Gives putative targets

• 0-3 mismatch with previous known miRNA

• miRNA lie on one arm of precursor

• High negative MFE

• <G mismatch between miRNA-mlRNA*

<4 mismatch between miRNA and target sequence miRNAs identification. One of such modified computational approach is miRTRAP. miRTRAP stands for miRNA Tests for Read Analysis and Prediction. This tool simplifies the systemic and whole-genome prediction of miRNAs by making use of high-throughput sequencing data. This tool utilizes a system of binary decisions that are based on biochemical mechanisms behind miRNA biogenesis (Hendrix et al. 2010). This program has been able to predict 400 putative miRNAs loci from simplest chordate Ciona intes-tinalis. No doubt in silico prediction provides an efficient and easy method for miRNA prediction and target identification. For organisms with incomplete genomic draft, in silico methods offer a promising approach to study genetic regulations. But an important fact to note is that all the data obtained by in silico predictions require experimental validations.

2.2 sRNA Library Preparation and miRNA Characterization

The experimental approach of miRNAs identification has been termed as RNomics. It deals with the preparation of cDNA libraries of sRNAs (Huttenhofer et al. 2005). In order to develop sRNA libraries, a number of strategies have been adopted. Various commercial kits are available that specifically isolate sRNAs fraction from the sample. Other method is to isolate total RNA and then purification of sRNAs fraction from the large RNA fractions. sRNAs fraction is usually resolved on 15% polyacrylamide gel and then eluted. sRNAs range from 18 to 26 nts in length, as a result before cloning, adaptors are ligated to them. Before ligations, sRNAs are often polyade-nylated to avoid circularization of linker RNAs (Devor et al. 2009). Ligation of adaptors on both

5' and 3' ends helps in the visualization of sRNAs in the further steps. Ligated product is reverse-transcribed using adaptor-specific primers. Resulting PCR product is used for cloning purpose and cloned into the desired vector (Sunkar et al. 2005). Prepared sRNA clones are then sequenced.

Sequencing of clones using BLAST analysis against Genbank genome assemblies helps to determine its nature, whether it is a transposon, any degraded product or already known noncod-ing RNAs except miRNAs/siRNAs. Candidates with perfect matches against genome sets are further used for fold-back secondary structure predictions using MFOLD 3.1 software (Zuker 2003). sRNA sequences are folded with flanking sequences in five contexts: (1) 300 bp upstream and 20 bp downstream, (2) 150 bp upstream and 20 bp downstream; (3) 150 bp upstream and 150 downstream, (4) 20 bp upstream and 150 downstream, and (5) 20 bp upstream and 300 bp downstream (Lu et al. 2005). Candidate clones satisfying all the criteria are considered as potential miRNAs. Further target for these miRNAs is determined using software miRU2. sRNAs isolation and cloning is a laborious job. Also it does not always ensure that each clone is a miRNA. As a result, in silico prediction of miRNAs and their experimental validation outshine the above technique.

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