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• TRACERx 100: RNAseq data from the first 100 TRACERx tumours

  TRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC). This study looks at the multi-region RNAseq data from the TRACERx100 cohort with high enough quality RNA available. There is RNAseq data from 164 regions (64 patients).

  Study EGAS00001003458

• Exome_sequencing_of_a_cohort_of_Rett_syndromelike_patients

  The aim of the project is the definition of the molecular defect in a cohort of Rett-like patients negative for mutations in known disease genes. To this aim, a number of unrelated trios (patients plus parents) will be analysed by exome sequencing. 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 EGAS00001002059

• Whole genome and RNA sequencing of paediatric glioblastoma in the ICGC PedBrain project

  Paediatric glioblastoma (pedGBM) is characterised by substantial intertumoural heterogeneity. In order to identify the genetic lesions underlying tumourigenesis, we analysed whole genomes and transcriptomes of pedGBMs and in vitro model systems by next generation sequencing. Novel fusions of the receptor tyrosine kinase MET lead us to investigate targeted therapy using MET inhibitors. This study is part of the International Cancer Genome Consortium PedBrain Tumor Project (ICGC PedBrain, http://www.pedbraintumor.org/).

  Study EGAS00001001139

• Sequencing of liver cancer cell lines

  Liver cancer is a dreadful disease with limited therapeutic options. How to translate genomic findings into biomarkers and precision treatment is challenging. A large cell line-based platform was established, termed as Liver Cancer Model Repository. Heterogeneities in patients were faithfully recapitulated by 81 liver cancer cell lines at genomic and transcriptomic levels. This platform provides a rich resource and models to study cancer drivers and drug responses in liver cancer.

  Study EGAS00001002237

• Molecular subtypes of malignant peritoneal mesothelioma

  Malignant Peritoneal Mesothelioma (PeM) is a rare but frequently fatal cancer that originates from the peritoneal lining of the abdomen. Standard treatment of PeM is limited to cytoreductive surgery and/or chemotherapy, and no targeted therapies for PeM yet exist. This study performs comprehensive integrative analysis of genome, transcriptome, and proteome of treatment-naive PeM tumors with the aim of identifying mesothelioma-related molecular alterations and potentially identifying novel treatment strategies.

  Study EGAS00001002820

• Whole genome sequencing of patients affected by acute intermittent porphyria

  Acute intermittent porphyria (AIP) is an hereditary rare metabolic disorder of incomplete penetrance, affecting the biosynthesis of heme. IAP is an autosomal dominant disorder, resulting in a substantial reduction of the activity of the hydroxymethylbilane synthase coding gene (HMBS). Most HMBS mutation carriers are asymptomatic and only between 10% and 20% of patients present acute attacks of the associated symptoms. Here, we sequenced whole genomes of 16 AIP patients.

  Study EGAS00001004999

• glioblastoma single cell RNAseq

  Understanding the cellular origin and differentiation status of glioblastoma is critical to resolve the etiology of the disease. we profile 18 patient glioblastomas by single cell RNA sequencing (scRNAseq). From this, we uncovered two principal cell-of-origin relations. Each lineage displays unique directional differentiation trajectories and transcriptional cores from the naïve cell populations. Thus, glioblastoma is defined by robust cell lineage features which may provide insights into the cell origin of the diseases.

  Study EGAS00001006236

• RNA sequencing of NK cells in human lung

  Human lung tissue-resident NK cells (trNK cells) are likely to play important roles in viral infections, inflammation and cancer. However, knowledge about lung trNK cells is lacking but is fundamental for exploiting these cells in therapeutic approaches. Here we analysed the transcriptome of CD69+CD49a+CD103+CD16-CD56bright, CD69+CD49a+CD103-CD16-CD56bright, CD69+CD49a-CD103-CD16-CD56bright, CD69-CD49a-CD103-CD16-CD56bright, and CD56dimCD16+NKG2A+CD57- NK cells isolated from human lung.

  Study EGAS00001003544

• RNA sequencing in primary human macrophages overexpressing ETS2

  We investigated an intergenic haplotype on chr21q22, linked to five different inflammatory diseases, and discovered a mechanism that orchestrates macrophage responses during chronic inflammation. We delineated how the risk haplotype increases expression of the causal gene, ETS2, and demonstrated that ETS2 is necessary for inflammatory macrophage effector functions. To establish whether ETS2 is sufficient to drive inflammatory responses, we overexpressed ETS2 in a dose-dependent manner and performed RNA-sequencing to characterise the transcriptional effects.

  Study EGAS00001007554

• The Gut Microbiome of liver transplant recipients – Cross-sectional + Longitudinal (renal and liver)

  TransplantLines is designed as a single-center, prospective cohort study and biobank including all different types of solid organ transplant recipients as well as living organ donors. In the TransplantLines gut microbiome study the gut microbiome of solid organ transplant recipients is characterized and linked to clinical phenotypes. This batch contains the cross-sectional data from liver transplant recipients and longitudinal data from renal and liver transplant recipients.

  Study EGAS00001006258

• CRU303 GWAS Raw Data Batch 1 Cases

  Raw data files of samples in batch 1 of CRU303 GWAS

  Dataset EGAD00010001954

• Mesothelioma Whole Genomes

  Genomic architecture of mesothelioma parent study is project 925. This project is set up in parallel to project 925 in order to Whole genome sequence ten of the 59 tumours in that project.

  Dataset EGAD00001001265

• The HLA-DQβ1 insertion is a strong achalasia risk factor and displays a geospatial north-south gradient among Europeans

  Idiopathic achalasia is a severe motility disorder of the esophagus and is characterized by a failure of the lower esophageal sphincter to relax due to a loss of neurons in the myenteric plexus. Most recently, we identified an 8 amino acid insertion in the cytoplasmic tail of HLA-DQβ1 as strong achalasia risk factor in a sample set from Central Europe, Italy, and Spain. Here, we tested whether the HLA-DQβ1 insertion also confers achalasia risk in the Polish and Swedish population. We could replicate the initial findings and the insertion shows strong achalasia association in both samples (Poland P = 1.84×10-04, Sweden P = 7.44×10-05). Combining all five European data sets – Central Europe, Italy, Spain, Poland, and Sweden – the insertion is achalasia associated with Pcombined = 1.67×10-35. In addition, we observe that the frequency of the insertion shows a geospatial north-south gradient. The insertion is less common in northern (around 6-7% in patients and 2% in controls from Sweden and Poland) compared to southern Europeans (around 16% in patients and 8% in controls from Italy) and shows a stronger attributable risk (AR) in the southern European population. Our study provides evidence that the prevalence of achalasia may differ between populations.

  Study EGAS00001001515

• Transcriptome of (peripheral blood), from donor Sample A, replicate 1 FACS processing technical replicates

  FACS processing technical replicates

  Dataset EGAD00001010086

• German early-onset prostate cancer cohort of the Pan-Prostate Cancer Genome (PPCG) project

  Prostate cancer is the most frequent malignant tumor in males and the second most frequent cause of cancer-related death. Currently, in Germany, more than 60,000 prostate cancers are diagnosed every year. Although most of these patients are treated in a curative attempt, more than 10,000 German men die from prostate cancer annually. Owing to the demographic changes of our society, a further doubling of prostate cancer incidences during the next 20 years is expected. Prostate cancer is generally considered a tumor of elderly men. However, a fraction of prostate cancers are diagnosed at the age of 55 years or less. For several reasons, these “early onset prostate cancers” may represent a key entity for the understanding of prostate cancer biology. First, it is likely that early onset prostate cancers represent a distinct molecular subgroup of prostate cancer (PCa), potentially characterized by relatively small numbers of genetic changes, some of which may be particularly strong driver mutations for PCa development. Second, a fraction of prostate cancers in young individuals could represent classical prostate cancers that are detected at a very early stage and might therefore have accumulated molecular changes/mutations occurring that are most instrumental for prostate cancer early detection. Third, PCa with hereditary backgrounds are likely to accumulate in the age group below 55 years. A comparison with other sample sets (e.g. from other ICGC consortia), a systematic genomic analysis of young men with PCa could therefore lead to the detection of mechanisms for hereditary PCa. Fourth, a better understanding of these tumors is particularly relevant as finding optimal treatment regimens is most critical in young cancer patients. We are analyzing the entire genomic DNA sequences of at least 200 PCas (and matched non-tumorous DNA) of young men (≤ 55y), to at least 30 fold coverage, and integrate single-nucleotide variants and genomic structural variations with differential methylation, mRNA and miRNA expression data.Tissues were collected at the University Medical Center Hamburg-Eppendorf. Sequencing was performed at DKFZ and NCT (Heidelberg), EMBL (Heidelberg), and MPIMG (Berlin). Data management and bioinformatic data analyses is being conducted at DKFZ.

  Study EGAS00001003373

• Epigenetic memory of SARS-CoV-2 mRNA vaccination in monocyte-derived macrophages

  Immune memory is key to effective antimicrobial responses, but the impact of mRNA vaccines on this process is not fully understood. Our research shows that SARS-CoV-2 mRNA vaccines alter the epigenetic profile of human macrophages, specifically enhancing histone acetylation, which is linked to immune training. Significant epigenetic changes, along with increased cytokine release, require two vaccine doses. However, these effects diminish over time but can be restored with a booster dose six months later, maintaining a strong pro-inflammatory response.

  Dataset EGAD50000000495

• 120 individuals from the TEENAGE study (Ntalla et al., 2013) have been genotyped on the Illumina HumanCoreExome-12v1-1_A array. This is a population-based study of adolescents from the Attica region in Greece

  120 individuals from the TEENAGE study (Ntalla et al., 2013) have been genotyped on the Illumina HumanCoreExome-12v1-1_A array. This is a population-based study of adolescents from the Attica region in Greece

  Study EGAS00001001733

• The Role of E2F4 in Controlling Resistance to Irinotecan (CPT-11) in Human Colon Cancer

  This study reports the results of RNA sequencing (RNA-seq) experiments performed on three isogenic clones of the human HT29 colon cancer cell line, genetically engineered to achieve the selective inactivation of the adenovirus early 2 (E2) gene transcription factor 4 (E2F4) protein, a key functional component of the multimeric transcriptional repression complex known as the dimerization partner (DP), retinoblastoma-like (RB-like), E2F and multi-vulva class B (MuvB) complex (DREAM). In previous studies, we identified E2F4 as a key regulator of colorectal cancer (CRC) resistance to irinotecan (CPT-11), a cytotoxic agent that stands at the backbone of multiple chemotherapy regimens for the treatment of metastatic CRCs. The molecular mechanisms linking E2F4 to irinotecan resistance, however, remain poorly understood. The present study was designed to identify which genes are under the transcriptional control of E2F4, either at baseline or following exposure to SN38 (i.e., the active metabolite of irinotecan, generated in vivo by biotransformation in the liver) in order to clarify the role played by E2F4 in shaping colon cancer resistance to chemotherapy. The cell line used for this study (HT29) is aneuploid and contains three copies of the E2F4 gene, because of a trisomy of chromosome 16, where the E2F4 gene is located (Kawai et al., Genes, Chromosomes & Cancer, 34:1-8, 2002; PMID: 11921276). HT29 cells were infected with lentivirus vectors encoding for CRISPR/Cas9 constructs designed to selectively inactivate the E2F4 gene, using three distinct guide-RNAs (gRNAs), each targeting a different sequence within the proximal exons of the E2F4 gene (exon 1, exon 2). Lentivirus-infected cells were sub-cloned by single-cell fluorescence-activated cell sorting (FACS), based on the differential expression of a fluorescent reporter (EGFP) expressed in tandem with the gRNAs. Isogenic clones (EGFP+) were then screened to identify those with a tri-allelic knock-out, as revealed by both genetic sequencing of the E2F4 gene (revealing the presence of three distinct frameshift mutations) and Western blot (revealing complete loss of E2F4 protein expression). The study includes twelve (n=12) RNA-seq experiments, representative of 3 experimental replicates (i.e., three distinct isogenic clones of the HT29 cell line, each displaying complete loss of E2F4 expression) and 3 negative controls (i.e., three independent preparations of the parent HT29 cell line, infected with a lentivirus vector encoding for a non-targeting gRNA) exposed to 2 distinct in vitro cell culture conditions: a) treatment with SN38 resuspended in dimethyl sulfoxide (DMSO); or b) treatment with DMSO alone (negative control). The in vitro treatment regimen used for SN38 (32 nM, 24 hours) was designed so that the exposure to SN38 experienced in vitro by HT29 cells would mirror the exposure to SN38 experienced in vivo by malignant tissues, as measured, for example, in metastatic CRC patients receiving a standard dose of irinotecan (180 mg/m2) administered by intravenous infusion over 90 minutes (Deyme et al., Cancer Chemotherapy and Pharmacology, 88:247-258, 2021; PMID: 33912999).

  Study phs003560

• Phase 1/2 Study of the Indoleamine 2,3-Dioxygenase 1 Inhibitor Linrodostat Mesylate Combined With Nivolumab or Nivolumab and Ipilimumab in Advanced Solid Tumors or Hematologic Malignancies

  Purpose: To evaluate the safety, efficacy, pharmacokinetics, pharmacodynamics, and biomarker data of linrodostat mesylate, a selective, oral indoleamine 2,3-dioxygenase 1 (IDO1) inhibitor, combined with nivolumab ± ipilimumab in advanced solid tumors and hematologic malignancies. Patients and Methods: In this phase 1/2 study, patients received once-daily (QD) linrodostat (part 1 [escalation], 25-400 mg; part 2 [expansion], 100 or 200 mg) plus nivolumab (480 mg every [Q] 4 weeks [W] or 240 mg Q2W) or triplet therapy (part 3, linrodostat 20-100 mg QD; nivolumab 360 mg Q3W or 480 mg Q4W; ipilimumab 1 mg/kg Q6W or Q8W). Endpoints included safety and efficacy (co-primary; parts 2, 3), pharmacokinetics, pharmacodynamics, biomarkers, and efficacy (part 1). Results: Fifty-five, 494, and 41 patients were enrolled in parts 1, 2, and 3, respectively. Linrodostat exposures exceeded predicted therapeutic target concentrations starting at 50 mg. Rates of grade 3/4 adverse events were 50.1%-63.4%. The maximum tolerated linrodostat dose was 200 mg; dose-limiting toxicities were primarily immune related. Responses were observed across different cohorts, study parts, and tumor types, particularly in immunotherapy-naive patients. Kynurenine decreased with linrodostat + nivolumab, regardless of response. In contrast, interferon gamma (IFN-γ) gene expression signature was associated with response; in nonmelanoma patients, a composite of low tryptophan 2,3-dioxygenase (TDO2) gene expression plus high IFN-γ signature was associated with response. Conclusions: Linrodostat + nivolumab ± ipilimumab demonstrated a manageable safety profile. Kynurenine changes supported IDO1 pathway inhibition but did not correlate with response. A composite biomarker of low TDO2 expression plus high IFN-γ gene expression may predict response to linrodostat + nivolumab.

  Study EGAS50000000710

• Variation in the Glucose Transporter gene SLC2A2 is associated with glycaemic response to metformin

  Metformin is the first-line antidiabetic drug with over 100 million users worldwide, yet its mechanism of action remains unclear1. Here the Metformin Genetics (MetGen) Consortium reports a three-stage genome wide association study (GWAS), consisting of 13,123 participants of different ancestries. The C-allele of rs8192675 in the intron of SLC2A2, which encodes the facilitated glucose transporter GLUT2, was associated with a 0.17% (p=6.6x10-14) greater metformin induced HbA1c reduction in 10,577 participants of European ancestry. rs8192675 is the top cis-eQTL for SLC2A2 in 1,226 human liver samples, suggesting a key role for hepatic GLUT2 in regulation of metformin action. In obese individuals C-allele homozygotes at rs8192675 had a 0.33% (3.6mmol/mol) greater absolute HbA1c reduction than T-allele homozygotes.This is about half the effect seen with the addition of a DPP-4 inhibitor, and equates to a dose difference of 550mg of metformin, suggesting rs8192675 as a potential biomarker for stratified medicine.

  Study EGAS00001001875

• A_study_of_the_genetic_basis_of_evation_by_Acute_Myeloid_Leukaemia_of_Graft_vs_Leukaemia_effects_after_allogeneic_bone_marrow_transplantation

  Acute myeloid leukaemia (AML) is an aggressive and molecularly diverse disease with a poor overall survival of 20-25%. With an annual incidence of 2.9 per 100,000, AML is currently the commonest myeloid malignancy in Europe, yet the two main therapeutic options for this disease, anthracyclines and purine analogues, have remained unchanged for over 20 years. Currently patients are stratified at diagnosis according to a series of clinicopathological parameters (e.g. age, white cell count and presence/absence of previous clonal haematological disease) and molecular markers (e.g. chromosomal translocations/deletions, aneuploidy and mutations in genes such as FLT3 and NPM1). Patients with adverse prognostic features, whose prognosis is particularly poor (e.g. <15% long-term survival) are offered treatment with allogeneic bone marrow transplantation (allo-BMT) if a sibling or unrelated donor is available. This can significantly improve survival (e.g. up to 40% long-term survival in some contexts), albeit at the expense of significant toxicity and transplant-related mortality (TRM). Allo-BMT is thought to work in part by allowing the delivery of large doses of chemotherapy followed by haemopoietic "rescue" with donor haemopoietic stem cells (haemopoietic failure would otherwise ensue). However, potentially the most potent effect of allo-BMT is the cytotoxic effect of donor lymphocytes against AML blasts, a phenomenon known as graft-vs-leukaemia (GVL) effect. Increasingly, transplants using reduced chemotherapy intensity (mini-allografts) are being used that partially circumvent the toxicity from chemotherapy and rely on GVL to effect cure. Nevertheless, AML relapse after allo-BMT still occurs at a significant rate of up to 80% depending on the type of transplant. There is accumulating evidence that genetic events in residual leukaemic cells enable them to evade immunodetection and therefore survive the GVL effect and expand to cause relapse. The most striking example of this is the loss of HLA antigens after transplants in which donor and recipient are not fully HLA-matched. In these cases, the leukaemia "deletes" the genomic region containing the disparate HLA antigen which was preferentially targeted as "foreign" by the GVL effect. However, the genetic basis of immune evasion in the majority of transplants, which are fully HLA matched, is not known. One possibility is that loss of genes coding for antigens outside the HLA locus but which are also targets of GVL may operate, alternatively genetic events that affect processes downstream of immunological cytotoxicity may be responsible.The identification of genetic events that mediate immune evasion would not only facilitate the understanding of this process but can help plan therapeutic interventions that improve the outcomes of allogeneic transplantation for AML and other disorders. We intend to study this by conducting exome sequencing on 6 cases of AMLs from patients that attend my clinic at Addenbrooke's hospital and have relapsed after allogeneic transplantation. Samples from AML diagnosis, remission/normal and AML relapse (total n=18) will be studied to identify somatic mutations in the primary AML and those acquired by the relapsed clone. The 18 samples will also be studied by array CGH to detect regions of genomic amplification or deletion.

  Study EGAS00001000145

• Genetic analysis of short stature using whole exome sequencing

  The aim of the study is to find the genetic aetiologies of short stature, using whole-exome sequencing technology.

  Study EGAS50000000578

• Somatic_mutations_in_twin_breast_cancers

  The aim of this study is to investigate the somatic mutations in twins with BRCA1/2 negative breast cancer with no strong family history.

  Study EGAS00001002379

• Whole genome sequencing (bam files) of 5 samples of myxofibrosarcoma and 5 matched pairs

  Myxofibrosarcoma (MFS) is a rare subtype of sarcomas in the elderly, whose genetic basis is poorly understood. To elucidate it, the whole genome sequence was performed.

  Dataset EGAD00001007825

• Programmatic submission based on XML

  Programmatic submissions (XML based) For further information please check our Submission FAQs, submission quickguide as well as submission terms! Introduction Besides the Submitter Portal tool, EGA supports programmatic sequence and clinical data metadata submissions. If you are not sure what this means, you may want to explore our brief metadata introduction. Programmatic submissions are recommended for array-based submission. Moreove, it may be of help if your submission is recurrent or it is difficult to manage manually due to its sheer size. Otherwise, we highly recommend using the Submitter Portal to perform submissions. In this page we will guide you through the required steps to programmatically submit data to the EGA. Programmatic submissions require your metadata to be structured for an easy and straightforward validation and archival. It basically consists in formatting your metadata as Extensible markup language (XML) files and submitting them to the EGA using the WEBIN Before submitting metadata to the EGA, it is important to ensure that the information in your XML files is compliant with our standards. You can see further details on how these standards are maintained at EGA at our EGA Schemas documentation page. Using WEBIN, you can validate your XML files against EGA's schemas to ensure that your metadata is compliant before submission. WEBIN services WEBIN production service WEBIN test service We advise you to submit your metadata to the test service when submitting to the production service for the first time. The test service is identical to the production service except that all submissions will be discarded in the following 24 hours. This allows you to learn about the submission process without having to worry about data being submitted. Authentication Authentication is required each time a submission is made. The submission service uses HTTPS protocol for metadata encryption and identification to provide a secure submission environment. Data file upload Both Runs and Analyses reference files (e.g. FASTQ need to be uploaded to the EGA before these metadata objects are submitted. In other words, if you submit a Run that references a file that we cannot find associated with your account, the metadata submission will fail. See further details on how to upload your files in our File Upload documentation. Metadata model of the EGA Our metadata model is formed by multiple metadata objects. Check further details in our documentation at our EGA Schema documentation page. Working with EGA XMLs files Now that the basic concepts of the EGA metadata have been described, you can start preparing your programmatic submission through XML. Here you will find the guidance on how to prepare the XML files. Programmatic Submission Tutorial Video Take a look at the Programmatic Submission Tutorial Video, which explains the workflow of a programmatic submission and goes over an example metadata submission. Programmatic Submission Tutorial Video. When building your XML files, we recommend using text editors (e.g.Sublime Text or VisualStudio) that allow you to visualise the structure of the XML with ease. Furthermore, these editors constantly check the consistency of the XML structure. Alternatively, and if the submission consists of a big number of objects (specially analyses), you may find the tool star2xml handy. This tool allows for a direct conversion between metadata in a tabular format (e.g. a spreadsheet) into XMLs. Identifying objects: Aliases and center names Every EGA object must be uniquely identified within the submission account using their alias attribute. The aliases can be used in submissions to make references between EGA objects. Let us dig into EGA's use of aliases and center names: alias: every object should have a name that is unique within your submission account. Once submitted successfully, every alias will be assigned a unique and permanent accession (EGA ID). refname: when an object references another by its alias, the alias of the referenced object goes into the "refname" attribute of the referencing object. For example, if a sample has the alias "sample1", and an experiment uses this sample, then the experiment's "EXPERIMENT/SAMPLE/refname" attribute should be "sample1". center_name: The "center_name" attribute is required within the submission XML and, if not provided when the object is submitted, it will be automatically filled using your default EGA account center_name. This element is the "controlled vocabulary acronym or abbreviation that is provided to the account holder when the account is first generated". If the submitter is brokering a submission for another institute, the submitter should use their special broker account name in broker_name while the data centre acronym remains in center_name. Log-in details should have been provided when you requested a submission account. Please contact our Helpdesk team if you have any questions. run_center: Many submitting centers contract out the actual sample sequencing to another center. In these cases, the sequencing center should be acknowledged in the run_center attribute. Again, this is controlled vocabulary and the acronym should be sought from EGA helpdesk before submitting. Please contact our Helpdesk team if you have any questions. Prepare your XMLs The goal of this section is to provide sufficient information to be able to create the metadata XML documents required for programmatic submissions. Please note, the EGA utilises the XML schemas maintained at the European Nucleotide Archive (ENA). It is important due to the fact that by using a similar system, some pieces of documentation from the ENA's programmatic submission can also help you with your programmatic submission to the EGA. For example, you can submit programmatically without using a Submission XML by following the steps at Submission actions without submission XML. A submission does not have to contain all different types of XMLs. For example, it is possible to submit only a few samples; or a study that is later to be referenced. You can submit each object one by one, or submit all in a batch: you choose what method of submission works best for you. We do recommend, nevertheless, that you submit the objects to be referenced (e.g. samples or studies) first, and the objects that reference these (e.g. experiments or datasets) afterwards. You can see a graphical view of these objects and their relationships at our EGA Schemas page. Independently of the submission scenario, you will always require a Dataset XML. The entity of a dataset is what is used to control access to the given data, in the form of runs or analyses. In other words, when a requester is granted access, it is through the dataset and the objects (e.g. runs or analyses) that the dataset contains, granting access to them in one go. Given the nature of the EGA, a dataset XML will always be required for the data access. First, we will differentiate between submissions of "raw" and "processed" data: Runs and Analyses, respectively. Run data submissions Raw data derives from instruments "as is". For example, a plain sequence file (e.g. FASTQ or unaligned BAM files) would be considered raw data. A typical raw (unaligned) sequence read submission consists of 8 XMLs: Submission Study Sample Experiment Run DAC Policy Dataset When technical reads (e.g. barcodes, adaptors or linkers) are included in the submitted raw sequences, a spot descriptor must be submitted to describe the position of the technical reads so that they can be removed. The following data files can be submitted without providing spot descriptor information in the experiment/run XML: BAM files (single reads) SFF files (single reads without barcodes) FastQ files (single reads without any technical reads) Complete Genomics files Analysis data submissions Processed data is, in some way, refined raw data. This includes raw data that has been processed by some form of analysis method (e.g. alignment, noise reduction, etc.). For example, an aligned sequence (e.g. BAM file), that was created using raw FASTQ files, would be a processed file. This category includes most types of data: sequence alignment files (e.g. BAM or CRAM), clinical data (e.g. phenopackets), sequence variation files (e.g. VCF), sequence annotation, etc. A typical EGA analysis data submission consists of 7 EGA XML: Submission Study Sample Analysis DAC Policy Dataset We accept three different types of analysis data submissions: BAM files (for multiple read alignments) VCF files (for sequence variations) Phenotype files (in any format) In anycase, keep in mind that samples must be created in order to be referenced in the analyses. In other words, the provenance of the information within the BAM, VCF and phenotype files Example XMLs Below you can find a non-extensive list of example XMLs with descriptive fields (i.e. explaining what to provide in each field). Furthermore, you can also find real examples (i.e. the true value of the provided fields) in our GitHub repository. Submission XML The submission XML is used to validate, submit or update any number of other objects. The submission XML refers to other XMLs. New submissions use the ADD action to submit new objects. Object updates are done using the MODIFY action and objects can be validated using the VERIFY action. Descriptive submission XML example True values submission XML example Study XML The study XML is used to describe the study containing a title, a study type and abstract as it would appear in a publication. Descriptive study XML example True values study XML example Please use the following notation within the property "STUDY_LINKS" when including PubMed citations in the Study XML: <STUDY_LINKS> <STUDY_LINK> <XREF_LINK> <DB>PUBMED</DB> <ID>18987735</ID> </XREF_LINK> </STUDY_LINK> </STUDY_LINKS> Sample XML The sample XML is used to describe the samples used to obtain the data, whether they were sequenced, measured in any other way, or have an associated phenotype. The mandatory fields include information about the taxonomy of the sample, sex, subject ID and phenotype. For example, the mandatory attribute fields for each sample would look like these, within the array of "SAMPLE_ATTRIBUTES": <SAMPLE_ATTRIBUTES> <SAMPLE_ATTRIBUTE> <TAG>subject_id</TAG> <VALUE>free text!</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>sex</TAG> <VALUE>female/male/unknown</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>phenotype</TAG> <VALUE>Free text, EFO terms (e.g. EFO:0000574) are recommended</VALUE> </SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTES> Sample is one of the most important objects to be described biologically, it is highly recommended that “TAG-VALUE” pairs are generated as SAMPLE_ATTRIBUTES to describe the sample in as much detail as possible. For example, were we to give the population ancestry of the sample, we could add a new attribute to the array, in which, for example, we would indicate that the sample derives from an individual of "Mende in Sierra Leone" (MSL), with an african ancestry: <SAMPLE_ATTRIBUTE> <TAG>Population</TAG> <VALUE>MSL</VALUE> </SAMPLE_ATTRIBUTE> Given that VALUE and TAG are free text, the combinations are limitless in order to give you full flexibility on the information you want to provide. We recommend you use the Experimental Factor Ontology (EFO) to describe the phenotypes of your samples. You can provide more than one phenotype by adding more items to the array of SAMPLE_ATTRIBUTES. Phenotypes considered essential for understanding the data submission should be provided. Each phenotype described should be listed as a separate sample attribute <SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTE>. There is no limit to the number of phenotypes that can be submitted. If a suitable EFO accession cannot be found for your phenotype attribute, please consider using another controlled ontology database (e.g. HPO, MONDO, etc.) before using free text. Descriptive sample XML example True values sample XML example Experiment XML The experiment XML is used to describe the experimental setup, including instrument platform and model details, library preparation details, and any additional information required to correctly interpret the submitted data. Where any of these values differ between runs, a new experiment object must exist, since runs are grouped by experiments. Each experiment references a study and a sample by alias, or if previously-submitted, by accession. Pooled data must be demultiplexed by barcode for submission. Descriptive experiment ( Illumina paired read ) XML example True values experiment ( Illumina paired read ) XML example Run XML The run XML is used to associate data files with experiments and typically comprises a single data file (e.g. a FASTQ file). Please note that pooled samples should be de-multiplexed prior submission and submitted as different runs. Descriptive run XML example True values run XML example Analysis XML Given that an analysis can be used to submit any type of processed data to the EGA, we will list below an example of each of the three most common types of analysis XMLs submitted to the EGA: sequence alignments (e.g. BAM files); sequence variation (e.g. VCF files); and clinical metadata or phenotypes (e.g. phenopackets). Regardless of the type of processed data submitted in the analysis, the analysis must be associated with a Study and can reference multiple types of other objects, from samples to experiments, if they are available at the EGA. Just like with Runs, whenever a file is submitted to the EGA through an analysis object, the file MD5 checksums must be present, in order for the EGA to validate file integrity upon transfer. This also includes index files when applicable (e.g. .bai.md5 files). Ideally, any analysis that uses a reference sequence for some kind of alignment (e.g. BAM, CRAM or VCF files), would contain metadata about the alignment, such as INSDC reference assemblies and sequences, by either using accessions (e.g. CM000663.1) or common labels (e.g. GRCh37). Read alignment (BAM) Analysis XML The Analysis can be used to submit BAM alignments to EGA. Only one BAM file can be submitted in each analysis and the samples used within the BAM read groups must be associated with Samples. Descriptive bam alignments XML example True values bam alignments XML example Sequence variation (VCF) Analysis XML The Analysis can be used to submit VCF files to EGA. Only one VCF file can be submitted in each analysis and the samples used within the VCF files must be associated with Samples. Download analysis XML (VCF) Phenotype files The Analysis XML can be used to submit phenotype files to the EGA. Only one phenotype file can be submitted in each analysis and the samples used within the phenotype files must be associated with EGA Samples. Download analysis XML (Phenotype) DAC XML The DAC XML describes the Data Access Committee (DAC) affiliated to the data submission. The DAC may consist of a group or a single individual and is responsible for the data access decisions based on the application procedure described in the POLICY.XML. As with any other object, if it was already submitted to the EGA, there is no need to submit it again: you can reference an existing object within the EGA. Hence, A DAC XML does not need to be provided if your submission is affiliated to an existing EGA DAC.. Further information on DACs can be found here, and you can always contact our Helpdesk team if you have further inquiries. Descriptive dac XML example True values dac XML example Policy XML The Policy XML describes the Data Access Agreement (DAA) to be affiliated to the named Data Access Committee. Descriptive policy XML example True values study XML example Dataset XML The dataset XML describes the data files, defined by the Run.XML and Analysis.XML, that make up the dataset and links the collection of data files to a specified Policy. The dataset xml is commonly the last metadata object to be submitted, since it references multiple other entities. Please consider the number of datasets that your submission consists of. For example, a case-control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Descriptive dataset XML example True values dataset XML example Validating and submitting your EGA Validating EGA's XMLs through Webin After you have ensured that the XMLs are properly formatted and contain all the required information. You can proceed to validate and submit your data. Use the curl command to validate your XML file: Once you have prepared your XML file and asserted you have access to Webin, you can validate your XML file programmatically against EGA's schemas using the curl command. There are multiple ways in which you can validate your XMLs. This variety has to do with the fact that: (1) there are 2 instances of Webin (test and production); and (2) that validation is a default step during submission. In other words, any time that you submit your data through Webin, it will be validated automatically before being accepted. This allows for 4 possible routes of validation, all having the same validation result: validating or submitting to either the production service or the test service of Webin. For example, directly validating a "study" object XML in the testing service (wwwdev…) would look like the following: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" In this command, you would need to replace <USERNAME> and <PASSWORD> with your EGA account username and password, respectively. You would also replace <INPUT_FILE> with the path to your XML file. A mock example would look like the following: curl -u ega-test-data@ebi.ac.uk:egarocks -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" The validation attempt can have different results depending on the given arguments: If your XML file is valid according to EGA's schemas, you will see a message indicating that your XML file is compliant. For example, see below for our mock example, where the "success" was "true" (i.e. no validation errors found). Nevertheless, notice how the "<STUDY accession=" is empty: it is because we were simply validating, so the study did not get an accession or ID. <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:19:28.850+01:00" submissionFile="submission-EBI-TEST_1681222768850.xml" success="true"> <STUDY accession="" alias="Mock example" status="PRIVATE"/> <SUBMISSION accession="" alias="SUBMISSION-11-04-2023-15:19:28:840"/> <MESSAGES> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If there are any errors or warnings, the tool will display them, allowing you to correct them before submitting your data to EGA. For example, in the following response, it is said that the object we were trying to submit was already existing, and therefore the "success" was "false". <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:12:35.609+01:00" submissionFile="submission-EBI-TEST_1681222355609.xml" success="false"> <STUDY alias="Example!_Human Microbiome Project SP56J" status="PRIVATE" holdUntilDate="2023-03-11Z"/> <SUBMISSION alias="SUBMISSION-11-04-2023-15:12:35:576"/> <MESSAGES> <ERROR>In study, alias: "Example!_Human Microbiome Project SP56J". The object being added already exists in the submission account with accession: "ERP127584".</ERROR> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If the curl command retrieves no response at all, please double check if your username and password are correctly provided. Also notice the "ACTION=..." argument passed to the Curl command. This specifies the action to take during the call to Webin, so we do not need a "Submission" XML just for a validation attempt. See more at submission actions without submission XML. Furthermore, validation of multiple files or objects (e.g. sample, experiment, study…) can be done in a single command by adding more arguments (i.e. '-F'). For example: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" -F "SAMPLE=@sample.xml" -F "DATASET=@dataset.xml" As mentioned above, beside "validate" action in the test environment, you can also validate your metadata by three other methods: "Validate" in the production server. From our example above, you simply need to take the "dev" away from the URL. curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the development server. From our example above, you would simply need to replace the action: from "validate" to "add". Whatever is submitted to this service will be discarded in 24h, so whether something gets submitted or not would not matter in the long run. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the productionserver. A combination of the previous two methods, which would render this attempt into a submission. This path is just to be taken when you are sure your metadata is compliant and what you want to submit. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" What happens after the submission of a dataset XML? Once you have completed the registration of your dataset/s please contact our Helpdesk Team to provide a release date for your study. Please note that all datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitter or DAC contact contact the EGA Helpdesk for the study to be released. We strongly advise you not to delete your data until EGA Helpdesk confirms that your data has been successfully archived.

  Documentation submission/metadata/submission/programmatic-submission-xml