Comprehensive Overview and Assessment of microRNA Target Prediction Tools in Homo sapiens and Drosophila melanogaster
Background: MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression
at the post-transcriptional level through complementary base pairing with the target
mRNA, leading to mRNA degradation and blocking translation process. Many dysfunctions of
these small regulatory molecules have been linked to the development and progression of several
diseases. Therefore, it is necessary to reliably predict potential miRNA targets.
Objective: A large number of computational prediction tools have been developed which provide a
faster way to find putative miRNA targets, but at the same time, their results are often inconsistent.
Hence, finding a reliable, functional miRNA target is still a challenging task. Also, each tool is
equipped with different algorithms, and it is difficult for the biologists to know which tool is the
best choice for their study.
Method: We analyzed eleven miRNA target predictors on Drosophila melanogaster and Homo
sapiens by applying significant empirical methods to evaluate and assess their accuracy and performance
using experimentally validated high confident mature miRNAs and their targets. In addition,
this paper also describes miRNA target prediction algorithms, and discusses common features
of frequently used target prediction tools.
Results: The results show that MicroT, microRNA and CoMir are the best performing tool on
Drosopihla melanogaster; while TargetScan and miRmap perform well for Homo sapiens. The
predicted results of each tool were combined in order to improve the performance in both the datasets,
but not any significant improvement is observed in terms of true positives.
Conclusion: The currently available miRNA target prediction tools greatly suffer from a large
number of false positives. Therefore, computational prediction of significant targets with high statistical confidence is
still an open challenge.
Journal Title: Current Bioinformatics