ArticleNucleic acids research2014
A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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62 citing papers in PubMed, 113 citations in OpenAlex.
- Article
- Exploring the roles of conserved context-dependent cis-regulatory elements (cdCREs) in multicellularity, human health and disease.The FEBS journal · 2025Review
- Quantifying transcription factor specificity with advanced DNA universal microarrays featuring long and modified binding sites.Nucleic acids research · 2025Article
- Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors.Communications biology · 2025Article
- TRAFICA: an open chromatin language model to improve transcription factor binding affinity prediction.Bioinformatics (Oxford, England) · 2025Article
- A KAN-based hybrid deep neural networks for accurate identification of transcription factor binding sites.PloS one · 2025Article
- A generative framework for enhanced cell-type specificity in rationally designed mRNAs.bioRxiv : the preprint server for biology · 2024Article
- Cross-platform DNA motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors.bioRxiv : the preprint server for biology · 2024Article
- Decoding Non-coding Variants: Recent Approaches to Studying Their Role in Gene Regulation and Human Diseases.Frontiers in bioscience (Scholar edition) · 2024Review
- Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.Briefings in bioinformatics · 2024Article
- HOCOMOCO in 2024: a rebuild of the curated collection of binding models for human and mouse transcription factors.Nucleic acids research · 2024Article
- Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models.Nucleic acids research · 2023Article
- Databases and prospects of dynamic gene regulation in eukaryotes: A mini review.Computational and structural biotechnology journal · 2023Review
- Review
- Bacterial Transcriptional Regulators: A Road Map for Functional, Structural, and Biophysical Characterization.International journal of molecular sciences · 2022Review
- Persistence and plasticity in bacterial gene regulation.Nature methods · 2021Article
- Systematic identification of non-canonical transcription factor motifs.BMC molecular and cell biology · 2021Article
- Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins.Bioinformatics (Oxford, England) · 2021Article
- Bayesian Markov models improve the prediction of binding motifs beyond first order.NAR genomics and bioinformatics · 2021Article
- DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence.International journal of molecular sciences · 2021Article
2 more citing papers are in PubMed but not listed here.
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2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Understanding gene regulation is a key challenge in today's biology. The new technologies of protein-binding microarrays (PBMs) and high-throughput SELEX (HT-SELEX) allow measurement of the binding intensities of one transcription factor (TF) to numerous synthetic double-stranded DNA sequences in a single experiment. Recently, Jolma et al. reported the results of 547 HT-SELEX experiments covering human and mouse TFs. Because 162 of these TFs were also covered by PBM technology, for the first time, a large-scale comparison between implementations of these two in vitro technologies is possible. Here we assessed the similarities and differences between binding models, represented as position weight matrices, inferred from PBM and HT-SELEX, and also measured how well these models predict in vivo binding. Our results show that HT-SELEX- and PBM-derived models agree for most TFs. For some TFs, the HT-SELEX-derived models are longer versions of the PBM-derived models, whereas for other TFs, the HT-SELEX models match the secondary PBM-derived models. Remarkably, PBM-based 8-mer ranking is more accurate than that of HT-SELEX, but models derived from HT-SELEX predict in vivo binding better. In addition, we reveal several biases in HT-SELEX data including nucleotide frequency bias, enrichment of C-rich k-mers and oligos and underrepresentation of palindromes.
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