12214 results for "cancer rna-seq"

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• Wilm's tumor sequencing data

  This study shares DNA and RNA sequencing data from Wilm's tumor patients

  Study EGAS00001005690

• Alveolar Rhabdomyosarcoma sequencing data

  This study shares DNA and RNA sequencing data from alveolar rhadbomyosarcoma patients

  Study EGAS00001005387

• Embryonal Rhabdomyosarcoma sequencing data

  This study shares DNA and RNA sequencing data from embryonal rhadbomyosarcoma patients

  Study EGAS00001005502

• Epithelioid sarcoma sequencing data

  This study shares DNA and RNA sequencing data from Epithelioid sarcoma patients

  Study EGAS00001005983

• Rhabdoid tumor sequencing data

  This study contains sequencing DNA and RNA sequencing data for rhabdoid tumors

  Study EGAS00001006351

• Dataset for "Genomic landscape of oral cancers" (Illumina RNA)

  Dataset for "Genomic landscape of oral cancers" (Illumina RNA)

  Dataset EGAD00001004366

• HipSci - Monogenic Diabetes - RNA Sequencing - April 2015

  HipSci - Monogenic Diabetes - RNA Sequencing - April 2015

  Dataset EGAD00001001953

• HipSci - Monogenic Diabetes - RNA Sequencing - January 2016

  HipSci - Monogenic Diabetes - RNA Sequencing - January 2016

  Dataset EGAD00001001955

• RNA sequencing of uveal melanoma metastases.

  RNA sequencing of uveal melanoma metastases.

  Dataset EGAD00001007912

• The genomic landscape of acute lymphoblastic leukemia with intrachromosomal amplification of chromosome 21

  Use scRNA-seq data from 4 iAMP21 patients to study the clonal evolution of iAMP21.

  Study EGAS00001006577

• CTCF scD&D-seq of IDH2 CHIP

  CTCF single-cell D&D-seq with GoT-ChA targeted genotyping of IDH2 R140Q CHIP variant from primary blood sample

  Dataset EGAD50000002273

• Single-cell CITE-seq from MDS patients with SF3B1 mutations

  Single-cell CITE(cellular indexing of transcriptomes and epitopes)-seq from MDS (n = 2, MDS02 in 2 replicates). cDNA from 10x Genomics 3' V3.

  Dataset EGAD00001011281

• Characterization of a human iPSC-derived islet differentiation model

  To explore the contribution of islet development to T2D pathogenesis, we characterised the transcriptomes of 3 human iPSC lines (from independent donors) differentiated along the islet development lineage. Whole transcriptome RNA-seq was performed at 7 developmental stages: definitive endoderm, primitive gut tube, posterior foregut (PF), pancreatic endoderm, endocrine progenitors, endocrine-like cells, and beta-like cells (BLC). Differentially-expressed (ΔExp) genes were assigned to the stage in which they were most upregulated (versus baseline iPSC profile), and used to assess the enrichment of T2D GWAS genes, as well as to predict the upstream transcription factors at each developmental stage.We found 9409 ΔExp genes across all stages, including known markers of islet development (NEUROG3, INS). Genes ΔExp in the most-differentiated (BLC) stage were significantly enriched for genes within the 99% credible sets of T2D GWAS loci. Despite this enrichment in BLC only, over 70% of genes mapping within the credible sets were ΔExp before this stage, highlighting the relevance of key T2D GWAS genes in islet development: e.g. expression of TCF7L2 peaked during the PF stage (log2FC=1.2; q=8.5×10-10). REST and LMNA are also highlighted by the analyses as having a potential function in islet development, considering their pattern of expression and those of the genes they regulate. Transcriptomic signatures derived from this iPSC differentiation model highlight T2D-associated GWAS loci, monogenic diabetes genes and potential master regulators of gene networks with plausible function in islet development. All these provide clues for developmental mechanisms contributing to T2D diabetes pathology.

  Study EGAS00001002721

• A unidirectional histone code in bidirectional promoters across cell types

  Recent studies indicate that thousands of genes are expressed from bidirectional promoters (BDPs). Gene regulation at BDPs is poorly understood, in particular how the cell is able to regulate them differently. Here we investigate the effect of histone Modifications in BDP regulation. In this study, we model the gene activity using different gene expression assays, such as RNA-Seq, GRO-cap, and CAGE around the BDP transcription start sites (TSSs) using different histone modifications in various cell types. We develop a new statistical approach that links histone modifications to gene expression at BDPs. It improves over previous methods, because it is able to capture spatial dependencies of histone modifications along a promoter and leads to more interpretable results. We predict a general histone code that is independent of transcript orientation, cell type, and promoter configuration. The histone code at BDPs reveals that promoters are regulated unidirectionally, such that the majority of histone marks associated with gene expression occur downstream of the gene's TSS. By contrasting associations of histone marks with steady-state levels of capped RNAs in CAGE and nascent capped RNAs in GRO-cap, we show which histone marks have a preference for initiating polymerases and actively elongating polymerases. Using single-cell data we show that the bidirectional histone signal of activating marks is often due averaging of a heterogeneous cell population. Our results have implications for other experiments where the relationship between histone modification and gene initiation or gene elongation is investigated on a genome-wide scale.

  Study EGAS00001001656

• Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

  Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

  Dataset EGAD50000000226

• RNA sequencing of colon tumor/normal sample pairs

  RNA sequencing of colon tumor/normal sample pairs

  Dataset EGAD00001000215

• HipSci - Primary Immune Deficiency - RNA Sequencing - July 2017

  HipSci - Primary Immune Deficiency - RNA Sequencing - July 2017

  Dataset EGAD00001003536

• HipSci - Hereditary Spastic Paraplegia - RNA Sequencing - July 2017

  HipSci - Hereditary Spastic Paraplegia - RNA Sequencing - July 2017

  Dataset EGAD00001003537

• HipSci - Hereditary Cerebellar Ataxias - RNA Sequencing - July 2017

  HipSci - Hereditary Cerebellar Ataxias - RNA Sequencing - July 2017

  Dataset EGAD00001003538

• HipSci - Bardet-Biedl Syndrome - RNA Sequencing - April 2015

  HipSci - Bardet-Biedl Syndrome - RNA Sequencing - April 2015

  Dataset EGAD00001001952

• HipSci - Bardet-Biedl Syndrome - RNA Sequencing - January 2016

  HipSci - Bardet-Biedl Syndrome - RNA Sequencing - January 2016

  Dataset EGAD00001001954

• SATB1 KO Treg and Teff cells: ATAC-seq

  To study the global effects of SATB1 on gene networks in fully differentiated, ex vivo expanded human CD4 T effector cells and Treg cells, we performed ATAC-seq. We systematically assessed the impact of SATB1 on the chromatin levels in mature human Treg and CD4 T effector cells.

  Study EGAS50000000876

• BLUEPRINT September 2016, ChIP-Seq for conventional dendritic cell from cord blood, on Genome GRCh38

  ChIP-Seq data for 1 sample(s) for conventional dendritic cell from cord blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001002948

• BLUEPRINT September 2016, ChIP-Seq T-cell Acute Lymphocytic Leukemia from capillary blood, on Genome GRCh38

  ChIP-Seq data for 4 sample(s) T-cell Acute Lymphocytic Leukemia from capillary blood, on Genome GRCh38. 7 run(s), 7 experiment(s), 7 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001002952

• BLUEPRINT September 2016, ChIP-Seq for memory B cell from venous blood, on Genome GRCh38

  ChIP-Seq data for 1 sample(s) for memory B cell from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001002950

• BLUEPRINT September 2016, ChIP-Seq Acute Lymphocytic Leukemia for precursor B cell from venous blood, on Genome GRCh38

  ChIP-Seq data for 1 sample(s) Acute Lymphocytic Leukemia for precursor B cell from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001002949

• H3K27ac ChIP-seq in primary prostate tumours

  H3K27ac ChIP-seq and input genome sequencing was performed in 19 primary prostate tumours classified as intermediate risk. Sequencing of ChIP DNA was performed on an Illumina HiSeq 2000 as either single end 50 bp reads (for 7 samples) or paired end 100 bp reads (for 12 samples). Input DNA from all samples was sequenced using single-end 50 bp reads. The files provided are in fastq format.

  Dataset EGAD00001003461

• cell-free Nucleosome ChIP-seq

  Dataset includes cell-free ChIP-seq data of 268 samples (from 61 self-declared healthy donors, four patients with acute myocardial infarction, 29 patients suffering from autoimmune, metabolic, or viral liver diseases and 56 metastatic colorectal carcinoma (CRC) patients). DNA libraries preparation is documented in the methods section. Libraries were paired end sequenced by Illumina NextSeq 500 and aligned to the human genome (hg19) using bowtie2 (2.3.4.3) with ‘no-mixed’ and ‘no-discordant’ flags. This dataset includes fastq and BAM files of all samples.

  Dataset EGAD00001006811

• scRNA-seq dataset of patient with immune dysregulation

  scRNA-seq dataset on a patient (P_IKZF2-het) presenting with immune dysregulation. This dataset contains raw and processed files from scRNA-seq performed on samples using the 10x Genomics Chromium Controller with the Chromium Single Cell 3′ Reagent Kit (v3 chemistry). There are three paired-end (75 bp) fastq files (I1, R1, R2) and three processed files generated with 10x Genomics Cell Ranger v3.0.2 software against GRCh38 human reference transcriptome (scrnaseq_P_IKZF2-het_barcodes.tsv.gz, scrnaseq_P_IKZF2-het_features.tsv.gz, scrnaseq_P_IKZF2-het_matrix.mtx.gz).

  Dataset EGAD00001008443

• TARGET-seq+ single-cell genotyping

  Single-cell genotyping data for bone marrow samples from 9 cases with clonal hematopoiesis and 1 control sample. The TARGET-seq+ protocol was used to generate plate-based 3' transcriptome data. For details on cell sorting and the TARGET-seq+ protocol see the methods section of the manuscript. One FASTQ file is provided per cell. Cells are named with their plate and well IDs and the subject ID. Empty wells (no-cell controls) are named "blank". Corresponding transcriptome files use the same naming with the "_transcriptome" suffix.

  Dataset EGAD00001011150

• 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

• Human dorsal root ganglia after plexus injury - RNA-fragment sequencing from histological slices

  Human dorsal root ganglia from patients with brachial plexus injury and from subjects from forensic autopsy were extracted and fixed with paraformaldehyde. The cellular composition of these DRG was analysed in histopathological slices. From some of the slices, total RNA was extracted and total RNAseq was performed. The RNA sequencing data represent DRG with neuronal preservation (n=6) or neuronal loss (n=2) versus control (n=6). Details can be found in the corresponding publication and supplement to this publication.

  Study EGAS50000000682

• Title: Divergent levels of CD112 and INKA1 define a distinct subset of human long-term hematopoietic stem cells

  The data provided here was critical in establishing that human long-term hematopoietic stem cells (LT-HSC), previously described as the most primitive HSC population, is actually composed of distinct subsets that can be prospectively isolated. Via mechanistic studies centering around the Rho-GTPase effector kinase PAK4 and its inhibitor INKA1, we identified the immune checkpoint ligand CD112 as a marker for hematopoietic stem and progenitor cells, that is highest expressed on LT-HSC. More importantly, CD112 can be used to stratify functionally distinct subsets within LT-HSC: In response to regeneration-mediated stress, the CD112low subset exhibits a transient restraint (termed latency) before contributing to hematopoietic reconstitution, while the CD112high subset is primed to respond rapidly. High resolution RNA-seq of the CD112 surface expression spectrum within rare LT-HSC subsets (human umbilical cord blood) demonstrated that more genes are differentially upregulated in the deeper quiescent and less metabolic active subset. Genes enriched in this subset centre around cell adhesion and Rho-GTPase signaling. This is in agreement with the scRNAseq data from human G-CSF mobilized peripheral blood (mPB) generated here that was used as an model of in vivo activation/priming revealing via RNA-velocity and pseudo-time analysis that INKA1high versus PAK4high, CDK6high and CD112high enrichment are either detected early or late in diffusion pseudotime indicative of quiescent versus primed cell status, respectively. RNAseq following INKA1 overexpression in LT-HSC and ST-HSC revealed by GSEA an overall stemness preserving phenotype and particularly in LT-HSC, but not in short-term HSC (ST-HSC), suppression of transcriptional programs linked to activation. Collectively, our data decipher the molecular intricacies underlying HSC heterogeneity and self-renewal regulation and point to latency as an orchestrated physiological response that integrates quiescence control with HSC fate choices to preserve a stem cell reservoir.

  Dataset EGAD00001006541

• Ewing's sarcoma sequencing data

  This study shares DNA and RNA sequencing data for patients with Ewing's sarcoma

  Study EGAS00001005689

• BCG-Flu_Challenge_Study_RNAseq_Human_01

  RNA sequencing data generated from human samples collected in the BCG-Flu Challenge study.

  Study EGAS50000001677

• Single cell sequences in patients with malignant tumors

  We perfomed single cell RNA and TCR sequencing of 2 lung adenocarcinoma patients and 3 head and neck squamous cell carcinoma to assess the detailed biological mechanisms of resistance to immune checkpoint inhibitors.

  Study JGAS000480

• RNA sequencing of high hyperdiploid and ETV6/RUNX1-positive pediatric acute lymphoblastic leukemia

  We performed RNA-sequencing of high hyperdiploid and ETV6/RUNX1-positive ALL cases by using the Human Ribo-Zero rRNA Removal Kit to investigate the gene expression in high hyperdiploid ALL.

  Study EGAS00001003079

• RNA-sequencing of platelets and immortalized megakaryocyte cell lines for inherited thrombocytopenia

  Total RNA-sequencing of platelets and immortalized megakaryocyte cell lines for inherited thrombocytopenia. Libraries were prepared using Illumina TruSeq Stranded Total RNA with Ribo-Zero Gold kits. The samples were paired-end sequenced using the NovaSeq 6000 platform at a depth of 60 million reads per sample with a read length of 100 base pairs.

  Dataset EGAD50000001818

• RNA-sequencing of a normal CD34+ cells

  Sample libraries were prepped using 500 ng of input RNA according to the KAPA RNA HyperPrep Kit with RiboErase (HMR) (Roche) using Unique Dual Index adapters (Integrated DNA Technologies, Inc.). Amplified sample libraries were paired-end sequenced (2x100 bp) on the Novaseq 6000 platform (Illumina) and aligned against the human genome (hg19) using STAR v2.5.4b2.

  Dataset EGAD00001007646

• Chromatin accessibility profiling of primary human hepatocytes

  The chromatin accessibility profiling of primary human hepatocytes using Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq).

  Study EGAS50000001230

• A distinct monocyte cellular state links systemic immune dysregulation to pulmonary impairment in long COVID

  single cell multiome dataset (snRNA-seq + snATAC-seq) from PBMC of patients with long COVID disease.

  Dataset EGAD50000001729

• scATAC-seq of sorted CD45+ cells from blood and tissues

  scATAC-seq by 10xGenomics Droplet Sequencing from two human donors. scATAC sequences are available for FACS-sorted CD45+ cells from blodd, skin and vat (visceral adipose tissue)

  Dataset EGAD50000000510

• ChIP-Seq data for the paper titled "Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors"

  ChIP-Seq data for the paper titled "Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors"

  Dataset EGAD00001003432

• 4C-seq data

  4C-seq data was generated for regions of interest to confirm enhancer-gene promoter interactions

  Dataset EGAD00001001847

• UCSF brain tumor data

  Single Nuclei ATAC seq data from GBM tumor samples. NovaSeq6000 was used for ATAC seq. The files uploaded are bam files created with grch38 reference.

  Dataset EGAD00001005314

• ATAC-seq

  ATAC-seq data for 26 CLL samples (7 controls, 19 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005967

• Targeted sequencing of genes recurrently mutated in AML

  Background Massively parallel sequencing technology has transformed cancer genomics. It is now feasible, in a clinically relevant time-frame, for a clinically manageable cost, to screen DNA from patient tumours for mutations essentially genome-wide. The challenge for personalised medicine will be to increase the sample size to thousands or tens of thousands of well-characterised cases in order to attain sufficient statistical power to stratify patients accurately across the complexity and genomic heterogeneity expected for most of the common tumour types. Currently, whole genome sequencing on this scale is not feasible, and targeted sequencing of relevant portions of the genome will be required. Pilot data We have developed protocols for large-scale, multiplexed sequencing of 100-200 genes in thousands of samples. Essentially, using robotic technology, genomic DNA from the cancer specimen is processed into sequencing libraries with unique DNA barcodes, thereby allowing sequencing reads to be attributed to the sample they derive from. Currently, these sequencing libraries can be generated in a 96-well format using fully automated protocols, and we are exploring methods to expand this to a 384-well format. The sequencing libraries are pooled and hybridized to custom sets of RNA baits representing the genomic regions of interest. Sequencing of the pulled-down libraries is done in pools of 48-96 samples per lane of an Illumina Hi-Seq. This protocol is already implemented at the Sanger Institute. We have published proof that somatic mutations in novel cancer genes can be identified from exome-wide sequencing. In unpublished pilot data, we have established the feasibility of robotic library production, custom pull-down, and multiplexed sequencing of barcoded libraries for 100 known myeloid cancer genes across 760 myelodysplasia samples. Highlights of the data thus far analysed reveal that the coverage is remarkably even between samples; when 96 samples are run, average coverage per lane of sequencing is ~250, with 90-95% of targeted exons covered by >25 reads; known mutations can be discovered in the data set; and the protocol is amenable to whole genome amplified DNA. The bioinformatic algorithms for identification of substitutions and indels in pull-down data are well-established; we have pilot data proving that copy number changes, LOH and genomic rearrangements in specific regions of interest can also be identified by tiling of baits across the relevant loci. Proposal We propose to apply this methodology to 10000 samples from patients with AML enrolled in clinical trials over the last 10-20 years. Oncogenic point mutations and potentially genomic rearrangements will be identified, and linked to clinical outcome data, with a view to undertaking the following sorts of analyses: ? Identification of co-occurrence, mutual exclusivity and clusters of driver mutations. ? Correlation of prognosis with driver mutations and potentially gene-gene interactions ? Exploration of genomic markers of drug response Ultimately, we would like to be in a position to release the mutation data together with matched clinical outcome data to genuine medical researchers via a controlled access approach, possibly within the COSMIC framework (www.sanger.ac.uk/genetics/CGP/cosmic/). The vision here is to generate a portal whereby a clinician faced with an AML patient and his / her mutational profile can obtain a ?personalised? prediction of outcome, together with a fair assessment of the uncertainty of the estimate. With a sufficient sample size, there would also be the potential to develop decision support algorithms for therapeutic choices based on such data.

  Dataset EGAD00001000606

• Gene Expression Signatures in CATHGEN

  Case control cohort: The details have been previously published: American Heart Journal. 2010;160:371-379 e372. Briefly, a nested case:control cohort of CATHGEN participants who had experienced death or MI (n = 250) after their index catheterization and age-, sex-, and race-matched controls (n = 250) who were free of death/MI > 2 years after cardiac catheterization was identified as part of the MURDOCK Horizon 1 Cardiovascular Disease Study.21 Sufficient RNA for microarray analysis was available in 447 members of this cohort. Observational cohort: 224 sequential CATHGEN samples were selected for whole blood RNA analysis, of which, 191 had sufficient RNA for microarray analysis.

  Study phs000551

• Acute myeloid leukemia sequencing data

  This study contains tumor DNA and RNA sequencing data for 1 AML patient

  Study EGAS00001006354

• Small RNA and MicroRNA sequencing data

  Small RNA and MicroRNA sequencing data was generated for the multiomics analysis of Eurotarget

  Dataset EGAD50000002294