ANCORP: a high-resolution approach that generates distinct chromatin state models from multiple genome-wide datasets.

Abstract

Chromatin components can be extensively modified and dynamically regulated by a plethora of catalytic complexes. The numerous modifications may form a type of molecular pattern that defines particular local and global 'chromatin states' through extensive cross-talk. Analyses that can integrate multiple genome-wide datasets are essential to determine the interactions and biological function of chromatin modifications in various contexts. Through a combination of hierarchical clustering and pattern visualization, we categorized all annotated Arabidopsis genes into 16 chromatin state clusters using combinations of four chromatin marks (H3K4me3, H3K36me2, H3K27me3 and cytosine methylation) using publicly available data. Our results suggest that gene length may be an important factor in shaping chromatin states across transcription units. By analysis of two rare chromatin states, we found that the enrichment of H3K36me2 around the transcription start site is negatively correlated with transcriptional activities. High-resolution association analyses in the context of chromatin states have identified inter-correlations between chromatin modifications. H3K4me3 were found to be under-represented in actively transcribed regions that are modified by DNA methylation and the H3K36me2 mark, concomitant with increased nucleosome occupancy in these regions. Lastly, quantitative data from transcriptome analyses and gene ontology partitioning were integrated to determine the possible functional relevance of the corresponding chromatin states. We show that modelling the plant epigenome in terms of chromatin states and combining correlative visualization methods can be a productive approach to unravel complex relationships between epigenomic features and the functional output of the genome.