Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Detecting circular RNAs: bioinformatic and experimental challenges

Key Points

  • In 2012, genome-wide statistical analysis of splicing led to the discovery of the global expression of circular RNA (circRNA) in eukaryotes and found that, in hundreds of human genes, circRNA constitutes the major isoform. circRNA expression was previously overlooked owing to a combination of biases in library preparation and heuristic filters imposed by algorithms to detect unannotated splicing events.

  • Assigning reads to the correct splice junction is complicated by experimental artefacts, sequence homology and degenerate sequences at exon boundaries. Even accurate assignment to annotated splice junctions, a seemingly straightforward task compared with identifying unannotated splice events, has not been solved.

  • Common RNA sequencing (RNA-seq) protocols introduce technical artefacts that can appear to be putative novel splice events, including circRNA. Statistical approaches can be used to test for these artefacts to avoid high false-positive rates, without the reduced sensitivity that comes with applying stringent bioinformatic filters.

  • Read count is an unreliable metric when assessing whether a splice junction is truly expressed. Statistical approaches that reduce reliance on read count have improved the accuracy of novel linear splice detection, enabled the discovery of circRNAs spliced by the U12 (minor) spliceosome, and reduced false-positive circRNA owing to highly expressed homologous genes.

  • There is little overlap in the predictions between published circRNA detection algorithms, and the field lacks a clear gold standard for assessing the accuracy of their genome-wide predictions. RNase R resistance is useful for validating a predicted circRNA, but more work is needed on normalization and appropriate enrichment tests for RNase R to be useful for assessing genome-wide accuracy.

  • The ubiquitous expression of circRNA, as well as high circRNA expression from specific genes, is conserved across highly diverged eukaryotes. Conservation, as well as evidence of tissue- or development-specific regulation, provides circumstantial evidence that circRNAs are functional, although the function of most remains unknown.

Abstract

The pervasive expression of circular RNAs (circRNAs) is a recently discovered feature of gene expression in highly diverged eukaryotes. Numerous algorithms that are used to detect genome-wide circRNA expression from RNA sequencing (RNA-seq) data have been developed in the past few years, but there is little overlap in their predictions and no clear gold-standard method to assess the accuracy of these algorithms. We review sources of experimental and bioinformatic biases that complicate the accurate discovery of circRNAs and discuss statistical approaches to address these biases. We conclude with a discussion of the current experimental progress on the topic.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Circular RNA.
Figure 2: Challenges for circRNA detection in RNA-seq.
Figure 3: Multiple circRNAs can be generated from a single locus.
Figure 4: Statistical considerations when using RNase R enrichment to assess genome-wide accuracy.

Similar content being viewed by others

References

  1. Salzman, J., Gawad, C., Wang, P. L., Lacayo, N. & Brown, P. O. Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PloS One 7, e30733 (2012). This article provided the first demonstration that circRNA was a ubiquitous and overlooked feature of eukaryotic gene expression.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Lasda, E. & Parker, R. Circular RNAs: diversity of form and function. RNA 20, 1829–1842 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Jeck, W. R. et al. Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 19, 141–157 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Zhang, X. O. et al. Complementary sequence-mediated exon circularization. Cell 159, 134–147 (2014).

    Article  CAS  PubMed  Google Scholar 

  5. Szabo, L. et al. Statistically based splicing detection reveals neural enrichment and tissue-specific induction of circular RNA during human fetal development. Genome Biol. 16, 126 (2015). The first published circRNA algorithm to develop a statistical score independent of read count for identifying true and false positives.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Salzman, J., Chen, R. E., Olsen, M. N., Wang, P. L. & Brown, P. O. Cell-type specific features of circular RNA expression. PLoS Genet. 9, e1003777 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Westholm, J. O. et al. Genome-wide analysis of Drosophila circular RNAs reveals their structural and sequence properties and age-dependent neural accumulation. Cell Rep. 9, 1966–1980 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Veno, M. T. et al. Spatio-temporal regulation of circular RNA expression during porcine embryonic brain development. Genome Biol. 16, 245 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Ivanov, A. et al. Analysis of intron sequences reveals hallmarks of circular RNA biogenesis in animals. Cell Rep. 10, 170–177 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Liang, D. & Wilusz, J. E. Short intronic repeat sequences facilitate circular RNA production. Genes Dev. 28, 2233–2247 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Memczak, S. et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495, 333–338 (2013).

    Article  CAS  PubMed  Google Scholar 

  12. Capel, B. et al. Circular transcripts of the testis-determining gene Sry in adult mouse testis. Cell 73, 1019–1030 (1993).

    Article  CAS  PubMed  Google Scholar 

  13. Hansen, T. B. et al. miRNA-dependent gene silencing involving Ago2-mediated cleavage of a circular antisense RNA. EMBO J. 30, 4414–4422 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Nigro, J. M. et al. Scrambled exons. Cell 64, 607–613 (1991).

    Article  CAS  PubMed  Google Scholar 

  15. Cocquerelle, C., Daubersies, P., Majerus, M. A., Kerckaert, J. P. & Bailleul, B. Splicing with inverted order of exons occurs proximal to large introns. EMBO J. 11, 1095–1098 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Caldas, C. et al. Exon scrambling of MLL transcripts occur commonly and mimic partial genomic duplication of the gene. Gene 208, 167–176 (1998).

    Article  CAS  PubMed  Google Scholar 

  17. Li, X. F. & Lytton, J. A circularized sodium-calcium exchanger exon 2 transcript. J. Biol. Chem. 274, 8153–8160 (1999).

    Article  CAS  PubMed  Google Scholar 

  18. Surono, A. et al. Circular dystrophin RNAs consisting of exons that were skipped by alternative splicing. Hum. Mol. Genet. 8, 493–500 (1999).

    Article  CAS  PubMed  Google Scholar 

  19. Houseley, J. M. et al. Noncanonical RNAs from transcripts of the Drosophila muscleblind gene. J. Hered. 97, 253–260 (2006). This study reports the first evidence of a highly enriched circRNA from the fly.

    Article  CAS  PubMed  Google Scholar 

  20. Burd, C. E. et al. Expression of linear and novel circular forms of an INK4/ARF-associated non-coding RNA correlates with atherosclerosis risk. PLoS Genet. 6, e1001233 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Rybak-Wolf, A. et al. Circular RNAs in the mammalian brain are highly abundant, conserved, and dynamically expressed. Mol. Cell 58, 870–885 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Alhasan, A. A. et al. Circular RNA enrichment in platelets is a signature of transcriptome degradation. Blood 127, e1–e11 (2015).

    Article  PubMed  CAS  Google Scholar 

  23. Hansen, T. B. et al. Natural RNA circRNAs function as efficient microRNA sponges. Nature 495, 384–388 (2013).

    Article  CAS  PubMed  Google Scholar 

  24. Ashwal-Fluss, R. et al. circRNA biogenesis competes with pre-mRNA splicing. Mol. Cell 56, 55–66 (2014).

    Article  CAS  PubMed  Google Scholar 

  25. Hoffmann, S. et al. A multi-split mapping algorithm for circular RNA, splicing. trans-splicing and fusion detection. Genome Biol. 15, R34 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Guo, J. U., Agarwal, V., Guo, H. & Bartel, D. P. Expanded identification and characterization of mammalian circular RNAs. Genome Biol. 15, 409 (2014). This paper provides a comprehensive controlled analysis of the enrichment in circRNAs from microRNA binding sites.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Gao, Y., Wang, J. & Zhao, F. CIRI: an efficient and unbiased algorithm for de novo circular RNA identification. Genome Biol. 16, 4 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Cheng, J., Metge, F. & Dieterich, C. Specific identification and quantification of circular RNAs from sequencing data. Bioinformatics 32, 1094–1096 (2016).

    Article  CAS  PubMed  Google Scholar 

  29. Kramer, M. C. et al. Combinatorial control of Drosophila circular RNA expression by intronic repeats, hnRNPs, and SR proteins. Genes Dev. 29, 2168–2182 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wang, P. L. et al. Circular RNA is expressed across the eukaryotic tree of life. PLoS ONE 9, e90859 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Yang, S., Tang, F. & Zhu, H. Alternative splicing in plant immunity. Int. J. Mol. Sci. 15, 10424–10445 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Filichkin, S., Priest, H. D., Megraw, M. & Mockler, T. C. Alternative splicing in plants: directing traffic at the crossroads of adaptation and environmental stress. Curr. Opin. Plant Biol. 24, 125–135 (2015).

    Article  CAS  PubMed  Google Scholar 

  33. Meyer, K., Koester, T. & Staiger, D. Pre-mRNA splicing in plants: in vivo functions of RNA-binding proteins implicated in the splicing process. Biomolecules 5, 1717–1740 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wang, Y. & Wang, Z. Efficient backsplicing produces translatable circular mRNAs. RNA 21, 172–179 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Engstrom, P. G. et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods 10, 1185–1191 (2013). Competition-style independent evaluation of linear spliced alignment algorithms identifying systematic discrepancies and blind spots in all algorithms.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Hayer, K. E., Pizarro, A., Lahens, N. F., Hogenesch, J. B. & Grant, G. R. Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data. Bioinformatics 31, 3938–3945 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Carrara, M. et al. Alternative splicing detection workflow needs a careful combination of sample prep and bioinformatics analysis. BMC Bioinformatics 16, S2 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Liu, R., Loraine, A. E. & Dickerson, J. A. Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems. BMC Bioinformatics 15, 364 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Chandramohan, R., Wu, P. Y., Phan, J. H. & Wang, M. D. Benchmarking RNA-seq quantification tools. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2013, 647–650 (2013).

    PubMed Central  Google Scholar 

  40. Hatem, A., Bozdag, D., Toland, A. E. & Catalyurek, U. V. Benchmarking short sequence mapping tools. BMC Bioinformatics 14, 184 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Hansen, T. B., Veno, M. T., Damgaard, C. K. & Kjems, J. Comparison of circular RNA prediction tools. Nucleic Acids Res. 44, e58 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Luo, G. X. & Taylor, J. Template switching by reverse transcriptase during DNA synthesis. J. Virol. 64, 4321–4328 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Houseley, J. & Tollervey, D. Apparent non-canonical trans-splicing is generated by reverse transcriptase in vitro. PLoS ONE 5, e12271 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Roy, C. K., Olson, S., Graveley, B. R., Zamore, P. D. & Moore, M. J. Assessing long-distance RNA sequence connectivity via RNA-templated DNA–DNA ligation. eLife 4, e03700 (2015). This study provided important biochemical evidence for artefactual splicing from RNA-seq and technological solution.

    Article  PubMed Central  Google Scholar 

  45. Cocquet, J., Chong, A., Zhang, G. & Veitia, R. A. Reverse transcriptase template switching and false alternative transcripts. Genomics 88, 127–131 (2006).

    Article  CAS  PubMed  Google Scholar 

  46. Yu, C. Y., Liu, H. J., Hung, L. Y., Kuo, H. C. & Chuang, T. J. Is an observed non-co-linear RNA product spliced in trans, in cis or just in vitro? Nucleic Acids Res. 42, 9410–9423 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Quail, M. A. et al. A large genome center's improvements to the Illumina sequencing system. Nat. Methods 5, 1005–1010 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kelleher, C. D. & Champoux, J. J. Characterization of RNA strand displacement synthesis by Moloney murine leukemia virus reverse transcriptase. J. Biol. Chem. 273, 9976–9986 (1998).

    Article  CAS  PubMed  Google Scholar 

  49. Pease, J. & Sooknanan, R. A rapid, directional RNA-seq library preparation workflow for Illumina® sequencing. Nat. Methods 9 (2012).

  50. Mohr, S. et al. Thermostable group II intron reverse transcriptase fusion proteins and their use in cDNA synthesis and next-generation RNA sequencing. RNA 19, 958–970 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Lahens, N. F. et al. IVT-seq reveals extreme bias in RNA sequencing. Genome Biol. 15, R86 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Jiang, H. & Salzman, J. A penalized likelihood approach for robust estimation of isoform expression. Stat. Interface 8, 437–445 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Wang, K. et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 38, e178 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Koch, P. et al. Identification of a novel putative Ran-binding protein and its close homologue. Biochem. Biophys. Res. Commun. 278, 241–249 (2000).

    Article  CAS  PubMed  Google Scholar 

  55. Vincent, H. A. & Deutscher, M. P. Substrate recognition and catalysis by the exoribonuclease RNase, R. J. Biol. Chem. 281, 29769–29775 (2006).

    Article  CAS  PubMed  Google Scholar 

  56. Stephan-Otto Attolini, C., Pena, V. & Rossell, D. Designing alternative splicing RNA-seq studies. Beyond generic guidelines. Bioinformatics 31, 3631–3637 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Jeck, W. R. & Sharpless, N. E. Detecting and characterizing circular RNAs. Nat. Biotechnol. 32, 453–461 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Chen, I., Chen, C. Y. & Chuang, T. J. Biogenesis, identification, and function of exonic circular RNAs. Wiley Interdiscip. Rev. RNA 6, 563–579 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Hesselberth, J. R. Lives that introns lead after splicing. Wiley Interdiscip Rev. RNA 4, 677–691 (2013).

    Article  CAS  PubMed  Google Scholar 

  60. Witten, D. & Tibshirani, R. A comparison of fold-change and the t-statistic for microarray data analysis. Tech. Report (Stanford Univ., 2007).

  61. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Salzman, J., Klass, D. M. & Brown, P. O. Improved discovery of molecular interactions in genome-scale data with adaptive model-based normalization. PLoS ONE 8, e53930 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Li, P., Piao, Y., Shon, H. S. & Ryu, K. H. Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-seq data. BMC Bioinformatics 16, 347 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Zyprych-Walczak, J. et al. The impact of normalization methods on RNA-seq data analysis. Biomed. Res. Int. 2015, 621690 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Erhard, F. & Zimmer, R. Count ratio model reveals bias affecting NGS fold changes. Nucleic Acids Res. 43, e136 (2015).

    PubMed  PubMed Central  Google Scholar 

  66. Wu, C. S. et al. Integrative transcriptome sequencing identifies trans-splicing events with important roles in human embryonic stem cell pluripotency. Genome Res. 24, 25–36 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Grant, G. R. et al. Comparative analysis of RNA-Seq alignment algorithms and the RNA-seq unified mapper (RUM). Bioinformatics 27, 2518–2528 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Simpson, E. H. The interpretation of interaction in contingency tables. J. R. Statist. Soc. 13, 238–241 (1951).

    Google Scholar 

  69. Boeckel, J. N. et al. Identification and characterization of hypoxia-regulated endothelial circular RNA. Circ. Res. 117, 884–890 (2015).

    Article  CAS  PubMed  Google Scholar 

  70. Petkovic, S. & Muller, S. RNA circularization strategies in vivo and in vitro. Nucleic Acids Res. 43, 2454–2465 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank G. Hsieh and P. Wang for useful discussion and comments. This work was supported by NCI grant R00 CA168987-03, NIGMS grant R01 GM116847, a JIMB seed grant and an NSF CAREER award to J.S. and McCormick-Gabilan and a Baxter Family Fellowship. The authors would also like to acknowledge the support of the Stanford Center for Computational, Evolutionary and Human Genomics. J.S. is an Alfred P. Sloan fellow in Computational & Evolutionary Molecular Biology.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Julia Salzman.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

FURTHER INFORMATION

Rateratio test

R code

PowerPoint slides

Glossary

Splice signals

Conserved sequences delineating introns in pre-mRNA and recognized by the spliceosome. Nearly all introns contain a GU at the 5′ end of the intron and an AG at the 3′ end (canonical U2 splice signal); the U12 splice signal is (A|G)TATCCT(C|T), and is present in a minority of exons.

RNA sequencing

(RNA-seq). A technique to obtain the sequence of the transcriptome (all expressed RNA) in a sample. It enables the identification and quantification of alternative splicing, as well as gene-level expression.

MicroRNA sponges

An RNA molecule containing microRNA-binding sites that sequesters the microRNA away from its target in a sequence-specific manner.

Indels

Insertions and deletions in the sequenced genome compared with a reference genome.

Oligo(dT) priming

Priming with a primer that hybridizes to the poly(A) tail of mRNA.

Wobble bases

The third position in a 3 nt codon in which more than one nucleotide in this position codes for the same amino acid.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Szabo, L., Salzman, J. Detecting circular RNAs: bioinformatic and experimental challenges. Nat Rev Genet 17, 679–692 (2016). https://doi.org/10.1038/nrg.2016.114

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg.2016.114

This article is cited by

Search

Quick links

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