A reversed framework for the identification of microRNA-target pairs in plants

Most plant microRNAs (miRNAs) perform their repressive regulation through target cleavages. The resulting slicing sites on the target transcripts could be mapped by sequencing of the 3'-cleavage remnants, called degradome sequencing. The high sequence complementarity between miRNAs and their targets has greatly facilitated the development of the target prediction tools for plant miRNAs. The prediction results were then subjected to degradome sequencing data-based validation, through which numerous miRNA-target interactions have been extracted. However, some drawbacks are unavoidable when using this forward approach. Essentially, a known list of plant miRNAs should be obtained in advance of target prediction and validation. This becomes an obstacle to discover novel miRNAs and their targets. Here, after reviewing the current available algorithms for reverse identification of miRNA-target pairs in plants, a case study was performed by using a newly established framework with adjustable parameters. In this workflow, integration of degradome and ARGONAUTE 1-enriched small RNA sequencing data was recommended to do a relatively comprehensive and reliable search. Besides, several computational algorithms such as BLAST, target plots and RNA secondary structure prediction were used. The results demonstrated the prevalent utility of the reversed approach for uncovering miRNA-target interactions in plants.