DNA microarrays vs RNAseq — The winner and new heavyweight champion is?... It’s a draw.
In the past year or so there have been several articles stating that the death of microarray technology is growing near. These proclamations are due to the more recently introduced methodology referred to as RNAseq. At first glance I wrote these claims off as being silly and premature. Over time though I am starting to appreciate that while the claim is still clearly wrong, the issue isn’t about technology displacement at all. My group works on a wide variety of gene expression problems ranging from the simple in vitro microbial gene expression studies to problems involving metagenomic samples of enormous complexity (http://pfgrc.jcvi.org). In my experience, the decision of whether to use DNA microarrays or RNAseq seems straight-forward and unambiguous. In reality the two technologies couldn’t be more complementary. Given the simple in vitro gene expression study as an example, the low cost, short turn-around time, exceptional quantitative accuracy and ease of data generation all make the glass slide microarray the clear choice.
About three years ago our laboratory began thinking about how to examine gene expression of pathogenic bacteria in the context of host infection. The challenge here is related to assay sensitivity since any RNA preparation derived from such an infection will yield host RNAs in an abundance 100 to 1000 times greater than that obtained from the infectious agent. Labeled RNAs from such an experiment would yield little useful information about the bacterial gene expression using standard DNA microarray procedures. This represents a clear case for RNAseq. The bewildering number of sequence reads we have come to enjoy from NextGen sequencing platforms is only going to get better. The extra bonus of applying RNAseq is that both the host and infectious agent can be profiled at the same time. There are still many technical problems to work out for routine use of RNAseq, such as effective rRNA removal and the development of appropriate data analysis tools, but the effort required seems quite justifiable.
I can think of only one application that is beginning to take on momentum where an investigator may truly ponder which strategy makes the most sense to apply. The approach is one that mimics EST sequencing as a means of defining genes and gene limits. Our ability to properly identify coding DNA sequences (CDS) in genomes ranges from, very good to relatively poor, depending on the genome in question. Members of the parasite research community, to name one, have struggled with this problem often. Generally speaking, substantial over-calling of genes occurs making it difficult for scientists to begin down the path of functional characterization of their favorite genome. We have worked with such groups recently to provide an independent means of substantiating gene calls via evidence of RNA expression. The design of such studies involves generating RNAs from a wide variety of experimental conditions to enhance the frequency for evidence based gene calls. DNA microarrays designed as a low or high density tiling array can be acquired at a reasonable cost and with good experimental outcomes. The case for applying RNAseq rests on the increased ability to detect transcripts that are expressed at low levels that defy routine detection using DNA microarrays.
In summary, I find very few instances where one might reasonably stop to wonder which technology are best suited for the biological/technical problem at hand. When sensitivity isn’t limiting, use DNA microarrays. When sensitivity is everything, look toward the short read sequencing technologies. In the end it turns out that it wasn’t really a contest at all. We should all feel fortunate that each strategy has its appropriate time and place for use. Those researchers, like myself, that have invested much time and effort working with DNA microarrays have nothing to fear, we just have more options now. This is a good thing to say the least. Most of our gene expression work is supported through a contract from NIAID to the PFGRC under contract N01-A115447.