Discovering Binding Cores in Protein-DNA Binding Using Association Rule Mining with Statistical Measures

Abstract

Understanding binding cores is of fundamental importance in deciphering Protein-DNA (TF-TFBS) binding and for the deep understanding of gene regulation. Traditionally, binding cores are identified in resolved high-resolution 3D structures. However, it is expensive, labor-intensive and time-consuming to obtain these structures. Hence, it is promising to discover binding cores computationally on a large scale. Previous studies successfully applied association rule mining to discover binding cores from TF-TFBS binding sequence data only. Despite the successful results, there are limitations such as the use of tight support and confidence thresholds, the distortion by statistical bias in counting pattern occurrences, and the lack of a unified scheme to rank TF-TFBS associated patterns. In this study, we proposed an association rule mining algorithm incorporating statistical measures and ranking to address these limitations. Experimental results demonstrated that, even when the threshold on support was lowered to one-tenth of the value used in previous studies, a satisfactory verification ratio was consistently observed under different confidence levels. Moreover, we proposed a novel ranking scheme for TF-TFBS associated patterns based on p-values and co-support values. By comparing with other discovery approaches, the effectiveness of our algorithm was demonstrated. Eighty-four binding cores with PDB support are uniquely identified.