Quantifying RNA Editing in Deep Transcriptome Datasets

Claudio Lo Giudice, Domenico Alessandro Silvestris, Shalom Hillel Roth, Eli Eisenberg, Graziano Pesole, Angela Gallo, Ernesto Picardi

Research output: Contribution to journalArticlepeer-review


Massive transcriptome sequencing through the RNAseq technology has enabled quantitative transcriptome-wide investigation of co-/post-transcriptional mechanisms such as alternative splicing and RNA editing. The latter is abundant in human transcriptomes in which million adenosines are deaminated into inosines by the ADAR enzymes. RNA editing modulates the innate immune response and its deregulation has been associated with different human diseases including autoimmune and inflammatory pathologies, neurodegenerative and psychiatric disorders, and tumors. Accurate profiling of RNA editing using deep transcriptome data is still a challenge, and the results depend strongly on processing and alignment steps taken. Accurate calling of the inosinome repertoire, however, is required to reliably quantify RNA editing and, in turn, investigate its biological and functional role across multiple samples. Using real RNAseq data, we demonstrate the impact of different bioinformatics steps on RNA editing detection and describe the main metrics to quantify its level of activity.

Original languageEnglish
Article number194
JournalFrontiers in Genetics
StatePublished - 6 Mar 2020


  • Alu editing index
  • RNA editing
  • RNAseq
  • deep sequencing
  • transcriptome


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