11925 results for "cancer rna-seq"

in 25.77 milliseconds.

• Reference epigenome Islet-derived-MSC04 input ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 input ChIP-Seq paired end data

  Dataset EGAD00001007435

• Reference epigenome Islet-derived-iPSC01 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007439

• Reference epigenome Islet-derived-iPSC02 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007444

• Reference epigenome Islet-derived-MSC01 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007402

• Reference epigenome Islet-derived-MSC01 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007403

• Reference epigenome Islet-derived-MSC01 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007405

• Reference epigenome Islet-derived-MSC01 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007406

• Reference epigenome Islet-derived-MSC01 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007407

• Reference epigenome Islet-derived-MSC02 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007409

• Reference epigenome Islet-derived-MSC01 input ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 input ChIP-Seq paired end data

  Dataset EGAD00001007408

• Reference epigenome Islet-derived-MSC02 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007410

• Reference epigenome Islet-derived-MSC02 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007411

• Reference epigenome Islet-derived-MSC02 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007413

• Reference epigenome Islet-derived-MSC02 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007414

• Reference epigenome Islet-derived-MSC03 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007417

• Reference epigenome Islet-derived-MSC03 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007425

• Reference epigenome Islet-derived-MSC03 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007426

• Reference epigenome Islet-derived-MSC03 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007427

• Reference epigenome Islet-derived-MSC04 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007432

• Reference epigenome Islet-derived-MSC03 input ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 input ChIP-Seq paired end data

  Dataset EGAD00001007428

• Reference epigenome Islet-derived-MSC04 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007429

• Reference epigenome Islet-derived-iPSC01 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007436

• Reference epigenome Islet-derived-iPSC01 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007437

• Reference epigenome Islet-derived-iPSC01 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007438

• Reference epigenome Islet-derived-iPSC01 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007440

• Reference epigenome Islet-derived-iPSC01 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007441

• Reference epigenome Islet-derived-iPSC01 input ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 input ChIP-Seq paired end data

  Dataset EGAD00001007442

• Reference epigenome Islet-derived-iPSC02 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007443

• Reference epigenome Islet-derived-iPSC02 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007448

• Reference epigenome Islet-derived-iPSC02 input ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 input ChIP-Seq paired end data

  Dataset EGAD00001007449

• ALI culture bronchial cells and alveolar lung surgical resection scRNA-Seq

  ALI culture bronchial cells and alveolar lung surgical resection scRNA-Seq

  Dataset EGAD00001006185

• Reference epigenome Islet-derived-MSC01 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007404

• Reference epigenome Islet-derived-MSC02 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007412

• Reference epigenome Islet-derived-iPSC02 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007445

• Reference epigenome Islet-derived-iPSC02 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007446

• single nuclei multiomics (ATAC-RNA) of KOLF2.1J human induced pluripotent stem cells-derived differentiated dopaminergic neurons

  We processed nuclei from hIPSCs differentiated optimized protocols for generating dopaminergic neurons. Cells differentiated with a first iteration were RNA and ATAC-sequenced, to understand our protocol caveats to address for optimization. Cells differentiated with a second protocol and in vivo were RNA-sequenced to validate the identities of our cells.

  Study EGAS50000001418

• Bulk RNA sequencing of ALS patients

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

  Study JGAS000851

• RNA Sequencing of paediatric patients with B lymphoblastic leukemia

  RNA sequencing was performed on 148 Bone marrow of peripheral blood samples of paediatric patients with B-ALL.

  Study EGAS00001004532

• Sample Multiplexing Oligo Comparison

  In this study, we compared various multiplexing reagents, including MULTI-Seq, Hashtag antibody, and CellPlex, in patient-derived xenografts (PDXs). We found that all multiplexing reagents worked well in cell types robust to ex vivo manipulation but suffered from signal-to-noise issues in more delicate sample types. We also compared the performance of fixed scRNA-Seq kits and CRISPR-based destruction of non-informative genes.

  Study EGAS50000000153

• RNA sequencing of Ewing sarcoma and CIC-DUX4 sarcoma

  RNA sequencing of a collection of 6 Ewing sarcoma and 3 CIC-DUX4 sarcoma tumoroids plus 3 matched patient tumors. Tumoroids were established from digested patient tumor material as 3D culture in Matrigel. After 2-3 month of growth, RNA was isolated from individual tumoroid models using the RNeasy Kit (Qiagen) and used for whole exome sequencing. RNA from corresponding patient tumors was tested in parallel, if available.

  Dataset EGAD50000000557

• MiRNome_pituitary_adenoma

  Small RNA (<100 bases in length) were purified from total RNA using miRNeasy kit (Qiagen), then sequenced. Libraries were prepared at the Genomics Platform of the Cochin Institute, following the TruSeq small RNA protocol (Illumina), starting from 1 µg of high quality total RNA. Single read (1 × 75 bp) sequencing was performed on a Nextseq 500 platform (Illumina). FASTQ sequences were aligned on miRBase v.2052, then counted with STAR (v.2.5.2a).

  Dataset EGAD00001004998

• Transcriptome_analysis_of_LCM_samples_

  We aim to set up a RNA pipeline for LCM samples to study transcriptome landscape of tumor and normal tissues. RNA was extracted from Lazer capture micro-dissection tissues and cDNA libraries were prepared with SMARTseq2 protocol. Libraries are pooled to be sequenced.

  Study EGAS00001003862

• scRNA-seq data of BALF and blood cells from COPD and control

  Chronic obstructive pulmonary disease (COPD) is ranked the third leading cause of death worldwide and hence constitutes a significant burden, both medically and financially. To date, the cellular mechanism underlying COPD is poorly understood and thus no effective therapy is currently available. To broaden the current knowledge of the immune landscape in the alveolar space, we performed scRNA-Seq of CD45-enriched bronchoalveolar lavage fluid (BALF) cells and isolated blood leukocytes obtained from COPD patients and healthy controls using both the Seq-Well and the Chromium (10x Genomics) technology.

  Study EGAS00001004369

• Lipomatous tumors with 12q amplification

  The project concerns whole genome sequencing (short-read and long-read) and RNA-sequencing of lipomatous tumors with 12q-amplification. A subset of lipomatous tumors is driven by amplification of genes mapping to chromosome arm 12q, including the MDM2 gene. The goals of the study were to compare expression levels of genes mapping to 12q in tumors with amplification in rod-shaped or circularized chromosomes as well as to assess and compare the structural variants in those tumors. In total, 20 samples were analyzed, and the data were correlated with genomic data on bulk and single cell DNA from the same tumors. The fastq files from the tumors were uploaded to EGA.

  Study EGAS50000000062

• Enhancer Mapping in Chronic Lymphocytic Leukemia

  In this study we profile the epigenomic enhancer landscapes of CLL B cells (CD19+/CD5+) harvested from peripheral blood of patients from our Center. Included are results of ChIPseq profiling using chromatin immunoprecipitation of the enhancer histone mark H3K27ac (acetylated lysine 27 on histone H3), and open chromatin profiles using ATAC-seq (assay for transposase accessible chromatin). These profiles are used to define the global enhancer and super enhancer landscape of CLL B cells, and to derive active transcription factor networks associated with this disease. Also included are H3K27ac ChIP-seq and ATAC-seq datasets for non-CLL B cells obtained from the peripheral blood of normal adult donors.

  Study phs001704

• WGBS data (CancerLocator study) of cell-free DNA derived from human blood

  The blood samples of eight lung cancer patients and one benign lung tumor patient are collected for this dataset. Blood samples were centrifuged first at 1,600 × g for 10 minutes, and then the plasma was transferred into new micro tubes and centrifuged at 16,000 × g for another 10 minutes. The plasma was collected and stored at -80⁰C. CfDNA was extracted from 5 ml plasma using the Qiagen QIAamp Circulating Nucleic Acids Kit and quantified by Qubit 3.0 Fluoromter (Thermo Fisher Scientific). Bisulfite conversion of cfDNA was performed by using EZ-DNA-Methylation-GOLD kit (Zymo Research). After that, Accel-NGS Methy-Seq DNA library kit (Swift Bioscience) was used to prepare the sequencing libraries. The DNA libraries were then sequenced with 150bp paired-end reads.

  Dataset EGAD00001003168

• RNA-seq data of bone marrow CD34-positive cells from 57 patients with myelodysplastic syndromes (MDS) and 5 healthy individuals (62 participants in total)

  Genetic mutations in hematopoietic neoplasms, seen in disease-specific and non-specific ways, are key drivers in pathogenesis. Typically, these mutations alone don't cause neoplasms; rather, multiple mutations or breakdowns in regulatory mechanisms occur, progressing the disease through stages. Genetic mutation patterns vary even within subtypes, suggesting shared gene expression abnormalities from different mutations may drive neoplasm development. Identifying these "common factors" is vital for effective molecular-targeted therapy development. Our study aims to elucidate neoplasm pathogenesis by analyzing patient genetic abnormalities, pinpointing "common factors" in each subtype for novel targeted therapy development.

  Study JGAS000724

• COVID-19 severity correlates with airway epithelium–immune cell interactions identified by single-cell analysis

  To investigate the immune response and mechanisms associated with severe COVID-19, we performed single-cell RNA-seq on nasopharyngeal and bronchial samples from 19 clinically well-characterized patients with moderate or critical disease and from 5 healthy controls. We identified airway epithelial cell types and states vulnerable to SARS-CoV-2 infection. In COVID-19 patients, epithelial cells showed an average threefold increase in expression of the SARS-CoV-2 entry receptor ACE2, which correlated with interferon signals by immune cells. Compared with moderate cases, critical cases exhibited stronger interactions between epithelial and immune cells, as indicated by ligand–receptor expression profiles, and activated immune cells , including inflammatory macrophages expressing CCL2, CCL3, CCL20, CXCL1, CXCL3, CXCL10, IL8, IL1B and TNF . The transcriptional differences in critical cases compared with moderate cases likely contribute to clinical observations of heightened inflammatory tissue damage, lung injury and respiratory failure. Our data suggest that pharmacologic inhibition of the CCR1 and/or CCR5 pathways may suppress immune hyperactivation in critical COVID-19.

  Dataset EGAD00001006339

• single-cell ATAC-seq

  Single-cell ATAC-seq data for 5 CLL samples (2 controls, 3 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005965

• Primary Neuroblastoma Circle-seq Data

  Circle-seq data for 21 primary neuroblastoma samples supporting Koche et al. Extrachromosomal circular DNA drives oncogenic genome remodeling in neuroblastoma (2020).

  Dataset EGAD00001006580

• release_2: ICGC PedBrain: ChIP-Seq

  release_2: ICGC PedBrain: ChIP-Seq

  Dataset EGAD00001001621

• miRNA seq data of 43 cases out of dataset EGAD00001000650 (+ one read_me file)

  miRNA seq data of 43 cases out of dataset EGAD00001000650 (MMML)

  Dataset EGAD00001001619

• ATAC-seq

  This dataset contains the raw sequencing data (Runs) for all 10x Genomics single-cell ATAC-seq Experiment.

  Dataset EGAD00001009822

• ATAC-seq of cohesin-mutated and -wildtype adult AMLs

  ATAC-seq (Illumina TDE1 Transposase) to profile accessible chromatin regions of cohesin-mutated (STAG2 or RAD21 mutations) and -wildtype adult AMLs.

  Dataset EGAD00001011267

• Aberrant RNA-splicing (neojunctions) offers a new source for targets, and our neoantigen discovery platform (SNIPP) characterizes a novel class of clonally-expressed splicing-derived neoantigens that elicit a CD8+ T-cell-mediated tumor killing response.

  RESULTS: Our pipeline identified 789 public neojunctions, with 32 neojunctions concurrently identified in transcriptomic and proteomic glioma data and predicted to be presented by HLA-A*02:01 with high confidence. IVS and subsequent 10x VDJ scRNA-seq identified TCR clonotypes reactive against neojunctions in RPL22 (n=7) and GNAS (n=1), the latter being highly intratumorally-conserved (detected in > 90% of spatially-mapped biopsies across 17/56 patients (26.78%)). TCR-transduced T-cells demonstrated recognition and immunogenic activation against endogenously processed and presented neoantigens in multiple GBM PDX cell lines. Furthermore, IDH1-mutant oligodendroglioma samples demonstrated significantly elevated expression of neojunctions over IDH1-mutant astrocytoma and IDH1wt subtypes. Differential gene expression (DESeq2) identified decreased expression of splicing factors due to oligodendroglioma-specific co-deletion of Chromosomes 1p/19q. siRNA knockdown of these splicing factors (e.g. SF3A3, SNRPD2) in IDH1wt glioma cells resulted in significantly increased expression of corresponding neojunctions.

  Study EGAS00001007986

• 1M-scBloodNL

  Here we present the 1M-scBloodNL study, in which we performed single-cell RNA-seq on 120 individuals of the Northern Netherlands population cohort Lifelines. For each individual peripheral blood mononuclear cells (PBMCs) were sequenced in an unstimulated condition, and after 3 and 24 hour in vitro stimulation with C. albicans (CA), M. tuberculosis (MTB) and P. aeruginosa (PA), totalling approximately 1.3 million cells. scRNA-seq was conducted with the 10X Genomics 3'-end v2 (72 libraries) and v3 (33 libraries) technology. In general, each library contains PBMCs from 8 donors and 2 different stimulation-timepoint combinations. Donors were demultiplexed using a combination of SoupOrCell (https://www.nature.com/articles/s41592-020-0820-1) and genotype information to assign the correct donor to a donor-specific cell cluster.

  Dataset EGAD00001007764

• RNA sequencing reads

  Paired-end, ribosome depleted, total RNA Sequencing

  Dataset EGAD00001004327

• Single Cell RNA Sequencing of Human Hematopoiesis

  This study includes single cell RNA sequencing of human CD34+ hematopoietic stem and progenitor cells throughout human life spanning midgestation into mature adulthood.

  Study phs002750

• Pilocytic Astrocytoma RNA sequencing

  This dataset contains RNA sequencing samples from the Duesseldorf/LIsbon pilocytic astrocytoma cohort which was profiled using Proteogenomics (RNA Sequencing, proteomics and methylation). There are 48 samples which were sequenced using paired-end sequencing at the University Hopstial Dusseldorf.

  Dataset EGAD00001009053

• RNA sequencing data pre-treatment and post-treatment for NABUCCO cohort 1

  NABUCCO cohort 1 sequencing data. The dataset includes RNA sequencing pre-treatment on tumor samples (n=18) and RNA sequencing post-treatment on bladder tumor samples (n=18).

  Dataset EGAD00001006855

• RNA seq on 19 samples of Radiation-Induced Meningiomas

  Purpose: Majority of pediatric cancers require the irradiation of the central nervous system (CNS), and as more patients survive into adulthood from improved oncological therapy the sequelae of brain radiation are increasing in prevalence. Radiation-induced meningiomas (RIMs), one such secondary effect, demonstrate a clinically more aggressive behaviour than sporadic meningiomas (SMs). We aimed to describe the genomic mutational landscape of RIMs.Methods: We analyzed a principal cohort of 18 RIMs, with 31 RIMs overall, from patients who received childhood radiation therapy and 30 SMs, as a comparator population. We performed a multiplatform integrative genomic analysis; including methylation, whole exome and RNA sequencing. Results: RIMs exhibited a five-fold increase in copy number alterations, commonly the loss of chromosome 1p (17/18 RIMs) and 22q (17/18 RIMs), which was significantly more than observed in sporadic meningiomas. Furthermore, RNA sequencing data revealed an NF2 gene fusion event in 35.3% of RIMs. In all 6 cases, there was a complete NF2 exon spliced into a complete exon of a reciprocal gene, suggesting that the breakpoints of genomic rearrangement are intronic. All tumours with the NF2 fusion also possessed monosomy of chromosome 22q, rendering the cells with homozygous disruption of NF2. Clinically, RIMs with the NF2 fusion exhibited ill-defined borders and a tendency to develop in anatomic frontal location. Also, targeted sequencing panel confirmed that RIMs had fewer nonsynonymous NF2 mutations (6.5% vs. 30% in SM) and absence of mutations in TRAF7, SMO, KLF4, PIK3CA and AKT1, genes traditionally involved in SMs.Conclusion: Our study demonstrates that RIMs have distinct genomic drivers of oncogenesis as compared to SMs, specifically NF2 inactivation through fusion event. Radiation therapy possibly triggers genomic structural rearrangements through error-prone repair of double-stranded DNA breaks.

  Study EGAS00001002318

• Genomic Analysis of Follicular Lymphoma

  We included tumors from newly diagnosed or untreated follicular lymphoma in this study. Samples consented to the University of Iowa and Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE) were included for data sharing. RNA was extracted from 1) CD19 sorted B cells from tumor cell suspension frozen in dimethyl sulfoxide (DMSO; RNA Tumor B Cell, n=77), 2) tumor cell suspensions frozen in DMSO (RNA Tumor DMSO, n=78), and 3) tumor tissue sections frozen in optimal cutting temperature compound (OCT; RNA Tumor OCT, n=53). For RNA sequencing, library preparation was done using the Illumina TruSeq RNA Exome Kit and sequenced with 100 nucleotide paired-end reads using the HiSeq 4000. DNA was extracted from 1) tumor cell suspensions frozen in DMSO (DNA Tumor DMSO, n=78), 2) tumor tissue sections frozen in OCT (DNA Tumor OCT, n=33), and 3) germline DNA from peripheral blood (n=102) and from normal tissue adjacent tumor (n=9). For whole exome sequencing (WES), library preparation was done using the Agilent SureSelect XT kit and sequenced with 100 nucleotide paired-end reads using the Illumina HiSeq 4000.

  Study phs002989

• Mapping the epigenomic landscape of human monocytes following innate immune activation reveals context-specific mechanisms driving endotoxin tolerance

  Background Monocytes are key mediators of innate immunity to infection, undergoing profound and dynamic changes in epigenetic state and immune function which are broadly protective but may be dysregulated in disease. Here, we aimed to advance understanding of epigenetic regulation following innate immune activation, acutely and in endotoxin tolerant states. Methods We exposed human primary monocytes from healthy donors (n=6) to interferon-γ or differing combinations of endotoxin (lipopolysaccharide), including acute response (2hr) and two models of endotoxin tolerance: repeated stimulations (6+6hr) and prolonged exposure to endotoxin (24hr). Another subset of monocytes was left untreated (naïve). We identified context-specific regulatory elements based on epigenetic signatures for chromatin accessibility (ATAC-seq) and regulatory non-coding RNAs from total RNA sequencing. Results We present an atlas of differential gene expression for endotoxin and interferon response, identifying widespread context specific changes. Across assayed states, only 24-29% of genes showing differential exon usage are also differential at the gene level. Overall, 19.9% (6884 of 34616) of repeatedly observed ATAC peaks were differential in at least one condition, the majority upregulated on stimulation and located in distal regions (64.1% vs 45.9% of non-differential peaks) within which sequences were less conserved than non-differential peaks. We identified enhancer-derived RNA signatures specific to different monocyte states that correlated with chromatin accessibility changes. The endotoxin tolerance models showed distinct chromatin accessibility and transcriptomic signatures, with integrated analysis identifying genes and pathways involved in the inflammatory response, detoxification, metabolism and wound healing. We leveraged eQTL mapping for the same monocyte activation states to link potential enhancers with specific genes, identifying 1,946 unique differential ATAC peaks with 1,340 expression associated genes. We further use this to inform understanding of reported GWAS, for example involving FCHO1 and coronary artery disease. Conclusion This study reports context-specific regulatory elements based on transcriptomic profiling and epigenetic signatures for enhancer-derived RNAs and chromatin accessibility in immune tolerant monocyte states, and demonstrates the informativeness of linking such elements and eQTL to inform future mechanistic studies aimed at defining therapeutic targets of immunosuppression and diseases.

  Study EGAS00001007362

• Pathogenic Neurofibromatosis type 1 (NF1) RNA splicing resolved by targeted RNAseq

  Neurofibromatosis type 1 (NF1) is caused by loss-of-function variants in the NF1 gene. Approximately 10% of these variants affect RNA splicing and are either missed by conventional DNA diagnostics or are misinterpreted by in silico splicing predictions. Therefore, a targeted RNAseq-based approach was designed to detect pathogenic RNA splicing and associated pathogenic DNA variants. an in-house developed tool (QURNAS) was used to calculate the enrichment score (ERS) for each splicing event. RNA enrichment of NF1 and SPRED1 was done using SPET (NUGEN - NF1 only) and using SureSelect (Agilent - NF1 and SPRED1).

  Study EGAS00001005530

• RNA-Seq of Whole Blood from Patients with Intracranial Aneurysms

  Rupture of intracranial aneurysms (IAs) causes intracranial hemorrhaging that leads to high rates of neurological deficits and death. Although rupture rates are low, clinicians must decide whether to treat or monitor these potentially dangerous lesions. In the current clinical practice, the most common metric to measure risk of rupture is aneurysm size. However, clinical data show that small aneurysms can also rupture. As a result, alternative clinical stratification scores have been proposed, including the PHASES (Population, Hypertension, Age, Size, Earlier subarachnoid hemorrhage, and Site) score based on patient demographics and IA characteristics to stratify ruptured and unruptured IAs, and the Rupture Resemblance Score (RRS) that stratifies ruptured and unruptured IAs based on hemodynamic and morphological properties. However, all metrics require imaging on digital subtraction angiography (DSA), which is invasive, expensive, requires the use of X-rays, and is associated with transient or permanent neurological and non-neurological complications. We hypothesize that individuals with dangerous IAs have detectable gene expression differences in their blood that could be used as biomarkers to determine rupture risk. We propose to use whole blood transcriptomes to develop a "one-stop" diagnostic test that can detect the presence of IAs and determine the risk of rupture based on circulating RNA expression biomarkers using our prototype AneuScreenTM platform. We aim to develop and validate biomarkers to predict risk, as calculated by the currently-used metric of aneurysm size, the clinical PHASES score, and the RRS. Here, we collected blood samples from consented individuals who were receiving cerebral imaging at the Gates Vascular Institute (Buffalo, NY) for intracranial aneurysm. RNA extracted from blood samples (n=43) was subjected to RNA-sequencing and added to our existing database (n=44). When combined with our previous data, we had transcriptome data from 68 aneurysms (after additional sample filtering). This dataset was stratified into low- and high-risk according to IA size, PHASES score, and RRS score. Differentially expressed genes were identified and used to construct predictive models. Fastq files and basic demographic information for the 43 newly-sequenced intracranial aneurysm samples on this project will be available through dbGaP.

  Study phs003072

• Genomic Characterization of Head and Neck Squamous Cell Carcinoma Cell Lines

  Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer by incidence worldwide(1). Various chemical carcinogens (tobacco, alcohol and betel nut), human papillomavirus (HPV) infection, and genetic predisposition contribute to the etiology of HNSCC, and to the complex genetic alterations in tumor subsets that differ in prognosis and response to therapies (2). Recently, a comprehensive landscape of genomic and transcriptomic alterations in HNSCC tumors has emerged from The Cancer Genome Atlas (TCGA) Network (3). TCGA revealed novel and previously recognized gene and chromosomal region copy number alterations (CNAs), mutations, and expression clusters, and defined their frequency, co-occurrence, and relationship to common and rare subtypes of HPV(-) and (+) tumors that vary in prognosis. To identify cell line models for determining the functional role and therapeutic importance of these alterations, we are performing whole exome and RNA sequencing and bioinformatic analysis of an expanded panel of 15 HPV(-) and 11 HPV(+) HNSCC cell lines and primary oral keratinocytes. We find that the recurrent genomic alterations in cell lines are remarkably consistent with those found in more aggressive tumors, from which cell lines have traditionally been most readily adapted to culture (4). Genome-wide correlation of CN (copy number) with expression identified a suite of potential drivers or modifier genes that differ by HPV status, and are of potential biologic and therapeutic relevance. Further, our findings elucidate and validate genomic alterations underpinning numerous discoveries made with these widely-used and recently derived HNSCC lines, and provide a roadmap for their potential use as models for future studies of tumor subtypes with worse prognosis. References Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87-108. Van Waes C, Musbahi O. Genomics and advances towards precision medicine for head and neck squamous cell carcinoma. Laryngoscope Investig Otolaryngol. 2017;2(5):310-9. Cancer Genome Atlas N. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature. 2015;517(7536):576-82. White JS, Weissfeld JL, Ragin CC, Rossie KM, Martin CL, Shuster M, et al. The influence of clinical and demographic risk factors on the establishment of head and neck squamous cell carcinoma cell lines. Oral Oncol. 2007;43(7):701-12.

  Study phs001581

• MINCR is a MYC-induced lncRNA able to modulate MYC’s transcriptional network in Burkitt lymphoma cells

  Despite the established role of the transcription factor MYC in cancer, little is known about the impact of a new class of transcriptional regulators, the long non-coding RNAs (lncRNAs), on the way MYC is able to influence cellular transcriptome. To this aim we have intersected RNA-sequencing data from two MYC-inducible cell lines and from a cohort of 91 mature B-cell lymphomas carrying, or not carrying, genetic variants resulting in MYC over-expression. By this approach, we identified 13 lncRNAs differentially expressed in IG-MYC-positive Burkitt lymphoma and regulated in the same direction by MYC in the model cell lines. Among them we focused on a lncRNA that we named MINCR, for MYC-Induced long Non-Coding RNA, showing a strong correlation with MYC expression in MYC-positive lymphomas and also in pancreatic ductal adenocarcinomas. To understand its cellular role we performed RNA interference (RNAi) experiments and found that MINCR knock-down is associated with a reduction in cellular viability, due to an impairment in cell cycle progression. Differential gene expression analysis following RNAi showed a strongly significant enrichment of cell cycle genes among the genes down-regulate following MINCR knock-down. Interestingly these genes are enriched in MYC binding sites in their promoters, suggesting that MINCR acts as a modulator of MYC transcriptional program. Accordingly, following MINCR knock-down, we observed a reduction in the binding of MYC to the promoters of selected cell cycle genes. Finally we provide evidences that down-regulation of AURKA, AURKB and CTD1 may explain the reduction in cellular proliferation observed upon MINCR knock-down. We therefore suggest that MINCR is a newly identified player in the MYC transcriptional network able to control the expression of cell cycle genes.

  Dataset EGAD00001001441

• 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

• ATAC-seq samples from 9 cHL cell lines

  The dataset contains 21 ATAC-seq samples from 9 cHL cell lines. Files within the dataset are raw fastq files for each cell line.

  Dataset EGAD50000001273

• WGS seq data (56 tumor/control pairs) for study EGAS00001001394

  Part of WGS seq data of Maligant Lymphoma study (ICGC)

  Dataset EGAD00001001673

• IVF MeDIP-seq bam files

  Bam files consisting of aligned MeDIP-seq reads from cord blood cells and cord blood mononuclear cells of twins conceived through in vitro fertilisation

  Dataset EGAD00001003158

• ChIP-Seq data for the paper titled "Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors"

  ChIP-Seq data for the paper titled "Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors"

  Dataset EGAD00001003432

• Intellance-2: TruSight Tumor 170 panel based DNA-seq

  Intellance-2: TruSight Tumor 170 panel based DNA-seq

  Dataset EGAD00001007945

• Microbiomic and immunogenic biomarkers of adjuvant chemotherapy efficacy in stage III colorectal cancer

  The standard treatment for stage III colorectal cancer (CRC) is surgery, followed by adjuvant chemotherapy (AC). However, the efficacy of AC is limited, and approximately 30% of patients experience recurrence. Therefore, easily assessable molecular biomarkers that can predict response to AC are needed. Here, we aimed to identify predictive biomarkers of response to AC using tumor transcriptomic data. Tumor specimens from 253 patients who underwent surgery for stage III CRC between 1997 and 2019 were analyzed using RNA sequencing. Transcriptional subtyping and analyses of gut microbiome, immune cell fractions, and T-cell receptor (TCR) were performed to identify factors associated with recurrence-free survival (RFS) in patients treated with or without AC. Of 253 patients, 118 (46.6%) received AC. Twenty phyla and 799 species of the microbiomes were identified, among which Fusobacteria were identified in 247 patients (97.6%). Multivariable Cox proportional hazards regression analysis of patients who received AC showed that intratumoral Fusobacterium and M1 macrophage abundance and diversity of TCR alpha and delta (TRAD) were independently associated with RFS. Fusobacterium and M1 macrophage abundance, and TRAD diversity were associated with the response to AC, suggesting that these factors could serve as biomarkers for personalized treatment in stage III CRC.

  Study JGAS000875

• Type 2 Diabetes Starr County GWAS and Exome Sequencing

  The T2D Seq GWAS Starr County Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" section of this top-level study page phs001166 T2D Seq GWAS Starr County Cohort. phs000143 CIDR T2D Hanis phs001099 T2D GENES Starr County

  Study phs001166

• Type 2 Diabetes in African Americans, GWAS and Exome Sequencing

  The T2D Seq GWAS AA Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs001167 T2D Seq GWAS AA Cohort. phs000140 CIDR T2D Bowdens phs001102 T2D GENES AA

  Study phs001167

• Evolving Cell States and Oncogenic Drivers during the Progression of IDH-Mutant Gliomas

  The study aims to understand the changes in cell states during the progression of IDH mutant gliomas. We generated scATAC-seq data from tumor samples of patients with IDH mutant gliomas, ranging from grade 2 to grade 4, including both IDH mutant oligodendroglioma and IDH mutant astrocytoma samples. Additionally, we have generated scRNA-seq data from patient-derived organoids that were treated with a DNMT1 inhibitor.

  Study phs003697

• Gene regulation of human stimulated and cultured CD4+ Treg cells

  This dataset maps gene expression regulation in human primary regulatory CD4+ T cells (Tregs) stimulated using PMA and ionomycin or cultured without stimulus for 18 hours. It includes whole genome sequence data for ChM-seq (18 H3K4me3, 20 H3K27ac and 3 inputs), ATAC-seq (42 samples) and whole transcriptome (39 samples). All individuals were genotyped using coreExome Illumina SNP chip array (included in EGAS00001003516).

  Study EGAS00001003515

• ChIP-Seq on multiple myeloma and plasma cell leukaemia cell lines

  ChIP-seq data (H3K4Me1, H3K4Me3, H3K27Ac histone modifications) in experimental triplicates on multiple myeloma cell line KMS11 and plasma cell leukaemia cell lines L363 and JJN3. ChIP reactions were performed on a Diagenode SX-8G IP-Star Compact using Diagenode automated Ideal Kit. ChIP libraries were generated using HTP Illumina library preparation kit, and sequenced using Illumina HiSeq 2000 with 100 bp single-ended reads. ChIP-seq files are in BED format.

  Dataset EGAD00001003349

• ATAC-seq dataset of patient and healthy donors

  ATAC-seq dataset on a patient (P) presenting with defects of immunity and two (C5, C6) healthy donors. This dataset contains raw and processed files from ATAC-seq chromatin accessibility analysis. There are 3 single-read (50 bp) fastq files (1 per patient/ donor). Processed files consist of narrowPeak files (1 per patient/ donor) and one file that contains read counts in consensus peaks.

  Dataset EGAD00001008370

• BLUEPRINT September 2016, ChIP-Seq for thymocyte from thymus, on Genome GRCh38

  ChIP-Seq data for 5 sample(s) for thymocyte from thymus, on Genome GRCh38. 17 run(s), 17 experiment(s), 17 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001002947

• ATAC-seq data from primary AML samples with t(3;8)

  Open chromatin regions in the MYC super-enhancer region were investigated by ATAC-seq in t(3;8) AML. ATAC-seq was performed as described (Buenrostro et al, 2013) with a modification in the lysis buffer (0.30 M sucrose, 10 mM Tris pH 7.5, 60 mM KCl, 15 mM NaCl, 5 mM MgCl2, 0.1 mM EGTA, 0.1% NP40, 0.15 mM Spermine, 0.5 mM Spermidine, 2 mM 6AA) to reduce mitochondrial DNA contamination.

  Dataset EGAD00001007910

• Bulk RNA Sequencing of 86 Human Donor Lungs

  The goal of this study was to characterize the effect of age on gene expression in normal human lung. We collected distal lung samples from 86 human donors distributed in age between 16 and 76 years and extracted RNA for Illumina-based sequencing.

  Study phs002484

• metastatic uveal melanoma tumour biopsies

  Tumour biopsies were obtained from uveal melanoma patients 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.

  Study EGAS50000000864

• metastatic uveal melanoma tumour biopsies (n=35)

  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.

  Study EGAS50000000836

• RNA004 Nanopore DRS of peripheral blood

  Direct RNA Sequencing offers the capability of standard transcriptomic analyses + RNA modification analysis, which holds promising applications for future clinical workflows. To this end, we tested the newly available RNA004 kit on human peripheral blood and provide orthogonal GLORI measurement for benchmarking of m6A-detection.

  Study EGAS50000001201

• Spatial RNA-sequencing of metastatic melanoma

  We performed RNA sequencing of 48 different regions sub-sampled from a metastatic melanoma biopsy that was treated with anti-PD-1 inhibitor. RNAseq was performed on samples with a minimum RNA integrity number (RIN) of 5.5 except for two cases (6A10 and 8A3) with RINs greater than 3. A minimum of 700ng of RNA were required for all samples undergoing RNAseq. Paired-end transcriptome reads were aligned using TopHat2, to the UCSC hg19 reference genome.

  Dataset EGAD00001005820

• Evaluation of protocols for rRNA depletion-based RNA sequencing of nanogram inputs of mammalian total RNA

  Study EGAS00001003849

• MuTHER adipose tissue small RNA expression

  MuTHER adipose tissue small RNA expression

  Dataset EGAD00001000105

• Bulk RNA sequencing of hematological toxicity following CAR-T cells injection

  Bone marrow aspirations obtained from patients treated with CAR-T cells were analyzed using bulk RNA sequencing (CD3+, CAR T+, CD34+ sorted populations).

  Study EGAS50000000777

• Extracellular Vesicle miRNA Sequencing with Qiaseq and NextSeq 550

  This dataset includes small RNA sequencing data from extracellular vesicle-derived RNA. miRNA libraries were prepared using the Qiaseq miRNA Library Kit with adaptations for low RNA input and sequenced on the Illumina NextSeq 550 platform

  Dataset EGAD50000001504

• RNA sequencing of skin tissue and peripheral blood mononuclear cells from patients with atopic dermatitis and healthy controls.

  We performed RNA-sequencing of skin tissue obtained from patients with atopic dermatitis, psoriasis and controls in Japanese population.

  Study JGAS000780

• Pediatric B-cell precursor acute lymphoblastic leukemia RNA sequencing

  This dataset included 33 childhood B-cell precursor acute lymphoblastic leukemia RNA sequencing samples. All samples were subjected to Illumina pair-end sequencing.

  Study EGAS50000000763

• Single-cell RNA-sequencing for peripheral blood mononuclear cells from COVID-19 patients and healthy controls of Japanese

  We performed single-cell RNA-sequencing of peripheral blood mononuclear cells obtained from COVID-19 patients and healthy controls.

  Study JGAS000593

• Single-cell RNA-sequencing for peripheral blood mononuclear cells from COVID-19 patients and healthy controls of Japanese

  We performed single-cell RNA-sequencing of peripheral blood mononuclear cells obtained from COVID-19 patients and healthy controls.

  Study JGAS000543

• Single cell RNA-sequencing of GSCs and GBM tumours

  Characterization of glioblastoma tumors and glioblastoma stem cell (GSCs) lines with single cell or single nuclei RNA-sequencing.

  Study EGAS00001004656

• RNA sequencing of AD, MCI and control OM cells

  Total RNA sequencing of cultured OM cells derived from patients with Alzheimer's disease (AD), individuals with mild cognitive impairment (MCI) and cognitively healthy controls.

  Study EGAS00001006195

• Bulk RNA from sorted CD8+ T cells after 48h co-culture with human Mito-DsRed MSCs.

  For human samples, total cellular RNA was isolated from post-sorted Mito+ and Mito CD8+ cells using the RNeasy Mini Kit (Qiagen). Stranded RNA libraries were created using SMARTSeq Stranded.

  Dataset EGAD00001011082

• non-IVF MeDIP-seq bam files

  Bam files consisting of aligned MeDIP-seq reads from cord blood cells and cord blood mononuclear cells of twins not conceived through in vitro fertilisation

  Dataset EGAD00001003159

• ATAC-seq of spermatogonia

  Raw sequencing reads of ATAC-seq of spermatogonia in FASTQ format, comprising 6 samples sequenced on the Illumina HiSeq 4000 platform.

  Dataset EGAD00001007759