Abstract

Current genome-wide ChIP-seq experiments on different epigenetic marks aim at unraveling the interplay between their regulation mechanisms. Published evaluation tools, however, allow testing for predefined hypotheses only. Here, we present a novel method for annotation-independent exploration of epigenetic data and their inter-correlation with other genome-wide features. Our method is based on a combinatorial genome segmentation solely using information on combinations of epigenetic marks. It does not require prior knowledge about the data (e.g. gene positions), but allows integrating the data in a straightforward manner. Thereby, it combines compression, clustering and visualization of the data in a single tool. Our method provides intuitive maps of epigenetic patterns across multiple levels of organization, e.g. of the co-occurrence of different epigenetic marks in different cell types. Thus, it facilitates the formulation of new hypotheses on the principles of epigenetic regulation. We apply our method to histone modification data on trimethylation of histone H3 at lysine 4, 9 and 27 in multi-potent and lineage-primed mouse cells, analyzing their combinatorial modification pattern as well as differentiation-related changes of single modifications. We demonstrate that our method is capable of reproducing recent findings of gene centered approaches, e.g. correlations between CpG-density and the analyzed histone modifications. Moreover, combining the clustered epigenetic data with information on the expression status of associated genes we classify differences in epigenetic status of e.g. house-keeping genes versus differentiation-related genes. Visualizing the distribution of modification states on the chromosomes, we discover strong patterns for chromosome X. For example, exclusively H3K9me3 marked segments are enriched, while poised and active states are rare. Hence, our method also provides new insights into chromosome-specific epigenetic patterns, opening up new questions how “epigenetic computation” is distributed over the genome in space and time.

Highlights

  • Genome-wide measurement and analysis of transcript levels have led to a different understanding of transcriptional regulation in mammalian cells (ENCODE) [1,2]

  • We introduce i) a genome segmentation method based on multiple histone modification data in different cell types and ii) a method to compress the modification pattern of the resulting thousands of segments into two-dimensional images which allows a sample-to-sample comparison of the different modifications in the different cell types

  • We found that self-organizing maps (SOMs)-training splits part of the islands referring to a particular combinatorial epigenetic profile (CEP) into two subregions distinguished with the postfixes ‘‘a’’ and ‘‘b’’

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Summary

Introduction

Genome-wide measurement and analysis of transcript levels have led to a different understanding of transcriptional regulation in mammalian cells (ENCODE) [1,2]. In order to understand these regulatory effects of chromatin, new assays for studying genome-wide chromatin modification have been introduced [3,4]. Part of the regulatory effects is ascribed to histone modifications. Namely H2A, H2B, H3, and H4, can be modified at multiple sites, i.e. specific amino acid residues. Chemical groups, such as acetyl and methyl groups, biotin, small proteins, or sugars become attached to target sites. We will consider a specific modification at a specific residue of one of the histones as an epigenetic mark

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