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A Random-Forest Based Algorithm for Prediction of Enhancers From Histone Modifications

ABSTRACT: Transcriptional enhancers play critical roles in regulation of gene expression, but their identification has remained a challenge. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of histone modifications have previously been investigated for this purpose, leaving the questions answered whether there exist an optimal set of histone modifications that could improve the enhancer prediction. Here, we address this issue by exploring a rich dataset produced by the human Epigenome Roadmap Project. Specifically, we examined genome-wide profiles of 24 histone modifications in human embryonic stem cells and fibroblasts, and developed a Random-Forest based algorithm to integrate histone modification profiles for identification of enhancers.As a training set, we used histone modification profiles at genome-wide binding sites of p300 in the two cell types identified using ChIP-seq. We show that this algorithm not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify an optimal set of three chromatin marks for enhancer prediction.

ORGANISM(S): Homo sapiens

PROVIDER: GSE37858 | GEO | 2012/05/10

SECONDARY ACCESSION(S): PRJNA165163

REPOSITORIES: GEO

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A Random-Forest Based Algorithm for Prediction of Enhancers From Histone Modifications

Project description:Transcriptional enhancers play critical roles in regulation of gene expression, but their identification has remained a challenge. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of histone modifications have previously been investigated for this purpose, leaving the questions answered whether there exist an optimal set of histone modifications that could improve the enhancer prediction. Here, we address this issue by exploring a rich dataset produced by the human Epigenome Roadmap Project. Specifically, we examined genome-wide profiles of 24 histone modifications in human embryonic stem cells and fibroblasts, and developed a Random-Forest based algorithm to integrate histone modification profiles for identification of enhancers.As a training set, we used histone modification profiles at genome-wide binding sites of p300 in the two cell types identified using ChIP-seq. We show that this algorithm not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify an optimal set of three chromatin marks for enhancer prediction. ChIP-Seq Analysis of p300 in hESC H1 and IMR90 cells. Sequencing was done on the Illumina Genome Analyzer II platform for the H1 data and Illumina HiSeq for IMR90.Data was mapped to hg18 using Bowtie.

2012-05-09 | E-GEOD-37858 | biostudies-arrayexpress

Genome-wide histone modification and accessibility maps of mESCs, in the pluripotent state and after induced differentiation

Project description:Gene expression networks are tightly regulated by transcription factors (TF) and their targeted regulatory genomic elements (enhancers), which are known to correlate with specific histone modifications and chromatin accessibility. The identification of enhancers on a genome wide level is an important prerequisite for many functional studies, however the prediction of enhancer elements, especially with in response to changing conditions, remains challenging. Here we report the generation of a genome-wide mESC enhancer prediction map, comparing the pluripotent state and induced differentiation, by the integration of data on transcription, chromatin accessibility and multiple histone modifications.

2019-09-30 | GSE120376 | GEO

Homo sapiens

Project description:A Random-Forest Based Algorithm for Prediction of Enhancers From Histone Modifications

| PRJNA165163 | ENA

Histone acetylation H2BK20ac marks cell-state specific active regulatory elements

Project description:Characterisation of different histone modifications is crucial to understand gene regulation. In order to study the most predictive histone modification for active enhancers we created unbiased set of enhancers and used machine learning approach. Our approach revealed an unconventional histone modification H2BK20ac as most efficient marker of active enhancers. H2BK20ac also showed superior coverage of tissue specific active enhancers in complex invivo samples. Adding H2BK20ac to set of conventional histone modifications lead to identification of new chromatin state which could be active enhancers. H2BK20ac tends to occur only at cell-type specific active promoters and showed higher specificity for related disease mutations than H3K27ac and other histone modifications. Using transient state of BV2 microglia cells after lipopolysaccharide based activation, we found that H2BK20ac also marks cell-state specific cis-regulatory elements. Further analysis using inhibition of TGF-beta pathway in BV2 cells and LPS stimulation, revealed differential patterns of H2BK20ac and H3K27ac at genome locations associated with opposite roles response to stmulation. Our study about H2BK20ac hints about a new mechanism of regulation of cell-type specificity and a distinct mode of action of pathways to maintain balance between cell-responses. Chip-seq of H2BK20ac and other histone modifcation was performed in 3 cell types and embryonic mouse forebrain. The sensitivity for active enhancers was compared for different histone modification ChIP-seq.The level of H2BK20ac at promoters and enhancers was assesed for relationship to cell-type specific expression. H2BK20ac signals were also analysed during cell-state transition when microgila are stimulated by LPS

2016-02-25 | E-GEOD-72886 | biostudies-arrayexpress

Identification of candidate enhancer elements in unstimulated and stimulated dendritic cells and fibroblasts

Project description:To identify candidate enhancer elements we analyzed the distribution of two histone modifications associated with enhancers - H3K4me1 and H3K27ac - and one histone modification associated with active transcription - H4 acetylation. ChIP-seq for H3K4me1, H3K27ac and H4ac, and input DNA controls, from 2 cell types (DCs & fibroblasts) under 2 conditions (unstimulated & stimulated)

2012-11-19 | E-GEOD-32380 | biostudies-arrayexpress

Histone H3K27ac separates active from poised enhancers and predicts developmental state (gene expression data)

Project description:Developmental programs are controlled by transcription factors and chromatin regulators, which maintain specific gene expression programs through epigenetic modification of the genome. These regulatory events at enhancers contribute to the specific gene expression programs that determine cell state and the potential for differentiation into new cell types. While enhancer elements are known to be associated with certain histone modifications, and transcription factors, the relationship of these modifications to gene expression and developmental state has not been clearly defined. Here we interrogate the epigenetic landscape of enhancer elements in embryonic stem cells and several adult tissues in the mouse. We find that histone H3K27ac distinguishes active enhancers from inactive/poised enhancer elements, thus providing clues to current cell state and further developmental potential. Gene expression profiling was performed in mouse ES, NPC, liver, and pro-B

2010-10-31 | E-GEOD-23907 | biostudies-arrayexpress

Histone acetylation H2BK20ac marks cell-state specific active regulatory elements

2016-02-25 | GSE72886 | GEO

Identification of candidate enhancer elements in unstimulated and stimulated dendritic cells and fibroblasts

2012-11-19 | GSE32380 | GEO

Dynamic-network-guided CRISPRi screen identifies CTCF loop-constrained 1 nonlinear enhancer-gene regulatory activity in cell state transitions

Project description:Enhancers play key roles in gene regulation. However, comprehensive enhancer discovery is challenging because most enhancers, especially those affected in complex diseases, have weak effects on gene expression. Through gene regulatory network modeling, we identified that dynamic cell state transitions, a critical missing component in prevalent enhancer discovery strategies, can be utilized to improve the cells’ sensitivity to enhancer perturbation. Guided by the modeling results, we performed a mid-transition CRISPRi-based enhancer screen utilizing human embryonic stem cell definitive endoderm differentiation as a dynamic transition system. The screen discovered a comprehensive set of enhancers (4 to 9 per locus) for each of the core lineage-specifying transcription factors (TFs), including many enhancers with weak to moderate effects. Integrating the screening results with enhancer activity measurements (ATAC-seq, H3K27ac ChIP-seq) and three-dimensional enhancer-promoter interaction information (CTCF looping, Hi-C), we were able to develop a CTCF loop-constrained Interaction Activity (CIA) model that can better predict functional enhancers compared to models that rely on Hi-C-based enhancer-promoter contact frequency. Together, our dynamic network-guided enhancer screen and the CIA enhancer prediction model provide generalizable strategies for sensitive and more comprehensive enhancer discovery in both normal and pathological cell state transitions.

2023-06-17 | PXD043070 | Pride

Epigenetic and genetic features that lead to discovery of enhancer function

Project description:The ability to measure epigenetic features, such as histone modifications and occupancy by transcription factors and co-activators, on a genome-wide scale is advancing the accuracy of CRM predictions. While integration of signals from multiple features is expected to improve predictions, the contribution of each feature to prediction accuracy is not known. We began with predictions of 4,915 erythroid enhancers based on genomic occupancy by TAL1, a key hematopoietic transcription factor that is strongly associated with gene induction in erythroid cells. Seventy of these DNA segments occupied by TAL1 (TAL1 OSs) were tested by transient transfections of cultured hematopoietic cells, and 56% of these were active as enhancers. Sixty-six TAL1 OSs were evaluated in transgenic mouse embryos, and 65% of these were active enhancers in various tissues. Inclusion of additional epigenetic features improved the prediction accuracy, with combinations of TAL1, GATA1, EP300, H3K4me1, and H3K27ac giving high accuracy of enhancer prediction (70%-75% success depending on method of clustering) while maintaining good sensitivity and specificity. Motifs that distinguish active from inactive TAL1 OSs implicate IRFs, STATs, and FOX protein families as candidate positive co-factors with TAL1, while REST (NRSF) and HOX family proteins are implicated in inactivity. While signals for evolutionary constraint were weak over the entire TAL1-bound DNA segments regardless of activity in either assay, phylogenetic preservation of a TF-binding site motif was associated with enhancer activity. The contribution of 8 epigenetic features including H3K27ac to identification of enhancers in 24h-induced G1E-ER4 cells.

2015-04-27 | E-GEOD-61349 | biostudies-arrayexpress

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