ArticleNature communications2021
Local DNA shape is a general principle of transcription factor binding specificity in Arabidopsis thaliana.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Functional and regulatory complexity: challenges and prospects for understanding rice WRKY transcription factors.Journal of experimental botany · 2026Review
- Motif-based models accurately predict cell type-specific distal regulatory elements.Nature communications · 2025Article
- Uncovering the Mechanistic Landscape of Regulatory DNA with Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- Mechanisms driving functional divergence of transcription factor paralogs.The New phytologist · 2025Review
- Systematic DNA nicking reveals the structural logic of protein recognition.bioRxiv : the preprint server for biology · 2025Article
- Many transcription factor families have evolutionarily conserved binding motifs in plants.Plant physiology · 2025Article
- Enhancers in Plant Development, Adaptation and Evolution.Plant & cell physiology · 2025Review
- Article
- Optimized DNA affinity purification sequencing determines relative binding affinity of transcription factors.Biology methods & protocols · 2025Article
- A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.Nucleic acids research · 2024Article
- START domains generate paralog-specific regulons from a single network architecture.Nature communications · 2024Article
- Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery.Heliyon · 2024Article
- Deep DNAshape webserver: prediction and real-time visualization of DNA shape considering extended k-mers.Nucleic acids research · 2024Article
- PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants.Briefings in bioinformatics · 2024Article
- Commonly used software tools produce conflicting and overly-optimistic AUPRC values.Genome biology · 2024Article
- Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation.Nucleic acids research · 2024Article
- 3D genome structural variations play important roles in regulating seed oil content of Brassica napus.Plant communications · 2024Article
- Incorporating Sequence-Dependent DNA Shape and Dynamics into Transcriptome Data Analysis.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Predictions of DNA mechanical properties at a genomic scale reveal potentially new functional roles of DNA flexibility.NAR genomics and bioinformatics · 2023Article
- Designing artificial synthetic promoters for accurate, smart, and versatile gene expression in plants.Plant communications · 2023Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding gene expression will require understanding where regulatory factors bind genomic DNA. The frequently used sequence-based motifs of protein-DNA binding are not predictive, since a genome contains many more binding sites than are actually bound and transcription factors of the same family share similar DNA-binding motifs. Traditionally, these motifs only depict sequence but neglect DNA shape. Since shape may contribute non-linearly and combinational to binding, machine learning approaches ought to be able to better predict transcription factor binding. Here we show that a random forest machine learning approach, which incorporates the 3D-shape of DNA, enhances binding prediction for all 216 tested Arabidopsis thaliana transcription factors and improves the resolution of differential binding by transcription factor family members which share the same binding motif. We observed that DNA shape features were individually weighted for each transcription factor, even if they shared the same binding sequence.
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