9337 results for "cancer rna-seq"

in 34.99 milliseconds.

• CD4 T-Cell RNA-Seq

  CD4 T-Cell RNA-Seq

  Dataset EGAD00001002687

• RNA-seq Revision

  RNA-seq Revision

  Dataset EGAD00001008951

• Identification of point mutations, expression perturbations, and gene fusions in T-cell acute lymphoblastic leukemia by RNA-seq

  RNA-seq is a promising technology to re-sequence protein-coding genes for the identification of single nucleotide variants, while simultaneously obtaining information on structural variations and gene expression perturbations. T-cell acute lymphoblastic leukemia (T-ALL) is caused by a combination of gene fusions leading to the over-expression of transcription factors reinforced by point mutations in oncogenes and tumor suppressor genes. We asked whether RNA-seq is suitable for the detection of driver mutations in these leukemias. We analyzed 31 T-ALL patient samples and 18 T-ALL cell lines by high-coverage paired-end RNA-seq.

  Study EGAS00001000536

• LGG Epilepsy Cohort RNA-Seq

  LGG Epilepsy Cohort RNA-Seq

  Dataset EGAD00001001666

• RNA-Seq and WXS from 6 glioblastoma patients

  RNA-Seq and WXS from 6 glioblastoma patients

  Dataset EGAD00001003293

• RNA-seq of human astroblastomas

  RNA-seq for fusion gene discovery of human astroblastomas

  Dataset EGAD00001009664

• Sputum RNA-Seq from Asthmatic Patients for Microbes and Genes

  RNA-seq files and other data associated with an investigation of the relationship between microbes, genes and immune cells present in the sputum of humans with asthma.

  Study phs002224

• Paired RNA-Seq of fCAB treated and bisulfite treated VDH01, partly depleted for NSUN3

  Paired RNA-seq of bisulfite treated VDH01 samples control or depleted for NSUN3 (4 replicates each). The samples were prepped with NEBNext Ultra II DNA library prep kit and sequenced on MiSeq. Paired RNA-seq of fCAB treated vdh01 samples control or depleted for NSUN3(4 replicates each). the samples were prepped with NEBNext Ultra II DNA library prep kit and sequenced on MiSeq. Paired RNA-seq of fCAB treated VDH01 samples (3). the samples were prepped with NEBNext Ultra II DNA library prep kit and sequenced on MiSeq. Paired RNA-seq of bisulfite treated VDH01 samples (3). the samples were prepped with NEBNext Ultra II DNA library prep kit and sequenced on MiSeq.

  Dataset EGAD00001008742

• Clinical Cancer Genomic Profiling by Three-Platform Sequencing of Whole Genome, Whole Exome and Transcriptome

  To evaluate the potential of an integrated clinical test to detect diverse classes of somatic and germline mutations relevant to pediatric oncology, we performed three-platform whole-genome (WGS), whole exome (WES) and transcriptome (RNA-Seq) sequencing of tumors and normal tissue from 78 pediatric cancer patients in a CLIA-certified, CAP-accredited laboratory. Our analysis pipeline achieves high accuracy by cross-validating variants between sequencing types, thereby removing the need for confirmatory testing, and facilitates comprehensive reporting in a clinically-relevant timeframe. Three-platform sequencing has a positive predictive value of 97-99%, 99% and 91% for somatic SNVs, indels and structural variations, respectively, based on independent experimental verification of 15,225 variants. We report 240 pathogenic variants across all cases, including 84 of 86 known from previous diagnostic testing (98% sensitivity). Combined WES and RNA-Seq, the current standard for precision oncology, achieved only 78% sensitivity. These results emphasize the critical need for incorporating WGS in pediatric oncology testing.

  Study EGAS00001002217

• Transcriptomic profiling of lymphedema

  RNA-seq of skin from human subjects with and without lymphedema

  Study EGAS00001004516

• RNA-seq

  RNA-seq data

  Dataset EGAD00001008959

• Additional RNA-seq, ChIP-seq, and ATAC-seq files for PCGP SJERG

  This dataset contains RNA-seq, ATAC-seq, and ChIP-seq samples from the SJERG cohort. We applied ChIP-Seq for Dux4 on two B-cell ALL cell-lines(REH, Nalm6) along with INPUT. ATAC-Seq on two B-cell ALL cell-lines(REH, Nalm6) and xenograft of a B-cell ALL patient(ERG000016).

  Dataset EGAD00001002654

• RNA-seq dataset

  HTG EdgeSeq fastq files of bulk baseline tumors from IMbassdor250: a randomised phase 3 trial comparing atezolizumab with enzalutamide vs enzalutamide alone in patients with metastatic castration-resistant prostate cancer.

  Dataset EGAD00001007685

• bulk Exome-seq of the 5 HCC patients which also have the single cell T cell RNA-seq data

  bulk Exome-seq data of the 5 HCC patinets. Single cell RNA seq data of these patients was under the accession number EGAD00001003337

  Dataset EGAD00001004149

• Whole Genome Sequencing of HCC

  The French ICGC project on liver tumors is coordinated by Pr Jessica Zucman-Rossi and funded by Inca (French Institute for Cancer). The aim of the present project is to identify the catalog of somatic and germline mutations in liver tumors using whole genome (WGS) and whole exome sequencing (WGS), integrated with DNA methylation and RNA sequencing (RNA-seq) data. The present series corresponds to 24 whole genome sequencing (12 Tumoral/Non-tumoral pairs). Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before.

  Study EGAS00001002888

• CCA RNA-seq data (60 CCA, 6 normal, 16 cell-line)

  Fastq files for RNA-seq for 60 CCAs, 6 normal bile duct tissues, 14 CCA cell-lines (including replicates), and 2 normal cholangiocyte cell-lines (including replicates). RNA was extracted using the Qiagen RNeasy Mini kit. Illumina Tru-Seq Stranded Total RNA kit (Illumina, San Diego, California, USA) was used to prepare RNA libraries from 1 µg of total RNA. Paired-end 150 bp sequencing was performed using Illumina HiSeq4000 sequencer with the paired-end 150 bp read option.

  Dataset EGAD00001011998

• Ewings Sarcoma RNA-seq drug sensitivity

  Illumina RNA-Seq will be performed on four Ewing's sarcoma cell lines and two control cell lines. RNA was extracted from all the lines using a basic Trizol extraction protocol.

  Dataset EGAD00001000337

• RNA-seq from normal human tissues

  RNA-seq from normal human tissues (2 x 75 bp)

  Dataset EGAD00001001057

• RNA-seq Phase Ib of olaparib and capivasertib

  RNA-seq Phase Ib of olaparib and capivasertib

  Dataset EGAD00001006840

• RNA-seq from normal human tissues

  RNA-seq from normal human tissues (2 x 250 bp)

  Dataset EGAD00001001922

• RNA-seq from melanoma biopsies

  RNA-seq data for melanoma biopsies at baseline and after treatment

  Dataset EGAD00001009059

• RNA-Seq of response to Tozinameran vaccination in individuals with KTx/dialysis or healthy controls.

  Low-input RNA-Seq (~200 cells per sample) of CD4+ T cells in patients with kidney transplants, dialysis, or healthy controls after the second dose of Tozinameran COVID-19 vaccine. RNA-Seq was performed using the Smart-Seq v4 ultra-low input protocol. The dataset includes 4 healthy control samples, 3 kidney transplant recipients, and 4 patients undergoing dialysis.

  Dataset EGAD00001007689

• Transcriptomic Characterization of Human Innate T Cells

  We performed low input RNA-seq and single-cell RNA-seq on 7 lymphocyte populations with the objective of finding the common transcriptional programs shared among innate T cells. In brief, we drew blood from healthy donors and isolated CD4+ T cells, CD8+ T cells, Natural Killer cells, invariant natural killer T (iNKT) cells, mucosal-associated invariant T (MAIT) cells, and two γδ T cell populations: Vδ1 and Vδ2. We performed low input RNA-seq on samples with 1000 cells each, for 6 healthy individuals, in duplicate. We performed single-cell RNA-seq with DNA-barcoded hashing antibodies to identify the cell type of origin for each cell. We found a common transcriptional program across lymphocytes that is gradually turned on as lymphocytes have a more "innateness" state, promoting effector functions and sacrificing proliferative capacity. Additionally, we performed low input RNA-seq for Vδ3-expressing γδ T cells and δ/αβ T cells for one healthy individual in duplicate (1000 cells per sample). This revealed δ/αβ T cells are transcriptionally more adaptive-like and Vδ3 more innate-like.

  Study phs002007

• RNA-seq iNKT, T and C1R cells

  RNA-seq (Ribodepleted Directional -75 PE- Hiseq 4000) data of purified and expanded iNKt and T cells from normal donor, and RNA-seq (poly-A 100-PE Hiseq 2500) data from C1R cell line. Data set consist of 3 pairs of fastq files, one pair per sample

  Dataset EGAD00001004331

• Short-read (RNA-seq)

  The goal of this project was to perform long-read RNA sequencing (LR-seq, PacBio) in combination with short-read RNA-seq for systematic characterization of the isoform diversity in primary breast tumor samples. We sequenced the full-length transcriptomes of 26 breast tumors and 4 normal breast samples.

  Dataset EGAD00001006596