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• Germline Sequencing for Aggressive Prostate Carcinoma

  This study aims to elucidate the genetic determinants of susceptibility to aggressive prostate carcinoma. To this end, germ line whole exome sequence has been generated from patients with aggressive prostate cancer. Whole genome sequence for a representative subset of tumors from patients whose cancer was resistant to primary therapy is included as well. This deep genomic interrogation will allow the identification of rare, functionally disruptive variants that may play a role in disease progession and response to treatment.

  Study phs000661

• NHLBI TOPMed: Trans-Omics for Precision Medicine (TOPMed) Whole Genome Sequencing Project: Cardiovascular Health Study

  Participants from the Cardiovascular Health Study (CHS), a large population-based longitudinal cohort study (phs000287), have been included in the TOPMed project. Whole genome sequencing will be performed to contribute to multiple analyses, including cardiovascular disease risk factors, subclinical disease measures, the occurrence of myocardial infarction (MI) and stroke, and analyses of venous thromboembolism (VTE).Comprehensive phenotypic and pedigree data for study participants are available through dbGaP phs000287.

  Study phs001368

• Identification of Novel Therapeutic Targets for Calcific Aortic Valve Stenosis Using Integrative Genomics

  In order to identify novel therapeutic targets for calcific aortic valve stenosis, we have generated a large transcriptomic dataset from RNA sequencing of human aortic valve tissues from 500 individuals undergoing aortic valve replacement or heart transplant at the Institut universitaire de cardiologie et de pneumologie de Québec – Université Laval (QUEBEC-CAVS-RNA). This dataset represents a unique resource to understand the molecular mechanisms underlying aortic valve diseases.

  Study phs003541

• Transcriptome-Wide Association Study (TWAS) to Identify Susceptibility Genes for Colorectal Cancer

  This study included genotype data from 420 study participants of East Asian descent, along with RNA-seq (n=300) and small RNA seq data (n=400) generated from their colorectal tissue samples. We used the transcriptome data and genotype data from these individuals to build a prediction model for each coding gene and long non-coding RNA, and perform a Transcriptome-Wide Association Study (TWAS) of colorectal cancer in East Asian populations.

  Study phs002813

• An efficient procedure for the recovery of DNA from formalin-fixed paraffin-embedded tissue sections

  We investigated the effects of different incubation temperatures and duration, and formalin scavenger (Tris) concentrations on the recovery of DNA from FFPE tissue sections. We optimized the conditions for preserving the integrity and maximizing the yield of DNA from FFPE tissue sections. Furthermore, DNA obtained using the optimized procedure was compared with that obtained using a commonly used extraction procedure with regard to their performance in whole-genome and targeted gene panel sequencing.

  Study JGAS000520

• DNA methylation database for gynecological cancer detection, classification and assay development

  DNA methylation changes are associated with cancer the best predictor of the presence of cancer. In order to detect and classify gynecological cancer, we performed MeD-seq to generate DNA methylation profiles of vulvar, cervical, endometrial, fallopian tube and ovarian cancers and their associated healthy tissues. DNA methylation profiles were used to find general cancer, regional cancer and specific cancer biomarkers that can be used for cancer detection, classification and treatment monitoring.

  Study EGAS50000000417

• Targeted Validation Samples

  The dataset consists of targeted sequencing data obtained from an independent cohort of 11 patients to validate discriminative DMRs (differentially methylated regions) for Acute coronary syndrome (ACS) subtypes. The cohort includes 2 healthy subjects, 4 STEMI (ST-segment elevation myocardial infarction), 3 NSTEMI (non-ST-segment elevation myocardial infarction), and 2 UA (unstable angina) patients. The sequencing panel targeted 18,831 CpGs for analysis, reaching at least 5 reads coverage for 75% of the targeted CpGs in each sample. The sequencing was performed using the NEBNext Enzymatic Methyl-seq Module and the Nonacus Cell3TMTarget: Library Preparation kit, followed by probe hybridization, capture enrichment, and sequencing on the Novaseq 6000 platform, generating 400 million reads. The dataset is in raw fastq format.

  Dataset EGAD00001010934

• Variant calls for "Quality of Whole Genome Sequencing from Blood versus Saliva Derived DNA in Cardiac Patients"

  Paired blood and saliva samples from five unrelated individuals were directly compared for quality of whole genome sequencing. Two (Sample Pairs 1 and 2) were female probands diagnosed with tetralogy of Fallot, a type of congenital heart disease, and three (Sample Pairs 3, 4 and 5) were male probands diagnosed with hypertrophic cardiomyopathy. WGS was performed using Illumina HiSeq X to a target average coverage depth of 30x and a read length of 150 bp. The resulting reads were not filtered for minimum quality in order to avoid losing possible contaminant reads. Sequencing read alignment was done using Isaac Aligner to human genome build hg19. Short variant i.e. single-nucleotide variant (SNV) and small insertion-deletion (indel) calling was performed using Isaac Variant Caller with default parameters.

  Dataset EGAD00001005772

• Hofer et al., A specific subpopulation of cancer-associated fibroblasts promotes resistance to chemotherapy in triple-negative breast cancer by upregulating G0S2 protein

  Chemotherapy is the standard of care for patients with triple-negative breast cancer (TNBC), but the mechanisms of chemoresistance remain poorly understood. Here, we investigated the role of cancer associated fibroblasts (CAFs) in the chemotherapy response of TNBC, by combining in vivo analyses of TNBC patients with ex vivo cancer modeling, using tumor-on-chip (ToC) technology. Transcriptomic analysis revealed that the proportion of a specific CAF population, the extracellular matrix-producing myofibroblasts (ECM-myCAFs), is reduced after chemotherapy in chemo-sensitive, but not in chemo-resistant TNBC patients. In ToC devices, primary ECM-myCAFs promoted TNBC cell survival under Doxorubicin and Paclitaxel treatment. The mechanism of chemoresistance was elucidated by using single-cell RNA sequencing, advanced cell imaging and functional assays: ECM-myCAFs upregulate the G0/G1 switch 2 (G0S2) protein in TNBC cells, via activation of SRC family kinases. Altogether, our work identifies G0S2 as a new player in CAF mediated chemotherapy resistance in TNBC.

  Study EGAS50000000886

• Submitting array based metadata

  Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

  Documentation submission/metadata/submission/array