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Identifying RNA editing sites using RNA sequencing data alone

Abstract

We show that RNA editing sites can be called with high confidence using RNA sequencing data from multiple samples across either individuals or species, without the need for matched genomic DNA sequence. We identified many previously unidentified editing sites in both humans and Drosophila; our results nearly double the known number of human protein recoding events. We also found that human genes harboring conserved editing sites within Alu repeats are enriched for neuronal functions.

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Figure 1: Identification and validation of A-to-I RNA editing sites using RNA-seq data from human brain tissues.
Figure 2: Accurate identification of RNA editing sites in the primate lineage.
Figure 3: RNA editing site identification in Drosophila.

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Acknowledgements

We thank E. Levanon, A. Fire and members of the Li lab for constructive discussions, S. Blair for assistance with data set curation, and modENCODE Consortium for the use of Drosophila RNA-seq data sets. G.R. and P.D. were supported by the Stanford Genome Training Program funded by the US National Institutes of Health. R.Z. was partially supported by a Dean's fellowship from Stanford University School of Medicine. R.P. was supported by a fellowship from the German Academic Exchange Service. This work was supported by startup funds from Stanford University Department of Genetics and Ellison Medical Foundation (to J.B.L.) and Medical Research Council, UK (to M.A.O.).

Author information

Authors and Affiliations

Authors

Contributions

G.R. and R.Z. performed computational analyses with help from R.P., P.D. and J.B.L.; R.Z. and G.R. carried out the validation experiments; L.P.K. and M.A.O. generated RNA-seq data for wild-type and Adar5G1 flies; and G.R., R.Z. and J.B.L. wrote the paper with input from other authors.

Corresponding author

Correspondence to Jin Billy Li.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–19, Supplementary Tables 1–10, Supplementary Notes 1–5 (PDF 2289 kb)

Supplementary Data 1

A-to-G mismatches identified in lymphoblastoid cell lines (XLSX 23087 kb)

Supplementary Data 2

Non-A-to-G mismatches identified in lymphoblastoid cell lines (XLSX 8502 kb)

Supplementary Data 3

A-to-G mismatches identified in human brain tissues (XLSX 50144 kb)

Supplementary Data 4

Non-A-to-G mismatches identified in human brain tissues (XLSX 11477 kb)

Supplementary Data 5

A-to-G mismatches identified in other human tissues (XLSX 31524 kb)

Supplementary Data 6

Non-A-to-G mismatches identified in other human tissues (XLSX 9841 kb)

Supplementary Data 7

A-to-G mismatches identified in primate cross-species comparison (XLSX 1479 kb)

Supplementary Data 8

Non-A-to-G mismatches identified in primate cross-species comparison (XLSX 160 kb)

Supplementary Data 9

A-to-G mismatches identified in Drosophila cross-species comparison (XLSX 70 kb)

Supplementary Data 10

Non-A-to-G mismatches identified in Drosophila cross-species comparison (XLSX 35 kb)

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Ramaswami, G., Zhang, R., Piskol, R. et al. Identifying RNA editing sites using RNA sequencing data alone. Nat Methods 10, 128–132 (2013). https://doi.org/10.1038/nmeth.2330

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