ArticleCell systems2016
DNA Shape Features Improve Transcription Factor Binding Site Predictions In Vivo.
Article in Cell systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.
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Who cites it
79 citing papers in PubMed.
- ShapeME: A Tool and Web Front-end for De Novo Discovery of Structural Motifs Underpinning Protein-DNA Interactions.Journal of molecular biology · 2026Article
- The adaptive landscapes of three globaleLife · 2026Article
- Nonconsensus flanking sequence of hundreds of base pairs around in vivo binding sites: statistical beacons for transcription factor scanning.Nucleic acids research · 2026Article
- DNA Mechanical Strain Steers Transcription Factor Recognition.Research square · 2026Article
- JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models.Nucleic acids research · 2026Article
- Relevance of DNA tridimensional shape in RNA:DNA:DNA triple helix formation.Computational and structural biotechnology journal · 2026Article
- 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
- Mechanisms for DNA Interplay in Eukaryotic Transcription Factors.Annual review of biophysics · 2025Review
- Article
- Aberrant homeodomain-DNA cooperative dimerization underlies distinct developmental defects in two dominantGenome research · 2025Article
- ShapeME: A tool and web front-end for de novo discovery of structural motifs underpinning protein-DNA interactions.bioRxiv : the preprint server for biology · 2025Article
- A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.Nucleic acids research · 2024Article
- Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation.Nucleic acids research · 2024Article
- Aberrant homeodomain-DNA cooperative dimerization underlies distinct developmental defects in two dominantbioRxiv : the preprint server for biology · 2024Article
- Predicting DNA structure using a deep learning method.Nature communications · 2024Article
- Harnessing regulatory networks in Actinobacteria for natural product discovery.Journal of industrial microbiology & biotechnology · 2024Review
- Dissection of integrated readout reveals the structural thermodynamics of DNA selection by transcription factors.Structure (London, England : 1993) · 2024Article
- Inferred regulons are consistent with regulator binding sequences in E. coli.PLoS computational biology · 2024Article
- Incorporating Sequence-Dependent DNA Shape and Dynamics into Transcriptome Data Analysis.Methods in molecular biology (Clifton, N.J.) · 2024Article
19 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
Interactions of transcription factors (TFs) with DNA comprise a complex interplay between base-specific amino acid contacts and readout of DNA structure. Recent studies have highlighted the complementarity of DNA sequence and shape in modeling TF binding in vitro. Here, we have provided a comprehensive evaluation of in vivo datasets to assess the predictive power obtained by augmenting various DNA sequence-based models of TF binding sites (TFBSs) with DNA shape features (helix twist, minor groove width, propeller twist, and roll). Results from 400 human ChIP-seq datasets for 76 TFs show that combining DNA shape features with position-specific scoring matrix (PSSM) scores improves TFBS predictions. Improvement has also been observed using TF flexible models and a machine-learning approach using a binary encoding of nucleotides in lieu of PSSMs. Incorporating DNA shape information is most beneficial for E2F and MADS-domain TF families. Our findings indicate that incorporating DNA sequence and shape information benefits the modeling of TF binding under complex in vivo conditions.
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