  Chromatin immunoprecipitation and high throughput sequencing (ChIP-seq) is
the de facto standard method to map chromatin features on genomes. The output
of ChIP-seq is quantitative within a single genome-wide profile, but there is
no natural way to compare experiments, which is why the data is often
discretized as present/absent calls. Many tools perform this task efficiently,
however they process a single input at a time, which produces discretization
conflicts among replicates. Here we present the implementation of a Hidden
Markov Model (HMM) using mixture negative multinomial emissions to discretize
ChIP-seq profiles. The method gives meaningful discretization for a wide range
of features and allows to merge datasets from different origins into a single
discretized profile, which resolves discretization conflicts. A quality control
step performed after the discretization accepts or rejects the discretization
as a whole. The implementation of the model is called jahmm, and it is
available as an R package. The source can be downloaded from
http://github.com/gui11aume/jahmm
