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• Whole blood RNAseq from a large ALS case-control study at Univ of Michigan

  This dataset consists of 694 bulk RNA-seq data (sorted aligned BAM files) from whole blood of ALS participants meeting the Gold Coast definition of ALS during their clinical visit (n=422) and healthy controls (n=272) at the University of Michigan (UM). Samples were collected as part of the UM Pranger ALS Clinic between 2011-2021, and prepared either using poly-A selection of rRNA depletion (see Experiments). RNAseq was performed on the NovSeq 6000 S4 at the UM Advanced Genomics Core, resulting in 150bp paired-end reads. Raw reads were trimmed using Cutadapt (v2.3) and aligned to hg38 using STAR (v2.5.3a). Age provided is the age at blood sample collection date.

  Dataset EGAD50000001488

• 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 “Authorized Access” 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 “ischemic HF”, 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 × 106 cells), autologous CPCs (target dose, 5 × 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

• Exome_sequencing_of_thyroid_disease_in_Val_Borbera

  The Val Borbera is a region characterized by low iodine and high prevalence of thyroid disorders, the commonest endocrine disorders in the general population. About 30% of the participants of the Val Borbera Project were affected by such disorders and were characterized by several parameters, TSH level, anti TPO antibodies, echography, family origin. Individuals with extreme phenotypes were identified and could be clustered based on family origin and genotype. We propose to exome sequence 6 of them, affected with true goiter, at high dept (40-60x) to obtain information on exonic rare variants. Due to the family structure and to the availability of whole genome sequence information on 110 individuals from the isolated population we expect to be able to identify putative causative variants for thyroid disorders that may be studied in the remaining affected individuals.

  Study EGAS00001000344

• DAC Portal documentation

  DAC Portal Welcome to the DAC Portal documentation! If you are involved in governance or legal aspects, technical or operational aspects, or serving as a data steward, this page will be helpful for you. By exploring these materials, you can define your own Data Access Committee (DAC) and policies, understand the minimal requirements for a DAC and a policy object, and comprehend how the EGA data access requests are managed. DAC Portal Index Setting up your account DACs and Policies Pending Request table Manage Data Requests History page Audit your DACs, policies, and datasets Deprecation User Preferences DAC API - A programmatic approach Setting up your account Register yourself as an EGA user. The Helpdesk team will validate your account (this could take up to 48 hours). After validation, you will receive an email with a link to verify your email - Make sure you click on the link to verify your account! If you don’t receive the email, please check your spam folder. Once your account is active you can login to the DAC Portal. We recommend you to check out the Take The Tour! DACs and Policies Create a DAC Click on Create a DAC Add the title and description to your DAC. Once ready, click on the ‘Create’ button. You will have to wait until the Helpdesk Team validates the creation of your DAC. Once approved by Helpdesk, your DAC will be assigned with a persistent identifier (EGAC). Edit a DAC Once your DAC is registered and approved, you can edit it and add contacts. Remember, you can add as many contacts as needed. And, you can also remove/add contacts anytime. To add a contact, you must write the username or email of a registered EGA user in the Members field. Once you have typed the whole username or email, a drop-down menu will appear where you can select the contact. Select a contact from the drop-down menu. Make sure you write the full username or email of the contact so it appears as an option. Make sure that you select one main contact. Set up the role you want to grant to the new contact of the DAC. We recommend you to check out the FAQ to learn more about the different roles! After adding all the necessary contacts and assigning roles, click on Update. This will send an email notification to the new DAC member, letting them know about the invitation to managing the DAC. Once they log into the DAC Portal, they will be able to either accept or turn down the invitation. Create a policy First, please note that all information registered in a policy metadata object will be publicly available on the EGA website. For each released dataset, all users will be able to read the policy under the “Data Access” tab. Select Create a Policy from the Policies tab. Select the DAC that you want your policy to depend on. Add the title and terms and conditions for accessing and using your data. If you already have an external website for data access requests, you can add the URL directly here (see example here). You can find a template of a Data Access Agreement (DAA) document in our Policy documentation page. The DAA template is provided for guidance only and should be adapted to suit your own purpose depending on the security policies, terms for publication or embargoes, and restrictions on data use or sharing. You can also add Data Use Ontologies (DUO) to your policy. These DUO codes are used to semantically tag the terms and conditions of using the data linked to the policy (example). For more information, you can refer to the Data Uso Ontology documentation. Once completed, make sure to click the Create button in order to register your policy. A persistent identifier (EGAP) will be assigned automatically. Edit a policy Please, note that you will not be able to edit the policy if it shows in orange. Meaning that you are a member of the DAC linked to that policy. Members don’t have edit rights. Only admins can edit objects. You can check your registered policies on My policies, available at the menu in the top-right corner. Select your policy. It will then display the information of that policy, allowing you to edit the information. Once you are done with the modifications, click Update. Pending Requests Table Table Settings Upon selecting a specific DAC, you will be presented with a customisable table showcasing all pending data access requests. This table allows you to tailor the displayed information according to your preferences by selecting desired columns. Available Columns (Displayed with the Eye Icon): Date: The date when the data access request was submitted. Full Name: Name and surname of the user submitting the request. Email: Email address associated with the user's request. Username: The username linked to the EGA account of the user submitting the request. Organisation: Affiliated organisation of the user submitting the request. Dataset: EGA accession ID for the requested EGA dataset. Dataset Title: Title of the requested dataset. DAC Comment: Space provided for internal comments related to the request. Expiration: Option to specify an expiration date for granted permissions. Check more information on the pending request table here! Table Filters By default, all data access requests are displayed. However, you have the option to apply filters to refine your view. Follow these steps to apply filters: Click on the More button. This action will reveal the different columns available for applying filters. As you select columns, the checked fields will be displayed at the top of the request table. Choose the specific values within the selected columns that you want to filter for. Click the Search button to apply the selected filters. Do you find yourself frequently applying the same filters? You can save multiple combinations of filters for easy access at any time! Here's how: Click the Save button. Provide a descriptive name for the filter combination. Click Save to confirm. To load saved filters, simply select the saved filter from the dropdown menu to apply it automatically. Check more information on filters here! Advanced Search For users requiring more granular control over their data access requests, the DAC Portal offers an Advanced Search feature using JIRA Query Language (JQL). With JQL, you can craft precise and complex queries to filter and retrieve specific data access requests based on various criteria such as date, user details, dataset attributes, and more. Using JQL's flexibility, you can create custom queries to meet your specific needs, allowing for advanced filtering options beyond the standard filters provided. You can find the Advanced Search feature following these steps: Click the … button Select Advanced To view the allowed fields for filtering and explore all available options, simply click on the info icon. Once you have written the filtering values, click the Search button to apply. Check more information on filters here! Manage Data Requests Upon logging into the DAC Portal, you'll notice a sand clock icon next to the DAC ID, indicating the number of pending requests. Click on the DAC to review these requests. Accept requests Click on the right side of the toggle button to grant access to the user. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with granting permissions. Deny requests Click on the left side of the toggle button to deny access to the user. Provide a reason for the denial. Note that the user will receive an email with this reason. Click on the Done button. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with denying permissions. Tips! Apply a filter to view all requests to be managed at once. Use the toggle in the row with column names to grant or deny permissions for multiple requests simultaneously. You can grant and deny permissions in the same action, simplifying the process. The confirmation box will provide a summary of all actions, including grants, denials, DAC comments, and expiration dates. History Page The History page serves as a dedicated space to view information regarding all requests managed by all DAC members. Here, you can review active permissions and revoke them as needed. Go to the History page by clicking on the "HISTORY" button from a DAC page in the DAC Portal. Here are the different row types you may find: Current permissions: row in green with a toggle button to revoke permissions on the right. Approved requests: row in green, with no toggle button. Request denied: row in red with “request denied” on the right. Permission revoked: row in red with “permission revoked” on the right. Distinctions to Note! Between Request Denied and Permission Revoked: Request Denied: Refers to requests that were rejected from the outset, indicating that access to the dataset was never granted. Permission Revoked: Indicates that permissions were previously granted but have since been revoked. Users with permissions revoked have previously accessed the dataset. Between Approved Request and Current Permissions: Approved Request: Represents an entry when a data access request has been approved in the past. Current Permissions: Denotes ongoing permissions where a user has present access to the dataset. An approved request may now appear as Permission Revoked in the present. By observing these different rows, users can gain insight into the complete history of a user's interactions and permissions regarding a dataset over time. To revoke access to a specific user for a dataset, follow these steps: Go to History page Look for the row with the specific permissions. You can use the filters! Click on the toggle button to revoke access. Add a denial reason. Bear in mind that the requester will receive the denial reason! Click on the Apply button. Check more information on the Hisotry page here! Audit your metadata objects In the DAC Portal, you can efficiently manage and audit various metadata objects pertinent to your role as a Data Controller. Upon accessing the DAC Portal, you will encounter three primary tabs on the homepage: DAC: Contains information about Data Access Committees (DACs). Policies: Provides insights into linked policies. Datasets: Displays datasets and relevant details. Within each tab, you'll find a comprehensive list of the objects you manage, these being grouped by type (DAC, Policies, Datasets), as well as by your role (member/admin). Whilst the lists give you a quick look, to check how things are connected, we've added a table at the bottom DACs and policies. DACs Let’s check which policies are linked to a specific DAC: Go to "My DACs." Select a DAC. Click on "EDIT" to see more details. You are now on this page: Scroll down to see the linked policies. Click on “List of linked policies of this DAC” and you will see a list of all policies linked to your selected DAC. Policies Do you want to view a list of datasets connected to a particular policy? Follow the same steps mentioned earlier, but head to the policy tab this time. In the policy tab, you'll find a list of all your policies. Here, you may notice two different icons next to the policy ID (EGAP): DAC Icon (): This represents the DAC. Hover your mouse over the icon to see the DAC ID. Dataset icon (): This indicates datasets falling under that policy. The number next to the icon tells you how many datasets are linked to the policy. For example, if you see "2" next to the icon, it means there are two datasets linked to that policy. If you want to check the linked datasets and their relevant information, simply click on a specific policy. You'll then find the "List of linked datasets of this policy" at the bottom of the page. Datasets Finally, in the dataset tab, by default you will see a list of all the datasets you can manage with all your DACs and policies. Yet, we've included two handy ways to organise them: DAC vs. policy: You can group by datasets, either by DAC or policy. Released vs. unreleased: You can sort out datasets based on their release status. Feel free to experiment with both options! For instance, if you want to see which DACs have unreleased datasets, simply select DAC and unreleased, and you'll get the details you need! Deprecation Do you have a bunch of metadata objects like DACs and policies that you don’t need anymore? This section shows you how to get rid of them! But what does "deprecation" mean for EGA? It's basically changing the status of a metadata object to "deprecated," which means we won't be using it in the future. In simple terms, it's like saying these objects are no longer useful. However, because we believe in making metadata FAIR, once an object has a persistent identifier, we can't just delete it. So, instead of deleting, we deprecate it. Here's a helpful tip! If you want to make a metadata object disappear from the DAC Portal, deprecate it. You won’t see it in the portal anymore! Let’s say you want to deprecate a DAC. Let’s do it! Go to "My DACs." Select a DAC. Click "EDIT" to see more details. You are now in this page: Click on the Deprecate button It will then appear a message. There are two options here: Your DAC is not linked to any policy, hence it’s ready to be deprecated. Click on “Yes. Confirm” to deprecate your DAC object! Your DAC is linked to at least one policy, and you need to either: Deprecate the linked policy first, or Link the policy to a different DAC Now, let’s say you don’t want to get rid of the policy altogether, but you want to change the DAC it’s linked to. Here's how: Go to the policy tab. Find the policy you want to change (for example, EGAP50000000019). Choose a new DAC to link it to. Click on Update. See the Edit Policy section for more details. After ensuring that the DAC we want to deprecate isn’t linked to any policy, return to the "My DACs" section and follow the steps outlined previously. This will lead you to a confirmation message. You can only deprecate DACs and Policies. To deprecate a dataset, please contact our Helpdesk team. Check more information on how deprecation works here! User Preferences We have implemented email notifications in the DAC Portal. Here’s the complete list: DACs: Pending requests Approved by Helpdesk Rejected by Helpdesk DAC Invitation Requesters: Data access request approved Data access request denied Permissions revoked Upcoming expiration date As a DAC member, you will be able to decide whether you want to receive the DAC notifications or not. For that, go to the top-right corner menu, select User Preference. You will be able to decide whether you want to receive notifications for: Approved by Helpdesk Rejected by Helpdesk DAC Invitation For pending requests, you will be able to select how often do you want to receive the notification: Daily Weekly Fortnightly As a DAC member, you are responsible for managing data access requests. Consequently, you will receive notifications for pending requests. If you prefer not to manage these requests, please arrange to be removed as a contact for your DAC. Here's a tip! Do you have a pending request that you don’t want to receive a notification? Add a DAC comment! 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! DAC API - A programmatic approach In addition to the new DAC Portal, we are excited to announce the release of the DAC API. This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can click the button below. Check out the DAC API specification!

  Documentation access/data-access-committee/dac-portal

• National Cancer Institute (NCI) TARGET: Therapeutically Applicable Research to Generate Effective Treatments

  The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) Initiative seeks to accelerate research in novel marker and drug development, along with understanding the molecular basis of pediatric malignancy, through identification of genomic changes associated with the following childhood cancers: Acute Lymphoblastic Leukemia (ALL) - A fast-growing type of blood cancer in which too many immature white blood cells are found in the blood and bone marrow. Acute Myeloid Leukemia (AML), including AML Induction Failure cases (AML-IF) - Another type of blood cancer marked by too many myeloblasts, an alternate type of immature white blood cell, are found in the blood and bone marrow. Upon diagnosis, AML patients without high-risk genetic markers undergo standard chemotherapy regimen, called the primary induction phase, to eliminate most cancer cells and induce a remission state. Clear Cell Sarcoma of the Kidney (CCSK) - A rare type of kidney cancer, in which the inside of the cells looks clear when viewed under a microscope. Clear cell sarcoma can spread from the kidney to other organs, most commonly the bone, but also including the lungs, brain, and soft tissues of the body. Neuroblastoma (NBL) - Cancer of cells of the sympathetic nervous system. Cancer Model Systems (MDLS), including PPTP - MDLS includes acute leukemia cell lines and xenografts with matched primary and/or relapsed tumor information, NBL cell lines and xenografts with matched controls, kidney tumor cell lines, normal brain tissues, and embryonic stem cell lines that were sequenced for some TARGET disease projects. Osteosarcoma (OS) - A cancer of the bone that primarily affects children and adolescents. Rhabdoid Tumor (RT) - A rare, fast-growing cancer that usually forms in the kidney or central nervous system (the brain and spinal cord) but can also form in soft tissues in other areas of the body. Rhabdoid tumors tend to spread quickly, are hard to treat, and have a poor prognosis. Wilms' Tumor (WT) - A cancer of cells in the kidney that can spread to the liver, lung and lymph nodes. More information can be found about each TARGET subproject by following the links on the top of this page. Together these cancers account for the majority of the more than 10,000 childhood cancer cases diagnosed in the United States each year. TARGET has employed a set of advanced and complementary genome analysis technologies, including large scale 2nd and 3rd generation genome sequencing, to strategically characterize alterations in both gene expression and in genomic structure (such as deletions and amplification) that are involved in childhood cancers. The goal of this coordinated effort has been to create a comprehensive genomic and transcriptomic profile of each cancer. Integrated analysis of the TARGET data has identified those genes that are either altered in their expression level or mapped to the chromosome regions of deletion/amplification/translocation, as these genes represent strong candidates for therapeutic targeting. To learn more about the TARGET project, which is now completed, visit the website at https://www.cancer.gov/ccg/research/genome-sequencing/target. TARGET data is accessible at the NCI's Genomic Data Commons (GDC) via the GDC's Data Portal and from each of the TARGET Publication Pages at the GDC (Please see the "Supplemental Links" section of any TARGET publication's "Publication Information and Associated Data Files page" at the GDC.). Available datasets include raw sequencing data, datasets generated by the original TARGET research teams (fully annotated clinical information and higher-level/analyzed molecular characterization data - gene expression, copy number variation, epigenetics, targeted sequencing), as well as higher level data generated by the GDC. Please see the TARGET Use and Publication Guidelines for updated details on the sharing of any TARGET substudy data, including how to cite TARGET.

  Study phs000218

• A compendium of mutational signatures due to environmental exposures

  Whole-genome-sequencing (WGS) of human tumours has revealed distinct mutation patterns that hint at the causative origins of cancer. We examined mutational-signatures in 324 WGS of human induced pluripotent stem cells (iPSCs) following exposure to known or suspected environmental carcinogens. 79 agents were tested with or without metabolic activation at concentrations that produced measurable cytotoxicity; 41 yielded characteristic substitution mutational signatures. Some exhibit similarity with signatures found in human tumours. Additionally, 6 agents produced double-substitution signatures and 8 produced indel signatures. Investigating mutation asymmetries across genome topography reveals fully functional mismatch and transcription-coupled repair pathways in iPSCs. Primary adducts induced by environmental carcinogens can be resolved by disparate repair/replicative pathways, resulting in an assortment of signature outcomes even for a single mutagen. This compendium of experimentally-induced mutational-signatures permits further exploration of roles of environmental agents in cancer aetiology, and underscores how human stem cell DNA is directly vulnerable to environmental agents.

  Dataset EGAD00001004583

• Transcriptomics-driven classification of ALAL

  Acute leukemia of ambiguous lineage (ALAL) is a rare, poor-prognosis subtype of acute leukemia that cannot be assigned to a single hematopoietic lineage. Although ALAL patients are typically treated with acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL) regimens, optimal treatment choice is hindered by their lineage ambiguity. Therefore, we investigated the added value of transcriptomics for improving lineage assignment, currently based mainly on surface markers. First, we used an in-house pipeline to comprehensively detect genetic lesions in RNA-sequencing data (n=23) with a sensitivity >90%, as confirmed by targeted DNA sequencing. Second, we compared ALAL gene expression profiles (GEPs) with representative AML (n=145), B-ALL (n=223) and T-ALL (n=85) cases. In a principal component analysis, ALALs did not form a separate group, but clustered with AML, B-ALL or T-ALL. Accordingly, a machine learning classifier trained with GEPs of acute leukemias segregated 20/23 ALALs into myeloid-, B- or T-lymphoid leukemia. These 20 cases harbored genetic abnormalities consistent with the classifier-assigned leukemia. Furthermore, ALAL GEPs were deconvoluted with single-cell transcriptional profiles of normal hematopoietic cells using CIBERSORTx, revealing enrichment for signatures of lineages corresponding to the leukemic type predicted by our algorithm. The classifier was validated on an external ALAL cohort (n=24), assigning 75% of the patients to a lineage matching their immunophenotypic and methylation profiles. In conclusion, integrative analysis of RNA-sequencing data can accurately classify most ALAL cases. The pipeline and classifier developed in our study are valuable tools to improve ALAL diagnosis and guide therapeutic decisions.

  Study EGAS00001007967

• Hyperfibrinolysis

  As part of the Bloodomics collaboration we have several categories of pedigrees with diseases/syndromes relevant to cardiovascular diseases (CVD). One such pedigree, hyperfibrinolysis is associated with increased fibrinolytic activity, resulting in increased bleeding. Exome sequencing has been performed as part of a discovery program to ascertain potential causative variants of the clinical phenotype.

  Study EGAS00001000104

• Whole exome sequencing in familial Multiple Sclerosis

  The purpose of the study is to determine whether variants of genes are associated with greater risk of Multiple Sclerosis (MS). We used whole-exome sequencing in 138 individuals from 23 families including at least 2 members with MS. We compare patients with MS, patients with other autoimmune diseases (AID), and unaffected individuals.

  Study EGAS00001004204

• Bleeding

  As part of the Bloodomics collaboration we have several categories of pedigrees with diseases/syndromes relevant to cardiovascular diseases (CVD). One such group, is from individuals who exhibit severe bleeding with an unknown cause. Exome sequencing has been performed as part of a discovery program to ascertain potential causative variants of the clinical phenotype.

  Study EGAS00001000106

• Whole-genome and transcriptome sequencing of tumor-stage mycosis fungoides

  Mycosis fungoides (MF) is the most common cutaneous T-cell lymphoma (CTCL), a group of malignancies derived from skin-homing malignant T cells. We subjected tumor biopsies from MF patients to whole-genome sequencing and RNA-sequencing to investigate genomic alterations and deregulated gene expression in the disease.

  Study EGAS00001002860

• Various_Platelet_Disorders

  As part of the Bloodomics collaboration we have several categories of pedigrees with diseases/syndromes relevant to cardiovascular diseases (CVD). One such group, is from individuals who have a platelet disorder with an unknown cause. Exome sequencing has been performed as part of a discovery program to ascertain potential causative variants of the clinical phenotype.

  Study EGAS00001000107

• Multi-omics of Richter syndrome

  Richter syndrome (RS) is the aggressive transformation of Chronic Lymphocytic Leukemia, mostly with Diffuse Large B-Cell Lymphoma histology. While histologically similar, RS and de novo DLBCLs present with different clinical courses. We characterized the RS epigenome by combining genome-wide DNA methylation with transcriptome-sequencing on a large RS cohort.

  Study EGAS00001005495

• Whole genome sequencing of Osteosarcoma clonal evolution

  Multiple large-scale genomic profiling efforts have been undertaken in osteosarcoma to define the genomic drivers of tumorigenesis, therapeutic response, and disease recurrence. The spatial and temporal intratumor heterogeneity could also play a role in promoting tumor growth and treatment resistance. Here, we conducted longitudinal whole-genome sequencing of 37 tumor samples from eight patients with osteosarcoma that relapsed or became refractory to initial therapy. We found that the chemoresistant population in recurrent osteosarcoma is subclonal at diagnosis, emerges at the time of primary resection due to selective pressure from neoadjuvant chemotherapy, and is characterized by unique oncogenic amplifications.

  Dataset EGAD00001011285

• HELIUS DAC The HELIUS data are owned by the Amsterdam University Medical Centers, location AMC in Amsterdam, The Netherlands. To allow sharing of microbiome data collected in HELIUS with (inter)national researchers, 16s rRNA sequence analysis has been stored at the European genome-phenome archive (EGA; accession code EGAD00001004106). This requires that access needs to be granted, also because the HELIUS data are stored with relevant phenotypical variables. Access is granted to all researchers affiliated with an internationally recognized research institution who request to use the HELIUS data within the EGA context, after having signed the data transfer agreement. Any researcher can request the data by submitting a proposal to the HELIUS Executive Board as outlined at http://www.heliusstudy.nl/en/researchers/collaboration, by email: heliuscoordinator at amsterdamumc dot nl. The HELIUS Executive Board will check proposals if they do not conflict with ethical approvals and informed consent forms of the HELIUS study.

  Dac EGAC00001000895

• Resuscitation Outcomes Consortium (ROC) Amiodarone, Lidocaine or Neither for Out-Of-Hospital Cardiac Arrest Due to Ventricular Fibrillation or Ventricular Tachycardia (ALPS)(ROC-ALPS-BioLINCC)

  Accessing Data Please refer to “Authorized Access” below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP. Objective To compare the effects of amiodarone, lidocaine, and placebo on survival to hospital discharge after out-of-hospital cardiac arrest due to shock-refractory ventricular fibrillation or pulseless ventricular tachycardia. Background Ventricular fibrillation (VF) or pulseless ventricular tachycardia (VT) are common causes of out-of-hospital cardiac arrest, but are considered the most responsive to shock and therefore the most treatable. Nonetheless, most defibrillation attempts do not result in sustained return of spontaneous circulation, and VF or VT may persist or recur after shock. There is also evidence that longer durations of VF or VT are associated with decreases in the likelihood of resuscitation. Amiodarone and lidocaine are commonly used to promote successful defibrillation of shock-refractory VF or VT and prevent recurrences. Previous trials have shown amiodarone to be more effective than placebo or lidocaine for return of spontaneous circulation and survival at hospital admittance. This study sought to further extend the research and examine whether amiodarone would improve survival to hospital discharge and neurologic outcomes, as compared to placebo or lidocaine. Participants 3,026 eligible participants were enrolled, with 974 assigned to amiodarone, 993 assigned to lidocaine, and 1,059 assigned to placebo. An additional 1,627 participants that received a study intervention, but did not meet eligibility criteria, were included in analysis of the intention-to-treat population. Design The study interventions (amiodarone, lidocaine, and saline) were packaged in indistinguishable sealed kits and randomly distributed in to Emergency Medical Services (EMS) providers in a 1:1:1 ratio, stratified by participating site and agency. Each kit contained three syringes, and each syringe held 3 ml of colorless fluid containing 150 mg of amiodarone, 60 mg of lidocaine, or normal saline. Participants with out-of-hospital cardiac arrest were treated in accordance with local EMS protocols, in compliance with American Heart Association (AHA) guidelines. If VF or VT persisted or recurred after one or more shocks, eligible participants received a vasopressor and the masked kit containing amiodarone, lidocaine, or placebo. Approximating current clinical practice, the initial dose consisted of two syringes administered by rapid bolus. If the estimated body weight of the patient was less than 100 lbs., then one syringe was used. If VF or VT persisted, standard resuscitation measures, additional shocks, and an additional syringe of the study drug were administered. At that point the trial interventions were completed and standard interventions for advanced life support were employed. Upon arrival at the hospital, providers were notified of the patient's enrollment in the trial and encouraged to provide usual care in accordance with AHA guidelines, including open-label amiodarone or lidocaine if necessary. Components of hospital care were monitored but not standardized by the trial protocol. Participants, providers, and trial personnel were blinded to the trial drug assignments, with the exception of treating physicians if emergency un-blinding was required for care. Data from pre-hospital patient care records, CPR process measures, and hospital medical records were collected. The primary outcome of the trial was survival to hospital discharge, and the secondary outcome was survival with favorable neurologic status at discharge, defined as a score on the modified Rankin scale of 3 or less. Conclusions Neither amiodarone nor lidocaine resulted in a significantly higher rate of survival to hospital discharge or favorable neurologic outcome, as compared to placebo, among participants with out-of-hospital cardiac arrest due to initial shock-refractory ventricular fibrillation or pulseless ventricular tachycardia.

  Study phs003784

• Heart Failure Network - Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Diastolic Heart Failure (HFN RELAX-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. Related StudyEcho images are available through HFN RELAX-ImagingObjectivesThe RELAX trial tested the hypothesis that chronic phosphodiesterase type-5 inhibitor therapy with sildenafil would improve exercise capacity and clinical status in heart failure patients with normal ejection fraction, as compared to placebo. Background Heart failure (HF) with preserved ejection fraction (HFpEF) is a common and highly morbid condition that is characterized by chronic exercise intolerance, progressive functional decline and a high rate of readmission. At the time of the RELAX trial, clinical trials of renin-angiotensin system antagonists had not demonstrated improvement in outcomes or clinical status in HFpEF, and effective therapies were needed. Phosphodiesterase type-5 (PDE-5) metabolizes the nitric oxide (NO) and natriuretic peptide (NP) systems' second messenger cyclic guanosine monophosphate (cGMP), and thus may limit beneficial NO and NP actions in the heart, vasculature and kidney. Pre-clinical studies suggest that inhibition of PDE-5 reverses adverse cardiac structural and functional remodeling and enhances vascular, neuroendocrine and renal function. In clinical studies, PDE-5 inhibitor therapy improved exercise tolerance and clinical status in patients with idiopathic pulmonary arterial hypertension and in patients with HF and reduced ejection fraction (HFrEF). A small, single-center study in HFpEF observed improved hemodynamics, left ventricular (LV) diastolic function, right ventricular (RV) systolic function, LV hypertrophy and lung function with chronic PDE-5 inhibition as compared to placebo. In aggregate, these studies suggested the potential for PDE-5 inhibition to ameliorate several key pathophysiological perturbations in HFpEF, and thus improve exercise capacity and clinical status. ParticipantsA total of 216 participants were enrolled in the trial with 113 in the Sildenafil group and 103 in the placebo group. Design Participants who met screening criteria underwent baseline studies including a history and physical examination, cardiopulmonary exercise test (CPXT), six-minute walk distance, Minnesota Living with Heart Failure Questionnaire (MLWHFQ), echocardiography, cardiac magnetic resonance imaging, and phlebotomy for biomarkers. Subjects were then randomly assigned, in a 1:1 ratio, to either the sildenafil or placebo intervention group. The study drug was administered orally at 20 mg three times daily (TID) for 12 weeks. If the dose was well tolerated at 12 weeks, it was increased to 60 mg TID for another 12 weeks. If side effects developed, study staff could recommend discontinuation or return to a lower or previously tolerated dose of study drug. Sildenafil levels 2 hours after a scheduled dose of study drug were obtained at 12 and 24 weeks. The primary endpoint was exercise capacity determined by change in peak oxygen consumption during the CPXT after 24 weeks of therapy. Secondary endpoints included change in six-minute walk distance at 12 and 24 weeks, change in peak oxygen consumption at 12 weeks, and a three tier score reflective of clinical status where patients were ranked based on time to death (lowest tier), time to cardiovascular or cardiorenal hospitalization (middle tier), and change in the MLWHFQ for patients alive without cardiovascular or cardiorenal hospitalization after 24 weeks (highest tier). Conclusions Chronic phosphodiesterase type-5 inhibitor therapy with sildenafil for 24 weeks did not alter exercise capacity or clinical status compared to placebo in patients with heart failure and preserved ejection fraction.

  Study phs003565

• Coronary Artery Risk Development in Young Adults (CARDIA-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.Related Studies: Other CARDIA data available include:phs000236.v2.p2 PAGE: CALiCo: Coronary Artery Risk Development in Young Adults(CARDIA) phs000309.v3.p2 The CARDIA-GENEVA Study phs000399.v1.p2 NHLBI GO-ESP: Heart Cohorts Exome Sequencing Project (CARDIA) phs000613.v1.p2 NHLBI CARDIA Candidate Gene Association Resource (CARe) phs003675.v1.p1 CARDIA Multi-omics Obesity & CVD Substudy – Year 20 Untargeted Metabolomics Data phs001612.v3.p2 NHLBI TOPMed: CARDIA (Coronary Artery Risk Development in Young Adults) phs003045.v1.p1 Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Coronary Artery Risk Development in Young Adults Study (CARDIA) phs000285.v4.p3 CARDIA_Cohort Available Data: Available data for request include exam data through Year 30 and outcomes and death data through 2022. Additionally, data from 28 ancillary studies are available for request.Objectives: The original objectives of CARDIA were to document levels of risk factors for coronary artery disease and potential determinants of these risk factors in young adults; to study the interrelationships of risk factors and lifestyles and to document behavioral and environmental changes during the transition from young adulthood to middle age; to compare cross-sectional and longitudinal data on age-related trends in cardiovascular disease risk factors; and to compare levels and evolution of risk factors between men and women, blacks and whites, and in groups of differing socioeconomic status. Goals of the study have evolved to emphasize understanding determinants of left ventricular mass, emerging obesity and hypertension, and sequelae of hypertension in pregnancy.Background: CARDIA is designed to increase understanding of contributors to changes in cardiovascular disease (CVD) risk factors during the critical years of transition from young adulthood to middle age. CARDIA was funded initially in 1983 for a five-year cycle that included two rounds of examinations. Contract renewals have allowed for subsequent re-examinations.Participants: Black and white men and women; ages 18-30 years at entry with a range of attained education; original sample size: 5,115.Design: CARDIA is a population-based observational study of 5,115 participants aged 18-30 years recruited in 1985-1986. The sample was designed to achieve approximately balanced subgroups of race, gender, education (high school or less and more than high school) and age (18-24 and 25-30). Forty percent of the cohort had no more than a high school education. A total of nine examinations have been completed in the cohort with examination cycles at year 2 of the project and years 5, 7, 10 , 15, 20, 25, and 30.In addition to standard measurements of blood pressure, anthropometry, blood lipids, smoking behavior, physical activity, diet, pulmonary function, and many psychological factors, CARDIA has other included measurements (in subsets or in the full cohort) to obtain unique information on other aspects of risk factor development and early morbidity. These have included: graded exercise treadmill testing; echocardiography, particularly for measurement of left ventricular mass; cardiovascular reactivity; serum cotinine; Lp(a), apoE phenotype, apolipoprotein A1 and B; homocysteine; skin reflectance; body composition by dual X-ray absorptiometry; glucose tolerance testing; vascular resistance and compliance; and plasma renin activity and sympathetic nervous system activity.

  Study phs003739

• TIRE-seq_PDN

  TIRE-seq is a novel RNA sequencing method that integrates mRNA purification directly into library preparation, eliminating separate RNA extraction. The technique demonstrated utility across three biological applications, including capturing transcriptional changes in human T cells, identifying genes driving murine dendritic cell differentiation, and analyzing the dose-response effects of temozolomide on patient-derived neurospheres. With its simplified approach and high sequencing efficiency, TIRE-seq offers a cost-effective solution for large-scale gene expression studies.

  Dataset EGAD50000001261

• WGS of off-target analysis of base editing in organoids

  Seven clonal organoid lines and one bulk wild-type control sample were paired-end whole-genome sequenced using the Illumina Novaseq 6000 system. We sequenced four clonal intestinal organoid lines harbouring engineered TP53 and FBXW7 mutations as well as three lines targeted for oncogenic APC/TP53/PIK3CA/SMAD4 mutations. The reads were mapped to hg38 genome assembly and data is provided as BAM files.

  Dataset EGAD00001009827

• Single cell RNA sequencing of colorectal cancer

  To understand intrinsic cancer cell signatures and the surrounding microenvironemt and their interactions, we performed single-cell RNA sequencing on 63,689 cells from 23 patients with 23 primary colorectal cancer and 10 matched normal mucosa samples. Analyzing of primary colorectal cancer and normal mucosa samples show a comprehensive cellular landscape of colon cancer, which is a valuable resource for the development of therapeutic strategies.

  Dataset EGAD00001005198

• Anaplastic Thyroid Cancer aligned sequence data

  Sequence data in fastq format was aligned to the GRCH38 reference genome. Aligned sequence was preprocessed with GATK for Indel Realignment and Base Quality Score Recalibration. Duplicates were marked with Picard Mark Duplicates. Aligned sequence is in bam format. Details of the alignment can be found int he bam header. In total, data generated from 174 tumour samples 102 matched blood normal controls was aligned. Tumour samples were classified as Anaplastic Thyroid, Poorly-differentiated or well-differentiated cancers.

  Dataset EGAD00001004126

• Functionally-defined Therapeutic Targets in Diffuse Intrinsic Pontine Glioma (DIPG)

  Diffuse Intrinsic Pontine Glioma (DIPG) is a universally fatal childhood cancer. Here, we performed a chemical screen in patient-derived DIPG cell cultures along with RNAseq expression analysis and integrated computational modeling to identify potentially effective therapeutic strategies. Panobinostat, among the more promising agents identified, demonstrated efficacy in pontine orthotopic xenograft models of both H3K27M and histone WT DIPG. These data suggest the potential utility of specific drug combinations and provides evidence of in vivo treatment efficacy of the multi-histone deacetylase inhibitor panobinostat. We are depositing to dbGaP deep sequencing whole exome data for 22 patient tumor samples and 13 matched normals, along with RNAseq data for 12 patient tumor samples and 6 normal pediatric brain tissue samples. In addition, we are depositing 22 RNAseq samples from DIPG cell lines before and after panobinostat treatment.

  Study phs000900

• Epigenetic profiling of colorectal cancer initiating cells (CC-ICs) to identify bivalently marked genes (H3K4me3 and H3K27me3 ChIP-seq), and investigation of changes in transcriptome following EZH2 inhibition using RNA-seq.

  Epigenetic regulation of transcription plays a crucial role in lineage commitment of embryonic stem cells. Promoters of key lineage-specific differentiation genes are found in a repressed bivalent state, having both activating H3K4me3 and repressive H3K27me3 histone marks, making them poised for transcription upon loss of H3K27me3 in response to environmental cues. Whether the tumour-initiating, self-renewing, cancer-initiating cells (C-ICs) have similar epigenetic regulatory mechanism that prevent lineage commitment is unknown. In order to investigate bivalently marked and repressed promoters, we used a patient-derived CC-IC enriched model to identify the changes in transcriptome following inhibition of EZH2, the H3K27 methyltransferase. We also performed ChIP-seq for H3K27me3 and H3K4me3 at baseline in order to identify repressed and bivalently marked promoters.

  Study EGAS00001003003

• Pan Cancer Investigation of Human Leukocyte Antigen Loss of Heterozygosity

  Cancer cells can escape immune recognition through human leukocyte antigen (HLA) loss of heterozygosity (LOH) which results in deletion of HLA alleles, causing a reduction in presentation of tumor neoantigens. Despite its influence on immunotherapy response, few methods exist to detect HLA LOH, and accuracy of the approaches is poorly defined. Here, we present DASH (Deletion of Allele-Specific HLAs), a machine learning-based algorithm developed to detect HLA LOH from paired tumor-normal sequencing data. Using DASH to characterize HLA LOH in 610 patients across 15 cancer types, we found that 18% of patients had loss of heterozygosity in at least one HLA allele, suggesting the HLA LOH is a widespread, and potentially key immune resistance strategy in multiple cancers.

  Study phs002783