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Reconstructing A/B compartments as revealed by Hi-C using long-range correlations in epigenetic data.

Fortin JP, Hansen KD - Genome Biol. (2015)

Bottom Line: Analysis of Hi-C data has shown that the genome can be divided into two compartments called A/B compartments.These compartments are cell-type specific and are associated with open and closed chromatin.We do this by exploiting that the structure of long-range correlations differs between open and closed compartments.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Road, Baltimore, 21205, MD, USA. fortin@jhsph.edu.

ABSTRACT
Analysis of Hi-C data has shown that the genome can be divided into two compartments called A/B compartments. These compartments are cell-type specific and are associated with open and closed chromatin. We show that A/B compartments can reliably be estimated using epigenetic data from several different platforms: the Illumina 450 k DNA methylation microarray, DNase hypersensitivity sequencing, single-cell ATAC sequencing and single-cell whole-genome bisulfite sequencing. We do this by exploiting that the structure of long-range correlations differs between open and closed compartments. This work makes A/B compartment assignment readily available in a wide variety of cell types, including many human cancers.

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Related in: MedlinePlus

Comparison of the methylation levels and the Hi-C compartment signal for chromosome 14. The figure displays data from 36.4 to 69.8 Mb on chromosome 14 at 100-kb resolution. a The first eigenvector from the HiC-IMR90-2014 dataset. b Average methylation on the beta scale for ten selected samples from the 450 k-fibroblast dataset; each sample is a line and divergent colors are used to distinguish the different levels of methylation in the different samples. c The first eigenvector from the HiC-EBV-2014 data. d Like (b), but for ten samples from the 450 k-EBV dataset; the samples from the two datasets are unrelated. On (d) we depict four different bins. Scatterplots between methylation values in different bins across all samples in the dataset are shown in (e–g). e For two bins in the closed compartment. g For one bin in the open and one bin in the closed compartment. g For two bins in the open compartment. The figure shows that samples have roughly the same ranking inside each closed compartment
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Fig6: Comparison of the methylation levels and the Hi-C compartment signal for chromosome 14. The figure displays data from 36.4 to 69.8 Mb on chromosome 14 at 100-kb resolution. a The first eigenvector from the HiC-IMR90-2014 dataset. b Average methylation on the beta scale for ten selected samples from the 450 k-fibroblast dataset; each sample is a line and divergent colors are used to distinguish the different levels of methylation in the different samples. c The first eigenvector from the HiC-EBV-2014 data. d Like (b), but for ten samples from the 450 k-EBV dataset; the samples from the two datasets are unrelated. On (d) we depict four different bins. Scatterplots between methylation values in different bins across all samples in the dataset are shown in (e–g). e For two bins in the closed compartment. g For one bin in the open and one bin in the closed compartment. g For two bins in the open compartment. The figure shows that samples have roughly the same ranking inside each closed compartment

Mentions: To understand what drives the correlation between loci within the closed compartment, we carefully examined the DNA methylation data in these genomic regions. Figure 6 shows a very surprising feature of the data, which explains the long-range correlations. In this figure, we have arbitrarily selected ten samples and we plot their methylation levels across a small part of chromosome 14, with each sample having its own color. Data from both EBV-transformed lymphocytes and fibroblasts are depicted. While the same coloring scheme has been used for both cell types, there is no correspondence between the samples assayed in the different experiments. The figure shows that the ten samples have roughly the same ranking inside each region in the closed compartment. This illustrates a surprising genome-wide ranking between samples in the closed compartment.Fig. 6


Reconstructing A/B compartments as revealed by Hi-C using long-range correlations in epigenetic data.

Fortin JP, Hansen KD - Genome Biol. (2015)

Comparison of the methylation levels and the Hi-C compartment signal for chromosome 14. The figure displays data from 36.4 to 69.8 Mb on chromosome 14 at 100-kb resolution. a The first eigenvector from the HiC-IMR90-2014 dataset. b Average methylation on the beta scale for ten selected samples from the 450 k-fibroblast dataset; each sample is a line and divergent colors are used to distinguish the different levels of methylation in the different samples. c The first eigenvector from the HiC-EBV-2014 data. d Like (b), but for ten samples from the 450 k-EBV dataset; the samples from the two datasets are unrelated. On (d) we depict four different bins. Scatterplots between methylation values in different bins across all samples in the dataset are shown in (e–g). e For two bins in the closed compartment. g For one bin in the open and one bin in the closed compartment. g For two bins in the open compartment. The figure shows that samples have roughly the same ranking inside each closed compartment
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4574526&req=5

Fig6: Comparison of the methylation levels and the Hi-C compartment signal for chromosome 14. The figure displays data from 36.4 to 69.8 Mb on chromosome 14 at 100-kb resolution. a The first eigenvector from the HiC-IMR90-2014 dataset. b Average methylation on the beta scale for ten selected samples from the 450 k-fibroblast dataset; each sample is a line and divergent colors are used to distinguish the different levels of methylation in the different samples. c The first eigenvector from the HiC-EBV-2014 data. d Like (b), but for ten samples from the 450 k-EBV dataset; the samples from the two datasets are unrelated. On (d) we depict four different bins. Scatterplots between methylation values in different bins across all samples in the dataset are shown in (e–g). e For two bins in the closed compartment. g For one bin in the open and one bin in the closed compartment. g For two bins in the open compartment. The figure shows that samples have roughly the same ranking inside each closed compartment
Mentions: To understand what drives the correlation between loci within the closed compartment, we carefully examined the DNA methylation data in these genomic regions. Figure 6 shows a very surprising feature of the data, which explains the long-range correlations. In this figure, we have arbitrarily selected ten samples and we plot their methylation levels across a small part of chromosome 14, with each sample having its own color. Data from both EBV-transformed lymphocytes and fibroblasts are depicted. While the same coloring scheme has been used for both cell types, there is no correspondence between the samples assayed in the different experiments. The figure shows that the ten samples have roughly the same ranking inside each region in the closed compartment. This illustrates a surprising genome-wide ranking between samples in the closed compartment.Fig. 6

Bottom Line: Analysis of Hi-C data has shown that the genome can be divided into two compartments called A/B compartments.These compartments are cell-type specific and are associated with open and closed chromatin.We do this by exploiting that the structure of long-range correlations differs between open and closed compartments.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Road, Baltimore, 21205, MD, USA. fortin@jhsph.edu.

ABSTRACT
Analysis of Hi-C data has shown that the genome can be divided into two compartments called A/B compartments. These compartments are cell-type specific and are associated with open and closed chromatin. We show that A/B compartments can reliably be estimated using epigenetic data from several different platforms: the Illumina 450 k DNA methylation microarray, DNase hypersensitivity sequencing, single-cell ATAC sequencing and single-cell whole-genome bisulfite sequencing. We do this by exploiting that the structure of long-range correlations differs between open and closed compartments. This work makes A/B compartment assignment readily available in a wide variety of cell types, including many human cancers.

Show MeSH
Related in: MedlinePlus