11750 results for "cancer rna-seq"

in 17.34 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

• Characterization of OCIAML-22 Fractions via ATAC-Seq, RNA-Seq and WGS

  ATAC-Seq on OCIAML-22 CD34+, CD34-, and Bulk Fractions RNA-Seq on OCIAML-22 CD34+/CD38-, CD34+/CD38+, CD34-/CD38+, CD34-/CD38- Fractions WGS on Donor Bulk, OCIAML-22 Bulk, and CD34+ and CD34- Fractions out of OCIAML-22 Xenografts

  Dataset EGAD00001009271

• scRNA-seq, WES, and bulk RNA-seq on longitudinal samples from 7 Lymphoma patients treated with CD20xCD3 bispecific antibodies

  We performed scRNA-seq on 15 fresh lymph node core biopsies from 7 Lymphoma patients treated with CD20xCD3 bispecific antibodies. We also performed whole-exome sequencing (WES) and bulk RNA-seq on formalin-fixed paraffin embedded (FFPE) tumor samples from these patients. WES was additionally performed on matched germline samples (blood) for each patient.

  Dataset EGAD00001011350

• The cellular state space of AML unveils novel NPM1 subtypes with distinct clinical outcomes and immune evasion properties

  Acute myeloid leukemia (AML) is a genetically and cellularly heterogeneous disease. We characterized 120 AMLs using bulk genomic and transcriptomic analyses (WES, MP-WGS, RNA-seq), and single-cell RNA sequencing (scRNA-seq). Our results reveal an extensive cellular heterogeneity that distorts the bulk transcriptomic profiles. Notably, NPM1 mutated AML could be stratified into two novel, clinically relevant classes (NPM1class I and NPM1class II).

  Study EGAS50000001084

• Landscape of somatic mutations and DNA copy number alterations and transcriptomic profiling identifies metabolic reprogramming as a hallmark of ibrutinib resistance

  Whole-exome sequencing (WES) was conducted on the clinical specimens of 14 cases that had sufficient isolated tumor DNA, including 7 ibrutinib-sensitive cases and 7 ibrutinib-resistant cases. Whole transcriptome sequencing (RNA-seq) was performed on clinical specimens isolated from 14 ibrutinib-sensitive and 7 ibrutinib-resistant cases. Unsupervised hierarchical clustering of MCL tumors using RNA-seq gene expression data showed a response-specific gene expression signature.

  Study EGAS00001003418

• Evolution of Core Archetypal Phenotypes in High-Grade Serous Ovarian Cancer (HGSOC)

  The evolution of resistance in high-grade serous ovarian cancer (HGSOC) cells in response to chemotherapy is only partially understood. To uncover phenotypic changes associated with chemotherapy resistance, we performed single-cell RNA-sequencing (scRNA-seq) or whole-genome sequencing of 23 total patients, with longitudinal samples collected from 9, treatment-naïve samples from 8 patients, and post-treatment samples from 6 patients. Our study revealed that the HGSOC transcriptome could be described using three core archetypal phenotypes, defined as oncogenic tasks that describe majority of the transcriptional data. We implemented a multi-task learning approach to identify the biological tasks of each archetype and identified metabolism and proliferation, cellular defense response, and DNA repair signaling. The “metabolism and proliferation” archetype evolved during treatment and was enriched in cancer cells from patients that received multiple-lines of treatment and had elevated tumor burden indicated by CA-125 levels. The emergence of archetypes was not consistently linked to specific whole-genome driver mutations. However, archetypes were closely associated with sub-clonal clusters at the single-cell level, indicating that cancer cell sub-populations specialize in particular biological tasks. Our study reveals a defining principle of ovarian tumor evolution using temporal scRNA-seq profiles, identifies the key phenotypic features across patients and links metabolic shifts with a response to chemotherapy.

  Study phs002294

• 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

• Reference Epigenomes and Regulatory Landscape of CLL

  Analysis of the reference epigenomes and regulatory landscape of CLL as a whole and its major clinico-biological subtypes (with mutated and unmutated IGHV) in the light of the normal B-cell differentiation. We have extensively characterized the reference epigenomes of seven primary chronic lymphocytic leukemia samples (CLLs) with mutated (n=5) and unmutated IGHV (n=2) as well as several mature B-cell subpopulations (naive B cells from blood and tonsil, germinal center B cells, memory B cells and plasma cells from tonsil) using genome-wide maps of six histone marks (H3K4me3, H3K4me1, H3K27ac, H3K36me3, H3K9me3 and H3K27me3), DNA accessibility (ATAC-seq), DNA methylation (whole-genome bisulfite sequencing) and gene expression (RNA-seq). Furthermore, we have mapped the regulatory chromatin landscape of 100 additional CLL cases using chIP-seq of H3K27ac and ATAC-seq and linked these data to additional layers of information (whole-genome and/or whole-exome sequencing (WGS/WES), RNA-seq and DNA methylation microarrays) studied in the context of the International Cancer Genome Consortium (ICGC).

  Dataset EGAD00001004046

• BIH COVID-19 airway single-cell, long-read RNA-seq

  We performed single-cell, long-read RNA-seq on nine nasopharyngeal swabs from patients hospitalized at Charite to study splicing alterations induced by SARS-CoV-2 infection. Additionally, three samples from healthy controls were included.

  Dac EGAC50000000757

• WGS and RNA-Seq data from a GBM patient PT-LC6372

  WGS and RNA-Seq data from a GBM patient PT-LC6372

  Dataset EGAD00001008522

• PCa-LINES: rRNA-minus RNA-seq of PCa cell-lines (VCaP & PC346c) and 5 additional patient samples

  PCa-LINES: rRNA-minus RNA-seq of PCa cell-lines (VCaP & PC346c) and 4 additional patient samples

  Dataset EGAD00001006366

• Human normal brain bulk RNA-seq

  We profiled 43 normal human adult brain and 11 normal human fetal brain specimens by bulk RNA-seq. The raw fastqs are provided.

  Dataset EGAD00001005130

• WGS and RNA-Seq data from a GBM patient PT-DS9789

  WGS and RNA-Seq data from a GBM patient PT-DS9789

  Dataset EGAD00001004233

• Human Pancreatic Beta Cell lncRNAs Control Cell-Specific Regulatory Networks - Whole Human Islet paired-ended RNA-seq assays

  Whole Human Islet paired-ended RNA-seq of 64 human pancreatic donors.

  Dataset EGAD00001003992

• WGS and RNA-Seq data from a GBM patient PT-GJ3716

  WGS and RNA-Seq data from a GBM patient PT-GJ3716

  Dataset EGAD00001004240

• WGS and RNA-Seq data from a GBM patient PT-JW6420

  WGS and RNA-Seq data from a GBM patient PT-JW6420

  Dataset EGAD00001004248

• WGS and RNA-Seq data from a GBM patient PT-KM5291

  WGS and RNA-Seq data from a GBM patient PT-KM5291

  Dataset EGAD00001004249

• WGS/RNA-seq pair of an inflammatory hepatocellular adenoma (IHCA)

  This study contain the WGS and RNA-seq aligned bam files for this particular inflammatory hepatocellular adenoma sample.

  Dataset EGAD00001004141

• WGS and RNA-Seq data from a GBM patient PT-SK0976

  WGS and RNA-Seq data from a GBM patient PT-SK0976

  Dataset EGAD00001004260

• WGS and RNA-Seq data from a GBM patient PT-PD6881

  WGS and RNA-Seq data from a GBM patient PT-PD6881

  Dataset EGAD00001004255

• WGS and RNA-Seq data from a GBM patient PT-LC3356

  WGS and RNA-Seq data from a GBM patient PT-LC3356

  Dataset EGAD00001004250

• WGS and RNA-Seq data from a GBM patient PT-EV3071

  WGS and RNA-Seq data from a GBM patient PT-EV3071

  Dataset EGAD00001004234

• RNA-seq data from 195 pediatric BCP-ALL cases

  RNA-seq data from 195 pediatric BCP-ALL cases. Alignment: TopHat 2.0.7. Reference genome: hg19.

  Dataset EGAD00001002112

• Human tumor bulk RNA-seq

  We profiled 7 patient neuroblastoma-FOXR2 tumor samples by bulk RNA-seq. The raw fastqs are provided.

  Dataset EGAD00001010901

• single cell RNA-seq and ATAC-seq of human fetal forebrain tissue DAC

  Dac EGAC00001002599

• RNA-Seq data from tumor samples collected from 12 UTSW translocation renal cell carcinoma (tRCC) patients.

  This study investigates differentially expressed genes associated with the progression of translocation renal cell carcinoma (tRCC) by analyzing RNA-Seq data from 23 tumor samples obtained from 12 patients. The samples were collected from affiliated hospitals of the University of Texas Southwestern Medical Center (UTSW), including Parkland Hospital and Children’s Medical Center. RNA isolated from tumor samples was sequenced using the Illumina platform.

  Study EGAS50000000127