BMC Genomics 2005, 6:88 http://www.biomedcentral.com/1471-2164/6/88 The MovingTargets software allows individual researchers to specify which constraints the software should enforce. Dramatically different predictions will result by adjusting the parameter values used here. This capacity of the soft- ware has two notable benefits. First, it provides the means to adjust the parameter values as the rules of miRNA/tar- get interactions become better understood. Second, the adjustability of the algorithm facilitates less constrained searches that focus on a particular mRNA or miRNA. For example, if experimental evidence suggests that an mRNA is regulated by miRNAs, yet it is not among the group pre- dicted using the stringent screening parameters, then relaxing different parameters one by one would produce a set of candidate miRNA regulators. Here the drawback of increased sensitivity – an increased number of predictions with an unknown fraction presumed to be false – would be acceptable. We were able to identify miRNAs targeting ttk using this strategy. What is the minimal quality of miRNA/target site interaction sufficient for regulation? In the initial examples of miRNA-dependent regulation, the miRNAs very efficiently blocked accumulation of the proteins encoded by the target mRNAs. These dramatic effects could be the normal mode of miRNA-dependent regulation: when some quality of miRNA/mRNA target interaction occurs, then protein accumulation is blocked. Alternatively, the degree of regulation could be directly correlated with the quality of the individual interactions (or their sum), and several mutation studies have shown that the level of translational repression of a miRNA target varies in relation to the quality of miRNA targeting, as defined by such characteristics as free energy of hybridiza- tion, consecutive 5' base pairing, and number of target sites [3,22,39]. In our experiments to validate the predicted miRNA targets we tested a miRNA, miR-92b, not predicted to interact with the ab 3' UTR. Although miR-92b did not produce the strong inhibitory effect of let-7, it did cause some inhibition. One interpretation is that miR-92b weakly interacts with the ab 3' UTR, and that the weak interaction is sufficient for a weak regulatory effect. When the 'number of target sites' parameter of MovingTargets was relaxed to two, rather than three, we found that miR- 92b is predicted to target ab at two sites that satisfy the remaining strict biological constraints. Thus our results are also consistent with the notion that there is a correlation between the overall strength of miRNA/mRNA interactions and the degree of regulation. An implication of this conclusion is that even weak interactions may have consequences, and that many or even most of all cellular mRNAs may be regulated, but that the degree of regula- tion may vary substantially. Comparison of different prediction results The high-likelihood miRNA targets predicted in this paper using strict biological constraints produce very different results in comparison to the other Drosophila miRNA tar- get prediction algorithms. The simplest forms of compar- isons are not possible, because the other predictions produce a ranked list of predicted targets for each miRNA, while our method identifies a group of miRNA targets that adhere to specific constraints. Nevertheless, when consid- ering our group of 83 predicted miRNA targets, only 11 are included among the top 20 targets for any individual miRNA predicted by Enright et al. [41], only 13 appear in the top 50 targets for individual miRNAs predicted by Stark et al. [45], and only 4 of the 83 are predicted by Rehmsmeier et al. [46]. There are several possible reasons for the differences in the predictions by the different algorithms. First, only our approach goes beyond thermodynamic stability consider- ations in imposing a penalty on G:U base pairs involving the 5' part of the miRNA. Second, the value of multiple target sites is treated differently (there is experimental evi- dence of synergy between multiple target sites and most known miRNA targets have multiple predicted target sites [2,21,23-28]. For example, Enright et al. reward for multi- ple sites by summing a score for all complementary sites in the target 3' UTR, whereas our algorithm requires a miRNA/target pair to have a user-specified absolute number of target sites each meeting a user-defined set of biological constraints. A third difference centers on the importance of extensive base pairing in the miRNA 5' region, for which there is both experimental evidence and the precedent of the predicted interactions between known target mRNAs and their miRNAs [2,23-28]. Our algorithm requires a miRNA-target interaction to have a user-specified minimum absolute number of consecutive and total base pairs in the miRNA 5' region. In contrast, Enright et. al. appear not to heavily weight this feature, since many of their top-rated miRNA/target interactions have significant gaps in the miRNA 5' region. Five other miRNA target prediction methods for animals have been published, but the predictions cannot be com- pared directly to ours since four of the five examined mammalian mRNAs and the other tested only a small number of Drosophila genes [38,43,47-49]. None of these approaches is identical to ours, and so if used with Drosophila mRNAs and miRNAs, each would be expected to provide results not identical with ours, just as for the published examples. Conclusion Prediction of animal miRNA targets is a challenging task due to the incomplete and interrupted base pairing between a miRNA and its target. We developed the Mov- Page 9 of 11 (page number not for citation purposes)