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Biology and Biotechnology of Environmental Stress Tolerance in Plants, Volume 3

are involved in the regulation of targets of those miRNAs which are not

widely conserved (Debernardi et al., 2012; Zhao et al., 2016). Accelerated

bioinformatics and computational biology enable the opportunity to study

molecular mechanisms related to various stress responses in plants. For a

proper understanding of the molecular mechanism of plant stress responses

under salinity, miRNA-based, and si-RNA based computational strategies

are widely used. Direct cloning, forward genetics, and bioinformatics are

the most common methods for identifying and screening miRNAs, and

these methods have resulted in the discovery of a vast number of miRNAs.

There are several tools and techniques that are available to analyze other

kinds of sRNAs and also to study the gene regulations control by them.

However, still, the identification of sRNA molecules remains quite a diffi­

cult task. The first step for identifying small RNAs is to obtain a collection

of sequences that might contain small RNA transcripts and the genome

sequences (if available), transcriptome data, and expressed sequence tags

(ESTs) can all be used for discovering small RNA genes (Li et al., 2012).

To date, in terms of reliability and sensitivity, the best choice for obtaining

a sequence of small RNAs is the sRNA library. By comparative analysis

of newly found sequences to those in databases and detecting overlap in

genomic location between the new data and databases, miRNAs can be

classified into several categories. The unannotated sequences are utilized

by the self-developed program Mireap to predict new miRNAs (Fu et

al., 2017). An in-depth study of the miRNA-mRNA regulatory network

provides knowledge about post-transcriptional fine adjustment of gene

expression. However, in silico predictions about the interaction between

miRNA and mRNA do not address the specific transcriptomic situation of

a particular biological system and are often influenced by false positive

(Meyer et al., 2014). Genome sequences can also be exploited to find small

RNA genes, and they offer the possibility of finding all small RNA genes,

but a high rate of false positives needs to be considered (Li et al., 2012). In

addition to the NCBI genome database, a few data centers are also helpful to

find small RNA genes or small RNA sequences. The plant microRNA data­

base (PMRD, http://bioinformatics.cau.edu.cn/PMRD) now contains over

10,000 miRNAs from 121 plant species (Zhang et al., 2010). Bioinformatics

techniques like EST, GSS (genome survey sequences), and many others are

used to discover miRNAs, including probable miRNA precursors capable

of forming a hairpin-like secondary structure (Sunkar & Jagadeeswaran,

2008; Zhang et al., 2005) (Tables 9.2–9.4).