DEEP: a general computational framework for predicting enhancers

Dimitrios A. Kleftogiannis, Panos Kalnis, Vladimir B. Bajic

Research output: Contribution to journalArticlepeer-review

95 Scopus citations


Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer's properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at
Original languageEnglish (US)
Pages (from-to)e6-e6
Number of pages1
JournalNucleic Acids Research
Issue number1
StatePublished - Nov 5 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: KAUST Research Funds via AEA KAUST-Stanford Round 3 Global Collaborative Research Program [to P.K., V.B.B.]; KAUST Base Research Funds [to P.K., V.B.B.]. Funding for open access charge: KAUST Research Funds via AEA KAUST-Stanford Round 3 Global Collaborative Research Program [to V.B.B.].


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