When RNA sequencing first emerged as an application for next-generation sequencing (NGS), there was much speculation about advantages over existing methods and the impact these would have on genomics research. In the years since, these advantages have translated to countless discoveries of novel features across a broad range of samples including multiple tumor subtypes, drastically improved genome annotation across a wide variety of species, much richer output from gene expression profiling studies, and more robust identification of potential biomarkers. It is no surprise that RNA-Seq has quickly become researchers’ preferred platform for transcriptome analysis.
One important indicator for the scientific community’s support for an application is observable trends in grant support. The figure below was generated based on public data available through the NIH website. Shown are the relative portions of funding awarded to new grants (in their first year of funding) in which RNA-Seq versus gene expression arrays were scoped. The analysis was run using the keywords “gene expression” or “gene regulation,” filtering for hits with or without the search terms “microarray” or “RNA-Seq or RNA sequencing.” The award funding returned by the RNA-Seq search filter is shown in red, while that returned by “gene expression array” filter is shown in blue. The analysis reveals that the portion of funding allocated to new, RNA-Seq vs. GEX array-inclusive grants has been trending towards RNA-Seq for the last several years, and as of 2013, constitutes the majority. These data suggest that selecting RNA-Seq versus gene expression arrays for grant proposals has increasingly provided a competitive advantage.
Also consistent with the research community’s increasing preference for RNA-Seq is the steady uptick of publications that include this type of data. The analysis below was performed using the website HighWire, searching the keyword “RNA-Seq” by publication year from 2008 through 2012. The very sharp ramp rate reflects a rapidly increasing rate of RNA-Seq adoption, as well as the reality that the benefits of RNA-Seq are no longer enjoyed by only a minority of researchers but are rather quickly becoming the accepted standard for transcriptome analysis.
The observed trends above are likely driven not only by increased recognition of RNA-Seq benefits, but also by the fact that RNA-Seq has become a practical solution for a broader range of study designs. Since it was introduced, substantial enhancements in both sample preparation chemistry and instrument output have markedly impacted data quality, speed, and affordability. For example, using Rapid Run mode on the HiSeq 2500, one can run as many as 24 samples at 25 million paired-end 75 basepair reads (50 million total reads) per sample – a depth and format at which published studies have reported the detection of novel features such as gene fusions and alternative transcripts - in less than 24 hours and in a single run. With these trends unlikely to reverse and sequencing improvements still to come, RNA-Seq will continue to empower a broadening range of study designs in the near future.