BACKGROUND: Cell-specific gene expression is controlled by epigenetic 
modifications and transcription factor binding. While genome-wide maps for these 
protein-DNA interactions have become widely available, quantitative comparison 
of the resulting ChIP-Seq data sets remains challenging. Current approaches to 
detect differentially bound or modified regions are mainly borrowed from RNA-Seq 
data analysis, thus focusing on total counts of fragments mapped to a region, 
ignoring any information encoded in the shape of the peaks.
RESULTS: Here, we present MMDiff, a robust, broadly applicable method for 
detecting differences between sequence count data sets. Based on quantifying 
shape changes in signal profiles, it overcomes challenges imposed by the highly 
structured nature of the data and the paucity of replicates.We first use a 
simulated data set to compare the performance of MMDiff with results obtained by 
four alternative methods. We demonstrate that MMDiff excels when peak profiles 
change between samples. We next use MMDiff to re-analyse a recent data set of 
the histone modification H3K4me3 elucidating the establishment of this prominent 
epigenomic marker. Our empirical analysis shows that the method yields 
reproducible results across experiments, and is able to detect functional 
important changes in histone modifications. To further explore the broader 
applicability of MMDiff, we apply it to two ENCODE data sets: one investigating 
the histone modification H3K27ac and one measuring the genome-wide binding of 
the transcription factor CTCF. In both cases, MMDiff proves to be complementary 
to count-based methods. In addition, we can show that MMDiff is capable of 
directly detecting changes of homotypic binding events at neighbouring binding 
sites. MMDiff is readily available as a Bioconductor package.
CONCLUSIONS: Our results demonstrate that higher order features of ChIP-Seq 
peaks carry relevant and often complementary information to total counts, and 
hence are important in assessing differential histone modifications and 
transcription factor binding. We have developed a new computational method, 
MMDiff, that is capable of exploring these features and therefore closes an 
existing gap in the analysis of ChIP-Seq data sets.