10156 results for "cancer rna-seq"

in 45.81 milliseconds.

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

  Total RNA sequencing was performed on whole blood samples from 74 Lupus nephritis (LN) patients and 20 healthy controls. Differential expression analysis and weighted gene co-expression network analysis were performed to characterise expression changes of long non-coding RNAs (lncRNAs) in LN and identify lncRNAs with a key role in disease activity that could be used as potential blood-based biomarkers.

  Study EGAS00001007117

• DAC for TCR-seq analysis of human intestinal Trm cells

  Dac EGAC00001001219

• scBCR-seq data from human donors

  Dataset EGAD00001005057

• Human Inflammatory Skin Disease scRNA-seq

  Study EGAS00001005271

• Presence of bacterial infection in brains of patients with Huntington's disease (HD)

  Bacterial isolation in infected brains in patients with Huntington's disease. Here we used next generation sequencing of 16S ribosomal RNA gene PCR amplicons (NGS 16S amplicon analysis)

  Study EGAS00001003678

• Presence of bacterial infection in brains of patients with Parkinson's disease (PD)

  Isolation of bacteria in infected brains in patients with Parkinson's disease. Here we used next generation sequencing of 16S ribosomal RNA gene PCR amplicons (NGS 16S amplicon analysis).

  Study EGAS00001003643

• A method for multiplexed full-length single-molecule sequencing of the human mitochondrial genome

  Here we describe a method to target, multiplex and sequence full-length, native single-molecule the human mitochondrial genome utilizing the RNA-guided DNA endonuclease Cas9.

  Study EGAS00001006203

• Small molecule screen reveals synergy of cell cycle checkpoint kinase inhibitors with DNA-damaging chemotherapies in medulloblastoma (methylation dataset)

  human medulloblastoma xenograft isolated from mouse brain was frozen and genomic DNA or RNA was extracted. Bisulfite converted DNA was processed and hybridised to illumina EPIC or 450K arrays using standard protocols. RNAseq was performed on total RNA.

  Dataset EGAD00001006583

• Single cell RNA-sequencing of treatment naïve PDAC patient samples

  Single cell RNA-sequencing of treatment naïve PDAC patient samples. We have 10 samples, sequenced using the 10X genomics chromium platform with 3 prime chemistry. We are submitting FASTQ files representing the index files (I1), Read1 (R1) and Read2 (R2).

  Dataset EGAD00001008961

• Single-cell RNA-sequencing of peripheral blood mononuclear cells from patients with achalasia

  The data published here contains single-cell RNA-sequencing (scRNAseq) data as obtained using the 3' scRNAseq using Chromium Single Cell 3’ Reagent from 10X Genomics on peripheral blood mononuclear cells (PBMC) from patients with achalasia. Sequencing was performed in a paired-ended fashion on the NovaSeq6000.

  Dataset EGAD50000000250

• RNA Sequencing of AD OM cells exposed to traffic-related air pollutants

  Total RNA sequencing of olfactory mucosa (OM) cells derived from individuals diagnosed with Alzheimer's disease exposed to traffic-related ultrafine particles (UFPs) for 24-h and 72-h in submerged cultures. The UFPs used for exposures were: A0, A20 and Euro6. Exposures were compared to the corresponding blank samples.

  Dataset EGAD50000000648

• Primary_DIPG_expression_profiles

  In this study, we used RNA-sequencing gene expression profiling in order to characterize specific phenotypic traits in DIPG. Seventeen primary tumor samples stereotactically biopsied at diagnosis by neurosurgeons in the Necker-Enfants Malades hospital (Paris, France) and snap-frozen for RNA extraction and subsequent poly-A mRNA purification.

  Dataset EGAD00001011080

• Optimisation of CITE-seq on liquid and solid tissues

  Study EGAS00001005849

• Bolleboom-Gao peri-tumoral snRNA-seq glioblastoma dataset 2022/A - Data Access Committee

  Dac EGAC00001003049

• MeDIP-seq data in the Peri/postnatal Epigenetics Twins Study (PETS)

  Dac EGAC00001000596

• EMBL genome biology research group for structural variation (Strand-seq application)

  Dac EGAC00001001091

• METHYLATION_TUMOR

  Methylation data from tumor tissue

  Dataset EGAD00010001674

• Atrial appendage miRNA-seq in non-, paroxysmal and persistent atrial fibrillation

  Study EGAS00001005296

• Peripheral clonal expansion of T cells (scRNA-seq)

  Study EGAS00001003993

• 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

• 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+ 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' 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

• Seminal Plasma RNA DAC

  Dac EGAC00001002111

• Adult Eosinophilic Esophagitis Registry Atlas

  Eosinophilic esophagitis (EoE) arises in the setting of type 2 cytokine-driven inflammation, is characterized by the infiltration of eosinophils in the esophageal mucosa, and is associated with dysphagia and fibrostenotic complications. Disease development and progression involve complex interactions between epithelial cells, stromal cells and immune cells. Indeed, increased epithelial permeability is consistently found in EoE, potentially leading to increased antigen exposure and inflammation. Persistent inflammation can in turn lead to fibroblast activation and fibrosis, which, together with the mediators produced by eosinophils and mast cells, leads to dysmotility. To decipher the interactions between these cell types across distinct disease states, we obtained biopsies from 7 healthy controls and 15 EoE subjects, including both active disease and clinical remission, to perform single cell RNA-sequencing (scRNA-seq).Each biopsy sample was enzymatically digested within 3 hours of collection with collagenase/dispase to generate a single cell suspension. Suspensions were then loaded on a 10x Chromium controller, and libraries were generated with the 3' Chemistry pipeline (v2 or v3) before sequencing on an Illumina sequencer. The resulting sequence data was demultiplexed and analyzed with the CellRanger pipeline to generate raw fastq files and processed gene-count matrices for each sample. Altogether, our study generated transcriptomic profiles from 421,312 individual cells after quality control and filtering.Our analyses reveal 60 cell subsets and functional alterations in cell states, compositions, and interactions that highlight previously unknown features of EoE. Active disease displayed enrichment of ALOX15+ macrophages, novel PRDM16+ dendritic cells expressing the EoE risk gene ATP10A, and cycling mast cells, with concomitant reduction of TH17 cells. Receptor–ligand expression uncovered eosinophil recruitment programs, increased fibroblast interactions in disease, and IL-9+IL-4+IL-13+TH2 and endothelial cells as potential mast cell interactors. Resolution of inflammation-associated signatures included mast and CD4+ Tissue-resident memory T (TRM) cell contraction and cell type-specific downregulation of eosinophil chemoattractant, growth, and survival factors.

  Study phs003574

• Repurposing azacitidine and carboplatin to prime immune checkpoint blockade-resistant melanoma for anti-PD-L1 re-challenge

  Purpose: Drug repurposing offers the opportunity for chemotherapy to be used to re-establish sensitivity to immune checkpoint blockade (ICB) therapy. Here we investigated the clinical and translational aspects of an early phase II study of azacitidine and carboplatin priming for anti-PDL1 immunotherapy (Avelumab) in patients with advanced ICB-resistant melanoma. Experimental Design: 20 participants with ICB resistant metastatic melanoma received 2 x 4-week cycles of azacitidine and carboplatin followed by ICB re-challenge with anti-PD-L1 avelumab. The primary objective was overall response rate after priming and ICB re-challenge. Secondary objectives were Clinical benefit rate (CBR), progression-free survival (PFS) and overall survival (OS). Translational correlation analysis of HLA-A and PD-L1 expression, RNA-seq and reduced representation bisulfite sequencing (RRBS) of biopsies at baseline, after priming and after 6 cycles of avelmuab was performed. Results: The ORR determined after azacitidine and carboplatin priming was 10% (2/20) with 2 partial responses (PR). The ORR determined after priming followed by 6 cycles of avelumab (week 22) was 10%, with 2/20 participants achieving iPR. The CBR for azacitidine and carboplatin priming was 65% (13/20) and after priming followed by 6 cycles of avelumab CBR was 35% (n = 7/20). The median PFS was 18.0 weeks (95% CI: 14.87 – 21.13 weeks) and the median OS was 47.86 weeks (95% CI: 9.67 – 86.06 weeks). Translational correlation analysis confirmed HLA-A generally increased after priming with azacitidine and carboplatin, particularly if it was absent at the start of treatment. Average methylation of CpGs across the HLA-A locus was decreased after priming and T-cells, in particular CD8+, showed the greatest increase in infiltration. Conclusions: Priming with azacitidine and carboplatin can induce disease stabilization and re-sensitisation to ICB for metastatic melanoma.

  Study EGAS00001006419

• Repurposing azacitidine and carboplatin to prime immune checkpoint blockade-resistant melanoma for anti-PD-L1 re-challenge

  Purpose: Drug repurposing offers the opportunity for chemotherapy to be used to re-establish sensitivity to immune checkpoint blockade (ICB) therapy. Here we investigated the clinical and translational aspects of an early phase II study of azacitidine and carboplatin priming for anti-PDL1 immunotherapy (Avelumab) in patients with advanced ICB-resistant melanoma. Experimental Design: 20 participants with ICB resistant metastatic melanoma received 2 x 4-week cycles of azacitidine and carboplatin followed by ICB re-challenge with anti-PD-L1 avelumab. The primary objective was overall response rate after priming and ICB re-challenge. Secondary objectives were Clinical benefit rate (CBR), progression-free survival (PFS) and overall survival (OS). Translational correlation analysis of HLA-A and PD-L1 expression, RNA-seq and reduced representation bisulfite sequencing (RRBS) of biopsies at baseline, after priming and after 6 cycles of avelmuab was performed. Results: The ORR determined after azacitidine and carboplatin priming was 10% (2/20) with 2 partial responses (PR). The ORR determined after priming followed by 6 cycles of avelumab (week 22) was 10%, with 2/20 participants achieving iPR. The CBR for azacitidine and carboplatin priming was 65% (13/20) and after priming followed by 6 cycles of avelumab CBR was 35% (n = 7/20). The median PFS was 18.0 weeks (95% CI: 14.87 – 21.13 weeks) and the median OS was 47.86 weeks (95% CI: 9.67 – 86.06 weeks). Translational correlation analysis confirmed HLA-A generally increased after priming with azacitidine and carboplatin, particularly if it was absent at the start of treatment. Average methylation of CpGs across the HLA-A locus was decreased after priming and T-cells, in particular CD8+, showed the greatest increase in infiltration. Conclusions: Priming with azacitidine and carboplatin can induce disease stabilization and re-sensitisation to ICB for metastatic melanoma.

  Study EGAS00001006420

• Genetic and epigenetic characterization of adenoid cystic carcinoma

  Translocation events are frequent in cancer and may create chimeric fusions or regulatory rearrangements that drive oncogene overexpression. Although regulatory rearrangements are increasingly recognized in hematopoietic and even solid tumors, the underlying mechanisms remain obscure. Here we identify super-enhancer translocations that drive overexpression of the oncogenic transcription factor MYB as a unifying theme in adenoid cystic carcinoma (ACC). Whole genome sequencing data and chromatin state maps for 13 primary tumors and xenografts reveal distinct chromosomal rearrangements that juxtapose super-enhancer clusters to the MYB locus. Chromosome conformation capture confirms that the translocated enhancers physically interact with the MYB promoter. Remarkably, the MYB product itself binds to the translocated enhancers, thus creating a positive feedback loop that sustains its own expression. MYB also binds a large number of active enhancers that drive opposing regulatory programs in alternate cell lineages in ACC. MYB cooperates with the transcription factor TP63 in the myoepithelial component of the tumors, but promotes a Notch program in the luminal epithelial component. Bromodomain inhibitors, which disrupt enhancer function, slow tumor growth in ACC primagraft models in vivo, but are ineffective against high grade tumors that harbor activating mutations in the Notch pathway. Thus, our study identifies super-enhancer translocations as a unifying feature of ACC, and provides insight into the mechanism by which sustained MYB overexpression drives alternate cell fates in this disease.Processed ChIP-seq data is available on GEO under accession number GSE76465.

  Study EGAS00001001457

• Single Cell Genotypic and Phenotypic Analysis of Measurable Residual Disease in Acute Myeloid Leukemia

  Acute myeloid leukemia (AML) is a group of blood cancers with high mortality. Even though complete remission is often achieved, majority of the AML patients still relapse. Measurable residual disease (MRD), defined as the population of cancer cells which persists following chemotherapy, is responsible for AML relapse. Understanding the biology enabling MRD clones to resist therapy is necessary to guide the development of curative treatments more effectively. Discriminating residual leukemic clones, preleukemic clones, and normal precursors remains a challenge with current MRD tools. We developed a single cell MRD (scMRD) assay by combining flow cytometric enrichment of the targeted precursor/blast population with integrated single cell DNA sequencing (scDNA-seq) and immunophenotyping (Tapestri, Missionbio). The platform utilizes a custom amplicon panel covering 109 amplicons across 31 genes frequently mutated in AML and a panel of 42 oligo-conjugated antibodies against surface makers. We applied our scMRD assay to sequence 30 MRD samples (multiplexed into 6) from 29 AML patients to study the clonal architecture and protein correlation. In addition, 2 MRD samples were sequenced with scDNA+protein sequencing technology without enrichment or multiplexing. Five patients with AML at diagnosis were also sequenced with scDNA+protein sequencing technology without enrichment or multiplexing. In order to establish the sensitivity of our scMRD assay, we spiked AML cells into normal bone marrow cells. We sequenced 12 such spiked samples after multiplexing.

  Study phs003233

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

  Dac EGAC00001001105

• whole exome seq

  whole exome sequencing data

  Dataset EGAD00001004464

• Exome

  Exome Seq for EGAS00001001845

  Dataset EGAD00001002160

• Comprehensive molecular profiling of ETMR

  ETMR RIPSeq

  Dataset EGAD00001004802

• DAC for scRNA-seq and scp-MS data on human bone marrow

  Dac EGAC00001003505

• Small intestinal neuroendocrine tumors

  The goal of this study was to gain insights into the transcriptomes, mutations and copy number variants present in small intestinal neuroendocrine tumors. The present dataset contains RNA-sequencing performed on seven such tumors.

  Study EGAS00001003358

• Illumina 5'end RNA sequencing

  The dataset contains 200 fastq files of Illumina 5'end RNA sequencing data of 50 PBMC samples. The paired-end data includes 100 fastq files (R1 and R2) of 50 full-length cDNA sequencing libraries and 100 fastq files (R1 and R2) of 50 HLA amplicon sequencing libraries.

  Dataset EGAD00001006838

• Blood and skin fibroblasts PSA study

  This dataset contains RNA-sequencing of blood samples from Healthy Controls (n=7), PSA patients (n=27) and RA patients (n=9). It also contains RNA-sequencing data of skin fibroblasts from healthy controls (n=3) and PSA patients (n=3).

  Dataset EGAD00001009069

• Single-cell RNA sequencing of chronic-phase chronic myeloid leukemia patients

  Single-cell RNA sequencing of viably frozen cells from 12 samples from four chronic-phase chronic myeloid leukemia patients. The raw data is available as fastq files.

  Dataset EGAD00001009086

• Ovarian Carcinosarcoma DNA and RNA sequencing of patient samples in the UK cohort.

  Ovarian Carcinosarcoma DNA and RNA sequencing of patient samples in the UK cohort (n=18).

  Dataset EGAD00001011068

• RNA sequencing data from children with febrile illness and multisystem inflammatory syndrome in children (MIS-C)

  RNA sequencing data from children with febrile illness and multisystem inflammatory syndrome in children (MIS-C). Samples used were Whole Blood. Febrile illness controls include children with bacterial and viral infections and healthy controls. This dataset contains samples from patients recruited into the DIAMONDS study.

  Dataset EGAD00001011134

• 5' single cell RNA sequencing

  Pared-end fastq files of 22 AML and 2 normal bone marrow samples were generated using 10x Genomics 5' single cell RNA sequencing following manufacturer's protocol (CG000086 User Guide RevG) for chemistry version 1.0 and targeting 10000 cells per sample. Libraries were sequenced using Illumina HiSeq 4000 using recommended cycling parameters.

  Dataset EGAD00001007672

• Single-cell RNA sequencing generated from 11 glioma patient samples

  This dataset includes paired-end fastq files from 6 IDH-mutant, 5 IDH-wild-type glioma patient samples of unmatched initial and recurrent timepoints profiled using single-cell RNA sequencing.

  Dataset EGAD00001007772

• PEDS-PLAN - Pediatric Precision Laboratory Advanced Neuroblastoma Therapy

  A multi-center clinical trial for newly diagnosed high-risk neuroblastoma patients. Molecular tumor boards selected one of six targeted agents based on tumor-normal whole exome sequencing and tumor RNA sequencing data.

  Study phs002303

• 3D-GSC_expression_profiles

  In this study, we used RNA-sequencing gene expression profiling in order to characterize specific phenotypic traits in DIPG-derived glioma stem cell (GSC) models. Twenty-two primary GSC models derived from biopsies collected at diagnosis were cultured in an ECM-mimicking compound before for RNA extraction and subsequent rRNA-depletion and sequencing .

  Dataset EGAD00001011079

• Myeloid cell programming in patients with non-medullary thyroid carcinoma

  This dataset contains single-cell RNA sequencing data from patients with thyroid cancer (n=7), multinodal Goiter (n=3) and healthy individuals (n=5). Mononuclear cells were taken from both the peripheral blood and the bone marrow compartments. We used a pooled single-cell design where multiple individuals were pooled in a single sample for sequencing (NextSeq 500-V2) and later demultiplexed using their genotype data. Associated metadata contains information on the phenotypes per individual, the pooling design and the linkage between the supplied files and sequenced pools. Due to limitations from EGA in uploading single-cell data, the raw fastq files were processed as follows: (i) I1/I2/R1/R2 fast files were concatenated over the different lanes. (ii) Concatenated I1 and I2 files were interleaved, as were the concatenated R1 and R2 files to generate two fastq files per pool containing all the information. To interleave the fastq files, the BBmap tool bbmap/reformat.sh was used, which can also be used to de-interleave the files.

  Dataset EGAD00001008108

• Dataset for other_cancer-RNA

  Rare cancer sequencing data of 95 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008847

• Dataset for head_and_neck_cancer-RNA

  Rare cancer sequencing data of 92 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008861

• Whole exome sequencing and RNA sequencing of cervical cancer

  We designed this study to identify molecular markers involved in the development and prognosis of cervical adenocarcinoma. In this study, we would like to elucidate the molecular biological characteristics of cervical adenocarcinoma in relation to the carcinogenesis of other cervical cancers (squamous cell carcinoma), vaginal and vulvar cancers. In addition, some squamous cell carcinomas of the cervix develop at a young age and have a poor prognosis. Furthermore, small cell carcinoma of the cervix also progresses rapidly and has a poor prognosis, making the establishment of diagnostic and therapeutic methods an urgent task.

  Study JGAS000582

• Whole exome sequencing and RNA sequencing of cervical cancer

  We designed this study to identify molecular markers involved in the development and prognosis of cervical adenocarcinoma. In this study, we would like to elucidate the molecular biological characteristics of cervical adenocarcinoma in relation to the carcinogenesis of other cervical cancers (squamous cell carcinoma), vaginal and vulvar cancers. In addition, some squamous cell carcinomas of the cervix develop at a young age and have a poor prognosis. Furthermore, small cell carcinoma of the cervix also progresses rapidly and has a poor prognosis, making the establishment of diagnostic and therapeutic methods an urgent task.

  Study JGAS000586

• 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

• A Platform Study of Combination Immunotherapy for the Neoadjuvant and Adjuvant Treatment of Patients with Surgically Resectable Adenocarcinoma of the Pancreas

  This is a single-institutional, randomized, open label platform clinical trial testing various immunotherapy combinations. Patients with newly diagnosed and surgically resectable pancreatic ductal adenocarcinoma (PDA) and scheduled to undergo the Whipple procedure at Johns Hopkins Hospital were eligible to participate in this study. Criteria for determining resectability strictly followed National Comprehensive Cancer Network (NCCN) guidelines. The study consisted of 6 parts: parts 1-5 constituted the Prime Phase (see below), and part 6 was the Extended Treatment Phase. Part 1: Participants received one cycle of immunotherapy two weeks prior to undergoing the Whipple procedure. Part 2: Subjects underwent the Whipple procedure. Part 3: Subjects received a second cycle of immunotherapy 4-10 weeks following the Whipple procedure (2-4 weeks prior to adjuvant chemoradiation). Part 4: Subjects underwent chemoradiation. Part 5: Subjects received 4 additional 28-day cycles of immunotherapy beginning 1-2 months after completing chemoradiation for a total of six cycles (two before chemoradiation and 4 following chemoradiation). Eligible subjects were randomized to Arms A and B in a 1:1 ratio, stratified up-front by age (≤65, >65). Arm A participants received Cy/GVAX. Arm B participants received the combination of Cy/GVAX and nivolumab. GVAX Vaccine consists of equal numbers (2.5 x 108 each) of Panc 6.03 pcDNA1GM-CSF and Panc 10.05 pcDNA1GM-CSF combined into a single vaccination. Each vaccination consisted of six total intradermal injections. A single intravenous (IV) dose of 200 mg/m2 Cy (CytoxanR) was given one day prior to GVAX. Anti-PD-1 Therapeutic Antibody Nivolumab 480 mg every 4 weeks was administered as an intravenous infusion. The primary objective for Arms A and B was to compare IL17 expression (Th17) in vaccine-induced lymphoid aggregates between resected PDAs from patients treated with the combination of Cy/GVAX and anti-PD-1 blockade antibody vs. the treatment of Cy/GVAX alone. Secondary objectives were to assess the safety, overall survival (OS), and disease free survival (DFS) of patients treated with each of the study drug combinations, and to assess the effects of each of the immunotherapy study drug combinations on PD-L1/PD-1 associated pathways, vaccine-induced immune regulatory signatures, and peripheral and intratumoral antigen specific T cell responses. The exploratory objective was to explore the effects of therapy on tumor and peripheral blood and tumor infiltrating immune cells, and to explore potential molecular determinants of response, progression and disease stability. To these ends, tumor infiltrating immune cells (TIICs) were obtained from surgically resected tumors following the neoadjuvant treatment of the study immunotherapy. TIICs were sorted into CD3+CD4+ T cells, CD3+CD8+ T cells, CD3-CD19+ B cells, and CD3-CD19-CD11b+ myeloid cells by flow cytometry. The sorted immune cell subtype pellets were sent to the commercial vendor (Eurofins) for RNA extraction and sequencing. SMART-seq v4 Ultra Low Input mRNA Sample Prep Kit was used for library preparation with 100bp paired end reads. FASTQ files, that were returned from the vendor, are deposited here.

  Study phs003002

• RNA sequencing of undifferentiated sarcomas

  Dac EGAC00001001055

• Human Biofluid RNA Atlas DAC

  Dac EGAC00001001338

• AML Proteogenomic Landscape Whole-transcriptome RNA-Sequencing

  Whole transcriptome RNA-sequencing of purified bone marrow blasts of 136 de novo, treatment naive AML patients. For further details, we refer to the manuscript "The Proteogenomic Landscape of AML" by Jayavelu, Wolf, Buettner et al. mRNA extraction and whole transcriptome sequencing For transcriptome analysis the TruSeq Total Stranded RNA kit was used, starting with 250ng of total RNA, to generate RNA libraries following the manufacturer’s recommendations (Illumina, San Diego, CA, USA). 100bp paired-end reads were sequenced on the NovaSeq 6000 (Illumina) with a median of 57 mio. reads per sample. RNA Data Analysis Data quality control was performed with FastQC v0.11.9. Reads were aligned to the human reference genome (Ensembl GRCh38 release 82) using STAR v2.6.1. Gene count tables were generated while mapping, using Gencode v31 annotations. All downstream analyses were carried out using R v4.0 and BioConductor v3.12 (Huber et al., 2015; R Core Team, 2020). Size-factor based normalization was performed using DESeq2 v1.28.1(Love et al., 2014).

  Dataset EGAD00001008484

• 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

• Genetic characterization of B-cell prolymphocytic leukemia: a hierarchical prognostic model involving MYC and TP53 abnormalities - RNA-seq

  B-cell prolymphocytic leukemia (B-PLL) is a rare disease, whose molecular pathology is largely unknown. We report here the cytogenetic and molecular findings in a large series of 34 B-PLL. Karyotype (K) was complex (≥3 abnormalities) in 73%, and highly complex (HCK≥5) in 45%. The most frequent chromosomal aberrations were: translocation targeting the MYC gene [t(MYC)] (62%), 17p deletion including TP53 gene (38%), trisomy 18/18q (30%), 13q14 deletion (29%), trisomy 3 (24%), trisomy 12 (24%) and 8p deletion (23%). Whole-Exome Sequencing performed in 16 patients revealed recurrent mutations in TP53 (6/16, 38%), MYD88 (n=4), BCOR (n=4), MYC (n=3), SF3B1 (n=3), FAT1 (n=3), SETD2 (n=2), CHD2 (n=2), CXCR4 (n=2) and BCLAF1 (n=2). The main group of patients (21/34, 62%) had a t(MYC) associated with a higher percentage of prolymphocytes (p=0.03), CD38 expression (p<0.001), lower K complexity (p=0.0004), mutations in MYC and in genes involved in RNA metabolism and chromatin remodeling. Principal component analysis of gene expression data showed that patients with t(MYC) clustered together. A second group with MYC gain (5/34, 15%), was associated with HCK≥5 (p=0,01) and trisomy 3 (p=0,008). Altogether, 26/34 patients (76%) had a MYC activation, translocation or gain, that were mutually exclusive. We identified 3 distinct cytogenetic prognostic groups (p=0.0006): lower risk: absence of MYC activation (median not reached); intermediate risk: MYC activation without del17p (125 months); high risk: MYC activation + del17p (11 months). Our results show that cytogenetic analysis is a useful diagnostic tool in B-PLL that improves prognostic stratification.

  Study EGAS00001003274

• Epitope-linked Ig-seq of self-reactive plasma cells in celiac disease

  Study EGAS00001003658

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

  Dac EGAC00001001875

• EGFR Mutant SCLC transformed exome seq

  Dataset EGAD00001001436

• Exome

  Exome Seq for Study EGAS00001001844

  Dataset EGAD00001002159

• Exome

  Exome Seq from EGAS00001001846

  Dataset EGAD00001002162

• ATRT ChIPSeq H3K27ac

  ATRT ChIPSeq H3K27ac

  Dataset EGAD00001004836

• ChIP_PEAKS

  ChIP-seq narrowPeaks. Software: MACS2 v2.1.2

  Dataset EGAD00010001923

• Profiles of Extracellular RNA in Cerebrospinal Fluid and Plasma from Subarachnoid Hemorrhage Patients

  Brain injury resulting from hemorrhagic stroke is clinically challenging to manage and results in high rates of morbidity and mortality. The pathophysiology of brain damage resulting from aneurysmal subarachnoid hemorrhage (aSAH) is largely unknown, and methods to treat and monitor patients are variable with no meaningful correlations to patient outcome. Prediction of patient risk for serious neurological complications is currently a significant clinical obstacle. An extracellular RNA (exRNA) biomarker to predict onset and severity of brain damage would improve patient outcomes. We sequenced plasma and CSF samples from adult patients with SAH. Samples were collected from post bleed day 1 to day 7. Total exRNA was isolated from each sample. In addition, we prepared a subset of 140 CSF samples, isolating the RNA contained within extracellular vesicles and vesicle-depleted biofluid.

  Study phs001759

• RNA sequencing in primary inflammatory (TPP) macrophages following deletion of a disease-associated gene desert at chr21q22, disruption of ETS2, or treatment of ETS2-edited macrophages with a HIF1α stabiliser.

  GWAS studies in five different inflammatory diseases have identified a strong genetic association at a gene desert at the chr21q22 locus. We have shown that this locus contains a monocyte/macrophage-specific enhancer that regulates ETS2 - a gene whose role in primary human monocytes/macrophages is incompletely understood. We therefore used a CRISPR-Cas9-based approach to delete the enhancer region or to disrupt ETS2, and performed RNA-sequencing to examine the transcriptional consequences. One effect of ETS2 disruption was upregulation of genes involved in aerobic respiration and oxidative phosphorylation. We therefore treated ETS2-edited inflammatory macrophages with roxadustat, a HIF1α stabiliser that can promote glycolysis via HIF1α-mediated metabolic reprogramming, and performed RNA-sequencing to determine whether this drug might rescue the transcriptional effects of ETS2 disruption.

  Study EGAS00001007553

• prDLBCL WXS/RNA Data Commitee

  Dac EGAC50000000284

• GLASS-NL RNA-sequencing Study

  Study EGAS00001007551

• Single cell analyses of transcriptome and epigenome in neuroblastoma infiltrated bone marrow

  We report transcriptional and genome-wide ATAC-seq analyses at single cell resolution of 16 bone marrow samples with and without neuroblastoma infiltration. Our samples span across several neuroblastoma models, including MYCN amplified, ATRX mutated, and sporadic (lacking either alteration).

  Study EGAS00001006106

• BLUEPRINT release August 2014, ChIP-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell

  ChIP-Seq data for 1 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 7 run(s), 7 experiment(s), 7 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_chipseq_analysis_ebi_20140811

  Dataset EGAD00001000916

• BLUEPRINT release August 2014, ChIP-Seq for CD14-positive, CD16-negative classical monocyte

  ChIP-Seq data for 9 CD14-positive, CD16-negative classical monocyte sample(s). 59 run(s), 55 experiment(s), 55 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_chipseq_analysis_ebi_20140811

  Dataset EGAD00001000913

• BLUEPRINT release August 2014, ChIP-Seq for CD8-positive, alpha-beta T cell

  ChIP-Seq data for 2 CD8-positive, alpha-beta T cell sample(s). 13 run(s), 13 experiment(s), 13 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_chipseq_analysis_ebi_20140811

  Dataset EGAD00001000936

• BLUEPRINT release August 2014, ChIP-Seq for CD4-positive, alpha-beta T cell

  ChIP-Seq data for 3 CD4-positive, alpha-beta T cell sample(s). 21 run(s), 21 experiment(s), 21 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_chipseq_analysis_ebi_20140811

  Dataset EGAD00001000925

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

  ChIP-Seq data for 2 effector memory CD8-positive, alpha-beta T cell sample(s). 10 run(s), 10 experiment(s), 10 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 EGAD00001001127

• BLUEPRINT release January 2015, ChIP-Seq for endothelial cell of umbilical vein (proliferating)

  ChIP-Seq data for 2 endothelial cell of umbilical vein (proliferating) sample(s). 13 run(s), 13 experiment(s), 13 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 EGAD00001001136

• BLUEPRINT release January 2015, ChIP-Seq for central memory CD4-positive, alpha-beta T cell

  ChIP-Seq data for 1 central memory CD4-positive, alpha-beta T cell sample(s). 6 run(s), 6 experiment(s), 6 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 EGAD00001001144

• BLUEPRINT release January 2015, ChIP-Seq for CD4-positive, alpha-beta T cell

  ChIP-Seq data for 7 CD4-positive, alpha-beta T cell sample(s). 46 run(s), 45 experiment(s), 45 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 EGAD00001001147

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

  ChIP-Seq data for 5 CD8-positive, alpha-beta T cell sample(s). 28 run(s), 28 experiment(s), 28 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 EGAD00001001154

• BLUEPRINT release January 2015, ChIP-Seq for cytotoxic CD56-dim natural killer cell

  ChIP-Seq data for 4 cytotoxic CD56-dim natural killer cell sample(s). 16 run(s), 16 experiment(s), 16 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 EGAD00001001158

• BLUEPRINT release January 2015, ChIP-Seq for CD14-positive, CD16-negative classical monocyte

  ChIP-Seq data for 10 CD14-positive, CD16-negative classical monocyte sample(s). 73 run(s), 69 experiment(s), 69 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 EGAD00001001179

• BLUEPRINT release January 2015, ChIP-Seq for CD34-negative, CD41-positive, CD42-positive megakaryocyte cell

  ChIP-Seq data for 1 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 7 run(s), 7 experiment(s), 7 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 EGAD00001001182

• BLUEPRINT release January 2015, ChIP-Seq for endothelial cell of umbilical vein (resting)

  ChIP-Seq data for 2 endothelial cell of umbilical vein (resting) sample(s). 10 run(s), 10 experiment(s), 10 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 EGAD00001001183

• BLUEPRINT release January 2015, ChIP-Seq for CD38-negative naive B cell

  ChIP-Seq data for 4 CD38-negative naive B cell sample(s). 14 run(s), 14 experiment(s), 14 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 EGAD00001001207

• BLUEPRINT release August 2015, ChIP-Seq for neutrophilic metamyelocyte, on genome GRCh38

  ChIP-Seq data for 4 neutrophilic metamyelocyte sample(s). 18 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 EGAD00001001495

• BLUEPRINT release August 2015, ChIP-Seq for germinal center B cell, on genome GRCh38

  ChIP-Seq data for 2 germinal center B cell sample(s). 12 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 EGAD00001001502

• BLUEPRINT release August 2015, ChIP-Seq for mature neutrophil, on genome GRCh38

  ChIP-Seq data for 9 mature neutrophil sample(s). 48 run(s), 45 experiment(s), 45 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 EGAD00001001508

• BLUEPRINT release August 2015, ChIP-Seq for alternatively activated macrophage, on genome GRCh38

  ChIP-Seq data for 4 alternatively activated macrophage sample(s). 22 run(s), 22 experiment(s), 22 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 EGAD00001001514

• BLUEPRINT release August 2015, ChIP-Seq for neutrophilic myelocyte, on genome GRCh38

  ChIP-Seq data for 4 neutrophilic myelocyte sample(s). 14 run(s), 14 experiment(s), 14 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 EGAD00001001517

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

  ChIP-Seq data for 6 naive B cell sample(s). 34 run(s), 28 experiment(s), 28 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 EGAD00001001519

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

  Bisulfite-Seq data for 1 CD34-negative, CD41-positive, CD42-positive megakaryocyte cell sample(s). 14 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 EGAD00001001509

• BLUEPRINT release August 2015, ChIP-Seq for band form neutrophil, on genome GRCh38

  ChIP-Seq data for 4 band form neutrophil sample(s). 18 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 EGAD00001001511

• BLUEPRINT release August 2015, ChIP-Seq for mature eosinophil, on genome GRCh38

  ChIP-Seq data for 2 mature eosinophil 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 EGAD00001001539

• 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, Bisulfite-Seq for central memory CD8-positive, alpha-beta T cell, on genome GRCh38

  Bisulfite-Seq data for 1 central memory CD8-positive, alpha-beta T cell sample(s). 13 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 EGAD00001001553

• BLUEPRINT release August 2015, ChIP-Seq for adult endothelial progenitor cell, on genome GRCh38

  ChIP-Seq data for 1 adult endothelial progenitor cell sample(s). 8 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 EGAD00001001554

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

  Bisulfite-Seq data for 1 central memory CD4-positive, alpha-beta T cell 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 EGAD00001001563

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

  Bisulfite-Seq data for 1 effector memory CD4-positive, alpha-beta T cell 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 EGAD00001001567

• 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, Bisulfite-Seq for CD8-positive, alpha-beta T cell, on genome GRCh38

  Bisulfite-Seq data for 4 CD8-positive, alpha-beta T cell sample(s). 56 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_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001571

• BLUEPRINT release August 2015, ChIP-Seq for macrophage, on genome GRCh38

  ChIP-Seq data for 12 macrophage sample(s). 49 run(s), 49 experiment(s), 49 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 EGAD00001001576

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

  Bisulfite-Seq data for 1 effector memory CD8-positive, alpha-beta T cell sample(s). 11 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 EGAD00001001583

• 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