14977 results for "cancer studies using rna-seq"

in 21.48 milliseconds.

• RNA-sequencing

  RNA-sequencing for a cohort of B-ALL samples : 5 Down Syndrome (DS), 16 Hyperdiploid (HeH), 6 iAMP21, 9 other. RNA-sequencing for B-cell progenitors from 3 healthy. It also contains RNA-sequencing datasets of Patient-Derived Xenografts (X) developed from the B-ALL samples : 4 Down Syndrome (DS), 4 Hyperdiploid (HeH), 1 iAMP21, 3 other.

  Dataset EGAD00001005426

• reChIP-seq reveals widespread bivalency of H3K4me3 and H3K27me3 in CD4+ memory T-Cells

  Identification of genomic loci exhibiting the physical co-localization of two distinct DNA-associated proteins remains a technical challenge. To tackle this challenge we have developed a novel reChIP-seq approach that enriches for two distinct DNA-associated proteins in an unbiased and genome-wide manner. We illustrate the utility of our approach by identifying nucleosomes bivalently modified by H3K4me3 and H3K27me3 in primary human CD4+ central memory T-cells (TCMs). We unravel widespread bivalency of many unmethylated CpG-rich regions in TCMs overlapping with transcriptional start sites of developmental regulators. Furthermore, reChIP-seq uncovers two yet unobserved classes of bivalency, where either H3K4me3 or H3K27me3 is mixed with low but detectable bivalency. We provide evidence that bivalency is established by an interplay between genome and epigenome and thus attains stability. Our described reChIP-seq approach augments conventional ChIP-seq and is broadly applicable in the study of co-localization of DNA-associated proteins to unravel the intricate possibilities of combinatorial action.

  Study EGAS00001001568

• Genomic gain of EBV's LMP-1 in NKTCL

  The study found a high recurrence of genomic gain of LMP1 locus within EBV-associated NKTCL. The pilot data set and extended dataset of 77 WGS NKTCLs found 18/77 tumoral genomes to harbour genomic gain of LMP-1 locus within the EBV viral genomes within NKTCL tumoral sequencing data. The study also found 1/10 NKTCL cell lines to be positive for LMP-1 locus gain within EBV genomes too. This dataset includes solely the confirmatory WGS data of LMP-1 gain from NKYS cell lines using Oxford Nanopore long-read sequencing technology.

  Study EGAS50000000260

• NanoSring of PBMC from bladder cancer and RCC patients

  PBMCs from patients were isolated using 50 mL Leucosep™ tubes (Greiner Bio-One International, Germany) and Ficoll-Paque™ PLUS (GE Healthcare, Sweden). Whole blood drawn into sodium heparin blood collection tubes were diluted 3x with phosphate-buffered saline (PBS) without calcium or magnesium (Lonza, Walkersville, MD). Diluted cell suspensions were carefully layered on Leucosep tubes and centrifuged for 15 minutes at 800 x g at room temperature (RT). Interphase containing PBMCs were harvested and washed with PBS and subsequently centrifuged for 10 minutes at 250 x g at RT before further processing.

  Study EGAS00001004229

• Dataset for the Pearl study

  Biomarkers to identify patients without benefit from adding everolimus to endocrine treatment in metastatic breast cancer (MBC) are needed. We report the results of the Pearl trial conducted in five Belgian centers assessing 18F-FDG-PET/CT non-response (n=45) and ctDNA detection (n=46) after 14 days of exemestane-everolimus (EXE-EVE) to identify MBC patients who will not benefit. Metabolic non-response rate was 66.6%. Median PFS in non-responding patients (using as cut-off 25% for SUVmax decrease) was 3.1 months compared to 6.0 months in those showing response (HR: 0.77, 95% CI: 0.40-1.50, p=0.44). Difference was significant when using a “post-hoc” cut-off of 15% (PFS 2.2 months vs 6.4 months). ctDNA detection at D14 was associated with PFS: 2.1 months vs 5.0 months (HR-2.5, 95% CI: 1.3-5.0, p=0.012). Detection of ctDNA and/or the absence of 18F-FDG-PET/CT response after 14 days of EXE-EVE identifies patients with a low probability of benefiting from treatment. Independent validation is needed.

  Dataset EGAD00001007979

• Spatiotemporal immune atlas of the first clinical-grade gene-edited pig-to-human kidney xenotransplant

  Pig-to-human xenotransplantation is rapidly approaching the clinical arena; however, it is unclear which immunomodulatory regimens will effectively control human immune responses to pig xenografts. We transplanted a gene-edited pig kidney into a brain-dead human recipient on pharmacologic immunosuppression and studied the human immune response to the xenograft using spatial transcriptomics and single-cell RNA sequencing. Human immune cells were uncommon in the porcine kidney cortex early after xenotransplantation and consisted of primarily myeloid cells. Both the porcine resident macrophages and human infiltrating macrophages expressed genes consistent with an alternatively activated, anti-inflammatory phenotype. No significant infiltration of human B or T cells into the porcine kidney xenograft was detected. Altogether, these findings provide proof of concept that conventional pharmacologic immunosuppression is sufficient to restrict infiltration of human immune cells into the xenograft early after compatible pig-to-human kidney xenotransplantation.

  Dataset EGAD50000000359

• RNAseq data from the study - Molecular patterns of response and treatment failure after frontline venetoclax combinations in older patients with AML

  This study assessed molecular determinants of response in a cohort of patients with AML that were treated with venetoclax in combination with either DNA methyltransferase inhibitors or low dose cytarabine. RNA sequencing was performed on 31 patients from three different response classes [10 Group A - Durable remission (n=10), Group B - Relapsed (n=10) and Group C - Refractory (n=11)]. Library preparation and sequencing was performed at the Australian Genome Research Facility, using the Truseq Stranded mRNA library kit. Technical and batch replicate samples are included. Gene count data are provided with the original publication. The use of the sequencing data is subject to a data transfer agreement and is restricted to ethically approved research into blood cell malignancies and cannot be used to assess germline variants.

  Dataset EGAD00001005949

• Atypical teratoid/rhabdoid tumors (ATRT) are comprised of three epigenetic subgroups with distinct enhancer landscapes

  Atypical teratoid/rhabdoid tumor (ATRT) is one of the most common brain tumors in infants and young children. Although the prognosis of ATRT patients is poor, some patients respond very well to current treatments, suggesting inter-tumor molecular heterogeneity. To investigate this further, we genetically and epigenetically analyzed a large cohort of ATRTs (n = 170). Three distinct molecular subgroups of ATRTs, associated with differences in demographics, tumor location and type of SMARCB1 alterations, were identified using DNA-methylation or gene expression analyses. Whole genome DNA- and RNA-sequencing found no other recurrent mutations explaining the differences between subgroups. However, whole genome bisulfite-sequencing and H3K27Ac ChIP-sequencing of primary tumors revealed clear differences in methylation patterns and enhancer landscapes, leading to the identification of subgroup-specific regulatory networks.

  Dataset EGAD00001001444

• ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  Dataset EGAD00001000281

• Genetic Mechanisms of Disease Lab DAC

  Data access for raw and processed H3K27ac ChIP-seq and ATAC-seq data from "Effect of ETS2 modulation on chromatin accessibility and enhancer activity in human macrophages".

  Dac EGAC50000000087

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

  A KNIH009 mRNA-seq paired end data for preadipocytes

  Dataset EGAD00001002175

• Reference epigenome SMC07_smRNA-Seq data generated from KEP study

  A SMC07_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003864

• Reference epigenome SMC03_smRNA-Seq data generated from KEP study

  A SMC03_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003860

• Reference epigenome SMC08_smRNA-Seq data generated from KEP study

  A SMC08_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003865

• Reference epigenome SMC09_smRNA-Seq data generated from KEP study

  A SMC09_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003866

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

  A KNIH007 mRNA-seq paired end data for adipocytes

  Dataset EGAD00001002173

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

  A KNIH009 miRNA-seq single end data for preadipocytes

  Dataset EGAD00001002768

• Reference epigenome SMC09 h3k9me3 ChIP-Seq data generated from KEP study

  SMC09 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007354

• Reference epigenome ADMSC06 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007385

• Reference epigenome SMC01_smRNA-Seq data generated from KEP study

  A SMC01_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003858

• Reference epigenome SMC04 h3k4me1 ChIP-Seq data generated from KEP study

  SMC04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007328

• Reference epigenome SMC01 h3k9me3 ChIP-Seq data generated from KEP study

  SMC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007312

• Reference epigenome SMC02 h3k36me3 ChIP-Seq data generated from KEP study

  SMC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007315

• Reference epigenome ADMSC01 input ChIP-Seq data generated from KEP study

  ADMSC01 input ChIP-Seq paired end data

  Dataset EGAD00001007361

• Reference epigenome SMC03 h3k9me3 ChIP-Seq data generated from KEP study

  SMC03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007324

• Reference epigenome SMC04 h3k36me3 ChIP-Seq data generated from KEP study

  SMC04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007327

• Reference epigenome SMC04 h3k4me3 ChIP-Seq data generated from KEP study

  SMC04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007329

• Reference epigenome SMC04 h3k9me3 ChIP-Seq data generated from KEP study

  SMC04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007330

• Reference epigenome SMC08 h3k27ac ChIP-Seq data generated from KEP study

  SMC08 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007344

• Reference epigenome ADMSC02 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007363

• Reference epigenome ADMSC01 h3k27ac ChIP-Seq data generated from KEP study

  ADMSC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007356

• Reference epigenome ADMSC06 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007386

• Reference epigenome ADMSC06 h3k27me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007382

• Reference epigenome ADMSC06 input ChIP-Seq data generated from KEP study

  ADMSC06 input ChIP-Seq paired end data

  Dataset EGAD00001007387

• Reference epigenome ADMSC08 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007398

• Reference epigenome SMC05 h3k4me1 ChIP-Seq data generated from KEP study

  SMC05 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007334

• Reference epigenome ADMSC02 h3k27ac ChIP-Seq data generated from KEP study

  ADMSC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007362

• Reference epigenome ADMSC02 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007364

• Reference epigenome ADMSC02 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007365

• Reference epigenome ADMSC05 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC05 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007378

• Reference epigenome ADMSC05 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC05 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007379

• Reference epigenome SMC02 h3k4me1 ChIP-Seq data generated from KEP study

  SMC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007316

• Reference epigenome SMC04_smRNA-Seq data generated from KEP study

  A SMC04_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003861

• Reference epigenome SMC05_smRNA-Seq data generated from KEP study

  A SMC05_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003862

• Reference epigenome SMC06_smRNA-Seq data generated from KEP study

  A SMC06_smRNA-Seq single end data for skeletal muscle cells

  Dataset EGAD00001003863

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

  Islet-derived-MSC03 miRNA-Seq single end data

  Dataset EGAD00001007183

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

  Islet-derived-iPSC02 miRNA-Seq single end data

  Dataset EGAD00001007186

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

  Islet-derived-iPSC01 miRNA-Seq single end data

  Dataset EGAD00001007185

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

  Islet-derived-MSC02 miRNA-Seq single end data

  Dataset EGAD00001007182

• Reference epigenome SMC03 h3k4me3 ChIP-Seq data generated from KEP study

  SMC03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007323

• Reference epigenome SMC09 input ChIP-Seq data generated from KEP study

  SMC09 input ChIP-Seq paired end data

  Dataset EGAD00001007355

• Reference epigenome SMC08 input ChIP-Seq data generated from KEP study

  SMC08 input ChIP-Seq paired end data

  Dataset EGAD00001007349

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

  Islet-derived-MSC01 miRNA-Seq single end data

  Dataset EGAD00001007181

• Reference epigenome SMC01 h3k27ac ChIP-Seq data generated from KEP study

  SMC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007308

• Reference epigenome SMC01 h3k4me1 ChIP-Seq data generated from KEP study

  SMC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007310

• Reference epigenome SMC01 input ChIP-Seq data generated from KEP study

  SMC01 input ChIP-Seq paired end data

  Dataset EGAD00001007313

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

  ADMSC07 input ChIP-Seq paired end data

  Dataset EGAD00001007394

• Reference epigenome ADMSC08 h3k27Ac ChIP-Seq data generated from KEP study

  ADMSC08 h3k27Ac ChIP-Seq paired end data

  Dataset EGAD00001007395

• Reference epigenome SMC02 h3k4me3 ChIP-Seq data generated from KEP study

  SMC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007317

• Reference epigenome SMC02 h3k9me3 ChIP-Seq data generated from KEP study

  SMC02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007318

• Reference epigenome ADMSC08 h3k27me3 ChIP-Seq data generated from KEP study

  ADMSC08 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007396

• Reference epigenome ADMSC08 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007397

• Reference epigenome ADMSC08 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC08 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007399

• Reference epigenome ADMSC08 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC08 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007400

• Reference epigenome SMC03 h3k27ac ChIP-Seq data generated from KEP study

  SMC03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007320

• Reference epigenome SMC03 h3k4me1 ChIP-Seq data generated from KEP study

  SMC03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007322

• Reference epigenome SMC03 h3k36me3 ChIP-Seq data generated from KEP study

  SMC03 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007321

• Reference epigenome SMC04 h3k27ac ChIP-Seq data generated from KEP study

  SMC04 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007326

• Reference epigenome SMC05 h3k27ac ChIP-Seq data generated from KEP study

  SMC05 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007332

• Reference epigenome SMC05 h3k36me3 ChIP-Seq data generated from KEP study

  SMC05 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007333

• Reference epigenome SMC07 h3k27ac ChIP-Seq data generated from KEP study

  SMC07 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007338

• Reference epigenome SMC05 input ChIP-Seq data generated from KEP study

  SMC05 input ChIP-Seq paired end data

  Dataset EGAD00001007337

• Reference epigenome SMC07 h3k4me1 ChIP-Seq data generated from KEP study

  SMC07 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007340

• Reference epigenome SMC07 h3k36me3 ChIP-Seq data generated from KEP study

  SMC07 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007339