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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

Estimated A/B compartments across several human cancers. The figure displays data on all of chromosome 14 at 100-kb resolution. Each track represents the first eigenvector of the methylation correlation matrix for the corresponding dataset. The datasets depicted in (a) and (b) are the 450 k-EBV and 450 k-fibroblast datasets. The datasets in (c–m) are cancer samples from TCGA for different cancers: (c) bladder urothelial carcinoma (BLCA), (d) breast invasive carcinoma (BRCA), (e) colon adenocarcinoma (COAD), (f) head and neck squamous cell carcinoma (HNSC), (g) kidney renal clear cell carcinoma (KIRC), (h) kidney renal papillary cell carcinoma (KIRP), (i) liver hepatocellular carcinoma (LIHC), (j) lung adenocarcinoma (LUAD), (k) lung squamous cell carcinoma (LUSC), (l) prostate adenocarcinoma (PRAD), and (m) uterine corpus endometrial carcinoma (UCEC)
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Fig13: Estimated A/B compartments across several human cancers. The figure displays data on all of chromosome 14 at 100-kb resolution. Each track represents the first eigenvector of the methylation correlation matrix for the corresponding dataset. The datasets depicted in (a) and (b) are the 450 k-EBV and 450 k-fibroblast datasets. The datasets in (c–m) are cancer samples from TCGA for different cancers: (c) bladder urothelial carcinoma (BLCA), (d) breast invasive carcinoma (BRCA), (e) colon adenocarcinoma (COAD), (f) head and neck squamous cell carcinoma (HNSC), (g) kidney renal clear cell carcinoma (KIRC), (h) kidney renal papillary cell carcinoma (KIRP), (i) liver hepatocellular carcinoma (LIHC), (j) lung adenocarcinoma (LUAD), (k) lung squamous cell carcinoma (LUSC), (l) prostate adenocarcinoma (PRAD), and (m) uterine corpus endometrial carcinoma (UCEC)

Mentions: Using the method we have developed in this manuscript, it is straightforward to estimate A/B compartments across a wide variety of human cancers using data from TCGA. Figure 13 displays the smoothed first eigenvectors for chromosome 14 at 100-kb resolution for 11 different cancers. Regions of similarity and differences are readily observed. We emphasize that TCGA does not include assays measuring chromatin accessibility such as DNase or various histone modifications. The extent to which these differences are associated with functional differences between these cancers is left for future work. Estimated compartments for all these cancer datasets are available online (see “Materials and methods”).Fig. 13


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

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

Estimated A/B compartments across several human cancers. The figure displays data on all of chromosome 14 at 100-kb resolution. Each track represents the first eigenvector of the methylation correlation matrix for the corresponding dataset. The datasets depicted in (a) and (b) are the 450 k-EBV and 450 k-fibroblast datasets. The datasets in (c–m) are cancer samples from TCGA for different cancers: (c) bladder urothelial carcinoma (BLCA), (d) breast invasive carcinoma (BRCA), (e) colon adenocarcinoma (COAD), (f) head and neck squamous cell carcinoma (HNSC), (g) kidney renal clear cell carcinoma (KIRC), (h) kidney renal papillary cell carcinoma (KIRP), (i) liver hepatocellular carcinoma (LIHC), (j) lung adenocarcinoma (LUAD), (k) lung squamous cell carcinoma (LUSC), (l) prostate adenocarcinoma (PRAD), and (m) uterine corpus endometrial carcinoma (UCEC)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig13: Estimated A/B compartments across several human cancers. The figure displays data on all of chromosome 14 at 100-kb resolution. Each track represents the first eigenvector of the methylation correlation matrix for the corresponding dataset. The datasets depicted in (a) and (b) are the 450 k-EBV and 450 k-fibroblast datasets. The datasets in (c–m) are cancer samples from TCGA for different cancers: (c) bladder urothelial carcinoma (BLCA), (d) breast invasive carcinoma (BRCA), (e) colon adenocarcinoma (COAD), (f) head and neck squamous cell carcinoma (HNSC), (g) kidney renal clear cell carcinoma (KIRC), (h) kidney renal papillary cell carcinoma (KIRP), (i) liver hepatocellular carcinoma (LIHC), (j) lung adenocarcinoma (LUAD), (k) lung squamous cell carcinoma (LUSC), (l) prostate adenocarcinoma (PRAD), and (m) uterine corpus endometrial carcinoma (UCEC)
Mentions: Using the method we have developed in this manuscript, it is straightforward to estimate A/B compartments across a wide variety of human cancers using data from TCGA. Figure 13 displays the smoothed first eigenvectors for chromosome 14 at 100-kb resolution for 11 different cancers. Regions of similarity and differences are readily observed. We emphasize that TCGA does not include assays measuring chromatin accessibility such as DNase or various histone modifications. The extent to which these differences are associated with functional differences between these cancers is left for future work. Estimated compartments for all these cancer datasets are available online (see “Materials and methods”).Fig. 13

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