12215 results for "cancer rna-seq"

in 29.54 milliseconds.

• OICR: Molecular Pathological Epidemiology of Colorectal Cancer

  Colorectal cancer, the second most commonly diagnosed cancer, is a biologically heterogeneous disease. To characterize the molecular attributes of colorectal tumors, we conducted targeted sequencing at the Ontario Cancer Research Institution (OICR) of tumor and matched normal DNA samples from 2,542 colorectal cancer cases in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) [phs001078] and the Colon Cancer Family Registry (CCFR). These consortia are based on cohort and population-based case-control studies.

  Study phs002050

• Pancreatic Cancer Case Control Association Study

  Pancreatic cancer is the fourth leading cause of cancer death in the United States. This is in large part due to the rapidly fatal course of this disease, as the vast majority of patients die within months of diagnosis and the five-year survival rate is less than 5. We have brought together over 8000 participants from 9 studies of pancreatic cancer, to conduct a GWAS of pancreatic cancer. The studies were drawn from the Pancreatic Cancer Case-Control consortium and include case-control studies from the United States, Europe and Australia. Sites include: Johns Hopkins National Familial Pancreas Tumor Registry, Mayo Clinic Biospecimen Resource for Pancreas Research, Ontario Pancreas Cancer Study (OPCS), Yale University, MD Anderson Case Control Study, Queensland Pancreatic Cancer Study, University of California San Francisco Molecular Epidemiology of Pancreatic Cancer Study, International Agency of Cancer Research and Memorial Sloan Kettering Cancer Center.

  Study phs000648

• Whole Transcriptome Sequencing of Resectable Stage III/IV Melanoma Evaluated After Starting Hu14.18-IL2 Predicts Outcome

  Background: We analyzed whole transcriptome sequencing in tumors from 23 patients with stage III or IV melanoma from a pilot trial of the anti-GD2 immunocytokine, hu14.18-IL2, to identify predictive immune and/or tumor biomarkers in melanoma patients at high risk for recurrence. Methods: Patients were randomized to receive the first of 3 monthly-courses of hu14.18-IL2 immunotherapy either before (Group A) or after (Group B) complete surgical resection of all known disease. Tumors were evaluated by histology and whole transcriptome sequencing. Results: We report here that tumor infiltrating lymphocyte (TIL) levels directly associate with relapse-free survival (RFS) and overall survival (OS) in resected tumors from Group A, where early responses to the immunotherapy agent could be assessed. TIL levels directly associated with a previously reported immune signature which associated with RFS and OS, particularly in Group A tumors. In Group A tumors there were decreased cell cycling gene RNA transcripts, but increased RNA transcripts for repair and growth genes. We found that outcome (RFS and OS) was directly associated with several immune signatures and immune-related RNA transcripts and inversely associated with several tumor growth-associated transcripts, particularly in Group A tumors. Most of these associations were not seen in Group B tumors. Conclusion: We interpret these data to signify that both immunologic and tumoral cell processes, as measured by RNAseq analyses detected shortly after initiation of hu14.18-IL2 therapy are associated with long term survival and could potentially be used as prognostic biomarkers in tumor resection specimens obtained after initiating neoadjuvant immunotherapy. Note: RNA sequences and expression data can be found at NCBI GEO GSE133713

  Study phs001947

• Blood Transcriptome Profiling Following Seizures

  The retrospective diagnosis or confirmation of a seizure, after the event, is very challenging. Many times clinicians can only use a patient's self-reported history. To further complicate the diagnosis, it is known that 20-30% of patients reporting a seizure actually suffer from psychogenic non-epileptic seizure (PNES). The patient has the behavior of a seizure, but does not suffer from brain seizure activity. Considerable time and resources are used to monitor PNES, and many patients undergo unnecessary therapy with anti-epileptic drugs. These resources could be more effectively used if we could confirm a seizure after the event (retrospective diagnosis). Here we propose a novel approach to distinguish seizures from PNES after the seizure event has happened.We have developed an accurate blood biomarker technology to diagnose acute brain injury, and assess chronic progression following brain injury. Previous studies support the premise that seizures affect blood RNA expression; here we assess temporal RNA expression pattern changes following seizure. We present preliminary preclinical data showing the discrimination of electric-stimulation evoked animal seizures. Here, we validate this maturing technology to assist with the discrimination of epileptic seizures from psychogenic non-epileptic seizures. We will analyze blood samples from patients undergoing video EEG monitoring in an epilepsy monitoring unit (Emory University Hospital, Atlanta GA). We expect 20% of these patients will have PNES. Once a patient has an EEG verified seizure, we will collect blood at various time points and identify biomarker RNA molecules in the blood. Blood samples will be collected from patients with non-verified seizures as control. These RNA expression values will be used to develop a predictive test for the occurrence of seizures. A biomarker gene panel will be developed from this exploratory project for validation in a larger patient population.

  Study phs003460

• scRNA-seq data of Anti-Her2-CAR T cells treated with immunomodulatory metabolites

  Anti-Her2-CAR T cells produced from T cells derived from healthy human donors were treated with DMSO (control) or either of the immunomodulatory metabolites indole‐3‐carboxaldehyde (ICA), valeric acid (VA) and isovaleric acid (IVA) to assess effects on CAR T cell function. scRNA-seq libraries were generated on a 10X Chromium controller with On-Chip-Multiplexing. The individual samples in a library are distinguished during analysis on the single-cell level via the cell barcode class.

  Dataset EGAD50000002012

• EM-seq datatset of primary human thymocyte subsets

  FASTQ-files of EM-seq experiments of 6 primary human thymocyte subsets, isolated by flow-cytometry (FACS) from neonatal thymus biopsies. All samples were sorted to be negative for the expression of the lineage surface markers CD11c, CD19, CD56, CD141 and CD303 and additionally had the following surface phenotypes: DN2 (CD4-CD8-CD7+CD1a-CD161-CD38+CD3-TCRab-); DN3 (CD4-CD8-CD7+CD1a+); ISP (CD4+CD8-CD3-TCRab-CD1a+); DPE (CD4+CD8+TCRab-CD69-); DPL (CD4+CD8+TCRab+CD69+); SP4 (CD4+CD8-). Samples were sorted from 1-2 pediatric patients undergoing heart surgery.

  Dataset EGAD50000001600

• (ATAC-seq) Shifted assembly and function of mSWI/SNF family subcomplexes underlie targetable dependencies in endometriod carcinomas

  Raw and processed ATAC-seq data from the VOA1066 undifferentiated endometrial carcinoma cell-line (10 samples). Samples were treated with DMSO, dBRD9, or compound 12, without doxycycline; some samples instead received doxycycline-induced ARID1A treatment, or doxycycline alone. Samples were sequenced on Illumina NextSeq 500 using 37 bp paired-end sequencing parameters. Raw fastq fastq files, bigwig files giving normalized genomic coverage per region, and called broad peaks are included for each sample.

  Dataset EGAD50000001472

• mRNA-seq data from ALL patients with NUP214::ABL1 disease

  Dataset contains mRNA-sequencing data (fastq files) from 2 patients with NUP214::ABL1 disease (one T-ALL and one B-ALL patient). Patient samples (peripheral blood or bone marrow) were taken at diagnosis (B-ALL) or relapse (T-ALL). mRNA was extracted from lymphoblasts and underwent paired-end sequencing (75bp) using the Illumina NextSeq platform. Transcriptomic data was used for identification of gene fusions, SNVs and InDels. mRNA-seq data for an additional B-ALL patient with NUP214::ABL1 disease utilised in this study has already been published (EGAS00001006460).

  Dataset EGAD50000001395

• SATB1 KO Treg and Teff cells: ATAC-seq

  We expanded human Treg and CD4+ Teff cells for 9 days, re-activated the cells and performed CRISPR/Cas9 RNP-mediated KO at day 11. The cells were treated with a pro-inflammatory cytokine IL-12 on day 12. 4 days after editing, living cells were FACS-sorted, the cell nuclei were extracted and prepared for ATAC-seq. AAVS1 KO served as control. The transcription factors SATB1 and two other targets (included to increase the robustness of the dataset) were knocked-out. Three biological replicates have been applied.

  Dataset EGAD50000001276

• ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  Dataset EGAD00001000281

• snATAC-seq BAM files from 10x Multiome profiling of human fetal liver hematopoiesis

  This dataset provides snATAC-seq BAM files from a single-nucleus multiome study of 53 human fetal liver samples (5–18 post-conception weeks). Live CD45+CD235- cells were sorted from dissociated tissue, and isolated nuclei were processed using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression kit. Raw sequencing data were processed and aligned to GRCh38-2020-A-2.0.0 using Cell Ranger ARC v2.0.2.

  Dataset EGAD50000002571

• BLUEPRINT release January 2015, ChIP-Seq for effector memory CD8-positive, alpha-beta T cell, terminally differentiated

  ChIP-Seq data for 1 effector memory CD8-positive, alpha-beta T cell, terminally differentiated 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_chipseq_analysis_ebi_20140811

  Dataset EGAD00001001195

• BLUEPRINT release August 2015, ChIP-Seq for CD3-negative, CD4-positive, CD8-positive, double positive thymocyte, on genome GRCh38

  ChIP-Seq data for 1 CD3-negative, CD4-positive, CD8-positive, double positive thymocyte sample(s). 2 run(s), 2 experiment(s), 2 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001570

• BLUEPRINT release August 2015, ChIP-Seq for effector memory CD8-positive, alpha-beta T cell, terminally differentiated, on genome GRCh38

  ChIP-Seq data for 1 effector memory CD8-positive, alpha-beta T cell, terminally differentiated sample(s). 4 run(s), 4 experiment(s), 4 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001577

• BLUEPRINT release August 2015, ChIP-Seq for mesenchymal stem cell of the bone marrow, on genome GRCh38

  ChIP-Seq data for 1 mesenchymal stem cell of the bone marrow sample(s). 9 run(s), 7 experiment(s), 7 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001578

• BLUEPRINT release August 2015, ChIP-Seq for effector memory CD8-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 2 effector memory CD8-positive, alpha-beta T cell sample(s). 10 run(s), 10 experiment(s), 10 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001472

• BLUEPRINT release August 2015, ChIP-Seq for CD4-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 7 CD4-positive, alpha-beta T cell sample(s). 39 run(s), 39 experiment(s), 39 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001505

• BLUEPRINT release August 2015, ChIP-Seq for central memory CD4-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 1 central memory CD4-positive, alpha-beta T cell sample(s). 9 run(s), 7 experiment(s), 7 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001499

• BLUEPRINT release August 2015, ChIP-Seq for CD3-positive, CD4-positive, CD8-positive, double positive thymocyte, on genome GRCh38

  ChIP-Seq data for 1 CD3-positive, CD4-positive, CD8-positive, double positive thymocyte 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001503

• BLUEPRINT release August 2015, ChIP-Seq for CD8-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 5 CD8-positive, alpha-beta T cell sample(s). 26 run(s), 26 experiment(s), 26 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001513

• BLUEPRINT release August 2015, ChIP-Seq for cytotoxic CD56-dim natural killer cell, on genome GRCh38

  ChIP-Seq data for 4 cytotoxic CD56-dim natural killer cell sample(s). 17 run(s), 17 experiment(s), 17 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001518

• BLUEPRINT release August 2015, ChIP-Seq for mature neutrophil - G-CSF/Dex. Treatment (16-20 hrs), on genome GRCh38

  ChIP-Seq data for 3 mature neutrophil - G-CSF/Dex. Treatment (16-20 hrs) sample(s). 18 run(s), 18 experiment(s), 18 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001527

• BLUEPRINT release August 2015, ChIP-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell, on genome GRCh38

  ChIP-Seq data for 1 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 7 run(s), 6 experiment(s), 6 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001557

• BLUEPRINT release August 2015, ChIP-Seq for CD14-positive, CD16-negative classical monocyte, on genome GRCh38

  ChIP-Seq data for 9 CD14-positive, CD16-negative classical monocyte sample(s). 56 run(s), 53 experiment(s), 53 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_chipseq_analysis_ebi_20150820

  Dataset EGAD00001001552

• BLUEPRINT release August 2016, ChIP-Seq for effector memory CD8-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 3 effector memory CD8-positive, alpha-beta T cell sample(s). 16 run(s), 15 experiment(s), 15 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002283

• BLUEPRINT release August 2016, ChIP-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell, on genome GRCh38

  ChIP-Seq data for 3 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 19 run(s), 18 experiment(s), 18 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002362

• BLUEPRINT release August 2016, ChIP-Seq for CD3-negative, CD4-positive, CD8-positive, double positive thymocyte, on genome GRCh38

  ChIP-Seq data for 2 CD3-negative, CD4-positive, CD8-positive, double positive thymocyte sample(s). 7 run(s), 5 experiment(s), 5 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002369

• BLUEPRINT release August 2016, ChIP-Seq for mesenchymal stem cell of the bone marrow, on genome GRCh38

  ChIP-Seq data for 2 mesenchymal stem cell of the bone marrow sample(s). 16 run(s), 14 experiment(s), 14 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002381

• BLUEPRINT release August 2016, ChIP-Seq for effector memory CD8-positive, alpha-beta T cell, terminally differentiated, on genome GRCh38

  ChIP-Seq data for 2 effector memory CD8-positive, alpha-beta T cell, terminally differentiated sample(s). 6 run(s), 6 experiment(s), 6 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002503

• BLUEPRINT release August 2016, ChIP-Seq for Germinal Center B-Cell-Like Diffuse Large B-Cell Lymphoma, on genome GRCh38

  ChIP-Seq data for 3 Germinal Center B-Cell-Like Diffuse Large B-Cell Lymphoma sample(s). 10 run(s), 10 experiment(s), 10 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002506

• BLUEPRINT release August 2016, ChIP-Seq for effector memory CD4-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 2 effector memory CD4-positive, alpha-beta T cell sample(s). 8 run(s), 7 experiment(s), 7 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002514

• BLUEPRINT release August 2016, ChIP-Seq for central memory CD4-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 3 central memory CD4-positive, alpha-beta T cell sample(s). 11 run(s), 9 experiment(s), 9 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002439

• BLUEPRINT release August 2016, ChIP-Seq for CD8-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 7 CD8-positive, alpha-beta T cell sample(s). 38 run(s), 38 experiment(s), 38 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002455

• BLUEPRINT release August 2016, ChIP-Seq for mature conventional dendritic cell - GM-CSF_IL4_T=6_days_R848_T=24hrs, on genome GRCh38

  ChIP-Seq data for 3 mature conventional dendritic cell - GM-CSF_IL4_T=6_days_R848_T=24hrs sample(s). 20 run(s), 19 experiment(s), 19 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002462

• BLUEPRINT release August 2016, ChIP-Seq for cytotoxic CD56-dim natural killer cell, on genome GRCh38

  ChIP-Seq data for 6 cytotoxic CD56-dim natural killer cell sample(s). 34 run(s), 34 experiment(s), 34 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002463

• BLUEPRINT release August 2016, ChIP-Seq for mature neutrophil - G-CSF/Dex. Treatment (16-20 hrs), on genome GRCh38

  ChIP-Seq data for 3 mature neutrophil - G-CSF/Dex. Treatment (16-20 hrs) sample(s). 23 run(s), 23 experiment(s), 23 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002470

• BLUEPRINT release August 2016, ChIP-Seq for CD14-positive, CD16-negative classical monocyte, on genome GRCh38

  ChIP-Seq data for 10 CD14-positive, CD16-negative classical monocyte sample(s). 80 run(s), 76 experiment(s), 76 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002484

• BLUEPRINT release August 2016, ChIP-Seq for central memory CD8-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 3 central memory CD8-positive, alpha-beta T cell sample(s). 4 run(s), 4 experiment(s), 4 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002450

• BLUEPRINT release August 2016, ChIP-Seq for CD3-positive, CD4-positive, CD8-positive, double positive thymocyte, on genome GRCh38

  ChIP-Seq data for 3 CD3-positive, CD4-positive, CD8-positive, double positive thymocyte sample(s). 11 run(s), 11 experiment(s), 11 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002445

• BLUEPRINT release August 2016, ChIP-Seq for CD4-positive, alpha-beta T cell, on genome GRCh38

  ChIP-Seq data for 9 CD4-positive, alpha-beta T cell sample(s). 68 run(s), 63 experiment(s), 63 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_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002444

• scRNA-seq raw data

  scRNA-seq data of B-lineage cells from the cerebrospinal fluid of 21 patients with multiple sclerosis. The data was generated with the Smart-seq2 protocol and sequenced on Illumina NextSeq500.

  Dataset EGAD00001008481

• H3K27ac ChIP-seq in a selected group of AML patients

  ChIP-seq data were generated for a number of selected patients to investigate changes in enhancer and promoter regions. ChIP was performed as described previously with slight modifications27. Briefly, cells were crosslinked with 1% formaldehyde for 10 minutes at room temperature and the reaction was quenched with glycine at a final concentration of 0.125 M. Chromatin was sheared using the Covaris S220 focused-ultrasonicator to an average size of 250–350 bp. A total of 2.5 µg of antibody against H3K27ac (Abcam, ab4729) was added to sonicated chromatin of 2 × 106 cells and incubated overnight at 4 °C. Protein A sepharose beads (GE healthcare) were added to the ChIP reactions and incubated for 2 h at 4 °C. Beads were washed and chromatin was eluted. After crosslink reversal, RNase A and proteinase K treatment, DNA was extracted with the Monarch PCR & DNA Cleanup kit (NEB). Sequencing libraries were prepared with the NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB) according to the manufacturer’s instructions. The quality of dsDNA libraries was analyzed using the High Sensitivity D1000 ScreenTape Kit (Agilent) and concentrations were assessed with the Qubit dsDNA HS Kit (Thermo Fisher Scientific). Libraries were single-end sequenced on a HiSeq 4000 (Illumina). ChIP-seq reads were aligned to the human reference genome build hg19 with bowtie

  Dataset EGAD00001007582

• Single-cell RNA sequencing of airway epithelial cells derived from patient-specific and gene-corrected human iPSCs carrying a CCNO variant

  This study aims to elucidate the molecular mechanisms underlying the pathology of Primary Ciliary Dyskinesia (PCD) caused by disease-associated gene variants. To this end, we will establish patient-specific iPSCs and gene-corrected iPSC lines, differentiate them into airway epithelial cells, and perform single-cell RNA sequencing for comparative transcriptomic analysis. Insights gained from this study are expected to contribute to the development of novel diagnostic and therapeutic strategies for PCD.

  Study JGAS000846

• RNA sequencing of CD8 T cells from melanoma patients prior to and during checkpoint immunotherapy and untreated healthy controls

  We are interested in inter-individual variation in transcriptional response to immune checkpoint blockade. We have analysed poly A purified RNA expression from CD8 T cells (297 transcriptomes in total) isolated from metastatic melanoma patients (n=106) prior to and during treatment with either single agent (Pembrolizumab) or combination (Ipilimumab/ Nivolumab) immune checkpoint blockade. We compare expression at different stages of treatment and additionally contrast this with that from healthy controls (n=68).

  Study EGAS00001004081

• Deep RNA sequencing in CLL

  Chronic lymphocytic leukemia (CLL) is a B-cell neoplasm with a heterogeneous clinical and biological behavior. We have performed deep RNA sequencing in different subpopulations of B-lymphocytes from healthy individuals and CLL cells from a cohort of 98 patients, and characterized the CLL transcriptional landscape with unprecedented resolution. The transcriptomic architecture of CLL uncovered here refines the biological characterization of the disease and opens new perspectives for the clinical management of patients.

  Study EGAS00001000374

• RNA sequencing in blood samples of cluster headache patients

  Cluster headache is a relatively rare headache disorder, typically characterized by multiple daily, short-lasting attacks of excruciating, unilateral (peri-)orbital or temporal pain associated with autonomic symptoms and restlessness. To better understand the pathophysiology of cluster headache, we used RNA sequencing to identify differentially expressed genes and pathways in whole blood of patients with episodic (n = 19) or chronic (n = 20) cluster headache in comparison with headache-free controls (n = 20). Blood samples were subjected to globin reduction before sequencing.

  Study EGAS00001001918

• Single-cell RNA sequencing reveals cell-type specific eQTLs in peripheral blood mononuclear cells

  This proof-of-concept study shows that single-cell RNA sequencing in ~25,000 peripheral blood mononuclear cells (PBMCs) of 45 donors allows for the identification of cell type-specific cis expression quantitative trait loci effects (cis-eQTLs). Moreover, the significant top-eQTL effects show consistent allelic direction (94%) with previously published whole blood deepSAGE data. These cell type-specific eQTLs may give us additional insight into the downstream consequences of genetic risk factors for immune-mediated diseases.

  Study EGAS00001002560

• Illumina RNA sequencing for HLA expression qauntification

  In this study, we quantified mRNA expression levels of HLA class I and II genes from peripheral blood samples of 50 healthy individuals. The gene- and allele-specific mRNA expression was assessed using unique molecular identifiers, which enabled PCR bias removal and calculation of the number of original mRNA transcripts. We identified differences in mRNA expression between different HLA genes and alleles. Our results suggest that HLA alleles are differentially expressed and these differences in expression levels are quantifiable using RNA sequencing technology.

  Study EGAS00001004931

• Single-cell RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015713

• Identification of Long Non-coding RNA Biomarker of Human Lupus Nephritis Disease Activity - Data Access Committee

  Dac EGAC00001003281