Recent research has already shown that circular RNAs (circRNAs) are functional in gene expression regulation and potentially related to diseases. Due to their stability, circRNAs can also be used as biomarkers for diagnosis. However, the function of most circRNAs remains unknown, and it is expensive and time-consuming to discover it through biological experiments. In this paper, we predict circRNA annotations from the knowledge of their interaction with miRNAs and subsequent miRNA-mRNA interactions. First, we construct an interaction network for a target circRNA and secondly spread the information from the network nodes with the known function to the root circRNA node. This idea itself is not new; our main contribution lies in proposing an efficient and exact deterministic procedure based on the principle of probability-generating functions to calculate the p-value of association test between a circRNA and an annotation term. We show that our publicly available algorithm is both more effective and efficient than the commonly used Monte-Carlo sampling approach that may suffer from difficult quantification of sampling convergence and subsequent sampling inefficiency. We experimentally demonstrate that the new approach is two orders of magnitude faster than the Monte-Carlo sampling, which makes summary annotation of large circRNA files feasible; this includes their reannotation after periodical interaction network updates, for example. We provide a summary annotation of a current circRNA database as one of our outputs. The proposed algorithm could be generalized towards other types of RNA in way that is straightforward.

Department of Computer Science Faculty of Electrical Engineering Czech Technical University Prague Prague Czech Republic

Department of Genomics Institute of Hematology and Blood Transfusion Prague Czech Republic

Zobrazit více v PubMed

Dong R, Ma X-K, Li G-W, Yang L. CIRCpedia v2: an updated database for comprehensive circular RNA annotation and expression comparison. Genom Proteom Bioinform. 2018;16(4):226–233. doi: 10.1016/j.gpb.2018.08.001. PubMed DOI PMC

Verduci L, Tarcitano E, Strano S, Yarden Y, Blandino G. CircRNAs: role in human diseases and potential use as biomarkers. Cell Death Dis. 2021;12(5):468. doi: 10.1038/s41419-021-03743-3. PubMed DOI PMC

Han B, Chao J, Yao H. Circular RNA and its mechanisms in disease: from the bench to the clinic. Pharmacol Ther. 2018;187:31–44. doi: 10.1016/j.pharmthera.2018.01.010. PubMed DOI

Wang C-C, Han C-D, Zhao Q, Chen X. Circular RNAs and complex diseases: from experimental results to computational models. Briefings in Bioinformatics (2021). 10.1093/bib/bbab286. bbab286. https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbab286/39715891/bbab286.pdf PubMed PMC

Meng S, Zhou H, Feng Z, Xu Z, Tang Y, Li P, Wu M. CircRNA: functions and properties of a novel potential biomarker for cancer. Mol Cancer. 2017;16(1):94. doi: 10.1186/s12943-017-0663-2. PubMed DOI PMC

Zhang Z, Yang T, Xiao J. Circular RNAs: promising biomarkers for human diseases. EBioMedicine. 2018;34:267–274. doi: 10.1016/j.ebiom.2018.07.036. PubMed DOI PMC

Pearson WR. An introduction to sequence similarity ("homology") searching. Curr Protoc Bioinformatics. 2013; Chapter 3: Unit3.1. 10.1002/0471250953.bi0301s42. PubMed PMC

Panda AC. Circular RNAs act as miRNA sponges. In: Xiao J, editor. Circular RNAs: biogenesis and functions. Singapore: Springer; 2018. pp. 67–79. PubMed

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

Cardenas J, Balaji U, Gu J. Cerina: systematic circRNA functional annotation based on integrative analysis of ceRNA interactions. Sci Rep. 2020;10(1):22165. doi: 10.1038/s41598-020-78469-x. PubMed DOI PMC

Li S, Chen L, Xu C, Qu X, Qin Z, Gao J, Li J, Liu J. Expression profile and bioinformatics analysis of circular RNAs in acute ischemic stroke in a South Chinese Han population. Sci Rep. 2020;10(1):10138. doi: 10.1038/s41598-020-66990-y. PubMed DOI PMC

Ding Y, Chen B, Lei X, Liao B, Wu F-X. Predicting novel CircRNA-disease associations based on random walk and logistic regression model. Comput Biol Chem. 2020;87:107287. doi: 10.1016/j.compbiolchem.2020.107287. PubMed DOI

Fang Z, Lei X. Prediction of miRNA-circRNA associations based on DOI

Glaab E, Baudot A, Krasnogor N, Schneider R, Valencia A. EnrichNet: network-based gene set enrichment analysis. Bioinformatics. 2012;28(18):451–457. doi: 10.1093/bioinformatics/bts389. PubMed DOI PMC

Lei X, Bian C. Integrating random walk with restart and PubMed DOI PMC

Oliver S. Guilt-by-association goes global. Nature. 2000;403(6770):601–602. doi: 10.1038/35001165. PubMed DOI

Segal BD, Braun T, Elliott MR, Jiang H. Fast approximation of small PubMed DOI

Silva I, Assunção R, Costa M. Power of the sequential monte Carlo test. Seq Anal. 2009;28(2):163–174. doi: 10.1080/07474940902816601. DOI

Silva IR, Assunção RM. Optimal generalized truncated sequential monte Carlo test. J Multivar Anal. 2013;121:33–49. doi: 10.1016/j.jmva.2013.06.003. DOI

Feller W. Introduction to Probability Theory and Its Applications, (1966)

Li Y, Xu J, Shao T, Zhang Y, Chen H, Li X. RNA function prediction. In: Kaufmann M, Klinger C, Savelsbergh A, editors. Functional genomics: methods and protocols. New York, NY: Springer; 2017. pp. 17–28. PubMed

Manly B, Navarro Alberto J. Randomization, Bootstrap and Monte Carlo methods in biology. 4th ed. London: Chapman and Hall/CRC; 2020.

Phipson B, Smyth GK. Permutation PubMed DOI

Keller A, Backes C, Lenhof H-P. Computation of significance scores of unweighted gene set enrichment analyses. BMC Bioinform. 2007;8(1):290. doi: 10.1186/1471-2105-8-290. PubMed DOI PMC

Eddelbuettel D, Balamuta JJ. Extending R with C++: a brief introduction to Rcpp. Am Stat. 2018;72(1):28–36. doi: 10.1080/00031305.2017.1375990. DOI

Dudekula DB, Panda AC, Grammatikakis I, De S, Abdelmohsen K, Gorospe M. CircInteractome: a web tool for exploring circular RNAs and their interacting proteins and microRNAs. RNA Biol. 2016;13(1):34–42. doi: 10.1080/15476286.2015.1128065. PubMed DOI PMC

Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microrna targets. Cell. 2005;120(1):15–20. doi: 10.1016/j.cell.2004.12.035. PubMed DOI

Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, Vlachos IS, Tastsoglou S, Kanellos I, Papadimitriou D, Kavakiotis I, Maniou S, Skoufos G, Vergoulis T, Dalamagas T, Hatzigeorgiou AG. DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res. 2017;46(D1):239–245. doi: 10.1093/nar/gkx1141. PubMed DOI PMC

Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2008;37(suppl–1):105–110. doi: 10.1093/nar/gkn851. PubMed DOI PMC

Hsu S-D, Lin F-M, Wu W-Y, Liang C, Huang W-C, Chan W-L, Tsai W-T, Chen G-Z, Lee C-J, Chiu C-M, Chien C-H, Wu M-C, Huang C-Y, Tsou A-P, Huang H-D. miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res. 2010;39(suppl–1):163–169. doi: 10.1093/nar/gkq1107. PubMed DOI PMC

Ru Y, Kechris KJ, Tabakoff B, Hoffman P, Radcliffe RA, Bowler R, Mahaffey S, Rossi S, Calin GA, Bemis L, Theodorescu D. The multiMiR R package and database: integration of microRNA-target interactions along with their disease and drug associations. Nucleic Acids Res. 2014;42(17):133. doi: 10.1093/nar/gku631. PubMed DOI PMC

Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006;34(suppl–1):140–144. doi: 10.1093/nar/gkj112. PubMed DOI PMC

Binns D, Dimmer E, Huntley R, Barrell D, O’Donovan C, Apweiler R. QuickGO: a web-based tool for gene ontology searching. Bioinformatics. 2009;25(22):3045–3046. doi: 10.1093/bioinformatics/btp536. PubMed DOI PMC

Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739–1740. doi: 10.1093/bioinformatics/btr260. PubMed DOI PMC

Reimand J, Isserlin R, Voisin V, Kucera M, Tannus-Lopes C, Rostamianfar A, Wadi L, Meyer M, Wong J, Xu C, Merico D, Bader GD. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat Protoc. 2019;14(2):482–517. doi: 10.1038/s41596-018-0103-9. PubMed DOI PMC

Dunn OJ. Multiple comparisons among means. J Am Stat Assoc. 1961;56(293):52–64. doi: 10.1080/01621459.1961.10482090. DOI

Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological) 1995;57(1):289–300.

Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One. 2010;5(11):1–12. doi: 10.1371/journal.pone.0013984. PubMed DOI PMC

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–2504. doi: 10.1101/gr.1239303. PubMed DOI PMC

Glažar P, Papavasileiou P, Rajewsky N. circBase: a database for circular RNAs. RNA. 2014;20(11):1666–1670. doi: 10.1261/rna.043687.113. PubMed DOI PMC

Fagerberg L, Hallström BM, Oksvold P, Kampf C, Djureinovic D, Odeberg J, Habuka M, Tahmasebpoor S, Danielsson A, Edlund K, Asplund A, Sjöstedt E, Lundberg E, Szigyarto CA-K, Skogs M, Takanen JO, Berling H, Tegel H, Mulder J, Nilsson P, Schwenk JM, Lindskog C, Danielsson F, Mardinoglu A, Sivertsson Å, von Feilitzen K, Forsberg M, Zwahlen M, Olsson I, Navani S, Huss M, Nielsen J, Ponten F, Uhlén M. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol Cell Proteom. 2014;13(2):397–406. doi: 10.1074/mcp.M113.035600. PubMed DOI PMC

Liu S, Li B, Li Y, Song H. Circular rna circ\_0000228 promotes the malignancy of cervical cancer via microrna-195-5p/ lysyl oxidase-like protein 2 axis. Bioengineered. 2021;12(1):4397–4406. doi: 10.1080/21655979.2021.1954846. PubMed DOI PMC

Pareto V. Cours Deconomie Politique. Geneva: Librairie Droz; 1964. pp. 299–345.

Zhao B-W, Hu L, Hu P-W, You Z-H, Su X-R, Li D-X, Chen Z-H, Zhang P. MRLDTI: a meta-path-based representation learning model for drug-target interaction prediction. In: Huang D-S, Jo K-H, Jing J, Premaratne P, Bevilacqua V, Hussain A, editors. Intelligent computing theories and application. Cham: Springer; 2022. pp. 451–459.

Zhang M-L, Zhao B-W, Hu L, You Z-H, Chen Z-H. Predicting drug-disease associations via meta-path representation learning based on heterogeneous information net works. In: Huang D-S, Jo K-H, Jing J, Premaratne P, Bevilacqua V, Hussain A, editors. Intelligent computing theories and application. Cham: Springer; 2022. pp. 220–232.

Vural H, Kaya M, Alhajj R. A model based on random walk with restart to predict circRNA-disease associations on heterogeneous network. In: Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining. ASONAM ’19, pp. 929–932. Association for Computing Machinery, New York, NY, USA (2019). 10.1145/3341161.3343514. https://doi.org/10.1145/3341161.3343514

Brin S, Page L. The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems. 1998;30(1):107–17. 10.1016/S0169-7552(98)00110-X (Proceedings of the Seventh International World Wide Web Conference).

Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, Li M, Wang G, Liu Y. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2008;37(suppl–1):98–104. doi: 10.1093/nar/gkn714. PubMed DOI PMC

Huang Z, Shi J, Gao Y, Cui C, Zhang S, Li J, Zhou Y, Cui Q. HMDD v3.0: a database for experimentally supported human microRNA-disease associations. Nucleic Acids Res. 2018;47(D1):1013–1017. doi: 10.1093/nar/gky1010. PubMed DOI PMC

Piñero J, Queralt-Rosinach N, Bravo A, Deu-Pons J, Bauer-Mehren A, Baron M, Sanz F, Furlong LI. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database, 2015; 2015. 10.1093/database/bav028. bav028. https://academic.oup.com/database/article-pdf/doi/10.1093/database/bav028/16975988/bav028.pdf PubMed PMC

Lan W, Zhu M, Chen Q, Chen B, Liu J, Li M, Chen Y-PP. CircR2Cancer: a manually curated database of associations between circRNAs and cancers. Database, 2020; 2020. 10.1093/database/baaa085. baaa085. https://academic.oup.com/database/article-pdf/doi/10.1093/database/baaa085/34283838/baaa085.pdf PubMed PMC

Yao D, Zhang L, Zheng M, Sun X, Lu Y, Liu P. Circ2Disease: a manually curated database of experimentally validated circRNAs in human disease. Sci Rep. 2018;8(1):11018. doi: 10.1038/s41598-018-29360-3. PubMed DOI PMC

Fan C, Lei X, Fang Z, Jiang Q, Wu F-X. CircR2Disease: a manually curated database for experimentally supported circular RNAs associated with various diseases. Database. 2018. 10.1093/database/bay044. PubMed PMC

Ghosal S, Das S, Sen R, Basak P, Chakrabarti J. Circ2Traits: a comprehensive database for circular RNA potentially associated with disease and traits. Front Genet. 2013 doi: 10.3389/fgene.2013.00283. PubMed DOI PMC

Lei X, Fang Z, Chen L, Wu F-X. PWCDA: path weighted method for predicting circRNA-disease associations. Int J Mol Sci. 2018;19(11):3410. doi: 10.3390/ijms19113410. PubMed DOI PMC

Zhao Q, Yang Y, Ren G, Ge E, Fan C. Integrating bipartite network projection and KATZ measure to identify novel circRNA-disease associations. IEEE Trans Nanobiosci. 2019;18(4):578–584. doi: 10.1109/TNB.2019.2922214. PubMed DOI

Lei X-J, Fang Z, Guo L. Predicting circRNA-disease associations based on improved collaboration filtering recommendation system with multiple data. Front Genet. 2019 doi: 10.3389/fgene.2019.00897. PubMed DOI PMC

Lu C, Zeng M, Wu F-X, Li M, Wang J. Improving circRNA-disease association prediction by sequence and ontology representations with convolutional and recurrent neural networks. Bioinformatics. 2020;36(24):5656–5664. doi: 10.1093/bioinformatics/btaa1077. PubMed DOI

Zhang H-Y, Wang L, You Z-H, Hu L, Zhao B-W, Li Z-W, Li Y-M. iGRLCDA: identifying circRNA-disease association based on graph representation learning. Brief Bioinform. 2022 doi: 10.1093/bib/bbac083. PubMed DOI

Zhao B-W, Hu L, You Z-H, Wang L, Su X-R. HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks. Brief Bioinform. 2021 doi: 10.1093/bib/bbab515. PubMed DOI

Li G, Luo J, Wang D, Liang C, Xiao Q, Ding P, Chen H. Potential circRNA-disease association prediction using DeepWalk and network consistency projection. J Biomed Inform. 2020;112:103624. doi: 10.1016/j.jbi.2020.103624. PubMed DOI

Li G, Yue Y, Liang C, Xiao Q, Ding P, Luo J. NCPCDA: network consistency projection for circRNA-disease association prediction. RSC Adv. 2019;9(57):33222–33228. doi: 10.1039/C9RA06133A. PubMed DOI PMC

Zhang Y, Lei X, Fang Z, Pan Y. Circrna-disease associations prediction based on metapath2vec++ and matrix factorization. Big Data Min Anal. 2020;3(4):280–291. doi: 10.26599/BDMA.2020.9020025. DOI

Deepthi K, Jereesh AS. An ensemble approach for circrna-disease association prediction based on autoencoder and deep neural network. Gene. 2020;762:145040. doi: 10.1016/j.gene.2020.145040. PubMed DOI

Wang L, You Z-H, Huang D-S, Li J-Q. MGRCDA: Metagraph recommendation method for predicting circRNA-disease association. IEEE Trans Cybern. 2021 doi: 10.1109/TCYB.2021.3090756. PubMed DOI

Zheng K, You Z-H, Li J-Q, Wang L, Guo Z-H, Huang Y-A. iCDA-CGR: Identification of circRNA-disease associations based on chaos game representation. PLoS Comput Biol. 2020;16(5):1–22. doi: 10.1371/journal.pcbi.1007872. PubMed DOI PMC

Expression of circular RNAs in myelodysplastic neoplasms and their association with mutations in the splicing factor gene SF3B1