DEEP: a general computational framework for predicting enhancers

By Dimitrios Kleftogiannis, Panos Kalnis, Vladimir B. Bajic. 2014

123 citations recorded by the source corpus.

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 http://cbrc.kaust.edu.sa/deep/.

Subfields: Molecular Biology

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In Molecular Biology

1. Wenqiang Shi
2. Yifeng Li
3. Wyeth W. Wasserman
4. Wenjie Shu
5. Chao Ren
6. Feng Liu
7. Xiaochen Bo
8. Hao Li

Citations in the corpus

• PEDLA: predicting enhancers with a deep learning-based algorithmic framework (2016, methodology): “RFECS (40), DELTA (41) and DEEP (44) use ensemble techniques to train classifiers with unbalanced classes by combining a set of individual classifiers.”
• Machine Learning and Integrative Analysis of Biomedical Big Data (2019, background): “, the dataset has many more negative samples (non-enhancer) compared to positive samples (enhancer) [12,139].”
• Genome-wide prediction of cis-regulatory regions using supervised deep learning methods (2016, methodology): “An ensemble support vector machine method suggested the potential to distinguish enhancers based on such data [24].”
• Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences (2017, unknown): “…predict and visualize transcription factor binding, DNase I hypersensitivity sites, enhancers, and sites of DNA methylation (Alipanahi et al. 2015; Kleftogiannis et al. 2015; Zhou and Troyanskaya 2015; Kelley et al. 2016; Lanchantin et al. 2016; Liu et al. 2016; Quang and Xie 2016; Wang et al.…”