CircRNAFisher: a systematic computational approach for de novo circular RNA identification

Authors: Guo-yi Jia1,2, Duo-lin Wang3, Meng-zhu Xue2, Yu-wei Liu2, Yu-chen Pei2, Ying-qun Yang2,4, Jing-mei Xu2,4, Yan-chun Liang3, Peng Wang2,4
1 School of Life Sciences, Shanghai University, Shanghai 200444, China
2 Laboratory of Systems Biology, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
3 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
4 School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China
Correspondence to: Yan-chun Liang:, Peng Wang:,
DOI: 10.1038/s41401-018-0063-1
Received: 18 December 2017
Accepted: 5 June 2018
Advance online: 16 July 2018


Circular RNAs (circRNAs) are emerging species of mRNA splicing products with largely unknown functions. Although several computational pipelines for circRNA identification have been developed, these methods strictly rely on uniquely mapped reads overlapping back-splice junctions (BSJs) and lack approaches to model the statistical significance of the identified circRNAs. Here, we reported a systematic computational approach to identify circRNAs by simultaneously utilizing BSJ overlapping reads and discordant BSJ spanning reads to identify circRNAs. Moreover, we developed a novel procedure to estimate the P-values of the identified circRNAs. A computational cross-validation and experimental validations demonstrated that our method performed favorably compared to existing circRNA detection tools. We created a standalone tool, CircRNAFisher, to implement the method, which might be valuable to computational and experimental scientists studying circRNAs.
Keywords: circRNA; RNA-Seq; alternative splicing; pipeline

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