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. 2013 Apr;19(4):467-74.
doi: 10.1261/rna.035634.112. Epub 2013 Feb 12.

Searching the coding region for microRNA targets

Ray M Marín  1 , Miroslav Sulc, Jirí Vanícek
Affiliations

Searching the coding region for microRNA targets

Ray M Marín et al. RNA. 2013 Apr.

Abstract

Finding microRNA targets in the coding region is difficult due to the overwhelming signal encoding the amino acid sequence. Here, we introduce an algorithm (called PACCMIT-CDS) that finds potential microRNA targets within coding sequences by searching for conserved motifs that are complementary to the microRNA seed region and also overrepresented in comparison with a background model preserving both codon usage and amino acid sequence. Precision and sensitivity of PACCMIT-CDS are evaluated using PAR-CLIP and proteomics data sets. Thanks to the properly constructed background, the new algorithm achieves a lower rate of false positives and better ranking of predictions than do currently available algorithms, which were designed to find microRNA targets within 3' UTRs.

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Figures

FIGURE 1.
FIGURE 1.
(A) Distribution of predicted miRNA-gene pairs according to their PSH values using four different background models. Percentages are computed with respect to the total number of predicted pairs. (B) Distribution of predicted miRNA-gene pairs for the real and two random genomes. PSH in the three genomes was computed using the CU background. RG1(PS + CU) and RG2(CU) were generated by shuffling the real sequences 1000 times.
FIGURE 2.
FIGURE 2.
(A) Distribution of predicted miRNA-gene pairs for the real genome and random genome RG1(PS + CU). Results with and without considering site conservation are shown for both genomes. In all cases, PS + CU background is used. (B) Comparison of the signal-to-noise ratios obtained from curves in panel A. The real genome is considered as the signal, whereas RG1(PS + CU) is considered as the noise.
FIGURE 3.
FIGURE 3.
Precision vs. sensitivity curves for three different shuffling methods of PACCMIT-CDS. In all cases, the results with and without the conservation requirement are shown. Flat regions in the case of “CU with conservation” and “NR with conservation” represent the cases in which the resolution obtained with 108 random sequences is not sufficient to establish a ranking (PSH < 10−8).
FIGURE 4.
FIGURE 4.
Effect of the length of the seed match on the precision and sensitivity of PACCMIT-CDS. In all cases, site conservation was required and PS + CU background was used. Results are shown for (A) the PAR-CLIP and (B) the proteomics data sets.
FIGURE 5.
FIGURE 5.
Comparison of different miRNA target prediction algorithms. (A) Precision vs. sensitivity curves for the different methods. (B) Numbers of true positives found before the first, second, and third false positives are found.
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