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

	author = {Guo-yi Jia and Duo-lin Wang and Meng-zhu Xue and Yu-wei Liu and Yu-chen Pei and Ying-qun Yang and Jing-mei Xu and Yan-chun Liang and Peng Wang},
	title = {CircRNAFisher: a systematic computational approach for de novo circular RNA identification},
	journal = {Acta Pharmacologica Sinica},
	volume = {40},
	number = {1},
	year = {2018},
	keywords = {},
	abstract = {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.},
	url = {}