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Genome-wide quantification of ADAR adenosine-to-inosine RNA editing activity


Adenosine-to-inosine (A-to-I) RNA editing by the adenosine deaminase that acts on RNA (ADAR) enzymes is a common RNA modification, preventing false activation of the innate immune system by endogenous double-stranded RNAs. Methods for quantification of ADAR activity are sought after, due to an increasing interest in the role of ADARs in cancer and autoimmune disorders, as well as attempts to harness the ADAR enzymes for RNA engineering. Here, we present the Alu editing index (AEI), a robust and simple-to-use computational tool devised for this purpose. We describe its properties and demonstrate its superiority to current quantification methods of ADAR activity. The AEI is used to map global editing across a large dataset of healthy human samples and identify putative regulators of ADAR, as well as previously unknown factors affecting the observed Alu editing levels. These should be taken into account in future comparative studies of ADAR activity. The AEI tool is available at

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Fig. 1: The AEI.
Fig. 2: Robustness of the AEI.
Fig. 3: The human Alu editome.
Fig. 4: Global editing effects.

Data availability

The protected RNA-seq data for the GTEx project are available via access request to dbGaP accession number phs000424.v8.p2. All other datasets analyzed in this study are public and published in other papers referenced in the Methods (see URLs and Supplementary Note 5).

Code availability

The AEI implemented as a stand-alone application, including an installation code (which downloads the needed data-tables), is available on GitHub at The AEI code is freely available for noncommercial use.


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We thank O. Gabay, I. Buchumenski, A. Fieglin, Y. Pinto and other laboratory members for assistance in data analysis and many helpful comments. E.Y.L. was supported by JDRF Innovative Grant (no. 1-INO-2018-639-A-N) and the Israel Science Foundation (grant no. 1380/14). E.E. was supported by the Israel Science Foundation (nos. 2673/17 and 1945/18).

Author information




E.Y.L. and E.E. conceived the study and designed the analyses. S.H.R. designed and wrote the software and performed computational analyses. S.H.R., E.Y.L. and E.E. wrote the paper.

Corresponding author

Correspondence to Eli Eisenberg.

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The authors declare no competing interests.

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Peer review information Nicole Rusk was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Strand decision accuracy.

(a) Distribution of the noise levels (C-to-T index), calculated with and without sequencing-strand information, for paired-end stranded data (Wenric et al. 2017; Sci. Rep. 7, 17452). (b) Relative error in AEI due to the absence of strand sequencing information for the same samples. Similar to the data presented in Fig. 2a, the noise level increases substantially, but the signal is under-estimated by a few per-cent only, suggesting that our strand-selection scheme is adequate for unstranded RNA-seq data.

Supplementary Figure 2 AEI results in a clean and stable signal for all alignment procedures tested.

(a) The absolute value of AEI does change as a function of the alignment scheme, but the SNR is about the same. (b) Using STAR, AEI SNR is maximized requiring a clean alignment cutoff, 95% of matches or more. AEI and SNR are presented for n=10 test-set samples per tissue-type.

Supplementary Figure 3 Robustness to alignment procedure.

AEI values calculated from alignments by different aligners are highly correlated for all six aligner-comparisons (n=30 test set samples, Pearson’s correlations, r values are presented in each panel; all p-values < 2.2e-82).

Supplementary Figure 4 Robustness of AEI to batch effect.

AEI was calculated for two biological samples (Sample A: Universal Human Reference RNA from Stratagene and ERCC Spike-In controls. Sample B: Human Brain Reference RNA from Ambion and ERCC Spike-In controls), each split into five different libraries prepared using different protocols, and sequenced in up to seven different genomic centers (SEQC Consortium data, Nat. Biotech. 32, 903–914, 2014). The coefficient of variance over the different centers ranges between 0.011-0.055 for the ten combinations of sample and library-preparation protocol (average, 0.037), demonstrating a rather small batch effect.

Supplementary Figure 5 Editing at conserved sites is not a good measure for global editing levels.

The correlation of Alu editing index to the average editing levels across 36 mammalian conserved CDS editing sites (Pinto et al 2014; Genome Biol. 15, R5) is not very high for the 30 test set samples (Pearson correlation, R=0.48, p=0.007). Inset: Alu editing index is also more robust, showing a lower sample-to-sample variation than the average over conserved sites. CoV: Coefficient of Variation.

Supplementary Figure 6 AEI-ADARs correlation for the different GTEx tissue types.

Spearman correlation coefficient between the AEI and the expression level (RPKM) of each of the three ADAR enzymes. Only post-mortem samples are included. Number of samples per tissue type are given in Supplementary Table 5. Non-significant correlations (Benjamini-Hochberg-adjusted p>0.05; adjusted for 47 tests, for each enzyme separately) are not shown. The association between editing levels and ADARs expression is complex. The correlation coefficients vary, and for each ADAR enzyme there are many tissues that do not exhibit a significant correlation. ADAR3 is correlated to the AEI in only three of the 47 tissue types.

Supplementary Figure 7 AEI dependence on ADAR expression in three selected tissues.

To demonstrate the variability in AEI-ADAR relations, we present the raw scatter plot for three tissues. Only post-mortem samples are included. The p’s are adjusted Benjamini-Hochberg p-values, adjusted for 47 tests, for each enzyme separately. In breast mammary tissue, only ADAR1 is somewhat correlated with the AEI. On the other hand, in testis all three ADARs show a clear correlation (positive for ADAR1 and ADAR2, negative for ADAR3). In blood, ADAR2 is the only ADAR enzyme that is (very weakly) correlated with AEI.

Supplementary Figure 8 Clustering of GTEx tissues by AEI.

AEI values in different tissues of the same donor are positively correlated. Heat map of R2 Pearson tissue-tissue correlations of AEI values, for all GTEx samples originating from the same donor, shows that nearly all significant correlations are positive. Clustering analysis results in a clear separation of brain and non-brain tissues. For each tissue-pair, the number of donors for which the two tissues are available is given in Supplementary Fig. 9. Correlation was not calculated for tissue-pairs in which data was available for less than 10 donors (grey). A dash indicates non-significant correlations (Benjamini-Hochberg FDR>0.05, correcting for multiple testing over 1081 tissue-pairs). The bars on the left present the expression level of the three ADAR enzymes (left to right: ADAR1, ADAR2, ADAR3; color scale on the right). The correlation across different tissues of the same donor goes beyond ADAR expression levels, suggesting genetic or environmental components contributing to the variability in Alu editing. We verified that the clustering pattern is not explained by the availability of same-donor samples, see Supplementary Fig. 9.

Supplementary Figure 9 Clustering of GTEx tissues by data availability.

Clustering GTEx tissues according to the number of common donors available for each tissue-pair. The pattern observed does not reproduce the clustering based on AEI correlations.

Supplementary Figure 10 Most editing activity observed in mRNA-seq data occurs outside exons.

Breakdown of mismatch sites detected, editing events, and Alu coverage to exons, introns and intergenic regions, shown per tissue-type. In all tissues, the majority of editing activity comes from the lowly-covered introns and intergenic regions.

Supplementary Figure 11 Post-mortem samples exhibit a higher AEI.

Distribution of AEI values, per tissue type, split to post-mortem samples (left, rectangular boxes) versus organ donors and surgical samples (right, notched boxes). Distributions are presented as box-and-whisker plots (center line, median; box limits correspond to the first and third quartiles; whiskers, 1.5x interquartile range; points, outliers). Consistently, post-mortem AEI values are higher (Number of samples, averages and standard deviation per tissue-type per category are presented in Supplementary Table 5). All GTEx brain samples are postmortem, and therefore brain tissues are not presented in this graph.

Supplementary Figure 12 Correlation of the AEI with the expression level of three putative editing regulators.

Multiple regression analysis (Supplementary Table 8) supports the identification of EIF2S1, SF3B4 and XPO5 as putative editing regulators (Fig. 4d). Here we present the raw scatter plot showing strong expression-AEI Pearson’s correlation for the three regulators, in 97 post-mortem testis samples. The p’s are raw p-values.

Supplementary Figure 13 Editing in mouse SINEs.

Only B1 and B2 SINE elements in the mouse show a sizable editing signal, allowing for calculation of the editing index with an appreciable SNR (data presented for 6 RNA-seq samples of mouse brain and 6 samples of central nervous system from the ENCODE project; Supplementary Table 10). The bars show the mean± one standard deviation of the 12 samples. These families of repeats are used for assessing global editing in mouse.

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Supplementary Tables 1–11.

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Roth, S.H., Levanon, E.Y. & Eisenberg, E. Genome-wide quantification of ADAR adenosine-to-inosine RNA editing activity. Nat Methods 16, 1131–1138 (2019).

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