Project description:This data set comprises population (47 samples) measurements of transcription factor DNA binding (PU.1 and RPB2) and histone modification (H3K27ac, H3K4me1 and H3k4me3) levels for a subset of the 1000 Genomes Project CEPH samples. This data was generated as part of the following study: - Population Variation and Genetic Control of Modular Chromatin Architecture in Humans. Cell. 2015 Aug 27;162(5):1039-50. doi: 10.1016/j.cell.2015.08.001. Epub 2015 Aug 20. An additional set of 111 samples from the 1000 Genomes Project (GBR and TSI populations) were also assayed for three histone modifications (H3K27ac, H3K4me1 and H3k4me3). This data was generated as part of the following study: - Chromatin 3D interactions mediate genetic effects on regulatory networks.
Project description:H3k27ac assessment by ChIPseq was performed on 16h-activated naïve CD4+ T cells with variables of RARα expression and RA concentration.
Project description:To determine the positions of promoters and enhancers in developing Xenopus laevis epithelial progenitors, we performed ChIPseq on the histone modifications H3K4me3 and H3K27ac. We also performed ChIPseq on the transcription factors foxj1 (in the presence or absence of rfx2), myb (in the presence or absence of multicilin), and rad21. Some embryos were harvested as wild-types; in other experiments, we injected embryos with mRNAs encoding FLAG-foxj1 (with and without rfx2 morpholino) or GFP-myb (with and without an inducible form of multicilin (mcidas-HGR)). We then isolated epithelial progenitors surgically and, when injected with multicilin, induced at mid-stage 11. We then harvested chromatin at 9 hours after induction (roughly stage 18) and performed ChIPseq using antibodies against endogenous targets (H3K4me3, H3K27ac, rad21) or protein tags (FLAG, GFP). We then sequenced these libraries, aligned the reads to the X. laevis genome (version 9.1) with bwa mem and called peaks with HOMER, using input as background.
Project description:ChIPseq data for human glioblastoma patients, EGAS00001003953. Mix of input, H3K27ac, H3K27me1, H3K27me3, H3K36me3, H3K4me1, H3K4me3, H3K9me3 and BRD, 20 human samples, 2 cell lines (LN229, ZH487).
Project description:In cancer cells, enhancer hijacking mediated by chromosomal alterations and/or increase of histone H3 lysine 27 acetylation (H3K27ac) can support oncogene expression. However, how the chromatin conformation of enhancer-promoter interactions is affected by these events is unclear. Here, by comparing chromatin structure and H3K27ac levels in normal and lymphoma B-cells, we show that enhancer-promoter interacting regions assume different conformations according to the local abundance of H3K27ac. Genetic or pharmacologic depletion of H3K27ac decreases the frequency and the spreading of these interactions, altering oncogene expression. Moreover, enhancer hijacking mediated by chromosomal translocations influences the epigenetic status of the regions flanking the breakpoint, prompting the formation of distinct intra-chromosomal interactions in the two homologous chromosomes. These interactions are accompanied by allele-specific gene expression changes. Overall, our work indicates that H3K27ac dynamics modulate interaction frequency between regulatory regions and can lead to allele-specific chromatin configurations to sustain oncogene expression.
Project description:Analysis of ETO2, MYB, EP300 binding as well as H3K27ac, H3K4me1 and H3M27me3 occupancy by ChIP-seq in HEL cells treated with DMSO or dCBP-1 (0.5uM) for 3h or expressing shRNA targeting MYB (shMYB) or genetically inactivated for ETO2 (ETO2ko)
Project description:MotivationChIPseq is rapidly becoming a common technique for investigating protein-DNA interactions. However, results from individual experiments provide a limited understanding of chromatin structure, as various chromatin factors cooperate in complex ways to orchestrate transcription. In order to quantify chromtain interactions, it is thus necessary to devise a robust similarity metric applicable to ChIPseq data. Unfortunately, moving past simple overlap calculations to give statistically rigorous comparisons of ChIPseq datasets often involves arbitrary choices of distance metrics, with significance being estimated by computationally intensive permutation tests whose statistical power may be sensitive to non-biological experimental and post-processing variation.ResultsWe show that it is in fact possible to compare ChIPseq datasets through the efficient computation of exact P-values for proximity. Our method is insensitive to non-biological variation in datasets such as peak width, and can rigorously model peak location biases by evaluating similarity conditioned on a restricted set of genomic regions (such as mappable genome or promoter regions). Applying our method to the well-studied dataset of Chen et al. (2008), we elucidate novel interactions which conform well with our biological understanding. By comparing ChIPseq data in an asymmetric way, we are able to observe clear interaction differences between cofactors such as p300 and factors that bind DNA directly.AvailabilitySource code is available for download at http://sonorus.princeton.edu/IntervalStats/IntervalStats.tar.gz.Supplementary informationSupplementary data are available at Bioinformatics online.