Abstract
Modeling the binding of transcription factors helps to decipher the control logic behind transcriptional regulatory networks. Position weight matrix is commonly used to describe a binding motif but assumes statistical independence between positions. Although current approaches take within-motif dependence into account for better predictive performance, these models usually rely on prior knowledge and incorporate simple positional dependence to describe binding motifs. The inability to take complex within-motif dependence into account may result in an incomplete representation of binding motifs. In this work, we applied association rule mining techniques and constructed models to explore within-motif dependence for transcription factors in Escherichia coli. Our models can reflect transcription factor-DNA recognition where the explored dependence correlates with the binding specificity. We also propose a graphical representation of the explored within-motif dependence to illustrate the final binding configurations. Understanding the binding configurations also enables us to fine-tune or design transcription factor binding sites, and we attempt to present the configurations through exploring within-motif dependence.
Highlights
In addition to the two sequence feature-based strategies, an alternative approach is to include DNA shape-based features when describing a binding motif
We introduce a novel approach to explore within-motif dependence of transcription factors (TFs) binding. (Fig. 1)
The explored within-motif dependence has a high correlation with the TF binding specificity and known base readout in the TF-DNA recognition
Summary
In addition to the two sequence feature-based strategies, an alternative approach is to include DNA shape-based features when describing a binding motif. These shape features can be derived from DNA sequences[14] and incorporated into the binding motif modeling These shape features encode the within-motif dependence implicitly to improve the performance of the model[15]. Other dependencies may exist for structural requirement such as the specific bending of DNA molecule for Fis binding[19]. Such dependencies cannot be directly retrieved by the aforementioned approaches. We developed a strategy that can explore underlying within-motif dependence and present the binding configurations in TF-DNA recognition. The core scripts of our proposed approach and the constructed ELRMs are publicly available at https://github.com/chiyang/ELRM
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