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

Bioinformatics. 2020 May 1;36(9):2796-2804. doi: 10.1093/bioinformatics/btaa066.


Motivation: 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.

Results: 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.

Availability and implementation: REDITs are implemented as functions in R and freely available for download at The repository also provides a code example for leveraging parallelization using multiple cores.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Base Sequence
  • RNA Editing*
  • RNA* / genetics
  • Sequence Analysis, RNA
  • Software


  • RNA