9337 results for "cancer rna-seq"

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• Reference epigenome IPS-Fibroblast02 h3k27ac ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007270

• Reference epigenome IPS-Fibroblast02 input ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 input ChIP-Seq paired end data

  Dataset EGAD00001007276

• Reference epigenome IPS-ENeuron02 h3k27ac ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007298

• ATAC-Seq of human stimulated and cultured CD4+ Treg cells

  This dataset includes whole genome sequence data for ATAC-seq (42 samples) of human stimulated and cultured CD4+ Treg cells.

  Dataset EGAD00001004829

• 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

• Osteosarcoma sequencing data

  This study shares DNA and RNA sequencing data from embryonal rhadbomyosarcoma patients

  Study EGAS00001005600

• HipSci - Primary Immune Deficiency - RNA Sequencing - July 2017

  HipSci - Primary Immune Deficiency - RNA Sequencing - July 2017

  Dataset EGAD00001003536

• HipSci - Hereditary Spastic Paraplegia - RNA Sequencing - July 2017

  HipSci - Hereditary Spastic Paraplegia - RNA Sequencing - July 2017

  Dataset EGAD00001003537

• HipSci - Hereditary Cerebellar Ataxias - RNA Sequencing - July 2017

  HipSci - Hereditary Cerebellar Ataxias - RNA Sequencing - July 2017

  Dataset EGAD00001003538

• RNA sequence of HUB_5 cell culture

  RNA sequence data for conditionally reprogrammed cells from patient HUB_5

  Dataset EGAD00001001923