Search Results - EGA European Genome-Phenome Archive

Covid19 WGS variants analysis

483 samples were collected for WGS and aligned with GRCh38 human genome. The variants were called using GATK (Sentieon v. 201808.03) in GVCF and VCF format.

Dataset EGAD00001010138

Irish Covid19 WGS Raw Reads

Dataset contains 483 Irish origin Individuals with Covid19. paired end sequencing has been performed For WGS and later processed by using sention v 201808.03.

Dataset EGAD00001010186

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

Cellular indexing of transcriptomes and epitopes sequencing of peritoneal fluid from patients with achalasia

The data published here contains cellular indexing of transcriptomes and epitopes sequencing (CITEseq) using the oligo-tagged TotalSeq™-B Human Universal Cocktail, V1.0 from BioLegend on peritoneal fluid (PF) from patients with achalasia. Sequencing was performed in a paired-ended fashion on the NovaSeq6000. Below an overview of the epitopes to oligotags as obtained from BioLegend: DNA_ID Description clone Sequence Ensembl ID B0006 anti-human CD86 IT2.2 GTCTTTGTCAGTGCA ENSG00000114013 B0007 anti-human CD274 (B7-H1, PD-L1) 29E.2A3 GTTGTCCGACAATAC ENSG00000120217 B0020 anti-human CD270 (HVEM, TR2) 122 TGATAGAAACAGACC ENSG00000157873 B0023 anti-human CD155 (PVR) SKII.4 ATCACATCGTTGCCA ENSG00000073008 B0024 anti-human CD112 (Nectin-2) TX31 AACCTTCCGTCTAAG ENSG00000130202 B0026 anti-human CD47 CC2C6 GCATTCTGTCACCTA ENSG00000196776 B0029 anti-human CD48 BJ40 CTACGACGTAGAAGA ENSG00000117091 B0031 anti-human CD40 5C3 CTCAGATGGAGTATG ENSG00000101017 B0032 anti-human CD154 24-31 GCTAGATAGATGCAA ENSG00000102245 B0033 anti-human CD52 HI186 CTTTGTACGAGCAAA ENSG00000169442 B0034 anti-human CD3 UCHT1 CTCATTGTAACTCCT ENSG00000167286 B0046 anti-human CD8 SK1 GCGCAACTTGATGAT ENSG00000153563 B0047 anti-human CD56 (NCAM) 5.1H11 TCCTTTCCTGATAGG ENSG00000149294 B0050 anti-human CD19 HIB19 CTGGGCAATTACTCG ENSG00000177455 B0052 anti-human CD33 P67.6 TAACTCAGGGCCTAT ENSG00000105383 B0053 anti-human CD11c S-HCL-3 TACGCCTATAACTTG ENSG00000140678 B0058 anti-human HLA-A,B,C W6/32 TATGCGAGGCTTATC ENSG00000206503 B0063 anti-human CD45RA HI100 TCAATCCTTCCGCTT ENSG00000081237 B0064 anti-human CD123 6H6 CTTCACTCTGTCAGG ENSG00000185291 B0066 anti-human CD7 CD7-6B7 TGGATTCCCGGACTT ENSG00000173762 B0068 anti-human CD105 43A3 ATCGTCGAGAGCTAG ENSG00000106991 B0070 anti-human/mouse CD49f GoH3 TTCCGAGGATGATCT ENSG00000091409 B0071 anti-human CD194 (CCR4) L291H4 AGCTTACCTGCACGA ENSG00000183813 B0072 anti-human CD4 RPA-T4 TGTTCCCGCTCAACT ENSG00000010610 B0073 anti-mouse/human CD44 IM7 TGGCTTCAGGTCCTA ENSG00000026508 B0081 anti-human CD14 M5E2 TCTCAGACCTCCGTA ENSG00000170458 B0083 anti-human CD16 3G8 AAGTTCACTCTTTGC ENSG00000203747 B0085 anti-human CD25 BC96 TTTGTCCTGTACGCC ENSG00000134460 B0087 anti-human CD45RO UCHL1 CTCCGAATCATGTTG ENSG00000081237 B0088 anti-human CD279 (PD-1) EH12.2H7 ACAGCGCCGTATTTA ENSG00000188389 B0089 anti-human TIGIT (VSTM3) A15153G TTGCTTACCGCCAGA ENSG00000181847 B0090 Mouse IgG1, κ isotype MOPC-21 GCCGGACGACATTAA B0091 Mouse IgG2a, κ isotype MOPC-173 CTCCTACCTAAACTG B0092 Mouse IgG2b, κ isotype MPC-11 ATATGTATCACGCGA B0095 Rat IgG2b, κ isotype RTK4530 GATTCTTGACGACCT B0100 anti-human CD20 2H7 TTCTGGGTCCCTAGA ENSG00000156738 B0101 anti-human CD335 (NKp46) 9E2 ACAATTTGAACAGCG ENSG00000189430 B0124 anti-human CD31 WM59 ACCTTTATGCCACGG ENSG00000261371 B0127 anti-Human Podoplanin NC-08 GGTTACTCGTTGTGT ENSG00000162493 B0134 anti-human CD146 P1H12 CCTTGGATAACATCA ENSG00000076706 B0136 anti-human IgM MHM-88 TAGCGAGCCCGTATA ENSG00000211899 B0138 anti-human CD5 UCHT2 CATTAACGGGATGCC ENSG00000110448 B0141 anti-human CD195 (CCR5) J418F1 CCAAAGTAAGAGCCA ENSG00000160791 B0142 anti-human CD32 FUN-2 GCTTCCGAATTACCG ENSG00000143226 B0143 anti-human CD196 (CCR6) G034E3 GATCCCTTTGTCACT ENSG00000112486 B0144 anti-human CD185 (CXCR5) J252D4 AATTCAACCGTCGCC ENSG00000160683 B0145 anti-human CD103 (Integrin αE) Ber-ACT8 GACCTCATTGTGAAT ENSG00000083457 B0146 anti-human CD69 FN50 GTCTCTTGGCTTAAA ENSG00000110848 B0147 anti-human CD62L DREG-56 GTCCCTGCAACTTGA ENSG00000188404 B0149 anti-human CD161 HP-3G10 GTACGCAGTCCTTCT ENSG00000111796 B0151 anti-human CD152 (CTLA-4) BNI3 ATGGTTCACGTAATC ENSG00000163599 B0152 anti-human CD223 (LAG-3) 11C3C65 CATTTGTCTGCCGGT ENSG00000089692 B0153 anti-human KLRG1 (MAFA) SA231A2 CTTATTTCCTGCCCT ENSG00000139187 B0154 anti-human CD27 O323 GCACTCCTGCATGTA ENSG00000139193 B0155 anti-human CD107a (LAMP-1) H4A3 CAGCCCACTGCAATA ENSG00000185896 B0156 anti-human CD95 (Fas) DX2 CCAGCTCATTAGAGC ENSG00000026103 B0158 anti-human CD134 (OX40) Ber-ACT35 (ACT35) AACCCACCGTTGTTA ENSG00000186827 B0159 anti-human HLA-DR L243 AATAGCGAGCAAGTA ENSG00000204287 B0160 anti-human CD1c L161 GAGCTACTTCACTCG ENSG00000158481 B0161 anti-human CD11b ICRF44 GACAAGTGATCTGCA ENSG00000169896 B0162 anti-human CD64 10.1 AAGTATGCCCTACGA ENSG00000150337 B0163 anti-human CD141 (Thrombomodulin) M80 GGATAACCGCGCTTT ENSG00000178726 B0164 anti-human CD1d 51.1 TCGAGTCGCTTATCA ENSG00000158473 B0165 anti-human CD314 (NKG2D) 1D11 CGTGTTTGTTCCTCA ENSG00000213809 B0167 anti-human CD35 E11 ACTTCCGTCGATCTT ENSG00000203710 B0168 anti-human CD57 Recombinant QA17A04 AACTCCCTATGGAGG ENSG00000109956 B0170 anti-human CD272 (BTLA) MIH26 GTTATTGGACTAAGG ENSG00000186265 B0171 anti-human/mouse/rat CD278 (ICOS) C398.4A CGCGCACCCATTAAA ENSG00000163600 B0174 anti-human CD58 (LFA-3) TS2/9 GTTCCTATGGACGAC ENSG00000116815 B0176 anti-human CD39 A1 TTACCTGGTATCCGT ENSG00000138185 B0179 anti-human CX3CR1 K0124E1 AGTATCGTCTCTGGG ENSG00000168329 B0180 anti-human CD24 ML5 AGATTCCTTCGTGTT ENSG00000272398 B0181 anti-human CD21 Bu32 AACCTAGTAGTTCGG ENSG00000117322 B0185 anti-human CD11a TS2/4 TATATCCTTGTGAGC ENSG00000005844 B0187 anti-human CD79b (Igβ) CB3-1 ATTCTTCAACCGAAG ENSG00000007312 B0189 anti-human CD244 (2B4) C1.7 TCGCTTGGATGGTAG ENSG00000122223 B0206 anti-human CD169 7-239 TACTCAGCGTGTTTG ENSG00000088827 B0214 anti-human/mouse integrin β7 FIB504 TCCTTGGATGTACCG ENSG00000139626 B0215 anti-human CD268 (BAFF-R) 11C1 CGAAGTCGATCCGTA ENSG00000159958 B0216 anti-human CD42b HIP1 TCCTAGTACCGAAGT ENSG00000203618 B0217 anti-human CD54 HA58 CTGATAGACTTGAGT ENSG00000090339 B0218 anti-human CD62P (P-Selectin) AK4 CCTTCCGTATCCCTT ENSG00000174175 B0219 anti-human CD119 (IFN-γ R α chain) GIR-208 TGTGTATTCCCTTGT ENSG00000027697 B0224 anti-human TCR α/β IP26 CGTAACGTAGAGCGA B0236 Rat IgG1, κ isotype RTK2071 ATCAGATGCCCTCAT B0238 Rat IgG2a, κ Isotype RTK2758 AAGTCAGGTTCGTTT B0242 anti-human CD192 (CCR2) K036C2 GAGTTCCCTTACCTG ENSG00000121807 B0246 anti-human CD122 (IL-2Rβ) TU27 TCATTTCCTCCGATT ENSG00000100385 B0352 anti-human FcεRIα AER-37 (CRA-1) CTCGTTTCCGTATCG ENSG00000179639 B0353 anti-human CD41 HIP8 ACGTTGTGGCCTTGT ENSG00000005961 B0355 anti-human CD137 (4-1BB) 4B4-1 CAGTAAGTTCGGGAC ENSG00000049249 B0358 anti-human CD163 GHI/61 GCTTCTCCTTCCTTA ENSG00000177575 B0359 anti-human CD83 HB15e CCACTCATTTCCGGT ENSG00000112149 B0363 anti-human CD124 (IL-4Rα) G077F6 CCGTCCTGATAGATG ENSG00000077238 B0364 anti-human CD13 WM15 TTTCAACGCCCTTTC ENSG00000166825 B0367 anti-human CD2 TS1/8 TACGATTTGTCAGGG ENSG00000116824 B0368 anti-human CD226 (DNAM-1) 11A8 TCTCAGTGTTTGTGG ENSG00000150637 B0369 anti-human CD29 TS2/16 GTATTCCCTCAGTCA ENSG00000150093 B0370 anti-human CD303 (BDCA-2) 201A GAGATGTCCGAATTT ENSG00000198178 B0371 anti-human CD49b P1E6-C5 GCTTTCTTCAGTATG ENSG00000164171 B0373 anti-human CD81 (TAPA-1) 5A6 GTATCCTTCCTTGGC ENSG00000110651 B0384 anti-human IgD IA6-2 CAGTCTCCGTAGAGT ENSG00000211898 B0385 anti-human CD18 TS1/18 TATTGGGACACTTCT ENSG00000160255 B0386 anti-human CD28 CD28.2 TGAGAACGACCCTAA ENSG00000178562 B0389 anti-human CD38 HIT2 TGTACCCGCTTGTGA ENSG00000004468 B0390 anti-human CD127 (IL-7Rα) A019D5 GTGTGTTGTCCTATG ENSG00000168685 B0391 anti-human CD45 HI30 TGCAATTACCCGGAT ENSG00000081237 B0393 anti-human CD22 S-HCL-1 GGGTTGTTGTCTTTG ENSG00000012124 B0394 anti-human CD71 CY1G4 CCGTGTTCCTCATTA ENSG00000072274 B0396 anti-human CD26 BA5b GGTGGCTAGATAATG ENSG00000197635 B0398 anti-human CD115 (CSF-1R) 9-4D2-1E4 AATCACGGTCCTTGT ENSG00000182578 B0404 anti-human CD63 H5C6 GAGATGTCTGCAACT ENSG00000135404 B0406 anti-human CD304 (Neuropilin-1) 12C2 GGACTAAGTTTCGTT ENSG00000099250 B0407 anti-human CD36 5-271 TTCTTTGCCTTGCCA ENSG00000135218 B0408 anti-human CD172a (SIRPα) 15-414 CGTGTTTAACTTGAG ENSG00000198053 B0419 anti-human CD72 3F3 CAGTCGTGGTAGATA ENSG00000137101 B0420 anti-human CD158 (KIR2DL1/S1/S3/S5) HP-MA4 TATCAACCAACGCTT ENSG00000125498 B0446 anti-human CD93 VIMD2 GCGCTACTTCCTTGA ENSG00000125810 B0575 anti-human CD49a TS2/7 ACTGATGGACTCAGA ENSG00000213949 B0576 anti-human CD49d 9F10 CCATTCAACTTCCGG ENSG00000115232 B0577 anti-human CD73 (Ecto-5'-nucleotidase) AD2 CAGTTCCTCAGTTCG ENSG00000135318 B0579 anti-human CD9 HI9a GAGTCACCAATCTGC ENSG00000010278 B0581 anti-human TCR Vα7.2 3C10 TACGAGCAGTATTCA B0582 anti-human TCR Vδ2 B6 TCAGTCAGATGGTAT B0591 anti-human LOX-1 15C4 ACCCTTTACCGAATA ENSG00000173391 B0592 anti-human CD158b (KIR2DL2/L3, NKAT2) DX27 GACCCGTAGTTTGAT ENSG00000243772 B0599 anti-human CD158e1 (KIR3DL1, NKB1) DX9 GGACGCTTTCCTTGA ENSG00000167633 B0822 anti-human CD142 NY2 CACTGCCGTCGATTA ENSG00000117525 B0830 anti-human CD319 (CRACC) 162.1 AGTATGCCATGTCTT ENSG00000026751 B0864 anti-human CD352 (NTB-A) NT-7 AGTTTCCACTCAGGC ENSG00000162739 B0867 anti-human CD94 DX22 CTTTCCGGTCCTACA ENSG00000134539 B0871 anti-human CD162 KPL-1 ATATGTCAGAGCACC ENSG00000110876 B0896 anti-human CD85j (ILT2) GHI/75 CCTTGTGAGGCTATG ENSG00000104972 B0897 anti-human CD23 EBVCS-5 TCTGTATAACCGTCT ENSG00000104921 B0902 anti-human CD328 (Siglec-7) 6-434 CTTAGCATTTCACTG ENSG00000168995 B0918 anti-human HLA-E 3D12 GAGTCGAGAAATCAT ENSG00000204592 B0920 anti-human CD82 ASL-24 TCCCACTTCCGCTTT ENSG00000085117 B0944 anti-human CD101 (BB27) BB27 CTACTTCCCTGTCAA ENSG00000134256 B1046 anti-human CD88 (C5aR) S5/1 GCCGCATGAGAAACA ENSG00000197405 B1052 anti-human CD224 KF29 CTGATGAGATGTCAG ENSG00000100031

Dataset EGAD50000000252

Federated EGA

Federated EGA Federated EGA Index Overview Map of FEGA status across the world Current status of the FEGA Network Documentation Overview Federated EGA is a global network of repositories enabling secure discovery and access to sensitive human data. Our vision is to accelerate scientific discovery and healthcare breakthroughs by creating the go-to worldwide sensitive human data resource. Our values are: Privacy & Trust: Ensuring privacy of data subjects, security of services and acting with integrity to build community trust.FAIRness: Driving reuse of data through standardisation of metadata and clear access policies.Diversity: Expanding our network and representing global diversity in the data we host to bring benefits worldwide. Federated EGA - European by name, global by nature Federated EGA collaborates with European and global initiatives (GA4GH, ELIXIR, 1+ Million Genomes Framework, GDI), demonstrating that a network for transnational discovery of and access to human data is possible. By providing a solution to emerging challenges around secure and efficient management of human omics and associated data, the FEGA Network fosters data reuse, enables reproducibility, and accelerates biomedical research. For general enquiries or to request more information about Federated EGA, please send an email to fega-info [at] lists [dot] ega-archive [dot] org. Read more about the Federated EGA in our commentary in Nature Genetics! Learn more about setting up a Federated EGA Node in our onboarding documentation. Map of FEGA status across the world Current status of the FEGA Network Node Description Joined CEGA Central EGA is managed by the Centre for Genomic Regulation (CRG) and EMBL-EBI. The EGA has been a trusted repository for sensitive data since 2008. CEGA coordinates the FEGA network, hosts central services and the EGA catalogue. 2022 Finland FEGA Finland is hosted by CSC - IT Center for Science Ltd., which is the national ELIXIR Node in Finland. CSC is a state-owned company specialising in providing high-quality IT infrastructure and services for Finnish higher education and research. 2022 Germany The German Human Genome-Phenome archive (GHGA) is part of the German National Research Data Infrastructure (NFDI). It is coordinated by the German Cancer Research Center (DKFZ) and includes over 20 academic partners. 2022 Norway FEGA Norway is a service by ELIXIR Norway, the Norwegian node of the European organisation ELIXIR. University of Oslo as a partner institution in ELIXIR Norway is hosting the service relying on the TSD infrastructure for sensitive data. ELIXIR Norway is a consortium of 5 Norwegian universities and is coordinated by University of Bergen. 2022 Spain FEGA Spain is co-hosted by the Barcelona Supercomputer Center (BSC) and the Centre for Genomic Regulation (CRG), both part of ELIXIR Spain and the Spanish National Infrastructure for Personalised Medicine associated with Science and Technology (IMPaCT). 2022 Sweden FEGA Sweden is hosted by the National Bioinformatics Infrastructure Sweden (NBIS), which legally is part of Uppsala University. NBIS forms the bioinformatics platform at SciLifeLab and constitutes the Swedish node of the European organisation ELIXIR. 2022 Poland FEGA Poland is hosted by Biobank Lodz, which is part of University of Lodz. Biobank Lodz is an element of the infrastructure of the Regional Digital Medicine Centers established by the Medical Research Agency. 2022 Portugal FEGA Portugal is managed by BioData.pt, the distributed infrastructure for Life and Health data for Portugal. This entity is a non-profit association of 15 life sciences R&I organisations spread across the country, and the home of ELIXIR Node in Portugal. 2023 Canada Canadian Genome-phenome Archive is part of the Pan Canadian Genome Library, with the mandate to allow the secure and responsible sharing and analysis of Canadian genomic information. 2024 Switzerland Swiss FEGA is hosted by SIB Swiss Institute of Bioinformatics, which is the national ELIXIR Node in Switzerland, on behalf of the Swiss FEGA Partnership which includes 4 other key partners (ETH Zurich, Health 2030 Genome Center, Swiss Data Science Center and Switch). 2025 Documentation Title Version Description Mission, vision, values Federated EGA Mission, vision, values 1.0 The official Mission-Vision-Values statement for FEGA, produced and formally approved by the FEGA Strategic Committee in September 2025. Structure and Organisation Federated EGA Structure and Organisation 2.0 The structure of the FEGA network and service expectations, responsibilities and commitments. FEGA is organised into three tiers: Central EGA, FEGA Nodes and FEGA Affiliates. Strategic Committee Federated EGA Strategic Committee 1.1 Terms of reference for the FEGA Strategic Committee describe the purpose and objectives of the committee, which is to provide direction and strategic planning for the FEGA network. The committee receives input from, and provides feedback for the EGA Strategic Committee. Operations Committee Federated EGA Operations Committee 1.1 Terms of reference for the FEGA Operations Committee describe the purpose and objectives of the committee, which is to review operational performance and coordinate technical roadmaps of FEGA Nodes and Central EGA. The committee receives advice from, and provides operational reporting to the FEGA Strategic Committee. Guidelines Node Operations guidelines 2.1 An overview of the operational areas which require resources in order to create a FEGA Node. The document is based on more than 15 years' experience of EMBL-EBI and CRG operating the EGA and initial expericences of the inaugural FEGA Nodes. The operational areas of responsability are in line with the FEGA Maturity Model, which guides establishment and operation of FEGA Nodes. Collaboration Federated EGA Node Collaboration Agreement 1.3 Nodes are welcome to make a copy of this current version of the CA to start its review with their legal teams and understand the responsibilities of joining FEGA. Nevertheless, this version (the one with a watermark) shall not be signed: the official version needs to be obtained from FEGA prior signing through its official channels.

Documentation about/projects-and-funders/federated-ega

LBC1936 fastq files

1075 members of the LBC1936 were sequenced using the Illumina HiSeq X platform. This dataset contains the paired fastq files.

Dataset EGAD00001008774

Longitudinal Study of Fluoride and Other Factors Related to Dental Fluorosis, Dental Caries, and Bone Health

This project is a combination of the Iowa Fluoride Study and the Iowa Bone Development Study from 1992 - 2019. The Iowa Fluoride Study (IFS) began in 1992 with recruitment of 1,882 mothers of newborns from eight Iowa hospital post-partum wards from March 1992 to February 1995. The focus of the IFS was to collect detailed fluoride, general dietary intake, and other data several times per year from age 6 weeks on and relate them to dental fluorosis and dental caries (cavities). Data collection occurred 3-5 times per year initially, and then twice per year after ~ age 4. Dental exams of the primary (baby) teeth were initiated at ~ age 5.The Iowa Bone Development Study (IBDS) grew out of the IFS in 1998 when we added a "normal bone development" focus, and soon after the age 5 dental exams, we began to collect genetic samples and accelerometry (motion sensor) data. All then-active parents of IFS-participating children were invited to have their child join the IBDS, and the majority agreed to do so. The majority of participating children received both ~ age 5 dental exams and bone densitometry assessments. The bone densitometry at age 5 was determined by dual-energy X-ray absorptiometry (DXA) of the hip, lumbar spine, and whole body.Dental exams for caries and fluorosis were performed again at age 8-9, along with DXA bone scans and accelerometry. DXA scans and accelerometry were performed again at ages 11 and 15 years, along with peripheral quantitative computed tomography (pQCT) scans of the distal tibia and radius, but no dental exams were performed.At ages 13 and 17, dental exams, DXA and pQCT scans, as well as accelerometry, were obtained.At age 19, no dental exams were conducted, but repeat DXA and pQCT scans and accelerometry were obtained, along with multi-slice detector CT (MDCT) of the distal tibia.At age 23, we conducted the final wave of exams, which included dental, DXA, pQCT, MDCT, and accelerometry assessments.In addition, parents were invited to participate once with DXA bone scans and to also provide limited questionnaire data about their own fluoride and dietary exposures and physical activity.Note: Please refer to the document Iowa Fluoride Study/Iowa Bone Development Study (IFS/IBDS) - dbGaP Data Tables: A User's Guide - Version 2.0 for a detailed reference manual on all of the clinical data submitted for this project.Note: No genetic data or specimens were submitted with this study. However, many of the child/parent participants in the study were genotyped. These genotyping data are linked to dbGaP Study Accession: phs000095 under Dr. Mary Marazita: "Dental Caries: Whole Genome Association and Gene X Environment Studies".<img style="width: 1155px;" 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

Study phs002203

CD_prognosis_UKB

Crohn's disease DNA samples genotyped using UK Biobank Axiom array

Dataset EGAD00010001155

Clonal dynamics of normal haematopoiesis across the human lifespan

Age-related loss of function in the human haematopoietic system is well documented, manifesting as reduced regenerative capacity, age-related cytopenias and immune dysfunction. However, the cellular and population level changes that underpin both this functional decline and the increased risk of clonal haematopoiesis and blood cancer in the elderly remain elusive. Here we performed whole genome sequencing on >3350 single haematopoietic stem cell / multipotent progenitors (HSC/MPP) derived colonies across 10 haematologically normal subjects aged 0 to 81. We found that HSC/MPPs accumulated 17 single nucleotide variants per year post birth and had a reduction in telomere length of 50bp per year throughout young adult life. We reconstructed phylogenies of the sampled HSC/MPPs to interrogate changes in clonal dynamics through life. Haematopoiesis in adults aged less than 65 was predominantly polyclonal, with few known driver mutations. In contrast, individuals aged over 75 displayed a profound change in clonal structure, with frequent clonal expansions, many unexplained by known driver mutations. The ratio of non-synonymous to synonymous mutations revealed widespread positive selection, estimating around 1000 driver mutations in the dataset (10-fold more than the number of known drivers). We identified novel genes ZNF318 and HIST2H3D as being under positive selection, despite not being enriched in myeloid malignancies. Our data show that HSC clonal dynamics is more complex than previously thought. One implication is that by old age, the majority of HSCs carry at least one of a number of largely undescribed driver mutations, which may underlie aspects of their functional decline.

Dataset EGAD00001007851

BC Cancer, part of the Provincial Health Services Authority, Technology Development Office, Data Access Committee (PHSA TDO DAC)

Access to data generated by BCCA is made available by completing the data access agreement for review by the data access committee and will be granted to qualified investigators for appropriate use.

Dac EGAC00000000011

15070 results for "cancer studies using rna-seq"

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