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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

A novel method for comparative analysis of epigenomic groups.(a) Starting with a single gene (NRXN1) and epigenome (cultured ganglionic cells), we represent the epigenome as the per cent coverage of each chromatin state at that gene. (b) Then, we repeat the process for all 127 epigenomes and 19,935 protein-coding genes, resulting in a matrix of 127 epigenomes by 299,025 features. (c) After normalizing and correcting each column in the matrix for covariate factors, we compare the female and male epigenomes of the original 127 epigenomes to identify features that exhibit different behaviour in female and male epigenomes. (d) The density plot of corrected P values from all features shows 536 out 299,025 features that significantly differ between the 2 groups. (e) Of the real biological comparisons that we tried, we found distinguishing epigenomic differences over 70% of the time. (f) Distinguishing features were found for randomized groupings only 10 out of 1,700 times or <1% of the time.
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f1: A novel method for comparative analysis of epigenomic groups.(a) Starting with a single gene (NRXN1) and epigenome (cultured ganglionic cells), we represent the epigenome as the per cent coverage of each chromatin state at that gene. (b) Then, we repeat the process for all 127 epigenomes and 19,935 protein-coding genes, resulting in a matrix of 127 epigenomes by 299,025 features. (c) After normalizing and correcting each column in the matrix for covariate factors, we compare the female and male epigenomes of the original 127 epigenomes to identify features that exhibit different behaviour in female and male epigenomes. (d) The density plot of corrected P values from all features shows 536 out 299,025 features that significantly differ between the 2 groups. (e) Of the real biological comparisons that we tried, we found distinguishing epigenomic differences over 70% of the time. (f) Distinguishing features were found for randomized groupings only 10 out of 1,700 times or <1% of the time.

Mentions: To capture epigenomic differences between groups of epigenomes, we focus on the set of chromatin states associated with each protein-coding gene (Fig. 1), while generating an information-theoretic encoding of these chromatin states and correcting for external factors to isolate differences due to the comparison. We leverage the multiple samples available in each pairwise group comparison to evaluate the statistical significance of such recurrent changes, and the multiple genes to evaluate the statistical significance of biological pathways. However, our methods are generally and broadly applicable to various regulatory genomic regions, beyond the gene-centric approach taken here.


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

Yen A, Kellis M - Nat Commun (2015)

A novel method for comparative analysis of epigenomic groups.(a) Starting with a single gene (NRXN1) and epigenome (cultured ganglionic cells), we represent the epigenome as the per cent coverage of each chromatin state at that gene. (b) Then, we repeat the process for all 127 epigenomes and 19,935 protein-coding genes, resulting in a matrix of 127 epigenomes by 299,025 features. (c) After normalizing and correcting each column in the matrix for covariate factors, we compare the female and male epigenomes of the original 127 epigenomes to identify features that exhibit different behaviour in female and male epigenomes. (d) The density plot of corrected P values from all features shows 536 out 299,025 features that significantly differ between the 2 groups. (e) Of the real biological comparisons that we tried, we found distinguishing epigenomic differences over 70% of the time. (f) Distinguishing features were found for randomized groupings only 10 out of 1,700 times or <1% of the time.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1: A novel method for comparative analysis of epigenomic groups.(a) Starting with a single gene (NRXN1) and epigenome (cultured ganglionic cells), we represent the epigenome as the per cent coverage of each chromatin state at that gene. (b) Then, we repeat the process for all 127 epigenomes and 19,935 protein-coding genes, resulting in a matrix of 127 epigenomes by 299,025 features. (c) After normalizing and correcting each column in the matrix for covariate factors, we compare the female and male epigenomes of the original 127 epigenomes to identify features that exhibit different behaviour in female and male epigenomes. (d) The density plot of corrected P values from all features shows 536 out 299,025 features that significantly differ between the 2 groups. (e) Of the real biological comparisons that we tried, we found distinguishing epigenomic differences over 70% of the time. (f) Distinguishing features were found for randomized groupings only 10 out of 1,700 times or <1% of the time.
Mentions: To capture epigenomic differences between groups of epigenomes, we focus on the set of chromatin states associated with each protein-coding gene (Fig. 1), while generating an information-theoretic encoding of these chromatin states and correcting for external factors to isolate differences due to the comparison. We leverage the multiple samples available in each pairwise group comparison to evaluate the statistical significance of such recurrent changes, and the multiple genes to evaluate the statistical significance of biological pathways. However, our methods are generally and broadly applicable to various regulatory genomic regions, beyond the gene-centric approach taken here.

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