Mining of cis-Regulatory Motifs Associated with Tissue-Specific Alternative Splicing

Abstract

Alternative splicing (AS) is an important post-transcriptional mechanism that increases protein diversity and may affect mRNA stability and translaftion efficiency. Despite its importance, our knowledge about its mechanism and regulation is very limited. Although it is known that the regulation of AS is influenced by multiple factors, most previous studies have focused on analyzing an individual regulator. In this dissertation, we apply three types of association rule mining techniques to discover cis-regulatory motifs or motif groups that are associated with specific AS patterns in mouse. General association rule mining for categorical attributes is used to find “motif=>motif†rules in gene groups that show similar exon skipping patterns. This method provides candidates for interacting motifs. Discretization-based and distribution-based quantitative association rule mining techniques are used to find “motif => exon skipping profile†rules. Many of the discovered motif candidates coincide with known splicing factor binding sites. Our ultimate goal is to find motifs and motif combinations that are involved in the dynamic regulation of AS. Based on our observations we hypothesize that some cis-regulatory elements affect AS only in combination with other elements. Interacting motifs show interesting differences to motifs that act individually. For example, interacting motif pairs are more conserved, they occur on average closer to the splice sites, motif pairs derived from distribution-based association rule mining, occur also in higher multiplicity. Based on these observations, we hypothesize that interacting cis-regulatory motifs might often correspond to weaker binding sites that occur in clusters close to the regulated splice sites.