Investigating RNA editing in deep transcriptome datasets with REDItools and REDIportal

Abstract

RNA editing is a widespread post-transcriptional mechanism able to modify transcripts through insertions/deletions or base substitutions. It is prominent in mammals, in which millions of adenosines are deaminated to inosines by members of the ADAR family of enzymes. A-to-I RNA editing has a plethora of biological functions, but its detection in large-scale transcriptome datasets is still an unsolved computational task. To this aim, we developed REDItools, the first software package devoted to the RNA editing profiling in RNA-sequencing (RNAseq) data. It has been successfully used in human transcriptomes, proving the tissue and cell type specificity of RNA editing as well as its pervasive nature. Outcomes from large-scale REDItools analyses on human RNAseq data have been collected in our specialized REDIportal database, containing more than 4.5 million events. Here we describe in detail two bioinformatic procedures based on our computational resources, REDItools and REDIportal. In the first procedure, we outline a workflow to detect RNA editing in the human cell line NA12878, for which transcriptome and whole genome data are available. In the second procedure, we show how to identify dysregulated editing at specific recoding sites in post-mortem brain samples of Huntington disease donors. On a 64-bit computer running Linux with ≥32 GB of random-access memory (RAM), both procedures should take ~76 h, using 4 to 24 cores. Our protocols have been designed to investigate RNA editing in different organisms with available transcriptomic and/or genomic reads. Scripts to complete both procedures and a docker image are available at https://github.com/BioinfoUNIBA/REDItools.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Overview of the bioinformatics workflow to preprocess data.
Fig. 2: Detection of RNA editing by REDItools.
Fig. 3: Filtering of REDItools tables to call RNA editing events.
Fig. 4: Differential RNA editing using REDItools and REDIportal.

Data availability

Example datasets that include NA12878 RNAseq/WGS reads and RNAseq reads from BioProject PRJNA316625 are freely available at the ENA database (https://www.ebi.ac.uk/ena) or the SRA archive (https://www.ncbi.nlm.nih.gov/sra).

Code availability

REDItools source code is available at the GitHub website (https://github.com/BioinfoUNIBA/REDItools) under the MIT License. The code in this protocol has been peer reviewed.

References

  1. 1.

    Saletore, Y., Meyer, K., Korlach, J., Vilfan, I. D., Jaffrey, S. & Mason, C. E. The birth of the Epitranscriptome: deciphering the function of RNA modifications. Genome Biol. 13, 175 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Boccaletto, P. et al. MODOMICS: a database of RNA modification pathways. 2017 update. Nucleic Acids Res. 46, D303–D307 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Jantsch, M. F. & Schaefer, M. R. Mining the epitranscriptome: detection of RNA editing and RNA modifications. Methods 156, 1–4 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Benne, R. et al. Major transcript of the frameshifted coxII gene from trypanosome mitochondria contains four nucleotides that are not encoded in the DNA. Cell 46, 819–826 (1986).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Gott, J. M. & Emeson, R. B. Functions and mechanisms of RNA editing. Annu. Rev. Genet. 34, 499–531 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Eisenberg, E. & Levanon, E. Y. A-to-I RNA editing—immune protector and transcriptome diversifier. Nat. Rev. Genet. 19, 473–490 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Rosenberg, B. R., Hamilton, C. E., Mwangi, M. M., Dewell, S. & Papavasiliou, F. N. Transcriptome-wide sequencing reveals numerous APOBEC1 mRNA-editing targets in transcript 3’ UTRs. Nat. Struct. Mol. Biol. 18, 230–236 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Nishikura, K. A-to-I editing of coding and non-coding RNAs by ADARs. Nat. Rev. Mol. Cell Biol. 17, 83–96 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Bazak, L. et al. A-to-I RNA editing occurs at over a hundred million genomic sites, located in a majority of human genes. Genome Res. 24, 365–376 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Picardi, E. et al. Profiling RNA editing in human tissues: towards the inosinome Atlas. Sci. Rep. 5, 14941 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Mallela, A. & Nishikura, K. A-to-I editing of protein coding and noncoding RNAs. Crit. Rev. Biochem. Mol. Biol. 47, 493–501 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Mannion, N. M. et al. The RNA-editing enzyme ADAR1 controls innate immune responses to RNA. Cell Rep. 9, 1482–1494 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Gallo, A., Vukic, D., Michalik, D., O’Connell, M. A. & Keegan, L. P. ADAR RNA editing in human disease; more to it than meets the I. Hum. Genet. 136, 1265–1278 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Silvestris, D. A. et al. Dynamic inosinome profiles reveal novel patient stratification and gender-specific differences in glioblastoma. Genome Biol. 20, 33 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Ramaswami, G. et al. Accurate identification of human Alu and non-Alu RNA editing sites. Nat. Methods 9, 579–581 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Eisenberg, E. Bioinformatic approaches for identification of A-to-I editing sites. Curr. Top. Microbiol. Immunol. 353, 145–162 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Diroma, M. A., Ciaccia, L., Pesole, G. & Picardi, E. Elucidating the editome: bioinformatics approaches for RNA editing detection. Brief. Bioinform. 20, 436–447 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Nigita, G., Alaimo, S., Ferro, A., Giugno, R. & Pulvirenti, A. Knowledge in the investigation of A-to-I RNA editing signals. Front. Bioeng. Biotechnol. 3, 18 (2015).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Nigita, G. et al. ncRNA editing: functional characterization and computational resources. in Computational Biology of Non-Coding RNA (eds Lai, X., Gupta, S. K. & Vera, J.) 133–174 (Humana Press, 2019).

  20. 20.

    Picardi, E. & Pesole, G. REDItools: high-throughput RNA editing detection made easy. Bioinformatics 29, 1813–1814 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Picardi, E., D’Erchia, A. M., Lo Giudice, C. & Pesole, G. REDIportal: a comprehensive database of A-to-I RNA editing events in humans. Nucleic Acids Res. 45, D750–D757 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68 (2015).

    Article  CAS  Google Scholar 

  23. 23.

    Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, 6 (2018).

    Google Scholar 

  24. 24.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Wang, M. & Kong, L. pblat: a multithread blat algorithm speeding up aligning sequences to genomes. BMC Bioinformatics 20, 28 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Kent, W. J. BLAT-the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Ross, C. A. & Tabrizi, S. J. Huntington’s disease: from molecular pathogenesis to clinical treatment. Lancet Neurol. 10, 15 (2011).

    Article  Google Scholar 

  30. 30.

    Hodges, A. et al. Regional and cellular gene expression changes in human Huntington’s disease brain. Hum. Mol. Genet. 15, 965–977 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Valor, L. M. Transcription, epigenetics and ameliorative strategies in Huntington’s Disease: a genome-wide perspective. Mol. Neurobiol. 51, 406–423 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Marti, E. et al. A myriad of miRNA variants in control and Huntington’s disease brain regions detected by massively parallel sequencing. Nucleic Acids Res. 38, 7219–7235 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Luthi-Carter, R. et al. Decreased expression of striatal signaling genes in a mouse model of Huntington’s disease. Hum. Mol. Genet. 9, 1259–1271 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Lin, L. et al. Transcriptome sequencing reveals aberrant alternative splicing in Huntington’s disease. Hum. Mol. Genet. 25, 3454–3466 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Annese, A. et al. Whole transcriptome profiling of late-onset Alzheimer’s disease patients provides insights into the molecular changes involved in the disease. Sci. Rep. 8, 4282 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    D’Erchia, A. M. et al. Massive transcriptome sequencing of human spinal cord tissues provides new insights into motor neuron degeneration in ALS. Sci. Rep. 7, 10046 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Khermesh, K. et al. Reduced levels of protein recoding by A-to-I RNA editing in Alzheimer’s disease. RNA 22, 290–302 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Srivastava, P. K. et al. Genome-wide analysis of differential RNA editing in epilepsy. Genome Res. 27, 440–450 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    DeLuca, D. S. et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28, 1530–1532 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

  43. 43.

    Patel, R. K. & Jain, M. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PlOS One 7, e30619 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Wu, T. D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2011).

    Article  CAS  Google Scholar 

  45. 45.

    Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 2 (2012).

    Article  CAS  Google Scholar 

  47. 47.

    Yu, C. et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25, 2 (2009).

    Article  CAS  Google Scholar 

  48. 48.

    John, D., Weirick, T., Dimmeler, S. & Uchida, S. RNAEditor: easy detection of RNA editing events and the introduction of editing islands. Brief. Bioinform. 18, 8 (2012).

    Article  Google Scholar 

  49. 49.

    Wang, Z. et al. RES-Scanner: a software package for genome-wide identification of RNA-editing sites. Gigascience 5, 37 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Zhang, Q. & Xiao, X. Genome sequence-independent identification of RNA editing sites. Nat. Methods 12, 347 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Piechotta, M., Wyler, E., Ohler, U., Landthaler, M. & Dieterich, C. JACUSA: site-specific identification of RNA editing events from replicate sequencing data. BMC Bioinform. 18, 7 (2017).

    Article  CAS  Google Scholar 

  52. 52.

    Kim, M. S., Hur, B. & Kim, S. RDDpred: a condition-specific RNA-editing prediction model from RNA-seq data. BMC Genomics 17(Suppl 1)), 5 (2016).

  53. 53.

    Xiong, H. et al. RED-ML: a novel, effective RNA editing detection method based on machine learning. Gigascience https://doi.org/10.1093/gigascience/gix012 (2017).

  54. 54.

    Ouyang, Z. et al. Accurate identification of RNA editing sites from primitive sequence with deep neural networks. Sci. Rep. 8, 6005 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Porath, H. T., Carmi, S. & Levanon, E. Y. A genome-wide map of hyper-edited RNA reveals numerous new sites. Nat. Commun. 5, 4726 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Zhang, F., Lu, Y., Yan, S., Xing, Q. & Tian, W. SPRINT: an SNP-free toolkit for identifying RNA editing sites. Bioinformatics 33, 3538–3548 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Kiran, A. M., O’Mahony, J. J., Sanjeev, K. & Baranov, P. V. Darned in 2013: inclusion of model organisms and linking with Wikipedia. Nucleic Acids Res. 41, D258–261 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Ramaswami, G. & Li, J. B. RADAR: a rigorously annotated database of A-to-I RNA editing. Nucleic Acids Res. 42, D109–113 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Picardi, E., Horner, D. S. & Pesole, G. Single cell transcriptomics reveals specific RNA editing signatures in the human brain. RNA 23, 860–865 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Rossetti, C. et al. RNA editing signature during myeloid leukemia cell differentiation. Leukemia 31, 2824–2832 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Pinto, Y., Buchumenski, I., Levanon, E. Y. & Eisenberg, E. Human cancer tissues exhibit reduced A-to-I editing of miRNAs coupled with elevated editing of their targets. Nucleic Acids Res. 46, 71–82 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Lin, C.-H. & Chen, S. C.-C. The Cancer Editome Atlas: a resource for exploratory analysis of the adenosine-to-inosine RNA editome in cancer. Cancer Res. 79, 3001–3006 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Porath, H. T. et al. RNA editing is abundant and correlates with task performance in a social bumblebee. Nat. Commun. 10, 1605 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Liu, H. et al. A-to-I RNA editing is developmentally regulated and generally adaptive for sexual reproduction in Neurospora crassa. Proc. Natl Acad. Sci. USA 114, E7756–E7765 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Liew, Y. J., Li, Y., Baumgarten, S., Voolstra, C. R. & Aranda, M. Condition-specific RNA editing in the coral symbiont Symbiodinium microadriaticum. PLOS Genet. 13, e1006619 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Sapiro, A. L. et al. Illuminating spatial A-to-I RNA editing signatures within the Drosophila brain. Proc. Natl Acad. Sci. USA 116, 2318–2327 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Picardi, E. et al. Large-scale detection and analysis of RNA editing in grape mtDNA by RNA deep-sequencing. Nucleic Acids Res. 38, 4755–4767 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Wu, B. et al. Identification of symmetrical RNA editing events in the mitochondria of Salvia miltiorrhiza by strand-specific RNA sequencing. Sci. Rep. 7, 42250 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Picardi, E., D’Erchia, A. M., Montalvo, A. & Pesole, G. Using REDItools to detect RNA editing events in NGS datasets. Curr. Protoc. Bioinforma. 49, 12.12.1–12.12.15 (2015).

    Article  Google Scholar 

  70. 70.

    Picardi, E., D’Erchia, A. M., Gallo, A., Montalvo, A. & Pesole, G. Uncovering RNA editing sites in long non-coding RNAs. Front. Bioeng. Biotechnol. 2, 64 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Pinto, Y., Cohen, H. Y. & Levanon, E. Y. Mammalian conserved ADAR targets comprise only a small fragment of the human editosome. Genome Biol. 15, R5 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Deininger, P. Alu elements: know the SINEs. Genome Biol. 12, 236 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Porath, H. T., Knisbacher, B. A., Eisenberg, E. & Levanon, E. Y. Massive A-to-I RNA editing is common across the Metazoa and correlates with dsRNA abundance. Genome Biol. 18, 185 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Tran, S. S. et al. Widespread RNA editing dysregulation in brains from autistic individuals. Nat. Neurosci. 22, 25 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Yang, H. & Wang, K. Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Nat. Protoc. 10, 1556 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Tan, M. H. et al. Dynamic landscape and regulation of RNA editing in mammals. Nature 550, 249 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We kindly thank the ReCaS computing center at University of Bari for computational and technical assistance. We also acknowledge the PRACE project 2016163924 for computing resources. This work was supported by Elixir IIB and PRACE projects 2016163924 and 2018194670.

Author information

Affiliations

Authors

Contributions

G.P. and E.P. conceived the project. C.L.G. and E.P. designed and wrote the full analysis pipeline. C.L.G. performed extensive user testing of the software. M.A.T. created the docker image and implemented the statistical tests to calculate differential RNA editing. E.P. wrote the manuscript with input and editing from all authors.

Corresponding author

Correspondence to Ernesto Picardi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Trees-Juen Chuang, Erez Levanon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Related links

Key references using this protocol

D’Erchia, A. M. et al. Sci. Rep. 7, 10046 (2017): https://doi.org/10.1038/s41598-017-10488-7

Picardi, E., Horner, D. S. and Pesole, G. RNA 23, 860–865 (2017): https://doi.org/10.1261/rna.058271.116

Rossetti, C. et al. Leukemia 31, 2824–2832 (2017): https://doi.org/10.1038/leu.2017.134

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lo Giudice, C., Tangaro, M.A., Pesole, G. et al. Investigating RNA editing in deep transcriptome datasets with REDItools and REDIportal. Nat Protoc 15, 1098–1131 (2020). https://doi.org/10.1038/s41596-019-0279-7

Download citation

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing