Up to now, 21264 hairpins and 25141 mature miRNAs in 193 species have been deposited in miRBase (miRBase release 19, 2012, http: //www. mirbase. org/) (Griffiths-Jones et al. 2008), and 9277 mature plant miRNAs have been deposited in Plant MicroRNA Database, PMRD (http: //bioinformatics. cau. edu. cn/PMRD/) (Zhang et al. 2010). A variety of strategies and approaches have been developed to identify novel miRNAs in diverse plant species (Bonnet et al. 2004; Jones-Rhoades and Bartel 2004; Wang et al. 2004; Adai et al. 2005; Zhang et al. 2005; Zhao et al. 2007; Meyers et al. 2008; Zhang et al. 2008; Zhou et al. 2010; Wang et al. 2011). This increasing knowledge of plant miRNAs and well-developed common tools have provided great opportunities for identification of miRNAs in switchgrass.

MiRNAs have been identified by three common approaches: direct cloning, forward genetics and bioinformatics predication followed by experimental validation (Jones-Rhoades et al. 2006). Forward genetics is rarely used for plant microRNA discovery (Jones-Rhoades et al. 2006). Cloning is the most direct and initial method for large-scale miRNA discovery (Reinhart et al. 2002; Jones-Rhoades et al. 2006). It includes isolation of small RNAs, ligation of small RNAs to adaptor oligonucleotides, reverse transcription, amplification and sequencing (Jones-Rhoades et al. 2006). The early sequencing method is conventional Sanger sequencing, which was successful in identifying some conserved miRNAs (Reinhart et al. 2002; Sunkar and Zhu 2004; Axtell and Bartel 2005), but is relatively in low-depth and not ideal for discovering evolutionarily young miRNAs with low abundance (Moxon et al. 2008). The next generation sequencing (NGS), such as the 454 technology and the Solexa platform, provides a powerful high throughput tool, which has greatly facilitated the identification of novel miRNAs (Lu et al. 2005; Rajagopalan et al. 2006; Fahlgren et al. 2007; Yao et al. 2007; Moxon et al. 2008; Sunkar et al. 2008; Zhao et al. 2010; Chi et al. 2011).

However, cloning method suffers from several limitations such as sequence-based biases during the cloning procedures, and difficulty in detecting miRNAs expressed in low levels or only in response to certain stressors (Jones-Rhoades et al. 2006). Bioinformatics approaches can be a great complement to overcoming these limitations (Jones-Rhoades and Bartel 2004; Jones-Rhoades et al. 2006; Gebelin et al. 2012). Thus, a combination of bioinformatics prediction and experimental validation approaches is often used to identify plant miRNAs.

With the access of the complete genome sequencing data, it is possible to predict and identify a complete set of conserved miRNAs using bioinformatics approaches (Matts et al. 2010; Thakur et al. 2011). However, currently there have been neither complete genome sequencing data, nor large genomic fragmented data (genomic survey sequences, whole-genome shortgun reads and high throughput genomic sequences) available for switchgrass (Matts et al. 2010). Although lack of genomic resources can be a challenge for identifying the complete set of conserved switchgrass miRNAs, the availability of expressed sequence tags (ESTs) deposits (currently 720,590 ESTs deposited in NCBI database) can be a viable source for miRNA discovery. Moreover, previous research has already led to discovery of conserved miRNAs from diverse plant species by EST database mining (Zhang et al. 2006; Sunkar and Jagadeeswaran 2008; Matts et al. 2010; Gebelin et al. 2012).