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

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 vs. control) or covary with biological factors such as age. However, RNA-seq has technical features that incumbent tests (e.g. t-test, 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, REDITs (RNA editing tests), 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 https://github.com/gxiaolab/REDITs. The repository also provides a code example for leveraging parallelization using multiple cores.


SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.