14659 results for "PLOS+ONE+Pennsylvania+German+ACS+language+code+pdc+open+access+study"

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• CTSP: Clinical Trial Sequencing Project

  For the Clinical Trials Sequencing Project (CTSP), National Cancer Institute (NCI) will utilize whole genome sequencing and/or whole exome sequencing in conjunction with transcriptome sequencing to try to identify recurrent genetic alterations (mutations, deletions, amplifications, rearrangements) and/or gene expression signatures that would be important to the hypothesis(es) submitted by the investigators. The samples will be processed and submitted for genomic characterization using pipelines and procedures established within The Cancer Genome Analysis (TCGA) project. Data analysis will be performed as a collaboration between the National Clinical Trials Network (NCTN) Group and its investigators submitting the proposal. The investigators at the NCI-sponsored Genomic Data Analysis Center (GDAC) will characterize the samples. The NCTN Group will be responsible for providing the clinical data needed for the proposal to the open clinical system maintained by NCI CCG's Biospecimen Core Resource (BCR) at Nationwide Children's Hospital in Columbus, Ohio. The project team (Network Group/investigators and GDAC) will analyze the data together. Additionally, clinical and genomic data related to the analyses will also need to be registered by NCI and will be made available to qualified researchers via a controlled-access database (e.g., dbGaP) upon publication of the primary analysis described in the study proposal. A substudy description and its molecular data information are provided under its own page: phs001184 CTSP Diffuse Large B-Cell Lymphoma (DLBCL) CALGB 50303

  Study phs001175

• Germline variants in patients with rare cancers and their implications for precision cancer medicine: experiences from the Multicenter MASTER Trial by the German Cancer Consortium (HIPO_021)

  The era of precision oncology entails an increasing amount of molecular data from tumor sequencing in research and diagnostics. In this setting, germline variant analysis may add clinical benefits for patients and family members. In this study, we evaluated germline variants from genome/exome matched tumor-blood sequencing in 1.485 advanced cancer patients within a prospective observational study carried out by the German Cancer Consortium. Included patients had cancers of rare entities (79.2%) or were under the age of 51 at first diagnosis (76.6%). Ten percent of the patients harbored pathogenic germline variants in 35 tumor predisposition genes with autosomal-dominant inheritance such as BRCA1, BRCA2, TP53, RB1, CHEK2, ATM, PALB2, SDHB etc., while 4.6% of the patients were carriers of pathogenic germline variants in 18 genes with autosomal-recessive inheritance of tumor predisposition. Based on pathogenic germline variants, 14% of the patients received genetic counselling recommendations in the molecular tumor board report. Forty five percent of pathogenic germline variants supported a molecular treatment recommendation, which was implemented for 28 patients. Furthermore, we compared our approach and results to previous studies, broadening the insight into pan cancer germline findings but also revealing differences between studies on various levels, such as cohort demographics, tumor entities, gene nominations, variant detection and variant interpretation. Our data support the importance of germline variant interpretation in precision oncology studies for patients and their families, if applicable. However, guidelines for evaluation and reporting of germline variants in the context of genetic tumor profiling need to be established and further research on the relevance of germline mutations as biomarkers for treatment recommendations is needed.

  Study EGAS00001005537

• Study of Controlled Human Malaria Infections to Evaluate Protection After Intravenous or Intramuscular Administration of PfSPZ Vaccine in Malaria-Naive Adults

  The study was performed to evaluate whole-blood transcriptomic profiles via RNA-seq before, during, and after immunization with the PfSPZ Vaccine in malaria-naïve, health adults enrolled in VRC 314 (NCT02015091). VRC 314 was designed as an open-label evaluation of the safety, tolerability, immunogenicity, and protective efficacy of the PfSPZ Vaccine. This study was designed to substantiate the initial results with the IV vaccination route for protection against CHMI that was observed in VRC 312 (NCT01441167). Based on the potential importance of dose and schedule in optimizing sustained immunity with this vaccine, an increase in PfSPZ IV dosage on schedules of 3 to 5 vaccinations was evaluated for protection against CHMI conducted early (about 3 weeks) and late (about 24 weeks) after completion of vaccinations. To assess if a higher dose given by another route confers protection, one group received PfSPZ IM, with half of the amount administered in each arm on a schedule with 4 vaccination. The primary objectives of the study were related to the safety and tolerability of vaccinations by the IV and IM routes of administration and protection against Plasmodium falciparum (Pf) challenge performed via a well-established CHMI procedure early (2-4 weeks) after completing schedules of 3 to 5 vaccinations. The secondary objective was related to the durability of protection at 20-26 weeks after the last vaccination, and exploratory objectives were related to the immunogenicity of the PfSPZ Vaccine and identifying potential immune correlates of protection. To further determine potential molecular correlates of immunogenicity and/or protection, RNA-seq was performed on whole blood collected from subjects in all dose groups at time points before, during, and after immunization.

  Study phs002423

• A Phase I Study with a Personalized Neoantigen Cancer Vaccine in Melanoma

  Effective anti-tumor immunity in humans has been associated with presence of T cells targeting neoantigens that arise from non-silent tumor-specific mutations. Here we conducted whole-exome sequencing of tumor and normal cells from individual patients to identify mutations. We assessed the expression of mutated alleles by RNA-sequencing of tumor. We demonstrated the feasibility, safety and immunogenicity of a vaccine that targets up to 20 personal neoantigens predicted to be presented by the autologous patient tumor.Antigen-specific CD8+ T cells that can recognize and eliminate cancer cells play a crucial role in anti-tumor immune responses. Here we conducted single-cell sequencing of cells infiltrating melanoma specimens originating from 4 patients and determine the TCR clonality of the CD8+ tumor infiltrating cells. We investigated the relationship between T cell clonality and phenotypic cell states coupling single-cell transcriptome to single-cell TCR seq. The surface expression of a panel of protein was detected in parallel using CITE-Seq antibodies. Sorted tumor and peripheral blood specimens were processed in parallel.In order to discover cancer antigens derived from annotated and unannotated protein-coding regions of the genome, we carried out matched ribosome profiling (Ribo-seq), RNA-sequencing, and whole genome sequencing on one of the patients, as well as Ribo-seq on an additional patient. We used it to discover novel or unannotated open reading frames (nuORFs), their expression levels, as well as somatic mutations within them to predict potential neoantigens.

  Study phs001451

• Investigating low frequency variants in CAD/MI cases, controls and pedigrees using whole exome sequencing and custom pulldowns

  The Cardiogenics re-sequencing study will consist of three parts: Eight pools of 25 individuals will be sequenced using a Nimblegen hybrid-capture solution specific to miRNA sequences, 80 pools of 25 individuals will be sequenced using a custom Agilent SureSelect array covering genes associated with coronary artery disease (CAD) and myocardial infarction (MI), 10 individuals from families with a history of CAD/MI will be exome sequenced using the Sanger exome array. The experiment will use the early onset patients from the German MI cohort and the UK BHF CAD/MI cohort both of which have strong family history. For controls we will consider individuals from the UKBS and KORA cohorts.

  Dataset EGAD00001000400

• What is a DAC?

  What is a DAC? Given the complexity, scale, and diversity of global submitters and studies, the EGA operates a distributed data access model in which requests are made to the data controller, not to the EGA. The European Commission defines a data controller, in the General Data Protection Regulation (GDPR), as the person that determines the purposes for which and the means by which personal data is processed. A Data Access Committees, commonly referred to as DACs, comprise on or more individuals (or data controllers) that review data access requests and make decisions on who can access personally identifiable genetic, phenotypic, and clinical data deposited at the EGA. Therefore, the members of a DAC should be individuals who have the authority to approve data access requests. The animation describes how you can authorise access to your sensitive data with the help of Data Access Committee and Authorisation tool. Acknowledgement to CSC - IT Center for Finland, Elixir Finland, Elixir Europe. Frequently Asked Questions How can I create a Data Access Committee? How can I create a Data Access Committee? The members of a DAC can come from different areas of expertise, such as data management, data analysis, information technology, legal and compliance, subject matter experts, privacy and security, and representatives from the organisations or individuals that provide data to the DAC. The specific members of a DAC can vary depending on the needs of the organisation and the type of data being managed. The EGA strongly suggests checking with your organisation to align with its regulations How should a DAC be named? The chosen name must be informative to the applicant.  For example, internal identifiers, such as grant numbers, should not be used. Individual PI names should also not be used. DAC's are often named after the organisation or department of the data source.  Browse the full list of DAC names currently in the EGA. How can I become an EGA DAC contact? To register a DAC at the EGA you must create first as an EGA user. Once your EGA user has been approved by the Helpdesk team, you will be able to log in to the DAC Portal. How can I register a DAC? To register a DAC, follow the DAC Portal instructions.  You will be required to provide a DAC name, name of the individual(s) that make up your DAC and contact details for your DAC including your Institutional email(s). Wherever possible, the DAC should make sure that all points of contact are readily available and able to answer any initial data requests/queries in < 2 weeks.  Once your DAC is registered, you will have to wait upon the validation from our Helpdesk team. As soon as all the validations have been completed, your DAC will be activated. Alternatively, you can also establish a DAC at the EGA during a programmatic submission through Webin API. Which are the possible roles of a DAC contact? There are two possible roles for DAC contacts: member and admin. An admin has additional privileges compared to a member: An “admin” can manage data requests, create and edit policies, edit the content of the DAC, add or remove contacts, and decide the role of each contact. A “member” can manage data requests and create policies. However, a member does not have permission to modify DAC details, edit information from policies where they are not admins, or add/remove contacts. There is no limit to the number of admins in a DAC, and each admin is responsible for deciding who should have editing privileges. This allows for a more decentralised and democratic approach to managing the DAC. How can I modify the information of a DAC? To modify a DAC, follow the instructions here. Keep in mind that only DAC contact with an admin role can modify the information of a DAC. If your DAC was registered before the lauch of the DAC Portal, and its ID is EGAC0 (not EGAC5), you must use the programmatic submission to modify it. Please, do not hesitate to contact our Helpdesk team if you need help with this! To prevent potential data breaches and ensure adherence to GDPR regulations, it is essential that the European Genome-Phenome Archive (EGA) is informed via the Helpdesk team of any changes to the Data Access Committee (DAC). This should be done in addition to any changes being made on the DAC portal. Data Controllers (as per the definition in the DPA) are also responsible for notifying the previous DAC of any modifications. Without proper notification, changes might not be automatically updated in our system, leading to the risk of incorrect permissions being applied and potential data access issues. Therefore, it is imperative that all Data Controllers follow this protocol to maintain data integrity and security. What’s the link between DAC, policy and dataset? A dataset is linked to one single policy. At the same time, one policy has a one to one relationship with a DAC. In this example, you can see that in this dataset page, we are only showing the information of one DAC (1 dataset - 1 DAC). However, the ratio of objects does not work the same in the other direction. One DAC can own multiple policy objects. And each policy object can be reused in several datasets. Thus, one DAC can manage one or more datasets. In this example, you can see that in this DAC page, we are showing all the datasets that are managed by one DAC (1 DAC - >400 datasets). EGA Data Access Committee Best Practices Which are the EGA DAC best practices? Refer to DAC Best Practices What happens if a DAC member changes institutions? EGA is committed to the protection and ownership of the data stored in our systems. We respect the institution's ownership of the data, and as such, if a DAC member changes institutions, the ownership of the data will not be transferred to the new institution. Therefore, before changing institutions, we request that the DAC contact add a new member who will replace them once they no longer work at the institution. This ensures that the data remains protected and is accessible to authorised personnel at the institution. To prevent potential data breaches and ensure adherence to GDPR regulations, it is essential that the European Genome-Phenome Archive (EGA) is informed via the Helpdesk team of any changes to the Data Access Committee (DAC). This should be done in addition to any changes being made on the DAC portal. Data Controllers (as per the definition in the DPA) are also responsible for notifying the previous DAC of any modifications. Without proper notification, changes might not be automatically updated in our system, leading to the risk of incorrect permissions being applied and potential data access issues. Therefore, it is imperative that all Data Controllers follow this protocol to maintain data integrity and security. What happens if EGA detects an unresponsive DAC? EGA defines an unresponsive DAC as a DAC with one or more contacts who do not respond to data access requests. EGA has procedures in place to identify these types of DACs, escalate the issue, and attempt to reassign the DAC to a responsive contact. This is a crucial step in ensuring that data can be accessed and utilised by researchers. If EGA identifies an unresponsive DAC, the organisation will first try to resolve the issue by escalating it to the appropriate parties. This may involve attempting to reassign the DAC to a more responsive contact. Unfortunately, in situations where we cannot reassign the DAC, the dataset will be withdrawn from the public website and the files will be removed from our system. If an EGA ID is referenced in a publication, the EGA will take extra steps to ensure that the public is made aware of the data's unavailability. I don't want to receive an email notification for pending requests. How can I do that? If you are an EGA DAC with pending requests, you will always receive emails for new data access requests. However, the EGA understands that a request resolution can take some time, for this reason, if you add a comment (make sure you save it by clicking the APPLY button!) we will filter those requests at the time of sending the notification! How can I manage data access requests? What documentation does the DAC need to provide? Each dataset that is submitted to the EGA must be linked to a policy object. The policy is a Data Access Agreement (DAA), which defines the terms and conditions of using the dataset, such as how the data files should be stored once downloaded or details of publication embargoes that should be observed by the approved user. As part of the Data Access Agreement, information regarding the application can be captured to help inform the DAC when making its decision. For example, requestors could be asked to provide a proposed title for their research and a proposal of how the data will be used. By asking for provision of such information the DAC can be assured that the requestor fully understands any consents associated with the data. It is important that accounts created at the EGA, are created solely for those individuals that will be downloading the data from the EGA. As part of the data access request, we strongly encourage you to identify individuals that will need an account at the EGA in order to prevent sharing of login details, which is strictly prohibited under EGA user account policy. Such information can easily be captured in the DAA. NOTICE The data access agreement template below is provided for guidance only and should be adapted as you see fit to suit your own purpose. In the interest of promoting data sharing, we suggest that if an agreement cannot be met around clause 19 in this example that both parties should agree to remain silent, and that the clause should be removed from the agreement. Example DAA How can the DAC provide the DAA? The DAC should provide their own DAA when registering a policy. Data requestors will download this document and should fill it in and send it back to the DAC. Data access decisions should be based on such documentation. The DAA can be downloaded through the request data webpage. Once it has been filled in, the signed copy of the DAA can be uploaded back to the request data webpage and sent to the DAC for review. How can I grant access to the data? Once you receive a data access request, you can login to the DAC Portal. In this portal you will see all your pending requests and will be able to grant or decline access to the requestors. I am a member of the Data Access Committee. Could I approve somebody else to deal with the requests on my behalf? If you want to delegate data access decisions to someone else, make sure that the individual's account is officially registered as a member of the DAC. Remember that a DAC contact with an "admin" role can always add new members to an existing DAC, remove members, and modify contact details through the DAC Portal. Can I automatise the process of managing data access requests? The answer is yes! You can use a programmatic approach using our DAC API! Check out the DAC API specification! Data Breach What should a DAC do if they suspect a breach? If a DAC suspects a data breach of one or more of their datasets, they should immediately contact the EGA Helpdesk team at this link. The DAC must provide the following information when contacting the EGA Helpdesk team: A list of affected datasets An estimated date of the data breach (or interval of dates) A list of unauthorised users who accessed the data (if available). Otherwise, they can provide a list of authorised users for the affected datasets Any observations they would like to raise to the EGA team Once the DAC has contacted the EGA team, we will respond within 48 hours (please allow some leeway during peak times) and activate our data breach protocol. What can I expect from the EGA if they detect a breach? Once the EGA determines that a security incident has occurred, we will notify all DAC members that a data breach has been detected, and take steps to contain the incident. Containment approaches may include: Revoking a data provider's access to the EGA resources, such as by changing passwords. Removing affected EGA datasets from distribution, such as by withdrawing a dataset. Disabling certain functions or services, such as the EGA ingestion pipeline. Shutting down the system or disconnecting it from the network. After the incident has been contained, the EGA will determine whether it is necessary to eradicate components related to the incident. Finally, the EGA will enable recovery of the service to normal operation and confirm that all services are functioning normally.

  Documentation access/data-access-committee/what-is-dac

• Beacon v2 and Federated EGA, part of the GDI project Starter Kit to enable access to genomic and phenotypic data across borders

  The European Genomic Data Infrastructure project has released a ‘Starter Kit’ based on the rare disease and cancer proof-of-concepts from the Beyond 1 Million Genomes (B1MG) project. The Starter Kit, freely available to any interested parties, contains a set of software applications and components co-developed by the 20 GDI nodes and based on open community standards from the Global Alliance for Genomics and Health (GA4GH). Products in the Starter Kit give all countries the technical capability to access synthetic genomic and phenotypic data across borders. The image above shows the full workflow of the GDI Starter Kit user journey. The dotted circles are not included in the Starter Kit yet The EGA’s work is represented in the Starter Kit with Beacon v2 and Federated EGA among the software applications and components. About Beacon v2 Beacon is a protocol for data discovery. This API allows the search of genomic variants and associated information without breaching the datasets privacy. The European Genome-phenome Archive (EGA) is a key contributor to the Beacon v2 Protocol, the Beacon v2 Reference Implementation (B2RI) and the EGA Beacon(s). About Federated EGA The Federated EGA network offers a tool for data storage and sharing. Officially launched in 2022, this network of connected resources enables transnational discovery of and access to human omics data for research, while also respecting jurisdictional data protection regulations. The Federated EGA Onboarding Website contains all the information related to the establishment of a Federated Node.

  Blog beacon-v2-and-federated-ega-part-of-the-gdi-project-starter-kit

• PAGE: Global Reference Panel

  Stanford contributed samples to the PAGE study that can act as a population reference dataset across the globe. Therefore this dataset includes reference individuals, without phenotypes, chosen to help infer ancestry that will help us understand the diverse samples available in PAGE. The complete dataset comprises individuals of European, African, Asian, Oceanian, and Native American descent, from a total of over 50 populations. A subset of these individuals from Puno, Peru and Easter Island (Rapa Nui), Chile, are included in the PAGE samples that were whole genome sequenced in 2015. Additional details are available in the Study Acknowledgments. The Global Reference Panel comprises 6 sample sets: A population sample of Andean individuals primarily of Quechuan/Aymaran ancestry from Puno, Peru A population sample of Easter Island (Rapa Nui), Chile Individuals of indigenous origin from Oaxaca, Mexico Individuals of indigenous origin from Honduras Individuals of indigenous origin from Colombia Individuals of indigenous origin from the Nama and Khomani KhoeSan populations of the Northern Cape, South Africa In addition, we genotyped publicly available samples that will be hosted on the Bustamante lab website (https://bustamantelab.stanford.edu/). These comprise large public datasets to provide an open reference dataset for the world: The additional related individuals from the Americas in the Human Genome Diversity Panel (H952) plus all additional samples from the Americas A subset of the unrelated individuals from the Maasai in Kinyawa, Kenya (MKK) dataset from the International Hapmap Project hosted at Coriell Additional samples will be available for restricted use with a data access agreement with the Bustamante Lab. This study is part of the Population Architecture using Genomics and Epidemiology (PAGE) study (phs000356).

  Study phs001033

• How to request data

  Request Data Access If you're looking to access data from the EGA for your research, you are in the right documentation page! In this page you will find a step by step guide on how to request data archived at the EGA, as well as some frequently asked questions you might find useful. When requesting data, you'll need to provide information about your research project and intended use of the data. Keep in mind that your data access request may require additional approvals or agreements depending on the datasets and data providers involved. Step by step guide Register yourself as an EGA user. Validate your account. A validation link will be sent to your email to activate your EGA user account. Log into the EGA page. Search the dataset of interest from the dataset catalogue or search bar. Click on “Request access” Add a comment requesting these files and send your request Your request is successfully registered, and the DAC will receive a notification about your request In addition to submitting your request, you should also contact the DAC directly via e-mail. You can access the DAC's contact information from the relevant dataset page. Frequently Asked Questions (FAQ) Which is the role of the EGA in the data access process? The European Genome-phenome Archive (EGA) facilitates the secure distribution under controlled access of personally identifiable genetic and phenotypic data. Data Access Committee (DAC) is a body of one or more individuals who are responsible for data release to external requestors, based on participant consent and/or National Research Ethics terms. How do I request access to multiple datasets? To request access for multiple datasets managed by the same Data Access Committee (DAC), please start by visiting the DAC catalogue to locate the DAC of your choice. Once you've found the relevant DAC, proceed to its dedicated page where you'll find a "Request access" button. After logging in to the EGA website, you'll be presented with a comprehensive list of datasets under the chosen DAC. Select the datasets you're interested in, and then compose a message indicating your access request. Click the "Send" button to submit your request, which will include the chosen datasets. Please be sure to review the data usage policies for the requested datasets, as policies can vary among datasets managed by the same DAC. I am a co-applicant. Will I be given a joint access account? Each applicant needs to create an EGA account themselves, with unique login details. User log-in details are for individual use only and should never be shared, under any circumstances. EGA services are only provided to EGA account holders. Therefore, the co-applicant will send a formal request to the DAC. What happens if a co-applicant leaves or a new member needs to be added to an existing DAC application? The appropriate Data Access Committee (DAC) should be contacted and informed about the required changes so that the application can be updated. I have changed my place of work since my EGA account was created. Can I still have access to data? You must contact the relevant Data Access Committee (DAC). The DAC did not reply to my data access request. How should I proceed? There is no estimated time for DAC response. Please allow up to four weeks for a response. In the meantime, please send them a weekly reminder of the status of your request by emailing them directly.If the DAC becomes unresponsive, meaning that they fail to communicate with a requestor within the specified time frame, please contact the EGA Helpdesk team. I have an account with the EGA and would like access to further datasets. How should I proceed? You need to reapply to the relevant Data Access Committee (DAC). Once the DAC has approved your application your account will be updated and an email will be sent to notify you of a change made to your account. How do I download files of the datasets I have been granted access to? The preferred method for data download and decryption is the EGA Secure Download Client. Please carefully review the provided documentation. How do I download metadata? Metadata is retrieved from the EGA webpage; you will not need to use any download client. Be informed that you will only be able to download metadata of your approved datasets. For such download, you should first log in the EGA webpage with your credentials. Then browse the metadata's dataset you have gained access and you will find a metadata download button. You may also want to have a look our metadata schema. I have forgotten or misplaced my user account password; how do I get a new one? Click on “Log in”, located in the top right corner of the EGA homepage. Then click “Forgot password”, type in your registered institutional email address, and instructions for resetting your password will be sent to you. Please check both regular and junk/spam folders. I was sent a link to set a password for my download account, but it seems like the link has expired. What should I do? The procedure is the same as the one for a forgotten password, explained in the previous question.

  Documentation access/request-data/how-to-request-data

• RNA sequencing data of 142 samples from 142 patients with HER2+ breast cancer treated with letrozole or tamoxifen (SOLTI PAMELA trial)

  PAMELA (NCT01973660) is a non-randomized, open-label, multicentric, prospective translational research study in stage I-IIIA HER2+ breast cancer designed to evaluate the ability of the PAM50 intrinsic subtypes to predict pCR in the breast (pCRB) following 18 weeks of neoadjuvant lapatinib and trastuzumab (+/- hormonal therapy if hormone receptor [HR] positive). Patients with HR+ disease received letrozole (if postmenopausal) or tamoxifen (if pre-menopausal). The primary objective was to compare the pCRB rates of the HER2-E versus the non-HER2-E subtypes in the intent-to-treat population.

  Study EGAS00001006410

• PEVOsq

  The PEVOData PEVOsq basket trial is an open-label, multi-center, phase II trial investigating the combination of pembrolizumab (an immune checkpoint inhibitor) with vorinostat (a histone deacetylase inhibitor). This trial focuses on treating patients with recurrent or metastatic squamous carcinomas, including cancers of the cervix, head and neck, anus, vulva/vagina, penis, and lung. The aim is to evaluate the efficacy and safety of this combination, with pembrolizumab administered every 3 weeks and vorinostat daily. Outcomes include overall response rate and progression-free survival.

  Study EGAS50000000731

• Prematurity and Respiratory Outcomes Program (PROP) Core Database Protocol (PROP-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: The Prematurity and Respiratory Outcomes Program (PROP) was performed to identify suitable predictors of respiratory outcomes that may serve as surrogate endpoints in future trials of prevention and therapy of respiratory diseases in preterm infants.Background: Acute and chronic respiratory morbidities are common in premature births, and can pose significant risk to the infant's health, particularly during the first two years of life. One such condition is bronchopulmonary dysplasia (BPD) where infants require oxygen therapy due to abnormal repair and impaired lung development after acute lung injury. BPD poses a high mortality risk, even in infants that survive the initial hospitalization. Regardless of BPD diagnosis, preterm infants frequently return for medical care due to symptoms of post-prematurity respiratory disease (PRD), which includes intermittent or chronic wheezing, cough without cold, poor growth, apnea and cyanosis, and lower respiratory tract infections. Impaired lung function can persist into adulthood, contributing to chronic respiratory diseases including asthma and emphysema.However, at the time of the PROP study, there were no objective measures to predict which preterm infants would have persistent respiratory problems after discharge from the hospital. Furthermore, improved survival rates of premature infants and the high prevalence of lasting respiratory morbidities highlight the need for more comprehensive strategies to address both short-term and long-term respiratory complications. Thus, the Prematurity and Respiratory Outcomes Program (PROP) was formed to investigate multiple research hypotheses on the molecular mechanisms that contribute to respiratory disease risk of premature neonates over the first year of life. Specifically, PROP investigators hypothesized that in survivors of extreme prematurity to 36 weeks postmenstrual age (PMA), specific biologic, physiologic and clinical data obtained during the initial hospitalization will predict respiratory morbidity between discharge and 1 year corrected age. Participants: A total of 835 infants were enrolled.Design: PROP was a collaboratively developed multicenter prospective cohort study of very preterm infants from birth through the time of discharge from the NICU and up to one year corrected age.Clinical data included maternal and infant demographics, co-morbidities, and daily infant respiratory, nutritional, and medication data until discharge. These were prospectively collected from birth using medical record review and family interviews. After discharge, phone interviews were conducted with the infant's main caregiver at 3, 6, 9 and 12 months corrected age to assess domains of respiratory morbidity. At 6 and 12 months corrected age, a survey of environmental respiratory irritant exposures and an assessment for gastroesophageal reflux disease were also completed. A standardized physical exam was performed at 36-40 weeks PMA and again at one year corrected age. The exam focused on anthropometrics, vital signs, and respiratory system signs. At 34-41 weeks PMA and within one week of anticipated discharge based on physiologic stability, a set of non-invasive respiratory assessments were performed to assess physiologic biomarkers in infants that were not mechanically ventilated or receiving non-invasive positive pressure ventilation. Respiratory inductance plethysmography (RIP) was performed before and after inhaled albuterol to assess potential airway reactivity. During the RIP study, continuous pulse oximetry was performed during quiet sleep in order to analyze oxygen desaturations and apneas. Standardized oxygen requirement challenge tests were performed at about 36 weeks PMA, and at about 40 weeks PMA if the infant was still hospitalized and was not eligible for, or failed, the previous challenge. Failure was defined as SpO2 20 seconds at any point in the testing. Infants breathing ambient air or that passed the 36 week challenge test underwent a hypoxia challenge consisting of a 15-minute trial of FiO2 of 0.15. Failure was defined as SpO2 The primary outcome was respiratory morbidity, as measured by the presence or absence of substantial post-prematurity respiratory disease (PRD) up to one year corrected age. PRD was classified as a positive response in at least one of four morbidity domains during at least two separate parental interviews. The domains were respiratory medications, hospitalizations for cardiopulmonary cause, respiratory symptoms, and home technology dependence (oxygen, ventilator, or CPAP/BiPAP). Mortality from a respiratory cause was also incorporated.

  Study phs004117

• National Institute of Dental and Craniofacial Research (NIDCR) Sjögren's International Collaborative Clinical Alliance (SICCA): Center for Inherited Disease Research (CIDR) Genome-Wide Genotyping

  The Sj&#246;gren&apos;s International Collaborative Clinical Alliance (SICCA) is a multisite observational cohort study that recruited a large cohort of geographically diverse participants. Enrollment of participants began in late 2004 at five (one domestic and four international) sites, in which all groups used the same protocol-directed methods to provide uniform evaluations; collect oral, ocular, and rheumatologic data; and collect specimens. The sites were located at the University of Buenos Aires, Argentina; Peking Union Medical College, Beijing, China; Rigshopitalet (formerly at Glostrup Hospital), Copenhagen, Denmark; Kanazawa Medical University, Ishikawa, Japan; King&#39;s College, London, UK (joined in 2007); and the University of California, San Francisco (UCSF). In 2009, Aravind Eye Hospital, Madurai, India; Johns Hopkins University, Baltimore, MD; and University of Pennsylvania, Philadelphia, PA were added as additional SICCA sites. UCSF is the coordinating center for SICCA. All specimens and data collected for SICCA are housed at UCSF. To facilitate the research focused on understanding the genetics of Sj&#246;gren&apos;sSj&#246;gren&apos;s syndrome, high-density SNP genotype data and SICCA clinical information are being made available to the research community. This includes participants recruited from all SICCA research groups (RG). These participants (3,382), blood relatives (439), and unrelated healthy controls (25) had their whole blood or saliva sample (Oragene) extracted for DNA for the GWAS at the UCSF DNA Bank. Eighty eight percent of the participants are women, most are of European or Asian ancestry and the median age is 55. To assess potential batch effects when doing case-control comparisons using planned external controls, 30 DNA samples from each of three studies were genotyped with SICCA DNA samples: The Genetic Architecture of Smoking and Smoking Cessation - Collaborative Genetic Study of Nicotine Dependence (COGEND) - PI: Laura Bierut. NEI-Age-related disease study (AREDS) - Genetic Variation in Refractive Error Substudy - PI: Dwight Stambolian. Controls from the National Institute of Mental Health&apos;s (NIMH&apos;s) Human Genetics Initiative. Genome-Wide Association Study of Schizophrenia - PI: Pablo V. Gejman. Molecular Genetics of Schizophrenia - nonGAIN Sample (MGS_nonGAIN) - PI: Pablo V. Gejman. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data quality control, dbGaP preparation and posting, and imputation to 1000 Genomes to increase SNP density was performed by the Center for Biomedical Statistics at the University of Washington. Data analysis was performed at UCSF using software PLINK, EIGENSOFT, and SNPTEST.

  Study phs000672

• Single cell epigenomic study of H3-K27M mutant diffuse midline glioma across age and location

  This dataset contains the open chromatin profiles of 8 patient H3-K27M mutant DMGs utilizing the single-cell/nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq)

  Dataset EGAD00001010170

• Sequence Data for DEEP Release August 2016

  DEEP (German Epigenome Project) sequence data of following samples (Sequencing Types: Chip-Seq, WGBS-Seq, RNA-Seq, sncRNA-Seq, NOMe-Se, DNase-Seq): 41_Hf01_LiHe_Ct, 41_Hf02_LiHe_Ct, 41_Hf03_LiHe_Ct, 01_HepG2_LiHG_Ct1, 01_HepG2_LiHG_Ct2, 01_HepaRG_LiHR_D31, 01_HepaRG_LiHR_D32, 01_HepaRG_LiHR_D33, 43_Hm01_BlMo_Ct, 43_Hm03_BlMo_Ct, 43_Hm05_BlMo_Ct, 43_Hm03_BlMa_Ct, 43_Hm05_BlMa_Ct, 43_Hm03_BlMa_TO, 43_Hm05_BlMa_TO, 43_Hm03_BlMa_TE, 43_Hm05_BlMa_TE, 51_Hf01_BlCM_Ct, 51_Hf03_BlCM_Ct, 51_Hf04_BlCM_Ct, 51_Hf02_BlCM_Ct, 51_Hf05_BlCM_Ct, 51_Hf06_BlCM_Ct, 51_Hf06_BlCM_T1, 51_Hf06_BlCM_T2, 51_Hf03_BlEM_Ct, 51_Hf04_BlEM_Ct, 51_Hf02_BlEM_Ct, 51_Hf05_BlEM_Ct, 51_Hf06_BlEM_Ct, 51_Hf06_BlEM_T1, 51_Hf06_BlEM_T2, 51_Hf03_BlTN_Ct, 51_Hf04_BlTN_Ct, 51_Hf02_BlTN_Ct, 51_Hf05_BlTN_Ct, 51_Hf06_BlTN_Ct, 51_Hf06_BlTN_T1, 51_Hf06_BlTN_T2, 51_Hf07_BmTM4_Ct, 51_Hf08_BlTM4_Ct, 51_Hf08_BmTM4_SP1, 51_Hf08_BmTM4_SP2, 51_Hf05_BlTA_Ct, 44_Mm01_WEAd_C2, 44_Mm03_WEAd_C2, 44_Mm02_WEAd_C2, 44_Mm07_WEAd_C2, 44_Mm04_WEAd_C1, 44_Mm05_WEAd_C1

  Dataset EGAD00001002527

• Cardiovascular Cell Therapy Research Network (CCTRN): A Phase II, RCT of Mesenchymal Stem Cells & Cardiac Stem Cells in Subjects With Ischemic HF (CONCERT HF-BioLINCC)

  Data Access Note: Please refer to the &#8220;Authorized Access&#8221; section below for information about how access to the data from this accession differs from many other dbGaP accessions. ObjectivesTo assess the feasibility, safety, and efficacy of autologous mesenchymal stromal cells (MSCs) and c-kit positive cardiac cells (CPCs), alone or in combination, in participants with ischemic heart failure.Background The prognosis of heart failure (HF) caused by chronic ischemic cardiomyopathy (coronary artery disease and prior myocardial infarction), hereby referred to as &#8220;ischemic HF&#8221;, remains poor. Many studies have explored the use of various types of stem or progenitor cells in participants with chronic ischemic HF, with encouraging results. Several clinical trials have suggested that MSCs and CPCs are both safe and beneficial in participants with ischemic HF. At the time of this study, no information was available on the efficacy of MSCs and CPCs in humans, however, both cell types have been shown to attenuate left ventricle (LV) dysfunction in animal models. Preclinical models indicate that combining MSCs and CPCs increases the therapeutic effects, but this had not yet been tested in humans. The CONCERT-HF study was initiated to assess whether autologous MSCs and CPCs, alone or in combination, can be manufactured and delivered to participants with ischemic HF; are well-tolerated; and improve LV function, quality of life, and functional capacity, and/or reduce scar size. Participants A total of 125 participants were randomized with 33 participants randomized to the MSCs and CPCs group, 29 participants to the MSCs alone group, 31 participants to the CPCs alone group, and 32 participants to the placebo group. Design The CONCERT-HF study was a multi-center Phase II, double-blind, randomized, placebo-controlled trial designed to evaluate the feasibility, safety, and efficacy of MSCs alone, CPCs alone, and their combination compared with placebo as well as each other in patients with ischemic HF. In Stage 1 (open label, lead-in study) participants were randomized 1:1 to either a standard-of-care control group (i.e., they did not undergo harvest, mapping, or injection procedures) or combination therapy (MSCs + CPCs, as described for Stage 2) to complete safety assessments. Once approval was granted for Stage 2, participants were randomized (1:1:1:1) to one of four treatments: placebo, autologous MSCs (target dose, 150 &#215; 106 cells), autologous CPCs (target dose, 5 &#215; 106 cells), or a combination of autologous MSCs and CPCs. At the harvest visit, right ventricular endocardial biopsy (EMB) was performed in participants randomized to receive CPCs alone or a combination of MSCs and CPCs. Participants randomized to receive MSCs alone or placebo had a sham procedure (right heart catheterization without EMB). All participants underwent a bone marrow aspiration and approximately 14 weeks later had transendocardial, electromechanically-guided injections of study product. Visits occurred at 1 day, 1 week, and 1, 3, 6, and 12 months after study product injection. A telephone contact took place at 24 months to assess the participant's current medications, as well as morbidity and mortality. Study endpoints included measures of safety, feasibility, and efficacy. Safety outcomes included all adverse events grade 2 and higher, including major adverse cardiac events (MACE) related to HF (death, hospitalization for worsening HF, and HF exacerbation not requiring hospitalization). Efficacy endpoints included quality of life, MRI measures of LV function and structure, functional capacity, and N-terminal pro-brain natriuretic peptide (NT-proBNP) levels. Conclusions In patients with ischemic HF, autologous MSCs and CPCs, alone or in combination, are safe and feasible; CPCs were associated with a reduction in the incidence of MACE related to HF compared to placebo; MSCs, either alone or in combination with CPCs, were associated with improved quality of life; these seemingly beneficial effects of CPCs and MSCs on clinical outcome were not associated with changes in LV function or structure; and combination therapy with MSCs and CPCs was associated with the best clinical outcomes with respect to both MACE related to HF and quality of life.

  Study phs004055

• FFPE, buffycoat and cell free DNA from N. German esophagus cancer patients - 2021

  We recruited 28 patients and sequenced samples from their FFPE tissue, full blood buffy coat and plasma cell free DNA from diagnosis and other, longitudinal time points. The cell free DNA was sequenced ultra-deeply. The IDT PanCancer panel was used for targeted enrichment. Aims: detection of primary tumor mutations in cell free DNA, overlap and difference in mutation profiles between primary tumor, metastases and cell free DNA.

  Study EGAS00001006813

• Mosaic structural variation sample

  Mosaic Normal-Tumor genomes are generated from real normal-tumor pairs with validated somatic variants. In this case, validated variants from the GRIDSS2 paper. Mosaic genomes were generated using GenomeMosaicMaker, an open-source software tool designed and implemented for this specific purpose. The variants provided in the VCF truth file for each mosaic tumor/normal pair were annotated using funnSV for SVs and VEP for SNVs and indels. All CRAM files are aligned to GRCh37. Please see https://github.com/EUCANCan/oncoliner and https://github.com/Computational-Genomics-BSC/GenomeMosaicMaker for more information.

  Study EGAS50000000460

• Resuscitation Outcomes Consortium (ROC) Trauma Epidemiologic Registry (Trauma Epistry) (ROC-Trauma Epistry-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: This dataset only includes traumatic injury participants. For ROC cardiac arrest Epistry data please see: ROC-Cardiac Epistry 1 and 2 and ROC-Cardiac Epistry 3.Objectives: To build a prospective population-based registry of participants with out-of-hospital traumatic injury 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 people that experience traumatic injury; to more fully establish the burden of traumatic injury; and to examine the relationships between variation in EMS structure and process, regional and periodic factors, and participant outcomes.Background: Traumatic injury is a major public health problem generating substantial morbidity, mortality, and economic burden on society. The majority of seriously injured persons are initially evaluated and cared for by prehospital providers, however the effect of EMS systems, EMS clinical care, and EMS interventions on trauma patient outcomes is largely unknown. 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.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. Previous trauma registries have generally focused primarily on hospitalized patients with limited prehospital information. Registries may also exclude trauma cases at far ends of the spectrum, such as those who die in the field or in a non-trauma center and/or patients that are treated and released. These limitations do not allow for detailed, outcome-based assessments of EMS system factors necessary to define prehospital resuscitation best practices. Therefore there was a need for standardized data collection of out-of-hospital traumatic injuries matched to hospital-based outcomes in diverse geographic locations in order to identify the independent effects of prognostic or treatment factors accounting for variations in survival. Participants: The registry included 13,830 traumatic events from 264 EMS agencies transporting to 287 acute care hospitals from the following regional centers: Birmingham, Alabama; Dallas, Texas; Iowa City, Iowa; Milwaukee, Wisconsin; Pittsburgh, Pennsylvania; Portland, Oregon; San Diego, California; Seattle/King County, Washington; Ottawa, Ontario; Toronto, Ontario; and Vancouver, British Columbia.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 traumatic injuries in the ROC regions. Cases were identified through review of emergency response system records including dispatch centers, EMS ground agencies, and air medical services. 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. Sites submitted data using a web-based interface or batch uploads (Newgard, et al., 2008, PMID: 18482792).

  Study phs003809

• National Emphysema Treatment Trial (NETT-BioLINCC)

  Data Access NOTE: Please refer to the &#8220;Authorized Access&#8221; section below for information about how access to the data from this accession differs from many other dbGaP accessions. Available Data: The current release of the NETT study dataset includes follow-up data through May of 2013.Objectives: To compare lung-volume-reduction surgery with medical therapy for severe emphysema, and to identify participant selection criteria for lung volume reduction surgery.Background: Lung-volume-reduction surgery has been proposed as a palliative treatment for severe emphysema. Effects on mortality, the magnitude and durability of benefits, and criteria for the selection of participants have not been established.Participants: A total of 1,218 participants with severe emphysema underwent pulmonary rehabilitation and were randomly assigned at 17 centers to undergo lung-volume-reduction surgery (bilateral stapled wedge resection) or to receive continued medical treatment. Participants were randomized after a 6-10 week pulmonary rehabilitation period and participants with a forced expiratory volume in one second (FEV1) that was 20 percent or less of predicted and a homogeneous distribution of emphysema or carbon monoxide diffusing capacity 20 percent or less of predicted were not eligible for randomization due to poor post-surgery prognosis for death or functional improvement.Conclusions: Overall, lung-volume-reduction surgery increases the chance of improved exercise capacity but does not confer a survival advantage over medical therapy. It does yield a survival advantage for participants with both predominantly upper-lobe emphysema and low base-line exercise capacity. Participants previously reported to be at high risk and those with non-upper-lobe emphysema and high base-line exercise capacity are poor candidates for lung-volume-reduction surgery, because of increased mortality and negligible functional gain. (NEJM 2003;348:2059-2073).

  Study phs004077

• Whole Genome Sequencing Analysis in a Family of Discordant Twins With Non-Syndromic Microtia and Hemifacial Microsomia: Identification of Novel Candidate Genes and Variants

  This research is a non-NIH effort to discover the pathogenesis of non-syndromic microtia and to detect potential candidate genes. Microtia (OMIM 600674, 612290, 251800) is a spectrum of congenital anomalies and facial abnormalities, 90% of the cases followed by hearing loss. Two types of the disease are described in terms of connection to the affected syndromes: anomalies (syndromic) and only clinical manifestation (non-syndromic) associated with other symptoms. Though the exact pathogenesis of microtia remains unknown, pathogenic genes of majority of the microtia syndromic types have been identified. However, the genetic mutation of the non-syndromic microtia has not been confirmed. In this study, whole genome sequencing (WGS) was utilized to screen mutations in monozygotic discordant twins of congenital microtia-atresia and their family members. One of the twins, along with her grandfather, was discordant for microtia-atresia. By bioinformatic analysis of the WGS data from monozygotic twins with microtia and their non-microtia relatives, we proposed that among 28 identified potential genes, 4 of them (CNOT1, ODAD4, TBX15, and GIPC3) could be more responsible for non-syndromic microtia development and phenotypic features. Interested investigators may request to access the DNA genomic sequencing data of the study participants via dbGaP authorized access. In addition to raw genomic sequencing data, datasets of germline mutations and candidate gene mutations may also be available for investigators to request.

  Study phs003216

• FEGA and European GDI: working together to improve human health

  The Federated EGA is a global resource for discovery of and access to sensitive human omics and associated data consented for secondary use, through a network of human data repositories to accelerate biomedical research and improve human health. The Federated EGA network was launched in September 2022 with five inaugural nodes, and since 2023 seven operational nodes can share data across national borders in adherence to European and national laws. A few weeks after FEGA's official launch, in November 2022, the European Genomic Data Infrastructure (GDI) project was kicked-off . This European Commission co-funded project, coordinated by ELIXIR, is aimed to deliver federated, sustainable and secure data infrastructure to access genomic and related phenotypic and clinical data across Europe. This project supports the aim of the 1+MG initiative (25 EU countries, Norway and UK) to enable personalised medicine and health through a shared framework and infrastructure for securely accessing and integrating high quality genomic data and other health data across borders.1+MG will be an integral component of the European Health Data Space (EHDS) for secondary use (Healthdata@EU) as an authorised participant. How are Federated EGA and GDI similar? Given their shared visions, the Federated EGA and European GDI networks have a lot in common. To begin, they share the same overall goal of establishing networks of “nodes” that host sensitive human data within a jurisdiction and connecting these nodes in a global network to support data discovery and promote human genome data access for research and healthcare. FEGA and GDI are both initially focused on enabling access to human genomic data, while FEGA ambition is to expand the scope to clinical research data and other omics data in the future. FEGA and GDI are both built on open and interoperable software solutions, a subset of which are based on the LocalEGA components. FEGA and GDI implementation solutions are based on international community standards, for example those developed by the Global Alliance for Genomics and Health, which contributes to making them interoperable. Both FEGA and GDI allow for the nodes to make use of any solution that is fully compatible. As illustrated in Figure 1, a final key point is the significant overlap of institutions involved in envisioning and operating FEGA and GDI nodes, with the clear wish to keep them interoperable in the future. What distinguishes Federated EGA from GDI? Despite being largely similar, there are some differences between the FEGA and GDI networks, which we aim to clarify in this post. The first difference is the governance model that the nodes will operate to comply with national laws and GDPR. In the FEGA network, nodes have taken inspiration from the EGA data access model, where the infrastructure is a data processor of the hosted data and data controllership remains with the originating Data Access Committees (i.e. Data controllers) for each dataset. On the other hand, in the GDI network the controllership of datasets will be transferred to a 1+MG European Digital Infrastructure Consortium (EDIC) legal entity created by the Member States (MS), who will make data access decisions, with data holders having veto powers for their datasets. Importantly, FEGA nodes have the flexibility to choose another model to fit with their data protection framework, including becoming data controllers for their datasets. The second difference is the inclusion criteria for data. While FEGA nodes are designed to accept almost any type of omics data in need of control access (e.g. genomics, transcriptomics, genotyping, single cells sequencing, patient-tracked metagenomics), GDI nodes are initially, but not only, focused on accepting whole genome and exome sequencing data and affiliated data from sources such as (i) the Genome of Europe use case of the 1+MG initiative which specifically aims to fulfil the mission of building “a European network of national genomic reference cohorts of at least 500,000 citizens”) (ii) data collection of other types of genomic data identified by countries through the 1+MG dashboard and (iii) genomic data coming from data holders which would need to fulfil EHDS requirements. The third difference is the maturity of the software stacks. The FEGA network provides a set of software for data and metadata submission, storage, permissions management, and file distribution (the LocalEGA package). GDI is building a complete set of open source reference for the five functionalities covering the full data life cycle (a few more compared to the LocalEGA) including federated processing (analytics, AI/ML), which is still under active development. Notably, one of the GDI functionalities - storage and interfaces - can be satisfied by using the LocalEGA storage solution, highlighting the ability of FEGA and GDI to be interoperable. In both FEGA and GDI, nodes are allowed to use alternative solutions as long as they are fully interoperable with the networks. Figure 1 provides a simple overview of these described commonalities and differences. Figure1: schematic overview of the main commonalities and difference among a FEGA and a GDI node Can the same institute run a FEGA and a GDI node at the same time? The answer is Yes! We believe this is entirely possible and encouraged. As long as 1+MG requirements are continuously fulfilled in the implementation. The same trained personnel could operate the infrastructure and leverage the same national funding to run a FEGA and a GDI node. Several European nodes, especially GDI vanguard nodes like Norway, Sweden, Finland and Spain are following this model. The idea is that a lot of the work can be reused, given the overlapping scope and the interoperable technology. Thus, the hosted datasets can be discovered via the two catalogues, perhaps accessible under the same or different governance models. From a FEGA node perspective, the GDI datasets could simply be a subset of the datasets hosted in the FEGA node, which have their specific governance as any other. What's next? So, the amazing teams of people building the Federated EGA network and the European GDI network are working together to create infrastructures able to provide secure access to human genomic and associated data around Europe. Because we all know how much human genomics can improve healthcare and precision medicine. And we all want to collaborate to make it happen. This article has been reviewed collectively by members of the EGA and the GDI coordination team.

  Blog fega-and-gdi

• Whole transcriptome and 97 antibodies of one healthy bone marrow

  Whole transcriptome sequencing using BD Rhapsody with 97 antibodies of a healthy young adult bone marrow (Young3/BM3) mononuclear cells from iliac crest aspirations. Please note raw and integrated gene expression data, cell type annotation, metadata and dimensionality reduction are available as Seurat v3 objects through figshare. Access links is https://figshare.com/s/901bcddb9ee18e226031.

  Dataset EGAD00001008186

• Single cell epigenomic study of H3-K27M mutant diffuse midline glioma across age and location

  Histone 3 lysine27-to-methionine (H3-K27M) mutations most frequently occur in childhood diffuse midline gliomas (DMGs) of the pons, but are also increasingly recognized in adult DMGs. Their potential heterogeneity at different ages and midline locations are vastly understudied. Here, through dissecting the single-cell epigenomic architectures of a comprehensive cohort of patient H3-K27M DMGs, we delineate how age and anatomical location shape glioma cell-intrinsic and -extrinsic features in light of the shared driver mutation. We analyzed the open chromatin profiles of 8 tumors utilizing the single-cell/nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq)

  Study EGAS00001007035

• Enhancer Mapping in Chronic Lymphocytic Leukemia

  In this study we profile the epigenomic enhancer landscapes of CLL B cells (CD19&#43;/CD5&#43;) harvested from peripheral blood of patients from our Center. Included are results of ChIPseq profiling using chromatin immunoprecipitation of the enhancer histone mark H3K27ac (acetylated lysine 27 on histone H3), and open chromatin profiles using ATAC-seq (assay for transposase accessible chromatin). These profiles are used to define the global enhancer and super enhancer landscape of CLL B cells, and to derive active transcription factor networks associated with this disease. Also included are H3K27ac ChIP-seq and ATAC-seq datasets for non-CLL B cells obtained from the peripheral blood of normal adult donors.

  Study phs001704