With the rapid rise of next-generation sequencing (NGS), one of its technologies, RNA sequencing (RNA-Seq), has taken center stage for analyzing whole transcriptomes. Although RNA-Seq is still the new kid on the block, this technology, its proponents say, could revolutionize transcriptomics, revealing the architecture of gene expression in unprecedented detail.
RNA-Seq applications are proliferating and include the elucidation of disease processes, targeted drug development, and personalized medicine. RNA-Seq also has veterinary and agricultural applications. RNA-Seq, however, will realize its promise only if certain impediments are overcome. These include consistency of methodology, cost, and optimization of data analysis.
To orient researchers who are unfamiliar with the differences between RNA-Seq platforms, Kelli Bramlett, R&D scientist, Life Technologies, poses two key questions:
1. Are you interested in pure discovery, in a nonguided fashion, of every RNA species present in your test samples?
2. Are you mainly focused on measuring expression levels of well-annotated coding RNA transcripts?
“You might have a set of genes crucial to identifying a disease state, or profiling the stage of a specific type of cancer, or monitoring development in your experimental system,” elaborates Dr. Bramlett. “You then would want to employ a system that allows you to quickly and efficiently focus on just your genes of interest and screen through many different samples in a short amount of time.”
Researchers who are clear about their experimental goals are better able to choose the technology that best suits their needs.
“Some RNA-Seq allows for true discovery but will require greater sequencing depth to get all the information. Oftentimes, this type of deep sequencing requires significant additional time for analysis,” continues Dr. Bramlett. “If a focused panel targeting specific RNAs will better meet your needs, this can be accomplished on systems with much faster turnaround time and less sequencing depth. Also, the analysis of a less complex targeted library is much more straightforward.”