Improved regulatory element prediction based on tissue-specific local epigenomic signatures

He, Yiping; Gorkin, David U.; Dickel, Diane E.; Nery, Joseph R.; Castanon, Rosa; Lee, Ah Young; Shen, Yin; Visel, Axel; Pennacchio, Len A.; Ren, Bing; Ecker, Joseph R.

doi:10.1073/pnas.1618353114

Cited by 78 publications

(142 citation statements)

References 71 publications

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“…Testing whether a RE is active in a cellular context, say by editing the RE in a cell line, is time consuming experimentally. As an alternative, genomewide inference of active REs is usually done based on ChIP-seq signals for selected chromatin regulators (e.g., P300), histone modification marks (e.g., H3K4me3, H3K27ac) (10), and local methylation signal (11). Thus, the knowledge of which CRs have been recruited to a RE is informative on the activity status of that RE.…”

Section: Bhlhe40mentioning

confidence: 99%

Modeling gene regulation from paired expression and chromatin accessibility data

Duren

Chen

Jiang

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

186

198

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The rapid increase of genome-wide datasets on gene expression, chromatin states, and transcription factor (TF) binding locations offers an exciting opportunity to interpret the information encoded in genomes and epigenomes. This task can be challenging as it requires joint modeling of context-specific activation of cis-regulatory elements (REs) and the effects on transcription of associated regulatory factors. To meet this challenge, we propose a statistical approach based on paired expression and chromatin accessibility (PECA) data across diverse cellular contexts. In our approach, we model (i) the localization to REs of chromatin regulators (CRs) based on their interaction with sequence-specific TFs, (ii) the activation of REs due to CRs that are localized to them, and (iii) the effect of TFs bound to activated REs on the transcription of target genes (TGs). The transcriptional regulatory network inferred by PECA provides a detailed view of how trans-and cis-regulatory elements work together to affect gene expression in a context-specific manner. We illustrate the feasibility of this approach by analyzing paired expression and accessibility data from the mouse Encyclopedia of DNA Elements (ENCODE) and explore various applications of the resulting model. gene regulation | transcription factor | regulatory element | chromatin regulator | chromatin activity E ver since the emergence of high-throughput gene expression experiments (1), computational biologists have been interested in the inference of gene regulatory relationships from gene expression data across diverse cellular contexts corresponding to diverse cell types and experimental conditions ( Fig. 1, red boxes). However, progress has been hindered by the fact that gene expression measurements provide little information on underlying regulatory mechanisms such as transcription factor binding and chromatin modification. To fill this gap, chromatin immunoprecipitationbased methods (2, 3) have been developed for the genome-wide mapping of transcriptional regulator binding locations and the detection of epigenetic marks characteristic of specific chromatin states. For example, by performing thousands of ChIP-seq experiments, the Encyclopedia of DNA Elements (ENCODE) consortium has generated such data for many chromatin marks and transcriptional regulators on a small number of cell lines (Fig. 1, green boxes). However, because a large number of transcriptional regulators and chromatin marks have to be analyzed one by one, it is unlikely that such comprehensive data will become available for many other cell lines. For most cellular contexts, the desired data will remain missing in the foreseeable future (Fig. 1, gray boxes).On the other hand, it is known that many of the protein-DNA interactions important for gene regulation occur in regulatory elements (REs) such as enhancers and insulators, which compose only a small portion of the noncoding sequences in a genome. The REs active in gene regulation in a given cellular state tend to have an open chromatin struc...

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Section: Bhlhe40mentioning

confidence: 99%

Modeling gene regulation from paired expression and chromatin accessibility data

Duren

Chen

Jiang

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

186

198

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“…Unlike RNA signatures, epigenomic profiling uniquely identifies the marks of gene regulatory elements that drive cell type differences and these can be used to create new tools to achieve cell type specific expression (Visel et al, 2007). Computational approaches that combine various epigenomic signatures such as chromatin modifications/structure and DNA methylation variation allow pinpointing of relatively small (~500–1000 bp) cell-type specific enhancers (Dickel et al, 2016; He et al, 2017; Monti et al, 2017). Similar collections of brain cell type-specific enhancers would provide a means to generate new reagents to target transgene expression to brain regions either in transgenic mice as shown by the Nelson group (Shima et al, 2016) and others (Silberberg et al, 2016), or with viral vectors (Dimidschstein et al, 2016), linking back diverse cell types to their locations/anatomical features in the whole brain.…”

Section: Single-cell Epigenomicsmentioning

confidence: 99%

The BRAIN Initiative Cell Census Consortium: Lessons Learned toward Generating a Comprehensive Brain Cell Atlas

Ecker

Geschwind

Kriegstein

et al. 2017

Neuron

Self Cite

259

199

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A comprehensive characterization of neuronal cell types, their distributions and patterns of connectivity is critical for understanding the properties of neural circuits and how they generate behaviors. Here we review the experiences of the BRAIN Initiative Cell Census Consortium – ten pilot projects funded by the U.S. BRAIN Initiative – in developing, validating and scaling up emerging genomic and anatomical mapping technologies for creating a complete inventory of neuronal cell types and their connections in multiple species and during development. These projects lay the foundation for a larger and longer-term effort to generate whole-brain cell atlases in species including mice and humans.

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“…Nucleosome occupancy exerts additional influence on TF binding (Kornberg and Lorch 1999;Pique-Regi et al 2011). Epigenetic marks are cell type-specific signatures (He et al 2017) that contribute to cell type-specific protein binding events ).…”

Section: Introductionmentioning

confidence: 99%

Relationship between histone modifications and transcription factor binding is protein family specific

Xin

Rohs

2018

Genome Res.

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The very small fraction of putative binding sites (BSs) TFs displayed protein family-specific preferences for HM patterns surrounding BSs, with high agreement among cell lines. Moreover, compared to models based on DNA sequence and shape at flanking regions of BSs, HM-augmented quantitative machine-learning methods resulted in increased performance in a TF family-specific manner. Analysis of the relative importance of features in these models indicated that TFs, displaying larger HM pattern differences between BSs and non-BSs, bound DNA in an HM-specific manner on a protein family-specific basis. We propose that TF family-specific HM preferences reveal distinct mechanisms that assist in guiding TFs to their cognate BSs by altering chromatin structure and accessibility.

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Improved regulatory element prediction based on tissue-specific local epigenomic signatures

Cited by 78 publications

References 71 publications

Modeling gene regulation from paired expression and chromatin accessibility data

Modeling gene regulation from paired expression and chromatin accessibility data

The BRAIN Initiative Cell Census Consortium: Lessons Learned toward Generating a Comprehensive Brain Cell Atlas

Relationship between histone modifications and transcription factor binding is protein family specific

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