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.
Highlights
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
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 again find that differential principal component analysis (dPCA) has a higher average similarity score among unrelated comparisons than ChromDiff for both gene and MSigDB results (Fig. 7e). These results show that for the Roadmap Epigenomics and ENCODE data sets, ChromDiff is more powerful than dPCA: ChromDiff can identify genes showing important epigenomic changes even when dPCA does not have enough power, and ChromDiff identifies more specific and relevant gene sets than dPCA, likely due to its ability to correct for covariates
Summary
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. 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. Our methodology should be broadly applicable for epigenomic comparisons and provides a powerful new tool for studying chromatin state differences at the genome scale. Epigenomic data sets provide a rich resource for understanding genome activity across both genes and regulatory regions in response to developmental, environmental or genetic signals. No methods have yet been developed to enable group-wise chromatin state comparisons based on these combinatorial segmentations. As the availability of data increased rapidly in recent years, methods tackling combinatorial approaches to histone modification data to identify patterns across many histone marks for one biological condition or sample have been developed[35,36,37,38], including the aforementioned segmentation methods[21,22,23]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
