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Unknown,Transcriptomics,Genomics,Proteomics

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Modeling genome-wide transcriptional cis-regulation in n LNCaP-abl cell line after siRNA knock down of a series of gene factors [RNA-seq]

ABSTRACT: We describe, MARGE, Model-based Analysis of the Regulation of Gene Expression, a robust methodology that leverages a large library of genome-wide H3K27ac ChIP-seq profiles to predict key regulated genes and cis-regulatory regions in human or mouse. MARGE adopts a gene centric approach to define a regulatory potential that summarizes the aggregate activity of multiple cis-regulatory elements on each gene. This model is effective in describing cis-regulatory activity and, unlike the super-enhancer based approach, is highly predictive of gene expression changes in response to BET-bromodomain inhibitors. We show that linear combinations of H3K27ac defined regulatory potentials, selected from an extensive database of published H3K27ac profiles, can accurately model diverse gene sets derived from differential gene expression experiments. In addition, we demonstrate a novel semi-supervised learning approach for identifying transcription factor binding sites associated with the set of transcription factors that regulate the gene set. MARGE leverages published H3K27ac ChIP-seq data to enhance the interpretation of newly generated H3K27ac ChIP-seq profiles. MARGE can also be used to analyze gene expression studies, without the production of matched H3K27ac ChIP-seq data. Transcriptome profiling in LNCaP-abl cell line after siRNA knock down of a series of gene factors.

ORGANISM(S): Homo sapiens

SUBMITTER: Chongzhi Zang

PROVIDER: E-GEOD-72534 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Modeling transcriptional cis-regulation with a library of genome-wide histone H3K27ac profiles

Project description:We describe, MARGE, Model-based Analysis of the Regulation of Gene Expression, a robust methodology that leverages a large library of genome-wide H3K27ac ChIP-seq profiles to predict key regulated genes and cis-regulatory regions in human or mouse. MARGE adopts a gene centric approach to define a regulatory potential that summarizes the aggregate activity of multiple cis-regulatory elements on each gene. This model is effective in describing cis-regulatory activity and, unlike the super-enhancer based approach, is highly predictive of gene expression changes in response to BET-bromodomain inhibitors. We show that linear combinations of H3K27ac defined regulatory potentials, selected from an extensive database of published H3K27ac profiles, can accurately model diverse gene sets derived from differential gene expression experiments. In addition, we demonstrate a novel semi-supervised learning approach for identifying transcription factor binding sites associated with the set of transcription factors that regulate the gene set. MARGE leverages published H3K27ac ChIP-seq data to enhance the interpretation of newly generated H3K27ac ChIP-seq profiles. MARGE can also be used to analyze gene expression studies, without the production of matched H3K27ac ChIP-seq data. Identifying genomic profiles of histone modification H3K27ac in LNCaP-abl cell line after siRNA knock down of a series of gene factors.

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Modeling genome-wide transcriptional cis-regulation in n LNCaP-abl cell line after siRNA knock down of a series of gene factors [RNA-seq]

2016-05-16 | GSE72534 | GEO

Integrative analysis identifies targetable CREB1/FoxA1 transcriptional co-regulation as a predictor of prostate cancer recurrence

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Regulation of Histone Acetylation by SUMOylation Through TFAP2C Binding to the Chromatin and Recruitment of HDAC Complex

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2012-04-03 | E-GEOD-35126 | biostudies-arrayexpress

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Project description:Millions of cis-regulatory sequences have recently been found in the human genome, but the function of most cis-elements are not yet clear, in part due to the difficulty in determining their regulatory targets, which are often located millions of base pairs away and separated by one or more unrelated genes. To address this problem, the Hi-C method has been developed to identify long-range looping interactions in a genome-wide, unbiased fashion. However, current data analysis of Hi-C datasets cannot fully resolve regulatory interactions between enhancers and promoters due to the low resolution. Here, we generated a high-depth Hi-C dataset and applied a new analysis method that offers improved resolution permitting genome-wide identification of nearly one million chromatin interactions. We demonstrated the use of Hi-C to identify target promoters of enhancers regulated by NF-M-NM-:B signaling and signal-dependent dynamic chromatin interaction at these enhancers in human cells. Surprisingly, our results showed that most NF-M-NM-:B binding sites are looped to their regulatory targets prior to activation of the signaling pathway, and appear to undergo little change during signaling. This observation suggests that the chromatin organization landscape, once established in a cell type, is rather stable and may influence the selection and activation of target genes by a transcription factor. We performed Hi-C analysis using a human fibroblast cell line IMR90 before and after NF-M-NM-:B activation. In the meantime, we also performed ChIP-seq experiments to map the location of NF-M-NM-:B p65 subunit, RNA polymerase II, p300, and several histone modifications (including H3K4me1, H3K4me3, H3K27ac and H3K36me3) in IMR90 cells before and after transient TNF-M-NM-1 stimulation. Additionally, to monitor the dynamic transcription profiles, we also performed Global Run-On sequencing (GRO-seq).

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