Finding Associations among Histone Modifications Using Sparse Partial Correlation Networks

Lasserre, Julia; Chung, Ho‐Ryun; Vingron, Martin

doi:10.1371/journal.pcbi.1003168

“…The positive correlations we found among activating histone modifications (H3K4me1, H3K4me3, H3K27ac, H3K36me3), among repressive histone modifications (H3K9me3, H3K27me3, 5mC) and between H3K36me3 and repressive marks are in qualitative agreement with previous studies conducted with ESCs and other cell types (62,64,65).…”

Section: Comparison Of Chromatin Domains In Escs Versus Ncssupporting

confidence: 92%

Retrieving chromatin patterns from deep sequencing data using correlation functions

Molitor

¹

,

Mallm

²

,

Rippe

³

et al. 2016

Preprint

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Epigenetic modifications and other chromatin features partition the genome on multiple length scales. They define chromatin domains with distinct biological functions that come in sizes ranging from single modified DNA bases to several megabases in the case of heterochromatic histone modifications. Due to chromatin folding, domains that are well separated along the linear nucleosome chain can form long-range interactions in three-dimensional space. It has now become a routine task to map epigenetic marks and chromatin structure by deep sequencing methods. However, assessing and comparing the properties of chromatin domains and their positional relationships across data sets without a priori assumptions remains challenging. Here, we introduce multiscale correlation evaluation (MCORE), which uses the fluctuation spectrum of mapped sequencing reads to quantify and compare chromatin patterns over a broad range of length scales in a model-independent manner. We applied MCORE to map the chromatin landscape in mouse embryonic stem cells and differentiated neural cells. We integrated sequencing data from chromatin immunoprecipitation, RNA expression, DNA methylation, and chromosome conformation capture experiments into network models that reflect the positional relationships among these features on different genomic scales. Furthermore, we used MCORE to compare our experimental data to models for heterochromatin reorganization during differentiation. The application of correlation functions to deep sequencing data complements current evaluation schemes and will support the development of quantitative descriptions of chromatin networks.

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“…The positive correlations we found among activating histone modifications (H3K4me1, H3K4me3, H3K27ac, and H3K36me3), among repressive histone modifications (H3K9me3, H3K27me3, and 5mC), and between H3K36me3 and repressive marks are in qualitative agreement with previous studies conducted with ESCs and other cell types (62,64,65). Genomewide colocalization of marks that were originally thought to affect transcription antagonistically might reflect the additional functions of these marks that are unrelated to the regulation of gene expression.…”

Section: Comparison Of Chromatin Domains In Escs Versus Ncssupporting

confidence: 91%

Retrieving Chromatin Patterns from Deep Sequencing Data Using Correlation Functions

Molitor

¹

,

Mallm

²

,

Rippe

³

et al. 2017

Biophysical Journal

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Epigenetic modifications and other chromatin features partition the genome on multiple length scales. They define chromatin domains with distinct biological functions that come in sizes ranging from single modified DNA bases to several megabases in the case of heterochromatic histone modifications. Due to chromatin folding, domains that are well separated along the linear nucleosome chain can form long-range interactions in three-dimensional space. It has now become a routine task to map epigenetic marks and chromatin structure by deep sequencing methods. However, assessing and comparing the properties of chromatin domains and their positional relationships across data sets without a priori assumptions remains challenging. Here, we introduce multiscale correlation evaluation (MCORE), which uses the fluctuation spectrum of mapped sequencing reads to quantify and compare chromatin patterns over a broad range of length scales in a model-independent manner. We applied MCORE to map the chromatin landscape in mouse embryonic stem cells and differentiated neural cells. We integrated sequencing data from chromatin immunoprecipitation, RNA expression, DNA methylation, and chromosome conformation capture experiments into network models that reflect the positional relationships among these features on different genomic scales. Furthermore, we used MCORE to compare our experimental data to models for heterochromatin reorganization during differentiation. The application of correlation functions to deep sequencing data complements current evaluation schemes and will support the development of quantitative descriptions of chromatin networks.

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“…It has been observed that different histone PTMs at the same loci often show significant correlation with each other (115). This interplay influences both modification states and modification turnover rates.…”

Section: Ptm Synergy and Antagonismmentioning

confidence: 99%

Metabolic Regulation of Histone Post-Translational Modifications

Fan

¹

,

Krautkramer

²

,

Feldman

³

et al. 2015

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Histone post-translational modifications regulate transcription and other DNA-templated functions. This process is dynamically regulated by specific modifying enzymes whose activities require metabolites that either serve as co-substrates or act as activators/inhibitors. Therefore, metabolism can influence histone modification by changing local concentrations of key metabolites. Physiologically, the epigenetic response to metabolism is important for nutrient sensing and environment adaption. In pathologic states, the connection between metabolism and histone modification mediates epigenetic abnormality in complex disease. In this review, we summarize recent studies of the molecular mechanisms involved in metabolic regulation of histone modifications and discuss their biological significance.

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Finding Associations among Histone Modifications Using Sparse Partial Correlation Networks

Cited by 43 publications

References 45 publications

Retrieving chromatin patterns from deep sequencing data using correlation functions

Retrieving chromatin patterns from deep sequencing data using correlation functions

Retrieving Chromatin Patterns from Deep Sequencing Data Using Correlation Functions

Metabolic Regulation of Histone Post-Translational Modifications

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