9987 results for "cancer rna-seq"

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• Reference epigenome Islet-derived_iPSC04 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007891

• Reference epigenome Islet-derived_iPSC04 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007892

• Reference epigenome Islet-derived_iPSC04 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007893

• Reference epigenome Islet-derived_iPSC04 input ChIP-Seq data generated from KEP study

  Islet-derived_iPSC04 input ChIP-Seq paired end data

  Dataset EGAD00001007894

• Reference epigenome Islet-derived_MSC06 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007895

• Reference epigenome Islet-derived_MSC06 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007896

• Reference epigenome Islet-derived_MSC06 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007897

• Reference epigenome Islet-derived_MSC06 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007898

• Reference epigenome Islet-derived_MSC06 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007899

• Reference epigenome Islet-derived_MSC06 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007900

• Reference epigenome Islet-derived_MSC06 input ChIP-Seq data generated from KEP study

  Islet-derived_MSC06 input ChIP-Seq paired end data

  Dataset EGAD00001007901

• Reference epigenome Islet-derived_MSC08 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007902

• Reference epigenome Islet-derived_MSC08 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007903

• Reference epigenome Islet-derived_MSC08 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007904

• Reference epigenome Islet-derived_MSC08 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007905

• Reference epigenome Islet-derived_MSC08 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007906

• Reference epigenome Islet-derived_MSC08 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007907

• Reference epigenome Islet-derived_MSC08 input ChIP-Seq data generated from KEP study

  Islet-derived_MSC08 input ChIP-Seq paired end data

  Dataset EGAD00001007908

• H3K27ac ChIP-seq in Insulinomas

  H3K27ac ChIP-seq datasets in human insulinoma samples

  Dataset EGAD50000000461

• scRNA-seq data of human nuclei collected from the temporal cortex of 17 individuals.

  Transcriptome analysis of single cells has tremendously increased our understanding of cellular heterogeneity in complex tissues. However, whole-cell transcriptomics cannot be applied to frozen tissue due to poor cell recovery after freezing. Human tissue is usually limited to tissue banks containing frozen material, and since single nuclei can be readily isolated from frozen tissues, human cell nuclei can substitute whole-cell analysis to extract single cell transcriptomes from human cells. Despite the importance of human tissue analysis, single-nucleus RNA-sequencing protocols are less developed than those for whole cells. SMARTSeq, either in form of custom-made SMARTSeq2 or as part of the Fluidigm C1 platform, provides a high resolution view of the transcriptome, has proven effective for whole-cell analysis and has recently been adapted to nuclei. Here, we show that the standard SMARTSeq2 protocol applied to single nuclei biases the amplification of single-nucleus transcriptomes due to unspecific capturing of transcripts, which can comprise the majority of reads in single-nucleus RNA-sequencing libraries. We demonstrate that modification of the SMARTSeq2 protocol with respect to template switching and cDNA amplification conditions generates sequencing libraries of higher complexity, allows for detection of more genes and reduces biases of the amplified transcriptome. In addition, modification of SMARTSeq2 chemistry allows for gene detection at shallower sequencing depth, thus increasing efficiency and decreasing cost.

  Study EGAS00001002882

• RNA Splicing Dysregulation in the Pathogenesis of Chronic Lymphocytic Leukemia

  RNA splicing dysregulation is a hallmark of cancers, promoting the onset and progression of disease. In chronic lymphocytic leukemia (CLL), spliceosome mutations leading to aberrant splicing occurs in approximately 20% of patients. However, the underlying mechanism for splicing defects in spliceosome unmutated CLL cases remains elusive. To discover posttranscriptional regulation of RNA splicing, we performed RNA sequencing using RNA derived from B cells from 6 healthy donors and 36 CLL patients, along with MAZTER sequencing from 9 CLL patients (8 matched with RNA sequencing cohort) and B cells from 5 healthy donors. We found that CLL cells have dysregulated m6A modification sites on transcripts from splicing factors, suggesting a critical role of m6A modification in RNA splicing dysregulation.

  Study phs003191

• Myeloid-specific KDM6B inhibition sensitizes Glioblastoma to PD1 blockade

  This data set include FASTQ files for five experiments: Human scRNA-Seq data (6 samples), Human Visium spatial transcriptomic data (3 samples), Mouse scRNA-seq data (4 samples), Mouse scATAC-seq data (2 samples), Mouse ChIP-seq data (8 samples). For the mouse scATAC-seq data (2 samples), there are originally three FASTQ files, R1, R2 and R3 files. The R1 and R2 FASTQ files were merged into one larger file ("R1R2") per sample for submission as paired-end sequencing setting to follow the EGA guidelines. They can be split into individual R1 and R2 files in order to be processed by Cell Ranger software. The files with IC1 suffix are CHIP-seq Input control for anti-KDM6B antibody pull down experiments and the files with IC2 suffix are CHIP-seq Input control for anti-H3K27ME3 antibody pull down experiments in murine model.

  Dataset EGAD00001010073

• Plasma RNA sequencing

  Plasma RNA sequencing

  Study EGAS00001000731

• STAG1 and STAG2-ChIP-seq in RAD21-mutant adult acute myeloid leukemia

  STAG1 and STAG2-ChIP-seq in RAD21-mutant adult acute myeloid leukemia

  Dataset EGAD00001015361

• BLUEPRINT EpiVar ChIP-seq for naive CD4+ T-cells

  BLUEPRINT EpiVar ChIP-seq for naive CD4+ T-cells

  Study EGAS00001000753