Abstract
Analysis of circular RNA (circRNA) expression from RNA-Seq data can be performed with different algorithms and analysis pipelines, tools allowing the extraction of heterogeneous information on the expression of this novel class of RNAs. Computational pipelines were developed to facilitate the analysis of circRNA expression by leveraging different public tools in easy-to-use pipelines. This chapter describes the complete workflow for a computationally reproducible analysis of circRNA expression starting for a public RNA-Seq experiment. The main steps of circRNA prediction, annotation, classification, sequence reconstruction, quantification, and differential expression are illustrated.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Salzman J (2016) Circular RNA expression: its potential regulation and function. Trends Genet 32:309–316
Szabo L, Salzman J (2016) Detecting circular RNAs: bioinformatic and experimental challenges. Nat Rev Genet 17:679–692
Hansen TB (2018) Improved circRNA identification by combining prediction algorithms. Front Cell Dev Biol 6:20
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
Cheng J, Metge F, Dieterich C (2016) Specific identification and quantification of circular RNAs from sequencing data. Bioinformatics 32:1094–1096
Sun P, Li G (2019) CircCode: a powerful tool for identifying circRNA coding ability. Front Genet 10:981
Feng J, Xiang Y, Xia S et al (2018) CircView: a visualization and exploration tool for circular RNAs. Brief Bioinform 19:1310–1316
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. https://doi.org/10.3390/ncrna3010008
Jakobi T, Uvarovskii A, Dieterich C (2019) circtools-a one-stop software solution for circular RNA research. Bioinformatics 35:2326–2328
Humphreys DT, Fossat N, Demuth M et al (2019) Ularcirc: visualization and enhanced analysis of circular RNAs via back and canonical forward splicing. Nucleic Acids Res 47:e123–e123
Ferrero G, Licheri N, Tarrero LC et al (2019) Docker4Circ: a framework for the reproducible characterization of circRNAs from RNA-seq data. Int J Mol Sci 21:293
Kulkarni N, Alessandrì L, Panero R et al (2018) Reproducible bioinformatics project: a community for reproducible bioinformatics analysis pipelines. BMC Bioinformatics 19:349
Beccuti M, Cordero F, Arigoni M et al (2018) SeqBox: RNAseq/ChIPseq reproducible analysis on a consumer game computer. Bioinformatics 34:871–872
Zeng K, He B, Yang BB et al (2018) The pro-metastasis effect of circANKS1B in breast cancer. Mol Cancer 17:160
Gao Y, Zhang J, Zhao F (2018) Circular RNA identification based on multiple seed matching. Brief Bioinform 19:803–810
Akers NK, Schadt EE, Losic B (2018) STAR chimeric post for rapid detection of circular RNA and fusion transcripts. Bioinformatics 34:2364–2370
Coscujuela Tarrero L, Ferrero G, Miano V et al (2018) Luminal breast cancer-specific circular RNAs uncovered by a novel tool for data analysis. Oncotarget 9:14580–14596
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Ferrero, G., Licheri, N., De Bortoli, M., Calogero, R.A., Beccuti, M., Cordero, F. (2021). Computational Analysis of circRNA Expression Data. In: Picardi, E. (eds) RNA Bioinformatics. Methods in Molecular Biology, vol 2284. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1307-8_10
Download citation
DOI: https://doi.org/10.1007/978-1-0716-1307-8_10
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-1306-1
Online ISBN: 978-1-0716-1307-8
eBook Packages: Springer Protocols