Transcriptome Analysis of a Cultivar of Green Perilla (Perilla frutescens) Using Genetic Similarity with Other Plants via Public Databases

Abstract

Ensuring stable and increasing crop yields are important problems within agriculture. In many cases, studies on the cultivation of crops have a long history; however, few studies have focused on the changes occurring in crops at the genetic level. Gene expression profiles in vivo are thought to assist in understanding plant conditions, so as to stabilize and increase yield. Recently, comprehensive analyses such as genomics, proteomics, and metabolomics have advanced the understanding of information in vivo and provided a lot of information through one-time analysis. Transcriptome analysis by RNA-seq, an omics analysis technique, is widely used in studies of animals, plants, and insects (Scherf et al., 2000; Rifkin et al., 2003; Lister et al., 2008). Bioinformatics approaches are used to elucidate biological implications. There is abundant genetic information available for model plants such as Arabidopsis thaliana, as well as a reference sequence (RefSeq) supporting highly accurate analysis (Fiehn et al., 2001). Software applications for visualizing the analyzed data and genetic information have been built into public databases, offering the ability to assess large quantities of data. However, there is little information for cultivars. On the other hand, basic mechanisms and genes involved in plant processes, such as those involved in growth metabolism, are conserved in many species. Therefore, the use of information available in databases is very helpful in building and improving the cultivation environment of cultivars. The use of model-plant genetic analysis techniques within cultivars has a strong potential to increase yield and improve crop quality. Although species differ, the functions of genes tend to be similar as they are evolutionarily conserved (Tanigaki et al., 2014). In plants, the amino acid or nucleotide sequences of ribulose-1,5-bisphosphate carboxylase/ oxygenase (RuBisCO) are evolutionarily conserved (Tabita et al., 2007). In some cases, this similarity is low because of differences in rates of molecular evolution (Xiang et al., 2004). However, species-specific genes are difficult to analyze by expression estimation and mapping to metabolic pathways. Using omics data for species-specific genes via genetic functional analysis is time-consuming. Moreover, genes specific to a particular cultivar can facilitate speciesspecific de novo genetic analysis (Hong et al., 2015). Conventional farmers seeking methods to improve crop growth require data rapidly (within a single planting sea-