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• CHEWIE ctDNA in Rhabdomyosarcoma

  Targeted DNA based panel of multiple ctDNA samples from 10 patients througout clinical care to assess treatment response. The panel is custom-designed and property of UGS, IC.

  Dataset EGAD00001011127

• Paediatric glioma cell line WGS

  Whole genome sequencing of 5 paediatric glioma cells lines - KNS42, SF188, UW479, RES186 and RES259. Illumina paired end sequencing is provided as 5 BAM files aligned to hg19 with bwa.

  Dataset EGAD00001004123

• TCRab sequencing of CML patients

  This dataset contains 15 TCRab sequencing samples from 6 CML patients before and after TKI-cessation. The raw data is available as fastq files.

  Dataset EGAD00001012841

• Liver Tumours WGS (2020-02-20)

  The aim of this study is to define the mutational landscape of human liver tumours. . This dataset contains all the data available for this study on 2020-02-20.

  Dataset EGAD00001005993

• TCRab sequencing of RCC patients

  TCRab sequencing was performed on viable frozen tumor dissociated cells from three RCC patients. The raw data is available as fastq files.

  Dataset EGAD00001011046

• EGAD00000000048

  Sequencing data from oestrogen-receptor-alpha-positive metastatic lobular breast cancer sample

  Dataset EGAD00000000048

• EGAD00010000722

  Pilot experiment on functional genomics in osteoarthritis (coreex)

  Dataset EGAD00010000722

• Transcriptome of (peripheral blood), from donor Sample A, replicate 2 Freezing replicates

  Freezing replicates

  Dataset EGAD00001010085

• Transcriptome of (peripheral blood), from donor Sample A, replicate 2 Technical replicates

  Technical replicates

  Dataset EGAD00001010083

• chr1_allsamples_imputated

  Chromosome 1 imputed genotypes of samples genotyped on the Axiom Human Genotyping SARS-CoV-2 array (GRCh38)

  Dataset EGAD00010002525

• LOPEZ_2019.vcf.gz

  Genotype data for new samples in Lopez et al 2021

  Dataset EGAD00010002100

• Mutation and Microsatellite Burden Predict Response to PD-1 Inhibition in Children with Germline DNA Replication Repair Deficiency: An Observational Registry Study

  Study EGAS00001005579

• Ovarian subtypes tumor methylation data

  This dataset contains ovarian endometriod and ovarian, stomach, pancreatic and colorectal mucinous tumor tissue samples

  Dataset EGAD00010002773

• Metadata file

  "Master" file of patient clinical characteristics and outcomes, samples, and the results of certain analyses, including immunohistochemistry.

  Dataset EGAD00001008792

• Oxford Nanopore sequencing for APL

  Raw fast5 file of Oxford Nanopore sequencing for an APL patient sample

  Dataset EGAD00001008151

• Reference epigenome OB56_N_PreA_WGBS data generated from KEP study

  A OB56_N_PreA_WGBS paired end data for Preadipocytes(fat)

  Dataset EGAD00001003479

• RPPA analysis + clinical data

  RPPA analysis from FAIRLANE Trial of Neoadjuvant Ipatasertib Plus Paclitaxel for Triple-Negative Breast Cancer.

  Dataset EGAD00001008507

• Durvalumab Plus Tremelimumab Alone or in Combination with Low-Dose or Hypofractionated Radiotherapy in Metastatic Non-Small-Cell Lung Cancer Refractory to Previous PD(L)-1 Therapy: an Open-Label, Multicentre, Randomised, Phase 2 Trial

  Brief SummaryThis randomized phase II trial studies the side effects of durvalumab and tremelimumab and to see how well they work with or without high or low-dose radiation therapy in treating patients with colorectal or non-small cell lung cancer that has spread to other parts of the body (metastatic). Immunotherapy with durvalumab and tremelimumab, may induce changes in body's immune system and may interfere with the ability of tumor cells to grow and spread. Radiation therapy uses high energy x-rays to kill tumor cells and shrink tumors. Giving durvalumab and tremelimumab with radiation therapy may work better in treating patients with colorectal or non-small cell lung cancer.Detailed DescriptionPRIMARY OBJECTIVES:I. To assess safety and tolerability of combined checkpoint blockade with MEDI4736 (durvalumab) and tremelimumab alone or with high or low-dose radiation in non-small cell lung cancer (NSCLC). (NSCLC Cohort) II. To compare the overall response (excluding the irradiated lesion[s]) between combined checkpoint blockade with MEDI4736 and tremelimumab alone or combined checkpoint blockade with low or high dose radiation. (NSCLC Cohort) III. To assess safety and tolerability of combined checkpoint blockade with MEDI4736 and tremelimumab with high or low-dose radiation. (Colorectal Cohort) IV. To determine the overall response rate (excluding the irradiated lesion[s]) with combined checkpoint blockade with MEDI4736 and tremelimumab with either low or high dose radiation. (Colorectal Cohort)SECONDARY OBJECTIVES:I. To estimate median progression-free survival and overall survival. (NSCLC Cohort) II. To determine local control within the irradiated field(s) and abscopal response rates. (NSCLC Cohort) III. To evaluate associations between PD-L1 expression as well as levels of infiltrating CD3+, CD8+ T-cells and overall response. (NSCLC Cohort) IV. To explore changes in PD-L1 expression, circulating T-cell populations, T-cell infiltration, ribonucleic acid (RNA) expression, spatial relationship of immune markers, and mutational burden as a result of low or high dose radiation. (NSCLC Cohort) V. To estimate median progression-free survival and overall survival. (Colorectal Cohort) VI. To determine local control within the irradiated field and abscopal response rates. (Colorectal Cohort) VII. To evaluate associations between PD-L1 expression as well as levels of infiltrating CD3+ CD8+ T-cells and overall response. (Colorectal Cohort) VIII. To evaluate changes between PD-L1 expression as well as levels of infiltrating CD3+, CD8+ T-cells induced by targeted low or high dose radiation. (Colorectal Cohort) IX. To explore changes in circulating T-cell populations, T-cell infiltration, RNA expression, spatial relationship of immune markers, and mutational burden as a result of low or high dose radiation. (Colorectal Cohort)OUTLINE: Patients are assigned to 1 of 2 cohorts. NOTE: This Study is only for Cohort 1. Please see phs003294 for Cohort 2COHORT 1: Patients with NSCLC are randomized to 1 of 3 arms.ARM A: Patients receive tremelimumab and durvalumab and as in Arm C. Beginning at week 2, patients receive high dose radiation therapy once per day (QD) over 10 days for up to 3 fractions.ARM B: Patients receive tremelimumab and durvalumab and as in Arm C. Beginning at week 2, patients receive low dose radiation therapy every 6 hours twice per day (BID) on weeks 2, 6, 10 and 14.ARM C: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses.COHORT 2: Patients with CRC are randomized to 1 of 2 arms.ARM A: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses. Beginning at week 2, patients receive high dose radiation therapy once per day (QD) over 10 days for up to 3 fractions.ARM B: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses. Beginning at week 2, patients receive low dose radiation therapy every 6 hours twice per day (BID) on weeks 2, 6, 10 and 14.After completion of study treatment, patients are followed for up to 12 weeks.Official TitleA Phase 2 Study of MEDI4736 (Durvalumab) and Tremelimumab Alone or in Combination With High or Low-Dose Radiation in Metastatic Colorectal and NSCLC Conditions Metastatic Colorectal CarcinomaMetastatic Lung Non-Small Cell CarcinomaStage IV Colorectal Cancer AJCC v7Stage IV Lung Non-Small Cell Cancer AJCC v7Stage IVA Colorectal Cancer AJCC v7Stage IVB Colorectal Cancer AJCC v7 Intervention/Treatment Biological: DurvalumabRadiation: Radiation TherapyBiological: Tremelimumab Other Study ID Numbers NCI-2016-01325NCI-2016-01325 ( Registry Identifier ) (REGISTRY: CTRP (Clinical Trial Reporting Program))17-71910021 ( Other Identifier ) (OTHER: Dana-Farber - Harvard Cancer Center LAO)10021 ( Other Identifier ) (OTHER: CTEP) Molecular Technologies / AssaysCyTOFIHCmIFOlinkRNAseqWES

  Study phs003295

• Early progression to active tuberculosis is a highly heritable trait driven by 3q23 in Peruvians

  In a genome-wide genetic study of early progression to active tuberculosis (TB), we genotyped 4,002 active TB cases and their household contacts in Lima, Peru. We first established TB progression has a strong genetic basis, and is comparable to traits with well-established genetic bases (h2g=0.212). We identified a novel association between early TB progression and variants located in a putative enhancer region on chromosome 3q23 (rs73226617, OR = 1.18; P = 3.93 x 10-8). With in silico and in vitro analyses we identified genetic variant rs73226617 or rs148722713 as the likely functional variant and ATP1B3 as a potential causal target gene with monocyte specific function.

  Study phs002025

• EGA FUSE Client

  EGA FUSE Client What is EGA FUSE Client? The EGA FUSE Client is a Java Native Runtime (JNR) based Filesystem in Userspace (FUSE) client that allows users to access authorised EGA files by presenting them in a virtual directory. This means that users can use authorised EGA files like regular files without having to download them first. The EGA FUSE Client is a useful tool for researchers who need to access and work with large amounts of EGA data. Why use EGA FUSE Client? The EGA FUSE Client provides several benefits for researchers working with EGA data: Efficient use of storage space: With the EGA FUSE Client, users can access authorised EGA files without having to download them first. This means that users can save storage space on their local machines. Faster access to data: Since users can access authorised EGA files directly through the EGA FUSE Client, they can work with the data more quickly than if they had to download the files first. Simplified workflow: The EGA FUSE Client presents authorised EGA files in a virtual directory, allowing users to work with the files like regular files. This simplifies the workflow for researchers who need to access and work with large amounts of EGA data. Get started with EGA FUSE Client To get started with EGA FUSE Client visit the EGA FUSE Client GitHub repository for more information and installation instructions.

  Documentation access/download/visualisation/fuse-client

• An exome sequencing study of the HIV elite-long term non progressors and rapid progressors (CASCADE cohorts)

  Background: A rare subgroup of HIV infected individuals naturally controls infection without treatment. These ?elite controllers? constitute an important model for the natural control of HIV infection. Indeed, the study of these individuals may provide insights into strategies for the development of HIV vaccines. Although several HLA and chemokine alleles are known to be over-represented in elite controllers, only a small portion of HIV phenotypic variation is explained by known genetic variants. The elite controller phenotype is rare and distinct, representing the extreme of an infectious disease trait. As such, this phenotype may be partly explained by variation in host immune control, which may be characterized by differences in rare functional genetic variants. Genomic regions underlying elite control can be potentially identified by comparing the presence or frequency of variants in this group to that representing the opposite extreme. In this context, ?rapid progressors? is a group defined by its rapid immunological and clinical disease progression. Aim: To extend an existing study, in order to identify DNA sequence variants involved in the control of HIV infection with greater statistical resolution. Specifically, we aim to sequence up to 200 exomes from multiple cohort studies within the EuroCoord CASCADE collaboration (a collaboration of 25 HIV seroconversion cohort studies across Europe).

  Dataset EGAD00001002179

• Clonal haematopoiesis in patients with AAA (2019-04-03)

  Recent advances in genomics have demonstrated that clonal haemopoiesis driven by leukaemia associated somatic mutations is a relatively common phenomenon that increases in frequency with advancing age. Whilst individuals with clonal haemopoiesis have an increased risk of developing haematological malignancies, they also have an increased mortality from other causes. Additionally, certain mutations are almost exclusively seen in individuals aged 70 years or older, whilst others are seen in individuals with non-haematological cancers including breast and ovarian. Recently, clonal haemopoiesis was found to be associated with a significantly increased risk of atherosclerotic cardiovascular disease. This association is thought to be causative with clonally-derived macrophages showing elevated expression of several chemokine and cytokine genes that contribute to atherosclerosis. Another vascular pathology, abdominal aortic aneurysm (AAA), increases with age and shares risk factors with atherosclerosis (including smoking, male sex, high cholesterol). However, the impact of these risk factors and the overlap between AAA and atherosclerosis is poorly understood. To investigate a possible link between clonal haemopoiesis and AAA, we will study DNA samples from 300 patients with AAA and up to 200 controls for evidence of clonal haemopoiesis. This will be done using target DNA enrichment with biotinylated RNA baits followed by high throughput sequencing. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004895

• EGAD00000000049

  RNA-SEQ data from oestrogen-receptor-alpha-positive metastatic lobular breast cancer sample

  Dataset EGAD00000000049

• EGAD00010000636

  WTCCC2 Visceral Leishmaniasis samples from Brazil using Illumina 670k

  Dataset EGAD00010000636

• EGAD00010000638

  WTCCC2 Visceral Leishmaniasis samples from Indial using Illumina 670k

  Dataset EGAD00010000638

• EGAD00010000640

  WTCCC2 Visceral Leishmaniasis samples from Sudanl using Illumina 670k

  Dataset EGAD00010000640

• Non Tumour Renal Cell Line Sequencing

  Non Tumour Renal Cell Line Sequencing

  Dataset EGAD00001000091

• Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments

  Dataset EGAD00001001602

• Second generation noninvasive fetal genome analysis reveals de novo mutations, single-base parental inheritance, and preferred DNA ends

  Dataset EGAD00001003778

• Medulloblastoma Illumina WGS

  Illumina whole genome sequencing of Medulloblastoma Blood, Primary tumor, and Relapse tumor

  Dataset EGAD00001009424

• Medulloblastoma Illumina RNA-Seq

  Illumina RNA-sequencing of Medulloblastoma primary and relapse tumor.

  Dataset EGAD00001009425

• Study1_PeaFiber

  SOMAscan plasma proteome datasets generated from participants consuming the pea fibre snack prototype (study 1)

  Dataset EGAD00010002133

• RNAseq for 4 pdx and 1 cell-line

  RNAseq for #1049, #111, #1217, #206, COV362

  Dataset EGAD50000000032

• Immune deconvolution output

  Results of comprehensive immune deconvolution analysis through the TIMER2 web portal with algorithms specified in the publication.

  Dataset EGAD00001008789

• The UCSF Glioblastoma Genome Project #1

  Dataset contains MeDIP-Seq, MRE-Seq and H3K4me3 ChIP-Seq data on 5 GBM patients.

  Dataset EGAD00001000777

• OT2_Solid_WXS_BL

  Whole-exome sequencing on AB 5500xl Genetic Analyzer of Blood EDTA (OT2_cohort)

  Dataset EGAD00001003385

• OT2_Illumina_WXS_CONT

  Whole-exome sequencing on Illumina HiSeq2000/2500 of normal colon control tissue (OT2_cohort)

  Dataset EGAD00001003378

• Whole genomes from Sahel

  The dataset includes 43 high coverge (30x) whole genome samples mostly from the Sahelian belt.

  Dataset EGAD00001011812

• Purple copy number segments for EGAS00001004572

  Bulk copy number segments from Purple analysis in EGAS00001004572

  Dataset EGAD00001006908

• DAC for "HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling"

  This is the DAC for the study "HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling" of PD Dr. Aurélie Ernst (a.ernst@dkfz.de) B420, DKFZ.

  Dac EGAC50000000452

• Akershus University Hospital Data Access Committee for Immunoglobulin Heavy-Chain locus in Multiple Sclerosis datasets in FEGA Norway

  This is the Data Access Committee appointed by Akershus University Hospital (AHUS) for datasets from Immunoglobulin Heavy-Chain locus in Multiple Sclerosis studies.

  Dac EGAC50000000659

• DAC for lymphoma IFZ Essen

  This DAC is set in place to handle acess to data on lymphomas and immune cells submitted by scientists from the Institute of Cell Biology (Cancer Research) at the University of Duisburg-Essen in Essen, Germany.

  Dac EGAC50000000521

• Whole genome sequencing in myasthenia gravis

  This dataset contains whole genome sequencing data on 25 individuals with myasthenia gravis. The data was generated using Illumina sequencing technology and is presented as BAM files for each sample.

  Dataset EGAD00001005262

• Esophageal Adenocarcinoma Organoid scRNAseq data

  This is 10X single cell RNA-seq data of esophageal adenocarcinoma organoids.

  Dataset EGAD00001007525

• Whole Exome Sequencing of gliomas

  15 whole exome sequencing datasets from five patients. Data is provided as bam files. Libraries were generated using the SeqCap EZ Exome v3.0 kit and sequenced on an Illumina sequencer

  Dataset EGAD00001003763

• WES data

  This is the Whole Exome Sequencing (WES) data from 59 samples from 11 patients with lung adenocarcinomas including 48 tumor samples and 11 peripheral white blood cell samples

  Dataset EGAD00001000984

• OAfg-M-1

  Methylation profiling of hip osteoarthritis patients who have undergone total joint replacement

  Dataset EGAD00010001291

• scoop-G-1

  Human Core Exome Genotyping for 1456 severe early onset obesity cases (SCOOP)

  Dataset EGAD00010001623

• WP8_DNA_Methylation_450K_UCL

  BLUEPRINT DNA methylation profiles of monocytes, T cells and B cells in type 1 diabetes-discordant monozygotic twins

  Dataset EGAD00010001025

• Average_5mC_genome_wide

  Average genome-wide methylation levels per sample at different time points using nanopolish calls. Data from 8 individuals.

  Dataset EGAD00010002412

• Average_5hmC_genome_wide

  Average genome-wide hydroxymethylation levels per sample at different time points using megalodon calls. Data from 8 individuals.

  Dataset EGAD00010002419

• Batch1-2_Genotypes_PostQC

  Combined genotypes for Batch 1 and 2 after quality control. All individuals included in analyses.

  Dataset EGAD00010002127

• RNA-sequençing of 21 inflammatory hepatocellular adenomas and 1 non-tumoral sample

  Dataset EGAD00001005284

• Leeds GLASS Data Release 1

  Paired end Illumina whole exome sequencing of 9 GBM trios (blood, primary and recurrent tumour).

  Dataset EGAD00001004856

• Whole-genome sequencing

  Variant calls from whole-genome sequencing of Ly1-07 using Complete Genomics paired-end sequencing service.

  Dataset EGAD00001004783

• Reference epigenome ADMSC07 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC07 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007391

• RNAseq metadata

  Meta-data/patient information for the bulk RNAseq data

  Dataset EGAD00001008815

• RCRF release 1

  Rare Cancer Research Foundation: leiomyosarcoma, large cell neuroendorcrine carcinoma, clear cell carcinoma

  Dataset EGAD00001010199

• Reference epigenome IPS06_X_ENeuron_WGBS data generated from KEP study

  A IPS06_X_ENeuron_WGBS paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003478

• OT2_Illumina_WGS_CONT

  Whole-genome sequencing on Illumina HiSeq2000/2500 of normal colon control tissue (OT2_cohort)

  Dataset EGAD00001003371

• Reference epigenome OB56_N_PreA_mRNA-Seq data generated from KEP study

  A OB56_N_PreA_mRNA-Seq paired end data for Preadipocytes(fat)

  Dataset EGAD00001003486

• 20180221_EGA_OESO

  Illumina platform sequencing of whole genome libraries prepared from normal, Barrett's oesophagus and oesophageal cancer samples from 44 donors

  Dataset EGAD00001003996

• Reference epigenome IPS05_X_NPC_WGBS data generated from KEP study

  A IPS05_X_NPC_WGBS paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003477

• RCRF release 1

  exome data from leiomyosarcoma, large cell neuroendocrine carcinoma, and clear cell sarcoma

  Dataset EGAD00001010197

• SSBP1 sample

  The dataset includes exome sequencing results for a patient with SSBP1 mutations that cause a complex optic atrophy spectrum disorder

  Dataset EGAD00001005475

• Germline snv g.vcf for EGAS00001004572

  Bulk Germline snv vcfs from haplotypecaller analysis in EGAS00001004572

  Dataset EGAD00001006910

• dataset1

  Contains 4 clonal organoid samples + 1 bulk healthy tissue sample

  Dataset EGAD00001009485

• Coagulation and Fibrinolysis in a Pediatric Insulin Titration Trial

  Summary of the Design and Aims: The Coagulation and Fibrinolysis in a Pediatric Insulin Titration Trial (CAF-PINT) is an ancillary study to the HALF PINT (NCT01565941) randomized, controlled trial that was originally designed to study changes in inflammatory and thrombosis pathways in the setting of an interventional trial. The parent HALF PINT study was designed to study the impact of tight glycemic control (TGC) using an explicit insulin titration algorithm to safely achieve a normal glucose in target range of 80-110 mg/dL versus 150-180mg/dL on clinical outcomes among critically ill hyperglycemic children with Heart and Lung Failure. The CAF PINT study collected blood samples pre randomization and at 2 and 4 days after randomization. Blood samples collected in EDTA tubes were centrifuged at local sites to separate the plasma from cell pellets and frozen prior to shipment. In addition, 0.5 mL of whole blood collected in PAXgene tubes modified for use in pediatrics frozen 24 hours after collection prior to shipment.Population Information: Setting: Twenty-one academic children's hospitals (HALF-PINT) and 1 academic children's hospital (IIT-SBPP). Participants: Critically ill hyperglycemic children requiring mechanical ventilation/vasoactive support (n=293).Interventions and Exposures: Patients in HALF PINT were randomized to achieve a low target glucose (80-110 mg/dL) vs. a higher target (150-180 mg/dL). The primary trial was stopped early due to low likelihood of achieving a statistically significant difference in the primary outcomes. Molecular Technologies Employed: Plasma biomarkers were assayed on thawed plasma using a Luminex panel. RNA was extracted from PAXgene Blood RNA Kits modified for pediatric use. Extracted RNA was sequenced with the Novaseq S4 system (Illumina) to generate 2 x 150 base paired end reads to a target depth of 50 million read-pairs per sample.Principal Findings of the Study: We tested for heterogeneity of treatment effects according to baseline blood inflammatory proteins using logistic regression with individual biomarkers as interaction terms and Cox regression analysis using biomarker-derived latent classes. Children with higher inflammatory proteins including TNFR-1, IL-6, IL-8, and IL-10 had lower mortality when treated with insulin to a target glucose of 80-110 mg/dL as compared to 150-180 mg/dL (interaction p<0.05). Causal forest estimates ranged from a 40% mortality reduction to a 15% mortality increase with TGC, depending on each patient’s individual biomarker profile. Latent class categorization strongly interacted with TGC with respect to mortality (Cox regression interaction p=0.005). Specifically, patients with higher inflammation benefited from TGC (6% mortality with TGC vs. 48% mortality, p=0.004) whereas hypoinflamed patients showed a trend towards harm (14% mortality with TGC vs. 7% mortality, p=0.055).Data available through dbGaP: Transcriptomic (Gene Counts) and phenotype data

  Study phs003016

• RNASeq from PC12 cell lines (derived from PPGL) cultivated under 21% (normoxia) and 1% (hypoxia) oxygen conditions.

  RNASeq fastq files from PC12 cells (derived from rat PPGL) were cultivated in normoxia conditions (37˚C, 5% CO2 and 21% O2 balanced with N2) or in hypoxia conditions in a hypoxic incubator (37˚C, 5% CO2 and 1% O2 balanced with N2), for short time (12h, 24h, and 48h) or prolonged time (36 days). Samples at each timepoint were cultured in triplicate (total 24 paired end fastq files). Sequencing was performed with the Illumina NovaSeq6000.

  Dataset EGAD50000001200

• Analysis of ATL progression by CD30 signaling and its biomarkers (2)

  Adult T-cell leukemia/lymphoma (ATL) develops via stepwise accumulation of gene mutations and chromosome aberrations. However, the molecular mechanisms underlying this tumorigenic process are poorly understood. We aim to elucidate the mechanism of the tumorigenic process involving genomic aberrations by CD30 signaling in HTLV-1���infected T-cells.

  Study JGAS000537

• Gene expression analysis of clear cell renal cell carcinoma

  To identify genes that are differentially expressed across different conditions (cancer vs normal, normoxic vs hypoxic), we performed RNA-seq in 3 ccRCC cell lines (786-O, A498, UMRC-2), and 1 normal kidney epithelium cell line (HK-2), cultured under normoxic (20% O2) and hypoxic (0.05% O2) conditions.

  Study EGAS50000001324

• Patients with metastatic urothelial carcinoma

  Samples from three patients with urothelial carcinoma and paired lymph node metastases and distant metastases. Patient 1: Muscle invasive tumor and lymph node metastasis. Patient 2: Muscle invasive tumor, lymph node metastasis, and distant metastasis in the small intestine. Patient 3: T1b tumor, lymph node metastasis, and distant metastasis in the lung

  Study EGAS00001002114

• Whole Exome Sequencing of cohorts of Mutant Braf mouse model melanoma DNA and germline DNA.

  Whole Exome Sequencing of cohorts of Mutant Braf mouse model melanoma DNA and germline DNA. The cohorts are (1) Mutant Braf mouse model melanomas, (2) Mutant Braf mouse model melanomas from UVR exposed mice and (3) Mutant Braf mouse model melanomas from UVR exposed, sunscreen protected mice.

  Study EGAS00001000729

• Anal SCC cell line and parent tumour comparative whole exome sequencing

  1) To perform whole exome sequencing on a panel of five human anal squamous cell carcinoma lines and their respective parent tumours. 2) To perform a comparative analysis between the lines and their parent tumours. 3) Specifically assess the somatic nucleotide variants, copy number variants, mutational burden and mutational signatures.

  Study EGAS00001005077

• bulk TCR-seq data IMCISION

  We performed bulk TCR sequencing (Adaptive Biotechnologies, Seattle, WA) on peripheral blood monocytic cells (PBMCs) from 9 HNSCC patients enrolled in the IMCISION trial (Vos et al. 2021) that responded to anti-PD-1 and anti-CTLA4 combination immunotherapy. DNA was extracted from PBMCs that were isolated from pre-treatment and on-treatment blood.

  Study EGAS00001007367

• Raw microarray gene expression data from NPC samples_Cohort 2

  We retrospectively collected 150 non-metastatic, pretreatment, formalin-fixed, paraffin-embedded (FFPE) nasopharyngeal carcinoma (NPC) samples as validation cohort 1. Also, we prospectively collected 32 FFPE samples from NPC patients enrolled in a trial evaluating anti-PD-1 antibody as validation cohort 2. Total RNA was extracted and hybridised to an Affymetrix HTA 2.0 microarray. In this study, we investigated the immune status of the tumour microenvironment (TME) based on gene expression profiles to classify NPC into biologically distinct immune subtypes, and clarify their associations with prognosis and immunotherapy response.

  Dataset EGAD00001006272

• This dataset contains WES data 5 patients and WGS data of 1 patient with Lynch Syndrome from the INFORM registry.

  This dataset contains paired whole exome sequence data of 5 patients (control/tumor pairs) and paired whole genome sequencing data of 1 patient (control/tumor pair) with Lynch Syndrome from the INFORM registry. Paired sequencing was done mostly on Illumina HiSeq 4000, few on HiSeq2500 and NovaSeq 6000. The library preparation was either with Agilent SureSelect Human_All_Exon V5 (hg19) or with Agilent SureSelectXT HS Human_All_Exon V7 (hg19). The WGS samples were prepared with Agilent SureSelect WGS.

  Dataset EGAD00001011098

• scRNAseq of ALS patients

  The data set contains information from 9 individuals (5 ALS + 4 controls) using single cell RNA sequencing in combination with TCR V(D)J sequencing to study the immune profile of the central nervous compartment (CSF). Sequencing was done using 10x Genomics platform (5’ scRNAseq & V(D)J Reagent Kits v1.1). 5P and TCR libraries were then pooled and for sequencing on the NovaSeq sequencer. Provided files are in .fastq.gz format and per individual four files are available (Read 1&2 and Lane 1&2).

  Dataset EGAD00001009623

• Solve-RD_GENTURIS_cohort-1_DF1+2_V1

  Solve-RD data submitted to the ERN-GENTURIS cohort for re-analysis (Data freeze 1+2) v1

  Dataset EGAD00001009767

• Processed microarray gene expression data from NPC samples_Cohort 2

  We retrospectively collected 150 non-metastatic, pretreatment, formalin-fixed, paraffin-embedded (FFPE) nasopharyngeal carcinoma (NPC) samples as validation cohort 1. Also, we prospectively collected 32 FFPE samples from NPC patients enrolled in a trial evaluating anti-PD-1 antibody as validation cohort 2. Total RNA was extracted and hybridised to an Affymetrix HTA 2.0 microarray. In this study, we investigated the immune status of the tumour microenvironment (TME) based on gene expression profiles to classify NPC into biologically distinct immune subtypes, and clarify their associations with prognosis and immunotherapy response.

  Dataset EGAD00001006274

• BLUEPRINT release August 2015, ChIP-Seq for CD3-positive, CD4-positive, CD8-positive, double positive thymocyte, on genome GRCh38

  ChIP-Seq data for 1 CD3-positive, CD4-positive, CD8-positive, double positive thymocyte sample(s). 3 run(s), 3 experiment(s), 3 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001503

• BLUEPRINT release January 2015, ChIP-Seq for effector memory CD8-positive, alpha-beta T cell, terminally differentiated

  ChIP-Seq data for 1 effector memory CD8-positive, alpha-beta T cell, terminally differentiated sample(s). 4 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001195

• BLUEPRINT release August 2015, ChIP-Seq for central memory CD4-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 1 central memory CD4-positive, alpha-beta T cell sample(s). 9 run(s), 7 experiment(s), 7 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001499

• BLUEPRINT release August 2015, ChIP-Seq for effector memory CD8-positive, alpha-beta T cell, terminally differentiated, on genome GRCh38

  ChIP-Seq data for 1 effector memory CD8-positive, alpha-beta T cell, terminally differentiated sample(s). 4 run(s), 4 experiment(s), 4 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001577

• BLUEPRINT release August 2015, ChIP-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell, on genome GRCh38

  ChIP-Seq data for 1 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 7 run(s), 6 experiment(s), 6 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001557

• BLUEPRINT release August 2015, ChIP-Seq for CD3-negative, CD4-positive, CD8-positive, double positive thymocyte, on genome GRCh38

  ChIP-Seq data for 1 CD3-negative, CD4-positive, CD8-positive, double positive thymocyte sample(s). 2 run(s), 2 experiment(s), 2 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001570

• BLUEPRINT release August 2015, ChIP-Seq for mesenchymal stem cell of the bone marrow, on genome GRCh38

  ChIP-Seq data for 1 mesenchymal stem cell of the bone marrow sample(s). 9 run(s), 7 experiment(s), 7 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001578

• BLUEPRINT September 2016, ChIPmentation for effector memory CD8-positive, alpha-beta T cell, terminally differentiated from venous blood, on Genome GRCh38

  ChIPmentation data for 1 sample(s) for effector memory CD8-positive, alpha-beta T cell, terminally differentiated from venous blood, on Genome GRCh38. 5 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002943

• Normalized read counts for 84 PDAC samples

  Bulk RNAseq analysis of 84 PDAC samples : Normalized read counts

  Dataset EGAD50000000537

• SF4324 Primary Wildtype GBM Female, 57.

  Single Cell RNA-Seq of Primary GBM. Gender Female, Age, 57.

  Dataset EGAD00001006017

• RBSC11

  One retinoblastoma sample was studied by single-cell RNA-sequencing (10X genomics Chromium).

  Dataset EGAD00001007497

• GCAT| Sex and Age

  Sex, age at recruitment (2014-2018), and birthdate of GCAT Cohort individuals.

  Dataset EGAD00001007729

• Raw count table

  Raw count matrix of the 124 bulk tumor RNAseq samples from patients with hormone sensitive or castration resistant prostate cancer.

  Dataset EGAD00001008762

• A non-canonical lymphoblast in refractory childhood T-cell leukaemia

  This dataset contains 75 WGS samples of T-ALL (including matched remission samples where available).

  Dataset EGAD00001015727

• HipSci - Macular Dystrophy - RNA Sequencing - July 2017

  HipSci - Macular Dystrophy - RNA Sequencing - July 2017

  Dataset EGAD00001003541

• HipSci - Monogenic Diabetes - Exome Sequencing - January 2016

  HipSci - Monogenic Diabetes - Exome Sequencing - January 2016

  Dataset EGAD00001001951

• RNA sequence of HUB_5 cell culture

  RNA sequence data for conditionally reprogrammed cells from patient HUB_5

  Dataset EGAD00001001923

• HipSci - Monogenic Diabetes - Exome Sequencing - April 2015

  HipSci - Monogenic Diabetes - Exome Sequencing - April 2015

  Dataset EGAD00001001949

• SCANDARE MACARON project

  Among gynecological cancers, high-grade serous ovarian cancer has the highest prevalence and more than 75% of patients have advanced stage at diagnosis with a 5-year survival of 40%. When the initial complete surgical removal is not possible (60% of patients), an interval surgery can be considered after three to four cycles of neoadjuvant chemotherapy to reduce the tumor mass. However, in approximately 50% of cases the tumor has little or no response to neoadjuvant chemotherapy. Therefore, identification of potential drug targets, biomarkers and/or pathways whose modulation can help in the prevention, interception, or curing of the disease is essential to address the unmet need. Therefore, MACARON project aims to address these issues through the identification of shared neoantigens arising out of somatic alterations and aberrant transcriptional programs in ovarian cancer DNA/RNA datasets.

  Dac EGAC50000000104

• Exome sequencing of early and late passage Patient Derived Xenogratf Tumoroids with matched Patient Derived Xenogratfs and matched normal liver

  The breadth and depth at which cancer models are interrogated contribute to successful translation of drug discovery efforts to the clinic. In colorectal cancer (CRC), model availability is limited by a dearth of large-scale collections of patient-derived xenografts (PDXs) and paired tumoroids from metastatic disease, the setting where experimental therapies are typically tested. XENTURION is a unique open-science resource that combines a platform of 129 PDX models and a sister platform of 129 matched PDX-derived tumoroids (PDXTs) from patients with metastatic CRC, with accompanying multidimensional molecular and therapeutic characterization. In this specific dataset we focused our attention on early (passage 3) and late (passage 8-12) PDXTs with their matched PDXs and normal liver

  Dataset EGAD50000000266