TRUEE; a bioinformatic pipeline to define the functional microRNA targetome of Arabidopsis

Abstract

Central to plant microRNA (miRNA) biology is the identification of functional miRNA-target interactions (MTIs). However, the complementarity basis of bioinformatic target prediction results in mostly false positives, and the degree of complementarity does not equate with regulation. Here, we develop a bioinformatic workflow named TRUEE (Targets Ranked Using Experimental Evidence) that ranks MTIs on the extent to which they are subjected to miRNA-mediated cleavage. It sorts predicted targets into high (HE) and low evidence (LE) groupings based on the frequency and strength of miRNA-guided cleavage degradome signals across multiple degradome experiments. From this, each target is assigned a numerical value, termed a Category Score, ranking the extent to which it is subjected to miRNA-mediated cleavage. As a proof-of-concept, the 428 Arabidopsis miRNAs annotated in miRBase were processed through the TRUEE pipeline to determine the miRNA 'targetome'. The majority of high-ranking Category Score targets corresponded to highly conserved MTIs, validating the workflow. Very few Arabidopsis-specific, Brassicaceae-specific, or Conserved-passenger miRNAs had HE targets with high Category Scores. In total, only several hundred MTIs were found to have Category Scores characteristic of currently known physiologically significance MTIs. Although non-exhaustive, clearly the number of functional MTIs is much narrower than many studies claim. Therefore, using TRUEE to numerically rank targets directly on experimental evidence has given insights into the scope of the functional miRNA targetome of Arabidopsis.

Keywords: Arabidopsis thaliana; bioinformatics; degradome; microRNA; microRNA-target interaction; target prediction.