miRNAs regulate gene expression through targeting mRNA for cleavage, translational repression or mRNA decay. Thus, identifying miRNA targets is a very important step to study miRNA functions in animal and plant development. In the past years, several approaches have been employed to identify miRNA targets. A genetic approach is the first approach to identify miRNA targets, which is based on the abnormal expression of targeted mRNAs in the miRNA loss-of-function mutants. This approach has been used to identify several miRNA targets that play an important role in worm development (Lee et al., 1993; Wightman et al., 1993; Reinhart et al., 2000; Johnston and Hobert, 2003). For all known miRNA targets, they have conserved perfect or near-perfect complementary sites of miRNAs (Llave et al., 2002; Pasquinelli and Ruvkun, 2002; Saxena et al., 2003; Bartel, 2004; Mallory et al., 2004a; Meister et al., 2004b; Ota et al., 2004; Vella et al., 2004; Bagga et al., 2005; Brown and Sanseau, 2005; Millar and Waterhouse, 2005), especially for plant miRNAs (Aukerman and Sakai, 2003; Wang et al., 2005a; Williams et al., 2005b). This suggests a powerful strategy to predict miRNA targets by computational approaches. Based on this characteristic, several laboratories have developed different computational strategies to predict miRNA targets in available genome database, and successfully identified 100s of miRNA targets for given miRNAs (Rhoades et al., 2002; Enright et al., 2003; Lewis et al., 2003; Stark et al., 2003; Axton, 2004; Bonnet et al., 2004; John et al., 2004; Jones-Rhoades and Bartel, 2004; Kiriakidou et al., 2004; Lai, 2004; Rajewsky and Socci, 2004; Rehmsmeier et al., 2004; Wang et al., 2004; Axtell and Bartel, 2005; Bentwich, 2005; Brennecke et al., 2005; Brown and Sanseau, 2005; Burgler and Macdonald, 2005; Grun et al., 2005; Hariharan et al., 2005; Kawasaki and Taira, 2005; Krek et al., 2005; Legendre et al., 2005; Lewis et al., 2005; Li and Zhang, 2005; Nakahara et al., 2005; Robins et al., 2005; Saetrom et al., 2005; Williams et al., 2005a; Xie et al., 2005; Yoon and De Micheli, 2005; Zhang, 2005). These computer software programs include TargetScan (Lewis et al., 2003), TargetScanS (Lewis et al., 2005), miRanda (Enright et al., 2003; John et al., 2004), MovingTargets (Burgler and Macdonald, 2005), PicTar (Grun et al., 2005; Krek et al., 2005), RNAhybrid (Rehmsmeier et al., 2004), DIAN-AmicroT (Lim et al., 2005) for animals; and MIRcheck (Jones-Rhoades and Bartel, 2004), findMiRNA (Adai et al., 2005), miRU (Zhang, 2005), and PatScan* (Dsouza et al., 1997; Rhoades et al., 2002) for plants. Microarray technology was also recently employed to identify miRNA targets, and successfully identified 100s of miRNA targets (Lim et al., 2005).
Predicting and identifying miRNA targets by computational approaches is much easier in plants than in animals. This is due to the fact that complementarity between miRNAs and targeted mRNAs is much higher in plants than in animals for a majority of targets (Carrington and Ambros, 2003; Ambros, 2004; Bartel, 2004). Thus, for a majority of plant miRNAs have predicted targets although some of them have not been validated by experimental approaches. However, there are no targets found for a majority of animal miRNAs although more miRNAs are identified in animals than in plants. There are only one or a few targets for a majority of plant miRNAs (Rhoades et al., 2002), while there are lots, even 100s of targets for each animal miRNA (Enright et al., 2003; Lewis et al., 2003, 2005; Stark et al., 2003; John et al., 2004; Lai, 2004; Rajewsky and Socci, 2004; Bentwich, 2005; Krek et al., 2005).
Currently, a majority of plant miRNAs have identified targets. For example, miR 172 targets AP2 in Arabidopsis thaliana and gossy15 in maize for regulating flower development and developmental timing switch (Aukerman and Sakai, 2003; Chen, 2004; Lauter et al., 2005). For animal miRNAs, a majority of miRNAs have not been experimentally identified their targets although 100s of targets are predicted for each miRNA. However, about 30% of protein-coding genes are predicted to be negatively regulated by miRNAs (Lewis et al., 2005). This suggests miRNAs are the biggest regulator in gene regulation.