ArticleNature biotechnology2013
Evaluation of methods for modeling transcription factor sequence specificity.
Article in Nature biotechnology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 226 papers.
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Who cites it
226 citing papers in PubMed, 407 citations in OpenAlex.
- ShapeME: A Tool and Web Front-end for De Novo Discovery of Structural Motifs Underpinning Protein-DNA Interactions.Journal of molecular biology · 2026Article
- GHT-SELEX demonstrates unexpectedly high intrinsic sequence specificity and complex DNA binding of many human transcription factors.Nature methods · 2026Article
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- De novo design of functional nucleic acids of aptamers.Nature computational science · 2026Article
- Sequence-based modeling of low-affinity transcription factor-DNA binding through deep learning.NAR genomics and bioinformatics · 2026Article
- OptimDase: An Algorithm for Predicting DNA Binding Sites with Combined Feature Encoding.Interdisciplinary sciences, computational life sciences · 2025Article
- Quantifying transcription factor specificity with advanced DNA universal microarrays featuring long and modified binding sites.Nucleic acids research · 2025Article
- Identifying a novel Mecp2-mediated epigenetic mechanism controlling Lonp1 in the hippocampus and its disruption by aging.Scientific reports · 2025Article
- Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors.Communications biology · 2025Article
- Accurate affinity models for SH2 domains from peptide binding assays and free-energy regression.Protein science : a publication of the Protein Society · 2025Article
- TRAFICA: an open chromatin language model to improve transcription factor binding affinity prediction.Bioinformatics (Oxford, England) · 2025Article
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- Benchmarking transcription factor binding site prediction models: a comparative analysis on synthetic and biological data.Briefings in bioinformatics · 2025Article
- Exploring the complexity of MECP2 function in Rett syndrome.Nature reviews. Neuroscience · 2025Review
- Characterization of non-coding variants associated with transcription-factor binding through ATAC-seq-defined footprint QTLs in liver.American journal of human genetics · 2025Article
- A systematic survey of TF function in E. coli suggests RNAP stabilization is a prevalent strategy for both repressors and activators.Nucleic acids 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
- Predicting CTCF cell type active binding sites in human genome.Scientific reports · 2024Article
- The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes.BMC bioinformatics · 2024Article
166 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
16 authors at 8 institutions in 4 countries.
Funding
Abstract
Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein's DNA-binding specificity, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For nine TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro-derived motifs performed similarly to motifs derived from the in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices trained by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (<10% of the TFs examined here). In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.
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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 OpenQuestion graph.