Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms

We present a new microRNA target prediction algorithm called TargetBoost, and show that the algorithm is stable and identifies more true targets than do existing algorithms. TargetBoost uses machine learning on a set of validated microRNA targets in lower organisms to create weighted sequence motifs that capture the binding characteristics between microRNAs and their targets. Existing algorithms require candidates to have (1) near-perfect complementarity between microRNAs' 5' end and their targets; (2) relatively high thermodynamic duplex stability; (3) multiple target sites in the target's 3' UTR; and (4) evolutionary conservation of the target between species. Most algorithms use one of the two first requirements in a seeding step, and use the three others as filters to improve the method's specificity. The initial seeding step determines an algorithm's sensitivity and also influences its specificity. As all algorithms may add filters to increase the specificity, we propose that methods should be compared before such filtering. We show that TargetBoost's weighted sequence motif approach is favorable to using both the duplex stability and the sequence complementarity steps.