ArticleNucleic acids research2017
Expanding the repertoire of DNA shape features for genome-scale studies of transcription factor binding.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed.
- iEnhancer-Flow: Integrating Transformer-Based Sequence Learning with DNA Shape Insights for Robust Enhancer Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Nonconsensus flanking sequence of hundreds of base pairs around in vivo binding sites: statistical beacons for transcription factor scanning.Nucleic acids research · 2026Article
- Sequence-based modeling of low-affinity transcription factor-DNA binding through deep learning.NAR genomics and bioinformatics · 2026Article
- Relevance of DNA tridimensional shape in RNA:DNA:DNA triple helix formation.Computational and structural biotechnology journal · 2026Article
- Inferring binding specificities of human transcription factors with the wisdom of crowds.bioRxiv : the preprint server for biology · 2025Article
- TRAFICA: an open chromatin language model to improve transcription factor binding affinity prediction.Bioinformatics (Oxford, England) · 2025Article
- Sequence-Dependent Shape and Stiffness of DNA and RNA Double Helices: Hexanucleotide Scale and Beyond.Journal of chemical information and modeling · 2025Article
- Mechanisms driving functional divergence of transcription factor paralogs.The New phytologist · 2025Review
- Fundamentals of DNA methylation in development.Pediatric research · 2025Review
- Consecutive low-frequency shifts in A/T content denote nucleosome positions across microeukaryotes.iScience · 2025Article
- Mechanisms for DNA Interplay in Eukaryotic Transcription Factors.Annual review of biophysics · 2025Review
- Systematic analysis of specificities and flanking sequence preferences of bacterial DNA-(cytosine C5)-methyltransferases reveals mechanisms of enzyme- and sequence-specific DNA readout.Nucleic acids research · 2025Article
- START domains generate paralog-specific regulons from a single network architecture.Nature communications · 2024Article
- DNA breathing integration with deep learning foundational model advances genome-wide binding prediction of human transcription factors.Nucleic acids research · 2024Article
- RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.Briefings in bioinformatics · 2024Article
- Systematic dissection of sequence features affecting binding specificity of a pioneer factor reveals binding synergy between FOXA1 and AP-1.Molecular cell · 2024Article
- Deep DNAshape webserver: prediction and real-time visualization of DNA shape considering extended k-mers.Nucleic acids research · 2024Article
- Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation.Nucleic acids research · 2024Article
- Epigenetic MLH1 silencing concurs with mismatch repair deficiency in sporadic, naturally occurring colorectal cancer in rhesus macaques.Journal of translational medicine · 2024Article
- Advancing Transcription Factor Binding Site Prediction Using DNA Breathing Dynamics and Sequence Transformers via Cross Attention.bioRxiv : the preprint server for biology · 2024Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Uncovering the mechanisms that affect the binding specificity of transcription factors (TFs) is critical for understanding the principles of gene regulation. Although sequence-based models have been used successfully to predict TF binding specificities, we found that including DNA shape information in these models improved their accuracy and interpretability. Previously, we developed a method for modeling DNA binding specificities based on DNA shape features extracted from Monte Carlo (MC) simulations. Prediction accuracies of our models, however, have not yet been compared to accuracies of models incorporating DNA shape information extracted from X-ray crystallography (XRC) data or Molecular Dynamics (MD) simulations. Here, we integrated DNA shape information extracted from MC or MD simulations and XRC data into predictive models of TF binding and compared their performance. Models that incorporated structural information consistently showed improved performance over sequence-based models regardless of data source. Furthermore, we derived and validated nine additional DNA shape features beyond our original set of four features. The expanded repertoire of 13 distinct DNA shape features, including six intra-base pair and six inter-base pair parameters and minor groove width, is available in our R/Bioconductor package DNAshapeR and enables a comprehensive structural description of the double helix on a genome-wide scale.
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Registered trials
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 Socratic graph.