ArticleBMC bioinformatics2015
MARZ: an algorithm to combinatorially analyze gapped n-mer models of transcription factor binding.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Experimental approaches to investigate biophysical interactions between homeodomain transcription factors and DNA.Biochimica et biophysica acta. Gene regulatory mechanisms · 2025Review
- Investigating the sequence landscape in thePeerJ · 2023Article
- JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework.Nucleic acids research · 2018Article
- Article
- Spatial distribution of predicted transcription factor binding sites in Drosophila ChIP peaks.Mechanisms of development · 2016Article
- Nucleotide Interdependency in Transcription Factor Binding Sites in the Drosophila Genome.Gene regulation and systems biology · 2016Article
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3 authors.
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Abstract
backgroundA key challenge in understanding the molecular mechanisms that control gene regulation is the characterization of the specificity with which transcription factor proteins bind to specific DNA sequences. A number of computational approaches have been developed to examine these interactions, including simple mononucleotide and dinucleotide position weight matrix models.
resultsHere we develop a novel, unbiased computational algorithm, MARZ, that systematically analyzes all possible gapped matrices across a fixed number of nucleotides. In addition, to evaluate the ability of these matrix models to predict in vivo binding sites, we utilize a new scoring system and, in combination with established scoring methods and statistical analysis, test the performance of 32 different gapped matrices on the well characterized HUNCHBACK transcription factor in Drosophila.
conclusionsOur results indicate that in many cases gapped matrix models can outperform traditional models, but that the relative strength of the binding sites considered in the analysis can profoundly influence the predictive ability of specific models.
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