22938 results for "ega"

in 38.23 milliseconds.

• Targeted capture ctDNA Library CRCQV42Run050-12

  Targeted capture ctDNA Library CRCQV42Run050-12 from patient PBC0001135, buffy coat sample

  Dataset EGAD00001010785

• Targeted capture ctDNA Library CRCQV42Run050-13

  Targeted capture ctDNA Library CRCQV42Run050-13 from patient PBC0002625, buffy coat sample

  Dataset EGAD00001010786

• Targeted capture ctDNA Library CRCQV42Run050-14

  Targeted capture ctDNA Library CRCQV42Run050-14 from patient PBC0001065, buffy coat sample

  Dataset EGAD00001010787

• Targeted capture ctDNA Library CRCQV42Run050-21

  Targeted capture ctDNA Library CRCQV42Run050-21 from patient PBC0001828, buffy coat sample

  Dataset EGAD00001010794

• Targeted capture ctDNA Library CRCQV42Run050-4

  Targeted capture ctDNA Library CRCQV42Run050-4 from patient PBC0001776, buffy coat sample

  Dataset EGAD00001010798

• Targeted capture ctDNA Library CRCQV42Run050-5

  Targeted capture ctDNA Library CRCQV42Run050-5 from patient PBC0001242, buffy coat sample

  Dataset EGAD00001010799

• Targeted capture ctDNA Library CRCQV42Run050-6

  Targeted capture ctDNA Library CRCQV42Run050-6 from patient PBC0001666, buffy coat sample

  Dataset EGAD00001010800

• Targeted capture ctDNA Library CRCQV42Run050-17

  Targeted capture ctDNA Library CRCQV42Run050-17 from patient PBC0001329, buffy coat sample

  Dataset EGAD00001010790

• Targeted capture ctDNA Library CRCQV42Run42-12

  Targeted capture ctDNA Library CRCQV42Run42-12 from patient PBC0001375, plasma baseline sample

  Dataset EGAD00001010824

• Targeted capture ctDNA Library CRCQV42Run051-22

  Targeted capture ctDNA Library CRCQV42Run051-22 from patient PBC0002989, buffy coat sample

  Dataset EGAD00001010815

• Targeted capture ctDNA Library CRCQV42Run051-23

  Targeted capture ctDNA Library CRCQV42Run051-23 from patient PBC0001516, buffy coat sample

  Dataset EGAD00001010816

• TXT_CD138N_15

  TPM matrices of counts from RNA sequencing (RNAseq) from longitudinal CD138(–) BM fractions.

  Dataset EGAD00001011145

• Reference epigenome Pancreas-Islet08 WGBS data generated from KEP study

  Pancreas-Islet08 WGBS paired end data

  Dataset EGAD00001007887

• Reference epigenome Islet-derived_MSC08 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007904

• Targeted capture ctDNA Library CRCQV42Run047-6

  Targeted capture ctDNA Library CRCQV42Run047-6 from patient PBC0001255, buffy coat sample

  Dataset EGAD00001010738

• Ovarian cancer EV RNA-seq

  Ovarian cancer EV RNA-seq

  Dataset EGAD00001008972

• Targeted capture ctDNA Library CRCQV34Run002-12

  Targeted capture ctDNA Library CRCQV34Run002-12 from patient PBC0002459, plasma baseline sample

  Dataset EGAD00001010329

• Targeted capture ctDNA Library CRCQV34Run002-13

  Targeted capture ctDNA Library CRCQV34Run002-13 from patient PBC0002595, plasma baseline sample

  Dataset EGAD00001010330

• Targeted capture ctDNA Library CRCQV34Run002-10

  Targeted capture ctDNA Library CRCQV34Run002-10 from patient PBC0002108, plasma baseline sample

  Dataset EGAD00001010327

• Targeted capture ctDNA Library CRCQV34Run002-11

  Targeted capture ctDNA Library CRCQV34Run002-11 from patient PBC0002429, plasma baseline sample

  Dataset EGAD00001010328

• Targeted capture ctDNA Library CRCQV34Run002-6

  Targeted capture ctDNA Library CRCQV34Run002-6 from patient PBC0001627, plasma baseline sample

  Dataset EGAD00001010339

• Targeted capture ctDNA Library CRCQV34Run007-22

  Targeted capture ctDNA Library CRCQV34Run007-22 from patient PBC0002816, buffy coat sample

  Dataset EGAD00001010359

• Targeted capture ctDNA Library CRCQV42Run016-11

  Targeted capture ctDNA Library CRCQV42Run016-11 from patient PBC0001467, plasma baseline sample

  Dataset EGAD00001010399

• Targeted capture ctDNA Library CRCQV42Run021-13

  Targeted capture ctDNA Library CRCQV42Run021-13 from patient PBC0001299, plasma baseline sample

  Dataset EGAD00001010436

• Targeted capture ctDNA Library CRCQV42Run026-4

  Targeted capture ctDNA Library CRCQV42Run026-4 from patient PBC0002872, plasma baseline sample

  Dataset EGAD00001010496

• Targeted capture ctDNA Library CRCQV42Run029-23

  Targeted capture ctDNA Library CRCQV42Run029-23 from patient PBC0004350, plasma baseline sample

  Dataset EGAD00001010516

• Targeted capture ctDNA Library CRCQV42Run033-13

  Targeted capture ctDNA Library CRCQV42Run033-13 from patient PBC0002429, buffy coat sample

  Dataset EGAD00001010552

• Targeted capture ctDNA Library CRCQV42Run033-14

  Targeted capture ctDNA Library CRCQV42Run033-14 from patient PBC0002459, buffy coat sample

  Dataset EGAD00001010553

• Targeted capture ctDNA Library CRCQV42Run034-14

  Targeted capture ctDNA Library CRCQV42Run034-14 from patient PBC0004414, buffy coat sample

  Dataset EGAD00001010568

• Targeted capture ctDNA Library CRCQV42Run039-6

  Targeted capture ctDNA Library CRCQV42Run039-6 from patient PBC0002853, plasma baseline sample

  Dataset EGAD00001010622

• Targeted capture ctDNA Library CRCQV42Run039-8

  Targeted capture ctDNA Library CRCQV42Run039-8 from patient PBC0003334, plasma baseline sample

  Dataset EGAD00001010624

• Targeted capture ctDNA Library CRCQV42Run048-24

  Targeted capture ctDNA Library CRCQV42Run048-24 from patient PBC0005531, buffy coat sample

  Dataset EGAD00001010756

• Targeted capture ctDNA Library CRCQV42Run049-22

  Targeted capture ctDNA Library CRCQV42Run049-22 from patient PBC0002481, buffy coat sample

  Dataset EGAD00001010775

• Targeted capture ctDNA Library CRCQV42Run049-23

  Targeted capture ctDNA Library CRCQV42Run049-23 from patient PBC0001409, buffy coat sample

  Dataset EGAD00001010776

• Targeted capture ctDNA Library CRCQV42Run049-4

  Targeted capture ctDNA Library CRCQV42Run049-4 from patient PBC0004537, buffy coat sample

  Dataset EGAD00001010777

• Targeted capture ctDNA Library CRCQV42Run050-19

  Targeted capture ctDNA Library CRCQV42Run050-19 from patient PBC0001527, buffy coat sample

  Dataset EGAD00001010792

• Targeted capture ctDNA Library CRCQV42Run051-19

  Targeted capture ctDNA Library CRCQV42Run051-19 from patient PBC0002294, buffy coat sample

  Dataset EGAD00001010812

• SNF_RNAseq_CD138n_20f

  TPM matrices of counts from RNA sequencing (RNAseq) from baseline CD138(–) BM fractions.

  Dataset EGAD00001011142

• The Ashkenazi Genome Consortium: Phase I

  Whole genome, high coverage, sequencing of 128 Ashkenazi Jewish controls

  Dataset EGAD00001000781

• Van Hippel-Lindau syndrome multi-region exome sequencing of two patients

  Van Hippel-Lindau syndrome multi-region exome sequencing of two patients

  Dataset EGAD00001000973

• FFPE Normal Panel V3 Cancer Panel

  FFPE normal panel generation for use with V3 cancer panel 0618521

  Dataset EGAD00001001122

• Cloning of the breakpoint of a novel translocation associated with T-acute lymphoblastic leukaemia

  Single case of T-ALL carrying t(4;6), a novel translocation.

  Dataset EGAD00001002193

• WXS files for Zhang PanNBL

  WXS files for Zhang PanNBL paper titled "Pan-neuroblastoma analysis reveals age- and signature-associated driver alterations"

  Dataset EGAD00001005484

• 108 samples liver cancer and normal controls (54 pairs), whole exome sequencing

  liver cancer paired with normal controls, viral and non-viral origin

  Dataset EGAD00001006005

• Additional Neuroblastoma whole genome sequencing data

  Additional Neuroblastoma whole genome sequencing data

  Dataset EGAD00001006196

• Single Cell RNAseq of PBMC from renal cancer patients

  Single Cell RNAseq of PBMC from renal cancer patients

  Dataset EGAD00001006175

• H3Africa NEEDI SNPs and INDELs

  SNPs and INDELs of novel hereditary neurological disease genes in Mali using Beckman 8800, ABI 3730/3730xl.

  Dataset EGAD00001006295

• Strelka somatic snv vcfs for EGAS00001004572

  Bulk Strelka somatic snv vcfs from tumour-normal analysis in EGAS00001004572

  Dataset EGAD00001006907

• STAG1 and STAG2-ChIP-seq in RAD21-mutant adult acute myeloid leukemia

  STAG1 and STAG2-ChIP-seq in RAD21-mutant adult acute myeloid leukemia

  Dataset EGAD00001015361

• RNA-Seq files for SJOS study

  RNA-Seq files for SJOS study

  Dataset EGAD00001003154

• 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

• iNHL WXS Data Commitee

  The iNHL WXS Data Commitee is responsible for managing access to datasets submitted to the European Genome-phenome Archive (EGA). Our committee ensures that access requests are evaluated in compliance with ethical guidelines, regulatory requirements, and the conditions specified by data submitters. Researchers requesting access to these datasets must provide a clear and valid scientific purpose, demonstrate affiliation with a recognized institution, and agree to the terms of use outlined in the Data Access Agreement. The iNHL WXS Data Commitee works to uphold data privacy and security while facilitating responsible data sharing to advance scientific discovery. For further information on our policies or the application process, please contact Hauke Busch (hauke.busch@uni-luebeck.de).

  Dac EGAC50000000488

• DFCI Gynecological Oncology Data Access Committee

  This Data Access Committee is responsible for reviewing requests to access controlled human genomic data generated as part of the RESOLVE clinical trial at DFCI. The committee ensures that access complies with participant consent, institutional policies, and applicable data protection regulations (e.g., GDPR). Data access decisions are made by a multidisciplinary panel including clinical researchers, bioinformaticians, and data governance officers from Dana-Farber Cancer Institute. The committee reviews each access request to determine whether the proposed research aligns with the study’s consent terms and the data access policy. If approved, access is granted via the European Genome-phenome Archive (EGA) in accordance with EGA’s data use and security guidelines.

  Dac EGAC50000000712

• GenomeEUtwin Data Access Committee

  Access to data generated by the GenomeEUtwin is available by emailing application to the data access committee and will be granted to qualified investigators for appropriate use.

  Dac EGAC00000000007

• Dr. Fatima Mechta-Grigoriou, PhD, DR1 Head of Stress and Cancer Laboratory Deputy Director Genetic and Biology of Cancer Dpt Institut Curie - Inserm U830

  Dac EGAC00001000581

• Concepción Aguilera belongs to the Department of Biochemistry and Molecular Biology II from the University of Granada and is head of the molecular biology research group BIONIT (CTS-461).

  Dac EGAC00001000779

• EGAD00010000602

  WTCCC2 Reading and Mathematics ability (RM) samples from UK using the Affymetrix 6.0 array

  Dataset EGAD00010000602

• EGAD00010000270

  Metabric breast cancer samples (Images)

  Dataset EGAD00010000270

• Mutational Screening of Human Acute Myleloid Leukaemia Samples

  Mutational Screening of Human Acute Myleloid Leukaemia Samples

  Dataset EGAD00001000054

• Integrative Oncogenomics of Multiple Myeloma

  Integrative Oncogenomics of Multiple Myeloma

  Dataset EGAD00001000074

• Splenic Marginal Zone Lymphoma with villous lymphocytes exome sequencing

  Splenic Marginal Zone Lymphoma with villous lymphocytes exome sequencing

  Dataset EGAD00001000081

• UK10K_NEURO_MUIR REL-2012-01-13

  UK10K_NEURO_MUIR REL-2012-01-13

  Dataset EGAD00001000170

• Evaluation of PCR library method on whole genome samples

  Evaluation of PCR library method on whole genome samples

  Dataset EGAD00001000252

• Data set for Whole-genome-Sequencing of adult medulloblastoma

  Data set for Whole-genome-Sequencing of adult medulloblastoma

  Dataset EGAD00001000275

• ICGC prostate cancer whole genome sequencing

  ICGC prostate cancer whole genome sequencing

  Dataset EGAD00001000306

• UK10K_NEURO_MUIR REL-2012-11-27

  UK10K_NEURO_MUIR REL-2012-11-27

  Dataset EGAD00001000322

• Myeloproliferative Neoplasms (MPN) Exome Validation Study

  Experiments using targeted pulldown methods will be sequenced to validate findings in the exomes of patients with Myeloproliferative Neoplasms (MPN).

  Dataset EGAD00001000619

• Human Epidermis scRNA

  Single cell RNA-seq analysis of human skin.

  Dataset EGAD00010001620

• Imputed_genetics

  Imputed genetic data for INTERVAL proteomics cohort

  Dataset EGAD00010001544

• Normalised mRNA expressions

  Quantile-normalised and batch corrected

  Dataset EGAD00010001561

• Russian_TB_6.0

  Russian Tuberculosis samples using Affymetrix 6.0

  Dataset EGAD00010000901

• APP-G-1

  Array data from 4778 individuals from general population of rural Uganda

  Dataset EGAD00010000965

• BMI EWAS summary stats

  Summary statistics for a multi-cohort epigenome-wide association study. This includes summary statistics (effect-size, standard error, p-value) for 470,000 methylation markers.

  Dataset EGAD00010001029

• mtDNA-Roma

  mtDNA variant positions vcf files for 86 human samples

  Dataset EGAD00010001844

• Gene Expression Profiling for study EGAS00001004165

  Gene Expression Profiling from 44 Mantle Cell Lymphoma cases

  Dataset EGAD00010001842

• UAE genotype dataset

  This dataset contains PLINK processed (PED and MAP) genotype data, from 1000 samples from the UAE using the Illumina Omni5 Exome Bead Chip

  Dataset EGAD00010001886

• methylation_risk_reducing_surgery_breast

  Raw methylation data from breast tissue collected during risk-reducing surgery in BRCA1/2 mutation carriers.

  Dataset EGAD00010002075

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D1, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008038

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D3, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008057

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D1, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008037

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D2, replicate 1, glucose concentration 5mM

  Dataset EGAD00001008047

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D2, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008048

• SNParray_ASD

  ASD samples using Illumina Infinium Human Core-24 BeadChip platform

  Dataset EGAD00010002229

• Average_5hmC_DIFF_Roadmap

  Average hydroxymethylation difference 12 months vs 0 months at Roadmap Epigenomics chromatin state annotations from different cell types. Data from 8 individuals.

  Dataset EGAD00010002415

• cfDNA exercise - methylation array

  methylation array data of cfDNA from plasma samples of individuals after running a marathon, a 40 min run and resting

  Dataset EGAD00010002497

• Sequencing data for oesophageal and related samples - OACs release 2 (RNA)

  Dataset EGAD00001003839

• Sequencing data for oesophageal and related samples - OACs release 1 (RNA)

  Dataset EGAD00001002259

• Illumina BeadArray SNP arrays

  SNP measurement. Illumina BeadArray SNP arrays for the study "Molecular characteristics in Burkitt lymphoma over age groups"

  Dataset EGAD00010002137

• GM

  metabolite levels measured by general metabolomics (Boston, USA) (the data is raw abundance. Mapping was applied on log10 transformed data)

  Dataset EGAD00010002148

• INMUNGEN_CoV2_genotype

  Cohort: Raw genotype files for INMUNGEN_CoV2 cohort. Genotype Chip: HumanCore Exome Chip (Illumina) and Axiom Spanish Biobank Array (Thermofisher) genomic build: b37

  Dataset EGAD00010002174

• Transcriptome of human CD4+ T-lymphyocyte (memory-T-cells - untreated) from donor D3, replicate 2, glucose concentration 5mM

  Dataset EGAD00001008058

• Array data for oesophageal and related samples – kno_paper_methyl_release

  Array data for oesophageal and related samples – kno_paper_methyl_release

  Dataset EGAD00010001795

• Knoll et al Identification of drug candidates targeting monocyte reprogramming in people living with HIV scRNAseq

  single-cell RNAseq dataset

  Dataset EGAD50000000067

• Converging and evolving immuno-genomic routes towards immune escape in breast cancer

  Data access committe for the project: Converging and evolving immuno-genomic routes towards immune escape in breast cancer

  Dac EGAC50000000074

• Spatial transcriptomic data of HGSOC patients before and after treatment

  Spatial transcriptomic data of HGSOC patients before and after treatment

  Dataset EGAD50000000190

• Data Access Committee of Biobank Lab, Department of Oncobiology and Epigenetics, University of Lodz

  Dac EGAC50000000080

• Long-read dataset

  Long-read transcriptomic data of control and FECD exp positive CECs

  Dataset EGAD50000000435

• Data used in "BaTwa" populations from Zambia retain ancestry of past hunter-gatherer groups

  BaTwa genotype data using the H3Africa array.

  Dataset EGAD50000000555

• TwinsUK_Whole_Skin_DNA_Methylation_Microarray_Data

  Whole-skin DNA Methylation profiled using Illumina Infinium HumanMethylation450 BeadChip Arrays. Methylation was quantified in beta values, which were normalised using the regRCPqn algorithm.

  Dataset EGAD00010002671

• RNA-seq data of CRC-PDX tumors

  We obtained bulk RNAseq data of CRC-PDX tumors and performed molecular subtype classification.

  Dataset EGAD50000000850