Accurate identification of DNA regulatory elements becomes an urgent need in the 
post-genomic era. Recent genome-wide chromatin states mapping efforts revealed 
that DNA elements are associated with characteristic chromatin modification 
signatures, based on which several approaches have been developed to predict 
transcriptional enhancers. However, their practical application is limited by 
incomplete extraction of chromatin features and model inconsistency for 
predicting enhancers across different cell types. To address these issues, we 
define a set of non-redundant shape features of histone modifications, which 
shows high consistency across cell types and can greatly reduce the 
dimensionality of feature vectors. Integrating shape features with a 
machine-learning algorithm AdaBoost, we developed an enhancer predicting method, 
DELTA (Distal Enhancer Locating Tool based on AdaBoost). We show that DELTA 
significantly outperforms current enhancer prediction methods in prediction 
accuracy on different datasets and can predict enhancers in one cell type using 
models trained in other cell types without loss of accuracy. Overall, our study 
presents a novel framework for accurately identifying enhancers from epigenetic 
data across multiple cell types.