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Comparative Study
. 2005 Jul;11(7):995-1003.
doi: 10.1261/rna.7290705. Epub 2005 May 31.

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

Ola Saetrom  1 , Ola Snøve Jr, Pål Saetrom
Affiliations
Comparative Study

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

Ola Saetrom et al. RNA. 2005 Jul.

Abstract

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. (TargetBoost is available as a Web tool from http://www.interagon.com/demo/.).

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Figures

FIGURE 1.
FIGURE 1.
Overall ROC-curves for each algorithm.
FIGURE 2.
FIGURE 2.
ROC-curves. A–D compare the performance of RNAhybrid, Nucleus, and TargetBoost at predicting the true target sites of let-7, lin-4, miR-13a, and bantam.
FIGURE 2.
FIGURE 2.
ROC-curves. A–D compare the performance of RNAhybrid, Nucleus, and TargetBoost at predicting the true target sites of let-7, lin-4, miR-13a, and bantam.
FIGURE 2.
FIGURE 2.
ROC-curves. A–D compare the performance of RNAhybrid, Nucleus, and TargetBoost at predicting the true target sites of let-7, lin-4, miR-13a, and bantam.
FIGURE 2.
FIGURE 2.
ROC-curves. A–D compare the performance of RNAhybrid, Nucleus, and TargetBoost at predicting the true target sites of let-7, lin-4, miR-13a, and bantam.
FIGURE 3.
FIGURE 3.
ROC-curves comparing different parameter settings on RNAhybrid (A) and Nucleus (B). We can see increased sensitivity for high-specificity values for RNAhybrid in A.
FIGURE 3.
FIGURE 3.
ROC-curves comparing different parameter settings on RNAhybrid (A) and Nucleus (B). We can see increased sensitivity for high-specificity values for RNAhybrid in A.
FIGURE 4.
FIGURE 4.
MicroRNA target query examples. (A) General pattern generated after the first boosting iteration in TargetBoost. This pattern has been translated using the let-7 miRNA in a complemented form. (B) The translated query matched against the lin-14 target site.
FIGURE 4.
FIGURE 4.
MicroRNA target query examples. (A) General pattern generated after the first boosting iteration in TargetBoost. This pattern has been translated using the let-7 miRNA in a complemented form. (B) The translated query matched against the lin-14 target site.
FIGURE 5.
FIGURE 5.
Aligned D. melanogaster target sites.
FIGURE 6.
FIGURE 6.
The grammar (A) and semantics (B) of the pattern language used by TargetBoost. The grammar and semantics are explained in the main text.
FIGURE 7.
FIGURE 7.
Two example patterns from our pattern language. Pi denotes nucleotide i in the miRNA counted from the 3′ end.
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