Chromatin state discovery and characterization

 ChromHMM is software for learning and characterizing chromatin states.
 ChromHMM can integrate multiple chromatin datasets such as ChIP-seq data of
 various histone modifications to discover de novo the major re-occuring
 combinatorial and spatial patterns of marks. ChromHMM is based on a
 multivariate Hidden Markov Model that explicitly models the presence or
 absence of each chromatin mark. The resulting model can then be used to
 systematically annotate a genome in one or more cell types. By automatically
 computing state enrichments for large-scale functional and annotation datasets
 ChromHMM facilitates the biological characterization of each state. ChromHMM
 also produces files with genome-wide maps of chromatin state annotations that
 can be directly visualized in a genome browser.