The advent of high-throughput technologies such as ChIP-seq has made possible 
the study of histone modifications. A problem of particular interest is the 
identification of regions of the genome where different cell types from the same 
organism exhibit different patterns of histone enrichment. This problem turns 
out to be surprisingly difficult, even in simple pairwise comparisons, because 
of the significant level of noise in ChIP-seq data. In this paper we propose a 
two-stage statistical method, called ChIPnorm, to normalize ChIP-seq data, and 
to find differential regions in the genome, given two libraries of histone 
modifications of different cell types. We show that the ChIPnorm method removes 
most of the noise and bias in the data and outperforms other normalization 
methods. We correlate the histone marks with gene expression data and confirm 
that histone modifications H3K27me3 and H3K4me3 act as respectively a repressor 
and an activator of genes. Compared to what was previously reported in the 
literature, we find that a substantially higher fraction of bivalent marks in ES 
cells for H3K27me3 and H3K4me3 move into a K27-only state. We find that most of 
the promoter regions in protein-coding genes have differential 
histone-modification sites. The software for this work can be downloaded from 
http://lcbb.epfl.ch/software.html.