Statistical inference of differential RNA-editing sites from RNA-sequencing data by hierarchical modeling

RNA-sequencing (RNA-seq) enables global identification of RNA-editing sites in biological systems and disease. A salient step in many studies is to identify editing sites that statistically associate with treatment (e.g. case versus control) or covary with biological factors, such as age. However, RNA-seq has technical features that incumbent tests (e.g. t-test and linear regression) do not consider, which can lead to false positives and false negatives.

In this study, we demonstrate the limitations of currently used tests and introduce the method, RNA-editing tests (REDITs), a suite of tests that employ beta-binomial models to identify differential RNA editing. The tests in REDITs have higher sensitivity than other tests, while also maintaining the type I error (false positive) rate at the nominal level. Applied to the GTEx dataset, we unveil RNA-editing changes associated with age and gender, and differential recoding profiles between brain regions.

REDITs are implemented as functions in R and freely available for download at https://github.com/gxiaolab/REDITs. The repository also provides a code example for leveraging parallelization using multiple cores.