A comprehensive overview and evaluation of circular RNA detection tools

By Xiangxiang Zeng, Wei Lin, Maozu Guo, Quan Zou. PLoS Comput. Biol., 2017

320 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d4d5bf837c57154c2ffd4a7de6590c580db1c57c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Wanying Wu
2. Peifeng Ji
3. Fangqing Zhao

In Molecular Biology

1. Wanying Wu
2. Peifeng Ji
3. Fangqing Zhao
4. Leyi Wei

Citations in the corpus

• CircAtlas: an integrated resource of one million highly accurate circular RNAs from 1070 vertebrate transcriptomes (2020, methodology): “Briefly, the circRNAs in each species were identified using four detection algorithms, including CIRI2 [26, 27], find_circ [6], CIRCexplorer2 [28], and DCC [29], which have been widely used and extensively tested by previous studies [1, 30 – 32].”
• Past, present, and future of circRNAs (2019, unknown): “Nowadays, there are a number of pipelines for annotating and quantifying circRNAs (reviewed by Hansen et al , 2016; Zeng et al , 2017; and compared by Hansen, 2018) as well as databases for circRNA reference (reviewed by Xu, 2017).”
• RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans (2018, methodology): “In the past decades, various applications have been developed via computer-aided methods in medical/biomedical image processing, cellular biology domains (Zeng et al., 2017; Hong et al., 2020a,b; Song et al., 2020a,b, 2021).”
• The bioinformatics toolbox for circRNA discovery and analysis (2020, methodology): “Based on comparison results, MapSplice is more reliable but timeconsuming [78, 87].”