Advertisement

RNA sequencing and Prediction Tools for Circular RNAs Analysis

  • Elena López-Jiménez
  • Ana M. Rojas
  • Eduardo Andrés-LeónEmail author
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1087)

Abstract

Circular RNAs (circRNAs) are noncoding and single-stranded RNA transcripts able to form covalently circular-closed structures. They are generated through alternative splicing events and widely expressed from human to viruses. CircRNAs have been appointed as potential regulators of microRNAs (miRNAs), RNA-binding proteins (RPBs), and lineal protein-coding transcripts. Although their mechanism of action remains unclear, the deregulation of circular RNAs has been confirmed in different diseases such as Alzheimer or cancer.

The introduction of high-throughput next-generation sequencing (NGS) technology provides millions of short RNA sequences at single-nucleotide level, allowing an accurate and proficient method to measure circular RNAs. Novel protocols based on non-polyadenylated RNAs, rRNA-depleted, and RNA exonuclease-based enrichment approaches (RNase R) have taken even further the possibility of detecting circRNAs.

Besides, the identification of circRNAs presence requires the development of specific bioinformatics tools to detect junction-spanning sequences from transcriptome deep-sequencing samples. Thus, recently established bioinformatics’ approaches have permitted the discovery of an elevated number of different circRNAs in diverse organisms. In that sense, recent studies have compared different methods and advocate the simultaneous use of more than one prediction tool. For that reason, we want to highlight pipelines such as miARma-Seq that is able to execute various circular RNA identification algorithms in an easy way, without the tedious installation of third-party prerequisites.

Keywords

CircRNAs CircRNA RNA-seq CircRNA prediction tools 

Notes

Acknowledgments

We wish to thank the No Surrender Cancer Trust for supporting the position and projects of ELJ at Imperial College London.

Competing Financial Interests

The authors declare no competing financial interests.

References

  1. 1.
    Nigro JM, Cho KR, Fearon ER et al (1991) Scrambled exons. Cell 64(3):607–613CrossRefGoogle Scholar
  2. 2.
    Cocquerelle C, Mascrez B, Hetuin D et al (1993) Mis-splicing yields circular RNA molecules. FASEB J 7(1):155–160CrossRefGoogle Scholar
  3. 3.
    Salzman J, Gawad C, Wang PL et al (2012) Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PLoS One 7(2):e30733CrossRefGoogle Scholar
  4. 4.
    Abu N, Jamal R (2016) Circular RNAs as promising biomarkers: a mini-review. Front Physiol 7:355CrossRefGoogle Scholar
  5. 5.
    Morris KV, Mattick JS (2014) The rise of regulatory RNA. Nat Rev Genet 15(6):423–437CrossRefGoogle Scholar
  6. 6.
    Lasda E, Parker R (2014) Circular RNAs: diversity of form and function. RNA 20(12):1829–1842CrossRefGoogle Scholar
  7. 7.
    Chen LL (2016) The biogenesis and emerging roles of circular RNAs. Nat Rev Mol Cell Biol 17(4):205–211CrossRefGoogle Scholar
  8. 8.
    Gruner H, Cortes-Lopez M, Cooper DA et al (2016) CircRNA accumulation in the aging mouse brain. Sci Rep 6:38907CrossRefGoogle Scholar
  9. 9.
    Lukiw WJ (2013) Circular RNA (circRNA) in Alzheimer’s disease (AD). Front Genet 4:307PubMedPubMedCentralGoogle Scholar
  10. 10.
    Memczak S, Jens M, Elefsinioti A et al (2013) Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495(7441):333–338CrossRefGoogle Scholar
  11. 11.
    Umekage S, Uehara T, Yoshinobu F et al (2012) In vivo circular RNA expression by the permuted intron-exon method. Innov Biotechnol 76:1Google Scholar
  12. 12.
    Ashwal-Fluss R, Meyer M, Pamudurti NR et al (2014) circRNA biogenesis competes with pre-mRNA splicing. Mol Cell 56(1):55–66CrossRefGoogle Scholar
  13. 13.
    Jeck WR, Sharpless NE (2014) Detecting and characterizing circular RNAs. Nat Biotechnol 32(5):453–461CrossRefGoogle Scholar
  14. 14.
    Zaphiropoulos PG (1996) Circular RNAs from transcripts of the rat cytochrome P450 2C24 gene: correlation with exon skipping. Proc Natl Acad Sci U S A 93(13):6536–6541CrossRefGoogle Scholar
  15. 15.
    Salzman J, Chen RE, Olsen MN et al (2013) Cell-type specific features of circular RNA expression. PLoS Genet 9(9):e1003777CrossRefGoogle Scholar
  16. 16.
    Jeck WR, Sorrentino JA, Wang K et al (2013) Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 19(2):141–157CrossRefGoogle Scholar
  17. 17.
    Hansen TB, Jensen TI, Clausen BH et al (2013) Natural RNA circles function as efficient microRNA sponges. Nature 495(7441):384–388CrossRefGoogle Scholar
  18. 18.
    Yap KL, Li S, Munoz-Cabello AM et al (2010) Molecular interplay of the noncoding RNA ANRIL and methylated histone H3 lysine 27 by polycomb CBX7 in transcriptional silencing of INK4a. Mol Cell 38(5):662–674CrossRefGoogle Scholar
  19. 19.
    Burd CE, Jeck WR, Liu Y et al (2010) Expression of linear and novel circular forms of an INK4/ARF-associated non-coding RNA correlates with atherosclerosis risk. PLoS Genet 6(12):e1001233CrossRefGoogle Scholar
  20. 20.
    Li Z, Huang C, Bao C et al (2015) Exon-intron circular RNAs regulate transcription in the nucleus. Nat Struct Mol Biol 22(3):256–264CrossRefGoogle Scholar
  21. 21.
    Braunschweig U, Barbosa-Morais NL, Pan Q et al (2014) Widespread intron retention in mammals functionally tunes transcriptomes. Genome Res 24(11):1774–1786CrossRefGoogle Scholar
  22. 22.
    Rybak-Wolf A, Stottmeister C, Glazar P et al (2015) Circular RNAs in the mammalian brain are highly abundant, conserved, and dynamically expressed. Mol Cell 58(5):870–885CrossRefGoogle Scholar
  23. 23.
    Huang S, Yang B, Chen BJ et al (2017) The emerging role of circular RNAs in transcriptome regulation. Genomics 109(5–6):401–407CrossRefGoogle Scholar
  24. 24.
    Du WW, Yang W, Liu E et al (2016) Foxo3 circular RNA retards cell cycle progression via forming ternary complexes with p21 and CDK2. Nucleic Acids Res 44(6):2846–2858CrossRefGoogle Scholar
  25. 25.
    Chen CY, Sarnow P (1995) Initiation of protein synthesis by the eukaryotic translational apparatus on circular RNAs. Science 268(5209):415–417CrossRefGoogle Scholar
  26. 26.
    Legnini I, Di Timoteo G, Rossi F et al (2017) Circ-ZNF609 is a circular RNA that can be translated and functions in myogenesis. Mol Cell 66(1):22–37 e29CrossRefGoogle Scholar
  27. 27.
    Pamudurti NR, Bartok O, Jens M et al (2017) Translation of CircRNAs. Mol Cell 66(1):9–21 e27CrossRefGoogle Scholar
  28. 28.
    Yang Y, Fan X, Mao M et al (2017) Extensive translation of circular RNAs driven by N(6)-methyladenosine. Cell Res 27(5):626–641CrossRefGoogle Scholar
  29. 29.
    Guo JU, Agarwal V, Guo H et al (2014) Expanded identification and characterization of mammalian circular RNAs. Genome Biol 15(7):409CrossRefGoogle Scholar
  30. 30.
    Zhang XO, Wang HB, Zhang Y et al (2014) Complementary sequence-mediated exon circularization. Cell 159(1):134–147CrossRefGoogle Scholar
  31. 31.
    Labaj PP, Leparc GG, Linggi BE et al (2011) Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics 27(13):i383–i391CrossRefGoogle Scholar
  32. 32.
    Suzuki H, Zuo Y, Wang J et al (2006) Characterization of RNase R-digested cellular RNA source that consists of lariat and circular RNAs from pre-mRNA splicing. Nucleic Acids Res 34(8):e63CrossRefGoogle Scholar
  33. 33.
    Danan M, Schwartz S, Edelheit S et al (2012) Transcriptome-wide discovery of circular RNAs in Archaea. Nucleic Acids Res 40(7):3131–3142CrossRefGoogle Scholar
  34. 34.
    Wang K, Singh D, Zeng Z et al (2010) MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 38(18):e178CrossRefGoogle Scholar
  35. 35.
    Roy CK, Olson S, Graveley BR et al (2015) Assessing long-distance RNA sequence connectivity via RNA-templated DNA-DNA ligation. elife 4:e03700CrossRefGoogle Scholar
  36. 36.
    Cocquet J, Chong A, Zhang G et al (2006) Reverse transcriptase template switching and false alternative transcripts. Genomics 88(1):127–131CrossRefGoogle Scholar
  37. 37.
    Capel B, Swain A, Nicolis S et al (1993) Circular transcripts of the testis-determining gene Sry in adult mouse testis. Cell 73(5):1019–1030CrossRefGoogle Scholar
  38. 38.
    Braissant O, Wahli W (1998) A simplified in situ hybridization protocol using non-radioactively labeled probes to detect abundant and rare mRNAs on tissue sections. Biochemica 1:6Google Scholar
  39. 39.
    Schindler CW, Krolewski JJ, Rush MG (1982) Selective trapping of circular double-stranded DNA molecules in solidifying agarose. Plasmid 7(3):263–270CrossRefGoogle Scholar
  40. 40.
    Awan AR, Manfredo A, Pleiss JA (2013) Lariat sequencing in a unicellular yeast identifies regulated alternative splicing of exons that are evolutionarily conserved with humans. Proc Natl Acad Sci U S A 110(31):12762–12767CrossRefGoogle Scholar
  41. 41.
    Chen BJ, Mills JD, Takenaka K et al (2016) Characterization of circular RNAs landscape in multiple system atrophy brain. J Neurochem 139(3):485–496CrossRefGoogle Scholar
  42. 42.
    Glazar P, Papavasileiou P, Rajewsky N (2014) circBase: a database for circular RNAs. RNA 20(11):1666–1670CrossRefGoogle Scholar
  43. 43.
    Zhang XO, Dong R, Zhang Y et al (2016) Diverse alternative back-splicing and alternative splicing landscape of circular RNAs. Genome Res 26(9):1277–1287CrossRefGoogle Scholar
  44. 44.
    Hansen TB, Veno MT, Damgaard CK et al (2016) Comparison of circular RNA prediction tools. Nucleic Acids Res 44(6):e58CrossRefGoogle Scholar
  45. 45.
    Zeng X, Lin W, Guo M et al (2017) A comprehensive overview and evaluation of circular RNA detection tools. PLoS Comput Biol 13(6):e1005420CrossRefGoogle Scholar
  46. 46.
    Szabo L, Morey R, Palpant NJ et al (2015) Statistically based splicing detection reveals neural enrichment and tissue-specific induction of circular RNA during human fetal development. Genome Biol 16:126CrossRefGoogle Scholar
  47. 47.
    Izuogu OG, Alhasan AA, Alafghani HM et al (2016) PTESFinder: a computational method to identify post-transcriptional exon shuffling (PTES) events. BMC Bioinform 17:31CrossRefGoogle Scholar
  48. 48.
    Langmead B, Salzberg SL (2012) Fast gapped-read alignment with bowtie 2. Nat Methods 9(4):357–359CrossRefGoogle Scholar
  49. 49.
    Langmead B, Trapnell C, Pop M et al (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25CrossRefGoogle Scholar
  50. 50.
    Gao Y, Wang J, Zhao F (2015) CIRI: an efficient and unbiased algorithm for de novo circular RNA identification. Genome Biol 16:4CrossRefGoogle Scholar
  51. 51.
    You X, Conrad TO (2016) Acfs: accurate circRNA identification and quantification from RNA-Seq data. Sci Rep 6:38820CrossRefGoogle Scholar
  52. 52.
    Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25(9):1105–1111CrossRefGoogle Scholar
  53. 53.
    Kim D, Salzberg SL (2011) TopHat-fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol 12(8):R72CrossRefGoogle Scholar
  54. 54.
    Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15–21CrossRefGoogle Scholar
  55. 55.
    Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14):1754–1760CrossRefGoogle Scholar
  56. 56.
    Otto C, Stadler PF, Hoffmann S (2014) Lacking alignments? The next-generation sequencing mapper segemehl revisited. Bioinformatics 30(13):1837–1843CrossRefGoogle Scholar
  57. 57.
    Yeo G, Burge CB (2004) Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J Comput Biol 11(2–3):377–394CrossRefGoogle Scholar
  58. 58.
    Andres-Leon E, Nunez-Torres R, Rojas AM (2016) miARma-Seq: a comprehensive tool for miRNA, mRNA and circRNA analysis. Sci Rep 6:25749CrossRefGoogle Scholar
  59. 59.
    Andres-Leon E, Gonzalez Pena D, Gomez-Lopez G et al (2015) miRGate: a curated database of human, mouse and rat miRNA-mRNA targets. Database (Oxford) 2015:bav035CrossRefGoogle Scholar
  60. 60.
    Andres-Leon E, Gomez-Lopez G, Pisano DG (2017) Prediction of miRNA-mRNA interactions using miRGate. Methods Mol Biol 1580:225–237CrossRefGoogle Scholar
  61. 61.
    Anders S (2010) FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc
  62. 62.
    Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17:1CrossRefGoogle Scholar
  63. 63.
    Davis MP, van Dongen S, Abreu-Goodger C et al (2013) Kraken: a set of tools for quality control and analysis of high-throughput sequence data. Methods 63(1):41–49CrossRefGoogle Scholar
  64. 64.
    Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140CrossRefGoogle Scholar
  65. 65.
    Tarazona S, Garcia-Alcalde F, Dopazo J et al (2011) Differential expression in RNA-seq: a matter of depth. Genome Res 21(12):2213–2223CrossRefGoogle Scholar
  66. 66.
    Xuan L, Qu L, Zhou H et al (2016) Circular RNA: a novel biomarker for progressive laryngeal cancer. Am J Transl Res 8(2):932–939PubMedPubMedCentralGoogle Scholar
  67. 67.
    Lin J, Li J, Huang B et al (2015) Exosomes: novel biomarkers for clinical diagnosis. ScientificWorldJournal 2015:657086Google Scholar
  68. 68.
    Lyu D, Huang S (2017) The emerging role and clinical implication of human exonic circular RNA. RNA Biol 14(8):1000–1006CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Elena López-Jiménez
    • 1
  • Ana M. Rojas
    • 2
  • Eduardo Andrés-León
    • 3
    Email author
  1. 1.Imperial College LondonLondonUK
  2. 2.Computational Biology and Bioinformatics GroupInstitute of Biomedicine of SevilleSevilleSpain
  3. 3.Bioinformatics UnitInstituto de Parasitología y Biomedicina “López-Neyra”, Consejo Superior de Investigaciones Científicas (IPBLN-CSIC)GranadaSpain

Personalised recommendations