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• Providing safe access to sensitive human data across borders: Federated EGA becomes a reality

  Enabling discovery and access to sensitive data across national boundaries is vital for improving human health. It enables more powerful and efficient research by increasing the volume and diversity of data available for analysis. It allows us to better understand the causes of diseases – cancer, rare diseases, infectious diseases like COVID-19 – and develop new medicines and treatments. Sensitive human omics data are typically generated by research initiatives and shared using specialist repositories which provide services for data submission, discovery, and access. The EGA is one such repository. Established in 2008 at EMBL’s European Bioinformatics Institute (EMBL-EBI) in the UK, since 2012 the EGA (“Central EGA”) has been jointly managed by EMBL-EBI and the Centre for Genomic Regulation (CRG) in Spain. Many countries have emerging personalised medicine programmes which generate data from national initiatives. These programmes are driving a transition in human genomics from being research-driven to receiving funding through healthcare. Data generated in a clinical context are subject to stricter governance than research data and must follow national data protection legislation. To solve these challenges, the Federated EGA provides a network of connected resources to enable transnational discovery of and access to human omics data for research, while also respecting jurisdictional data protection regulations. In this way, the Federated EGA infrastructure supports the goals of European initiatives such as the 1+ Million Genomes initiative (1+MG), the European Health Data Space, and a number of EU-funded 1+MG implementation projects including Beyond 1 Million Genomes. The Federated EGA is made up of “Nodes” – typically nationally funded and operated – which store and manage data locally while allowing global discovery within the Federated EGA network. Since 2016, multiple parallel efforts, supported by ELIXIR and other transnational and national initiatives, have created technical and legal frameworks for establishing the Federated EGA. In 2022, the first five Nodes – Finland, Germany, Norway, Spain, and Sweden – officially joined the Federated EGA by signing Federated EGA Collaboration Agreements with Central EGA. The Finnish FEGA Node is operated by CSC - IT Center for Science and provides data management services according to national laws and the requirements of the EU General Data Protection Regulation (GDPR). These services provide tools and support for the whole life-cycle of sensitive research data from collating to analysis, publication, and authorised re-use. The development of the services has been a joint effort with the other Nordic nodes within NeIC's Tryggve and Heilsa projects, and funded by Finnish Ministry of Education and Culture and projects coordinated by ELIXIR Finland. Read more about Finnish FEGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. In Norway, a key component of the infrastructure is the services of The Sensitive Data Service (TSD) offered by USIT at University of Oslo. The Federated EGA Norway Node is developed by ELIXIR Norway and operated by the University of Oslo as the responsible legal entity. Core software modules are developed jointly with the other Nordic nodes in the NeIC Tryggve and Heilsa projects. Read more about FEGA Norway signing. The Spanish FEGA (es-FEGA) is a national service for storing sensitive biomedical data in Spain. Supported by the Spanish Institute of Bioinformatics (INB) in collaboration with Central EGA, sensitive research datasets are primarily hosted at the Barcelona Supercomputing Centre facilities. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. By providing a solution for secure and efficient management of human omics data, the Federated EGA aims to foster data reuse, enable reproducibility, accelerate biomedical research, and improve human health. Find out more Interested in setting up your own Federated EGA Node? Check out the FEGA Onboarding Knowledge Base for more information. The ELIXIR Federated Human Data Community is a great entry point for anyone interested in learning more about the Federated EGA. You can: Join the ELIXIR Federated Human Data Community mailing list (select “Human Data”) Attend the ELIXIR Federated Human Data Community calls

  Blog safe-access-to-sensitive-human-data-federated-ega

• Genome-Wide Approach to Measure Variant-Based Heritability of Drug Outcome Phenotypes

  Current pharmacogenetic approaches often use a few large-effect alleles to predict drug outcomes. However, many drug outcome phenotypes are determined by complex pathways that may be influenced by common single-nucleotide variants (SNVs) across the genome. We ascertained how much variation in drug response is explained by common SNVs for pharmacokinetic (drug levels) and pharmacodynamic phenotypes (highest creatinine) for 4 drugs: vancomycin, gentamicin, cyclosporine and tacrolimus.

  Study phs002506

• cfDNA methylation profiling on longitudinally collected blood plasma of patients with esophageal adenocarcinoma

  Esophageal adenocarcinoma (EAC) is an aggressive cancer characterized by a high risk of relapse post-surgery. Analyzing cell-free DNA (cfDNA) from blood plasma emerges as a promising avenue for therapy response monitoring. This study aims to evaluate the cost-effective and genome-wide cell-free reduced representation bisulfite sequencing (cfRRBS) method combined with computational deconvolution for effective disease monitoring in EAC patients.

  Study EGAS50000000514

• IBD_Whole_Genome_Sequencing

  We will sequence at 15X coverage the genomes of 1536 IBD patients. These samples are currently onsite at Sanger and made available for sequencing via our collaboration with the UK IBD Genetics consortium. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002238

• Genomewide detection of cytosine methylation by single molecule real-time sequencing

  5-methylcytosine (5mC) is an important type of epigenetic modification. In this study, we enhanced 5mC detection using SMRT sequencing by holistically analyzing kinetic signals of a DNA polymerase and sequence context for every base within a measurement window. We employed a convolutional neural network to train a methylation classification model. This methodology has provided a system for simultaneous genome-wide genetic and epigenetic analyses. 

  Study EGAS00001004642

• Multidimensional Proteomics analysis of intractable cancers with prospective observational cohort for precision medicine

  Multi-omics project of Non small cell Lung Cancer. Exome and RNAseq datasets were generated from normal-tumor pairs of Non small cell Lung Cancer patients from the retrospective cohort.

  Study EGAS50000000592

• Single cell ATAC sequencing

  Paired-end fastq files of 22 AML and 2 normal bone marrow samples were generated using 10x Genomics single cell ATAC sequencing following manufacturer's protocol (CG000168 User Guide RevB) for chemistry version 1.0 and targeting 10000 nuclei per sample. Libraries were sequenced using Illumina HiSeq4000 using recommended cycling parameters.

  Dataset EGAD00001007675

• 5' single cell RNA sequencing

  Pared-end fastq files of 22 AML and 2 normal bone marrow samples were generated using 10x Genomics 5' single cell RNA sequencing following manufacturer's protocol (CG000086 User Guide RevG) for chemistry version 1.0 and targeting 10000 cells per sample. Libraries were sequenced using Illumina HiSeq 4000 using recommended cycling parameters.

  Dataset EGAD00001007672

• scRNA-seq from brain metastasis micorenvironment and cerebrospinal fluid

  A total of 9 brain metastasis were sequenced. For 6/9 a matched cerebrospinal fluid sample, prior to surgery and in two cases after surgery (+1 month from surgery) and after treatment (+3 month) were collected. Single-cell gene expression was produced using the Chromium Single Cell 5’ Library and sequenced on an Illumina NovaSeq 6000.

  Dataset EGAD00001006452

• Single cell BCR sequencing

  Paired-end fastq files of 22 AML and 2 normal bone marrow samples were generated using 10x Genomics single cell BCR sequencing following manufacturer's protocol (CG000086 User Guide RevG) for chemistry version 1.0 and targeting 10000 cells per sample. Libraries were sequenced using Illumina NovaSeq 6000 using recommended cycling parameters.

  Dataset EGAD00001007673