RNA studies under fire
High-throughput RNA sequencing has yielded some unexpected results in the past few years — including some that seem to rewrite conventional wisdom in genetics. But a few of those findings are now being challenged, as computational biologists warn of the statistical pitfalls that can lurk in data-intensive studies.
For their study, Dulac and Gregg used high-throughput RNA sequencing to search mouse RNA for single nucleotide polymorphisms (SNPs) — one-letter variations in genetic sequence. The researchers then asked whether the SNPs they found for each gene could be traced to one or to both parents. If the SNPs were encoded mainly by one parent’s copy of the gene, the team concluded that the gene was imprinted (see ‘The silence of the genes’).
…The debate has implications for any sequencing-based study that requires statisticians to identify rare genetic phenomena using relatively new methods. “If you don’t deal with the analytical details very carefully, you’re going to get into trouble because of the low signal-to-noise ratio” in these types of experiments, says Jin Billy Li, a genomicist at Stanford University who was one of the critics of Cheung’s RNA-editing paper.