Skip to main content
SpringerLink
Log in

NGS Methodologies and Computational Algorithms for the Prediction and Analysis of Plant Circular RNAs

  • Protocol
  • First Online:
Plant Circular RNAs

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2362))

Abstract

Circular RNAs (circRNAs) are a class of single-stranded RNAs derived from exonic, intronic, and intergenic regions from precursor messenger RNAs (pre-mRNA), where a noncanonical back-splicing event occurs, in which the 5′ and 3′ ends are attached by covalent bond. CircRNAs participate in the regulation of gene expression at the transcriptional and posttranscriptional level primarily as miRNA and RNA-binding protein (RBP) sponges, but also involved in the regulation of alternative RNA splicing and transcription. CircRNAs are widespread and abundant in plants where they have been involved in stress responses and development. Through the analysis of all publications in this field in the last five years, we can summarize that the identification of these molecules is carried out through next generation sequencing studies, where samples have been previously treated to eliminate DNA, rRNA, and linear RNAs as a means to enrich circRNAs. Once libraries are prepared, they are sequenced and subsequently studied from a bioinformatics point of view. Among the different tools for identifying circRNAs, we can highlight CIRI as the most used (in 60% of the published studies), as well as CIRCExplorer (20%) and find_circ (20%). Although it is recommended to use more than one program in combination, and preferably developed specifically to treat with plant samples, this is not always the case. It should also be noted that after identifying these circular RNAs, most of the authors validate their findings in the laboratory in order to obtain bona fide results.

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. Ye CY, Chen L, Liu C et al (2015) Widespread noncoding circular RNAs in plants. New Phytol 208:88–95. https://doi.org/10.1111/nph.13585

    Article  CAS  PubMed  Google Scholar 

  2. Xiao MS, Ai Y, Wilusz JE (2020) Biogenesis and functions of circular RNAs come into focus. Trends Cell Biol 30:226–240

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Zhang P, Li S, Chen M (2020) Characterization and function of Circular RNAs in plants. Front Mol Biosci 7:91

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Jeck WR, Sorrentino JA, Wang K et al (2013) Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 19:141–157. https://doi.org/10.1261/rna.035667.112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Memczak S, Jens M, Elefsinioti A et al (2013) Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495:333–338. https://doi.org/10.1038/nature11928

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Guo JU, Agarwal V, Guo H et al (2014) Expanded identification and characterization of mammalian circular RNAs. Genome Biol 15:409. https://doi.org/10.1186/s13059-014-0409-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chen LL (2016) The biogenesis and emerging roles of circular RNAs. Nat Rev Mol Cell Biol 17:205–211

    Article  CAS  PubMed  Google Scholar 

  9. Suzuki H, Tsukahara T (2014) A view of pre-mRNA splicing from RNase R resistant RNAs. Int J Mol Sci 15:9331–9342

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 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:e30733. https://doi.org/10.1371/journal.pone.0030733

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Rybak-Wolf A, Stottmeister C, Glažar P et al (2014) Circular RNAs in the mammalian brain are highly abundant, conserved, and dynamically expressed. Mol Cell 58:870–885. https://doi.org/10.1016/j.molcel.2015.03.027

    Article  CAS  Google Scholar 

  12. Venø MT, Hansen TB, Venø ST et al (2015) Spatio-temporal regulation of circular RNA expression during porcine embryonic brain development. Genome Biol 16:245. https://doi.org/10.1186/s13059-015-0801-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhang P, Liu Y, Chen H et al (2020) CircPlant: an integrated tool for CircRNA detection and functional prediction in plants. Genomics Proteomics Bioinformatics 18(3):352–358. https://doi.org/10.1016/j.gpb.2020.10.001

    Article  PubMed  PubMed Central  Google Scholar 

  14. Wang PL, Bao Y, Yee MC et al (2014) Circular RNA is expressed across the eukaryotic tree of life. PLoS One 9:e90859. https://doi.org/10.1371/journal.pone.0090859

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kristensen LS, Andersen MS, Stagsted LVW et al (2019) The biogenesis, biology and characterization of circular RNAs. Nat Rev Genet 20:675–691

    Article  CAS  PubMed  Google Scholar 

  16. Li T, Shao Y, Fu L et al (2018) Plasma circular RNA profiling of patients with gastric cancer and their droplet digital RT-PCR detection. J Mol Med 96:85–96. https://doi.org/10.1007/s00109-017-1600-y

    Article  CAS  PubMed  Google Scholar 

  17. Sun X, Wang L, Ding J et al (2016) Integrative analysis of Arabidopsis thaliana transcriptomics reveals intuitive splicing mechanism for circular RNA. FEBS Lett 590:3510–3516. https://doi.org/10.1002/1873-3468.12440

    Article  CAS  PubMed  Google Scholar 

  18. Zhang Y, Zhang XO, Chen T et al (2013) Circular Intronic Long Noncoding RNAs. Mol Cell 51:792–806. https://doi.org/10.1016/j.molcel.2013.08.017

    Article  CAS  PubMed  Google Scholar 

  19. Zhao T, Wang L, Li S et al (2017) Characterization of conserved circular RNA in polyploid Gossypium species and their ancestors. FEBS Lett 591:3660–3669

    Article  CAS  PubMed  Google Scholar 

  20. Zhao W, Cheng Y, Zhang C et al (2017) Genome-wide identification and characterization of circular RNAs by high throughput sequencing in soybean. Sci Rep 7:1–11. https://doi.org/10.1038/s41598-017-05922-9

    Article  CAS  Google Scholar 

  21. Zhu YX, Jia JH, Yang L et al (2019) Identification of cucumber circular RNAs responsive to salt stress. BMC Plant Biol 19:1–18. https://doi.org/10.1186/s12870-019-1712-3

    Article  Google Scholar 

  22. Lu T, Cui L, Zhou Y et al (2015) Transcriptome-wide investigation of circular RNAs in rice. RNA 21:2076–2087. https://doi.org/10.1261/rna.052282.115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wang X, Chang X, Jing Y et al (2020) Identification and functional prediction of soybean CircRNAs involved in low-temperature responses. J Plant Physiol 250:153188. https://doi.org/10.1016/j.jplph.2020.153188

    Article  CAS  PubMed  Google Scholar 

  24. Szcześniak MW, Kabza M, Pokrzywa R et al (2013) ERISdb: A database of plant splice sites and splicing signals. Plant Cell Physiol 54:e10. https://doi.org/10.1093/pcp/pct001

    Article  CAS  PubMed  Google Scholar 

  25. Ye CY, Zhang X, Chu Q et al (2017) Full-length sequence assembly reveals circular RNAs with diverse non-GT/AG splicing signals in rice. RNA Biol 14:1055–1063. https://doi.org/10.1080/15476286.2016.1245268

    Article  PubMed  Google Scholar 

  26. Chen L, Yu Y, Zhang X et al (2016) PcircRNA-finder: A software for circRNA prediction in plants. Bioinformatics 32:3528–3529. https://doi.org/10.1093/bioinformatics/btw496

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Starke S, Jost I, Rossbach O et al (2015) Exon circularization requires canonical splice signals. Cell Rep 10:103–111. https://doi.org/10.1016/j.celrep.2014.12.002

    Article  CAS  PubMed  Google Scholar 

  28. 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 16:126. https://doi.org/10.1186/s13059-015-0690-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Chen G, Cui J, Wang L et al (2017) Genome-wide identification of circular RNAs in Arabidopsis thaliana. Front Plant Sci 8:1–12. https://doi.org/10.3389/fpls.2017.01678

    Article  Google Scholar 

  30. Li QF, Zhang YC, Chen YQ et al (2017) Circular RNAs roll into the regulatory network of plants. Biochem Biophys Res Commun 488:382–386. https://doi.org/10.1016/j.bbrc.2017.05.061

    Article  CAS  PubMed  Google Scholar 

  31. Litholdo CG, da Fonseca GC (2018) Circular RNAs and plant stress responses. Adv Exp Med Biol 1087:345–353. https://doi.org/10.1007/978-981-13-1426-1_27

    Article  CAS  PubMed  Google Scholar 

  32. Westholm JO, Miura P, Olson S et al (2014) Genome-wide analysis of drosophila circular RNAs reveals their structural and sequence properties and age-dependent neural accumulation. Cell Rep 9:1966–1980. https://doi.org/10.1016/j.celrep.2014.10.062

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Fan X, Zhang X, Wu X et al (2015) Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol 16:148. https://doi.org/10.1186/s13059-015-0706-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Hansen TB, Jensen TI, Clausen BH et al (2013) Natural RNA circles function as efficient microRNA sponges. Nature 495:384–388. https://doi.org/10.1038/nature11993

    Article  CAS  PubMed  Google Scholar 

  35. Zuo J, Wang Q, Zhu B et al (2016) Deciphering the roles of circRNAs on chilling injury in tomato. Biochem Biophys Res Commun 479:132–138. https://doi.org/10.1016/j.bbrc.2016.07.032

    Article  CAS  PubMed  Google Scholar 

  36. Ashwal-Fluss R, Meyer M, Pamudurti NR et al (2014) CircRNA Biogenesis competes with Pre-mRNA splicing. Mol Cell 56:55–66. https://doi.org/10.1016/j.molcel.2014.08.019

    Article  CAS  PubMed  Google Scholar 

  37. Huang S, Yang B, Chen BJ et al (2017) The emerging role of circular RNAs in transcriptome regulation. Genomics 109:401–407

    Article  CAS  PubMed  Google Scholar 

  38. Conn VM, Hugouvieux V, Nayak A et al (2017) A circRNA from SEPALLATA3 regulates splicing of its cognate mRNA through R-loop formation. Nat Plants 3:17053. https://doi.org/10.1038/nplants.2017.53

    Article  CAS  PubMed  Google Scholar 

  39. Pamudurti NR, Bartok O, Jens M et al (2017) Translation of CircRNAs. Mol Cell 66:9–21.e7. https://doi.org/10.1016/j.molcel.2017.02.021

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 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. https://doi.org/10.1016/j.molcel.2017.02.017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Yang Y, Fan X, Mao M et al (2017) Extensive translation of circular RNAs driven by N 6 -methyladenosine. Cell Res 27:626–641. https://doi.org/10.1038/cr.2017.31

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wang Y, Yang M, Wei S et al (2017) Identification of circular RNAs and their targets in leaves of Triticum aestivum L. under dehydration stress. Front Plant Sci 7:2024. https://doi.org/10.3389/fpls.2016.02024

    Article  PubMed  PubMed Central  Google Scholar 

  43. Dong Y, Chen H, Gao J et al (2019) Bioactive ingredients in Chinese herbal medicines that target non-coding RNAs: promising new choices for disease treatment. Front Pharmacol 10:1–30. https://doi.org/10.3389/fphar.2019.00515

    Article  CAS  Google Scholar 

  44. Zhang P, Meng X, Chen H et al (2017) PlantCircNet: a database for plant circRNA-miRNA-mRNA regulatory networks. Database (Oxford) 2017:1–8. https://doi.org/10.1093/database/bax089

    Article  CAS  Google Scholar 

  45. Chu Q, Zhang X, Zhu X et al (2017) PlantcircBase: a database for plant circular RNAs. Mol Plant 10:1126–1128

    Article  CAS  PubMed  Google Scholar 

  46. Gao Z, Li J, Luo M et al (2019) Characterization and cloning of grape circular rnas identified the cold resistance-related vv-circats1. Plant Physiol 180:966–985. https://doi.org/10.1104/pp.18.01331

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Philips A, Nowis K, Stelmaszczuk M et al (2020) Arabidopsis thaliana cbp80, c2h2, and flk knockout mutants accumulate increased amounts of circular RNAs. Cell 9:1937. https://doi.org/10.3390/cells9091937

    Article  CAS  Google Scholar 

  48. Wang Y, Wang Q, Gao L et al (2017) Integrative analysis of circRNAs acting as ceRNAs involved in ethylene pathway in tomato. Physiol Plant 161:311–321. https://doi.org/10.1111/ppl.12600

    Article  CAS  PubMed  Google Scholar 

  49. Wang Y, Xiong Z, Li Q et al (2019) Circular RNA profiling of the rice photo-thermosensitive genic male sterile line Wuxiang S reveals circRNA involved in the fertility transition. BMC Plant Biol 19:1–16. https://doi.org/10.1186/s12870-019-1944-2

    Article  CAS  Google Scholar 

  50. Tan J, Zhou Z, Niu Y et al (2017) Identification and functional characterization of tomato CircRNAs derived from genes involved in fruit pigment accumulation. Sci Rep 7:8594. https://doi.org/10.1038/s41598-017-08806-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Zhang XO, Bin WH, Zhang Y et al (2014) Complementary sequence-mediated exon circularization. Cell 159:134–147. https://doi.org/10.1016/j.cell.2014.09.001

    Article  CAS  PubMed  Google Scholar 

  52. Hoffmann S, Otto C, Doose G et al (2014) A multi-split mapping algorithm for circular RNA, splicing, trans-splicing and fusion detection. Genome Biol 15:r34. https://doi.org/10.1186/gb-2014-15-2-r34

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Gao Y, Wang J, Zhao F (2015) CIRI: An efficient and unbiased algorithm for de novo circular RNA identification. Genome Biol 16:4. https://doi.org/10.1186/s13059-014-0571-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Gao Y, Zhang J, Zhao F (2018) Circular RNA identification based on multiple seed matching. Brief Bioinform 19:803–810. https://doi.org/10.1093/bib/bbx014

    Article  CAS  PubMed  Google Scholar 

  55. Wang K, Singh D, Zeng Z et al (2010) MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 38:e178. https://doi.org/10.1093/nar/gkq622

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. López-Jiménez E, Rojas AM, Andrés-León E (2018) RNA sequencing and prediction tools for circular RNAs analysis. In: Advances in experimental medicine and biology. Springer New York LLC, New York, pp 17–33

    Google Scholar 

  57. Meng X, Zhang P, Chen Q et al (2018) Identification and characterization of ncRNA-associated ceRNA networks in Arabidopsis leaf development. BMC Genomics 19:1–10. https://doi.org/10.1186/s12864-018-4993-2

    Article  CAS  Google Scholar 

  58. Liu T, Zhang L, Chen G et al (2017) Identifying and characterizing the circular RNAs during the lifespan of Arabidopsis leaves. Front Plant Sci 8:1–9. https://doi.org/10.3389/fpls.2017.01278

    Article  Google Scholar 

  59. Kent WJ (2002) BLAT-The BLAST-like alignment tool. Genome Res 12:656–664. https://doi.org/10.1101/gr.229202

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Chen L, Zhang P, Fan Y et al (2018) Circular RNAs mediated by transposons are associated with transcriptomic and phenotypic variation in maize. New Phytol 217:1292–1306. https://doi.org/10.1111/nph.14901

    Article  CAS  PubMed  Google Scholar 

  61. Zeng X, Lin W, Guo M et al (2017) A comprehensive overview and evaluation of circular RNA detection tools. PLoS Comput Biol 13:e1005420

    Article  PubMed  PubMed Central  Google Scholar 

  62. Hansen TB (2018) Improved circRNA identification by combining prediction algorithms. Front Cell Dev Biol 6:20. https://doi.org/10.3389/fcell.2018.00020

    Article  PubMed  PubMed Central  Google Scholar 

  63. Hansen TB, Venø MT, Damgaard CK et al (2015) Comparison of circular RNA prediction tools. Nucleic Acids Res 44:e58. https://doi.org/10.1093/nar/gkv1458

    Article  PubMed  PubMed Central  Google Scholar 

  64. Chen X, Sun S, Liu F et al (2019) A transcriptomic profile of topping responsive non-coding RNAs in tobacco roots (Nicotiana tabacum). BMC Genomics 20:1–14. https://doi.org/10.1186/s12864-019-6236-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. https://doi.org/10.1186/s13059-014-0550-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Robinson MD, McCarthy DJ, Smyth GK (2009) edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140. https://doi.org/10.1093/bioinformatics/btp616

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Fan J, Quan W, Li GB et al (2020) CircRNAs are involved in the rice-Magnaporthe oryzae interaction. Plant Physiol 182:272–286. https://doi.org/10.1104/pp.19.00716

    Article  CAS  PubMed  Google Scholar 

  68. Zuo J, Wang Y, Zhu B et al (2019) Network analysis of noncoding RNAs in pepper provides insights into fruit ripening control. Sci Rep 9:1–11. https://doi.org/10.1038/s41598-019-45427-1

    Article  CAS  Google Scholar 

  69. Alexa A, Rahnenfuhrer J (2020) topGO: Enrichment Analysis for Gene Ontology. R package version 2.42.0

    Google Scholar 

  70. Conesa A, Götz S (2008) Blast2GO: A comprehensive suite for functional analysis in plant genomics. Int J Plant Genomics 2008:619832. https://doi.org/10.1155/2008/619832

    Article  CAS  PubMed  Google Scholar 

  71. Young MD, Wakefield MJ, Smyth GK et al (2010) Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol 11:r14. https://doi.org/10.1186/gb-2010-11-2-r14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Tian T, Liu Y, Yan H et al (2017) AgriGO v2.0: A GO analysis toolkit for the agricultural community, 2017 update. Nucleic Acids Res 45:W122–W129. https://doi.org/10.1093/nar/gkx382

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Xie C, Mao X, Huang J et al (2011) KOBAS 2.0: A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res 39:W316. https://doi.org/10.1093/nar/gkr483

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57. https://doi.org/10.1038/nprot.2008.211

    Article  CAS  Google Scholar 

  75. Chen L, Ding X, Zhang H et al (2018) Comparative analysis of circular RNAs between soybean cytoplasmic male-sterile line NJCMS1A and its maintainer NJCMS1B by high-throughput sequencing. BMC Genomics 19:1–14. https://doi.org/10.1186/s12864-018-5054-6

    Article  CAS  Google Scholar 

  76. Wang Y, Wang H, Xi F et al (2020) Profiling of circular RNA N6-methyladenosine in moso bamboo (Phyllostachys edulis) using nanopore-based direct RNA sequencing. J Integr Plant Biol. https://doi.org/10.1111/jipb.13002

  77. Prats AC, Prats H (2002) Translational control of gene expression: Role of IRESs and consequences for cell transformation and angiogenesis. Prog Nucleic Acid Res Mol Biol 72:367–413

    Article  CAS  PubMed  Google Scholar 

  78. Dai X, Zhuang Z, Zhao PX (2018) PsRNATarget: A plant small RNA target analysis server (2017 release). Nucleic Acids Res 46:W49–W54. https://doi.org/10.1093/nar/gky316

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Xiaochen B, Wang S (2005) TargetFinder: A software for antisense oligonucleotide target site selection based on MAST and secondary structures of target mRNA. Bioinformatics 21:1401–1402. https://doi.org/10.1093/bioinformatics/bti211

    Article  CAS  Google Scholar 

  80. Wu HJ, Ma YK, Chen T et al (2012) PsRobot: A web-based plant small RNA meta-analysis toolbox. Nucleic Acids Res 40:w228. https://doi.org/10.1093/nar/gks554

    Article  CAS  Google Scholar 

  81. Agarwal V, Bell GW, Nam JW et al (2015) Predicting effective microRNA target sites in mammalian mRNAs. Elife 4:e05005. https://doi.org/10.7554/eLife.05005

    Article  PubMed Central  Google Scholar 

  82. Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: A software Environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Li JH, Liu S, Zhou H et al (2014) StarBase v2.0: Decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 42:D92–D97. https://doi.org/10.1093/nar/gkt1248

    Article  CAS  PubMed  Google Scholar 

  84. Pan T, Sun X, Liu Y et al (2018) Heat stress alters genome-wide profiles of circular RNAs in Arabidopsis. Plant Mol Biol 96:217–229. https://doi.org/10.1007/s11103-017-0684-7

    Article  CAS  PubMed  Google Scholar 

  85. Ghosal S, Das S, Sen R et al (2013) Circ2Traits: A comprehensive database for circular RNA potentially associated with disease and traits. Front Genet 4:283. https://doi.org/10.3389/fgene.2013.00283

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Zhuo C, Ke L, Zhangming Y et al (2015) nc2Cancer:a database for cancer-associated human ncRNAs. China J Bioinforma:77–81

    Google Scholar 

  87. Glažar P, Papavasileiou P, Rajewsky N (2014) CircBase: A database for circular RNAs. RNA 20:1666–1670. https://doi.org/10.1261/rna.043687.113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Liu YC, Li JR, Sun CH et al (2016) CircNet: A database of circular RNAs derived from transcriptome sequencing data. Nucleic Acids Res 44:D209–D215. https://doi.org/10.1093/nar/gkv940

    Article  CAS  PubMed  Google Scholar 

  89. Zheng LL, Li JH, Wu J et al (2016) deepBase v2.0: Identification, expression, evolution and function of small RNAs, LncRNAs and circular RNAs from deep-sequencing data. Nucleic Acids Res 44:D196–D202. https://doi.org/10.1093/nar/gkv1273

    Article  CAS  PubMed  Google Scholar 

  90. Dudekula DB, Panda AC, Grammatikakis I et al (2016) Circinteractome: A web tool for exploring circular RNAs and their interacting proteins and microRNAs. RNA Biol 13:34–42. https://doi.org/10.1080/15476286.2015.1128065

    Article  PubMed  Google Scholar 

  91. Chen X, Han P, Zhou T et al (2016) CircRNADb: A comprehensive database for human circular RNAs with protein-coding annotations. Sci Rep 6:34985. https://doi.org/10.1038/srep34985

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Li S, Li Y, Chen B et al (2018) ExoRBase: A database of circRNA, lncRNA and mRNA in human blood exosomes. Nucleic Acids Res 46:D106–D112. https://doi.org/10.1093/nar/gkx891

    Article  CAS  PubMed  Google Scholar 

  93. Xia S, Feng J, Chen K et al (2018) CSCD: A database for cancer-specific circular RNAs. Nucleic Acids Res 46:D925–D929. https://doi.org/10.1093/nar/gkx863

    Article  CAS  PubMed  Google Scholar 

  94. Liu M, Wang Q, Shen J et al (2019) Circbank: a comprehensive database for circRNA with standard nomenclature. RNA Biol 16:899–905. https://doi.org/10.1080/15476286.2019.1600395

    Article  PubMed  PubMed Central  Google Scholar 

  95. Zhang J, Hao Z, Yin S et al (2020) GreenCircRNA: a database for plant circRNAs that act as miRNA decoys. Database (Oxford) 2020:baaa039. https://doi.org/10.1093/database/baaa039

    Article  CAS  Google Scholar 

  96. Ye J, Wang L, Li S et al (2019) AtCircDB: A tissue-specific database for Arabidopsis circular RNAs. Brief Bioinform 20:58–65. https://doi.org/10.1093/bib/bbx089

    Article  CAS  PubMed  Google Scholar 

  97. Wang K, Wang C, Guo B et al (2019) CropCircDB: A comprehensive circular RNA resource for crops in response to abiotic stress. Database 2019:1–7. https://doi.org/10.1093/database/baz053

    Article  CAS  Google Scholar 

  98. Meng X, Hu D, Zhang P et al (2019) CircFunBase: A database for functional circular RNAs. Database 2019:1–6. https://doi.org/10.1093/database/baz003

    Article  CAS  Google Scholar 

  99. Wang H, Wang H, Zhang H et al (2019) The interplay between microRNA and alternative splicing of linear and circular RNAs in eleven plant species. Bioinformatics 35:3119–3126. https://doi.org/10.1093/bioinformatics/btz038

    Article  CAS  PubMed  Google Scholar 

  100. Tong W, Yu J, Hou Y et al (2018) Circular RNA architecture and differentiation during leaf bud to young leaf development in tea (Camellia sinensis). Planta 248:1417–1429. https://doi.org/10.1007/s00425-018-2983-x

    Article  CAS  PubMed  Google Scholar 

  101. Guria A, Velayudha Vimala Kumar K, Srikakulam N et al (2019) Circular RNA profiling by illumina sequencing via template-dependent multiple displacement amplification. Biomed Res Int 2019:2756516. https://doi.org/10.1155/2019/2756516

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Zhang P, Fan Y, Sun X et al (2019) A large-scale circular RNA profiling reveals universal molecular mechanisms responsive to drought stress in maize and Arabidopsis. Plant J 98:697–713. https://doi.org/10.1111/tpj.14267

    Article  CAS  PubMed  Google Scholar 

  103. Liu Y, Yu X, Feng Y et al (2017) Physiological and transcriptome response to cadmium in cosmos (Cosmos bipinnatus Cav.) seedlings. Sci Rep 7:1–16. https://doi.org/10.1038/s41598-017-14407-8

    Article  CAS  Google Scholar 

  104. Kulcheski FR, Christoff AP, Margis R (2016) Circular RNAs are miRNA sponges and can be used as a new class of biomarker. J Biotechnol 238:42–51. https://doi.org/10.1016/j.jbiotec.2016.09.011

    Article  CAS  PubMed  Google Scholar 

  105. Zhang X, Ma X, Ning L et al (2019) Genome-wide identification of circular RNAs in peanut (Arachis hypogaea L.). BMC Genomics 20:1–10. https://doi.org/10.1186/s12864-019-6020-7

    Article  CAS  Google Scholar 

  106. Capelari ÉF, da Fonseca GC, Guzman F et al (2019) Circular and micro RNAs from Arabidopsis thaliana flowers are simultaneously isolated from AGO-IP libraries. Plan Theory 8:302. https://doi.org/10.3390/plants8090302

    Article  CAS  Google Scholar 

  107. Darbani B, Noeparvar S, Borg S (2016) Identification of circular RNAs from the parental genes involved in multiple aspects of cellular metabolism in barley. Front Plant Sci 7:1–8. https://doi.org/10.3389/fpls.2016.00776

    Article  Google Scholar 

  108. Wang Y, Gao Y, Zhang H et al (2019) Genome-wide profiling of circular RNAs in the rapidly growing shoots of moso bamboo (Phyllostachys edulis). Plant Cell Physiol 60:1354–1373. https://doi.org/10.1093/pcp/pcz043

    Article  CAS  PubMed  Google Scholar 

  109. Tang B, Hao Z, Zhu Y et al (2018) Genome-wide identification and functional analysis of circRNAs in Zea mays. PLoS One 13:1–17. https://doi.org/10.1371/journal.pone.0202375

    Article  CAS  Google Scholar 

  110. Luo Z, Han L, Qian J et al (2019) Circular RNAs exhibit extensive intraspecific variation in maize. Planta 250:69–78. https://doi.org/10.1007/s00425-019-03145-y

    Article  CAS  PubMed  Google Scholar 

  111. Zhang J, Liu R, Zhu Y et al (2020) Identification and characterization of circRNAs responsive to methyl jasmonate in Arabidopsis thaliana. Int J Mol Sci 21:792. https://doi.org/10.3390/ijms21030792

    Article  CAS  PubMed Central  Google Scholar 

  112. Liang Y, Zhang Y, Xu L et al (2019) CircRNA expression pattern and ceRNA and miRNA-mRNA networks involved in anther development in the CMS line of Brassica campestris. Int J Mol Sci 20:4808. https://doi.org/10.3390/ijms20194808

    Article  CAS  PubMed Central  Google Scholar 

  113. Hu H, Wang M, Ding Y et al (2018) Transcriptomic repertoires depict the initiation of lint and fuzz fibres in cotton (Gossypium hirsutum L.). Plant Biotechnol J 16:1002–1012. https://doi.org/10.1111/pbi.12844

    Article  CAS  PubMed  Google Scholar 

  114. Zhang G, Duan A, Zhang J et al (2017) Genome-wide analysis of long non-coding RNAs at the mature stage of sea buckthorn (Hippophae rhamnoides Linn) fruit. Gene 596:130–136. https://doi.org/10.1016/j.gene.2016.10.017

    Article  CAS  PubMed  Google Scholar 

  115. Zhang G, Diao S, Zhang T et al (2019) Identification and characterization of circular RNAs during the sea buckthorn fruit development. RNA Biol 16:354–361. https://doi.org/10.1080/15476286.2019.1574162

    Article  PubMed  PubMed Central  Google Scholar 

  116. Li C, Qin S, Bao L et al (2020) Identification and functional prediction of circRNAs in Populus euphratica Oliv. heteromorphic leaves. Genomics 112:92–98. https://doi.org/10.1016/j.ygeno.2019.01.013

    Article  CAS  PubMed  Google Scholar 

  117. Qin SW, Jiang RJ, Zhang N et al (2018) Genome-wide analysis of RNAs associated with Populus euphratica Oliv. heterophyll morphogenesis. Sci Rep 8:1–9. https://doi.org/10.1038/s41598-018-35371-x

    Article  CAS  Google Scholar 

  118. Zeng RF, Zhou JJ, Hu CG et al (2018) Transcriptome-wide identification and functional prediction of novel and flowering-related circular RNAs from trifoliate orange (Poncirus trifoliata L. Raf.). Planta 247:1191–1202. https://doi.org/10.1007/s00425-018-2857-2

    Article  CAS  PubMed  Google Scholar 

  119. Yin J, Liu M, Ma D et al (2018) Identification of circular RNAs and their targets during tomato fruit ripening. Postharvest Biol Technol 136:90–98. https://doi.org/10.1016/j.postharvbio.2017.10.013

    Article  CAS  Google Scholar 

  120. Zhou R, Xu L, Zhao L et al (2018) Genome-wide identification of circRNAs involved in tomato fruit coloration. Biochem Biophys Res Commun 499:466–469. https://doi.org/10.1016/j.bbrc.2018.03.167

    Article  CAS  PubMed  Google Scholar 

  121. Zuo J, Grierson D, Courtney LT et al (2020) Relationships between genome methylation, levels of non-coding RNAs, mRNAs and metabolites in ripening tomato fruit. Plant J 103:980–994. https://doi.org/10.1111/tpj.14778

    Article  CAS  PubMed  Google Scholar 

  122. Xu Y, Ren Y, Lin T et al (2019) Identification and characterization of CircRNAs involved in the regulation of wheat root length. Biol Res 52:19. https://doi.org/10.1186/s40659-019-0228-5

    Article  PubMed  PubMed Central  Google Scholar 

  123. Wang Z, Liu Y, Li D et al (2017) Identification of circular rnas in kiwifruit and their species-specific response to bacterial canker pathogen invasion. Front Plant Sci 8:413. https://doi.org/10.3389/fpls.2017.00413

    Article  PubMed  PubMed Central  Google Scholar 

  124. Sun Y, Zhang H, Fan M et al (2020) Genome-wide identification of long non-coding RNAs and circular RNAs reveal their ceRNA networks in response to cucumber green mottle mosaic virus infection in watermelon. Arch Virol 165:1177–1190. https://doi.org/10.1007/s00705-020-04589-4

    Article  CAS  PubMed  Google Scholar 

  125. Xiang L, Cai C, Cheng J et al (2018) Identification of circularRNAs and their targets in Gossypium under Verticillium wilt stress based on RNA-seq. PeerJ 2018:1–25. https://doi.org/10.7717/peerj.4500

    Article  CAS  Google Scholar 

  126. Wang J, Yang Y, Jin L et al (2018) Re-analysis of long non-coding RNAs and prediction of circRNAs reveal their novel roles in susceptible tomato following TYLCV infection. BMC Plant Biol 18:1–16. https://doi.org/10.1186/s12870-018-1332-3

    Article  CAS  Google Scholar 

  127. Hong YH, Meng J, Zhang M et al (2020) Identification of tomato circular RNAs responsive to Phytophthora infestans. Gene 746:144652. https://doi.org/10.1016/j.gene.2020.144652

    Article  CAS  PubMed  Google Scholar 

  128. Zhou R, Zhu Y, Zhao J et al (2018) Transcriptome-wide identification and characterization of potato circular RNAs in response to Pectobacterium carotovorum subspecies brasiliense infection. Int J Mol Sci 19:1–12. https://doi.org/10.3390/ijms19010071

    Article  CAS  Google Scholar 

  129. Ghorbani A, Izadpanah K, Peters JR et al (2018) Detection and profiling of circular RNAs in uninfected and maize Iranian mosaic virus-infected maize. Plant Sci 274:402–409. https://doi.org/10.1016/j.plantsci.2018.06.016

    Article  CAS  PubMed  Google Scholar 

  130. Wang W, Wang J, Wei Q et al (2019) Transcriptome-Wide Identification and Characterization of Circular RNAs in Leaves of Chinese Cabbage (Brassica rapa L. ssp. pekinensis) in Response to Calcium Deficiency-Induced Tip-burn. Sci Rep 9:1–9. https://doi.org/10.1038/s41598-019-51190-0

    Article  CAS  Google Scholar 

  131. Zuo J, Wang Y, Zhu B et al (2018) Analysis of the coding and non-coding RNA transcriptomes in response to bell pepper chilling. Int J Mol Sci 19:1–15. https://doi.org/10.3390/ijms19072001

    Article  CAS  Google Scholar 

  132. Fu XZ, Zhang XY, Qiu JY et al (2019) Whole-transcriptome RNA sequencing reveals the global molecular responses and ceRNA regulatory network of mRNAs, lncRNAs, miRNAs and circRNAs in response to copper toxicity in Ziyang Xiangcheng (Citrus junos Sieb. Ex Tanaka). BMC Plant Biol 19:1–20. https://doi.org/10.1186/s12870-019-2087-1

    Article  CAS  Google Scholar 

  133. He X, Guo S, Wang Y et al (2020) Systematic identification and analysis of heat-stress-responsive lncRNAs, circRNAs and miRNAs with associated co-expression and ceRNA networks in cucumber (Cucumis sativus L.). Physiol Plant 168:736–754. https://doi.org/10.1111/ppl.12997

    Article  CAS  PubMed  Google Scholar 

  134. Lv L, Yu K, Lü H et al (2020) Transcriptome-wide identification of novel circular RNAs in soybean in response to low-phosphorus stress. PLoS One 15:1–18. https://doi.org/10.1371/journal.pone.0227243

    Article  CAS  Google Scholar 

  135. Yang Z, Yang Z, Xie Y et al (2020) Systematic identification and analysis of light-responsive circular RNA and co-expression networks in Lettuce (Lactuca sativa). G3 (Bethesda) 10:2397–2410. https://doi.org/10.1534/g3.120.401331

    Article  CAS  Google Scholar 

  136. Wang J, Lin J, Wang H et al (2018) Identification and characterization of circRNAs in Pyrus betulifolia Bunge under drought stress. PLoS One 13:1–13. https://doi.org/10.1371/journal.pone.0200692

    Article  Google Scholar 

  137. Yang Z, Li W, Su X et al (2019) Early response of radish to heat stress by strand-specific transcriptome and mirna analysis. Int J Mol Sci 20:3321. https://doi.org/10.3390/ijms20133321

    Article  CAS  PubMed Central  Google Scholar 

  138. Ren Y, Yue H, Li L et al (2018) Identification and characterization of circRNAs involved in the regulation of low nitrogen-promoted root growth in hexaploid wheat. Biol Res 51:1–9. https://doi.org/10.1186/s40659-018-0194-3

    Article  CAS  Google Scholar 

  139. Han Y, Li X, Yan Y et al (2020) Identification, characterization, and functional prediction of circular RNAs in maize. Mol Gen Genomics 295:491–503. https://doi.org/10.1007/s00438-019-01638-9

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bioinformatics Unit, Instituto de Parasitología y Biomedicina “López-Neyra”, Consejo Superior de Investigaciones Científicas (IPBLN-CSIC), Granada, Spain

    Laura Carmen Terrón-Camero & Eduardo Andrés-León

Authors
  1. Laura Carmen Terrón-Camero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eduardo Andrés-León
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eduardo Andrés-León .

Editor information

Editors and Affiliations

  1. Oncativo, Argentina

    Luis María Vaschetto

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Terrón-Camero, L.C., Andrés-León, E. (2021). NGS Methodologies and Computational Algorithms for the Prediction and Analysis of Plant Circular RNAs. In: Vaschetto, L.M. (eds) Plant Circular RNAs. Methods in Molecular Biology, vol 2362. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1645-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1645-1_8

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1644-4

  • Online ISBN: 978-1-0716-1645-1

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation