12221 results for "cancer rna-seq"

in 11.55 milliseconds.

• Mapping_regulatory_variation_in_sensory_neurons_using_IPS_lines_from_the_HIPSCI_project

  Sensory neurons are nerve cells that are activated by sensory input such as heat, light and convey information to the brain. Although a key cell type in complex organisms, human sensory neurons are challenging to study because they are impossible to obtain from living donors. We have collaborated with the Neucentis Pharmaceutical Research Unit to differentiate sensory neuron like cells from human induced pluripotent stem cells derived as part of the Human Induced Pluripotent Stem Cells Initiative. We will sequence RNA from 100 IPS lines derived from healthy individuals and perform RNA-seq on the differentiated cells to identify noncoding variants that alter gene expression in human sensory neurons.

  Study EGAS00001001149

• Raw multi-omics dataset from the lungNENomics cohort of lung neuroendocrine tumours

  This study contains raw multi-omics data generated as part of the lungNENomics project, a multi-omics study aimed at deep molecular profiling of lung neuroendocrine tumours, including typical carcinoids (grade 1) and atypical carcinoids (grade 2). The study includes whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and Infinium MethylationEPIC array data from samples in the lungNENomics cohort. These raw data complement the associated processed molecular data released on Zenodo (10.5281/zenodo.19366762) and the corresponding whole-slide pathology images deposited in the EBI BioImage Archive (10.6019/S-BIAD3143). See Sexton-Oates et al medrxiv 2025 (https://www.medrxiv.org/content/10.1101/2025.07.18.25331556v1) for details.

  Study EGAS00001005979

• Childhood Cancer Model Atlas

  we establish a single-site collection of 243 cell lines, including 207 paediatric cancer cell lines representing 14 distinct extracranial and brain childhood tumour types. We subjected 164 paediatric cancer cell lines to multi-omic analyses across three dimensions (DNA-sequencing, RNA-sequencing, DNA methylation) to classify them based on clinically relevant molecular subtypes.

  Study EGAS00001006320

• Genomic Studies of Bipolar Disorder in a Large Cohort from The Netherlands

  The transcriptomes of whole blood samples of a large cohort of individuals from the Netherlands were assayed using RNA-seq. These whole blood samples included patients diagnosed with schizophrenia or bipolar disorder, family members of patients, and healthy controls. Within the whole blood dataset, about 600 of the samples were sequenced to an average of 13.9 million mapped reads per sample, while about 2,000 were sequenced to an average of 5.9 million mapped reads per sample, in order to assess changes in power to detect eQTLs based on sample size. We find that expression, defined as the log transcripts per million reads (TPMs), is highly correlated when comparing the moderate (13.9M) and lower (5.9M) coverage datasets. Additionally, a subset of about 150 individuals provided fibroblast cell lines which were assessed for the levels of gene expression using RNA-seq at an average of 50 million mapped reads per sample. We use downsampling techniques to generate synthetic datasets derived from this real fibroblast RNA-Seq data to explore the relationship between coverage and number individuals in an eQTL analysis. Corresponding individual-level genotypes for the samples included in the expression matrix are also made available. The genotypes were derived from multiple other project cohorts, each using different genotyping platforms including OmniExpress Exome, Global Screening Array, Illumina550, and PsychChip. A Plink file set of these cohorts is provided here after merging the separate project file sets and extracting only the individuals who have expression data also provided here; quality control for SNP-missingness is recommended. We demonstrate in this study that given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample to allow for an a greater number of individuals sequenced. 

  Study phs002856

• DNA methylation database for gynecological cancer detection, classification and assay development

  MeD-seq data (fastq files) from gynecological cancers and associated healthy tissues. In total 292 fastq files generated by MeD-seq are deposited consisting of: healthy tissues (vulva n=11, cervix n=15, endometrium n=13, fallopian tube n=18 and ovary n=13), precursor lesions of cancer (vulva n=23 and cervix n=46) and cancer (vulva n=21, cervix n=45, endometrium n=26, fallopian tube n=8 and ovary n=33)

  Dataset EGAD50000000611

• Single-Cell RNA-Sequencing of Tumor and Immune Cells from Patients with Asymptomatic Waldenstrom's Macroglobulinemia

  We performed single-cell RNA-seq on tumor and immune cells from 30 patients with IgM Monoclonal Gammopathy of Undetermined Significance (IgM MGUS), Smoldering Waldenstrom's Macroglobulinemia (SWM), and overt Waldenstrom's Macroglobulinemia (WM) to identify tumor-intrinsic and immune biomarkers of disease progression in patients with asymptomatic WM. We identified subtypes within MYD88-mutant WM that are associated with clinical presentation and/or patient outcomes. We also characterized immune dysregulation in patients with asymptomatic WM, and demonstrated disordered interferon stimulation in T and NK cells of patients.

  Study phs003787

• RNA-sequencing of gluten-reactive and non-reactive T-cells from blood samples of treated CeD patients during a gluten-challenge

  Gluten-specific CD4+ T cells are drivers of celiac disease (CeD). Here we aimed to characterize such cells in blood of treated CeD patients during gluten challenge with comparison to similar cells of untreated disease. Treated CeD patients underwent a 3-day gluten-challenge with blood sampling at baseline and day 6 (d6). Blood cells were stained with HLA-DQ:gluten tetramers (Tetramer). CD4+, gut-homing Tetramer+ and Tetramer- effector-memory T (TEM) cells at d6 were subjected to bulk RNA-seq (n=7).

  Study EGAS00001004988

• Single-cell RNA sequencing of control and Notch inhibitor treated HCC PDX model

  We generated single-cell RNA-seq data for anti-Notch2 (n=3), anti-JAG1 (n=1) and anti-gD (n=4) treated LIV78 tumors. NCR nude mice bearing similarly sized LIV78 tumors (volumes between 300-900mm3) were injected intravenously with 30mg/kg body weight of either anti-Notch2, anti-Jag1 or anti-gD control antibody 72hrs prior to tumor harvest and tumor cell isolation. Following the depletion of CD45+ and Ter119+ cells, the samples were processed for single-cell RNA-seq (scRNAseq) as described previously (Long et al., 2019) using the Chromium Single Cell 3’ Library and Gel bead kit v2, following the manufacturer’s manual. cDNAs and libraries were prepared following the manufacturer’s manual (10X Genomics). Libraries were profiled by Bioanalyzer High Sensitivity DNA kit (Agilent Technologies) and quantified using Kapa Library Quantification Kit (Kapa Biosystems, Wilmington, MA). Each library was sequenced in one lane of HiSeq 2500 (Illumina) following the manufacturer’s sequencing specification (10X Genomics) to produce the resulting FASTQ files.

  Dataset EGAD50000000738

• Convergent genetic adaptation in human tumors developed under systemic hypoxia and in populations living at high altitudes

  Genomic files, including whole-exome and RNA-seq data from tumors and cell lines, were generated and analyzed in this study.

  Dac EGAC50000000484

• RNA-seq of murine osteosarcoma cell line genetically modified for CYR61

  Transcriptomic analysis of K7M2 murine osteosarcoma cells stably modified to overexpress or repress CYR61

  Study EGAS50000000526