10173 results for "cancer rna-seq"

in 15.69 milliseconds.

• ATAC-seq in colon normal mucosa

  ATAC-seq

  Dataset EGAD00001007690

• MTLE small RNA-sequencing and RNA-sequencing Data Access Committee

  Dac EGAC00001001427

• SEGA RNA-sequencing and small RNA-sequencing Data Access Commitee

  Dac EGAC00001001467

• Genomic Analysis of Follicular Lymphoma

  We included tumors from newly diagnosed or untreated follicular lymphoma in this study. Samples consented to the University of Iowa and Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE) were included for data sharing. RNA was extracted from 1) CD19 sorted B cells from tumor cell suspension frozen in dimethyl sulfoxide (DMSO; RNA Tumor B Cell, n=77), 2) tumor cell suspensions frozen in DMSO (RNA Tumor DMSO, n=78), and 3) tumor tissue sections frozen in optimal cutting temperature compound (OCT; RNA Tumor OCT, n=53). For RNA sequencing, library preparation was done using the Illumina TruSeq RNA Exome Kit and sequenced with 100 nucleotide paired-end reads using the HiSeq 4000. DNA was extracted from 1) tumor cell suspensions frozen in DMSO (DNA Tumor DMSO, n=78), 2) tumor tissue sections frozen in OCT (DNA Tumor OCT, n=33), and 3) germline DNA from peripheral blood (n=102) and from normal tissue adjacent tumor (n=9). For whole exome sequencing (WES), library preparation was done using the Agilent SureSelect XT kit and sequenced with 100 nucleotide paired-end reads using the Illumina HiSeq 4000.

  Study phs002989

• BLUEPRINT: DNaseI-seq for monocytes

  DNaseI-seq for monocytes

  Dataset EGAD00001000674

• Transcriptome_analysis_of_LCM_samples_

  We aim to set up a RNA pipeline for LCM samples to study transcriptome landscape of tumor and normal tissues. RNA was extracted from Lazer capture micro-dissection tissues and cDNA libraries were prepared with SMARTseq2 protocol. Libraries are pooled to be sequenced.

  Study EGAS00001003862

• MiRNome_pituitary_adenoma

  Small RNA (<100 bases in length) were purified from total RNA using miRNeasy kit (Qiagen), then sequenced. Libraries were prepared at the Genomics Platform of the Cochin Institute, following the TruSeq small RNA protocol (Illumina), starting from 1 µg of high quality total RNA. Single read (1 × 75 bp) sequencing was performed on a Nextseq 500 platform (Illumina). FASTQ sequences were aligned on miRBase v.2052, then counted with STAR (v.2.5.2a).

  Dataset EGAD00001004998

• RNA sequencing of Ewing sarcoma and CIC-DUX4 sarcoma

  RNA sequencing of a collection of 6 Ewing sarcoma and 3 CIC-DUX4 sarcoma tumoroids plus 3 matched patient tumors. Tumoroids were established from digested patient tumor material as 3D culture in Matrigel. After 2-3 month of growth, RNA was isolated from individual tumoroid models using the RNeasy Kit (Qiagen) and used for whole exome sequencing. RNA from corresponding patient tumors was tested in parallel, if available.

  Dataset EGAD50000000557

• RNA Sequencing of paediatric patients with B lymphoblastic leukemia

  RNA sequencing was performed on 148 Bone marrow of peripheral blood samples of paediatric patients with B-ALL.

  Study EGAS00001004532

• METABRIC

  The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (CNVs, SNPs) and acquired somatic copy number aberrations (CNAs) were associated with expression in 40% of genes, although the landscape was dominated by cis and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP, and MAP2K4. Unsupervised analysis of paired DNA/RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, ER-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration 0152hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the 0152CNA-devoid sub-group and a Basal-specific chromosome 5 deletion-driven mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic copy number aberrations on the transcriptome.

  Study EGAS00000000083

• Combining transcription factor binding affinities with open chromatin data for accurate gene expression prediction

  The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

  Study EGAS00001002073

• HCA_Reproductive_Tract_Adult_RNA

  HCA Reproductive Tract Adult RNA

  Study EGAS00001006636

• scRNA/TCR-seq of CD8+ T cells from a melanoma patient

  Specific in vitro stimulation of patient-derived PBMC with the neoantigens SYTL4-S363F and KIF2C-P13L was followed by enriching for CD137+ activated T cells. Restimulated T cells as well as an unstimulated patient PBMC sample from the same time point were used for scRNA-Seq/scTCR-Seq.

  Dataset EGAD50000000854

• McGill EMC Release 4 for assay "ChIP-Seq Input"

  McGill EMC Release 4 for assay "ChIP-Seq Input"

  Dataset EGAD00001001293

• Reference epigenome IPS-NPC02 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007288

• 5 human plasma cell-free DNA cases (BS-seq)

  5 human plasma cell-free DNA cases (BS-seq)

  Dataset EGAD00001008809

• Pathogenic Neurofibromatosis type 1 (NF1) RNA splicing resolved by targeted RNAseq

  Neurofibromatosis type 1 (NF1) is caused by loss-of-function variants in the NF1 gene. Approximately 10% of these variants affect RNA splicing and are either missed by conventional DNA diagnostics or are misinterpreted by in silico splicing predictions. Therefore, a targeted RNAseq-based approach was designed to detect pathogenic RNA splicing and associated pathogenic DNA variants. an in-house developed tool (QURNAS) was used to calculate the enrichment score (ERS) for each splicing event. RNA enrichment of NF1 and SPRED1 was done using SPET (NUGEN - NF1 only) and using SureSelect (Agilent - NF1 and SPRED1).

  Study EGAS00001005530

• reChIP-seq reveals widespread bivalency of H3K4me3 and H3K27me3 in CD4+ memory T-Cells

  Identification of genomic loci exhibiting the physical co-localization of two distinct DNA-associated proteins remains a technical challenge. To tackle this challenge we have developed a novel reChIP-seq approach that enriches for two distinct DNA-associated proteins in an unbiased and genome-wide manner. We illustrate the utility of our approach by identifying nucleosomes bivalently modified by H3K4me3 and H3K27me3 in primary human CD4+ central memory T-cells (TCMs). We unravel widespread bivalency of many unmethylated CpG-rich regions in TCMs overlapping with transcriptional start sites of developmental regulators. Furthermore, reChIP-seq uncovers two yet unobserved classes of bivalency, where either H3K4me3 or H3K27me3 is mixed with low but detectable bivalency. We provide evidence that bivalency is established by an interplay between genome and epigenome and thus attains stability. Our described reChIP-seq approach augments conventional ChIP-seq and is broadly applicable in the study of co-localization of DNA-associated proteins to unravel the intricate possibilities of combinatorial action.

  Study EGAS00001001568

• Single cell RNA-sequencing of GSCs and GBM tumours

  Characterization of glioblastoma tumors and glioblastoma stem cell (GSCs) lines with single cell or single nuclei RNA-sequencing.

  Study EGAS00001004656

• Reference epigenome ADMSC04_smRNA-Seq data generated from KEP study

  A ADMSC04_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003870

• DNA-seq from plasma of 14 liver transplantation patients

  DNA-seq from plasma of 14 liver transplantation patients

  Dataset EGAD00001004590

• mRNA-Seq dataset

  mRNA-Seq, HiSeq 2000 dataset of the Cell-line use case

  Dataset EGAD00001002250

• ATAC-Seq datasets in human islets

  13 ATAC-Seq datasets of human pancreatic islets from 13 donors

  Dataset EGAD00001005201

• Reference epigenome ADMSC02 mRNA-Seq data generated from KEP study

  ADMSC02 mRNA-Seq paired end data

  Dataset EGAD00001007151

• Reference epigenome ADMSC05 miRNA-Seq data generated from KEP study

  ADMSC05 miRNA-Seq single end data

  Dataset EGAD00001007177