3795 results for "RNA AND sequencing OR transcriptome"

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• Clinical validity of post-surgery circulating tumor DNA (ctDNA) in stage III colon cancer patients treated with adjuvant chemotherapy: the PROVENC3 study

  The PROVENC3 study aimed to determine the clinical validity of post-surgery ctDNA testing in patients with stage III colon cancer treated with adjuvant chemotherapy (ACT). Blood was collected pre-surgery, post-surgery and post-adjuvant chemotherapy. Tumor-informed plasma ctDNA detection was performed through integrated whole genome sequencing (WGS) analyses of formalin-fixed paraffin-embedded tumor tissue DNA (80x), white blood cell germline DNA (40x) and plasma cell-free DNA (30x). These raw WGS data for 209 patients are shared in .cram format in this EGA submission. For 196/209 of these patients there is overlapping FFPE tumor RNA sequencing data available, shared in .fastq format in this EGA submission.

  Study EGAS50000000804

• Whole-genome sequencing of rhabdomyosarcoma tumouroids (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumoroids data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015707

• Detection of Gene Fusions using Targeted Next-Generation Sequencing

  Five commercially available parallel sequencing assays were evaluated for their ability to detect gene fusions in eight cell lines and 18 FFPE tissue samples carrying a variety of known gene fusions. Four RNA-based assays and one DNA-based assay were compared; two were hybrid capture-based, TruSight Tumor 170 Assay (Illumina) and SureSelect XT HS Custom Panel (Agilent), and three were amplicon-based, Archer FusionPlex Lung Panel (ArcherDX), QIAseq RNAscan Custom Panel (Qiagen) and Oncomine Focus Assay (Thermo Fisher Scientific).

  Dataset EGAD00001006913

• Whole Genome-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015709

• Whole Genome-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015716

• Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses

  Background Single-cell RNA sequencing (scRNAseq) is a powerful tool for studying complex biological systems, such as tumour heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumour tissues and patient-derived mouse xenografts for scRNAseq, are not well understood. Here, we used low temperature (6°C) protease and collagenase (37°C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNAseq dataset comprising 128,481 cells from patient cancer tissues, patient-derived breast cancer xenografts and cancer cell lines.Results We observe substantial variation in standard quality control (QC) metrics of cell viability across conditions and tissues. From FACS sorted populations gated for cell viability, we identify a sub-population of dead cells that would pass standard data filtering practices, and quantify the extent to which their transcriptomes differ from live cells. We identify a further subpopulation of transcriptomically “dying” cells that exhibit up-regulation of MHC class I transcripts, in contrast with live and fully dead cells. From the contrast between tissue protease dissociation at 37°C or 6°C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37°C), which are minimized by dissociation with a cold active protease (6°C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues. We observe that the yield of cancer and non-cancer cell types varies between tissues and dissociation methods.Conclusions The method and conditions of tumour dissociation influence cell yield and transcriptome state and are both tissue and cell type dependent. Interpretation of stress pathway expression differences in cancer single cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with identification of such effects in dissociated scRNA-seq experiments.

  Study EGAS00001003753

• NINDS Deep Sequencing for the Detection of Viral Sequences in Primary Progressive Multiple Sclerosis Brains

  Deep sequencing allows for a rapid, accurate characterization of microbial DNA and RNA sequences in many types of samples. Deep sequencing (also called next generation sequencing or NGS) is being developed to assist with the diagnosis of a wide variety of infectious diseases. In this study, seven frozen brain samples (n = 7) from deceased subjects with recent encephalitis were investigated. RNA from each sample was extracted, randomly reverse transcribed, and sequenced. The sequence analysis was performed in a blinded fashion and confirmed with pathogen-specific PCR. This analysis successfully identified measles virus sequences in two brain samples and herpes simplex virus type-1 sequences in three brain samples. No pathogen was identified in the other two brain specimens. These results were concordant with pathogen-specific PCR and partially concordant with prior neuropathological examinations, demonstrating that deep sequencing can accurately identify viral infections in frozen brain tissue (Chan et al., 2014). Data from the primary progressive multiple sclerosis (PPMS) brain specimens (n = 14) and Normal Control brain specimens (n = 14) are also submitted here. All the subjects studied here (encephalitis, PPMS, and controls) were selected from existing collections at the Rocky Mountain and UCLA brain banks. These selections were performed by the directors at the two centers (Dr. Corboy at RM and Nagra at UCLA) based on clinic information provided at the time of collection. All the specimens are from deidentified deceased individuals. The submitting investigators have no identifying information on any of these subjects. To avoid confusion, the dbGaP Subject IDs are identical to the brain bank identifying numbers.

  Study phs000715

• Phase 2 Study of Nivolumab and Entinostat in Unresectable or Metastatic Cholangiocarcinoma and Pancreatic Adenocarcinoma

  The study was a single-center, open-label, phase II trial (registration no. NCT03250273) designed to evaluate the efficacy of entinostat in combination with nivolumab in patients with advanced pancreatic ductal adenocarcinoma (PDAC). The aim was to assess the objective response rate using RECIST v1.1 criteria as the primary endpoint. A total of 27 patients with advanced PDA were enrolled from November 2017 to November 2020. Their treatment involved a 14-day lead-in with entinostat monotherapy, followed by a combination with nivolumab. The study utilized several molecular technologies to assess immune profiles and tumor microenvironment (TME) changes: high-dimensional mass cytometry by time of flight (CyTOF), Luminex for chemokine analysis in plasma, multiplexed immunohistochemistry (mIHC), image cytometry-based quantification, bulk RNA sequencing analysis. The objective response rate (ORR) was 11% with three patients showing a partial response (PR). The median duration of response was 10.2 months. Grade ≥ 3 treatment-related adverse events (TRAEs) occurred in 63% of patients, with the most common being decreased lymphocyte count, anemia, hypoalbuminemia, and hyponatremia. Gene expression analysis showed an enrichment in inflammatory response signaling pathways with the combination treatment. RNA sequencing data with clinical and demographic patient information will be available through dbGaP. For expression summaries, please refer to GEO submission GSE248014.

  Study phs003615

• COVID-19 Multiomics Atlas

  We provide a single-nuclei RNA-sequencing (snRNA-seq) dataset derived from four COVID-19 patients, generated using the 10x Chromium Next GEM Single Cell v3.1 kit. For our study, these were integrated with snRNA-seq data with 12 publicly available sc/snRNA-seq datasets, comprising organ donor lung samples (n=89) and COVID-19 lung tissue samples (n=51).

  Dataset EGAD00001015602

• Measuring the level of relatedness between NGS datasets

  Sequencing technologies are providing increasingly detailed insight in genetic makeup are paving their way into molecular diagnostics. The field will benefit from rigorous and bias-free measures for the quality of sequence data and for the proper representation of the complexity of the original samples. While current methodologies rely on the availability of a well-characterized reference genome, we propose kMer profiling for alignment-free assessment of the quality, comparability, and complexity of sequencing datasets. We show that kMer detects technical artefacts such as high duplication rates, library chimaeras, and differences in library preparation protocols in whole-genome, whole-exome, and RNA sequencing data. Additionally, it successfully captures the complexity and diversity of microbiomes. Thus, kMer allows for a robust evaluation of the quality and complexity of sequencing data without relying on any prior information and opens the way to a more reliable biological reasoning.

  Study EGAS00001000600