3659 results for "RNA AND sequencing OR transcriptome"

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• Genetic drivers of epigenetic and transcriptional variation of human immune responses to infection (RNA-seq, ATAC-seq and ChIPmentation)

  RNA-seq, ATAC-seq and ChIPmentation data from monocyte-derived macrophages that were infected with Influenza A virus strain PR8WT, or a matching non-infected control.

  Dataset EGAD00001008422

• Detection of Gene Fusions using Targeted Next-Generation Sequencing

  Five commercially available parallel sequencing assays were evaluated for their ability to detect gene fusions in eight cell lines and 18 FFPE tissue samples carrying a variety of known gene fusions. Four RNA-based assays and one DNA-based assay were compared; two were hybrid capture-based, TruSight Tumor 170 Assay (Illumina) and SureSelect XT HS Custom Panel (Agilent), and three were amplicon-based, Archer FusionPlex Lung Panel (ArcherDX), QIAseq RNAscan Custom Panel (Qiagen) and Oncomine Focus Assay (Thermo Fisher Scientific).

  Dataset EGAD00001006913

• Whole-genome sequencing of rhabdomyosarcoma tumouroids (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumoroids data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015707

• Whole Genome-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015709

• Whole Genome-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015716

• Developing Targeted Therapy for Patients with Multiple Myeloma and Gain or Amplification of Chr1q (1q+)

  Multiple myeloma (MM) frequently presents with gain or amplification of chr1q (1q+), which is associated with inferior response to treatment and poor outcomes. To develop targeted therapy for MM with 1q+, we leveraged external and in-house dependency screens and drug screens, along with single-cell sequencing of patient tumors with subclonal 1q+. We demonstrated that MM with 1q+ exhibits higher sensitivity to MCL1 and PI3K inhibitors, which may be synergistic and act through established and novel mechanisms. Two human MM cell lines with 1q+, KMS12BM and KMS11, were profiled with either single-cell RNA-sequencing (scRNA-seq) alone (KMS12BM, KMS11) or both scRNA-seq and whole-genome sequencing (WGS; KMS12BM) to understand the factors that determine response to MCL1 and PI3K inhibitors and how these inhibitors work.

  Study phs003886

• Egyptref: An integrated personal and population-based Egyptian genome reference

  North African individuals are not represented in current genetic data sets. To address this issue, we generated an integrated personal and population-based Egyptian genome reference called Egyptref. Towards this end, we performed a human de novo assembly of an Egyptian individual using long PacBio reads (99x genome coverage) and polished it using Illumina short reads (90x). Variants were phased using 10x Genomics linked reads (80x). This personal genome was complemented with whole genome sequencing-based variant data of 109 further Egyptians and mitochondrial haplogroups from mtDNA sequencing of 326 further Egyptians (of which 100 individuals from EGAD00001001372/EGAD00001001380) to obtain a population-based genome. We used Egyptref for assessing the impact of genetic variation, e.g. by integration of blood RNA sequencing data of the assembly individual.

  Study EGAS00001004303

• Measuring the level of relatedness between NGS datasets

  Sequencing technologies are providing increasingly detailed insight in genetic makeup are paving their way into molecular diagnostics. The field will benefit from rigorous and bias-free measures for the quality of sequence data and for the proper representation of the complexity of the original samples. While current methodologies rely on the availability of a well-characterized reference genome, we propose kMer profiling for alignment-free assessment of the quality, comparability, and complexity of sequencing datasets. We show that kMer detects technical artefacts such as high duplication rates, library chimaeras, and differences in library preparation protocols in whole-genome, whole-exome, and RNA sequencing data. Additionally, it successfully captures the complexity and diversity of microbiomes. Thus, kMer allows for a robust evaluation of the quality and complexity of sequencing data without relying on any prior information and opens the way to a more reliable biological reasoning.

  Study EGAS00001000600

• Single Cell and Spatial Transcriptomic Profiling of Haemophilus ducreyi Infection

  We investigated the immune response in end-point pustules formed by Haemophilus ducreyi in volunteers using an experimental infection model. Six volunteers were infected in the skin of the upper arm at three sites with 41-85 CFU of H. ducreyi loaded on an allergy testing device; a sham-inoculated (e.g., wound) site was also performed. Five volunteers (4 men and 1 woman; 2 Asians and 3 Whites; aged 27-46 years) formed pustules 6-8 days post infection; 1 volunteer spontaneously resolved all sites. If a volunteer formed pustules, biopsies were performed to collect their pustule and wound sites. Biopsy pairs for all 5 pustule formers were subjected to single cell (sc) RNA-seq. Sequencing libraries were prepared using the 10X Genomics Single-cell 3' RNA-seq kit. Spatial transcriptomics was performed on biopsy pairs from 4 of the 5 pustule formers. Ten micron sections were placed on 10X Genomics Visium slides for fresh-frozen tissue, and libraries were prepared with the 10X Genomics Visium spatial 3' gene expression kit. All samples were sequenced on an Illumina NovaSeq 6000. Count tables were produced with CellRanger and SpaceRanger, respectively, and Seurat was used to analyze the data. We identified 13 major cell types by scRNA-seq; the proportion of some immune cell subsets was increased in pustules compared to wound sites. We also localized the major cell types within the tissue by using the scRNA-seq data to deconvolve the spatial transcriptomic data. The FASTQ files and count tables from CellRanger and SpaceRanger are provided.

  Study phs003754

• COVID-19 Multiomics Atlas

  We provide a single-nuclei RNA-sequencing (snRNA-seq) dataset derived from four COVID-19 patients, generated using the 10x Chromium Next GEM Single Cell v3.1 kit. For our study, these were integrated with snRNA-seq data with 12 publicly available sc/snRNA-seq datasets, comprising organ donor lung samples (n=89) and COVID-19 lung tissue samples (n=51).

  Dataset EGAD00001015602