11769 results for "cancer rna-seq"

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• Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer

  We generated a collection of patient-derived pancreatic normal and cancer organoids. We performed whole genome sequencing, targeted exome sequencing, and RNA sequencing on organoids as well as matched tumor and normal tissue if available. This dataset is a valuable resource for pancreas cancer researchers, and those looking to compare primary tissue to organoid culture. In our linked publication, we show that pancreatic cancer organoids recapitulate the mutational spectrum of pancreatic cancer. Furthermore, RNA sequencing of organoids demonstrates the presence of both transcriptional subtypes of pancreas cancer.

  Study phs001611

• RNA-seq data from 27 glioblastoma samples

  27 Fresh frozen tissue specimens were crushed by mortar and pestle, homogenized using the QIAShredder kit (Qiagen), and genomic DNA and total RNA were extracted using the AllPrep DNA/RNA Mini kit (Qiagen), according to the manufacturer’s instructions. RNA libraries were synthesized using 200 ng of total RNA using the Ilumina TruSeq Stranded RNA LT Sample Prep Kit (Illumina), and subsequently sequenced on the NextSeq550 platform to a read depth of 80 million clusters and 160 million paired end reads (75 bp X 75 bp) using V2 chemistry.

  Dataset EGAD00001008362

• RNA-seq data of CRC-PDX tumors

  We obtained bulk RNAseq data of CRC-PDX tumors and performed molecular subtype classification.

  Dataset EGAD50000000850

• Single-cell RNA sequencing of human IL-18R supported CAR T cells targeting oncofetal Tenascin C

  scRNA-seq raw data

  Dataset EGAD50000001134

• RNA-Seq of human stimulated and cultured CD4+ Treg cells

  This dataset includes whole transcriptome data of human stimulated and cultured CD4+ Treg cells (39 samples).

  Dataset EGAD00001005964

• Follicular T cell lymphoma

  Paired-end RNA-seq of follicular T cell lymphoma for the discovery of fusion transcripts

  Dataset EGAD00001006379

• Recurrent somatic mutations in POLR2A define a distinct subset of meningiomas: RNAseq variants

  Variants called from RNA-seq data of meningioma tumors.

  Dataset EGAD00001002649

• Genetic and Genomic Analysis of Primary Human Chondrocytes

  Osteoarthritis (OA) is a common, degenerative joint disease characterized by the gradual breakdown of the cartilage in the joints. Despite osteoarthritis affecting over 500 million individuals, the lack of a comprehensive mechanistic understanding of the disease has greatly hindered the development of adequate treatment options. To address this, we generated RNA-seq, Hi-C, and ATAC-seq data in primary human chondrocytes treated with either PBS as resting or a fibronectin fragment (FN-f) to model the OA chondrocyte phenotype. We mapped expression quantitative trait loci and splicing quantitative trait loci, and performed colocalization with OA GWAS data to identify putative OA risk genes. Integration of ATAC-seq and Hi-C data provided insights into the regulatory mechanisms. This study revealed both expression-based and splicing-based OA risk genes, several of which had not been previously identified in chondrocyte studies.

  Study phs003581

• Genomic Studies of Bipolar Disorder in a Large Cohort from The Netherlands

  The transcriptomes of whole blood samples of a large cohort of individuals from the Netherlands were assayed using RNA-seq. These whole blood samples included patients diagnosed with schizophrenia or bipolar disorder, family members of patients, and healthy controls. Within the whole blood dataset, about 600 of the samples were sequenced to an average of 13.9 million mapped reads per sample, while about 2,000 were sequenced to an average of 5.9 million mapped reads per sample, in order to assess changes in power to detect eQTLs based on sample size. We find that expression, defined as the log transcripts per million reads (TPMs), is highly correlated when comparing the moderate (13.9M) and lower (5.9M) coverage datasets. Additionally, a subset of about 150 individuals provided fibroblast cell lines which were assessed for the levels of gene expression using RNA-seq at an average of 50 million mapped reads per sample. We use downsampling techniques to generate synthetic datasets derived from this real fibroblast RNA-Seq data to explore the relationship between coverage and number individuals in an eQTL analysis. Corresponding individual-level genotypes for the samples included in the expression matrix are also made available. The genotypes were derived from multiple other project cohorts, each using different genotyping platforms including OmniExpress Exome, Global Screening Array, Illumina550, and PsychChip. A Plink file set of these cohorts is provided here after merging the separate project file sets and extracting only the individuals who have expression data also provided here; quality control for SNP-missingness is recommended. We demonstrate in this study that given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample to allow for an a greater number of individuals sequenced. 

  Study phs002856

• RNA-seq consisting of FASTQ paired-end reads from cancer samples

  Efforts to precisely identify tumor human leukocyte antigen presented peptides (HLAp) capable of mediating T cell based tumor rejection still face important challenges. Recent reports suggest that non-canonical cancer HLAp could be immunogenic but their identification requires highly sensitive and accurate mass-spectrometry (MS)-based proteogenomics approaches. Here, we present a MS-based analytical pipeline that can precisely characterize the non-canonical HLAp repertoire, incorporating whole exome sequencing, bulk and single cell transcriptomics, ribosome profiling, and a combination of two MS/MS search tools. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLAp. Albeit often at low levels and in distinct subpopulations of cells, numerous non-canonical HLAp are shared across tumors. This analytical platform holds great promise for the discovery of novel cancer antigens for cancer immunotherapy.

  Study EGAS00001003724