9182 results for "cancer rna-seq"

in 45.51 milliseconds.

• ChIP_2PEAKS

  Second batch of ChIP-seq narrowPeaks. Software: MACS2 v2.1.2

  Dataset EGAD00010002051

• Rna_expression

  RNA expression microarray

  Dataset EGAD00010000869

• The Extracellular RNA Quality Control (exRNAQC) study (phase 1)

  Study EGAS00001005263

• The Extracellular RNA Quality Control (exRNAQC) study (phase 2)

  Study EGAS00001006499

• Identification of cardiovascular biomarkers through an integrative omics approach

  Recent GWAS studies have made extensive use of large eQTL data sets to functionally annotate index SNPs. With a large number of association signals located outside coding regions there has been an intense search among sequence variants affecting gene expression at the transcriptional level. However, little progress has been made in mapping regulatory variants that affect protein levels at the translational or post-translational level. It is now possible to undertake a protein QTL scan for focused sets of e.g. oxidized proteins by mass spectrometry. We have established a collaboration with a longitudinal, family-based study in France, the Stanislas cohort, which comprises circa 1000 nuclear families (4,295 individuals) and has follow up data for 10 years (three visits). We have undertaken a pilot study in a focus set of 257 subjects from 79 families with the aim to integrate GWAS, transcriptomic and DNA methylation data with proteomic data on a set of 100 proteins measured in PBMCs. We have already generated GWAS data using Illumina's core-exome chip as well as DNA methylation profiles with the 450K array. We propose to use RNA seq to generate transcriptomic data of the corresponding PBMCs. 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/

  Dataset EGAD00001002197

• RNA-seq study of longitudinal blood cell samples from children at risk of type 1 diabetes

  The appearance of type 1 diabetes (T1D)-associated autoantibodies is the first and only measurable parameter to predict progression toward T1D in genetically susceptible individuals. However, autoantibodies indicate an active autoimmune reaction, wherein the immune tolerance is already broken. Therefore, there is a clear and urgent need for new biomarkers that predict the onset of the autoimmune reaction preceding auto-antibody positivity or reflect progressive b-cell destruction. Here we report the mRNA sequencing–based analysis of 306 samples including fractionated samples of CD4+ and CD8+ T cells as well as CD4, CD8 cell fractions and unfractionated PBMC samples longitudinally collected from seven children who developed beta-cell autoimmunity (Case subjects) at a young age and matched control subjects.

  Study EGAS00001004071

• RNA sequencing, ATACseq, and TCR-seq of Tfh cells and CXCR5- CD4+ T cells in HIV infected lymph nodes

  The high dimensional analyses of CD4+ T cells in human lymph nodes are case-based analyses of the TCR repertoire, transcriptome, and epigenetic signatures of various CD4+ T cells in human lymph nodes. The study aims to determine clonal overlap between phenotypically distinct CD4+ T cells and the similarities between these populations on transcriptional and epigenetic levels. This study performs TCR repertoire sequencing using Molecular Identifier Clustering-based Immune Repertoire Sequencing (MIDCIRS). We found clonal overlap between a population of CXCR5- T cells and follicular helper T (Tfh) cells. We also show epigenetic and transcriptional similarities between CXCR5-PD-1+ T cells and Tfh cells. This study shares an overlapping set of donors with phs001548.

  Study phs001849

• TRACERx NSCLC, multiregion sequencing of the first 100 tumors

  BACKGROUND TRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC). METHODS Multiregion high-depth whole-exome sequencing (M-seq) was performed on 100 early stage NSCLC tumors resected prior to systemic therapy. A total of 327 tumor regions were sequenced and analyzed to define evolutionary histories, obtain a census of clonal and subclonal events, and assess the relationship between ITH and recurrence-free survival (RFS). RESULTS Widespread ITH was observed for both somatic copy number alterations (median 48% [0.03-88%]) and mutations (median 30% [0.5-93%]). Driver mutations in EGFR, MET, BRAF and TP53 were almost always clonal. However, heterogeneous driver alterations occurring later in evolution were found in over 75% of tumors and were common in PIK3CA, NF1 and genes involved in chromatin modification and DNA response and repair. Genome doubling and ongoing dynamic chromosomal instability (CIN), illustrated by mirrored subclonal allelic imbalance, were identified as causes of ITH resulting in parallel evolution of driver copy number events, including amplifications of CDK4, FOXA1, and BCL11A. Elevated copy number heterogeneity was associated with shorter RFS (HR=4.9, P=0.00044), which remained significant in a multivariate analysis. CONCLUSIONS ITH mediated through CIN, rather than point mutational heterogeneity, was associated with increased risk of relapse, supporting its value as a prognostic predictor, and the need to target this high-risk phenotype.

  Dataset EGAD00001003206

• Genome Sequencing of Circulating Tumor Cells for Minimally Invasive Molecular Characterization of Multiple Myeloma Pathology

  ​​Multiple Myeloma (MM) develops from well-defined precursor stages known as Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering MM (SMM); however, invasive bone marrow (BM) biopsy limits screening and monitoring strategies for patients. In this study, we enumerated circulating tumor cells (CTCs) from patients with precursor and overt diseases, and developed a novel approach, MinimuMM-seq, which enables detection of translocations and copy number abnormalities through whole-genome sequencing of highly pure CTCs. In a cohort of 51 patients, 24 with paired BM, we applied our method MinimuMM-seq and detected 100% of clinically reported BM biopsy events. The next-generation sequencing (NGS) assay could replace molecular cytogenetics by Fluorescence In Situ Hybridization (FISH) for diagnostic yield and risk classification. In 8 patients we could apply MinimuMM-seq on serially collected samples to track and monitor major clones in peripheral blood. We describe clonal evolution and shifting dynamics of subclones over time, as well as in between blood and marrow compartments. All whole-genome sequencing (WGS) data generated in this study will be made accessible through dbGaP.

  Study phs003084

• RNA sequencing of AD, MCI and control ONS cells

  Study EGAS00001006594

• RNAseq data of polyA+ RNA from Leukocytes from 624 individuals of the ProgeNIA cohort.

  We sequenced the polyA+ fraction of the RNA of the leukocytes from 624 sardinian individuals with RNAseq. Prior to library preparation we added either ERCC ExFold RNA Spike-In. An average of 60M reads per samples with 51 bp paired-end reads were generated on a HiSeq 2000 (Illumina). Sequencing reads were then aligned using STAR-2.2.0c2 to the h37d5 reference genome supplemented with the ERCC spike-ins sequences. We further provided an exon-exon junction database that we generated from the GENCODE v14 annotation. In order to remove a contamination from a parallel experiment, we discarded any reads that mapped to the genomic regions of CBLB (chr3:105370773-105592330) and BCL11A (chr2:60672555-60784156). Filtered aligned reads (bam format) are shared.

  Dataset EGAD00001003102

• DNA and RNA sequencing data from Ovarian Carcinosarcoma patients from the Glasgow Cohort.

  In this study, we analysed DNA and RNA sequencing data from Ovarian Carcinosarcoma patients, from isolated carcinoma and sarcoma components and investigated DNA variants as well as gene expression. Genomic analyses from this data and further investigation from preclinical models of ovarian carcinosarcoma support the conversion theory for disease development and indicate that microtubule inhibitors could be used to suppress EMT and stimulate anti-tumour immunity.

  Study EGAS00001006605

• Single Cell RNA sequencing of Organoids from TSC2+/- patient IPSCs in H- and L-medium

  This dataset contains single cell RNA sequencing data from Organoids grown in high nutrient (H) and low nutrient (L) medium. Organoids were grown for 110 days. Organoids were grown from a patient IPS cell line with a heterozygous mutation in TSC2 (Patient 1 TSC2+/- iPSCs) and an isogenic control cell line (TSC2+/+).

  Dataset EGAD00001006332

• Dataset on keratinocytic gene expression pattern in Hidradenitis suppurativa

  Human skin samples were obtained from HS patients after informed consent (Ethical vote, University of Würzburg; No. 306/12). Lesional and perilesional were taken and epidermis and dermis separated. Isolated epidermal keratinocytes were further processed for RNA isolation. mRNA was extracted from five pairwise-matched lesional and perilesional epidermal HS pellets and RNA sequencing was performed.

  Dataset EGAD00001008005

• RNA-sequencing data of post-mortem brain tissue taken from individuals with SCA3 and controls

  Spinocerebellar ataxia type 3 (SCA3) is the most common autosomal dominant inherited ataxia worldwide, caused by a CAG repeat expansion in the Ataxin-3 gene resulting in a polyglutamine (polyQ)-expansion in the corresponding protein. Here we have RNA-sequencing data from the cerebellum of individuals with SCA3 and matched controls.

  Dataset EGAD00001009317

• Total NF1 sequence in the patients with neurofaibromatosis type1

  <html>We analyzed whole <i>NF1</i> gene region at DNA (including whole exon and intron sequences) and RNA (full length cDNA of leukocyte) level for 20 patients with neurofibromatosis type 1.</html>

  Study JGAS000288

• Rapid Idiosyncratic Mechanisms of Clinical Resistance to KRAS G12C Inhibition

  Using deep RNA and whole exome sequencing of pre- and post-treatment autopsy samples, we reveal diverse clonal populations that occurred through altered cell intrinsic, tumor microenvironment and immunologic remodeling mechanisms of resistance.

  Study phs002734

• Centroblast Cohort

  miRNA-seq Cohort of 15 Benign Centroblasts

  Dataset EGAD00001001075

• scRNA-seq of patient-derived PDAC organoids

  Dataset EGAD00001006448

• Dataset that contains analyses of submission 27

  Description of analyses dataset of submission 27

  Dataset EGAD50000000113

• Dataset that contains analyses of submission 129

  Description of analyses dataset of submission 129

  Dataset EGAD50000000212

• The PEMDAC phase 2 study of pembrolizumab and entinostat in patients with metastatic uveal melanoma

  Patients with metastatic uveal melanoma (UM) have an abysmal prognosis. Preclinical studies have suggested that epigenetic therapy could enhance immunogenicity of cancer cells. Here we test if epigenetic therapy would enhance PD-1 immunotherapy in patients with metastatic UM. We report the results of the PEMDAC phase 2 clinical trial (n=29; NCT02697630) where the HDAC inhibitor entinostat was combined with the PD-1 inhibitor pembrolizumab in patients with metastatic UM. The primary endpoint was objective response rate (ORR), and was met with an ORR of 14%. The clinical benefit rate at 18 weeks was 28%, median progression free survival was 2.1 months and the median overall survival was 13.4 months. Toxicities were manageable, and there were no treatment-related deaths. Extensive genomics studies were performed using DNA/RNA and single cell sequencing and flow cytometry. Objective responses and prolonged survival were seen in three patients with BAP1 wildtype tumors, and in one patient with an iris melanoma that exhibited a UV signature. Longer survival also correlated with low baseline ctDNA levels or LDH. In conclusion, HDAC inhibition and anti-PD1 immunotherapy results in durable responses in a subset of patients with metastatic UM. Further exploration of combined immunotherapy and epigenetic therapy in metastatic UM is warranted.

  Study EGAS00001005478

• Longitudinal evaluation of serum microRNAs as biomarkers for neuroblastoma burden and therapeutic p53 reactivation

  Accurate assessment of treatment response and residual disease is indispensable for the evaluation of cancer treatment efficacy. However, performing tissue biopsies for longitudinal follow-up poses a major challenge in the management of solid tumours like neuroblastoma. In the present study, we evaluated whether circulating miRNAs are suitable to monitor neuroblastoma tumour burden and whether treatment-induced changes of miRNA abundance in the tumour are detectable in serum. We performed small RNA sequencing on longitudinally collected serum samples from mice carrying orthotopic neuroblastoma xenografts that were exposed to treatment with idasanutlin or temsirolimus. We identified 57 serum miRNAs to be differentially expressed upon xenograft tumour manifestation, out of which 21 were also found specifically expressed in the serum of human high-risk neuroblastoma patients. The murine serum levels of these 57 miRNAs correlated with tumour tissue expression and tumour volume, suggesting potential utility for monitoring tumour burden. In addition, we describe serum miRNAs that dynamically respond to p53 activation following treatment of engrafted mice with idasanutlin. We identified idasanutlin-induced serum miRNA expression changes upon one day and 11 days of treatment. By limiting to miRNAs with a tumour-related induction, we put forward hsa-miR-34a-5p as a potential pharmacodynamic biomarker of p53 activation in serum.

  Study EGAS00001006678

• Stratifying and Targeting Pediatric Medulloblastoma through Genomics

  In this project, genomic analyses of pediatric medulloblastoma samples, obtained through the international medulloblastoma consortium, will be performed. RNA and miRNA expression profiles of 1000 samples, representing all four subgroups (Wnt, Shh, Group C, and D), will be studied to identify novel subtypes within each subgroup. The resulting subtype-specific expression profiles will support the development of reliable and robust biomarkers to more accurately and reliably classify medulloblastomas for treatment in clinical trials. For that purpose, two assays will be developed: an antibody-based immunohistochemical assay and an orthogonal nucleic acid-based hybridization assay. Additional genomic DNA analysis of the 300 high risk subgroup cases will support the discovery of subgroup specific somatic mutations in order to inform current clinical trials of targeted therapies, and to identify genes and pathways already targeted in other diseases. Such therapies could be rapidly transitioned to Phase II trials in medulloblastoma. Furthermore, the discovery of somatic mutations could be used for developing as well as validating specific biomarkers. The project team will also try to identify risk factors that predispose children to this type of cancer.

  Study EGAS00001000273

• Characterization of Structural Variants in Acute Myeloid Leukemia Patients

  Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults and is expected to increase in frequency as the population ages. Most subtypes of AML are difficult to treat, in part due to a lack of understanding of possible molecular targets. Furthermore, the peak incidence of AML is in the sixth decade, and patients over the age of 65 do not tolerate chemotherapeutic regimens well. Moreover, AML has been highly studied as a model for other cancers. An RNA Biology Program is established aiming to develop comprehensive datasets through the profiling of all different classes of RNA expression, RNA editing, alternative splicing, and non-coding RNAs in CD34&#43; normal hematopoietic stem cells (HSC), their differentiated progeny, and AML clinical samples that could be utilized to understand leukemia. These datasets will allow us to computationally identify and characterize various RNA species and processes to enable the development of effective therapeutics to AML, leukemia and other types of cancers. Epigenetic factors such as methylation and histone modifications will also be characterized from these clinical samples allowing for integrated dataset analysis to look at the cross-talk between RNA and DNA/epigenomics in leukemia. The following is a subsidiary study of the above study description where whole genome sequencing was performed on two collected AML patients&#39; samples using Illumina and Oxford Nanopore sequencing technologies. The aim is to characterize the roles of structural variants (SV) in RNA biology which are potentially important for AML development using our own established long-read SV characterization tool, NanoVar. The advent of Oxford Nanopore sequencing technologies has opened a new avenue for better SV characterization by having longer sequencing reads. NanoVar is an accurate and rapid SV characterization tool that utilizes low-depth Nanopore sequencing data. NanoVar makes use of split-reads and hard-clipped reads for SV discovery and employs a simulation-trained neural network classifier for true positive enrichment. NanoVar exhibited higher accuracy and faster computational speed amongst other long-read SV detection tools in simulated data. In AML patient data, NanoVar displayed excellent accuracy in SV characterization (16/16 SVs validated in two patients) by using only 4x depth sequencing data. In summary, NanoVar improves the accuracy and speed of SV characterization at a lower sequencing cost, an approach compatible with clinical studies.

  Study phs001847

• ChIP-seq of plasma cell-free nucleosomes identifies gene expression programs of the cells-of-origin

  Cell-free DNA in human plasma provides access to molecular information about the pathological processes in the organs or tumors from which it originates. These DNA fragments are derived from fragmented chromatin in dying cells, and retain some of the cell of origin histone modifications. Here, we apply chromatin immunoprecipitation of cell-free nucleosomes carrying active chromatin modifications followed by sequencing (cfChIP-seq) to 268 human samples. In healthy donors, we identified bone marrow megakaryocytes, but not erythroblasts, as major contributors to the cfDNA pool. In patients with a range of liver diseases, we show that we can identify pathology-related changes in hepatocyte transcriptional programs. In metastatic colorectal carcinoma patients, we detected clinically relevant, and patient-specific information, including transcriptionally active HER2 amplifications. Altogether, cfChIP-seq using low sequencing depth, provides systemic and genome-wide information, can inform diagnosis, and facilitate interrogation of physiological and pathological processes using blood samples.

  Study EGAS00001004913

• Performance assessment of total RNA sequencing of human biofluids and extracellular vesicles

  Dac EGAC00001001591

• Total RNA-Sequencing of patient-derived xenograft models of myxoid liposarcoma

  Study EGAS00001004901

• BLUEPRINT Bisulfite-seq (CNAG)

  Bisulfite-Seq

  Study EGAS00001000418

• H3K27ac ChIP-seq in primary inflammatory (TPP) macrophages

  We investigated an intergenic haplotype on chr21q22, linked to five different inflammatory diseases, and identified the molecular mechanism by which the risk haplotype increases expression of the causal gene, ETS2. This mechanism was predicted to alter enhancer activity at the disease-associated locus. We therefore performed H3K27ac ChIP-seq in inflammatory macrophages from minor and major allele homozygotes at the causal allele and investigated the effect on enhancer activity. In doing so, we mechanistically delineated how the risk haplotype increases expression of ETS2.

  Study EGAS00001007562

• 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 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 endothelial cell of umbilical vein (proliferating), on genome GRCh38

  ChIP-Seq data for 2 endothelial cell of umbilical vein (proliferating) sample(s). 12 run(s), 12 experiment(s), 12 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 EGAD00001001487

• 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 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 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 endothelial cell of umbilical vein (resting), on genome GRCh38

  ChIP-Seq data for 2 endothelial cell of umbilical vein (resting) sample(s). 11 run(s), 11 experiment(s), 11 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 EGAD00001001559

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

  Bisulfite-Seq data for 1 effector memory CD8-positive, alpha-beta T cell, terminally differentiated sample(s). 15 run(s), 1 experiment(s), 1 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_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001541

• 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 2015, ChIP-Seq for CD8-positive, alpha-beta thymocyte, on genome GRCh38

  ChIP-Seq data for 1 CD8-positive, alpha-beta 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 EGAD00001001568

• 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 CD4-positive, alpha-beta thymocyte, on genome GRCh38

  ChIP-Seq data for 1 CD4-positive, alpha-beta 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 EGAD00001001584

• BLUEPRINT release August 2015, ChIP-Seq for CD38-negative naive B cell, on genome GRCh38

  ChIP-Seq data for 6 CD38-negative naive B cell sample(s). 20 run(s), 20 experiment(s), 20 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 EGAD00001001594

• BLUEPRINT release August 2015, ChIP-Seq for segmented neutrophil of bone marrow, on genome GRCh38

  ChIP-Seq data for 4 segmented neutrophil of bone marrow sample(s). 20 run(s), 19 experiment(s), 19 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 EGAD00001001588

• snRNA-seq in white matter post-mortem tissue from MS and controls

  This Dataset is currently hosted by the European Nucleotide Archive. To access the data contained within the Dataset please follow the link below: https://www.ebi.ac.uk/ena/browser/view/PRJEB39323 Dataset consists of 20 snRNA-seq bam files from 10X v2. 5 samples from postmortem white matter tissue from non-neurological controls and15 samples from different MS lesions from the white matter tissue of 4 postmortem progressive MS patients.

  Dataset EGAD00001004544

• BLUEPRINT release August 2016, ChIP-Seq for Acute Promyelocytic Leukemia - MC2884 (24h), on genome GRCh38

  ChIP-Seq data for 1 Acute Promyelocytic Leukemia - MC2884 (24h) 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 EGAD00001002312

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

  Bisulfite-Seq data for 2 CD3-positive, CD4-positive, CD8-positive, double positive thymocyte sample(s). 29 run(s), 2 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_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002325

• 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