3885 results for "RNA AND sequencing OR transcriptome"

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• HipSci - Healthy Normals - RNA Sequencing - July 2017

  HipSci - Healthy Normals - RNA Sequencing - July 2017

  Dataset EGAD00001003529

• Small RNA Sequencing across Diverse Biofluids Identifies Optimal Methods for exRNA Isolation

  We tested the efficiency and reproducibility of 10 RNA isolation methods on 5 different biofluids. Pooled human plasma and serum, bile and urine were used along with cell culture supernatants from human embryonic stem cells (hESC), primary neonatal rat ventricular myocytes (NRVM) and human cholangiocarcinoma cells (KMBC). 4 RNA isolation methods designed to isolate total RNA from bio fluids without enrichment of exRNA carriers were used. 6 methods that preferentially enriched for exRNA carriers were also compared. Small RNA libraries were constructed using the NEBNext small-RNA library prep. The efficiency and reproducibility of the different RNA isolation methods was compared using small-RNA seq.

  Study phs002143

• Calprotectin in vitro effects on human early hematopoiesis

  Dataset content : Raw data from sequencing (fastq) 184 samples from 4 experiments : - Bulk mRNA seq - 24h and 48h, CTRL, CAL, IL6 and combination treatment - single-cell RNAseq - 7days CTRL, CAL, IL6 and combination treatment - ATACseq - 7days CTRL, CAL, IL6 and combination treatment To explore the effects of calprotectin (CAL) on early hematopoiesis, we incubated CD34+ cells from four healthy donors for 7 days in the presence of stem cell factor (SCF), FLT3-ligand and thrombopoietin (TPO) without and with CAL, interleukin-6 (IL6) and the IL6_CAL combination before collecting cells. We performed the same experiment with CD34+ cells collected from three patients with JAK2-V617F-mediated myelofibrosis (MF). A total of 135,545 cells from healthy donors and 111,725 cells from myelofibrosis patients were analyzed by single-cell RNA sequencing (scRNA-seq) using the 10X Chromium droplet-based platform.

  Dataset EGAD50000000659

• Patient tumour data (RNAseq, WGBS, ChIPseq, WGS)

  Content: 60 GB patient tumours and 4 normal brain samples combined in pairs by region (x2=8 total input samples). RNAseq: 1 lane per sample, total strand-specific rRNA-depleted (normal samples were combined = 2 lanes/samples per brain region). WGBS: 2 lanes per sample (normal samples were combined = 2 lanes/samples per brain region). ChIPseq (histone mark): a subset of 20 GB samples were profiled. For the same modification were multiplexed and sequenced on 4 lanes each (H3K27ac, H3K4me1) or a single lane (all others). WGS: used as matching input control for the 20 ChIPseq samples. Data type and technology: RNA-seq: PE 100bp sequenced on HiSeq2000. WGBS: PE 100bp sequenced on HiSeq2000/4000. ChIPseq: SE 50bp sequenced on HiSeq2000/4000. WGS: PE 150bp sequenced on HiSeq X.

  Dataset EGAD00001005492

• RNAseq data from the study - Molecular patterns of response and treatment failure after frontline venetoclax combinations in older patients with AML

  This study assessed molecular determinants of response in a cohort of patients with AML that were treated with venetoclax in combination with either DNA methyltransferase inhibitors or low dose cytarabine. RNA sequencing was performed on 31 patients from three different response classes [10 Group A - Durable remission (n=10), Group B - Relapsed (n=10) and Group C - Refractory (n=11)]. Library preparation and sequencing was performed at the Australian Genome Research Facility, using the Truseq Stranded mRNA library kit. Technical and batch replicate samples are included. Gene count data are provided with the original publication. The use of the sequencing data is subject to a data transfer agreement and is restricted to ethically approved research into blood cell malignancies and cannot be used to assess germline variants.

  Dataset EGAD00001005949

• Spatial multi-omic map of human myocardial infarction

  We applied an integrative single-cell genomics strategy with single nucleus RNA sequencing (snRNA-seq) and single nucleus Assay for Transposase-Accessible Chromatin sequencing (snATAC-seq) together with spatial transcriptomics from the same tissue mapping human cardiac cells in homeostasis and after myocardial infarction (MI) at unprecedented spatial and molecular resolution. We profiled in total 31 samples from 23 patients including four non-transplanted donor hearts as controls and samples from tissues with necrotic tissue areas (ischemic zone, IZ), border zone (BZ), and the non-affected left ventricular myocardium (remote zone, RZ) of patients with acute MI.

  Dataset EGAD00001008952

• Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsis

  This dataset contains raw sequencing files associated with the paper "Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsis" (https://doi.org/10.1038/s41590-023-01490-5). It is composed of two experiments, which are as follows: 1. Single-cell profiling of whole blood leukocytes: This experiment comprises sequencing files generated using the BD Rhapsody single-cell multi-omics profiling (RNA + protein) platform. This platform was used to profile the whole blood leukocyte population in a cohort of sepsis patients, cardiac surgery controls and healthy controls. There are 48 samples and four files per sample: two paired-end FASTQ files (R1 and R2) corresponding to the RNA-seq library, and two paired-end FASTQ files (R1 and R2) corresponding to the protein profiling (ADT-based AbSeq) library. 2. Single-cell profiling of circulating HSPCs: This experiment comprises sequencing files generated using the 10X single-cell multi-omics (RNA-seq + ATAC-seq) profiling platform. This platform was used to profile circulating HSPCs in a cohort of sepsis patients and healthy controls. There are 5 samples (or plexes), each of which consists of a pool of individuals for whom cells were multiplexed and sequenced as a single sample. There are four files per sample: two paired-end FASTQ files (R1 and R2) corresponding to the RNA-seq library, and two paired-end FASTQ files (R1 and R2) corresponding to the ATAC-seq library. Because these samples consists of multiplexed pools, index files are also provided. These can be used for sample deconvolution using the Cell Ranger pipeline. NOTE: Due to EGA's constrains in the number of files permitted per sample, index files (I1 and I2) for the HSPC data set are provided as a separate experiment named "FASTQ index files (I1 and I2) for deconvolution of 10X single-cell multiomics libraries".

  Dataset EGAD00001010927

• Comprehensive Epigenetic Landscape of Rheumatoid Arthritis Fibroblast-Like Synoviocytes

  We characterized the epigenetic landscape of rheumatoid arthritis fibroblast-like synoviocytes (FLS) compared with osteoarthritis FLS. Multiple technologies were used, including ChIP-seq to assay H3K27ac, H3K4me1, H3K4me3, H3K36me3, H3K27me3, and H3K9me3, ATAC-seq for chromatin accessibility, the transcriptome by RNA-seq and whole genomic bisulfite sequencing for DNA methylation. Integrative analysis was performed using a novel unbiased method to identify regions of the genome that have similar epigenetic marks. The regions that distinguished RA and osteoarthritis cells were primarily located in active enhancers and promoters. The regions and genes identified included immunological pathways. In addition, some unexpected pathways, most notably "Huntington's Disease Signaling", were discovered. The Huntington's Disease pathway was biologically validated for Huntingtin-interacting protein-1, which regulated invasive behavior of FLS. For a complete description, see R. Ai et al., Comprehensive epigenetic landscape of rheumatoid arthritis fibroblast-like synoviocytes. Nat Commun 9, 1921 (2018). Sequencing data of study participants are available through dbGaP's Authorized Access portal, while analyses of the sequencing data may be obtained through NCBI's GEO portal under GSE112658.

  Study phs001615

• 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

• scRNAseq of colonic organoids derived from biopsies taken from healthy human individuals treated with IL22

  scRNAseq dataset of colonic organoids derived from epithelium from biopsies taken from three healthy human individuals. The organoids have either been grown in standard conditions (control) or treated with IL22 (treated). Includes 6 samples in total, one control from each individual (ctrl1, ctrl2, ctrl3) and one treated from each (treat1, treat2, treat3). The samples have been multiplexed using the antibody hashing technique. The 6 samples have been pooled into the one organoids sample. In order to analyse the raw files, they have to be demultiplexed first. Information necessary for demultiplexing, as well as which files belong to which sample, can be found in the map_file.csv, attached to each sample. Dataset includes raw Fastq files and processed csv count matrices. Fastq files are divided into HTO (hashtag) and RNA (transcriptome) files. HTO has one index (I1) and two read (R1, R2) files and RNA has two index (I1, I2) and two read (R1, R2) files. The fastq files are for the pooled (organoids) sample and need to be demultiplexed. Count matrices contain comma-separated values with cell barcodes as column names and gene names as row names. Since count matrices have been created after the demultiplexing step, there’s one matrix for each of the 6 individual samples. scRNA-seq data from human colon organoids was analysed in the same manner as for the Colitis dataset, apart from the following changes. Data was generated with the Cell Hashing technique, which uses oligo-tagged antibodies against surface proteins to barcode single cells. This allows for samples to be multiplexed together and run in a single experiment. The data was demultiplexed using the HTODemux() function from Seurat (Hao et al., 2021).

  Dataset EGAD00001010168