title: PEDLA: predicting enhancers with a deep learning-based algorithmic framework.
abstract: 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.
authors.affiliation: Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Liu
authors.affiliation: Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China.
authors.forename: Chao
authors.initials: C
authors.lastname: Ren
authors.affiliation: Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China.
authors.forename: Xiaochen
authors.initials: X
authors.lastname: Bo
authors.affiliation: Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China.
authors.forename: Wenjie
authors.initials: W
authors.lastname: Shu