11979 results for "cancer rna-seq"

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• Reference epigenome Islet-derived-iPSC01 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007439

• Reference epigenome Islet-derived-iPSC02 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007444

• Reference epigenome Islet-derived-MSC01 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007403

• Reference epigenome Islet-derived-MSC01 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007402

• Reference epigenome Islet-derived-MSC01 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007405

• Reference epigenome Islet-derived-MSC01 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007406

• Reference epigenome Islet-derived-iPSC02 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007448

• Reference epigenome Islet-derived-MSC01 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007407

• Reference epigenome Islet-derived-MSC01 input ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 input ChIP-Seq paired end data

  Dataset EGAD00001007408

• Reference epigenome Islet-derived-MSC02 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007409

• Reference epigenome Islet-derived-MSC02 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007410

• Reference epigenome Islet-derived-MSC02 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007411

• Reference epigenome Islet-derived-MSC02 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007413

• Reference epigenome Islet-derived-MSC03 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007417

• Reference epigenome Islet-derived-MSC02 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007414

• Reference epigenome Islet-derived-MSC03 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007425

• Reference epigenome Islet-derived-MSC03 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007426

• Reference epigenome Islet-derived-MSC03 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007427

• Reference epigenome Islet-derived-iPSC01 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007438

• Reference epigenome Islet-derived-MSC04 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007429

• Reference epigenome Islet-derived-MSC04 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-MSC04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007432

• Reference epigenome Islet-derived-MSC03 input ChIP-Seq data generated from KEP study

  Islet-derived-MSC03 input ChIP-Seq paired end data

  Dataset EGAD00001007428

• Reference epigenome Islet-derived-iPSC01 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007436

• Reference epigenome Islet-derived-iPSC01 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007437

• Reference epigenome Islet-derived-iPSC01 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007440

• Reference epigenome Islet-derived-iPSC01 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007441

• Reference epigenome Islet-derived-iPSC01 input ChIP-Seq data generated from KEP study

  Islet-derived-iPSC01 input ChIP-Seq paired end data

  Dataset EGAD00001007442

• Reference epigenome Islet-derived-iPSC02 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007443

• ALI culture bronchial cells and alveolar lung surgical resection scRNA-Seq

  ALI culture bronchial cells and alveolar lung surgical resection scRNA-Seq

  Dataset EGAD00001006185

• Reference epigenome Islet-derived-MSC01 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007404

• Reference epigenome Islet-derived-MSC02 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007412

• Reference epigenome Islet-derived-iPSC02 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007445

• Reference epigenome Islet-derived-iPSC02 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-iPSC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007446

• 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

• 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

• Single Cell ATAC-Seq of MELAS

  This dataset includes combined single Assay for Transposase-Accessible Chromatin, using sequencing (ATAC-seq), and mitochondrial DNA sequencing of peripheral blood mononuclear cells from three individuals with m.3243A>G associated MELAS (Mitochondrial Encephalopathy, Lactic Acidosis, and Stroke-like episodes).

  Study phs002217

• scRNA-seq of Patient-derived tumor fragments (PDTFs)

  scRNA-seq of Patient-derived tumor fragments (PDTFs) which were treated with either Isotype (DP47), Isotype-IL2v (DP47-IL2v), anti-PD1 (PembroPGLALA) or PD1-IL2v (PembroPGLALA-IL2v) for 48 hours. Dataset content: 10 samples from 3 donors File type: paired-end fastq files Protocol: Chromium Single Cell 3' Reagent Kits User Guide (Dual Index) with Feature Barcoding technology for Cell Surface Protein (CG000317) Technology: Illumina sequencing Experimentation: scRNA-seq using the 10X technology

  Dataset EGAD50000000584

• Chromatin segmentation of myometrium and UL subclasses

  Histone modification ChIP-seq for chromatin state annotations of myometrium and UL subclasses. Altogether 5 analyses and 210 runs. Includes fastq files; both ChIP and Input for each sample. Some Inputs are used for multiple ChIP experiments. ChIP-seq alignment files for trimmed, mapping q20 and nonredundant reads; both ChIP and Input for each sample. Software: Trim Galore v0.6.7; Bowtie 2 v2.5.0; samtools v1.6

  Dataset EGAD50000001443

• COVID-19 Postmortem Lung snRNA-seq

  A deeper understanding of the pathological mechanisms of SARS-CoV-2 infection is required to combat COVID-19. Through this dataset, we analyze postmortem lung cells from patients that are infected/uninfected with SARS-CoV-2 with snRNA-seq.

  Study EGAS00001004689

• 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

• Human Embryonic Multi-tissue ChIP-seq (Manchester)

  Histone ChIP-seq of 13 human embryonic tissues from weeks 6-8 of gestation. H3K4me3, H3K27me3 and H3K27ac. Biological replicates (n=2) for 11 tissues. Tissues (n): Brain (2); Retinal Pigmented Epithelium (eye)(2); Palate(2); Tongue (1); Left ventricle (heart)(2); Lung(2); Liver(2); Pancreas (2*); Stomach(1); Upper limb (2); Lower limb (2); Adrenal gland (2); Kidney (2)

  Dataset EGAD00001004335

• DNA methylation (MCIP-seq) of human acute leukemias and healthy donors

  MCIP-seq of 77 primary samples obtained from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). Moreover, MCIP-seq of CD34+ HSPCs from 3 healthy donors is included.

  Dataset EGAD00001011052

• Single Cell Dissection of the Tumour Microenvironment Reveals Dynamic Interplay Shaping the Tumour Immunity Continuum in Ovarian Cancer

  Diverse tumour immune phenotypes with distinct T cell infiltration patterns result in different responses to cancer immunotherapies. However, the key determinants and biology underpinning these phenotypes remain elusive. Here, we provide a high-resolution dissection of the tumour microenvironment representative of different tumour immune phenotypes. By performing single-cell RNA sequencing (scRNAseq) of the tumour, immune, and stromal compartments from 15 ovarian cancer patients, we characterized the diverse cellular and functional phenotypes, as well as dynamic interplay within and between these components. Infiltrated and excluded tumours markedly differed from desert tumours in several tumour cell intrinsic features, including high interferon response, antigen presentation and oxidative phosphorylation. Moreover, these tumours were characterized by tumour associated macrophage (TAM)-like myeloid subsets, while desert tumours were enriched in myeloid derived suppressive cell (MDSC)-like myeloid subsets. Infiltrated and excluded tumours differed in their T cell composition and fibroblast subsets. While dysfunctional CD8+ GZMB T cells and IL1-activated fibroblasts (IL1 CAFs) were associated with infiltrated tumours, pre-dysfunctional CD8+ GZMK T cells and TGFB-activated fibroblasts (TGFB CAFs) were enriched in excluded tumours. These findings validated in bulk transcriptomic profiles of 1071 ovarian tumours and using in situ hybridization. Furthermore, our study revealed chemokine receptor-ligand interactions within and across compartments as potential mechanisms mediating immune cell infiltration, exemplified by the tumour cell-T cell crosstalk via a CXC16-CXCR6 axis, stromal-immune cell crosstalk via CXCL12/14-CXCR4 signalling, and TAM-like macrophage-T cell crosstalk through CXCR3-CXCL9/10/11 signalling. Our in-depth characterization of the tumour environment and intercellular interactions provides novel insights into potential molecular mechanisms that shape the distinct biology of tumour immune phenotypes. Our findings may inform novel therapeutic strategies for potentially shifting tumours along the immunity continuum to improve clinical benefit from cancer immunotherapies.

  Study EGAS00001004935

• RNA sequencing reads

  Paired-end, ribosome depleted, total RNA Sequencing

  Dataset EGAD00001004327

• An autoinflammatory RIG-I variant causing Singleton-Merten Syndrome associates with small non-coding Y-RNAs

  The RNA sensor RIG-I performs a critical role in surveying the cytoplasm for the presence of viral nucleic acids and initiating the downstream anti-viral type I interferon pathway. Through recognition of specific features, such as the presence of a 5’ di/triphosphate motif or highly structured base-paired regions, RIG-I effectively differentiates between RNA of viral- and self-origin. In Singleton-Merten syndrome (SMS), gain-of-function variants in RIG-I lead to a breakdown in this surveillance system and results in aberrant sensing of self-RNAs and deleterious upregulation of type I interferons. The identity of the self-RNAs binding to gain-of-function RIG-I mutants has remained elusive and would provide a greater understanding of the aetiology of SMS. Here we used an infrared individual-nucleotide resolution UV-crosslinking and immunoprecipitation (irCLIP) approach to determine the RNA profile bound to a previously characterised ATPase-deficient SMS variant, RIG-IC268F. irCLIP identified a broad array of self-RNAs, primarily those transcribed by RNA polymerase III, that were bound to RIG-I. Subsequent native RNA immunoprecipitation confirmed a prominent and specific interaction between RIG-IC268F and Y-RNAs, a family of four structurally similar non-coding RNAs. Manipulation of Y-RNAs alone by targeting either Y-RNA transcripts or Y-RNA stabilising proteins was insufficient to negate RIG-I induce interferon, hinting at a broader profile of RNA polymerase III-derived RNAs being influential in driving the sterile activation of gain-of-function RIG-I variants.

  Study EGAS50000001660

• The Environmental Determinants of Diabetes in the Young Study (TEDDY)

  Study Overview The Environmental Determinants of Diabetes in the Young (TEDDY) Study is a longitudinal study that investigates genetic and genetic-environmental interactions, including gestational events, childhood infections, dietary exposures, and other environmental factors after birth, in relation to the development of islet autoimmunity and type 1 diabetes (T1D). A consortium of six clinical centers assembled to participate in the development and implementation of the study to identify environmental triggers for the development of islet autoimmunity and T1D in genetically susceptible individuals. Beginning in 2004, the TEDDY study screened over 400,000 newborns for high-risk HLA-DR, DQ genotypes from both the general population and families already affected by T1D. The TEDDY study enrolled around 8,676 participants across six clinical centers worldwide (Finland, Germany, Sweden and three in the United States) in the 15-year prospective follow-up. Participants are followed every three months for islet autoantibody (IA) measurements with blood sampling until four years of age and then at least every six months until the age of 15. After the age of four, autoantibody positive participants continue to be followed at three month intervals and autoantibody negative participants are followed at six-month intervals. In addition to the analysis of autoantibodies, additional data and sample collection are performed at each visit. Parents collect monthly stool samples in early childhood. The parents also fill out questionnaires at regular intervals in connection with study visits and record information about diet and health status in the child's TEDDY Book between visits. Continued long-term follow-up of the currently active TEDDY participants will provide important scientific information on early childhood diet, reported and measured infections, vaccinations, and psychosocial stressors that may contribute to the development of type 1 diabetes and islet autoimmunity. Additional information on the TEDDY study is available in the following articles: Rewers et al., 2008, PMID: 19120261 and Hagopian et al., 2006, PMID: 17130573. Details of the TEDDY protocol can be found in Hagopian et al., 2011, PMID: 21564455. TEDDY data currently available in dbGaP include: gene expression, SNPs, exome, microbiome (gut, nasal, and plasma), RNA sequencing, and whole genome sequencing. For more information on TEDDY Study version history please refer to TEDDY Study dbGaP README File. ImmunoChip SNP DNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for SNP genotyping. Genotyping was performed by the Center for Public Health Genomics at the University of Virginia using the Illumina ImmunoChip SNP array, which contains around 196,000 SNPs from 186 regions associated with 12 autoimmune diseases (Hadley et al., 2015, PMID: 26010309). Data cleaning and validation included the removal of subjects with a low call rate (< 5% SNPs missing) and differences in reported sex and prior genotyping at the TEDDY HLA laboratory. Additionally, SNPs with a low call rate or Hardy-Weinberg equilibrium P value < 10-6, except for chromosome 6 due to HLA eligibility requirements, were removed from the final dataset (Törn et al., 2015, PMID: 25422107).TEDDY-T1DExome ArrayDNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for genotyping. Genotyping was performed by the University of Virginia using the Illumina TEDDY-T1DExome array. The TEDDY-T1DExome array is a custom chip that contains 550,601 markers from the Infinium CoreExome-24 v1.1 BeadChip and an additional 90,214 tagSNPs specifically selected by the TEDDY investigators based on their associations with nutrients, vitamins, type 2 diabetes, autoimmune diseases, body-mass index, or other exposures and phenotypes measured by TEDDY study.The Illumina GenTrain2 algorithm was used for genotype calling. Sample quality control metrics included sample call rate, heterozygosity rate and concordance of gender between the information reported and genotyped. Gene Expression The TEDDY study collected peripheral blood for the extraction of total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA (200 ng) was further used for cRNA amplification and labeling with biotin using Target Amp cDNA synthesis kit (Epicenter catalog no. TAB1R6924). Labeled cRNA was hybridized to the Illumina HumanHT-12 Expression BeadChips based on the manufacturer's instructions. The HumanHT-12 Expression BeadChip provides coverage for more than 47,000 transcripts and known splice variants across the human transcriptome. Microbiome The TEDDY microbiome study aimed to characterize the longitudinal development of the microbiome, including bacteria, viruses and other microorganisms in the gut, plasma, and nasal cavity of prediabetic and diabetic subjects compared to autoantibody negative non-diabetic subjects. Stool samples used were collected monthly from 3 to 48 months, after which stool samples were collected every 3 months. Nasal swab samples were collected every 3 months starting at 9 months of age until 48 months, after which nasal swabs were collected every 6 months. Plasma samples were collected every 3 months starting at 3 months of age until 48 months, after which plasma samples were collected every 6 months. If the subject was autoantibody positive at 48 months then they remained on the 3 month collection interval for nasal swab and plasma samples. Samples underwent 16s rRNA gene sequencing, DNA and viral RNA metagenomics shotgun sequencing, and sequencing of the internal transcribed spacer (ITS) regions. Additional information on the TEDDY microbiome data is available in the following articles: Vatanen et al., 2018, PMID: 30356183, Stewart et al., 2018, PMID: 30356187, and Vehik et al., 2020, PMID: 31792456. RNA Sequencing The TEDDY study aimed to characterize the transcriptome in subjects with islet autoimmunity and type 1 diabetes compared to matched control subjects. Peripheral blood was collected to extract total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA was then sent to the Broad Institute for the generation of the TEDDY RNA sequencing (RNA-Seq) data. The RNA samples were prepped using Superscript III reverse transcriptase and Illumina's TruSeq Stranded mRNA Sample Prep Kit. The TruSeq libraries were run on the Illumina HiSeq2500 platform. Whole Genome Sequencing The TEDDY study aimed to conduct deep whole genome sequencing and examine the genomic variations in subjects with islet autoimmunity and type 1 diabetes compared to matched autoantibody negative and non-diabetic children. DNA from whole blood was obtained from TEDDY children for whole genome sequencing. The WGS data were generated on the Illumina HiSeq X Ten system.

  Study phs001442

• Sample Multiplexing Oligo Comparison

  In this study, we compared various multiplexing reagents, including MULTI-Seq, Hashtag antibody, and CellPlex, in patient-derived xenografts (PDXs). We found that all multiplexing reagents worked well in cell types robust to ex vivo manipulation but suffered from signal-to-noise issues in more delicate sample types. We also compared the performance of fixed scRNA-Seq kits and CRISPR-based destruction of non-informative genes.

  Study EGAS50000000153

• Single Cell RNA Sequencing of Human Hematopoiesis

  This study includes single cell RNA sequencing of human CD34+ hematopoietic stem and progenitor cells throughout human life spanning midgestation into mature adulthood.

  Study phs002750

• Pilocytic Astrocytoma RNA sequencing

  This dataset contains RNA sequencing samples from the Duesseldorf/LIsbon pilocytic astrocytoma cohort which was profiled using Proteogenomics (RNA Sequencing, proteomics and methylation). There are 48 samples which were sequenced using paired-end sequencing at the University Hopstial Dusseldorf.

  Dataset EGAD00001009053