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• scRNA-seq of total bone marrow and T cells from multiple myeloma long-term survivors

  scRNA This dataset contains 50 scRNA-seq samples from bone marrow aspirates of 11 multiple myeloma patients experiencing long-term survival and 3 healthy donors. For each donor, total bone marrow and CD3+ T cells were sequenced. For multiple myeloma patients, paired samples were collected at initial diagnosis and between 7-17 years after first-line therapy. Bone marrow mononuclear cells were isolated by Ficoll density gradient centrifugation. For sorting of total bone marrow cells singlet, live cells were gated and sorted, for sorting of T cells CD45+, CD3+ cells were gated and sorted on either FACSAria Fusion or FACSAria II. Single-cell RNA sequencing were generated using 10x Genomics single-cell RNAseq technology (Chromium Single Cell 3’ Solution v2) according to the manufacturer’s protocol and sequenced on an Illumina HiSeq4000 (paired end, 26 and 74 bp). Bulk RNA Singlet, live CD3+CD4- CXCR3+CD8+ and CD3+CD4- CXCR3-CD8+ cells were sorted from 7 bone marrow and 3 peripheral blood samples of 7 multiple myeloma patients using a FACSAria Fusion machine. Bulk-RNA sequencing libraries were generated using the SMART Seq Stranded Total RNA-Seq kit (Takara) and sequenced using the Illumina NovaSeq 6000 platform (2 x 100 bp).

  Dataset EGAD00001010025

• Genomic heterogeneity recapitulated in a PDXovo model - WXS mapped reads

  Ultra-Fast Patient-Derived Xenografts Identify Functional and Spatial Tumour Heterogeneities that Drive Therapeutic Resistance - WXS mapped reads

  Dataset EGAD00001003589

• CNS Embryonal tumors

  The dataset represents a total of 58 DNA samples from 16 male and 12 female pediatric patients affected with embryonal central nervous system tumors. The samples were subject to whole genome sequencing, WGS, [48 samples, (representing 12 male and 11 female individuals)] and whole exome sequencing, WES, [10 samples, (representing 4 male and 1 female individuals)]. One tumor tissue sample and one peripheral blood sample were analyzed from each of 26 patients, whereas two tumor tissue samples and one peripheral blood sample were analyzed from two patients. The WGS samples were sequenced 2x150 bp paired-end on an Illumina HiSeqX v2.5 instrument, and the WES samples were sequenced 2x100 bp paired-end on an Illumina HiSeq 2500 instrument. The FASTQ files generated were aligned to the human reference genome sequence GRCh38/hg38 using bwa-mem, with the ALT-aware option turned on. Sorting of reads and marking of PCR duplicates was performed with GATK. Base quality score recalibration and joint realignment of reads around insertions and deletions (indels) were conducted using GATK tools. The dataset consists of 58 files in the CRAM format (lossless compression) with a total file size of ~8,8 TB. All CRAM files but one, are derived from one sequence run and one sample. P4551_227N_P4552_112N is a CRAM file where 2 sequence runs (P4551_227N and P4552_112N) from peripheral blood samples from the same individual, P019, were aligned into one single CRAM file. Additional genomic and molecular data (FASTQ, BAM, IDAT, and VCF files) and limited clinical data can be requested by ethically approved projects conducting research in the field of pediatric cancer.

  Dataset EGAD50000000298

• sWGS of HGSOC samples for fixative comparison study

  Shallow whole-genome sequencing of samples from the study "Methanol-based fixation is superior to buffered formalin for next-generation sequencing of DNA from clinical cancer samples". DNA from each sample (100ng) was sheared on Covaris S220 (Covaris): duty cycle - 10%, intensity -5.0, bursts per sec - 200, duration - 300 sec, mode - frequency sweeping, power - 23V, temperature -5:5 C to 6 C, water level - 13. Libraries were prepared with the TruSeq Nano DNA LT Sample Prep Kit (Illumina) using a modi?ed protocol - Sample Puri?cation Beads were replaced by Agencourt AMPure XP beads (Beckman Coultier) and size selection after the End Repair was done to remove only the short fragments. Quality and quantity for contructed libraries were assessed with DNA 7500 kit on Agilent 2100 Bioanalyzer and with Kapa Quanti?cation kit (KAPA Biosystems) on 7900HT Fast Real-Time PCR System (Applied Biosystems) according to the supplier's recommendations, respectively. Libraries from 18 barcoded samples were pooled together in equimolar amounts and each pool was loaded on a single lane of a HiSeq Single End Flowcell (Illumina), followed by cluster generation on a cBot (Illumina) and sequencing on a HiSeq 2500 (Illumina) in a single-read 50bp mode. Reads were aligned using bwa-mem v0.7.12-r1039 to the 1000 genomes version of human genome build GRCh37. Picard (http://picard.sourceforge.net) was used to remove duplicate reads.

  Dataset EGAD00001001938

• Whole Genome Sequencing of 317 individuals from the Pacific region

  Although genetic studies of modern Oceanians have revealed varying levels of admixture with populations of East Asian ancestry, attributed to the Austronesian expansion, the early peopling history of Oceania remains poorly characterized. Additionally, it is currently unknown whether and how the settlement of the Pacific was accompanied by genetic adaptation to island environments, and if archaic introgression facilitated this process in Oceanians, who present the highest worldwide levels of combined Neanderthal and Denisovan ancestry. This study reports the first whole genome-based survey that addresses a wide range of questions related to the demographic and adaptive history of Pacific populations.

  Study EGAS00001004540

• Sensitive neoantigen discovery by real-time mutanome-guided immunopeptidomics - RNAseq

  Targeting cancer-specific HLA-peptide complexes is a promising immunotherapy approach, with mutated neoantigens offering high value for their immunogenicity and cancer-specificity. Selecting immunogenic targets requires personalized prioritization of cancer-specific immunopeptides. Mass spectrometry (MS)-based immunopeptidomics supports this process by directly identifying cancer-specific antigens and informing global presentation patterns to refine immunogenicity predictions. Clinical pipelines, however, must balance global depth and target sensitivity compounded by low input samples and time constraints. Here, we present NeoDiscMS, an extension of the NeoDisc pipeline, enabling personalized immunopeptidomics data acquisition. By leveraging real-time NGS-guided spectral acquisitions, NeoDiscMS maximizing sensitivity with minimal loss of global depth. NeoDiscMS improves TAA-derived peptide detection up to 20% and enhances neoantigen identification confidence compared to the clinical gold standard method. Designed for effectiveness and ease of use, it requires minimal effort for implementation. NeoDiscMS advances personalization in clinical antigen discovery by enabling more sensitive neoantigen detection while seamlessly integrating into existing workflows.

  Study EGAS50000000977

• Sensitive neoantigen discovery by real-time mutanome-guided immunopeptidomics - WES

  Targeting cancer-specific HLA-peptide complexes is a promising immunotherapy approach, with mutated neoantigens offering high value for their immunogenicity and cancer-specificity. Selecting immunogenic targets requires personalized prioritization of cancer-specific immunopeptides. Mass spectrometry (MS)-based immunopeptidomics supports this process by directly identifying cancer-specific antigens and informing global presentation patterns to refine immunogenicity predictions. Clinical pipelines, however, must balance global depth and target sensitivity compounded by low input samples and time constraints. Here, we present NeoDiscMS, an extension of the NeoDisc pipeline, enabling personalized immunopeptidomics data acquisition. By leveraging real-time NGS-guided spectral acquisitions, NeoDiscMS maximizing sensitivity with minimal loss of global depth. NeoDiscMS improves TAA-derived peptide detection up to 20% and enhances neoantigen identification confidence compared to the clinical gold standard method. Designed for effectiveness and ease of use, it requires minimal effort for implementation. NeoDiscMS advances personalization in clinical antigen discovery by enabling more sensitive neoantigen detection while seamlessly integrating into existing workflows.

  Study EGAS50000000976

• Genomic heterogeneity recapitulated in a PDXovo model - WXS unaligned reads

  Ultra-Fast Patient-Derived Xenografts Identify Functional and Spatial Tumour Heterogeneities that Drive Therapeutic Resistance - WXS unaligned reads

  Dataset EGAD00001003590

• 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

• WES Elucidation of Pathogenetic Mechanisms for Immune System Disorders and NIAID Pilot of Genetic Incidental Finding Management

  Background: - There are many types of immune disorders. These range from rare immune deficiencies to allergies to autoimmune disease like rheumatoid arthritis. Genes are the instructions our body uses to work and develop. A new technology called whole exome sequencing may help find the cause of these disorders. Whole exome sequencing is a way to look at many genes at once for errors. Researchers hope to find new gene changes that lead to immune disorders. Additionally, researchers are interested in finding the best way to manage unexpected but important findings by whole exome sequencing. Objectives: - To better understand genetic causes of immune system disorders. Also, to better understand people s thoughts and feelings about immune system disorders and new genomic testing. Eligibility: - People ages 0 100 with an immune disorder or a relative with an immune disorder. People must be at least 2 to be evaluated at the NIH clinical center. People must be at least 12 to do the survey/interview portion of the study. Design: Participants will have their genes sequenced. They may be asked for a new sample of blood. If participants cannot come for a study visit, they can have a blood sample collected by their local lab or doctor and sent by mail. Researchers may or may not find the cause of the participant s immune disorder. Participants will learn that information. Some participants may be asked to return to NIH to get results and have more tests. Researchers may share information with other studies. The data will be anonymous. For the survey part of the study, participants will answer questions about their or their relative s immune disorder. They will also answer about their thoughts and feelings about genomic testing. Some participants will be asked for a brief interview to ask more about the survey topics. There may be more follow-up after several months. From clinicaltrials.gov

  Study phs001561