DIANA miRNA targets being successfully predicted with the

and TARGETScan are two of the most commonly employed tools for the prediction
of microRNA (miRNA) targets. MiRNAs are noncoding RNA molecules (approx. 22
nucleotides in length) that interact post-translationally with mRNAs,
influencing the stability of the target mRNA molecule, and thus controlling gene
expression. (Witkos, et al. 2011) Bioinformatical software differ from platform
to platform incorporating varying algorithms to allow for the identification
and prediction of miRNA targets. The involvement of miRNAs in several disease
states drives the need for accurate, reliable identification of miRNA: RNA interaction
for experimental validation and for the development of new drugs and therapies.
There are several challenges involved in the development of these platforms as miRNAs
have multiple mRNA targets and each mRNA is the target of multiple miRNAs. (Gururajan,
2017) Each program has its advantages and weakness and selection should be
based on the requirements by the individual researcher.

was created in 2003 and was the first computational software tool used to
predict miRNA targets. (Schnall-Levin, et al. 2011) The program has evolved
over the past 14years, adding new algorithms and continually improving its prediction
accuracy. The current web version of TARGETScan is easily accessible to
scientists with two main search options (a) gene symbol (b) species-specific
miRNA (human, rat, zebrafish, etc.). The seed region of miRNA sequence, between
nucleotides 2 and 8 from 5′-3′ end, is a highly conserved segment incorporated into
the algorithm of TARGETScan. This feature allows for the distinction between
families 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 predicted
with the programs algorithms. The perfect Watson-Crick base pairing employed
within TRAGETScans algorithms is limited by the G:U wobble in the seed region, this
can result in the failure to detect some pairs in the 3′ compensatory site and
must be taken into consideration when using the program. Less conserved targets
also may not be picked up by the algorithms increasing the chances of false

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DIANA uses the
micro-T algorithm for miRNA target predications employing computational and
experimental approaches. The algorithm predicts all C. elegans miRNA
target sites, and identified seven mammalian miRNA target genes that have been
validated. The program uses miRNA-recognition elements
(MREs), using a 38nt-long frame that is moved along 3′ untranslated
region (UTR) of the potential target.  In contrast to TARGETScan, the program uses weak
binding at 5′ seed, involving G:U wobble pairs, if there exists additional base
paring between the miRNA 3′ end and target gene. (Min, et al. 2010)