11750 results for "cancer rna-seq"

in 12.26 milliseconds.

• WGS and RNA-Seq data from a GBM patient PT-WR7927

  WGS and RNA-Seq data from a GBM patient PT-WR7927

  Dataset EGAD00001008531

• STAR outputs from RNA-seq

  STAR outputs from RNA-seq of 84 pancreatic neuroendocrine neoplasms, 10 normal islet samples and 4 cell line samples.

  Dataset EGAD00001006991

• WGS and RNA-Seq data from a GBM patient PT-GI2070

  WGS and RNA-Seq data from a GBM patient PT-GI2070

  Dataset EGAD00001008520

• WGS and RNA-Seq data from a GBM patient PT-JR9883

  WGS and RNA-Seq data from a GBM patient PT-JR9883

  Dataset EGAD00001008521

• WGS and RNA-Seq data from a GBM patient PT-ML9537

  WGS and RNA-Seq data from a GBM patient PT-ML9537

  Dataset EGAD00001008523

• Intellance-2: TruSight Tumor 170 panel based RNA-seq

  Intellance-2: TruSight Tumor 170 panel based RNA-seq

  Dataset EGAD00001007944

• RNA-Seq datasets in human islets cultured in high glucose conditions

  7 RNA-Seq datasets in human pancreatic islets from 7 donors, where islets were treated in high (11mM) glucose conditions

  Dataset EGAD00001005207

• Smart-seq2 single-cell RNA-seq of human liver non-parenchymal cells

  Smart-seq2 single-cell RNA-seq of human liver non-parenchymal cells from lean and obese individuals

  Dataset EGAD00001010301

• WGS and RNA-Seq data from a GBM patient PT-HO0394

  WGS and RNA-Seq data from a GBM patient PT-HO0394

  Dataset EGAD00001004243

• WGS and RNA-Seq data from a GBM patient PT-CA2271

  WGS and RNA-Seq data from a GBM patient PT-CA2271

  Dataset EGAD00001004230

• WGS and RNA-Seq data from a GBM patient PT-GC1519

  WGS and RNA-Seq data from a GBM patient PT-GC1519

  Dataset EGAD00001004239

• WGS and RNA-Seq data from a GBM patient PT-TM5196

  WGS and RNA-Seq data from a GBM patient PT-TM5196

  Dataset EGAD00001004262

• WGS and RNA-Seq data from a GBM patient PT-VO7089

  WGS and RNA-Seq data from a GBM patient PT-VO7089

  Dataset EGAD00001004263

• WGS and RNA-Seq data from a GBM patient PT-MD9088

  WGS and RNA-Seq data from a GBM patient PT-MD9088

  Dataset EGAD00001004254

• WGS and RNA-Seq data from a GBM patient PT-JE6375

  WGS and RNA-Seq data from a GBM patient PT-JE6375

  Dataset EGAD00001004246

• WGS and RNA-Seq data from a GBM patient PT-RD1291

  WGS and RNA-Seq data from a GBM patient PT-RD1291

  Dataset EGAD00001004256

• RNA-Seq of pancreatic neuroendocrine tumors mutated in MEN1/DAXX/ ATRX

  RNA-Seq of of 11 primary pancreatic neuroendocrine tumors mutated in MEN1/DAXX/ ATRX used to identify molecular subtypes

  Dataset EGAD00001015695

• WGS and RNA-Seq data from a GBM patient PT-WT4796

  WGS and RNA-Seq data from a GBM patient PT-WT4796

  Dataset EGAD00001008532

• RNA-Seq datasets in human islets cultured in low glucose conditions

  7 RNA-Seq datasets in human pancreatic islets from 7 donors, where islets were using low (4mM) glucose conditions.

  Dataset EGAD00001005208

• Human tumour bulk RNA-seq

  We profiled 18 high-grade gliomas patient tumor samples by bulk RNA-seq. The raw fastqs are provided.

  Dataset EGAD00001006098

• Tissue Sampling and Biorepository to improve Cancer Cell Therapy

  This study focuses on the elucidation of determinants of response and resistance to anti-CD19 CAR T-cells in large B-cell lymphoma. Plasma samples were collected prior to treatment and serially post CAR T-cell infusion. Pre-treatment and relapse tumor samples were also obtained as available. Germ-line DNA isolated from Peripheral Blood Mononuclear Cells (PBMCs) was collected for all patients when available. Circulating tumor DNA (ctDNA) and tumor DNA were sequenced using a target hybrid capture oligonucleotide pool. RNA-Seq was also performed on tumor isolated RNA. The authors identified several recurrent genomic alterations associated with relapse, and identified molecular thresholds that were predictive of response.

  Study phs003145

• Single-cell RNA and ATAC sequencing data analysis of human postmenopausal fallopian tube and ovary

  In this study, we used a combination of technologies, including Drop-seq, 10X single-cell RNA-seq (scRNA-seq), and single-cell assay on transposase accessible chromatin (scATAC-seq) to generate  a high-quality cell atlas that consists of four tissue sites from the reproductive tract of postmenopausal women. This atlas, by far the most comprehensive map of the postmenopausal female reproductive system to date, will pave the way to a better understanding of the pathophysiology of many diseases affecting older women.

  Study EGAS00001006780

• 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

• RNA-seq as a tool for evaluating human embryo competence

  The majority of embryos that are created through IVF do not implant. It seems plausible that rates of implantation would improve if we had a better understanding of molecular factors affecting embryo competence. Currently, the process of selecting an embryo for uterine transfer utilizes an ad-hoc combination of morphological criteria, the kinetics of development, and genetic testing for aneuploidy. However, no single criterion can ensure selection of a viable embryo. In contrast, RNA-sequencing of embryos could yield highly dimensional data, which may provide additional insight and illuminate the discrepancies among current selection criteria. Indeed, recent advances enabling the production of RNA-sequencing (RNA-seq) libraries from single cells have facilitated the application of this technique to the study of some transcriptional events in early human development. However, these studies have not assessed the quality of their constituent embryos relative to commonly used embryological criteria. Here, we perform proof-of-principle advancement to clinical selection procedures by generating high quality RNA-seq libraries from a trophectoderm biopsy as well as the remaining whole embryo. We combine state-of-the-art embryological methods with low-input RNA-seq to develop the first transcriptome-wide approach for use in future predictive embryology studies. Specifically, we demonstrate the capacity of RNA-seq as a promising tool in preimplantation screening by showing that biopsies of an embryo can capture valuable information content available in the whole embryo from which they are derived. Furthermore, we show that this technique can be used to generate a RNA-based digital karyotype, and to identify candidate competence-associated genes. Together, these data establish the foundation for a future RNA-based diagnostic in IVF.

  Dataset EGAD00001005044

• Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts

  Whole Exome Sequencing was performed in a dilution series containing known amounts of human and mouse DNA, 3x 100% human 0% mouse, 2x 90/10, 3x 50/50, 2x 25/75 and 3x 0/100. A set of breast cancer clinical samples, matched normal tissue and matched PDTXs (total number = 14) were also analysed. Paired-end 75bp sequences for the dilution series and paired-end 125bp for the clinical samples were obtained on Illumina HiSeq2500; fastq files are provided. A triplicate analysis of the transcriptome using RNA-seq was also performed for the Universal Human RNA Reference and the Universal Mouse RNA Reference samples. Paired-end 150bp fastq files obtained on Illumina HiSeq4000 are provided.

  Dataset EGAD00001003800