Search Results - EGA European Genome-Phenome Archive

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

Whole Genome - HAP-1 clones

Whole genome sequencing on HAP-1 clones wild-type or knockout for MBD4 and/or TDG. Libraries were prepared using the Kapa HyperPrep kit (Roche, 07962363001). Paired-end libraries (2 x 100 bp) were sequenced on a NovaSeq 6000 instrument (Illumina).

Dataset EGAD50000000765

Methylation Array of pre- and post-5ZA-treated head and neck cancer patients refractory to anto-PD-1 therapy

Illumina MethylationEpic Beachip data generated from tissue samples collected from head and neck cancer patients refractory to anti-PD-1 therapy, collected before and after the 5AZA treatment

Dataset EGAD00010002718

Cancer RNA-seq consisting of FASTQ single-end reads from colon cancer sample

Cancer RNA-seq consisting of FASTQ single-end reads from 1 colon-cancer individual RNA-seq was performed on illumina This dataset contains reads from a single region.

Dataset EGAD00001007949

10x Genomics Single Cell Gene Expression for SA1096BX1XB02037

10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1096 passage 1

Dataset EGAD00001009137

10x Genomics Single Cell Gene Expression for SA1053FX1XB01611

10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1053 passage 1

Dataset EGAD00001009128

10x Genomics Single Cell Gene Expression for SA1096CX1XB02039

10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1096 passage 1

Dataset EGAD00001009136

RNAseq of NSCLC and melanoma tumor xenografts treated with ADC

RNAseq of tumours derived from NSCLC (n=3 PDX; n=1 CDX) and melanoma (n=1 CDX) xenograft models treated with ADC or controls (64 samples; 189 FASTQ files).

Dataset EGAD00001006620

10x Genomics Single Cell Gene Expression for SA1051DX1XB01482

10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1051 passage 1

Dataset EGAD00001009133

WGS data of XPC-ko human small intestinal organoid cultures

Whole genome sequencing (WGS) data of human small intestinal organoid cultures, which were deleted for the XPC gene using CRISPR-Cas9. Contains WGS data of 1 clone and 1 subclone.

Dataset EGAD00001003779

RNA sequencing data pre-treatment and post-treatment for NABUCCO cohort 1

NABUCCO cohort 1 sequencing data. The dataset includes RNA sequencing pre-treatment on tumor samples (n=18) and RNA sequencing post-treatment on bladder tumor samples (n=18).

Dataset EGAD00001006855

Transcriptomic data for Human proximal tubular epithelial cell interleukin-1 receptor signalling triggers cell cycle arrest during hypoxic kidney injury

Transcriptomic data for manuscript titled: Human proximal tubular epithelial cell interleukin-1 receptor signalling triggers cell cycle arrest during hypoxic kidney injury.

Dataset EGAD00001015460

Synergy study: "Tissue resident CD8+ T cell clonal expansion in advanced triple negative breast cancer is associated with response to chemoimmunotherapy"

Dataset of the study "Synergy study: "Tissue resident CD8+ T cell clonal expansion in advanced triple negative breast cancer is associated with response to chemoimmunotherapy". This dataset contains the cellranger's gene-expression matrices for the 5 prime single-cell RNA sequencing experiments

Dataset EGAD50000000748

Comprehensive analyses of clinicopathological features and genomic mutations of combined hepatocellular-cholangiocarcinoma

Combined hepatocellular and cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer. We performed whole exome sequencing to clarify the relationship between genetic mutations and clinicopathological features in cHCC-CCA.

Study JGAS000599

Early-onset Colorectal Cancer Study TOGETHER Data Access Committee

The Early-onset Colorectal Cancer Study TOGETHER Data Access Committee (DAC) is responsible for reviewing requests to access sequencing and clinical data from the study. Access to the data is restricted to researchers with approved ethical clearance and scientifically valid research projects. Each request will be evaluated by the DAC to ensure compliance with participants’ informed consent and all applicable data protection regulations.

Dac EGAC50000000752

Data Access Committee for Genomics of bone marrow failure (BMF) and myelodysplastic syndromes (MDS)

The Data Access Committee for the genomics of bone marrow failure and myelodysplastic syndrome project is responsible for reviewing applications for access to controlled human genomic and phenotypic datasets generated as part of this project. The DAC ensures that data sharing with qualified researchers is conducted in accordance with participant consent, institutional ethical approvals, and all applicable data protection regulations.

Dac EGAC50000000754

PDAC Phenotype and Germline Genotype Data Access Committee

This Data Access Committee (DAC) is responsible for reviewing and approving requests for access to controlled phenotype metadata and derived germline FUT2/FUT3 genotype data from patients with pancreatic ductal adenocarcinoma (PDAC). Access is granted to bona fide researchers for non-commercial research purposes only, subject to approval by the DAC and in accordance with the original informed consent and applicable ethical regulations.

Dac EGAC50000000893

Rare_renal_tumours_RNA_

Rare renal tumours are by hard to classify and there is very little available information regarding prognosis and sensitivity to treatment. Here we endevour to understand their genomic properties in order to better understand their biology.

Study EGAS00001004323

Rare_renal_tumours_WGS_

Rare renal tumours are by hard to classify and there is very little available information regarding prognosis and sensitivity to treatment. Here we endevour to understand their genomic properties in order to better understand their biology.

Study EGAS00001004322

Cancer Genomics of the Kidney

The aim of CAGEKID is to carry out comprehensive detection of DNA markers for conventional (clear cell) renal carcinoma. The project includes complete analysis of somatic and constitutional DNA variation, methylation patterns and expression in a large number of constitutional/tumor pairs. CAGEKID is a part of the International Cancer Genome Consortium, ICGC.

Dataset EGAD00001004018

Sperm sequencing reveals extensive positive selection in the human germline -WGS

Mutations that occur during human ageing in the cell lineages of sperm or eggs have the potential to be transmitted to offspring. In males, positive selection of driver mutations during spermatogenesis is known to increase the birth prevalence of certain developmental disorders and cancer predisposition syndromes. However, direct observation of the scope of this selection in sperm has been limited by the error rates of sequencing technologies. Using the duplex sequencing method known as NanoSeq, we sequenced bulk sperm samples from individuals aged 24 to 75 years. Our findings revealed a linear accumulation mutations per year and mutational signatures consistent with pedigree studies. Deep targeted and deep exome NanoSeq of sperm samples identified genes subject to significant positive selection in the male germline, which are linked to diverse cellular pathways including RAS/MAPK and BMP signaling. Strikingly, nearly all positively selected genes are associated with developmental or cancer predisposition disorders in children. We find that this effect drives an elevated risk of known likely disease-causing mutations in sperm across a wide age range. This implies that the disorders attributed to these genes likely exhibit elevated birth prevalence in human populations, particularly among older fathers. These findings shed light on the dynamics of germline mutations and highlight an increased disease risk for children born to fathers of advanced age.

Dataset EGAD00001015592

Sperm sequencing reveals extensive positive selection in the human germline -NanoSeq

Mutations that occur during human ageing in the cell lineages of sperm or eggs have the potential to be transmitted to offspring. In males, positive selection of driver mutations during spermatogenesis is known to increase the birth prevalence of certain developmental disorders and cancer predisposition syndromes. However, direct observation of the scope of this selection in sperm has been limited by the error rates of sequencing technologies. Using the duplex sequencing method known as NanoSeq, we sequenced bulk sperm samples from individuals aged 24 to 75 years. Our findings revealed a linear accumulation mutations per year and mutational signatures consistent with pedigree studies. Deep targeted and deep exome NanoSeq of sperm samples identified genes subject to significant positive selection in the male germline, which are linked to diverse cellular pathways including RAS/MAPK and BMP signaling. Strikingly, nearly all positively selected genes are associated with developmental or cancer predisposition disorders in children. We find that this effect drives an elevated risk of known likely disease-causing mutations in sperm across a wide age range. This implies that the disorders attributed to these genes likely exhibit elevated birth prevalence in human populations, particularly among older fathers. These findings shed light on the dynamics of germline mutations and highlight an increased disease risk for children born to fathers of advanced age.

Dataset EGAD00001015590

Atezolizumab Plus Chemotherapy With or Without Bevacizumab in Advanced Biliary Tract Cancer: Clinical and Biomarker Data From the Randomized Phase II IMbrave151 Trial

PURPOSE Biliary tract cancers (BTCs) harbor an immunosuppressed tumor microenvironment (TME) and respond poorly to PD-1/PD-L1 inhibitors. Bevacizumab (anti-VEGF) plus chemotherapy can promote anticancer immunity, augmenting response to PD-L1 inhibition. PATIENTS AND METHODS This randomized, double-blind, proof-of-concept Phase II study enrolled patients (n=162) with previously untreated advanced BTC (IMbrave151; NCT04677504). Patients were randomized 1:1 to receive 3-weekly cycles of atezolizumab (1200 mg) plus bevacizumab (15 mg/kg) or atezolizumab plus placebo until disease progression or unacceptable toxicity. All patients received cisplatin (25 mg/m2) plus gemcitabine (1000 mg/m2; CisGem) on Days 1 and 8 every 3 weeks for up to 8 cycles. Stratification of patients was by disease status, geographic region, and primary tumor location. The primary endpoint was progression-free survival (PFS). No formal hypothesis testing was performed. Exploratory correlative biomarker analysis was undertaken using transcriptome analysis (n=95) and mutation profiling (n=102) on baseline tumor samples. RESULTS Between February and September 2021, 162 patients were enrolled. Median PFS was 8.3 months in the bevacizumab arm and 7.9 months in the placebo

Study EGAS50000000387

University of Miami Study on Genetics of Autism and Related Disorders (AutismDisorders)

Autism spectrum disorders (ASD) are neurodevelopmental in origin and characterized by impairments in reciprocal social interaction and communication accompanied by restricted and repetitive patterns of interest or behavior. With improved surveillance and a broadening of the diagnostic criteria, the most recent prevalence studies suggest that ASD may affect as many as 1/166 children in the US. Treatments are limited resulting in little impact on the profound morbidity. Little is known about the etiology of ASD, but there is a strong genetic component. Genetic studies over the past decade have failed to identify definitive ASD genes, but have clearly shown that the underlying genetics are more complex than anticipated with the likelihood that multiple genes are acting independently and/or interactively. With this realization the field of ASD genetics is at a critical juncture. To move forward we must embrace new and creative paradigms to successfully dissect the genetic etiology of this disease. Specifically the current study will: Extend our ascertainment to the full range of ASD. We will increase our ASD dataset by collecting 600 families who meet clinical diagnostic criteria for the three most common ASDs: autism (AUT), Asperger disorder (ASP) and Pervasive Developmental Disorder-NOS (PDD-NOS). Phenotype assessment will be expanded to include the Social Responsiveness Scale (SRS) and detailed seizure data. Ascertainment will focus on parent-child trios. Further characterize the ASD-associated variations in the Gamma-Aminobutyric Acid (GABA) receptor (GABR) subunits. We will complete our GABR subunit variant analysis, including coding, UTR, regulatory, and conserved intronic sequence, concentrating on GABRA4 and GABRB1. We will also investigate the role of variation in other GABR subunits. All AUT specific findings will be examined in the entire ASD dataset. Identify the chromosome 19p ASD gene. Multiple genome-wide screens have provided significant evidence for linkage to Chromosome 19p and we have found evidence for significant association in the linked families within an ~3 Mb region. We will genotype 768 SNPs spaced at ~4 kb to refine the candidate region and delineate specific candidate genes for further testing. We will screen the strongest associated candidate genes for additional variants and evaluate evidence for association in the entire ASD dataset (SA5). Develop and apply the Phenotypic Homogeneity Distinction (PHD) algorithm for identification of the phenotypic signature of linkage and/or association signals. The PHD algorithm identifies phenotypic covariates that discriminate a target sample subset (e.g. the positively linked families or families carrying the associated risk allele) from its complement. The resulting PHD discriminators will define a homogeneous subset from our newly-ascertained ASD dataset to use, as a confirmatory dataset and for localization within genes under study in our other aims. Test for gene-gene interactions. To fully explain the spectrum of autism risk, we will test for interactions between genetic variations associated with ASD (such as those in GABR subunit genes). We will build upon our experience using the MDR method and extend our analyses across biological pathways using the entire ASD data set.

Study phs000436

Environmental Influences on Child Health Outcomes (ECHO) - PATHWAYS - Conditions Affecting Neurocognitive Development and Learning in Early Childhood (CANDLE)

Pregnant women are exposed daily to multiple chemical and non-chemical stressors, including air pollutants, phthalates, and psychosocial stress. The Developmental Origins of Health and Disease model (DOHaD) states that exposure to these stressors in pregnancy affects fetal development in a manner that impacts offspring health across the life course. Yet epidemiologic data in these areas is limited, particularly with U.S. samples. It is also poorly understood whether these exposures prenatally affect childhood neurodevelopment and airway health in an independent or combined manner, and likely moderators of effects, such as the sex of the child, are infrequently addressed. We propose to unify three diverse extant pregnancy cohorts: TIDES (N=717, 2010-12), GAPPS (N=1133, 2012-16), and CANDLE (N=1385, 2007-11), for a combined sample size of 3235 mother-child dyads in the PATHWAYS study. PATHWAYS will investigate how chemical (air pollutants and phthalates) and non-chemical (psychosocial stress) exposures during pregnancy are related to placental gene expression (transcriptome) and childhood neurodevelopment and airway health (at ages 4-6, 8-9, and 10-11 years). Each cohort has rich resources of prenatal data and banked specimens (urine, blood, and placenta) that will be harmonized for the PATHWAYS study and will contribute to the ECHO consortium. We will develop a national model with high spatiotemporal resolution of key air pollutants and assess urinary markers of maternal exposure. A composite measure will capture multilevel maternal psychosocial stress across pregnancy, and urinary phthalate and blood stress hormone [Corticotropin-releasing hormone (CRH)] levels in the second and third trimesters will provide individual assessment of those exposures in potential critical periods. We will characterize the placental transcriptome using RNA sequencing (RNA-Seq) and will assess neurodevelopment and airway health prospectively into middle childhood. PATHWAYS will examine how these prenatal exposures are related to the placental transcriptome and child health outcomes in main effect and interactive models, with emphasis on sex-specific associations. For both neurodevelopment and airway health, we propose to measure both phenotypic precursors of health outcomes (i.e. fluid cognition, lung function growth), which yield dimensional tests of proposed associations, as well as clinically meaningful and policy relevant outcomes (i.e. asthma, mental health). Our study is powered to assess interactive effects of chemical and non-chemical stressors and will be the first study to characterize how prenatal environmental exposures relate to placental transcriptome pathways in relation to childhood health outcomes. This represents a significant scientific advance in testing DOHaD hypotheses. Major contributions to the ECHO Consortium include: 1) the development of a state of the art national model of air pollution, 2) a large, diverse pregnancy cohort with extensive biorepositories and extant prenatal and postnatal biomarkers, placental transcriptome, psychosocial and environmental data, and 3) an experienced, interdisciplinary team that will contribute meaningfully to the ECHO program of work.The sequencing data in this submission pertains only to the 794 subjects from the CANDLE study for whom placental tissue transcriptomics was performed.

Study phs003619

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