Computational prediction of miRNA precursors and miRNA targets
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MicroRNA is small 22 nucleotides long non-coding RNA which regulates genes by targeting mRNA especially the 3’ UTR region. Identification of miRNAs and their targets by laboratory experiment has had limited success especially for lowly expressed therefore computational prediction approaches are needed. In this study grouping technique for miRNA precursor prediction is introduced. Compared with global alignment, grouping miRNA by classes yield a better sensitivity with very high specificity for pre-miRNA prediction even when a simple positional based secondary and primary structure alignment are used. The program TarSpec was developed to predict miR-1a and miR-124 targets based on common features of miRNA and target binding characteristics observed from alignment between miRNA and the 3’UTR targets. TarSpec obtained 78% and 77% sensitivity for miR-1a and Mir-124 targets, and 98% specificity for both. TarSpec was used to scan the Platypus 3’UTR regions. This approach predicted 734 novel potential target of miR-1a in Platypus 3’UTR regions where 98 of them are in the well annotated chromosomal region and 124 novel potential target of miR-124 where 32 of them are in the well annotated chromosomal region. Some miRNAs are derivations of transposable elements (TE). In human these TE derived miRNAs have a potential to regulate thousands of human genes. Therefore TEs as potential miRNA targets were investigated using an L2 derived miRNA miR-28. Three different miRNA target prediction programs miTarget, miRanda, and RNAhybrid were used to predict a potential miRNA-28 targets in human L2 transposable elements. It was demonstrated that the human TE is also a potential target for miRNAs; subsequently 1,094 of potential target were predicted in human L2.