ArticleGenome biology2020
Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.
- Genome-wide meta-analysis of monoclonal gammopathy of undetermined significance (MGUS) identifies risk loci impacting IRF-6.Blood cancer journal · 2022Pooled it
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- A massively parallel reporter assay ofbioRxiv : the preprint server for biology · 2026Article
- A structure-guided approach to noncoding variant evaluation for transcription factor binding using AlphaFold 3.Nucleic acids research · 2026Article
- Inferring binding specificities of human transcription factors with the wisdom of crowds.bioRxiv : the preprint server for biology · 2025Article
- Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors.Communications biology · 2025Article
- Variations in flanking or less conserved positions of Reb1 and Abf1 consensus binding sites lead to major changes in their ability to modulate nucleosome sliding activity.Biological research · 2025Article
- Benchmarking transcription factor binding site prediction models: a comparative analysis on synthetic and biological data.Briefings in bioinformatics · 2025Article
- Mechanisms for DNA Interplay in Eukaryotic Transcription Factors.Annual review of biophysics · 2025Review
- Asymmetry of Motif Conservation Within Their Homotypic Pairs Distinguishes DNA-Binding Domains of Target Transcription Factors in ChIP-Seq Data.International journal of molecular sciences · 2025Article
- Pax proteins mediate segment-specific functions in proximal tubule survival and response to ischemic injury.American journal of physiology. Renal physiology · 2025Article
- Benchmarking tools for transcription factor prioritization.Computational and structural biotechnology journal · 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
- Perspectives on Codebook: sequence specificity of uncharacterized human transcription factors.bioRxiv : the preprint server for biology · 2024Article
- Identifying transcription factors with cell-type specific DNA binding signatures.BMC genomics · 2024Article
- Review
- Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data.NAR genomics and bioinformatics · 2024Article
- TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors.Genome biology · 2024Article
- 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
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Authors and funding
12 authors at 9 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPositional weight matrix (PWM) is a de facto standard model to describe transcription factor (TF) DNA binding specificities. PWMs inferred from in vivo or in vitro data are stored in many databases and used in a plethora of biological applications. This calls for comprehensive benchmarking of public PWM models with large experimental reference sets.
resultsHere we report results from all-against-all benchmarking of PWM models for DNA binding sites of human TFs on a large compilation of in vitro (HT-SELEX, PBM) and in vivo (ChIP-seq) binding data. We observe that the best performing PWM for a given TF often belongs to another TF, usually from the same family. Occasionally, binding specificity is correlated with the structural class of the DNA binding domain, indicated by good cross-family performance measures. Benchmarking-based selection of family-representative motifs is more effective than motif clustering-based approaches. Overall, there is good agreement between in vitro and in vivo performance measures. However, for some in vivo experiments, the best performing PWM is assigned to an unrelated TF, indicating a binding mode involving protein-protein cooperativity.
conclusionsIn an all-against-all setting, we compute more than 18 million performance measure values for different PWM-experiment combinations and offer these results as a public resource to the research community. The benchmarking protocols are provided via a web interface and as docker images. The methods and results from this study may help others make better use of public TF specificity models, as well as public TF binding data sets.
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