3662 results for "RNA AND sequencing OR transcriptome"

in 10.65 milliseconds.

• Transcriptomic signatures of CD4+ T cells from visceral leishmaniasis (VL) patients

  Bulk RNA-sequencing was performed on CD4+ T cells isolated from the blood of visceral leishmaniasis patients (n = 12) and endemic controls (EC; n = 12). CD4+ T cells were obtained by magnetic-activated cell sorting (MACS). Alterations in the transcripts of T helper (Th) cells during infection were identified.

  Dataset EGAD00001005807

• Transcriptional programming in whole blood reveals changes in pro-inflammatory phenotype explained by changed food and changed life style.

  We here focused on whole blood from paxgene tubes from healthy Tanzanians and the impact of urbanization and diet on innate immune responses. We performed RNA-sequencing of whole blood from paxgene tubes from healthy Tanzanians and investigate that transcriptional changes depend of diet and location.

  Dataset EGAD00001006051

• Sequencing data for oesophageal and related samples - Nowicki-Osuch, Zhuang et al (bulk RNA)

  Data supporting: “Deep molecular phenotyping reveals the identity of Barrett’s esophagus and its malignant transition.” Nowicki-Osuch, Zhuang et al. RNAseq (BAM files) 241 tumour samples

  Dataset EGAD00001005388

• RNA-Seq files for St. Jude Clinical Pilot

  This dataset contains whole genome sequencing BAM files for 78 tumor-normal pairs (a total of 156 samples) used in the St. Jude Clinical Pilot. Mapping was performed using BWA. This dataset accompanies the paper "Clinical Cancer Genomic Profiling by Three-Platform Sequencing of Whole Genome, Whole Exome and Transcriptome"

  Dataset EGAD00001004280

• Natural Killer Cell Plasticity During IL-15-driven Homeostatic Proliferation

  Here we performed single-cell RNA sequencing to address repertoire stability and subset plasticity during IL-15 driven homeostatic proliferation. Sorted NK cell subsets representing discrete stages of NK cell differentiation are compared with the corresponding subsets after proliferation and further sorted into two subsets depending on the rate of proliferation.

  Dataset EGAD00001005420

• RNA-seq of T-ALL patient-derived xenograft (PDX) samples

  This dataset includes 4 samples profiled by high-throughput Illumina sequencing, in bam format, aligned to GRCh37. Human patient T-ALL samples were serially propagated as xenografts in immunodeficient mice. The samples were collected after development of frank leukemia in recipient mice.

  Dataset EGAD00001009749

• Whole blood RNA-sequencing of covid-19 patients and healthy controls

  This dataset contains whole blood transcriptome data generated from 93 patients with COVID-19 across a range of severities and 23 healthy controls. All patients were PCR positive for SARS-CoV-2 and disease severity ranged from asymptomatic to severe disease requiring ventilation. Individuals without symptoms, or with mild symptoms, were recruited from routine screening of healthcare workers, while COVID-19 patients were recruited at or soon after admission to Addenbrooke’s or Royal Papworth hospitals. Blood samples were taken at recruitment and then again four weeks later. Further details of the cohort and the generation of the RNA-Sequencing data can be obtained from Bergamaschi, L. et al. Longitudinal analysis reveals that delayed bystander CD8+ T cell activation and early immune pathology distinguish severe COVID-19 from mild disease. Immunity 54, 1257-1275 e8 (2021).

  Dataset EGAD00001007776

• An Integrated Approach to Patient Stratification and Therapy Selection in Acute Myeloid Leukemia

  As more clinically-relevant genomic features of myeloid malignancies are revealed, it has become clear that targeted clinical genetic testing is inadequate for risk stratification. Here, we developed and validated a clinical transcriptome-based assay for stratification of acute myeloid leukemia (AML). Comparison of RNA-Seq to whole genome and exome sequencing revealed that as a standalone assay, RNA-Seq offered the greatest diagnostic return, enabling identification of expressed gene fusions, single nucleotide and short insertion/deletion variants, and whole-transcriptome expression information. Expression data were used to develop a novel risk score which, when combined with molecular risk guidelines, allowed for the re-stratification of 22.1 to 25.3% of AML patients from three independent cohorts into correct risk groups. Within the adverse-risk subgroup, we identified a subset of patients characterized by dysregulated integrin signaling and RUNX1 or TP53 mutation. We show that these patients may benefit from therapy with inhibitors of focal adhesion kinase (PTK2), demonstrating additional utility of transcriptome-based testing for therapy selection in myeloid malignancy.

  Dataset EGAD00001006701

• Development of hunanized mice for human hematopoisis and immunity research

  We obtained peripheral blood or bone marrow samples from 112 acute myeloid leukemia patients and examined sensitivity and resistance of the AML samples to molecular targeting drugs. By doing RNA sequencing using AML cells, we found heterogenous gene expression patterns in AML with different mutational landscapes. From the RNA sequencing, we identified anti-apoptosis, cell cycle progression, and immune-related signals as pathways up-regulated in AML cells as compared with normal CD34+ cells. From the pathway analyses, we further setup in vitro experiments to find vulnerability genes in clinically-aggressive AML. BIRC, BCL2, MCL1, AURKB, and KIF10 may be essential molecules for AML cell survival and proliferation.

  Study JGAS000253

• RNA-Seq of human longitudinal whole blood samples from PCR-positive and PCR-negative adults following controlled inoculation with Influenza A H3N2 virus.

  Understanding of the transcriptomic profile of individuals in early influenza infection is limited. To investigate this, longitudinal whole-blood samples (n=178) were taken from adult participants following controlled inoculation with Influenza A H3N2 virus (sampling at baseline (0) and days 1, 2, 3, 7, 10 and 14 post-challenge). Most participants became influenza PCR-positive; a minority remained PCR-negative. Total RNA was extracted from PAXgene tubes before undergoing globin and rRNA depletion. DNA libraries were constructed using the NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina. All samples were then sequenced using Illumina NovaSeq 6000.

  Dataset EGAD50000000956

• Ribosomal profiling on Epstein-Barr virus transformed B-lymphoblastoid cell lines.

  This dataset contains Ribo-seq BAM files of Epstein-Barr virus-transformed B-lymphoblastoid cell lines from six individuals (HG00114, HG00282, NA12005, NA12044, NA12717 and NA12751) of the GEUVADIS Project. For each EBV-LCL, 20 million cells were treated with 2 μg/ml harringtonine and 100 μg/ml cycloheximide. After lysing cells in lysis buffer supplemented with 100 μg/ml cycloheximide, ribosome complexes were purified by density purification. Small RNA molecules were isolated using the NucleoSpin miRNA Kit (Bioke, Leiden, Netherlands), followed by RNA PAGE gel separation. Universal linkers were added after dephosphorylation. After reverse transcription, the cDNA was circularized and, after ribosomal RNA depletion, barcoded and sequenced on Illumina NextSeq 500.

  Dataset EGAD50000000465

• RNA-seq of oropharynx cancer cases from University of Michigan

  This dataset consists of 67 bulk RNA-seq data (sorted aligned BAM files) from formalin-fixed paraffin-embedded (FFPE) oropharynx tumor samples from the University of Michigan (UM). Samples were collected as part of the UM Head and Neck SPORE between 2008-2014. Library preparation was performed using the ribo-depletion method with the FastSelect kit and sequenced on the NovaSeq 6000 at the UM Advanced Genomics Core, resulting in 150bp paired-end reads. Raw reads were trimmed using Cutadapt (v3.4) and aligned to hg38 and high-risk HPV genomes using STAR (v2.7.9a). 62 of 67 samples were determined to be HPV RNA-positive.

  Dataset EGAD50000001306

• RNA-seq and Hi-C data of a chromothripsis patient's iPS cells

  This dataset contains RNA-seq and Hi-C data files of induced pluripotent stem (iPS) cells and iPS cell-derived neural progenitors (NPCs) derived from a germline chromothripsis patient and both parents. iPS cells of the patient (cell lines 14 and 15), the father (lines 23 (with two replicates) and 32) and mother (line 30) were differentiated to NPCs and RNA was collected on day 0, day 7 and day 10 of differentiation. In addition, Hi-C data for two iPS cell-derived NPC lines from the patient (14 and 15) and two lines from the father (23 and 32) was generated.

  Dataset EGAD00001002242

• Natural genetic variation of the cardiac transcriptome in non-diseased donors and patients with dilated cardiomyopathy

  Background: Genetic variation is an important determinant of RNA transcription and splicing, which in turn contributes to variation in human traits including cardiovascular diseases.Results: Here we report the first in-depth survey of heart transcriptome variation using RNA-sequencing in 149 (97)* patients with dilated cardiomyopathy and 113 (108)* non-diseased controls. We reveal extensive differences of gene expression and splicing between dilated cardiomyopathy patients and controls, affecting known as well as novel dilated cardiomyopathy genes. Moreover, we show a widespread effect of genetic variation on the regulation of transcription, isoform usage and allele specific expression. Systematic annotation of genome wide association SNPs identifies 60 functional candidate genes for heart phenotypes, representing 20% of all published heart genome wide association loci. Focusing on the dilated cardiomyopathy phenotype we found that eQTL variants are also enriched for dilated cardiomyopathy genome wide association signals in two independent cohorts.Conclusions: RNA transcription, splicing and allele specific expression are each important determinants of the DCM phenotype and are controlled by genetic factors. Our results represent a powerful resource for the field of cardiovascular genetics. (*) numbers in parentheses represent samples that the publication is based on.

  Study EGAS00001002454

• Transcriptome of memory B cells with autoproliferation in MS

  Transcriptional profile of resting/hi and autoproliferating/Dim memory B cells in Multiple sclerosis patients. In brief, non-proliferating (CFSEhi) and AP+ (CFSEdim) CD19+ CD27+ B cells from PBMCs of 5 natalizumab-treated female RRMS (NAT) patients were sorted following 7 days of autoproliferation. Transcriptome libraries were constructed from the same samples, except for one sample pair, which was excluded due to limited input material (5 donors as for Methylome, D1; D2, D3, D5, D6; samples from donor D4 unfortunately technically failed). SMARTer Total Stranded RNA-seq library construction with ribosomal depletion was performed and then sequenced on NovaSeq S4 lane.

  Dataset EGAD50000001270

• Single-nucleus RNA-sequencing data of kidney biopsies from patients with primary FSGS, maladaptive FSGS, proteinuric controls and healthy controls

  This dataset includes FASTQ files of single-nucleus RNA sequencing of cryopreserved kidney biopsy cores from adult patients with diagnosed primary FSGS (n = 9, all nephrotic), maladaptive FSGS (n = 9, not nephrotic), proteinuric controls (PLA2R-positive membranous nephropathy, n = 3), and healthy controls (n = 4). A total of 120,751 high-quality nuclei were identified, including 2,471 podocytes and 1,574 parietal epithelial cells (PECs). In addition to the raw FASTQ files, the dataset includes processed data files from all 25 samples, generated using Seurat in R: barcode files, features, count matrices, and associated metadata. Details regarding the bioinformatics pipeline can be found at https://github.com/lambrechtslab/FSGS_Deleersnijder_et_al

  Dataset EGAD50000001557

• Bulk RNAseq FASTQ files of three PDAC organoid lines treated with LGK974 or DMSO for 24h

  This dataset contains raw FASTQ files from bulk RNA sequencing of three patient-derived pancreatic ductal adenocarcinoma (PDAC) organoid lines (P28, P40, P47). Each organoid line was treated with either the WNT secretion inhibitor LGK974 or DMSO for 24 hours, with three biological replicates per treatment condition. RNA was extracted using the Qiagen QiaSymphony SP system, and libraries were prepared with the Illumina Truseq stranded polyA kit. Sequencing was performed on an Illumina NextSeq2000 platform using single-end 50bp reads. Quality control was conducted with FastQC, and reads were trimmed with TrimGalore as appropriate. Data are controlled-access and intended for transcriptomic analysis of WNT pathway perturbation in PDAC organoid models.

  Dataset EGAD50000002219

• Host-microbe interactions in a novel SARS-CoV-2 human challenge model

  This research leverages previously collected clinical data and samples from a first-in-human SARS-CoV-2 challenge study conducted in healthy adults without serological evidence of prior infection or vaccination. We performed shotgun metagenomic and RNA sequencing of serially collected mid-turbinate nasal and throat swab samples from 34 adults who were inoculated with a wild-type virus, 18 (53%) of whom developed PCR-confirmed infection. By integrating microbiome and host transcriptome data, this research will provide insight into the complex interplay between the upper respiratory microbiota and the host immune system, offering a deeper understanding of factors that influence susceptibility to and severity of SARS-CoV-2 and other respiratory viral infections.

  Study EGAS50000001440

• Single-cell transcriptomic analyses of peritoneal fluid from patients with colorectal cancer metastasized to the peritoneum

  The data published here contains single-cell RNA-sequencing (scRNAseq) data as obtained using the 3' scRNAseq using Chromium Single Cell 3’ Reagent from 10X Genomics on peritoneal fluid (PF) from patients with colorectal cancer (CRC) and peritoneal metastases (PM). Sequencing was performed in a paired-ended fashion on the NovaSeq6000.

  Dataset EGAD50000000248

• Genomic and Transcriptomic Data in GBM Patients Undergoing anti-PDL1 Therapy

  This dataset comprises whole exome sequencing data from 29 tumor-normal pairs and RNA-seq data from 39 tumors of newly diagnosed IDH-wt GBM patients. It includes analyses such as variant calls, copy number variations, the transcriptomic expression matrix, and patient metadata and outcomes.

  Dataset EGAD50000001154

• 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

• Epigenome of sorted human muscle stem cell

  This dataset contain RNA-seq, ChIP-seq, WGBS and ATAC-seq of 1 human muscle stem cell sample. NCAM+ITGB1+ CD31−CD45−CD34− were used as the sorting strategy for the sample. H3K27ac, H3K27me3, H3K4me1, H3K4me3, H3K36me3 and H3K9me3 are the targets of ChIP-seq.

  Dataset EGAD00001008686

• Next Generation Sequencing Characterization of Tregs in Human Peripheral Blood during Autoimmunity

  Tregs were sorted as CD4+CD25+CD127- cells from peripheral blood of 14 healthy individuals, 8 patients with systemic lupus erythematosus, 9 patients with rheumatoid arthritis, and 11 patients with multiple sclerosis. RNA was extracted and polyA libraries were prepared using the Illumina Truseq sample preparation kit v.2. Single-end sequencing was performed on NextSeq500.

  Dataset EGAD00001006193

• Molecular characterization of T-PLL by Next-Generation Sequencing

  Patients with T-cell prolymphocytic leukemia (T-PLL) were profiled with multiple OMICS approaches based on Next-Generation Sequencing (NGS). In total, data from RNA-Seq, Whole-Exome Sequencing, Whole-Genome Sequencing and amplicon panel analyses in 134 samples are available. All samples were processed as paired-end libraries on Illumina sequencing machines. The data are available as paired FastQ files.

  Dataset EGAD00001003825

• Spatial transcriptome analysis of aging of healthy skin samples

  Previous studies have revealed that skin T cells accumulate and maintain immune responses in the elderly. However, we questioned why these functional T cells fail to recognize and eliminate malignant cells, making elderly skin more prone to developing malignant tumors. To address this question, we examined the overall skin microenvironment in aging using Xenium digital spatial RNA sequencing.

  Study JGAS000771