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Systematic chromatin state comparison of epigenomes associated with diverse properties including sex and tissue type.

Yen A, Kellis M - Nat Commun (2015)

Bottom Line: By applying ChromDiff to the 127 epigenomes from the Roadmap Epigenomics and ENCODE projects, we provide novel group-wise comparative analyses across sex, tissue type, state and developmental age.Remarkably, we find that distinct sets of epigenomic features are maximally discriminative for different group-wise comparisons, in each case revealing distinct enriched pathways, many of which do not show gene expression differences.Our methodology should be broadly applicable for epigenomic comparisons and provides a powerful new tool for studying chromatin state differences at the genome scale.

View Article: PubMed Central - PubMed

Affiliation: 1] Electrical Engineering and Computer Science Department, Computer Science and Artificial Intelligence Laboratory, MIT, 32 Vassar Street, 32D-524, Cambridge, Massachusetts 02139, USA [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.

ABSTRACT
Epigenomic data sets provide critical information about the dynamic role of chromatin states in gene regulation, but a key question of how chromatin state segmentations vary under different conditions across the genome has remained unaddressed. Here we present ChromDiff, a group-wise chromatin state comparison method that generates an information-theoretic representation of epigenomes and corrects for external covariate factors to better isolate relevant chromatin state changes. By applying ChromDiff to the 127 epigenomes from the Roadmap Epigenomics and ENCODE projects, we provide novel group-wise comparative analyses across sex, tissue type, state and developmental age. Remarkably, we find that distinct sets of epigenomic features are maximally discriminative for different group-wise comparisons, in each case revealing distinct enriched pathways, many of which do not show gene expression differences. Our methodology should be broadly applicable for epigenomic comparisons and provides a powerful new tool for studying chromatin state differences at the genome scale.

No MeSH data available.


Related in: MedlinePlus

Transcriptional differences dominate brain and GI tissue comparison.Comparison of brain and gastrointestinal epigenomes reveal (a) coordinated chromatin state changes that co-occur within groups of genes, as well as cluster-specific transcriptional differences at associated genes based on (b) most abundant chromatin state and (d) gene expression data (when available). Five of the six identified gene groups have significantly different expression between brain and GI samples, with asterisks indicating P<0.05 based on the two-sided Mann–Whitney-Wilcoxon test. (c) Identified genes are enriched for brain- (dark blue stars) and gastric-specific (light blue stars) purposes and gene sets, as evidenced by the top 10 gene set annotations. (e) Genes in each epigenomic cluster contain different gene set annotations, such as cancer-related and cell cycle gene sets (cluster A), gastric-specific (cluster E) and brain-specific (cluster D) gene sets, genes related to the nervous system (cluster C), genes associated with histone marks (clusters C and F) and membrane genes (cluster F).
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f3: Transcriptional differences dominate brain and GI tissue comparison.Comparison of brain and gastrointestinal epigenomes reveal (a) coordinated chromatin state changes that co-occur within groups of genes, as well as cluster-specific transcriptional differences at associated genes based on (b) most abundant chromatin state and (d) gene expression data (when available). Five of the six identified gene groups have significantly different expression between brain and GI samples, with asterisks indicating P<0.05 based on the two-sided Mann–Whitney-Wilcoxon test. (c) Identified genes are enriched for brain- (dark blue stars) and gastric-specific (light blue stars) purposes and gene sets, as evidenced by the top 10 gene set annotations. (e) Genes in each epigenomic cluster contain different gene set annotations, such as cancer-related and cell cycle gene sets (cluster A), gastric-specific (cluster E) and brain-specific (cluster D) gene sets, genes related to the nervous system (cluster C), genes associated with histone marks (clusters C and F) and membrane genes (cluster F).

Mentions: Over 40% (2,274 of 5,533 genes) of the genes distinguishing brain from GI tissues involve multiple chromatin states for each gene. Of the 5,079 genes associated with the 10,000 sampled features, 6 groups of genes emerge, representing genes with distinguishing features involving: (a) promoter and enhancer regions, (b) weakly transcribed and quiescent regions; (c) enhancer and weakly transcribed regions, (d) enhancer regions only, (e) Polycomb-repressed and active transcription start site regions, and (f) genetic enhancer regions (Fig. 3a, left to right, Supplementary Fig. 4). These results highlight the powerful ability of ChromDiff to identify relationships between chromatin states: these gene groups suggest combinations of chromatin states that act in coordinated ways to complement and/or reinforce one another.


Systematic chromatin state comparison of epigenomes associated with diverse properties including sex and tissue type.

Yen A, Kellis M - Nat Commun (2015)

Transcriptional differences dominate brain and GI tissue comparison.Comparison of brain and gastrointestinal epigenomes reveal (a) coordinated chromatin state changes that co-occur within groups of genes, as well as cluster-specific transcriptional differences at associated genes based on (b) most abundant chromatin state and (d) gene expression data (when available). Five of the six identified gene groups have significantly different expression between brain and GI samples, with asterisks indicating P<0.05 based on the two-sided Mann–Whitney-Wilcoxon test. (c) Identified genes are enriched for brain- (dark blue stars) and gastric-specific (light blue stars) purposes and gene sets, as evidenced by the top 10 gene set annotations. (e) Genes in each epigenomic cluster contain different gene set annotations, such as cancer-related and cell cycle gene sets (cluster A), gastric-specific (cluster E) and brain-specific (cluster D) gene sets, genes related to the nervous system (cluster C), genes associated with histone marks (clusters C and F) and membrane genes (cluster F).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4557131&req=5

f3: Transcriptional differences dominate brain and GI tissue comparison.Comparison of brain and gastrointestinal epigenomes reveal (a) coordinated chromatin state changes that co-occur within groups of genes, as well as cluster-specific transcriptional differences at associated genes based on (b) most abundant chromatin state and (d) gene expression data (when available). Five of the six identified gene groups have significantly different expression between brain and GI samples, with asterisks indicating P<0.05 based on the two-sided Mann–Whitney-Wilcoxon test. (c) Identified genes are enriched for brain- (dark blue stars) and gastric-specific (light blue stars) purposes and gene sets, as evidenced by the top 10 gene set annotations. (e) Genes in each epigenomic cluster contain different gene set annotations, such as cancer-related and cell cycle gene sets (cluster A), gastric-specific (cluster E) and brain-specific (cluster D) gene sets, genes related to the nervous system (cluster C), genes associated with histone marks (clusters C and F) and membrane genes (cluster F).
Mentions: Over 40% (2,274 of 5,533 genes) of the genes distinguishing brain from GI tissues involve multiple chromatin states for each gene. Of the 5,079 genes associated with the 10,000 sampled features, 6 groups of genes emerge, representing genes with distinguishing features involving: (a) promoter and enhancer regions, (b) weakly transcribed and quiescent regions; (c) enhancer and weakly transcribed regions, (d) enhancer regions only, (e) Polycomb-repressed and active transcription start site regions, and (f) genetic enhancer regions (Fig. 3a, left to right, Supplementary Fig. 4). These results highlight the powerful ability of ChromDiff to identify relationships between chromatin states: these gene groups suggest combinations of chromatin states that act in coordinated ways to complement and/or reinforce one another.

Bottom Line: By applying ChromDiff to the 127 epigenomes from the Roadmap Epigenomics and ENCODE projects, we provide novel group-wise comparative analyses across sex, tissue type, state and developmental age.Remarkably, we find that distinct sets of epigenomic features are maximally discriminative for different group-wise comparisons, in each case revealing distinct enriched pathways, many of which do not show gene expression differences.Our methodology should be broadly applicable for epigenomic comparisons and provides a powerful new tool for studying chromatin state differences at the genome scale.

View Article: PubMed Central - PubMed

Affiliation: 1] Electrical Engineering and Computer Science Department, Computer Science and Artificial Intelligence Laboratory, MIT, 32 Vassar Street, 32D-524, Cambridge, Massachusetts 02139, USA [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.

ABSTRACT
Epigenomic data sets provide critical information about the dynamic role of chromatin states in gene regulation, but a key question of how chromatin state segmentations vary under different conditions across the genome has remained unaddressed. Here we present ChromDiff, a group-wise chromatin state comparison method that generates an information-theoretic representation of epigenomes and corrects for external covariate factors to better isolate relevant chromatin state changes. By applying ChromDiff to the 127 epigenomes from the Roadmap Epigenomics and ENCODE projects, we provide novel group-wise comparative analyses across sex, tissue type, state and developmental age. Remarkably, we find that distinct sets of epigenomic features are maximally discriminative for different group-wise comparisons, in each case revealing distinct enriched pathways, many of which do not show gene expression differences. Our methodology should be broadly applicable for epigenomic comparisons and provides a powerful new tool for studying chromatin state differences at the genome scale.

No MeSH data available.


Related in: MedlinePlus