12000 results for "cancer rna-seq"

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

  Islet-derived_MSC06 miRNA-Seq single end data

  Dataset EGAD00001007881

• Reference epigenome CKD23_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003497

• Reference epigenome CKD27_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003500

• Reference epigenome CKD23_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003481

• Reference epigenome KNIH010 mRNA-seq data generated from KEP study

  A KNIH010 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002176

• Reference epigenome KNIH011 mRNA-seq data generated from KEP study

  A KNIH011 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002177

• Reference epigenome CKD27_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003484

• Histone marks

  EZH2, H3K4me3, H3K27ac and H3K27me3 ChIP-seq data consisting of fastq single-end reads from peripheral blood CLL cells

  Dataset EGAD00001004063

• Reference epigenome KNIH011 miRNA-seq data generated from KEP study

  A KNIH011 miRNA-seq single end data for podocytes

  Dataset EGAD00001002770

• Single cell RNA sequencing of human cord Blood CD34 Cells

  We performed single cell RNA sequencing and ATAC e valuation of human umbilical cord blood. To explore the differentiation pathway for human lymphoid lineage, our target cells contain CD34-positive cord blood. That population is evaluated by single cell RNA sequencing and single cell ATAC.

  Study JGAS000528

• Whole genome sequencing of pheochromocytoma and paraganglioma arising from germline SDHB mutations

  Paired-end 150 basepair whole genome sequencing of 94 SDHB-deficient non-metastatic and metastatic pheochromocytoma and paraganglioma. Libraries were prepared using the Illumina® TruSeq™ DNA Nano library preparation method according to the manufacturer’s instructions at The University of Melbourne Centre for Cancer Research (UMCCR) using 200ng input DNA and a 550 base pair insert size. Samples were sequenced in separate batches on the Illumina® Nova-Seq 6000 according to manufacturer’s instructions (Illumina, USA). Included in this dataset are FASTQ format files containing raw read data, CRAM format files containing reads aligned to GRCh38, and VCF format files containing germline variants calls.

  Dataset EGAD50000000502

• RNA-sequencing of tumors from 45 patients with recurrent or metastatic gastric cancer treated with immune checkpoint inhibitors

  Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this work, we used our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identified four molecular subtypes that were prognostic for survival. We then built a support vector machine with linear kernel and the binary classifier to generate a risk score that is prognostic for 5-year overall survival and validated the risk score using three independent datasets. We also found that the molecular subtypes predicted response to adjuvant 5-fluorouracil and platinum after gastrectomy and immune checkpoint inhibitors in patients with metastatic or recurrent disease. The 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated in a prospective manner. This EGA archive includes both St.Mary hospital cohort samples (n=28) and Yonsei immunotherapy patient samples (n=17).

  Study EGAS00001005588

• ChIP-Seq files for RMS.

  ChIP-Seq files accompanying the paper titled "Identification of Therapeutic Targets in Rhabdomyosarcoma Through Integrated Genomic, Epigenomic, and Proteomic Analyses".

  Dataset EGAD00001006398

• Dataset for B-ALL scRNA-seq

  10x Genomics 5' library scRNA-seq data for 85 B-ALL patients with different subtypes

  Dataset EGAD00001011327

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

  Fat-adipocyte04 mRNA-Seq paired end data

  Dataset EGAD00001007158

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

  Pancreas-Islet08 mRNA-Seq paired end data

  Dataset EGAD00001007168

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

  Fat-adipocyte03 mRNA-Seq paired end data

  Dataset EGAD00001007157

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

  Fat-adipocyte05 mRNA-Seq paired end data

  Dataset EGAD00001007159

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

  Islet-derived_MSC06 mRNA-Seq paired end data

  Dataset EGAD00001007878

• Reference epigenome SMC05 h3k9me3 ChIP-Seq data generated from KEP study

  SMC05 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007336

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

  Pancreas-Islet07 mRNA-Seq paired end data

  Dataset EGAD00001007167

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

  Pancreas-Islet06 mRNA-Seq paired end data

  Dataset EGAD00001007166

• Reference epigenome SMC01 h3k4me3 ChIP-Seq data generated from KEP study

  SMC01 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007311

• Reference epigenome OB56_N_PreA_smRNA-Seq data generated from KEP study

  A OB56_N_PreA_smRNA-Seq single end data for Preadipocytes(fat)

  Dataset EGAD00001003495

• Reference epigenome OB57_D_PreA_mRNA-Seq data generated from KEP study

  A OB57_D_PreA_mRNA-Seq paired end data for Preadipocyte(fat)

  Dataset EGAD00001003487

• Reference epigenome CKD25_C_Podo_smRNA-Seq data generated from KEP study

  A CKD25_C_Podo_smRNA-Seq single end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003499

• Reference epigenome CKD25_C_Podo_mRNA-Seq data generated from KEP study

  A CKD25_C_Podo_mRNA-Seq paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003483

• ChIP-Seq files for PCGP ATRX study

  ChIP-Seq files for PCGP ATRX study paper titled "MYCN Amplification and ATRX Mutations are Incompatible in Neuroblastoma"

  Dataset EGAD00001004429

• Reference epigenome CKD24_C_Podo_mRNA-Seq data generated from KEP study

  A CKD24_C_Podo_mRNA-Seq paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003482

• Reference epigenome OB57_D_PreA_smRNA-Seq data generated from KEP study

  A OB57_D_PreA_smRNA-Seq single end data for Preadipocyte(fat)

  Dataset EGAD00001003496

• Reference epigenome CKD24_C_Podo_smRNA-Seq data generated from KEP study

  A CKD24_C_Podo_smRNA-Seq single end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003498

• ATAC-seq for placental and buffycoat cells.

  We performed ATAC-seq experiments using 2 placental samples and 2 buffycoat samples.

  Dataset EGAD00001004291

• ChIP-Seq files for RMS.

  ChIP-Seq files accompanying the paper titled "Identification of Therapeutic Targets in Rhabdomyosarcoma Through Integrated Genomic, Epigenomic, and Proteomic Analyses".

  Dataset EGAD00001004312

• Reference epigenome ADMSC05 h3k27me3 ChIP-Seq data generated from KEP study

  ADMSC05 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007375

• 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

• Single-cell RNA-sequencing of a multi-region pleural mesothelioma case

  Single-cell RNA-sequencing from three distant regions of an epithelioid pleural mesothelioma case.

  Study EGAS00001008062

• Combining transcription factor binding affinities with open chromatin data for accurate gene expression prediction

  The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

  Study EGAS00001002073

• Dataset for desmoplastic small round cell tumor - RNA

  This dataset contains 32 RNA sequencing samples of patients with desmoplastic small round cell tumor. Sequencing was performed on Illumina HiSeq 2500, Illumina HiSeq 4000, Illumina HiSeq X and NovaSeq 6000 using Illumina TruSeq RNA and TruSeq Stranded RNA Kit. The sequencing was always paired.

  Dataset EGAD50000000912

• RNA-sequencing

  RNA-sequencing for a cohort of B-ALL samples : 5 Down Syndrome (DS), 16 Hyperdiploid (HeH), 6 iAMP21, 9 other. RNA-sequencing for B-cell progenitors from 3 healthy. It also contains RNA-sequencing datasets of Patient-Derived Xenografts (X) developed from the B-ALL samples : 4 Down Syndrome (DS), 4 Hyperdiploid (HeH), 1 iAMP21, 3 other.

  Dataset EGAD00001005426

• Altered oligodendrocyte heterogeneity in Multiple sclerosis revealed by single nuclei RNA sequencing

  This study is currently hosted by the European Nucleotide Archive. To access the data contained within the Study please follow the link below: https://www.ebi.ac.uk/ena/browser/view/PRJEB39323 Oligodendrocyte (OL) pathology is increasingly implicated in neurodegenerative diseases, as they are involved in metabolic support of axons and functional cross-talk with other brain cells. Rodent OLs are heterogeneous, with developmental and biological differences, but the extent of heterogeneity in the normal human brain and its contribution to any changes to disease remains unknown. Here we performed single nuclei RNA-sequencing (snRNA-seq) from white matter (WM) areas of post mortem human brain both in control (Ctr) and multiple sclerosis (MS) patients. We identified several sub-clusters of oligodendroglia in the Ctr human WM, some similar to those in mouse, and defined new markers for these cell states. Strikingly, some of these sub-clusters were under-represented in MS tissue, while others were more prevalent than in controls. We found a lack of OL precursor cells (OPCs) and an OL subcluster in an intermediate stage of differentiation in MS lesions and in normal appearing white matter (NAWM), suggesting either depletion by the disease or by a regenerative response. The differences in mature OL sub-clusters indicate different functional states of OLs in MS tissue and, as this is similar in NAWM to lesions, that MS is a more diffuse brain disease than the focal demyelinating lesions suggest. We were also able to identify new putative markers of different MS lesion subtypes. Our findings of an altered heterogeneity of oligodendroglia in MS may have an important contribution to our understanding of disease progression and may alter therapeutic approaches to MS.

  Study EGAS00001003412

• 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

• ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing

  Dataset EGAD00001000281

• Genetic Mechanisms of Disease Lab DAC

  Data access for raw and processed H3K27ac ChIP-seq and ATAC-seq data from "Effect of ETS2 modulation on chromatin accessibility and enhancer activity in human macrophages".

  Dac EGAC50000000087

• Reference epigenome SMC01 h3k9me3 ChIP-Seq data generated from KEP study

  SMC01 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007312

• Reference epigenome SMC08 h3k27ac ChIP-Seq data generated from KEP study

  SMC08 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007344

• Reference epigenome ADMSC06 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007386

• Reference epigenome SMC09 h3k9me3 ChIP-Seq data generated from KEP study

  SMC09 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007354

• Reference epigenome SMC02 h3k36me3 ChIP-Seq data generated from KEP study

  SMC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007315

• Reference epigenome SMC04 h3k9me3 ChIP-Seq data generated from KEP study

  SMC04 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007330

• Reference epigenome ADMSC06 input ChIP-Seq data generated from KEP study

  ADMSC06 input ChIP-Seq paired end data

  Dataset EGAD00001007387

• Reference epigenome ADMSC01 h3k27ac ChIP-Seq data generated from KEP study

  ADMSC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007356

• Reference epigenome ADMSC01 input ChIP-Seq data generated from KEP study

  ADMSC01 input ChIP-Seq paired end data

  Dataset EGAD00001007361

• Reference epigenome ADMSC02 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC02 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007363

• Reference epigenome ADMSC06 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007385

• Reference epigenome ADMSC08 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC08 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007398

• Reference epigenome SMC04 h3k4me1 ChIP-Seq data generated from KEP study

  SMC04 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007328

• Reference epigenome SMC04 h3k36me3 ChIP-Seq data generated from KEP study

  SMC04 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007327

• Reference epigenome SMC03 h3k9me3 ChIP-Seq data generated from KEP study

  SMC03 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007324

• Reference epigenome SMC04 h3k4me3 ChIP-Seq data generated from KEP study

  SMC04 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007329

• Reference epigenome ADMSC02 h3k4me1 ChIP-Seq data generated from KEP study

  ADMSC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007364

• Reference epigenome ADMSC06 h3k27me3 ChIP-Seq data generated from KEP study

  ADMSC06 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007382

• Reference epigenome ADMSC02 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007365

• Reference epigenome ADMSC02 h3k27ac ChIP-Seq data generated from KEP study

  ADMSC02 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007362

• Reference epigenome ADMSC05 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC05 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007378

• Reference epigenome ADMSC05 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC05 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007379

• Reference epigenome SMC02 h3k4me1 ChIP-Seq data generated from KEP study

  SMC02 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007316

• Reference epigenome SMC05 h3k4me1 ChIP-Seq data generated from KEP study

  SMC05 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007334

• Reference epigenome Islet-derived-MSC03 miRNA-Seq data generated from KEP study

  Islet-derived-MSC03 miRNA-Seq single end data

  Dataset EGAD00001007183

• Reference epigenome Islet-derived-iPSC01 miRNA-Seq data generated from KEP study

  Islet-derived-iPSC01 miRNA-Seq single end data

  Dataset EGAD00001007185

• Reference epigenome Islet-derived-iPSC02 miRNA-Seq data generated from KEP study

  Islet-derived-iPSC02 miRNA-Seq single end data

  Dataset EGAD00001007186

• Reference epigenome SMC03 h3k27ac ChIP-Seq data generated from KEP study

  SMC03 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007320

• Reference epigenome SMC03 h3k36me3 ChIP-Seq data generated from KEP study

  SMC03 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007321

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

  Islet-derived-MSC02 miRNA-Seq single end data

  Dataset EGAD00001007182

• Reference epigenome SMC03 h3k4me3 ChIP-Seq data generated from KEP study

  SMC03 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007323

• Reference epigenome SMC09 input ChIP-Seq data generated from KEP study

  SMC09 input ChIP-Seq paired end data

  Dataset EGAD00001007355

• Reference epigenome SMC08 input ChIP-Seq data generated from KEP study

  SMC08 input ChIP-Seq paired end data

  Dataset EGAD00001007349

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

  Islet-derived-MSC01 miRNA-Seq single end data

  Dataset EGAD00001007181

• Reference epigenome SMC01 h3k4me1 ChIP-Seq data generated from KEP study

  SMC01 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007310

• Reference epigenome SMC02 h3k9me3 ChIP-Seq data generated from KEP study

  SMC02 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007318

• Reference epigenome SMC01 h3k27ac ChIP-Seq data generated from KEP study

  SMC01 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007308

• Reference epigenome ADMSC07 input ChIP-Seq data generated from KEP study

  ADMSC07 input ChIP-Seq paired end data

  Dataset EGAD00001007394

• Reference epigenome ADMSC08 h3k27Ac ChIP-Seq data generated from KEP study

  ADMSC08 h3k27Ac ChIP-Seq paired end data

  Dataset EGAD00001007395

• Reference epigenome SMC01 input ChIP-Seq data generated from KEP study

  SMC01 input ChIP-Seq paired end data

  Dataset EGAD00001007313

• Reference epigenome ADMSC08 h3k36me3 ChIP-Seq data generated from KEP study

  ADMSC08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007397

• Reference epigenome SMC02 h3k4me3 ChIP-Seq data generated from KEP study

  SMC02 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007317

• Reference epigenome ADMSC08 h3k27me3 ChIP-Seq data generated from KEP study

  ADMSC08 h3k27me3 ChIP-Seq paired end data

  Dataset EGAD00001007396

• Reference epigenome ADMSC08 h3k4me3 ChIP-Seq data generated from KEP study

  ADMSC08 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007399

• Reference epigenome ADMSC08 h3k9me3 ChIP-Seq data generated from KEP study

  ADMSC08 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007400

• Reference epigenome SMC03 h3k4me1 ChIP-Seq data generated from KEP study

  SMC03 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007322

• Reference epigenome SMC04 h3k27ac ChIP-Seq data generated from KEP study

  SMC04 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007326

• Reference epigenome SMC05 h3k27ac ChIP-Seq data generated from KEP study

  SMC05 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007332

• Reference epigenome SMC05 h3k36me3 ChIP-Seq data generated from KEP study

  SMC05 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007333

• Reference epigenome SMC05 input ChIP-Seq data generated from KEP study

  SMC05 input ChIP-Seq paired end data

  Dataset EGAD00001007337

• Reference epigenome SMC07 h3k36me3 ChIP-Seq data generated from KEP study

  SMC07 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007339

• Reference epigenome SMC07 h3k4me1 ChIP-Seq data generated from KEP study

  SMC07 h3k4me1 ChIP-Seq paired end data

  Dataset EGAD00001007340

• Reference epigenome SMC07 h3k27ac ChIP-Seq data generated from KEP study

  SMC07 h3k27ac ChIP-Seq paired end data

  Dataset EGAD00001007338

• Reference epigenome SMC08 h3k36me3 ChIP-Seq data generated from KEP study

  SMC08 h3k36me3 ChIP-Seq paired end data

  Dataset EGAD00001007345

• Reference epigenome SMC07 h3k4me3 ChIP-Seq data generated from KEP study

  SMC07 h3k4me3 ChIP-Seq paired end data

  Dataset EGAD00001007341

• Reference epigenome SMC07 h3k9me3 ChIP-Seq data generated from KEP study

  SMC07 h3k9me3 ChIP-Seq paired end data

  Dataset EGAD00001007342

• Reference epigenome SMC07 input ChIP-Seq data generated from KEP study

  SMC07 input ChIP-Seq paired end data

  Dataset EGAD00001007343