Search Results - EGA European Genome-Phenome Archive

Long read mRNA sequencing of human neural retinal samples - Usher syndrome transcript landscape

Long-read (PacBio) RNA sequencing dataset of three neural retinal samples. Three PacBio libraries were prepared according the 'standard workflow' optimized for sequencing transcripts centered around 2kb. Additionally one Library (input HNR_S2) was prepared according an optimized 'long workflow' to enrich for larger transcripts up to >10kb. To further enhance the capture of full-length transcripts for USH2A and ADGRV1 (USH2C) genes, we also employed a targeted enrichment approach using the Samplix Xdrop System, followed by PacBio long-read sequencing. USH2A and ADGRV1 enrichment was performed by targeting '5- mid and 3' targets of the respective genes. Finally, we also added ONT (Oxford Nanopore technology) long-read mRNA sequencing of three independent neural retina samples. These data were used for the validation of events observed from the Iso-Seq and Samplix data. The ONT datasets contain sequencing data of the Usher-associated genes. Files are raw BAM, and CRAM format files generated by a Sequel II machine. Additionally, the ccs3 BAM format files are included.

Dataset EGAD50000000720

Acute myeloid leukemia sequencing data

This study contains tumor DNA and RNA sequencing data for 1 AML patient

Study EGAS00001006354

Genomic Analysis of Relapsed/Refractory DLBCL

Molecular phenotyping techniques have established a landscape of genomic variants in diagnostic Diffuse Large B Cell Lymphoma (DLBCL), however these have not yet been applied in large scale studies of relapsed/refractory DLBCL resulting in incomplete characterization of mechanisms driving tumor progression and treatment resistance. Here, we performed an integrated multiomic analysis on relapsed/refractory DLBCL samples. Tumors from 87 relapsed/refractory DLBCL patients were included in this study and were available for data deposition [Tumor whole exome sequencing (WES): n=75, Germline WES: n=73, RNA sequencing (RNA-Seq): n=72]. Tumor DNA was extracted from formalin-fixed, paraffin embedded (FFPE) tissue sections, germline DNA was extracted from blood, and WES of all samples was performed using the Agilent SureSelect XT All Exon v6 kit and sequencing was carried out on an Illumina NovaSeq, 100 x 2 paired end reads. Tumor RNA was extracted from FFPE tissue sections and RNA-Seq was performed using the Illumina TruSeq RNA Exome Kit (Illumina) for library preparation, sequencing platform HiSeq 4000, 100 x 2 paired end reads.

Study phs003868

Single-cell RNA-sequencing for peripheral blood mononuclear cells and plasma proteomics data from COVID-19 patients and healthy controls of Japanese

We performed single-cell RNA-sequencing of peripheral blood mononuclear cells and measured plasma protein expression from COVID-19 patients and healthy controls.

Study JGAS000783

Small RNA sequencing of human oocytes and early embryos

This study looks at small RNA expression in human oocytes (n=12), zygotes (n=5), and embryos (n=10). The sample preparation and sequencing were done in two batches.

Study EGAS50000000157

Bulk RNA-sequencing of PDX models

This project contains bulk RNA-seq of PDX models containing grafts of ATC tumours.

Study EGAS50000001492

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

Clear cell sarcoma sequencing data

This study shares DNA and RNA sequencing data from Clear cell sarcoma patients

Study EGAS00001006072

HipSci - Congenital Hyperinsulinia - RNA Sequencing - July 2017

Dataset EGAD00001003534

HipSci - Embryonic Stem Cells - RNA Sequencing - April 2016

Dataset EGAD00001002000

single cell RNA sequencing and ATAC sequencing, and Whole Genome sequencing of ALS patients

To investigate ALS pathology at the cellular level, we conducted a multiome (single-nucleus RNA sequencing and assay for transposase-accessible chromatin using sequencing) analysis using the motor cortex and spinal cord of patients with ALS and control individuals.

Study JGAS000852

Single cell RNA sequencing of mononuclear cells from synovial fluid of patients with rheumatoid arthritis

Mononuclear cells were collected from synovial fluid of anti-citrullinated positive antibodies (ACPA)-positive rheumatoid arthritis patients (n=8) and ACPA-negative RA patients (n=8). Global cell types (i.e., no enrichment) were obtained from these cryopreserved synovial fluid mononuclear cells (SFMC) samples and immediately fixed and processed using the GEM-X Flex Gene Expression Reagent Kits (10x Genomics) according to protocol. Following Gel Bead-in Emulsion (GEM) generation, samples were processed using the standard manufacturer’s protocol. Once sequencing libraries passed standard quality control metrics, libraries were sequenced on an Illumina NextSeq2000 P4 100 cycle reagent kit with the following read structure: R1: 28, R2: 90, I1: 10, I2: 10. Libraries were sequenced to obtain a read depth greater than 10,000 reads/cell for gene-expression (GEX). FASTQ files are made available. More detailed information can be obtained in Argyriou A. et al, Annals of the Rheumatic Disease, 2025.

Dataset EGAD50000001801

A single-cell multi-omic atlas of nodal B-cell non-Hodgkin lymphomas

CITE-Seq (cellular indexing of transcriptomes and epitopes) of 51 lymph node (LN) samples from including mantle cell lymphoma (MCL, n = 8), follicular lymphoma (FL, n = 12), germinal center (GCB, n = 5) or activated B-cell (non-GCB/ABC, n = 7) diffuse large B-cell lymphoma (DLBCL), and marginal zone lymphomas (MZL, n = 11), in addition to non-malignant reactive lymph nodes (rLN, n = 8). Of the malignant LN samples, 20 were collected at the time of initial diagnosis and 23 were from patients who had previously undergone one or more lines of systemic treatment. Relapse samples were collected at least 3 months after cessation of systemic treatment. This dataset also includes 5 prime RNA sequencing and immune receptor sequencing (10X Genomics) for 11 of these samples (2 rLN, 2 MCL, 3 FL, 2 DLBCL (GCB) and 2 MZL).

Dataset EGAD50000000497

Dataset for cancer_of_unknown_primary-RNA

Rare cancer sequencing data of 58 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

Dataset EGAD00001008857

Dataset for breast_cancer-RNA

Rare cancer sequencing data of 59 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

Dataset EGAD00001008850

Somatic L1 retrotransposition in normal colorectal epthelium

Throughout an individual’s lifetime, genomic alterations accumulate in somatic cells. However, the mutational landscape induced by retrotransposition of long interspersed nuclear element-1 (L1), a widespread mobile element in the human genome, is poorly understood in normal cells. Here, we explored the whole-genome sequences of 406 normal colorectal clones, 12 MUTYH-associated adenomatous clones, and 19 matched colorectal cancer tissues. In addition, we analyzed promoter DNA methylation status of retrotransposition-competent L1 (in 139 clones) and read-through RNA expression profiles (in 116 clones) to investigate the epigenetic regulation of L1 activity.

Dataset EGAD00001010183

Pseudogene_RNAseq

RNA sequencing to validate findings of somatic pseudogenes acquired during cancer development

Study EGAS00001000606

Genomic Analysis of Nucleic Acid Sequences from Pancreatic Cancer

Our study aims to perform multi-omics analysis of pancreatic ductal adenocarcinoma. We performed dual single-cell RNA sequencing and whole genome sequencing on fresh tumor specimens and dual bulk RNA-sequencing and 16S amplicon sequencing on frozen tumor specimens to identify human and non-human nucleic acid sequences. Additionally, we identified microbial sequences and found that bacterial taxonomic profiles from the same sample strongly correlated across technologies.

Study phs003035

Chromatin run-on and RNA sequencing of fibrolamellar carcinoma

Fibrolamellar carcinoma (FLC) is a rare, therapeutically intractable liver cancer that disproportionately affects youth. Here we used chromatin run-on sequencing to characterize the enhancer landscape in FLC and RNA sequencing to identify dysregulated genes in FLC.

Study EGAS00001004169

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

FBSeq: RNA sequencing of human fetal brain.

RNA sequencing of 120 human fetal brains (aged 12-19 post-conception weeks) was performed as part of a Medical Research Council (U.K.) funded project (MR/L010674/2) to investigate genetic effects on gene expression in the developing human brain and their role in neuropsychiatric disorders. Human fetal brain tissue from elective terminations of pregnancy was provided by the Human Developmental Biology Resource (HDBR) (http://www.hdbr.org), with ethical approval (#08/H0712/34), and with consent for use of fetal material for medical research provided by female donors. Total RNA was extracted from half of the available brain tissue from each fetus using Tri-Reagent and treated with TURBO DNase. RNA-Seq libraries were prepared using 1µg of purified total RNA and the Illumina TruSeq Stranded Total RNA Library Prep kit, following ribosomal RNA depletion. Libraries were sequenced on the Illumina HiSeq 2500 or HiSeq 4000 systems, generating at least 50 million read pairs per sample.

Study EGAS00001003214

3884 results for "RNA AND sequencing OR transcriptome"

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