12322 results for "cancer rna-seq"

in 13.07 milliseconds.

• Human tumour ChIP-seq.

  We profiled 16 patient tumour samples by ChIP-seq. H3K27ac and Input are provided for 16 samples and H3K27me3 is provided for 14 samples. Among the 16 samples, 9 are G34WT and 7 are G34R/V. The raw fastq or bam files are provided.

  Dataset EGAD00001006100

• 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

• EM-seq data from plasma cfDNA samples of ALS patients, control and C9-carriers

  This dataset contains targeted EM-seq data from 61 plasma cfDNA samples spanning 20 sporadic ALS, 10 C9orf72-ALS, 10 asymptomatic C9orf72 expansion carriers, and 21 non-disease controls. Libraries were prepared with the NEBNext Enzymatic Methyl-seq (EM-seq) kit, captured using the Twist Human Methylome Panel (3.98 M CpGs; 123 Mb), and sequenced on Illumina NovaSeq 6000 (150 bp paired-end; ~45× on-target depth) with unmethylated lambda DNA spike-in and a 20% PhiX balance library. The dataset includes per-sample gzipped FASTQ files (R1/R2) and minimal metadata.

  Dataset EGAD50000001808

• scRNA-seq and scATAC-seq data of bone marrow

  17 scRNA-seq and 16 scATAC-seq datasets on bone marrows derived from 16 patients. The sequencing dataset consists of 5 samples without bone marrow infiltration, defined as controls (C1, C2, C3, C4, and C5) and 11 neuroblastoma infiltrated bone marrow samples from patients with MYCN amplification (M1, M2, M3, M4), ATRX mutations (A1, A2), and cases lacking either alteration (S1, S2, S3, S4, S5).

  Dataset EGAD00001008635

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

  Dac EGAC00001001427

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

  Dac EGAC00001001467

• single nuclei multiomics (ATAC-RNA) of KOLF2.1J human induced pluripotent stem cells-derived differentiated dopaminergic neurons

  We processed nuclei from hIPSCs differentiated optimized protocols for generating dopaminergic neurons. Cells differentiated with a first iteration were RNA and ATAC-sequenced, to understand our protocol caveats to address for optimization. Cells differentiated with a second protocol and in vivo were RNA-sequenced to validate the identities of our cells.

  Study EGAS50000001418

• 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

• TRanscriptomic ANalySis of left ventriCulaR gene Expression (TRANSCRibE)

  Myocardial ischemia occurs when there is a mismatch between coronary oxygen delivery and metabolic requirements of the myocardium, which may be clinically manifested during angina, coronary angioplasty or cardiopulmonary bypass (CPB). Myocardial ischemia may lead to a spectrum of myocardial stunning, hibernating myocardium, and ultimately cell death if the ischemic insult is severe. In the human heart, irreversible damage begins after approximately 20 to 40 minutes of oxygen deprivation. Observed molecular and cellular changes of myocardial ischemia are characteristic of an inflammatory response, but the exact mechanisms that underlie this pathological process are unclear and may not be full emulated by animal models of ischemia or infarction. Thus, we felt it valuable to investigate a human ischemia model. During cardiac surgery, CPB with aortic cross-clamping (AoXC) and cardioplegic arrest is associated with excellent clinical outcomes and suitable operative conditions. However, despite the use of cardioprotective strategies, AoXc during CPB is accompanied by a variable, yet obligate ischemic period lasting 1 to 3 hours, resulting in hypoxia, metabolic substrate depletion, reperfusion injury, apoptosis, and necrosis. Cardiac specific biomarkers of ischemia and infarction, including troponin, are elevated even after routine coronary artery bypass graft surgery and correlate with the duration of ischemia from AoXc.This process of CPB provides us with the ability to examine the transcriptional profile before and after an expected, consistent, and reproducible human ischemic event, albeit induced by cold cardioplegic arrest and not coronary occlusion. In addition, the absence of reperfusion in this time period allows us to examine the transcriptomic response to intermittent ischemia, without having to account for the perturbations of reperfusion injury. Although various animal models have been used to examine the effects of ischemia on cardiac function, no human data exist which examine the early transcriptomic response to a left ventricular (LV) ischemic insult. We therefore characterized the effect of cold cardioplegia induced acute ischemia on the transcriptional profile of the LV by performing whole transcriptome next-generation RNA-sequencing (RNA-seq) in patients undergoing cardiac surgery by sampling human LV tissue prior to, and after, the obligate ischemia during AoXC. We hypothesized that the cold cardioplegia induced ischemic injury will dramatically alter transcription in the human myocardium, and that we would identify genes and pathways, which will identify interventional targets for pharmacological therapy. Methods:We have collected left ventricle tissue samples and blood sample from patients undergoing heart surgery. We obtained punch biopsies (~3-5μg total RNA content) from the site of a routinely placed surgical vent in the anterolateral apical left ventricular wall of patients undergoing elective aortic valve replacement surgery with cardiopulmonary bypass. After an average of 79 minutes of aortic cross-clamping with intermittent cold blood cardioplegia for myocardial protection every 20 minutes, a second biopsy was obtained in the same manner. Tissue samples were immediately placed in RNAlater® (Ambion, ThermoFisher Scientific, Waltham, MA), and after 48 hours at +4°C were stored at -80°C until RNA extraction. Total RNA was isolated with Trizol and RNA quality was assessed using the Agilent Bioanalyzer 2100 (Agilent, Santa Clara, CA). Libraries were prepared by poly(A) mRNA isolation and reverse transcription Polymerase Chain Reaction (RT-PCR), then sequenced on the Illumina HiSeq2000 or HiSeq2500 (Illumina, San Diego, CA). As samples were analyzed at different times, different read lengths were employed, initially using single-end reads (n=20) and then transitioning to paired end reads (n=216), ranging from 36 - 100 base pairs. Raw sequencing files were processed using Sickle, Skewer, and STAR software, and aligned to GrCh37 or UCSC Hg19. DNA was isolated from whole blood using standard methods. SNP genotyping was performed using the Illumina Omni2.5Exome-8 BeadChip array with additional exome content (Illumina, San Diego, CA) chip, version 1.1. We first phased and imputed 93 subjects using a phasing tool called SHAPEIT and an imputation tool called MINIMAC, with 1000 Genomes phase 1 version 3 for the reference panel. We then phased and imputed 26 more subjects using SHAPEIT, an imputation tool called IMPUTE2, and 1000 Genomes phase 3 version 5.

  Study phs001679

• mRNA-Seq dataset

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

  Dataset EGAD00001002250