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• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Mediators of Atherosclerosis in South Asians Living in America Study (MASALA)

  Cohort Description The Mediators of Atherosclerosis in South Asians Living in America (MASALA) study is a prospective cohort of South Asians that aims to identify risk factors for heart disease in a large, growing Asian American subgroup. MASALA enrolled 906 South Asians in 2010-2013 and then added a new wave of 258 South Asian participants from 2017-2018, for a full cohort size of 1,164. Data Being Submitted Wave 1 questionnaire data includes 3967 variables for up to 431 MASALA participants in C4R. Wave 2 questionnaire data includes 448 variables for up to 313 MASALA participants in C4R. Dried Blood Spot/Serosurvey data includes 7 variables for up to 248 MASALA participants in C4R. Derived data includes 43 variables for up to 528 MASALA participants in C4R. Phenotype data includes 113 variables for up to 429 MASALA participants in C4R.

  Study phs002980

• COVID-19: Post-Hospital Thromboprophylaxis A Multicenter, Adaptive, Prospective, Randomized Trial Evaluating the Efficacy and Safety of Antithrombotic Strategies in Patients with COVID-19 Following Hospital Discharge (ACTIV-4C)

  This study is an adaptive, prospective, randomized trial designed to compare the effectiveness and safety of antithrombotic therapy with no antithrombotic therapy after hospitalization for 48 hours or longer for COVID-19. For Stage 1 of this study, participants will be randomized to either prophylactic anticoagulation or no anticoagulant therapy for 30 days, and then followed for an additional 60 days after the completion of treatment (total duration of follow-up, approximately 90 days). Biobanking of samples for future biomarker and mechanistic studies will be available for centers able to participate and collect samples from eligible participants. Samples will be collected at the time of enrollment and after the completion of 30 days of therapy.

  Study phs003063

• WGS files for paper titled "Preclinical Pediatric Molecular Analysis for Therapy Choice (MATCH)"

  WGS files for paper titled "Preclinical Pediatric Molecular Analysis for Therapy Choice (MATCH)"

  Dataset EGAD00001015697

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Atherosclerosis Risk in Communities Study (ARIC)

  Cohort Description The Atherosclerosis Risk in Communities (ARIC) study began in the mid-1980s with the initial aims being to describe the presence of subclinical atherosclerosis, the progression of atherosclerosis to clinical cardiovascular disease (CVD), and the association of novel risk factors with CVD. ARIC recruited its cohort of men and women aged 45-64 in 1987-89 from four communities (Forsyth County, NC; Jackson, MS; suburban Minneapolis, MN; and Washington County, MD). As of 2025, ARIC counts over 4,000 participants.Data Submitted Wave 1 questionnaire data includes 356 variables for up to 5763 ARIC participants in C4R. Wave 2 questionnaire data includes 367 variables for up to 5000 ARIC participants in C4R. Wave 3 questionnaire data includes 81 variables for up to 3450 participants in C4R.Dried Blood Spot/Serosurvey data includes 8 variables for up to 2437 ARIC participants in C4R. Derived data includes 62 variables for up to 18765 observations. Phenotype data includes 163 variables for up to 5937 ARIC participants in C4R. Brain MRI data includes 13 variables for up to 2550 participants in C4R.HCU data includes 21 variable for up to 631 ARIC participants in C4R.Lung CT data includes 15 variables for 1329 ARIC participants in C4R.Neurocognitive data includes 16 variables and 49826 observations.Serosurvey data includes 20 variables for 1707 ARIC participants in C4R.

  Study phs002988

• Single cell and plasma RNA sequencing

  Single cell and plasma RNA sequencing for RNA liquid biopsy for hepatocellular carcinoma

  Study EGAS00001005194

• Dataset for Manuscript: Cancer genome standards for long-read sequencing using cancer cell line mixtures

  Dataset for Manuscript: Cancer genome standards for long-read sequencing using cancer cell line mixtures

  Dataset EGAD00001015628

• Dac for "MediMer: A versatile do-it-yourself peptide-receptive MHC class I multimer platform for tumor neoantigen-specific T cell detection"

  Dac for the Study "MediMer: A versatile do-it-yourself peptide-receptive MHC class I multimer platform for tumor neoantigen-specific T cell detection" registered for Frank Momburg, f.momburg@dkfz-heidelberg.de Marten Meyer, marten.meyer@dkfz-heidelberg.de

  Dac EGAC50000000064

• FFPE WGS for optimizing mutation signature extraction from archival HGSC samples

  HGSC cases were selected for which matched fresh frozen and FFPE samples were available from the same tissue specimens. Fresh frozen, FFPE and normal blood were subject to WGS for the purpose of assessing the possibility of using FFPE WGS in place of fresh frozen for somatic mutation calling

  Study EGAS00001007521

• Molecular profiling of DLBCL patients treated in the HOVON84 trial

  Using a comprehensive NGS assay for simultaneous screening of mutations, copy number alterations, and translocations, 5 biology-based risk profiles could be reproduced in a Dutch DLBCL population and validated for outcome prediction. This approach will enable application as a diagnostic tool to select DLBCL patients for risk adapted treatment and help to develop biomarker-driven clinical trials for personalized medicine.

  Study EGAS00001003949

• 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