12004 results for "cancer rna-seq"

in 16.64 milliseconds.

• ChIP-seq data of Hodgkin lymphoma cell line L-428

  ChIP-seq data (H3K4Me3, H3K27Ac histone modifications) of Hodgkin lymphoma cell line L-428. Samples were processed as previously described (Sud et al., 2018). The files are in bam format, aligned to build 37.

  Study EGAS00001003033

• H3K27ac ChIP-seq of human acute leukemias and healthy donors

  H3K27ac ChIP-seq of 79 primary samples derived from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). In addition, 4 samples derived from CD34+ cord blood cells of healthy donors were included.

  Dataset EGAD00001011060

• Human and rat skeletal muscle multiomic profiling sequencing data

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

  Dataset EGAD00001008323

• Chromatin accessibility (ATAC-seq) of human acute leukemias and healthy donors

  ATAC-seq of 79 primary samples obtained from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). ATAC-seq of CD34+ HSPCs from 3 healthy donors is also included.

  Dataset EGAD00001011050

• Exome and RNA sequencing of Greenlanders

  This study contains whole exome sequence of 27 Greenlanders and deep RNA sequencing of 17 Greenlanders

  Study EGAS00001002727

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

  RNA sequencing data of a collection of 6 pediatric ependymoma cases

  Study EGAS00001006535

• Characterization of genetic intratumor heterogeneity in colorectal cancer and matching patient-derived spheroid cultures.

  Patient-derived in vitro cultures of colorectal cancer (CRC) may help guide treatment strategies prior to patient treatment. However, most previous studies have been performed on a single biopsy per tumor. The purpose of this study was to analyze multiple spatially distinct biopsies from CRCs and see how well intratumor heterogeneity (ITH) was recapitulated in matching patient-derived spheroids. Three to five biopsies were collected from six CRC tumors. Each biopsy was split in two; one half was used for spheroid culturing, the other half was used for direct DNA and RNA purification. For two of the patients, lymph node metastases were analyzed. Somatic mutations were called from whole exome sequencing data. Each tumor contained multiple mutations shared across all biopsies and spheroids, including major CRC drivers such as APC, KRAS, and TP53. At the same time, all tumors exhibited ITH on both mutation and copy number level. The concordance between biopsies and spheroids ranged between 40-70% for coding mutations. For three of the patients the biopsy and spheroid from matching areas clustered together, meaning that the spheroid resembled the area of origin more than the other areas. However, all biopsies and spheroids contained private mutations. Thereby, multiple cultures from spatially distinct sites of the tumor increase the insight into the genetic profile of the entire tumor. RNA sequencing based molecular subtyping was stable and seemed independent of genetic ITH. In conclusion, all examined CRC tumors contained genetic ITH on mutation and copy number level. Spheroid cultures partly reflected this ITH, meaning that multiple cultures from distinct sites of the tumor improved the representation of the different genetic subclones in the primary tumor. This should be taken in to account when establishing patient-derived models for drug screening.

  Study EGAS00001002684

• Sequencing_component_for_the_whole_genome_methylation_analysis_in_PBMCs_and_cell_subsets__pilot_study_

  DNA methylation has been shown to play a major role in determining cellular phenotype by regulating gene expression. Moreover, dysregulation of differentially methylated genes has been implicated in disease pathogenesis of various conditions including cancer development as well as autoimmune diseases such as systemic Lupus erythematosus and rheumatoid arthritis. Evidence is rapidly accumulating for a role of DNA methylation in regulating immune responses in health and disease. However, the exact mechanisms remain unknown. The overall aim of the project is to investigate the role of epigenetic mechanisms in regulating immunity and their impact on autoimmune disease pathogenesis.The aim of this pilot study is to perform whole genome methylation analysis in peripheral blood mononuclear cells (PBMCs) and cell subsets (CD4, CD8, CD14, CD19, CD16 and whole PBMCs) obtained from 6 healthy volunteers. Whole genome methylation analysis will be performed using two methodological approaches, the Infinium Methylation Bead Array K450 (Illumina) and MeDIP-seq. mRNA expression arrays will also be performed in order to correlate DNA methylation with gene expression as well as genotyping on the Illumina OmniExpress chip

  Study EGAS00001000490

• ICGC MMML-seq Data Freeze November 2012 whole genome sequencing

  ICGC MMML-seq Data Freeze November 2012 whole genome sequencing

  Dataset EGAD00001000278

• ICGC MMML-seq Data Freeze November 2012 whole exome sequencing

  ICGC MMML-seq Data Freeze November 2012 whole exome sequencing

  Dataset EGAD00001000279

• ICGC MMML-seq Data Freeze March 2013 whole genome sequencing

  ICGC MMML-seq Data Freeze March 2013 whole genome sequencing

  Dataset EGAD00001000355

• ICGC MMML-seq Data Freeze July 2013 transcriptome sequencing

  ICGC MMML-seq Data Freeze July 2013 transcriptome sequencing

  Dataset EGAD00001000648

• EM-seq converted WGS for CSF-derived cfDNA from pediatric brain tumor patients

  EM-seq converted WGS for CSF-derived cfDNA from pediatric brain tumor patients

  Dataset EGAD50000001975

• Single cell RNAseq of PDAC organoids

  To study the role of WNT7B, three organoid lines were sequenced using SORT-Seq

  Study EGAS50000001544

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

  IPS-NPC02 input ChIP-Seq paired end data

  Dataset EGAD00001007290

• Reference epigenome Pancreas-Islet03 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet03 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007201

• Reference epigenome Fat-Preadipocyte01 h3k36me3 ChIP-Seq data generated from KEP study

  Fat-Preadipocyte01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007223

• Reference epigenome Fat-Preadipocyte01 h3k4me3 ChIP-Seq data generated from KEP study

  Fat-Preadipocyte01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007225

• Patient data used in FLTseq paper

  Apply newly developed FLT-seq protocol and package FLAMES on a primary patient sample

  Study EGAS00001005597

• Reference epigenome Kidney-Podocyte01 h3k4me1 ChIP-Seq data generated from KEP study

  Kidney-Podocyte01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007231

• Reference epigenome Kidney-mesangial02 h3k36me3 ChIP-Seq data generated from KEP study

  Kidney-mesangial02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007258

• Reference epigenome Pancreas-Islet07 input ChIP-Seq data generated from KEP study

  Pancreas-Islet07 input ChIP-Seq paired end data

  Dataset EGAD00001007463

• Reference epigenome Pancreas-Islet07 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007462

• Reference epigenome Pancreas-Islet02 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007198

• Reference epigenome Pancreas-Islet02 input ChIP-Seq data generated from KEP study

  Pancreas-Islet02 input ChIP-Seq paired end data

  Dataset EGAD00001007199

• Reference epigenome Pancreas-Islet03 h3k27ac ChIP-Seq data generated from KEP study

  Pancreas-Islet03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007200

• Reference epigenome Kidney-Podocyte01 h3k27me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007229

• 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-NPC02 h3k27ac ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007284

• Reference epigenome IPS-ENeuron02 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007302

• Reference epigenome Pancreas-Islet06 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007452

• Reference epigenome Pancreas-Islet06 h3k4me1 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007453

• Reference epigenome Pancreas-Islet07 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007461

• Reference epigenome Pancreas-Islet04 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007211

• Reference epigenome Kidney-Podocyte03 h3k36me3 ChIP-Seq data generated from KEP study

  Kidney-Podocyte03 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007237

• Reference epigenome Pancreas-Islet08 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007466

• Reference epigenome Fat-adipocyte04 h3k27ac ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007478

• Reference epigenome Pancreas-Islet08 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007469

• Reference epigenome Fat-adipocyte03 h3k27ac ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007471

• Reference epigenome Fat-adipocyte04 input ChIP-Seq data generated from KEP study

  Fat-adipocyte04 input ChIP-Seq paired end data

  Dataset EGAD00001007484

• Reference epigenome Fat-adipocyte04 h3k4me1 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007481

• Reference epigenome Fat-adipocyte04 h3k9me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007483

• Reference epigenome Fat-adipocyte05 h3k27ac ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007485

• Reference epigenome Fat-adipocyte05 h3k27me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007486

• Reference epigenome Fat-adipocyte05 h3k36me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007487

• Reference epigenome Pancreas-Islet07 h3k27ac ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007457

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

  IPS-NPC01 input ChIP-Seq paired end data

  Dataset EGAD00001007283

• Reference epigenome Pancreas-Islet08 h3k4me1 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007467

• Reference epigenome Fat-adipocyte03 h3k27me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007472

• Reference epigenome Pancreas-Islet03 h3k4me1 ChIP-Seq data generated from KEP study

  Pancreas-Islet03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007203