11750 results for "cancer rna-seq"

in 17.62 milliseconds.

• Leiden University Medical Center Adipose tissue RNA-seq

  Dac EGAC00001000503

• Board considering access to BELOB RNA-seq data

  Dac EGAC00001001686

• RNA-seq data from 27 glioblastoma samples

  Study EGAS00001005807

• Genomic, transcriptomic and epigenomic profiling of GCTB

  Giant cell tumor of bone is a rare bone neoplasm driven by oncogenic hotspot G34W/L/V mutations in the non-canonical histone variant H3.3. This study provides primary data of genomic (WGS), transcriptomic (RNA-seq) and epigenomic (histone mark ChIP-seq, WGBS, ATAC-seq) profiling of primary GCTB samples from several cohorts in Germany and Korea. We confirm the H3.3-G34 mutations as sole genetic drivers of GCTB, and describe the epigenetic alterations in mutant GCTB stromal cells.

  Study EGAS00001003730

• GEP-NEN Bulk-RNA-seq dataset

  This dataset entails 40 Bulk-RNA sequenced patient-derived gastro-intestinal neuroendocrine (GEP-NEN) neoplasms.

  Dataset EGAD00001006657

• Pharmacogenomics of T-ALL

  Whole exome seq (N=21) and RNA-seq (N=36) data of additional T-ALL

  Dataset EGAD00001006434

• RNA-seq analysis of human skin

  RNA sequencing analysis comparing epidermal subpopulations in normal and inflamed skin.

  Study EGAS00001002927

• Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models: validation cohort

  Total RNA sequencing (SMARTer Stranded Total RNA-Seq Kit v2) data of extracellular RNA (exRNA) from liquid biopsies of the validation PDX/CDX cohort

  Dataset EGAD00001009413

• Nasal MicroRNA during Bronchiolitis and Age 6y Asthma: MARC-35 Cohort

  We analyzed data from the 35th Multicenter Airway Research Collaboration (MARC-35) study to examine the relationship between nasal airway microRNAs (miRNAs) and the risk of developing childhood asthma. MARC-35 is a multicenter prospective cohort study which enrolled infants (age Structured interviews of the parents/guardians were performed by site investigators and clinical details were collected via emergency department and hospital inpatient chart reviews. Following the index hospitalization, trained study personnel conducted telephone interviews with parents/legal guardians at six-month intervals. Participants also underwent nasal swab sampling conducted by trained site personnel during the index bronchiolitis hospitalization and during an in-person follow-up examination at about age 6 years. Total RNA was extracted from nasal swab specimens using Trizol LS reagent (ThermoFisher Scientific, Waltham, MA) and the Direct-zol RNA Miniprep Kit (Zymo Research, Irvine, CA). Small RNA-seq was performed using the PerkinElmer NEXTFLEX® small RNA-seq v3 kit with Unique Dual Indexes (PerkinElmer, Waltham, MA) and sequenced on an Illumina NovaSeq6000 sequencer using an S2 50bp PE Flowcell (Illumina, San Diego, CA). Through dbGaP, limited de-identified phenotypic data (e.g., demographics) and RNA-seq FASTQ files will be made available. Among a cohort of infants hospitalized with bronchiolitis, we applied a small RNA sequencing approach and found miRNAs were associated with asthma risk factors, and highly expressed in lung tissue and immune cells (e.g., TH cells, neutrophils). They also played key roles in mechanisms relating to asthma, such as innate immunity, airway remodeling, and IgE regulation.

  Study phs003564

• DAC for RNA-Seq Transcriptomic CD4+ T data from EGAS00001004152

  Dac EGAC00001001462

• FIMM AML Single-Cell RNA-seq Data Access Committee

  Dac EGAC00001001709

• RNA-Seq of clonal endocrine therapy resistant cell lines

  Study EGAS00001007123

• Cancer and germline exomes, and cancer RNA-seq consisiting of FASTQ paired-end reads from melanoma, lung and colon cancer samples

  Discovery of patient-specific tumor antigens usually requires in vitro-expanded autologous tumor infiltrating lymphocytes (TILs), among which tumor antigen-specific T cells are however often rare, thus limiting sensitivity. We designed in vitro culture conditions to improve the identification of rare tumor antigen-specific CD8 TILs. This innovative yet accessible pipeline allows highly-sensitive identification of tumor antigens and cognate T cell receptors (TCRs), greatly improving the selection of candidates for personalized cancer vaccines and TCR-based cellular immunotherapies.

  Study EGAS00001005513

• RNA-seq analysis of metastatic prostate cancer solid tumor biopsies

  The prevalence and features of treatment-emergent small-cell neuroendocrine prostate cancer (t-SCNC) are not well characterized in the era of modern androgen receptor (AR)–targeting therapy. We sought to characterize the clinical and genomic features of t-SCNC in a multi-institutional prospective study. Patients with progressive, metastatic castration-resistant prostate cancer (mCRPC) underwent metastatic tumor biopsy and were followed for survival. t-SCNC is present in nearly one fifth of patients with mCRPC and is associated with shortened survival.

  Study EGAS00001006369

• RNA-seq of pcALCL

  RNA sequencing of frozen tumor biopsies from patients with primary cutaneous anaplastic large cell lymphoma. 12 samples. Illumina HiSeq 4000.

  Dataset EGAD00001006178

• rna-seq data of multiple myeloma patients

  6 samples from individuals with multiple myeloma with selective elimination of immunosuppressive T cells, rna sequencing

  Dataset EGAD00001006866

• MOSAIC Window DLBCL Data

  This dataset will include Spatial Transcriptomics from the 10x Genomics Visium SD technology, Single-Cell RNA-Seq from the 10x Genomics Chromium Flex protocol, Bulk RNA-Seq (sequenced on Illumina), WES (sequenced on Illumina), H&E, and Clinical data from 10 DLBCL patients. For Visium and Chromium data we are sharing the outputs of respectively Space Ranger and Cell Ranger. For Bulk RNA-Seq we are including processed files such as counts.tsv, FPKM.tsv, fusion_genes.tsv, TPM count matrix.tsv, DESeq2 count matrix.tsv, multiqc_report. For WES, we are sharing the presence or absence of single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number variants (CNVs).* Researchers from private or public institutions outside the MOSAIC Consortium will be able to apply to access this data and, pending approval, use the data for their research.

  Dataset EGAD50000001702

• MOSAIC Window Ovarian Data

  This dataset will include Spatial Transcriptomics from the 10x Genomics Visium SD technology, Single-Cell RNA-Seq from the 10x Genomics Chromium Flex protocol, Bulk RNA-Seq (sequenced on Illumina), WES (sequenced on Illumina), H&E, and Clinical data from 15 Ovarian patients. For Visium and Chromium data we are sharing the outputs of respectively Space Ranger and Cell Ranger. For Bulk RNA-Seq we are including processed files such as counts.tsv, FPKM.tsv, fusion_genes.tsv, TPM count matrix.tsv, DESeq2 count matrix.tsv, multiqc_report. For WES, we are sharing the presence or absence of single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number variants (CNVs).* Researchers from private or public institutions outside the MOSAIC Consortium will be able to apply to access this data and, pending approval, use the data for their research.

  Dataset EGAD50000001704

• MOSAIC Window Mesothelioma Data

  This dataset will include Spatial Transcriptomics from the 10x Genomics Visium SD technology, Single-Cell RNA-Seq from the 10x Genomics Chromium Flex protocol, Bulk RNA-Seq (sequenced on Illumina), WES (sequenced on Illumina), H&E, and Clinical data from 10 Mesothelioma patients. For Visium and Chromium data we are sharing the outputs of respectively Space Ranger and Cell Ranger. For Bulk RNA-Seq we are including processed files such as counts.tsv, FPKM.tsv, fusion_genes.tsv, TPM count matrix.tsv, DESeq2 count matrix.tsv, multiqc_report. For WES, we are sharing the presence or absence of single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number variants (CNVs).* Researchers from private or public institutions outside the MOSAIC Consortium will be able to apply to access this data and, pending approval, use the data for their research.

  Dataset EGAD50000001706

• RNA-seq data from the EUROBATS Project in four tissues: Fat, LCL, Skin and whole Blood.

  EUROBATS consists of RNA-seq data for female twins in lisphoblastoide cell lines (LCL), abdominal adipose tissue (Fat), abdominal skin tissue (Skin) and whole blood (Blood). The samples are a combination of MZ twins, DZ twins and singletons. The goal of the project is to find biomarkers related with the ageing process and cellular senescence.

  Study EGAS00001000805

• single-cell RNA-seq Case-Control study of children progressing to Type1 diabetes

  It was a single-cell RNA sequencing study on the PBMC samples from four Finnish children at risk of developing Type 1 diabetes and their gender age and HLA matched control children. All four Case children were positive for multiple islet specific autoantibodies and two of them also progressed to clinical disease during the follow up whereas the control children remain negative for all autoantibodies. Single-cell analysis confirmed some of the signatures obtained from the bulk data. It identified that high IL32 in case samples in the bulk RNA-seq was contributed mainly by activated T cells and NK cells. Trajectory analysis of the scRNA-seq data suggested that IL32 expression increased as the T cells moved towards activated state.

  Dataset EGAD00001005768

• Circulating RNAs prior to endometrial cancer diagnosis

  We analyzed circulating RNA levels using small RNA sequencing, targeting RNA in the size range 17 to 47 nucleotides, in samples collected prior to endometrial cancer diagnosis compared to cancer-free controls. The analysis included 316 cases with samples collected 1-11 years prior to diagnosis , and 316 matched controls, both from the Janus Serum Bank Cohort in Norway.

  Study EGAS50000000267

• Single-cell RNA and DNA sequencing data obtained after genome-and-transcriptome separation.

  This dataset contains single-cell RNA and DNA sequencing data (fastq, n=928) obtained after genome-and-transcriptome separation. The RNA-seq data was obtained by Smart-seq2 amplification and Nextera XT library preparation. The DNA-seq data was obtained either by Gtag library preparation and amplification or by PicoPlex whole-genome amplification followed by Nextera XT library preparation. The single cells originate from a human PDX melanoma model and from HCC38 and HCC38 BL cell lines. The sequencing libraries were sequenced with Illumina instruments.

  Dataset EGAD00001010096

• Processed Total RNA Counts for the RNA Profiling from Indian HFrEF Cohort Dataset

  This dataset contains processed RNA count data for the 49 Heart Failure samples profiled by RNAseq for the Indian HFrEF Cohort from the study Integrated Transcriptomic and Regulatory RNA Profiling Reflects Complex Pathophysiology and Uncovers a Conserved Gene Signature in End Stage Heart Failure RNA-Seq data.

  Dataset EGAD50000001193

• Transcriptional profiling and clonality analysis of peripheral blood B cells and small intestinal plasma cells in celiac disease

  Single-cell RNA-seq and BCR sequencing and/or inference of sorted IgA+ peripheral blood B-lineage cells and small intestinal plasma cells of untreated celiac disease patients.

  Study EGAS50000000229

• Spatially resolved transcriptomics reveals profound subclonal heterogeneity and T cell dysfunction in extramedullary myeloma

  Investigating the spatial architecture and microenvironment of EMD lesions in Multiple Myeloma patients using spatial whole transcriptome sequencing (10X Visium), tomography for spatially resolved transcriptomics (TOMO-seq), and single-cell RNA sequencing (scRNAseq)

  Study EGAS50000000227

• InsPIRE islets

  We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors and 26 FAC sorted beta-cells.

  Study EGAS00001003997

• BLUEPRINT release August 2016, RNA-Seq for common myeloid progenitor, on genome GRCh38

  RNA-Seq data for 3 common myeloid progenitor sample(s). 3 run(s), 3 experiment(s), 3 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_rnaseq_analysis_crg_20160816

  Dataset EGAD00001002478

• BLUEPRINT release August 2014, RNA-Seq for megakaryocyte-erythroid progenitor cell

  RNA-Seq data for 4 megakaryocyte-erythroid progenitor cell sample(s). 4 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000915

• BLUEPRINT release August 2015, RNA-Seq for common myeloid progenitor, on genome GRCh38

  RNA-Seq data for 3 common myeloid progenitor sample(s). 3 run(s), 3 experiment(s), 3 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001538

• RNA-Seq of novel miR (nmiR-1 and nmiR-2) overexpression and knockdown in BL

  After overexpression and knockdown of both described novel miRs nmiR-1 and nmiR-2 in BL cell lines (SU-DHL4 for nmiR-1 and Raji for nmiR-2), we performed regular RNA-Seq (including Mock controls for all cell lines) to identify their direct and indirect downstream mRNA targets.

  Dataset EGAD00001001612

• BLUEPRINT release January 2015, RNA-Seq for megakaryocyte-erythroid progenitor cell

  RNA-Seq data for 4 megakaryocyte-erythroid progenitor cell sample(s). 4 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001140

• BLUEPRINT release January 2015, RNA-Seq for hematopoietic multipotent progenitor cell

  RNA-Seq data for 3 hematopoietic multipotent progenitor cell sample(s). 9 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001001186

• BLUEPRINT release August 2014, RNA-Seq for hematopoietic multipotent progenitor cell

  RNA-Seq data for 3 hematopoietic multipotent progenitor cell sample(s). 9 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release August 2014. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_rnaseq_analysis_crg_20140811

  Dataset EGAD00001000919

• Ewing's Sarcoma RNA-Seq

  28 Pretreated Ewing sarcoma tumor blood samples were collected from the Hospital for Sick Children (SickKids) and Mount Sinai Hospital in Toronto, Canada in accordance with each institution’s Research Ethical Board (REB) guidelines. Detailed clinical information (age at presentation, gender, tumor site, stage, etc.) were obtained from the corresponding institutional tumor banks. Transcriptome (RNA-Seq) sequencing was performed using established protocols on Illumina instruments.

  Dataset EGAD00001004188

• ALS-FTD brain motor cortex single-nucleus RNA-seq of NeuN+ nuclei sorted for TDP-43 low vs. high

  A dataset of single-nucleus RNA-seq data of nuclei from the post-mortem human primary motor cortex from patients with ALS-FTD. The nuclei were sorted by flow cytomotetry into NeuN+ (neuronal) TDP-43 low vs. TDP-43 high fractions.

  Dataset EGAD50000002243

• Breast Cancer Single-Cell RNA-Seq Dataset

  Single-cell RNA-Sequencing of 26 primary breast cancers from Wu et al. (2021) study. Data was generated using the Chromium controller (10X Genomics) and sequenced on the NextSeq 500 platform.

  Dataset EGAD00001007495

• Sequencing Lymphoma

  This repository contains sequencing data to describe the genomic profiles of several B-cell lymphomas including follicular lymphoma and Hodgkin lymphoma. We have used several different high throughput sequencing technologies to interrogate genomic DNA and RNA from tumor cells obtained from lymph node biopsies and normal cells obtained from skin biopsies and peripheral blood mononuclear cells. A number of sequencing platforms for DNA, RNA, and protein were used to generate the data. These platforms include exome sequencing and a custom capture panel (NimbleGen) that targets 7.05 MB corresponding to the exons and splice sites of 1716 genes related to lymphoma biology (WUSM-LP). Other platforms include single-cell RNA-seq and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), which was used to describe transcriptional and proteomic changes in immune cell subsets within lymphoma patients pre- and post-therapy.

  Study phs001229

• RNA-Seq Single End

  RNA analysis of six patients 34, 35, 36, 37, 38 and 39 with WGS done on Illumina HiSeq2500. For research purpose and authorised user only.

  Dataset EGAD00001003430

• Childhood Cancer Data Initiative (CCDI): Pediatric In Vivo Testing Program –Sarcoma, Kidney, and Liver Cancers

  The goal was to complete whole exome sequencing (WES) and RNA sequencing (RNA-Seq) on patient derived xenograft samples. Cancer in children is rare with approximately 15,700 new cases diagnosed annually in children 21 years or younger in the U.S. Through use of multimodality therapy (surgery, radiation therapy, and aggressive chemotherapy), 70% of patients will be 'cured' of their disease, and 5-year Event-Free Survival (EFS) exceeds 80%. Consequently, the number of patients that can be enrolled in phase I/II clinical trials is small, and most patients will have been extensively treated, hence drug/radiation resistant. Thus, preclinical studies that accurately translate into effective clinical therapy are an essential component of pediatric drug development. Our group has contributed to studies in the Pediatric Preclinical Testing Program (PPTP) and Pediatric Preclinical Testing Consortium (PPTC) that have led to clinical studies through Children's Oncology Group (COG). Of importance, we have developed and characterized over 330 Patient Derived Xenografts (PDX), developed from tumors both at diagnosis and relapse, that can be used to facilitate pediatric drug development as directed by FDA under the Research to Accelerate Cures and Equity for Children Act (RACE for Children Act).

  Study phs003160

• H005 RNA-seq CLL

  Paired-end RNA sequencing using total RNA from 136 primary lymphoma samples. Sequencing was performed on the Illumina HiSeq2000 with 300bp insert size. The dataset contains FASTQ files.

  Dataset EGAD00001002056

• Colorectal cancer functional annotation - ChIP

  ChIP-seq data for multiple colorectal cancer cell lines (H3K4me1, H3K4me3, H3K27ac, H3K27me3, H3K36me3, CTCF)

  Study EGAS50000000207

• RNA-seq of PDAC patient-derived xenograft tumors

  Study EGAS00001007070

• Data access to Small RNA-Seq of MicroRNA's in Tear EV's of Ushers Syndrome patients

  Via this DAC you can get access to Small RNA-Seq dataset of MicroRNA's in Tear EV's of Ushers Syndrome patients. The study involved genetically confirmed USH1B patients and their healthy relatives, with additional fibroblast samples from Spanish collaborators, to ensure a robust comparison of small non-coding RNAs (sncRNAs). Tear and blood samples were collected using non-invasive methods, and extracellular vesicles (EVs) were isolated from both tears and hiPSC-derived retinal pigment epithelium (RPE) cells. RNA was extracted from these EVs and assessed for quality before preparing libraries using the TruSeq Small RNA Library Preparation Kit. Sequencing was performed on the Illumina NextSeq 2000 platform, generating data for comparative analysis of sncRNA profiles between patients and controls.

  Dac EGAC50000000711

• Exploration of coding and non-coding variants in cancer using GenomePaint.

  GenomePaint (https://proteinpaint.stjude.org/genomepaint) is a dynamic visualization platform for whole-genome, whole-exome, transcriptome, and epigenomic data, featuring a novel design that captures the inter-relatedness between DNA variations and RNA expression. Regulatory non-coding variants can be inspected and discovered along with coding variants, and their functional impact further explored by examining 3D genome and/or ChIP-seq data generated from cancer cell lines. Further, GenomePaint correlates mutation and expression patterns with patient outcomes, and can display external data such as adult cancer datasets and user-provided custom tracks. We used GenomePaint to analyze multi-omics data from 3,652 pediatric cancers representing 16 histotypes, and demonstrate the visualization features through examples, including two that led to new insights into oncogenic mechanisms in pediatric cancer. The first is the discovery of a new class of pathogenic recurrent variants that cause aberrant splicing, disrupting the RING domain of CREBBP, a driver gene frequently mutated in relapsed pediatric leukemia. The second is the cis-activation of the MYC oncogene in a subset of B-lineage acute lymphoblastic leukemia (B-ALL) via duplication of the NOTCH1-MYC enhancer (N-ME), previously discovered only in T-lineage ALL. The regulatory impact of N-ME enhancer amplification was initially confirmed by allelic imbalance in published gene expression and ChIP-seq data and verified by additional Capture-C and fluorescence in situ hybridization data generated by follow-up experiments. These examples demonstrate the power of GenomePaint in enabling not only data visualization but also integrative genomic analysis that can lead to novel biological insight for follow-up experimental validation.

  Study EGAS00001004669

• Functional Enhancer Elements Drive Subclass-Selective Expression From Mouse to Human Neocortex

  A complete epigenomic map of human brain cell types will be necessary to fully understand their genetic regulation, and to create the next generation of non-species-restricted genetic tools for their functional characterization. Here we performed both bulk and single-nucleus ATAC-seq studies of fresh human neocortical samples excised during neurosurgery, in order to discover the functional genomic elements that discriminate cell types. This dataset contains 78 bulk layer-dissected ATAC-seq samples, and 3660 single-nucleus ATAC-seq samples from fresh acutely isolated human neurosurgical samples. Furthermore, from these data we identified and validated several enhancers for use in AAV context. We also provide validation single cell RNA-seq for 456 human acutely labeled neurosurgical neurons labeled by 3 unique enhancer-AAV vectors, and single cell RNA-seq for 1483 mouse neurons labeled in vivo by 11 unique enhancer-AAV vectors. Together these datasets provide high-resolution maps of single human neocortical cell types, as well as proof of the functional utility of the genomic elements discovered. As a whole these data will be important to furnish the next generation of cell type-specific functional tools and therapies in neuroscience.  

  Study phs002292

• HCC.GNE RNA-Seq

  RNA-Sequencing data (raw read sequences) for 23 samples, from 12 patients, for the study "Diverse modes of genomic alterations in Hepatocellular Carcinoma"

  Dataset EGAD00001000886

• RNA profiles of human placentas

  This DAC addresses the control of the access to the RNA-seq of the human placentas. It covers the project "Decoding human placental cellular and molecular responses to maternal obesity and fetal growth"

  Dac EGAC50000000505

• RNA-seq of pcAECyTCL

  RNA sequencing of frozen tumor biopsies from patients with primary cutaneous CD8+ aggressive epidermotropic cytotoxic T-cell lymphoma. 6 samples. Illumina HiSeq 4000.

  Dataset EGAD00001006085

• RNA-Seq of paediatric patients with B lymphoblastic leukemia

  Whole transcriptome RNA-Sequencing was performed on 148 bone marrow or peripheral blood samples of B-ALL patients.

  Dataset EGAD00001006257