RNA editing is a widespread post-transcriptional mechanism able to modify transcripts through insertions/deletions or base substitutions. It is prominent in mammals, in which millions of adenosines are deaminated to inosines by members of the ADAR family of enzymes. A-to-I RNA editing has a plethora of biological functions, but its detection in large-scale transcriptome datasets is still an unsolved computational task.
To this aim, researchers from the National Research Council, Italy developed REDItools, the first software package devoted to the RNA editing profiling in RNA-sequencing (RNAseq) data. It has been successfully used in human transcriptomes, proving the tissue and cell type specificity of RNA editing as well as its pervasive nature. Outcomes from large-scale REDItools analyses on human RNAseq data have been collected in the specialized REDIportal database, containing more than 4.5 million events. Here the researchers describe in detail two bioinformatic procedures based on our computational resources, REDItools and REDIportal. In the first procedure, they outline a workflow to detect RNA editing in the human cell line NA12878, for which transcriptome and whole genome data are available. In the second procedure, they show how to identify dysregulated editing at specific recoding sites in post-mortem brain samples of Huntington disease donors. On a 64-bit computer running Linux with ≥32 GB of random-access memory (RAM), both procedures should take ~76 h, using 4 to 24 cores. These protocols have been designed to investigate RNA editing in different organisms with available transcriptomic and/or genomic reads.
Differential RNA editing using REDItools and REDIportal
REDItools and REDIportal can be used in combination to identify differential RNA editing at known sites, i.e., genomic positions in which RNA editing levels are statistically different between two or more conditions. The procedure requires aligned RNAseq data and begins with the selection of known events to explore from REDIportal. Then, REDItools are launched on each RNAseq sample, providing a series of output tables (Steps 1–4, Procedure 2). All REDItools tables are parsed and compared according to metadata, returning a final list of known sites with indications of differential editing (Steps 5 and 6, Procedure 2).
Availability – Scripts to complete both procedures and a docker image are available at https://github.com/BioinfoUNIBA/REDItools