ArticleiScience2023
DeepBend: An interpretable model of DNA bendability.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.Cell systems · 2026Article
- Visualizing poloidal orientation in DNA minicircles.Biophysical journal · 2026Article
- Searching for sequence features that control DNA cyclizability.PNAS nexus · 2026Article
- cgNA+min: computation of sequence-dependent dsDNA energy-minimizing minicircles.Nucleic acids research · 2026Article
- DNAcycP2: improved estimation of intrinsic DNA cyclizability through data augmentation.Nucleic acids research · 2025Article
- Probing mechanical selection in diverse eukaryotic genomes through accurate prediction of 3D DNA mechanics.bioRxiv : the preprint server for biology · 2024Article
- Employing bimodal representations to predict DNA bendability within a self-supervised pre-trained framework.Nucleic acids research · 2024Article
- Predicting DNA structure using a deep learning method.Nature communications · 2024Article
- Predictions of DNA mechanical properties at a genomic scale reveal potentially new functional roles of DNA flexibility.NAR genomics and bioinformatics · 2023Article
- Assessing base-resolution DNA mechanics on the genome scale.Nucleic acids research · 2023Article
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Authors and funding
4 authors.
Funding
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Abstract
The bendability of genomic DNA impacts chromatin packaging and protein-DNA binding. However, we do not have a comprehensive understanding of the motifs influencing DNA bendability. Recent high-throughput technologies such as Loop-Seq offer an opportunity to address this gap but the lack of accurate and interpretable machine learning models still remains. Here we introduce DeepBend, a convolutional neural network model with convolutions designed to directly capture the motifs underlying DNA bendability and their periodic occurrences or relative arrangements that modulate bendability. DeepBend consistently performs on par with alternative models while giving an extra edge through mechanistic interpretations. Besides confirming the known motifs of DNA bendability, DeepBend also revealed several novel motifs and showed how the spatial patterns of motif occurrences influence bendability. DeepBend's genome-wide prediction of bendability further showed how bendability is linked to chromatin conformation and revealed the motifs controlling the bendability of topologically associated domains and their boundaries.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.