BACKGROUND: Recent genomic scale survey of epigenetic states in the mammalian 
genomes has shown that promoters and enhancers are correlated with distinct 
chromatin signatures, providing a pragmatic way for systematic mapping of these 
regulatory elements in the genome. With rapid accumulation of chromatin 
modification profiles in the genome of various organisms and cell types, this 
chromatin based approach promises to uncover many new regulatory elements, but 
computational methods to effectively extract information from these datasets are 
still limited.
RESULTS: We present here a supervised learning method to predict promoters and 
enhancers based on their unique chromatin modification signatures. We trained 
Hidden Markov models (HMMs) on the histone modification data for known promoters 
and enhancers, and then used the trained HMMs to identify promoter or enhancer 
like sequences in the human genome. Using a simulated annealing (SA) procedure, 
we searched for the most informative combination and the optimal window size of 
histone marks.
CONCLUSION: Compared with the previous methods, the HMM method can capture the 
complex patterns of histone modifications particularly from the weak signals. 
Cross validation and scanning the ENCODE regions showed that our method 
outperforms the previous profile-based method in mapping promoters and 
enhancers. We also showed that including more histone marks can further boost 
the performance of our method. This observation suggests that the HMM is robust 
and is capable of integrating information from multiple histone marks. To 
further demonstrate the usefulness of our method, we applied it to analyzing 
genome wide ChIP-Seq data in three mouse cell lines and correctly predicted 
active and inactive promoters with positive predictive values of more than 80%. 
The software is available at http://http:/nash.ucsd.edu/chromatin.tar.gz.