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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

ChromDiff identifies more specific results than dPCA.After filtering out pairs of comparisons with shared epigenomic groups, we find that (a). dPCA's gene results are less specific than (b). ChromDiff's gene results for unrelated comparisons. Similarly, (c). dPCA's gene set enrichments are less specific than (d). ChromDiff's gene set enrichments for unrelated comparisons. (e) We quantify this result by confirming that dPCA's results have higher mean similarity scores for unrelated comparisons than ChromDiff does, with bars displaying s.e. of the sample mean. These scores were calculated from the 53 and 58 pairs of unrelated comparisons for ChromDiff and dPCA results, respectively, as shown in a–d.
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f7: ChromDiff identifies more specific results than dPCA.After filtering out pairs of comparisons with shared epigenomic groups, we find that (a). dPCA's gene results are less specific than (b). ChromDiff's gene results for unrelated comparisons. Similarly, (c). dPCA's gene set enrichments are less specific than (d). ChromDiff's gene set enrichments for unrelated comparisons. (e) We quantify this result by confirming that dPCA's results have higher mean similarity scores for unrelated comparisons than ChromDiff does, with bars displaying s.e. of the sample mean. These scores were calculated from the 53 and 58 pairs of unrelated comparisons for ChromDiff and dPCA results, respectively, as shown in a–d.

Mentions: To quantify this lack of specificity from dPCA, we calculated the Jaccard similarity score of the lists of distinguishing genes for pairs of comparisons, and as expected, we see higher similarity scores for the dPCA results than the ChromDiff results (Supplementary Fig. 6a,b). Even more strikingly, we see very high similarity between the enriched MSigDB gene sets for the genes identified by dPCA (Supplementary Fig. 6c), while ChromDiff returns gene set enrichments specific to that comparison (Supplementary Fig. 6d). Since some of these comparisons also share epigenomic groups (for example, the same brain epigenomes are used for Brain/ESC and Brain/GI), we filter out similarity scores for pairs of comparisons with overlapping groups (Fig. 7a–d). After filtering, we again find that dPCA has a higher average similarity score among unrelated comparisons than ChromDiff for both gene and MSigDB results (Fig. 7e).


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

Yen A, Kellis M - Nat Commun (2015)

ChromDiff identifies more specific results than dPCA.After filtering out pairs of comparisons with shared epigenomic groups, we find that (a). dPCA's gene results are less specific than (b). ChromDiff's gene results for unrelated comparisons. Similarly, (c). dPCA's gene set enrichments are less specific than (d). ChromDiff's gene set enrichments for unrelated comparisons. (e) We quantify this result by confirming that dPCA's results have higher mean similarity scores for unrelated comparisons than ChromDiff does, with bars displaying s.e. of the sample mean. These scores were calculated from the 53 and 58 pairs of unrelated comparisons for ChromDiff and dPCA results, respectively, as shown in a–d.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f7: ChromDiff identifies more specific results than dPCA.After filtering out pairs of comparisons with shared epigenomic groups, we find that (a). dPCA's gene results are less specific than (b). ChromDiff's gene results for unrelated comparisons. Similarly, (c). dPCA's gene set enrichments are less specific than (d). ChromDiff's gene set enrichments for unrelated comparisons. (e) We quantify this result by confirming that dPCA's results have higher mean similarity scores for unrelated comparisons than ChromDiff does, with bars displaying s.e. of the sample mean. These scores were calculated from the 53 and 58 pairs of unrelated comparisons for ChromDiff and dPCA results, respectively, as shown in a–d.
Mentions: To quantify this lack of specificity from dPCA, we calculated the Jaccard similarity score of the lists of distinguishing genes for pairs of comparisons, and as expected, we see higher similarity scores for the dPCA results than the ChromDiff results (Supplementary Fig. 6a,b). Even more strikingly, we see very high similarity between the enriched MSigDB gene sets for the genes identified by dPCA (Supplementary Fig. 6c), while ChromDiff returns gene set enrichments specific to that comparison (Supplementary Fig. 6d). Since some of these comparisons also share epigenomic groups (for example, the same brain epigenomes are used for Brain/ESC and Brain/GI), we filter out similarity scores for pairs of comparisons with overlapping groups (Fig. 7a–d). After filtering, we again find that dPCA has a higher average similarity score among unrelated comparisons than ChromDiff for both gene and MSigDB results (Fig. 7e).

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