11932 results for "cancer rna-seq"

in 18.05 milliseconds.

• Deciphering Intratumoral Molecular Heterogeneity in Clear Cell Renal Cell Carcinoma with a Radiogenomics Platform

  We used 200 ccRCC samples from 51 tumors to simultaneously isolate DNA, RNA, and protein according to established protocol. RNA quality was assessed using an Agilent Bioanalyzer, and total RNA with RIN>7 was used for further RNA sequencing. 184 ccRCC samples from 49 tumors passing initial quality control underwent RNA sequencing at Admera Health Inc. (Genohub Inc., Austin, TX). RNA sequencing libraries were prepared using the Illumina TruSeq Stranded mRNA high throughput (HT) sample preparation kit following the manufacturers’ protocol. Pair-end RNA Seq data was deposited in this cohort.

  Dataset EGAD00001005356

• RNA-seq data from diagnosis and relapse samples of Down Syndrome acute lymphoblastic leukemia.

  Dataset EGAD00001003280

• sc-RNA-Seq soft tissue sarcoma tumoroid biobank

  Dataset EGAD00001009002

• UTSW KCP DAC

  This DAC manages access to WES and RNA-Seq data from studies approved under the IRBs of KCP principal investigators.

  Dac EGAC50000000203

• IMpower133 subtype assignments

  This dataset contains subtype assignments for 271 tumor samples profiled by RNA-seq.

  Dataset EGAD00001006926

• UROMOL 2020 - RNA-seq data for validation

  Total RNAseq data from 47 patients with NMIBC.

  Study EGAS00001005050

• RNA-seq FASTQ files from newborn screening dried blood spot samples

  Paired-end RNA-seq FASTQ files from 21 newborn screening dried blood spot (DBS) samples. These DBS samples were obtained from extremely low gestional age newborns, where 10 of them were affected by a fetal inflammatory response (FIR) before birth, and 11 were unaffected. Total RNA was sequenced using an Illumina NextSeq-500 instrument. The sample preparation protocol included the depletion of rRNA and globin mRNA using the Globin Zero Gold rRNA Removal Kit from Illumina. Libraries were prepared using the NebNext Ultra TM II Directionl RNA LIbrary Prep Kit (New England Biolabs). Each sample was sequenced in 4 lanes, leading to 8 FASTQ files per sample and a total of 21x8=168 FASTQ files. There is an additional number of 8 FASTQ files corresponding to sample BS13, which was downsampled to 1/4 of its original depth (see BS13_README file for details).

  Dataset EGAD00001004991

• Mutational Landscape of Plasmablastic Lymphoma

  Plasmablastic lymphoma (PBL) represents a clinically heterogeneous subtype of aggressive B-cell non-Hodgkin lymphoma. Although targeted sequencing studies and a single center whole exome sequencing (WES) study in HIV+ patients recently revealed several genes, associated with PBL pathogenesis, the global mutational landscape and transcriptional profile of PBL remain elusive. To inform on disease-associated mutational drivers, mutational patterns and perturbed pathways in HIV+ and HIV- PBL we performed WES and RNA-sequencing (RNA-seq) of 34 PBL tumors.

  Study EGAS00001004906

• RNA-seq

  Our cohort represents infants with ALL in whom KMT2Ar was not detected by FISH or by standard cytogenetics. Whole-transcriptome sequencing (WTS) libraries were prepared using the NEBNext Ultra II RNA Directional library kit for samples with at least 100ng RNA (n=19), the Clontech double stranded cDNA conversion kit plus the Nextera XT library protocol for samples with less than 100ng RNA (n=4), or ribodepletion using NEBNext rRNA Depletion Kit v2 (Human/Mouse/Rat) for Total RNA (n=1). Libraries were paired-end sequenced using the Illumina HiSeq 2500.

  Dataset EGAD50000000595

• RCC Infiltrated Macrophages and Monocytes

  Tumor infiltrated Macrophages and Monocytes were sorted on Aria II (Becton Dickinson) into TRIzol LS and flash frozen. RNA was extracted with chloroform. Isopropanol and linear acrylamide were added, and the RNA was precipitated with 75% ethanol. Total RNA (0.649–1 ng) with RNA integrity numbers 6.8 to 10 underwent amplification using the SMART-Seq v4. Ultra Low Input RNA Kit (Clontech; cat. #63488). Amplified cDNA (15 ng) was used to prepare libraries with the KAPA Hyper Prep Kit (Kapa Biosystems, KK8504) using 8 cycles of PCR.

  Dataset EGAD00001009393

• Sequence Data for DEEP Release August 2016

  DEEP (German Epigenome Project) sequence data of following samples (Sequencing Types: Chip-Seq, WGBS-Seq, RNA-Seq, sncRNA-Seq, NOMe-Se, DNase-Seq): 41_Hf01_LiHe_Ct, 41_Hf02_LiHe_Ct, 41_Hf03_LiHe_Ct, 01_HepG2_LiHG_Ct1, 01_HepG2_LiHG_Ct2, 01_HepaRG_LiHR_D31, 01_HepaRG_LiHR_D32, 01_HepaRG_LiHR_D33, 43_Hm01_BlMo_Ct, 43_Hm03_BlMo_Ct, 43_Hm05_BlMo_Ct, 43_Hm03_BlMa_Ct, 43_Hm05_BlMa_Ct, 43_Hm03_BlMa_TO, 43_Hm05_BlMa_TO, 43_Hm03_BlMa_TE, 43_Hm05_BlMa_TE, 51_Hf01_BlCM_Ct, 51_Hf03_BlCM_Ct, 51_Hf04_BlCM_Ct, 51_Hf02_BlCM_Ct, 51_Hf05_BlCM_Ct, 51_Hf06_BlCM_Ct, 51_Hf06_BlCM_T1, 51_Hf06_BlCM_T2, 51_Hf03_BlEM_Ct, 51_Hf04_BlEM_Ct, 51_Hf02_BlEM_Ct, 51_Hf05_BlEM_Ct, 51_Hf06_BlEM_Ct, 51_Hf06_BlEM_T1, 51_Hf06_BlEM_T2, 51_Hf03_BlTN_Ct, 51_Hf04_BlTN_Ct, 51_Hf02_BlTN_Ct, 51_Hf05_BlTN_Ct, 51_Hf06_BlTN_Ct, 51_Hf06_BlTN_T1, 51_Hf06_BlTN_T2, 51_Hf07_BmTM4_Ct, 51_Hf08_BlTM4_Ct, 51_Hf08_BmTM4_SP1, 51_Hf08_BmTM4_SP2, 51_Hf05_BlTA_Ct, 44_Mm01_WEAd_C2, 44_Mm03_WEAd_C2, 44_Mm02_WEAd_C2, 44_Mm07_WEAd_C2, 44_Mm04_WEAd_C1, 44_Mm05_WEAd_C1

  Dataset EGAD00001002527

• Exploring the cell-free total RNA transcriptome in diffuse large B-cell lymphoma and primary mediastinal B-cell lymphoma patients as biomarker source in blood plasma liquid biopsies

  The goal of this study was to investigate cell-free RNA (cfRNA) biomarkers in DLBCL and PMBCL patients. Blood plasma samples (n=168) and matched diagnostic formalin-fixed paraffin-embedded (FFPE) tissue samples (n=69) of DLBCL patients, PMBCL patients and healthy controls were collected between 2016-2021. Plasma samples were collected at diagnosis, at interim evaluation, after treatment, and in case of refractory or relapsed disease. RNA was extracted from 200 µl plasma using the miRNeasy serum/plasma kit and from FFPE tissue using the miRNeasy FFPE kit. RNA was subsequently sequenced on a NovaSeq 6000 instrument using the SMARTer Stranded Total RNA-seq pico v3 library preparation kit.

  Study EGAS00001007585

• Impact of Glycolysis Inhibition on the Epigenome of Synovial Fluid T Cells in Juvenile Idiopathic Arthritis

  Juvenile Idiopathic Arthritis (JIA) encompasses a group of childhood-onset autoimmune conditions characterized by joint inflammation and mononuclear cell infiltration, especially of activated CD4+ memory/effector T (Tmem/Teff) cells in synovial fluid (SF). JIA CD4+ T cells exhibit a distinct inflammation-associated epigenomic profile, though the underlying molecular mechanisms remain unclear. This study investigates the role of glycolysis in shaping the epigenomic landscape of JIA SF CD4+ T cells, with a focus on H3K27ac histone modifications. Using an ex vivo model of antigen stimulation, CD4+ T cells were isolated from peripheral blood (PB) of healthy controls (HC) and synovial fluid from JIA patients. Cells were activated in vitro with anti-CD3/CD28 for 24 hours, and changes in H3K27ac were assessed by chromatin immunoprecipitation followed by sequencing (ChIP-seq). To explore the specific role of glycolysis, cells were also treated with 2-deoxyglucose (2DG), an inhibitor of glucose-6-phosphate production. This inhibition impacted both H3K27ac acetylation patterns and gene expression associated with the activated epigenomic regions. Additionally, HC CD4+ T cells were activated in vitro in the presence of 30% SF or 30% plasma from JIA patients. RNA sequencing (RNA-seq) was performed to examine SF-induced transcriptional changes, providing insight into the metabolic dysregulation and epigenomic alterations observed in JIA SF CD4+ T cells. This dataset includes ChIP-seq and RNA-seq data that collectively advance our understanding of how glycolysis and the SF environment contribute to the epigenetic and transcriptional regulation in JIA CD4+ T cells.

  Study EGAS50000000808

• DSRCT RNA genomic sequencing

  Purpose: To further understand the molecular pathogenesis of desmoplastic small round cell tumor (DSRCT), a rare but often fatal malignancy occurring primarily in young males, we used next-generation RNA sequencing to investigate the gene expression profiles intrinsic to this disease.Experimental Design: RNA from DSRCT tumor samples were obtained from the Cooperative Human Tissue Network biorepository. RNA sequencing was completed using the Illumina HiSeq 2000 system. Presence of the EWSR1-WT1 fusion was verified using both RNA-sequencing and PCR. Validation studies, including WT1 ChIP-seq, RNA sequencing, and EWS-WT1 knockdown experiments, were performed on an established DSRCT cell line, JN-DSRCT-1. Protein expression of clinically relevant genes determined by RNA sequencing was confirmed using immunohistochemistry. A panel of immune signature genes was also evaluated to identify possible immune therapeutic targets. Results: We analyzed the RNA from 14 tumor samples, of which two were confirmed negative for the diagnostic EWSR1-WT1 translocation. Principal component analysis of the sequencing data indicated that fusion-negative and fusion-positive tumors are transcriptionally distinct entities. Overlapping the significant ChIP-seq peaks with genes found to be highly expressed in the RNA sequencing data revealed that IGF2 and FGFR4 were both highly expressed, and targets of the EWS-WT1 fusion gene. This finding has potential clinical relevance for targeted therapeutic intervention. In addition, we identified the immune checkpoints CD200 and CD276 as potentially targetable genes whose expression is independent of the EWS-WT1 fusion gene in vitro.Conclusions: Despite the transcriptional complexity of this disease, we were able to identify IGF2, FGFR4, CD200, and CD276 as potential therapeutic targets for patients suffering from DSRCT.

  Study EGAS00001002770

• RNA-sequencing of Human Thyroid cancer cell lines

  RNA-sequencing data of 5 human thyroid cancer cell lines cultured in control conditions

  Dataset EGAD00001011677

• SPECTA Lung cancer RNA FASTQ files

  SPECTA Lung cancer RNA FASTQ files (Illumina TST170 targeted analysis)

  Dataset EGAD00001006222

• Evaluation of triple negative breast cancer with heterogeneous immune infiltration

  Identification of potential RNA markers fir TILs infiltration in patients with triple negative breast cancer with heterogeneous immune infiltration.

  Study EGAS00001007159

• Baulac Lab - Paris Brain Institute

  Raw 10X single-nucleus RNA-seq data from human postoperative brain samples of patients with epilepsy and cortical malformations.

  Dac EGAC50000000546

• Transcriptomic profiling of lymphedema

  RNA-seq of skin from human subjects with and without lymphedema

  Dataset EGAD00001006246

• RNA_Seq_in_Patients_with_Primordial_Dwarfism

  Transcriptome studies in patients with rare genetic diseases can potentially aid in theinterpretation of likely causal genetic variation through identification of altered transcriptabundance and/or structure. RNA-Seq is the most sensitive assay for both investigatingtranscript structure and abundanceThe primary aim of this pilot project is to investigate to what degree integrating exome-Seqand RNA-Seq data on the same individual can accelerate the identification of causal allelesfor rare genetic diseases. There are two main strands to this: (i) identifying which variantsdiscovered in exome-seq appear to be having a functional impact on transcripts, and (ii)identifying transcript outliers, especially among known causal genes, that may not necessarilyhave a causal variant identified from exome sequencing. The latter may identify the presenceof causal variants that lie far from coding regions (e.g. the formation of cryptic splice sitesdeep within introns, or loss of long range regulatory elements), which can be confirmed withfurther targeted genetic assays. Just over 50% of all disease-causing variants recorded in theHuman Gene Mutation Database (HGMD) affect transcript structure and abundance (e.g.nonsense SNVs, essential splice site SNVs, frameshifting indels, CNVs).This pilot project will study RNA from lymphoblastoid cell-lines from 12 patients withprimordial dwarfism syndromes, for 10 of these samples we have previously generate exomedata as part of our collaboration with the group of Prof Andrew Jackson. The two remainingsamples are positive controls where the causal mutation is known, and is known to affecttranscript structure and/or abundance.Primordial dwarfism is a prime candidate for these RNA-seq studies because all knowncausal mutations to date have key roles in DNA replication and thus, unsurprisingly, theproducts of the causal genes are typically ubiquitously expressed.Each RNA will be sequenced, with two technical replicates (independent RT-PCR and libraries) persample, and each replicate run in 1/2 of a HiSeq lane using 100bp paired reads. Samples preparation was as follows :The cells were grown to confluency, then pellets frozen at -80. RNA samples were prepared using the Qiagen RNeasy kit, then nanodropped and analyzed using the bioanalyzer to determine concentration and purity.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001000283

• Newcastle COVID-19 Single-cell PBMC

  Single-cell multi-omic profiling of healthy controls, asymptomatic and hosptial-admitted COVID19 patients recruited from Newcastle University hospitals. These data also include healthy control volunteers treated with IV-LPS as inflammatory controls. Data represent RNA-seq, surface protein measurements (CITE-seq) of 192 antibody targets, along with VDJ-seq profiling of single T cell and B cell receptors.

  Dataset EGAD00001007866

• ImmuNEO CD8 cell lines

  Paired RNA-Seq data from 16 samples of different tumors CD8+ T cells added to the study "Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification (H021)". Sequencing was performed on Illumina NextSeq 500. The sequencing was always paired

  Dataset EGAD50000000193

• RNA-Seq dataset for malignant pleural and peritoneal mesothelioma

  This dataset contains RNA sequencing data of pleural and peritoneal mesothelioma. The number of samples is 18. Sequencing was performed on Illumina NovaSeq 6000 and Illumina NovaSeqXPlus. The sequencing was always paired.

  Dataset EGAD50000001342

• Dual RNA-seq data of chlamydia infected M2 macrophages

  This dataset contains 8 unpaired fastq files sequenced with Ilumina NextSeq 2000. The files contain transcriptome data of host and pathogen from infected and uninfected samples from extracted total RNA at 30 hours post infection.

  Dataset EGAD50000001398

• Whole Transcriptome Sequencing

  RNA-seq data of 15 snap-frozen tissue of peritoneal mesothelioma. The strand specific RNA library prepared using TruSeq (Illumina) and pair-end sequencing performed in Illumina HiSeq 4000. The datasets contains paired fastq files for each of 15 tumor samples.

  Dataset EGAD00001004504