It isn’t always a good thing to get the end of something, but researchers say that by grabbing onto 3’ ends of transcripts and tagging them, DeepSAGE digs deep, getting low-level transcripts and getting new info on their “back-ends.” That could be important for figuring out exactly how gene expression changes with disease.
“It was already known that variants that are associated with diseases often influence gene expression,” says Daria Zhernakova, who is at the University of Groningen, The Netherlands. “In this work, we have shown that some of these variants are also affecting polyadenylation, adding a new layer of complexity to our knowledge of disease mechanisms.”
The Dutch team used DeepSAGE, a next-generation sequencing method, which captures information from 3’ ends, to see how genetic variants, such as SNPs and CNVs, affect gene expression. They used this method on blood samples from 94 healthy people.
Other researchers had used RNA-seq to study expression quantitative trait loci (eQTLs), but that method doesn’t have good coverage at 3’ ends.
The team also combined the two methods for part of the analysis. Here’s some of what they found in this new study:
- They identified about a thousand unique cis-regulated tags. They found more eQTLs with DeepSAGE (and more with low expression levels) than with microarrays, the old standby method.
- DeepSAGE detected more non-coding (like lncRNAs and antisense genes) and novel transcripts than microarrays. They also got eQTLs in transposable elements.
- They saw changes in the use of alternative polyadenylation sites that were genotype-dependent with DeepSAGE.
- When the researchers combined DeepSAGE and RNA-seq data in a meta-analysis, they saw that the two methods were complementary.
Dig in to all the details at PLOS Genetics, June 2013.