12004 results for "cancer rna-seq"

in 15.52 milliseconds.

• RNA-seq of small intestinal neuroendocrine tumors.

  RNA-seq of seven small intestinal neuroendocrine tumors, sequenced with illumina Nextseq 500.

  Dataset EGAD00001004491

• WGS and RNA-Seq data from a GBM patient PT-RW9277

  WGS and RNA-Seq data from a GBM patient PT-RW9277

  Dataset EGAD00001004259

• Single Cell RNA Seq LGG

  Single Cell RNA sequencing for 5 low grade glioma samples. NovaSeq6000 was used for scRNA Seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005368

• Single Cell RNA Seq GBM

  Single Cell RNA sequencing for 4 high grade glioma samples. NovaSeq6000 was used for snRNA Seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005369

• DIPG WES and RNA-Seq

  We characterised H3K27M-mutant diffuse intrinsic pontine glioma (DIPG, n=21) and RNA-Seq (n=26 DIPG, 12 normal brain)

  Dataset EGAD00001006450

• Bulk RNA sequencing of Singapore colorectal cancer patients (SG-BULK)

  RNA-seq libraries were prepared using the KAPA Stranded RNA-Seq Kit with RiboErase (Kapa Biosystems, Wilmington, MA) and sequenced to a target depth of 200-M reads on the Illumina HiSeq platform (Illumina, San Diego, CA).

  Dataset EGAD00001008512

• Genomics of Acute Myeloid Leukemia

  Sequence data from the Genomics of AML PPG program at Washington University since 2009 is made available. This repository contains data from tumors (bone marrow or peripheral blood) from Acute Myeloid Leukemia (AML) and several other related disorders - myelodysplastic syndromes (MDS), secondary AML (sAML) and therapy-related AML (tAML). It also contains matched normal data (skin or buccal swab) and healthy donors for comparison. A variety of DNA sequencing assays were used, including whole genome sequencing (WGS), exome sequencing, targeted panels of AML genes, or targeted panels for validation. Other modalities, including RNA-seq, small RNA-seq, whole genome bisulfite sequencing, CHIP-seq, ATAC-seq, and single-cell RNAseq are also represented.

  Study phs000159

• Comparison Between qPCR and RNA-Seq Reveals Challenges of Quantifying HLA Expression

  Human leukocyte antigen (HLA) class I and HLA class II loci are essential elements of innate and acquired immunity. Their exceptional influence on disease outcome is well documented by GWAS and candidate gene studies. The impact of HLA allelic variation on human disease through allele-specific presentation of antigenic peptides to T cells has been the main focus to determine HLA effects on disease susceptibility/pathogenesis. However, HLA expression levels have also been implicated in disease, adding another dimension to the extreme diversity of HLA that impacts variability in immune responses across individuals. HLA expression levels routinely rely on quantitative PCR (qPCR). An alternative is adoption of high throughput technologies such as RNA-Seq. This provides the opportunity to quantify HLA expression in large datasets, but also allows comparison between RNA-Seq and qPCR. In this study, we analyze expression data for HLA Class I genes for a matched set of individuals (N = 96) by RNA-Seq, qPCR and cell surface expression. Samples were obtained from the Research Donor Program (NCI-Frederick). We observed a moderate correlation between RNA-Seq and qPCR. We suggest technical and biological factors that might contribute to differences in the techniques.

  Study phs003177

• Single cell RNA seq of breast cancer stem cells

  RNA sequences of breast cancer have been investigated for many patients. Recently it has been shown that there are intra-heterogeneity in tumor tissues. When the tumor cells were sequenced in bulk condition, only the major populations' mRNA will be detected. With respect to breast cancer, it has been also reported that cancer cells can sub-gropued into cancer stem cells and non-cancer stem cells. Although the mRNA expressions' difference between this two groups has been profoundly investigated, whether each group is composed by unique cell or not is yet to be elucidated. Since cancer stem cells are the thought to be the major contributor of metastasis and/or recurrence, we focused on cancer stem cells in this study. We separated cancer cells into cancer stem cells and non-cancer stem cells exploiting cancer stem cell specific surface markers. Thereafter, we sequenced mRNA of cancer stem cells in single cell resolution. From this result, we examined whether these cells can be sub-grouped or not.

  Study JGAS000305

• RNA-Seq data from study: Molecular underpinnings of dedifferentiation and aggressiveness in chromophobe renal cell carcinoma

  The ChRCC study RNA-Seq dataset contains raw whole transcriptome sequencing data of 16 tumor and 6 adjacent normal samples from 7 UTSW patients, who have consented to depositing their genomic data to public repository. RNA-Seq was performed using 50bp single-end on a HiSeq2500 platform (Illumina, San Diego, CA, USA). 50M reads per sample on average. The raw data is in fastq format.

  Dataset EGAD50000000416