A survey of recently emerged genome-wide computational enhancer predictor tools

Leonard Whye Kit LIM, Hung Hui CHUNG, Yee Ling CHONG, Nung Kion LEE

Research output: Contribution to journalArticlespeer-review

26 Citations (Scopus)


The race for the discovery of enhancers at a genome-wide scale has been on since the commencement of next generation sequencing decades after the discovery of the first enhancer, SV40. A few enhancer-predicting features such as chromatin feature, histone modifications and sequence feature had been implemented with varying success rates. However, to date, there is no consensus yet on the single enhancer marker that can be employed to ultimately distinguish and uncover enhancers from the enormous genomic regions. Many supervised, unsupervised and semi-supervised computational approaches had emerged to complement and facilitate experimental approaches in enhancer discovery. In this review, we placed our focus on the recently emerged enhancer predictor tools that work on general enhancer features such as sequences, chromatin states and histone modifications, eRNA and of multiple feature approach. Comparisons of their prediction methods and outcomes were done across their functionally similar counterparts. We provide some recommendations and insights for future development of more comprehensive and robust tools. Copyright © 2018 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)132-141
JournalComputational Biology and Chemistry
Early online dateMar 2018
Publication statusPublished - Jun 2018


Lim, L. W. K., Chung, H. H., Chong, Y. L., & Lee, N. K. (2018). A survey of recently emerged genome-wide computational enhancer predictor tools. Computational Biology and Chemistry, 74, 132-141. doi: 10.1016/j.compbiolchem.2018.03.019


  • Enhancer prediction
  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning


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