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• Single cell RNAseq FASTQ files of three PDAC organoid lines (P28, P40, P47) using SORT-seq

  This dataset contains raw FASTQ files from single cell RNA sequencing of three patient-derived pancreatic ductal adenocarcinoma (PDAC) organoid lines (P28, P40, P47) cultured under standard conditions. For each line, single cells were FACS sorted into 384-well capture plates, with each well containing a 50nl droplet of barcoded primers. Plates were processed following an adapted SORT-seq protocol, and cDNA libraries were generated using CEL-Seq2 with TruSeq small RNA primers (Illumina). Sequencing was performed on an Illumina NextSeq500 platform using paired-end reads (read 1: 26 cycles, index read: 6 cycles, read 2: 60 cycles). Data are controlled-access and intended for single cell transcriptomic analysis of PDAC organoid heterogeneity and WNT pathway activity.

  Dataset EGAD50000002220

• Whole-exome/genome sequencing of childhood acute leukemia in Iraq

  This dataset contains 26 whole-genome sequencing (13 paired tumor and normal), 106 whole-exome sequencing (53 paired tumor and normal), and 43 targeted sequencing data. Sequencing was performed using an Illumina platform. The data are BAM files aligned to the hg19 reference genome.

  Dataset EGAD00001007873

• RNA-seq samples

  Raw FASTQ files obtained by RNA sequencing of tumor samples from patients (age 12-29) with newly diagnosed, recurrent intermediate or high-grade sarcoma.

  Dataset EGAD00001008393

• fragmentomic features of individuals with different cfDNA concentrations

  The uploaded data includes sequencing data of 862 individuals from the nasopharyngeal carcinoma (NPC) screening study. Samples from this cohort were sequenced using targeted sequencing methods for the Epstein Barr Virus (EBV) and selected autosomal DNA, but only ‘off-target’ reads were used for fragmentomic analyses. We have also performed genome-wide (non-targeted sequencing) for 1) Individuals with the highest and lowest cell-free DNA concentration (40 individuals); 2) A subsequent collection of the subjects with the highest and lowest DNA concentrations after six years (26 individuals); 3) 30 cases of pregnancy and 4) 20 patients with hepatocellular carcinoma. All sequencing data are of extracted plasma cfDNA from human subjects. The targeted sequencing samples from 862 individuals were sequenced on the NextSeq500 System (Illumina) and aligned to the EBV genome (AJ507799.2) and the human genome (hg19). The alignments were provided in bam format. The original fastq files of the remaining non-target sequencing samples were provided.

  Dataset EGAD50000000970

• Center for Common Disease Genomics [CCDG] - Cardiovascular: Genetic and Phenotypic Determinants of Blood Pressure and Other Cardiovascular Risk Factors

  This study is a part of NHGRI's Center for Common Disease Genomics, which is a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Current estimates anticipate that the CCDG program will sequence approximately 140K whole genomes and 225K whole exomes during the life of the project. The Cardiovascular Disease working group of the CCDG considered five diseases: early-onset coronary artery disease (EOCAD), stroke, atrial fibrillation, congestive heart failure and type 2 diabetes. Atrial fibrillation will affect between 6-12 million individuals in the US by 2050. AF also is associated with increased risks of stroke, dementia, heart failure, death, and high health care costs. Many risk factors for AF have been identified, including advancing age, cardiovascular disease (CVD), and CVD risk factors. However, there is little knowledge how to prevent AF. Furthermore, therapies for AF are only partially effective, and are themselves associated with substantial morbidity. Previously, heritable forms of AF have been considered rare; yet in the last decade, it has been established that AF, and in particular early-onset forms of AF, are heritable. Genome-wide association studies (GWAS) provide a powerful tool to identify common variants underlying disease risk. The AFGen Consortium currently consists of investigators from more than 25 studies with >20,000 individuals with AF and >100,000 without AF. In the latest analyses, 14 loci have been identified for AF1. Broadly, the loci implicate genes related to cardiopulmonary development, cardiac-expressed ion channels, and cell signaling molecules. Source: ccdg.rutgers.edu The GAPP study is an ongoing national population-based prospective cohort study conducted in the Principality of Liechtenstein. Between 2010 and 2014, all inhabitants of Liechtenstein aged 25-41 years old were asked to participate in the study, and 2170 could be enrolled. The aim of the study is to identify the determinants of blood pressure and other cardiovascular risk factors and their progression over time. A large number of baseline characteristics and health information as well as several blood, urinary and genetic markers were collected. The samples were sent to The Broad Institute of MIT and Harvard to generate genetic data using whole exome sequencing and whole genome genotyping (Infinium Global Screening Array); all samples serving as controls.

  Study phs002236

• 10x Genomics raw data of intestinal plasma cells

  The dataset contains raw fastq files (fastq.gz) for Chromium Single Cell 5’ gene expression (GEX), human B cell VDJ and feature barcode (CSP) sequencing from transglutaminase 2-specific and other small intestinal plasma cells isolated from four untreated celiac disease patients. Single cell 5’ gene expression, V(D)J-enriched and cell surface protein libraries were generated using Chromium single cell kits, and barcoded cDNA from a total of 5,000-10,000 cells per sample was generated using the 10x Genomics Chromium Controller. The libraries were pooled prior to sequencing on a NovaSeq 6000 instrument (Illumina) using the following configuration: read 1: 26 cycles, read 2: 89 cycles, index read 1: 8 cycles.

  Dataset EGAD50000000342

• Federated discovery and sharing of genomic data using Beacons

  The Beacon Project is a Global Alliance for Genomics & Health (GA4GH) initiative that enables genomic and clinical data sharing across federated networks. The project is working toward developing regulatory, ethics and security guidance to ensure proportionate safeguards for distribution of data according to the GA4GH-developed “Framework for Responsible Sharing of Genomic and Health-Related Data”. Within this Nature Biotechnology Correspondence a description of the Beacon protocol and how it can be used as a model for the federated discovery and sharing of genomic data is offered to the community. A Beacon is defined as a web-accessible service that can be queried for information about a specific allele. A user of a Beacon can pose queries of the form “Have you observed this nucleotide (e.g., C) at this genomic location (e.g., position 32,936,732 on chromosome 13)?” to which the Beacon responds with either “yes” or “no.” In this way, a Beacon allows allelic information of interest to be discovered by a remote searcher with no reference to a specific sample or patient, thereby mitigating privacy risks. The Beacon API (represented as a RESTful web application) provides a technical specification that a Beacon server must implement. The specification is open source and available online here. To simplify the process of lighting a Beacon, a free, open-source reference implementation of the latest specification has been developed. GA4GH is promoting different levels of data access (open, registered, and controlled) for convenience and for compatibility across its projects. Each so-called access tier has distinct visibility and requirements for authorization. For example, ‘open access’ Beacons are accessible to anonymous users of the internet, whereas ‘registered access’ Beacons are accessible to registered users (for example, bona fide researchers and clinicians) who have agreed to a set of conditions of data use. Many of the largest genomic archives, such as dbGaP, the European Genome-phenome Archive and the European Variation Archive, have provided access to variation data through Beacons for some or all of their datasets. Beacons can be interconnected through the Beacon Network, a directory and search engine for Beacons. Although individual Beacons answer the question “Have you observed this allele?”, the Beacon Network answers the question “Who has observed this allele?”. Beacons can be freely registered to the Beacon Network and can be searched independently or in aggregate with other connected Beacons.

  Blog federated-discovery-using-beacons

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 17)

  The dataset contains whole genome sequencing data of 26 high-grade serous carcinoma (HGSC) patients sequenced with Novoseq 6000. The 86 samples are either fresh frozen tumour samples or blood samples. The files provided are paired fastq files.

  Dataset EGAD50000002409

• WES of der(1;7)(q10;p10)

  This dataset contains the BAM files of WES of 26 myeloid neoplasm patients with der(1;7)(q10;p10)(+) cases. The dataset was used to identify key driver genes in patients with der(1;7)(q10;p10). Already known drivers along with novel driver genes were identified through the use of this WES.

  Dataset EGAD50000000986

• EGA FUSE Client

  EGA FUSE Client What is EGA FUSE Client? The EGA FUSE Client is a Java Native Runtime (JNR) based Filesystem in Userspace (FUSE) client that allows users to access authorised EGA files by presenting them in a virtual directory. This means that users can use authorised EGA files like regular files without having to download them first. The EGA FUSE Client is a useful tool for researchers who need to access and work with large amounts of EGA data. Why use EGA FUSE Client? The EGA FUSE Client provides several benefits for researchers working with EGA data: Efficient use of storage space: With the EGA FUSE Client, users can access authorised EGA files without having to download them first. This means that users can save storage space on their local machines. Faster access to data: Since users can access authorised EGA files directly through the EGA FUSE Client, they can work with the data more quickly than if they had to download the files first. Simplified workflow: The EGA FUSE Client presents authorised EGA files in a virtual directory, allowing users to work with the files like regular files. This simplifies the workflow for researchers who need to access and work with large amounts of EGA data. Get started with EGA FUSE Client To get started with EGA FUSE Client visit the EGA FUSE Client GitHub repository for more information and installation instructions.

  Documentation access/download/visualisation/fuse-client