MicroRNAs (miRNAs) are a class of endogenous, small regulatory RNA averaging 22 nucleotides in length that mediate the post-transcriptional regulation of messenger RNAs. They bind to target messages in a sequence-specific manner, and induce translational repression or endonucleolytic cleavage. The first two miRNAs, lin-4 and let-7 were discovered some seven years apart in the worm C. elegans, in genetic screens for mutants with disrupted developmental timing 12. There has since been an explosion of interest in the field, and the identification of hundreds of miRNAs in metazoans as disparate as vertebrates, arthropods, nematodes, and viruses 3 has established miRNAs as pervasive regulators of gene expression. For recent reviews, see 456.

Functions have only been experimentally assigned to a small fraction of the few thousand known miRNAs 7. Of the experimental strategies available to investigate miRNA function, stringent genetic tests that link miRNA loss-of-function mutants to misregulated targets, and point mutations in miRNA binding sites to specific phenotypes are impractical on a genomic scale in any animal species 8. Tissue-culture assays using reporter gene constructs fused to target sequences are an easier alternative, but their reliance on ectopic miRNA expression harbours the danger of measuring what may be a nonphysiological interaction between two molecules with complementary surfaces 9. Computational approaches are thus likely to remain an important means of studying miRNA targets for the forseeable future, not least as a means of directing wet-lab experiments. These predictions are no doubt hampered by the fact that animal miRNAs – in contrast to plant miRNAs – tend to be only partially complementary to their target mRNAs. This fact, compounded by the small size of these molecules, precludes the use of standard sequence comparison methods.

Several algorithms have been developed to predict miRNA targets in animal species; these are listed in Table 1. A common strategy in several of these programs is to rank target 3' untranslated region (UTR) complementarity by some combination of duplex free energy and/or pairing requirements at the 5' end (seed region) of the miRNA 8. For instance, TargetScan 10 combines requirements for conserved perfect Watson-Crick pairing at positions 2–8 of the miRNA with estimates of the free energy of isolated miRNA-target site interactions, ignoring initiation free energy. While in vitro tests have shown sites containing G:U base-pairs to be functional but impaired 11, recent in vivo experiments have demonstrated them to be efficiently downregulated 9. Taken together with the presence of a G:U base-pair in the seed region of a functional let-7 binding site in the lin-41 3'-UTR 12, these results make a case for the inclusion of seeds with G:U wobbles in target prediction algorithms.

The PicTar 1314 algorithm defines seeds as heptamers with Watson-Crick or G:U pairings at positions 1–7 or 2–8 from the miRNA 5' end. It combines seed searches with RNA duplex free energy filters, evolutionary conservation requirements, and a probabilistic scoring mechanism to predict targets that are under combinatorial control by co-expressed miRNAs. However, it makes use of RNAHybrid 15, an algorithm that approximates RNA duplex free energies by discarding intramolecular hybridizations in order to achieve linear time complexity.

Robins et al. 16 incorporate mRNA secondary structure computed from 3'-UTRs in their target prediction algorithm, but require perfect Watson-Crick complementarity in the seed site. Furthermore, the use of isolated 3'-UTRs is likely to produce structures very different from the structure of 3'-UTRs in folds that use complete mRNA sequences.

While most of the tools listed in Table 1 are accessible as web services, only miRanda 17 and RNAHybrid are available as downloadable software that can be modified, extended and run on custom datasets. Most listed algorithms also rely on target conservation across two or more species as a filter. While this is necessary to distinguish functional targets from a vast array of candidates, it results in the unavoidable omission of real targets that are not thus conserved.

Here we present MicroTar, an miRNA target prediction program that does not rely on evolutionary conservation. Through the use of the partial complementarity of miRNAs to their target messages, and the predicted free energy of complete mRNA molecules, we are able to address the problem of the prediction of targets that are not conserved across different genomes. Moreover, harnessing the power of parallel computing obviates the need for introducing approximations that discard intramolecular base pairs in estimates of miRNA-mRNA duplex free energy; we thus implicitly incorporate the accessibility of 3'-UTRs in the algorithm. MicroTar source code – available under an open-source licence – and predictions can be accessed at the MicroTar website 18.

MicroTar does not rely on evolutionary conservation to filter predicted targets and is able to address the problem of the prediction of targets that are not conserved across different genomes. Parallel computing makes feasible the use of complex energy prediction algorithms on a large scale, and by using estimates of miRNA-mRNA duplex free energy that allow intramolecular pairings, MicroTar implicitly incorporates the accessibility of 3'-UTRs. In tests on three datasets of experimentally verified miRNA targets in C. elegans, Drosophila and mouse, MicroTar displays greater sensitivity than previously developed target prediction programs.

Project name: MicroTar

Project home page: http://tiger.dbs.nus.edu.sg/microtar/

Operating systems: Linux, UNIX

Programming language: C

Other requirements: GNU autoconf/automake

Licence: New BSD licence

Any restrictions to use by non-academics: None (check ViennaRNA licence, however)

MTT and RT planned the project. RT acquired the data and implemented the algorithm. Both authors prepared and approved the final manuscript.