Skip to main content
SpringerLink
Log in

Bioinformatic Analysis of Circular RNA Expression

  • Protocol
  • First Online:
Long Non-Coding RNAs in Cancer

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2348))

Abstract

Circular RNAs (circRNAs) are stable RNA molecules generated by backsplicing that play regulatory functions through interaction with other RNA and proteins, as well as by encoding peptides. Dysregulation of circRNA expression can drive cancer development and progression with different mechanisms. CircRNAs are currently regarded as extremely attractive molecules in cancer research for the identification of new and possibly targetable disease regulatory networks and for the development of biomarkers for cancer diagnosis, prognosis definition, and monitoring. Using specific experimental and computational protocols, circRNAs can be identified through RNA-seq by spotting the reads spanning backsplice junctions, which are specific to circular molecules. In this chapter, we report a state-of-the-art computational protocol for a genome-wide analysis of circRNAs from RNA-seq data, which considers circRNA detection, quantification, and differential expression testing. Finally, we indicate how to determine circular transcript sequences and the resources for an in silico functional characterization of circRNAs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Article  CAS  PubMed  Google Scholar 

  2. Gaffo E, Boldrin E, Dal Molin A et al (2019) Circular RNA differential expression in blood cell populations and exploration of circRNA deregulation in pediatric acute lymphoblastic leukemia. Sci Rep 9:14670

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Hansen TB, Jensen TI, Clausen BH et al (2013) Natural RNA circles function as efficient microRNA sponges. Nature 495:384–388

    Article  CAS  PubMed  Google Scholar 

  4. Schneider T, Hung L-H, Schreiner S et al (2016) CircRNA-protein complexes: IMP3 protein component defines subfamily of circRNPs. Sci Rep 6:31313

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rossi F, Legnini I, Megiorni F et al (2019) Circ-ZNF609 regulates G1-S progression in rhabdomyosarcoma. Oncogene 38:3843–3854

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 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 66:22–37.e9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Chekulaeva M, Rajewsky N (2019) Roles of long noncoding RNAs and circular RNAs in translation. Cold Spring Harb Perspect Biol 11:a032680

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Pan Z, Cai J, Lin J et al (2020) A novel protein encoded by circFNDC3B inhibits tumor progression and EMT through regulating snail in colon cancer. Mol Cancer 19:71

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Bonizzato A, Gaffo E, Te Kronnie G, Bortoluzzi S (2016) CircRNAs in hematopoiesis and hematological malignancies. Blood Cancer J 6:e483

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sadeghi H, Heiat M (2020) A novel circular RNA hsa_circ_0060927 may serve as a potential diagnostic biomarker for human colorectal cancer. Mol Biol Rep 47(9):6649–6655. https://doi.org/10.1007/s11033-020-05716-9

    Article  CAS  PubMed  Google Scholar 

  11. Chen P, Yao Y, Yang N et al (2020) Circular RNA circCTNNA1 promotes colorectal cancer progression by sponging miR-149-5p and regulating FOXM1 expression. Cell Death Dis 11:557

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Xia B, Zhao Z, Wu Y et al (2020) Circular RNA circTNPO3 regulates paclitaxel resistance of ovarian cancer cells by miR-1299/NEK2 signaling pathway. Mol Ther Nucleic Acids 21:780–791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhang M, Zhao K, Xu X et al (2018) A peptide encoded by circular form of LINC-PINT suppresses oncogenic transcriptional elongation in glioblastoma. Nat Commun 9:4475

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Dal Molin A, Bresolin S, Gaffo E et al (2019) CircRNAs are here to stay: a perspective on the recombinome. Front Genet 10:88

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Wen J, Liao J, Liang J et al (2020) Circular RNA HIPK3: a key circular RNA in a variety of human cancers. Front Oncol 10:773

    Article  PubMed  PubMed Central  Google Scholar 

  16. Papaioannou D, Volinia S, Nicolet D et al (2020) Clinical and functional significance of circular RNAs in cytogenetically normal AML. Blood Adv 4:239–251

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hirsch S, Blätte TJ, Grasedieck S et al (2017) Circular RNAs of the nucleophosmin (NPM1) gene in acute myeloid leukemia. Haematologica 102:2039–2047

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wu Z, Sun H, Liu W et al (2020) Circ-RPL15: a plasma circular RNA as novel oncogenic driver to promote progression of chronic lymphocytic leukemia. Leukemia 34:919–923

    Article  PubMed  Google Scholar 

  19. Jeck WR, Sorrentino JA, Wang K et al (2013) Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 19:141–157

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Jeck WR, Sharpless NE (2014) Detecting and characterizing circular RNAs. Nat Biotechnol 32:453–461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Toubia J, Conn VM, Conn SJ (2018) Don’t go in circles: confounding factors in gene expression profiling. EMBO J 37:e97945

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Wang J, Liu K, Liu Y et al (2017) Evaluating the bias of circRNA predictions from total RNA-Seq data. Oncotarget 8:110914–110921

    Article  PubMed  PubMed Central  Google Scholar 

  23. Gao Y, Zhang J, Zhao F (2018) Circular RNA identification based on multiple seed matching. Brief Bioinform 19:803–810

    Article  CAS  PubMed  Google Scholar 

  24. Li H (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM

    Google Scholar 

  25. Quinlan AR (2014) BEDTools: the Swiss-Army tool for genome feature analysis. Curr Protoc Bioinformatics 47:11.12.1–11.1234

    Article  Google Scholar 

  26. Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Babraham Bioinformatics - FastQC A Quality Control tool for High Throughput Sequence Data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 18 Aug 2020

  29. Leinonen R, Sugawara H, Shumway M, on behalf of the International Nucleotide Sequence Database Collaboration (2011) The sequence read archive. Nucleic Acids Res 39:D19–D21

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Gao Y, Wang J, Zheng Y et al (2016) Comprehensive identification of internal structure and alternative splicing events in circular RNAs. Nat Commun 7:12060

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chen L, Wang C, Sun H et al (2020) The bioinformatics toolbox for circRNA discovery and analysis. Brief Bioinform. https://doi.org/10.1093/bib/bbaa001

  33. Love M, Anders S, Huber W (2014) Differential analysis of count data--the DESeq2 package. Genome Biol 15:10–1186

    Article  CAS  Google Scholar 

  34. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140

    Article  CAS  PubMed  Google Scholar 

  35. Ritchie ME, Phipson B, Wu D et al (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Hardcastle TJ (2012) baySeq: empirical Bayesian analysis of patterns of differential expression in count data. R package version 2

    Google Scholar 

  37. Liang D, Tatomer DC, Luo Z et al (2017) The output of protein-coding genes shifts to circular RNAs when the pre-mRNA processing machinery is limiting. Mol Cell 68:940–954.e3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  39. Enright AJ, John B, Gaul U et al (2003) MicroRNA targets in Drosophila. Genome Biol 5:R1

    Article  PubMed  PubMed Central  Google Scholar 

  40. Kertesz M, Iovino N, Unnerstall U et al (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39:1278–1284

    Article  CAS  PubMed  Google Scholar 

  41. Yu H, Wang J, Sheng Q et al (2019) beRBP: binding estimation for human RNA-binding proteins. Nucleic Acids Res 47:e26

    Article  CAS  PubMed  Google Scholar 

  42. Pan X, Rijnbeek P, Yan J, Shen H-B (2018) Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks. BMC Genomics 19(1):511

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Sun P, Li G (2019) CircCode: a powerful tool for identifying circRNA coding ability. Front Genet 10:981

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Xia S, Feng J, Chen K et al (2018) CSCD: a database for cancer-specific circular RNAs. Nucleic Acids Res 46:D925–D929

    Article  CAS  PubMed  Google Scholar 

  45. Dori M, Bicciato S (2019) Integration of Bioinformatic predictions and experimental data to identify circRNA-miRNA associations. Genes (Basel) 10:642. https://doi.org/10.3390/genes10090642

    Article  CAS  Google Scholar 

  46. Gaffo E, Bonizzato A, Kronnie GT, Bortoluzzi S (2017) CirComPara: a multi-method comparative bioinformatics pipeline to detect and study circRNAs from RNA-seq data. Noncoding RNA 3:8. https://doi.org/10.3390/ncrna3010008

    Article  CAS  PubMed Central  Google Scholar 

  47. Metge F, Czaja-Hasse LF, Reinhardt R, Dieterich C (2017) FUCHS-towards full circular RNA characterization using RNAseq. PeerJ 5:e2934

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Jakobi T, Uvarovskii A, Dieterich C (2019) Circtools—a one-stop software solution for circular RNA research. Bioinformatics 35:2326–2328

    Article  CAS  PubMed  Google Scholar 

  49. Chen L, Wang F, Bruggeman EC et al (2020) circMeta: a unified computational framework for genomic feature annotation and differential expression analysis of circular RNAs. Bioinformatics 36:539–545

    Article  CAS  PubMed  Google Scholar 

  50. Ma X-K, Wang M-R, Liu C-X et al (2019) CIRCexplorer3: a CLEAR pipeline for direct comparison of circular and linear RNA expression. Genomics Proteomics Bioinformatics 17:511–521

    Article  PubMed  Google Scholar 

  51. Hansen TB (2018) Improved circRNA identification by combining prediction algorithms. Front Cell Dev Biol 6:20

    Article  PubMed  PubMed Central  Google Scholar 

  52. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Vromman M, Vandesompele J, Volders P-J (2020) Closing the circle: current state and perspectives of circular RNA databases. Brief Bioinform 22(1):288–297. https://doi.org/10.1093/bib/bbz175

    Article  CAS  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Molecular Medicine, University of Padova, Padova, Italy

    Enrico Gaffo, Alessia Buratin, Anna Dal Molin & Stefania Bortoluzzi

  2. Department of Biology, University of Padova, Padova, Italy

    Alessia Buratin

  3. Interdepartmental Research Center for Innovative Biotechnologies (CRIBI), University of Padova, Padova, Italy

    Stefania Bortoluzzi

Authors
  1. Enrico Gaffo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alessia Buratin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anna Dal Molin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stefania Bortoluzzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefania Bortoluzzi .

Editor information

Editors and Affiliations

  1. Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain

    Alfons Navarro

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Gaffo, E., Buratin, A., Dal Molin, A., Bortoluzzi, S. (2021). Bioinformatic Analysis of Circular RNA Expression. In: Navarro, A. (eds) Long Non-Coding RNAs in Cancer. Methods in Molecular Biology, vol 2348. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1581-2_22

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1581-2_22

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1580-5

  • Online ISBN: 978-1-0716-1581-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation