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Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

Lookup NU author(s): Dr Michael Jackson, Dr Mauro Santibanez Koref


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© 2023, The Author(s), under exclusive licence to Springer Nature America, Inc. The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation.

Publication metadata

Author(s): Vromman M, Anckaert J, Bortoluzzi S, Buratin A, Chen C-Y, Chu Q, Chuang T-J, Dehghannasiri R, Dieterich C, Dong X, Flicek P, Gaffo E, Gu W, He C, Hoffmann S, Izuogu O, Jackson MS, Jakobi T, Lai EC, Nuytens J, Salzman J, Santibanez-Koref M, Stadler P, Thas O, Vanden Eynde E, Verniers K, Wen G, Westholm J, Yang L, Ye C-Y, Yigit N, Yuan G-H, Zhang J, Zhao F, Vandesompele J, Volders P-J

Publication type: Article

Publication status: Published

Journal: Nature Methods

Year: 2023

Volume: 20

Pages: 1159-1169

Online publication date: 13/07/2023

Acceptance date: 12/06/2023

ISSN (print): 1548-7091

ISSN (electronic): 1548-7105

Publisher: Nature Research


DOI: 10.1038/s41592-023-01944-6


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