Location: Grain, Forage, and Bioenergy ResearchTitle: Characterizing differential gene expression in polyploid grasses lacking a reference transcriptome) Author
Submitted to: OA Biotechnology
Publication Type: Peer reviewed journal
Publication Acceptance Date: 1/8/2014
Publication Date: 1/10/2014
Publication URL: http://handle.nal.usda.gov/10113/59006
Citation: Asmaradasa, B.S., Donze-Reiner, T., Heng-Moss, T., Sarath, G., Amundsen, K. 2014. Characterizing differential gene expression in polyploid grasses lacking a reference transcriptome. OA Biotechnology. 3:1. Interpretive Summary: Plants respond to diverse stresses by changing the levels and types of gene expression. During gene expression, the DNA sequence that encodes a gene is converted to a RNA molecule called as a messenger RNA (mRNA, also known as a transcript). It is possible to selectively isolate mRNA from tissues and biochemically convert them into DNA (called cDNA). This cDNA can be sequenced to obtain information on the genes coding for mRNAs that were specifically turned-on (higher transcript levels) or turned off (lower transcript levels) in response to stress. Utilizing next-generation DNA sequencing instruments (NGS), it is possible to obtain millions of reads from a single sample of a tissue. However, to convert this sequencing data into useable information requires the selection of the appropriate tools in bioinformatics. In addition, bioinformatic analysis of NGS datasets from genomically poorly characterized species, such as polyploid grasses is quite challenging. In this study, it has been shown that these large NGS datasets obtained from the amenity grass, buffalograss, can be analyzed to provide important data on genes that are specifically modulated in response to different stresses (fungal or insect attack) and in male or female plants. These strategies will be broadly applicable to other polyploid plant species for which there is limited genomic information.
Technical Abstract: Basal transcriptome characterization and differential gene expression in response to varying conditions are often addressed through next generation sequencing (NGS) and data analysis techniques. While these strategies are commonly used, there are countless tools, pipelines, data analysis methods and levels of data interpretation making a unified best practice workflow difficult to resolve. Transcriptional characterization from NGS data is increasingly more difficult as organismal genome complexity increases. The process is even more challenging when a suitable reference genome or transcriptome does not exist for the organism being studied. This review discusses strategies used recently to resolve differential gene expression in buffalograss, a polyploid obligate outcrossing amenity grass in response to three separate conditions. We illustrate the value of using a de novo transcriptome assembly as a reference and the effectiveness of using data from one NGS experiment to draw meaningful conclusions.