15041 results for "cancer studies using rna-seq"

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• RNA-Seq of acute lymphoblastic leukemia in LLAG-0707 study

  This dataset contains RNA-Seq data of acute lymphoblastic leukemia in LLAG-0707 study. Diagnostic samples of 188 patients were sequenced. Reads were aligned to hg19 genome reference using STAR. Aligned bam files are provided in this dataset.

  Dataset EGAD50000001181

• Human tumor single-cell RNAseq

  We profiled 7 high-grade gliomas patient brain tumor samples by single-cell RNA-seq and 18 single-nuclei RNA-seq using 10X Chromium 3' techonology. The raw fastq files are provided.

  Dataset EGAD00001008351

• Human tumour and matched-normal single-cell RNAseq

  We profiled 16 high-grade gliomas patient tumour samples by single-cell and single-nuclei RNA-seq and 3 normal-matched single-cell RNA-seq using 10X Chromium 3'. The fastq files are provided.

  Dataset EGAD00001006097

• Genetic and Genomic Analysis of Primary Human Chondrocytes

  Osteoarthritis (OA) is a common, degenerative joint disease characterized by the gradual breakdown of the cartilage in the joints. Despite osteoarthritis affecting over 500 million individuals, the lack of a comprehensive mechanistic understanding of the disease has greatly hindered the development of adequate treatment options. To address this, we generated RNA-seq, Hi-C, and ATAC-seq data in primary human chondrocytes treated with either PBS as resting or a fibronectin fragment (FN-f) to model the OA chondrocyte phenotype. We mapped expression quantitative trait loci and splicing quantitative trait loci, and performed colocalization with OA GWAS data to identify putative OA risk genes. Integration of ATAC-seq and Hi-C data provided insights into the regulatory mechanisms. This study revealed both expression-based and splicing-based OA risk genes, several of which had not been previously identified in chondrocyte studies.

  Study phs003581

• Cancer RNA-seq consisting of FASTQ paired-end reads from melanoma and lung cancer sample

  Cancer RNA-seq consisting of FASTQ paired-end reads from 6 individuals (4 melanoma, 1 lung cancer). RNASEQ was performed on illumina, Truseq capture kit, 40M-80M clusters. 2 Melanoma datasets contain reads from 1 tumor region and from a tumor derived cell line 2 Melanoma, 1 Colon and 1 lung datasets contain each reads from a single region.

  Dataset EGAD00001007951

• 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

• Innate and Adaptive Immunity in Parkinson Disease-P20

  Increasing evidence supports the role of brain and systemic inflammation in the etiology of Parkinson disease (PD). We used gene expression profiling (RNA-seq) to examine the activation state of peripheral blood monocytes in 18 patients with early, untreated PD and 16 healthy control (HC) subjects. Monocytes were isolated by negative selection, and gene expression studied by RNA-seq and gene set enrichment analysis. A computational model that incorporated case/control status, sex, and the interaction between case/control status and sex was utilized. We found that there was a striking effect of sex on monocyte gene expression. There was inflammatory activation of monocytes in females with PD, with enrichment of gene sets associated with interferon gamma stimulation. In males, the activation patterns were more heterogeneous. These data point to the importance of systemic monocyte activation in PD, and the importance of studies which examine the differential effects of sex on pathophysiology of the disease.

  Study phs002063

• RNA-Seq data from 34 CAF-S3 subset in human breast and ovarian cancers

  This dataset is related to publications Costa et al. Cancer Cell 2018 and Givel et al. Nat. Commun. 2018 which describe the identification of 4 Cancer Associated Fibroblasts (CAF) in breast and ovarian cancer. This dataset contains transcriptomic profiles obtained by RNA-Seq of 34 CAF-S3 samples from breast and ovarian Tumors.

  Dataset EGAD00001004810

• RNA-seq libraries from FFPE samples using Illumina Ribo-Zero Plus kit

  RNA was extracted from FFPE human tissue samples and used to prepare RNA-seq libraries with the Illumina Stranded Total RNA Prep Ligation kit with Ribo-Zero Plus. cDNA synthesis was performed starting from 100 ng of DNA-free RNA. Libraries were amplified with 16 PCR cycles and sequenced in paired-end mode (2×150 bp) on the Illumina NextSeq 550 platform. Library quality was assessed with Bioanalyzer and Qubit.

  Dataset EGAD50000001553

• DNA and RNA Profiling Using Simultaneous Sequencing (Simul-seq)

  Paired DNA and RNA profiling is increasingly employed in genomics research to uncover molecular mechanisms of disease and to explore personal genotype and phenotype correlations. We developed a novel simultaneous DNA and RNA sequencing approach (Simul-seq) that enables comprehensive genomic and transcriptomic profiles from small quantities of cells or tissues. In this study, Simul-seq was performed on patient-derived fibroblast cells as well as an esophageal adenocarcinoma tumor sample and compared with standard DNA and RNA-sequencing approaches.

  Study phs001214