14980 results for "cancer studies using rna-seq"

in 14.21 milliseconds.

• subset of RNA-Seq data from umbrella study EGAS00001004338, used in EGAS00001004786

  linking 55 samples/74 RNA-Seq runs - out of EGAS00001004338 Umbrella study to EGAS0001004786

  Dataset EGAD00001006615

• RNA-seq colorectal carcinomas NKI-AvL TGO series Gut2009

  The dataset "RNA-seq colorectal carcinomas NKI-AvL TGO series Gut2009 " includes 2 x 29 fastq files from paired-end mRNA sequencing on Illumina HiSeq2500 for 29 snap-frozen colorectal carcinomas.

  Dataset EGAD00001004059

• RNA-seq colorectal adenomas NKI-AvL TGO series Gut2009

  The dataset "RNA-seq colorectal adenomas NKI-AvL TGO series Gut2009" includes 2 x 32 fastq files from paired-end mRNA sequencing on Illumina HiSeq2500 for 32 snap-frozen colorectal adenomas.

  Dataset EGAD00001004058

• WGS and RNA-Seq data from a GBM patient PT-PR5617

  WGS and RNA-Seq data from a GBM patient PT-PR5617

  Dataset EGAD00001008525

• WGS and RNA-Seq data from a GBM patient PT-CM3220

  WGS and RNA-Seq data from a GBM patient PT-CM3220

  Dataset EGAD00001008517

• WGS and RNA-Seq data from a GBM patient PT-DM9089

  WGS and RNA-Seq data from a GBM patient PT-DM9089

  Dataset EGAD00001008518

• RNA-seq data from paired tumour and germline samples from mesothelioma patients for study EGAS00001005196

  RNA-seq data from paired tumour and germline samples from mesothelioma patients for study EGAS00001005196

  Dataset EGAD00001007874

• WGS and RNA-Seq data from a GBM patient PT-CM1209

  WGS and RNA-Seq data from a GBM patient PT-CM1209

  Dataset EGAD00001004231

• RNA-seq

  This dataset contains the raw sequencing data (Runs) for all 10x Genomics single-cell RNA-seq Experiments.

  Dataset EGAD00001009823

• Single-cell RNA-seq analysis of cutaneous immune cells isolated from skin biopsies of psoriasis patients undergoing IL-23 blockade

  Single-cell RNA-seq analysis of cutaneous immune cells isolated from skin biopsies of psoriasis patients undergoing IL-23 blockade

  Dataset EGAD00001011103

• PFA ependymoma study -RNA-seq data

  RNA-seq fastq files for the 16 samples used in Michealraj et al. Cell 2020. The samples include PFA and ST ependymoma tissues, normal pediatric brain as control and PFA ependymoma lines.

  Dataset EGAD00001006046

• IMpower133 processed RNA-seq data for genes utilized for cluster assignments

  This dataset contains log2(TPM + 1) for 271 tumor samples profiled by RNA-seq for the subset of genes used for validation of the NMF cluster assignments.

  Dataset EGAD00001006928

• Genetic Studies of Chronic Kidney Disease (CKD)

  The Genetic Studies of Chronic Kidney Disease (CKD) is an ongoing genetic research and biobanking protocol which aims to elucidate the genetic basis CKD. The study recruits patients seen at the Columbia University Medical Center (CUMC) Nephrology Division for the evaluation and care of CKD. All patients clinically diagnosed with CKD are eligible to participate; there are no inclusion or exclusion criteria related to age, sex/gender, and/or race/ethnicity. In addition to office visits, patients are recruited in contexts including perinatal care and nonprofit events for patients with various forms of CKD; recruitment tools include bilingual informational brochures, flyers, and consent forms. All individuals provide informed consent; for minors, signature of the parent/guardian is required. Consent includes: use of biosamples given for genetic studies, and the request to be re-contacted if medically actionable genetic findings are identified. In the case of the latter, such research-level findings are first validated in a clinically certified (CLIA) laboratory environment, using a second sample given by the individual at the time of re-contact; following CLIA validation, the individual is once again re-contacted, and, assuming he or she still desires return of these results, they can then be returned. Following the above consent process to participate in the study, individuals provide a single biosample (generally, a venous blood sample), from which DNA is extracted using standard protocols and used for genetic studies, such as exome sequencing. Basic clinical and demographic data, including sex/gender, age, and race/ethnicity are also collected. All data collected is stored in a secure computerized database, with patient samples anonymized using numerical identifiers; the relation of this identifier to patients' names is kept in a locked computer file in a centralized database. Electronic data complies with all HIPAA and other privacy and security regulations, and resides on a firewall server maintained by the CUMC IT department.

  Study phs001828

• ctDNA mutation analysis using the SiMSen-seq approach

  Mutation analysis in plasma samples with low ctDNA levels using a molecular barcoding technology, i.e. the single target approach SiMSen-seq (Simple, multiplexed, PCR-based barcoding of DNA for sensitive mutation detection using sequencing).

  Dataset EGAD00001006104

• RNA-sequencing of adult T-cell leukemia/lymphoma samples

  Adult T-cell leukemia/lymphoma (ATL) is a highly aggressive hematological malignancy derived from mature CD4+ T-lymphocytes. In this study, we performed RNA-sequencing (RNA-seq) analysis for ATL samples including 9 primary tumors and 1 ATL cell line to clarify gene expression profiles in this disease.

  Study EGAS00001003575

• Gene Expression and Regulatory Networks in Human Leukocytes - Immunological Variation Consortium

  Profiling of gene expression with microarrays holds great potential for human health, for illuminating disease pathways or providing biomarkers to monitor disease or its resolution. Using high-throughput approaches for genotyping, immunophenotyping and gene expression analysis, the project will examine the basis for the control of gene expression in human immune cells, and how it is influenced by natural genetic variation or aging. In practice, the project will combine several cross-informative approaches: 1) Building on the established sample/data pipelines and robust protocols of the Immunological Genome (ImmGen) project, and on established cohorts of ethnically diverse healthy volunteers at hand, microarray techniques will be used to generate whole-genome expression profiles from purified naive CD4+ lymphocytes and monocytes from 600 healthy volunteers. A dense genetic map will be established for all donors. The results will elucidate how variation in the human genome affects the expression of immune genes, of key importance in understanding gene variants that bring susceptibility to immune or inflammatory disease. Computational analysis of these rich data will allow the reconstruction of regulatory connections between genes, helping to establish general modules and those specific of a given immune cell type. These data will be complemented by an orthogonal data group, generated from a restricted subset of 10 individuals, in which we will profile a larger set of 28 carefully delineated cell populations that exist in human blood. This work will also benefit from powerful interspecies comparison with similar experiments being performed in mice by ImmGen. 2) In addition, RNA from the same set of 28 defined cell populations will be probed with microarrays that explore other aspects of the transcriptome: i) microRNAs and other non-coding RNAs; ii) exon or splice junction arrays that will establish a map of differential splicing in human blood leukocyte. 3) The composition and reactivity of blood cells from the same donors will be established at the time of sample collection using high-throughput flow cytometry, correlating immune phenotypes with gene expression and genetic variation. 4) Genetic variability conditions the baseline levels of gene expression, but also the responsiveness to activating challenges. With samples from the same donors, NanoString technology for transcript counting will be used to analyze the transcriptional response of defined gene sets, representing response signatures of T or dendritic cells, for a fine-grained dissection of responses to different triggers (different bacterial ligands for dendritic cells, different cytokine environment for T cells). In keeping with the resource aspect of this project, all data and interpretations will be made publicly available rapidly upon curation, allowing public querying and browsing of the data, by using and evolving the existing web architectures of the ImmGen project, of the Broad Institute, and of international repositories. RELEVANCE: Exploration with DNA chips of the genome's expression microarrays holds great potential for human health, to better understand disease and to serve as diagnostic tools. Using a combination of high-throughput genomic techniques and computational biology, we will perform a broad exploration of gene expression in human blood cells across groups of African-American, Asian and European ancestry, asking how these profiles are affected by genetic variation or by age. These results will provide an invaluable reference benchmark for the interpretation of genetic and immunological studies. Additionally, over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, in a second study we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution. 15% of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (ATAC-QTLs). ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression, and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression. In another study we analyzed RNA-seq profiles from primary CD4+ T cells of two healthy donors at six time points from 0 to 72 hours after stimulation with bead-bound antibodies against CD3 and CD28.

  Study phs000815