PEDLA: predicting enhancers with a deep learning-based algorithmic framework
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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.
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Massively parallel reporter assays coupled with machine learning have proven useful in addressing similar voids by producing quantitative biological insight that would be difficult to achieve through traditional approaches.
“Massively parallel reporter assays (MPRAs) – methods that assess thousands to millions of sequence variants in a single experiment – coupled with machine learning have proven useful in addressing similar voids by producing quantitative biological insight that would be difficult to achieve through traditional approaches3–9.”
From Human 5′ UTR design and variant effect prediction from a massively parallel translation assay (2019) · cites this paper as [7]
The Abstract reports that PEDLA uses deep learning and heterogeneous genomic data to improve enhancer prediction, supporting the general point that machine learning can yield useful biological insight. But the paper does not report coupling machine learning with massively parallel reporter assays; its Materials and Methods describe other data types and validation using enhancer markers. The MPRA-specific part of the attributed claim is therefore unsupported. (LLM, conf=0.85)
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- Authors
- Liu, F., Li, H., Ren, C., Bo, X., & Shu, W.
- Affiliations
- Beijing Radiation Center (CN)
- Corresponding author
- Bo, Xiaochen
- Shu, Wenjie
- Venue
- Scientific Reports
- Published
- 2016-06-22
- Volume
- 6
- Issue
- 1
- Pages
- 28517-28517
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4916453
- OpenAlex
- W2950980382
- Cited by
- 101
- References
- 12
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 4 · 2023: 10 · 2022: 10 · 2021: 10 · 2020: 17 · 2019: 14 · 2018: 21 · 2017: 8 · 2016: 1
- FWCI
- 6.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (U1435222, 2014DFB30020, 2015AA020108)
- Major Research Plan (U1435222)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- enhancer prediction, enhancers, deep learning, regulatory elements, non-coding DNA, heterogeneous data integration
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