11925 results for "cancer rna-seq"

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• Transcriptomic Characterization of Human Innate T Cells

  We performed low input RNA-seq and single-cell RNA-seq on 7 lymphocyte populations with the objective of finding the common transcriptional programs shared among innate T cells. In brief, we drew blood from healthy donors and isolated CD4+ T cells, CD8+ T cells, Natural Killer cells, invariant natural killer T (iNKT) cells, mucosal-associated invariant T (MAIT) cells, and two γδ T cell populations: Vδ1 and Vδ2. We performed low input RNA-seq on samples with 1000 cells each, for 6 healthy individuals, in duplicate. We performed single-cell RNA-seq with DNA-barcoded hashing antibodies to identify the cell type of origin for each cell. We found a common transcriptional program across lymphocytes that is gradually turned on as lymphocytes have a more "innateness" state, promoting effector functions and sacrificing proliferative capacity. Additionally, we performed low input RNA-seq for Vδ3-expressing γδ T cells and δ/αβ T cells for one healthy individual in duplicate (1000 cells per sample). This revealed δ/αβ T cells are transcriptionally more adaptive-like and Vδ3 more innate-like.

  Study phs002007

• RNA-seq FASTQ files studied in Generation of hypoimmunogenic induced pluripotent stem cells by CRISPR-Cas9 system and detailed evaluation for clinical application

  This data set contains 12 paired fastq files (RNA-seq) from one wild-type iPS cell, two HLA-KO iPS clones and three CD14+ monocytes differentiated from them. The RNA-seq libraries were generated with Illumina TruSeq Stranded Total RNA library prep kit. The libraries were sequenced with Illumina NovaSeq 6000 in paired-end mode.

  Dataset EGAD50000001700

• RNA-seq data of proliferative vitreoretinal diseases and healthy human retinal pigment epithelium

  This dataset contains RNA-seq data generated from surgical specimens of proliferative vitreoretinal diseases (including proliferative vitreoretinopathy and epiretinal membranes) and from retinal pigment epithelial cells isolated from post-mortem donor eyes. Total RNA was extracted and cDNA libraries were prepared using the Ovation Solo RNA-seq Kit (Tecan). Sequencing was performed on the Illumina NovaSeq 6000 platform. Raw FASTQ files are included to enable downstream analyses such as gene expression profiling.

  Dataset EGAD50000001780

• mapped Bam files from whole transcriptome RNA-seq

  In this study we characterized genomic alterations in two to five metachronous bladder tumors from 29 patients initially diagnosed with early stage disease. Fourteen patients (32 tumors) had non progressive disease (NPD) and 15 patients (34 tumors) had progressive disease (PD). Whole exome sequencing (WES, ~50x mean read depth and whole transcriptome RNA-seq was performed (RNA was not advalible for 4 tumors). Data provided here consist of 71 mapped Bam files form whole transcriptome RNA-seq.

  Dataset EGAD00001002718

• unmapped Bam files from whole transcriptome RNA-seq

  In this study we characterized genomic alterations in two to five metachronous bladder tumors from 29 patients initially diagnosed with early stage disease. Fourteen patients (32 tumors) had non progressive disease (NPD) and 15 patients (34 tumors) had progressive disease (PD). Whole exome sequencing (WES, ~50x mean read depth and whole transcriptome RNA-seq was performed (RNA was not advalible for 4 tumors). Data provided here consist of 71 unmapped Bam files form whole transcriptome RNA-seq.

  Dataset EGAD00001002717

• Human Epidermis scRNA

  Single cell RNA-seq analysis of human skin.

  Dataset EGAD00010001620

• Exome reads and RNA-seq

  Exome sequencing for 26 patients with matched blood RNA-seq for 41 patients

  Dataset EGAD00001002722

• GCLJ RNA-Seq data

  RNA-Seq data from 6 Giant Cell Lesions of the Jaw (GCLJ) samples.

  Dataset EGAD00001004320

• nccRCC RNA-Seq data of consented samples

  nccRCC RNA-Seq data of consented samples

  Dataset EGAD00001001022

• All available datasets of DEEP

  All available datasets of DEEP. There are several Reference Epigenomes available. For metadata check out www.deep.dkfz.de One reference epigenome represents a set of 6 ChIP-Seqs, WGBS, RNA-Seq, smallRNA-Seq and sometimes DNase1 and/or NOME-Seq.

  Study EGAS00001001608