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• Metadata Public API

  Metadata Rest Endpoints Common Aspects Functionality Identifiers Requests and Responses Request Response Querying Querying by Object Cross-Querying by Object Common Aspects Functionality The Metadata REST API allows EGA users to request metadata from the EGA. Using this api you will be able to obtain the publicly available information from EGA study, sample, experiment, run, analysis, policy, dac and dataset. The API also allows for objects to be cross-referenced in order to obtain for example all the datasets linked to a dac. Identifiers EGA objects can be identified by their unique accession. These are ID's displayed everywhere, shared among all EGA locations and specific for each data type (More information on the list below) EGA Accession ID EGA Object description EGAS EGA Study Accession ID EGAC EGA DAC Accession ID EGAP EGA Policy Accession ID EGAN EGA Sample Accession ID EGAR EGA Run Accession ID EGAX EGA Experiment ID EGAZ EGA Analysis Accession ID EGAD EGA Dataset Accession ID EGAB EGA Submission ID EGAF EGA File Unique Accession ID Server Request and Response Request All the endpoints implement these two HTTP methods, GET and HEAD. Moreover, there is an authentication layer that allows to disclose the private information about each object if the user runs the endpoint with their token and has permissions to the related dataset. HTTP Method Description GET Returns the objects and their public information (including private information if the user has permissions) HEAD Returns the count of objects to be returned Response API responses are diverse but they always return a status call. Below you have a summary on what these codes mean and some tips that can help you troubleshooting them: HTTP Status Code Description Resolution 200 OK No error handling necessary 206 Partial Content Expected status code when paginating the results. No error handling necessary 400 BAD REQUEST Request incorrectly formulated 401 UNAUTHORISED/FORBIDDEN You do not have permissions over the object you are trying to access. / The authorised user is not permitted to make the given request. 404 NOT FOUND The object/resource you are trying to access does not exist. 500 INTERNAL SERVER ERROR This is a server-side error, try later. If the issue persists, contact the Heldpesk. Querying Querying by Object In the below documentation, worked examples can be found on how to use the API, to query about each unique EGA object. https://metadata.ega-archive.org/analyses/{id} https://metadata.ega-archive.org/dacs/{id} https://metadata.ega-archive.org/datasets/{id} https://metadata.ega-archive.org/runs/{id} https://metadata.ega-archive.org/samples/{id} https://metadata.ega-archive.org/studies/{id} Worked example for the EGA data access committee(DAC) EGAC00001000514 https://metadata.ega-archive.org/dacs/EGAC00001000514 Points to notice Please, be advised that all EGA objects can be queried by using the same endpoint. For example: https://metadata.ega-archive.org/datasets Returns all the datasets registered at the EGA. https://metadata.ega-archive.org/datasets?limit=5&offset=15 Returns 5 results skipping the initial 15 results. Cross-Querying by Object The EGA REST API Metadata also allows for the crossquerying of public metadata. For example, should I want to query the datasets included in the ICGC DAC: https://metadata.ega-archive.org/dacs/EGAC00001000010/datasets Below, you can find worked examples on how to perform the call to the API. https://metadata.ega-archive.org/datasets/EGAD00001000645/analyses https://metadata.ega-archive.org/datasetsEGAD00001000645/files https://metadata.ega-archive.org/datasets/EGAD00001000620/runs https://metadata.ega-archive.org/datasets/EGAD00001000620/samples https://metadata.ega-archive.org/samples/EGAN00001092114/datasets https://metadata.ega-archive.org/files/EGAF00000056146/dacs https://metadata.ega-archive.org/files/EGAF00000056146/datasets https://metadata.ega-archive.org/files/EGAF00000056146/studies There are more endpoints included in this category. Please, find all the possibilities (green tick) in the table below. Table The table displays in a ordered fashion the objects that can be crossreferenced using the metadata API. BY Analysis Dac Dataset Experiment Policy Run Sample Study File Analysis ✔ ✔ Dac ✔ ✔ Dataset ✔ ✔ ✔ ✔ Experiment ✔ ✔ ✔ ✔ Policy ✔ Run ✔ ✔ ✔ Sample ✔ ✔* ✔ Study ✔ ✔ ✔ ✔ ✔ ✔* File ✔ *: These cross queries do not include data from Array-File submissions. Check out the SPEC

  Documentation discovery/metadata/public-metadata-api

• Predicting the Prevalence of Complex Genetic Diseases from Individual Genotype Profiles Using Capsule Networks

  Diseases with a complex genetic architecture, such as amyotrophic lateral sclerosis (ALS) as a most prominent example, are hard to disentangle. "Human mind-friendly" methods, such as linear approaches used in genome wide association studies (GWAS), remain blind to many genetic variants that matter. On the other hand, methods that are sufficiently sophisticated to entirely capture the characteristic interplay of genetic variants remain black boxes for the human mind. Here, we give this a decided, and as we argue, a very promising try. We present DiseaseCapsule, a capsule network-based approach that enables us to predict prevalence/occurrence of a genetically complex disease from individual genotype profiles. Importantly, capsule networks are considerably easier to decompose into their components than ordinary deep neural networks, which promotes the interpretability of their results. The data we used are from Project MinE, a large-scale study that aims to reveal the genetic and epigenetic mechanisms that underlie ALS in the framework of a globally concerted collaboration. Specifically, we used data from the Dutch cohort of the project, which contains 7213 healthy ('control') individuals and 3192 individuals affected with ALS. The cohort includes 5208 females and 5197 males. All participants of the study were genotyped using an Illumina 2.5M SNP array.

  Study phs003146

• Center for Common Disease Genomics (CCDG) - Cardiovascular: Emory Cohort

  The National Human Genome Research Institute (NHGRI) has funded the Centers for Common Disease Genomics (CCDG), a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to common disease phenotypes and to better understand the general principles of genomic architecture underlying common, complex inherited diseases. This study contains variant calls derived from the whole genome sequence data of an early-onset coronary artery disease case-control cohort selected from Emory University Cardiovascular Biobank. Case status was assigned based on the presence of myocardial infarction, coronary artery stenosis greater than 70% in at least one coronary artery, and/or revascularization (coronary angioplasty with or without stent placement or coronary artery bypass grafting) at an early age. For these samples "early age" is defined as men 60 years of age or younger and women 70 years of age or younger. The National Human Genome Research Institute (NHGRI) has funded the Centers for Common Disease Genomics (CCDG), a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to common disease phenotypes and to better understand the general principles of genomic architecture underlying common, complex inherited diseases.

  Study phs001880

• GATA2 Deficiency

  The aim of this study is to identify somatic mutations that contribute to the progression of GATA2 Deficiency patients from bone marrow failure to myelodysplastic syndrome (MDS) to acute myeloid leukemia (AML) or chronic myelomonocytic leukemia (CMML). We investigated somatic mutations in >100 patients by whole exome and myeloid malignancy targeted array sequencing to identify acquired mutations associated with myeloid malignancies. MDS was the most common diagnosis (~45%), followed by GATA2 Bone Marrow Immunodeficiency Disorder (G2BMID) (~35%). Approximately ten percent of our cohort were unaffected "carrier" family members with no disease manifestations. In contrast, about ten percent had progressed to a proliferative leukemia. There were no correlations with GATA2 mutation, patient age or gender and disease progression or survival. Somatic Recurrent somatic mutations were uncommon in cancer associated genes, and virtually absent in asymptomatic patients. However, mutations in the cohesin gene STAG2 and chromatin remodeling gene ASXL1 were detected in ~20% of patients. Patients with ASXL1 mutations had a lower survival. ASXL1 mutations were ~10-times more prevalent in female patients than males. Other recurrent mutations were in the DNMT3A, BCOR, and SETBP1 genes. There was a conspicuous absence of the most commonly mutated genes associated with myeloid malignancies, including TET2, RUNX, IDH1/2 and splicing factor genes. 

  Study phs002311

• 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

• Trisomy21: Risk Factors for Chromosome Nondisjunction (T21NDJ)

  The overall goal of the project is to identify genetic risk factors associated with chromosome 21 nondisjunction in the oocyte. The dataset derives from multi-site collection of live birth probands with Down syndrome due to standard trisomy 21 (T21) and their biological parents. The type of nondisjunction error (NDJ)(maternal or paternal) in all cases has been determined to be maternal in origin based on the chromosome 21 variants contributed from parent to proband. The Center of Inherited Disease with support from NICHD has conducted genome-wide genotyping using the Illuminia Human OmniExpress Plus Exome array on approximately 800 women who have been identified through their offspring with DS and have been characterized as having a maternal meiois I (MI) or meiosis II (MII) nondisjunction error. Genotypes from biological fathers of the offspring with DS can be used with the data on mothers to better define the type of nondisjunction error (MI or MII) and to refine the chromosome 21 recombination profile. We provide the type of nondisjunction error, knowing that this will be updated based on the new panel. We do not provide the recombination profile, as this can be best defined using the new comprehensive set of SNPs in the OmniExpress panel.

  Study phs000718

• Integrated Personal omics Processing (iPoP) Longitudinal multi-omics profiling of prediabetes

  Type 2 diabetes mellitus (T2D) is a significant health problem. Close to 20 million individuals in the United States have T2D, and 79 million aged 20 years or older are clinically pre-diabetic, with a 5-year conversion rate of 10% to 23% from prediabetes to T2D. In a collaborative effort to systematically understand diabetes and its etiology, the team is comprised of leading experts in research on both the human host as well as the microbiome, as properties of both are likely relevant in T2D development. For a better elucidation of mechanisms of onset and progression of T2D disease, the group is performing a detailed analysis of the biological processes that occur in the microbiome and human host by longitudinal profiling of patients at risk for T2D. Both microbiome and host profiles are being analyzed by state-of-the-art omics platforms, and these large-scale and diverse data sets will be integrated to determine the dynamic pathways that change during the onset and progression of T2D, especially during viral infections and other stresses. This longitudinal study is expected to reveal changes in the microbiome and host at an unprecedented level of detail, and identify molecules and pathways that play important roles in diabetes onset and progression.

  Study phs001719

• Germline Mutations and Developmental Mosaicism Underlying EGFR-Mutant Lung Cancer

  We designed this study to examine the etiology of multiple lung tumors in EGFR-mutant non-small cell lung cancer patients who undergo surgical resection. Participants were 52-85 years old never or former smokers, who had at least 2 surgical lung tumor pathology specimens available in the Massachusetts General Hospital biobank, at least one of which had an EGFR mutation by routine clinical genotyping. 10 patients were selected, along with two patients from a family with an inherited EGFR-T790M mutation leading to multiple lung tumors. These patient tumor samples and matched normal lung samples were microdissected to collect DNA.In addition to obtaining clinical histories, we performed whole exome sequencing to evaluate the genetic profile of matched tumor and normal samples. The WES data are available on dbGaP. Our data showed increasing mutational burden and copy number alteration with increasing invasiveness, as compared to normal tissues, for the subset of samples that were assessed for tumor invasiveness. We also observed a range of numbers of shared mutations between tumors in a single patient, indicating different degrees of relatedness between lung tumors. We hope that this resource will help researchers understand non-small cell lung cancer presentations with multiple lung tumors. These data may aid in clinical decision making for such patients.

  Study phs003379

• Chronic Renal Insufficiency Cohort (CRIC) - GWAS

  The Chronic Renal Insufficiency Cohort (CRIC study) was established in 2001 by the National Institute of Diabetes, Digestive, and Kidney Diseases (NIDDK) to improve the understanding of the relationship between chronic kidney disease and cardiovascular disease. The goals of the CRIC Study are to examine risk factors for progression of chronic kidney disease and cardiovascular disease among patients with chronic kidney disease and to develop predictive models to identify high-risk subgroups, informing future treatment trials and increasing application of available preventive therapies. Data available at dbGaP include genotype data for 3527 participants released in version 1. Plasma metabolomics and plasma proteomics data will be available at dbGaP in "Chronic Renal Insufficiency Cohort (CRIC) Study Metabolomics and Proteomics". Additional phenotypic, clinical, and outcomes data are available at the NIDDK Central Repository and can be linked to data available at dbGaP. More than 330 manuscripts have been published, reporting on the effects of a broad range of risk factors (from molecular biomarkers of disease pathways to clinical, demographic, and behavioral characteristics) on the progression of CKD and other health consequences. The full CRIC bibliography can be found at http://cristudy.org/Chronic-Kidney-Disease/Chronic-Renal-Insufficiency-Cohort-Study/bibliography-2024-2023.

  Study phs000524

• Molecular characteristics in Burkitt lymphoma over age groups

  Treatment optimization has improved the overall survival in Burkitt lymphoma (BL) patients but there are still relevant outcome differences between pediatric and adult patients. In both age groups survival remains poor for patients who suffer relapse. Therefore, new treatment approaches are warranted to improve survival. Thus, we conducted a comparative study in the largest pediatric cohort to date including 191 cases and 97 samples from adult patients and analyzed the mutational landscape. We detected overlapping molecular profiles but also significant differences in the mutational pattern. Samples from pediatric patients show a higher frequency of mutations in ID3, DDX3X and BAF-complex components ARID1A and SMARCA4, while YY1AP1 and BCL2 are more frequently mutated in cases from adult BL patients. Correlation of the mutational profile with clinical data was available for the pediatric cohort and identified TP53 mutations to be highly significantly associated with the incidence of relapse thus identifying the first molecular marker to be associated with the incidence of relapse in pediatric BL. As the hotspot analysis of TP53 mutations showed mostly gain of function mutations, it is tempting to speculate that drugs decreasing the amount of malignant TP53 or increase the abundance of wildtype TP53 could be a promising approach for future treatment regimens to further improve prognosis.

  Study EGAS00001005270