HOT or not: examining the basis of high-occupancy target regions

Wreczycka, Katarzyna; Franke, Vedran; Uyar, Bora; Wurmus, Ricardo; Akalin, Altuna

doi:10.1101/107680

Cited by 3 publications

(5 citation statements)

References 71 publications

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“…Indeed, we found that ChIP-seq peaks that do not contain a predicted TFBS were significantly enriched at HOT/XOT regions (odds ratio = 1.43 for HOT and 1.44 for XOT, p-value < 2.2e-16, hypergeometric test, Supplementary Table S5). This observation, combined with a previous study describing that HOT/XOT regions are likely to be derived from ChIP-seq artifacts (6) , suggests that HOT/XOT regions are not derived from the direct binding of TFs.…”

Section: High-occupancy Target Regions Are Likely Not Derived From DIsupporting

confidence: 59%

A map of direct TF-DNA interactions in the human genome

Gheorghe

Sandve

Khan

et al. 2018

Preprint

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Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the most popular assay to identify genomic regions, called ChIP-seq peaks, that are bound in vivo by transcription factors (TFs). These regions are derived from direct TF-DNA interactions, indirect binding of the TF to the DNA (through a co-binding partner), nonspecific binding to the DNA, and noise/bias/artifacts. Delineating the bona fide direct TF-DNA interactions within the ChIP-seq peaks remains challenging. We developed a dedicated software, ChIP-eat, that combines computational TF binding models and ChIP-seq peaks to automatically predict direct TF-DNA interactions. Our work culminated with predicted interactions covering >4% of the human genome, obtained by uniformly processing 1,983 ChIP-seq peak data sets from the ReMap database for 232 unique TFs. The predictions were a posteriori assessed using protein binding microarray and ChIP-exo data, and were predominantly found in high quality ChIP-seq peaks. The set of predicted direct TF-DNA interactions suggested that high-occupancy target regions are likely not derived from direct binding of the TFs to the DNA. Our predictions derived co-binding TFs supported by protein-protein interaction data and defined cis -regulatory modules enriched for disease-and trait-associated SNPs. Finally, we provide this collection of direct TF-DNA interactions and cis -regulatory modules in the human genome through the UniBind web-interface ( http://unibind.uio.no ).

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Section: High-occupancy Target Regions Are Likely Not Derived From DIsupporting

confidence: 59%

A map of direct TF-DNA interactions in the human genome

Gheorghe

Sandve

Khan

et al. 2018

Preprint

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“…2E). The features of ChIP-seq-specific peaks (enrichment of H3K4me3, close proximity to TSS, low CpG methylation, high expression of target genes) resembles the regions described as "hyper-ChIPable" or "phantom peaks" in yeast and in fly (Park et al 2013;Teytelman et al 2013;Jain et al 2015) and, more recently, as "HOT regions" in worm, mouse, and human ChIP-seq data (Wreczycka et al 2017). Notably, ∼51% of the mouse HOT regions overlapped with POU5F1 ChIP-seq-specific peaks, while only ∼3% of them overlapped with POU5F1 DamID-specific peaks (Fig.…”

Section: Optimization Of Damid-seq Protocolmentioning

confidence: 96%

“…(E) Expression changes of genes in D, 24 h after knockdown of Pou5f1 in ESCs (King and Klose 2017). (F ) DamID/ChIP-seq overlapping and specific peaks including mouse ChIP-seq HOT regions(Wreczycka et al 2017). 10 6 NSC SOX2 DamID-seq in comparison with ChIP-seq.…”

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confidence: 99%

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Mapping transcription factor occupancy using minimal numbers of cells in vitro and in vivo

et al. 2018

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The identification of transcription factor (TF) binding sites in the genome is critical to understanding gene regulatory networks (GRNs). While ChIP-seq is commonly used to identify TF targets, it requires specific ChIP-grade antibodies and high cell numbers, often limiting its applicability. NAdenine ethyltransferaseentification (DamID), developed and widely used in , is a distinct technology to investigate protein-DNA interactions. Unlike ChIP-seq, it does not require antibodies, precipitation steps, or chemical protein-DNA crosslinking, but to date it has been seldom used in mammalian cells due to technical limitations. Here we describe an optimized DamID method coupled with next-generation sequencing (DamID-seq) in mouse cells and demonstrate the identification of the binding sites of two TFs, POU5F1 (also known as OCT4) and SOX2, in as few as 1000 embryonic stem cells (ESCs) and neural stem cells (NSCs), respectively. Furthermore, we have applied this technique in vivo for the first time in mammals. POU5F1 DamID-seq in the gastrulating mouse embryo at 7.5 d post coitum (dpc) successfully identified multiple POU5F1 binding sites proximal to genes involved in embryo development, neural tube formation, and mesoderm-cardiac tissue development, consistent with the pivotal role of this TF in post-implantation embryo. This technology paves the way to unprecedented investigation of TF-DNA interactions and GRNs in specific cell types of limited availability in mammals, including in vivo samples.

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“…We further demonstrate the ability of the bimodal network to deconvolve the sequence and prior On the other hand, some highly accessible active promoters and enhancers are bound even with weaker sequence signatures, as defined by low activation scores from the BichromSEQ sub-network in our model. We note that some TF-bound regions with high preexisting chromatin (BichromCHR) sub-network activations and low sequence (BichromSEQ) sub-network activations might represent artifactual ChIP-seq enrichment 59 .…”

Section: Discussionmentioning

confidence: 90%

An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced TF binding

Srivastava

Aydın

Mazzoni

et al. 2019

Preprint

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AbstractTranscription factor (TF) binding specificity is determined via a complex interplay between the TF’s DNA binding preference and cell type-specific chromatin environments. The chromatin features that correlate with TF binding in a given cell type have been well characterized. For instance, the binding sites for a majority of TFs display concurrent chromatin accessibility. However, concurrent chromatin features reflect the binding activities of the TF itself, and thus provide limited insight into how genome-wide TF-DNA binding patterns became established in the first place. To understand the determinants of TF binding specificity, we therefore need to examine how newly activated TFs interact with sequence and preexisting chromatin landscapes.Here, we investigate the sequence and preexisting chromatin predictors of TF-DNA binding by examining the genome-wide occupancy of TFs that have been induced in well-characterized chromatin environments. We develop Bichrom, a bimodal neural network that jointly models sequence and preexisting chromatin data to interpret the genome-wide binding patterns of induced TFs. We find that the preexisting chromatin landscape is a differential global predictor of TF-DNA binding; incorporating preexisting chromatin features improves our ability to explain the binding specificity of some TFs substantially, but not others. Furthermore, by analyzing site-level predictors, we show that TF binding in previously inaccessible chromatin tends to correspond to the presence of more favorable cognate DNA sequences. Bichrom thus provides a framework for modeling, interpreting, and visualizing the joint sequence and chromatin landscapes that determine TF-DNA binding dynamics.

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HOT or not: examining the basis of high-occupancy target regions

Cited by 3 publications

References 71 publications

A map of direct TF-DNA interactions in the human genome

A map of direct TF-DNA interactions in the human genome

Mapping transcription factor occupancy using minimal numbers of cells in vitro and in vivo

An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced TF binding

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