12080 results for "cancer rna-seq"

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• DAC for Single cell RNA sequencing of peripheral blood HSPC from patients with sickle cell anemia and healthy donors.

  Data access comittee for Single cell RNA sequencing of peripheral blood HSPC from patients with sickle cell anemia and healthy donors.

  Dac EGAC50000000650

• RNA sequencing of mCRPC patient biopsies

  RNA sequencing of mCRPC patient biopsies obtained for the profiling of the prevalence of immune transcripts. Manuscript can be found at PMID: 35491356

  Dataset EGAD50000001811

• SIA Subtyping RNA Sequencing

  Associated with Molecular classification of small intestinal adenocarcinomas. RNA sequencing generated 849 .fastq files from 137 primary and recurrent small intestine adenocarcinoma samples from 135 patients.

  Dataset EGAD50000001762

• Single cell RNA sequencing of samples from core and periphery of human glioblastoma

  Samples taken from various regions of three human glioblastomas were subject to single cell RNA sequencing.

  Dataset EGAD50000002080

• Multiome sequencing of primary colon tissue and derived organoids

  Multiome (ATAC + RNA) sequencing of primary colon tissue and organoids derived from the same donors.

  Study EGAS00001008110

• Long non-coding RNA capture

  Capture lncRNA and totalRNA sequencing of various sample types (including plasma, FFPE and high quality RNA).

  Dataset EGAD00001007819

• Single-cell RNA sequencing for metastatic gastric adenocarcinoma

  Single-cell RNA sequencing was performed on a total of 20 PC specimens.

  Dataset EGAD00001006172

• RNA-sequencing of cohesin-mutated and wildtype adult AMLs

  Transcriptomic data generated by RNA-sequencing for adult human AMLs with STAG2 or RAD21 mutations or no cohesin mutations (CTRL-AMLs).

  Dataset EGAD00001011197

• release_2: ICGC PedBrain: RNA sequencing

  release_2: ICGC PedBrain: RNA sequencing

  Dataset EGAD00001001620

• Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

  Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

  Dataset EGAD50000000226