Enhancers play a pivotal role in regulating the transcription of distal genes. 
Although certain chromatin features, such as the histone acetyltransferase P300 
and the histone modification H3K4me1, indicate the presence of enhancers, only a 
fraction of enhancers are functionally active. Individual chromatin marks, such 
as H3K27ac and H3K27me3, have been identified to distinguish active from 
inactive enhancers. However, the systematic identification of the most 
informative single modification, or combination thereof, is still lacking. 
Furthermore, the discovery of enhancer RNAs (eRNAs) provides an alternative 
approach to directly predicting enhancer activity. However, it remains 
challenging to link chromatin modifications to eRNA transcription. Herein, we 
develop a logistic regression model to unravel the relationship between 
chromatin modifications and eRNA synthesis. We perform a systematic assessment 
of 24 chromatin modifications in fetal lung fibroblast and demonstrate that a 
combination of four modifications is sufficient to accurately predict eRNA 
transcription. Furthermore, we compare the ability of eRNAs and H3K27ac to 
discriminate enhancer activity. We demonstrate that eRNA is more indicative of 
enhancer activity. Finally, we apply our fibroblast trained model to six other 
cell-types and successfully predict eRNA synthesis. Thus, we demonstrate the 
learned relationships are general and independent of cell-type. We provided a 
powerful tool to identify active enhancers and reveal the relationship between 
chromatin modifications, eRNA production and enhancer activity.