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• Reference epigenome KNIH011 WGBS data generated from KEP study

  A KNIH011 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for podocytes

  Dataset EGAD00001002759

• Reference epigenome KNIH007 miRNA-seq data generated from KEP study

  A KNIH007 miRNA-seq single end data for adipocytes

  Dataset EGAD00001002766

• Reference epigenome KNIH008 miRNA-seq data generated from KEP study

  A KNIH008 miRNA-seq single end data for adipocytes

  Dataset EGAD00001002767

• Reference epigenome KNIH009 miRNA-seq data generated from KEP study

  A KNIH009 miRNA-seq single end data for preadipocytes

  Dataset EGAD00001002768

• Dataset for the study of ulcerative colitis

  Whole exome sequencing and RNAseq data.

  Dataset EGAD00001005237

• Whole Exome Sequencing

  Whole Exome Sequencing for a cohort of 20 B-ALL samples : 5 Down syndrome (DS), 7 Hyperdiploid (HeH), 3 iAMP21 and 5 others.

  Dataset EGAD00001005425

• Sporadic multiple meningioma Genomics

  Wes for 15 multiple meningioma samples from 6 individual

  Dataset EGAD00001008710

• PDX WES

  2 patient-derived xenograph tumours, and associated normal blood samples. Duplicated samples for each gave 8 pairs of fastq files

  Dataset EGAD00001009657

• WES data for HCC patients from Y90+Nivolumab trial

  This dataset includes WES fastq files of pre-treatment and on-treatment samples from 29 patients

  Dataset EGAD00001010133

• Variant Calls

  Variant Calls for all 97 consenting participants in the study.

  Dataset EGAD00001009971

• Validation of a genome-wide polygenic score for body mass index in South Asians

  Genotyping data of BMI model markers generated using Global Screening Array (Illumina Inc.)

  Dataset EGAD00001015700

• Ipilimumab plus Decitabine for Patients with MDS or AML in Post-Transplant or Transplant Naïve Settings

  Design and AimsThis study was a phase 1 clinical trial investigating the combination of ipilimumab (IPI), a CTLA-4 checkpoint inhibitor, and decitabine (DEC), a hypomethylating agent, for treating relapsed/refractory (R/R) myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). The goal was to determine whether DEC could enhance the immune response to IPI without causing excessive immune toxicity. This study provides important insights into the use of immune checkpoint inhibitors with hypomethylating agents in treating high-risk MDS/AML and highlights the need for further research on optimizing immune-based therapies in these settings.Population InformationPost-HSCT arm: 25 patients (23 AML, 2 MDS; all relapsed/refractory).Transplant-naïve arm: 23 patients (15 AML, 8 MDS; 20 relapsed/refractory, 3 untreated).Data available through dbGaPDeidentified individual participant data, Multiplex immunofluorescence staining, Olink, RNA-Seq and Whole Exome SequencingPrincipal FindingsSafety & Tolerability: The maximum tolerated dose (MTD) for IPI was determined to be 10 mg/kg, though higher doses were associated with increased immune-related adverse events (irAEs).Efficacy: The overall response rate (ORR) was higher in transplant-naïve patients (52%) compared to post-HSCT patients (20%).Responses were more frequent at higher IPI doses (10 mg/kg). Some patients achieved durable remission (>1 year), often linked to irAE development.Toxicity: The most common grade ≥3 treatment-emergent adverse events included neutropenia (32%-48%), thrombocytopenia (28%-48%), and febrile neutropenia (36%-61%). irAEs were observed in 44%-48% of patients, with GVHD occurring only in the post-HSCT group.Survival Outcomes: Median overall survival and progression-free survival did not significantly differ between the two arms. However, in transplant-naïve patients, 1-year survival was higher in those who developed irAEs (72.7%) versus those who did not (33.3%).

  Study phs003292

• Spatial Transcriptomics of Vaccine Therapy With or Without Cyclophosphamide in Treating Patients Undergoing Chemotherapy and Radiation Therapy for Stage I or Stage II Pancreatic Cancer That Can Be Removed by Surgery

  Pancreatic ductal adenocarcinoma (PDAC) is a rapidly progressing cancer that responds poorly to immunotherapies. Intratumoral tertiary lymphoid structures (TLS) have been associated with rare long-term PDAC survivors, but the role of TLS in PDAC and their spatial relationships within the context of the broader tumor microenvironment remain unknown. Here, spatial transcriptomics data were generated from 16 pancreatic cancer patients treated with GVAX, whose tumors presented with induced intratumoral TLS. Resected tumor specimens were embedded in OCT media and freshly frozen. Sections were cut into 6.5 x 6.5 mm dimensions compatible with Visium spatial transcriptomics slides. TLS-positive tumor sections from 14 patients (14 tumor samples in total) and lymph nodes from remaining patients (2 lymph node samples in total) were selected by an expert pathologist for profiling. We used 4 Visium slides and covered the 16 capture areas with sections cut in a cryostat at 10 µm. The fresh frozen Visium protocol was followed according to manufacturer's recommendations (10x Genomics Spatial 3' v1). Tissue permeabilization and library construction was carried out according to the manufacturer's protocols. Permeabilization time was optimized to 30 minutes. The captured tissue sections were permeabilized to allow RNA to be captured onto the slide where it reverse-transcribed to cDNA. The cDNA was then amplified, and sequencing libraries were prepared using the 10x Genomics Visium Spatial Gene Expression Reagent Kit for Library Preparation. The libraries were then sequenced using an Illumina NovaSeq sequencer. The raw data obtained from the sequencing were processed using the SpaceRanger (spaceranger-1.2.0) pipeline for alignment onto the GRCh38-2020-A reference transcriptome quantification of gene expression. Unsupervised learning generated a TLS-specific spatial gene expression signature that significantly associates with improved survival in PDAC patients. These analyses demonstrate TLS-associated intratumoral B cell maturation.

  Study phs003862

• DNA Methylation in Prostate Tumor and Paired Benign Tissue for African and European Ancestry Men

  We recruited prostate cancer (PCa) patients undergoing robotic-assisted laparoscopic radical prostatectomy at the University of Chicago Medical Center Urology Clinic between 2011 and 2017. Our study includes 76 African American (AA) and 75 European American (EA) men with PCa of Gleason Score (GS) ≥7, recruited by the Epidemiology Research Recruitment Core at the University of Chicago. All patients consented to the collection of questionnaire data, prostate tissue, and medical records. The study was approved by the Institutional Review Board of the University of Chicago. Following prostatectomy, prostate tissue was sent to the Human Tissue Research Center (HTRC) at the University of Chicago. Each sample underwent histological examination and Gleason scoring by University of Chicago genitourinary pathologists. The presence of adenocarcinoma was confirmed by overexpression of alpha-methylacyl-coenzyme-A racemase (AMACR) and areas for DNA extraction were marked. Tissue samples used for DNA extraction were obtained using with either a 1mm biopunch or laser microdissection of a 100 µm2 area of tissue. Genome-wide DNA methylation profiles were generated using Illumina's Infinium MethylationEPIC BeadChip array kit. Genome-wide SNP data was generated (from benign tissue DNA) using the Illumina Infinium Multi-Ethnic Global-8 v1.0 array at the University of Chicago Genomics Core Facility. We performed imputation using the Haplotype Reference Consortium (HRC, Version r1.1 2016) panel, which includes all samples from the 1,000 Genomes Project (Phase 3), using the Michigan Imputation Server. The imputed SNP data for mQTL analysis was imputed based on genotype SNP data which was all from benign tissue DNA and the methylation data was identified in both tumor and benign tissue, both AA and EA men, and identified QTLs co-occuring with prostate cancer susceptibility loci.Data including SNP genotypes, DNA methylation, tissue type, and participant characteristics will be available in dbGaP.

  Study phs003516

• Na?ve Treg-specific genomic DNA hypomethylation for autoimmune disease susceptibility

  Naturally occurring regulatory T (Treg) cells, which are specifically expressing the transcription factor FOXP3, CD25 and CTLA-4, are indispensable for the maintenance of immunological self-tolerance and homeostasis. Mutations of the FOXP3, CD25 and CTLA4 genes compromise the function of natural FOXP3+CD25+CD4+ Treg cells and cause severe autoimmune diseases showing Mendelian inheritance. Also, altered Treg-specific epigenetic changes, such as DNA hypomethylation and enhancer activation, impair the expression of these molecules in developing Treg cells, causing autoimmune diseases. It is obscure, however, whether Treg cells significantly contribute to the development of polygenic common autoimmune diseases (such as type 1 diabetes and rheumatoid arthritis), which afflict ~10% of the population. Recent genome-wide association study (GWAS) of common autoimmune diseases has revealed many autoimmune disease-associated single nucleotide polymorphisms (SNPs) in enhancer regions of immune cells, for example, in non-coding regions of T cell activation-associated genes such as CD25 and CTLA4. These findings, especially the involvement of common T-cell genes in the functions of both Treg cells and effector T cells, prompt one to ask whether autoimmune disease-associated causative SNPs impact on the functions of either disease-causing effector T cells or disease-suppressing Treg cells. Here, we have explored epigenetic profiles of human Treg and conventional T (Tconv) cells in na?ve or activated states, and found that autoimmune disease-associated SNPs are predominantly enriched in Treg-specific CpG demethylated regions (Treg-DRs), which were mostly included in Treg-specific super-enhancers. Treg-DRs are developmentally established in na?ve Treg cells before antigenic stimulation, distinct from activation-induced CpG hypomethylation in Treg or Tconv cells, and highly associated with Treg cell-specific transcriptional and epigenetic changes. Taken together, genetic variants affecting endogenous development of natural Treg cells and their Treg-intrinsic functions are causative of common autoimmune diseases, indicating natural Treg cells as a key target for their treatment and prevention.

  Study JGAS000145

• A prospective study identifies MisMatch Repair genes as candidate predisposing genes for uveal melanoma.

  Objective: The aim of this study is to identify new predisposing genes for uveal melanoma (UM), the most common intraocular malignancy in adults. Design: Prospective cohort study from Institut Curie. Participants: 381 unselected patients diagnosed with UM between July 2021 and February 2023 at Institut Curie. Methods: All patients received genetic counseling and consented to extended genetic testing. A panel of 122 genes was analyzed by targeted sequencing on germline DNA from these patients. Main outcomes measures: UM was diagnosed by ophthalmologists at Institut Curie, and a senior pathologist confirmed the diagnosis when tumor or biopsy was available. Classification of the germline genetic variants was performed according to the ACMG guidelines. Results: We identified at least one pathogenic variant (PV) in 79 patients. Among those variants, 20 were found in clinically relevant genes for the patient’s follow-up and genetic counseling. We found a significant enrichment of germline pathogenic variants in Mismatch Repair (MMR) MLH1, MSH2 and MSH6 genes in this UM cohort, suggesting that MMR germline alterations could predispose to UM. From this cohort, one tumor was available from a patient carrying a MLH1 germline mutation. The tumor was characterized by a monosomy 3, in line with a loss of heterozygosity (LOH) of MLH1, located on chromosome 3. Loss of expression of MLH1 and PMS2 was observed by immunohistochemistry, and MMR mutational signatures SBS6, ID1 and ID2 were identified from the whole genome sequencing of the tumor. These data suggest that MLH1 is implicated in the oncogenesis of this UM. Conclusions: This study is the largest prospective constitutional study performed on UM patients, and the first demonstrating that MMR germline alterations are significantly enriched among these patients. Further analyses will be needed to estimate the risk of developing UM for patients with Lynch syndrome

  Study EGAS50000000914

• 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

• Randomized Evaluation of Sedation Titration for Respiratory Failure (RESTORE-BioLINCC)

  Data Access NOTE Please refer to the "Authorized Access" section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives The primary aim of the RESTORE clinical trial was to determine whether critically ill children managed with a nurse-implemented, goal-directed sedation protocol would experience fewer days of mechanical ventilation than participants receiving usual care. Background Ensuring the safety and comfort of critically ill infants and children supported by mechanical ventilation is integral to the practice of pediatric critical care. Although sedation therapy benefits young participants who cannot understand the imperative nature of invasive life-sustaining therapies, sedative use is also associated with untoward adverse effects. Specifically, opioids and benzodiazepines commonly used for pediatric sedation may impair bedside neurological assessment, depress spontaneous ventilation, and prolong mechanical ventilation. Over time, drug tolerance develops, which may precipitate iatrogenic withdrawal syndrome when sedation therapy is no longer necessary.Numerous studies in adult critical care support a minimal yet effective approach to sedation management. Sedation goals for mechanically ventilated adults have shifted from an unresponsive state to a calm, easily aroused, readily evaluated participant. Studies in adult participants evaluating targeted sedation, daily interruption and/or titration of sedation, pairing of spontaneous awakening with breathing, and no sedation have reported improved clinical outcomes, including decreased length of mechanical ventilation when compared with usual care. In contrast, few data inform sedation practices in pediatric critical care, and international studies describe significant practice variation. Given unique biobehavioral differences, knowledge generated in adult critical care may not translate to the care of critically ill children. The RESTORE study was conducted to test the effect of a nurse-implemented, goal-directed sedation protocol on clinical outcomes in pediatric participants with acute respiratory failure. Participants There was a total of 2,449 participants (mean age: 4.7 years; range: 2 weeks to 17 years). Design The RESTORE study was a cluster randomized clinical trial conducted in 31 US PICUs. Intervention PICUs (17 sites; 1,225 participants) used a protocol that included targeted sedation, arousal assessments, extubation readiness testing, sedation adjustment every 8 hours, and sedation weaning. Control PICUs (14 sites; 1,224 participants) managed sedation per usual care. The primary outcome variable was duration of mechanical ventilation. Conclusions Among children undergoing mechanical ventilation for acute respiratory failure, the use of a sedation protocol compared with usual care did not reduce the duration of mechanical ventilation. Exploratory analyses of secondary outcomes suggest a complex relationship among wakefulness, pain, and agitation (JAMA 2015; 313(4):379-89).

  Study phs003783

• MediMer: A versatile do-it-yourself peptide-receptive MHC class I multimer platform for tumor neoantigen-specific T cell detection

  Peptide-loaded MHC class I (pMHC-I) multimers have revolutionized our capabilities to monitor disease-associated T cell responses with high sensitivity and specificity. To improve the discovery of T cell receptors (TCR) targeting neoantigens of individual tumor patients with recombinant MHC molecules, we developed a peptide-loadable MHC class I platform termed MediMer. MediMers are based on soluble disulfide-stabilized β2-microglobulin/heavy chain ectodomain single-chain dimers (dsSCD) that can be easily produced in large quantities in eukaryotic cells and tailored to individual patients’ HLA allotypes with only little hands-on time. Upon transient expression in CHO-S cells together with ER-targeted BirA biotin ligase, biotinylated dsSCD are purified from the cell supernatant and are ready to use. We show that CHO-produced dsSCD are free of endogenous peptide ligands. Empty dsSCD from more than 30 different HLA-A,B,C allotypes, that were produced and validated so far, can be loaded with synthetic peptides matching the known binding criteria of the respective allotypes, and stored at low temperature without loss of binding activity. We demonstrate the usability of peptide-loaded dsSCD multimers for the detection of human antigen-specific T cells with comparable sensitivities as multimers generated with peptide-tethered β2m-HLA heavy chain single-chain trimers (SCT) and wild-type peptide-MHC-I complexes prior formed in small-scale refolding reactions. Using allotype-specific, fluorophore-labeled competitor peptides, we present a novel dsSCD-based peptide binding assay capable of interrogating large libraries of in silico predicted neoepitope peptides by flow cytometry in a high-throughput and rapid format. We discovered rare T cell populations with specificity for tumor neoepitopes and epitopes from shared tumor-associated antigens in peripheral blood of a melanoma patient including a so far unreported HLA-C*08:02-restricted NY-ESO-1-specific CD8+ T cell population. Two representative TCR of this T cell population, which could be of potential value for a broader spectrum of patients, were identified by dsSCD-guided single-cell sequencing and were validated by cognate pMHC-I multimer staining and functional responses to autologous peptide-pulsed antigen presenting cells. By deploying the novel CHO producer cell-based dsSCD MHC-I MediMer platform, we hope to significantly improve success rates for the discovery of personalized neoepitope- specific TCR in the future by being able to also cover rare HLA allotypes.

  Study EGAS50000000065

• The CCA Genome Core is responsible for archiving the sequencing data generated by the Cancer Centre Amsterdam (CCA).

  Dac EGAC00001000181

• tRCC UTSW 2024 Cohort

  This submission contains the WES and RNA-Seq raw data for the study titled 'Comparative Genomics of a Translocation Renal Cell Carcinoma Mouse Model Reveals Molecular Mechanisms of Tumorigenesis.

  Dac EGAC50000000105

• Walter and Eliza Hall Institute - University of Melbourne

  Data Access Committee for Gocuk, Lancaster et al. 2024: nanopore sequencing data of patients with choroideremia and X-linked retinitis pigmentosa, to determine X inactivation skew.

  Dac EGAC50000000301

• Psoriasis PBMCs

  This DAC is for the purpose of controlling access to Psoriasis PBMC scRNA-seq data of the study Spermidine/spermine N1-acetyltransferase controls tissue-specific regulatory T cell function in chronic inflammation

  Dac EGAC50000000470

• HUG-CELL ASD Data Access Committee

  Data Access Committee for data generated by projects involving ASD and associated phenotypes by the Human Genome and Stem Cell Research Center (HUG-CELL), University of São Paulo

  Dac EGAC50000000475

• RCC trial WES dataset

  This dataset includes WES data for 116 runs, corresponding to 58 pairs of normal(PBMC)/tumor samples from 58 patients (116 BAM files).

  Dataset EGAD50000000953

• BROCA cancer panel sequencing for 15 samples

  BROCA panel sequencing using the BROCA-HR v8 and BROCA-GO v1 versions of the gene panel

  Dataset EGAD50000000035

• MS-Twins-EPIC-PBMCs

  Genome-wide DNA methylation profiles of MZ twins clinically discordant for MS generated using Illumina’s Infinium MethylationEPIC BeadChip assay (EPIC array)

  Dataset EGAD00010001618

• GR@ACE_StageI.v01

  Binary Plink files for pre-GWAS quality control in 7409 samples genotyped using Axiom 815K Spanish Biobank array (Thermo Fisher)

  Dataset EGAD00010001654

• GR@ACE_StageI.CEL_files

  Intensity calculation on the pixel values of the DAT file for 7409 samples genotyped using Axiom 815K Spanish Biobank array (Thermo Fisher)

  Dataset EGAD00010001655

• Exome chip analysis file

  This data set includes the following summary level data file used for the exome chip analysis: exome_chip.sv.assoc.txt: results from single variant association analysis in exome chip

  Dataset EGAD00010001187

• ChIP-Seq files for RMS.

  ChIP-Seq files accompanying the paper titled "Identification of Therapeutic Targets in Rhabdomyosarcoma Through Integrated Genomic, Epigenomic, and Proteomic Analyses".

  Dataset EGAD00001006398

• Sequencing data for ICGC Oesophageal Adenocarcinoma tissue samples - side_cases_barretts

  Dataset EGAD00001001379

• HIV exome pilot, exome data hs37d5

  exome sequence data for 57 HIV elite long term non-progressors and rapid progressors. Complete dataset of improved BAMs mapped to hs37d5 and including phenotype information.

  Dataset EGAD00001003345

• Illumina HiSeq X Ten

  Three files of patients 10, 11 and 13 with WGS done on Illumina HiSeq X Ten. For research purpose and authorised user only.

  Dataset EGAD00001003439

• Reference epigenome IPS02_N_NPC_mRNA-Seq data generated from KEP study

  A IPS02_N_NPC_mRNA-Seq paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003489

• Reference epigenome IPS03_N_ENeuron_mRNA-Seq data generated from KEP study

  A IPS03_N_ENeuron_mRNA-Seq paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003490

• Reference epigenome IPS05_X_NPC_mRNA-Seq data generated from KEP study

  A IPS05_X_NPC_mRNA-Seq paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003492

• Reference epigenome IPS06_X_ENeuron_mRNA-Seq data generated from KEP study

  A IPS06_X_ENeuron_mRNA-Seq paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003493

• Reference epigenome IPS02_N_NPC_smRNA-Seq data generated from KEP study

  A IPS02_N_NPC_smRNA-Seq single end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003502

• Reference epigenome IPS03_N_ENeuron_smRNA-Seq data generated from KEP study

  A IPS03_N_ENeuron_smRNA-Seq single end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003503

• Reference epigenome IPS05_X_NPC_smRNA-Seq data generated from KEP study

  A IPS05_X_NPC_smRNA-Seq single end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003505

• Reference epigenome IPS06_X_ENeuron_smRNA-Seq data generated from KEP study

  A IPS06_X_ENeuron_smRNA-Seq single end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003506

• Oxford Human islet ATAC-seq dataset of 5 human islets

  ATAC-seq data for 5 non-diabetic human pancreatic islet samples

  Dataset EGAD00001003759

• Benchmark and validation of whole exome sequencing of a trio and singleton for mobile element detection.

  Illumina HiSeq paired-end exome sequencing of a trio and singleton.

  Dataset EGAD00001000883

• Exome

  Exome Seq for Study EGAS00001001844

  Dataset EGAD00001002159

• Reference epigenome KNIH001 mRNA-seq data generated from KEP study

  A KNIH001 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002167

• Reference epigenome KNIH002 mRNA-seq data generated from KEP study

  A KNIH002 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002168

• Reference epigenome KNIH003 mRNA-seq data generated from KEP study

  A KNIH003 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002169

• Reference epigenome KNIH004 mRNA-seq data generated from KEP study

  A KNIH004 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002170

• Reference epigenome KNIH005 mRNA-seq data generated from KEP study

  A KNIH005 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002171

• Reference epigenome KNIH006 mRNA-seq data generated from KEP study

  A KNIH006 mRNA-seq paired end data for beta cells

  Dataset EGAD00001002172

• Melanoma - Exome sequencing

  whole exome sequencing of tumor- as well as PBMC-derived DNA of five melanoma patients for identification of naturally presented patient-specific neoepitopes

  Dataset EGAD00001002731

• ATAC-Seq of human stimulated and cultured CD4+ Treg cells

  This dataset includes whole genome sequence data for ATAC-seq (42 samples) of human stimulated and cultured CD4+ Treg cells.

  Dataset EGAD00001004829

• Reference epigenome KNIH001 miRNA-seq data generated from KEP study

  A KNIH001 miRNA-seq single end data for islet cells

  Dataset EGAD00001002760

• Reference epigenome KNIH002 miRNA-seq data generated from KEP study

  A KNIH002 miRNA-seq single end data for islet cells

  Dataset EGAD00001002761

• Reference epigenome KNIH003 miRNA-seq data generated from KEP study

  A KNIH003 miRNA-seq single end data for islet cells

  Dataset EGAD00001002762

• Reference epigenome KNIH004 miRNA-seq data generated from KEP study

  A KNIH004 miRNA-seq single end data for islet cells

  Dataset EGAD00001002763

• Reference epigenome KNIH005 miRNA-seq data generated from KEP study

  A KNIH005 miRNA-seq single end data for islet cells

  Dataset EGAD00001002764

• Reference epigenome KNIH006 miRNA-seq data generated from KEP study

  A KNIH006 miRNA-seq single end data for beta cells

  Dataset EGAD00001002765

• Leeds Paired Primary and Recurrent GBM RNAseq

  Whole transcriptome, strand-directional RNAseq data for paired primary dn recurrent samples from patients with glioblastoma

  Dataset EGAD00001005224

• Melanoma post mortem analysis

  We have been applying whole genome and transcriptome sequencing across metastases collected during post mortem. Herein we show the findings for the first such patient.

  Dataset EGAD00001005072

• Reconstructed BCRs

  Computationally reconstructed B-cell receptor sequences (using BraCeR) from scRNA-seq data for all cells passing quality control.

  Dataset EGAD00001008456

• Clinical data

  Clinical data for KATHERINE: Clinical data include Treatment Arm, Invasive Disease Free Survival (IDFS), Clinical Stage at Presentation, Hormone Receptor Status, Preoperative HER2 Directed Therapy, Pathological Node.

  Dataset EGAD00001008785

• TLR7 variants in human lupus patients

  Set of 2 bam files from patients affected with Lupus. Fastq alignments for exonic variants present in TLR7 gene.

  Dataset EGAD00001008534

• Additional Sanger Sequencing and qPCR data to PSCNL samples

  Sanger sequencing and RT-qPCR data for validation used in Primary lymphomas of the central nervous system (PCNSL).

  Dataset EGAD00001008583

• ITS amplicon sequencing dataset

  This dataset contains raw ITS amplicon sequencing data for 719 sputum samples from individuals in Guangdong province, China.

  Dataset EGAD00001009817

• COMET TCRseq raw data

  This is the raw TCRseq data for the manuscript T cell receptor repertoire sequencing reveals chemotherapy-driven clonal expansion in colorectal liver metastases.

  Dataset EGAD00001010269

• A Platform Study of Combination Immunotherapy for the Neoadjuvant and Adjuvant Treatment of Patients with Surgically Resectable Adenocarcinoma of the Pancreas

  This is a single-institutional, randomized, open label platform clinical trial testing various immunotherapy combinations. Patients with newly diagnosed and surgically resectable pancreatic ductal adenocarcinoma (PDA) and scheduled to undergo the Whipple procedure at Johns Hopkins Hospital were eligible to participate in this study. Criteria for determining resectability strictly followed National Comprehensive Cancer Network (NCCN) guidelines. The study consisted of 6 parts: parts 1-5 constituted the Prime Phase (see below), and part 6 was the Extended Treatment Phase. Part 1: Participants received one cycle of immunotherapy two weeks prior to undergoing the Whipple procedure. Part 2: Subjects underwent the Whipple procedure. Part 3: Subjects received a second cycle of immunotherapy 4-10 weeks following the Whipple procedure (2-4 weeks prior to adjuvant chemoradiation). Part 4: Subjects underwent chemoradiation. Part 5: Subjects received 4 additional 28-day cycles of immunotherapy beginning 1-2 months after completing chemoradiation for a total of six cycles (two before chemoradiation and 4 following chemoradiation). Eligible subjects were randomized to Arms A and B in a 1:1 ratio, stratified up-front by age (≤65, >65). Arm A participants received Cy/GVAX. Arm B participants received the combination of Cy/GVAX and nivolumab. GVAX Vaccine consists of equal numbers (2.5 x 108 each) of Panc 6.03 pcDNA1GM-CSF and Panc 10.05 pcDNA1GM-CSF combined into a single vaccination. Each vaccination consisted of six total intradermal injections. A single intravenous (IV) dose of 200 mg/m2 Cy (CytoxanR) was given one day prior to GVAX. Anti-PD-1 Therapeutic Antibody Nivolumab 480 mg every 4 weeks was administered as an intravenous infusion. The primary objective for Arms A and B was to compare IL17 expression (Th17) in vaccine-induced lymphoid aggregates between resected PDAs from patients treated with the combination of Cy/GVAX and anti-PD-1 blockade antibody vs. the treatment of Cy/GVAX alone. Secondary objectives were to assess the safety, overall survival (OS), and disease free survival (DFS) of patients treated with each of the study drug combinations, and to assess the effects of each of the immunotherapy study drug combinations on PD-L1/PD-1 associated pathways, vaccine-induced immune regulatory signatures, and peripheral and intratumoral antigen specific T cell responses. The exploratory objective was to explore the effects of therapy on tumor and peripheral blood and tumor infiltrating immune cells, and to explore potential molecular determinants of response, progression and disease stability. To these ends, tumor infiltrating immune cells (TIICs) were obtained from surgically resected tumors following the neoadjuvant treatment of the study immunotherapy. TIICs were sorted into CD3+CD4+ T cells, CD3+CD8+ T cells, CD3-CD19+ B cells, and CD3-CD19-CD11b+ myeloid cells by flow cytometry. The sorted immune cell subtype pellets were sent to the commercial vendor (Eurofins) for RNA extraction and sequencing. SMART-seq v4 Ultra Low Input mRNA Sample Prep Kit was used for library preparation with 100bp paired end reads. FASTQ files, that were returned from the vendor, are deposited here.

  Study phs003002

• Data Access Committee for Deciphering Developmental Disorders (DDD) Project

  Dac EGAC00001000282

• DAC policy The Kids Research Institute Australia

  DAC policy related to the study: Visium CytAssist Spatial Gene Expression analysis for FFPE glioblastoma samples. Committee: Emily Fletcher emily.fletcher@thekids.org.au Anya Jones anya.jones@thekids.org.au Emma Stone emma.stone@thekids.org.au

  Dac EGAC50000000734

• Brain Mapping by Integrated Neurotechnologies for Disease Studies: Human Brain Aging Imaging Study

  Compare the MRI data with amyloid PET images and cognitive data.

  Study JGAS000277

• RNA sequencing study for 8 pairs of primary NSCLCs and distant metastases

  This study is to understand of the molecular mechanisms driving metastasis.

  Study EGAS00001004078

• Whole exome sequencing study for 8 pairs of primary NSCLCs and distant metastases

  This study is to understand of the molecular mechanisms driving metastasis.

  Study EGAS00001004077

• Low coverage whole genome sequencing form CSF-derived cell free DNA

  Low coverage whole genome sequencing form CSF-derived cell free DNA for matching samples with EM-seq cohort

  Dataset EGAD50000002148

• Picuris Pueblo Genomic Project – Ancient Dataset

  Ancient individual from Picuris Pueblo. Please refer to paper for more information.

  Dataset EGAD50000001246

• WES PTCL Data

  Whole Exome Sequencing (WES) of 153 FFPE DNA samples of Peripheral T-Cell Lymphoma. Associated with "Molecular biomarkers for stratification periheral T cell lymphoma" study.

  Dataset EGAD50000001250

• APOBEC Breast Cancer Whole Genome

  Five tumor-normal human breast cancer cases driven by APOBEC mutagenesis, including normal tissue, primary, and metastatic samples for each patient.

  Dataset EGAD50000001275

• sWGS for 92 samples of mCRC

  Shallow Whole Genome Sequencing of 92 mCRC samples. Chromosomal copy number alterations analysis was performed with the samples to predict response to bevacizumab.

  Dataset EGAD50000000992

• SMPaeds PanelSeq of cfDNA at relapse with UMIs

  Deep sequencing of a targeted panel of 67 paediatric cancer related genes for cfDNA with UMIs at approximately 1500xUMI coverage.

  Dataset EGAD50000000781

• RNA-seq of murine osteosarcoma cell line genetically modified for CYR61

  Transcriptomic analysis of K7M2 murine osteosarcoma cells stably modified to overexpress or repress CYR61

  Dataset EGAD50000000747

• Whole exome sequencing data for two samples

  Whole exome sequencing of two human samples run on the Illumina HiSeq2500 platform. It contains two BAM files aligned to the refrence genoeme GRCh38.

  Dataset EGAD50000000825

• Single Cell RNA-Seq

  Examination of Sample Multiplexing Reagents for Single Cell RNA-Seq. Nine techniques applied to samples from four PDX models: #105, #177, #233, #264

  Dataset EGAD50000000216

• SAFIR02_Cytoscan

  Genomics to select patients with metastatic breast cancer for targeted therapy (microarray_cytoscan)

  Dataset EGAD00010002239

• Dataset for desmoplastic small round cell tumor array data

  This dataset contains 24 methylation array samples of patients with desmoplastic small round cell tumor measured on Illumina 850k.

  Dataset EGAD00010002689

• SNParray_PGT_samples_scGBS

  PGT samples for haplotyping and copy-number profiling via SNP array

  Dataset EGAD00010002169

• SNParray_HapMap_samples_scGBS

  HapMap samples for haplotyping and copy-number profiling via SNP array

  Dataset EGAD00010002168

• Batch2_Genotypes_Raw

  Genotypes generated for Puno cohort Batch 2. Case (PRE) and control (PUN) families were recruited in hospital. Raw genotypes no QC. Includes unrelated genotyping controls (HG).

  Dataset EGAD00010002124

• GR@ACE_StageI.v02

  Binary Plink files for post-GWAS quality control in 7409 samples genotyped using Axiom 815K Spanish Biobank array (Thermo Fisher)

  Dataset EGAD00010001653

• Copy Number Arrays for study EGAS00001004165

  Copy Number Alterations arrays from 153 samples performed by Affymetrix 6.0 and Oncoscan CNV platforms

  Dataset EGAD00010001841

• Oncotrack_450K_tumor

  Oncotrack primary tumor samples using 450K. The dataset includes shared AF analysis files oncotrackDNAmAnalysis.R and oncotrackDNAmBetaScores.txt which were applied for both Oncotrack_450K_tumor (EGAD00010001162) and Oncotrack_450K_metastatic (EGAD00010001161) datasets.

  Dataset EGAD00010001162

• Whole genome sequencing of 63 single cells isolated from bone marrow aspirates of six non-metastatic breast cancer patients after staining for disseminated tumor cells.

  Dataset EGAD00001002745

• Exome sequencing of relapsed/refractory DLBCL

  This dataset consists of the exome sequencing data for 30 tumour and germline DNA pairs derived from relapsed/refractory DLBCL.

  Dataset EGAD00001003395

• RNA-Seq Single End

  RNA analysis of six patients 34, 35, 36, 37, 38 and 39 with WGS done on Illumina HiSeq2500. For research purpose and authorised user only.

  Dataset EGAD00001003430

• Reference epigenome DB31_N_Alpha_mRNA-Seq data generated from KEP study

  A DB31_N_Alpha_mRNA-Seq paired end data for alpha cells(PSA-NCAM(-), pancreas)

  Dataset EGAD00001003485

• Reference epigenome DB31_N_Alpha_smRNA-Seq data generated from KEP study

  A DB31_N_Alpha_smRNA-Seq single end data for alpha cells(PSA-NCAM(-), pancreas)

  Dataset EGAD00001003494

• Reference epigenome ADMSC01_smRNA-Seq data generated from KEP study

  A ADMSC01_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003867

• Reference epigenome ADMSC02_smRNA-Seq data generated from KEP study

  A ADMSC02_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003868

• Reference epigenome ADMSC03_smRNA-Seq data generated from KEP study

  A ADMSC03_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003869

• ChIP-Seq files for RMS.

  ChIP-Seq files accompanying the paper titled "Identification of Therapeutic Targets in Rhabdomyosarcoma Through Integrated Genomic, Epigenomic, and Proteomic Analyses".

  Dataset EGAD00001004312

• Whole exome sequencing of inflammatory bowel disease cases

  Whole exome sequencing of around 700 inflammatory bowel disease cases.This data can only be used for the identification of IBD/immune-mediated disease loci.

  Dataset EGAD00001001354