Search Results - EGA European Genome-Phenome Archive

Coenzyme Q10 (CoQ) in Huntington's Disease (HD) (2CARE)

This is a multi-center, randomized, double-blind, placebo-controlled trial of CoQ involving 609 ambulatory subjects with HD who are not requiring skilled care or institutionalization. Eligible subjects were randomized to receive either CoQ 2400 mg/day or matching placebo. Subjects were followed prospectively and systematically for 60 months of double-blind observation. The primary outcome variable is a combination of time to death (for subjects who die) and the change from baseline to Month 60 in Total Functional Capacity (TFC) score (for subjects who survive). The primary hypothesis of the study is that chronic treatment of early-stage HD subjects with high-dosage CoQ will slow the progressive functional decline of HD.

Study phs001065

Genetics of diffuse large B-cell lymphoma in Japan

Diffuse large B-cell lymphoma (DLBCL) is a common hematological malignancy. Treatment with standard chemotherapy results in long-term remission in approximately 60% of patients. Whole-exome sequencing studies of large DLBCL cohorts revealed the biological diversity of tumors and facilitated the identification of genetic subtypes based on genetic alterations. Despite the progress facilitated by NGS, genetic classification is difficult to apply in clinical practice because of the complexity of the clustering algorithm. Furthermore, only a few reports included large Asian cohorts. Thus, a genetic overview of Japanese patients with DLBCL is still needed. Thus, in this study, we aimed to establish the clinical application of our simplified genetic classification algorithm in Japanese DLBCL.

Study JGAS000307

Star2xml: metadata converter into XML

Star2xml: metadata converter into XML Welcome to the landing page for star2xml - a tool developed by the European Genome-phenome Archive (EGA) that enables users to convert metadata into XML format that is compliant with EGA's programmatic submission system. For starters, if you are not sure what metadata is in this context, you may want to explore our brief metadata introduction. . Star2xml is useful for users who have already submitted their data to the EGA and want to also submit their metadata programmatically without having to create the XML files manually. The need for these XMLs containing metadata is explained in the EGA's metadata schemas and programmatic submission guidelines. In summary, star2xml allows for users to deposit metadata in a tabular format (e.g. excel spreadsheet) and transform it into a compliant XML format. This saves time in large submissions, where the sheer size of metadata makes it difficult to manage. For smaller submissions, it is recommended to use EGA's Submitter Portal (SP) instead. The installation of the tool is straightforward, and it contains detailed documentation on how to use it in its GitHub repository . While star2xml can help users convert metadata formats, it does not validate the resulting metadata against EGA's schemas. Therefore, after creating your XMLs, you should still validate your metadata before submitting it to EGA, as described in the documentation for programmatic metadata validation.

Documentation tools/star2xml

EGA submission account terms

Submission account terms Each EGA submission account has a dedicated submission box. To ensure continued service to all submitters EGA submission boxes should not exceed 8Tb in size, and cannot exceed 12Tb. Those that are near to or exceeding this value will be monitored on a weekly basis by the helpdesk team and the submitter will be contacted and guidance provided accordingly on completing their metadata registration. You will be able to register your metadata associated with the files in your submission account via EGA Submitter Portal, Webin or the EGA REST API. EGA also requests that metadata is submitted within 60 days of upload data to a submission box. When we detect files that are over 60 days old in a submission box we will notify the submitter requesting that metadata be submitted or the files removed from the submission box. It is important to note that data is not archived at the EGA until the metadata describing the files has been associated and the archival process has completed. It is only when associated metadata has been submitted that the archival process into EGA is fully triggered. EGA removes files from the submission box once they have been successfully archived, so no action is required by a submitter to remove successfully archived files. Files over 90 days old will be deleted from the submission box unless EGA Helpdesk has been contacted and confirmed an exemption with the submitter.

Documentation submission/terms

ICGC Oesophageal adenocarcinoma - pilot samples

The oesophageal project will focus on adenocarcinoma which is increasing in incidence in the UK and other developed countries and has a very poor outcome. The primary aims of this project are to deeply sequence tumour and normal genomic DNA (including the precursor condition Barrett's oesophagus when material is available) to provide a comprehensive catalogue of somatic mutations. This will be achieved through a UK-wide network of hospitals involved in a research collaboration called the OCCAMS consortium. The goal of this project is to use high quality clinical material with accurately annotated clinic-pathological, treatment and outcome data.

Study EGAS00001000559

ESGI_Identification_of_novel_genes_and_mechanisms_leading_to_Primary_Ciliary_Dyskinesia

Approximately 80% of clinically clearly diagnosed patients suffering from primary ciliary dyskinesia (PCD) cannot be assigned to a specific gene defect. Despite extensive research on PCD and despite the increasing number of PCD genes and knowledge about their sites of action as e.g structural component or cytoplasmic pre-assembly factor, the biology of motile cilia and the pathomechanism leading to PCD is largely unknown. The aim of this study is to identify novel PCD related genes and processes relevant for motile cilia function.We will perform exome sequencing, aiming on the analysis of family trios. In these families, the diagnosis of PCD is secured, but the underlying gene defects has so far not been identified.

Study EGAS00001000523

ICGC Oesophageal adenocarcinoma - Barrett's samples

The oesophageal project will focus on adenocarcinoma which is increasing in incidence in the UK and other developed countries and has a very poor outcome. The primary aims of this project are to deeply sequence tumour and normal genomic DNA (including the precursor condition Barrett’s oesophagus when material is available) to provide a comprehensive catalogue of somatic mutations. This will be achieved through a UK-wide network of hospitals involved in a research collaboration called the OCCAMS consortium. The goal of this project is to use high quality clinical material with accurately annotated clinic-pathological, treatment and outcome data.

Study EGAS00001000726

Deregulation of DUX4 and ERG in acute lymphoblastic leukemia

Chromosomal rearrangements deregulating hematopoietic transcription factors are common leukemia-initiating lesions in acute lymphoblastic leukemia (ALL). Here, we show that deregulation of the homeobox transcription factor gene DUX4 and the ETS transcription factor gene ERG are hallmarks of a subtype of B-progenitor ALL with a distinct gene expression profile that comprises up to 7% of B-ALL. DUX4 rearrangement and overexpression is present in all cases, and is accompanied by profound transcriptional deregulation of ERG, frequent ERG deletion and expression of a truncated ERG isoform, ERGalt. ERGalt utilizes a non-canonical first exon that is bound by DUX4 and induces its expression....

Study EGAS00001001923

GoDARTS T2D-GENES Exome Sequencing Study is a subset of the ~52,000 Type 2 diabetes exome sequencing project.

The GoDARTS T2D-GENES Exome sequencing study is a subset of a larger Type 2 Diabetes Exome Sequencing project. This effort is a collaboration of six consortia with various funding mechanisms that have joined together to investigate genetic variants for type 2 diabetes (T2D) using the largest T2D case/control sample set compiled to date and includes samples from: T2D-GENES, GoT2D, ESP, SIGMA T2D, LuCAMP, and ProDIGY. This data generated from the Genetics of Diabetes and Audit Research Tayside Study (GoDARTS) cohort was part of the T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples) consortium, which is a NIDDK-funded international research consortium that seeks to identify genetic variants for T2D through multiethnic sequencing studies. Sequencing for the GoDARTS study was performed at the Broad Institute using Illumina Rapid Capture on Illumina HiSeq machines.

Study EGAS00001002937

Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

Dataset EGAD50000000226

COLO-829/COLO-829BL

COLO-829 is a publicly available immortal cancer cell line and COLO-829BL is a lymphoblastoid cell line derived from the same patient

Dataset EGAD00000000055

UNC Tumor Donation Program Set 2021

The goal of this study is to study gene expression patterns in primary, metastatic tumors, and normal tissues.

Study phs002429

California Pacific Medical Center Research Breast Health Cohort

Description of Cohort: The California Pacific Medical Center (CPMC) Breast Health Cohort is a cohort study based at CPMC and is linked to the San Francisco Mammography Registry, one of the sites of the NCI-funded Breast Cancer Screening Consortium (U01CA063740). CPMC is a community hospital in San Francisco, which has one of the highest volumes for mammography in San Francisco. Between September 2004 and June 2007, >90,000 mammograms were performed at CPMC. The CPMC breast health cohort collects demographic and risk factor data on women receiving mammography through participation in the San Francisco Mammography Registry, as part of the Breast Cancer Screening Consortium (U01CA063740). The SFMR database collects information from all sources, including a questionnaire on demographic and risk factor information, the clinical results of the breast examination, the measures of breast density by Dr. John Shepherd and the women who agreed to donate a blood sample. By merging these various sources of information we have very efficiently developed a large sample of women who have donated blood and have had a measure of mamographic density. Blood Collection: Dr. Steve Cummings is leading an effort to collect and archive blood samples from women who are receiving mammography screening. All women who are sent for a screening mammogram at CPMC are considered eligible. Since the cohort began collecting blood samples in July 2004 until June 2007, samples have been collected from over 11,000 women. Measurement of Breast Density: Dr. John Shepherd is currently measuring breast density in a large fraction of the cohort using an automated approach with single X-ray absorptiometry. Dr. Shepherd has established a link with the CPMC mammography center that allows him to collect routine digital mammography information. Using the data from the mammogram, Dr. Shepherd and his group have developed the single X-ray absorptiometry (SXA) technique for measuring density which is described in more detail below. The table demonstrates the distribution of demographic variables and some breast cancer risk factors of women who donated blood and had a breast density measurement in the CPMC breast health cohort. Nearly 80% of the participants are Caucasian and most of the women are post-menopausal with a median age of ~52. Since it will be difficult to accrue a large enough sample from each ethnic group, our study will focus only on Caucasian women. Table: Demographic variables, reproductive history and family history of breast cancer among 2962 women participating in the CPMC cohort study who contributed blood samples between 1994-1997. Variable Median/Percentage Age (Median/IQR) 52 (46-59) Ethnicity Caucasian/White 0.76 Asian/Pacific Islander 0.141 Hispanic 0.029 Mixed Race/Ethnicity 0.039 African American/Black 0.022 American Indian 0.001 Other 0.009 First degree relative with breast cancer 0.17 Age at first birth Nulliparous 0.39 Age<20 0.043 Age>40 0.032 Age<30, ≥20 0.251 Age>30, ≤40 0.282 Measurement of Breast Density in Cohort: Measurement of breast density is accomplished using an automated technique for all mammograms obtained by Dr. Shepherd using Single X-Ray absorptiometry (SXA). SXA measurement of breast density is done on approximately 30% of all screening mammograms. Below we describe the method for measurement of breast density by SXA by Dr. Shepherd's group and its validation and association with breast cancer. As we demonstrate below, breast density, as measured by SXA, is an automated, highly reproducible measure of the density of breast tissue and is associated with breast cancer risk. SXA for Quantifying Breast Density: Single x-ray absorptiometry (SXA) was initially developed for measuring bone density. SXA can determine the fraction of each of two densities simultaneously using the fact the sample is a constant thickness, the thickness in known, and the total attenuation is known. In applying this technique to breast density, we assume a two compartment model: fat and non-fatty (fibroglandular tissue). We use a reference material composed of various concentrations of two materials: one which is the same density as fat and another which is the same density as fibroglandular tissue. The reference material (phantom) is placed in the X-ray field with each mammogram. We have been able to implement this in a way that is unintrusive to the patient and technologist at CPMC. Assuming this two-compartment model and a constant known breast thickness, we can then calculate the percent density at any region of the breast based on the assumption that % pixel grey-scale is proportion to the mass fractions of breast fat and lean tissue. If reference materials (a phantom) of fat and fibroglandular tissue are imaged with the patient's breast and the reference materials have the same thickness as the patient's breast, then the breast's grey-scale values can be converted to fat/fibroglandular mass fractions by interpolating between those two references. The total percent density is found by averaging the volume fraction over all breast pixels. The phantom being used for breast density assessment at CPMC began to be used in September 2004. The phantom does not have to be manipulated by the technologist and stays attached on the mammography device during standard craniocaudal (CC) views. Thus it creates minimal to no interference with the clinical mammogram. Reproducibility of breast density measures: Traditional measures of mammographic density require some human interpretation. A human reader outlines the area perceived to be dense and a computer then calculates the percent area outlined as a percent of the entire image. Thus, while traditional mammographic density is associated with breast cancer risk, it has some limitations. In a study by Drs. Shepherd, Kerlikowske, et al., the correlation coefficient (Pearson's R) between different readers was 0.8-0.9. In contrast to the traditional mammographic density measurement, the SXA measurement is fully automated and, therefore, the reproducibility of the measurement is higher. Dr. Shepherd and colleagues have performed a replication study of SXA as a measurement of breast density. They have estimated the correlation coefficient of the SXA measurement of breast density to be >0.98. Thus, as expected for an automated measure, SXA is a highly reproducible measure of mammographic breast density. Drs. Shepherd and Kerlikowske have recently analyzed the association between breast cancer risk and breast density as measured by SXA (Shepherd et al., Cancer Epi Biomarkers and Prev, 2011, PMID: 21610220). They found that women in the highest quintile of % volumetric density had an odds ratio of 4.1 (95% CI: 2.3 - 7.2) for breast cancer risk compared to women in the lowest quintile of volumetric density. Thus volumetric density appears to be a highly reproducible, automated measure of breast cancer.

Study phs000395

Genomic sequencing of Ewing's Sarcoma

The Ewing sarcoma family of tumors (EFT) is a group of malignant small round blue cell tumors that arise in bone or soft tissue. Ewing sarcoma (ES) is the second most common type of primary bone tumor to affect children and adolescents and accounts for 2.9% of all childhood cancers. This data set reports whole-genome sequencing of 6 primary Ewing's Sarcoma samples and matching germline samples.

Study phs000768

Dana-Farber Cancer Institute (DFCI) Brown Lab CLL Sequencing Study

Chronic Lymphocytic Leukemia (CLL) is a hematological malignancy characterized by accumulation of CD5+CD19+ B cells in peripheral lymphoid organs, bone marrow and blood. In this study we analyze matched whole genome sequencing and RNA-seq data from CLL patients to characterize the mutational processes operative throughout tumor evolution. Source of tumor and normal DNA used is patient PBMC or B cells and saliva respectively.

Study phs000879

Clinical and Genetic Analysis of Retinopathy of Prematurity (ROP)

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide and is treated based on the presence of symptoms of "plus disease", a dilation and tortuosity of retinal vessels. This project aims to identify the genetic and clinical underpinnings of the disease by comparing the germline variants of ~1,400 premature babies. 100 samples have been selected for WES and all have been genotyped.

Study phs002047

CLDN14 mutation identifed from Japanese sensorineural hearing loss patient.

The CLDN14 gene, encoding claudin 14, is reported to be one of the rare causes of non-syndromic autosomal dominant sensorineural hearing loss. The detailed clinical characteristics of CLDN14 associated hearing loss is still remain unclear. In the present study, we performed CLDN14 mutation analysis using massively parallel DNA sequencing (MPS) to reveal the clinical characteristics of CLDN14-related hearing loss in a large hearing loss population.

Study JGAS000191

Benchmarking V(D)J Repertoire Reconstruction: Bulk RNA-Seq vs PCR-Based RepSeq Validated by SMRT Sequencing

The aim of this study is to benchmark methods for immunoglobulin repertoire profiling using human B-cell samples. We compare targeted VDJ mRNA sequencing and repertoire reconstruction from bulk RNA-seq data to PCR-independent full-length transcript sequencing using Single-Molecule Real-Time technology. The objective is to assess methodological biases and establish a robust reference framework for immunoglobulin repertoire analysis.

Study EGAS50000001541

ATAC_SEQ_MAIN___PHASE_1

This is a study to test ATAC-seq protocols. CD4+ and CD8+ cells have been obtained from three different anatomical compartments. We aim to assay open-chromatin regions across these cells and perform comparative analyses.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 EGAS00001000947

Spontaneous mutations in the single TTN gene represent high tumor mutation burden

Tumor mutation burden (TMB) is an emerging biomarker, whose calculation requires targeted sequencing of many genes. We investigated if the measurement of mutation counts within a single gene is representative of TMB. Whole exome sequencing (WES) data from the pan-cancer cohort (n=10,224) of TCGA, and targeted sequencing (tNGS) and TTN gene sequencing from 24 colorectal cancer samples (AMC cohort) were analyzed.

Study EGAS00001004009

The Landscape of Genetic Alterations in Hepatocellular Carcinoma, 88 matched HCC tumour/normal pairs WGS belongs to ICGC LICA-CN project

Hepatocellular carcinoma (HCC) is the most common malignancy of the liver. Genomic analysis is conducted to identify genetic alterations in driver genes which are all druggable targets for cancer therapy. Whole genome shotgun sequencing (WGS) and the landscaple of somatic mutations in 88 paired samples from HCC patients including tumors and matched adjacent normal tissues using Illumina HiSeq 2000 platform were performed.

Study EGAS00001002218

Multiomics Characterization of Low-grade Serous Ovarian Carcinoma

Low-grade serous ovarian carcinoma (LGSOC) is a rare tumor subtype with high case fatality rates. There is a pressing need to develop effective treatments using newly available preclinical models for therapeutic discovery and drug evaluation. In this study, we use multiomics integration of whole exome sequencing, RNA sequencing, and mass spectrometry-based proteomics on fourteen LGSOC patient derived cell lines to elucidate novel biomarkers and therapeutic vulnerabilities.

Study EGAS00001004724

Jeju Genome Project DAC

This Data Access Committee (DAC) evaluates and manages access requests for large-scale genomic and phenotypic datasets generated as part of the Jeju Genome Project (JGP). The committee's mission is to facilitate responsible data sharing to advance precision medicine and population genetics. The DAC ensures that data access is granted only to bona fide researchers who agree to the terms of the Data Access Agreement (DAA) and whose proposed research is strictly consistent with the informed consent provided by the participants and the ethical guidelines of the project.

Dac EGAC50000000938

NHGRI Genome Integrity of iPSCs Study

The purpose of this study is to compare mutation load in induced pluripotent stem cells derived from skin fibroblasts.

Study phs001277

Kidney_tumour_DNA

This is a bulk DNA and RNA sequencing study of human renal tumours

Study EGAS00001002486

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