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  • DEEP: A general computational framework for predicting enhancers

    Dimitrios Kleftogiannis, Panos Kalnis and Vladimir B. Bajic

    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 of specificity and sensitivity across 36 different tissues. We further present satisfactory results on in vivo enhancer data achieved in VISTA database.
  • Contents :
    DEEP workflow : DEEP_framework
  • This work is part of a joint project between CBRC and InfoCloud at King Abdullah University of Science and Technology

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