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• Comprehensive biomarker analysis from phase II study of nivolumab in patients with thymic carcinoma

  In a phase II trial of nivolumab in advanced thymic carcinoma (UMIN000022007), long SD (SD for more than 24 weeks) was seen in three patients and irAE (Gr2 or higher) was seen in four patients among 15 patients. We conducted preplanned comprehensive biomarker analyses. We obtained whole blood for Immuno Pharmacogenomic analysis using PBMC DNA. Immuno-PGx analysis showed non-synonymous SNVs in ITGAX and PDCD1 had some correlation with PFS.

  Study JGAS000588

• Bone morphogenetic protein-9 controls pulmonary vascular growth and remodeling

  Human PMECs from 10 donor lungs were isolated, cultured (passages 3 to 4), synchronized by serum starvation (0.3% FCS), and stimulated with 10 ng/mL BMP-9 for 4 h. Single-cell profiling was performed using the 10X Genomics Chromium RNA 3' V3.1 kit. Five experimental groups were analyzed using cell hashing and sequenced on an Illumina NextSeq500. Data were processed with Cell Ranger and Seurat for differential gene expression and functional enrichment analysis.

  Study EGAS50000001026

• Exome_sequencing_of_a_cohort_of_Rett_syndromelike_patients

  The aim of the project is the definition of the molecular defect in a cohort of Rett-like patients negative for mutations in known disease genes. To this aim, a number of unrelated trios (patients plus parents) will be analysed by exome sequencing. 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 EGAS00001002059

• WGS/RNA-seq pair of an inflammatory hepatocellular adenoma (IHCA)

  A 49 years old female patient consulted for diarrhea, rectal bleeding and lower limbs edema and was diagnosed with systemic AA amyloidosis. An inflammatory hepatocellular adenoma was further identified and resected, resulting in the improvement of amyloidosis-related symptoms. Molecular analyses of the tumor tissue revealed a somatic chromosome rearrangement responsible for interleukin-6 mRNA stabilization leading to an inflammatory acute response with serum amyloid A protein production by both neoplastic and normal hepatocytes.

  Study EGAS00001003025

• PRDM9_loss_of_function_follow_up_from_Born_in_Bradford_Autozygosity_sequencing

  In the autozygosity exome sequencing of Born-in-Bradford samples of Pakistani origin thereis a mother who is homozygous for an apparent truncating stop codon in PRDM9, the generesponsible for localising recombination during meiosis. We plan to deep sequence motherand child with X10, and physically phase the mother with PacBio sequencing.We will use this data to identify recombination locations, and test whether these areconsistent with the known fine scale recombination map.

  Study EGAS00001001301

• BCR_repertoire_sequencing

  Isotype-specific B-cell receptor repertoire sequencing in six immune-mediated diseases at diagnosis and during therapy reveals an unexpectedly complex B-cell architecture, which may provide a platform for a better understanding of pathological mechanisms and treatment responses. 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 EGAS00001003185

• International consensus definition of DNA methylation subgroups in juvenile myelomonocytic leukemia

  Known clinical and genetic markers have limitations in predicting disease course and outcome in juvenile myelomonocytic leukemia (JMML). DNA methylation (DNAme) patterns in JMML have correlated with outcome across multiple studies, suggesting it as a biomarker to improve patient stratification. However, standardized approaches to classify JMML based on DNAme patterns are lacking. We therefore sought to define an international consensus for DNAme subgroups in JMML and develop classification methods for clinical implementation.

  Study EGAS00001004682

• RNA sequencing data of in vitro differentiated megakaryocyte cells transduced with E527K and WT SRC

  For RNA sequencing (RNAseq), CD34+ hematopoietic stem cells (HSC) were isolated from healthy controls before transduction with WT-SRC and E527K-SRC lentiviral vectors in triplicate and differentiation to MK. RNA was extracted from the complete cell population as the aim was to detect differences in genes regulating MK maturation and differentiation. A total of six WT-SRC and six E527K-SRC RNA samples were used for RNAseq to identify differentially expressed genes.

  Study EGAS00001005808

• Resequencing candidate genes for male spermatogenic impairment

  This study aims to identify novel candidate variants from human Y-chromosomal genes DAZ, BPY2 and CDY1/2 by resequencing the coding regions of these genes from male patients with spermatogenic impairment. The coding regions of the genes plus a selection of phylogenetically informative Y-chromosomal markers have been amplified by standard PCR, amplicon lengths range from 178 to 486 bp. Amplicons were quantified by gel electrophoresis and pooled in approx. equimolar concentrations per patient. For each of the 480 submitted samples, approx. 1 microgram of amplified DNA pool was provided in a total volume of 120 microlitres. The samples were indexed and libraries prepared for PE250bp Illumina MiSeq runs. 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/ .

  Dataset EGAD00001006784

• PCA Atlas Visium spatial transcriptomics data (FASTQs, spaceranger BAMs and Visium mapping tables)

  This dataset contains Visium spatial transcriptomics data for the PCA Atlas study EGAS00001008332. It includes: - Raw Visium FASTQ runs (EGAR00004172272–EGAR00004172283) for all libraries. - spaceranger alignment BAM analyses for those libraries where BAM output was generated. - visium.csv: a slide-level mapping table linking Visium slide samples to EGA sample accessions, sequencing runs/experiments and spaceranger BAM analyses. - visium_tma.csv: a TMA mapping table describing how mini-TMA slides relate slide-level Visium libraries to tissue-level samples/cores. Together these objects provide the raw and aligned Visium data and the technical/biological mappings needed to understand and reuse the spatial transcriptomics component of the PCA Atlas. High resolution histology images are available from the following Zenodo repository, along with additional spaceranger outs for each slide: - https://zenodo.org/records/17411292

  Dataset EGAD00001015796

• Organ maturation in preparation for birth (Peds RFA) to develop a tissue resource and a single-cell atlas of organ development and maturation for dissemination among the scientific and clinical community: RNA (2025-10-14)

  Knowledge about abnormal organ development is important to understand pathology and to develop novel treatment approaches for individuals with congenital and acquired disease. Most of our current understanding is based on examination of tissues from the embryo and early foetus, collected from women undergoing termination of pregnancy in the first trimester (third) of pregnancy. There is very little known about normal and abnormal organ development from a developmental perspective during the crucial last two-thirds of pregnancy when much remodelling of foetal tissues occurs. This study will generate a single-cell atlas of late-foetal lungs, blood, heart, bone and immune organs. . This dataset contains all the data available for this study on 2025-10-14.

  Dataset EGAD00001015737

• scRNA-seq of total bone marrow mononuclear cells and CD3+ T cells of multiple myeloma patients and healthy donors

  The study includes 50 scRNA-seq samples of bone marrow aspirates of 11 multiple myeloma patients and 3 healthy donors generated with the 10x Genomics platform (Chromium Single Cell 3’ Solution v2). For the multiple myeloma patients, paired longitudinal samples are available from initial diagnosis and long-term surivor state. For each patient and timepoint, total bone marrow mononuclear cells and CD3+ T cells have been sequenced separately.

  Study EGAS00001006980

• 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

• LCCC1122: Defining the Triple Negative Breast Cancer Kinome Response to GSK1120212

  In this study the investigators looked at adaptive reprogramming impact on the kinome when a MEK inhibitor called GSK1120212 (trametinib) was administered in a "window of opportunity" trial. GSK1120212 is not yet approved by the FDA for use in breast cancer patients. The investigators gave GSK1120212 for a short period of time (one week) to examine MEK and the other kinase expression in cancer cells both before and after the study drug is given. The investigators gave this drug for research purposes only. The length of time it was given is not intended to treat cancer. Recently researchers at UNC developed a process that can comprehensively profile the majority of the individual kinases in the kinome and examine the impact on kinase expression of kinase inhibitors (Duncan et al, Cell 2012, PMID: 22500798). This can tell us which kinases need to be concurrently blocked to augment responsiveness and prevent acquired resistance so that the investigators can design the best combinations of kinase blocking drugs for triple negative breast cancer. This is especially important for individuals with triple negative breast cancer (TNBC) because there are no targeted drugs available that can block molecules that affect tumor growth. The investigators believe that kinase-blocking drugs have the potential to be a more effective treatment for people with TNBC. In this recently published study (Zawistowski et al, Cancer Discovery 2017, PMID: 28108460), TNBC patients treated with trametinib for 7 days resulted in a transcriptional response characterized by significant reprogramming of the tyrosine kinome, and this adaptive bypass response in human tumors was found to be similar to that seen in preclinical models including TNBC cell lines and mouse xenografts. In this study we also examined whether reprogramming differed between TNBC molecular subtypes, finding that basal-like and claudin-low human TNBC cells and mouse tumor subtypes had different adaptive transcriptional responses to MEK-ERK inhibition. Mechanistically we found that genome-wide enhancer remodeling drove the adaptive transcriptional response, suggesting that epigenetic approaches to reprogramming may be more durable than kinase inhibitor polypharmacology.

  Study phs001405

• Genome-Wide Pleiotropy Scan Across Multiple Cancers

  A whole-exome sequencing (WES) study was conducted in 3,233 cases diagnosed with multiple primary cancers and 3,229 matched cancer-free controls (90% non-Hispanic white, 3% African-American, 3% East Asian, and 4% Latino) selected from individuals in the Kaiser Permanente Research Bank (KPRB) who were members of the Kaiser Permanente Northern California (KPNC) health plan. Cancer-free controls were matched to cases on age at specimen collection (within 2 years), sex, genotyping array (which matched on self-reported race/ethnicity), closest distance using the first two principal components for genetic ancestry, and reagent kit. Cases and controls were drawn from two prospective KPRB cohorts: the Research Program on Genes, Environment and Health (RPGEH) and the ProHealth study. Participants were sequenced by the Regeneron Genetics Center using the Illumina NovaSeq 6000 platform, and sample preparation and quality control were performed using a high-throughput, fully-automated system [PMID: 33087929]. Reads were aligned to the GRCh38 reference genome, and variants were called using WeCall [PMID: 33087929]. Participants with sex discordance, 20x coverage at less than 80% of targeted sites, and/or contamination greater than 5% were excluded. After quality control, we retained n = 6,247 (3,111 cases, 3,136 controls) individuals for downstream analyses. Among participants selected for this WES study, n = 5,432 (2,299 cases; 3,133 controls) consented to deposition of data to the National Institutes of Health (NIH).Further quality control was applied to filter low quality variants. Genotype calls with low depth of coverage (DP) were updated to missing (DP < 7 for SNPs and DP < 10 for indels), after which sites with low allele balance (AB) - variants without at least one sample having AB ≥ 15% for SNPs or AB ≥ 20% for indels - were removed. Lastly, variants with missingness > 10% and Hardy-Weinberg equilibrium p-value < 10-15 were excluded. Further description of quality control and downstream single-variant and gene-based analyses is available in Cavazos et al, 2022 [medRxiv].

  Study phs002809

• 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

• TK-EPN862 - Patient-dervied xenograft model of Posterior Fossa A Ependymoma - RNAseq

  Ependymomas (EPN) are the third most common malignant brain cancer in children. Treatment strategies for pediatric EPN have remained unchanged over recent decades, with 10-year survival rates stagnating at just 67% for children aged 0-14 years. Moreover, patients who survive treatment for the most common subtype of pediatric EPN (posterior fossa A (PFA)), often suffer long-term neurological side effects as a result of therapy. It is evident that there is a need for safer, more effective treatments for pediatric EPN patients. There are ten distinct subgroups of EPN, each with their own molecular and prognostic features. To identify and facilitate the testing of new treatments for EPN, in vivo laboratory models representative of the diverse molecular subtypes are required. Here, we describe the establishment of a patient-derived orthotopic xenograft (PDOX) model of PFA EPN, derived from a metastatic cranial lesion. Patient and PDOX tumors were analyzed using immunohistochemistry, DNA methylation profiling, whole genome sequencing (WGS) and RNA sequencing. Both patient and PDOX tumors classified as PFA EPN by methylation profiling, and shared similar histological features consistent with this molecular subgroup. RNA sequencing revealed that gene expression patterns were maintained across the primary and metastatic tumors, as well as the PDOX. Copy number profiling revealed gains of chromosomes 7, 8 and 19, and loss of chromosomes 2q and 6q in the PDOX and matched patient tumor. No clinically significant single nucleotide variants were identified, consistent with the low mutation rates observed in PFA EPN. Overexpression of EZHIP RNA and protein, a common feature of PFA EPN, was also observed. Despite the aggressive nature of the tumor in the patient, this PDOX was unable to be maintained past two passages in vivo. Others who have successfully developed PDOX models report some of the lowest success rates for EPN compared to other pediatric brain cancer types attempted, with loss of tumorigenicity not uncommon, highlighting the challenge in propagating these tumors in the laboratory. Here, we discuss our collective experiences with PFA EPN PDOX model generation and propose potential approaches to improve future success in establishing preclinical EPN models

  Study EGAS00001006844

• Array data for oesophageal and related samples – sj_paper_methyl_normal_release

  Array data for oesophageal and related samples – sj_paper_methyl_normal_release

  Dataset EGAD00010001834

• Mutation detection of T follicular helper cell lymphomas

  Whole-exome sequencing data for study of tumor heterogeneity and immune-evasive microenvironment in T follicular helper cell lymphomas

  Study EGAS50000000276

• Roifman_DAC

  This Data Access Committee (DAC) will be set up to review applications, and render decisions for requests submitted to EGA to access data deposited into ega-box-1592 by Dr. Chaim Roifman at the Hospital for Sick Children in Toronto, Canada. Contact: Dr. Chaim Roifman. email:chaim.roifman@sickkids.ca

  Dac EGAC50000000243

• Young-Boost Whole Exome Sequencing (WES)

  Datasets associated with Young Boost Trial for Breast Cancer patients. Whole Exome Sequencing (WES) performed on 109 Samples (75 Tumor and 34 Normal). WES was performed to identify potential predictive biomarkers for treatment response.

  Dataset EGAD50000001171

• Clinical and ctDNA data for IMpassion031

  Clinical and ctDNA data for IMpassion031, including survival, response, and ctDNA data from baseline through post-surgery time points. 222 samples run on Signatera assay. File type is csv.

  Dataset EGAD50000001420

• Dataset for "HPV integration induces gene fusions" (ONT)

  ONT whole-genome sequencing data for "HPV integration induces gene fusions" . We sequenced five HPV-positive head and neck cancer samples using Oxford Nanopore platform. The sequences was submitted in fastq format.

  Dataset EGAD50000001302

• DNAmet

  DNAmet analysis of olfactory mucosa (OM) cells derived from cognitively healthy and individuals with AD exposed to traffic-related ultrafine particles (UFPs) for 72h in submerged cultures. The UFPs used for exposures were: A0 and A20. Exposures were compared to the corresponding blank samples.

  Dataset EGAD50000001531

• WGS dataset for two NUP98-Rearranged Acute Myeloid Leukemia PDX samples

  This dataset includes whole genome sequencing of two patient-derived xenograft (PDX) samples with NUP98-Rearranged Acute Myeloid Leukemia. Bam files are provided for each sample.

  Dataset EGAD50000001563

• Chromatin accessibility profiling of primary human hepatocytes

  The chromatin accessibility profiling of primary human hepatocytes using Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq).

  Study EGAS50000001230

• snRNAseq prostate cancer

  single-nucleus RNA seq characterization of human fresh frozen prostate cancer samples and non-malignant controls for the same patients. Biopsies were sectioned in 100 micron thick sections, adjacent to those used for spatial transcriptomics.

  Dataset EGAD50000001633

• Whole exome sequence analysis in multiple system atrophy

  To identify a susceptible gene for multiple system atrophy, exome sequence analysis was conducted in 14 patients and 7 controls.

  Study JGAS000009

• Bulk RNA sequencing of ALS patients

  We performed RNA sequencing to screen for alternative splicing alterations in patients with ALS to identify those linked to ALS pathology.

  Study JGAS000851

• Multi-omics characterisation of immune cells in Long Covid

  We generated 10X Multiomics (paired RNA+ATAC) for Long Covid patients with up to 14 months follow-up.

  Study EGAS50000000142

• WTCCC2 Pre-eclampsia Study

  A WTCCC2 project genome-wide association study for pre-eclampsia (PA) in 4375 individuals from Colombia, genotyped on the Affymetrix 6.0 array.

  Study EGAS00001003349

• Indonesian Genome Diversity Project 2, genotyping data

  This study reports Illumina Omni 2.5M genotyping chip data for 48 individuals from Aru, Mentawai, New Guinea and Sumba islands.

  Study EGAS00001003670

• scATAC sequence runs of 29 samples of RRMM (multiple myeloma) tumors

  scATAC sequencing was performed of 29 samples of RRMM tumors using 10xGenomics for the preparation and NovaSeq6000 for sequencing.

  Dataset EGAD00001009683

• A95626A

  Whole genome sequencing for single cells for library A95626A 1040 cells; filetype=bam

  Dataset EGAD00001007103

• A96199B

  Whole genome sequencing for single cells for library A96199B 1170 samples; filetype=bam

  Dataset EGAD00001008251

• A96205A

  Whole genome sequencing for single cells for library A96205A 1907 samples; filetype=bam

  Dataset EGAD00001008253

• 10x Genomics Single Cell Gene Expression for SA1051AX1XB01452

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1051 passage 1

  Dataset EGAD00001009124

• 10x Genomics Single Cell Gene Expression for SA1035X8XB03425

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 8

  Dataset EGAD00001009148

• 10x Genomics Single Cell Gene Expression for SA1052DX1XB01519

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1052 passage 1

  Dataset EGAD00001009122

• 10x Genomics Single Cell Gene Expression for SA1050FX1XB01446

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1050 passage 1

  Dataset EGAD00001009125

• 10x Genomics Single Cell Gene Expression for SA1093CX1XB01917

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1093 passage 1

  Dataset EGAD00001009129

• A96146A

  Whole genome sequencing for single cells for library A96146A 1195 samples; filetype=bam

  Dataset EGAD00001008233

• 10x Genomics Single Cell Gene Expression for SA1184SA

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient SA1184

  Dataset EGAD00001009145

• A118869B

  Whole genome sequencing for single cells for library A118869B 1124 cells; filetype=bam

  Dataset EGAD00001007100

• A96225C

  Whole genome sequencing for single cells for library A96225C 1034 cells; filetype=bam

  Dataset EGAD00001007120

• Single Cell Genome Sequence for DLP+ library A95632C

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA609 passage 6 on DLP+ library A95632C

  Dataset EGAD00001009322

• 10x Genomics Single Cell Gene Expression for OV2295

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma cell line OV2295

  Dataset EGAD00001009143

• A98244A

  Whole genome sequencing for single cells for library A98244A 1271 cells; filetype=bam

  Dataset EGAD00001007628

• Single Cell Genome Sequence for DLP+ library A96141A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 8 on DLP+ library A96141A

  Dataset EGAD00001009777

• A96184A

  Whole genome sequencing for single cells for library A96184A 1733 samples; filetype=bam

  Dataset EGAD00001008248

• A96172B

  Whole genome sequencing for single cells for library A96172B 1694 samples; filetype=bam

  Dataset EGAD00001008241

• A96220B

  Whole genome sequencing for single cells for library A96220B 1267 samples; filetype=bam

  Dataset EGAD00001008258

• A96244A

  Whole genome sequencing for single cells for library A96244A 1374 samples; filetype=bam

  Dataset EGAD00001008259

• 10x Genomics Single Cell Gene Expression for SA604X8XB02164

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA604 passage 8

  Dataset EGAD00001009118

• 10x Genomics Single Cell Gene Expression for SA1049AX1XB01417

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1049 passage 1

  Dataset EGAD00001009121

• 10x Genomics Single Cell Gene Expression for SA1091AX1XB01790

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1091 passage 1

  Dataset EGAD00001009130

• 10x Genomics Single Cell Gene Expression for SA1052BX1XB01516

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1052 passage 1

  Dataset EGAD00001009127

• 10x Genomics Single Cell Gene Expression for SA1096AX1XB02034

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1096 passage 1

  Dataset EGAD00001009132

• 10x Genomics Single Cell Gene Expression for SA1035X4XB02879

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 4

  Dataset EGAD00001009147

• A98290B

  Whole genome sequencing for single cells for library A98290B 869 cells; filetype=bam

  Dataset EGAD00001007635

• Single Cell Genome Sequence for DLP+ library A95670A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 5 on DLP+ library A95670A

  Dataset EGAD00001009448

• Dataset for atypical teratoid rhabdoid tumors transcriptome sequencing

  This dataset contains transcriptome sequencing for 12 samples from atypical teratoid rhabdoid tumors (ATRTs). The sequencing was performed on Illumina HiSeq 4000. The sequencing was always paired.

  Dataset EGAD00001015542

• A110632B

  Whole genome sequencing for single cells for library A110632B 892 cells; filetype=bam

  Dataset EGAD00001007095

• A96186A

  Whole genome sequencing for single cells for library A96186A 850 samples; filetype=bam

  Dataset EGAD00001008249

• Single Cell Genome Sequence for DLP+ library A95628A

  Single Cell Genome Sequence for triple negative breast cancer patient SA1162SB on DLP+ library A95628A

  Dataset EGAD00001009319

• A96211C

  Whole genome sequencing for single cells for library A96211C 1397 samples; filetype=bam

  Dataset EGAD00001008255

• A96201A

  Whole genome sequencing for single cells for library A96201A 536 samples; filetype=bam

  Dataset EGAD00001008252

• V4 Colorectal panel test (2018-03-07)

  Targeted gene screen of cell line tumours for testing the new V4 Colorectal gene panel. . This dataset contains all the data available for this study on 2018-03-07.

  Dataset EGAD00001004000

• ICGC Benchmark 1 (CLL)

  Raw fastq files from WGS sequencing of CLL and matching blood normal for the ICGC Techval Benchmark1 study. Sequence data was provided to multiple centers for independent analysis and comparison.

  Dataset EGAD00001001858

• Human Developmental Cell Atlas_HDCA - WGS (2019-04-11)

  Single cell + bulk genomics study for immune and hematopoietic organs during human fetal development . This dataset contains all the data available for this study on 2019-04-11.

  Dataset EGAD00001004953

• Single Cell RNA Seq LGG

  Single Cell RNA sequencing for 5 low grade glioma samples. NovaSeq6000 was used for scRNA Seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005368

• Single Cell RNA Seq GBM

  Single Cell RNA sequencing for 4 high grade glioma samples. NovaSeq6000 was used for snRNA Seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005369

• sWGS for genome-wide copy number profiling

  Shallow whole-genome sequencing (sWGS) data for the identification of somatic copy number alterations (SCNA) and the estimation of tumor fractions in plasma DNA of metastatic colorectal cancer patients (mCRC).

  Dataset EGAD00001006384

• A98283A

  Whole genome sequencing for single cells for library A98283A 1137 cells; filetype=bam

  Dataset EGAD00001007130

• Sequencing data for "Replication timing alterations are associated with mutation acquisition during tumour evolution in breast and lung cancer"

  Repli-seq data for "Replication timing alterations are associated with mutation acquisition during tumour evolution in breast and lung cancer"

  Dataset EGAD00001015363

• University of Washington Center for Mendelian Genomics (UW-CMG): Atrioventricular Septal Defects (AVSD) Study

  The Centers for Mendelian Genomics project uses next-generation sequencing and computational approaches to discover the genes and variants that underlie Mendelian conditions. By discovering genes that cause Mendelian conditions, we will expand our understanding of their biology to facilitate diagnosis and new treatments.

  Study phs001774

• ATAC sequencing raw data files of human Treg cell subsets

  Assay for Transposase-Accessible Chromatin using sequencing (ATAC) sequencing of human CD38+, HLA-DR+, CD38+ HLA-DR+, and CD38- HLA-DR- regulatory T (Treg) cell subsets sorted from peripheral blood pooled from 4 healthy donors. After isolation and purification of Treg cell subsets with flow cytometry, samples were frozen and transferred to Active Motif for ATAC sequencing. The dataset contains one raw sequencing file (fastq) of accesible regions for each of the above-mentioned Treg cell subsets.

  Dataset EGAD50000000664

• RNA-seq

  Our cohort represents infants with ALL in whom KMT2Ar was not detected by FISH or by standard cytogenetics. Whole-transcriptome sequencing (WTS) libraries were prepared using the NEBNext Ultra II RNA Directional library kit for samples with at least 100ng RNA (n=19), the Clontech double stranded cDNA conversion kit plus the Nextera XT library protocol for samples with less than 100ng RNA (n=4), or ribodepletion using NEBNext rRNA Depletion Kit v2 (Human/Mouse/Rat) for Total RNA (n=1). Libraries were paired-end sequenced using the Illumina HiSeq 2500.

  Dataset EGAD50000000595

• BELLINI clinical trial bulk RNA-Seq data: cohorts A, B & С

  RNA-Seq data of 78 breast cancer primary tumors obtained from 45 unique patients from BELLINI clinical trial. The data includes pre- and post-treatment samples. Patients in cohort A (15 patients, 25 samples) received nivolumab for 4 weeks, patients in cohort B (15 patients, 28 samples) received nivolumab + ipilimumab for 4 weeks, and patients in cohort C (15 patients, 25 samples) received nivolumab + ipilimumab for 6 weeks. The included raw transcriptome sequencing data in fastq format was generated using Illumina NovaSeq 6000.

  Dataset EGAD50000000808

• Profiling of human fecal microbiota for succinate consumption

  The objective of this study was to identify the bacterial taxa responsible for succinate consumption in human gut microbiomes. We enriched fresh stool from healthy donors in growth medium with and without supplemented succinate. We then measured metabolite concentrations and bacterial composition in the stool and enrichment samples.

  Study EGAS50000000519

• RNA-Seq Brest Patient-derived Tumor Organoids

  This dataset includes 14 RNASeq samples from 8 patients. For some patients, organoids at different passages have been profiled. They can be identified by their alias, composed of the following structure: <subjectId>_O<organoid_number>_<passage>_RNA. For example, ICSBCS002_O1_1_RNA indicates the earlier passage of organoid 1 for subject ICSBCS002, and ICSBCS002_O1_2_RNA indicates the later passage. Additionally, WCM2137_O1_RNA and WCM2137_O2_RNA indicate two organoids (O1 and O2) derived from different tissues. More information about the passages and tissues can be found in the sample information table.

  Dataset EGAD50000000960

• Illumina GSA-MD v3 genotyping arrays for 183 samples

  Genotyping arrays for 183 samples from patients with a neuroendocrine tumor of the small intestine. All samples have been hybridized on Illumina GSA-MD v3 arrays with standard automated protocols. Reading of the chips was performed on Illumina iScan+ scanners and this file is the result of primary analysis done using Illumina GenomeStudio software. It contains data for 147 tumors, 2 adenomas, 1 lymph node, 19 mesenteric nodules, 6 liver metastasis and 8 normal ileum samples.

  Dataset EGAD50000000905

• Sequencing data of the ampulla and fimbriae of the fallopian tube in pre-menopausal women

  A total of 10 pre-menopausal patients were recruited for this study. Sequencing of both the fimbriae and ampullary region of the fallopian tubes was performed using 10x Genomics single-cell RNA-seq and ATAC-seq. For one patient, data were sequenced across 4 lanes, while the remaining patients' data were sequenced in 2 lanes each, resulting in a total of 44 sequencing datasets for each platform.

  Dataset EGAD50000000889

• Clinical Outcomes for 344 Diffuse Large B-Cell Lymphoma Patients

  This dataset contains clinical outcomes for 344 Diffuse Large B-Cell Lymphoma patients. For each subject we have recorded AGE (binarized: > 60, <= 60), SEX (male or female), LDH (serum lactate dehydrogenase), ECOG (Performance status 3 or 4), STAGE (Disease stage III or IV according to the Ann Arbor staging system), EXBM (Two or more extranodal sites of disease (outside of a lymph node), BSYMP (Presence of B symptoms), and BM (Bone marrow involvement). All data are delivered as a single XLSX file with one row per patient.

  Dataset EGAD50000001536

• Genetic analysis in monozygotic twins discordant for bipolar disorder

  To identify genes relevant to bipolar disorder, an exome sequence analysis was conducted in 3 pairs of healthy monozygotic twins as control twins in this study. In addition to this data set, an exome sequence analysis was also conducted in several monozygotic twins discordant for bipolar disorder.

  Study JGAS000014

• comprehensive genetic analysis and database construction for head and neck cancer

  The main purpose is to conduct comprehensive genetic analysis of head and neck malignant tumor tissue and patient-derived blood, and to construct a database for it. By analyzing these databases, we aim to clinically identify recurrence and metastasis prediction markers and establish new molecular target therapies.

  Study JGAS000214

• RRBS of 58 pleural mesothelioma samples (Paired-end)

  The dataset contains RRBS data (bam files) for 58 pleural mesothelioma samples. The samples were sequenced on NovaSeq6000 (Illumina, San Diego, CA) in paired-end 150bp mode. RRBS raw reads were trimmed for adaptor sequences using trim galore (v. 0.6.5) (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) and filtered for low-quality sequences using fastQC (v. 0.11.8) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). High quality trimmed reads were mapped to the Human reference genome (UCSC genome assembly GRCh38/hg38) using Bismark (v. 0.22.3) with default parameters.

  Dataset EGAD50000002129

• TTFields Glioblastoma Data Access Committee, Charité – Universitätsmedizin Berlin

  This Data Access Committee is responsible for reviewing and approving data access requests for genomic and associated clinical data from patients with glioblastoma, IDH wildtype, treated with surgery, standard chemoradiotherapy and Tumor Treating Fields at Charité – Universitätsmedizin Berlin. The DAC ensures that any data use is consistent with the patients’ informed consent, the study’s ethical approval and applicable data protection regulations. Approved applicants may use the data only for biomedical research on brain tumours and related methods, must not attempt re-identification of participants, and may not share the data with unauthorized third parties

  Dac EGAC50000000843

• Transcriptomics for LUNG-NSCLC2 cohort

  The goal of IMMUcan is to understand how the immune system interacts with tumors and how therapeutic interventions influence this interaction. This cohort includes patients with non-small cell lung cancer who had surgical resection. This is a subcohort of the EORTC SPECTAlung study. This dataset contains transcriptomics data processed with the STAR pipeline. Two sequencing kits were employed: Roche KAPA for high-quality RNA samples and Takara SMART-Seq Stranded for samples with limited RNA quantities.

  Dataset EGAD50000002236

• CEITEC DAC

  A CEITEC Data Access Committee (DAC) is a body of one or more named individuals who are responsible for data release to external requesters. The members of DAC are selected members of research groups and their respective leaders. Multiple datasets may be affiliated to a single DAC. DAC continues communication with requester to understand the purpose of use, i.e. research setting the dataset is envisioned for by requester, to identify if data sharing is possible directly under current conditions for the dataset, or actions such as re-consenting is needed and desirable to accommodate the proposed research setting/data use.

  Dac EGAC50000000049

• MASQ targeted amplicon sequencing data of AML samples at presentation, remission, and relapse, and MASQ data demonstrating performance ranges of the method.

  MASQ targeted amplicon sequencing of AML blood or bone marrow at presentation, and relapse, when available, for 5 patients. Remission samples of both blood and bone marrow are included for 5 patients. Multiple batches (b1,b2) are used for 2 patients. There are 25 assays of AML data. MASQ data demonstrating sensitivity, input range, and batch size are also included, as 12 assays. All data is provided in paired FASTQ files.

  Dataset EGAD00001005121

• Panel-based NGS data for ADME genes in human liver samples

  Panel-based next-generation sequencing data of 150 human surgical liver samples from Caucasian donors. The panel was designed for 340 ADME (absorption, distribution, metabolism and excretion) and ADME-related genes. NGS was carried out on the Illumina HiSeq2500 system (Illumina Inc., San Diego, CA, United States) at high depth with 2 × 100 bps paired-end reads. Variants were called using samtools and varscan (2.3.5). Data on n=15,727 filtered variants for the 150 patients are comprised in one vcf file.

  Dataset EGAD00001005116

• BLUEPRINT September 2016, ChIPmentation for class switched memory B cell from venous blood, on Genome GRCh38

  ChIPmentation data for 1 sample(s) for class switched memory B cell from venous blood, on Genome GRCh38. 2 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002933

• BLUEPRINT September 2016, ChIPmentation for CD38-negative naive B cell from cord blood, on Genome GRCh38

  ChIPmentation data for 1 sample(s) for CD38-negative naive B cell from cord blood, on Genome GRCh38. 5 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002929