Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Open accessAbstract
Knowing the sequence specificities of DNA- and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery. Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data. We call this approach DeepBind and have built a stand-alone software tool that is fully automatic and handles millions of sequences per experiment. Specificities determined by DeepBind are readily visualized as a weighted ensemble of position weight matrices or as a 'mutation map' that indicates how variations affect binding within a specific sequence.
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Prior work has shown minimal variation in performance between chromosomes on the ENCODE problem, so training and test distributions can be approximated as identical.
“Prior work has shown that there is minimal variation in performance between chromosomes on this problem (Won et al., 2010; Alipanahi et al., 2015; Keilwagen et al., 2019), so we can approximate these training and test distributions as identical.”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Alipanahi et al., 2015
Machine-learning models trained to predict function from DNA sequences have been successful at characterizing regulatory syntax and interpreting genetic variant effects.
“Machine-learning models trained to predict function from DNA sequences have been successful at characterizing regulatory syntax and interpreting genetic variant effects.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [4]
Thus far, sequence-based models have focused on assays in which measured activity is proportional to local sequencing read counts.
“Thus far, such models have focused on assays in which measured activity is proportional to local sequencing read counts.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [4]
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 [4]
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BackgroundGraph convolutional networks for epigenetic state prediction using both sequence and 3D genome data
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A Review on Genomics Data Analysis using Machine Learning
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- Authors
- Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J.
- Affiliations
- University of Toronto (CA)
- Canadian Institute for Advanced Research (CA)
- Corresponding author
- Frey, Brendan J
- Venue
- Nature Biotechnology
- Published
- 2015-07-27
- Volume
- 33
- Issue
- 8
- Pages
- 831-838
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W1019830208
- Cited by
- 2,873
- References
- 8
- Citations by year
- 2026: 171 · 2025: 238 · 2024: 288 · 2023: 266 · 2022: 331 · 2021: 370 · 2020: 325 · 2019: 341 · 2018: 284 · 2017: 165 · 2016: 80 · 2015: 10 · 2014: 1 · 2013: 1
- FWCI
- 78.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Funders
- Canadian Institutes of Health Research (106690_1)
- 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
- DNA-binding proteins, RNA-binding proteins, position weight matrices, protein-DNA interactions, RNA-protein interactions, deep learning, binding site prediction
- MeSH terms
- DNA-Binding Proteins, Software, RNA-Binding Proteins, Computational Biology, Sequence Analysis, Protein, Position-Specific Scoring Matrices
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