12304 results for "cancer rna-seq"

in 10.99 milliseconds.

• Brain transcriptome of hereditary cerebral haemorrhage with amyloidosis–Dutch type (HCHWA-D)

  HCHWA-D is an early onset hereditary form of Cerebral Amyloid Angiopathy (CAA) caused by a point mutation resulting in an amino acid change (NP_000475.1:p.Glu693Gln) in the Amyloid Precursor Protein (APP). Post-mortem brain tissue (9 patients and 9 age-related controls; frontal and occipital cortex) was used for next generation sequencing of RNA (RNA-Seq with ribosomal RNA depletion).

  Study EGAS00001002730

• Bulk and RIP-seq transcriptome datasets from skin fibroblasts in PTBP1- and PTBP2-related disorders

  PTBP1- and PTBP2-related disorders are rare human conditions that cause variable neurodevelopmental phenotypes, including intellectual disability and behavioral problems such as autism spectrum disorder, anxiety, aggressiveness, and attention disorders, with or without hyperactivity. PTBP1-related disorder is also associated with skeletal dysplasia. In this study, we generated bulk RNA sequencing and RNA immunoprecipitation sequencing (RIP-seq) data from skin fibroblasts obtained from patients and healthy controls.

  Study EGAS50000001210

• Human transcriptomic (RNA-Seq) and epigenomic (ATAC-Seq) data of early B-cell lineage (bulk and single-cell) DAC

  Dac EGAC00001003265

• RNA alignment

  RNAseq aligned to hg38.p14 using nfcore/RNA-seq

  Dataset EGAD50000000428

• Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

  Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via "mapping" procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

  Study EGAS00001003452

• Inferring expressed genes by whole-genome sequencing of plasma DNA

  The analysis of cell-free DNA (cfDNA) in plasma represents a rapidly advancing field in medicine. cfDNA consists predominantly of nucleosome-protected DNA shed into the bloodstream by cells undergoing apoptosis. We performed whole-genome sequencing (WGS) of plasma DNA and identified two discrete regions at transcription start sites (TSS) where the nucleosome occupancy results in different read-depth coverage patterns in expressed and silent genes. By employing machine learning for gene classification we found that the plasma DNA read depth patterns from healthy donors reflected the expression signature of hematopoietic cells. In cancer patients with metastatic disease, we were able to classify expressed cancer driver genes in regions with somatic copy number gains with high accuracy. We could even determine the expressed isoform of genes with several TSSs as confirmed by RNA-Seq of the matching primary tumor. Our analyses provide functional information about the cells releasing their DNA into the circulation.

  Study EGAS00001001754

• RNA-seq study of human long-term and short-term hematopoietic stem cells from umblical cord blood with lentiviral overexpression of S1PR3

  To investigate the molecular and biological pathways altered by S1PR3OE in human hematopoietic stem cells (HSC), we performed RNA-sequencing (RNA-seq) of LT- and ST-HSC 3 days after transduction with control or S1PR3 overexpression (OE) lentiviral vectors. LT-HSC and ST-HSC from 3 pool of CB lin- were FACS-purified, cells were prestimulated for 4 hours and transduced with lentiviral vectors. At day 3, 2000-5300 BFP+ cells were FACS-purified for RNA isolation with a PicoPure kit. We were able to isolate only 1600-1800 BFP+ cells from LT-HSC control samples as opposed to 4000-5400 BFP+ cells from S1PR3OE samples. Thus, we pooled all control BFP+ LT-HSC cells into one sample for RNA-seq analysis. BFP- LT-HSC from control vector transduction were purified from CB1 as an additional LT-HSC control. Nextera libraries generated from 10 ng RNA from 5 LT-HSC samples (2 controls, 3 S1PR3OE) and 6 ST-HSC samples (3 controls, 3 S1PR3OE) were subjected to 125 bp, paired-end RNA-sequencing on the Illumina HiSeq 2500 with an average of 50 million reads/sample at the Center for Applied Genomics, Sick Kids Hospital.

  Study EGAS00001004798

• Colorectal cancer functional annotation - ATAC

  ATAC-seq data from multiple colorectal cancer cell lines

  Study EGAS50000000205

• Genomic and Immune Profiling of Breast Cancer Brain Metastases

  Previous work has shown that breast cancer brain metastases (BCBrM) can have unique molecular profiles compared to extracranial tumors (ECTs), resulting in some patients demonstrating subtype switching between primary/extracranial tumors and brain metastases. Description of the biology of BCBrM and associations between biological features and clinical outcomes, particularly between molecular intrinsic subtypes, will facilitate the design of rational therapies for patients with BCBrM. To better characterize the molecular profiles of BCBrM, we conducted whole exome sequencing (WES) and total RNA sequencing (RNA-Seq) on BCBrM tissues, regardless of clinical or molecular subtype, obtained from a cohort of 42 patients from Duke University Medical Center. This cohort adds valuable patient samples to the field and enables strengthened investigation of somatic mutations, copy number alterations, gene expression profiles, and immune populations within and between BCBrM patients and other similar cohorts. The Duke BCBrM cohort includes clinical and molecular annotation of the 42 patient specimens and their associated WES and RNA-Seq data. Clinical and molecular (PAM50) intrinsic subtypes in ECTs and brain metastases were largely concordant within patients. There were observed differences in intrinsic subtype calls between frozen and FFPE specimens of the same tissue, with discordance mostly observed with LumB calls. Copy number alterations in chromosomal regions included clinically relevant genes, and somatic alterations were largely conserved within patients across matched samples, with few genes, including TP53, being recurrently altered in the majority of patient specimens. In matched FFPE brain metastasis and ECT samples, we observed differentially expressed genes and pathways that had increased or decreased expression in brain metastasis, including in oxidative phosphorylation and DNA repair. Survival outcomes for patients differed by intrinsic molecular subtypes, with some association between expression of select gene pathways and survival. Data available through dbGaP will be the sequencing data and sample attributes from BCBrM tissues obtained from 42 patients from Duke University Medical Center.

  Study phs003673

• RNA-seq dataset of salivary gland tumor samples

  This dataset contains paired-end RNA-seq data of salivary gland tumor samples from 94 patients. Sequencing was performed on Illumina HiSeq 2500, HiSeq 4000, HiSeq X Ten or NovaSeq 6000.

  Dataset EGAD50000001191