DIANAand TARGETScan are two of the most commonly employed tools for the predictionof microRNA (miRNA) targets.
MiRNAs are noncoding RNA molecules (approx. 22nucleotides in length) that interact post-translationally with mRNAs,influencing the stability of the target mRNA molecule, and thus controlling geneexpression. (Witkos, et al. 2011) Bioinformatical software differ from platformto platform incorporating varying algorithms to allow for the identificationand prediction of miRNA targets.
The involvement of miRNAs in several diseasestates drives the need for accurate, reliable identification of miRNA: RNA interactionfor experimental validation and for the development of new drugs and therapies.There are several challenges involved in the development of these platforms as miRNAshave multiple mRNA targets and each mRNA is the target of multiple miRNAs. (Gururajan,2017) Each program has its advantages and weakness and selection should bebased on the requirements by the individual researcher.TARGETScanwas created in 2003 and was the first computational software tool used topredict miRNA targets. (Schnall-Levin, et al.
2011) The program has evolvedover the past 14years, adding new algorithms and continually improving its predictionaccuracy. The current web version of TARGETScan is easily accessible toscientists with two main search options (a) gene symbol (b) species-specificmiRNA (human, rat, zebrafish, etc.). The seed region of miRNA sequence, betweennucleotides 2 and 8 from 5′-3′ end, is a highly conserved segment incorporated intothe algorithm of TARGETScan. This feature allows for the distinction betweenfamilies and species during base pair analysis.
(Riffo-Campos ÁL, et al. 2016)TARGETScan has an estimated false-positive rate of approximately 22% in mammals(Min, et al 2010) with all known miRNA targets being successfully predictedwith the programs algorithms. The perfect Watson-Crick base pairing employedwithin TRAGETScans algorithms is limited by the G:U wobble in the seed region, thiscan result in the failure to detect some pairs in the 3′ compensatory site andmust be taken into consideration when using the program. Less conserved targetsalso may not be picked up by the algorithms increasing the chances of falsenegatives.DIANA uses themicro-T algorithm for miRNA target predications employing computational andexperimental approaches. The algorithm predicts all C.
elegans miRNAtarget sites, and identified seven mammalian miRNA target genes that have beenvalidated. The program uses miRNA-recognition elements(MREs), using a 38nt-long frame that is moved along 3′ untranslatedregion (UTR) of the potential target. In contrast to TARGETScan, the program uses weakbinding at 5′ seed, involving G:U wobble pairs, if there exists additional baseparing between the miRNA 3′ end and target gene. (Min, et al.