Comprehensive analysis of RNA-Seq data reveals extensive RNA editing in a human transcriptome

Journal name:
Nature Biotechnology
Year published:
Published online


RNA editing is a post-transcriptional event that recodes hereditary information. Here we describe a comprehensive profile of the RNA editome of a male Han Chinese individual based on analysis of ~767 million sequencing reads from poly(A)+, poly(A) and small RNA samples. We developed a computational pipeline that carefully controls for false positives while calling RNA editing events from genome and whole-transcriptome data of the same individual. We identified 22,688 RNA editing events in noncoding genes and introns, untranslated regions and coding sequences of protein-coding genes. Most changes (~93%) converted A to I(G), consistent with known editing mechanisms based on adenosine deaminase acting on RNA (ADAR). We also found evidence of other types of nucleotide changes; however, these were validated at lower rates. We found 44 editing sites in microRNAs (miRNAs), suggesting a potential link between RNA editing and miRNA-mediated regulation. Our approach facilitates large-scale studies to profile and compare editomes across a wide range of samples.

At a glance


  1. High-throughput sequencing and bioinformatics for profiling the RNA editome of an individual.
    Figure 1: High-throughput sequencing and bioinformatics for profiling the RNA editome of an individual.

    (a) Schematic depiction of the experimental design of the study. Total RNA was isolated from the lymphoblastoid cell line (LCL) derived from a male Han Chinese individual (YH) and further processed into three different libraries for high-throughput whole-transcriptome sequencing. (b) Overview of algorithm for calling RNA editing sites or RNA-centric SNVs. The pipeline takes raw sequencing reads as input, filters them on the basis of several stringent criteria and outputs the inferred variants that are to be analyzed further. (c) Accuracy and sensitivity of the pipeline for each given filter stage. As successive filters were applied to simulated reads (harboring Aright arrowG variants at known positions categorized in DARNED; see Methods), the performance of the approach was evaluated. Accuracy is defined as the false discovery rate (FDR; dotted lines). Sensitivity (SN; gray bars) equals positive calling rate of the simulated editing sites. Notably, the pipeline yielded candidates at a high sensitivity while significantly eliminating the false positives. (d) Validation of inferred editing sites from RNA-Seq by Sanger sequencing. Sequencing chromatogram traces from two exemplary gene loci, CLEC2D and PLEKHA9, are shown. The editing positions (located in the intron of CLEC2D and coding sequence of PLEKHA9) are highlighted by yellow shading. Note the clustering of editing sites in the CLEC2D transcript. Top trace is genomic DNA (gDNA), bottom trace cDNA.

  2. Characterization of the editing sites in poly(A)+ and poly(A)- RNAs.
    Figure 2: Characterization of the editing sites in poly(A)+ and poly(A) RNAs.

    (a,b) Distribution of editing sites in the poly(A)+ RNAs (left) and poly(A) RNAs (right) transcriptome by editing type (a) or incidence per unit length (Mbp) (b) of the indicated structure classes. 'Unknown' denotes editing sites located in regions with conflicting annotations in the database. (ce) Distribution of RNA editing levels in poly(A)+ RNAs (c), the protein-coding (CDS) region of mRNA (d) or poly(A) RNAs (e). (f) Two examples of noncoding RNA genes with multiple edits (Jpx/NR_024582, top; Malat1/NR_002819, bottom) that, to our knowledge, have not been reported to show evidence of RNA editing. RNA editing sites identified from YH RNA-Seq data are highlighted by red boxes. Green boxes denote editing sites from DARNED. (g,h) Frequency of nucleotides in the flanking sequences (10 bp both upstream and downstream) of the editing loci in poly(A)+ RNA (g) andin poly(A) RNA (h). The editing loci are denoted as nucleotide position 0. (i) The conservation of editing sites in poly(A)+ RNA (left) and poly(A) RNA (right). Total sites as well as the non-Alu component of the data set were analyzed independently, as indicated. Shown are fractions of all, Aright arrowG or non-Aright arrowG sites that are located in the most evolutionarily conserved regions (score ≥200 in the UCSC conservation table). Statistical significance of the Aright arrowG versus non-Aright arrowG comparisons was calculated by the Fisher's exact test.

  3. The overlap of RNA editing sites between different data sets.
    Figure 3: The overlap of RNA editing sites between different data sets.

    (a) Extent of overlap in editing sites between data sets in terms of nucleotide position ('site') and corresponding gene ('gene'). The YH data were compared with those of DARNED and the breast cancer RNA-Seq study. Proportions of sites and genes that are unique or common between data sets are shown. (b,c) Examples of genes with multiple editing sites. Distribution of sites (nucleotide positions indicated on the right) in an mRNA gene (ARPC2) (b) and a noncoding gene (SLC35E3) (c) is shown.

  4. Functional link of RNA editing to other post-transcriptional events.
    Figure 4: Functional link of RNA editing to other post-transcriptional events.

    (a) Distribution of editing sites relative to miRNA target sites in the 3′-UTR and possible consequence of RNA editing. In total, 1,905 possible miRNA target sites were predicted in the 3′-UTRs. RNA editing may disrupt or switch miRNA recognition if editing occurs at nucleotides in the miRNA seed region (“altered”). Editing on non-miRNA target sites (“no match”) may generate new miRNA targets (“new targets”). (b) Distribution of RNA edits identified from miRNAs. (c,d) Examples of miRNA species that harbor RNA edits. The most abundant perfect-match and single-mismatch reads from the hsa-mir-200b (c) and hsa-mir-548o (d) loci support Aright arrowI editing in the seed region and Gright arrowA editing, respectively.

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Author information

  1. These authors contributed equally to this work.

    • Zhiyu Peng,
    • Yanbing Cheng &
    • Bertrand Chin-Ming Tan


  1. BGI-Shenzhen, Shenzhen, China.

    • Zhiyu Peng,
    • Yanbing Cheng,
    • Lin Kang,
    • Zhijian Tian,
    • Yuankun Zhu,
    • Wenwei Zhang,
    • Yu Liang,
    • Xueda Hu,
    • Xuemei Tan,
    • Jing Guo,
    • Zirui Dong,
    • Yan Liang,
    • Li Bao &
    • Jun Wang
  2. Department of Biomedical Sciences and Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Tao-Yuan, Taiwan.

    • Bertrand Chin-Ming Tan
  3. The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.

    • Jun Wang
  4. Department of Biology, University of Copenhagen, Copenhagen, Denmark.

    • Jun Wang


Z.P., B.C.-M.T. and J.W. conceived and designed the experiment; Z.P., Y.C., B.C.-M.T., L.K. and Y.Z. performed data analysis and informatics; Z.T., Yu L., X.H., Yan L. and L.B. carried out sample preparation and sequencing experiments; Y.C., Z.T., W.Z., X.T., J.G. and Z.D. designed and executed experimental validation; Z.P., B.C.-M.T. and J.W. wrote the manuscript.

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The authors declare no competing financial interests.

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