Search Results - EGA European Genome-Phenome Archive

Environmental Arsenic and Diabetes Mellitus (Chihuahua Cohort)

The Chihuahua Cohort was established as part of a larger NIEHS-funded project that used laboratory models (tissue culture and mice) and human subjects to examine the association between exposure to arsenic and diabetes, including identification of mechanisms underlying this association. All procedures involving human subjects were approved by institutional review boards at the University of North Carolina at Chapel Hill and Cinvestav-IPN (Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, Mexico). All participants provided signed informed consent. A total of 1,160 adults (≥ 18 years old) with a minimum 5-year uninterrupted residency in the Chihuahua study area were recruited between 2008 and 2012. Samples of drinking water were obtained from the participants' households. Data on residency, occupation, drinking-water sources and use, smoking, alcohol consumption, and medical history were recorded by a study questionnaire. Spot urine and fasting venous blood were collected during medical examination that included an oral glucose tolerance test (OGTT) with blood drawn 2 hr after a 75-g glucose dose. Plasma from both fasting and 2-hr blood samples was collected. Body mass index (BMI) of each participant was calculated from body weight and height. BMI cutoffs of ≥ 25.0, ≥ 30, and < 18.5 kg/m2 were used to define overweight, obese, and underweight individuals, respectively. Participants' waist circumference was also measured. Blood pressure was assessed using a manual sphygmomanometer. Three measurements were taken at intervals of at least 1 min, with a 5-min rest before obtaining the first reading; the mean of the last two measurements was used. The concentrations of arsenic in drinking water and arsenic species in urine were measured by hydride generation-atomic absorption spectrometry. The limit of detection for As in water as well as for As species in urine was 0.01 µg As/L. Creatinine concentration in urine was determined using an established colorimetric assay. DNA was isolated from blood for genotyping analysis.The following measures were taken: glucose concentration in fasting blood (FBG) and in blood collected 2 hours into OGTT (2HBG); concentrations of triglycerides, HDL and total cholesterol (TC) in fasting plasma; and LDL was calculated using the Friedewald equation. Diabetes was classified by FPG ≥ 126 mg/dL, 2HPG ≥ 200 mg/dL, or self-reported diabetes diagnosis or medication use (WHO/International Diabetes Federation 2006). Prediabetes was defined as the absence of diabetes with FPG ≥ 110 mg/ dL or 2HPG ≥ 140 mg/dL. Elevated fasting levels of each lipid were defined as plasma TG ≥ 150 mg/dL, TC ≥ 200 mg/dL, and LDL ≥ 130 mg/dL. Fasting HDL < 40 mg/dL in men and < 50 mg/dL in women were designated as low. Hypertension was defined by systolic blood pressure (SBP) > 140 mm Hg, diastolic blood pressure (DBP) > 90 mmHg, or self-reported use of anti-hypertensive medication. Genotyping analysis, which will be available in version 2 of this study, was carried out in a subset of ~600 subjects with focus on enzymes involved in the metabolism of arsenic, including AS3MT, GSTs.All data collected during the medical exams or generated by the above-described chemical and biochemical analysis of water, urine and blood/plasma, as well as results of the genotyping analysis are included in dbGaP. Data generated by metabolomics and epigenetic analyses in small subsets of the Chihuahua cohort are not included, but are available in published articles.

Study phs002139

Rapid Acceleration of Diagnostics - Underserved Populations (RADx-UP): COVID-19 Testing and Prevention in Correctional Settings

This study will work with diverse correctional facilities across MN, RI, WA, and FL to characterize the incidence of COVID-19 disease progression and related outcomes. The goal is to identify barriers and facilitators to develop strategies to improve testing in these under-served populations by: Increasing the reach, access, uptake, and impact of COVID-19 testing among incarcerated people and staff in prison and jails. Individuals in congregate settings have been disproportionately affected by COVID-19, with correctional settings accounting for 39 of the 50 largest cluster outbreaks in the country to date. Schema of Correctional Facilities: Correctional facilities are amplifiers of infectious diseases for structural reasons: 1) Dormitory-style living and overcrowded conditions make social distancing nearly impossible 2) They have a focus primarily on security not health 3) Their medical departments are under-resourced and often disconnected from local public health agencies. Correctional facilities must be the focus of urgent intervention to understand the incidence of COVID-19, how it spreads in correctional facilities, and durable long-term strategies to reduce transmission, including testing and vaccines. However, our work from the COVID Prison Project shows that correctional systems have implemented a range of testing strategies, without coordination, such that synthesizing the information on COVID-19 incidence, outcomes and optimal testing strategies (including repeat testing and contact tracing) has been challenging. Incarcerated people decline testing due to concerns about confidentiality, implications for release, and fear of placement in solitary confinement. Through interviews we will identify ethical concerns and potential solutions for COVID-19 testing and vaccine strategies in correctional facilities using a community-engaged strategy. The project's work from the COVID Prison Project shows that correctional systems have implemented a range of testing strategies, without coordination, such that synthesizing the information on COVID-19 incidence, outcomes and optimal testing strategies (including repeat testing and contact tracing) has been challenging. Incarcerated people decline testing due to concerns about confidentiality, implications for release, and fear of placement in solitary confinement. Through interviews we will Identify ethical concerns and potential solutions for COVID-19 testing and vaccine strategies in correctional facilities using a community-engaged strategy. Goals: 1. Identify ethical concerns and potential solutions for COVID-19 testing and vaccine strategies in correctional facilities using a community-engaged strategy. 2. Characterize incidence of COVID-19, disease progression, and related-outcomes and effectiveness of testing and contact tracing in correctional facilities. Participant Selection: a. Potential subjects must be currently incarcerated at key facilities in Washington, Rhode Island, Minnesota, and Florida. These facilities are Everglades Correctional Institution and Homestead Correctional Institution in Florida, Shakopee and Stillwater correctional facilities in Minnesota, the Rhode Island Department of Corrections, and Yakima County Jail in Washington. b. Participating correctional facilities in Florida, Minnesota, Rhode Island, and Washington will provide de-identified data on approximately 16,859 incarcerated adults and correctional staff. Data sources include deidentified data from medical records, administrative data, and policy documents pertaining to COVID-19. c. 3 series of focus groups (6, 12, 18 months) x 3 distinct groups (incarcerated people, medical providers, and correctional leaders) x 4 sites=36 total focus groups over the study period. d. Will utilize 74,812 COVID-19 tests to test and re-test a total of 16,859 incarcerated people and correctional staff for active infection over a six-month period in the FL, MN, and RI state prison systems and the Yakima County Jail in WA.

Study phs003361

The Genomics and Randomized Trials Network (GARNET) Vitamin Intervention Stroke Prevention (VISP) Trial

The VISP trial (PI Jim Toole, M.D., Wake Forest University School of Medicine) was a multi-center, double-blind, randomized, controlled clinical trial that enrolled patients aged 35 or older with homocysteine (Hcy) levels above the 25th percentile at screening and a non-disabling cerebral infarction (NDCI) within 120 days of randomization (Toole, 2002, PMID: 12417369). The trial was designed to determine if daily intake of a multivitamin tablet with high dose folic acid, vitamin B6 and vitamin B12 reduced recurrent cerebral infarction (primary endpoint), and nonfatal myocardial infarction (MI) or mortality (secondary endpoints). Subjects were randomly assigned to receive daily doses of the high-dose formulation (n=1,827), containing 25mg pyridoxine (B6), 0.4mg cobalamin (B12), and 2.5mg folic acid; or the low-dose formulation (n=1,853), containing 200mcg pyridoxine, 6mcg cobalamin and 20mcg folic acid. Enrollment in VISP began in August 1997, and was completed in December 2001, with 3,680 participants enrolled. Within the trial, 2,164 participants from 46 clinic sites provided DNA and agreed for it to be shared for use in a genetic subset study of VISP. This study is part of Genomics and Randomized Trials Network (GARNET), funded by the National Human Genome Research Institute (NHGRI). The overarching goal is to identify novel genetic factors that contribute to stroke through large-scale genome-wide association studies of treatment response in randomized clinical trials. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were performed at the GARNET Coordinating Center at the University of Washington. The data of the VISP trial have been released to dbGaP users in several segments: Version 1 (phs000343.v1.p1), consisted of n=4 phenotype datasets, and all raw, cleaned and imputed genotype data. Version 2 (phs000343.v2.p1) included n=14 additional phenotype datasets (plus pedigree, consent, and sample-mapping data), and increased the available data to a total of n=970 phenotype variables. Version 3 (phs000343.v3.p1) included all n=36 phenotype datasets (plus pedigree, consent, and sample-mapping data), and increased the available data to a total of n=1918 phenotype variables.Version 4 (phs000343.v4.p1), the current release, includes all previous phenotypic datasets, includes one new phenotypic dataset for the untargeted metabolomics data of 50 individuals, includes DNA methylation profiles for n=180 individuals, and sequencing data for n=179 individuals.

Study phs000343

NHLBI TOPMed: The Cleveland Family Study (CFS)

The Cleveland Family Study (CFS) is one cohort involved in the WGS project. The CFS was designed to provide fundamental epidemiological data on genetic and non-genetic risk factors for sleep disordered breathing (SDB). In brief, the CFS is a family-based study that enrolled a total of 2284 individuals from 361 families between 1990 and 2006. The sample was selected by identifying affected probands who had laboratory diagnosed obstructive sleep apnea. All first degree relatives, spouses and available second degree relatives of affected probands were studied. In addition, during the first 5 study years, neighborhood control families were identified through a neighborhood proband, and his/her spouses and first degree relatives. Each exam, occurring at approximately 4 year intervals, included new enrollment as well as follow up exams for previously enrolled subjects. For the first three visits, data, including an overnight sleep study, were collected in the participants' homes while the last visit occurred in a general clinical research center (GCRC). Phenotypic characterization of the entire cohort included overnight sleep apnea studies, blood pressure, spirometry, anthropometry and questionnaires. The GCRC exam (n=735 selected individuals) included more comprehensive phenotype data on a focused subsample of the larger cohort, to permit linking SDB phenotypes with cardio-metabolic phenotypes, with an interest in identifying genetic loci that are associated with these related phenotypes. In this last round of data collection, a subset of 735 individuals was selected based on expected genetic informativity by choosing pedigrees where siblings had extremes of the apnea hypopnea index (AHI). Participants underwent detailed phenotyping including laboratory polysomnography (PSG), ECG, spirometry, nasal and oral acoustic reflectometry, vigilance testing, and blood and urine collection before and after sleep and after an oral glucose tolerance test. A wide range of biochemical measures of inflammation and metabolism were assayed by a Core Laboratory at the University of Vermont. 994 individuals were sequenced as part of TOPMed Phase 1, including 507 African-Americans and 487 European-Americans. Among the sequenced individuals, 156 were probands with diagnosed sleep apnea, an additional 706 were members of families with probands, and 132 were from neighborhood control families. 298 individuals were sequenced as part of TOPMed Phase 3.5, including 169 African-Americans and 129 European-Americans. Among the newly sequenced individuals, 33 were probands with diagnosed sleep apnea, an additional 214 were members of families with probands, and 51 were from neighborhood control families. Please note: Phenotype and pedigree data are available through "NHLBI Cleveland Family Study (CFS) Candidate Gene Association Resource (CARe)", phs000284.

Study phs000954

NEI CIDR Methylation Profiling of Primary Open Angle Glaucoma in NEIGHBOR Samples

This is a case-control study of Primary Open Angle Glaucoma (POAG). POAG is a progressive optic neuropathy that eventually leads to blindness. More than 30 million people worldwide have Primary Open Angle Glaucoma (POAG), of which greater than 3 million are blind. Gene expression changes in the retina have been observed for POAG. Several recent studies, including the NEIGHBOR consortium (NEI Glaucoma Human genetic collaboration) have used GWAS to identify correlative regions of the genome. Despite this, the genetic basis of Glaucoma is not well understood. Epigenetic variation may account for low heritability and environmental effects on human disease. Despite the significant advances being made in understanding the role of epigenetics in gene regulation in other fields, little is known about the relationship between DNA methylation patterns, retinal gene expression, and retinal disease. The goal of this study is to identify differentially methylated regions in the peripheral blood of patients with POAG. The case identification is as per the NEIGHBOR description: Cases and controls were recruited from ophthalmology clinics and were examined by ophthalmologists. For cases the clinical exam included intraocular pressure measurements, optic nerve assessment and visual field evaluation. Controls had no family history of glaucoma, normal intraocular pressure and normal optic nerves. POAG and control samples come from two sources: Duke University and Massachusetts Eye and Ear Infirmary (MEEI). These samples were collected and genotyped as part of the NEIGHBOR study. From the 219 POAG cases contributed to NEIGHBOR by Duke, 75 samples were selected for exome sequencing by Dr. Terry Gaasterland (UCSD) as part of an ARRA grant. These samples were selected to mirror the distribution of the entire set of Duke patients for variables such as age, cup-to-disc ratio, CCT, and IOP. From these 75 patients, 43 were selected for our study and an additional seven patients chosen at random, for a total 50 patients from Duke. From the 219 Duke controls, 50 were selected to match the age (at the time of blood draw) and sex of the 50 Duke POAG patients. From the NEIGHBOR control samples from MEEI, 27 were selected who best matched the age (at blood draw), time since sample collection and sex of the POAG probands from GLAUGEN. Additional individual-level phenotype and genotype data may be obtained through the authorized access portal of phs000238 (Neighbor - Glaucoma GWAS Study).

Study phs000458

Heart Failure Network: Functional Impact of GLP-1 for Heart Failure Treatment (HFN FIGHT-BioLINCC)

Objectives To test whether therapy with a GLP-1 agonist improves clinical stability following hospitalization for acute heart failure. Background Heart failure was the leading cause of hospitalization in the United States with more than 4 million admissions per year from 2003–2009. Abnormal cardiac metabolism contributes to the pathophysiology of advanced heart failure with reduced left ventricular ejection fraction (LVEF). As heart failure progresses, these abnormalities become more pronounced and are observed in both patients with and without type 2 diabetes. At this time of this study, there was no heart failure therapy that targeted these metabolic abnormalities; however, in earlier clinical studies, glucagon-like peptide 1 (GLP-1) agonists showed cardioprotective effects in patients with advanced heart failure, irrespective of type 2 diabetes status. Glucagon-like peptide 1 is an endogenous incretin hormone that improves insulin sensitivity with minimal risk of hypoglycemia. This study was created to determine if use of GLP-1 agonists improved clinical stability in recently hospitalized patients with acute heart failure and reduced LVEF. Participants There were 300 participants. Design Participants were identified by hospital admission records and were enrolled during either the last 24 hours of his or her hospitalization for heart failure or the 2-week interval after the hospitalization. Baseline evaluations were conducted, which included echocardiographic measures, the 6-minute walk test, the Kansas City Cardiomyopathy Questionnaire (KCCQ), and blood tests. After the baseline evaluation, patients were randomized in a 1:1 ratio to receive either the GLP-1 agonist liraglutide or placebo as a daily subcutaneous injection. The protocol involved titration of study drug dosage as tolerated every 14 days from 0.6 mg/d to 1.2 mg/d to 1.8 mg/d during the first 30 days of the trial (as tolerated). Follow-up testing was performed at 30-, 90-, and 180-day study visits. The primary end point was a global rank score in which all participants, regardless of treatment assignment, were ranked across 3 hierarchical tiers: time to death, time to rehospitalization for heart failure, and time-averaged proportional change in N-terminal pro-B-type natriuretic peptide (NT-proBNP) level from baseline to 180 days. Conclusions The GLP-1 agonist liraglutide did not improve post-hospitalization clinical stability in patients with advanced heart failure and reduced LVEF. There was no significant between-group difference in the global rank scores and furthermore, there were no significant differences detected in any of the secondary endpoints.

Study phs003542

Single cell genomic variation induced by mutational processes in cancer

How cell-to-cell copy number alterations that underpin genomic instability in human cancers drive genomic and phenotypic variation, and consequently cancer evolution, remains understudied. Here, applying scaled single cell whole genome sequencing to wildtype, TP53-deficient and TP53/BRCA1/2-deficient mammary epithelial cells (n=13,818 genomes) and primary triple negative breast (TNBC) and high grade serous ovarian cancers (HGSC) (n=22,057 genomes), we identified three distinct ‘foreground’ mutational patterns defined by cell-to-cell copy number and structural variation properties occurring in the ‘background’ of cancer-associated genomic instability. Cell and clone-specific high level amplifications (HLAMPs), parallel allele-specific copy number alterations and copy number segment length variation (serrate structural variations) exhibited measurable phenotypic and evolutionary consequences. Clone-specific HLAMPs in known oncogenes were highly prevalent in fold back inversion (FBI)-bearing tumors and associated with increased clone-to-clone phenotypic variation in gene expression inferred from matched single cell RNASeq. Parallel allele-specific alterations were linked with phylogenetically determined evolutionary diversity and clone-specific mono-allelic expression. Serrate patterns present across all clones were increased in FBI tumors, most prominently on a tetraploid background and were highly correlated with increased genomic diversity of cellular populations. Taken together, we show that cell-to-cell structural-copy number variation significantly impacts the phenotypes and evolutionary diversity of TNBC and HGSC, revealing previously hidden genomic states of cancer cells.

Study EGAS00001006343

HCA_Thymus_Disease_DiGeorge_Transplant_RNA

Single cell transcriptomics study of thymic transplant biopsies Allogeneic thymus transplantation is the only curative therapy for complete DiGeorge Syndrome (cDGS), a rare severe primary immunodeficiency characterised by athymia. GOSH is one of only two centres worldwide to offer this treatment. Despite a lack of major histocompatibility complex (MHC)-matching between donor and host, transplanted thymus becomes repopulated by recipient bone marrow derived precursor cells and supports development of functional T-cells. The mechanisms underlying thymopoiesis in this context are poorly understood, but over time we observe reconstitution of T-cell immunity, with the ability to produce host naïve T-cells showing a broad T-cell receptor (TCR) repertoire and to generate MHC-restricted T-cell proliferative responses. Although lifesaving, the achieved immunological reconstitution is typically not complete with circulating T-cell numbers usually remaining below the age related normal ranges. Additionally, we observe persistence of donor-derived T-cells of unknown clinical significance. To gain more insight into the mechanisms by which MHC-mismatched transplanted thymus supports T-cell development with self-tolerance, as well as into the basis of suboptimal T-cell immunity, we now aim to investigate immune reconstitution after thymus transplantation in further detail by using single-cell transcriptomics, applied to thymic transplant biopsies and peripheral blood samples collected during standard post-transplant patient care. By identifying which lineages of host- derived cells repopulate the thymic tissue after transplantation, we will be able to address the role of MHC in positive and negative T-cell selection during T-cell differentiation. We will also be able to clarify the exact ontogeny of the persistent donor T-cells, as well as their possible role. Understanding the mechanisms of action of HLA-mismatched transplanted thymus will contribute to treatment optimisation. Additionally, our research provides a unique opportunity to further investigate key immunological concepts, such as tolerance and autoimmunity, challenging existing paradigms in thymus immunology. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

Study EGAS00001004025

Lindsey E. Jones et al. Patient-derived cells from recurrent tumors that model the evolution of IDH-mutant glioma. Neuro-Oncology Advances, 2020. We established robust, versatile, and well-characterized Patient Derived Cells (PDC) of an IDH1-mutant astrocytoma and an IDH1-mutant oligodendroglioma that represents defined evolutionary stages of chemotherapy-induced hypermutation. The PDCs retain tumor subtype defining features over time as well as classic hallmarks of cancer, including anchorage independent growth and cell immortality. The integrated phylogenies composed of PDCs,single-cell-derived PDCs, patient-derived xenografts, and corresponding spatiotemporal tumor tissue samples also provide new insight into the clonality, evolutionary pattern, and immense mutational load of hypermutated IDH mutant gliomas.

IDH-mutant lower-grade gliomas can undergo malignant progression via temozolomide-driven hypermutation. Patient-derived cells (PDC) that model the genetically distinct hypermutated (HM) tumor subgroup are generally lacking, and few if any human brain tumor cell models are from defined evolutionary time points. Here, we characterize multiple PDC derived from independent surgical specimens of IDH1-mutant recurrences, including an ATRX and TP53-mutant astrocytoma and a 1p/19q co-deleted and TERT promoter-mutant oligodendroglioma. We determined the evolutionary time points represented by each PDC using exome sequencing and phylogenetic reconstruction, comparing the PDC and single cell clones of the PDC (scPDC) to multiple spatiotemporal tumor tissue samples, PDC-derived xenografts (PDX) and patient-matched blood. The tumor samples exhibited TMZ-induced mutagenesis and a branching pattern of evolution. We found clear evidence of two fully independent founder HM clones in the tumor tissue that are faithfully represented by independent PDC. The PDC, scPDC and PDX also shared the mutagenesis signature and represent the mid and later evolutionary time points of their corresponding tumors. The PDC maintained the tumor subtype-defining features over many passages, including heterozygosity of the IDH1 R132H mutation, production of 2-hydroxyglutarate (2-HG), and subtype-specific telomere maintenance mechanisms. The PDC from both tumors exhibited anchorage-independent growth in soft agar. The oligodendroglioma PDC formed infiltrative intracranial tumors with characteristic oligodendroglioma histology, initially with a long period to tumor formation. We conclude that the PDC, scPDC and PDX faithfully model the heterogeneous clonal origins of the corresponding tumor tissue. The multilevel analysis also provides new insight into the intratumoral heterogeneity and vast mutational load of HM glioma. The PDC from multiple evolutionary time points presented in the context of full clinical timelines may be useful to model evolution and intratumoral heterogeneity, important sources of therapeutic failure.

Study EGAS00001003992

Submitter Portal

Submitter Portal The Submitter Portal does not support array submissions! Please refer to the array documentationAny new objects registered in the Submitter Portal will not be available for use in an array submission For further information please check our Submission FAQs, submission quickguide as well as submission terms! The metadata submission process can be difficult and time-consuming. For this reason, the EGA has developed the Submitter Portal, a tool that was created to offer a simplified and user-friendly method of registering metadata. Our portal provides features that are intended to make the input of metadata easier, ensuring that your data is registered correctly and effectively. Our page is divided into logical sections and includes a helpful video instruction to further assist you as you complete the submission process. With the Submitter Portal, you can rest assured that the submission of your data is in good hands. Submitter Portal Index Previous steps Access to Submitter Portal Start a Submission Take The Tour Tutorial Previous steps Create your EGA account To submit data, you first need to create your EGA user. Then, once your account has been verified by the Help Desk team, you will be able to request a submitter role and send the EGA Data Processing Agreement (DPA). In the case you already have an EGA account, or an ega-box (submission account), you can skip this step. Upload your files Please note that all your files must be encrypted using the Crypt4GH tool before upload them.As soon as you are assigned with a submitter role and added your public key to your profile, you will be able to connect to the EGA inbox and upload your files. Understand the EGA metadata schema It's crucial to comprehend the EGA metadata schema, a set of rules that specify how data is organised, described, and shared inside the EGA, in order to get the most out of this resource. Learn all about EGA metadata schema! Register your DAC and policy There are two objects in the EGA metadata schema that are registered in a separate portal. All DACs and policy objects are registered using the DAC Portal, a tool developed by EGA to help data controllers manage their data stored at the EGA. You can find relevant information in the DAC Portal Guide. Access to Submitter Portal 1. Request Submitter Role Once you have created your EGA user, request a Submitter Role and complete relevant information such as submission type and size. 2. Signature of the Data Processing Agreement The Data Processing Agreement (DPA) signed by CRG and EMBL-EBI (Joint Data Processors for the EGA) can be downloaded to the submitter for its signature. Further information related to the DPA can be in the Privacy Notice section. The DPA should be signed by a legal representative of the submitter with power of attorney/appointment duly authorised to sign contracts on behalf of the institution. The submitter will return a fully-executed copy of the DPA to us. Please note that due to the high volume of submissions to EGA the DPA is non-negotiable. Once you have completed all the information and attached the DPA signed, send your Submitter request and the EGA Helpdesk Team will validate it and send a confirmation to start submitting your metadata. Start a Submission The steps below will show you how to start a submission and are accompanied with a tutorial and the Take the tour of Submitter Portal. 1. Login Go to the Submitter Portal interface and log in using your EGA account credentials. 2. Create a submission The Submitter Portal interface will show you any already existing open submission. To start a new submission click on “Create a Submission”.In this phase you have the option to add any collaborator(s) as well as their permissions (“Read only” or “Read & Write”). If you want to invite all the collaborators from a previous submission, you can add them by selecting said submission.In the event that the submission has been closed, you will need to reopen it. To do this, you will need to modify one of the objects in the submission, for example, copy and paste the title. Once the submission is open, you can add a collaborator. Further information can be found in the Submission Tour 3. Create Study Click on add Study and complete the form with the relevant information. The fields with an asterisk (*) are mandatory. Further information can be found in the Submission Tour4. Create Sample Sample registration can be done either individually or in batch by using a template. Batch upload: download the template and complete it with information about the sequencing samples. Upload the samples.csv and the sample will be created.Add single sample: complete the mandatory fields with the metadata information of your sample. Further information can be found in the Submission Tour5. Create Experiment Register an experiment with information about the sequencing methods, protocols and machines. Experiments generate the connection between samples and study. Further information can be found in the Submission Tour6. Create Run Samples, experiments and files are linked through runs. To register a run, you will be asked to select the appropriate experiment and the file format. Run registration can be done either individually or in batch by using a template. Batch upload: download the template and complete it with the sample - file linkage. Upload the run.csv and the runs will register.As the INBOX is a directory, bear in mind to include the file path (/). Example: /filename.c4gh. Add single run: select the file format, sample and experiment from the drop box. For re-using submitted samples, please use the EGA accession ID (EGANXXX). Please note that files must be uploaded in your inbox before linking your files and samples. Further information can be found in the Submission Tour7. Create Analysis Analysis should be only used for BAM/BAI or CRAM/CRAI pair, VCF and phenotype linkage to samples. The analysis is an EGA specific metadata object that links samples to files. This object also stores some metadata about your experiments, such as the experiment type, genome reference, or the platform used. **If only BAM or CRAM alignment files are submitted but not the original unaligned FASTQ files, then please make sure that the BAM or CRAM files also contain the unaligned reads. This is critical to enable primary re-analysis and re-alignment of the dataset using new tools or future genome assemblies.** Start by selecting the samples to be linked to the file, and then populate the required attribute fields. Please be aware of the existence of mandatory fields that must be populated.When populating the chromosome field (mandatory), please select the chromosomes from the checkbox. Please take in consideration that EGA allows for the re-use of any registered metadata. Therefore, a previously registered study or samples can be referenced for the analysis in the current data submission.Further information can be found in the Submission Tour 8. Create Dataset Dataset contains the collection of runs/analysis data files subjected to controlled access by being associated with a Policy. Complete the mandatory fields such as title, description, type and policy. Please, take in consideration that DAC and Policy must be registered in advance in the DAC Portal. Similar to other metadata objects, you can re-use previous runs or analyses, using their EGA accessions IDs (runs - EGAR, analyses - EGAZ). Further information can be found in the Submission Tour 9. Finalise submission Once the files are ingested please delete them from your SFTP INBOX before finalising your submission. This will prevent the files from reappearing in your Submitter Portal Inbox. Once you have completed your submission, click on the Finalise button, and schedule an estimated release date. Afterwards, your submission will be sent to Helpdesk for approval. Please note that once the Helpdesk has approved your submission, you will receive the identifiers for your data. See the Frequently Asked Questions (FAQ) for more information. 10. Update closed submission If you want to append more file to a closed submission, you will need to re-open the submission where the dataset was registered. Find the Submission: go to the Submitter Portal Menu -> Datasets section and find the submission id (EGA0XXX). Reopen the submission where the dataset is located: to reopen a submission, you must edit any object within the submission. For example, you can copy and paste the title. Take The Tour The Submitter Portal Take The Tour is a visual guidance of all the steps described previously about how to start a submission. Tutorial In this tutorial we will show you how to use the Submitter Portal to register your metadata. Please note the video focuses on the run-based submission (for raw files - fastq - and aligned data - BAM/CRAM) and analysis-based submission (for your BAM/BAI pairs, variation -VCF - and phenotype files).

Documentation submission/metadata/submission/sequencing-phenotype/submitter-portal

14970 results for "cancer studies using rna-seq"

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