ArticleNucleic acids research2018
Disentangling transcription factor binding site complexity.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Selective chr21 homolog silencing reveals polymorphisms influence the epigenetic silencing and functional dosage of RWDD2B.American journal of human genetics · 2026Article
- Selective chr21 homolog silencing reveals polymorphisms influence the epigenetic silencing and functional dosage of RWDD2B.bioRxiv : the preprint server for biology · 2025Article
- Harnessing regulatory networks in Actinobacteria for natural product discovery.Journal of industrial microbiology & biotechnology · 2024Review
- Position Weight Matrix or Acyclic Probabilistic Finite Automaton: Which model to use? A decision rule inferred for the prediction of transcription factor binding sites.Genetics and molecular biology · 2024Article
- Construction of the genetic switches in response to mannitol based on artificial MtlR box.Bioresources and bioprocessing · 2023Article
- Motif models proposing independent and interdependent impacts of nucleotides are related to high and low affinity transcription factor binding sites in Arabidopsis.Frontiers in plant science · 2022Article
- Decoding the temporal nature of brain GR activity in the NFκB signal transition leading to depressive-like behavior.Molecular psychiatry · 2021Article
- Bayesian Markov models improve the prediction of binding motifs beyond first order.NAR genomics and bioinformatics · 2021Article
- DNA-binding properties of the MADS-domain transcription factor SEPALLATA3 and mutant variants characterized by SELEX-seq.Plant molecular biology · 2021Article
- MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs.Bioinformatics (Oxford, England) · 2020Article
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1 author.
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Abstract
The binding motifs of many transcription factors (TFs) comprise a higher degree of complexity than a single position weight matrix model permits. Additional complexity is typically taken into account either as intra-motif dependencies via more sophisticated probabilistic models or as heterogeneities via multiple weight matrices. However, both orthogonal approaches have limitations when learning from in vivo data where binding sites of other factors in close proximity can interfere with motif discovery for the protein of interest. In this work, we demonstrate how intra-motif complexity can, purely by analyzing the statistical properties of a given set of TF-binding sites, be distinguished from complexity arising from an intermix with motifs of co-binding TFs or other artifacts. In addition, we study the related question whether intra-motif complexity is represented more effectively by dependencies, heterogeneities or variants in between. Benchmarks demonstrate the effectiveness of both methods for their respective tasks and applications on motif discovery output from recent tools detect and correct many undesirable artifacts. These results further suggest that the prevalence of intra-motif dependencies may have been overestimated in previous studies on in vivo data and should thus be reassessed.
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