Mining Spatial Association Rules for Composite Motif Discovery

Motif discovery in biological sequences is an important field in bioinformatics. Most of the scientific research focuses on the de novo discovery of single motifs, but biological activities are typically co-regulated by several factors and this feature is properly reflected by higher order structures, called composite motifs, or cis-regulatory modules or simply modules. A module is a set of motifs, constrained both in number and location, which is statistically overrepresented and hence may be indicative of a biological function. Several methods have been studied for the de novo discovery of modules. We propose an alternative approach based on the discovery of rules that define strong spatial associations between single motifs and suggest the structure of a module. Single motifs involved in the mined rules might be either de novo discovered by motif discovery algorithms or taken from databases of single motifs. Rules are expressed in a first-order logic formalism and are mined by means of an inductive logic programming system. We also propose computational solutions to two issues: the hard discretization of numerical intermotif distances and the choice of a minimum support threshold. All methods have been implemented and integrated in a tool designed to support biologists in the discovery and characterization of composite motifs. A case study is reported in order to show the potential of the tool.