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

## TL;DR

This paper introduces a new method called ChromDiff to compare chromatin states across different epigenomes, revealing how factors like sex and tissue type influence chromatin differences.

## Contribution

The novel contribution is the development of ChromDiff, a method that isolates relevant chromatin state changes by correcting for external covariates.

## Key findings

- Distinct sets of epigenomic features are maximally discriminative for different group-wise comparisons.
- Many discriminative chromatin state changes do not correlate with gene expression differences.
- ChromDiff reveals enriched pathways specific to comparisons of sex, tissue type, and developmental age.

## 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.

In contrast to genetic information, epigenetic state varies greatly under different conditions. Here, the authors develop ChromDiff and apply it to ENCODE and Roadmap Epigenomics datasets to find chromatin state differences associated with sex, tissue, and developmental age.