Background: Enhancers are short DNA regions that improve transcription efficiency by
recruiting transcription factors. Identifying enhancer regions is important to understand the process
of gene expression. As enhancers are independent of their distances and orientations to the target
genes, it is difficult to locate enhancers accurately. Recently, with the development of highthroughput
ChIP-seq (Chromatin Immunoprecipitation sequencing) technologies, several computational
methods were developed to predict enhancers. However, most of these methods rely on
p300 binding sites and/or DNase I hypersensitive sites (DHSs) for selecting positive training
samples, which is imprecise and subsequently leads to unsatisfactory prediction performance.
Besides, in the literature, there is no work that predicts enhancers from tissues across different
Methods: In this paper, we proposed a method based on support vector machines (SVMs) to
investigate enhancer prediction on cell lines and tissues from EnhancerAtlas. Specifically, we
focused on predicting enhancers on different developmental stages of heart and lung tissues.
Results and Conclusion: Our results show that 1) the proposed method achieves good
performance on most cell lines and tissues, especially it outperforms several state of the art
methods on heart and lung. 2) It is easier to predict enhancers from tissues of adult stage than from
tissues of fetal stage, which is proven on both heart and lung tissues.