ArticleNucleic acids research2013
A general approach for discriminative de novo motif discovery from high-throughput data.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Identification of methylation-sensitive human transcription factors using meSMiLE-seq.Nature communications · 2026Article
- Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors.Communications biology · 2025Article
- Cross-platform DNA motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors.bioRxiv : the preprint server for biology · 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
- ExplaiNN: interpretable and transparent neural networks for genomics.Genome biology · 2023Article
- A survey on algorithms to characterize transcription factor binding sites.Briefings in bioinformatics · 2023Review
- ProSampler: an ultrafast and accurate motif finder in large ChIP-seq datasets for combinatory motif discovery.Bioinformatics (Oxford, England) · 2019Article
- RECAP reveals the true statistical significance of ChIP-seq peak calls.Bioinformatics (Oxford, England) · 2019Article
- Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases.Scientific reports · 2019Article
- Accurate prediction of cell type-specific transcription factor binding.Genome biology · 2019Article
- EFFECTOR OF TRANSCRIPTION factors are novel plant-specific regulators associated with genomic DNA methylation in Arabidopsis.The New phytologist · 2019Article
- Genome-wide determinants of sequence-specific DNA binding of general regulatory factors.Genome research · 2018Article
- Insights from resolving protein-DNA interactions at near base-pair resolution.Briefings in functional genomics · 2018Review
- Systems and Synthetic Biology Approaches to Engineer Fungi for Fine Chemical Production.Frontiers in bioengineering and biotechnology · 2018Review
- Direct AUC optimization of regulatory motifs.Bioinformatics (Oxford, England) · 2017Article
- CircularLogo: A lightweight web application to visualize intra-motif dependencies.BMC bioinformatics · 2017Article
- Mapping specificity landscapes of RNA-protein interactions by high throughput sequencing.Methods (San Diego, Calif.) · 2017Article
- Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies.BMC bioinformatics · 2017Article
- Analysis of Genomic Sequence Motifs for Deciphering Transcription Factor Binding and Transcriptional Regulation in Eukaryotic Cells.Frontiers in genetics · 2016Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
De novo motif discovery has been an important challenge of bioinformatics for the past two decades. Since the emergence of high-throughput techniques like ChIP-seq, ChIP-exo and protein-binding microarrays (PBMs), the focus of de novo motif discovery has shifted to runtime and accuracy on large data sets. For this purpose, specialized algorithms have been designed for discovering motifs in ChIP-seq or PBM data. However, none of the existing approaches work perfectly for all three high-throughput techniques. In this article, we propose Dimont, a general approach for fast and accurate de novo motif discovery from high-throughput data. We demonstrate that Dimont yields a higher number of correct motifs from ChIP-seq data than any of the specialized approaches and achieves a higher accuracy for predicting PBM intensities from probe sequence than any of the approaches specifically designed for that purpose. Dimont also reports the expected motifs for several ChIP-exo data sets. Investigating differences between in vitro and in vivo binding, we find that for most transcription factors, the motifs discovered by Dimont are in good accordance between techniques, but we also find notable exceptions. We also observe that modeling intra-motif dependencies may increase accuracy, which indicates that more complex motif models are a worthwhile field of research.
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