Characterization and comparison of human nuclear and cytosolic editomes

Proc Natl Acad Sci U S A. 2013 Jul 16;110(29):E2741-7. doi: 10.1073/pnas.1218884110. Epub 2013 Jul 1.


We developed a robust computational statistical framework to identify RNA editing events from RNA-Seq data with high specificity. Our approach handles several outstanding challenges of genome-wide editing analyses, including the effect of editing on read alignment and the utilization of redundant reads. By applying this framework, we characterized the nuclear and cytosolic editomes of seven human cell lines. We found that 93.8-99.2% of the editing events are A-to-G (or A-to-I). Nuclear transcriptomes contain many more editing events than cytosolic transcriptomes. Most of the sites exhibiting nucleus-specific editing are in introns or novel intergenic transcripts that are preferentially localized in the nucleus regardless of their editing status, arguing against the role of editing in nuclear retention. In contrast, many sites that exhibit cytosol-specific editing show comparable nuclear and cytosolic expression, suggesting the differential subcellular compartmentalization of the edited and the unedited alleles. We found that RNA editing is globally associated with the modification of microRNA regulation in 3' untranslated regions, whereas editing events in coding regions are rare and tend to be synonymous. Interestingly, A-to-G editing at derived alleles in the human lineage tends to result in reversion back to the ancestral forms at the RNA level. This suggests that editing can mediate RNA memory on evolutionary time-scales to maintain ancestral genetic information.

Keywords: high-throughput sequencing; nucleo-cytoplasmic localization.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Base Sequence / genetics
  • Cell Line
  • Computational Biology / methods*
  • Evolution, Molecular*
  • High-Throughput Nucleotide Sequencing / methods
  • Humans
  • MicroRNAs / genetics*
  • Models, Genetic*
  • Molecular Sequence Data
  • RNA Editing / genetics*


  • MicroRNAs