9931 results for "cancer rna-seq"

in 48.91 milliseconds.

• Reference epigenome IPS-Fibroblast02 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007273

• Reference epigenome IPS-Fibroblast02 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007274

• Reference epigenome IPS-Fibroblast02 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-Fibroblast02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007275

• Reference epigenome IPS-NPC01 h3k27ac ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007277

• Reference epigenome IPS-NPC01 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007279

• Reference epigenome IPS-NPC01 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007280

• Reference epigenome IPS-NPC01 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007281

• Reference epigenome IPS-NPC01 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-NPC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007282

• Reference epigenome IPS-NPC01 input ChIP-Seq data generated from KEP study

  IPS-NPC01 input ChIP-Seq paired end data

  Dataset EGAD00001007283

• Reference epigenome IPS-NPC02 h3k27ac ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007284

• Reference epigenome IPS-NPC02 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007285

• Reference epigenome IPS-NPC02 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007286

• Reference epigenome IPS-NPC02 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007287

• Reference epigenome IPS-NPC02 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-NPC02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007289

• Reference epigenome IPS-NPC02 input ChIP-Seq data generated from KEP study

  IPS-NPC02 input ChIP-Seq paired end data

  Dataset EGAD00001007290

• Reference epigenome IPS-ENeuron01 h3k27ac ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007291

• Reference epigenome IPS-ENeuron01 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007292

• Reference epigenome IPS-ENeuron01 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007294

• Reference epigenome IPS-ENeuron01 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007295

• Reference epigenome IPS-ENeuron01 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007296

• Reference epigenome IPS-ENeuron01 input ChIP-Seq data generated from KEP study

  IPS-ENeuron01 input ChIP-Seq paired end data

  Dataset EGAD00001007297

• Reference epigenome IPS-ENeuron02 h3k27me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007299

• Reference epigenome IPS-ENeuron02 h3k36me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007300

• Reference epigenome IPS-ENeuron02 h3k4me1 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007301

• Reference epigenome IPS-ENeuron02 h3k4me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007302

• Reference epigenome IPS-ENeuron02 h3k9me3 ChIP-Seq data generated from KEP study

  IPS-ENeuron02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007303

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

  IPS-ENeuron02 input ChIP-Seq paired end data

  Dataset EGAD00001007304

• Reference epigenome Pancreas-Islet06 h3k27ac ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007450

• Reference epigenome Pancreas-Islet06 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007451

• Reference epigenome Pancreas-Islet06 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007452

• Reference epigenome Pancreas-Islet06 h3k4me1 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007453

• Reference epigenome Pancreas-Islet06 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007454

• Reference epigenome Pancreas-Islet06 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet06 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007455

• Reference epigenome Pancreas-Islet06 input ChIP-Seq data generated from KEP study

  Pancreas-Islet06 input ChIP-Seq paired end data

  Dataset EGAD00001007456

• Reference epigenome Pancreas-Islet07 h3k27ac ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007457

• Reference epigenome Pancreas-Islet07 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007458

• Reference epigenome Pancreas-Islet07 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007459

• Reference epigenome Pancreas-Islet07 h3k4me1 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007460

• Reference epigenome Pancreas-Islet07 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007461

• Reference epigenome Pancreas-Islet07 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet07 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007462

• Reference epigenome Pancreas-Islet07 input ChIP-Seq data generated from KEP study

  Pancreas-Islet07 input ChIP-Seq paired end data

  Dataset EGAD00001007463

• Reference epigenome Pancreas-Islet08 h3k27ac ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007464

• Reference epigenome Pancreas-Islet08 h3k27me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007465

• Reference epigenome Pancreas-Islet08 h3k36me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007466

• Reference epigenome Pancreas-Islet08 h3k4me1 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007467

• Reference epigenome Pancreas-Islet08 h3k4me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007468

• Reference epigenome Pancreas-Islet08 h3k9me3 ChIP-Seq data generated from KEP study

  Pancreas-Islet08 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007469

• Reference epigenome Pancreas-Islet08 input ChIP-Seq data generated from KEP study

  Pancreas-Islet08 input ChIP-Seq paired end data

  Dataset EGAD00001007470

• Reference epigenome Fat-adipocyte03 h3k27ac ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007471

• Reference epigenome Fat-adipocyte03 h3k27me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007472

• Reference epigenome Fat-adipocyte03 h3k36me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007473

• Reference epigenome Fat-adipocyte03 h3k4me1 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007474

• Reference epigenome Fat-adipocyte03 h3k4me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007475

• Reference epigenome Fat-adipocyte03 h3k9me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007476

• Reference epigenome Fat-adipocyte03 input ChIP-Seq data generated from KEP study

  Fat-adipocyte03 input ChIP-Seq paired end data

  Dataset EGAD00001007477

• Reference epigenome Fat-adipocyte04 h3k27ac ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007478

• Reference epigenome Fat-adipocyte04 h3k27me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007479

• Reference epigenome Fat-adipocyte04 h3k36me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007480

• Reference epigenome Fat-adipocyte04 h3k4me1 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007481

• Reference epigenome Fat-adipocyte04 h3k4me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007482

• Reference epigenome Fat-adipocyte04 h3k9me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007483

• Reference epigenome Fat-adipocyte04 input ChIP-Seq data generated from KEP study

  Fat-adipocyte04 input ChIP-Seq paired end data

  Dataset EGAD00001007484

• Reference epigenome Fat-adipocyte05 h3k27ac ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007485

• Reference epigenome Fat-adipocyte05 h3k27me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007486

• Reference epigenome Fat-adipocyte05 h3k36me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007487

• Reference epigenome Fat-adipocyte05 h3k4me1 ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007488

• Reference epigenome Fat-adipocyte05 h3k4me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007489

• Reference epigenome Fat-adipocyte05 h3k9me3 ChIP-Seq data generated from KEP study

  Fat-adipocyte05 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007490

• Reference epigenome Fat-adipocyte05 input ChIP-Seq data generated from KEP study

  Fat-adipocyte05 input ChIP-Seq paired end data

  Dataset EGAD00001007491

• 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

• TST170 Pilot RNA BAM

  TST170 Pilot RNA BAM files

  Dataset EGAD00001006044

• TSO500 on RNA

  TruSight Oncology 500 RNA probes panel on case 521

  Dataset EGAD50000000271

• Whole genome sequencing of adult glioblastoma nuclei

  Nuclei were extracted from three primary adult glioblastoma samples and processed for scATAC-seq, and residual nuclei left over from these same samples were used to extract DNA and processed and sequenced for WGS. This WGS data was then used to cross-validate assessment of large-scale copy number alterations with scATAC-seq using the Copy-scAT tool.

  Study EGAS00001005256

• RNA sequencing data from visceral and abdominal subcutaneous adipose tissue from morbidly obese women with normal glucose tolerance or type 2 diabetes

  The study group consisted of 17 obese women with normal glucose tolerance and 15 obese women with T2DM. Adipose tissue specimens were taken from the epigastric region of the abdominal wall (SAT) and from the major omentum (VAT). RNA was isolated and RNA sequencing was used to analyse the transcriptome. Dharuri H et al, Diabetologia. 2014;57(11):2384-92.

  Study EGAS00001001872

• ChIP-seq data of Hodgkin lymphoma cell line L-428

  ChIP-seq data (H3K4Me3, H3K27Ac histone modifications) of Hodgkin lymphoma cell line L-428. Samples were processed as previously described (Sud et al., 2018). The files are in bam format, aligned to build 37.

  Study EGAS00001003033

• DNA methylation (MCIP-seq) of human acute leukemias and healthy donors

  MCIP-seq of 77 primary samples obtained from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). Moreover, MCIP-seq of CD34+ HSPCs from 3 healthy donors is included.

  Dataset EGAD00001011052

• Chromatin accessibility (ATAC-seq) of human acute leukemias and healthy donors

  ATAC-seq of 79 primary samples obtained from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). ATAC-seq of CD34+ HSPCs from 3 healthy donors is also included.

  Dataset EGAD00001011050

• Non-coding RNAs Activated by the Wnt/Beta-catenin Signaling Pathway in Hepatoblastoma

  Hepatoblastoma (HB) is the most common pediatric liver tumor, affecting mostly children under 3 years of age. This rare tumor represents 1% of all pediatric cancers. Genetic studies have shown that HB is characterized by high frequency mutations of the CTNNB1 gene encoding beta-catenin (around 75%) and relative genomic stability. Here we have analyzed the transcriptional profile of 21 HBs compared to matched non-tumor livers by Cap Analysis of Gene Expression (CAGE), which provides accurate and quantitative profiling of all transcripts. CAGE analysis revealed strong upregulation of known Wnt target coding genes in most tumors analyzed, consistent with previous transcriptomic studies. To better define the Wnt-dependent transcriptional landscape of HB, we integrated CAGE data with TCF4 ChIP-seq data from a CTNNB1-mutated cancer cell line and with the FANTOM5 genomic coordinates of TCF/LEF binding motifs. Both TCF/LEF binding motifs and ChIP-seq peaks were strongly enriched in the immediate upstream region, not only for protein-coding genes, but also for non-coding transcripts. Among the selected 112 top Wnt target genes at FDR<1.0E-6 and fold change>8, we found clear over-representation (66%) of distant transcription start sites (TSSs) representing lncRNAs and enhancer RNAs, which raises the question of their role in HB pathogenesis. Analysis of the 112 promoters using CAGEd-oPOSSUM confirmed the predominant involvement of Tcf/Lef transcription factors, together with HNF4G, GATA2, Sox3 and Ets-related genes. Finally, the 112 Wnt target signature defined 3 tumor subclasses, T1, T2 and T3, characterized by progressive alteration of the non-coding part of the transcriptome and significant differences in clinical behavior.

  Study phs001433

• Gene Expression and Regulatory Networks in Human Leukocytes - Immunological Variation Consortium

  Profiling of gene expression with microarrays holds great potential for human health, for illuminating disease pathways or providing biomarkers to monitor disease or its resolution. Using high-throughput approaches for genotyping, immunophenotyping and gene expression analysis, the project will examine the basis for the control of gene expression in human immune cells, and how it is influenced by natural genetic variation or aging. In practice, the project will combine several cross-informative approaches: 1) Building on the established sample/data pipelines and robust protocols of the Immunological Genome (ImmGen) project, and on established cohorts of ethnically diverse healthy volunteers at hand, microarray techniques will be used to generate whole-genome expression profiles from purified naive CD4+ lymphocytes and monocytes from 600 healthy volunteers. A dense genetic map will be established for all donors. The results will elucidate how variation in the human genome affects the expression of immune genes, of key importance in understanding gene variants that bring susceptibility to immune or inflammatory disease. Computational analysis of these rich data will allow the reconstruction of regulatory connections between genes, helping to establish general modules and those specific of a given immune cell type. These data will be complemented by an orthogonal data group, generated from a restricted subset of 10 individuals, in which we will profile a larger set of 28 carefully delineated cell populations that exist in human blood. This work will also benefit from powerful interspecies comparison with similar experiments being performed in mice by ImmGen. 2) In addition, RNA from the same set of 28 defined cell populations will be probed with microarrays that explore other aspects of the transcriptome: i) microRNAs and other non-coding RNAs; ii) exon or splice junction arrays that will establish a map of differential splicing in human blood leukocyte. 3) The composition and reactivity of blood cells from the same donors will be established at the time of sample collection using high-throughput flow cytometry, correlating immune phenotypes with gene expression and genetic variation. 4) Genetic variability conditions the baseline levels of gene expression, but also the responsiveness to activating challenges. With samples from the same donors, NanoString technology for transcript counting will be used to analyze the transcriptional response of defined gene sets, representing response signatures of T or dendritic cells, for a fine-grained dissection of responses to different triggers (different bacterial ligands for dendritic cells, different cytokine environment for T cells). In keeping with the resource aspect of this project, all data and interpretations will be made publicly available rapidly upon curation, allowing public querying and browsing of the data, by using and evolving the existing web architectures of the ImmGen project, of the Broad Institute, and of international repositories. RELEVANCE: Exploration with DNA chips of the genome's expression microarrays holds great potential for human health, to better understand disease and to serve as diagnostic tools. Using a combination of high-throughput genomic techniques and computational biology, we will perform a broad exploration of gene expression in human blood cells across groups of African-American, Asian and European ancestry, asking how these profiles are affected by genetic variation or by age. These results will provide an invaluable reference benchmark for the interpretation of genetic and immunological studies. Additionally, over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, in a second study we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution. 15% of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (ATAC-QTLs). ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression, and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression. In another study we analyzed RNA-seq profiles from primary CD4+ T cells of two healthy donors at six time points from 0 to 72 hours after stimulation with bead-bound antibodies against CD3 and CD28.

  Study phs000815

• Patient data used in FLTseq paper

  Apply newly developed FLT-seq protocol and package FLAMES on a primary patient sample

  Study EGAS00001005597

• ICGC MMML-seq Data Freeze November 2012 whole genome sequencing

  ICGC MMML-seq Data Freeze November 2012 whole genome sequencing

  Dataset EGAD00001000278

• ICGC MMML-seq Data Freeze November 2012 whole exome sequencing

  ICGC MMML-seq Data Freeze November 2012 whole exome sequencing

  Dataset EGAD00001000279

• ICGC MMML-seq Data Freeze March 2013 whole genome sequencing

  ICGC MMML-seq Data Freeze March 2013 whole genome sequencing

  Dataset EGAD00001000355

• ICGC MMML-seq Data Freeze July 2013 whole genome sequencing

  ICGC MMML-seq Data Freeze July 2013 whole genome sequencing

  Dataset EGAD00001000645

• ICGC MMML-seq Data Freeze July 2013 transcriptome sequencing

  ICGC MMML-seq Data Freeze July 2013 transcriptome sequencing

  Dataset EGAD00001000648

• ICGC MMML-seq Data Freeze July 2013 miRNA sequencing

  ICGC MMML-seq Data Freeze July 2013 miRNA sequencing

  Dataset EGAD00001000650

• release_2: ICGC PedBrain: ChIP-Seq

  release_2: ICGC PedBrain: ChIP-Seq

  Dataset EGAD00001001621

• BLUEPRINT Bisulfite-seq and Whole Genome Sequencing of mantle cell lymphoma

  BLUEPRINT Bisulfite-seq and Whole Genome Sequencing of mantle cell lymphoma

  Dataset EGAD00001001892

• Reference epigenome Islet-derived-MSC01 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007402

• Reference epigenome Islet-derived-MSC01 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007403

• Reference epigenome Islet-derived-MSC01 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007404

• Reference epigenome Islet-derived-MSC01 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007405

• Reference epigenome Islet-derived-MSC01 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007406

• Reference epigenome Islet-derived-MSC01 h3k9me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007407

• Reference epigenome Islet-derived-MSC01 input ChIP-Seq data generated from KEP study

  Islet-derived-MSC01 input ChIP-Seq paired end data

  Dataset EGAD00001007408

• Reference epigenome Islet-derived-MSC02 h3k27ac ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007409

• Reference epigenome Islet-derived-MSC02 h3k27me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007410

• Reference epigenome Islet-derived-MSC02 h3k36me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007411

• Reference epigenome Islet-derived-MSC02 h3k4me1 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007412

• Reference epigenome Islet-derived-MSC02 h3k4me3 ChIP-Seq data generated from KEP study

  Islet-derived-MSC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007413