12215 results for "cancer rna-seq"

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• Bulk RNA-Seq

  Human data for transcriptome (bulk RNA-Seq) in eight B-cell precursors: HSC, CLP, pro-B, pre-B, Immature B, Transitional B, Naive B CD5-, Naive B CD5+ cells

  Dataset EGAD00001010906

• Gene Fusion Discovery through RNA Sequencing of Human Glioblastoma Stem Cell Lines

  We used massively parallel, paired-end sequencing of expressed transcripts (RNA-seq) to detect novel gene fusions in short-term cultures of glioma stem-like cells freshly isolated from nine patients carrying primary glioblastoma multiforme (GBM). The culture of primary GBM tumors under serum-free conditions selects cells that retain phenotypes and genotypes closely mirroring primary tumor profiles as compared to serum-cultured glioma cell lines that have largely lost their developmental identities.

  Study phs000505

• Expression profiling of Gorlin iPSCs in the osteoblast induction culture

  In this study, we utilized human induced pluripotent stem cell (iPSC)-based models of the two diseases, named Gorlin syndrome and McCune Albright synfrome, to understand the roles of Hh signaling in osteogenesis, especially in mineralization. To examine which signaling pathways and genetic networks were affected by Gorlin syndrome-associated PTCH1 mutation in human osteoblastogenesis, we performed RNA sequencing (RNA-seq) analysis in the osteoblast induction culture of WT and Gorlin iPSCs.

  Study JGAS000218

• Mutational landscape of the transcriptome offers putative targets for immunotherapy of myeloproliferative neoplasms

  In this study, we aimed to comprehensively characterize in 113 myeloproliferative neoplasms (MPN) patients the mutational landscape of the granulocyte transcriptome using RNA sequencing data. We implemented workflows for fusion and variant calling, and differential splicing analysis for patients with hotspot SF3B1 mutations. Finally, we characterized the neoantigen landscape on individualized level taking each patient's HLA genotype (derived from the same RNA-seq dataset) into account.

  Study EGAS00001003486

• RNAseq of 76 samples from Uveal Melanoma tumors

  Hotspot mutations in the spliceosome gene SF3B1 are reported in 20% of uveal melanomas. SF3B1 is involved in 3'-splice site (3'ss) recognition during RNA splicing; however, the molecular mechanisms of its mutation have remained unclear. Here we show, using RNA-Seq analyses of uveal melanoma, that the SF3B1 R625/K666 mutation results in deregulated splicing at a subset of junctions, mostly by the use of alternative 3'ss

  Study EGAS00001002932

• Histone and transcriptome profiling of glioblastoma initiating cells

  ChIP-seq and RNA-seq of glioblastoma initiating cells and their differentiated counterparts with and without inhibition/knockdown of KDMs.

  Dataset EGAD00001005124

• Paired RNA-Seq data of Primary lymphomas of the central nervous system (PCNSL)

  Primary lymphomas of the central nervous system (PCNSL) are diffuse large B-cell lymphomas (DLBCLs) which are confined to the central nervous system (CNS). Paired RNA-Seq sequencing was done on Illumina HiSeq2000 machines using Illumina TruSeq RNA library preparation kit. About 36 tumor samples were sequenced.

  Dataset EGAD00001007811

• RNA-Seq Profiles from CA017-003 Clinical Trial

  The dataset contains RNAseq profiles of 182 patients from the CA017-003 clinical trial. The Allprep DNA/RNA FFPE kit was used to simultaneously purify genomic DNA and total RNA from formalin-fixed, paraffin embedded (FFPE) tissue sections. RNAseq libraries (50PE, 50M) were constructed using Illumina TruSeq RNA Access method. Fastq files are included.

  Dataset EGAD50000000991

• Epigenome of sorted human muscle stem cell

  This dataset contain RNA-seq, ChIP-seq, WGBS and ATAC-seq of 1 human muscle stem cell sample. NCAM+ITGB1+ CD31−CD45−CD34− were used as the sorting strategy for the sample. H3K27ac, H3K27me3, H3K4me1, H3K4me3, H3K36me3 and H3K9me3 are the targets of ChIP-seq.

  Dataset EGAD00001008686

• Whole Genome, Exome, Transcriptome and Validation Sequencing of an Acute Lymphoblastic Leukemia

  We used massively parallel sequencing technology to profile the genomic DNA and RNA of tumor cells (leukemic bone marrow) and normal cells (skin biopsy) obtained from a single patient with Acute Lymphoblastic Leukemia (ALL), referred to throughout this study as 'ALL1'. Included in this study are samples obtained from a primary tumor, first relapse, second relapse and several intermediate timepoints. We identified somatic mutations present in each tumor by analysis of whole genome (WGS) and exome sequence data. Single nucleotide variants (SNVs) and small insertions and deletions were identified in both the exome and WGS data. Large copy number variations (CNVs) and structural variants (SVs) were identified in the WGS data. A custom capture reagent was designed to target most variants and used to generate deep validation sequence data. The expression status of all somatic variants was assessed by RNA-seq. The RNA-seq data was also used for gene expression analysis and gene fusion detection.

  Study phs001066