11925 results for "cancer rna-seq"

in 9.59 milliseconds.

• RNA-seq of multi-regional CRC samples

  BAM files of RNA sequencing (RNA-seq) experiment on multi-regional colorectal cancer (CRC) samples. 58 samples corresponding to 16 patients were sequenced and there is one BAM file for each sample.

  Dataset EGAD00001006164

• Cancer RNA-seq consisting of FASTQ paired-end reads from melanoma and lung cancer sample

  Cancer RNA-seq consisting of FASTQ paired-end reads from 6 individuals (4 melanoma, 1 lung cancer). RNASEQ was performed on illumina, Truseq capture kit, 40M-80M clusters. 2 Melanoma datasets contain reads from 1 tumor region and from a tumor derived cell line 2 Melanoma, 1 Colon and 1 lung datasets contain each reads from a single region.

  Dataset EGAD00001007951

• UROMOL 2020 - RNA-seq data validation

  For this project about non-muscle invasive bladder cancer (NMIBC), we analysed total RNA-seq data from 47 patients used for validation. Sequencing of total RNA was performed using KAPA RNA HyperPrep Kit with RiboErase HMR (Roche). RNA input was 100 to 500 ng. The dataset is composed of 94 fastq files.

  Dataset EGAD00001006967

• RNA-Seq data from 34 CAF-S3 subset in human breast and ovarian cancers

  This dataset is related to publications Costa et al. Cancer Cell 2018 and Givel et al. Nat. Commun. 2018 which describe the identification of 4 Cancer Associated Fibroblasts (CAF) in breast and ovarian cancer. This dataset contains transcriptomic profiles obtained by RNA-Seq of 34 CAF-S3 samples from breast and ovarian Tumors.

  Dataset EGAD00001004810

• RNA-seq of Liver Cancer

  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 161 RNA-seq samples from tumors with matched WES or WGS. 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 EGAS00001002879

• BarcUVa-Seq (Biology of Colorectal Cancer Risk Enhancers)

  This study was designed to generate a cohort of normal colon mucosal biopsies from the "average risk" population. Its primary aims included the development of an RNA-seq dataset of normal colon mucosal biopsies across three colon regions (ascending, transverse and descending). Further, we aimed to provide plausible prioritization to genetic components of colorectal cancer risk by employing methods such as expression quantitative trait loci analysis. In doing so, we provide a novel reference dataset for the study of colon cancer risk in the healthy colon epithelium. RNA-seq was performed in batches on an Illumina HiSeq 2500 (batch 1-7) or NovaSeq 6000 (batch 8) on a total of 463 samples. Matched genotyping data was generated on the Illumina OncoArray BeadChip. Phasing was carried out using Minimac 3 and imputation was performed using The Haplotype Reference Consortium panel on the Michigan Imputation Server.Genotype and RNA-seq data will be made available in dbGaP.

  Study phs003338

• Transcriptome profiling of prostate tissue samples from early onset prostate cancer patients and normal controls by RNA-seq

  The dataset contains RNA-seq data of 96 EOPC patients and 9 controls. For some patients multiple tissue samples were sequenced ("multi-area" samples). The RNA extraction and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.

  Dataset EGAD00001004791

• RNAseq data to study PRPF6 regulated splice forms in colon cancer cell lines

  Alternative splicing plays critical roles in differentiation, development, and cancer (Pettigrew et al., 2008; Chen and Manley, 2009). The recent identification of specific spliceosome inhibitors has generated interest in the therapeutic potential of targeting this cellular process (van Alphen et al., 2009). Using an integrated genomic approach, we have identified PRPF6, an RNA binding component of the pre-mRNA spliceosome, as an essential driver of oncogenesis in colon cancer. Importantly, PRPF6 is both amplified and overexpressed in colon cancer, and only colon cancer cells with high PRPF6 levels are sensitive to its loss. Our data clearly point to an important role for PRPF6 in colon cancer growth and suggest that a better understanding of its role in alternative splicing in colon cancer is warranted. To determine the specific alternative splice forms that PRPF6 regulates in colon cancer, we plan three experiments: 1. The first involves knocking down expression of PRPF6 in two different cancer cell lines with 3 different siRNAs, and then completing RNA-seq to determine the gene expression changes that occur relative to a non-targeting control siRNA. Because of the role for PRPF6 in pre-mRNA splicing, we especially want to quantify the changes in splice-specific forms of all genes genome-wide to identify genes whose splicing is altered upon PRPF6 knockdown. 2. The second involves immunoprecipitating PRPF6 from two different cancer cell lines and isolating any RNA that is bound to PRPF6, since PRPF6 is an RNA-binding protein. We then want to carry out RNA-seq to identify which RNA molecules co-immunoprecipitated with PRPF6. This will help us determine possible functions for PRPF6 in regulating colon cancer growth. 3. The third involves overexpressing PRPF6 in cell lines and then carrying out RNA-seq to identify any changes in splice-specific gene expression. This will allow us to determine whether increased PRPF6 expression is sufficient to drive alternative splicing changes.

  Study EGAS00001000761

• RNA Seq of 25 spheres derived from lymph nodes of lung cancer patients

  To further decipher the cell type composition within single spheres we performed RNA-Seq of 25 selected spheres of 4 different categories (including 8 signature-/balanced, 9 signature+/balanced, 1 signature-/aberrant, 7 signature+/aberrant). Samples were sequenced using Illumina NextSeq 2000.Fastq reads were processed using inhouse RNA-Seq workflow.

  Dataset EGAD00001011099

• Lung cancer RNA Seq Data Access Committee

  Dac EGAC00001003317

• HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling

  Single-cell DNA-sequencing (scDNA-seq) enables decoding somatic cancer variation. Existing methods are hampered by low throughput or cannot be combined with transcriptome sequencing in the same cell. We propose HIPSD&R-seq (HIgh-throughPut Single-cell Dna and Rna-seq), a scalable yet simple and accessible assay to profile low-coverage DNA and RNA in thousands of cells in parallel. Our approach builds on a modification of the 10X Genomics platform for scATAC and multiome profiling. In applications to human cell models and primary tissue, we demonstrate the feasibility to detect rare clones and we combine the assay with combinatorial indexing to profile over 17,000 cells.

  Study EGAS50000000691

• Profiling RNA Translation in Pediatric Medulloblastoma

  This study provides RNA-sequencing and ribosome profiling data for patient-derived cell lines and patient tissue samples for children with medulloblastoma. Ribosome profiling is a variant protocol of RNA-sequencing that directly sequences ribosome-bound RNA fragments only. Associated RNA-seq and Ribo-seq data obtained separately for some cancer cell lines can be found on the NCBI SRA as PRJNA957428. Samples were processed for poly-A mRNA sequencing using the Roche Kapa Kit. Ribosome profiling was performed as described in the manuscript referenced below (Hofman et al.) and based on the article by McGlincy et al., Methods (2017). Ribo-seq data were analyzed for sample quality using RiboseQC (Calviello, Nat Struct Mol Biol, 2020). These data were used to quantify P-sites of open reading frames. RNAseq and ribo-seq data were integrated and compared to determine translational efficiency values using TPM (Ribo-seq) over TPM (RNA-seq) as the metric. Ribo-seq data on 14 samples and RNAseq data on 21 samples derived from 16 fresh-frozen surgical samples for medulloblastoma and 5 autopsy samples are available through dbGaP. A second cohort of 4 medulloblastoma samples (RNAseq, Ribo-seq) related to this study can be found on the EGA at EGAS00001007426.

  Study phs003446

• bulk RNA-Seq of colorectal cancer patient samples

  Study EGAS00001006666

• Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression

  Noncoding RNAs (ncRNAs) are emerging as key molecules in human cancer, with the potential to serve as novel markers of disease and to reveal uncharacterized aspects of tumor biology. Here we discover 121 unannotated prostate cancer-associated ncRNA transcripts (PCATs) by ab initio assembly of high-throughput sequencing of polyA+ RNA (RNA-Seq) from a cohort of 102 prostate tissues and cells lines. We characterized one ncRNA, PCAT-1, as a prostate-specific regulator of cell proliferation and show that it is a target of the polycomb repressive complex 2 (PRC2). We further found that patterns of PCAT-1 and PRC2 expression stratified patient tissues into molecular subtypes distinguished by expression signatures of PCAT-1-repressed target genes. Taken together, our findings suggest that PCAT-1 is a transcriptional repressor implicated in a subset of prostate cancer patients. These findings establish the utility of RNA-Seq to identify disease-associated ncRNAs that may improve the stratification of cancer subtypes.

  Study phs000443

• Assessing gene expression profiling from FFPE Patient Samples: A Comparison of Two Library Preparation Approaches and Recommendations

  Next-Generation Sequencing (NGS) has transformed cancer research and clinical practice, with Whole Exome Sequencing (WES) driving advances in mutational profiling and personalized oncology. Yet, transcriptomic signatures remain essential for understanding disease mechanisms, including therapy resistance pathways. RNA sequencing (RNA-seq), however, faces unique challenges when dealing with low-input or degraded RNA, as often found in archival formalin-fixed paraffin-embedded (FFPE) tissues. Although previous studies have compared library preparation protocols, rapidly evolving technologies call for updated evaluations. Here, we present a direct comparison of two FFPE-compatible stranded RNA-seq library preparation kits: TaKaRa™ SMARTer® Stranded Total RNA-Seq Kit v2 (Kit A) and Illumina Stranded Total RNA Prep Ligation with Ribo-Zero Plus (Kit B). Both kits generated high-quality RNA-seq data, yet important differences emerged. Notably, Kit A achieved comparable gene expression quantification to Kit B while requiring 20-fold less RNA input, a crucial advantage for limited samples, albeit with increased sequencing depth. We critically discuss these results in relation to RNA availability, technical performance, cost-effectiveness, processing time, and automation potential, offering practical guidance for selecting optimal RNA-seq strategies in clinical and translational research settings

  Study EGAS50000001066