ArticleNucleic acids research2015
Varying levels of complexity in transcription factor binding motifs.
Article in Nucleic acids research, 2015. 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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Who cites it
33 citing papers in PubMed.
- Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data.NAR genomics and bioinformatics · 2024Article
- A statistical approach for identifying single nucleotide variants that affect transcription factor binding.iScience · 2024Article
- Peak Scores Significantly Depend on the Relationships between Contextual Signals in ChIP-Seq Peaks.International journal of molecular sciences · 2024Article
- Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models.Nucleic acids research · 2023Article
- CVD-associated SNPs with regulatory potential reveal novel non-coding disease genes.Human genomics · 2023Article
- A survey on algorithms to characterize transcription factor binding sites.Briefings in bioinformatics · 2023Review
- Double DAP-seq uncovered synergistic DNA binding of interacting bZIP transcription factors.Nature communications · 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
- DNA-binding properties of the MADS-domain transcription factor SEPALLATA3 and mutant variants characterized by SELEX-seq.Plant molecular biology · 2021Article
- Systematic Evaluation of DNA Sequence Variations onFrontiers in genetics · 2021Article
- Learning the Regulatory Code of Gene Expression.Frontiers in molecular biosciences · 2021Review
- 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
- Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network.Scientific reports · 2019Article
- Characterizing protein-DNA binding event subtypes in ChIP-exo data.Bioinformatics (Oxford, England) · 2019Article
- A map of direct TF-DNA interactions in the human genome.Nucleic acids research · 2019Article
- Identification of transcription factor binding sites using ATAC-seq.Genome biology · 2019Article
- Accurate prediction of cell type-specific transcription factor binding.Genome biology · 2019Article
- Disentangling transcription factor binding site complexity.Nucleic acids research · 2018Article
- A novel method for improved accuracy of transcription factor binding site prediction.Nucleic acids research · 2018Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Binding of transcription factors to DNA is one of the keystones of gene regulation. The existence of statistical dependencies between binding site positions is widely accepted, while their relevance for computational predictions has been debated. Building probabilistic models of binding sites that may capture dependencies is still challenging, since the most successful motif discovery approaches require numerical optimization techniques, which are not suited for selecting dependency structures. To overcome this issue, we propose sparse local inhomogeneous mixture (Slim) models that combine putative dependency structures in a weighted manner allowing for numerical optimization of dependency structure and model parameters simultaneously. We find that Slim models yield a substantially better prediction performance than previous models on genomic context protein binding microarray data sets and on ChIP-seq data sets. To elucidate the reasons for the improved performance, we develop dependency logos, which allow for visual inspection of dependency structures within binding sites. We find that the dependency structures discovered by Slim models are highly diverse and highly transcription factor-specific, which emphasizes the need for flexible dependency models. The observed dependency structures range from broad heterogeneities to sparse dependencies between neighboring and non-neighboring binding site positions.
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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.