A comprehensive overview and evaluation of circular RNA detection tools

• Published in: PLoS computational biology Vol. 13; no. 6; p. e1005420
• Main Authors: Zeng, Xiangxiang, Lin, Wei, Guo, Maozu, Zou, Quan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.06.2017; Public Library of Science (PLoS)
• Subjects: Acids; Algorithms; Base Sequence; Binding sites; Bioindicators; Bioinformatics; Biology; Biology and life sciences; Biomarkers; Circular RNA; Circularity; Civil engineering; Colleges & universities; Computer and Information Sciences; Database Management Systems; Databases, Nucleic Acid; Engineering and Technology; Gene expression; Genomes; HeLa Cells; Humans; Identification; Information science; Methods; MicroRNA; MicroRNAs; Mimicry; miRNA; Physiological aspects; Pipelines; Polyadenylation; Regulators; Research and analysis methods; Review; Ribonucleic acid; RNA; RNA - chemistry; RNA - genetics; RNA polymerase; RNA sequencing; Sensitivity; Sensitivity analysis; Sequence Alignment; Sequence Analysis, RNA; Sponges; Transcription
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1005420

Circular RNA (circRNA) is mainly generated by the splice donor of a downstream exon joining to an upstream splice acceptor, a phenomenon known as backsplicing. It has been reported that circRNA can function as microRNA (miRNA) sponges, transcriptional regulators, or potential biomarkers. The availability of massive non-polyadenylated transcriptomes data has facilitated the genome-wide identification of thousands of circRNAs. Several circRNA detection tools or pipelines have recently been developed, and it is essential to provide useful guidelines on these pipelines for users, including a comprehensive and unbiased comparison. Here, we provide an improved and easy-to-use circRNA read simulator that can produce mimicking backsplicing reads supporting circRNAs deposited in CircBase. Moreover, we compared the performance of 11 circRNA detection tools on both simulated and real datasets. We assessed their performance regarding metrics such as precision, sensitivity, F1 score, and Area under Curve. It is concluded that no single method dominated on all of these metrics. Among all of the state-of-the-art tools, CIRI, CIRCexplorer, and KNIFE, which achieved better balanced performance between their precision and sensitivity, compared favorably to the other methods.