3743 results for "RNA AND sequencing OR transcriptome"

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• CMF RNA sequencing

  RNA sequencing will be undertaken to reconstruct rearrangements at level of transcription to determine pathogenomic genomic events in chondromyxoid fibroma.

  Dataset EGAD00001000824

• HipSci - Healthy Normals - RNA Sequencing - January 2016

  HipSci - Healthy Normals - RNA Sequencing - January 2016

  Dataset EGAD00001001933

• Breast_cancer_topographs

  Breast cancer topography will be explored using a combination of multi-region whole geome sequencing and in-situ RNA sequencing of fixed tissue specimens.

  Study EGAS00001003698

• Clonal Evolution and Transcriptional Plasticity Shape Metastatic Dissemination Routes in Prostate Cancer

  This study provides comprehensive insights into the genetic and transcriptomic landscape of prostate cancer, highlighting significant clonal heterogeneity, transcriptional plasticity, and metastatic spread patterns. We examined the clonal evolution and metastatic dissemination of prostate cancer by integrating single-nuclei RNA sequencing (snRNA-seq) and low-pass whole-genome sequencing (WGS) data from 43 spatially distinct tumour samples in five patients with locally advanced disease. By reconstructing the evolutionary trajectories of each tumour, we identified distinct patterns of tumour evolution, including both monophyletic and polyphyletic metastatic dissemination, as well as ongoing primary tumour clonal evolution after metastatic spread. We observed converging changes leading to metastatic potential, such as androgen receptor independence and increased activity in the estrogen, WNT, and JAK/STAT pathways in identified seeding areas. These findings enhance our understanding of the disease's progression and may inform future therapeutic strategies.

  Study EGAS50000000927

• Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

  Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

  Dataset EGAD50000000226

• Long-read and short-read isoform sequencing in breast cancer

  The goal of this project was to perform long-read RNA sequencing (LR-seq, PacBio) in combination with short-read RNA-seq for systematic characterization of the isoform diversity in primary breast tumor samples. We sequenced the full-length transcriptomes of 26 breast tumors and 4 normal breast samples.

  Study EGAS00001004819

• Transcriptome Sequencing PPGL

  RNA library preparation of samples with RNA Integrity Number (RIN) > 5.5 (median 7.1; range 5.6-8.9) was performed as described in the TruSeq Stranded mRNA Library Prep Kit (Illumina, RS-122-2101). Briefly, 0.7-1 µg total RNA PolyA+ fraction was purified and randomly fragmented, converted to double stranded cDNA and processed through subsequent enzymatic treatments of end-repair, dA-tailing, and ligation to adapters. Adapter-ligated library was completed by PCR with Illumina PE primers. The resulting purified cDNA library was applied to an Illumina flow cell for cluster generation and sequenced on an Illumina HiSeq2500 on a 51bp single-read format following manufacturer's protocols. cDNA libraries from FFPE tumors and low integrity RNA’s (RIN<5.5) from FF tumors (median 2.4; range 1-5.5) but % of fragments > 200 nucleotides higher than 20% (median 57.5; range 20-96) were prepared using QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen, 015) with a UMI Second Strand Synthesis Module for QuantSeq FWD (Lexogen, 081), vendor’s protocol for low input/ low quality/ FFPE RNA using 200-500 ng of total RNA, according to availability, was used as starting material. PCR Add-on Kit for Illumina (Lexogen, 020) was used to adjust the library amplification number of cycles. Libraries were applied to an Illumina flow cell for cluster generation and sequenced on NovaSeq6000. Image analysis, per-cycle basecalling and quality score assignment was performed with Illumina Real Time Analysis software. Demultiplexing of BCL files to FASTQ format was performed with the bcl2fastq Software (Illumina).

  Study EGAS00001006044

• Massively Parallel Single-Cell RNA-Sequencing of Chronic Rhinosinusitis Nasal and Sinus Tissue

  Single-cell RNA sequencing (scRNA-seq) using the SeqWell V1 platform on sinus mast cells flow cytometrically sorted from the nasal polyps of patients with chronic rhinosinsusitis (CRS) with nasal polyps (CRSwNP) and aspirin-exacerbated respiratory disease (AERD).

  Study phs002333

• RNA sequencing of Ewing and CIC-DUX4 sarcoma tumoroid models

  Tumoroid models from Ewing sarcoma and CIC-DUX4 sarcoma models were generated from patient tumor biopsies. Tumoroids were established in 3D matrigel domes and then propagated on a matrige layer. RNA sequencing was performed with established tumoroid models.

  Study EGAS50000000380

• Single cell RNA sequencing of lung adenocarcinoma.

  We performed single cell RNA sequencing (scRNA-seq) for 208,506 cells derived from 58 lung adenocarcinomas from 44 patients, which covers primary tumour, lymph node and brain metastases, and pleural effusion in addition to normal lung tissues and lymph nodes.

  Study EGAS00001003681

• RNA-Sequencing data of patient derived normal fibroblasts (NFs), cancer associated fibroblasts (CAFs) and tumor spheroid samples

  RNA-Sequencing data of patient derived normal fibroblasts (NFs), cancer associated fibroblasts (CAFs) and tumor spheroid samples performed in the context of the characterization of an IL1R1 positive CAF population in CRC tumour samples.

  Study EGAS00001007205

• The mission of the BIOS Consortium is to create a large-scale data infrastructure and to bring together BBMRI researchers focusing on integrative omics studies in Dutch Biobanks.

  The advent of the genome-wide association study (GWAS) led to the successful identification of thousands of variants that are robustly associated with complex disease phenotypes. Dutch biobanks played a substantial role in these discoveries. For most of these variants, however, the mechanisms through which they contribute to these phenotypes remain unknown. The BIOS Consortium applies a functional genomics approach that integrates genome-wide genetic data with data on the epigenome and transcriptome to elucidate these mechanisms. Over 4000 samples from BBMRI-NL biobanks with in-depth information on disease phenotypes and GWAS data are being enriched with RNA-sequencing and genome-wide DNA methylation data. The same is true for samples with whole-genome sequencing data from GoNL. This unique data infrastructure provides a powerful platform to evaluate key questions in integrative omics from establishing comprehensive eQTL and meQTL catalogues to linking molecular pathways across omics levels to phenotypic outcomes.

  Study EGAS00001001077

• HipSci - Healthy Normals - RNA Sequencing - July 2017

  HipSci - Healthy Normals - RNA Sequencing - July 2017

  Dataset EGAD00001003529

• Single cell analysis reveals new evolutionary complexity in uveal melanoma

  Uveal melanoma (UM) is a rare eye cancer that is highly aggressive and leads to metastatic death in up to half of patients. Here, we conduct droplet-based single-cell RNA sequencing of 59,915 neoplastic and non-neoplastic cells from 8 primary and 3 metastatic UMs. Single-cell VDJ sequencing of RNA is conducted to determine clonally expanded B/T- cell populations. Single-cell DNA sequencing of 1 primary and 1 metastasis is utilized.

  Study phs001861

• Identification of Novel Therapeutic Targets for Calcific Aortic Valve Stenosis Using Integrative Genomics

  In order to identify novel therapeutic targets for calcific aortic valve stenosis, we have generated a large transcriptomic dataset from RNA sequencing of human aortic valve tissues from 500 individuals undergoing aortic valve replacement or heart transplant at the Institut universitaire de cardiologie et de pneumologie de Québec – Université Laval (QUEBEC-CAVS-RNA). This dataset represents a unique resource to understand the molecular mechanisms underlying aortic valve diseases.

  Study phs003541

• RNA-seq following TBL1XR1 KD in human CD34+CD38- cord blood cells

  We performed RNA-seq on CD34+CD38- human cord blood (CB) cells transduced with sh_Control or sh_TBL1XR1 lentiviruses to identify differentially expressed genes. Sorted CD34+CD38- cells from 3 independent cord blood pools were transduced with lentiviruses containing BFP reporter gene. BFP+ cells were sorted 3 days post-transduction and RNA was harvested for library preparation and sequencing.

  Study EGAS00001005869

• Genomic and Transcriptomic Landscape of Fibrolamellar Hepatocellular Carcinoma

  Fibrolamellar hepatocellular carcinoma (FL-HCC) is a rare liver tumor primarily affecting adolescents and young adults. Little is known of the molecular pathogenesis. To characterize the disease we performed RNA sequencing and whole genome sequencing on FL-HCC tumors and adjacent normal tissue. The results demonstrate few consistent differences on the chromosomal level and many hundreds of alterations in the expression of RNA transcripts.

  Study phs000709

• Oesophageal_Adenocarcinoma_Organoids_Iso_Seq

  RNA Sequencing of 5 patient derived organoid cell models. Each model was derived from a piece of patient tumour taken following surgical rescetion of the tumour. All model derivations took place with the CGaP facility in Sanger. This PacBio RNA data will be combined with other sequencing data in order to generate accurate reference cancer genomes and study how structural variants affect gene expression

  Study EGAS00001004051

• To_profile_the_landscape_of_sebaceous_tumours___RNA

  Sebaceous tumours are a rare cutaneous cancer with potential for aggressive behaviour. However, limited information is available on these cancers with few published cases. Here we wish to exome sequence these cancers to define the first genomic landscape for this malignancy. We will extract RNA from formalin-fixed, paraffin-embedded (FFPE) cores. Cores may be obtained from lesional and non-lesional tissues of primaries as well as matching metastases. The extracted RNA will be used for RNA sequencing.

  Study EGAS00001007803

• Identification of Novel Immunotherapy Targets in Myeloma

  RNA sequencing (RNA-Seq) was performed on 94 multiple myeloma (MM) patient samples. We used genomic subgroup and high-risk markers to identify therapeutic targets, including TNFRSF17, GPRC5D, ITGA4, and LAX1, with low predicted toxicity and high specificity to MM and genomic subgroups such as those with TP53 alterations.

  Study phs003772

• 41 fibroblast RNAseq samples of pediatric patients with childhood epilepsy and developmental delay

  We used RNA sequencing of patient fibroblasts to find a genetic diagnosis in patients with exome-negative childhood epilepsy. RNA extracted from 41 individuals' cultured fibroblasts (passage 3-6) was collected and sequenced at a depth of 90-110 paired reads per sample.

  Study EGAS50000000792

• Primary_DIPG_expression_profiles

  In this study, we used RNA-sequencing gene expression profiling in order to characterize specific phenotypic traits in DIPG. Seventeen primary tumor samples stereotactically biopsied at diagnosis by neurosurgeons in the Necker-Enfants Malades hospital (Paris, France) and snap-frozen for RNA extraction and subsequent poly-A mRNA purification.

  Study EGAS00001007181

• Dissecting Cell Composition and Drug Sensitivity in Human Adenoid Cystic Carcinomas (ACCs)

  The study reports the results of single-cell RNA sequencing (scRNA-seq), RNA sequencing (RNA-seq), and whole-exome sequencing (WES) experiments performed on malignant cells isolated by flow cytometry from seven patient-derived xenograft (PDX) lines established from human Adenoid Cystic Carcinomas (ACCs). Of these seven lines, five are characterized by well differentiated histology (ACCX5M1, ACCX6, ACCX14, ACCX22, SGTX6) and two by solid histology (ACCX9, ACCX11). Analysis by scRNA-seq of the well-differentiated PDX line ACCX22 led to identification of cell-surface markers (CD49f, KIT) that enabled the differential purification of the two major cellular components contained in well-differentiated ACCs: myoepithelial-like (CD49f-high/KIT-negative) and ductal-like (CD49f-low/KIT-positive) cells. The study reports the analysis by RNA-seq and WES of purified preparations of myoepithelial-like and ductal-like cells from well-differentiated PDX lines (ACCX5M1, ACCX6, ACCX14, ACCX22, SGTX6) and of ductal-like (CD49f-low/KIT-positive) cells from solid PDX lines (ACCX9, ACCX11).

  Study phs002764

• raw RNA-seq data from patients infected with COVID-19 or influenza

  Dataset consists of fastq files from bulk RNA-seq done on peripheral blood acquired from two well characterised hospitalised cohorts, a cohort of patients infected with influenza and a cohort of patients infected with SARS-CoV-2 during the first wave of the pandemic and prior to availability of COVID-19 treatments and vaccines.

  Dataset EGAD00001008505

• Whole blood RNAseq from a large ALS case-control study at Univ of Michigan

  This dataset consists of 694 bulk RNA-seq data (sorted aligned BAM files) from whole blood of ALS participants meeting the Gold Coast definition of ALS during their clinical visit (n=422) and healthy controls (n=272) at the University of Michigan (UM). Samples were collected as part of the UM Pranger ALS Clinic between 2011-2021, and prepared either using poly-A selection of rRNA depletion (see Experiments). RNAseq was performed on the NovSeq 6000 S4 at the UM Advanced Genomics Core, resulting in 150bp paired-end reads. Raw reads were trimmed using Cutadapt (v2.3) and aligned to hg38 using STAR (v2.5.3a). Age provided is the age at blood sample collection date.

  Dataset EGAD50000001488