  Motivation: Histone modification constitutes a basic mechanism for the
genetic regulation of gene expression. In early 2000s, a powerful technique has
emerged that couples chromatin immunoprecipitation with high-throughput
sequencing (ChIP-seq). This technique provides a direct survey of the DNA
regions associated to these modifications. In order to realize the full
potential of this technique, increasingly sophisticated statistical algorithms
have been developed or adapted to analyze the massive amount of data it
generates. Many of these algorithms were built around natural assumptions such
as the Poisson one to model the noise in the count data. In this work we start
from these natural assumptions and show that it is possible to improve upon
them. Results: The results of our comparisons on seven reference datasets of
histone modifications (H3K36me3 and H3K4me3) suggest that natural assumptions
are not always realistic under application conditions. We show that the
unconstrained multiple changepoint detection model, with alternative noise
assumptions and a suitable setup, reduces the over-dispersion exhibited by
count data and turns out to detect peaks more accurately than algorithms which
rely on these natural assumptions.
