Discovering approximate-associated sequence patterns for protein-DNA interactions.

Leung, K.-S., Wong, K.-C., Chan, T.-M., Wong, M.-H., Lee, K.-H., Lau, C.-K., et al. (2010). Discovering protein-DNA binding sequence patterns using association rule mining. Nucleic acids research, 38(19), 6324-37. doi:10.1093/nar/gkq500.

Abstract

Motivation: The bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) are fundamental protein-DNA interactions in transcriptional regulation. Extensive efforts have been made to better understand the protein-DNA interactions. Recent mining on exact TF-TFBS associated sequence patterns (rules) has shown great potentials and achieved very promising results. However, exact rules cannot handle variations in real data, resulting in limited informative rules. In this paper, we generalize the exact rules to approximate ones for both TFs and TFBSs, which are essential for biological variations.

Results: A progressive approach is proposed to address the approximation to alleviate the computational requirements. Firstly, similar TFBSs are grouped from the available TF-TFBS data (TRANSFAC database). Secondly, approximate and highly conserved binding cores are discovered from TF sequences corresponding to each TFBS group. A customized algorithm is developed for the specific objective. We discover the approximate TF-TFBS rules by associating the grouped TFBS consensuses and TF cores. The rules discovered are evaluated by matching (verifying with) actual protein-DNA binding pairs from Protein Data Bank (PDB) 3D structures. The approximate results exhibit many more verified rules and up to 300% better verification ratios than the exact ones. The customized algorithm achieves over 73% better verification ratios than traditional methods. 64% – 79% approximate rules are shown statistically significant. Detailed variation analysis and conservation verification on NCBI records demonstrate that the approximate rules reveal both the flexible and specific protein-DNA interactions accurately. The approximate TF-TFBS rules discovered show great generalized capability of exploring more informative binding rules.

Availability: Supplementary Data are available on Bioinformatics online and http://www.cse.cuhk.edu.hk/%7Etmchan/rules/