9337 results for "cancer rna-seq"

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• ICGC PCAWG Dataset: OV-AU_PCAWG_RNA-Seq_TopHat2

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using TopHat2. Project: OV-AU.

  Dataset EGAD00001003416

• ICGC PCAWG Dataset: RECA-EU_PCAWG_RNA-Seq_Star

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using Star. Project: RECA-EU.

  Dataset EGAD00001003558

• ICGC PCAWG Dataset: LIRI-JP_PCAWG_RNA-Seq_TopHat2

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using TopHat2. Project: LIRI-JP.

  Dataset EGAD00001003546

• ICGC PCAWG Dataset: LIRI-JP_PCAWG_RNA-Seq_Star

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using Star. Project: LIRI-JP.

  Dataset EGAD00001003547

• ICGC PCAWG Dataset: CLLE-ES_PCAWG_RNA-Seq_TopHat2

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using TopHat2. Project: CLLE-ES.

  Dataset EGAD00001003548

• ICGC PCAWG Dataset: CLLE-ES_PCAWG_RNA-Seq_Star

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using Star. Project: CLLE-ES.

  Dataset EGAD00001003549

• ICGC PCAWG Dataset: RECA-EU_PCAWG_RNA-Seq_TopHat2

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using TopHat2. Project: RECA-EU.

  Dataset EGAD00001003559

• ICGC PCAWG Dataset: MALY-DE_PCAWG_RNA-Seq_Star

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using Star. Project: MALY-DE.

  Dataset EGAD00001003560

• ICGC PCAWG Dataset: MALY-DE_PCAWG_RNA-Seq_TopHat2

  ICGC PCAWG Dataset for RNA-Seq BAM aligned using TopHat2. Project: MALY-DE.

  Dataset EGAD00001003561

• Whole-genome sequencing reveals genomic signatures associated with the inflammatory microenvironments in Chinese NSCLC patients

  We integrate genomic (whole-genome sequencing, WGS) and transcriptome (polyA-enriched RNA-Seq) sequencing from 90 NSCLC cases and comprehensively identified the distinct genomic features of Chinese NSCLC patients.

  Dataset EGAD00001004071

• macIDR validation data

  RNA-seq of 24 M-CSF differentiated human peripheral monocyte-derived macrophages (MDMs) activated with short exposure (3hours) to LPS, or long exposure (24 hours) to LPS, LPS with IFNγ, IFNγ, IL-4, IL-10, and dexamethasone.

  Dataset EGAD00001004584

• Human normal brain single-cell

  We profiled two human fetal brainstem specimens at 17 and 19 post-conception weeks by single-cell RNA-seq using 10X Chromium Single Cell 3'. The BAM files are provided.

  Dataset EGAD00001005128

• 17 scRNAseq samples

  17 samples were processed for single cell RNA sequencing (SORT-seq)

  Dataset EGAD00001007523

• Count table for OAK (GO28915)

  This dataset contains counts for 699 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from OAK (GO28915).

  Dataset EGAD00001008628

• CPM table for OAK (GO28915)

  This dataset contains counts per million for 699 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from OAK (GO28915).

  Dataset EGAD00001008629

• Count table for POPLAR (GO28753)

  This dataset contains counts for 192 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from POPLAR (GO28753).

  Dataset EGAD00001008630

• CPM table for POPLAR (GO28953)

  This dataset contains counts per million for 192 tumor samples profiled by RNA-seq for the entire transcriptome for samples originating from POPLAR (GO28753).

  Dataset EGAD00001008631

• RNA-Seq of primary pediatric kidney tumor controls for the soft tissue sarcoma tumoroid biobank

  Dataset EGAD00001008709

• Atypical teratoid/rhabdoid tumoroids reveal subgroup-specific drug vulnerabilities - RNA-Seq

  We demonstrate that ATRT tumoroids retain subgroup-specific epigenetic and gene expression profiles

  Dataset EGAD00001009794

• LEF1 knockdown effects on human T cell transcriptome and chromatin accessibility profiles.

  LEF1 (Lymphoid Enhancer-Binding Factor 1) is a key transcription factor in T cell development and function, and its dysregulation has been implicated in various immune-related disorders. This study aimed to comprehensively characterize the transcriptional and epigenetic changes induced by LEF1 knockdown in human T cells. CD3+ T cells isolated from three healthy donors were transfected with either LEF1-specific siRNA or control siRNA, followed by activation with CD3/CD28 Dynabeads and culture in IL-2-containing media. Cells were harvested at 24, 48 and 72 hours post-transfection for multi-omics analysis including bulk RNA-seq, bulk ATAC-seq, single-cell CITE-seq (For CITE-seq, 48 and 72 hours only). This dataset provides insights into LEF1-dependent gene regulatory networks and chromatin accessibility patterns in activated human T cells, contributing to our understanding of T cell biology and potential therapeutic targets. This dataset is part of a comprehensive study investigating functional roles of risk polymorphisms in autoimmune diseases, particularly focusing on the rs58107865 variant at the LEF1 locus.

  Study JGAS000818

• WGS,RNA data of patients with multiple myeloma (MM) refractory to immunomodulatory agents (IMiDs) and proteasome inhibitors (PIs)

  Paired WGS and RNA-Seq data of patients with multiple myeloma (MM) refractory to immunomodulatory agents (IMiDs) and proteasome inhibitors (PIs). We performed whole genome and transcriptome sequencing of 39 heavily pretreated RRMM patients with at least double refractoriness revealing complex structural changes and a high mutational load.

  Dataset EGAD00001006189

• RNAseq on 20 samples of multiple myeloma patients and 3 normal plasma cells

  RNAseq on 20 samples of multiple myeloma patients and 3 normal plasma cells. RNAseq was performed using 200 ng of total RNA by GATC Biotech. Directional libraries were performed after mRNA selection by polyA selection using UTP method. RNA-seq libraries were sequenced on HiSeq2500 Illumina machine using 100bp paired-end reads. Reads alignment was performed using the STAR aligner (version 2.4.0f1) and human genome hg19 as reference.

  Dataset EGAD00001007813

• Whole genome, RNA-seq and single-cell Multiome profile of multiple myeloma

  We analyzed multiple myeloma samples from two patients included in the observational prospective cohort MYRACLE before talquetamab treatment and after relapse. Five other myelomas from the same cohort were included for comparison. Normal plasma cells were also retrieved. All samples were analyzed by whole genome sequencing and single-nucleus Multiome, except one that could only be analyzed by bulk RNA sequencing.

  Dataset EGAD00001010057

• CRUK Accelerator: Non-small cell lung cancer whole exome and RNA sequencing

  Whole exome and RNASeq raw sequencing data for a cohort 24 patients with non-small cell lung cancer, 15 adenocarcinoma (8 female, 7 male) and 9 squamous cell carcinoma(5 female, 4 male). Median age at diagnosis was 69. Tumour tissue and PBMCs were used for whole exome sequencing and RNA sequencing. This data was generated as part of a study funded by a Cancer Research UK Centres Network Accelerator Award Grant (A21998).

  Dataset EGAD00001007934

• Gene Expression Signatures Characterized by Longitudinal Stability and Inter-Individual Variability Delineate Baseline Phenotypic Groups with Distinct Responses to Immune Stimulation

  RNA sequencing (RNAseq) of peripheral blood lymphocytes was used to develop a means to assess immune function in a way that can be used in discovery science and applied to patients individually in clinical settings. The premise is that profiles of RNA present in immune cells is reflective of the combined influence of genetic and environmental variation on immune potential of individuals and that this potential can be tapped to understand human immunity in a variety of biological contexts. CD4+ cells were isolated from fresh whole blood via positive magnetic bead selection and cell lysates were prepared using Qiazol (QIAGEN) and stored at -80ºC for 3 to 8 months. RNA was extracted in batches for cDNA library preparation and RNA-Seq. For this study, we developed standard operating procedures for handling human blood samples and determined: a) the best way to enrich for CD4+ T cells from whole blood and yield high quality RNA, b) the sensitivity of this RNA profiling strategy, and c) the reproducibility of generated immune profiles from healthy subjects. We then developed bioinformatics processes to establish immune response signatures and immune response phenotypes within cohorts of individuals.

  Study phs001512