BACKGROUND: Transcriptional regulation in multi-cellular organisms is a complex 
process involving multiple modular regulatory elements for each gene. Building 
whole-genome models of transcriptional networks requires mapping all relevant 
enhancers and then linking them to target genes. Previous methods of enhancer 
identification based either on sequence information or on epigenetic marks have 
different limitations stemming from incompleteness of each of these datasets 
taken separately.
RESULTS: In this work we present a new approach for discovery of regulatory 
elements based on the combination of sequence motifs and epigenetic marks 
measured with ChIP-Seq. Our method uses supervised learning approaches to train 
a model describing the dependence of enhancer activity on sequence features and 
histone marks. Our results indicate that using combination of features provides 
superior results to previous approaches based on either one of the datasets. 
While histone modifications remain the dominant feature for accurate 
predictions, the models based on sequence motifs have advantages in their 
general applicability to different tissues. Additionally, we assess the 
relevance of different sequence motifs in prediction accuracy showing that even 
tissue-specific enhancer activity depends on multiple motifs.
CONCLUSIONS: Based on our results, we conclude that it is worthwhile to include 
sequence motif data into computational approaches to active enhancer prediction 
and also that classifiers trained on a specific set of enhancers can generalize 
with significant accuracy beyond the training set.