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• Analysis of AR Gene Rearrangements in Prostate Cancer

  Molecularly-targeted therapies for advanced prostate cancer include castration modalities that suppress ligand-dependent transcriptional activity of the androgen receptor (AR). However, persistent AR signaling undermines therapeutic efficacy and promotes progression to lethal castration-resistant prostate cancer (CRPC), even when patients are treated with potent second-generation AR-targeted therapies abiraterone and enzalutamide. Here we define diverse AR genomic structural rearrangements (AR-GSRs) as a class of molecular alterations occurring in one third of CRPC-stage tumors. AR-GSRs occur in the context of copy-neutral and amplified AR and display heterogeneity in breakpoint location, rearrangement class, and sub-clonal enrichment in tumors within and between patients. Despite this heterogeneity, one common outcome in tumors with high sub-clonal enrichment of AR-GSRs is outlier expression of diverse AR variant species lacking the ligand binding domain and possessing ligand-independent transcriptional activity. Collectively, these findings reveal AR-GSRs as important drivers of persistent AR signaling in CRPC.

  Study phs001223

• The European MAPPYACTS trial: Precision Medicine Program in Pediatric and Adolescent Patients with Recurrent Malignancies

  MAPPYACTS (NCT02613962) is an international prospective precision medicine trial aiming to define tumor molecular profiles in pediatric patients with recurrent/refractory malignancies, in order to suggest the most adapted salvage treatment. From February 2016 to July 2020, 787 patients were included in France, Italy, Ireland and Spain. At least one genetic alteration leading to targeted treatment suggestion was identified in 436 patients (69%) with successful sequencing; 10% of these were considered "ready for routine use". Of 356 patients with follow-up beyond 12 months, 107 (30%) received one or more matched targeted therapies, 56% of them within early clinical trials, mainly in the AcSé-ESMART platform trial (NCT02813135). Overall, matched treatment resulted in a 17% objective response rate, those of patients with "ready for routine use" alterations was 38%. In patients with extra-cerebral tumors, 76% of actionable alterations detected in tumor tissue were also identified in circulating cell free DNA (cfDNA).

  Study EGAS00001005935

• Unraveling mutagenic processes influencing the tumor mutational patterns of individuals with Constitutional Mismatch Repair Deficiency

  Constitutional mismatch repair deficiency (CMMRD), caused by bi-allelic germline variants in one of the mismatch repair (MMR) genes, is a childhood cancer predisposition syndrome that often results in the development of multiple tumors early in life. A better understanding of mutational processes driving subsequent CMMRD tumors could advance optimal treatment. Therefore, we performed a genomic characterization of 41 tumors from 17 individuals with CMMRD. Mutational patterns in these tumors were found to be influenced by multiple factors, including the affected MMR genes, tumor types and somatic polymerase proofreading mutations. Temozolomide treatment left prominent mutational signatures in two second primary hematologic malignancies. Furthermore, a novel indel signature was found in 54% of the tumors, characterized by one base pair cytosine insertions in cytosine homopolymers. In conclusion, the analysis of sequential tumors in individuals with CMMRD reveals diverse mutational patterns influenced by the underlying mutated MMR gene, tumor type and treatment history.

  Study EGAS00001007660

• How to upload GPG files

  Uploading files Users that holds an ega-box-XXX account can upload files using either INBOX or FTP. Users who have a Submitter role associated with their email will only be able to upload files using INBOX. Before uploading your files please make sure that any files that will be uploaded to EGA do not use special characters in their naming convention such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &. This can cause issues with the archiving process, leading to problems for end users. The EGA is a shared, public service with limited storage. In order to manage the available resources, we enforce a limit of 10Tb per submission account at any one time. Please do not exceed this limit. INBOX FTP The FTP is only compatible with files encrypted using the EGACryptor tool Before uploading Once your submission files have been prepared using the EGAryptor, the resulting encrypted files and associated md5sum files can be uploaded to your submission account using Aspera or FTP. The EGA is a shared, public service with limited resources. In order to manage the available resources, EGA submission boxes should not exceed 8Tb in size, and cannot exceed 12Tb. If you are approaching this limit please contact contact EGA Helpdesk so that we can advise on how to register the associated metadata and trigger the archiving of files, so that you can continue with your submission. If we note that your submission account increases above 10Tb on a consistent base your password will be changed until metadata is associated. Aspera Download Aspera Using Aspera FTP FTP windows FTP Linux / Unix FTP client (Filezilla) FTP and TLS Troubleshooting Troubleshooting Aspera Download Aspera is a commercial file transfer protocol that may provide faster transfer speeds than ftp especially over longer distances. The Aspera ascp command line client. Please select Aspera Connect. The ascp command line client is distributed as part of the aspera connect highperformance transfer browser plugin and is free to use, without registration. The minimum required version of the IBM Aspera Cli is V4. Further instructions. Using the Aspera ascp command line program The location of the ascp program in the filesystem: Mac: on the desktop go cd /Applications/Aspera\ Connect.app/Contents/Resources/ there you'll see the command line utilities where you're going to use ascp. Windows: the downloaded files are a bit hidden. For instance, in Windows 7 the ascp.exe is located in the users home directory in: AppData\Local\Programs\Aspera\Aspera Connect\bin\ascp.exe Linux: should be in your user's home directory, cd /home/username/.aspera/connect/bin/ there you'll see the command line utilities where you're going to use ascp. Your command should look similar to this: ascp -P33001 -O33001 -QT -l300M -L- /path/file ega-box-N@fasp.ega.ebi.ac.uk:/path If you wish to upload several files without being requested the password, please use the below command : ASPERA_SCP_PASS=ega-box-password ascp -P33001 -O33001 -QT -l300M /path/file ega-box-N@fasp.ega.ebi.ac.uk:/path/ Explanation of parameters l300M option sets the upload speed limit to 30MB/s. You may wish to lower this value to increase the reliability of the transfer. L option is for printing logs out while transferring files to upload can be a file mask (e.g. '/homes/submitter/*.srf) or a list of files. ega-box-N is your submission account login. Add k2 switch for transfer restarts Check the command line transfer usage for more configuration details. Using FTP to upload your prepared files Use your preferred ftp client. For example, lftp is a popular choice for Linux and Mac users. Use binary mode for file transfers. Use ftp.ega.ebi.ac.uk as the target host. Login with your ega-box username and password. Upload files to your private ega-box upload area. Depending on your network setting you might wish to start FTP in passive or active mode. Using default FTP command line client in Windows Start the command line interpreter: press WinR, type cmd, hit enter Enter ftp ftp.ega.ebi.ac.uk Enter your submission username Enter your submission password Type binary to enter binary mode for transfer To see a list of available ftp commands type help. Type ls command to check the content of your submission account. Type prompt to switch off confirmation for each file uploaded. Use mput command to upload files: mput *.bam* Use bye command to exit the ftp client. Use exit command to exit the command line interpreter. Using default FTP command line client in Linux / Unix Open a terminal and type ftp ftp.ega.ebi.ac.uk Enter your submission username Enter your submission password Type binary to enter binary mode for transfer To see a list of available ftp commands type help. Type ls command to check the content of your submission account. Type prompt to switch off confirmation for each file uploaded. Use mput command to upload files: mput *.bam* Use bye command to exit the ftp client. Using FTP client FileZilla We recommend the use of FileZilla, a free FTP client . FileZilla is open source software distributed free of charge under the terms of the GNU General Public License. Use the following connection details (File - Site Manager) and add yoursubmission account username and password: Using FTP client FileZilla Select the files you wish to upload and then select upload: Using FTP client Filezilla Using LFTP with TLS We recommend the following to force the use of a secure connection. lftp > set ftp:ssl-force yes We also recommend setting the following for not encrypting the bulk data itself for performance reasons (theauthentication will still be encrypted): lftp > ftp:ssl-protect-data no In order to verify the certificate,the recommended way would be to use the CA certificates from your machine. To do that use this command in lftp adjusting the path to your ca-certificates location. lftp > ssl:ca-file "/etc/ssl/certs/ca-certificates.crt" If that is not possible or certificates are old and you can't update them, you can download the certificates needed from Quo Vadis Digital Repository The two certificates to download in PEM format are: QuoVadis Root CA2 G3 QuoVadis EV SSL ICA G3 Then you can concatenate them in a file one after the other and save it as lftp-certificates.pem Once this is done you have to point the ssl:ca-file variable to the path lftp > set ssl:ca-file "/path/to/lftp-certificates.pem" Also note that you can save this configuration at ~/.lftp/rc Another option is to download the certificates and add them to the ca-certificates of your machine. For example: In RHEL7 and cenots and others box the process to add the certificates globally is: Download the two certificates (in PEM or DER format, doesn't matter) and save them to "/etc/pki/ca-trust/source/anchors/" Run "update-ca-trust extract" Another less secure option is to turn off certificate verification with the following command: lftp > set ssl:verify-certificate false Troubleshooting If you are having problems with Aspera connection timeouts, it can be down to either one of the following. Transfers cannot start the connection fails instantly. Ensure that TCP traffic on port 33001 is allowed (open) for outbound connections through your computer's firewall and network's firewall. The connection is made, transfers are started, but 0 bytes (0%) are uploaded for each file. Ensure that UDP traffic on port 33001 is allowed (open) for outbound connections through your computer's firewall and network's firewal

  Documentation submission/data/uploading-files/ftp

• Expression data from the Neo-CheckRay trial

  ER+/HER2- early breast cancer were treated with neoadjuvant immune-modulating SBRT and anti-CD73. The RNAseq dataset contains fastq files for 234 samples corresponding to 134 patients. There are 208 breast cancer samples, taken at screening (N=136) or week 6 (N=76). In addition, there are 26 lymph node samples taken at screening.

  Dataset EGAD50000002552

• H3Africa H3AChipDesign Phenotype

  Basic phenotypic data (country, ethnicity and sex) for 348 samples of the H3Africa Chip Design Study. Divided into 8 datasets of 41 samples from Zambia, 24 samples from Cameroon, 50 samples from Mali, 26 samples from Cameroon, 49 samples from Nigeria, 48 samples from Botswana, 50 samples from Benin, 60 samples from Burkina Faso and Ghana.

  Dataset EGAD00001005310

• RNA-seq samples

  Raw FASTQ files obtained by RNA sequencing of tumor samples from patients (age 12-29) with newly diagnosed, recurrent intermediate or high-grade sarcoma.

  Dataset EGAD00001008393

• Whole-exome/genome sequencing of childhood acute leukemia in Iraq

  This dataset contains 26 whole-genome sequencing (13 paired tumor and normal), 106 whole-exome sequencing (53 paired tumor and normal), and 43 targeted sequencing data. Sequencing was performed using an Illumina platform. The data are BAM files aligned to the hg19 reference genome.

  Dataset EGAD00001007873

• Genome Sequencing of Pancreatic Ductal Adenocarcinoma at The Human Genome Sequencing Center of Baylor College of Medicine (HGSC-BCM)

  Pancreatic ductal adenocarcinomas (PDAC) is the 11th most common cancer in the USA but the 4th in fatalities. The onset and progression of cancer is driven by extensive rearrangement and mutation of the genome. We combined our capability to capture and enrich exome DNA with the next generation sequencing capacity to allow us to detect and characterize the somatic mutation profile of 24 patients with PDAC. Patient samples were collected by the Elkins Pancreatic Center in the Baylor College of Medicine Department of Surgery. Sequencing of the pancreatic ductal adenocarcinoma is one of the NHGRI Center Initiated Projects in progress in the Human Genome Sequencing Center at Baylor College of Medicine. These data are also contributed to an ICGC study and will be published with the ICGC collaborators.

  Study phs000516

• Exome Sequencing in Schizophrenia Families

  Schizophrenia is a complex neuropsychiatric disorder characterized by marked genetic heterogeneity. Much of the genetic architecture of the disorder has yet to be explained, but de novo mutations appear to play an important role. We used exome sequencing of parent-offspring quads and trios to detect de novo mutations in persons with schizophrenia. Patients were more likely to harbor one or more damaging de novo mutations, as compared to their healthy siblings. The genes disrupted by damaging mutations in patients operated in processes important to early brain development.

  Study phs000738

• Analysis of TKI resistant mechanism for gastrointstinal stromal tumor

  Gastrointestinal stromal tumors are the most common mesenchymal tumor of the digestive tract and the proliferation is driven by gain-of-function mutations in KIT. Despite its proven benefits, half of the patients treated with imatinib develop disease progression within 2 years due to secondary resistance mutations in the KIT kinase domains. To characterize the changes associated with imatinib resistance, we performed genomic and transcriptomic analyses of four resistant cell lines and one cell line briefly exposed to imatinib. We also performed exome sequencing of drug-resistant clinical samples.

  Study JGAS000039

• High-resolution lung adenocarcinoma expression subtypes identify tumors with dependencies on MET, CDK4, CDK6, and PD-L1

  Lung adenocarcinoma is one of the most common cancer types with various treatment modalities. However, better biomarkers to predict therapeutic response are still needed to improve precision medicine. We utilized a consensus hierarchical clustering approach on 509 LUAD cases from TCGA to identify five robust LUAD expression subtypes. We then integrated genomic (patient and cell line) and proteomic data to help define biomarkers of response to targeted therapies and immunotherapies. This approach defined subtypes with unique proteogenomic and dependency profiles.

  Study EGAS00001006461

• Raw ONT R10 data: Rapid brain tumor classification from sparse epigenomic data

  This dataset contains 207 samples sequenced using rapid-multiplexed barcoded library preparation on Nanopore PromethION R10 flow cells on P2 Solo and P24 systems. One unaligned BAM file per sample is provided.

  Dataset EGAD50000000791

• CYPTAM - PacBio SMRT sequencing

  Dataset contains CYP2D6 sequencing data of 566 individuals who used tamoxifen as adjuvant breast cancer therapy. Phenotype data consists of the ratio between the metabolites endoxifen and desmethyltamoxifen (Metabolic ratio (MR)) as a proxy for CYP2D6 enzyme activity. Each sample is linked to one bam-file containing the CYP2D6 sequence.

  Dataset EGAD00001005972

• RNA_Seq_in_Patients_with_Primordial_Dwarfism

  Transcriptome studies in patients with rare genetic diseases can potentially aid in theinterpretation of likely causal genetic variation through identification of altered transcriptabundance and/or structure. RNA-Seq is the most sensitive assay for both investigatingtranscript structure and abundanceThe primary aim of this pilot project is to investigate to what degree integrating exome-Seqand RNA-Seq data on the same individual can accelerate the identification of causal allelesfor rare genetic diseases. There are two main strands to this: (i) identifying which variantsdiscovered in exome-seq appear to be having a functional impact on transcripts, and (ii)identifying transcript outliers, especially among known causal genes, that may not necessarilyhave a causal variant identified from exome sequencing. The latter may identify the presenceof causal variants that lie far from coding regions (e.g. the formation of cryptic splice sitesdeep within introns, or loss of long range regulatory elements), which can be confirmed withfurther targeted genetic assays. Just over 50% of all disease-causing variants recorded in theHuman Gene Mutation Database (HGMD) affect transcript structure and abundance (e.g.nonsense SNVs, essential splice site SNVs, frameshifting indels, CNVs).This pilot project will study RNA from lymphoblastoid cell-lines from 12 patients withprimordial dwarfism syndromes, for 10 of these samples we have previously generate exomedata as part of our collaboration with the group of Prof Andrew Jackson. The two remainingsamples are positive controls where the causal mutation is known, and is known to affecttranscript structure and/or abundance.Primordial dwarfism is a prime candidate for these RNA-seq studies because all knowncausal mutations to date have key roles in DNA replication and thus, unsurprisingly, theproducts of the causal genes are typically ubiquitously expressed.Each RNA will be sequenced, with two technical replicates (independent RT-PCR and libraries) persample, and each replicate run in 1/2 of a HiSeq lane using 100bp paired reads. Samples preparation was as follows :The cells were grown to confluency, then pellets frozen at -80. RNA samples were prepared using the Qiagen RNeasy kit, then nanodropped and analyzed using the bioanalyzer to determine concentration and purity.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 EGAS00001000283

• MP2PRT: Genomic and Molecular Characterization of Biomarkers Associated with Tumor Angiogenesis, DNA Repair, and Immunologic Tolerance using Samples from the NRG Oncology Phase 3 Randomized Trial, GOG-0240 (NCT00803062)

  The Molecular Profiling to Predict Response to Treatment (MP2PRT) program is part of the NCI's Cancer Moonshot Initiative. The aim of this program is the retrospective characterization and analysis of biospecimens collected from completed NCI-sponsored trials of the National Clinical Trials Network and the NCI Community Oncology Research Program. This study is one of the selected studies under this program. For this study, genomic characterization (WGS, WXS, Total RNAseq, miRNAseq) has been performed on cervical tumor tissue from patients with advanced disease (persistent, recurrent, and metastatic) and matching germline samples. It is hypothesized that the characterization of these tissues using next generation sequencing will lead to the identification of relevant novel genetic aberrations within the immunologic, DNA repair, and tumor angiogenesis domains that dichotomize the study population into exceptional responders and non-responders to systemic therapy, with a spectrum of response manifesting between the two extremes.

  Study phs002293

• Whole Exome and Target Sequencing Data in 75 Samples from 5 Hepatocellular Carcinoma Patients.

  Whole Exome and Target Sequencing Data in 75 Samples from 5 Hepatocellular Carcinoma Patients. The sequencing was performed by Illumina HiSeq 4000. Background and aims: Intratumoral heterogeneity (ITH) challenges identifying mutations with target therapy potential whereas circulating cell-free DNAs (cfDNAs) could reflect nearly the entire mutation spectrum in given tumors. We investigated how to minimize the limit of ITH for profiling hepatocellular carcinoma (HCC).Methods: Thirty-two multi-regional HCC samples from five patients were subjected to whole exome sequencing (WES) and targeted deep sequencing (TDS). ITH extent was measured by the average percentage of non-ubiquitous mutations (present in parts of tumor regions). Matched cfDNAs were also analyzed by WES and TDS. Profiling efficiencies of single tumor specimen and cfDNA were compared and the one better depicted mutational landscape was selected to screen therapeutic targets.Results: We found variable extents of ITH in HCCs and observed branched and parallel evolution patterns. ITH level decreased at higher sequencing depth of TDS than that measured by WES (28.1% vs 34.9%, P &lt; 0.01) but it remained unchanged upon additional samples analyzed. TDS of single tumor specimen detected an average of 70% the total mutations in HCC. Although more mutations were detected in cfDNA under TDS than WES, an average of 47.2% total HCC mutations uncovered by cfDNA suggested tissue outperform cfDNA and the latter may serve as alternative in profiling HCC genome. Consequently, TDS of single tumor tissue in 66 patients and cfDNAs in four unresectable HCCs identified 38.6% (26/66 and 1/4) patients bearing therapeutic targets.Conclusions: TDS of single tumor specimen could largely circumvent ITH to uncover mutations indicative of target therapy in HCC.

  Dataset EGAD00001003278

• Homozygous Duplication Identified by Whole Genome Sequencing Causes LRBA Deficiency

  In more than one third of primary immunodeficiency (PID) patients, extensive genetic analysis including whole exome sequencing (WES) fails to identify the genetic defect. Whole genome sequencing (WGS) is able to detect variants missed by other genomics platforms, enabling the molecular diagnosis of otherwise unresolved cases. Here, we report two siblings, offspring of consanguineous parents, who experienced similar severe events encompassing early onset of colitis, lymphoproliferation and hypogammaglobulinemia, typical of lipopolysaccharide (LPS)-responsive and beige-like anchor protein (LRBA) or cytotoxic T-lymphocyte-associated protein 4 (CTLA4) deficiencies. WGS of these patients detected a 12.3 kb homozygous tandem duplication that was absent in control cohorts and is predicted to disrupt the reading frame of the LRBA gene.

  Study phs002557

• 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

• Longitudinal analysis of bone marrow heterogeneity reveals the co-evolution of malignant B cells and their T-cell niche supporting follicular lymphoma persistence

  Follicular Lymphoma (FL) is an indolent lymphoid malignancy characterized by cycles of remission and relapse, reflecting the inability of current therapies to fully eradicate tumor cells. Our hypothesis is that rare cancer precursor/persister cells (CPCs) evade treatment by homing to a protective tumor microenvironment, particularly within the bone marrow (BM), and/or by adopting specific cellular states, and form a rare reservoir of residual cells seeding lymphoma recurrence. To adress this, we collected paired BM samples at diagnosis and one year post-therapy from a clinical trial (FLIRT trial, NCT02303119) and applied multi-modal single-cell analysis to dissect the transcriptomics determinants of rare drug-tolerant persister cells and their surrounding T-cell niche.

  Study EGAS50000001295

• Comprehensive genetic analysis of uveal melanoma heterogeneity during metastatic progression

  Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Despite improvement of diagnosis and treatment of the primary tumor, there is no effective treatment of metastatic disease and approximately half of patients will die within one year or less following metastases detection. Tumor heterogeneity has been proposed as a key factor of drug resistance. However, it has been scarcely studied in UM. The present project aims searching for specific drivers of the metastatic progression, describing the genomic and transcriptomic landscape of metastatic UM, exploring tumor heterogeneity and investigating its role in drug resistance. Thus whole exome sequencing and transcriptomics have been performed on constitutional, primary tumor and metastatic samples from 28 UM patients.

  Study EGAS00001002761

• 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

• Test dataset: Sequence and variant data from public 1000 Genomes Project

  This is a test dataset derived from public data of the 1000 Genomes Project. Its purpose is not to allow for any inference about cohort data or results, but to aid bioinformaticians in the technical development and testing of tools, as well as data consumers in learning how to access information. This dataset consists of 2508 samples from the 1000 Genomes Project (https://www.nature.com/articles/nature15393). Samples' (e.g. NA18534) data can be accessed through the IGSR portal (e.g. https://www.internationalgenome.org/data-portal/sample/NA18534) or their corresponding folder at the 1000 Genomes' FTP site (e.g. http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000_genomes_project/data/CHB/NA18534/exome_alignment/). There are several different types of data this dataset encompasses: Variant Calling Format (VCF, or its binary counterparts BCF) files, both joint (e.g. ALL_chr22_20130502_2504Individuals.vcf.gz) and split (HG01775.chrY.vcf.gz); exome sequencing CRAM files (e.g. NA18534.GRCh38DH.exome.cram); whole genome sequencing CRAM/BAM files (e.g. NA19239.cram). Additionally, there are multiple files that were sliced to create shorter files, which allows for a quick download, formated as "{FILE-INFO}__{NUMBER-OF-READS}r__{CHR}.{START-COORDINATE}-{END-COORDINATE}.{FILETYPE}" (e.g. "HG01500.GRCh38DH__90r__3.10000-10500__4.10000-10500.cram"). These files can be downloaded directly through the EGA-download-client PyEGA3 (https://github.com/EGA-archive/ega-download-client).

  Dataset EGAD00001003338

• Pediatric Papillary Thyroid Carcinoma Whole Exome Sequencing

  BACKGROUND: Pediatric papillary thyroid carcinoma (PPTC) is distinct from adult-onset disease. Despite higher rates of regional and distal metastases and more frequent recurrence, there is, paradoxically, a highly favourable prognosis. Historically, only 40-50% of childhood PTC are driven by genomic variants common to adult PTC, while the oncogenic drivers in the remainder are unknown. METHODS: Fifty-two PPTCs were genotyped by candidate gene testing, followed, in a subset (n=32), by whole exome (WES) and transcriptome sequencing (RNASeq) to identify oncogenic variants in driver-unknown tumors and to explore gene expression patterns. FINDINGS: Thirty-one tumors (60%) carried variants identified by candidate gene testing, specifically: BRAFV600E(22%), NRASQ61R(2%), RET-CCDC6(19%), RET-NCOA4(15%) and PAX8-PPARG. Among the 21 remaining tumors with negative first-line genotyping, oncogenic variants were identified in 19 (90%). These were enriched with oncogenic fusions, with 11 non-recurrent fusion transcripts identified, including two that had not been previously described: STRN-RET and TG-PBF. Most fusions were associated with 3’ receptor tyrosine kinase (RTK) moieties: RET, MET, ALK and NTRK3. Gene expression analysis defined three distinct clusters, which demonstrated differing expression of genes involved in thyroid differentiation and MAPK signaling. INTERPRETATION: Exome and transcriptome-wide sequencing identify genomic drivers of PPTC in 98% of tumors, whereas conventional genotyping leaves many tumors genetically unexplained. PPTC is driven, in the majority of cases, by oncogenic fusion transcripts involving one of a limited number of RTKs. These observations have broad implications for diagnostic testing and for therapeutic options in children with advanced disease.

  Study EGAS00001005187

• Genetic Associations in Idiopathic Talipes Equinovarus (Clubfoot) - GAIT

  Isolated clubfoot or idiopathic talipes equinovarus (ITEV) is one of the five most common birth defects, affecting approximately 4,000 newborns each year in the US. While the orthopedic care of these children has improved, long-term problems persist and the health care costs are significant. Studies suggest that clubfoot is a complex disorder with segregation analyses and family studies indicating that genetic factors play an important etiologic role in the development of clubfoot. Only one environmental factor, maternal smoking during pregnancy, has been implicated. We postulate that a small number of genes account for a substantial fraction of clubfoot and that these genes can be identified in a defined population. The challenge now is to identify the genetic loci and, later, the effect of environmental exposures. To accomplish this task, it is important to have a well-defined population and the methodology to detect linkage with and without association. Towards these goals, we have identified and characterized multiplex clubfoot families, including two large clubfoot families, a resource which is among the largest ITEV populations in existence. We will perform a high-density SNP genome scan (GWAS) on our clubfoot dataset to identify chromosomal regions that may harbor clubfoot genes with subsequent interrogation of these regions and candidate genes. We are in a position with our unique clubfoot population and with the methodology in place to undertake this study. The study phases are: 1) continued ascertainment of multiplex and simplex families and 2) GWAS and characterization of identified chromosomal regions and candidate genes. The results of this study will provide data essential to the identification of the gene(s) contributing to the clubfoot phenotype. Identification of high-risk genotypes can lead to the development of prevention programs in selected populations and may suggest gene-based prevention strategies.

  Study phs000314