11750 results for "cancer rna-seq"

in 28.89 milliseconds.

• Effect of ETS2 modulation on chromatin accessibility and enhancer activity in primary human macrophages

  3 datasets included: - H3K27ac ChIP in ETS2-edited and unedited TPP macrophages from 3 biological replicates - H3K27ac ChIP in ETS2-overexpressing and control M0 (resting) macrophages from 3 biological replicates - ATAC-seq (deep sequencing) in ETS2-edited and unedited TPP macrophages from 3 biological replicates

  Dataset EGAD50000000154

• ScRNA-seq of kidney organoids expressing different APOL1 variants and treated with IFN-γ

  This dataset includes scRNA-seq fastq files and Seurat counts and metadata for 3 samples of hiPSC-derived kidney organoid cells cultured in vitro in presence of IFN-γ. Samples were obtained from kidney organoids with APOL1 G1G1 variant, APOL1 G2G2 variant and an isogenic control.

  Dataset EGAD50000001743

• CHROMATIN ACTIVATION PROFILING OF STEREOTYPED CHRONIC LYMPHOCYTIC LEUKEMIAS

  We used chromatin-immunoprecipitation followed by sequencing (ChIP-Seq) with an antibody for the H3K27ac (a bona fide histone mark for regulatory element activation) in sorted CLL cells from 15 CLL, including cases from stereotyped subsets #1, #2, #4, and #8. The samples were sequenced by Illumina HiSeq 2500.

  Dataset EGAD00001009082

• Oxford Human Islet ATAC-seq data of 18 human pancreatic islet preparations

  18 human pancreatic islet preparations derived from 17 donors were processed for ATAC-seq. The data was generated on an Illumina Hiseq 2500 sequencing machine to generate 50bp paired end read data. The resulting fastq.gz and mapped bam files were deposited.

  Dataset EGAD00001003947

• BLUEPRINT release January 2015, Bisulfite-Seq for Multiple myeloma

  Bisulfite-Seq data for 2 Multiple myeloma sample(s). 16 run(s), 2 experiment(s), 2 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_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001152

• BLUEPRINT release August 2014, Bisulfite-Seq for Multiple myeloma

  Bisulfite-Seq data for 2 Multiple myeloma sample(s). 16 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001000934

• BLUEPRINT release January 2015, Bisulfite-Seq for Acute promyelocytic leukemia

  Bisulfite-Seq data for 3 Acute promyelocytic leukemia sample(s). 24 run(s), 3 experiment(s), 3 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_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001167

• BLUEPRINT release January 2015, Bisulfite-Seq for Acute myeloid leukemia

  Bisulfite-Seq data for 1 Acute myeloid leukemia sample(s). 18 run(s), 1 experiment(s), 1 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_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001162

• MethylCap-seq based DNA methylation profiles of 65 glioblastoma and 5 non-tumoral tissues

  Data represent genome-wide DNA methylation profiles obtained by MethylCap-seq (Diagenode’s MethylCap-kit based purification followed by Illumina GAIIx sequencing), for 70 brain tissue samples, including 65 glioblastoma samples and 5 non-tumoral tissues (obtained from epilepsy surgery).

  Dataset EGAD00001001399

• ETMR ATACSeq

  ATACSeq library amplification was performed on 5 ETMR tumour samples using the NEBnext High Fidelity 2xPCR Master Mix (New England Biolabs, Cat#M0541S) according to the manufacturer’s protocol. ATAC-seq libraries were sequenced using single-end 50 bp reads on the Illumina HiSeq 2000 platform. ATAC-seq peaks analysed was conducted as published previously(Torchia et al. 2016).

  Dataset EGAD00001004805

• smMIP-seq of 18 FFPE prostate samples (normal and tumour pairs) to identify mutations

  smMIP-Seq was performed on radical prostatectomy formalin-fixed paraffin-embedded sample pairs (n = 18). Library prep was done by capture of targeted sequences with single molecule (unique molecular index) tagged molecular inversion probes (MIP) and sequenced as 75bp paired end on Illumina NextSeq 500. Sequences were aligned to the human genome (hg38) using BWA.

  Dataset EGAD00001006109

• Extramammary Paget Disease

  37 surgical samples were interrogated by WXS, and 50 formalin-fixed, paraffin-embedded samples were interrogated by target-seq. Agilent SureSelect XT kit and SureSelect Human Exon V6 were used to generate exome libraries. Agilent SureSelect XT low input kit and custom capture panel designed on SureDesign were used to generate target-seq libraries. All libraries were sequenced on Illumina HiSeq 2500 platform.

  Dataset EGAD00001006449

• smRNA-seq of human post-mortem brain data from the frontal lobe (goettingen part)

  This dataset contains smRNA-seq data from human post-mortem brain tissue of the frontal lobe of patients with FTD and healthy controls. These samples depict the data generated at the DZNE Göttingen and should be used together with the data generated at the DZNE Tübingen.

  Dataset EGAD00001006845

• Whole exome and RNA sequencing of organoid samples derived from TRACERx patients

  Immune checkpoint blockade in non-small cell lung cancer (NSCLC) rarely leads to complete tumour remission, suggesting that tumours harbour subpopulations of cancer cells that can escape immune predation. However, while tumours consist of multiple genetically distinct clones, the extent to which separate tumour subclones differ intrinsically in their functional capacity for immune evasion remains largely unexplored. This has been challenging to address due to an inability to isolate and propagate individual tumour subclones from human cancers. Here, we leverage the multi-region TRACERx lung cancer evolution study to generate a patient-derived platform that allows the functional analysis of immune escape at single clone resolution. We generated libraries of >20 separate NSCLC organoid lines per patient by isolating individual (clonal) organoids established from multiple spatially separated tumour regions. Each organoid subline was co-cultured with autologous tumour infiltrating lymphocytes (TIL) to evaluate their capacity to elicit a T cell response. Organoid lines derived from separate tumour regions, or from single clones within individual regions, differ vastly in their capacity to elicit a T cell response, showing the coexistence of intrinsically immune evading (‘hot’) and non-evading (‘cold’) clones in the same primary tumour. Organoid lines escaping T cell recognition represent genetically and transcriptionally unique subclones with a distinct evolutionary history, showing that evolutionary sculpting of subclonal diversity can have functional consequences for anti-tumour immunity. Notably, the identification of immune-evasive subclones would not have been possible using computational approaches alone, highlighting the unique value of functional co-culture platforms. This patient-derived functional co-culture platform reveals that tumour evolution can give rise to distinct cancer clones with intrinsic differences in immune evasion capacity and provides an approach to prospectively identify, isolate and characterise immune evading subclones from patients with cancer.

  Study EGAS00001008092

• ChIP-seq on Hodgkin lymphoma DAC

  Dac EGAC00001001007

• Henssen Lab Neuroblastoma Circle-seq Data

  Dac EGAC00001001803

• Henssen Lab Neuroblastoma Circle-seq Data

  Dac EGAC00001001804

• Human Inflammatory Skin Disease scRNA-seq

  Dac EGAC00001002309

• MS meninges single nuclei RNA sequencing

  10X 3' (RNA) samples of isolated single nuclei from highly inflamed leptomeninges and adjacent grey matter from 3 MS patients used in the paper.

  Dataset EGAD50000001235

• Bulk RNA-sequencing data from 9 cHL cell lines

  The dataset contains raw data of 27 bulk RNA-sequencing data from 9 cHL cell lines. Paired-end fastq with polyA capture are available for each cell line.

  Dataset EGAD50000001271

• RNA Sequencing of mCRC xenografts under cetuximab treatment.

  RNA sequencing on cetuximab treated, untreated and release samples of one metastatic colorectal xenograft. 3 cetuximab treated samples, 3 placebo and 3 release - paired fastq.

  Dataset EGAD00001005185

• Expression profiling of pediatric brain tumors Embryonal Tumor with Multilayered Rosettes (ETMR)

  RNA was prepared using the IlluminaTruSeq RNA sample preparation kit for poly-adenylated mRNA with an average of 97.64 million reads per sample respectively

  Dataset EGAD00001004803

• Single-cell RNA-sequencing reveals that glioblastoma recapitulates a normal neurodevetlopmental hierarchy

  10xGenomics single-cell RNA sequencing of glioblastoma patient tumor

  Dataset EGAD00001006206

• RNA data for EGAS00001004660

  RNA data for EGAS00001004660, "Aggressive PDACs show hypomethylation of repetitive elements and the execution of an intrinsic IFN program linked to a ductal cell-of-origin"

  Dataset EGAD00001006539

• RNA sequencing of 20 intra/extra hepatic bileduct organiods

  This dataset contains RNA sequencing data for 20 intra/extra hepatic bileduct organiods. Data is in BAM format and was processed by STAR.

  Dataset EGAD00001005225

• RNA-sequencing (Illumina HiSeq 4000) of tumor-stage mycosis fungoides

  This dataset contains RNA-sequencing data of tumors from 8 patients with mycosis fungoides. The data was generated using the Illumina HiSeq 4000 platform.

  Dataset EGAD00001003983

• Single-cell RNA sequencing of 43 BALF samples from CAPA and COVID-19 patients

  Single-cell RNA sequencing of 43 bronchoalveolar lavage fluid (BALF) samples from 15 CAPA and 22 COVID-19 mechanically ventilated patients.

  Dataset EGAD00001011342

• Hospital for Sick Children Infant Glioma RNA Sequencing

  Whole transcriptome (n=4) and targetted RNA sequencing (n=15) .bam files of infantile glioma samples reported on at the Hospital for Sick Children

  Dataset EGAD00001005092

• RNA-Sequencing data of Human Acute Myleloid Leukemia Stem Cells

  RNA-Sequencing of 27 functionally validated LSC and blast fractions from 9 AML patients. Three healthy hematopoietic stem and progenitor cells from age-matched controls.

  Dataset EGAD00001006112

• RNA sequencing data of pretreatment tumor biopsies of patients treated in the OpACIN-neo trial

  The dataset includes RNA sequencing on pre-treatment tumor biopsies of lymph node metastases (n=65)

  Dataset EGAD00001006731

• Melanoma Genome Sequencing Project

  Melanoma is the most prevalent cause of skin cancer morbidity and mortality. In order to characterize the full range of somatic mutations that may drive the growth of melanoma, we are sequencing tumor and normal DNA from a set of roughly 150 melanomas. For the majority of samples (approximately 90% of the cases), mutations in protein coding genes will be assessed in the exonic DNA of tumor-normal pairs using hybrid capture and paired-end DNA sequencing. For a few of DNA samples (approximately 10% of the cases), the entire genomes will be analyzed to assess the possible contribution of complex structural rearrangements contributing to oncogenesis. The sequencing data are supplemented by copy number profiling on the same tumors using high-density SNP arrays. Integration of these approaches will enable the unbiased and comprehensive characterization of both known and novel recurrent DNA alterations that arise in melanoma.Versions 1-4 contain whole exome and bulk RNA sequencing data for patients with melanoma.Version 5 - We compared somatic genomic profiles from matched pre-treatment and post-resistance tumor biopsies in patients with metastatic melanoma who exhibited heterogeneous responses to immune checkpoint inhibitors (ICIs). This version includes whole exome sequencing (WES) data from 14 patients.

  Study phs000452

• Stratifying and Targeting Pediatric Medulloblastoma through Genomics

  In this project, genomic analyses of pediatric medulloblastoma samples, obtained through the international medulloblastoma consortium, will be performed. RNA and miRNA expression profiles of 1000 samples, representing all four subgroups (Wnt, Shh, Group C, and D), will be studied to identify novel subtypes within each subgroup. The resulting subtype-specific expression profiles will support the development of reliable and robust biomarkers to more accurately and reliably classify medulloblastomas for treatment in clinical trials. For that purpose, two assays will be developed: an antibody-based immunohistochemical assay and an orthogonal nucleic acid-based hybridization assay. Additional genomic DNA analysis of the 300 high risk subgroup cases will support the discovery of subgroup specific somatic mutations in order to inform current clinical trials of targeted therapies, and to identify genes and pathways already targeted in other diseases. Such therapies could be rapidly transitioned to Phase II trials in medulloblastoma. Furthermore, the discovery of somatic mutations could be used for developing as well as validating specific biomarkers. The project team will also try to identify risk factors that predispose children to this type of cancer.

  Study EGAS00001000273

• The PEMDAC phase 2 study of pembrolizumab and entinostat in patients with metastatic uveal melanoma

  Patients with metastatic uveal melanoma (UM) have an abysmal prognosis. Preclinical studies have suggested that epigenetic therapy could enhance immunogenicity of cancer cells. Here we test if epigenetic therapy would enhance PD-1 immunotherapy in patients with metastatic UM. We report the results of the PEMDAC phase 2 clinical trial (n=29; NCT02697630) where the HDAC inhibitor entinostat was combined with the PD-1 inhibitor pembrolizumab in patients with metastatic UM. The primary endpoint was objective response rate (ORR), and was met with an ORR of 14%. The clinical benefit rate at 18 weeks was 28%, median progression free survival was 2.1 months and the median overall survival was 13.4 months. Toxicities were manageable, and there were no treatment-related deaths. Extensive genomics studies were performed using DNA/RNA and single cell sequencing and flow cytometry. Objective responses and prolonged survival were seen in three patients with BAP1 wildtype tumors, and in one patient with an iris melanoma that exhibited a UV signature. Longer survival also correlated with low baseline ctDNA levels or LDH. In conclusion, HDAC inhibition and anti-PD1 immunotherapy results in durable responses in a subset of patients with metastatic UM. Further exploration of combined immunotherapy and epigenetic therapy in metastatic UM is warranted.

  Study EGAS00001005478

• Longitudinal evaluation of serum microRNAs as biomarkers for neuroblastoma burden and therapeutic p53 reactivation

  Accurate assessment of treatment response and residual disease is indispensable for the evaluation of cancer treatment efficacy. However, performing tissue biopsies for longitudinal follow-up poses a major challenge in the management of solid tumours like neuroblastoma. In the present study, we evaluated whether circulating miRNAs are suitable to monitor neuroblastoma tumour burden and whether treatment-induced changes of miRNA abundance in the tumour are detectable in serum. We performed small RNA sequencing on longitudinally collected serum samples from mice carrying orthotopic neuroblastoma xenografts that were exposed to treatment with idasanutlin or temsirolimus. We identified 57 serum miRNAs to be differentially expressed upon xenograft tumour manifestation, out of which 21 were also found specifically expressed in the serum of human high-risk neuroblastoma patients. The murine serum levels of these 57 miRNAs correlated with tumour tissue expression and tumour volume, suggesting potential utility for monitoring tumour burden. In addition, we describe serum miRNAs that dynamically respond to p53 activation following treatment of engrafted mice with idasanutlin. We identified idasanutlin-induced serum miRNA expression changes upon one day and 11 days of treatment. By limiting to miRNAs with a tumour-related induction, we put forward hsa-miR-34a-5p as a potential pharmacodynamic biomarker of p53 activation in serum.

  Study EGAS00001006678

• Ewing and Ewing-like-sarcoma tumoroid biobank reveals distinct drug sensitivities: translocation makes the difference

  Ewing sarcoma (ES) and Ewing-like sarcomas (ELS) are highly aggressive tumors found in soft tissues and bone of mostly children and young adults. Despite being different in many aspects, including genetics, possible cell-of-origin, and pathology, patients with any of these entities all receive the same therapeutic regimen. Currently, there is a lack of preclinical cell models that can predict drug response of these entities in vitro. Here, we describe a pediatric ES and ELS tumor organoid (tumoroid) biobank containing tumoroids with different translocations, including EWSR1::FLI1, EWSR1::ERG, CIC::DUX4, and BCOR-rearrangements. Using histology, whole genome sequencing and RNA sequencing, we demonstrate that these tumoroids retain key properties of their matching patient tumors. In addition, we traced tumor subclones in the tumoroids across patient-matched longitudinal samples, which showed that cellular heterogeneity was maintained. We subsequently performed drug screening on the tumoroid models, which unveiled entity-specific drug sensitivity to various cytotoxic compounds and targeted compounds, including MCL-1 inhibitors for CIC::DUX4 sarcomas. Taken together, this newly established ES and ELS patient-derived tumoroid biobank generates a unique source of material for future basic cancer research and drug-screening.

  Study EGAS00001007941

• 5 scRNA-seq with TCR Enrichment of Tumour-Involved Lymph Nodes, Malignant Seromas and Patient-Derived Xenografts from 18 T-Cell Lymphoma Patients

  This raw data contains 5' scRNA-seq with TCR enrichment data from 18 individual patients analysing cells taken from tumour-involved lymph nodes (LN), malignant seromas and patient-derived xenografts (PDX) including systemic/nodal ALK+ ALCL (n=3 PDX), BIA-ALCL (n=2 seromas), PTCL-NOS (n=5 nodal tumours), nodal T follicular helper lymphoma - angioimmunoblastic type (nTFHL-AI) (n=1), nTFHL, not otherwise specified (nTFHL, NOS, n=1), TLBL (n=5) and 1 case of Sezary Syndrome (SS, cutaneous tumour). Droplet-based 5’ scRNA-seq with TCR enrichment (10x Chromium platform) was performed for all live cells, or the viable CD45+CD3- and CD45+CD3+ fractions following cell sorting by flow cytometry.

  Dataset EGAD00001015703

• Rheumatoid Arthritis Finger Stick RNA Sequence Data

  This is a longitudinal study of 355 blood samples collected weekly from 5 patients with Rheumatoid Arthritis with varying disease activity over the course of one to five years. Blood samples were self collected from finger sticks and mailed from home. RNA was extracted, purified and sequenced.

  Study phs003179

• RNA-sequencing of 82 pleural mesothelioma samples

  The dataset contains RNA-sequencing data (bam files) for 82 pleural mesothelioma samples. All the samples were sequenced on an Illumina NovaSeq 600 sequencer in paired-end mode, generating 100 nt length reads, to obtain an average of 60 million clusters for RNA. Demultiplexing was performed using Illumina bcl2fastq2. Fastq quality was assessed using fastQC (v. 0.11.8) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and low-quality reads were discarded. Sequence reads were aligned to Human reference genome (UCSC genome assembly GRCh38/hg38) using STAR (v. 2.7.0b).

  Dataset EGAD50000002131

• Single-cell bam files and RNA sequencing of viral RNA stocks

  244 infected single-cell alveolar bam files, 48 empty well bam files, and 52 RNA sequencing of amplicons (4 SARS-CoV-2 variants with 12 batches and 4 viral variants pool samples). 244 alveolar single cells were captured over 12 experimental batches and experimental condition is written in metadata uploaded as "infected_cells_final_revision.csv". on github (https://github.com/twkim-0510/SARS-CoV-2_viral_competition). Each bam file name corresponds to the sample_name column of the metadata.

  Dataset EGAD00001009711

• RNA sequencing of BCP-LBL and EM B-ALL patients samples

  In order to perform comprehensive transcriptomics and gene fusion analyses of a cohort of BCP-LBL and extramedullary (EM) manifestations of B-ALL patients, we performed RNA sequencing of 16 tissue samples from BCP-LBL and EM B-ALL patients. Because the material was available as FFPE, and had a relative low quality, we used a capture-based approach, where NGS libraries obtained from total RNA were captured in 4-plex, using a whole exome capture panel.

  Dataset EGAD50000002047

• Single-cell RNA-sequencing of rhabdomyosarcoma tumouroids (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumouroids data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015708

• Bulk RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the bulk RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015714

• Bulk RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the bulk RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015718

• Human Embryonic Multi-tissue ChIP-seq (Manchester)

  Dac EGAC00001000999

• Ig-seq celiac disease Data Access Committee

  Dac EGAC00001001208

• HDBR exome-seq data access committee

  Dac EGAC00001002703

• 16SV4 ribosomal RNA gene sequencing data

  16SV4 ribosomal RNA gene sequencing data of GI samples and associated mixed community cultures collected from pediatric patients at risk for IBD.

  Dataset EGAD50000000485

• 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

• RNA sequencing of pediatric BCP ALL

  RNA sequencing data of 33 B-cell precursor (BCP) acute lymphoblastic leukemia (ALL) cases were constructed on an Illumina nextseq 2000 Sequencing System.

  Dataset EGAD50000001125

• RNA sequencing of AML with 3q26 rearrangements

  RNA sequencing of 44 AML cases with diverse 3q26 rearrangements. This dataset is complementary to EGAD00001000726 (which only contains t(3;3)/inv(3)) and to EGAD00001006123.

  Dataset EGAD00001015629

• Characterizing the tumor immune microenvironment of ependymomas using targeted gene expression profiles and RNA-sequencing the pros and cons

  RNA sequencing data of a collection of 6 pediatric ependymoma cases

  Dataset EGAD00001009301

• The Extracellular RNA Quality Control (exRNAQC) study (phase 2)

  mRNA capture sequencing and small RNA sequencing data (FASTQ files) of the exRNAQC study phase 2 (interaction study)

  Dataset EGAD00001009724

• Whole exome and RNA sequencing of 5 samples of patient-derived xenograft (PDX).

  Whole exome and RNA sequencing of 5 samples of patient-derived xenograft (PDX). Available files are raw sequencing fastq files.

  Dataset EGAD00001010102

• Human serum small non-coding RNA sequencing

  Human small non-coding RNA sequencing of serum from sons of PCOS mothers (n=9) and sons of control mothers (n=9), see publication for details.

  Dataset EGAD00001010165

• Strand-specific RNA Sequencing of initial and recurrent gliomas

  Four RNA-sequencing datasets from two patients with initial low-grade glioma and copy number alteration at IDH1 upon recurrence. Data is provided as bam files.

  Dataset EGAD00001003764

• Poikiloderma syndrome RNAseq

  We will sequence the RNA of lymphoblast samples, transformed with EBV, which have poikiloderma syndrome with mutations in c16orf57. The aim of the experiment is to characterise RNA structural effects in this disease.

  Dataset EGAD00001000324

• Strand-specific RNA sequencing of 16 pairs of primary and relapsed IDH-wt glioblastomas

  This dataset contains strand-specific RNA sequencing data from 16 primary/relapsed sample pairs of IDH-wt glioblastomas

  Dataset EGAD00001004564

• RNA sequencing of paediatric glioblastoma in the ICGC PedBrain project

  RNA sequencing of paediatric glioblastoma in the ICGC PedBrain project

  Dataset EGAD00001001944

• A capture methyl-seq protocol with improved efficiency and cost-effectiveness using pre-pooling and enzymatic conversion

  The opportunities for sequencing-based methylome analysis of clinical samples are increasing. To reduce its cost and the amount of genomic DNA required for library preparation, we aimed to establish a capture methyl-seq protocol, which adopts pre-pooling of multiple libraries before hybridization capture and TET2/APOBEC-mediated conversion of unmethylated cytosine to thymine. We compared a publicly available dataset generated by the standard Agilent protocol of SureSelect XT Human Methyl-Seq Kit and our dataset obtained by our modified protocol, EMCap, that adopted sample pre-pooling and enzymatic conversion. We confirmed that the quality of DNA methylation data was comparable between the two datasets. As our protocol, EMCap, is more cost-effective and reduces the amount of input genomic DNA, it would serve as a better choice for clinical methylome sequencing.

  Study JGAS000605

• RNA sequencing, ATACseq, and TCR-seq of Tfh cells and CXCR5- CD4+ T cells in HIV infected lymph nodes

  The high dimensional analyses of CD4+ T cells in human lymph nodes are case-based analyses of the TCR repertoire, transcriptome, and epigenetic signatures of various CD4+ T cells in human lymph nodes. The study aims to determine clonal overlap between phenotypically distinct CD4+ T cells and the similarities between these populations on transcriptional and epigenetic levels. This study performs TCR repertoire sequencing using Molecular Identifier Clustering-based Immune Repertoire Sequencing (MIDCIRS). We found clonal overlap between a population of CXCR5- T cells and follicular helper T (Tfh) cells. We also show epigenetic and transcriptional similarities between CXCR5-PD-1+ T cells and Tfh cells. This study shares an overlapping set of donors with phs001548.

  Study phs001849

• RNA-seq study of longitudinal blood cell samples from children at risk of type 1 diabetes

  The appearance of type 1 diabetes (T1D)-associated autoantibodies is the first and only measurable parameter to predict progression toward T1D in genetically susceptible individuals. However, autoantibodies indicate an active autoimmune reaction, wherein the immune tolerance is already broken. Therefore, there is a clear and urgent need for new biomarkers that predict the onset of the autoimmune reaction preceding auto-antibody positivity or reflect progressive b-cell destruction. Here we report the mRNA sequencing–based analysis of 306 samples including fractionated samples of CD4+ and CD8+ T cells as well as CD4, CD8 cell fractions and unfractionated PBMC samples longitudinally collected from seven children who developed beta-cell autoimmunity (Case subjects) at a young age and matched control subjects.

  Study EGAS00001004071

• RNA-seq data

  RNAseq FASTq files of 181 bulk pre-treatment and 14 post-treatment tumors from GO30140 Ph1b group A and F and 177 bulk pre-treatment tumors of IMbrave150 PhIII

  Dataset EGAD00001008128

• Identification of cardiovascular biomarkers through an integrative omics approach

  Recent GWAS studies have made extensive use of large eQTL data sets to functionally annotate index SNPs. With a large number of association signals located outside coding regions there has been an intense search among sequence variants affecting gene expression at the transcriptional level. However, little progress has been made in mapping regulatory variants that affect protein levels at the translational or post-translational level. It is now possible to undertake a protein QTL scan for focused sets of e.g. oxidized proteins by mass spectrometry. We have established a collaboration with a longitudinal, family-based study in France, the Stanislas cohort, which comprises circa 1000 nuclear families (4,295 individuals) and has follow up data for 10 years (three visits). We have undertaken a pilot study in a focus set of 257 subjects from 79 families with the aim to integrate GWAS, transcriptomic and DNA methylation data with proteomic data on a set of 100 proteins measured in PBMCs. We have already generated GWAS data using Illumina's core-exome chip as well as DNA methylation profiles with the 450K array. We propose to use RNA seq to generate transcriptomic data of the corresponding PBMCs. 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 EGAD00001002197

• ChIP seq samples

  Dataset contains 12 ChIP-Seq samples sequenced on the Illumina NextSeq500 sequencer. 3 samples are control samples (HcAct-Chip 1,HcAct-Chip 2,HcAct-Imput 2) 9 samples are case samples (HcAct2DG-Chip 1,HcAct2DG-Chip 2,HcAct2DG-Imput 1,JIAAct-Chip1,JIAAct-Chip2,JIAAct-Imput2,JIAAct2DG-Chip1,JIAAct2DG-Chip2,JIAAct2DG-Imput2)

  Dataset EGAD50000001188

• Metadata and count matrix

  The dataset contains metadata for all cells before scRNA-seq quality control and for cells passing quality control. It also contains a count matrix with Salmon gene counts for all cells passing quality control, and reconstructed B-cell receptor sequences using the computational tool BraCeR. The scRNA-seq data was generated using the Smart-seq2 protocol and sequenced on Illumina NextSeq500.

  Dataset EGAD00001006435

• Whole genome sequencing of adult glioblastoma nuclei

  Three primary adult glioblastoma specimens were dissociated and nuclei were extracted. A portion of the nuclei was used for single-cell ATAC seq and the remainder were submitted for whole genome sequencing, to provide orthogonal validation of copy number variations in the samples compared to single-cell ATAC seq. Samples were sequenced on the Illumina NovaSeq 6000 in paired-end mode.

  Dataset EGAD00001007651

• Human tumor scMultiome

  We profile 10 high-grade gliomas patient brain tumor samples by single-cell multiome ATAC + gene expression, using the 10X Chromium technology. 3 sets of fastq are provided for each samples: R1 and R2 for gene expression, R1 and R2 for ATAC-seq as well as index1 and index2 for ATAC-seq.

  Dataset EGAD00001008349

• BLUEPRINT release August 2014, Bisulfite-Seq for erythroblast

  Bisulfite-Seq data for 1 erythroblast sample(s). 14 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001000909

• BLUEPRINT release August 2014, Bisulfite-Seq for macrophage

  Bisulfite-Seq data for 1 macrophage sample(s). 14 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001000923

• BLUEPRINT release January 2015, Bisulfite-Seq for erythroblast

  Bisulfite-Seq data for 2 erythroblast sample(s). 35 run(s), 2 experiment(s), 2 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_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001133

• BLUEPRINT release January 2015, Bisulfite-Seq for macrophage

  Bisulfite-Seq data for 5 macrophage sample(s). 72 run(s), 5 experiment(s), 5 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_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001192

• BLUEPRINT release August 2014, Bisulfite-Seq for inflammatory macrophage

  Bisulfite-Seq data for 3 inflammatory macrophage sample(s). 38 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001000914

• BLUEPRINT release January 2015, Bisulfite-Seq for inflammatory macrophage

  Bisulfite-Seq data for 3 inflammatory macrophage sample(s). 38 run(s), 3 experiment(s), 3 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_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001139

• BLUEPRINT release August 2014, Bisulfite-Seq for Plasma cell

  Bisulfite-Seq data for 1 Plasma cell sample(s). 11 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001000927

• BLUEPRINT release January 2015, Bisulfite-Seq for plasma cell

  Bisulfite-Seq data for 1 plasma cell sample(s). 11 run(s), 1 experiment(s), 1 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_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001160

• Whole-genome and transcriptome versus panel sequencing in precision oncology: A translational-clinical comparison

  Precision oncology offers new cancer treatment options. While the use of whole-genome (WGS) and transcriptome (TS) sequencing remains often confined to specific clinical programs, molecular analysis with gene panels is more common clinical practice. Methods: Twenty patients with rare or advanced tumors sequenced by WGS/TS within the DKFZ/NCT/DKTK-MASTER program from 2015 to 2020 (MASTER 1) were additionally sequenced with the TSO500 DNA and TST170 RNA Assay by Illumina. Original Molecular Tumor Board (MTB) therapy recommendations (TR) were juxtaposed with TR from NGS panel data analysis (comparison 1). To mitigate temporal differences between the two sequencings, WGS/TS was bioinformatically reanalyzed (MASTER 2), and new TR were again compared (comparison 2). Results: In comparison 1, 47.1% of the MASTER 1’s TR and 62.8% of the panel’s TR were identical, as were 45.9% of the TR of MASTER 2 and 54.9% of the panel in comparison 2. In both MASTER 1 and MASTER 2, 26.5% and 36.1% of TR, respectively, relied on Biomarkers (BM) which were not covered by the panel. Eight of the ten MTB-derived therapy implementations were also supported in the panel, the two remaining ones being based on BM absent from the panel. Conclusions: About half of the TR in WGS/TS and panel sequencing were identical. Around a third of TR issued by WGS/TS were based on BM which are not covered by the panel, leading to therapy implementations. The presented data underscores the clinical value of comprehensive molecular analyses in individuals with advanced cancers.

  Study EGAS50000000431

• Data Access Commitee for skin scRNA-seq

  Dac EGAC00001003129

• Erasmus MC, Dept. of Developmental Biology, MeD-seq

  Dac EGAC50000000291

• Epigenomic atlas of organoid development

  To investigfate the epigenomic basis of fate decisions in the central nerveous system, we recorded a epigenomic timecourse of human brain organoid development using Cut&Tag with paired scRNA-seq. We further performed perturbation experiment inhibiting EED and profiled the response with scRNA-seq and Cut&Tag. Finally, we profiled a sample of the developing human brain using multiome sequencing and Cut&Tag. In our study, we uncover epigenomic switches during brain region diversification and show that epigenetic activation precedes gene expression during neurogenesis in organoids and in primary tissue. Lastly, we show that transcriptional repression by H3K27me3 is required to stabilize neuroectoderm induction from pluripotency.

  Study EGAS50000000155

• Transcriptome - Uveal Melanomas

  Total RNA sequencing on 9 uveal melanomas. Libraries were prepared using the TruSeq Stranded Total RNA Library Prep Gold (Illumina, 20020599). Paired-end libraries (2 x 100 bp) were sequenced on a NovaSeq 6000 instrument (Illumina).

  Dataset EGAD50000000763

• Single cell RNA-sequencing of glioblastoma tumours

  Single cell RNA-sequencing of fresh glioblastoma tumor biopsies with 10x Genomics 3' expression (v2 chemistry). Aligned to GRCh38 reference genome using CellRanger.

  Dataset EGAD00001006803

• Bulk RNA-sequencing of nivolumab-treated glioblastoma

  Bulk RNA-sequencing of 30 relapse tumour samples. 20 patients were treated with nivolumab prior to relapse surgery, while 10 patients are control patients.

  Dataset EGAD00001010844

• Sequencing data for oesophageal and related samples - Ganguli et al (RNA)

  Data supporting: "TBC" Ganguli et al (RNA for 394 samples)

  Dataset EGAD00001011190

• TRACERx NSCLC, multiregion sequencing of the first 100 tumors

  BACKGROUND TRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC). METHODS Multiregion high-depth whole-exome sequencing (M-seq) was performed on 100 early stage NSCLC tumors resected prior to systemic therapy. A total of 327 tumor regions were sequenced and analyzed to define evolutionary histories, obtain a census of clonal and subclonal events, and assess the relationship between ITH and recurrence-free survival (RFS). RESULTS Widespread ITH was observed for both somatic copy number alterations (median 48% [0.03-88%]) and mutations (median 30% [0.5-93%]). Driver mutations in EGFR, MET, BRAF and TP53 were almost always clonal. However, heterogeneous driver alterations occurring later in evolution were found in over 75% of tumors and were common in PIK3CA, NF1 and genes involved in chromatin modification and DNA response and repair. Genome doubling and ongoing dynamic chromosomal instability (CIN), illustrated by mirrored subclonal allelic imbalance, were identified as causes of ITH resulting in parallel evolution of driver copy number events, including amplifications of CDK4, FOXA1, and BCL11A. Elevated copy number heterogeneity was associated with shorter RFS (HR=4.9, P=0.00044), which remained significant in a multivariate analysis. CONCLUSIONS ITH mediated through CIN, rather than point mutational heterogeneity, was associated with increased risk of relapse, supporting its value as a prognostic predictor, and the need to target this high-risk phenotype.

  Dataset EGAD00001003206

• Board considering access to van Hijfte snRNA-seq GBM dataset

  Dac EGAC00001003044

• Human LV H3K27ac ChIP-seq Data Access Commitee

  Dac EGAC00001001169

• Bulk ATAC-Seq on sorted cord blood hematopoietic populations

  Study EGAS00001004742

• MRC 60 snRNA-seq

  This dataset includes snRNA-seq generated from post-mortem brain and genotype from 60 individuals. The snRNA-seq data was generated using the 10X Single Cell Next GEM Chip targeting a minimum 5,000 nuclei per sample and libraries prepared using the Chromium Single Cell 3′ Library and Gel Bead v3 kit according to manufacturer’s instructions. cDNA libraries were sequenced using the Illumina NovaSeq 6000 system at a minimum sequencing depth of 30,000 paired-end reads per nucleus. Samples were pooled (max 4 per pool) and sequenced over 4 lanes. Pool-specific genotype files (containing genotype information at individual level) were used to demultiplex mapped pools into individual-level single-cell data. Donor DNA from samples processed at Imperial College were genotyped using the Illumina Infinium Global Screening Array v2.0.

  Dataset EGAD50000000965

• 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

• Human and rat skeletal muscle multiomic profiling sequencing data

  Illumina sequencing data (fastq files) representing single-nucleus (sn) ATAC-seq, snRNA-seq, bulk ATAC-seq, and snATACseq+snRNAseq multiomics data from human and rat skeletal muscle samples (19 libraries total). Includes a README file that describes the relationship between libraries, samples, and files.

  Dataset EGAD00001008323

• Dataset for WGS and RNA melanoma samples

  This dataset contains WGS and RNA sequencing data for 4 melanoma samples. Sequencing was performed on Illumina Novaseq 6000 and Illumina HiSeq X. The sequencing was always paired.

  Dataset EGAD50000000093

• Transcriptome - Uveal Melanomas MPI

  Total RNA sequencing on 4 uveal melanoma samples. Libraries were prepared using the TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Gold. Paired-end libraries (2 x 75 bp) were sequenced on HiSeq 4000 instrument (Illumina).

  Dataset EGAD50000001138

• B cell single cell sequencing (RNA + VDJ) - Autoproliferation

  10X 3' (RNA) and 10X 5' (VDJ) samples of B cells (non-proliferating/hi and autoproliferating/Dim) for healthy donor and Multiple sclerosis patients used in the paper.

  Dataset EGAD50000001237

• Hematological toxicity following CAR-T cells injection dataset

  This dataset containes 44 paired of bulk RNA sequencing and 42 parired of single cell RNA sequencing issued from bone marrow aspirations obtained from patients treated with CAR-T (CD3+, CAR T+, CD34+ sorted populations).

  Dataset EGAD50000001142

• bulk RNAseq from metastatic uveal melanoma tumour biopsies

  Tumour biopsies were obtained from uveal melanoma patients (n = 35) pre and 16 days post treatment with tebentafusp. Biopsies, which were either snap frozen or put in RNA later, were analysed by bulk RNA sequencing (50 million reads per sample) using the Illumina NovaSeq system.

  Dac EGAC50000000519

• Clear cell sarcoma dataset

  This deposit consists of DNA and RNA sequencing data from 67 CCS samples. 55 samples had tumor DNA sequencing data. 6 had matched normal sequencing data. 34 samples had tumor RNA sequencing data.

  Dataset EGAD00001008611

• Single nuclei RNA-sequencing of glioblastoma tumors

  Single nuclei RNA-sequencing of snap frozen glioblastoma tumor tissue with 10x Genomics 3' expression (v2 chemistry). Aligned to GRCh38 reference genome with intron using CellRanger.

  Dataset EGAD00001006802

• Osteosarcoma capture-based RNA sequencing

  This dataset includes 23 specimens from osteosarcoma patients (primary, relapsed, metastatic). It contains bam files from RNA sequencing using a library in which coding regions of cDNA are captured and short-read, paired-end sequencing.

  Dataset EGAD00001008434

• sc-RNA and sc-BCR sequencing of multiple myeloma and precursors

  bam files of sc-RNA and sc-BCR sequencing of multiple myeloma and precursors from 65 samples

  Dataset EGAD00001009648

• Single cell RNA-sequencing of glioblastoma stem cell lines

  Single cell RNA-sequencing of glioblastoma stem cell (GSC) lines with 10x Genomics 3' expression (v2 chemistry). Aligned to GRCh38 reference genome using CellRanger.

  Dataset EGAD00001006804