Genome wide ChIP-seq analysis of human TOP2B occupancy in MCF7 breast cancer epithelial cells
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Simon Cockell
PROVIDER: E-GEOD-66753 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:TOP2B is involved in transcriptional initiation in response to nuclear hormone ligands and plays a role in transcriptional elongation. Whole genome TOP2B ChIP-seq was carried out on human MCF7 cells in the presence and absence of the nuclear hormone estradiol. Three peak calling methods were used and the peaks identified by at least two methods were analyzed further. Approximately half of the peaks fell either within a gene or within 5Kb of a transcription start site. The coincidence of TOP2B peaks and gene promoters was analyzed; TOP2B peaks were less frequently associated with promoters in estradiol treated than in control cells, suggesting a role of TOP2B in repression of transcription or a transient role in estradiol induced transcriptional changes.
2015-09-21 | GSE66753 | GEO
Project description:SNAPc-dependent promoters are unique among cellular promoters in being very similar to each other, even though some of them recruit RNA polymerase II and other RNA polymerase III. We have examined all SNAPc-bound promoters present in the human genome. We find that there is a surprisingly small number of them, some 70 promoters. Among these, the large majority is bound by either RNA polymerase II or RNA polymerase III, as expected, but one gene hitherto considered an RNA polymerase III gene is also occupied by significant levels of RNA polymerase II, which synthesizes an RNA distinct from that synthesized by RNA polymerase III. Both RNA polymerase II and RNA polymerase III SNAPc- dependent promoters use a largely overlapping set of a few transcription activators, including GABP, a novel factor implicated in snRNA gene transcription. SNAPc-dependent promoters in the human genome
2012-11-22 | E-GEOD-38303 | biostudies-arrayexpress
Project description:Molecular characterization of tumors has been critical for identifying important genes in cancer biology and for improving tumor classification and diagnosis. Long non-coding RNAs (lncRNAs), as a new, relatively unstudied class of transcripts, provide a rich opportunity to identify both functional drivers and cancer-type specific biomarkers. However, despite the potential importance of lncRNAs to the cancer field, no comprehensive survey of lncRNA expression across various cancers has been reported. We used 3'-End Sequencing for Expression Quantification (3SEQ) to quantify transcript abundance across 64 solid tumors representing 17 diagnostic subtypes of adenocarcinomas, squamous cell carcinomas, and sarcomas. We identified hundreds of transcripts from among the known 1,065 lncRNAs surveyed that show variability in transcript levels between the tumor types, and therefore, make potential biomarker candidates. We discovered 1,071 novel intergenic transcribed regions and demonstrate that these show similar patterns of variability between tumor types. We find that many of these differentially expressed cancer transcripts are also expressed in normal tissues. One such novel transcript specifically expressed in breast tissue was further evaluated using RNA in situ hybridization on a panel of breast tumors and shown to correlate with low tumor grade and estrogen receptor expression, thereby representing a potentially important new breast cancer biomarker. This study provides the first large survey of lncRNA expression within a panel of solid cancers and also identifies a number of novel transcribed regions differentially expressed across distinct cancer types that represent candidate biomarkers for future research. 3SEQ was performed on 64 formalin-fixed, paraffin-embedded (FFPE) human tumors representing 17 diagnostic cancer subtypes. Duplicate libraries were prepared for two of the tumors (ESS STT5520 and LMS STT516). 3SEQ was also performed on 27 normal human tissue samples. RNA-Seq was performed on 6 breast cancer cell lines for the examination of the breast-specific transcript on chr10. Series supplementary files: 'GSE28866_raw_counts_54511_peaks_cancer_and_normal.txt': Raw_counts: Total 3SEQ reads in each peak for each sample. File includes read counts for 54,511 peaks for the 66 cancer libraries and the 27 normal libraries. 'GSE28866_36048_normalized_peaks_cancer_and_normal.txt': Normalized_peaks: Normalized 3SEQ expression data for the 36,048 filtered peaks for the 66 cancer libraries and the 27 normal libraries. Peaks were classified as coding, lncRNA (Rinn, Bartel, Hughes, Gencode_lnc), other known transcripts (ref_known_Gencode_nc, all_mrna, other known), downstream, intron, promoter, or novel_intergenic. Peaks determined to be differentially expressed in one of the 17 cancer subtypes using a series of 2-Class SAM analyses are noted along with the type of cancer showing upregulation. Expression data was normalized using the sequencing depth of each sample by scaling the data using the mean value of each sample. Data was further compressed to reduce outliers by taking the square root of each value.
2012-08-24 | E-GEOD-28866 | biostudies-arrayexpress
Project description:Genome-wide mapping of proteinM-bM-^@M-^SDNA interactions is essential for a full understanding of transcriptional regulation. A precise map of binding sites for transcription factors, core transcriptional machinery is vital for deciphering the gene regulatory networks that underlie various biological processes. Chromatin immunoprecipitation followed by sequencing (ChIPM-bM-^@M-^Sseq) is a technique for genome-wide profiling of DNA-binding proteins. However, our conventional ChIPM-bM-^@M-^Sseq occasionally gives wider peaks which might be due to overlapping binding sites of two or more transcription factors. Therefore, to improve the resolution of our conventional ChIPM-bM-^@M-^Sseq which have DNA-protein footprint of ~100 bp, we decreased the size of DNA-protein footprint to ~ 50 bp by DNaseI digestion of whole cell extract (WCE). ChIP-seq for Twist transcription factor in Drosophila embryos
2014-02-06 | E-GEOD-40664 | biostudies-arrayexpress
Project description:Chromatin immunoprecipitation against CTCF followed by Illumina High-throughput sequencing. Examination of CTCF binding in red blood cells at 2 stages of development
2011-04-15 | E-GEOD-27945 | biostudies-arrayexpress
Project description:The cellular epigenetic landscape changes as pluripotent stem cells differentiate to somatic cells or when differentiated cells transform to a cancerous state. These epigenetic changes are commonly correlated with differences in gene expression. Whether active DNA replication is also associated with distinct chromatin environments in these developmentally and phenotypically diverse cell types has not been known. Here, we used BrdU-seq to map active DNA replication loci in human embryonic stem cells (hESCs), normal primary fibroblasts and a cancer cell line, and correlated these maps to the epigenome. In all cell lines, the majority of BrdU peaks were enriched in euchromatin and at DNA repetitive elements, especially at microsatellite repeats, and coincided with previously determined replication origins. The most prominent BrdU peaks were shared between all cells but a sizable fraction of the peaks were specific to each cell type and associated with cell type-specific genes. Surprisingly, the BrdU peaks that were common to all cell lines were associated with H3K18ac, H3K56ac, and H4K20me1 histone marks only in hESCs but not in normal fibroblasts or cancer cells. Depletion of the histone acetyltransferases for H3K18 and H3K56 dramatically decreased the number and intensity of BrdU peaks in hESCs. Our data reveal a unique epigenetic signature that distinguishes active replication loci in hESCs from normal somatic or malignant cells. To investigate relationship between DNA replication and epigenetic modifications
2013-11-05 | E-GEOD-43152 | biostudies-arrayexpress
Project description:In order to understand how Sox6 coordinately regulates the transcription of multiple fiber type specific genes during muscle development, we have performed ChIP-seq analyses to identify Sox6 target genes as well as RNA polymerase II (Pol II) binding sites in mouse fetal myotubes. Examination of Sox6 and Pol II binding sites in mouse fetal primary myotubes
2011-10-06 | E-GEOD-32627 | biostudies-arrayexpress
Project description:The most recurrently mutated oncogene in T-cell acute lymphoblastic leukemia (T-ALL) is NOTCH1. The core Notch complex consists of an ICN protein, a Maml cofactor, and the DNA binding factor Rbpj. The known direct cofactors of Notch appear to act nonselectively, homogeneously driving Notch gene expression functions. It is unclear whether there are direct cofactors of Notch that act selectively and heterogeneously regulate ICN. We discovered that Zmiz1, a Protein Inhibitor of Activated STAT (PIAS)-like coactivator, directly bound ICN1. In order to determine whether this interaction occured at chromatin, we performed ChIP-Seq. We identified significant overlap between ICN1, Rbpj, and Zmiz1 peaks. 75% of overlapping ICN1/Rbpj peaks overlapped with Zmiz1 (HA) peaks (273 peaks). The size of Zmiz1 (HA) peaks had moderate correlation with the size of Rbpj and ICN1 peaks. Like Rbpj and ICN1 peaks, Zmiz1 (HA) peaks were associated with activating H3K27ac, H3K4me1, and H3K4me3 chromatin marks and were devoid of repressive H3K27me3 marks. These data suggest that Zmiz1 is a selective Notch regulator. It co-binds only a subset of ICN1 and Rbpj-regulated sites. Zmiz1, ICN1, Rbpj, histone mehylation ChIP-Seq in murine T-ALL cell line
2015-10-28 | E-GEOD-66146 | biostudies-arrayexpress
Project description:Epigenetic modification plays important roles in plant and animal development. DNA methylation can impact the transposable element (TE) silencing, gene imprinting and regulate gene expression.Through a genome-wide analysis, DNA methylation peaks were respectively characterized and mapped in maize embryo and endosperm genome. Distinct methylation level across maize embryo and endosperm was observed. The maize embryo genome contained more DNA methylation peaks than endosperm. However, the endosperm chloroplast genome contained more DNA methylation peaks to compare with the embryo chloroplast genome. DNA methylation regions were characterized and mapped in genome. More CG island (CGI) shore are methylated than CGI in maize suggested that DNA methylation level is not positively correlated with CpG density. The DNA methylation occurred more frequently in the promoter sequence and transcriptional termination region (TTR) than other regions of the genes. The result showed that 99% TEs we characterized are methylated in maize embryo, but some (34.8%) of them are not methylated in endosperm. Maize embryo and endosperm exhibit distinct pattern/level of methylation. The most differentially methylated two regions between embryo and endosperm are High CpG content promoters (HCPs) and high CpG content TTRs (HCTTRs). DNA methylation peaks distinction of mitochondria and chloroplast DNA were less than the nucleus DNA. Our results indicated that DNA methylation is associated with the gene silencing or gene activation in maize endosperm and embryo. Many genes involved in embryogenesis and seed development were found differentially methylated in embryo and endosperm. We found 17 endosperm-specific expressed imprinting genes were hypomethylated in endosperm and were hypermethylated in embryo. The expression of a maize DEMETER -like (DME-like) gene and MBD101 gene (MBD4 homolog) which direct bulk genome DNA demethylation were higher in endosperm than in embryo. These two genes may be associated with the distinct methylation level across maize embryo and endosperm.The methylomes of maize embryo and endosperm was obtained by MeDIP-seq method. The global mapping of maize embryo and endosperm methylation in this study broadened our knowledge of DNA methylation patterns in maize genome, and provided useful information for future studies on maize seed development and regulation of metabolic pathways in different seed tissues. Examination of DNA methylated modifications in 2 maize tissues.
2014-06-17 | E-GEOD-58549 | biostudies-arrayexpress
Project description:Analysis of transcriptional regulation in human cells has implicated a large number of promoter-specific DNA-binding proteins that regulate transcription via diverse mechanisms. In some cases, these DNA-sequence-specific factors associate with intermediaries that orchestrate interactions with the chromatin-modifying enzymes. One such intermediary is HCF-1 (host-cell factor-1; HCFC1). HCF-1, first identified for its involvement in herpes-simplex virus transcription and subsequently shown to be an important cell-cycle regulator, has a poorly defined role in genome-wide transcriptional regulation. We show here, by chromatin immunoprecipitation followed by high-throughput sequence analysis (ChIP-seq), that HCF-1 is a major transcriptional start site associated factor, whose promoter association correlates positively with transcriptional activity. Thus, in HeLa cells HCF-1 is observed on 5400 generally active CpG-island promoters. Examination of the DNA sequences underlying the HCF-1-binding sites revealed three sequence motifs associated with the binding of (i) ZNF143 and Ronin/THAP11, (ii) GABP, and (iii) YY1 sequence-specific transcription factors. Subsequent ChIP-seq analysis of these four transcription factors revealed a large co-association of HCF-1 with these four transcription factors at approximately 90% of HCF-1-bound promoters. These studies suggest that a relatively small number of transcription factors -- some (ZNF143 and Ronin/THAP11) in novel combinations -- play a major role in HeLa-cell transcriptional regulation in association with HCF-1. This experiment includes the sequencing data of material obtained from chromatin immunoprecipitation using antibodies against HCFC1(antibodies against the C-subunit and the N-subunit), PolII(antibody against the RPB2 subunit), H3K36ME3 and H3K4Me3 histone modifications, ZNF143, THAP11, GABPalpha and YY1. It also includes control data of the Input material. All of them were done using HeLa cycling cells.
2012-07-31 | E-GEOD-31417 | biostudies-arrayexpress