ArticleBioinformatics (Oxford, England)2013
Stability selection for regression-based models of transcription factor-DNA binding specificity.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed, 61 citations in OpenAlex.
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- DNA breathing integration with deep learning foundational model advances genome-wide binding prediction of human transcription factors.Nucleic acids research · 2024Article
- Site-Specific Acetylation of the Transcription Factor Protein Max Modulates Its DNA Binding Activity.ACS central science · 2024Article
- Advancing Transcription Factor Binding Site Prediction Using DNA Breathing Dynamics and Sequence Transformers via Cross Attention.bioRxiv : the preprint server for biology · 2024Article
- Predicting DNA structure using a deep learning method.Nature communications · 2024Article
- Article
- Developing an Improved Survival Prediction Model for Disease Prognosis.Biomolecules · 2022Article
- Motif-Raptor: a cell type-specific and transcription factor centric approach for post-GWAS prioritization of causal regulators.Bioinformatics (Oxford, England) · 2021Article
- Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning.Healthcare (Basel, Switzerland) · 2020Article
- Sequence and chromatin determinants of transcription factor binding and the establishment of cell type-specific binding patterns.Biochimica et biophysica acta. Gene regulatory mechanisms · 2020Review
- Toward deciphering the mechanistic role of variations in the Rep1 repeat site in the transcription regulation of SNCA gene.Neurogenetics · 2018Article
- Divergence in DNA Specificity among Paralogous Transcription Factors Contributes to Their Differential In Vivo Binding.Cell systems · 2018Article
- Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Trinucleotide Base Pair Stacking Free Energy for Understanding TF-DNA Recognition and the Functions of SNPs.Frontiers in chemistry · 2018Article
- Quantum annealing versus classical machine learning applied to a simplified computational biology problem.NPJ quantum information · 2018Article
- Genome-wide prediction of minor-groove electrostatic potential enables biophysical modeling of protein-DNA binding.Nucleic acids research · 2017Article
- Modeling protein-DNA binding via high-throughput in vitro technologies.Briefings in functional genomics · 2017Article
- Transcription factor family-specific DNA shape readout revealed by quantitative specificity models.Molecular systems biology · 2017Article
- Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences.Nucleic acids research · 2016Article
- Nucleotide Interdependency in Transcription Factor Binding Sites in the Drosophila Genome.Gene regulation and systems biology · 2016Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
motivationThe DNA binding specificity of a transcription factor (TF) is typically represented using a position weight matrix model, which implicitly assumes that individual bases in a TF binding site contribute independently to the binding affinity, an assumption that does not always hold. For this reason, more complex models of binding specificity have been developed. However, these models have their own caveats: they typically have a large number of parameters, which makes them hard to learn and interpret.
resultsWe propose novel regression-based models of TF-DNA binding specificity, trained using high resolution in vitro data from custom protein-binding microarray (PBM) experiments. Our PBMs are specifically designed to cover a large number of putative DNA binding sites for the TFs of interest (yeast TFs Cbf1 and Tye7, and human TFs c-Myc, Max and Mad2) in their native genomic context. These high-throughput quantitative data are well suited for training complex models that take into account not only independent contributions from individual bases, but also contributions from di- and trinucleotides at various positions within or near the binding sites. To ensure that our models remain interpretable, we use feature selection to identify a small number of sequence features that accurately predict TF-DNA binding specificity. To further illustrate the accuracy of our regression models, we show that even in the case of paralogous TF with highly similar position weight matrices, our new models can distinguish the specificities of individual factors. Thus, our work represents an important step toward better sequence-based models of individual TF-DNA binding specificity. AVAILABILITY: Our code is available at http://genome.duke.edu/labs/gordan/ISMB2013. The PBM data used in this article are available in the Gene Expression Omnibus under accession number GSE47026.
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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.