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• EGA metadata schema

  EGA Schemas In this page, you will find information on how the European Genome-phenome Archive (EGA) manages its metadata standards using both XML Schema Definition (XSD) and JavaScript Object Notation (JSON) formats. If you are not sure what this means, you may want to explore our brief metadata introduction. This information may be of your interest if you are planning to learn more about how the EGA is built and how to wrap around it for other processes. Nevertheless, if you are a common user (e.g. submitter or requester), you would not have to worry about these schemas nor their format, since they are implemented in user-friendly ways for you. Metadata standards are rules that define how to format and structure data of metadata objects (i.e. entities), like EGA's samples or experiments in a consistent manner. These objects are the nodes of the metadata model of the EGA (Figure 1). Figure 1. Diagram of EGA's metadata model. The model's building blocks are objects (e.g. sample), which can reference each other (e.g. an experiment referencing the used samples). Once your files are uploaded, they can also be referenced by Runs and Analyses. The submission object is an object itself that compiles many others. At EGA, we inherit our metadata schemas from the European Nucleotide Archive (ENA), and we have expanded them to include bespoke objects such as "Policy", "Dataset", and "DAC" (Data Access Committee) for our specific use-case: handling sensitive human data. See below a list of all our metadata objects and some context for each. Metadata Object EGA accession Description Examples of metadata fields Study EGAS… Information about the study Study type, study title, study abstract… Sample EGAN… Information about the used samples in the experiment or analysis Taxon ID, scientific name, biological sex, phenotype… Experiment EGAX… Information about the performed experiment Used libraries, sequencing platform, reference to the used samples… Analysis EGAZ… Contains information about the analysis Type of analysis, used assembly, reference sequence… Run EGAR… The run holds information about the files containing the raw reads generated in a run of sequencing Platform, spot descriptor, raw file references… DAC EGAC… Contains information about the Data Access Committee (DAC) DAC contacts, contact emails… Policy EGAP… Contains the Data Access Agreement (DAA) and policy which its usage complies with Policy text, data use ontologies (DUO) codes… Dataset EGAD… Contains the collection of runs/analysis to be subject to controlled access Dataset type, compilation of Run's and Analysis' IDs There are two different sets of schemas, based on their formats, in which the EGA accepts metadata: XSDs (for XML files) and JSON Schemas (for JSON files). EGA's XML Schema Definition . When programmatic submissions are pushed through the European Bioinformatics Institute (EBI) system, XML format is used. The schemas that are applied for this format are defined in XML Schema Definition (XSD) files, which can be found at ENA's GitHub repository. You can find more information on how to validate and submit your data programmatically in our programmatic submission documentation. Furthermore, see at our GitHub repository some XML examples with either made-up values (similar to what you would submit) or descriptive values for each field (just for documentation). EGA's JSON Metadata Schemas . When programmatic submissions are pushed through the Centre for Genomic Regulation (CRG) system, JSON format specifications are used instead. See the full JSON specifications for further details. In conclusion, the EGA metadata schemas are crucial for maintaining the quality and consistency of submitted data. By understanding and following the rules outlined in these schemas, you can ensure that your submissions comply with the EGA's standards and contribute to a valuable and accessible genomic resource. Sample checklists Besides the standards in our schemas, we have another layer called Sample checklists. These are another system that the EGA inherits from ENA, and specifies what attributes are required or allowed for a sample object. The EGA uses these checklists to enforce that, for example, a sample object has the three mandatory attributes: subject ID, sex and phenotype of the individual the sample was taken from. When you submit to EGA, our checklist is automatically selected by default.

  Documentation submission/metadata/ega-schema

• Genome-Wide Analysis for Addiction Susceptibility Genes

  Genome wide analysis for addiction susceptibility genes is an affected sibling pair linkage study using subjects who were patients in methadone replacement treatment programs (MMTP). Such patients are highly addicted to opioids and usually to other addicting substances as well. For entry into the programs, patients had to be at least 18 years of age and had to satisfy DSM-IV criteria for opioid dependence. Despite their common substance use disorder diagnosis, patients enrolled in MMTPs generally have a heterogeneous mix of co-morbid psychiatric disorders including anti-social personality disorder, major depression and anxiety disorders, and often satisfy DSM-IV criteria of substance use disorders other than opioid dependence. Genetic analysis was carried out using genotyping data obtained from a 10K SNP array, and non-parametric linkage was assessed using MERLIN. This resulted in the identification of a linkage peak on 14q that overlapped the NRXN3 gene (neurexin 3), which was previously identified as a potential candidate gene for addiction, and subsequently identified as a target gene in other addiction studies and autism. In version 2 of this study, a text file containing 7704 SNPs with large allele frequency discrepancy as compared to 1000 Genomes is included. Users have the option to exlude these 7704 SNPs.

  Study phs001266

• Gabriella Miller Kids First Pediatric Research Program for Infantile Hemangiomas Associated with Multi-Organ Structural Birth Defects

  The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). All of the WGS and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. Infantile hemangiomas are the most common benign vascular tumor in infants, affecting 4-5% of children. Thirty percent of segmental infantile hemangiomas on the face and scalp are associated with birth defects of multiple organs. This condition is known as PHACE, an acronym for posterior fossa brain malformations, segmental facial hemangiomas, arterial anomalies, cardiac defects, eye anomalies, and sternal clefting. Genomic analysis of PHACE will inform treatment and expand knowledge about the causes of birth defects affecting the brain, arteries, heart, eye, midline development and hearing. The knowledge gained in this study will be used to drive strategies for prevention and provide critical targets for treatments for a range of birth defects and infantile hemangiomas.

  Study phs001785

• Center for Common Disease Genomics [CCDG] Neuropsychiatric: Autism Spectrum Disorder (ASD) – Whole Exomes

  The National Human Genome Research Institute (NHGRI) has funded a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Called the Centers for Common Disease Genomics (CCDG), this initiative will explore a range of diseases with the ultimate goal of 1) undertaking variant discovery for enough different examples of disease architectures and study designs to better understand the general principles of the genomic architecture underlying common, complex inherited diseases, 2) understanding how best to design rare variant studies for common disease, and 3) developing resources, informatics tools, and innovative approaches and technologies for multiple disease research communities and the wider biomedical research community. The initial focus of the CCDGs will be in cardiovascular disease (early-onset coronary artery disease, atrial fibrillation, hemorrhagic stroke), neuropsychiatric disease (epilepsy, autism), and autoimmune/inflammatory disease (type 1 diabetes, inflammatory bowel disease). The Broad Institute is one of four selected CCDG project centers. The goal of the Autism Spectrum Disorder (ASD) program is to work together to utilize dramatic new advances in DNA sequencing technology to reveal the genetic architecture of autism through comprehensive examination of the exotic sequence of all genes.

  Study phs002502

• LySeqST: A targeted sequencing assay for robust genomic classification of diffuse large B-cell lymphoma

  Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous disease. Two genomic classification systems, LymphGen and DLBclass, are capable of reproducibly classifying single tumors into subtypes with prognostic relevance in the context of standard-of-care R-CHOP chemoimmunotherapy. Preliminary data from subgroup analyses of clinical trials suggest that the distinct biological features are targetable with drugs that exploit subtype-specific vulnerabilities, and large clinical trials are being designed to test this hypothesis. Although whole exome sequencing (WES) remains the gold standard for each of these classification systems, many laboratories may opt for smaller targeted sequencing panels that reduce costs associated with sequencing, data storage, and processing. Here, we describe the design and validation of a targeted sequencing panel (LySeqST) that captures the genomic features required specifically for LymphGen classification. We perform in silico and real-world validation vs. WES data and demonstrate that LySeqST can be used to accurately classify DLBCL tumors. In an unselected population-based cohort, we determine the real-world proportions and validate the prognostic relevance of LymphGen subtypes, confirming that only tumors expressing the dark zone gene expression signature are consistently associated with inferior outcomes. Our results support the use of LySeqST for accurate genomic classification of DLBCL.

  Study EGAS50000001601

• Rapid identification of somatic genome rearrangements as personalized biomarkers for blood-based cancer monitoring

  Increasing evidence shows the value of circulating tumour DNA (ctDNA) to detect cancer and monitor its progression. Somatic genomic structural variations (SVs) are promising personalized biomarkers for sensitive and specific detection of ctDNA in liquid biopsies. However, accurate, affordable, and fast identification of such SV biomarkers is challenging, which hinders routine use in the clinic. Here, we demonstrate a novel approach - termed SHARC - for rapid discovery of somatic SV breakpoints as personalized tumour biomarkers. SHARC combines low-coverage cancer genome sketching by using Oxford Nanopore portable sequencing with a random forest classification and a dedicated filtering pipeline to enrich for somatic SVs. Our method leverages the real-time and long-read capabilities of Nanopore sequencing to identify somatic SV breakpoints at nucleotide resolution from a tumour biopsy within two days. We applied SHARC to tumour samples of high-grade ovarian and prostate cancer and validated on average 10 somatic SVs per sample with the use of PCR mini-amplicons. Finally, we demonstrate that these somatic SV biomarkers can be used to detect tumour presence from liquid biopsies in a quantitative manner and we retrospectively monitored treatment response in patients with prostate cancer, demonstrating its potential benefit for clinical practice.

  Study EGAS00001003963

• Bisulfite-converted duplexes for the strand-specific detection and quantification of rare mutations

  The identification of mutations that are present at low frequencies in clinical samples is an essential component of precision medicine. The development of molecular barcoding for next generation sequencing has greatly enhanced the sensitivity of detecting such mutations by massively parallel sequencing. However, further improvements in specificity would be useful for a variety of applications. We herein describe a technology (BiSeqS) that can increase the specificity of sequencing by at least two orders of magnitude over and above that achieved with molecular barcoding and can be applied to any massively parallel sequencing instrument. BiSeqS employs bisulfite treatment to distinguish the two strands of molecularly barcoded DNA; its specificity arises from the requirement for the same mutation to be identified in both strands. Because no library preparation is required, the technology permits very efficient use of the template DNA as well as sequence reads, which are nearly all confined to the amplicons of interest. Such efficiency is critical for clinical samples, such as plasma, in which only tiny amounts of DNA are often available. We show here that BiSeqS can be applied to evaluate transversions, as well as small insertions or deletions, and can reliably detect one mutation among >10,000 wild type molecules.

  Study EGAS00001002406

• Hypoxia acts as an environmental cue for the human TRM differentiation program

  Tissue-resident memory T-cells (TRM) provide frontline defense against infectious diseases and contribute to anti-tumor immunity; however, aside from the necessity of TGF-β, knowledge regarding TRM-inductive cues remains incomplete, particularly for human cells. Oxygen tension is an environmental cue that distinguishes peripheral tissues from the circulation and here, we demonstrate that differentiation of human CD8+ T-cells in the presence of hypoxia and TGF-β1 led to the development of a TRM phenotype, characterized by a greater than five-fold increase in CD69+CD103+ cells expressing human TRM hallmarks and enrichment for endogenous human TRM gene signatures, including increased adhesion molecule expression and decreased expression of genes involved in recirculation. Hypoxia and TGF-β1 synergized to produce a significantly larger population of TRM phenotype cells than either condition alone, and comparison of these cells from the individual and combination conditions revealed distinct phenotypic and transcriptional profiles, indicating a programming response to milieu rather than a mere expansion. Our findings identify a previously unreported cue for the TRM differentiation program and can enable facile generation of human TRM phenotype cells in vitro for basic studies and translational applications such as adoptive cellular therapy.

  Study EGAS00001005286

• Metagenome shotgun sequencing of the Inflammatory Bowel Disease

  We performed a comprehensive metagenome-wide association study for inflammatory bowel disease by utilizing whole-genome shotgun sequencing.

  Study JGAS000530

• WTCCC2 Pharmacogenomic Response to Statins study

  A WTCCC2 project genome-wide study for pharmacogenomic response to statins (PR) in individuals from Scotland.

  Study EGAS00000000121

• WTCCC2 BO (Barretts oesophagus) samples

  A WTCCC2 project genome-wide case-control association study for Barretts oesophagus in individuals from the UK.

  Study EGAS00001000628

• ONT and PacBio data of 22q11 patient-parent duos/trios

  ONT and PacBio FASTQ files generated for de novo assembly and resulting de novo assemblies

  Study EGAS50000001647

• Multi-regional tumour biopsies of a RET fusion patient

  Identifying modes of resistance to a new experimental drug for a patient resistant to other therapies

  Study EGAS00001004023

• ChIP-seq in colorectal cancer and paired adjacent normal mucosa

  Chip-seq samples for 20 colorectal patients with paired adjacent normal mucosa to characterize H3K27ac and H3K4me1.

  Study EGAS00001005303

• small RNA sequencing for 6 patients

  small RNA sequencing data for 3 patients with type 2D diabetes, and 3 patients with non diabetes. Single end sequencing was performed with read length of 100.

  Dataset EGAD50000001259

• WES data for Heterogeneity of IKZF1 genomic alterations and risk of relapse in childhood BALL

  Whole exome sequencing data for 425 samples with B-cell acute lymphoblastic leukemia

  Dataset EGAD50000000150

• Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma

  Sequence reads for pediatric GBM samples for manuscript: Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma

  Dataset EGAD00001000134

• Sequencing data for Australian Ovarian Cancer study submitted 20121116

  Sequencing data for Australian Ovarian Cancer study submitted 20121116

  Dataset EGAD00001000293

• Adenoid Cystic Carcinoma

  Whole genome sequencing data for 25 adenoid cystic carcinoma samples. The samples were used for Illumina TruSeq library construction and were sequenced on an Illumina HiSeq 2000. The PE fastq files are provided.

  Dataset EGAD00001003959

• WES files for Newman MAP3K8 melanoma

  WES files for Newman MAP3K8 melanoma paper titled "Clinical genome sequencing uncovers potentially targetable truncations and fusions of MAP3K8 in spitzoid and other melanomas"

  Dataset EGAD00001004566

• WGS files for Newman MAP3K8 melanoma

  WGS files for Newman MAP3K8 melanoma paper titled "Clinical genome sequencing uncovers potentially targetable truncations and fusions of MAP3K8 in spitzoid and other melanomas"

  Dataset EGAD00001004579

• APCDR AGV Project: Low depth (4x) Illumina HiSeq sequence data for a Zulu population(BAMs)

  Low depth (4x) Illumina HiSeq raw sequence data for 100 unrelated Zulu from Durban area, South Africa.

  Dataset EGAD00001001007

• Whole genome data for study EGAS00001000824 (Diverse modes of genomic alterations in hepatocellular carcinoma)

  Whole genome data (Complete genomics platform) for the study EGAS00001000824

  Dataset EGAD00001001034

• DATA FILES FOR SJOS-WGS-2ndBatch

  DATA FILES FOR SJOS-WGS-2ndBatch

  Dataset EGAD00001001053

• McGill EMC Release 4 in tissue "venous blood" for cell type "CD4-positive helper T cell"

  McGill EMC Release 4 in tissue "venous blood" for cell type "CD4-positive helper T cell"

  Dataset EGAD00001001279