9985 results for "cancer rna-seq"

in 33.16 milliseconds.

• 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

• TARGET-seq+ single-cell genotyping

  Single-cell genotyping data for bone marrow samples from 9 cases with clonal hematopoiesis and 1 control sample. The TARGET-seq+ protocol was used to generate plate-based 3' transcriptome data. For details on cell sorting and the TARGET-seq+ protocol see the methods section of the manuscript. One FASTQ file is provided per cell. Cells are named with their plate and well IDs and the subject ID. Empty wells (no-cell controls) are named "blank". Corresponding transcriptome files use the same naming with the "_transcriptome" suffix.

  Dataset EGAD00001011150

• RNA sequencing of Resistant BCC samples.

  RNA sequencing of Resistant BCC samples.

  Dataset EGAD00001000881

• Dataset to study clonal evolution in TCF3:PBX1 patient SJE2A066 using scWGS-seq

  scWGS-seq of flow sorted blast and normal cells from SJE2A066 with 69 high quality cells sequenced (62 blast and 7 normal)

  Dataset EGAD00001011329

• Dataset to study clonal evolution in TCF3:PBX1 patient SJE2A067 using scWGS-seq

  scWGS-seq of flow sorted blast and normal cells from SJE2A067 with 78 high quality cells sequenced (73 blast and 5 normal)

  Dataset EGAD00001011330

• Dataset to study clonal evolution in TCF3:PBX1 patient SJE2A063 using scWGS-seq

  scWGS-seq of flow sorted blast and normal cells from SJE2A063 with 80 high quality cells sequenced (75 blast and 5 normal)

  Dataset EGAD00001011328

• 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

• UCSF brain tumor data

  Single Nuclei ATAC seq data from GBM tumor samples. NovaSeq6000 was used for ATAC seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005314

• RCC ATAC-seq data

  ATAC-seq data 72cases

  Dataset EGAD00001009868

• Gene expression in brain (Schizophrenia) study

  The study included post-mortem brain tissue samples from 68 schizophrenia patients and 44 age and sex matched control subjects. Whole transcriptome poly-A selected paired-end RNA sequencing was performed on tissue from prefrontal cortex and orbitofrontal cortex. RNA expression differences were detected between case and control individuals, focusing both on single genes and pathways.

  Study EGAS00001004199

• An Organoid Biobank of Rare Human Neuroendocrine Neoplasms Enables Genotype-Phenotype Mapping

  We performed whole genome sequencing and RNA-sequencing of neuroendocrine neoplasm. The aim of this study was to provide genetic landscape of neuroendocrine neoplasm, to construct available animal models, and to engineer neuroendocrine neoplasm from normal colon epithelium. The reference genomes used are GRCh37 for whole genome sequencing and GRCh38 for RNA-sequencing.

  Study JGAS000237

• Nasopharyngeal RNASeq Comparing SARS-CoV-2+ Patients and SARS-CoV-2 Negative Control Subjects

  The goal of this analysis was to compare RNAs enriched in the upper respiratory tract of SARS-CoV-2 patients and SARS-CoV-2 negative controls. RNA-sequencing of RNA isolated from the viral transport medium associated with nasopharyngeal swab samples from SARS-CoV-2+ patients and SARS-COV-2 negative controls.

  Study phs002433

• Single nucleus and bulk transcriptomics in prefrontal cortex of individuals with alpha synucleinopathies

  We explore cell-specific changes in gene expression across different α-synucleinopathies. We perform single nucleus RNA sequencing using 10X Genomics, and bulk RNA sequencing from the prefrontal cortex of individuals with alpha synucleinopathies - Parkinson's Disese (PD), monogenic LRRK2 PD, and multiple system atrophy (MSA), together with neurologically healthy controls.

  Study EGAS50000000301

• Single cell RNA sequencing of cerebrospinal fluid

  Single-cell RNA sequencing of cells isolated from cerebrospinal fluid from 81 individuals with multiple sclerosis (n=33) or other neurological disease (n=48) using 10x Genomics 3’ expression (v2 chemistry).

  Study EGAS00001007478

• Genome-wide DNA-methylation assessment by MethylCap-seq and Infinium HumanMethylation450 BeadChips: an independent large-scale comparison

  DNA-methylation is an important epigenetic feature in health and disease. Two cost-efficient genome-scale methodologies to assess DNA-methylation are MethylCap-seq and Illumina's Infinium HumanMethylation450 BeadChips (HM450). However, objective information regarding the best-suited methodology for a specific research question is scant. Therefore, we performed a large-scale evaluation on a set of 70 brain tissue samples obtained from 65 glioblastoma and 5 non-tumoral brain tissues, using a gold standard free Bayesian modeling procedure. While conditional specificity was adequate for both approaches, conditional sensitivity was systematically higher for HM450. Also the genome-wide characteristics were compared, revealing that the HM450 probes assess less than 10% of the regions identified as methylated by MethylCap-seq. Hence the latter method may detect more potentially relevant DNA-methylation, defined by either functional location or previously reported differentially methylated candidate regions. Our results therefore indicate that – at least for the tissue under study - both methodologies are complementary, with a higher sensitivity for HM450, but a far larger genome-wide coverage for MethylCap-seq. Note that here only the relevant MethylCap-seq data is deposited, for the HM450 data we refer to GEO (GSE60274).

  Study EGAS00001001191

• Osteosarcoma sequencing data

  This study shares DNA and RNA sequencing data from embryonal rhadbomyosarcoma patients

  Study EGAS00001005600

• Pilocytic Astrocytoma ICGC PedBrain RNA sequencing

  Pilocytic Astrocytoma ICGC PedBrain RNA sequencing

  Dataset EGAD00001000617

• RNA sequence of HUB_5 cell culture

  RNA sequence data for conditionally reprogrammed cells from patient HUB_5

  Dataset EGAD00001001923

• HipSci - Alport Syndrome - RNA Sequencing - July 2017

  HipSci - Alport Syndrome - RNA Sequencing - July 2017

  Dataset EGAD00001003532

• HipSci - Battens Disease - RNA Sequencing - July 2017

  HipSci - Battens Disease - RNA Sequencing - July 2017

  Dataset EGAD00001003533

• HipSci - Kabuki Syndrome - RNA Sequencing - July 2017

  HipSci - Kabuki Syndrome - RNA Sequencing - July 2017

  Dataset EGAD00001003535

• HipSci - Hypertrophic Cardiomyopathy - RNA Sequencing - July 2017

  HipSci - Hypertrophic Cardiomyopathy - RNA Sequencing - July 2017

  Dataset EGAD00001003540

• HipSci - Macular Dystrophy - RNA Sequencing - July 2017

  HipSci - Macular Dystrophy - RNA Sequencing - July 2017

  Dataset EGAD00001003541