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• Sensitive urothelial cancer detection via high volume urine DNA analysis

  Cystoscopy and imaging are the gold standard for urothelial cancer (UC) detection and surveillance, but cystoscopy is an uncomfortable procedure associated with adverse effects and a low diagnostic yield as only 10% of hematuria patients have UC. Urine tumor DNA (utDNA) is a non-invasive UC biomarker that has suffered from poor sensitivity for Ta stage tumors (<80%). We hypothesized that high urine volume analysis with a multi-gene panel could overcome these limitations, and developed a 100 mL utDNA laboratory platform (UroScout) that analyzes 25 commonly mutated UC genes and 8 copy number-altered loci for somatic alterations to detect UC.

  Study EGAS50000000630

• Resolving complex duplication variants using long read genome sequencing in autism spectrum disorder

  The goal of the study is to resolve complex genomic rearrangements in individuals diagnosed with autism spectrum disorder (ASD). Families were identified where at least one individual had previously been diagnosed with ASD, and carried a complex genomic rearrangement as determined by short-read whole genome sequencing. In this study the parents, proband and additional siblings, a total of 20 individuals, were analyzed using Oxford Nanopore Technologies long-read sequencing. Each individual was sequenced on one PromethION flow-cell, providing approximately 25-30X coverage of the genome. The data is deposited as nanopore BAM files with methylation information included.

  Study EGAS50000000390

• Single-cell/single-nucleus RNA-seq of Embryonal Tumor with Multilayered Rosettes (ETMR)

  Embryonal tumor with multilayered rosettes (ETMR) is a pediatric brain tumor with dismal prognosis. We analyzed eleven ETMR patient samples using single-cell transcriptomics. We reveal a spatially distinct cellular hierarchy that spans highly proliferative neural stem-like cells and more differentiated neuron-like cells. C19MC is predominantly expressed in stem-like cells and controls a transcriptional network governing stemness and lineage commitment. Systematic analysis of receptor-ligand interactions between malignant cell types reveals FGFR and NOTCH signaling as oncogenic pathways. Our study provides fundamental insights into ETMR pathobiology and a powerful rationale for more effective targeted therapies.

  Study EGAS50000000937

• Amplicon sequencing of melanoma samples

  The types of biomarkers for skin tumors have been 1) biochemical markers in blood, urine, and body fluid, 2) histopathological markers, 3) genetic examination of tumor tissue, 4) tumor-susceptibility assessed by germ_x0002_line genetic examination. Biomarkers in a blood are frequently used in the treatment of cutaneous malignancies, but are not suitable for early detection of malignancies. Research on circulating tumor cells, cell free DNA, exosomes has been promoted in recent years, but clinical application is halfway. Based on these circumstances, the purpose of this study is to search for biomarkers for diagnosis, therapeutic effect, and follow-up of skin tumors, and to verify their usefulness.

  Study JGAS000351

• Whole genome sequencing data of pediatric hypodiploid acute lymphoblastic leukemia

  Hypodiploid acute lymphoblastic leukemia (ALL) is a rare subtype of B-cell ALL characterized by chromosome number < 45 , encompassing near-haploid (24–29 chromosomes) and low-hypodiploid (30–39 chromosomes) subgroups. These subgroups display distinct genetic landscapes, including Ras and receptor tyrosine kinase pathway alterations in near-haploid ALL, and frequent IKZF2, RB1, and germline TP53 alterations in low-hypodiploid ALL. Clinically, hypodiploid ALL is associated with dismal prognosis, with 5-year overall survival rates often between 50-70% in children and markedly worse in adults. Despite advances in risk stratification and MRD-guided therapy, allogeneic stem cell transplantation remains the current standard recommendation for affected patients.

  Study EGAS50000001305

• Genomic analysis Nasopharyngeal cancer through whole exome sequencing and whole genomic sequencing.

  Nasopharyngeal carcinoma (NPC) is a squamous-cell carcinoma and has remarkable ethnic and geographic distribution, with a high prevalence in southern China, southeast Asia, northern Africa and Alaska. Although there are previous studies implicated NF-κB pathway play an important role in NPC. Newer research also found several important cellular processes and pathways in NPC, including chromatin modification, ERBB-PI3K signaling and autophagy machinery. Put these studies together, although, NPC is still poorly understood at the genetic level. Here we determined the mutational landscape of 553 cases with NPC using whole-genomic and whole-exome sequencing. These approaches revealed a distinct mutational signature and significantly mutated genes.

  Study EGAS00001002788

• Small intestinal plasma cells transcriptome profiles

  Objective: Plasma cells (PCs) are terminally differentiated B-lymphocytes that produce antibodies. The lamina propria of the small intestine is particularly rich in PCs. In coeliac disease (CeD) there is infiltration and increased density of PCs in the small intestinal lesion. Many of these PCs produce disease-specific autoantibodies targeting the enzyme transglutaminase 2 (TG2). The plasmacytosis and high numbers of disease-antigen reactive PCs in CeD motivated us to study the transcriptional programme of PCs from coeliac gut lesions. Design: We performed RNASeq analysis of IgA+ PCs of untreated CeD patients being specific or non-specific for TG2 and from healthy controls.

  Study EGAS00001003345

• Spatial and temporal genomic evolution in glioblastoma

  Tumor heterogeneity is a challenging barrier to successful targeted therapy in glioblastoma. Identifying the mechanisms that drive cancer cells as they genetically evolve and adapt in response to treatment is a vital goal of modern cancer research. Analysis of sequencing data provides a method for uncovering the mechanisms sustaining tumor heterogeneity and driving evolution. Here, we analyzed genomic profiles of GBM biopsies taken from pre- and post-treatment GBMs (with matched germline controls), to understand 1) the intratumoral clonal compositions of primary GBM and 2) how GBM responds to therapeutic intervention. Our results provide a molecular portrait of GBM recurrence.

  Study EGAS00001001033

• A personalised medicine approach for ponatinib-resistant chronic myeloid leukaemia

  Chronic myeloid leukaemia (CML) is characterised by the presence of a fusion driver oncogene, BCR-ABL1, which is a constitutive tyrosine kinase. Tyrosine kinase inhibitors (TKIs) are the central treatment strategy for CML patients and have significantly improved survival rates, but the T315I mutation in the kinase domain of BCR-ABL1 confers resistance to all clinically approved TKIs, except ponatinib. However, compound mutations can mediate resistance even to ponatinib and remain a clinical challenge in CML therapy. Here, we investigated a ponatinib-resistant CML patient through whole genome sequencing (WGS) to identify the cause of resistance and to find alternative therapeutic targets.

  Study EGAS00001001150

• Targeted_sequencing_of_blood_DNA_from_Human_twins_

  The goals of this study is to investigate the prevalence and heritability of age-related clonal haemopoeisis (ARCH) in healthy elderly individuals.We will use a bespoke bait set to pull down DNA regions of interest in whole blood samples combined with HiSeq at a deep level . By correlating findings from each individual to their respective twin we hope to elucidate whether heritable traits influence the development of ARCH. a. 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 EGAS00001002210

• Molecular profiling of longitudinally observed small colorectal polyps: a cohort study

  Knowledge of the natural history of colorectal adenomas is limited because these lesions are removed upon detection. The few studies in which small adenomas have been left in situ for a limited period of time have shown that most lesions remain stable or even completely vanish. Lesions that have grown in size more often turn out to be advanced adenomas when resected. Specific DNA copy number changes (cancer associated events or CAEs) are associated with progression of adenomas to cancer. The aim of the study was to evaluate whether growth of small colorectal polyps left in situ is associated with specific molecular features.

  Study EGAS00001003284

• ENU_CCK_81_cetuximab_pilot_project

  Cetuximab is a targeted monoclonal antibody against the epidermal growth factor receptor (EGFR) which is used therapeutically for the treatment of KRAS wild-type colorectal cancer (CRC). The Cetuximab sensitive KRAS wild-type CRC cell line NCI-H508 has been treated with a fixed concentration of ENU for 24 hours and then selected with Cetuximab until drug resistant clones were ready to be picked and grown up as sub-clones of the parental cell line. These will have genes causally implicated in cancer sequenced to identify common point mutations in multiple independently derived drug resistant clones as a forward genetic screen for mechanisms of resistance to Cetuximab in CRC.

  Study EGAS00001001743

• Native_American_Ancient_DNA_sequencing

  The morphology of the first humans in the Americas (Paleoamericans) differs from that of Native Americans, and has raised the question of whether or not there are also differences in origin or genetics. A few populations who survived until relatively recently have been suggested to retain Paleoamerican morphology. One of these populations is from La Jolla. Here, we have generated genome sequence data from four La Jolla individuals in order to investigate these questions 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 EGAS00001001802

• Integrative single-cell and cell-free plasma RNA transcriptomics elucidates placental cellular dynamics

  The human placenta is a dynamic and cellular heterogeneous organ, which is critical in fetomaternal homeostasis and the development of preeclampsia. Previous work has shown that placenta-derived cell-free RNA increases during pregnancy. We applied large-scale microfluidic single-cell transcriptomic technology to comprehensively characterize cellular heterogeneity of the human placentas and identified multiple placental cell-type–specific gene signatures. Analysis of the cellular signature expression in maternal plasma enabled noninvasive delineation of the cellular dynamics of the placenta during pregnancy and the elucidation of extravillous trophoblastic dysfunction in early preeclampsia.

  Study EGAS00001002449

• ENU_NCI_H508_cetuximab_fixed_concentration_project

  Cetuximab is a targeted monoclonal antibody against the epidermal growth factor receptor (EGFR) which is used therapeutically for the treatment of KRAS wild-type colorectal cancer (CRC). The Cetuximab sensitive KRAS wild-type CRC cell line NCI-H508 has been treated with a fixed concentration of ENU for 24 hours and then selected with Cetuximab until drug resistant clones were ready to be picked and grown up as sub-clones of the parental cell line. These will have genes causally implicated in cancer sequenced to identify common point mutations in multiple independently derived drug resistant clones as a forward genetic screen for mechanisms of resistance to Cetuximab in CRC

  Study EGAS00001001744

• ENU_NCI_H508_Cetuximab_SecondRound

  Cetuximab is a targeted monoclonal antibody against the epidermal growth factor receptor (EGFR) which is used therapeutically for the treatment of KRAS wild-type colorectal cancer (CRC). The Cetuximab sensitive KRAS wild-type CRC cell line NCI-H508 has been treated with a fixed concentration of ENU for 24 hours and then selected with Cetuximab until drug resistant clones were ready to be picked and grown up as sub-clones of the parental cell line. These will have genes causally implicated in cancer sequenced to identify common point mutations in multiple independently derived drug resistant clones as a forward genetic screen for mechanisms of resistance to Cetuximab in CRC

  Study EGAS00001001745

• CASCADE metastatic melanoma study

  In this study we analyse the evolution of human melanoma from early to late disease in patients by sampling their tumours at multiple sites and times. Whole exome and genome sequencing data from tumour samples reveals only limited gain of point mutations generally, with net mutational loss in some metastases. In contrast, melanoma evolution is dominated by whole genome doubling and large-scale aneuploidy, in which widespread loss of heterozygosity sculpts the burden of point mutations, neoantigens and structural variants even in treatment-naïve and primary cutaneous melanomas in some patients. These results imply that dysregulation of genomic integrity is a key driver of selective clonal advantage during melanoma progression.

  Study EGAS00001004950

• Control samples, breast cancer clinical samples and matched patient-derived tumour xenografts (PDTXs) to develop and test a computational approach to discriminate human and mouse sequences in PDTXs

  Patient-Derived Tumour Xenografts (PDTXs) have emerged as the pre-clinical models that best represent clinical tumour diversity and intra-tumour heterogeneity. The molecular characterization of PDTXs using High-Throughput Sequencing (HTS) is essential; however, the presence of mouse stroma is challenging for HTS data analysis. Indeed, the high homology between the two genomes results in a proportion of mouse reads being mapped as human. In this study we generated Whole Exome Sequencing (WES) and RNA sequencing (RNA-seq) data from samples with known mixtures of mouse and human DNA or RNA.

  Study EGAS00001002445

• Deciphering somatic mosaic structural variation in human blood lineages using single-cell multiomics

  Somatic mosaicism (SM), referring to the presence of somatic mutations in sub-populations of cells within healthy individuals, is associated with an increased risk of a variety of diseases, including cancer. Blood is at particularly high risk of SM, given its rapid turnover and functionally- heterogeneous cell-type composition. While the roles of point mutations and large-scale rearrangements in blood SM have been scrutinised in recent years, the functional impact of mosaic structural variants (mSVs) remains poorly understood. Using haplotype-resolved single-cell multi-omics, we explored the mSV landscape of human hematopoietic stem and progenitor cells (HSPCs).

  Study EGAS00001006567

• Single-cell transcriptomics identifies pathogenic T-helper 17.1 cells and pro-inflammatory monocytes in ICI-related pneumonitis

  ICI-pneumonitis is a frequent serious adverse event of cancer immunotherapy hinging on a cell-mediated immune response, though the exact pathophysiology is currently unknown. Using single-cell transcriptomics, an enrichment of pathogenic (TBX21, RORC, IFNG, IL17A, CSF2 expressing) T-helper 17.1 cells and pro-inflammatory (TNF, IL1B, IL6, IL23A expressing) monocytes was identified in ICI-pneumonitis bronchoalveolar lavage fluid, putatively engaging in a feedforward inflammatory loop. This finding yields several novel therapeutic targets for the treatment of ICI-pneumonitis. Most notably repurposing anti-IL-23 merits further research as a potential efficacious and safe treatment for ICI-pneumonitis.

  Study EGAS00001006762

• Transcriptome analysis of endometrial organoids derived from patients with Mayer-Rokitansky-Küster-Hauser syndrome

  The Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome is characterized by agenesis of the uterus and upper part of the vagina in females with normal ovarian function. The cause of the disease is still enigmatic. Here, we developed organoid cultures from endometrium found in uterine rudiment horns of MRKH patients. Phenotypically they share great similarity between healthy control organoids and are fully hormone responsive. Transcriptome analysis using RNA-seq identified possible disease-causing pathways altered in MRKH patients during development of the female reproductive tract. Thus, the organoid cultures provide a powerful research model for further insight into disease-causing alterations.

  Study EGAS00001005908

• 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

• Increased trunk fat is associated with altered gene expression in breast tissue of normal weight women

  Dac EGAC00001002031

• Oxidative phosphorylation is a key ontogenetic feature of monocyte immunometabolism promoting myeloid differentiation after birth

  Dac EGAC00001003420

• NPY methylated ctDNA is a promising biomarker for treatment response monitoring in metastatic colorectal cancer

  Study EGAS00001006820

• Frequent mutation of the major cartilage collagen gene, COL2A1, in chondrosarcoma

  Chondrosarcoma (CHS) is a heterogeneous collection of malignant bone tumours and is the second most common primary malignancy of bone after osteosarcoma. Recent work has identified frequent, recurrent mutations in IDH1/2 in nearly half of central CHS. However, there has been little systematic genomic analysis of this tumour type and thus the contribution of other genes is unclear. Here we report comprehensive genomic analyses of 49 cases of CHS. We identified hypermutability of the major cartilage collagen COL2A1 with insertions, deletions and rearrangements identified in 37% of cases. The patterns of mutation were consistent with selection for variants likely to impair normal collagen biosynthesis. In addition we identified mutations in IDH1/2 (59%), TP53 (20%), the RB1 pathway (27%) and hedgehog signaling (22%).

  Dataset EGAD00001000358

• Whole genome sequencing of pancreatic cystic fluid for early detection and diagnosis of pancreatic cancer

  Pancreatic cancer (PC) is a leading cause of cancer-related deaths globally. Accurate PC detection at early or premalignant stage, when surgery is effective, would increase survival rates and prevent unnecessary surgery or surveillance. Molecular diagnostic attempts using PCy fluid (PCyF), as a liquid biopsy, to analyze common mutations associated with PC development are yet to contribute to early diagnosis. Whole genome sequencing (WGS) is utilized in various cancers, PC inclusive, for detecting genetic changes associated with carcinogenesis, but not in PCyF due to technical limitations, including isolated DNA purity. The goal of the study was to achieve high quality of WGS from PCyF from 9 patients to enable development of signatures of malignancy. The WGS was performed using Illumina sequencing. Experimental design was at a 50x depth.

  Dataset EGAD50000000869

• PTEN homozygous deletion is a negative prognostic factor in Tumor Treating Fields-treated glioblastoma, IDH wildtype patients

  This study investigates genomic determinants of outcome in glioblastoma, IDH-wildtype patients treated with Tumor Treating Fields (TTFields) in addition to standard radiochemotherapy. Tumor tissue from primary glioblastoma at initial diagnosis was profiled using the Illumina TruSight Oncology 500 (TSO500) targeted sequencing panel to characterise somatic alterations. The overarching goal is to provide exploration of genomic determinants of survival and treatment response in TTFields-treated glioblastoma, IDH-wildtype.

  Study EGAS50000001469

• Acquisition of additional mutations drives accelerated progression of NPM1 positive CMML to AML

  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/ We performed exome sequencing on serial samples from a patient with CMML who progressed to AML. The exome sequencing suggests that NPM1, TET2 and DNMT3a mutations were present in the dominant clone in the CMML sample and that NRAS is a new subclonal mutation in the AML sample. Diagnostic data shows the presence of a FLT3-ITD mutation in the AML sample, which is likely to have driven progression. Here we are performing re-sequencing of the putative driver and some passenger mutations which appear to be in the same clone to validate these mutations and to verify the relative quantification of these abnormalities .

  Dataset EGAD00001002194

• RNA-seq study of longitudinal blood cell samples drawn from children at risk of type 1 diabetes

  The appearance of type 1 diabetes (T1D)-associated autoantibodies is the first and only measurable parameter to predict progression toward T1D in genetically susceptible individuals. However, autoantibodies indicate an active autoimmune reaction, wherein the immune tolerance is already broken. Therefore, there is a clear and urgent need for new biomarkers that predict the onset of the autoimmune reaction preceding auto-antibody positivity or reflect progressive b-cell destruction. Here we report the mRNA sequencing-based analysis of 306 samples including fractionated samples of CD4+ and CD8+ T cells as well as CD4, CD8 cell fractions and unfractionated PBMC samples longitudinally collected from seven children who developed beta-cell autoimmunity (case subjects) at a young age and matched control subjects.

  Dataset EGAD00001005767

• Profiling heterogeneity in Human derived IPSC-neurons (2020-05-18)

  The impact of genetic variants on molecular pathways that give rise to neurodegenerative diseases such as Alzheimer's and Parkinson's is best elucidated in the appropriate cell types and molecular contexts. Existing studies have focused on bulk profiling of mixed cell types, but have ignored assaying genetic effects across development and cell differentiation. At the core of this proposal is the idea to use single-cell assays to study genetic effects during differentiation of dopaminergic and cortical neurons to identify the sequence of molecular events from variants to healthy and diseased cell states in a cell-specific manner. 1) 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 please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2020-05-18.

  Dataset EGAD00001006157

• Evaluating gene expression in aggressive B-cell lymphoma using a quantitative nuclease protection assay

  Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma (NHL), comprising 25-30% of all NHL in developed countries with an annual incidence in the USA of 7 cases/100000 persons/year. Collectively, DLBCL is classified based on a common morphological appearance of diffuse growth of large transformed B-cells, immunophenotype, high proliferation rate and aggressive behaviour. Despite these similarities, DLBCLs are a heterogeneous collection of malignancies with distinct clinical and molecular characteristics that do not always correlate with immunohistological features. This gene expression dataset includes transcriptomes of ABC-DLBCLs and of GCB-DLBCLs where cell of origin is determined by the HTG-EdgeSeq quantitative nuclease protection assay. Also included are clonality results from BCR profiling from high-grade B-cell lymphomas sequenced using a NOVA sequencer

  Dataset EGAD00001011309

• Somatic mutation and clonal evolution in premalignant lung disease - WGS (2020-01-15)

  Cancer is a genetic disease caused by an accumulations of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from the lung tissue of individuals with a variety of lung diseases (COPD, UIP, IPF, Emphysema, pulmonary hypertension). This will allow us to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. Smoking is a large risk factor for developing many of these lung diseases so we are particularly keen to determining whether there is evidence of a smoking signature in these patients. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005786

• NOTCH1 fusion genes in pediatric T-cell lymphoblastic lymphoma hallmark a common high-risk subgroup with blood TARC levels as possible biomarker

  T-cell lymphoblastic lymphoma (T-LBL) is a common pediatric malignancy accounting for approximately 20% of the non-Hodgkin lymphomas during childhood. Survival rates of T-LBL are ~80%, but outcome after relapse is dismal, with salvage rates reaching only ~15. Considering the extremely poor prognosis after relapse and absence of clinically relevant high-risk genetics, there is an urgent need for the identification of molecular risk factors and new prognostic biomarkers in T-LBL, as well as identification of new therapeutic strategies. In this study we present a novel entity of high-risk pediatric T-LBL patients characterized by previously unknown NOTCH1 gene fusions and highly elevated blood TARC levels

  Dataset EGAD00001015255

• Organ maturation in preparation for birth (Peds RFA) to develop a tissue resource and a single-cell atlas of organ development and maturation for dissemination among the scientific and clinical community: RNA (2025-10-14)

  Knowledge about abnormal organ development is important to understand pathology and to develop novel treatment approaches for individuals with congenital and acquired disease. Most of our current understanding is based on examination of tissues from the embryo and early foetus, collected from women undergoing termination of pregnancy in the first trimester (third) of pregnancy. There is very little known about normal and abnormal organ development from a developmental perspective during the crucial last two-thirds of pregnancy when much remodelling of foetal tissues occurs. This study will generate a single-cell atlas of late-foetal lungs, blood, heart, bone and immune organs. . This dataset contains all the data available for this study on 2025-10-14.

  Dataset EGAD00001015737

• Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

  Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

  Dataset EGAD50000000226

• The chemotherapeutic CX-5461 is extremely mutagenic and may increase cancer risk

  Deposited here are whole-genome sequencing bam or fastq files from the experimental isogenic cell lines used in the study "The chemotherapeutic CX-5461 is extremely mutagenic and may increase cancer risk, Koh, Gene (2023)". The bam files were aligned to GRCh38/hg38 using BWA-MEM. The corresponding variant call data have been deposited on Mendeley Data, V2, doi: 10.17632/d58cv549v6.2

  Dataset EGAD50000000036

• Idiopathic Collapsing Glomerulopathy in Brazilian patients

  Collapsing glomerulopathy is a severe kidney disease that often leads to chronic dialysis or kidney transplantation. To expand its understanding, we performed a broad clinical and genetic analysis of a highly admixed patient population with idiopathic collapsing glomerulopathy.

  Study EGAS50000000064

• Genomic Sequencing of Pediatric Rhaboid Cancers

  In this study, we sequenced the exomes of 35 rhabdoid tumors, highly aggressive cancers of early childhood. This study is part of a larger effort to characterize pediatric cancers as part of the Slim Initiative for Genomic Medicine (SIGMA) project.

  Study phs000508

• The Normal Human Tissue Sequencing Project: Non-Diseased, Non-Malignant Tissues

  The Normal Human Tissue Sequencing Project provides RNA expression and DNA methylation data from normal (non-diseased) human tissues. This data set from a variety of source tissue is a valuable resource for human disease studies.

  Study phs000819

• Chromosome Errors in Human Eggs and Natural Fertility

  The overall aim of this project is to investigate chromosome errors in human oocytes as a function of age. We aimed to identify molecular mechanisms underpinning these errors and how they may shape the curve of natural fertility in humans.

  Study phs001922

• Genomic Characterization of Brain Metastases from Lung Cancer

  Metastatic disease to the brain is associated with a poor prognosis. Our understanding of the genetic drivers of metastasis remain limited. Whole-exome sequencing was performed from brain metastases from lung adenocarcinoma to identify putative drivers of metastatic progression.

  Study phs001920

• Ischemia Reperfusion Responses in Human Lung Transplants at the Single Cell Resolution

  Ischemia reperfusion is an unavoidable step of organ transplantation. Development of therapeutics for lung injury during transplantation has proved challenging; understanding lung injury from human data at the single cell resolution is required to accelerate the development of therapeutics. Donor lung biopsies from six human lung transplant cases were collected at the end of cold preservation and 2-hour reperfusion and underwent single cell RNA sequencing.

  Dataset EGAD50000000704

• Aberrant (pro)renin receptor expression induces genomic instability by chromatin remodeler SMARCA5 disruption during the pancreatic ductal adenocarcinoma

  The aim of the present study is to elucidate whether aberrant (P)RR expression induces genomic instability in human pancreatic ductal epithelial (HPDE) cells, which are transforming into pancreatic ductal adenocarcinoma (PDAC).

  Study JGAS000143

• Targeted Sequencing Data for RESOLVE Clinical Trial

  This is a Phase II clinical trial. This research study is studying a combination of targeted therapies as a possible treatment for estrogen-receptor positive (ER+) endometrial cancer and low-grade serous ovarian cancer. The drugs involved in this study are: - Abemaciclib (also known as Verzenio™) - Letrozole (also known as Femara®) - Metformin (also known as Glucophage®) - Zotatifin (also known as eFT226) - Gedatolisib (also known as PF-05212384)

  Dataset EGAD50000001711

• WGS

  A genome-wide approach based on next generation sequencing (NGS) is performed on trios ( Patient+ Father + Mother) with SAID in order to identify known and new gene variants associated with certain forms of the disease. Whole-genome sequencing (WGS) was performed on frozen PBMCs. DNA was extracted using Chemagic Prime ( Perkin Elmer) and quantified with nanodrop. The raw sequencing data is generated on the Novaseq 6000 using an S4 flowcell, with a sequencing depth of ~30X

  Dataset EGAD50000002026

• Comprehensive sequencing analyses of uterine and ovarian carcinosarcoma

  We collected 109 primary tumor and mached normal samples of uterine and ovarian carcinosarcoma. The purpose of this study is to find novel therapeutic options for carcinosarcoma as well as a clue to understand the molecular histogenic mechanism.

  Study JGAS000172

• Comprehensive analyses of genetic aberrations in gastroenterological tumors

  Intestinal metaplasia (IM) is a risk factor of gastric cancer in gastric mucosa with Helicobacter pylori infection. To explore the susceptibility to the cancer development in pure gastric IM, we investigated landscape of genetic alterations in gastric single-glands with IM.

  Study JGAS000269

• Genome-wide integrative analysis of pediatric pancreatoblastoma

  Pancreatoblastoma is the most common pancreatic malignancy of young children, but its molecular pathogenesis remains poorly understood. To understand the molecular pathogenesis of pancreatoblastoma, we comprehensively analysed the whole exome sequencing, RNA sequencing, SNP array, and methylation array data of pancreatoblastoma.

  Study JGAS000088

• Whole genome sequencing data of paediatric KMT2A-rearranged acute lymphoblastic leukemia

  Whole genome sequencing data from infant (<1 y) paediatric (< 18 y) KMT2A-rearranged acute lymphoblastic leukemias. Dataset includes fastq and BAM files from diagnostic and remission (control) samples of 3 patients. Dataset consists of two experiments depending on library preparation method; "Experiment 1" is made using normal WGS library preparation (pcr+), and "Experiment 2" is made using pcr-free library preparation method.

  Dataset EGAD50000001568