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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

Epigenomically distinguishing genes are enriched for differential expression.By analysing expression of genes that our method identifies as being part of distinguishing gene and chromatin state combinations, we find that our method both recaptures differential gene expression and identifies distinguishing epigenetic context not captured by differential gene expression. (We performed this analysis on the 12 comparisons that produced distinguishing epigenomic features.) (a) Overall, identified genes are more differentially expressed than non-distinguishing genes, although <50% of the genes identified are significantly differently expressed. (The three comparisons that resulted in no differentially expressed genes are excluded.) (b) In every comparison of our nine remaining comparisons, our identified genes were significantly enriched for differentially expressed genes overall (as designated by asterisks), based on calculation of the log odds ratio and the corresponding 95% confidence intervals, as shown by the error bars. (P<0.003 in all cases, two-sided z-test) (c). After correcting for covariates in expression data, ChromDiff identifies all of the differentially expressed genes in two of the remaining four cases. (The eight comparisons that yielded no significant expression differences are excluded.)
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f6: Epigenomically distinguishing genes are enriched for differential expression.By analysing expression of genes that our method identifies as being part of distinguishing gene and chromatin state combinations, we find that our method both recaptures differential gene expression and identifies distinguishing epigenetic context not captured by differential gene expression. (We performed this analysis on the 12 comparisons that produced distinguishing epigenomic features.) (a) Overall, identified genes are more differentially expressed than non-distinguishing genes, although <50% of the genes identified are significantly differently expressed. (The three comparisons that resulted in no differentially expressed genes are excluded.) (b) In every comparison of our nine remaining comparisons, our identified genes were significantly enriched for differentially expressed genes overall (as designated by asterisks), based on calculation of the log odds ratio and the corresponding 95% confidence intervals, as shown by the error bars. (P<0.003 in all cases, two-sided z-test) (c). After correcting for covariates in expression data, ChromDiff identifies all of the differentially expressed genes in two of the remaining four cases. (The eight comparisons that yielded no significant expression differences are excluded.)

Mentions: The genes identified from our comparisons often exhibited different expression levels between the groups compared. To quantify this, for each of the 12 comparisons with distinguishing features (Supplementary Table 1), we calculated how many of our identified genes had differential gene expression between the 2 groups of the comparison (Fig. 6a) (see Methods). Three comparisons that revealed epigenomic differences did not have any differentially expressed genes: Brain/ESC, Cell Line/Primary Culture and ESC/GI. Furthermore, in the nine cases with differentially expressed genes, the epigenomically distinguishing genes included proportionally more differentially expressed genes than the non-distinguishing genes, with log odds ratios ranging from 0.13–2.26 and 95% confidence intervals as shown (Fig. 6b). In all nine cases, the increased proportion of differentially expressed genes is found to be significant, with the hypothesis of ln(OR)=0, or equivalently odds ratio (OR)=1, falling outside the 95% confidence interval.


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

Yen A, Kellis M - Nat Commun (2015)

Epigenomically distinguishing genes are enriched for differential expression.By analysing expression of genes that our method identifies as being part of distinguishing gene and chromatin state combinations, we find that our method both recaptures differential gene expression and identifies distinguishing epigenetic context not captured by differential gene expression. (We performed this analysis on the 12 comparisons that produced distinguishing epigenomic features.) (a) Overall, identified genes are more differentially expressed than non-distinguishing genes, although <50% of the genes identified are significantly differently expressed. (The three comparisons that resulted in no differentially expressed genes are excluded.) (b) In every comparison of our nine remaining comparisons, our identified genes were significantly enriched for differentially expressed genes overall (as designated by asterisks), based on calculation of the log odds ratio and the corresponding 95% confidence intervals, as shown by the error bars. (P<0.003 in all cases, two-sided z-test) (c). After correcting for covariates in expression data, ChromDiff identifies all of the differentially expressed genes in two of the remaining four cases. (The eight comparisons that yielded no significant expression differences are excluded.)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f6: Epigenomically distinguishing genes are enriched for differential expression.By analysing expression of genes that our method identifies as being part of distinguishing gene and chromatin state combinations, we find that our method both recaptures differential gene expression and identifies distinguishing epigenetic context not captured by differential gene expression. (We performed this analysis on the 12 comparisons that produced distinguishing epigenomic features.) (a) Overall, identified genes are more differentially expressed than non-distinguishing genes, although <50% of the genes identified are significantly differently expressed. (The three comparisons that resulted in no differentially expressed genes are excluded.) (b) In every comparison of our nine remaining comparisons, our identified genes were significantly enriched for differentially expressed genes overall (as designated by asterisks), based on calculation of the log odds ratio and the corresponding 95% confidence intervals, as shown by the error bars. (P<0.003 in all cases, two-sided z-test) (c). After correcting for covariates in expression data, ChromDiff identifies all of the differentially expressed genes in two of the remaining four cases. (The eight comparisons that yielded no significant expression differences are excluded.)
Mentions: The genes identified from our comparisons often exhibited different expression levels between the groups compared. To quantify this, for each of the 12 comparisons with distinguishing features (Supplementary Table 1), we calculated how many of our identified genes had differential gene expression between the 2 groups of the comparison (Fig. 6a) (see Methods). Three comparisons that revealed epigenomic differences did not have any differentially expressed genes: Brain/ESC, Cell Line/Primary Culture and ESC/GI. Furthermore, in the nine cases with differentially expressed genes, the epigenomically distinguishing genes included proportionally more differentially expressed genes than the non-distinguishing genes, with log odds ratios ranging from 0.13–2.26 and 95% confidence intervals as shown (Fig. 6b). In all nine cases, the increased proportion of differentially expressed genes is found to be significant, with the hypothesis of ln(OR)=0, or equivalently odds ratio (OR)=1, falling outside the 95% confidence interval.

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