Adenosine deaminases acting on RNA (ADARs) convert adenosines to inosines in double-stranded RNA (dsRNA) in animals. Despite their importance, ADAR RNA substrates have not been mapped extensively in vivo. Here we develop irCLASH to map RNA substrates recognized by human ADARs and uncover features that determine their binding affinity and editing efficiency. We also observe a dominance of long-range interactions within ADAR substrates and analyze differences between ADAR1 and ADAR2 editing substrates. Moreover, we unexpectedly discovered that ADAR proteins bind dsRNA substrates tandemly in vivo, each with a 50-bp footprint. Using RNA duplexes recognized by ADARs as readout of pre-messenger RNA structures, we reveal distinct higher-order architectures between pre-messenger RNAs and mRNAs. Our transcriptome-wide atlas of ADAR substrates and the features governing RNA editing observed in our study will assist in the rational design of guide RNAs for ADAR-mediated RNA base editing.
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All relevant codes and data processing pipelines have been deposited to GitHub (https://github.com/SYSU-zhanglab/ irCLASH).
Gott, J. M. & Emeson, R. B. Functions and mechanisms of RNA editing. Annu. Rev. Genet. 34, 499–531 (2000).
Bass, B. L. RNA editing by adenosine deaminases that act on RNA. Annu. Rev. Biochem. 71, 817–846 (2002).
Nishikura, K. Functions and regulation of RNA editing by ADAR deaminases. Annu. Rev. Biochem. 79, 321–349 (2010).
Li, J. B. & Church, G. M. Deciphering the functions and regulation of brain-enriched A-to-I RNA editing. Nat. Neurosci. 16, 1518–1522 (2013).
Liddicoat, B. J. et al. RNA editing by ADAR1 prevents MDA5 sensing of endogenous dsRNA as nonself. Science 349, 1115–1120 (2015).
Nishikura, K. A-to-I editing of coding and non-coding RNAs by ADARs. Nat. Rev. Mol. Cell Biol. 17, 83–96 (2016).
Eisenberg, E. & Levanon, E. Y. A-to-I RNA editing - immune protector and transcriptome diversifier. Nat. Rev. Genet. 19, 473–490 (2018).
Wang, Q. et al. Stress-induced apoptosis associated with null mutation of ADAR1 RNA editing deaminase gene. J. Biol. Chem. 279, 4952–4961 (2004).
Paz-Yaacov, N. et al. Elevated RNA editing activity is a major contributor to transcriptomic diversity in tumors. Cell Reports 13, 267–276 (2015).
Ishizuka, J. J. et al. Loss of ADAR1 in tumours overcomes resistance to immune checkpoint blockade. Nature 565, 43–48 (2019).
Patterson, J. B., Thomis, D. C., Hans, S. L. & Samuel, C. E. Mechanism of interferon action: double-stranded RNA-specific adenosine deaminase from human cells is inducible by alpha and gamma interferons. Virology 210, 508–511 (1995).
Higuchi, M. et al. Point mutation in an AMPA receptor gene rescues lethality in mice deficient in the RNA-editing enzyme ADAR2. Nature 406, 78–81 (2000).
Mladenova, D. et al. Adar3 is involved in learning and memory in mice. Front. Neurosci. 12, 243 (2018).
Tan, M. H. et al. Dynamic landscape and regulation of RNA editing in mammals. Nature 550, 249–254 (2017).
Oakes, E., Anderson, A., Cohen-Gadol, A. & Hundley, H. A. Adenosine deaminase that acts on RNA 3 (ADAR3) binding to glutamate receptor subunit B pre-mRNA inhibits RNA editing in glioblastoma. J. Biol. Chem. 292, 4326–4335 (2017).
Nie, Y., Ding, L., Kao, P. N., Braun, R. & Yang, J.-H. ADAR1 interacts with NF90 through double-stranded RNA and regulates NF90-mediated gene expression independently of RNA editing. Mol. Cell. Biol. 25, 6956–6963 (2005).
Ota, H. et al. ADAR1 forms a complex with Dicer to promote microRNA processing and RNA-induced gene silencing. Cell 153, 575–589 (2013).
Wang, I. X. et al. ADAR regulates RNA editing, transcript stability, and gene expression. Cell Reports 5, 849–860 (2013).
Bahn, J. H. et al. Genomic analysis of ADAR1 binding and its involvement in multiple RNA processing pathways. Nat. Commun. 6, 6355 (2015).
Stellos, K. et al. Adenosine-to-inosine RNA editing controls cathepsin S expression in atherosclerosis by enabling HuR-mediated post-transcriptional regulation. Nat. Med. 22, 1140–1150 (2016).
Goldstein, B. et al. A-to-I RNA editing promotes developmental stage–specific gene and lncRNA expression. Genome Res. 27, 462–470 (2017).
Aktas, T. et al. DHX9 suppresses RNA processing defects originating from the Alu invasion of the human genome. Nature 544, 115–119 (2017).
Yang, C.-C. et al. ADAR1-mediated 3′ UTR editing and expression control of antiapoptosis genes fine-tunes cellular apoptosis response. Cell Death Dis. 8, e2833 (2017).
Helwak, A. & Tollervey, D. Mapping the miRNA interactome by cross-linking ligation and sequencing of hybrids (CLASH). Nat. Protoc. 9, 711 (2014).
Sugimoto, Y. et al. hiCLIP reveals the in vivo atlas of mRNA secondary structures recognized by Staufen 1. Nature 519, 491–494 (2015).
Zarnegar, B. J. et al. irCLIP platform for efficient characterization of protein-RNA interactions. Nat. Methods 13, 489–492 (2016).
Kudla, G., Granneman, S., Hahn, D., Beggs, J. D. & Tollervey, D. Cross-linking, ligation, and sequencing of hybrids reveals RNA-RNA interactions in yeast. Proc. Natl Acad. Sci. USA 108, 10010–10015 (2011).
Helwak, A., Kudla, G., Dudnakova, T. & Tollervey, D. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153, 654–665 (2013).
Wan, Y. et al. Landscape and variation of RNA secondary structure across the human transcriptome. Nature 505, 706–709 (2014).
Nishikura, K. et al. Substrate specificity of the dsRNA unwinding/modifying activity. EMBO J. 10, 3523–3532 (1991).
Lu, Z. et al. RNA duplex map in living cells reveals higher-order transcriptome structure. Cell 165, 1267–1279 (2016).
Merkle, T. et al. Precise RNA editing by recruiting endogenous ADARs with antisense oligonucleotides. Nat. Biotechnol. 37, 133–138 (2019).
Katrekar, D. et al. In vivo RNA editing of point mutations via RNA-guided adenosine deaminases. Nat. Methods 16, 239−242 (2019).
Danaee, P. et al. bpRNA: large-scale automated annotation and analysis of RNA secondary structure. Nucleic Acids Res. 46, 5381–5394 (2018).
Li, L. et al. The landscape of miRNA editing in animals and its impact on miRNA biogenesis and targeting. Genome Res. 28, 132–143 (2018).
Ramaswami, G. et al. Genetic mapping uncovers cis-regulatory landscape of RNA editing. Nat. Commun. 6, 8194 (2015).
Gu, T. et al. Genetic architectures of quantitative variation in RNA editing pathways. Genetics 202, 787–798 (2016).
Park, E. et al. Population and allelic variation of A-to-I RNA editing in human transcriptomes. Genome Biol. 18, 143 (2017).
Ramani, V., Qiu, R. & Shendure, J. High-throughput determination of RNA structure by proximity ligation. Nat. Biotechnol. 33, 980–984 (2015).
Aw, J. G. et al. In vivo mapping of eukaryotic RNA interactomes reveals principles of higher-order organization and regulation. Mol. Cell 62, 603–617 (2016).
Sharma, E., Sterne-Weiler, T., O’Hanlon, D. & Blencowe, B. J. Global mapping of human RNA-RNA interactions. Mol. Cell 62, 618–626 (2016).
Hsiao, Y. E. et al. RNA editing in nascent RNA affects pre-mRNA splicing. Genome Res. 28, 812–823 (2018).
Solomon, O. et al. RNA editing by ADAR1 leads to context-dependent transcriptome-wide changes in RNA secondary structure. Nat. Commun. 8, 1440 (2017).
Montiel-Gonzalez, M. F., Vallecillo-Viejo, I., Yudowski, G. A. & Rosenthal, J. J. C. Correction of mutations within the cystic fibrosis transmembrane conductance regulator by site-directed RNA editing. Proc. Natl Acad. Sci. USA 110, 18285–18290 (2013).
Fukuda, M. et al. Construction of a guide-RNA for site-directed RNA mutagenesis utilising intracellular A-to-I RNA editing. Sci. Reports 7, 41478 (2017).
Sinnamon, J. R. et al. Site-directed RNA repair of endogenous Mecp2 RNA in neurons. Proc. Natl Acad. Sci. USA 114, E9395–E9402 (2017).
Wettengel, J., Reautschnig, P., Geisler, S., Kahle, P. J. & Stafforst, T. Harnessing human ADAR2 for RNA repair - Recoding a PINK1 mutation rescues mitophagy. Nucleic Acids Res. 45, 2797–2808 (2017).
Vogel, P. et al. Efficient and precise editing of endogenous transcripts with SNAP-tagged ADARs. Nat. Methods 15, 535–538 (2018).
Ramaswami, G. & Li, J. B. RADAR: a rigorously annotated database of A-to-I RNA editing. Nucleic Acids Res. 42, D109–D113 (2014).
Ramaswami, G. et al. Accurate identification of human Alu and non-Alu RNA editing sites. Nat. Methods 9, 579–581 (2012).
Ramaswami, G. et al. Identifying RNA editing sites using RNA sequencing data alone. Nat. Methods 10, 128–132 (2013).
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).
Lin, Y. C. et al. Genome dynamics of the human embryonic kidney 293 lineage in response to cell biology manipulations. Nat. Commun. 5, 4767 (2014).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10−12 (2011).
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Au - Konig, J. et al. iCLIP - Transcriptome-wide mapping of protein-RNA interactions with individual nucleotide resolution. J. Vis. Exp. 50, e2638 (2011).
Rehmsmeier, M., Steffen, P., Hochsmann, M. & Giegerich, R. Fast and effective prediction of microRNA/target duplexes. RNA 10, 1507–1517 (2004).
Reuter, J. S. & Mathews, D. H. RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinformatics 11, 129 (2010).
Lorenz, R. et al. ViennaRNA Package 2.0. Algorithms Mol. Biol. 6, 26 (2011).
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).
Gruber, A. R., Findeiss, S., Washietl, S., Hofacker, I. L. & Stadler, P. F. RNAz 2.0: improved noncoding RNA detection. Pac. Symp. Biocomput. 2010, 69−79 (2009).
Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).
Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).
Zhou, K. I. et al. Regulation of co-transcriptional pre-mRNA splicing by m6A through the low-complexity protein hnRNPG. Mol. Cell 76, 70–81.e9 (2019).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
We thank T. Sun, B. Zarnegar, H. Chang and J. B. Li for their help on irCLASH method development, Q. Li and J. B. Li for sharing the GTEx A-to-I RNA-editing call data and critical discussion of the project. This study was supported by grants from National Key Research and Development Program of China (2018YFC1003100 to R.Z.), Guangdong Major Science and Technology Projects (2017B020226002 to R.Z.), Guangdong Innovative and Entrepreneurial Research Team Program (2016ZT06S638 to R.Z.), National Natural Science Foundation of China (31571341 and 91631108 to R.Z., 31900437 to Y.L.S.), and the Fundamental Research Funds for the Central Universities (R.Z. and Y.L.S.). The GTEx project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (commonfund.nih.gov/GTEx). Additional funds were provided by the National Cancer Institute (NCI), National Human Genome Research Institute (NHGRI), National Heart, Lung, and Blood Institute (NHLBI), National Institute on Drug Abuse (NIDA), National Institute of Mental Health (NIMH), and National Institute of Neurological Disorders and Stroke (NINDS). Donors were enrolled at Biospecimen Source Sites funded by NCISAIC-Frederick, Inc. (SAIC-F) subcontracts to the National Disease Research Interchange (10XS170) and Roswell Park Cancer Institute (10XS171). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to The Broad Institute, Inc. Biorepository operations were funded through a SAIC-F subcontract to Van Andel Institute (10ST1035). Additional data repository and project management were provided by SAIC-F (HHSN261200800001E). The Brain Bank was supported by a supplement to University of Miami grant DA006227.
Sun Yat-Sen University has filed a provisional patent application on the high-affinity substrates identified, and R.Z., W.Y. and Y.S. are named as the inventors.
Peer review information Anke Sparmann 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.
Extended Data Fig. 1 The subcellular location and editing activity of the exogenously expressed ADARs.
a, IB: western blot shows the over-expressed FLAG-tagged ADARs in HEK293 cells. IP: immunoprecipitation followed by western blot confirmed the effectiveness of anti-FLAG antibody. b, Top: example of western blot of endogenously and exogenously expressed ADAR1 protein in HEK293 cells. Bottom: quantifications of western blot with odyssey infrared imaging system. Values represent mean ± s.d. (n = 3 independent experiments). c, The immunofluorescence staining shows the localization of FLAG-tagged ADARs. Scale bar, 20 µm. d, Percentages of different types of RNA variants. Only a group of RNA variants with A-to-G/T-to-C sites that comprised over 80% of all mismatches were used to compile the master list of RNA editing sites. For example, in the control sample (empty vector), only A-to-G sites of Alu and repetitive non-Alu variants were considered as genuine RNA editing sites. Replicates were merged for variant call. e, Boxplot shows the overall editing levels in ADAR overexpressed cells or control cells (Control, n = 47,527; ADAR1, n = 58,157; ADAR2, n = 91,305; ADAR3, n = 73,086). Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. The significant difference between ADAR overexpressed cells and control cells was determined using two-sided Mann-Whitney U test. The sites covered by at least 30 reads were analyzed. Source data
a, The computational pipeline to identify RNA duplex. b, A diagram describing how to rescue “swapped” hybrid reads. c, Correlation analysis of the non-hybrid read counts on each gene between the replicates. For each gene, the transcript with the longest length was used for analysis. In each sample, the read count of each transcript was normalized by the total mapped read count.
a, The composition of sequence context in the arms of hybrid reads. Notably, the proportions of Alus bound by ADARs may be underestimated in our analysis, since many Alu reads were mapped to multiple positions in the transcripts and removed from the analysis. b, The proportions of Alu repeat in 5′ UTR, CDS, 3′ UTR and intron. Y-axis is defined as the relative proportion of Alu repeat in a genic region divided by the relative length of a genic region across the pre-mRNAs. c, Dot plots showing the minimum free energies of each of the hybrid reads calculated using different RNA prediction programs. Spearman’s correlation coefficient (ρ) was shown. d, Comparison of the minimum free energies of hybridization between the two arms of hybrid reads and randomly repositioned sequences using two additional RNA structure prediction programs (ADAR1, n = 5,783; ADAR2, n = 13,831; ADAR3, n = 13520). Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. Data analysis was performed as described in Fig. 1f. Source data
a, b, Metaprofiles of the base-pairing status of ADAR substrates (a) or non-Alu ADAR substrates (b). Only substrates with two arms located in the same gene were used for analysis. Position 0 means the center of substrates, and the 40 bp upstream and downstream were analyzed. Percentages of paired bases were calculated in a 5 bp sliding window. As a control, the sequence of one arm was shuffled 10 times, and each time the shuffled sequence and the sequence of the other arm were folded using RNAhybrid. c, Comparison of the percentage of substrates with editing sites in the second arm of an editing substrate. Control means the percentage of the repositioned second arms with editing sites. To include editing sites around the substrates for analysis, substrates and flanking 50 bp regions were selected. d, A species tree and the definition of editing sites in different conservation groups. N, any base; B, C or G or T. e, Comparison of the sequence conservation of substrates containing different groups of editing sites as defined in d. Two arms of a substrate were joined and the distance was calculated using MEGA7 software with Maximum Composite Likelihood model. Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. f, Comparison of the secondary structure conservation of substrates containing different groups of editing sites as defined in d. Two arms of a substrate were joined and the structure conservation index was calculated using RNAz software. Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. g, Distributions of interval distance of ADAR Alu substrates. Alu substrates were defined as substrates that are overlapped with Alus in the genome. h, The relationship between interval distances and arm lengths of ADAR substrates. A best-fit linear regression line was shown with Pearson correlation coefficient and p value. Source data
a, Validation of RNA editing substrates. Substrate 1–6 and 7–16 are the substrates of ADAR1 and ADAR2, respectively. The two arms of a substrate were synthesized and subcloned into pSilencer-2.1-U6-hygro vector. Following co-transfection with a plasmid encoding either ADAR1 or ADAR2 into HEK293 cells, RNA was isolated, reverse transcribed, amplified with target-specific primers, and submitted to sanger sequencing. For each substrate, editing status of a known editing site is selected to be shown. Depending on the sequencing primer used, the edited sites were identified as A+G or T+C peaks. b, Schematics of sequence-specific RNA base editing. adRNA comprises a specificity domain (complementary to RAB7A 3′ UTR sequence with a mismatched cytidine opposite to the target adenosine) and an ADAR-recruiting domain (duplexes derived from the high-affinity substrates of ADAR1 or GluR2 transcript). dsRBD, double-stranded RNA-binding domain of ADAR1 or ADAR2; A* or I*, adenosine or inosine base at target site, respectively. c, The predicted RNA secondary structures of selected high-affinity substrates. The sequences selected as adRNA ADAR-recruiting domain (highlighted in red) and their flanking regions were shown.
a, The cumulative distributions of the distances between the substrates and the nearest editing sites. Editing-dependent (ED) substrates were defined as the ones containing editing sites. Editing-independent (EI) substrates were defined as the ones without editing sites within 1 kb of both arms. b, Comparison of the repetitive sequence features between ED and EI substrates. c, Comparison of the minimum free energies of hybridization between the two arms of hybrid reads and randomly repositioned sequences. ED and EI substrates were analyzed, respectively. Reads from mRNAs and long non-coding RNAs were used. Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. P-values were calculated using two-sided Mann-Whitney U test. d, Comparison of the arm lengths between ED and EI substrates. The significant difference between different groups of substrates was determined using two-sided Mann-Whitney U test. e, Comparison of minimum free energies between ED and EI substrates. To control the effect of arm length, 15 bp upstream and downstream sequences of the center of each arm were used in this analysis. P-values were calculated using the Mann-Whitney U test. f, Comparison of ADAR binding affinities between ED and EI substrates. The significant difference between different groups of substrates was determined using two-sided Mann-Whitney U test. Source data
a, The number of crosslinking sites across ADAR substrates. b, The GC content across ADAR substrates. Only the positions with > 100 (ADAR1) or > 200 (ADAR2 and ADAR3) substrates were plotted. c, Sequence context around the peaks. 10 bp upstream and downstream of the peak summit were plotted with WebLogo v.3.5. d, The distribution of editing sites across RNA editing substrates. Position 0 is the center of the arm.
a, The distributions of editing levels in GTEx samples. Editing levels for the editing sites that are located within the ADAR substrates and flanking 100 bp region were plotted in each tissue. Editing sites were ranked by their mean editing levels in a tissue. The number of editing sites in each tissue is indicated. b, The number of edQTL associations shared by tissues. c, Boxplot showing the relationship between an edQTL SNP in the RPL37A gene and the associated editing site in the non-brain tissues. Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. d, The number of associated editing pairs shared by tissues.
Extended Data Fig. 9 Comparison of RNA substrates bound by endogenously and exogenously expressed ADAR1.
a, Comparison of sequence context in the arms of hybrid reads bound by endogenously expressed ADAR1 (EnEA) and exogenously expressed ADAR1 (ExEA). b, The proportion of the genic locations of the uniquely annotated arms of EnEA and ExEA hybrid reads. c, Comparison of the minimum free energies of hybridization between the two arms of EnEA hybrid reads and randomly repositioned sequences (n = 25,537). Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. Data analysis was performed as described in Fig. 1f. d, The cumulative distributions of the distance between EnEA hybrid arms and the nearest editing site or between repositioned arms and the nearest editing site. P-values were calculated using the Mann-Whitney U test. e, Median normalized PARS scores in EnEA duplexes and flanking regions. Data analysis was performed as described in Fig. 1h. f, Metaprofiles of the base-pairing status of EnEA and ExEA substrates. Data analysis was performed as described in Extended Data Fig. 4a. g, Distributions of arm lengths of EnEA and ExEA substrates. h, Distributions of interval distances of EnEA and ExEA substrates. i, The mean number of crosslinking events along EnEA substrates. Only editing-dependent substrates were plotted. Editing-independent substrates were not shown because of the limited positions that pass the coverage filter. Position 0 is the 5’start of the arm. A 5 nt sliding window was used and only positions with ≥ 10 crosslinking sites were plotted. j, The mean editing level and number of crosslinking events across RNA editing substrates. Position 0 is the center of the arm. A 5 nt sliding window was used. Editing levels in HEK293 cells were used for analysis. Positions with < 60 editing sites were not shown. k, The percentage of editing sites in different structural features of EnEA substrates. l, The proportions of different bulge types in EnEA substrates. m, The percentage and mean representative editing level of editing sites in internal loops of EnEA substrates. Loop types with length < 3 nt and with sites ≥ 5 were shown. The mean representative editing levels for each type were shown as diamond symbols. The edited “A” was highlighted in red. The control percentages were obtained as in k. Source data
a, Two editing substrates were selected and either the upstream or downstream region of the edited sites was deleted, separately. Following co-transfection with a plasmid encoding ADAR1 into HEK293 cells, RNA was isolated, reverse transcribed, amplified with target-specific primers, and submitted to sanger sequencing. b, Editing level measurement derived from sanger sequencing. Allelic ratio was measured using QSVanalyser. Values represent mean ± s.e.m. (n = 3 independent experiments). Source data
Supplementary Notes 1 and 2.
Comparison of CLASH, hiCLIP and irCLASH methods.
Library list and the mapping information.
Annotation of hybrid reads.
Annotation of ADAR substrates.
Sanger sequencing verification of base editing.
edQTL SNP information.
Statistical source data
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Song, Y., Yang, W., Fu, Q. et al. irCLASH reveals RNA substrates recognized by human ADARs. Nat Struct Mol Biol 27, 351–362 (2020). https://doi.org/10.1038/s41594-020-0398-4
Nature Structural & Molecular Biology (2020)