Noncoding DNA regions have central roles in human biology, evolution, and 
disease. ChromHMM helps to annotate the noncoding genome using epigenomic 
information across one or multiple cell types. It combines multiple genome-wide 
epigenomic maps, and uses combinatorial and spatial mark patterns to infer a 
complete annotation for each cell type. ChromHMM learns chromatin-state 
signatures using a multivariate hidden Markov model (HMM) that explicitly models 
the combinatorial presence or absence of each mark. ChromHMM uses these 
signatures to generate a genome-wide annotation for each cell type by 
calculating the most probable state for each genomic segment. ChromHMM provides 
an automated enrichment analysis of the resulting annotations to facilitate the 
functional interpretations of each chromatin state. ChromHMM is distinguished by 
its modeling emphasis on combinations of marks, its tight integration with 
downstream functional enrichment analyses, its speed, and its ease of use. 
Chromatin states are learned, annotations are produced, and enrichments are 
computed within 1 d.