12213 results for "cancer rna-seq"

in 59.83 milliseconds.

• Reference epigenome IPS-Fibroblast01 input ChIP-Seq data generated from KEP study

  IPS-Fibroblast01 input ChIP-Seq paired end data

  Dataset EGAD00001007269

• Reference epigenome IPS-Fibroblast02 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007271

• Reference epigenome IPS-Fibroblast02 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007272

• Reference epigenome IPS-Fibroblast02 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007273

• Reference epigenome IPS-Fibroblast02 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007274

• Reference epigenome IPS-Fibroblast02 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007275

• Reference epigenome IPS-NPC01 h3k27ac ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007277

• Reference epigenome IPS-NPC01 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007278

• Reference epigenome IPS-NPC01 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007279

• Reference epigenome IPS-NPC01 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007280

• Reference epigenome IPS-NPC01 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007281

• Reference epigenome IPS-NPC01 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007282

• Reference epigenome IPS-NPC01 input ChIP-Seq data generated from KEP study

  IPS-NPC01 input ChIP-Seq paired end data

  Dataset EGAD00001007283

• Reference epigenome IPS-NPC02 h3k27ac ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007284

• Reference epigenome IPS-NPC02 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007285

• oxBS-seq for APL

  Oxidative bisulfite sequencing (oxBS-Seq) for APL

  Dataset EGAD00001008135

• Paired-end ATAC-seq analysis of the 3D spatially mapped GBM samples.

  Dataset contains 70 paired-end ATAC-seq samples from 8 patients.

  Dataset EGAD00001010311

• Single-cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers

  Background The encoding of cell intrinsic drug resistance states in breast cancer reflects the contributions of genomic and non-genomic variations and requires accurate estimation of clonal fitness from co-measurement of transcriptomic and genomic data. Somatic copy number (CN) variation is the dominant mutational mechanism leading to transcriptional variation and notably contributes to platinum chemotherapy resistance cell states. Here, we deploy time series measurements of triple negative breast cancer (TNBC) single-cell transcriptomes, along with co-measured single-cell CN fitness, identifying genomic and transcriptomic mechanisms in drug associated transcriptional cell states. Results We present scRNA-seq data (53,641 filtered cells) from serial passaging TNBC patient-derived xenograft (PDX) experiments spanning 2.5 years, matched with genomic single-cell CN data from the same samples. Our findings reveal distinct clonal responses within TNBC tumors exposed to platinum. Clones with high drug fitness undergo clonal sweeps and show subtle transcriptional reversion, while those with weak fitness exhibit dynamic transcription upon drug withdrawal. Pathway analysis highlights convergence on epithelial-mesenchymal transition and cytokine signaling, associated with resistance. Furthermore, pseudotime analysis demonstrates hysteresis in transcriptional reversion, indicating generation of new intermediate transcriptional states upon platinum exposure. Conclusions Within a polyclonal tumor, clones with strong genotype-associated fitness under platinum remained fixed, minimizing transcriptional reversion upon drug withdrawal. Conversely, clones with weaker fitness display non-genomic transcriptional plasticity. This suggests CN-associated and CN-independent transcriptional states could both contribute to platinum resistance. The dominance of genomic or non-genomic mechanisms within polyclonal tumors has implications for drug sensitivity, restoration and re-treatment strategies.

  Study EGAS00001007242

• 41 fibroblast RNAseq samples of pediatric patients with childhood epilepsy and developmental delay

  We used RNA sequencing of patient fibroblasts to find a genetic diagnosis in patients with exome-negative childhood epilepsy. RNA extracted from 41 individuals' cultured fibroblasts (passage 3-6) was collected and sequenced at a depth of 90-110 paired reads per sample.

  Study EGAS50000000792

• Primary_DIPG_expression_profiles

  In this study, we used RNA-sequencing gene expression profiling in order to characterize specific phenotypic traits in DIPG. Seventeen primary tumor samples stereotactically biopsied at diagnosis by neurosurgeons in the Necker-Enfants Malades hospital (Paris, France) and snap-frozen for RNA extraction and subsequent poly-A mRNA purification.

  Study EGAS00001007181

• Whole genome sequencing of matched patient sample sets of normal and tumor DNA

  Sixteen matched, male and female patient sample sets, each comprising normal DNA, tumor DNA, and RNA, were processed for storage comparison studies. Upon receipt, 10 μl quality control aliquots were prepared from each sample. DNA samples were characterized using Qubit dsDNA High Sensitivity assay for quantification, Genomic DNA TapeStation for integrity assessment (DIN scores), and NanoDrop for purity evaluation. RNA samples were analyzed using Qubit RNA High Sensitivity assay, RNA TapeStation (RIN scores), and NanoDrop. Each sample was divided equally by volume for parallel processing.

  Dataset EGAD50000002441

• RNA sequencing data of pediatric T-cell acute lymphoblastic leukemia (set 3)

  The dataset includes RNA sequencing FASTQ files from diagnostic tumor samples of two T-ALL patients (ALLK-126, ALLT-503). Two diagnostic samples of ALLT-503 were sequenced: one extracted from the bone marrow (ALLT-503D-RNA) and one from the lymph node (ALLT-503lympD-RNA). Novogene's internal method was used in directional library preparation including rRNA removal. The diagnostic tumor samples were sequenced using Illumina Novaseq X Plus, and the target sequencing depth was 21 Gb per sample.

  Dataset EGAD50000002586

• 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

• RNA sequencing data from visceral and abdominal subcutaneous adipose tissue from morbidly obese women with normal glucose tolerance or type 2 diabetes

  The study group consisted of 17 obese women with normal glucose tolerance and 15 obese women with T2DM. Adipose tissue specimens were taken from the epigastric region of the abdominal wall (SAT) and from the major omentum (VAT). RNA was isolated and RNA sequencing was used to analyse the transcriptome. Dharuri H et al, Diabetologia. 2014;57(11):2384-92.

  Study EGAS00001001872

• RNA-Sequencing of cervical cancers

  Question: Can we identify cervical cancer patients who are at risk for distant metastatic (DM) recurrence following treatment with radiotherapy, concurrent weekly cisplatin and brachytherapy (RTCT). Findings: An immune-based 55 gene risk score was developed using a cohort of 81 patients treated with RTCT that was strongly predictive of DM and cause-specific survival (CSS). The risk score was validated in two independent patient cohorts. A high immune metastatic risk score was associated with a high tumor mutational burden and a ‘cold’, immune-excluded tumor microenvironment at diagnosis. Meaning: The immune gene expression risk score may help to identify patients at risk of DM and potential targets for mitigating this risk.

  Study EGAS50000000087

• RNA sequencing of undifferentiated sarcomas

  Undifferentiated sarcomas (USARCs) of adults are diverse, rare, and aggressive soft tissue cancers. Recent sequencing efforts have confirmed that USARCs exhibit one of the highest burdens of structural aberrations across human cancer. Here, we sought to unravel the molecular basis of the structural complexity in USARCs by integrating DNA sequencing, ploidy analysis, gene expression, and methylation profiling. We identified whole genome duplication as a prevalent and pernicious force in USARC tumorigenesis. Using mathematical deconvolution strategies to unravel the complex copy-number profiles and mutational timing models we infer distinct evolutionary pathways of these rare cancers. In addition, 15% of tumors exhibited raised mutational burdens that correlated with gene expression signatures of immune infiltration, and good prognosis.

  Study EGAS00001003291

• Single Cell ATAC-Seq of MELAS

  This dataset includes combined single Assay for Transposase-Accessible Chromatin, using sequencing (ATAC-seq), and mitochondrial DNA sequencing of peripheral blood mononuclear cells from three individuals with m.3243A>G associated MELAS (Mitochondrial Encephalopathy, Lactic Acidosis, and Stroke-like episodes).

  Study phs002217

• COVID-19 Postmortem Lung snRNA-seq

  A deeper understanding of the pathological mechanisms of SARS-CoV-2 infection is required to combat COVID-19. Through this dataset, we analyze postmortem lung cells from patients that are infected/uninfected with SARS-CoV-2 with snRNA-seq.

  Study EGAS00001004689

• Chromatin segmentation of myometrium and UL subclasses

  Histone modification ChIP-seq for chromatin state annotations of myometrium and UL subclasses. Altogether 5 analyses and 210 runs. Includes fastq files; both ChIP and Input for each sample. Some Inputs are used for multiple ChIP experiments. ChIP-seq alignment files for trimmed, mapping q20 and nonredundant reads; both ChIP and Input for each sample. Software: Trim Galore v0.6.7; Bowtie 2 v2.5.0; samtools v1.6

  Dataset EGAD50000001443

• ChIP-seq for 10 samples

  This dataset contains ChIP-seq data generated from two patient-derived xenograft (PDX) models. For each model, sequencing data are provided from input control (n=1), IgG control (n=2), and FOXF1 immunoprecipitation (n=2). All the 10 samples are in bam file type.

  Dataset EGAD50000001786

• scRNA-seq of Patient-derived tumor fragments (PDTFs)

  scRNA-seq of Patient-derived tumor fragments (PDTFs) which were treated with either Isotype (DP47), Isotype-IL2v (DP47-IL2v), anti-PD1 (PembroPGLALA) or PD1-IL2v (PembroPGLALA-IL2v) for 48 hours. Dataset content: 10 samples from 3 donors File type: paired-end fastq files Protocol: Chromium Single Cell 3' Reagent Kits User Guide (Dual Index) with Feature Barcoding technology for Cell Surface Protein (CG000317) Technology: Illumina sequencing Experimentation: scRNA-seq using the 10X technology

  Dataset EGAD50000000584

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

  Histone ChIP-seq of 13 human embryonic tissues from weeks 6-8 of gestation. H3K4me3, H3K27me3 and H3K27ac. Biological replicates (n=2) for 11 tissues. Tissues (n): Brain (2); Retinal Pigmented Epithelium (eye)(2); Palate(2); Tongue (1); Left ventricle (heart)(2); Lung(2); Liver(2); Pancreas (2*); Stomach(1); Upper limb (2); Lower limb (2); Adrenal gland (2); Kidney (2)

  Dataset EGAD00001004335

• DNA methylation (MCIP-seq) of human acute leukemias and healthy donors

  MCIP-seq of 77 primary samples obtained from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). Moreover, MCIP-seq of CD34+ HSPCs from 3 healthy donors is included.

  Dataset EGAD00001011052

• MATISSE bulk RNA-sequencing data

  Tumor availability: Bulk RNA sequencing was performed on tumor biopsies of 48 patients at baseline and 45 patients across time points (baseline n=48, week 1 n=38, week 2 n=34, week 4 n=37), including lymph node metastasis that were sequenced in addition to the primary tumor. Tumor RNA was extracted from formalin-fixed paraffin-embedded (FFPE) primary tumor and lymph node metastasis sections containing at least 10% viable tumor cells present in the sample, except for on-treatment samples with a complete pathological response in which we isolated samples with at least 10% immune-infiltration with no viable tumor cells. A pathologist (LS) scored the tumor percentage and indicated the most tumor-dense region on a hematoxylin and eosin (H&E) stain slide for subsequent RNA isolation. According to the manufacturer's protocol, five to 10 FFPE slides (10 µm) were used for RNA isolation using the AllPrep DNA/RNA FFPE isolation kit (Qiagen, 80234) and the QIAcube. Genetic Diagnostics and Sequencing Services (CeGaT) performed RNA sequencing in Germany. Data processing: Demultiplexing of the sequencing reads was performed with Illumina bcl2fastq (v2.20). Adapters of the reads (Pico v2 SMART adapter, first three nucleotides of the second sequencing read) were trimmed with Skewer (v0.2.2), without quality trimming. RNA-sequencing data was aligned to GRCh38 (Ensembl v109) using STAR (v2.7.9) in 2-pass mode with default settings. Gene counts were generated with the HTSeq (v2.0.2). FastQC (v0.12.1) and MultiQC (v1.14) were used for assessing data quality, on FASTQ files and intermediate processing steps.

  Dataset EGAD50000001470

• Leukemia stem cell containing fractions

  ATAC-seq performed on leukemic stem cell fractions with different engraftment potential. Leukemic cells were sorted out of Acute Myeloid Leukemia patient samples based on CD34/CD38 status. Samples were annotated based on an independent engraftment assay in mice as LSC+ (engrafting) and LSC- (not engrafting). Omni-ATAC-seq was carried out to compute differential enrichment of transposable elements in chromatin accessible regions.

  Study EGAS00001004893

• 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

• 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

• 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

• Human embryo ATAC+RNA single cell sequencing samples DAC (Linnarsson)

  DAC for human embryo ATAC+RNA single cell sequencing samples

  Dac EGAC50000000657

• Single-cell/single-nucleus RNA-seq of Embryonal Tumor with Multilayered Rosettes (ETMR)

  Embryonal tumor with multilayered rosettes (ETMR) is a pediatric brain tumor with dismal prognosis. We analyzed eleven ETMR patient samples using single-cell transcriptomics. We reveal a spatially distinct cellular hierarchy that spans highly proliferative neural stem-like cells and more differentiated neuron-like cells. C19MC is predominantly expressed in stem-like cells and controls a transcriptional network governing stemness and lineage commitment. Systematic analysis of receptor-ligand interactions between malignant cell types reveals FGFR and NOTCH signaling as oncogenic pathways. Our study provides fundamental insights into ETMR pathobiology and a powerful rationale for more effective targeted therapies.

  Study EGAS50000000937

• Systematic immune cell dysregulation and molecular subtypes revealed by single cell RNA-seq of subjects with type 1 diabetes

  Type 1 diabetes mellitus (T1DM) is a prototypic endocrine autoimmune disease resulting from an immune-mediated destruction of pancreatic insulin-secreting beta-cells. A comprehensive immune cell phenotype evaluation in T1DM has not been performed thus far at the single. In this cross-sectional analysis, we generated a single-cell transcriptomic dataset of peripheral blood mononuclear cells (PBMCs) from 46 manifest T1DM (Stage 3) cases and 31 matched controls.Our study reveals a surprisingly strong systemic dimension at the level of immune cell network in T1DM, defines disease-relevant molecular subtypes and has the potential to guide non-invasive test development and patient stratification.

  Study EGAS50000000231

• 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 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 h3k36me3 ChIP-Seq data generated from KEP study

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

  Dataset EGAD00001007404

• 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-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 h3k27ac ChIP-Seq data generated from KEP study

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

  Dataset EGAD00001007409

• 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 h3k4me1 ChIP-Seq data generated from KEP study

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

  Dataset EGAD00001007412

• 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-MSC02 input ChIP-Seq data generated from KEP study

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

  Dataset EGAD00001007415

• Reference epigenome Islet-derived-MSC03 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007416

• 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-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-MSC04 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007430

• Reference epigenome Islet-derived-MSC04 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007431

• 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-MSC04 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007433

• Reference epigenome Islet-derived-MSC04 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007434

• 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 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 h3k4me1 ChIP-Seq data generated from KEP study

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

  Dataset EGAD00001007439

• 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 h3k27me3 ChIP-Seq data generated from KEP study

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

  Dataset EGAD00001007444

• 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

• 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

• Cohort B RNA sequencing

  Cohort B includes IDH-wild type, EGFR amplified, surgically derived glioblastoma tissue samples from male and female individuals. RNA was isolated from FFPE glioma tissue of each sample from Cohort B.

  Study EGAS50000000953

• Cohort A RNA sequencing

  Cohort A includes glioma tumors of varied grading and pathology from male and female individuals. RNA was isolated from fresh frozen glioma tissue of each sample tumor block from Cohort A.

  Study EGAS50000000950

• RNA sequencing

  For RNA sequencing: All sample numbers starting with 6717 are tumor samples which has been sequenced using transcriptomics (see BAM files). It concerns biopsies of metastatic lesions from patients with BRAFV600 mutated melanoma, obtained before, during and after the study treatment (see samples metadata). RNA sequencing is performed using the Illumina Novaseq 6000 system.

  Dataset EGAD50000000383

• Therapy and RNA group Ghent DAC

  This is a Data Access Committee for datasets of the Therapy and RNA group Ghent (TaRGeT, Belgium).

  Dac EGAC50000000491

• Human iPSC-derived brain cell RNA sequencing data DAC at UEF (DAC-hiPSC)

  RNA sequencing data from human iPSC-derived cell model systems

  Dac EGAC50000000949

• Identification of Novel Therapeutic Targets for Calcific Aortic Valve Stenosis Using Integrative Genomics

  In order to identify novel therapeutic targets for calcific aortic valve stenosis, we have generated a large transcriptomic dataset from RNA sequencing of human aortic valve tissues from 500 individuals undergoing aortic valve replacement or heart transplant at the Institut universitaire de cardiologie et de pneumologie de Québec – Université Laval (QUEBEC-CAVS-RNA). This dataset represents a unique resource to understand the molecular mechanisms underlying aortic valve diseases.

  Study phs003541

• ChIP-Seq files for PCGP ATRX study

  ChIP-Seq files for PCGP ATRX study paper titled "MYCN Amplification and ATRX Mutations are Incompatible in Neuroblastoma"

  Dataset EGAD00001004429

• 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

• Single-cell DNA-seq

  Single-cell DNA sequencing (scDNA-seq) data.

  Dataset EGAD00001008779

• 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

• Mayo Clinic and Illumina Collaborative Early Stage Ovarian Cancer (ESOC) Study

  Twenty five patients with early stage carcinoma (Stage I and II) of the ovary were consented for this study. As part of a collaborative agreement with Illumina Inc, Tumor-DNA and patient matched normal DNA from blood underwent whole genome sequencing and microarray genotyping and RNA underwent RNASeq. Tissue also underwent IHC staining for TP53 mutations. The aim of the study was to profile the genomic landscape of these early tumors to discover biomarkers for early detection or subgrouping into different genomic or outcome subgroups. Analyses performed include somatic mutation analysis, RNASeq mutation analysis, LOH analysis, structural variant detection. The current release of this study focusses on 17 patients with serous carcinoma and stage 1 or 2 and with high grade (3 or 4).

  Study phs000897

• 3D-GSC_expression_profiles

  In this study, we used RNA-sequencing gene expression profiling in order to characterize specific phenotypic traits in DIPG-derived glioma stem cell (GSC) models. Twenty-two primary GSC models derived from biopsies collected at diagnosis were cultured in an ECM-mimicking compound before for RNA extraction and subsequent rRNA-depletion and sequencing .

  Study EGAS00001007182

• small RNA-sequencing and RNA-sequencing of human brain tissue with temporal lobe epilepsy

  This data set contains the raw .fastq files from two RNA-sequencing experiments and two small RNA-sequencing experiments. Both control brain tissue and tissue from sufferers of mesial temporal lobe epilepsy were sequenced. Two different brain regions were sequenced; the cortex and the hippocampus. For more details please see: Mills, James D., et al. "Coding and non-coding transcriptome of mesial temporal lobe epilepsy: Critical role of small non-coding RNAs." Neurobiology of disease 134 (2020): 104612.

  Dataset EGAD00001005735

• TSO500 on RNA

  TruSight Oncology 500 RNA probes panel on case 521

  Dataset EGAD50000000271

• RNA sequencing of myxofibrosarcoma

  RNA sequencing (fastq files) of 29 samples of myxofibrosarcoma

  Dataset EGAD00001004102

• Single-Cell Multi-Omic Analysis of the Vestibular Schwannoma Ecosystem Uncovers a Nerve Injury-Like State

  Vestibular schwannomas are benign tumors that can lead to significant morbidity, including hearing loss, facial nerve paralysis and obstructive hydrocephalus. The genomic landscape of vestibular schwannomas is relatively bland and the molecular pathophysiology of vestibular schwannomas is poorly understood. Therefore, we performed single cell RNA sequencing on 15 sporadic vestibular schwannomas, with paired single cell ATAC sequencing (n = 6) and whole exome sequencing (n = 12) to perform a detailed characterization of the vestibular schwannoma tumor microenvironment at the transcriptional and epigenetic level. We validated our results by analyzing bulk RNA sequencing data from 22 newly sequenced vestibular schwannomas and 173 previously sequenced bulk RNA sequencing data from 195 vestibular schwannomas. Data available in dbGaP include whole exome sequencing of 12 tumors and matched blood samples. Matching single cell RNA sequencing and single cell ATAC sequencing data is available via NCBI GEO.

  Study phs003318

• 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