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• Targeted_NanoSeq___Thyroid

  Targeted NanoSeq in thyroid from a number of donors, both healthy and with pathology.

  Study EGAS00001007647

• 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

• 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

• Data Access Committee of the MyPAC clinical research group (Sorbonne Universités, UPMC Univ Paris 06, GRC n°07, Groupe de Recherche Clinique sur les Myéloproliférations Aiguës et Chroniques MyPAC)

  Dac EGAC00001000480

• Data access committee (DAC) in the Graduate School of Medical Science and Engineering (GSMSE) at Korea Advanced Institute of Science and Technology (KAIST)

  Dac EGAC00001000587

• EGAD00000000056

  WTCCC project samples from the primary biliary cirrhosis cohort

  Dataset EGAD00000000056

• Determination of the molecular nature of the Vel blood group by exome sequencing

  Determination of the molecular nature of the Vel blood group by exome sequencing

  Dataset EGAD00001000025

• Hepatitis C IL28B pooled resequencing study with 100 responders and 100 non-responders

  Hepatitis C IL28B pooled resequencing study with 100 responders and 100 non-responders

  Dataset EGAD00001000032

• "Single nucleotide variant detection in multiple foci of three prostate cancer tumors"

  "Single nucleotide variant detection in multiple foci of three prostate cancer tumors"

  Dataset EGAD00001000035

• "Copy number variant detection in multiple foci of three prostate cancer tumors"

  "Copy number variant detection in multiple foci of three prostate cancer tumors"

  Dataset EGAD00001000036

• An evaluation of different strategies for large-scale pooled sequencing study design.

  An evaluation of different strategies for large-scale pooled sequencing study design.

  Dataset EGAD00001000037

• Mutational landscapes of primary triple negative breast cancers - Exomes

  Mutational landscapes of primary triple negative breast cancers - Exomes

  Dataset EGAD00001000113

• DATA_SET_ICGC_PedBrainTumor_Medulloblastoma

  DATA_SET_ICGC_PedBrainTumor_Medulloblastoma

  Dataset EGAD00001000122

• UK10K_RARE_SIR REL-2012-01-13

  UK10K_RARE_SIR REL-2012-01-13

  Dataset EGAD00001000153

• UK10K_RARE_HYPERCHOL REL-2012-01-13

  UK10K_RARE_HYPERCHOL REL-2012-01-13

  Dataset EGAD00001000167

• UK10K_RARE_CILIOPATHIES REL-2012-01-13

  UK10K_RARE_CILIOPATHIES REL-2012-01-13

  Dataset EGAD00001000168

• UK10K_RARE_HYPERCHOL REL-2012-02-22

  UK10K_RARE_HYPERCHOL REL-2012-02-22

  Dataset EGAD00001000186

• UK10K_RARE_SIR REL-2012-02-22

  UK10K_RARE_SIR REL-2012-02-22

  Dataset EGAD00001000188

• UK10K_RARE_CILIOPATHIES REL-2012-02-22

  UK10K_RARE_CILIOPATHIES REL-2012-02-22

  Dataset EGAD00001000191

• UK10K_OBESITY_SCOOP REL-2012-02-22

  UK10K_OBESITY_SCOOP REL-2012-02-22

  Dataset EGAD00001000193

• UK10K_RARE_HYPERCHOL REL-2012-07-05

  UK10K_RARE_HYPERCHOL REL-2012-07-05

  Dataset EGAD00001000207

• UK10K_RARE_CILIOPATHIES REL-2012-07-05

  UK10K_RARE_CILIOPATHIES REL-2012-07-05

  Dataset EGAD00001000217

• UK10K_RARE_SIR REL-2012-07-05

  UK10K_RARE_SIR REL-2012-07-05

  Dataset EGAD00001000218

• UK10K_RARE_HYPERCHOL REL-2012-11-27

  UK10K_RARE_HYPERCHOL REL-2012-11-27

  Dataset EGAD00001000295

• UK10K_RARE_CILIOPATHIES REL-2012-11-27

  UK10K_RARE_CILIOPATHIES REL-2012-11-27

  Dataset EGAD00001000296

• UK10K_RARE_SIR REL-2012-11-27

  UK10K_RARE_SIR REL-2012-11-27

  Dataset EGAD00001000334

• UK10K_OBESITY_SCOOP REL-2012-11-27

  UK10K_OBESITY_SCOOP REL-2012-11-27

  Dataset EGAD00001000336

• Dataset of CageKid Tumor RNA samples

  Dataset of CageKid Tumor RNA samples

  Dataset EGAD00001000718

• Whole exome sequencing of gallbladder cancer

  The dataset includes the whole exome sequencing data from32 pairs of gallbladder caner tissues and patient-matched normal tissues.

  Dataset EGAD00001000901

• Imputation analysis file

  This data set includes the following summary level data file used for the imputation data: imputation.sv.assoc.txt: results from single variant association analysis in imputed samples

  Dataset EGAD00010001184

• OA_hip-G-1

  Genotyping of hip osteoarthritis patients who have undergone total joint replacement

  Dataset EGAD00010001292

• RNA-SEQ data from 13 CAF (Cancer Associated Fibroblast) samples sorted from fresh high-grade ovarian carcinoma

  Dataset EGAD00001003460

• HGP-clean

  Himalayan population genetic study QC filtered data

  Dataset EGAD00010001471

• Gene Expression Profiling

  Gene Expression Profiling from 21 cases: 14 CCND1-negative Mantle Cell Lymphoma and 7 CCND1-positive Mantle Cell Lymphoma

  Dataset EGAD00010001582

• Affymetrix_HTA2.0

  Primary renal cell carcinoma (RCC) and RCC metastases by Affymetrix GeneChip HTA 2.0

  Dataset EGAD00010001589

• stilts-G-1

  Human Core Exome Genotyping for 1471 samples from the STudy Into Lean and Thin Subjects (STILTS) cohort

  Dataset EGAD00010001622

• Gencode_741K

  The individuals were genotyped for the Illumina Human Omni Express Bead Chip (OmniExpress), containing 741,000 SNPs. 22 samples were excluded with more than 10% missing genotypes

  Dataset EGAD00010001640

• WTCCC2_BS

  WTCCC2 Bacteraemia Susceptibility (BS) smaples using Affymetrix 6.0

  Dataset EGAD00010000950

• APCDR AGV Baganda

  APCDR AGV Project: Array data from 100 Baganda. Raw data, intensity files and post-QC Plink files.

  Dataset EGAD00010001055

• chad

  Genotype data from Chad, Lebanon, and Yemen

  Dataset EGAD00010001103

• lebanon

  Genotype data from Chad, Lebanon, and Yemen

  Dataset EGAD00010001102

• yemen

  Genotype data from Chad, Lebanon, and Yemen

  Dataset EGAD00010001101

• Array data for oesophageal and related samples – sj_paper_methyl_barretts_release

  Array data for oesophageal and related samples – sj_paper_methyl_barretts_release

  Dataset EGAD00010001838

• Whole-genome and transcriptome sequencing data of an undifferentiated gastric spindle cell sarcoma

  Dataset EGAD00001006014

• GWG_Dataset

  Patients with T1DM genotyped on Illumina HiScan using Illumina Infinium OmniExpress Exome-8 v1.4 arrays

  Dataset EGAD00010001874

• DLBCL gene expression

  DLBCL gene expression data using 133.plus.2 Affymetrix array

  Dataset EGAD00010001976

• PE-META-CENTRAL_ASIA-MATERNAL

  Fixed effect meta-analysis summary statistics combining GWAS of maternal preeclampsia cases and controls from Central Asia (Kazakhstan and Uzbekistan).

  Dataset EGAD00010001985

• BRCA-DS1 Buccal sample methylation data

  Raw methylation data from buccal samples.

  Dataset EGAD00010002231

• SAFIR02_Oncoscan

  Genomics to select patients with metastatic breast cancer for targeted therapy (microarray_oncoscan)

  Dataset EGAD00010002241

• Myeloma methylation

  Myeloma methylation data

  Dataset EGAD00010002259

• genotype500FG

  Genotype data from healthy Dutch individuals measured by Illumina humanOmniExpress Exome-8v1.0 SNP chip Calling by Opticall 7.0

  Dataset EGAD00010002149

• NIHR BioResource DAC

  Dac EGAC00001000259

• ONGOING CHROMOSOMAL INSTABILITY ACROSS ANEUPLOID SUBTYPES OF CHILDHOOD B-CELL ACUTE LYMPHOBLASTIC LEUKEMIA ASSOCIATES WITH DISEASE PROGRESSION

  This is the DAC for the scDNA data on the c-B-ALL datasets

  Dac EGAC50000000005

• CITE-seq raw count matrix

  Raw count matrix for CITE-seq data of PBMC collected from 113 patients.

  Dataset EGAD50000000141

• Raw count matrix scRNA-seq

  Raw count matrix for scRNA-seq data of PBMC collected from 113 patients.

  Dataset EGAD50000000142

• Exome sequencing of FFPE and patient-derived cultures

  Exome sequencing of FFPE and patient-derived cultures from patients enrolled in clinical study NCT03860376

  Dataset EGAD50000000245

• TCRseq

  TCRseq data from Lauss et al Nat Comm 2024: Molecular patterns of resistance to immune checkpoint blockade in melanoma.

  Dataset EGAD50000000379

• Medulloblastoma dataset

  This dataset contains a tumor + normal DNA sequence data and tumor RNA seq data for a medulloblastoma patient.

  Dataset EGAD50000000384

• WGS data of plasma cell free DNA

  Raw paired-end whole-genome sequencing data of plasma cell free DNA on the NovaSeq 6000.

  Dataset EGAD50000000257

• Papua New Guinean Lowlanders Dataset (PNGLD)

  DAC to oversee high-coverage whole genome sequences collected to study patterns of genomic variation across the southern lowland of Papua New Guinea.

  Dac EGAC50000000032

• Cancer Genomics, ICR, cell line data

  DAC for cell line (non patient) data from the Cancer Genomics group, Division of Genetics and Epidemiology at the Institute of Cancer Research, Sutton, UK.

  Dac EGAC50000000023

• BAM/BAI dataset of Colorectal Cancer Synthetic genomes

  This dataset contains binary alignment map (BAM) data with their respective index (BAI) from four patients synthetically generated.

  Dataset EGAD50000000316

• cfRRBSonCUPs

  FFPE tissue and liquid biopsy (blood, pleural and peritoneal effusions) samples, processed with the cfRRBS (cell-free reduced representation bisulfite sequencing) protocol.

  Dataset EGAD50000000378

• Duplex sequencing

  An additional 320 buccal swab samples were subjected to error-corrected sequencing (duplex sequencing).

  Study EGAS50000000443

• DAC Ghent University - Department of Movement and Sports Sciences

  DAC of Ghent University for RNA-seq data of human skeletal muscle biopsies collected at the Department of Movement and Sports Sciences

  Dac EGAC50000000263

• Exome sequencing identifies mutation of the ribosome in T-cell acute lymphoblastic leukemia

  Exome sequencing identifies mutation of the ribosome in T-cell acute lymphoblastic leukemia

  Dataset EGAD00001000291

• RNAseq counts

  RNA-seq data from ILC samples. This dataset includes raw, unnormalized counts per gene, per sample.

  Dataset EGAD50000000826

• DAC - Department of Periodontology, University of Gothenburg, Sweden

  Data Access Committee to manage external requests. Project: RNA-seq data from soft tissue biopsies harvested from dental implant sites with peri-implantitis

  Dac EGAC50000000241

• HYPOMAP snRNASeq - Tadross data

  In-house snRNASeq dataset generated from 8 post-mortem hypothalami

  Dataset EGAD50000000997

• Access to shallow whole genome sequencing data from DETECT

  Access to this dataset is subject to aceptance of the terms and conditions. Please summarise your project and sign the following document:

  Dac EGAC50000000553

• Single cell RNA-Seq of Clear Cell Sarcoma of the Kidney

  Dataset EGAD00001007498

• Primary T-cell differentiation to T helper subtypes

  human CD4+ T cells differentiated to diverse T helper subtypes

  Study EGAS50000000818

• Biomodal sequencing of normal blood and brain samples

  Whole-genome sequencing of normal blood and brain tissue using Biomodal multi-omics technology.

  Dataset EGAD50000001691

• WGS from KeyLargo patients

  WGS from KeyLargo from paired tumor (tissue) and normal (blood) samples

  Dataset EGAD50000001744

• Michigan-Georgetown Cancer DAC

  Genomics data from cancers and/or dysplasias for the collaborations between Maureen Sartor and Nisha D'Silva at the University of Michigan and Laura Rozek at Georgetown University

  Dac EGAC50000000397

• RNAseq for 208 patients with medulloblastoma cancer (MB_COMICS cohort)

  This dataset contains 208 paired fastq files sequenced with Illumina HiSeq 2500.

  Dataset EGAD50000000600

• Short-read scRNA-seq

  10x Chromium Single Cell 3′ single-cell cDNA sequencing data for adrenal tissue sampleA and sampleB.

  Dataset EGAD50000002210

• Targeted PacBio long-read scRNA-seq

  Twist custom capture–based targeted PacBio long-read scRNA-seq datasets for human adrenal sampleA and sampleB

  Dataset EGAD50000002212

• Small RNA and MicroRNA sequencing data

  Small RNA and MicroRNA sequencing data was generated for the multiomics analysis of Eurotarget

  Dataset EGAD50000002294

• Whole_genome_sequencing_of_rhabdomyosarcoma_tumouroids

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008270

• Paediatric IBD Mosaicism

  This is a DNA sequencing study investigating genetic mechanisms underpinning childhood inflammatory bowel disease.

  Study EGAS00001002489

• Validation_for_human_early_embryonic_substitutions_

  PCR and MiSeq validation for early embryonic substitution candidates from 400 Breast cancer patients

  Study EGAS00001001218

• Whole_Exome_Sequencing_of_INTERVAL

  Whole exome sequencing of a subset of participants from the INTERVAL study.

  Study EGAS00001000825

• Blina_Tumour_project

  The aim of this study is to reconstruct the embryology of childhood tumours.

  Study EGAS00001006486

• Genentech - Cell line exome sequencing

  Genentech cell line exome study. A wide variety of cell lines were sequenced.

  Study EGAS00001002554

• input

  Input dataset of human islets. To be used in conjuction with Cohesin and Mediator ChIP-Seq datasets.

  Dataset EGAD00001005209

• Low_input_LC__WGS_

  R&amp;D project to develop low input library construction methods.

  Study EGAS00001001855

• Exploring_the_landscape_of_somatic_mutations_in_normal_tissue___POL___TGS_

  Somatic mutations (burdens and signatures) and clonal dynamics in individuals with germline DNA polymerase mutations.

  Study EGAS00001003754

• Whole_Genome_sequencing_of_rhabdomyosarcoma_tumour_tissue

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008274

• Reference epigenome Fat-adipocyte03 miRNA-Seq data generated from KEP study

  Fat-adipocyte03 miRNA-Seq single end data

  Dataset EGAD00001007190

• Brain mets discovery cohort, orthogonal validation raw sequencing files

  These are the raw sequencing files for the orthogonal validation brain tumour samples in the discovery cohort.

  Dataset EGAD00001005985

• Genomics of drug sensitivity in acute lymphoblastic leukemia

  Genomics of drug sensitivity in acute lymphoblastic leukemia

  Study EGAS00001006336

• OAC WGS

  Please see our related publication, 'Molecular profiling to predict immunochemotherapy outcomes in esophageal adenocarcinoma'

  Study EGAS00001006470

• Acute myeloid leukemia sequencing data

  This study contains tumor DNA and RNA sequencing data for 1 AML patient

  Study EGAS00001006354

• Glioma sequencing data

  This study contains sequencing data for various glioma subtypes (astrocytoma, glioblastoma, diffuse midline glioma).

  Study EGAS00001006355

• Vitiligo exome sequencing

  Exome sequencing data for 14 Vitiligo samples

  Dataset EGAD00001006371

• Targeted capture ctDNA Library CRCQV42Run021-11

  Targeted capture ctDNA Library CRCQV42Run021-11 from patient PBC0001516, TNBC FFPE sample

  Dataset EGAD00001010434

• Brain mets external validation cohort targeted panel sequencing raw sequencing files

  Raw sequencing files for the 18 (brain tumour-only) samples within the external validation cohort.

  Dataset EGAD00001005984

• Targeted capture ctDNA Library CRCQV42Run018-18

  Targeted capture ctDNA Library CRCQV42Run018-18 from patient PBC0001859, TNBC FFPE sample

  Dataset EGAD00001010417