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Sep
6
by Jeffrey M. Perkel
When you get right down to it, the difference between a skin cell, say, and a kidney cell, is a matter of gene expression. All cells have the same DNA; it’s the proteins they produce that define their behavior. The instructions to build those proteins are carried by RNA, and researchers have long recognized the value of probing RNAs to gain insight into the expression differences that define tissues, developmental stages and disease.
RNA-Seq vs microarray
Just a few years ago, researchers who wanted to get a 30,000-foot overview of the transcriptional state of a cell—the so-called “transcriptome,” or cellular RNA content—had one option: DNA microarrays. But the rise of next-generation DNA sequencing (NGS) technologies, coupled with plummeting prices, has shifted the technology landscape.
Today, transcriptome analysis is performed most commonly using an NGS application called RNA-seq, in which some RNA pool—total RNA, messenger RNA or noncoding RNA, for instance—is reverse-transcribed into cDNA, converted into a sequencing library, sequenced and analyzed.
The technique offers several advantages over DNA microarrays, says John Marioni, research group leader at the European Bioinformatics Institute on the Wellcome Trust Genome Sciences Campus in Cambridge, UK Most obviously, RNA-seq works even for species for which no reference genome or DNA microarray exists. Microarrays cannot be built without at least a partial genome sequence and some understanding of what sequences the researcher is looking for. And microarray manufacturers produce chips mostly for the classic laboratory models—Drosophila and C. elegans, mouse and rat.
“If you want to look at organisms way down the evolutionary ladder, like sponges or marine mollusks, there’s no way to do that with arrays,” Marioni says.
In contrast, RNA-seq is unbiased. It reads whatever cDNA is in the sample, regardless of whether researchers have seen that DNA before or not.
Marioni, a statistician and computational biologist who develops tools for analyzing RNA-seq data, has been using the technique since 2008. This year he co-authored a paper in which he applied it to genetic differences and variation among 16 mammalian species, including 11 non-human primates, of which seven had “little or no genomic data . . . previously available.” [1]
His goal, he says, is to create tools that can turn raw data into biological insights. “The idea is you get counts of transcripts from primate livers, and you want to develop models to take in count data and get biological inferences out, so you can know these are not chance events and there is meaningful data in the numbers you’ve analyzed,” Marioni explains.
RNA-seq also offers other advantages over microarrays. It offers a wider dynamic range than microarrays and generally can pick up less abundant transcripts. And unlike microarrays, which report relative expression values based on fluorescence intensity, RNA-seq can report those abundances absolutely, because it counts the transcripts that it reads. Finally, RNA-seq can reveal transcript structure and splicing and can even identify novel isoforms, gene fusions, allele-specific variants and the like.
Naturally, given its growing popularity, tools for performing RNA-seq are widely available, and more are coming to market. Whether it’s sample preparation on the front-end or bioinformatics analysis on the back-end, you’re sure to find a tool to fit your needs.
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I wouldn’t necessarily say RNA-Seq is unbiased… there’s definitely some bias due to transcript length and GC content..