Prediction of constitutive A-to-I editing sites from human transcriptomes in the absence of genomic sequences
- Equal contributors
1 Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
2 State Key Laboratory of Molecular Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
Citation and License
BMC Genomics 2013, 14:206 doi:10.1186/1471-2164-14-206Published: 27 March 2013
Adenosine-to-inosine (A-to-I) RNA editing is recognized as a cellular mechanism for generating both RNA and protein diversity. Inosine base pairs with cytidine during reverse transcription and therefore appears as guanosine during sequencing of cDNA. Current approaches of RNA editing identification largely depend on the comparison between transcriptomes and genomic DNA (gDNA) sequencing datasets from the same individuals, and it has been challenging to identify editing candidates from transcriptomes in the absence of gDNA information.
We have developed a new strategy to accurately predict constitutive RNA editing sites from publicly available human RNA-seq datasets in the absence of relevant genomic sequences. Our approach establishes new parameters to increase the ability to map mismatches and to minimize sequencing/mapping errors and unreported genome variations. We identified 695 novel constitutive A-to-I editing sites that appear in clusters (named “editing boxes”) in multiple samples and which exhibit spatial and dynamic regulation across human tissues. Some of these editing boxes are enriched in non-repetitive regions lacking inverted repeat structures and contain an extremely high conversion frequency of As to Is. We validated a number of editing boxes in multiple human cell lines and confirmed that ADAR1 is responsible for the observed promiscuous editing events in non-repetitive regions, further expanding our knowledge of the catalytic substrate of A-to-I RNA editing by ADAR enzymes.
The approach we present here provides a novel way of identifying A-to-I RNA editing events by analyzing only RNA-seq datasets. This method has allowed us to gain new insights into RNA editing and should also aid in the identification of more constitutive A-to-I editing sites from additional transcriptomes.