptomics and T-cell receptor sequencing reveal heterogeneity of mycosis fungoides between and within patients and identify a CD4+ cytotoxic subtype"

  Dac for "Combined single-cell transcriptomics and T-cell receptor sequencing reveal heterogeneity of mycosis fungoides between and within patients and identify a CD4+ cytotoxic subtype" with Name: Menghong Yin Email address: menghong.yin@dkfz-heidelberg.de Associated affiliation: Translational Skin Cancer Research Lab (DKFZ)

  Dac EGAC50000000168

• GIST Comprehensive Cancer Panel

  DNA Sequencing of 864 genes from KiCS cancer panel. Multiple family members affected with multifocal GIST who underwent whole genome sequencing of the germline and tumor. Affected individuals with GIST harbored a germline variant found within exon 13 of the KIT gene, (c.1965T>G; p.Asn655Lys, p.N655K) and a variant in the MSR1 gene (c.877C>T; p.Arg293*, pR293X).

  Dataset EGAD50000000548

• SALTO: chromosomal copy number alterations to predict response to bevacizumab

  Study of the correlation between vessel formation and or CMS subtypes in relation to a predictive copy number marker for bevacizumab. Shallow Whole Genome Sequencing of 92 mCRC samples.

  Study EGAS50000000711

• Comparison between phenotypic-defined stage of blasts and transcriptional profile

  Established a proof-of-concept methodology with potential integration into flow cytometry software for objective AML subtype classification based on blast differentiation, offering valuable prognostic insights for elderly patients.

  Study EGAS50000000336

• Copy number profile of ctDNA in ovarian cancer patients

  This study analyzes the copy number profiles from circulating tumor DNA (ctDNA) in plasma samples withdrawn from ovarian cancer patients, in a subset of samples from the MITO16A clinical trial.

  Study EGAS00001008226