12213 results for "cancer rna-seq"

in 19.37 milliseconds.

• IBS-M RNA-seq raw data

  The data set consists of fastq raw files from RNA-seq of seven mucosal biopsies of the colon from seven patients, among them three patients with irritable bowel syndrome with mixed type symptoms. Paired end sequencing on Illumina NovaSeq 6000 was used.

  Dataset EGAD00001006646

• RNA-seq of HCC patients undergoing sorafenib treatment.

  RNA-seq dataset (BAM files) of 28 HCCs and 19 non-tumor livers derived from 8 patients undergoing sorafenib treatment.

  Dataset EGAD00001008204

• Pediatric high grade glioma RNA-Seq

  We performed RNA-Seq in DIPG and hemispheric HGG.

  Dataset EGAD00001008278

• Pheochromocytoma paraganglioma single nuclei RNA-seq

  Single nuclei RNA sequencing (snRNA-seq) on tissue samples from 11 patients (SDHB and RET). Files are fastq files of 10x-5'scRNAseq libraries.

  Dataset EGAD00001008739

• RNA-Seq data on multiple myeloma CD138positive bone marrow plasma cells

  RNA-Seq data on multiple myeloma CD138positive bone marrow plasma cells, 11 samples, sequenced on HiSeq 4000 and HiSeq X-Ten, using mostly the TruSeq stranded mRNA Kit.

  Dataset EGAD00001008619

• RNA-seq for identification of neotranscripts

  This dataset contains RNA-seq data (Fastq files of paired-end data) of 18 patient tumors used for identification of neotranscripts in 18 different types of fusion-driven sarcomas and other cancers as described in Vibert et al., Mol Cell 2022 (PMID: 35550257)

  Dataset EGAD00001008832

• Genomic characterization of Malignant Pleural Mesothelioma.

  Genomic characterization of Malignant Pleural Mesothelioma using RNA-seq, Exome and Illumina 2.5 M array.

  Study EGAS00001001563

• Development and Validation of Patient-Derived Xenografts from Fibrolamellar Carcinoma Human Tissue

  We developed and validated Patient-Derived Xenografts (PDX) of Fibrolamellar Carcinoma human tissue from surgical resections. Human tissues from primary liver tumor, recurrent liver, and liver metastases were implanted into NOD-SCID-Gamma (NSG) mice (Jackson laboratories) and monitored for tumor growth. Some of the original tissues were frozen for further analysis including RNA sequencing. Tumors that grew were then harvested and passaged into additional mice. Some tumor tissues from the PDX samples were frozen for RNA-Seq analysis. The PDX was validated by histologic, transcriptomic and proteomic analysis. The RNA-Seq analysis was performed on original tissue samples including primary liver tumor, recurrent liver, liver metastases, normal liver, and the derived PDX.

  Study phs002435

• Transcriptional and DNA binding profiling of CEBPE in REH acute lymphoblastic leukaemia cells using shRNA RNA-Seq and ChIP-Seq

  ChIP-Seq - CEBPE - REH. The ETV6/RUNX1 translocated acute lymphoblastic leukaemia cell line REH was used to perform ChIP-Seq using a CEBPE antibody. Cells were fixed in 1% formaldehyde for 10mins, prior to preparation of chromatin using Active Motif Express ChIP-IT. 2ug of antibody (anti CEBPE Atlas Antibodies HPA002928)was added to 25ug of chromatin O/N at 4C with rotation. Duplicate reactions were pooled and purified. 10ng of ChIPd and input DNA used for Illumina NGS preparation (NEBNext ChIP-Seq Library kit; New England Biolabs), CEBPE and Input DNA ChIP samples were sequenced on a MiSeq using 150bp Kit v3 paired end and a HiSeq 2500 using 2x101 version 4 paired end (Illumina) respectively. Reactions performed in duplicate. shCEBPE RNA-Seq - REH. REH cells were lentivirally transduced with a pTRIPZ shRNA vector for transcriptional profiling of CEBPE. Two controls (empty and non-targeting) and two CEBPE shRNAs (V3THS_150517(A13), V3THS_404312(G3) Dharmacon, GE) were transduced into REH cells. Cells were treated with 1ug/ml doxycyclin for 144hrs and total RNA purified. Libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kit and sequenced on an Illuimna HiSeq 2500 using 2x101 version 4 paired end chemistry. 3 biological replicates of each samples were prepared.

  Study EGAS00001002877

• Spatiotemporal single-cell analysis reveals a time-dependent immunological modulation by multi-fractionated radiotherapy in esophageal cancer

  Description Here, by integrative spatial and single-cell analyses using spatial transcriptome (VISIUM) and single-cell RNA sequencing (scRNA-seq), we examined the spatiotemporal dynamics of immune-response in the tumor microenvironment of esophageal squamous cell carcinoma (ESCC) before, during and after RT. The comprehensive single-cell analyses demonstrated the infiltration of immune cells into tumor with the dynamic change of immune-stimulatory and -suppressive gene expression after RT. ScRNA-seq analysis uncovered the time- and cell-type-dependent alteration of gene expression profile from during to after RT. This datasets in the present study will provide the information of RT-induced gene signature to seek an appropriate target of personalized immunotherapy combined with radiotherapy and of determining the timing of the combination therapy.

  Study JGAS000712

• Colorectal cancer functional annotation - ATAC

  ATAC-seq data from multiple colorectal cancer cell lines

  Study EGAS50000000205

• Contribution of specific cell types to the development of Barrett’s esophagus and carcinoma via germline genetic risk

  This study was designed to elucidate how individual cell types in Barrett’s esophagus (BE) and esophageal adenocarcinoma (EAC) contribute to disease development via germline genetic risk. We performed single cell RNA-sequencing (scRNA-seq) of BE, EAC and paired normal esophagus and gastric fundus tissue samples. After analyzing individual cell types, we integrated the scRNA-seq data and data of a genome-wide association study (GWAS) on BE/EAC to determine cell type-specific genetic risk. Raw data from the RNA-sequencing is provided here.

  Study EGAS50000000530

• RNA alignment

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

  Dataset EGAD50000000428

• Single-cell RNA-sequencing and cellular indexing of transcriptomes and epitopes of peripheral blood mononuclear cells and peritoneal fluid from patients with achalasia

  The data published here contains cellular indexing of transcriptomes and epitopes (CITE-seq) and single-cell RNA- sequencing (scRNAseq) data on patients with achalasia. Paired PBMC and PF were subjected to 3' scRNAseq and CITE-seq, respectively. All single-cell data was generated using the 10X Genomics (Chromium Single Cell 3’ Reagent) and sequenced in a paired-ended fashion on the NovaSeq6000.

  Study EGAS50000000174

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

  Dac EGAC00001003265

• 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

• 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

• 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

• 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

• Combined genetic and transcriptome analysis of patients with Systemic Lupus Erythematosus (SLE)

  A comprehensive profiling of the genomic architecture of Systemic Lupus Erythematosus (SLE) by combining genetic and transscriptomic analysis by RNA-seq.

  Study EGAS00001003662

• Patient-derived conditionally reprogrammed cells (CRCs) were established and characterized to assess their biological properties and to apply these to test the efficacies of drugs.

  RNA seq data was used for the characterization and profiling of the created patient-derived cells and in vivo tissue.

  Study EGAS00001001702

• 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

• 104 CRLM patients RNA-seq

  This dataset consists of RNA-seq data from 104 CRLM patients. Reads were mapped to the GRCh37 genome version using STAR. The data include patients with no treatment, as well as those treated with FOLFOX, FOLFOX-bevacizumab, atezolizumab, and FOLFOX-bevacizumab-atezolizumab.

  Dataset EGAD50000000954

• RNA-Seq of non-canonical t(7;12) AML

  We performed WGS on AutoMACS-separated BM leukemic blasts (CD34 + CD33+) to characterize the breakpoint and genes involved in the translocation. Additionally, we performed RNA-seq of the bone marrow blast cells to explore the functional consequences of the t(7;12) translocation

  Dataset EGAD50000000268