Predicting RNA hyper-editing with a novel tool when unambiguous alignment is impossible

Repetitive elements are now known to have relevant cellular functions, including self-complementary sequences that form double stranded (ds) RNA. There are numerous pathways that determine the fate of endogenous dsRNA, and misregulation of endogenous dsRNA is a driver of autoimmune disease, particularly in the brain. Unfortunately, the alignment of high-throughput, short-read sequences to repeat elements poses a dilemma: Such sequences may align equally well to multiple genomic locations. In order to differentiate repeat elements, current alignment methods depend on sequence variation in the reference genome. Reads are discarded when no such variations are present. However, RNA hyper-editing, a possible fate for dsRNA, introduces enough variation to distinguish between repeats that are otherwise identical.

To take advantage of this variation, we developed a new algorithm, RepProfile, that simultaneously aligns reads and predicts novel variations. RepProfile accurately aligns hyper-edited reads that other methods discard. In particular we predict hyper-editing of Drosophila melanogaster repeat elements in vivo at levels previously described only in vitro, and provide validation by Sanger sequencing sixty-two individual cloned sequences. We find that hyper-editing is concentrated in genes involved in cell-cell communication at the synapse, including some that are associated with neurodegeneration. We also find that hyper-editing tends to occur in short runs.

Previous studies of RNA hyper-editing discarded ambiguously aligned reads, ignoring hyper-editing in long, perfect dsRNA - the perfect substrate for hyper-editing. We provide a method that simulation and Sanger validation show accurately predicts such RNA editing, yielding a superior picture of hyper-editing.