Abstract
RNA editing by ADAR (adenosine deaminase acting on RNA) is gaining an increased interest in the field of post-transcriptional regulation. Fused to an RNA-binding protein (RBP) of interest, the catalytic activity of ADAR results in A-to-I RNA edits, whose identification will determine RBP-bound RNA transcripts. However, the computational tools available for their identification and differential RNA editing statistical analysis are limited or too specialised for general-purpose usage. Here we present hyperTRIBER, a flexible suite of tools, wrapped into a convenient R package, for the detection of differential RNA editing. hyperTRIBER is applicable to complex scenarios and experimental designs, and provides a robust statistical framework allowing for the control for coverage of reads at a given base, the total expression level and other co-variates. We demonstrate the capabilities of our approach on HyperTRIBE RNA-seq data for the detection of bound RNAs by the N6-methyladenosine (m6A) reader protein ECT2 in Arabidopsis roots. We show that hyperTRIBER finds edits with a high statistical power, even where editing proportions and RNA transcript expression levels are low, together demonstrating its usability and versatility for analysing differential RNA editing.
Competing Interest Statement
The authors have declared no competing interest.
