Computational prediction of miRNAs in Arabidopsis thaliana

Abstract

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in animals and plants. Comparative genomic computational methods have been developed to predict new miRNAs in worms, flies, and humans. Here, we present a novel single genome approach for the detection of miRNAs in Arabidopsis thaliana. This was initiated by producing a candidate miRNA-target data set using an algorithm called findMiRNA, which predicts potential miRNAs within candidate precursor sequences that have corresponding target sites within transcripts. From this data set, we used a characteristic divergence pattern of miRNA precursor families to select 13 potential new miRNAs for experimental verification, and found that corresponding small RNAs could be detected for at least eight of the candidate miRNAs. Expression of some of these miRNAs appears to be under developmental control. Our results are consistent with the idea that targets of miRNAs encompass a wide range of transcripts, including those for F-box factors, ubiquitin conjugases, Leucine-rich repeat proteins, and metabolic enzymes, and that regulation by miRNAs might be widespread in the genome. The entire set of annotated transcripts in the Arabidopsis genome has been run through findMiRNA to yield a data set that will enable identification of potential miRNAs directed against any target gene.

Fine tuning of auxin signaling by miRNAs

“…Several computational programs have been developed and successfully applied to predict miRNAs in a diverse animal and plant species. Some of the available softwares include, miRScan (Lim et al, 2003), miRseeker (Lai et al, 2003), findMiRNA (Adai et al, 2005), miRAlign (Wang et al, 2005b), ProMiR (Nam et al, 2005) and microHARVESTER (Dezulian et al, 2006). The application of computational methods are limited by the requirement of genome sequences, yet the EST and GSS databases have been employed to identify several miRNAs in 71 different plant species, (Zhang et al, , 2006a.…”

“…Another strategy is to predict miRNA targets by computational methods. MIRcheck (Jones- Rhoades and Bartel, 2004), findMiRNA (Adai et al, 2005) and miRU are the available software's for identifying miRNA targets in plants. The predicted targets can be subsequently verified by adopting PCR based strategies.…”

Evolutionary patterns of non-coding RNAs

“…[1]: starting from yeast rRNA methylation sites, they first identified homologous positions in D. melanogaster rRNAs and then use snoScan [255] to search for putative snoRNAs with binding motifs complementary to the putative methylation sites. MicroRNAs in plants can be found by extracting those hairpin structures that contain sequence motifs complementary to a mRNA, which is then a putative target [189,34,2].…”

The Cornucopia of Small RNAs in Plant Genomes

“…Deep sequencing across plant lineages has identified evolutionarily conserved miRNA families in gymnosperms, mosses, monocots, and dicots [37,38]. Notably, miRNA families miR156/157, miR159/319, miR160, miR165/166, miR390, and miR408 are also found in primitive land plants [31,[37][38][39][40][41][42]. Between Arabidopsis and rice, there are~20 miRNA families that are evolutionarily conserved (Table 1).…”

“…High-throughput sequencing such as massively parallel signature sequencing (MPSS), 454 pyrosequencing, and sequencing-by-synthesis (SBS) of small RNA libraries has substantially increased the rate of identification of miRNAs [34][35][36]. Deep sequencing across plant lineages has identified evolutionarily conserved miRNA families in gymnosperms, mosses, monocots, and dicots [37,38]. Notably, miRNA families miR156/157, miR159/319, miR160, miR165/166, miR390, and miR408 are also found in primitive land plants [31,[37][38][39][40][41][42].…”