PEDLA: predicting enhancers with a deep learning-based algorithmic framework

By Feng Liu, Hao Li, Chao Ren, Xiaochen Bo, Wenjie Shu. 2016

111 citations recorded by the source corpus.

Transcriptional enhancers are non-coding segments of DNA that play a central role in the spatiotemporal regulation of gene expression programs. However, systematically and precisely predicting enhancers remain a major challenge. Although existing methods have achieved some success in enhancer prediction, they still suffer from many issues. We developed a deep learning-based algorithmic framework named PEDLA (https://github.com/wenjiegroup/PEDLA), which can directly learn an enhancer predictor from massively heterogeneous data and generalize in ways that are mostly consistent across various cell types/tissues. We first trained PEDLA with 1,114-dimensional heterogeneous features in H1 cells, and demonstrated that PEDLA framework integrates diverse heterogeneous features and gives state-of-the-art performance relative to five existing methods for enhancer prediction. We further extended PEDLA to iteratively learn from 22 training cell types/tissues. Our results showed that PEDLA manifested superior performance consistency in both training and independent test sets. On average, PEDLA achieved 95.0% accuracy and a 96.8% geometric mean (GM) of sensitivity and specificity across 22 training cell types/tissues, as well as 95.7% accuracy and a 96.8% GM across 20 independent test cell types/tissues. Together, our work illustrates the power of harnessing state-of-the-art deep learning techniques to consistently identify regulatory elements at a genome-wide scale from massively heterogeneous data across diverse cell types/tissues.

Subfields: Molecular Biology

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Citations in the corpus

Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences (2017, unknown): “…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. 2016).”
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, background): “17 Other recent applications of deep learning models to prediction of regulatory elements and their interactions with the state-of-the-art performance include enhancer prediction [76-78]; classification of gene expression using histone modification data as input [60]; prediction of DNA methylation s”
Deep Learning based multi-omics integration robustly predicts survival in liver cancer (2017, methodology): “We stacked the three matrices that are unit-norm scaled by sample to form a unique matrix as reported before (35).”
Deep Learning and Its Applications in Biomedicine (2018, background): “Other applications include that of Li et al. [134] and Liu et al. [151,152], who proposed deep learning approaches for the identiﬁcation of cis -regulatory regions and replication timing domains, respectively.”
Genome-wide prediction of cis-regulatory regions using supervised deep learning methods (2016, methodology): “To prevent predictions from jumping between states, smoothing has been taken into consideration in a deep neural network combined with hidden Markov model [56, 57].”