15016 results for "cancer studies using rna-seq"

in 18.06 milliseconds.

• 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 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 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 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

• 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

• 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

• 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

• scRNA-seq of glioblastoma cells alone or after co-culture with cerebral organoid cells

  Dataset EGAD00001005315

• ChIPseq data

  ChIPseq data for CLL patients

  Dataset EGAD00001008665

• Dataset of PPB

  Whole exome sequencing and miRNA-seq data of PPB.

  Dataset EGAD00001000822

• ChIPseq data

  ChIPseq data for H3K4me1 and H3K9me3 in HT29; H3K4me1, H3K27me3, H3K9me3, H3K36me3 in LoVo.

  Dataset EGAD00001003258

• Multiomics analysis of PBMCs from healthy individuals

  We performed single-cell and bulk level multiomics analysis using PBMCs from healthy individuals to dipict the diversity of immune system.

  Study JGAS000637

• Multimodal single-cell analyses of peripheral blood mononuclear cells in COVID-19 patients

  10x scRNA and 10X scVDJ analyses were performed using PBMCs collected from COVID-19 patients in Japan.

  Study JGAS000546

• Chromatin 3D interactions mediate genetic effects on gene expression (genotypes)

  This dataset contains the imputed genotypes for the gencord samples. Genotyping was done using Illumina OMNI2.5M. Imputation was done using SHAPEIT2/IMPUTE2 with 1000 genomes project phase 3 reference panel.

  Dataset EGAD00001004790

• Single-cell RNA-sequencing of CSF cells and PBMCs from individuals with neurological disorders

  We undertook single-cell RNA sequencing to establish gene expression and lymphocyte receptor sequences of CSF and peripheral blood leukocytes derived from patients with a variety of inflammatory, infectious and non-inflammatory neurological disorders. The aim of the study was the investigation of functional changes of the cells involved in neuroinflammatory responses.

  Dataset EGAD50000001173

• Transcriptional and epigenetic profiling of bone marrow blood progenitors across age

  Bone marrow from donors without known malignancy was isolated and CD34-enriched, FACS-purified to further enrich for CD34+CD38- primitive cells, and then profiled by single-cell 10X genomics chromium Multiome (RNA+ATAC).

  Study EGAS50000001623

• Transcriptome profiling of human lung fibroblasts in COPD

  Primary lung fibroblast were isolated from well-matched control donors (no COPD, n=3) and patients with COPD (GOLD stage I-IV, n=8). Total RNA of cultured human lung fibroblast were isolated at passage 3 and rRNA was depleted. 75 bp single-end reads were generated from RNA libraries on Illumina NextSeq 500.

  Dataset EGAD00001009405

• Transcript read counts derived from RNA sequencing data collected for NABUCCO cohort 1 (NCT03387761)

  Data from NABUCCO cohort 1 (NCT03387761). This dataset includes the Transcript read counts derived from the RNA sequencing data. The samples are pre-treatment (n=18) and post-treatment (n=18), and not all samples are paired. The data processing pipeline can be found on the Methods section from the Nature Medicine paper (https://doi.org/10.1038/s41591-020-1085-z)

  Dataset EGAD00001006854

• FASTQ files of the cell-free RNA from the maternal blood

  cell-free RNA from the maternal plasma obtained at round the 12, 20, 28 and 36 week gestational age from the cases of preeclampsia combined with fetal growth restriction (n=39) and their matched controls (n=156). All samples are from the Pregnancy Outcome Prediction (POP) study.

  Dataset EGAD00001015416