And Characterization

With the initiation of miRNA research, a lot of work has been carried out since then. Fifteen classes of miRNAs have been identified and well characterized from the model plant Arabidopsis

(Bartel and Bartel 2003). Twenty miRNA families have been computationally predicted in case of Oryza, out of which 14 miRNAs have been experimentally isolated and characterized (Sunkar et al. 2005). Similarly, eight novel miRNAs have been isolated and characterized from Medicago (Szittya et al. 2008) . miRNAs have been identified by cloning as well as computational approaches. The two strategies for miRNAs identification are:

2.1 In Silico Prediction and Characterization

The very first miRNAs were experimentally isolated and cloned by biochemical and genetic approaches (Lee et al. 1993). But in cases of low expression and tissue-specific miRNAs, cloning is not an appropriate approach. As a result, it has been replaced by the method of computational predictions and experimental validations (Zhang et al. 2006). Since then computational approaches have dominated the field of miRNAs research. The traditional computational strategy was based on the decoded genome sequences of certain model species. In that case only completely sequenced genomes could be analyzed for miR-NAs prediction, leaving behind the species with unsequenced genomes (Zhang et al. 2005) . Because of the inefficiency associated with the traditional method, presently a new approach has been developed. It has been reported that within same kingdom as well as sometimes in between different kingdoms, miRNAs show evolutionary conservations. It gave the idea that comparative genomics could be a powerful strategy to identify miRNAs. So, this conservative nature was employed to predict miRNAs (Zhang et al. 2007). Most recently, genomic survey sequences (GSSs) and expressed sequence tags (ESTs) have been adopted for mining novel, undiscovered miRNAs (Zhang et al. 2005) .

Various steps involved in the in silico miRNAs prediction are: (1) miRNAs prediction using ESTs and GSSs analysis, (2) identification of potential miRNAs, and (2) miRNAs target identification.

2.1.1 miRNAs Prediction Using ESTs and GSSs Analysis

It has been known that ESTs are the cDNA sequences of the expressed genes. In case of organisms with incomplete genomic draft, ESTs have been considered as an alternative tool for gene discovery. Also, ESTs and expressed genes are actually obtained from true gene expression. Thus it justifies the use of ESTs to predict miRNAs for species with undiscovered genomes (Matukumalli et al. 2004; Zhang et al. 2005). National Center for Biotechnology Information (NCBI) contains 22,165,266 entries of ESTs from various organisms in its EST database (Boguski et al. 1993). ESTs could be obtained from EST database of NCBI, dbEST ( dbEST/). Initially, redundancy within the ESTs is removed by using software CAP3 (http:// This software presents the overlapping sequences as contigs and nonoverlapping sequences as singletons. The contigs are used for miRNAs prediction. These processed sequences, i.e., contigs are submitted to miRNA-finder (http://bioinfo3. This software predicts the possible miRNAs from the query sequence. In order to further remove the redundant and overlapping miRNAs from the predicted ones, a reference set of miRNAs is used. This reference miRNAs set belongs to any of the closely related organisms and can be obtained from miRNA registry database (http://miRNA. (Manila et al. 2009). A computational tool MicroHARVESTER (http://www. index/php) has been designed to search homol-ogy between the predicted miRNAs and previously detected miRNAs (Dezulian et al. 2006). While using GSSs, the complete sequence is used as miRNA precursor sequence. Overlapping as well as protein coding sequences is removed. The remaining nonprotein sequences are used for further proceedings (Sunkar and Jagadeeswaran 2008). The predicted miRNAs are then analyzed to determine the potential miRNAs among them.

2.1.2 Identification of Potential miRNAs

Potential miRNAs are predicted on the basis of data obtained from their secondary structures. For the predicted miRNAs or pre-miRNAs, secondary structure is determined by using software MFOLD 3.1 ( applications/mfold/rna/form1.cgi) (Mathews et al. 1999; Zuker 2003). In order to screen the miRNAs, following criteria are followed (Xie et al. 2007; Sunkar and Jagadeeswaran 2008):

1. Pre-miRNA sequences should fold into an appropriate stem-loop hairpin secondary structure that contains around 22 nts mature miRNA sequence within one arm of the hairpin.

2. Predicted mature miRNAs are allowed to have only 0-3 nts mismatch in comparison to the previously known plant mature miRNAs.

3. Predicted secondary structures should have higher negative minimal free energies (MFEs) and minimal free energy index (MFEIs) than the other different types of RNAs.

4. No loop or break is allowed in the miRNAs sequence.

5. miRNAs sequence should have 30-70% A+U content.

6. miRNAs should have no more than six mismatches with the opposite miRNA* sequence in the other arm.

miRNAs satisfying the following parameters are thus considered as the potential miRNAs.

2.1.3 miRNAs Target Prediction

It has been known that plant miRNAs have the tendency to bind to the protein coding regions of their mRNA targets. The binding of miRNA to target mRNA is based on the nature of complementarity between them, either perfect or nearly perfect (Xie et al. 2007). This reveals the usage of homology search to determine miRNA targets (Zhang et al. 2007). A software for miRNAs target prediction based upon above said approach is miRU2 ( This tool has been applied for the prediction of several miRNA targets (Fig. 12.1) (Manila et al. 2009). Presently, computational approaches are being further explored to ease the process of novel

Fig. 12.1 Flowchart showing computational tools and related criteria to predict potent miRNAs

• Sequence Input In FASTA format

• Target genome selected for comparing

• Reference genome to reduce false positive microHARVE

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