Genome Profiling (GP) Method Based Classification of Insects: Congruence with That of Classical Phenotype-Based One

Abstract

BackgroundRibosomal RNAs have been widely used for identification and classification of species, and have produced data giving new insights into phylogenetic relationships. Recently, multilocus genotyping and even whole genome sequencing-based technologies have been adopted in ambitious comparative biology studies. However, such technologies are still far from routine-use in species classification studies due to their high costs in terms of labor, equipment and consumables.Methodology/Principal FindingsHere, we describe a simple and powerful approach for species classification called genome profiling (GP). The GP method composed of random PCR, temperature gradient gel electrophoresis (TGGE) and computer-aided gel image processing is highly informative and less laborious. For demonstration, we classified 26 species of insects using GP and 18S rDNA-sequencing approaches. The GP method was found to give a better correspondence to the classical phenotype-based approach than did 18S rDNA sequencing employing a congruence value. To our surprise, use of a single probe in GP was sufficient to identify the relationships between the insect species, making this approach more straightforward.Conclusion/SignificanceThe data gathered here, together with those of previous studies show that GP is a simple and powerful method that can be applied for actually universally identifying and classifying species. The current success supported our previous proposal that GP-based web database can be constructible and effective for the global identification/classification of species.

Ordered genome change of plant and animal body cells revealed by the genome profiling method

“…Finally, the GP analysis was able to discriminate differences between close genomes (i.e., those generated during short term body growth or differentiation), such as neighboring leaf sections (~mm distance) and microcubes of brain slices (~200 μm distance). The GP method and genome distance have already been used with success in several other studies . This evokes a novel, difficult problem in rationalizing the total amount of mutation, μ(g), introduced in a previous study , where the genome distance between two leaves taken from different branches was dealt with as follows: where μ c and g represent the constant mutation rate (/base/generation) of cells and the number of generations of cells, respectively.…”

Genome profiling (GP) as an effective tool for monitoring culture collections: A case study with Trichosporon

“…As is the case with all methodologies, the GP method has its own limitation: it contains a stochastic property in the calculation of PaSS values (also their derivative genome distances) (Kouduka et al, 2006;Ahmed and Nishigaki, 2008), which becomes more significant when the relationship of two species is distant although such a stochastic effect is negligible for sufficiently close species as shown in this study (Watanabe et al, 2002). Empirically, we have already clarified that the range of effective applications is unexpectedly wide; e.g., the genera of insects (Odonata (i.e., dragonfly), Orthoptera (i.e., grasshopper), Hemiptera (i.e., cicada), Lepidoptera (i.e., butterfly), Coleoptera (i.e., beetle), and other related taxa) could be classified congruently with that provided by the classical phenotype-based method (Ahmed et al, 2011). Through the success of this study, fungal species of Trichosporon were shown to be genomically closer than the application limit of the GP approach.…”

“…In this context, a simple and universal method to identify and classify species has been developed: the genome profiling (GP) method (Ahmed et al, 2011;Hamano et al, 1996;Kouduka et al, 2007;Naimuddin et al, 2000;Watanabe et al, 2002) which is mainly based on the statistical concept of random sampling and the rapid acquisition of sequence information. GP can be highly powerful and reproducible in species identification due to i) reproducible sampling of DNA fragments from the original genomic DNA by random PCR, which can employ a defined set of primers for any kind of organisms, ii) acquisition of the sequence--5 -derived information without sequencing by using µTGGE (temperature gradient gel electrophoresis) to which random PCR products can be directly applied without purification of DNA fragments, and iii) normalization of the acquired data using internal references, which leads to obtaining species-specific data.…”

Mining Functional Elements in Messenger RNAs: Overview, Challenges, and Perspectives

“…There is a great need to explore more deeply the formation of these secondary structure patterns and their role in 3′-end maturation at the genome-wide level. It is clear from several studies that integrating secondary structure features of mRNA during siRNA design generates more functionally potent siRNA (Tafer et al, 2008; Ahmed and Raghava, 2011). Therefore, new algorithms integrating both sequence and structural features may be helpful for predicting more accurate PAS’s.…”