9296 results for "cancer rna-seq"

in 30.27 milliseconds.

• Single-cell RNA-seq of matched primary GBM tumours, patient-derived organoids, and gliomasphere lines

  Single-cell RNA-seq of primary GBM tumours and matched patient-derived organoids and gliomasphere lines. Obtained using the 10X Genomics single-cell 3' expression solution (v2 chemistry). Primary samples and PDOs from 12 tumours from 10 patients (10 primary, two recurrent), and gliomasphere lines from a subset of five tumours. Samples were obtained from two spatially distinct regions of each tumour.

  Dataset EGAD00001007936

• two tables containing RNASeq expression values to patients with RNA-Seq data in the study "Comprehensive genomic characterization of refractory multiple myeloma (H067)"

  two tables containing RNASeq expression values to patients with RNA-Seq data in the study "Comprehensive genomic characterization of refractory multiple myeloma (HIPO_067)". From the bam files gene expression was calculated with the annotation of Gencode.v19. Raw Counts and TPM values are given in one table, the other contains filtered TMM normalized CPM values (genes < 1CPM omitted).

  Dataset EGAD00001008363

• Single-cell RNA-seq of patient-derived organoids across three generations

  Single-cell RNA-seq of first-, second-, and third-generation patient-derived organoids. Obtained using the 10X Genomics single-cell 3' expression solution (v3 chemistry). First- and second-generation PDOs from one patient and first-, second-, and third-generation PDOs from three additional patients.

  Dataset EGAD00001008431

• Transcriptome analysis of Fabry disease iPSC-derived cardiomyocytes treated with splice-regulating compound; RECTAS

  In order to investigate transcriptome profiles in response to splice-regulating compounds, iPSC-derived cardyomyocytes were treated with RECTAS and transcriptome study was conducted by RNA-seq. Splicing profiles of cells in each condition were then analyzed.

  Study JGAS000225

• Transcriptome analysis of Familial dysautonomia patient cells treated with splice-regulating compounds

  In order to investigate familial dysautonomia patient fibroblasts in response to splice-regulating compounds, cells were treated with RECTAS or TG003 and transcriptome study was conducted by RNA-seq. Splicing profiles of cells in each condition were then analyzed.

  Study JGAS000170

• Methylation profiles in patients with blastic plasmacytoid dendritic cell neoplasm

  We added 7 additional samples to our previous cohort (EGAS00001006166). Each samples comprises RNA-seq and whole exome sequencing data. Methylation EPIC Array data have been deposited in Gene Expression Omnibus (GEO) under accession number GSE230487.

  Study EGAS00001007201

• scMultiome

  This dataset gather scMultiomic data including RNA-seq and ATAC-seq in MM.1S in control and dex condition at 1h and 4h

  Dataset EGAD00001011140

• Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

  Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via "mapping" procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

  Study EGAS00001003452

• Inferring expressed genes by whole-genome sequencing of plasma DNA

  The analysis of cell-free DNA (cfDNA) in plasma represents a rapidly advancing field in medicine. cfDNA consists predominantly of nucleosome-protected DNA shed into the bloodstream by cells undergoing apoptosis. We performed whole-genome sequencing (WGS) of plasma DNA and identified two discrete regions at transcription start sites (TSS) where the nucleosome occupancy results in different read-depth coverage patterns in expressed and silent genes. By employing machine learning for gene classification we found that the plasma DNA read depth patterns from healthy donors reflected the expression signature of hematopoietic cells. In cancer patients with metastatic disease, we were able to classify expressed cancer driver genes in regions with somatic copy number gains with high accuracy. We could even determine the expressed isoform of genes with several TSSs as confirmed by RNA-Seq of the matching primary tumor. Our analyses provide functional information about the cells releasing their DNA into the circulation.

  Study EGAS00001001754

• Comprehensive molecular and clinicopathological profiling of glioma

  Various molecular profiles are needed to classify malignant brain tumors, including gliomas, based on the latest classification criteria of the World Health Organization, and their poor prognosis necessitates new therapeutic targets. The Todai OncoPanel 2 RNA Panel (TOP2-RNA) is a custom-target RNA-sequencing (RNA-seq) using the junction capture method to maximize the sensitivity of detecting 455 fusion gene transcripts and analyze the expression profiles of 1,390 genes. This study aimed to classify gliomas and identify their molecular targets using TOP2-RNA. A total of 124 frozen samples of malignant gliomas were subjected to TOP2-RNA for classification based on their molecular profiles and the identification of molecular targets.

  Study JGAS000671

• Epigenome and transcriptome profiling of chronic lymphocytic leukemia patients

  Chronic lymphocytic leukemia (CLL) is characterized by substantial clinical heterogeneity, despite relatively few genetic alterations. To provide a basis for studying epigenome deregulation in CLL, we established genome-wide chromatin accessibility maps for 88 CLL samples from 55 patients using the ATAC-seq assay, and we also performed ChIPmentation and RNA-seq profiling for ten representative samples. Based on the resulting dataset, we devised and applied a bioinformatic method that links chromatin profiles to clinical annotations. Our analysis identified sample-specific variation on top of a shared core of CLL regulatory regions. IGHV mutation status – which distinguishes the two major subtypes of CLL – was accurately predicted by the chromatin profiles, and gene regulatory networks inferred for IGHV-mutated vs. IGHV-unmutated samples identified characteristic differences between these two disease subtypes. In summary, we discovered widespread heterogeneity in the chromatin landscape of CLL, established a community resource for studying epigenome deregulation in leukemia, and demonstrated the feasibility of chromatin accessibility mapping in cancer cohorts and clinical research.

  Study EGAS00001001821

• Characterization of a novel MEF2D-BCL9 fusion positive acute lymphoblastic leukemia cell line RNA-Seq

  Study EGAS00001006802

• Healthy human B-lymphopoiesis RNA-Seq reference using FACS sorted bone marrow aspirates

  Study EGAS00001007305

• Functional Enhancer Elements Drive Subclass-Selective Expression From Mouse to Human Neocortex

  A complete epigenomic map of human brain cell types will be necessary to fully understand their genetic regulation, and to create the next generation of non-species-restricted genetic tools for their functional characterization. Here we performed both bulk and single-nucleus ATAC-seq studies of fresh human neocortical samples excised during neurosurgery, in order to discover the functional genomic elements that discriminate cell types. This dataset contains 78 bulk layer-dissected ATAC-seq samples, and 3660 single-nucleus ATAC-seq samples from fresh acutely isolated human neurosurgical samples. Furthermore, from these data we identified and validated several enhancers for use in AAV context. We also provide validation single cell RNA-seq for 456 human acutely labeled neurosurgical neurons labeled by 3 unique enhancer-AAV vectors, and single cell RNA-seq for 1483 mouse neurons labeled in vivo by 11 unique enhancer-AAV vectors. Together these datasets provide high-resolution maps of single human neocortical cell types, as well as proof of the functional utility of the genomic elements discovered. As a whole these data will be important to furnish the next generation of cell type-specific functional tools and therapies in neuroscience.  

  Study phs002292

• A single-cell transcriptional gradient in human cutaneous memory T cells restricts Th17/Tc17 identity

  We identified a gradated transcriptional program of coordinately regulated inflammation-suppressive genes in psoriatic resident memory T cells. This dataset includes bulk RNA-seq for CRISPR-based deactivation of two core components of this inflammation-suppressive program, ZFP36L2 and ZFP36, in human PBMCs. It also includes scRNA-seq of cutaneous immune cells from psoriasis patients before and during therapeutic IL-23 blockade.

  Study EGAS00001006716

• Exome and RNA seq data for female patient

  Exome and RNA sequencing data for EGAS00001003776 - one female patient with neurofibroma/schwannoma hybrid nerve sheath tumor (N/S HNST)

  Dataset EGAD00001005249

• NICHE - RNA-seq of MMR proficient and MMR deficient early stage colon cancers

  The dataset includes 77 FASTQ files from single-end total RNA sequencing on Illumina HiSeq2500 for 39 patients.

  Dataset EGAD00001006042

• Richter Syndrome RNA-seq dataset

  Raw FASTQ files for 77 RS + DLBCL + CLL samples. RNA-sequencing with single-end 50 nt reads.

  Dataset EGAD00001007922

• single-cell RNA sequencing of human lung epithelial cells

  Single-cell RNA seq data of epithelial cells ((EpCAM-enriched by FACS) isolated from cryopreserved human lung tissue (3 samples, 3 donors)

  Dataset EGAD00001010251

• Natural variation of circulating RNAs in human serum

  Non-coding RNA (ncRNA) molecules have fundamental roles in cells and many are also stable in body fluids as extracellular RNAs. In these studies, we used RNA sequencing (RNA-seq) to investigate the profile of small non-coding RNA (sncRNA) in human serum. We analyzed more than 10 billion Illumina reads from more than 500 serum samples, included in the Norwegian population-based Janus Serum Bank (JSB). Our results suggest that many circulating RNAs in serum can be potential biomarkers and they are associated with different traits including age, sex, smoking, body size etc.

  Study EGAS00001002814

• RNA-seq of iPSC-derived hepatocyte-like cells

  We performed RNA-seq on polyA-enriched mRNA isolated from the original liver biopsy tissue (Liver tissue), primary liver cells (PLC), hepatocyte-like cells (HLCs) differentiated from induced pluripotent stem cells (iPSCs), and iPSCs. RNA libraries were prepared using the Illumina TruSeq Stranded mRNA Sample Preparation protocol (ref. RS-122-2101, Illumina, San Diego CA, US) and sequenced using the Illumina HiSeq2500 platform following the manufacturer’s protocol. Samples were sequenced in paired-end mode to a length of 2x76 base pairs. Images from the instrument were processed using the manufacturer’s software to generate FASTQ sequence files.

  Dataset EGAD00001003770

• RNA sequencing of genetically modified human iPSCs modeling patients with autism spectrum disorders (ASD)

  We generated two genetically modified iPSC lines. In each line, a single promoter variant identified in a patient with autism spectrum disorder (ASD) was introduced by using the CRISPR/Cas9 system. These mutant lines were analyzed by RNA-seq together with control lines to assess the impact of the introduced variant.

  Study JGAS000651

• Arriba: accurate and efficient detection of gene fusions from RNA-Seq (H021)

  The identification of gene fusions from RNA sequencing data is a routine task in cancer research and precision oncology. However, despite the availability of many computational tools, fusion detection remains challenging. Existing methods suffer from poor prediction accuracy and are computationally demanding. We developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime. When applied to a large collection of published pancreatic cancer samples (n=803), Arriba identified a variety of driver fusions, many of which affected druggable proteins, including ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, and ROS1. The fusions were significantly associated with KRAS wild-type tumors and involved proteins stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in KRAS. In addition, we confirmed the transforming potential of two novel fusions, RRBP1-RAF1 and RASGRP1-ATP1A1, in cellular assays. These results demonstrate Arriba’s utility in both basic cancer research and clinical translation.

  Study EGAS00001003554

• Detection and genomic analysis of BRAF fusions in Juvenile Pilocytic Astrocytoma through the combination and integration of multi-omic data

  Juvenile Pilocytic Astrocytoma (JPAs) are highly vascular tumors and show pervasive immune infiltration, which can lead to low tumor cell purity in clinical samples. In some instances, this results in gene fusions that are difficult to detect with conventional omics approaches including RNA-Seq. To this effect, we applied RNA-Seq as well as linked-read whole-genome sequencing and in situ Hi-C as new approaches to detect and characterize low-frequency gene fusions at the genomic, transcriptomic and spatial level. Overall, we demonstrate the power of integrating multi-omic datasets to identify low frequency fusions and characterize the JPA genome at high resolution.

  Study EGAS00001006388

• RNA-sequencing

  Raw reads from RNA-sequencing of monocyte-derived macrophages from three individuals with heterozygous TET2 loss (Ly9, Ly11, Ly14) and two wild-type controls (Ly8 and an unrelated control). Libraries were prepared using ScriptSeq RNA-Seq Library Preparation Kit and Illumina sequenced with paired-end 75bp reads.

  Dataset EGAD00001004784